aneela maqsood - higher education...

WORK ENVIRONMENT, BURNOUT, ORGANIZATIONAL COMMITMENT, AND ROLE OF PERSONAL VARIABLES AS MODERATORS

BY

ANEELA MAQSOOD

Dr. Muhammad Ajmal NATIONAL INSTITUTE OF PSYCHOLOGY

Center of Excellence Quaid-i-Azam University, Islamabad

2011


BY

ANEELA MAQSOOD

A dissertation submitted to the

Dr. Muhammad Ajmal NATIONAL INSTITUTE OF PSYCHOLOGY

Center of Excellence Quaid-i-Azam University, Islamabad

In partial fulfillment of the requirements for the

DEGREE OF PHILOSOPHY OF DOCTORATE

IN

PSYCHOLOGY

2011

CCEERRTTIIFFIICCAATTEE

Certified that Ph.D. Dissertation “Work Environment, Burnout,

Organizational Commitment, and Role of Personal Variables as Moderators”,

prepared by Ms. Aneela Maqsood has been approved for submission to Quaid-e-

Azam University, Islamabad.

Dr. Ghazala Rehman Supervisor

Dr. Rubina Hanif Co-Supervisor

WORK ENVIRONMENT, BURNOUT,

ORGANIZATIONAL COMMITMENT, AND ROLE OF PERSONAL VARIABLES AS MODERATORS

BBYY

AANNEEEELLAA MMAAQQSSOOOODD

Approved by

_________________ Supervisor

_________________ Co-Supervisor

_________________ External Examiner

_________________ External Examiner

_________________ Director NIP

Dedicated to

My Parents for ever strengthening support, impetus, and prayers behind being I am

CONTENTS

List of Tables iList of Figures viiList of Appendices viiiAcknowledgements ixAbstract x CHAPTER 1: INTRODUCTION

Work Environment: Nature and Dimensionality 4Theoretical Foundations of Work Environment 8 Work Environment of Academic Settings 14Measurement of Work Environment 18Burnout 20Theoretical Models of Burnout 24Measurement of Burnout 27Relationship of Work Environment and Burnout 29Organizational Commitment 33Theoretical Models of Organizational Commitment 35Measurement of Organizational Commitment 40Relationship of Work Environment and Organizational Commitment 42Role of Personal Variables in Relationship of Work Environment and its Outcomes

46

Ratioanle of the Study 58

CHAPTER II: OBJECTIVES, HYPOTHESES, OPERATIONAL

DEFINITIONS, AND RESEARCH DESIGN

68

Objectives of the Study 68Hypotheses 69Operational Definitions of Variables 69Research Design

76

CHAPTER III: PHASE I: PILOT STUDY

Pilot Testing of Study Measures and Preliminary Testing of the Model of Work Environment and Outcomes

77

Method 77 Participants 77 Meaures 79 Procedure 85

Results 87Discussion 106

CHAPTER IV: PHASE II: MAIN STUDY

123

Step I: Examining the Measurement Models of Constructs 123Objectives of Step I of the Main Study 124Method 124 Participants 124

Instruments 125 Procedure 126

Results Testing the Factor Structue of Work Environment, Burnout,

Organizational Commitmnet, and Personality Measures

127 127

Factor Structure of Work Environment Scale 130 Factor Structure of Maslach Burnout Inventory-Educators Survey 136 Factor Structure of Organizational Commitment Questionnaire 145 Factor Structure of Mini Markers Set 151Discussion Conclusion Step II: The Role of Work Environment in Predicting Burnout and Organizational Commitment and the Moderating Role of Personal Variables

155 169 171

Objectives of Step II of the Main Study 171Instruments 171Results 170 Descriptive Analysis 173 Predictive Relationship between Work Environment and Burnout 180 Predictive Relationship between Work Environment and Organizational Commitment

187

The Moderating Role of Personal Variables 193Discussion 235 Psychometric Issues 237 Predictive Impact of Work Environment on Burnout and Organizational

Commitment 242

Moderating Effects of Personality 254 Moderating Effects of Organizational and Demographic related Personal

variables 258

Implications of the Study 266Limitations and Future Research 266Conclusion

267

REFERENCES

269

APPENDICES 319

i

LIST OF TABLES

Table 1 Mean & SD on scores representing Levels of Work

Environment, Burnout, Organizational Commitment, and

Personality Dimensions (N = 102)

89

Table 2 Cronbach’s Alpha (on the diagonal) and Pearson Product

Moment Correlation Coefficients on Scores of Study

Variables (N = 102)

90

Table 3 Multiple Regression Analysis on scores of Emotional

Exhaustion by Work Environment (N = 102) 92

Table 4 Multiple Regression Analysis on scores of Depersonalization

by Work Environment (N = 102) 93

Table 5 Multiple Regression Analysis on scores of Personal

Accomplishment by Work Environment (N = 102) 94

Table 6 Multiple Regression Analysis on total scores of Burnout by

Work Environment (N = 102) 95

Table 7 Regression Analysis on Burnout and its components by total

scores of Work Environment (N = 102) 96

Table 8 Multiple Regression Analysis on Affective Commitment by


Table 9 Multiple Regression Analysis on Continuance Commitment


Table 10 Multiple Regression Analysis on Normative Commitment by


Table 11 Multiple Regression Analysis on total scores of

Organizational Commitment by Work Environment (N = 102) 100

Table 12 Regression Analysis on Organizational Commitment and its

components by total scores on Work Environment (N = 102) 101

Table 13 Goodness-of-fit statistics for ten-factor model of Work

Environment (N = 426) 127

ii

Table 14 Factor loadings and Standard Errors for ten factor model of


Table 15 Goodness-of-fit statistics for single, three and five-factor

models of MBI (N = 426) 135

Table 16 Factor loadings and Standard Errors for one factor model of

Maslach Burnout Inventory (N = 426) 137

Table 17 Factor loadings and Standard Errors for three factor model of


Table 18 Factor loadings and Standard Errors for five factor model of


Table 19 Goodness-of-fit statistics for a one-factor and three-factor

model of OCQ (N = 426) 142

Table 20 Factor loadings and Standard Errors for one factor model of

Organizational Commitment Questionnaire (N = 426) 144

Table 21 Factor loadings and Standard Errors for three factor model of

Organizational Commitment Questionnaire (N = 426) 146

Table 22 Goodness-of-fit statistics for five-factor models of MM (N =

426) 148

Table 23 Factor loadings and Standard Errors for five factor model of

Mini Markers Set (N = 426) 150

Table 24 Mean & SD on scores representing Levels of Work

Environment, Burnout, Organizational Commitment, and

Personality Variables (N = 426)

172

Table 25 Cronbach’s Alpha (on the diagonal), Pearson Product

Moment Correlations for Predictive, Criterion, and

Moderator Variables (N = 426)

173

Table 26 Multiple Regression Analysis on scores of Emotional

Exhaustion and its components by Work Environment (N =

426)

177

iii

Table 27 Multiple Regression Analysis on scores of Depersonalization


Table 28 Multiple Regression Analysis on scores of Personal

Accomplishment and its components by Work Environment

(N = 426)

180

Table 29 Multiple Regression Analysis on scores of Burnout by Work

Environment (N = 426) 181

Table 30 Regression Analysis on Burnout and its components by total

scores of Work Environment (N = 426) 183

Table 31 Multiple Regression Analysis on Affective Commitment by


Table 32 Multiple Regression Analysis on Continuance Commitment


Table 33 Multiple Regression Analysis on Normative Commitment by


Table 34 Multiple Regression Analysis on Organizational

Commitment by Work Environment (N = 426) 189

Table 35 Regression Analysis on Organizational Commitment and its

components by total scores on Work Environment (N = 426)

190

Table 36 Moderating Effects of Personality in predicting Burnout-

three-factor model (N = 426) 193

Table 37 Interaction Effects of Extraversion in predicting Work

Environment and Burnout Relationship 195

Table 38 Interaction Effects of Agreeableness in predicting Work


Table 39 Interaction Effects of Openness in predicting Work


Table 40 Moderating Effects of Personality in predicting Burnout-

five-factor model (N = 426) 200

iv


Environment and Burnout (five- factor model) Relationship 203





Table 44 Moderating Effects of Personality in predicting

Organizational Commitment (N = 425) 207


Environment and Organizational Commitment Relationship

(N = 426)

209



(N = 426)

210

Table 47 Interaction Effects of Conscientiousness in predicting Work


(N = 426)

211



(N = 426)

212

Table 49 Moderating Effects of Sector in predicting Burnout (N =

426) 216

Table 50 Interaction Effects of Sector in predicting Work Environment

and Burnout (three-factor model) Relationship 217

Table 51 Interaction Effects of Sector in predicting Work Environment

Burnout (five-factor model) 218

Table 52 Moderating Effects of Sector in predicting Organizational

Commitment (N = 426) 219

v

Table 53 Interaction Effects of Sector in predicting Work

Environment and Organizational Commitment (N = 426)

220

Table 54 Moderating Effects of Rank in predicting Burnout (N = 426) 221

Table 55 Moderating Effects of Rank in predicting Organizational


Table 56 Moderating Effects of Employment Duration in predicting

Burnout (N = 426) 222

Table 57 Moderating Effects of Employment Duration in predicting


Table 58 Moderating Effects of Faculties in predicting Burnout (N =

426) 223

Table 59 Moderating Effects of Faculties in predicting Organizational

Commitment (N = 426)

224

Table 60 Moderating Effects of Side Jobs in predicting Burnout (N =

426) 224

Table 61 Moderating Effects of Side Jobs in predicting Organizational


Table 62 Moderating Effects of Age in predicting Burnout (N = 426) 225

Table 63 Interaction Effects of Age in predicting Work Environment

and Burnout (three factor model) (N = 426) 226

Table 64 Interaction Effects of Age in predicting Work Environment

and Burnout (five-factor model) (N = 426) 227

Table 65 Moderating Effects of Age in predicting Organizational


Table 66 Moderating Effects of Gender in predicting Burnout (N =

426) 228

Table 67 Moderating Effects of Gender- Men (N = 268) vs. Women

(N = 158) in predicting Organizational Commitment 229

vi

Table 68 Moderating Effects of Education in predicting Burnout (N =

426) 229

Table 69 Moderating Effects of Education in predicting Organizational


Table 70 Moderating Effects of Marital Status in predicting Burnout

(N = 426) 230

Table 71 Moderating Effects of Marital Status in predicting


vii

LIST OF FIGURES

Figure 1 Conceptual Model of Organizational and Personal Factors and Outcomes

12

Figure 2 Theoretical conceptualization of the present study 67

Figure 3 Moderating Effects of Extraversion in predicting Burnout (three factor model)

195

Figure 4 Moderating Effects of Agreeableness in predicting Burnout (three factor model)

196

Figure 5 Moderating Effects of Openness in predicting Burnout (three factor model)

197

Figure 6 Moderating Effect of Extraversion in predicting Organizational Commitment

203

Figure 7 Moderating Effects of Agreeableness in predicting Organizational Commitment

204

Figure 8 Moderating Effects of Consciousness in predicting Organizational Commitment

205

Figure 9 Moderating Effects of Openness in predicting Organizational Commitment

209

Figure 10 Moderating Effects of Extraversion in predicting Burnout (five-factor model)

210

Figure 11 Moderating Effects of Agreeableness in predicting Burnout (five-factor model)

211

Figure 12 Moderating Effects of Openness in predicting Organizational Commitment

212

Figure 13 Moderating Effects of Sector in predicting Burnout (three factor model)

217

Figure 14 Moderating Effects of Sector in predicting Burnout (five-factor model)

218

Figure 15 Moderating Effects of Sector in predicting Organizational Commitment

220

Figure 16 Moderating Effects of Age in predicting Burnout (three factor model)

226

Figure 17 Moderating Effects of Age in predicting Burnout (five-factor model)

227

viii

LIST OF APPENDICES

Appendix A Consent Letter to Participate in the Research Study 320

Appendix B General Instructions & Demographic Information Sheet 321

Appendix C Work Environment Scale 322

Appendix D Work Environment Scale- Scoring Key 326

Appendix E MBI-Educators Survey 328

Appendix F Organizational Commitment Questionnaire 330

Appendix G MINI-MARKERS 332

Appendix H MINI-MARKERS- Scoring Key 334

Appendix I Descriptive Profile of Pilot Sample 335

Appendix J Descriptive Profile of Sample of Main Study 336

ix

ACKNOWLEDGEMENTS

The time, when I am finalized with the completion and reporting of the

dissertation, I am thankful to God Almighty for His Blessings- leading me towards

the way in my life up to the stage today. Though it was a long journey enchained

with different flavours of life; however, the “persistence” remains rewarding at the

end.

I feel immense gratitude to all those people who made valuable

contribution in completing this task. I would like to express my deepest gratitude

to my supervisors, Dr. Ghazala Rehman and Dr. Rubina Hanif, for their excellent

guidance, patience, and valuable critical evaluation in this learning process. They

remained a source of inspiration for me as well. I would never have been able to

complete my task without their critical evaluation.

I am thankful to the management of Nottingham Trent University, UK., for

giving me the opportunity of Ph.D fellowship. It was a great learning experience to

work with Dr. Glenn A. Williams, Senior Lecturer in Psychology at School of

Social Sciences, Nottingham Trent University. I speak very highly about him for

his professional commitment and expert guidance in data analysis, write up, and

interpretation of the results.

I would like to appreciate the management of universities of Pakistan for

provision of data collection and to all those teachers who participated in this study.

I am thankful to Mr. Abdul Qayoom for providing assistance in formatting issues

and to the library personnel of Nottingham Trent University, Uk., Fatima Jinnah

Women University, Rawalpindi, Quaid-i-Azam University, Islamabad, and

PASTIC Islamabad, for providing access to digital library resources.

My heartily acknowledgment to my parents for providing a supporting

environment for me and to whom I dedicate this thesis. I would never have been

x

able to complete my task without their moral support. Sweet cheers to my daughter

Malaika for the time and laughter she brought in my life.

Aneela Maqsood

xi

ABSTRACT

Theoretical orientation of psychosocial context of work place based on

Moos’ model of work environment (1986, 1994) explaining the interplay of work

environment and its outcomes was investigated in the context of academic settings

in Pakistan. In explaining the relative effect of work environment in predicting

burnout and organization commitment within academic settings, present study also

addressed the question of moderating role of personal variables which so far was

remained open in this process. Universities teachers (N = 420) employed in public

and private sector Universities located in Rawalpindi, Islamabad, and Lahore

cities were approached using opportunity sampling. The work environment was

assessed (Work Environment Scale: Moos, 1994) on basis of ten indicators

namely: involvement; co-worker cohesion; supervisor support; autonomy; task

orientation; work pressure; clarity; managerial control; innovation and physical

comfort. Teachers’ burnout was assessed using three-facet approach defining

burnout as emotional exhaustion, depersonalization and reduced sense of personal

accomplishment (Maslach Burnout Inventory–Educators Survey: Maslach,

Jackson, & Leiter, 1996). A three facet measure of organizational commitment

namely affective, continuance, and normative commitment (Organizational

Commitment Questionnaire: Meyer & Allen, 1990) was used. Personality

dimensions oriented to Big-Five theory of personality were assessed using Mini-

Markers Set (Saucier, 1994). The study was carried out in different phases. Phase I

of the study aimed to conduct pilot study (n = 102) for evaluating preliminary

psychometric issues and trend in data regarding the results of hypothesized

xii

relations of study variables. The phase II as main study (N = 426) was further

subject to two parts. The part I focusing to test the factor structure of study

measures using Confirmatory Factor Analysis demonstrated support for existing

theoretical structure of study measures. Results of fit indices, factor loadings,

consideration of reliability indices, and understanding of items in perspective of

our culture were used as decision criteria to retain or exclude items of respective

factors. The exclusion of items was discussed in perspective of use of these

measures in our culture. Findings of multiple regression analyses highlighted that

involvement as a negative predictor and work pressure as a positive predictor are

explaining variance in emotional exhaustion. Additionally, managerial control and

task orientation were negative predictors explaining the elaborative structure of

emotional exhaustion. For depersonalization, involvement is a negative predictor.

Co-worker cohesion and work pressure as positive predictors and physical comfort

as a negative predictor are explaining variance in personal accomplishment.

Additionally, task orientation explained variance in self related personal

accomplishment. In predicting affective commitment, autonomy is a positive

predictor. Co-worker cohesion and supervisor support as negative predictors and

clarity as positive predictor explains variance in continuance commitment. Results

of multiple moderated regression analyses provided evidences of moderation

effects of certain personality dimensions, age, and public and private sector for

relationship of work environment with burnout and organizational commitment.

Findings of the study were discussed in light of deducing implications for

improving the quality of work life of teachers.

1

Chapter I

INTRODUCTION

The research drift in occupational psychology from last two decades has

focused the construct of work environment as a mean for assessing employees’

perceptions about organizational processes influencing the employee and

organizational related outcomes (Carr, Schmidt, Ford, & DeShon, 2003: Kopelman,

Brief, & Guzzo, 1990; Moos, 1994; Ostroff, 1993; Ostroff, Kinicki, & Tamkins,

2003; Parker et al., 2003). In organizational settings, experiencing work environment

is considered as a dominating and central characteristic of many people lives

(Muchinsky, 2007). This recognizes the fact that employees do report contrasting

work experiences and impending influences in form of morale, commitment,

satisfaction, despair, feeling of underutilization of individual abilities, work pressure,

stress, burnout, alienation, etc. (Moos & Billings, 1991). As regard quality of work

life, it’s important to consider ‘contextual factors’ of work settings such as policies,

operational procedures, management style, and lot many other factors of working

conditions (Wadsworth, Chaplin, Allen, & Smith, 2010). Blum and Nayler (2004)

concluded that people prefer pleasant environment to work in. In other words,

workplace as a social setting exerts profound influence by means of physiological and

psychological processes and thereby influences employees’ reactions (Quick,

Simmons, & Nelson, 2000). Organizational management has emphasized increasing

concerns of employees with work related issues particularly their expectation and

demand of the better wok environment (Sverke, 2008).

Within this context, the construct of work environment as multifaceted nature

is further described under “molar” and “particular referent” approaches. The molar

2

approach focuses on multiple or diverse psychosocial dimensions of the work

environment oriented towards processes for organizational goals attainment (Carr,

Schmidt, Ford, & DeShon, 2003). Most theoretical models explain the work

environment within this molar domain (James & James, 1989; James, James, & Ashe,

1990; Moos, 1994; Newman, 1977; Ostroff’s, 1993). Focusing particular referent of

work environment defines a specific aspect e.g., climate for creativity (Amabile &

Gryskiewicz, as cited in Taylor & Gryskiewicz, 1993), and then looks for sub-

dimensions contributing in creativity environment.

The perceptions of work environment as a critical determinant of individual

behavior imply that issues of employees have direct relationship with the work

environment (Moos & Billings, 1991). For instance, employees’ health or well-being

has been associated with positive impact of work environment (Cooper & Cartwright,

1994; Kompier, 2005). Certain aspects of the work environment might be perceived

as demanding (Sears, Urizar, & Evans, 2000), or may be stressful (Sulsky & Smith,

2005) which may have negatively impact upon employees’ attitudes, e.g., burnout.

Furthermore, impact of work environment has also been associated with positive

emotional states and organizational productivity level variables e.g., satisfaction,

organizational commitment, turnover, absenteeism, and job performance (Leka &

Houdmont, 2010). This emphasizes the importance of evaluating the dynamics of

work environment; more specifically this aims to appreciate, diagnose and prioritize

improvements in managing human resource system e.g., in academic settings (Wilk,

& Redmon, 1998) and in health settings (Kotzer, Koepping, & LeDuc, 2006).

Therefore, for the past two decades focus of research attention has been

devoted to understand the role of psychosocial work environment in determining the

behavior and attitudes of employees concerning their work and the organization

3

(Moos, 1994; O’Driscoll & Evans, 1988). There has been increased attention on

exploring certain outcome variables particularly related to occupational health and

well-being (Hyvones, Feldt, Tolvanen, & Kinnunen, 2010; Parkes & Von Rabenau,

1993; Stansfeld & Candy, 2006). More specifically, these include both positive and

negative outcome measures, such as research attention has been given to outcome

variables including job stress (Haines, Williams, & Carson, 2004; Portello & Long,

2001); negative attitudinal outcomes including burnout (Adali et al., 2003; Boyas &

Wind, 2010; Kumar, Hatcher, Dutu, Fischer, & Ma’u, 2011; Turnispeed, 1998),

turnover (Hayhurst, Saylor, & Stuenkel, 2005; Hemingway & Smith, 1999); positive

attitudinal outcomes including job satisfaction (Blegan, 1993; Tumulty, Jernigan, &

Kohut, 1994; Westerman & Yamamura, 2007), organizational commitment (Grau,

Chandler, Burton, & Kolditz, 1991; Karsh, Booske, & Sainfort, 2005; Stewart, Bing,

Gruys, & Helford, 2007), job morale (Day, Minichiello, & Madison, 2007; Gaynor,

Verdin, & Bucko, 1995; Schaefer & Moos, 1996); and the ultimate concern of

organizational dynamics reflecting in performance (Cotton, Dollard, & De Jonge,

2002; Evans & Dion, 1991; Westerman & Simmons, 2007); etc.

With the recognition that psychosocial factors of work environment may affect

employee and organizational related outcomes, it became an important research

question to identify what aspects of a work environment contribute in determining

employees’ attitudes. This seems an important issue of work place management when

examined in context of a particular work setting, e.g., the academic workplaces.

Based on well established theoretical premises of Moos (1994), the present study

examined the model explaining the complex interplay of work environment and its

outcomes. The current study speculated that work environment factors may contribute

in affecting burnout and organizational commitment in context of academic work

4

place culture in Pakistan. This may investigate more thoroughly through examining

the possible moderating effects of personality and demographic variables.

Work Environment: Nature and Dimensionality

From the perspective of organizational behavior, concept of work environment

can be traced back in Lewin’s field observations of work environment in

organizational settings (Lewin, 1951), which suggest that …. ‘Behavior is a function

of environment or some part of the environment’, B = f (E). This conceptualization

explains work environment as a behavior setting or a small-scale social system

comprises of people and physical objects, governed by behavioral rules. In other

words this refers to a set of ‘routine’ activities shaping the behavior of people who

inhibit them (Barker, 1965). The pioneering research on work environment defines

work environment as an interaction between observable set of organizational

conditions and the perceptual interpretation of organizational characteristic features

by its participants (Guion, 1973; Hellreigel & Slocum, 1974; James & Jones, 1974;

Litwin & Stringer, 1968). There is emerging consensus that work environment can be

defined through employees’ perceptions about characteristic features of the

organization events, and processes (James & Jones, 1974; Schneider, 1990).

Hellriegel and Slocum (1974) elaborated that perceived characteristics of work

environment distinguish one organization from another, which may influence the

behavior of members of the organization. By the end of the 1970s, in literature of

work environment, researchers identified various dimensions or indicators to define or

describe the work environment. A meta-analytic review conducted by Parker et al.

(2003) mentioned that different terminologies are being used in literature when

5

referring to the work environment, e.g., psychological climate, organizational climate,

working conditions, or organizational culture. However, work environment has been

treated distinct from the organizational culture (Flarey, 1991). Owens (1998) research

attempted to provide further clarification: this defined “culture as the behavioral

norms, assumptions and beliefs of an organization, whereas environment refers to

perceptions of persons in the organization that reflect those norms, assumptions and

beliefs” (p. 165).

In organizational perspective, the concept of work environment is defined in

varied ways. Robbins and Coulter (1999) referred this as ‘a force that affects

organization’s performance’ and he tried to differentiate employees’ general and

specific environment. This definition explains that the general environment includes

factors outside the organization that affects the organization, e.g., economic factors,

political conditions, socio-cultural influences, globalization issues, and technological

factors; whereas, the specific environment has taken as an organizational part directly

relevant to the achievement of organizational goals.

Deer (1980) defined work environment as average of the individuals’

perceptions which they have about their daily work environments.

Moos and Billings (1991) defined work environment as the social-

psychological characteristics of work settings i.e. attitudes of employees toward their

job tasks and interpersonal communication.

James, James, and Ashe (1990) attempted to defined psychological

environment evaluated through individual’s cognitive appraisal of his or her

organizational environment, which helps assessing individual’s significance and

meaning of work environments.

6

Literature treated the construct of work environment as of multidimensional

nature (e.g., see James & James, 1989; James & Sells, 1981; Litwin & Stringer, 1968;

Moos, 1986; Ostroff, 1993). The construct has also treated as specific referent i.e.,

competitive work environment (Brown, Cron, & Slocum, 1998; Fletcher, Major, &

Davis, 2008; Fletcher & Nusbaum, 2010), and safety climate (Schneider, 2000).

Earlier, Litwin and Stringer (1968) attempted to explain the dimensionality of

work environment namely: structure, responsibility, reward, risk, warmth, support,

standards, conflict, and identity. James and James (1989) explained four dimensions

of work environment including: “(1) perceptual indicators of job attributes like job

challenge, job autonomy, (2) characteristics of leader and leadership processes, e.g.,

leader consideration and support, leader work facilitation, (3) workgroup

characteristics and processes, e.g., work group cooperation, workgroup esprit and (4),

interfaces between individuals and subsystems or organizations, e.g., role ambiguity,

fairness and equity of reward system” (p. 739). James and Sells (1981) proposed eight

factor model of psychosocial work environment including; work group cooperation

and friendliness, leadership facilitation and support, organizational concern and

identification, job challenge, job importance, job variety, role ambiguity, and role

conflict.

Furthermore, Ostroff’s (1993) taxonomy of work environment facets explained

three higher order facets of work environment namely affective, cognitive, and

instrumental climate perceptions including underlying overall 12 climate dimensions.

This taxonomy explains affective facet as interpersonal and social relations among

workers characterized with four underlying dimensions of participation, cooperation,

warmth, and social rewards. Whereas, cognitive facet represents those dimensions,

which are primarily related with involvement in work activities or with himself. This

7

comprised of four dimensions including growth, innovation, autonomy, and intrinsic

rewards. The third facet i.e. instrumental relates with involvement or getting things

done in the organization. Dimensions that fall under the instrumental facet include

achievement, hierarchy, structure, and extrinsic rewards. Brown and Leigh (1996)

explained six factor model of work environment factors, this include, management

support, clarity, self-expression, contribution, recognition, and challenge.

Moos (1986), one of the most influential contributors in research on work

environment, mentioned that each work setting develops a “style” or a work climate,

which influences the overall behavioral aspects of the management and the employee.

Work environment is the outgrowth of generalized attributions that stem from

judgments of particular environmental characteristics or events. Some pioneering

studies include research on physical features, organizational structure (Damanpour,

1991), policies and procedures, suprapersonal or collective attributes of its members

(Moos, 1986), varying tasks and demands (Wilkes, Stammerjohn, & Lalich, 1981),

values of organizations (Ashforth, 1985). These studies have identified potential

determinants and characteristics features of work environment of an organization.

The empirical framework proposed by Newman (1977) suggests that person's

own characteristics form the frames of reference for perceptual processes that in turn

determines persons’ evaluations of (attitudes toward) the work environment. This

further proposes that the evaluations of attitudes in notion of person-environment fit

are related to work motivation, behavioral intentions, absenteeism, performance, and

turnover. Whilst explaining the role of perception in conceptualizing the work

environment, Moos (1986) further explained his conceptualization of work

environment from Gestalt’s perspective:

8

Individuals try to create order by selecting and integrating specific perceptions

into meaningful patterns. These cognitive schemas or maps are the product of

a constructive process in which new information is interpreted in the light of

prior experience. In turn, these schemas guide subsequent information

processing and shape the way in which organizational factors alter an

individual’s mood and behavior. By comparing with functional perspective,

individuals need to learn about the environment so that they can behave

appropriately and attain homeostasis. Cognitive appraisal thus must be based

on reasonably accurate perceptions of environmental characteristics rather

than simply on idiosyncratic personal factors. But individuals are predisposed

to construct reality in terms that are compatible with their current needs and

beliefs. These needs may cause people to attend selectively to particular

aspects of their work environment (p. 12).

The definitional issues discussed above refer to the most dominant stance of

treating work environment as comprising multiple facets. The theoretical groundings

of the construct and the research question inquiring environment-outcome

relationships can be explained by different theoretical models explained below.

Theoretical Foundations of Work Environment

Theoretical perspectives explaining work environment and its implications

particularly in the form of environment-to-outcome relationships can be grouped

under broader categorization namely: the person-environment interactional

perspective and the environmental perspective. The person-environment interactional

models (Holland, 1985; Pervin, 1968; Stern, 1970) examine environment as the

9

product of interactions between individuals and environment. Holland’s (1985) model

assumes that environment can be defined by describing its participants. Furthermore,

personality orientation of individuals leads to develop characteristic work

environments namely realistic, investigative, artistic, social, enterprising, and

conventional environments. This is further clarified by Strange and Banning (2001);

they elaborated that individual performance is optimized when one’s needs and

abilities are congruent with the demands of the environment. Stern’s person-

environment model (1970) describes work environment in terms of characteristic

demands or features of the setting as perceived by its participants. This explains that

individual’s responses to activities are associated with a particular personal need

orientation e.g., achievement, adaptability, dominance, etc. Pervin (1968) proposes

that an environment which is stimulating for congruency between individual’s

perceived and ideal self serves an important determinant of individuals’ satisfaction

and productivity. Walsh and Betz (1994) criticized as interactional models do not

effectively describe developmental processes or work environment related outcomes.

The environmental perspective as proposed by researchers such as Karasek’s

(1979), Moos (1986), and Siegrist (1996) have taken a different position in this

regard, i.e. individual’s behavior is mainly a function of environmental or situational

factors. These models provide a focus on assessing perceptual attributes of

environments with an elaborated stance for linking these perceptions to behavioral

and attitudinal outcome variables.

The Demand-Control-Support Model. The demand-control-support model

proposed by Karasek (1979) describes psychosocial work environment as a

combination of the demands of the work situation and the amount of control

10

employees have to cope with these demands. It further explains that incompatibility

between job demands (the perceived psychological stressors) and job control act as

stressful situation leading to characteristic high and low strain jobs. The demand-

control-support model is well documented especially predicting health related

outcomes, such as risk of poor health and stress related problems (Vermeulen &

Mustard, 2000); and to lesser extent certain organizational related outcomes, e.g.,

productivity, motivation, and engagement at work (Demerouti, Bakker, deJonge,

Janssen, & Schaufeli, 2001).

The Effort-Reward Imbalance Model. Another model namely the effort-

reward imbalance model (Siegrist, 1996) maintains its position explaining the

outcomes of psychosocial wok place facets mainly in health domain. The model

explains that imbalance between efforts spent and rewards received in work settings

leads to a state of distress. Researches have shown that model predicts adverse health

effects, e.g., myocardial infarction (Peter et al., 2002); morbidity and mortality

(Oxenstierna, Widmark, Finnholm, & Elofsson, 2008); lifestyle risk factors, such as

smoking, unhealthy dietary habits, and sedentary behavior (Kouvonen et al., 2006).

Further, the model was extended to explain the role of personal characteristic namely

overcommitment as a third variable which includes attitudes, behaviors, and emotions

reflecting excessive striving in combination with a strong desire of being approved

and esteemed (Peter et al., 2002).

Oxenstierna, Widmark, Finnholm, and Elofsson (2008) proposed a new

expanded model, which is based on Karasek’s (1979) demand-control-support model

and Siegrist’s (1996) effort-reward imbalance model. This model explains the the

impact of work environment on employee’s health. This has further propose that

11

workplace factors (goals, structure, leadership, workplace freedom, democracy and

justice, conflict and handling of conflicts, and humanity and social support) and work

factors (skill discretion, work decision authority, demands, and resources) lead to

outcomes involving stress symptoms (exhaustion, burnout, cognitive disruption,

physical symptoms, insomnia and restlessness) and health (sick leave, self-rated

health, and self-rated work capacity). Study highlighted that among various variables

only humanity and social support (workplace factors) and demands (work factors) had

a direct connection. This model has apparently stimulated further research into

investigating new dimensions particularly conflict and its management, work-leisure

relationships, and employment security, in explaining the impact of work on health

outcomes.

The Social Ecological Model. Moos’s social ecological model (1986)

proposes that the way one perceives the environment tends to influence the way one

will behave in that environment. The model holds view that perceived environment in

which individuals live and work tends to have a significant impact on attitudes,

behavior, and physical and psychological well being. In order to explain the

development and outcomes of work environment, the model explains the interplay

between five systems namely: the organizational system; personal system; work

stressors; coping responses; and the individual adaptation or outcomes (see Figure 1).

The organizational system comprises of physical features, organizational structure and

policies, suprapersonal and work task factors, and work climate. Personal factors

include characteristics including employee’s job position and level of experience,

socio demographic background, personal resources such as self confidence, their

expectations and preferences about the work place, etc.

12

The model explains that the association between the organizational system and

contextual factors e.g., work environment (Panel I) leads to certain outcomes (Panel

V). The understanding of environment-to-outcome relationship is a complex one i.e.

routed through the complex interplay of employees’ personal system involving

demographic or personal variables (Panel II). Furthermore, the model extended to the

understanding of certain important dimensions, e.g., work stresses (Panel III), and

employees’ coping skills (Panel IV), which may exert impact on association between

environment (Panel I), personal system (Panel II), and its outcomes (Panel IV).

(Source: Moos, 1994, p. 29)

Figure 1. Conceptual Model of Organizational and Personal Factors and Outcomes

Major theories of organizational management are providing support to the

propositions of Moos’s model. For example, scientific management approach

contributes to see the environment as a set of task-relevant reinforces that can be used

PANEL I

ENVIRONMENTAL SYSTEM

ORGANIZATIONAL AND WORK CONTEXT

PANEL II

PERSONAL SYSTEM

TYPE OF JOB

AND WORK ROLE

DEMOGRAPHIC AND

PERSONAL FACTORS

PANEL V

INDIVIDUAL ADAPTATION

WORK MORALE AND

PERFORMANCE

OVERALL

WELL-BEING

AND HEALTH

PANEL IV

COPING

RESPONSES

PANEL III

WORK

STRESSORS

13

to regulate employees’ behavior. The human relation approach takes into account the

personal and social context of work. The socio-technical approach provides an

inclusive conception of the organizational environment as composed of the interplay

of task and social factors (Moos, 1994). This supports strong arguments in favor of

superiority of Moos’ model.

The environmental perspective posited by Moos (1986) provides a more

detailed description of development of work environment and its influence on

employee and organization related outcomes. Moos’ model is extended to explain the

possible moderating influence of employees’ personal variables (demographic and

certain other personal factors) while explaining the environment-to-outcome

relationships. Other approaches e.g., Karasek’s (1979) demand-control-support model

also occupies a dominant position in classical psychosocial work environment

research; however, the critique of this approach maintain that the concepts are too

general to be used to examine work environment issues (Oxenstierna, Widmark,

Finnholm, & Elofsson, 2008). Another critique of Karasek’s model (Sulsky & Smith,

2005) stated that model is conceptually very narrow as it considers few constructs

whilst this tend to ignore the complexity of operating dynamics of work environment

and role of confounding effects of socio-demographic factors. One of the advantages

of using Moos’s model highlights its usefulness in identifying strengths of the

environment (Flarey, 1991). Moos’s socio-ecological approach occupies a prominent

position partly because that model was extended to operationalized the construct of

work environment and has offered a multi-facet measure which so far has remained

widely used in literature (Belicki & Woolcott, 1996; Straker, 1989).

In reviewing empirical studies on work environment, an important

consideration or distinction is based on the broader classification of the setting itself,

14

e.g., academic settings, health settings, etc. Generally, academic settings within the

context of work environment is less explored area. In following, a comprehensive

review of studies particularly in academic settings is discussed.

Work Environment of Academic Settings

Research on work environment related issues is mainly focused on human

service related organizations particularly the health sector (see Chan & Huak, 2004;

Day, Minichiello, & Madison, 2007; Dickens, Sugarman, & Rogers, 2005; Kotzer,

Koepping, & LeDuc, 2006). Moos’s model of work environment is applied for

assessing the work environment of academic settings in varied directions. Some

contemporary empirical researches have explored the dynamics of academic work

environment on samples of teachers at post graduate teaching institutions/universities

(Goddard, O’Brien, & Goddard, 2006; Rehman & Maqsood, 2008). Studies have

focused on samples of teachers of secondary schools (Wu, 1998); Australian science

teachers of secondary schools (Fisher & Fraser, 1983); nursing faculty (Thompson, as

cited in Moos, 2008); university students (Cotton, Dollard, & de Jonge, 2002); nurse

students of medical university (Margall & Duquette, 2000); students at teaching

hospitals (Waryszak, 1999); university admission staff (Wilk & Redmon, 1998);

employees of language institutes (Walker, 2007); and academic centre of a service

oriented company (Miranda, as cited in Moos, 2008).

There are concerns that in the educational settings, monitoring of work

environment is recommended. This was revealed in a longitudinal study (Goddard et

al., 2006) on Australian university graduate teachers reporting their work environment

more negative over time. Differences in workplace may be attributed to teachers’

15

profile. For example, Shechtman, Levy, and Leichtentritt (2005) reported that teachers

who had high duration of training perceived their work environment as more positive

as being high on involvement, co-worker cohesion, supervisor support, task

orientation, clarity, managerial control, and innovation, and lower in work pressure

compared to their counterparts. The above mentioned studies and similar have

supported that management of academic settings should monitor workplace

periodically.

Studies recruiting involving teachers as participants have reported dominant

characteristics of the academic work environments. Wu (1998) mentioned that

teachers of secondary schools in England and Wales reported above average emphasis

on the dimensions of work environment including: involvement, coworker cohesion,

task orientation, and clarity, but also on work pressure and management control. The

findings of the study further elaborated that teachers reported below average emphasis

on supervisor support, autonomy, and physical comfort. Moos (1994) mentioned that

various comparisons of work environment assessments can be made: i.e., (i)

expectations of work environment (employees’ before entering the organization), (ii)

employees’ perceptions of their current operating work environment, and (iii)

employees’ aspirations or perceptions of ideal work environment. Thompson (as cited

in Moos, 2008) has assessed perceptions of expected work environment of a newly

established wing of nursing faculty and compared it with the existing perceptions of

work environment. The study reported that faculty reported overly positive

expectations with new work setting by reporting high emphasis for involvement,

autonomy, task orientation, clarity, and innovation, and less managerial control but

with high work demands compared to present in the workplace involving existing

wings.

16

Fraser, Docker, and Fisher (1989) comparing the real and ideal work

environmental characteristics of elementary and high school teachers. Findings

highlighted that teachers in different types of schools did not have significant

differences in their perception of real and ideal work environment. Further, study

revealed that teachers’ perceptions of the actual school setting varied markedly with

the type of school. Schools that had better staff development practices also had more

positive work environment. Similar findings are obtained in another study (Docker,

Fisher, & Fraser, 1989).

Academic research on work environment has recruited participants including

studies on university supporting staff (Okoh, 2007), university clerical employees

(Wilk & Redmon, 1998), comparing teachers, principals, and parents as members of

school advisory council (McClure & DePiano, 1983). Wilk and Redmon (1998)

reported that various aspects of the work environment involving increased interaction

between supervisors and employees, and greater clarity and specificity of work goals

were found to be linked with effectiveness of a behavior management intervention

program which was especially designed to improve the employees’ work productivity

and job satisfaction. McClure and DePiano (1983) reported that among members of

school advisory council, principals reported more negative experience of work

environment.

Margall and Duquette (2000) mentioned that student nurses in a university

hospital reported high levels of involvement and coworker cohesion and moderate

levels of supervisor support and autonomy. They also emphasized the importance of

task orientation and clarity in the work environment. Cotton, Dollard, and de Jonge

(2002) reported that within academic settings, work demands and less support are

linked with performance. Waryszak’s (1997) reported significant overall differences

17

between students’ expected, actual, and ideal work environments among Australian

business students. Perceived involvement, task orientation, and physical comfort

approximated students’ expectations and preferences, but students preferred more

supervisor support and innovation and less work pressure and control than they

actually experienced in the workplace. Some other studies on academic work

environment have focused on sample of students, e.g., graduate counseling students

(MacGuffie & Henderson, 1977); dental students (Lusk, Diserens, Cormier,

Geranmayeh, & Neves, 1983). Norton’s study conducted in 1989 focused on

assessing the work environment dimensions of medical institutes experienced by

students and their faculty. The study highlighted the contrasting differences among

both groups due to nature of their role requirements (as cited in Moos, 1994).

Pioneering research in Pakistan conducted by Rehman and Maqsood (2008)

explored that work environment was found to be linked with employees and

organizational outcomes among university teachers. The research further showed that

work environment exerts positive influence on employees’ job satisfaction and

showed link with work stress and employees turnover. The study highlighted

significant differences in perceptions of work environment of teachers of public and

private sector universities and post graduate institutions of Pakistan. This has further

suggested the need of enhancing the job morale of employees in teachers of public

sector. However, this study was limited in terms of sample size and locale. Another

pioneering research (Imam, 1993) examined the perception of men and women

college teachers using the Work Environment Scale (Moos, 1994). The study reported

that teachers perceived college environment as a dominant factor in controlling and

task oriented characteristics of work environment. The study highlighted the need to

deduce implications for improvements of academic work settings. Imam’s study was

18

based on college setting only and the intended environment of college may

significantly vary from university setting primarily due to difference in level of

education (e.g., higher education) to offer and the level of research output expected

from teachers. Keeping in view that college and university based institutions may

differ with respect to operating environment; the present study counts in very few

reported in the extensive literature on work environment particularly within academic

(university) settings.

Furthermore, Khan (1999) examined differences in the perceptions of work

environment of government and private school teachers using the Urdu version of the

Moos’ measure of work environment. The findings reported that teachers employed at

private schools perceived their work setting as dominant on the dimensions of work

pressure, control, innovation, and physical comfort. On other hand, environment of

government schools was reported to be high on involvement, peer cohesion,

supervisor support, autonomy, task orientation, and clarity. However, public and

private sectors revealed non-significant differences. The observation emerged out of

research direction in Pakistan highlighted the paucity of empirical research on work

environment issues. Evaluating aforesaid studies indicate the need to further extend

the research on work environment and outcome relationships using more

comprehensive approach i.e., employing larger sample and the strong methodological

approach.

Measurement of Work Environment

Several measures have been developed to evaluate the assessment of

institutional attributes of environments which adheres to perceived group consensus

19

as formal measurement of psychological properties of the environments (Betz &

Walsh, 1994). For instance, Newman’s (1977) measure namely Perceived Work

Environment (PWE) assesses the perceived work environment in terms of eleven

dimensions namely: supervisory style, task characteristics, performance-reward

relationships, co-worker relations, employee work motivation, equipment and

arrangement of people and equipment, employee competence, decision making

policy, work space, pressure to produce, and job responsibility/importance. Among

other multifacets measures, Psychological Climate Inventory by Gavin and Howe

(1975) measures six factors: spirit, managerial trust and consideration, rewards,

challenge and risk, clarity of structure and hindrance structure. The Michigan

Organizational Assessment Questionnaire (Camman, Fichman, Jenkins, & Klesh,

1983) assesses dimensions of work group cohesion, openness of communication,

internal fragmentation, supervisor-subordinate communication and consideration,

participation in decision making, production, orientation, role overload, role

conflict, role clarity, work group clarity, supervisor control, supervisor goal setting

and problem solving, and decision centralization. Koys and DeCotiis' (1991)

measure of work environment comprising thirty-five Likert scale items is based on

the eight global dimensions namely autonomy, cohesion, trust, pressure, support,

recognition, fairness, and innovation.

The Work Environment Scale (Insel & Moos, 1974; Moos, 1994) is

extensively utilized in studies conducted within health sector, academic settings,

service oriented organizations, and industrial settings. The author mentioned that the

measure discriminates among environments about as well as personality tests

discriminate among people. The measure highlights that employees are participant

observer in work milieu and are uniquely qualified to appraise it. The measure

20

assesses ten dimensions of work environment including clarity, managerial control,

innovation, and physical comfort. The internal consistencies of the subscales for

sample of teachers range from .60 to .84 (Fisher & Fraser, 1983, 1991), which were

quite similar to those of the original WES normative sample (Moos, 1994). Work

Environment Scale (WES) is used to collect data on the sample of present study. This

has been used extensively in researches conducted in social service settings including

studies that have linked characteristics of the work environment to burnout (the

outcome variable of the present study) particularly using Maslach Burnout Inventory

(Koran, Moos, Moos, & Zasslow, 1983; Wilber & Specht, 1994). In explaining

environment-to-outcome relationship, one of the areas of organizational research

relates to explain how employees experience and respond to their work environment

particularly in the form of job burnout (Swider & Zimmerman, 2010). The theoretical

review of job burnout as one of the outcome variables of the present study is

presented below.

Burnout

Burnout is considered as a serious mental health hazard in the workplace

(Pretty, McCarthy, & Catano, 1992). In the context of work of human service

professionals, burnout often develops as a result of emotionally charged contacts with

recipients of their services (Van Dierendonck, Schaufeli, & Buunk, 2001) and is

conceptualized as the result of interaction with clients, organizational demands,

inadequate support, and personal vulnerabilities (Wilber & Specht, 1994). Job burnout

effects both the organization and the employee, for instance, in terms of effecting

organizational commitment (Lee & Ashforth, 1996; Jones, Flynn, & Kelloway, 1995),

21

job satisfaction, turnover intentions (Lee & Ashforth, 1996), and performance

outcomes (Halbesleben & Bowler, 2007).

The term ‘Burnout’ appeared in the literature first time in 1969 when Bradley

published a paper on probation officers (as cited in Cooper, Dewe, & O’Driscoll,

2001) and further elaborated in 1974 as a description of the emotional and physical

depletion among human service employees, that resulted from the conditions of the

work environment (Freudenberger, 1974a). Burnout is distinct from the normal

experience of stress (Sulsky & Smith, 2005) and has treated differently from related

concepts e.g., depression, dissatisfaction, tension, conflict, pressure, and particularly

stress (Densten, 2001). It is conceptualized as a specific manifestation of job related

strain, which is considered as a “psychological process caused by unrelieved work

stress” (Posig & Kickul, 2003, p. 3). The experience of burnout is characterized with

cynicism, negativism, inflexibility, a know-it-all attitude, absenteeism, psychosomatic

complaints, and physical illnesses (Freudenberger, 1974a, 1974b).

There is consensus among researchers that burnout has taken as a negative

attitude or behavior resulting from excessive occupational demands or stressors

(Maslach & Jackson, 1984. The most cited definition as pointed out by Lee and

Ashforth (1990) and Maslach (1993) has taken the construct as a psychological

syndrome of emotional exhaustion, depersonalization of others, and a feeling of

reduced personal accomplishment.

The literature on environmental assessment has focused on exploring the

dynamic interplay of burnout in context of caregiver-client relationship as the

outcome of extensive contact with individuals having many complex needs (Adali et

al., 2003; Chan & Huak, 2004; Eastburg, Williamson, Gorsuch, & Ridley, 1994;

Miller, Birkholt, Scott, & Stage, 1995; Salyers & Bond, 2001). Human service

22

professionals are considered as more vulnerable to risk of burnout (Schaufeli, 2003),

including teachers (Fejgin, Ephraty, & Ben-Sira, 1995; Greenglass, Fiksenbaum, &

Burke, 1994; Peeters & Rutte, 2005), nurses (Haque & Khan, 2001; Hochwater et al.,

2004; Koniarek & Dudek, 1996); physicians (Barnett, Gareis, & Brennan, 1999; Van

Dierendonck, Schaufeli, & Buunk, 2001); salespersonnel (Sand & Miyazaki, 2000),

and school psychologists (Mills & Huebner, 1998; Sandoval, 1993).

During early 1980s, educational researchers became more interested in

examining the causes, intensity, and prevalence of burnout among teachers

(Golembiewski, Scherb, & Munzenrider, 1994; Iwanicki, 1982; Kottkamp &

Mansfield, 1985; Sahu & Misra, 1996; Winnubst, 1993). During current decade,

research impetus is more focus towards the work environment issues in academic

settings, i.e. studies comparing teachers of different work settings (i.e. elementary and

secondary school institutions). High levels of emotional exhaustion and

depersonalization is reported among elementary school teachers which seems to

negative impact on their involvement and innovative approach in classroom

management (Yavuz, 2009). Khan (2000) reported high indicators of burnout amongst

teachers of deaf and dumb schools. Studies on burnout have focused on school

counselors (Wilkerson, 2009); Chinese school teachers (Luk, Chan, Cheong, & Ko,

2010); academic teaching librarians (Sheesley, 2001); Pakistani teachers involved in

teaching at higher education (Basir, 2006) in Pakistan; university teachers in China

(Zhong et al., 2009); university professors (Otero-López, Mariño, & Bolaño, 2008),

etc. Other studies involving primary and high school teachers (Moghadam &

Tabatabaei, 2006) and Turkish pre-service and in-service preschool teachers

(Kabadayi, 2010) are highlighting the common denominator that significant

differences in burnout are found in contrast groups. Furthermore, students’ disruptive

23

classroom behavior and the teachers’ competence to cope with that behavior predicted

depersonalization and personal accomplishment among teachers (Ever, Tomic, &

Brouwers, 2004).

Maslach and her associates (Maslach, Jackson, & Leiter, 1996) conceptualized

burnout having three components: emotional exhaustion, depersonalization, and lack

of personal accomplishment. Cordes and Dougherty (1993) also supported this three

component nature of the construct. The first component exhaustion refers to the

depletion or draining of emotional resources (Van Dierendonck, Garssen, & Visser,

2005) caused by excessive psychological and emotional demands (Lee & Ashforth,

1993), and compassion fatigue because the employee is unable to give support and

care to his clients (McShane & Glinow, 2003). There is consensus amongst researches

that burnout results in physical, emotional, and mental exhaustion (Edelwich &

Brodsky, 1980; Maslach, 1982a, 1982b; Paine, 1981; Pines, Aronson, & Kafry, 1981).

Individual’s physical exhaustion may manifest in the forms of low energy, chronic

fatigue, weakness, accident-proneness, increased susceptibility to illness, weariness,

frequent headaches, nausea, muscle tension, alterations in eating habits and weight,

somatic complaints, and increased frequency of illnesses (Golembiewski,

Minzenrider, & Stevenson, 1986; Pines, Aronson, & Kafry, 1981). Emotional

exhaustion may involve feelings of depression, entrapment, hopelessness,

helplessness, and distress and may be demonstrated by decreased coping ability,

marital problems, substance abuse, and incessant crying (Jackson & Maslach, 1982;

Ratliff, 1988). Mental exhaustion is evidenced by negative attitudes towards work and

life in general. These attitudes may be demonstrated by tardiness, leaving work early,

taking long breaks, clock watching, a rigid by-the-book stance toward problematic

situations and clients, avoiding client contact, stereotyping clients, discussing clients

24

only in a detached manner, absenteeism, employee turnover, and the intention to leave

one’s job (Cherniss, 1980; Maslach, 1982a, 1982b; Pines et al., 1981).

Depersonalization, the second component of burnout viewed as a coping

mechanism (Cordes & Dougherty, 1993) characterized with distant attitude toward

work and the people on the job (Maslach & Leiter, 1997). Fox and Leif (as cited in

Akram, 2003) found that moderate levels of “detached concern” toward clients is

appropriate, necessary and effective performance in some occupation but excessive

detachment with too little concern is assumed to exist when a staff member reports

feelings of callousness and cynicism. The third component of reduced personal

accomplishment happens when individual experiences decline in feelings of

competence and success, as well as feelings of diminished competency (McShane &

Glinow, 2003). Individual view their contribution as unworthy letting to develop lack

of self-esteem and depression which further prevents individual from performing up

to his/her full potential (Hamann & Gordon, 2000). Substantial empirical evidence

signifies the importance of these components (emotional exhaustion,

depersonalization, and low personal accomplishment) of burnout (Bakker, Schaufeli,

Sixma, Bosveld, & Van Dierendonck, 2000).

Theoretical Models of Burnout

Various theoretical models (Cherniss, 1980; Golembiewski, Minzenrider, &

Stevenson, 1986; Hobfoll, 1989; Maslach, Jackson, & Leiter, 1996) of burnout are

proposed which explain the development of burnout within work settings

Cherniss’s Model. Cherniss’s model evolved in 1980 out of research focus on

human service employees suggested that aspects of the work environment

25

(orientation, workload, stimulation, scope of client contact, autonomy, individual

goals, leadership/supervision, social isolation), available resources, and characteristics

of the individual (career orientation, support/ demands outside work) can function as

sources of strain by creating doubts in the person’s mind about his or her competence,

bureaucratic interference with task completion or goal achievement, and lack of

collegial coworker relationships. Individuals endeavor to cope with work stressors in

a variety of ways, some of which may entail negative attitude changes, including

reducing work goals, taking less responsibility for work outcomes, becoming less

idealistic in one’s approach to the job, and becoming detached from clients or the job

itself. While critically analyzing, Cooper, Dewe, and O’Driscoll (2001) commented

that the distinctiveness of the construct of burnout is missing in this theory as it

includes variety of variables as explanation of burnout and make burnout

indistinguishable from job strain.

Hobfoll’s (1989) Conservation of Resources Theory. Conservation of

resources theory propounded by Hobfoll (1989) imply that burnout as an outcome of

depletion of resources leads to potential inadequateness to resolve any impending

demands when confronted with stressful situations at workplace. These resources

includes: material resources (e.g., house, car), conditions (e.g., status, social support),

personal characteristics (e.g., self-esteem and optimize), and various forms of energy

(e.g., money, favors owed by other persons). In a work setting, the major resources

include social support, personal control over jobs, involvement, and appropriate

reward system. The major demands that tend to relates to resources are role

ambiguity, role conflict, overload, inadequate resources to perform the job, and

excessive demands. While critically evaluating, Cooper, Dewe, and O’Driscoll (2001)

26

commented that the conservation of resource theory is a more general approach

compared to other models.

Maslach, Jackson, and Leiter’s (1996) Model. Originally Maslach and

Jackson (1981), and later Maslach, Jackson, and Leiter (1996) theorized a conceptual

model conceptualizing burnout as a response syndrome of emotional exhaustion

(feelings of being emotionally over extended and exhausted by one’s work),

depersonalization (impersonal responses towards the recipients of one’s work), and

reduced personal accomplishment (low feelings of competency and achievement in

one’s work). The model highlights the role of work demands and lack or resources as

predictors of burnout. It explains the impact of burnout in terms of organizational

costs mainly the organizational commitment. The model supports the current stance of

the study by highlighting that various aspects of work environment, e.g., excessive

work demands, personal conflict, and diminished social support, autonomy, or

involvement develops emotional exhaustion leading consequently to coping by

depersonalization, which then results in reduced personal accomplishment.

Maslach, Jackson, and Leiter’s (1996) model is important in systematically

measuring the three components of burnout using the most widely used measure of

occupational burnout (Densten, 2001; Worley, Vassar, Wheeler, & Barnes, 2008)

namely the Maslach Burnout Inventory developed earlier by Maslach and Jackson

(1981). Despite evidences of factorial validity of Maslach’s three component model of

burnout (Byrne, 1993; Evans & Fischer, 1993); Densten (2001) further proposed an

extension of the model. The author cited that emotional exhaustion being felt at

physical and psychological level has demonstrated relationship with psychological

strain and somatic complains, emerging an extended factor structure of the emotional

27

exhaustion namely psychological and somatic strain. Moreover, personal

accomplishment as being linked with self inefficacy as related to job success and

failure and learned helplessness in terms of job expectations and the work

environment may emerge sub-factors in form of self and others related components of

personal accomplishment (for complete detail, see Densten, 2001).

Measurement of Burnout

Different measures of burnout have focused primarily on individual’s own

reporting of their level of burnout. Among widely used measures, Maslach Burnout

Inventory (MBI: Maslach, Jackson, & Leiter, 1996) and the Burnout Index (BI; Pines,

Aronson, & Kafry, 1981) have dominated the burnout researches. Maslach Burnout

Inventory, by far, remains the most widely employed measure of burnout in almost

90% of all studies assessing occupational burnout (Schaufeli & Enzman, 1998). It

established three factors of burnout that measures emotional exhaustion,

depersonalization, and feelings of reduced personal accomplishment.

MBI as most widely acceptable measure (Posig & Kickul, 2003) is being

credited as psychometrically sound (Lindblom, Linton, Fedeli, & Bryngelsson, 2006).

Despite its wide applicability, its construct validity is not beyond question.

Exploratory factor analysis of MBI have tended to support the construct validity of

the measure, as well as it’s convergent and discriminant validity (Cordes &

Dougherty, 1993). Studies using confirmatory factor analyses (Byrne, 1993; Kim &

Ji, 2009; Lee & Ashforth, 1990; Schaufeli & Van Dierendonck, 1993) have identified

the original three factor model as superior to other alternative models. Substantial

studies involving sample of teachers have supported the three factor structure of the

28

inventory (Aluja, Blanch, & Garcia, 2005; Boles, Dean, Ricks, Short, & Wang, 2000;

Byrne, 1991, 1993, 1994; Evans & Fischer, 1993; Gold, 1984; Gold, Roth, Wright, &

Michael, 1991; Holland, Mishael, & Kim, 1994; Iwanicki & Schwab, 1981;

Kokkinos, 2006; Richardson & Martinussen, 2004; Schaufeli, Daamen, & Van

Mierlo, 1994). Studies (Iwanicki & Schwab, 1981; Powers & Gose, 1986) have

reported that MBI measures four factors, while others (Brookings et al., 1985;

Dignam, Barrera, & West, 1986; Green, Walkey, & Taylor, 1991; Kalliath,

O’Driscoll, Gillespie, & Bluedorn, 2000; (Walkey, & Green, 1992) maintained that

MBI measures only two factors. Recently, Densten (2001) supported the five factor

structure of MBI measure.

In a subsequent effort, Friedman’s (1995) proposed a self-report questionnaire

based on Cherniss’s conceptualization of burnout which measures four components of

burnout: exhaustion, aloofness, self-dissatisfaction, and deprecation. The first two of

elements represent internal (exhaustion) and external (aloofness) weariness, whereas

the remaining two reflect internal (self-dissatisfaction) and external (deprecation)

discontent. These experiences are quite similar to MBI measure as well.

Burnout Index (BI; Pines et al., 1981) as a second most widely used measure

of burnout initially labeled as tedium that was considered to apply to wide range of

situations, instead of burnout that was primarily associated with emotionally

demanding settings. However, author contended that both burnout and tedium are

identical constructs. The measure reflects the core dimension of exhaustion by

differentiating physical, mental, and emotional kinds of exhaustion. Henceforth, is

regarded as a uni-dimensional measure.

29

Relationship of Work Environment and Burnout

Aspects of the work environment have been found to be important in

maintaining the concept of healthy work among employees (Karasek & Theorell,

1990). Insel and Moos (1974) argued that environment and individual’s perception

about this are critically important in order to understand the development of

employees’ attitudes. Cherniss, (1980) endorsed that the extent of negative changes in

mood and behavior are strongly influenced by the nature of the work setting. Leiter

and Maslach (1988) stressed the relationship aspects particularly the interpersonal

contact with supervisors and coworkers as contributory factor in development of

burnout. Among early stimulating investigations, Savicki and Cooley (1987)

concluded that those work environments that are associated with low levels of burnout

are the ones in which workers are committed to their work, relationships with

coworkers are encouraged, and the supervisory relationship is supportive. Further,

high levels of burnout are found in those work environments where worker freedom

and flexibility are restricted, and there is a de-emphasis on planning and efficiency for

completion of work tasks. They also found high levels of burnout to be associated

with vague or ambiguous job expectations, and a lack of support or encouragement

for new ideas.

Furthermore, antecedents of job burnout grouped at organizational level

(Maslach, Schaufeli, & Leiter, 2001) have been associated with psychosocial work

environment (Pretty, McCarthy, & Catano, 1992), access to organizational resources

(Shirom, 2003), social support (Dick, 1992; Fong, 1993), etc. Literature review

reveals that various studies had supported the predictive relationship between work

environment facets and the burnout (Dorman, 2003; Escribà-Agüir, Martín-Beena,

30

Pérez-Hoyos, 2006; Hochwälder, 2007; Jaffe, 1995; Langballe, Innstrand, Aasland, &

Falkum, 2011; Turnipseed, 1994). Researches on burnout within academic settings

have focused on sample of elementary teachers (Peeters & Rutte, 2005); teachers of

public schools (Russell, Altmaier, & Van Velzen, 1987); elementary, intermediate,

and secondary teachers (Byrne, 1994); teachers of nursing faculty (Dick, 1986);

university teachers (Goddard et al., 2006); school psychologists (Huberty & Huebner,

1988); library and computing staff of universities of Pakistan (Munir, 2005); etc.

The work environment related factors associated with burnout within human

service professions include supervisory and peer support among Korean elementary

school teachers (Kim, Lee, & Kim, 2009); role ambiguity and group pressure among

Indian engineering college male teachers (Pandey & Tripathi, 2001) and among

nursing faculty (Goldenberg & Waddel, 1990); role overload due to time limitations

among nursing educators (Fong, 1993); etc. Different work environment facets, e.g.,

high work pressure (Brown & Pranger, 1992; Huberty & Huebner, 1988; Constable &

Russell, 1986; Turnipseed, 1994); low work involvement (Brown & Pranger, 1992);

the extent of social support (Hochwälder, 2007; Russell, Altmaier, & Van Velzen,

1987) particularly from supervisors (Boyas & Wind, 2010; Lee & Ashforth, 1996)

and with coworkers (Savicki & Cooley, 1987; Turnipseed, 1994); and factors related

to job enhancement, e.g., low levels of autonomy, clarity (Constable & Russell, 1986;

Turnipseed, 1994), task orientation, innovation, and physical comfort (Constable &

Russell, 1986) have found to be the strong predictors of burnout. Byrne (1994)

suggested that role conflict, work load, and co-workers support serves as stronger

predictors of burnout. Contrary to dominant trend in literature, Dick (1986) found that

development of burnout may be irrespective to workload.

31

The pattern of interpersonal relationships at work appears a dominating

contributor in developing feelings of emotional exhaustion and depersonalization

(Leiter & Maslach, 1988). The indicators of relationship dynamics, e.g., supervisors’

and co-workers’ support is found to has negatively impact upon emotional exhaustion

(Escribà-Agüir, Martín-Beena, & Pérez-Hoyos, 2006). Employees’ emotional health

is linked with high emphasis on task orientation and work pressure and less emphasis

on innovation (Chan & Huak, 2004). Adali et al. (2003) found that work places high

on involvement and clarity were leading towards better personal accomplishment and

less emotional exhaustion. Moreover, supervisor support, clarity, and managerial

control, and fewer work demands were linked with less emotional exhaustion. In a

study (Robinson et al., 1991), high work pressure and low work involvement were

diagnosed as problematic aspects which predict emotional exhaustion.

Different work environment facets including work load, coworkers support,

and role clarity were found to explain significant variance in emotional exhaustion

and depersonalization (Levert, Lucas, & Ortlepp, 2000). The extent of physical

comfort was found to be linked with depersonalization (Salyers & Bond, 2001).

Depersonalization was reported as negatively linked with managerial control,

involvement, and coworker cohesion (Adali et al., 2003).

A longitudinal study conducted by Goddard et al. (2006) reported that high

work pressure and less emphasis on innovation tended to predict increases in

emotional exhaustion and depersonalization and a decline in the sense of personal

accomplishment over two year period. Role clarity explains significant variance in

personal accomplishment (Levert et al., 2000). In another study of Savicki (2002) on

burnout involving treatment providers, educators, and managers of child and youth

care across thirteen cultures included Australia, Austria, Canada, Denmark, England,

32

Germany, Israel, Poland, Scotland, the Slovak Republic, and the United States. The

findings of the study suggested though somewhat variations with respect to different

cultures, that emotional exhaustion and depersonalization was linked with high work

pressure and less supervisor support, task orientation, and innovation. High coworker

cohesion and innovation was associated with a greater sense of personal

accomplishment at work. Robinson et al. (1991) also noted that depersonalization and

personal accomplishment were predicted by task orientation, work pressure, and

involvement. Literature review has suggested some contradictory findings in reporting

the relationship between work environment and burnout. For instance, study

conducted by Salgado, Remeseiro, and Iglesias (1996) have mentioned non-

significant relationship between work environment and burnout.

In context of Pakistan, a noticeable unpublished study (Munir, 2005) was an

attempt to examine Moos’s work environment model within academic settings.

Munir’s study (2005) on librarians reported that supervisor support is related with

depersonalization; task orientation is linked to each component of burnout; clarity as

linked with depersonalization and personal accomplishment; and managerial control

with emotional exhaustion. Evaluating the study implies that the study was limited in

scope due to sample size and the study design which included only few colleges and

universities. For examining burnout among teachers, unpublished study of Basir

(2006) also faces critic of using limited sample and locale.

Examining the direction of research on environment and burnout relationship

along with the demonstrated importance of examining burnout among teachers has

found to be lacking in our cultural context. This draws attention that organizational

research in Pakistan generally has done in varied directions that adds to inconsistency

in empirical support. This further supports the observation that a consistent pattern of

33

research in a particular area of investigation with objective to address gaps in

literature is generally missing. This provided strong impetus to extend the empirical

studies in environment and burnout relationship particularly within academic settings.

In explaining environment-to-outcome relationship, present study specified

“organizational commitment” as another dimension of employees’ attitudes.

Organizational Commitment

Organizational commitment as a critical employee attitude is regarded as key

component in human resource management (McKenna, 2000). This is related with

several key aspects of work behavior, e.g., employees’ performance (Herscovitch &

Meyer, 2002), job satisfaction and turnover (Cooper-Hakim & Viswesvaran, 2005),

organizational citizenship behaviors (Riketta, 2002), counterproductive behavior

(Dalal, 2005), organization’s well being (Guion, 1973), etc. The foundation work on

commitment (Becker, 1960) revealed that the term has been used in analyses of

variety of phenomena including occupation, power, religion, political behavior and so

on. The commitment of employees to their work may takes many forms including

career, occupation, organization, union, work ethic, job involvement, and other

conceptually related variables (Cooper-Hakim & Viswesvaran, 2005), which added in

conceptualizing commitment as a domain specific construct (Meyer, Allen, & Smith,

1993; Ellemers, Gilder, & Heuevl, 1998).

Furthermore, commitment is defined and measured differently (Meyer &

Allen, 1991; Morrow, 1993; Mowday, Porter, & Steers, 1982). Generally, it has taken

as the nature of the relationship of the member to the system as a whole (Grusky,

1966). An earlier definition grows out of Becker’s (1960) work viewed commitment

34

in terms of side bet or investments. This suggests that employees make side bets as

investments in form of tenure, status, organization specific skills, pensions, etc.,

which pull them to continue their affiliation with the organization. Some authors

(Salanick, 1977; Scholl, 1981) have viewed organizational commitment as a form of

attitude results from behavioral acts that the individual engages in. Porter and his

associates (1974) explain commitment as the result of three factors: 1) acceptance of

organizational goals and values; 2) willingness to help the organization to achieve its

goals; and 3) the desire to remain within the organization. This definition reflecting

the element of individual vs. organizational goal congruence was also supported by

Buchanan (1974), who defined organizational commitment in terms of effective

attachment to one’s role in relation to goals and values of the organization, apart from

its purely instrumental worth.

Definitional issues discussed above suggest that generally commitment is

viewed as employees’ psychological attachment or a bond (Armstrong, 1996). Mainly

these definitions differ in terms of how psychological bond to the organization

develops (Mitchell, 1979). The concept of organizational commitment explains the

core essence of the concept so that organizational commitment is distinguished from

existing conceptualizations of motivation, morale, and general attitude. Meyer and

Herscovitch’s (2001) clearly differentiate commitment from motivation and general

attitude. They viewed commitment as distinguishable from exchange-based forms of

motivation and suggested that commitment influences behavior without even in

absence of extrinsic motivation.

Commitment seems to develop gradually and change over the course of an

employee’s career (Reilly & Orsak, 1991). Miner (1992) suggested that initially

personal factors (values, beliefs, and personality) and organizational characteristics

35

interplay to develop the commitment which soon after passing sometime in the

organization became linked to employees’ work experiences (job, supervision, work

group, pay, and organization). Later on investments with passage of time fosters long

term commitment with the organization. Morrow (1993) commented that concept

redundancy has been noted as a major problem in commitment literature. In a meta-

analytic study (Cooper-Hakim & Viswesvaran, 2005) examining 997 studies

associated with organizational commitment. They have found the presence of a

common psychological construct underlying different commitment forms, with the

exception of calculative, continuance, and union commitment.

In the context of educational settings, organizational commitment is associated

with teachers’ empowerment (Bogler & Somech, 2004; Finegan, 2000); personal

factors, e.g., teachers' understandings of their perceived failures (Joffres & Haughey,

2001); normative value orientation among elementary and high school teachers (Shaw

& Reyes, 1992); teachers’ satisfaction and retention in contributing effective schools

(Singh & Billinsgley, 1998); students’ level of achievement and teachers’ job

satisfaction (Kushman, 1992); and studies in field of library management (Hovekamp,

1994; Karim & Noor, 2006; Rubin & Butllar, 1992).

Theoretical Models of the Organizational Commitment

Commitment appears as a complex and multifaceted construct (Meyer, Allen,

& Smith, 1993). It has been treated as a unidimensional construct (e.g., Becker, 1960;

Mowday, Steers, & Porter, 1979; Wiener, 1982) as well as multidimensional construct

(e.g., Allen & Meyer, 1990; O’Reilly & Chatman, 1986). Different efforts in

explaining the multidimeniusoality of the commitemt concept revealed somewhat

36

similarities among existing multidimensional models. Earlier researchers (e.g.,

McGee & Ford, 1987; Meyer & Allen, 1984) have emphasized that organizational

commitment has two and, possibly three components (Allen & Meyer, 1990)

including affective, continuance and normative. Later, several other multideminsionl

frameworks seems to have extended the existing conceptualization of the construct

(e.g., O’Reilly & Chatman, 1986; Angle & Perry, 1981; Jaros, Jermier, Koehler, &

Sincich, 1993; Mayer & Schooman, 1998).

Mowday, Porter, and Steers’ Model. Mowday, Porter, and Steers’ model.

The attitudinal and behavioral diemnsions of the commitement were distinguished by

Mowday, Porter, and Steers’ (1982) model. Attitudinal commitment refelects the

individual’s identification with organiztaional goals and his/her willingness to work

towards them. Whereas, behavioral commitment represents from the binding of

individuals to behvaioral acts. Mowday and his assocaites mentioned that reciprocal

relationship exists between both aspects of the commitment. Based on Organiztaioanl

Commitment Questionnaire (Mowday et al., 1979), Angle and Perry (1981) supported

two underlying factors of comitment namely, acceptance of organiztaional goals and

the willingness to exert effort (value commitment) and desire to maintain membership

(continuance commitment). In similar lines, subsequently, Mayer and Schoorman

(1992) model suggested that organizational commitment comprises two dimenisons

referred as continuance commitment (desire to remain) and value commitment

(willingess to exert extra effort).

O’Reilly and Chatman’s Model. This multidimensional framework (1986)

focuses on commitment as an attitude towards the organization that developes

37

through various mechanisms. The model argued that commitment could take three

distinct forms: compliance, identification, and internatlization. Compliance occurs

when attitudes, and corresponding behaviors, are adopted in order to gain specific

rewards. Identification occurs when an individual accepts influence to establish or

maintain a satisfying relationship. Finally, internalization occurs when influence is

accepted because the attitudes and behaviors one is being encouraged to adopt are

congruent with existing values. Vanderberg, Self, and Seo (1994) examined O'Reilly

and Chatman's compliance, identification, and internalization scales, and compared

the latter measures to the OCQ. Findings indicated that although reliable, the

identification measure was redundant with the OCQ. The internalization measure was

reliable and valid in that most items strongly loaded upon a different factor than did

items of all other measures and the compliance measure obtained some validity only

after the removal of two of its items, but possessed weak reliability throughout the

analysis.

Despite of psychometric support provided by O’Reilly and colleagues,

subsequent researches reported difficulty in distinguishing identification and

internalization (Vanderberg, Self, & Seo, 1994). As a result, O’Reilly and colleagues

combined the identification and internaliztion items to form the dimension which they

labelled as normative commitment; whereas, compliance is what referred to as

instrumental commitment.

Meyer and Allen’s Model. Meyer and Allen’s (1991) model of commitrment

integratres numerous definitions of commitment that had prolifereated in the literature

and can be conceptualized under three mainstreams namely affective, continunace,

and normative basis of commitment. Affective commitment is conceptualized that

38

nature and quality of work experiences affect employees’ positive emotional

attachment characterized by strong links, an identification with, and involvement in

the organizationan. Identification in this case, means the employees sense of unity

with the organization. In an organizational setting loyalty and feelings of attachment

develop as individuals share values in common with other members of the group. This

identification, expressed through the adoption of organizational goals, occurs when

individuals take pride in the organization, participate with intense interest in its

activities, and speak positively about their connection with the organization (Etzioni,

1975; Mowday et al., 1982). Tracing back, earlier work by Kanter (1968) provided

foundation for the conceptualization of this form of commitment. The concept was

further posited by Mowday, Porter, and Steers (1982) which was the main basis for

conceptualization of affective component of the Meyer and Allen’s model. Further,

Buchanan (1974) aslo viewed commitment as orietned to the goals and values of an

organization apart from its purely instrumental worth.

Continuance commitment is related to side bets approach of Beckers’theory

(1960) and Herbiniak and Alutto (1972) conceptualization of commitment as a cost

induced desire to remain in the organization. Continuance commitment refers to

strength of a person’s tendency as a need to continue working for an organization (as

cited in Greenberg & Baron, 1993) and as feeling “struck” in one’s present position

(Angle & Lawson,1993). Continuance commitment include perception of high

sacrifice and few alternatives (Reilly & Orsak, 1991).

Normative commitment reflects an employee’s feelings of obligation toward

the organization. Individual committed to the organization on a normative basis

engage in activities on the basis of a sense of duty. Wiener (1982) suggested that

employees behave in accordance with organizational goals because “they believe it is

39

the right and moral things to do (p. 421). Normative commitment describes a process

whereby organizational actions (e.g., selection, socialization, procedure) as well as

individual predispositions such as personal organizational value congruence and

generalized loyalty or duty attitude leads to the development of organizational

commitment (Mathieu & Zajac, 1990).

These three types of commitment reflect a link between an organization and an

employee and distinguish between commitment based on a desire to stay, need to

stay, and obligation to stay in an organization. Allen and Meyer (1990) provided

empirical support that each component represents a somewhat distinct link between

employees and an organization that develops as the result of different work

experiences. Therefore, the link between commitment and on-the-job behavior may

vary as a function of the strength of the three components. Further, these components

of commitment are not mutually exclusive: an employee can simultaneously be

committed to an organiztaion in an affective, continunace, and normative sense, at

varying level of intensity (as cited in Popper & Lipshitz, 1992).

Based on Allen and Meyer’s framework, Jaros et al. (1993) suggested a

multidimensional conceptualization namely, affective, continuance, and moral

commitment. The differences were in terms of defining the moral commitment which

corresponds more closely to Allen and Meyers’ definition of affective commitment

than to their definition of normative commitment.

In conclusion, all theoretical models presented above have their own strengths

and weaknesses; but Meyer and Allen’s model is the most widely used theoretical

framework in studies. WeiBo, Kaur, and Jun (2010) provided a critical review of

different models of orgnaiztaional commitment. They argued that measurment

approach offered by Porter et al. (1974) is not having good content and discriminant

40

validity. They further mentioned that O’Reilly and Chatman’s model (1986) is

relatively unclear in its porpositions; hencforth Meyer and Allen’s modle is more

empirically validated and its measurment approach has been crediated as of wide use

in most of studies.

Measurement of Organizational Commitment

Reichers (1985) pointed out that commitment literature had dominantly

focused on assessing intra-personal approaches of attitude and attribution formation to

measure commitment. Morrow (1983) identified over 25 commitment related

constructs and measures which highlighted the need to clarify the measurement of the

construct. In this regard, Whitener and Walz (1993) highlighted that effort of Meyer

and Allen (1991) is an important milestone in measurements of the construct. In

subsequent research, Allen and Meyer (1996) reviewed results from over 40 samples

and claimed that construct validity of the measure was strong enough to support the

continued use of the Organizational Commitment Questionnaire. The measure of

Organizational Commitment Questionnaire is of widespread usage in organizational

commitment research (Jaros, 2007). Allen and Meyer (1990) provided the preliminary

evidence that affective and continuance components of attitudinal commitment are

empirically distinguishable constructs. However, the affective and normative

components, although distinguishable, appear to be somewhat related as antecedents

of affective commitment have found to be highly correlated with both affective and

normative commitment. Recent study by Karim and Noor (2006) provided theoretical

support for measure including items of distinguishable components of affective and

continuance commitment on sample of academic librarians. A concern about

41

continuance commitment (Dunham, Grube, & Castaneda, 1994; Meyer, Stanley,

Herscovitch, & Topolnytsky, 2002; McGee & Ford, 1987) observed that continuance

commitment scale underlies two sub dimensions, (a) a low job alternatives and (b)

high personal sacrifice. However, Wasti (2002) supported that continuance

commitment is reflecting perceived cost reference to their specific source. Studies

have provided empirical support to demonstrate that the components of the measure

are distinguishable from one another (Dunham, Grube, & Castaneda, 1994; Karim &

Noor, 2006; McGee & Ford, 1987; Reilly & Orsak, 1991).

Porter and his colleagues developed the Organizational Commitment

Questionnaire (OCQ) to measure the affective approach to commitment (Mowday et

al., 1979). This 15-item measure has been used extensively in research and has

acceptable psychometric properties. A parallel measure developed by Cook and Wall

(1980) has also showed adequate and stable psychometric properties. Allen and

Meyer (1990) mentioned that other measures of affective commitment have not been

subjected to rigorous psychometric evaluation.

The measures based on perceived cost view of commitment by Ritzer and

Trice (1969), which was further modified by Hrebiniak and Alutto (1972), that

requires respondents to indicate the likelihood that they will leave the organization

given various inducements to do so e.g., increase in pay, status, freedom, promotional

opportunity. Meyer and Allen (1984) criticized that high scores on these scales reflect

an unwillingness to leave the organization, suggests that it may measure affective

commitment rather than, or in addition to, cost-induced commitment.

Wiener and Vardi (1980) developed a measure based on normative view of

commitment. The scale measures the extent to which employees feel a person should

be loyal to his/her organization, should make sacrifices on its behalf, and should not

42

criticize it. However, Allen and Meyer (1990) mentioned that other than internal

consistency, the psychometric properties of the measure have not reported

Relationship of Work Environment and Organizational Commitment

There is empirical evidence suggesting that organizational commitment can

develop by fostering a positive climate in the organization (Grau et al., 1991; see also

Witt, 1989). Organizational commitment as an outcome variables has been linked to

work environment variables (Brierley, 2000; Painter & Akroyd, 1998; Richards,

O’Brien & Akroyd, 1994); dimensions of organizational structure along with several

personal variables and organizational factors (Littler, 1985); supervision practices and

job control factors as influencing employees’ affective responses (Mobley, Griffith,

Hand, & Meglino, 1979); organizational support (Casper, Martin, Buffardi, &

Erdwins, 2002; Johnson & Chang, 2008), etc.

Holmergen, Hensing, & Dellve (2010) reported that work environment

particularly within public sector organizations influences employees’ attitudes namely

the organizational commitment. The study also highlighted that considering subgroup

of employees with respect to job ranks provide meaningful understanding of this

relationship. Karsh, Booske, and Sainfort (2005) suggested that in work setting,

quality improvement efforts to enhance employees’ intrinsic and extrinsic job

satisfaction and managing their intention to leave the job was supported by findings

that management of service oriented organization need to consider how high task

orientation, clarity, and innovation, and less work pressure have potential relation in

building employees’ strong identification with their organization. Mauser (as cited in

Moos, 2008) added that workplaces characterized with high involvement, cohesion,

43

clarity, and openness to change and less work pressure contribute in developing high

level of organizational commitment. For organizational improvement, Westerman and

Cyr (2004) concluded that the discrepancy between employees’ actual and preferred

work environment is needed to consider in order to manage their commitment with

the organization as found to be linked with their commitment especially in case of

those work settings where extensive contacts with one’s recipients of services are

involved.

In educational organization, work environment aspects which enhance

employees’ satisfaction contributes most to the development of affective commitment

and variation in psychosocial characteristics of environment effects emotional

attachment with the organization (Dramstad, 2004). More recently, Stewart, Bing,

Gruys, and Helford (2007) attempted to link the perceptions of work environment

(cohesion, trust, pressure, support, autonomy, recognition, fairness, and innovation)

with affective and continuance commitment among 553 employees. Results showed

that task-oriented dimension of organizational support was a significant predictor of

affective commitment whereas the relationship-oriented dimension of workplace

recognition was a significant predictor of continuance commitment.

While investigating a model of work environment and its outcomes, Clarke

and Iles (2000) attempted to investigated a model of work environment for diversity

(as assessed by perceptions of policy support, organizational justice, support for

diversity and recognition of the need for diversity) as showing strong predictors of the

presence of positive organizational, job and career attitudes. The findings further

suggested that the perception of positive organizational justice strongly predicts

organizational commitment, career commitment, career planning, job satisfaction and

satisfaction with supervisors, careers, and career future satisfaction. Among other

44

studies, Ervin and Langkamer (2008) assessed leadership as one of the indicators of

psychosocial work environment exerts impact on affective commitment. Brooks and

Seers (1991) suggested the relationship between facets of work environment as

predictor of organizational commitment in five different career stages within a sample

of 1536 employees. Within stage analysis showed that, relative to task challenge and

supervisory behavior, team cohesion had a larger effect.

Studies in context of Pakistan (Chughtai & Zafar, 2006; Hayat, 2004)

explained the dynamics of relationship between work environment facets and

organizational commitment. Chughtai and Zafar (2006) examined the variables of

satisfaction with promotion opportunities, pay, coworkers, actual work undertaken,

job security, supervision, working conditions, and training opportunities as related to

commitment among full time Pakistani university teachers taken from Lahore,

Islamabad/Rawalpindi, and Peshawar cities of Pakistan. Results revealed that

satisfaction with job security, supervision, training opportunities, and the actual work

undertaken are positively related with organizational commitment. However, working

conditions have showed the non-significant link. Overall, predictive variables were

responsible to explain 39% variance in organizational commitment. Contradictory

findings are reported in a study (Hayat, 2004) conducted on a sample of bank

employees. The findings indicated non-significant relationship between work

environment and organizational commitment. In this study, organizational

commitment was conceptualized as a single domain assessing the overall commitment

of employees.

In addition, studies on organizational commitment carried out in Pakistan are

in varied directions using varied samples. For example, an important milestone in

literature of organizational commitment was an effort to develop an indigenous

45

multidimensional organizational commitment questionnaire following theoretical

conceptualization of Porter et al. (1974). The measure comprises three dimensions

namely identification, involvement, and loyalty with adequate reliability and construct

validity indices (Tayyab & Tariq, 2001). The study further reported that executive

employees of public sector organizations are higher on organizational commitment

compared to the public sector executives. Nasir and Haque (1996) reported that job

stress is negatively related with organizational commitment among 50 federal

government officials. Shah, Kaur, and Haque (1992) reported a significant correlation

between intrinsic work values and commitment for the employees of public sector

industry. The differences on work values and commitment among public and private

sector employees were not significant.

In context of Pakistan, research evidence in establishing the relationship

between work environment and organizational commitment is lacking particularly

within academic settings. A study by Chughtai and Zafar (2006) reported that

research focus on organizational commitment in educational settings is not sufficient.

The trend of literature on environment and outcome relationship reinforces the

impression that consistent line of empirical evidence in a particular area of research is

generally lacking in organizational researches in Pakistan. This eventually provided

the theoretical stance for conducting the present study as an effort to explore work

environment and outcome relationships among working group of universities

teachers.

In establishing the environment and outcome relationship, proceeding section

of literature review will explain how personality, organizational, and demographic

related factors influence the relationship of work environment with burnout and

commitment.

46

Role of Personal Variables in Relationship of Work Environment and its

Outcomes

Personality variables. In organizational research, understanding the

dispositional basis provide an explanation of how employees respond to their work

experiences (Swider & Zimmerman, 2010). In conceptualization of this process,

Moos’s (1994) theoretical approach adheres to consider the role of personal variables

in explaining the interplay of environment-to-outcome relationship. Personality plays

an important role in determining how individuals encode and evaluate information

from their environment (as cited in Swider & Zimmerman, 2010); whereby,

individual differences have taken to influence job attitudes which are involved in

favorable or unfavorable evaluation of workplace facets (Meyer & Allen, 2006). In

understanding personality, trait approach has taken the position for consensus on basic

structure of personality converging on five basic traits referring to the Five Factor

Model (Mooradian & Nezlek, 1995). The Big Five personality factor model

represents the dominant conceptualization of personality structure which has received

immense empirical attention in organizational research (Barrick & Mount, 1991). The

Big Five factor model of personality has demonstrated cross culturally equivalence

(Digman, 1997; Nye, Roberts, Saucier, & Zhou, 2008). The leading theoretical

approaches to explain Big Five Model were proposed by Costa and McCrae (1995)

and Goldberg (1992). Costa and McCrae developed NEO-PI-R inventory with 240

items assessing 30 sub-facets to assess five factors of personality. Later on, a shorter

version (NEO-FFI) with 60-items version was developed. Goldberg (1992) proposed

a 100-item inventory, which later on was revised by Saucier (1994) in form of shorter

version.

47

The Five Factor Model comprises five relatively independent dimensions:

Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness

to Experience. Extroversion reflects the nature of relationship with ones social

environment enriched with energy, enthusiasm and confidence (Rolland, 2002).

Extraversion dimension reflects typical behavior tendencies including assertive,

talkative, sociable, gregarious, and active (Barrick & Mount, 1991). Agreeableness

refers to the nature of ones relationship with others (Rolland, 2002). Agreeableness

describing the human aspect of compliance associates with traits including

cooperative, courteous, flexible, trusting, good-natured, and tolerant (Barrick &

Mount, 1991). The dimension of Conscientiousness relates to dependability reflected

in traits including hardworking, achievement oriented, persevering, careful, and

responsible (Barrick & Mount, 1991). Emotional stability or neuroticism represents

the general tendency to experience negative affects associated with traits including

being anxious, depressed, angry, embarrassed, emotional, worried and insecure

(Barrick & Mount, 1991). Openness to experience is manifested in a wide range of

interest and eagerness to seek out and live new and unusual experiences without

anxiety. The acceptance of new experiences relate to different sphere of behavior

(Rolland, 2002). The behavioral tendencies typically associated with openness to

experience include being imaginative, cultured, curious, broad-minded, and intelligent

(Digman, 1990).

In perspective of understanding how personality or people influence

workplace, researches have conducted in varied directions. Generally, it is considered

that employees showed congruence to different facets of work settings in relation to

their personality traits (Chesney & Rosenman, 1980) and has taken as a source of

attribution in perceiving differences in workplace environment (McManus, Keeling,

48

& Paice, 2004). In this context, most of researches have devoted to study the impact

of disposition towards independence (Wetzel, 1978; Wetzel & Redmond, 1980),

achievement and competition (Chesney & Rosenman, 1980; Chesney, Sevelius,

Black, Ward, Swan, & Rosenman, 1981; hardiness (Thomson & Wendt, 1995); the

sense of mastery (Phelan, Bromet, Schwartz, Dew, Parkinson, & Curtis, 1993), etc.

Chesney and Rosenman (1980) reported that employees having extroverted assertive

Type A personality traits feel more comfortable in a cohesive and independent setting.

Whereas, Type B persons feel more satisfaction in a structures work setting

characterized with lack of autonomy and less interaction with coworker cohesion.

The role of personality has appreciated contextual variables of an

organizational environment while predicting burnout among teachers (Cano-Garcia,

Padillo, & Carrasco-Ortiz, 2004). Personality characteristics may potentially relate to

the experience of burnout particularly among teachers (Otero-López, Mariño, &

Bolaño, 2008). Researches had revealed that association of personality characteristics

and perception of work environment results in employees’ stress related outcomes

(Kobasa & Puccetti, 1983). Many researches (Buhler & Land, 2004; Downey,

Hemenover, & Rappoport, 2000; Harris & Lee, 2004) have been conducted to find the

relationship of personality disposition in experiencing burnout. Burnout is linked with

personality hardiness (Sahu & Misra, 2004) among teachers. Dodd and Jacobs (2003)

suggested that personality along with social support and workload predicts burnout. A

high level of burnout is caused by negative temperament and subjective workload, but

actual workload (academic and vocational) had no effect on burnout. In examining the

role of Big Five personality traits in explaining link between work environment and

burnout, Eastburg et al. (1994) reported that extraverted personality dimension in

association with peer support predicted less emotional exhaustion. The study

49

suggested that high on extrovert may require more coworker support to avoid work

related emotional exhaustion. Independently, emotional exhaustion was found to be

predicted by emotional stability; depersonalization was predicted by extraversion; and

personal accomplishment was predicted by extraversion and emotional stability

(Bakker, Van Der Zee, Lewig, & Dollard, 2006). Decrease in emotional exhaustion

and depersonalization and increase in personal accomplishment was linked with

emotional stability, extraversion, openness to experience, agreeableness and

conscientiousness (Rothman & Storm, 2003). Another study (Hochwarter, Zellars,

Perrewé, Hoffman, & Ford, 2004) highlighted that exhaustion and depersonalization

components of burnout was predicted by neuroticism; whereas, diminished personal

accomplishment was predicted by extraversion. A recent study (Swider &

Zimmerman, 2010) documented that each one of the traits of Five-Factor model of

personality have demonstrated relationship with burnout components.

Teaching as a highly stressful occupation remained the focus of various

studies while investigating the role of various personality characteristics.

Organizational factors including social support, workload (Dodd & Jacobs, 2003),

role expectations (Huebner & Mills, 1994), peer support (Eastburg et al., 1994) etc.,

have been studied in explaining the link between burnout and personality. Study of

Kim-Wan (1991) noted that personality had a significant as well as mediating role in

burnout among teachers. In another research, Kokkins (2005) reported that teachers

who were unable to manage misbehaved students, and were high on neuroticism were

found to be burned out. Study of Huebner and Mills (1994) on school psychologists

reported that higher levels of burnout were associated with high competitiveness and

egocentricity and low levels of extraversion and conscientiousness. The study

reported that psychologists effecting with burnout reported greater dissatisfaction

50

towards their professional roles. The study highlighted that personality variables

relate significantly to burnout more than demographic and work condition variables.

Literature review indicates that some preliminary exploratory researches have

been carried out in Southeast Asia. For instance, Sahu and Misra (2004) assessed

personality hardiness of 240 teachers from Lucknow District (India) as negatively

related to burnout. In Pakistan, Basir (2006) attempted to find out the association of

Big Five personality traits with burnout among male college and university teachers.

The study found significant relationship with personality trait of extraversion with

emotional exhaustion, depersonalization, and personal accomplishment. The study

further reported that agreeableness and conscientiousness showed significant

relationship with composite score of burnout.

Several personal characteristics, including various personality traits have been

consistently related to organizational commitment. For instance, while exploring the

role of Big Five personality dimensions in explaining relationship between work

environment and organizational commitment, Armstrong (1996) noted that it is

reasonable to believe that strong commitment to work is likely to result in

conscientious individuals who may exhibit self directed effort to do the job, showing

regular attendance, prefer nominal supervision and demonstrate a high level of effort.

Extraversion was significantly related to affective, continuance, and normative

commitment; while neuroticism, conscientiousness, and openness to experience were

all significantly related to continuance commitment along with showing agreeableness

as significantly linked to normative commitment (see Erdheim, Wang, & Zickar,

2006; see also Meyer & Allen, 2006). Meyer and Allen (2006) pointed out the

scarcity of empirical evidence and stressed upon need to further explore the dynamics

of Big Five personality traits and commitment.

51

The aforesaid review of literature for examining the moderating role of

personality variables, particularly the Big Five model of personality for relationship

between work environment and outcome variables, revealed the need to expand

research in this direction. This provided a direction for specifying the objectives of

present study.

Demographic variables. Investigating the impact of organizational and

demographic related personal factors, studies have reported contrasting and diverse

findings when examined in context of particular social environments. For instance,

employees with high job status do report experiencing more positive work

environment (McCrae, Prior, Silverman, & Banerjee, 2007). Whereas, Margall and

Duquette (2000) reported that more positive perceptions of the work environment is

associated with less professional experience. Similarly, Farid (2001) reported that

variation in employees’ work experience explains differences in perception of

workplace autonomy. Studies have reported that differences in work environment

perceptions may attribute to departmental differences (Avallone & Gibbon, 1998;

Maloney, Anderson, Gladd, Brown, & Hardy, 1996; Straker, 1989). Among

demographic factors, employees’ age, education, and gender were found to be

significantly linked with certain dimensions of work environment (Maqsood &

Rehman, 2004). However, Imam (1993) did not reported any differences in perception

of overall work environment among different age groups and also with respect to

gender when he studied college teachers in Pakistan. Okoh (2007) reported that highly

educated and professional staff tended to have a more negative view of their work

environment. Maloney et al. (1996) reported that employees’ with high education

level reported a more positive work environment, probably because of more

52

supervisory responsibilities. Studies addressing gender differences in environmental

dimension highlighted that men and women perceive, evaluate, and react to their work

environment differently (Kirschenbaum, 1991; Weisberg & Kirschenbaum, 1993)

partly because of differences in their work experiences (Repetti, Matthews, &

Waldron, 1989). However, Phelan et al. (1993) found that professional women and

men perceived the work place similarly.

Interaction effects of work environment factors particularly work pressure and

supervisor support, and demographic and job related variables influence burnout

(Constable & Russell, 1986). However, Fejgin, Ephraty, and Ben-Sira (1995)

suggested that burnout is independent of personal variables and job related variables.

Similarly, Patterniti, Niedhammer, Lang, and Consoli (2002) suggested that

association between psychosocial factors and employees’ health is independent of

moderating effects of occupational grade, stressful occupational events, working

hours, physical workload factors, age, education, income, marital status, and stressful

personal events. Kim, Lee, and Kim (2009) reported that teachers in upper-grade

experience high burnout compared to their counterparts. Long (1993) suggested that

time or job duration play a role in employee well being. In assessing work

environment and burnout relationship, comparisons on basis of public and private

sector organizations is an important consideration (Kim, 2011). A systematic

literature review on burnout among university teaching staff (Watts & Robertson,

2011) emphasized that research evidence is lacking for comparative studies across

different sectors. This review paper shared the comparable nature of burnout studies

on university teaching staff while generalizing findinlthcare sector and with teachers

of school level.

53

Examining the relationship between demographic factors and burnout

experienced by teachers, younger professionals tend to have higher burnout scores

than do older professionals (Russell, Atmaier, & Van Zelen, 1987; Watts &

Robertson, 2011). Ever, Tomic, and Brouwers (2004) suggested that teachers' age

may significantly link with their experience of personal accomplishment. Interaction

effects of age and experience of workplace situation with co-workers cohesion and

supervisors support has found to exert impact on burnout (Turnipseed, 1998).

Examining the direct effects of demographic factors, emotional exhaustion is

associated with employees’ age (Hochwälder, 2007). However, Armelius and

Jeanneau (2000) found that age and gender had no effects on burnout. Recently, it was

reported that age, marital status, job experience, education background and

satisfaction with income among teachers are significant factors explaining variation in

burnout (Luk, Chan, Cheong, & Ko, 2010). Haque and Khan (2001) reported that

within human service professionals in Pakistan, employees’ age and job experience

had found negatively linked with personal accomplishment and showed positive

relationship with depersonalization. Wilber and Specht (1994) reported that

employees’ education is able to produce a considerable variance in personal

accomplishment. Moghadam and Tabatabaei (2006) reported that teachers’ level of

education showed positive relationship with burnout scores.

Studies examining the impact of gender on burnout have revealed contrasting

findings. Recent study by Yavuz (2009) reported higher level of depersonalization

among male teachers compared to female teachers. Sahu and Misra (1996) reported

that female teachers are more vulnerable to emotional exhaustion and

depersonalization, but not to personal accomplishment as compared to males. Later,

on, Sahu and Misra (2004) reported non-significant impact of gender on three

54

dimensions of burnout. Moghadam and Tabatabaei (2006) reported high burnout

among male teachers and employees of the education organization compared to

females. Watts and Robertson (2011) suggested that generally male university

teachers endorse high on depersonalization, whereas female teachers reported high

emotional exhaustion. Seltzer and Numerof (1986) reported that married individuals

reported lower levels of burnout than those who were single. Unmarried teachers with

graduate level education, less teaching experience and lacking in social support were

more burned out (Kim-Wan, 1991). Haque and Sohail (1997) found that age and

marital status was significantly correlated with personal accomplishment dimension

of burnout among younger employees.

Among potential influential demographic factors influencing the

organizational commitment, job duration has often been used as a surrogate measure

of continuance commitment (Meyer & Allen, 1984). The study of Fresko, Kfir, and

Nasser (1997) reported that job experience of teachers has found to be negatively

associated with commitment. Job status was found positively related to affective and

normative forms of commitment and negatively related to continuance commitement

(Meyer et al., 1993). Clarke and Iles (2000) treated gender, age, ethnicity, marital

status, domestic care responsibilities, disability, management level, and work hours as

moderating variables in predicting the outcomes of the work environment. The study

indicated that gender and management level influenced both the diversity as indicator

of work environment and commitment related outcome variables. The study done by

Shirbagi (2007) provided insight to assess university teachers’ organizational

commitment while considering comparison across different universities on some

meaningful distinction. In examining work attitudes particularly organizational

55

commitment, Boardman, Bozeman, and Ponomariov (2010) suggested that

comparison of public and private sector is an important consideration.

In examining the moderating role of demographic variables, researches have

shown consistent as well as varied pattern of findings. In most of researches age

(Angle & Perry, 1981; Brimeyer, Perrucci, & Wadsworth, 2010) and job duration has

been consistently shown as positively linked with organizational commitment

probably because of having better autonomy and control on job with passage of time

(Brimeye et al., 2010). Based on two dimensional view of organizational

commitment, Mayer and Schoorman (1998) suggested that organizational tenure,

retirement benefits, education, and age were more strongly associated with

continuance commitment compared to value commitment. However, contradictory

findings have also been observed. For example, Chughtai and Zafar (2006) reported

that age, tenure, marital status, and the level of education were not significantly

contributing in explaining variance in organizational commitment. Findings of a

meta-analytic study (Mathieu & Zajac, 1990) highlighted negative relationship

between education and commitment with stronger relationship found for attitudinal

commitment compared to calculative commitment. Whereas, finidngs of a study

(Grau, Chandler, Burton, & Kolditz, 1991) suggested that less educated and older

epmloyees reorted more institutional loyality. Findings of a study (Mishra &

Srivastava, 2001) reported that age and education level have accounted for 53% of the

variance in employees’ commitment.

Interestingly, gender differentiation in organizational commitment have come

up with findings that women tend to be less committed to their jobs than men

(Karrasch, 2003) may be with this conceotion that women place greater emphasis on

family roles than men (Dodd-McCue & Wright, 1996). Researchers who appear to be

56

focused on the continuance component of commitment have often argued that women

are more committed to organizations than men (Stewart, Bing, Gruys, & Helford,

2007; Wahn, 1998). The finding of met-analytical research (Aven, Parker, &

McEvoy, 1993) has reported no gender differentiation in terms of affective

commitment. Researches (e.g., Meyer et al., 2002; Riketta, 2005; Thorsteinson, 2003)

have found that there were no gender differences in organizational

commitment. Further, some studies found that even when there was a mean difference

in organizational commitment between men and women, there was no gender effect

when predicting organizational commitment using control variables such as age, job

level, educational, job and organizational tenure (Abdulla & Shaw, 1999; Vander,

Bossink, & Jansen, 2003; Ngo & Tsang, 1998). In explaining modertaing role of

gender moderates, Witt (1989) found that relationship between psychological climate

and organizational commitment does change across gender as stronger for men than

for women, with the relationship being positive in both cases. Stewart, Bing, Gruys,

and Helford (2007) explored moderating effect of gender and reported that task-

oriented dimension which reflects in form of organizational support was a significant

predictor of affective commitment for men employees; whereas, the relationship-

oriented dimension which reflects in form of workplace recognition was a significant

predictor of affective commitment for women. Marital status has emerged as a

consistent predictor of organizational commitment. Findings reported by John and

Taylor (1999) and Tsui, Leung, Cheung, Mok, and Ho (1994) reported that married

people were more committed to their organization than unmarried people because of

having more family responsibilities which requires more stability and security in their

jobs; and therefore, they are likely to be more committed to their current organization.

57

Aforesaid description highlights that a consistent pattern of findings with

respect to impact of demographic variables in generally missing. The nature of

findings varies in different studies. Therefore, investigating the influence of possible

demographics adds in thorough understanding of the dynamics of work environment

and outcome relationships.

The literature review presented in this chapter highlights that work

environment may serves as potential causal factor in developing and determining

employees’ attitudes. The review highlights that burnout and organizational

commitment are important attitudinal variables that influences employees’ as well as

organizational well being particularly within working group of teachers. This

eventually serves as the basis for specifying the research question of present research

to explore which dimensions of workplace environments are contributive factors in

experience of burnout and for organizational commitment among teachers. Given that

gaps in existing knowledge in line with research question have identified in literature

review; it’s well considering to explore and build empirical evidence in establishing

work environment and outcome relationships through explaining the moderator

influences as well. More specifically, research evidence in case of work environment

and burnout has not well established among teachers in Pakistan. Focusing on

organizational commitment as an outcome variables with respect to multidimensional

approach of the construct is lacking empirical evidence in both West and Pakistan.

Literature highlights that in explaining the work environment and outcome

relationship, exploring the moderating role of dispositional factors is needed to

establish. The research question is a timely topic especially for our country where

strong academic research in occupational psychology is needed as a base for leading

to policy level decisions.

58

Rationale of the Study

Research has provided substantial empirical support to explore the impact of

work environment facets on employees’ behavior and attitudes. Moos (1988), one of

the dominant proponent in literature of pioneering research on environment, explains

that employees are receptive and reactive to influence of the social settings in which

they are involved. In past three decades research on work environment has tried to

conceptualize and explore work environments, its various facets, moderating

variables, and more specifically impact of these variables upon employee and

organizational related outcomes (Parker et al., 2003). The theoretical

conceptualization of work environment has underpinning in major perspectives of

organizational psychology including human relation, sociotechnical, and the social

information processing approaches (Moos, 1986). The model (Moos, 1994)

explaining the interplay of work environment, personal system, and organizational

and personal outcomes serves the theoretical basis of the present study.

Among various models explaining psychosocial work factors and outcomes,

Karasek’s (1979) job-demand-control support model and Siegrist’s (1996) effort-

reward imbalance model more specifically are oriented towards limited aspects, e.g.,

looking into outcomes as a result of the process of expectations of positive outcomes

and an imbalance of reward and effort (Lindblom, Linton, Fedeli, & Bryngelsson,

2006). Vanroelen, Levecque, Moors, Gadeyne, and Louckx (2009) criticized that that

demand control model is restricted in assessing range of occupational stressors. In

comparison, Moos’ approach carries much broader view of constructs explaining

work environment (Moos, 1990) providing relatively a big range of psychosocial

factors of work environment. Moos’s model stimulated a massive body of research

59

and most of studies investigating the impact of outcomes of work environment have

utilized Moos’ model (Chan & Huak, 2004; Goddard et al., 2006; Hemingway &

Smith, 1999; Karsh, Booske, & Sainfort, 2005; Long, 1993; Salyers & Bond, 2001;

Westerman & Cyr, 2004; Westerman & Yamamura, 2007; Wilber & Specht, 1994;

Wu, 1998). One of the advantageous of testing propositions of Moos’s model in our

cultural context is based on the assumption that the model extends to assess somewhat

a wide range of psychosocial factors assuming to offer a more comprehensive view of

the intended work environment. Keeping in view the significant role, the present

research has postulated its assumption on Moos’ theoretical model.

Within academic settings, research trend in West seems to have focus on

varied samples, i.e. the perceptions of teachers’ and principals’ of their workplace

(Docker, Fisher, & Fraser, 1989); schools advisory councils (McClure & De Piano,

1983); school psychologists in urban public schools (Lusk et al., 1983); students in

university settings (Cotton, Dollard, & de Jonge, 2002); faculty in medical and dental

schools (Berry, 1994; Lubbert, 1995); and university faculty members (Corley, 2005).

Substantial researches on work environment have focused on assessment, monitoring,

and improving of academic settings; however, the situation is quite different in

context of Pakistan. A pioneering research conducted by Imam (1993) proposed the

need of modifications in existing work environment of school teachers. Another study

involving private and public sector universities had focused on assessment of the

work environment and its relationship with job satisfaction, job morale, turnover, and

job stress (Rehman & Maqsood, 2008). Other studies had focused on assessment of

work environment involving banking sector (Farid, 2001; Hayat, 2004),

telecommunication sector (Maqsood & Rehman, 2004), industrial (Haq & Sheikh,

1992) and health care organizations (Khan, 1999). In context of Pakistan, the scarcity

60

of researches particularly on psychosocial work environment and its outcomes within

academic settings involving teachers of higher education provided rationale to

conduct the present study.

In understanding the interplay between work environment and its outcomes

among working group of teachers, burnout as a negative attitude is considered as an

important aspect of psychological health when examining work environment factors

as causal factors (Lindblom, Linton, Fedeli, & Bryngelsson, 2006). According to a

national study of United States done in 1983 by Gmelch, Wilke, and Lovrich (as cited

in Benjamin, 1987), stress faced by faculty in university settings seems to be a

discipline-specific problem. Faculty members involved in higher education in

university settings may be considered at high risk for burnout because the demanding

job functions of education, teaching, and research (Benjamin, 1987). Their daily work

life routine demands heavy interaction (Wood & McCarthy, 2002). Enhancing work

environment aspects, e.g., coworker cohesion, supervisor support, autonomy, and

clarity of job procedures help to decrease the prevalence and intensity of burnout

(Turnipseed, 1994). In Pakistan, studies have supported burnout as an important

concern reported by working group of teachers (Bashir, 2006; Khurshid, Butt, &

Malik, 2011; Qureshi & Hijazi, 2006). Research evidence highlighted that work

environment factors explain variation in burnout (Adali et al., 2003; Escribà-Agüir,

Martín-Beena, & Pérez-Hoyos, 2006; Goddard et al., 2006; Hochwälder, 2007;

Langballe, Innstrand,; Levert, Lucas, & Ortlepp, 2000; Munir, 2005; Salyers & Bond,

2001), and has also suggested by recent researches (Aasland, & Falkum, 2011; Boyas

& Wind, 2010).

While assessing outcome variables of work environment, it seems logical to

assess employees’ attitude namely organizational commitment. According to current

61

perspectives of work environment, commitment as a critical employee attitude is

needed to be managed for effective functioning of the organization (Gummer, 2001).

Studies have suggested association between work environment facets and

organizational commitment (Clarke & Iles, 2000; Grau, Chandler, Burton, & Kolditz,

1991; Stewart, Bing, Gruys, & Helford, 2007). In Pakistani context, studies on work

environment and its outcomes have found to be limited. One such study on bank

employees (Hayat, 2004) reported non-significant relationship between work

environment and organizational commitment. Moreover, this study was limited in

scope because it focused on the unidimenional model of organizational commitment.

Literature review identified gaps in existing literature when it comes to explicit the

relational dynamics between work environment variables and multidimensional view

of the organizational commitment. This provided the rationale in direction of need of

research exploring the predictive impact of work environment variables with different

domains of commitment particularly considering the widely used model of Allen and

Meyer (1990). Therefore, it seems meaningful to explore relatively less explored

outcome variable of organizational commitment particularly the facet based approach

in context of academic work environment. In work environment literature, research

evidence has treated organizational commitment as one of the outcome variables.

Therefore, present study specified organizational commitment as an outcome variable

rather than as a predictor or consequence of the burnout. Moreover, specification of

organizational commitment as outcome variable was built on the argument to address

the literature gap in environment and commitment relationship.

The aforesaid researches provide substantial ground to hypothesize that work

environment effects employee and organizational related outcomes e.g., burnout and

organizational commitment. According to Moos’s model of work environment

62

(1986), such general effects may be moderated by personal factors. Teachers’

experience of burnout and their commitment to the organization may be affected if

their personal disposition is not supported by the environment of the workplace.

Literature review carried out for present study pointed out that further investigation is

required to explore the dispositional basis of employees’ attitudes. Very few

researchers have taken into account the role of Big Five Factor model of personality

(extraversion, agreeableness, emotional stability, conscientiousness, and openness)

while explaining the link between work environment and burnout (Dodd & Jacobs,

2003: Eastburg et al., 1994). An important concern pointed out by Toppinen-Tanner,

Kalimo, & Mutanen (2002) highlighted the importance of considering dispositional

factors in models of organizational factors predicting burnout due to scarcity of

researches in this line. Present study is noteworthy because it addresses the

researchers’ concern based on meta-analytical studies as pointed out in a recent article

by Johnson and Chang (2008), that studies should move towards exploring interactive

relationship by considering moderator variables instead of only focusing the bivariate

relationships. It was also reported by mainstream contributors (Meyer & Allen, 2006),

about the scarcity of literature in linking Big Five model of personality with

organizational commitment.

A meta-analytic review (Parker et. al., 2003) emphasized the need to explore

the moderating effects considering organizational related personal factors in

explaining the association between work environment and its outcomes. Among

demographic variables, research evidence is available to investigate the moderating

link of organizational related and demographic variables while establishing the link

between work environment with burnout (see e.g., Turnipseed, 1994; Wilber &

Specht, 1994) and with organizational commitment (see Clarke & Iles, 2000). Leka

63

and Houdmont (2010) pointed out that individual differences and demographic factors

has received less attention by researchers say around 5% of total in published research

themes in area of occupational health psychology. Based on observation, present

study intends to explore the possible moderating impact of teachers’ involvement in

other paid jobs. However, no study was found for this particular variable. Literature

review reveals variation in specifying personal variables. For present study,

demographic, organizational related, and personality factors are treated as moderating

variables. Though, personal variables are too many, but primarily the reason was to

assess them as a mean to comprehensively evaluate the possible influential factors.

Instead of omitting personal variables, researcher stance is to carry and elaborate this

work in form of article publications.

It is to be noted that the model used for predictor variable is of

multidimensional nature. The review has mentioned in detail about the contribution of

sub-facets of work environment model. Though the number of predictor variables to

be examined are large; however, limiting the predictors may not be appreciated as the

study is focusing on the multidimensional model of work environment. This seems

particularly more relevant to explore interrelationships among each sub-dimension in

cases where empirical evidence is needed to generate. Even, in pilot results, if only

few predictors emerge; still decision to limit predictors might not be appreciative

primarily due to small sample size involved in the pilot study. Since, constructs of the

study are of multidimensional in nature; therefore, evaluating sub-dimensions

provides in-depth understanding. Moreover, the study will focus on formulating non-

directional hypotheses. This relates to the rationale that study objectives should open

for exploration rather than strict directional one, especially where research evidence is

lacking in particular cultural context. Further, literature highlights that the directions

64

of research studies on work environment assessments are in varied directions;

therefore, the present study is based on stance to formulate non-directional

hypotheses.

Another important aspect in conducting any research is to find out that

measures should be best fitted for intended sample as a way to evaluate the cross-

cultural applicability. Testing the factorial validity of study measures seems to have

an association with evaluating the psychometric feasibility of using scales in English

when their first language may not be English. This is especially important when

respondents may be operating under different cultural norms from some Western

countries. For present study, it is well understood that the sample of university

teachers at higher education level can understand English. As their own medium of

studies during higher education (Masters, M.Phil or Ph.D) is English and their

required mode of teaching instructions to students and syllabus is in English. It is of

more importance in case of adopting western models, even though having empirical

evidences of cross-cultural soundness, it would be logical to test the factorial structure

of these theoretical models using confirmatory factor analysis. Examining the

dominant factorial structure of study measures on sample of university teachers of

Pakistan is helpful to establish the meaningfulness of measures to be used in the

study.

The propositions of present study are also based on the established differences

found in work values of individualistic and collectivistic cultures. Based on

Hofstede’s work (2001), it may be assumed that within our collectivistic culture, the

work environment and outcomes relationship might yield different interpretation. This

is also supported by a meta-analytic review study which highlighted that majority of

researches in area of work environment perceptions and outcomes had employed

western cultures particularly the individualistic cultures (Parker et al., 2003), which in

65

turn may lead to assumption that collectivistic cultures could carry more stronger

effects. Henceforth, present study is of preliminary nature in Pakistan on basis of

paucity of academic researches particularly in industrial psychology as of the fact that

literature indicates majority of researches involving western samples. This especially

is more evident, when it comes to examine the construct validity of work areas related

constructs in particular cultural context (Tayyeb & Riaz, 2004). Another strong

impetus for doing present study was based on the fact that in Pakistan, there is strong

need to strengthen the academic research particularly within field of occupational

psychology partly due to realization of growing concern of its implications for human

resource management system. Moreover, in developing country like us where the job

market of industrial psychologists is not much explicit with defined job titles, there is

strong need to link the academic research with industry. For instance, conducting in

depth studies on psychosocial characteristics of workplace considering the host of

organizational and individual based factors will be helpful in deducing implications

for the human resource management systems of the higher education providing

institutes and universities. Within educational organizations, the need to utilize the

research based objective information is imperative to cater the work conditions

conducive to employees’ preferences. The present study is an effort to put the

academic management or policy makers in a position to realize and at least think

about their working culture in terms of psychosocial factors carrying power to

influence behavioral outcomes. The study may contribute positively to promote the

concern that research based objective information is conducive for the effective

management of employees.

The rationale of present study highlights that based on a well-established

theoretical framework (Moos, 1994), the present study aimed to address gaps in

existing literature evident through limited empirical support. This is especially more

66

evident in case of exploring the three dimensional model of organizational

commitment with work environment facets both in West as well as in Pakistan.

Moreover, investigating work environment and burnout relationship is lacking

empirical support in context of Pakistan particularly on working group of teachers.

Evaluating the workplaces and investigating its outcomes particularly within

academic settings is an important topic in terms of its scope in promoting research

within the field of Organizational/ Occupational Psychology, which as reported by a

recent study (Zadeh & Ghani, 2012), the field comparatively is of not well flourished

status in Pakistan. It should be noted that investigating the three facet model of

organizational commitment with work environment is of exploratory nature.

Furthermore, investigating the moderating interplay of dispositional factors which so

far was remained open for investigation strongly adds in qualifying the study as

indigenous one. Moreover, demographic and organizational related variables will be

examined for thorough assessment of work environment and outcome relationship.

The aforesaid aspects of study rationale credited the study as noteworthy contribution

in academic knowledge.

The theoretical conceptualization of the study is depicted below as Figure 2.

67

Demographic/ Personal Factors Sector, hierarchical status, job duration, departmental differences, involvement in side jobs, age, education,

gender, marital status

Criterion/ Outcomes Burnout Emotional Exhaustion Depersonalization Personal Accomplishment Organizational Commitment Affective Commitment Continuance Commitment Normative Commitment

Predictors Work Environment

Involvement Peer Cohesion Supervisor Support Autonomy Task Orientation Work Pressure Clarity Managerial Control Innovation

Physical Comfort

Figure 2: Theoretical conceptualization of the present study

68

Chapter II

OBJECTIVES, HYPOTHESES, OPERATIONAL DEFINITIONS,

AND RESEARCH DESIGN

Objectives of the Study

The study intends to pursue following objectives of the study.

1. To establish the psychometric properties of the measures (including reliability

and validity indices) on sample of University teachers in context of Pakistan.

2. To examine the factor structure of measurement models of study constructs to

see how well data supports the existing factor structure of the constructs.

3. To investigate the predictive relationship of perceived work environment

facets with employee and organizational related outcomes including burnout

(emotional exhaustion, depersonalization, and personal accomplishment) and

organizational commitment (affective, continuance, and normative

commitment).

4. To explore the moderating role of personality factors (extraversion,

agreeableness, emotional stability, conscientiousness, and openness) in

explaining predictive relationship of work environment with burnout and


5. To explore the moderating role of job related and demographic factors (public

vs. private sector, hierarchical status, job duration, departmental differences-

natural vs. social sciences, involvement in side jobs, age, education, gender

and marital status) while assessing the predictive relationship of work

environment with burnout and organizational commitment.

69

Hypotheses

The stance of non-directional hypotheses testing in present study looks into

exploring how data guides about the conceptual relationships of variables instead of

relying too heavily on directional ones within the consideration that research

methodology happens to be in a state of flux (see Davis & Smith, 2005). Below

mentioned are the non-directional declarative statements tested to investigate the

predictive relationship between work environment and its outcomes.

1. There exists predictive relationship in relative effects of perceived facets of

work environment on burnout (emotional exhaustion, depersonalization, and

personal accomplishment).

2. There exists predictive relationship in relative effects of perceived facets of

work environment on organizational commitment (affective, continuance, and

normative commitment).

3. Personal variables (e.g, dispositional, job related and demographic factors)

interact with work environment perceptions influencing the burnout and


Operational Definitions of Variables

Work Environment. Work environment is considered as the immediate

operating social work environment and refers to the psychosocial characteristics of a

work setting characterized with the way in which individuals in a setting relate to each

other (the relationship domain), the personal growth goals towards which a setting is

oriented (personal growth or goal oriented domain), and the amount of structure and

70

openness to change that characterize it (system maintenance and change domain

(Moos, 1994).

Work Environment was assessed using Work Environment Scale (WES:

Moos, 1994), which measures ten subscales which primarily fall into three broader

dimensions namely: (1) Relationship Dimensions, (2) Personal Growth Dimensions

and (3) System Maintenance and Change Dimensions. An above average score on the

overall sum of the subscales could be considered “positive”, whereas, a below average

score could be considered “negative”. Furthermore, high score on each of the

dimension will indicate high endorsement of employees on respective dimension of

the work environment.

The relationship dimensions. The relationship dimension is defined as the

nature and intensity of personal relationship in the environment and further taps the

concepts like involvement, peer cohesion, and supervisor support.

Involvement: The extent to which employees are concerned about and

committed to their job.

Peer Cohesion: How much employees are friendly and supportive of one

another.

Supervisor Support: The extent to which management is supportive of

employees and encourages employees to be supportive of one another.

Personal growth dimensions. The personal growth dimension is being defined

as the opportunities in the environment for personal growth and development and

explained by concepts like autonomy and task orientation.

71

Autonomy: How much employees are encouraged to be self-sufficient and to

make their own decisions.

Task Orientation: The degree of emphasis on good planning, efficiency, and

getting the job done.

Work Pressure: The degree to which high work demands and time pressure

dominate the job milieu.

System maintenance and system change dimensions. The system

maintenance and system change dimensions is defined as the extent to which the

environment is orderly and clear in its expectations, maintain control, and is

responsive to change. This dimension tapes concepts of clarity, managerial control,

innovation, and physical comfort.

Clarity: The extent to which employees know what to expect in their daily

routine and how explicitly rules and policies are communicated.

Managerial Control: How much management uses rules and procedures to

keep employees under control.

Innovation: The degree of emphasis on variety, change, and new approaches.

Physical Comfort: The extent to which the physical surroundings contribute to

a pleasant work environment.

Burnout. Educators’ burnout is defined as the subjects’ responses to the three

subscales (Emotional Exhaustion, Depersonalization, and Personal Accomplishment)

of the Maslach Burnout Inventory- Educator Survey (Maslach, Jackson, & Leiter,

1996). Emotional Exhaustion includes feelings of being emotionally overextended

and exhausted by one's work. Depersonalization refers to an unfeeling and impersonal

response toward recipients of one's service, care treatment, or instruction. Personal

72

Accomplishment means feelings of competence and successful achievement in one's

work and a reduced sense of personal accomplishment from the job is an indicator of

educators’ burnout.

The high, moderate, and low levels of burnout were computed using median as

cut-off score.

Organizational commitment. Meyer and Allen (1991) defined organizational

commitment as a three component construct namely affective commitment,

continuance, and normative commitment. The affective component represents

attachment to the organization because of a sense of unity and shared values and

employees’ willingness to remain in relationship. The continuance Component

represents a perceived cost of leaving an organization. The normative Commitment

develops as a result of socialization experiences and emphasizes the obligations of

remaining in the organization.

The assessment of affective, continuance, and normative commitment was

based on measure of Organizational Commitment Questionnaire (Allen & Meyer,

1990). Median was taken as the cut off score; where, the scores falling above the

median were considered as high score, showing high organizational commitment and

vice-versa.

Personal variables. Educators’ personal variables include dispositional, job

and demographic related factors.

In present study, the typological approach- Big Five personality model of

Saucier (1994) based on Goldberg’s approach (1992) provided theoretical base for

assessment of personality. The model comprises five relatively independent

73

dimensions: Extraversion, Agreeableness, Emotional Stability, Conscientiousness,

and Openness.

Extraversion. It is the quantity and intensity of interpersonal interaction,

activity level, and capacity of joy. A person scoring low on extraversion subscale of

the Mini-Marker Personality Inventory would be an Introvert who is less friendly,

prefers to be alone, and is more reserved.

Agreeableness. Agreeableness is the quality of interpersonal orientation along

a continuum from compassion to antagonism in thoughts, feelings, and actions. A

person scoring high on the agreeableness subscale would be altruistic, sympathetic,

and cooperative.

Conscientiousness. It describes the individual’s degree of organization,

persistence, dependability, and motivation in goal-directed behavior in a

conscientious person. The contrasting qualities of the trait marked by low scores on

the subscale give lackadaisical, impractical, and sloppy people.

Emotional Stability. It describes individual prone to psychological distress,

unrealistic ideas, and maladaptive coping responses. A high score on this trait would

be relaxed, secure, and self-satisfied.

Openness. Proactive seeking and appreciation of experience for its own sake,

tolerance for and exploration of the unfamiliar is openness. This is also considered as

a measure of intelligence. High scorers reporting high openness have the qualities of

being imaginative, creative, and introspective.

74

Organizational and demographic related personal variables. These

continuous and nominal variables included information related to employees’

organizational and demographic related personal factors. Below is the detail of each

variable.

Public vs. Private Sector. In education system of Pakistan, public sector

universities providing higher education are fully governed under regulations of the

Government. The private sector universities are playing a dominant role in providing

education and usually operate under private sector regulations. Therefore, present

study focused on examining the sector wise differences.

Hierarchical Status. In both public and private sector universities, the hiring

of teachers used to done against a standard hierarchical system with four different job

levels namely lecturers, assistant professors, associate professors, and professors. The

study divided the sample into two groups. The first group (entry level rank) on basis

of entry level status of teachers includes lecturers. The second group (high rank)

includes teachers of next promotional cadres, e.g., assistant professors, associate

professors, and professors.

Job Duration. The length of the service of employees was considered in terms

of job duration in the present organization.

Faculties. Mainly universities are involved to provide education in natural and

social sciences. However, certain universities are specialized in certain discipline as

well. To examine the impact of belongingness to natural vs. social disciplines, the

75

sample information related to affiliation with different departments was divided into

two major groups of faculties namely natural and social sciences.

Side Jobs. It was also in observation that teachers used to involve in certain

job assignments outside of their regular job. For instance, hiring teachers as Visiting

Faculty is being widely used both in public and private sector universities. Other than

paid teaching assignments, certain individuals may be involved in running their own

personal private type education or tuition systems or some else kind of paid or income

generating nature of job. Therefore, based on this observation, it was assumed that

involvement or non-involvement in paid side jobs could have an impact. Therefore,

based on this information, sample was divided in two groups; those who reported

involvement in paid side jobs and those who reported non-involvement in paid jobs.

Age. Information related to this continuous demographic variable was taken.

For analysis purpose in main study, this variable was also converted into nominal

variable by splitting sample into two groups. The first group represents younger group

of participants and the second group represents older participants.

Gender. Sample was divided into groups of men and women participants to

examine the impact of gender.

Education. For entry level teachers under the hierarchical status as lectures,

the 16 years of education (masters degree) is a basic requirement. However, certain

universities prefer to hire lectures having research degree (M.Phil or MS with 18

years of education). For hiring assistant professors, research degree and certain level

of experience is the basic criterion. For associate professors and professors, doctorate

76

degree with certain level of experience and publications is a basic criterion. Therefore,

education serves as an important indicator in teachers’ hiring and promotional system.

To examine the impact of education, the sample was divided into two groups. The

first group includes participants having masters degree and the second group

comprises participants with research degree or doctorate level education.

Marital Status. This demographic information categorized respondents in

groups of married vs. single participants.

Research Design

Present study was conducted in two phases. The phase I was conducted as

pilot study by involving a sample of university teachers (N = 102), aimed to determine

preliminary psychometric issues involving the reliability and validity indices of the

measures used in current investigation for our indigenous population. Further, the

pilot study step aimed to see the pattern of results for basic hypothesized predictive

and outcome relationship of study variables. The phase II of present research was

conducted as main study involving sample of university teachers (N = 426). The step I

of the main study aimed to examine the factorial validity of study measures to see

how well the sample of present study supports the existing structure of the constructs.

After scrutiny of study measures merged through confirmatory factor analyses, the

data was then subject to further analyses under step II of the study. In step II,

specifically, the hypothesized predictive and moderated relationships between study

variables were examined.

77

Chapter III

PHASE I: PILOT STUDY

Pilot Testing of Study Measures and Preliminary Testing of the Model of

Work Environment and Outcomes

The phase I of the study (i.e., pilot study) was conducted following two steps.

Step I of study aimed to achieve the following objective.

1. To examine the psychometric characteristics of the measures of the study i.e.,

estimates of reliability (Cronbach’s alpha reliability) and validity (inter-scale

correlations) of study measures for the sample of current study.

Step II of the study aimed at achieving the following objectives:

2. To investigate the predictive relationship of work environment with burnout.

3. To investigate the predictive relationship of work environment with


Method

Participants

The participants of pilot study comprised of full time permanent teachers of

public and private sector Universities of Pakistan. They belonged to the universities

and post-graduate colleges of Punjab Province of Pakistan; located in the cities of

Rawalpindi, Islamabad, and Lahore. In order to select comparable public and private

78

sector universities, the ranking of the Universities by Higher Education Commission

(HEC) of Pakistan was followed. This ranking system is based on assigned scores to

different universities based on the performance in different functional areas of output

(e.g., number of pass out graduates, publications, research output, etc). Since, allotted

scores were varied in magnitude; therefore, as an inclusion criteria only those

universities were selected which were showing above average scores (as per based on

the range of allotted scores to the universities). (i) full time permanent of universities

employed on the position of; lecturers, assistant professors, associate professors, and

professors; (ii) pay scale associated with rank, e.g., system of basic pay scale (BPS)

applicable in government sector. As part of initial try out study for examining the

feasibility of random sampling, return rate of the questionnaire by participants was

very low up to 10%. A very important factor of low response rate remained the lack

of cooperation of selected participants. Therefore, it was decided to adopt the non-

random convenience/ opportunity sampling procedure as a mean to enhance the

response rate.

In total, a sample of 150 university teachers was approached, who belonged to

six universities of Punjab province (from Rawalpindi, Islamabad and Lahore

provinces). Researcher contacted the teachers individually and briefed about the

objectives of the study. Only those participants were selected who showed their

consent to participate in the study. The participants were handed over with a

questionnaire pack (including informed consent form, demographic information sheet,

Work Environment Questionnaire, Maslach Burnout Inventory- Educators Survey,

Organizational Commitment Questionnaire, and Mini Marker Set). With response rate

of 70%, 105 university teachers returned the completed questionnaires; among these

three incomplete questionnaires were dropped. The sample of this phase of study

79

comprised of 102 teachers of universities of Punjab, who showed their willingness to

participate in to study and completed the written consent form. For detailed

descriptive profile of the sample, see Appendix I.

Measures

For nominal data related to respondents’ personal job related and demographic

information, a demographic sheet was used. This includes information related to

organizational variables (sector, hierarchical status, job duration in the present

organization, Natural vs. Social Sciences department, and teachers’ Involvement in

other paid jobs) and demographic variables (age, gender, education and marital

status). The respondents were asked to report the required information.

Following is the detailed description of the measures used in the study.

The Work Environment Scale (WES). Employee’s perception of work

environment was measured by using English version of the Work Environment Scale

(WES; Moos, 1994), a very popular well-known measure used for the assessment of

employees’ perception of their work environment and operating institutional attributes

or psychosocial properties of environments. Reason for using English version was that

that English is a medium of instructions at university level and all communication and

reading material is in English, therefore the researcher’s decided to use the original

version of WES, which is already being used in original form in earlier studies

80

(Maqsood & Rehman, 2004; Rehman & Maqsood, 2008) having quite satisfactory

validity indices.

WES measures how people perceive the environment and how these

perceptions influence their behavior. WES comprises ten subscales comprises nine

items in each (see Appendix D for details of corresponding items in each

subscale/dimensions). Sample items for each subscales are as follows: “The work is

really challenging” (for Involvement), “People go out of their way to help a new

employee feel comfortable” (for Coworker Cohesion), “Supervisors usually

compliment an employee who does something well” (for Supervisor Support),

“Employees have a great deal of freedom to do as they like” (for Autonomy),

“Getting a lot of work done is important to people” (for Task Orientation), “There

always seems to be an urgency about everything” (for Work Pressure), “things are

sometimes pretty disorganized” (for Clarity), “People are expected to follow set rules

in doing their work” (for Managerial Control), “Doing things in a different way is

valued” (for Innovation), “Work place is awfully crowded” (for Physical Comfort).

The participants of pilot study were administered original WES, this has

dichotomous (True/ False) response format; score ‘I’ is assigned to true items and ‘0’

to false items. The score of WES ranges from 0-9 on each nine subscales and total

score ranges between 0-90 on the aggregate. Items numbers are reverse scored (see

Appendix D). The total score for each subscale was obtained by summing the scores

on each item; the total score on subscales except for work pressure and managerial

control were collectively summed up. For work pressure and managerial control,

scores for each subscale were reversed by subtracting each of the subscale scores

from 9. Finally, adding the total of reverse scores for these two subscales to the total

scores of other subscales yielded the overall score. As regards interpretation of the

81

total sum of scores on WES, Moss’s recommendation was followed which explains a

“positive” work environment score can be designated to an above average score on

the overall sum of the subscales; whereas, a below average score could be considered

as “negative” (R. H. Moos, personal communication, March 10, 2009).

Moos (1994) has reported internal consistency reliabilities for 10 subscales of

WES ranged from .69 to .86 (N = 1045), and for subsequent sample (N = 742) range

from .68 to .82 Moos (1994) reports WES as a cross-culturally valid instrument. The

use of WES in present study is based on the evidence of demonstrated use in studies

in context of Pakistan. However, for present study, no committee based assessment

procedure was used to evaluate the face validity of the instrument to be used for

sample of teachers. Studies conducted in Pakistan using English version of WES in

various organizational settings yielded satisfactory evidence. For instance, Rehman

and Maqsood (2008) reported satisfactory estimates of reliability (Cronbach’s Alpha

coefficients for WES’s total scores (.78) and subscales of relationship dimension (.71)

personal growth (.52) and system maintenance change (.75) on a sample of 500

Pakistani university teachers. The study also reported the satisfactory ‘construct

validity’ of WES. Maqsood and Rehman, (2004) reported the satisfactory internal

consistency reliability estimates of WES on a sample (N = 130) of service provider

telecommunication company. Munir’s study (2005) has also used the WES in source

language within academic settings.

The Maslach Burnout Inventory-Educators Survey. Assessment of

Burnout was performed using 22 items Maslach Burnout Inventory (MBI-ES)–

Educators Survey (MBI: Maslach, Jackson, & Leiter, 1996). This is a popular

measure frequently used in academic settings. The MBI-ES comprises of three

82

subscales namely: Emotional Exhaustion (EE); Depersonalization (DP) and reduced

sense of Personal Accomplishment (PA). The emotional exhaustion subscale

(measured by item nos. 1, 2, 3, 6, 8, 13, 14, 16, 20) assesses feelings of being

emotionally over-extended and exhausted by one’s work. One example item for

emotional exhaustion is: “I feel fatigued when I get up in the morning and have to

face another day on the job”. The depersonalization subscale (measured by item # 5,

10, 11, 15, 22), measures an unfeeling and impersonal response toward recipients of

one’s service, care, treatment, or instruction assessed by items e.g., “I feel I treat some

students as if they were impersonal objects”. The Personal accomplishment (measured

by item # 4, 7, 9, 12, 17, 18, 19, 21) assesses feelings of competence and successful

achievement in one’s work with people assessed by items e.g., “I feel I’m positively

influencing other people’s lives through my work”. The inventory measures the

frequency of experiences of feelings related to each subscale using anchors ranged

from 0 (never) to 6 (always).

The possible score range on total MBI-ES is 0-88 and scores falling on and

above the median scores are considered as the cut-off scores. Some studies conducted

in Pakistan have reported yielded satisfactory internal consistency estimates of MBI.

Basir (2006) reported satisfactory estimates of reliability (Cronbach alpha coefficient)

for emotional exhaustion (.64) depersonalization (.34) personal accomplishment (.66)

and .61 for the total scores on MBI. Munir (2005) reported satisfactory estimates of

Cronbach’s alpha coefficient for emotional exhaustion (α = .79), depersonalization (α

= .62), and personal accomplishment (α = .71) on sample of university academic staff

of Library Sciences and Computer Sciences from a provincial university of Punjab

province of Pakistan. Substantial empirical evidence is available for satisfactory

validity estimates of the measure (Maslach, Jackson, & Leiter, 1996).

83

The Organizational Commitment Questionnaire. For the assessment of

organizational commitment the measure developed by Meyer and Allen (1993) was

used. The OCQ aims to measure employee’s experience of organizational

commitment as three simultaneous mindsets encompassing; affective, continuance,

and normative commitment. Originally, the questionnaire comprises of 24 items

(Meyer & Allen, 1991) with eight items in each sub domain. Meyer, Allen, and Smith

(1993) later revised a six -item measure of normative commitment. Studies conducted

in Pakistan, have mainly used 22-item OCQ (see e.g., Hussain, 2004; Hussan, 2008;

Rashid, 2000). Meyer and Allen (2004) reported variation in number of items in using

the OCQ questionnaire as a way of modification for reducing scale length which

thereof is important to test through pilot test.

The affective commitment is related with emotion-based view of commitment;

this includes items like “I feel a strong sense of belonging to (name of organization)”.

The continuance component represents a perceived cost of leaving an organization

and represents calculative and exchanged-based view of commitment to the

organization. This includes item like “Too much in my life would be disrupted if I

decided I wanted to leave (name of organization) now”. The normative component is

based on feelings of moral obligations or responsibilities and this is considered to

developed as a result of employee’s socialization experiences and emphasizes

employees obligations of ‘remaining’ in the organization. For instance, one item

states that “It would be wrong to leave (name of organization) right now because of

my obligation to the people in it”.

In the present study we have used 22-item OCQ, it’s a five point Likert type

scale, items ranging from Strongly agree to strongly disagree, with scoring of strongly

agree (5), agree (4), neutral (3), disagree (2), and strongly disagree (1). Some items

84

are reverse scored (i.e., no’s 1, 5, 10, 13, and 17). Items 1-9 correspond to Affective

component, 10-16 to Continuance Component, whereas items 17-22 correspond to

Normative Component. Rashid (2000) reported alpha coefficient equivalent to .57 for

total scores on OCQ, .73 for affective commitment, .58 for continuance commitment,

and .70 for normative commitment on sample of teachers in Pakistan.

The Mini-Markers Set. The Mini-Marker Set developed by Saucier (1994) is

based on Goldberg’s Big Five theory of personality (1992). The Mini-Markers subset

as an abbreviated version of Goldberg’s Big-Five Personality Inventory comprises 5

subscales with 8 items for each factor. Each subscale may have a score range of 8 to

40. The shorter bipolar inventory measures the five factors of personality by

adjectives like talkative, energetic (for extraversion), cooperative, cold (for

agreeableness), organized, practical (for conscientiousness), jealous, moody (for

emotional stability), and deep, imaginative (for openness). The sub-scale of

extroversion measures the extent to which an individual is sociable, active, optimistic

and fun loving. Agreeableness covers individual traits like helpful, trusting, kind and

cooperative. Conscientiousness primarily describes one’s task orientation, hard work,

reliability and socially required impulse control. Emotional Stability indicates one’s

capacity to remain calm and composed and being free from traits, which carry

negative emotional tone. Intellect or Openness scale represents creativity, originality,

imagination and complexity. The Mini Marker Set was used in this study with 9-point

Liket type scale. These include positive (efficient, kind) and negative (inefficient,

unsympathetic) items; which assess respondents’ response in varying degrees from

accurateness (extremely accurate = 9, very accurate = 8, moderately accurate = 7,

and slightly accurate = 6) to inaccurateness (extremely inaccurate = 1, very

85

inaccurate = 2, moderately inaccurate = 3, and slightly inaccurate = 4) along with a

middle value (neither inaccurate nor accurate = 5).

Saucier (1994) described that abbreviated MMS with 40 items has impressive

features. The subscales of abbreviated MMS are reported having significantly high

inter-item consistency reliability estimates than that of the 100 MMS, however indices

of alpha reliability coefficient are still constantly low (typically by 0.05 to 0.10).

Subscale scores of abbreviated MMS correspond closely with the scores of full set of

100 markers. Saucier (1994) reported that abbreviated factors scores of MMS

correlated at 0.92-0.96 in raw data and 0.91-0.96 on scored data, with the

corresponding factors of full MMS. Some advantages of the MMS are this has

reduced items and the time taken to complete MMS is reduced.

Based on an empirical procedure of face validity adopted in a study (Shahid,

2006), the author came with synonymous explanation for certain items of MM as

reported difficult in terms of understandability. The present study used the same

version keeping in view its demonstrated face validity to be used in context of

workplaces. Basir (2006) reported Cronbach’s alpha reliability coefficients of five

subscales of MM as follows: extraversion (α = .59), agreeableness (α = .47),

conscientiousness (α = .66), emotional stability (α = .36), and intellect or openness (α

= .65) on a sample of Pakistani university academics.

Procedure

For the purpose of data collection, researcher contacted the relevant

administrative staff of the universities of Punjab personally. These included Incharge

student affairs, respective Deans of the Faculties, Directors/ Head of the Departments,

86

Registrar, or Incharge of Faculty Affairs. The researcher debriefed them in writing

about the objectives and outcome of the study. Researcher also explained to them

about the estimated period for the data collection and she shared with them about the

study protocols and synopsis. After obtaining the written informed consent by the

respective authorities of each university, in agreement with respective

Deans/Registrars/In-charge Faulty to the respective HODs would then send formal

letters/Memos to the teachers, which introduces the researcher, explains the purpose

of study and the outcome of research. This has further facilitated the researcher for

contacting the participants of study.

The criterion of selection of participants of the study was that only those

University teachers were included who showed their willingness to participate in the

study. These were approached individually, they were briefed about the purpose and

objectives of the study. They were also assured about the confidentiality of the data

and that the information obtained would purely be used for research purpose. Those

who consented to take part in study, were requested to complete the written ‘informed

consent form’ and to confirm their availability (day/time) to participate in the study.

The participants were also given general instructions to complete the

questionnaires, were also requested to provide their comments regarding their

experience while responding to the study questionnaires. Majority of the participants

agreed to return the completed questionnaires to the researcher within a week time.

The participants were contacted after 3-4 days to enquire about the completion of the

questionnaires. Follow up telephonic reminders were helpful in getting the completed

questionnaires from the respondents on time. The researcher personally collected

these completed questionnaires from the participants of the study, whereas some

respondents preferred posting the completed questionnaires to the researcher.

87

Results

The data of pilot study (N = 102) collected from the university faculties of

public and private sector universities was analyzed using SPSS 15.0.

Descriptive Analysis

At initial level, descriptive analysis of the data included mean, standard

deviations, and skewness of scores distribution was computed. Furthermore, to

estimate the relationship between variables, Pearson Product Moment correlations

were computes. Cronbach’s Alpha coefficients were computed to see the internal

consistency of study measures.

Findings reported in Table 1 explains the descriptive trends in data set through

computing levels of variables (see high, medium, & low scores) with corresponding

mean and standard deviation values on measures of study i.e., work environment,

burnout, organizational commitment, and personality dimensions. This is particularly

meaningful to relate the general understanding of study variables how generally the

sample is showing orientation to what level of the variables. Median of scores were

considered as cut-off scores; i.e., scores falling above median scores were considered

as high and below median as low and falling on median as average scores. For

burnout, values within parentheses are based on previously established cut-off scores

computed on normative sample of teachers mentioned in MBI scoring sheet (Maslach,

Jackson, & Leiter, 1996). These cut-off scores comprising of the following: emotional

exhaustion (high 27 or above, moderate 17-26, low 0-16), depersonalization (high 14

or above, moderate 9-13, low 0-8), and personal accomplishment (for reversed

scoring, high 0-30, moderate 31-36, low 37 or over). The Table 1 displays general

88

trends of data of university teachers of Pakistani academic settings. The findings

indicate positive psychosocial facets of perceived work environment; as reflected in

higher mean values on positive dimension of work environment. Respondents were

high on personal accomplishment. Comparison based on cut-off scores derived from

normative sample of teachers, highlighted that present sample highly endorsed the

dimension of personal accomplishment. It is interesting that levels of

depersonalization have found to be similar when computed against median based cut-

off scores and cut-off scores representing normative sample. They endorsed higher

level of affective commitment compared to continuance and normative commitment.

Findings further highlighted that respondents strongly endorsed high level of

agreeableness.

Table 2 presents the pattern of relationship of predictive variables with

criterion and moderator variables along with obtained Cronbach’s alpha coefficients

explaining the internal consistency of study measures.

91

The above table indicates a significantly high magnitude of Cronbach’s alpha

reliability coefficient for the total scores of WES (α = .89) and high alpha coefficients

for primary subscales of Relationship Dimension (α = .79), Personal Growth

Dimension (α = .77), and System Maintenance and Change Dimension (α = .80). For

secondary subscales of WES, the magnitude of alpha coefficients ranged from .73 to

.53. Table 2 further indicates high magnitude of alpha reliability coefficient on total

scores of MBI (α = .81), it yields moderate magnitude of alpha reliability coefficients

for its subscales, which ranges from .60 to .67. High Alpha Reliability Coefficients is

yielded for Organizational Commitment Questionnaire (α = .76), for subscales of

OCQ (i.e.) it ranged from.54 to .71. The findings in Table 2 further indicates, that for

Mini Markers Set (MMS), the Cronbach’s alpha coefficients was high for

extraversion (α = .76), agreeableness (α = .80), conscientiousness (α = .83), openness

(α = .78), to low on emotional stability (α = .27).

The findings further indicated that certain dimensions of WES including

coworker cohesion (r = -.29, p < .01), clarity (r = -.35, p < .05), and physical comfort

(r = -.29, p < .01) have inverse relationship with emotional exhaustion. Whereas,

work pressure has positive association with emotional exhaustion (r = .40, p < .01)

depersonalization (r = .20, p < .05), involvement (r = -.24, p < .05), coworker

cohesion (r = -.25, p < .05), supervisor support (r = -.23, p < .05), task orientation (r =

-.21, p < .05), clarity (r = -.22, p < .05), and physical comfort (r = -.22, p < .05) have

found to be inversely linked with depersonalization. Task orientation (r = .24), clarity

(r = .22) and innovation (r = .23) have significant positive relationship with personal

accomplishment.

Burnout as a unitary factor is showing association with most of dimensions of

WES. Significant positive relationship is observed between each subscale of work

environment with affective commitment except for coworker cohesion and autonomy.

92

Supervisor support (r = .26, p < .01) and innovation (r = .20, p < .05) are significantly

related with normative commitment.

Organizational commitment is significantly related with involvement (r = .25,

p < .05), supervisor support (r = .26, p < .01), work pressure (r = .20, p < .05), clarity

(r = .27, p < .01), managerial control (r = .25, p < .05), innovation (r = .30. p < .01)

and physical comfort (r = .22, p < .05). Except extraversion and openness in most of

cases, other personality dimensions are showing significant associated with certain

dimensions of WES. For example, agreeableness (r = .32, p < .01) and

conscientiousness (r = .47, p < .01) are showing stronger relationship with totals

scores on WES. The interpretations of relationship between study variables discussed

above, may help interpreting the discriminant validity of the constructs (for

interpretation Pl see discussion).

Since, the obtained values of skewness are not greater than 1.0 and are less

than -1.0; therefore, it may be interpreted that the skewness is substantial and

distribution of scores is somewhat less symmetrical.

93

Impact of Work Environment on Burnout

The predictive relationship of work environment with outcome variables was

tested on pilot sample using multiple linear regression analysis. Since, the rationale of

research involves exploratory case as a base of paucity of research evidence in our

cultural context; therefore, methodology of regression analysis- Entry method was

preferred. This is especially more preferable in this research case to see the

contribution of each one of the predictor of WES model. Pallant (2007) suggested that

standard multiple regression analysis is preferable as it treats set of independent

variables as a group and evaluates the predictive power of each of the independent

variable.

The exact reflection of population data might not be a case with small pilot

sample; therefore, for pilot study, values of Adjusted R2 will be interpreted instead of

R square. Nicola, Richard, and Rosemerg (2006) suggested that Adjusted R2 provides

useful estimate of the success of the models as it takes into account the number of

predictor variables in the model and the number of participants.

Following tables (Table 3-to-7) presents the results of regression analyses on

scores of burnout components as well as on overall burnout scores regressed against

scores on work environment scale (subscales and total score).

94

Table 3

Multiple Regression Analysis on scores of Emotional Exhaustion by Work

Environment (N = 102)

Emotional Exhaustion

Work Environment Variables B SE B Β 95% CI

LL UL

Involvement -.76 .70 -.15 -2.15 .63

Coworker Cohesion -.71 .52 -.15 -1.74 .34

Supervisor Support .78 .53 .17 -.32 1.79

Autonomy .43 .48 .10 -.53 1.38

Task Orientation .27 .57 .06 -.87 1.41

Work Pressure 1.7 .43 .40** .82 2.51

Clarity -1.76 .54 -.44** -2.82 -.70

Managerial Control .15 .50 .03 -.84 1.14

Innovation .57 .43 .14 -.29 1.42

Physical Comfort -.18 .52 -.04 -1.21 .85

R = .60, R2= .36, ∆R2= .29 (F = 5.02**)

**p ≤ .00

Results in Table 3 indicated moderate association (Multiple R = .60) between

emotional exhaustion and work environment variables showing overall significant

model F(10, 91) = 5.02, p < .00. Magnitude of Adjusted R2 (∆R2= .26) implies that

together subscales of work environment (work pressure and clarity) accounts for 26%

change in emotional exhaustion. Results highlighted work pressure as significant

positive predictor of emotional exhaustion (β = .40, t = 3.91, p < .01). To elaborate

further, this indicates that one unit increase in work pressure will result in a 1.66

increase in emotional exhaustion (B = 1.66). The dimension of Clarity as negative

predictor of emotional exhaustion implies that introducing one unit increase in clarity

will decrease emotional exhaustion by 1.76 units (β = -.44, B = -1.76, t = 3.29, p <

95

.01). Evaluating the strength of individual predictors, clarity (β = -.44) is better

predictor compared to work pressure (β = .40). To ensure that multicollinearity was

not a problem in regression analysis, variance inflation factor (VIF) and tolerance

statistics was calculated for each regression coefficient. The potential values of

variance inflation factor should be below 10. Tolerance greater than .20 is considered

as satisfactory estimate (as cited in Field, 2005). The obtained values of VIF are in

acceptable range ranged from .38 up to .73 suggested that multicollinearity is not

likely a threat to the substantive conclusions drawn from the data.

Table 4

Multiple Regression Analysis on scores of Depersonalization by Work Environment

(N = 102)

Depersonalization

Work Environment Variables B SE B β 95% CI

LL UL

Involvement -.44 .40 -.17 -1.23 .35

Coworker Cohesion -.22 .30 -.09 -.81 .38

Supervisor Support -.19 .30 -.06 -.80 .41

Autonomy .48 .28 .21 -.07 1.03

Task Orientation -.49 .33 -.21 -1.14 .17

Work Pressure .62 .24 .29* .13 1.10

Clarity .01 .31 .01 -.59 .62

Managerial Control -.03 .29 -.01 -.60 .54

Innovation -.04 .25 -.02 -.52 .45

Physical Comfort -.10 .30 -.04 -.69 .49

R = .44, R2= .19, ∆R2= .10 (F = 2.16*)

*p < .05

96

Results in Table 4 indicated that association between depersonalization and

work environment variables (F(10, 91) = 2.16, p < .05) is a moderate fit, indicating

that the model explains 19% of the variance (∆R2= .29) in depersonalization. Work

pressure indicated significant positive association (β = .29, t = 2.54, p < .05) which

implies that increase in work pressure by one unit accounts for increase in

depersonalization by .62 units (B = .62). The values of tolerance fall within acceptable

range that is below 10.

Table 5

Multiple Regression Analysis on scores of Personal Accomplishment by Work


Personal Accomplishment

Work Environment Variables B SE B

Β

95% CI

LL UL

Involvement -.32 .56 -.09 -1.44 .79

Coworker Cohesion .29 .42 .09 -.54 1.12

Supervisor Support -.29 .43 -.10 -1.14 .56

Autonomy -.20 .39 -.07 -.97 .57

Task Orientation .78 .46 .25 -.13 1.70

Work Pressure .19 .34 .07 -.49 .87

Clarity .24 .43 .09 -.61 1.10

Managerial Control -.23 .40 -.07 -1.03 .56

Innovation .54 .35 .20 -.14 1.23

Physical Comfort .10 .42 .03 -.72 .93

R = .34, R2= .12, ∆R2= .02 (F = 1.22)

p = n.s

97

Findings in Table 6 indicated that work environment dimensions are not

significantly contributing in predicting personal accomplishment (∆R2= .02, F(10, 91)

= 1.22, p = n.s).

Table 6

Multiple Regression Analysis on total scores of Burnout by Work Environment (N =

102)

Burnout

Work Environment Variables B SE B Β 95% CI

LL UL

Involvement -.88 1.23 -.10 -3.33 1.57

Coworker Cohesion -1.21 .92 -.15 -3.05 .62

Supervisor Support .83 .94 .11 -1.03 2.70

Autonomy 1.11 .85 .15 -.58 2.79

Task Orientation -1.00 1.01 -.13 -3.01 1.01

Work Pressure 2.09 .75 .30* .60 3.58

Clarity -1.99 .95 -.30* -3.87 -.11

Managerial Control .35 .88 .04 -1.40 2.09

Innovation -.01 .76 -.00 -1.52 1.49

Physical Comfort -.38 .92 -.05 -2.20 1.43

R = .52, R2= .27, ∆R2= .19 (F = 3.38**)

*p < .05, **p = .00

Results in Table 6 indicate a moderate fit of association between burnout (total

score) and work environment variables (R = .52, F(10, 91) = 3.38, p < .01). The

model accounts for producing 19% variability in burnout scores (∆R2 = .19). Among

subscales of work environment, work pressure and clarity are significantly

contributing in producing variance in burnout scores. Work pressure as positive

98

predictor (β = .30*) explains increase in burnout scores by 2.09 units. Clarity as

negative predictor (β = -.30*) explains decrease in burnout by 1.99 units.

This seems interesting to examine the effect of work environment (as total

scores) on total scores of burnout and its individual components. Following analysis

shows pattern of findings for total scores on burnout, and its subscales namely

emotional exhaustion, depersonalization, and personal accomplishment regressed

against total scores on work environment.

Table 7

Regression Analysis on Burnout and its components by total scores of Work


Work Environment B SE Β 95% CI

LL UL

Burnout

-.32 .12 -.26 -.56 -.08

R = .26, R2= .07, ∆R2= .06 (F = 6.93*)


-.11 .08 -.14 -.25 .04

R = .14, R2= .02, ∆R2= .01 (F = 2.01)

Depersonalization

-.08 .04 -.21* -.16 -.01

R = .21, R2= .05, ∆R2= .05 (F = 4.74*)


.13 .05 .25* .03 .23

R = .25, R2= .06, ∆R2 = .054 (F = 6.82*)

*p < .05

Results in Table 7 indicate significant role of work environment (total scores)

to accounts for variation in overall burnout (∆R2 = .06, F(1, 100) = 6.93, p = .01).

99

Findings indicated non-significant predictive role of work environment in predicting

emotional exhaustion (∆R2 = .01, F(1, 100) = 2.01, p = n.s). For depersonalization,

the value of Adjusted R2 (∆R2 = .045) explains 4.5 percentage of variance in

depersonalization with significant model fit (F(1,100) = 4.74, p < .05). Evaluation of

unstandardized Beta weights (B = -.08) suggests that one unit increase in work

environment will result in .08 decrease in depersonalization. The significant t-value

revealed that work environment is a significant negative predictor of

depersonalization (β = -.21, t = 2.18, p < .05). Work environment as positive predictor

of personal accomplishment (β = .25) account for 5.4% (∆R2 = .054) variance with

significant model fit (F(1,100) = 6.82, p < .05) is significant. Beta weight indicated

that one unit increase in work environment will result in .13 increase in personal

accomplishment (B = .13, t = 2.61, p < .05).

Impact of Work Environment on Organizational Commitment

To assess the pattern of predictive relationship of work environment with

organizational commitment, following tables (8-12) present the findings of regression

analyses performed on scores of affective, continuance, normative, and overall

commitment regressed against the subscales of work environment.

100

Table 8

Multiple Regression Analysis on Affective Commitment by Work Environment (N =

102)

Affective Commitment

95% CI

LL UL Work Environment Variables B SE B Β

Involvement .55 .39 .20 -.22 1.32



Autonomy -.09 .27 -.04 -.62 .44


Work Pressure .24 .24 .11 -.22 .71

Clarity .25 .30 .11 -.34 .83

Managerial Control .72 .28 .26** .17 1.26

Innovation .47 .24 .22* -.00 .94


R = .56, R2= .31, ∆ R2= .24 (F = 4.15**)

*p ≤ .05, **p < .00

Findings in Table 8 indicate that work environment facets including

managerial control and innovation are significant positive predictors of affective

commitment. Both dimensions account for a moderate fit of the model (∆R2= .24,

F(10, 91) = 4.15, p < .00). Managerial control and innovation predicted 24% variance

in affective commitment. The unstanderized beta-value, scoring high on managerial

control scale by one unit implies that affective commitment will increase by .72 units

(t = 2.60, p < .05). This suggest high scores on innovation by one unit will result

increase in affective commitment by .47 units (B = .47, t = 1.98, p < .05). Comparing

the strength of significant predictor revealed that managerial control (β = .26) is

101

stronger predictor compared to innovation (β = .22). The obtained values of VIF are in

acceptable range.

Table 9

Multiple Regression Analysis on Continuance Commitment by Work Environment (N

= 102)

Continuance Commitment

95% CI

Work Environment Variables B SE B Β LL UL

Involvement -.28 .34 -.13 -.96 .39


Supervisor Support .21 .26 .12 -.30 .73

Autonomy .13 .23 .07 -.33 .60

Task Orientation -.13 .28 -.07 -.69 .42

Work Pressure .04 .21 .02 -.37 .45

Clarity .02 .26 .01 -.50 .53

Managerial Control .44 .24 .21 -.04 .92

Innovation .37 .21 .22 -.04 .79


R = .33, R2= .11, ∆ R2= .01 (F = 1.08)

p = n.s

Results of correlation matrix revealed that continuance commitment is not

significantly related with any of predictor variables (see Table 2). Consistent with

this, the results as shown in Table 9 indicated that work environment facets do not

significantly account for predicting variance in employees’ continuance commitment

(∆R2= .01, F(10, 91) = 1.08, p > .05).

102

Table 10

Multiple Regression Analysis on Normative Commitment by Work Environment (N =

102)

Normative Commitment

95% CI


Involvement -.19 .32 -.09 -.83 .46



Autonomy .38 .22 .22 -.06 .82

Task Orientation .12 .27 .07 -.41 .64

Work Pressure .12 .20 .07 -.27 .51

Clarity -.09 .25 -.06 -.58 .41


Innovation .26 .20 .16 -.14 .65


R = .31, R2= .10, ∆ R2= -.00 (F = .98)

p = n.s

Results as shown in Table 10 indicate that work environment variables do not

significantly account for predicting variance in employees’ normative commitment (∆

R2= -.00, F(10, 91) = .98, p > .05).

Following table represents the extent of predictive relationship between work

environment variables and taking organizational commitment as a composite factor.

103

Table 11

Multiple Regression Analysis on total scores of Organizational Commitment by Work



95% CI


Involvement .09 .75 .02 -1.41 1.58



Autonomy .42 .52 .10 -.61 1.45


Work Pressure .40 .46 .10 -.51 1.31

Clarity .18 .58 .04 -.97 1.32

Managerial Control 1.24 .54 .25* .18 2.30

Innovation 1.10 .46 .27* .18 2.02

Physical Comfort .23 .56 .05 -.88 1.34

R = .49, R2= .24, ∆ R2= .15 (F = 2.80*)

*p < .05

Regression analysis on total scores of organizational commitment by work

environment variables (Table 11) indicated that overall relationship is significant

(∆R2= .15, F(10, 91) = 2.80, p < .05). The magnitude of model fit is low indicating

that managerial control and innovation together accounts for producing 15.1%

variability in organizational commitment. Increase in managerial control by one unit

accounts for an increase of 1.24 units in organizational commitment (B = 1.24, t =

2.32, p < .05). Similarly, increase in innovation by one unit results an increase in

organizational commitment by 1.10 units (B = 1.10, t = 2.38, p < .05). The

unstanderized beta-values indicated that innovation (β = .27) is comparatively

104

stronger predictor than managerial control (β = .25). The values of tolerance fall

within acceptable range.

To estimate the pattern of findings for total scores on organizational

commitment and its subscales regressed against total scores on work environment,

bivariate regression analysis was performed.

Table 12

Regression Analysis on Organizational Commitment and its components by total

scores on Work Environment (N = 102)

Work Environment B SE B β 95% CI

LL UL Organizational Commitment

.27 .07 .36** .13 .41

R =.36, R2= .13, ∆R2= .12 (F = 15.27**) Affective Commitment .18 .04 .44** .11 .25 R = .44, R2= .19, ∆R2 = .19 (F = 24.61**) Continuance Commitment .03 .03 .08 -.04 .09 R = .08, R2= .01, ∆R2= -.00 (F = .65) Normative Commitment .07 .03 .23* .01 .12 R = .23, R2= .05, ∆R2 = .04 (F = 5.33*) *p < .05, **p ≤ .00

Findings as shown in Table 12 indicated that by taking work environment as a

composite factor to predict unitary organizational commitment, the magnitude of

model fit (∆R2 = .12) although not fairly high revealed the significant overall

relationship (F(1, 100) = 15.27, p < .00) by contributing 12 % of variability in the

organizational commitment. This implies that one unit increase in work environment

will result .27 increase in organizational commitment (B = .27). The associated t-

105

value (t(101) = 3.91, p = .00) reflects that work environment is a significant predictor

of organizational commitment. The value of Adjusted R2 (∆R2 = .19) with significant

F ratio (F(1, 100) = 24.61, p < .00) reflects that work environment significantly

accounts for 19% variance in affective commitment. Assessing beta weights reflect

that as work environment increases by one unit, affective commitment increase by .18

units (B = .18, β = .44, t = 4.96, p = .00). For normative commitment, work

environment accounts for marginal variance up to 4 % (∆R2= .04, F(1, 100) = 2.31, p

< .05). Assessing beta weights reflect that an increase of one unit in work

environment results in increase of normative commitment by .07 unit (B = .07, β =

.23, t = 2.31, p = .02). Further, results revealed work environment does not

significantly (B = .07, β = .23, t = .81, p > .05) accounts for any increase or decrease

unit changes in continuance commitment.

106

Discussion

The pilot study intended to explore the psychometric properties of the

measures used in study on the participants of University teachers. This also focused to

find out the pattern of results/findings in testing the model of work environment and

its outcomes along with personal variables as moderating variables.

Psychometric Issues

Investigating the psychometric properties of the measures in pilot testing is an

important preliminary step, which allows evaluating the quality and suitability of the

measures for the sample of the study. This further helps confident use of measures in

the main study on a large sample. Reliability indices of the measures calculated using

Cronbach’s Alpha Reliability coefficient. Moreover, the discriminant validity of the

measures would reflect the psychometric soundness of the measures. The internal

consistency reliability of the measures of study for the participants of study

(university teachers i.e., lecturers, assistant professor and professors) was reported.

Indices of Work Environment Scale (WES) indicate significant alpha reliability

indices for primary scales of Relationship Dimension (α = .79), Personal Growth

Dimension (α = .77) with 27 number of items each, and System Maintenance and

Change Dimension with 36 items (α = .80). The reliability coefficients of secondary

sub scales with nine items each ranged from .73 to .53. Here, this is to be mentioned

that magnitude of alpha coefficients in secondary subscales is low compared to

primary dimensions; this may be attributed to lesser number of items in secondary

107

sub-scales. Results indicate that the alpha reliability coefficient obtained for total

scores on English version of WES (α = .89) is high. English form of WES has been

used in Pakistan in various studies using sample of academicians (Imam, 1993;

Rehman & Maqsood, 2008). The results of pilot study indicate that scores on WES

present a high magnitude of alpha reliability coefficient on subscale of involvement (α

= .89) and relatively moderate on managerial control (α = .53). More recently, a study

(Rehman & Maqsood, 2008) reported somewhat similar trend of internal consistency

estimate for subscale of managerial control (α = .49) on sample of university teachers.

Evaluation of qualitative responses obtained during pilot study indicates that

the study questionnaires generally received a very positive response by the

participants of study. This further indicate that none of them had difficulty in

understanding the items or and reading the instructions. However, in case of WES,

72% of the respondents showed their concern that responding to items in simple true/

false option seems forced-choice response pattern the respondents tend to have

consensus on that it would have been better if these items were arranged in a Likert

Type scale allowing them a choice to explain their degree of agreement to

disagreement. However, none of them reported that they are not satisfied with tier

responses on WES. This issue was further discussed with judges following

‘Committee Approach’, Judges included five teachers teaching course of research

methods and psychometrics.

Based upon the recommendations of expert committee and consultation with

the author (Moos) and supervisors, we decided to use five-point Likert type response

format for WES. This revised format of WES (i.e., response categories true, mostly

108

true, false, and mostly false) was further administered on small pilot sample of

university teachers of public and private sector University (N = 40).

The inclusion criterion for sample of this stage of study was similar as applied

for sample of the main pilot testing. The data obtained by this pilot was analyzed by

using SPSS 15.0. Cronbach’s Alpha Reliability coefficients were calculated on the

scores of WES. In our study, obtained magnitude of Cronbach’s alpha reliability

coefficient is .68 for secondary subscale of involvement, .49 for coworker cohesion,

.59 for supervisor support, .46 for autonomy, .61 for task orientation, .64 for work

pressure, .67 for clarity, and a low of .38 for managerial control, .74 for innovation,

.52 for physical comfort, and .68 for total scores on WES. Subscales of clarity,

managerial control, innovation, and physical comfort comprise the primary dimension

of system maintenance and change dimension and as overall yielded high magnitude

of alpha coefficient equivalent to .82. Therefore, in case of managerial control, an

increase in magnitude of alpha coefficient may observe using larger sample in main

study. An earlier study of Abraham and Foley (1984) pointed out an important

concern that that reliability estimate of Likert-type format of WES may possibly be

low in magnitude compared to dichotomous options.

The present researcher fully acknowledge the concerns of WES’s author

(Moos) regarding changing the response format and its possible impact on reliability

and validity indices.’ In this context Moos was consulted; he communicated that

...’reliability and validity are joint function of scale items and response formats and of

the characteristics and diversity of specific samples (Moos, 1990). Straker (1989)

emphasized that in using WES, researchers should use some procedure to enhance the

response rate of the respondents because it has shown as the problematic aspect.

109

Following these comments, perhaps one of the reasons for preferring Likert response

format may serve as a way to enhance the response rate.

Mehwish (2006) used WES on a sample of university teachers. She

highlighted possible limitations in using WES is its dichotomous response format,

which restricts the subjects’ responses, she suggested using Likert-type format in

future researches. Moreover, qualitative responses of out participants of study

highlighted that majority of the respondents in pilot study showed preference for

variation in response format. Committee approach and recommendation of original

author R. H. Moos (personal communication, March 10, 2009), we decided to use

Likert response format with dichotomous scoring in main study.

The results of pilot study indicated satisfactory estimate of internal

consistency of the Maslach Burnout Inventory (MBI). Reliability analysis on scores of

MBI yielded high magnitude of alpha coefficient for total score (α = .81). The

moderate magnitude of alpha coefficient has obtained for the subscales of emotional

exhaustion (.60), depersonalization (.65), and for personal accomplishment (.67).

Here, comparatively the magnitude of alpha coefficient for emotional exhaustion is

low. Since, Cronbach’s alpha estimates of internal consistency are essentially an

average of inter-item correlations for a scale; therefore, examining the effect of

individual items on reliability coefficient would also be helpful in this regard. The

alpha coefficient was compute using SPSS function of ‘scale reliability if item

deleted’. It showed that excluding item 14 of emotional exhaustion “I feel I’m

working hard on my job”, yielded an increase in alpha coefficient up to .22.

Subsequently, confirmatory factor analysis will provide a more detailed analysis of

this item.

110

Moreover, studies using English version of MBI in Pakistan has reported the

magnitude of Cronbach’s alpha coefficients in somewhat similar manner compared to

present study. Basir (2006) reported Cronbach’s alpha coefficient of .64 for emotional

exhaustion, .34 for depersonalization, .66 for personal accomplishment, and .61 for

total scores on MBI for sample of university teachers on sample of university

teachers. On varied sample, Munir (2005) reports Cronbach alpha coefficient of .79

for emotional exhaustion, .62 for depersonalization, and .71 for personal

accomplishment.

The scores on Organizational Commitment Questionnaire (OCQ) yielded that

total scores produces an alpha coefficient of .76 along with its subscales off affective

commitment (α = .68), continuance commitment (α = .55), and normative

commitment (α = .71). However, comparatively low magnitude has obtained for

continuance commitment (α = .55). Examining the reliability analysis by estimating

the effect of individual item on scale reliability demonstrated that deletion of item 13

“It wouldn’t be too costly for me to leave the organization now” could change the

magnitude up to .58. Since, this increase is marginal; therefore, thorough picture will

be clear in main analysis. Consistent with this finding, a study using sample of

Pakistani school teachers (Rashid, 2000) reported relatively low magnitude of alpha

coefficient for continuance commitment (α = .58).

The five scales of Mini Markers Set demonstrated satisfactory internal

consistency except for the factor of emotional stability. The magnitude of alpha

reliability coefficient for extroversion (α = .76), agreeableness (α = .80),

conscientiousness (α = .83), and openness (α = .78) are high. The low magnitude of

alpha coefficient has obtained for subscale of emotional stability (α = .28). The

111

reliability analysis by estimating the effect of individual item on scale reliability

showed that deletion of adjective no. 31 “Touchy” related to the factor of emotional

stability would add to increase the alpha reliability magnitude up to .53, which is

worth considering. We will examine this aspect further while we analyze the data of

main study using larger sample size. In comparison, previous study (Basir, 2006)

done in context of Pakistan also reported low alpha coefficient for factor of emotional

stability (α = .36) with sample (N = 40) of graduate and post graduate level teachers.

One possible reason for low alpha coefficient for subscale of emotional

stability might be that alpha is very dependent on the variability of the item and

subscale scores in the particular sample employed. This may be the fact that we may

not find much variations in our sample of (N = 102) University teachers. In the next

chapters of the study, the detailed confirmatory factor analysis would probably help

further clarify issues related with reliability indices of emotional stability.

Findings shown in Table 2 explain scales-total correlation- that is correlation

of subscales of a measure with total score is one of the procedures to estimate the

construct validity of the measures (see, Anastasi & Urbina, 1997). For example, each

of the subscale of the WES is showing highly significant correlation with total score

on WES. For burnout, emotional exhaustion (r = .84, p < .01), depersonalization (r =

.75, p < .01), and personal accomplishment (r = .24, p < .05) are showing significant

relationship with the total score of MBI. This indicates that subcomponent of burnout

are closer in examining the construct of burnout. For organizational commitment,

affective commitment (r = .82, p < .01), continuance commitment (r = .62, p < .01),

112

and normative commitment (r = .74, p < .01) are significant related with total score of

OCQ.

Moreover, bivariate correlations between subscales of measures (Table 2) may

also lead to estimation of distinctiveness of the constructs. For example, theoretically

the constructs of Supervisor Support and Managerial Control seems to be different.

The results in Table 2 indicated very weak correlation (r = .00) between both

constructs. Similarly, Managerial Control as part of System Maintenance dimension is

showing lowest magnitude of correlation with Relationship (r = -.01) and Personal

Growth (r = .15) dimensions, and with subscales of Involvement (r = .07) and

Coworker Cohesion (r = -.09). Similarly, weakest relationship of Work Pressure with

Relationship dimension (r = .02), Coworker Cohesion (r = -.20), and Supervisor

Support (r = .03) and Autonomy (r = .05) provides satisfactory estimate regarding the

distinctiveness of the construct. Innovation is showing weak relationship with work

pressure (r = .06) and managerial control (r = -.08). Moreover, apparently different

constructs including Task Orientation and Innovation (r = .15) are showing weakest

correlation. The desirable discriminant validity of the Work Environment Scale may

be inferred from this estimate.

Moreover, the measure of Mini Markers comprises a set of five independent

trait factor structures. Theoretically, for a satisfactory estimate of discriminant

validity, there should be low or weak correlation between trait structures. Results

revealed that personality trait of extroverted is showing low correlation with

agreeableness (r = .18), emotional stability (r = .10) and openness (r = .14).

Extroverted is showing significant but relatively low magnitude of correlation

coefficient with conscientiousness (r = .24).

113

Descriptive Trends of Data

Data obtained on the pilot sample of study was descriptively analyzed to

understand the patterns of scores obtained from the participants (university teachers).

While evaluating the mean scores of different indices of the work environment; it

seems quite interesting to observe that the participants of our study have endorsed

positive psychosocial facets of their work environment. High mean values for positive

work environment seems encouraging, especially from employee and employer’s

perspective, and is useful for the management. Majority of the teachers have endorsed

positive aspects of their work environment. The participants of our study highly

endorsed the dimension of task orientation, which indicates a high emphasis on

planning, efficiency and satisfactory accomplishment of the job targets. Similarly, the

dimensions of involvement in job and clarity of work procedures were categorized as

‘dominant’ aspects of their work environment. The participants of our study endorsed

average level of managerial control and physical comfort. Whereas, coworker

cohesion and the innovation aspect of the job, seems to appear relatively less

emphasized indicators of work environment in universities.

The trend of mean scores reflected that our participants reported significantly

high on personal accomplishment (M = 42.52), and emotional exhaustion (M =

25.96); whereas, teachers reported low experience of depersonalization (M = 17.00).

Since, participants are reporting high sense of competence and achievement and

comparatively low on emotional exhaustion and depersonalization; therefore, it

provides an estimation that they are not experiencing high level of burnout. If we see,

participants have dominantly endorsed the high levels compared to moderate and

114

average scores on emotional exhaustion and depersonalization. This indicates that

perhaps a moderate level of burnout is being experienced by the respondents.

Descriptive trends of data highlighted that participants of our study reported strong

sense of affective commitment towards their organizations. This further highlights

high level of endorsement of our participants for emotional attachment and

willingness to continue/remain with the organization. It may be inferred that being

socialized in a collectivist culture, perhaps we in a social unit are probably more

oriented for group affiliations. Being high on emotional attachment may possibly be

related to the product of the cognitive schemas of individuals living in a collectivist

culture. Mean scores obtained on the measure of personality indicated that

agreeableness is highly endorsed characteristic by the participants of our study,

compared to other dimensions of personality. This finding reflects that the participants

of our study are dominant on trait of agreeableness. However, computing high,

medium, and low levels on each of the personality dimension indicated that

participants have dominantly endorsed for high levels. This provides somewhat a

general idea about the personality profile of university teachers. In other words, its

encouraging that dispositional characteristics of teachers may positively contribute in

their performance. Future research may also need to look into exploring this aspect as

well.

The pattern of relationship of work environment with burnout using Pearson

Product Moment correlations, suggests further exploring of the relationships through

complex methodology e.g., regression analyses. The pattern of correlation coefficients

indicates that burnout is showing negative associations with most of dimensions of

work environment except with work pressure. The positive relationship between

115

burnout and work pressure is logical; as increase in work pressure may lead to an

increase in burnout experience. Workplace dimensions of ‘autonomy’ and ‘task

orientation’ are not showing significant relationship with burnout. If we see,

respondents have reported that academic settings are high on ‘task orientation’,

dimension. This helps to infer that workplaces with high emphasis on work planning

and emphasis on efficiency to complete the job assignments may logically explains

that their employees will possibly have less experience of burnout. Work pressure is

also showing positive association with emotional exhaustion and depersonalization;

whereas, non-significant relationship has shown with personal accomplishment. This

seems satisfying that work pressure does not seem having any considerable role in

effecting teachers’ sense of personal accomplishment. In order to prevent or mange

the burnout among employees, management of academic settings need to plan

strategies to enhance and monitor the relationship dimensions of their workplace. For

example, management needs to prioritize the involvement of teachers in different

requirements of their job. The cohesiveness among coworkers should be more

strengthen to promote the positive work spirit. Moreover, the system maintenance and

system change dimension has also shown important considerations in this regard. For

example, clarity of organizational rules and procedures, along with improving the

physical aspects of the work environment may contribute very effectively in

managing the burnout.

Work environment facets including ‘clarity’ and ‘physical comfort’ are

showing inverse relationship with emotional exhaustion; whereas, involvement,

coworker cohesion, supervisors’ support, task orientation, clarity, and physical

comfort are inversely related with feelings of depersonalization in our participants of

116

study. Furthermore, the dimensions of task orientation, clarity, innovation, physical

comfort, and overall work environment showed positive association with teachers’

personal accomplishment. Since teachers have dominantly endorsed for the positive

dimensions of their work environment along with high on personal accomplishment;

therefore, personal accomplishment is neutral to the negative aspects of the work

environment e.g., work pressure and managerial control.

The relationship of work environment with organizational commitment

revealed that organizational commitment of our participants of study is linked with

high emphasis on workplace organizational facets; which include involvement of

employees and supervisors’ support, work pressure, clarity, managerial control,

innovation and physical comfort. Affective component of the commitment is a

powerful factor, which is associated with most of psychosocial factors of our

participants of study. However, being emotionally attached to one’s organization

seems neutral of the extent of coworker cohesion and amount of autonomy to perform

one’s job. This is interesting to observe that the continuance based view of

employees’ commitment is neutral of any facets of the work environment. For

normative dimension of commitment, supervisor support and innovation might prove

potential variables during regression analyses. The pattern of results demonstrated

that affective commitment might be a potential dimension in later regression analyses.

To qualify personality traits as moderators, there should not be strong

correlation with work environment components. Findings highlighted that

extroversion is showing weaker association with work environment as a whole and

with each of its dimension. Agreeableness is showing weak relationship with

dimensions of coworker cohesion, autonomy, work pressure, managerial control and

117

innovation. Conscientiousness is showing significant relationship with most of work

environment components except for work pressure and managerial control. Emotional

stability is showing non-significant relationship with total of personal growth

dimension, autonomy, work pressure, clarity, managerial control, innovation, and

physical comfort. Openness is showing weak relationship with all work environment

components except for total of personal growth dimension and subscales of task

orientation and work pressure. This description indicates that dispositional dimensions

have shown variation in findings; which in turn further strengthened the need to

explore the role of personality dimensions in work environment and outcome

relationships.

Impact of Work Environment on Burnout and Organizational Commitment

According to objectives of the pilot study, the pattern of results in

investigating the role of work environment in predicting burnout and organizational

commitment was determined on the findings of pilot study. For regression analysis,

individual work environment variable were regressed against each dimension of the

constructs as well as on total scores. In predicting burnout, the work environment

facets of work pressure (personal growth dimension) and clarity (system maintenance

and change dimension) produced a significant equation when regressed against the

emotional exhaustion dimension of burnout. Both dimensions of work environment

accounts for 26% change in emotional exhaustion. Emotional exhaustion is found

inversely associated with clarity of work procedures in the workplace. This further

explains that keeping the work procedures explicit and well communicated to

118

employees in turn help to reduce role ambiguity among employees. Previously,

inverse relationship between clarity and emotional exhaustion has been reported

(Adali et al., 2003).

It seems fair to assume that our participants of study (i.e., university teachers)

in case feel overburdened or are required to work under pressure; this may lead to

emotional exhaustion. The positive association between work pressure and emotional

exhaustion obtained in pilot study carries existing empirical support (Goddard,

O’Brien, & Goddard, 2006; Robinson et al., 1991). Furthermore, work pressure is

also found as a contributory factor influencing our participants’ feelings of

depersonalization. Empirically supported, it’s logical to link that being high on work

pressure may leads to increase in depersonalization (Levert, Lucas, & Ortlepp, 2000;

Savicki, 2002). However, findings highlighted that effect of work pressure on

depersonalization accounts for relatively low level of variation up to 10%. The

remaining amount of variance responsible to produce depersonalization accounts for

reasons other than the work pressure.

Previous findings have provided support that positive aspects of the work

environment especially those aspects which are influencing emotional exhaustion, and

depersonalization may also effect the sense of personal accomplishment (Savicki,

2002). However, findings of present study demonstrated that teachers’ reported sense

of personal accomplishment is independent of the influence of different facets of the

work environment. Examining the mean scores showed that teachers have reported

high level of reduced sense of personal accomplishment; however, complex analysis

highlighted its negligible effects. Maslach, Jackson and Leiter (1996) recommends

reporting ‘Personal Accomplishment’ by directly computing sum of item scores rather

119

than as Diminished Personal Accomplishment based on reversed items. However, for

composite score, scores of personal accomplishment are first reversed to add up in

total scores of emotional exhaustion and depersonalization.

It is also noteworthy to comment on positive dimension of teachers’ attitude

that despite of demonstrated effects of workplace environment (in form of clarity and

work pressure) in developing emotional exhaustion, teachers are reporting positive

sense of personal accomplishment.

Taking burnout as a composite factor, work pressure (personal growth

dimension) and clarity of work procedures (system maintenance and change

dimension) produced significant equation. These factors of work environment were

contributing their role to produce 19% variation in overall reported burnout. Analysis

on total scores of work environment showed marginal variations in scores of burnout

and depersonalization in negative direction and showed positive association with

personal accomplishment. In conclusion, findings of pilot study highlighted that work

environment factors including work pressure and clarity are powerful predictors in

producing variation in burnout and its components. For management system, these

work environment factors stands out as important concerns for effectively preventing

the burnout among teachers. In academic settings, keeping employees involved in

work activities might directly help them to reduce their sense of depersonalization and

detachment from work activities. An optimum level of work pressure is always

required in work setting to monitor the output of teachers. However, it is very

important that management should realistically understand that how employees

perceive the extent of work pressure as it might link to alleviate the burnout among

120

teachers. This further suggests that the clarity of work procedures requires being well-

managed to prevent the symptoms of burnout.

In order to assess the pattern of relationship between work environment and

organizational commitment, non-directional hypothesis was preferred partly because

of scarcity of literature explaining the dynamics of relationship. Predicting teachers’

organizational commitment revealed that work environment facets including the

extent of managerial control and opportunities of innovation (system maintenance

and change dimension) in work produced a significant equation when regressed

against the affective commitment dimension of organizational commitment. The

dimensions of managerial control and innovation emerged as positive predictors and

accounted for 24 % variations in affective commitment among participants of our

study. Both factors showed positive association when regressed against the total score

of organizational commitment and accounted for 15% variation.

Previously, supervision control (Mobley, Griffith, Hand, & Meglino, 1979) or

management styles of influence (Ervin & Langkamer, 2008) and innovation (Stewart,

Bing, Gruys, & Helford, 2007) found to be associated with employees’ affective

responses. From the results of pilot study, we may infer that characteristics of work

conditions lead to affective attachment among employees. Furthermore, work places

providing stimulating environment to use innovative approaches to perform work

tasks seem to play contributory role to foster employees’ emotional attachment. This

emphasis on innovative approaches to get the job done seems very practical keeping

in view the requirements of the teaching profession itself. For example, planning the

courses and assignments, preparing and delivering lectures, stimulating students’

121

creativity and involvement in classrooms demands innovative attitude toward one’s

job.

Analysis on total scores of work environment highlighted positive association

with total scores on organizational commitment, with a comparatively stronger effect

on affective commitment, and with a marginal variation in normative commitment.

Despite the empirical support especially for relating continuance commitment with

work environment facets (Stewart, Bing, Gruys, & Helford, 2007); its very interesting

to observe that the findings of pilot study highlighted that Continuance and Normative

dimensions of the Organizational Commitment showed non-significant relation with

any of the facets of the work environment. Evaluating the mean scores revealed that

majority of teachers reported moderate to high concern for continuance and normative

dimensions of commitment to their organizations. However, this may be inferred that

their reported levels of commitment based on perceived cost of leaving an

organization is irrespective of psychosocial factors of their work settings. Similarly,

commitment to organization based on moral obligations of remaining in the

organization seems indifference to any of operating characteristics of the work

environment.

The previously mentioned discussion highlights that initial scrutiny of

psychometric issues provided considerable support regarding suitability of study

measures. Taking pilot study as preliminary quality control test provides logic to

further extensively analyzed psychometric indices using larger sample of the main

study. Extending this to further validate measures for indigenous sample of university

teachers in the context of Pakistan; it will be useful to examine how well sample

confirms the factor structure of measures. The findings of pilot study pointed out that

122

among different facets of work environment only few indices have yielded significant

predictive power; which thereof relates to in support of the arguments (Proctor &

Capaldi, as cited in Davis & Smith, 2005) to be cautious about the high probability of

disconfirming the true hypotheses in cases where empirical evidence is lacking and

thereby supports the formulation of non-directional hypotheses.

In conclusion, the findings of pilot study demosntarted that emotional

exhaustion has found to be negatively linked to clarity; whereas, work pressure had

showed positive association with emotional exhaustion and depersonalization. Work

pressure does account for explaining variance in depersonalization. Managerial

control and innovation are positive predictors and accounted for relatively stronger

variance in affective commitment. Above discussion highlights that whilst assessing

work environment and outcome relationships, some subscales of WES were able to

produce significant variance in the criterion variables. This may be assumed that a

more detailed understanding of hypothesized relations may emerge when this

relationship is analyzed on a larger sample. However, initial understanding of the

factor structure of the study measures seems important to examine as a way to

confirm the existing structure of the constructs on sample of university teachers. This

validation of constructs would help further to have more in-depth understanding of the

hypothesized relationships of study variables.

The proceeding chapter of main study thoroughly explains the processes and

outcomes of the next phases of the study.

123

Chapter IV

PHASE II: MAIN STUDY

The main study was carried out in two steps: the first step aimed to examine

the factor structure of study measures on sample of university teachers; and at second

step after scrutiny of factorial validity, data was subject to main analyses related to

hypotheses testing.

Step I: Examining the Measurement Models of Constructs

The first step of the main study focused to examine the factorial validity of

study measures. Since, the measures used in the study were developed in other

cultures and were validated on varied sample; therefore, it was meaningful to see how

well the existing stricture of study measure may confirm with sample of university

teachers of Pakistan. Additionally, the measures used in the present study are in

English language; therefore, it will be meaningful to evaluate their psychometric

issues in terms of examining the cross-cultural transferability of tools through testing

their factor structure with working group in Pakistani cultural context. Moreover, key

arguments for testing theoretical structure of study measures on larger sample of main

study and then subjecting data to further analysis instead of using data of pilot sample

is to control error variance associated with sample characteristics. Moreover,

generally it is expected that estimation of model fit involving Maximum Likelihood

(ML) estimation should reasonably be about 200 observations (Hox & Bechger,

1998). Another support for relevant sample size was derived from a meta-analytic

124

study mentioned that studies using confirmatory factor analysis have used sample size

ranged from 133 to 1,590 (Worley, Vassar, Wheeler, & Barnes, 2008).

Following are the specific objectives targeted in this step.

Objectives of Step I of the Main Study

1. Testing the ten factor model of work environment.

2. Testing the factor structure of competing theories of burnout to see how well a

one factor, a three factor, and a five factor model of burnout may confirms

with the sample of University teachers in Pakistan.

3. Testing the factor structure of unitary and a three factor model of the


4. Testing the factor structure of Big Five personality model to see how well it

may support by the data of present study.

Method

Participants

The participants of the main study comprises University teachers (N = 426)

belonging to public (n = 212) and private (n = 214) sector universities of three cities

i.e., Islamabad, Rawalpindi and Lahore (Pakistan) during the year 2008. The 575 test

booklets (containing measures of the study) were handed over personally to the

sample of study after explaining the purpose of study and obtaining their informed

consent. In total, twelve comparable public and private universities were selected on

basis of performance ranking criteria provided by Higher Education Commission of

125

Pakistan. Out of 575 participants, 445 respondents returned the completed

questionnaires, and 19 incomplete questionnaires were discarded. To deal with

missing data, the scoring equivalent to middle or neutral level was assigned in case of

burnout, organizational commitment, and personality measures. For measure of work

environment, in case of few missing responses, the missing value was replaced with

the Mean value. There were only two missing responses in terms of demographic

information which were kept as intact. The response rate in non-random -

opportunity/convenient sampling procedure was 77.4%. For the purpose of analysis,

data was sub classified as following: The information related to employees’

organizational related personal factors e.g., hierarchical status i.e., lecturers (i.e., entry

level rank: n = 185) and assistant professors, associate professors, and professors (i.e.,

high rank: n = 241), duration of job in current organization (M = 5.09, SD = 3.00),

comparison between social sciences (n = 198) vs. natural sciences (n = 228), and

involvement in any paid side jobs other than their regular job (involvement: n = 21,

non-involvement: n = 405). The mean age of participants of the study was M = 36.57

(SD = 8.96). These included 268 men and 158 women. They were classified having

master degree (n = 112) vs. participants having post graduate research (M.Phil) or

doctorate degree (Ph.D: n = 314), and marital status (married: n = 280; unmarried: n =

143). These informations were obtained through demographic information sheet.

Instruments

Participants of the main study using self-administration procedure responded

to a questionnaire pack including following questionnaires with Demographic

Information Sheet attcahed on top;

1) Work Environment Scale (WES; Moos, 1994) measuring perceptions of work

environment;

126

2) Maslach Burnout Inventory– Educators Survey (Maslach, Jackson, & Leiter,

1996) measuring burnout;

3) Organizational Commitment Questionnaire (Meyer & Allen, 1991) measuring

organizational commitment;

4) Mini-Markers Set (Saucier, 1994) measuring Big-Five dimensions of

personality

(see complete detail of the instruments under pilot study).

Procedure

After seeking formal written consent from the management of the selected

universities, the teachers of respective universities were approached individually by

the researcher. In some universities, Deans, departmental heads and administrative in

charge of the faculty were contacted initially to obtain their informed consent. The

respondents were given an average time ranges from 2-3 days. Only those included in

the study who consented formally to participate in the study. The respondents were

briefed about the objectives of the study and were provided with written instructions

to complete the questionnaires of the study. Follow-up procedure was adopted via

telephonic contact. The questionnaires were returned back personally by the

researcher, and in some cases these were sent to the researcher via post. At the time of

return of these questionnaires, it was ensured to check that the forms are completed

duly.

127

Testing the Factor Structure of Work Environment, Burnout, Organizational

Commitment, and Personality Measures

The measurement models of study variables were examined through

Confirmatory Factor Analysis with Maximum Likelihood estimation procedure using

LISREL 8.80.

Confirmatory Factor Analyses (CFA). Confirmatory Factor Analysis (CFA)

as an example of the measurement model of the Structural Equation Modeling

assuming Maximum Likelihood estimation was done using program of Linear

Structural Relations abbreviated as LISREL. CFA was conducted to test how well

data supports the factor structure of the measures on individual item scores available

for 426 participants. The purpose of assessing a model’s overall fit is to determine the

degree to which the hypothesized model as a whole is consistent with the empirical

data at hand. Statistical tests of the model for all tests are tests of differences between

the variance/covariance matrix predicted by the model and the sample

variance/covariance matrix from the observed data. Those differences are referred to

as “fit” or “goodness of fit”, namely how similar the hypothesized model is to the

observed data (Maruyama, 1998). A wide range of goodness-of-fit indices can be used

as summary measures of a model’s overall fit. Its difficult to rely only on any of the

indices due to the fact that we can’t say that any one is superior to other. As they

operate somewhat differently based on given sample size, estimation procedure,

model complexity, violation of the underlying assumptions of multivariate normality

and variable independence, or any combination thereof (Diamantopoulos & Siguaw,

2000). The fit indices used in study were; chi-square statistic denoted as Minimum Fit

Function Chi-Square, the Root Mean Square Residual (RMR), Root Mean Square

128

Error of Approximation (RMSEA), Goodness of Fit Index (GFI), the Adjusted

Goodness of Fit Index (AGFI), Incremental Fit Index (IFI), Normed Fit Index (NFI),

Comparative Fit Index (CFI), Akaike’s Information Criterion (AIC), and Consistent

Version of AIC (CAIC).

Chi-square likelihood ratio statistic is highly sensitive to small differences

and, hence, misleading in large samples. It is suggested that instead of reading chi-

square as a test statistic, one should regard it as a goodness (or badness)-of-fit

measure in a sense that large chi-square values correspond to bad fit and small chi-

square values to good fit (Diamantopoulos & Siguaw, 2000). One of the goodness-of-

fit indices based on residuals is root mean square residual (RMR), which is suitable

for judging between the fit of different models to the same data; the smaller the value,

the better the fit. There are no cut-off points as it happens to be in case of some of

other indices (Kline, 1993). Standardised RMR is used to overcome the problem

which can be raised in case of RMR due to effect on its value as a result of unit of

measurement. The values of Standardised RMR below .05 are indicative of acceptable

fit (Maruyama, 1998). For root mean square error of approximation (RMSEA)

coefficient values less than 0.05 are indicative of good fit, between 0.05 and under

0.08 of reasonable fit, between 0.08 and 0.10 of mediocre, and values fall greater than

0.10 indicates poor fit. The RMSEA is generally considered as one of the most

informative fit indices (Diamantopoulos & Siguaw, 2000). Cabrera-Nguyen (2010)

cited recommended cut-off values for RMSEA (≤ .06) and CFI (≥ .95).

The goodness of fit (GFI) and adjusted goodness of fit (AGFI) are other

widely used indices of goodness-of-fit indices based on residuals. The GFI should be

between 0 and 1. The data probably do not fit the model if the GFI is negative or

much larger than 1. The AGFI is the GFI adjusted for the degrees of freedom of the

model. The AGFI should be between 0 and 1. The data probably do not fit the model

if the AGFI is negative or much larger than 1 (Diamantopoulos & Siguaw, 2000). Hu

129

and Bentler (1995) mentioned that values greater than .90 denote acceptable fit for

GFI and AGFI.

Relative fit indices also known as “incremental” or “comparative” fit indices

which assess how much better the model fits compared to a baseline model, usually

the independence model. Incremental fit index (IFI), normed fit index (NFI), non-

normed fit index (NNFI), parsimonious normed fit index (PNFI), and the comparative

fit index (CFI) comes under this category of relative fit indices. All the indices in this

group have value range from 0 to 1, with values closer to 1 interpreted as good fit.

However, NNFI can take value greater than 1. The lower value of PNFI is desirable

compare do its non-parsimonious counterpart- NFI. Literature recommends that NNFI

and CFI are dominantly relied upon compared to other indices of this group

(Diamantopoulos & Siguaw, 2000). Hu and Bentler (1995) mentioned that values

greater than .95 are set as acceptable fit for CFI and greater than .90 for NFI.

The next set of fit indices based on information criteria incorporate the

parsimony of the hypothesized model in assessing its fit in comparison with other

models including independence model (baseline model) and the saturated model.

Independence model hypothesized that all variables are uncorrelated; while, saturated

model on another extreme proposes that number of parameters to be estimated is

equal to the number of variances and covariances among observed variables. Akaike's

Information Criterion (AIC) used in this perspective is interpreted for selecting the

best model among a number of candidate models. It’s desirable that model value of

AIC should be lower than independence and saturated model. The model that yields

the smallest value of AIC is considered the best. Another index namely consistent

version of AIC (CAIC) adjusts the AIC for sample size effects. Smaller value of

CAIC denoted as Model CAIC compared to Independence CAIC and Saturated CAIC

is preferable.

130

Factor Structure of Work Environment Scale

A dominant and mostly used measure of psychosocial work environment

namely Work Environment Scale (Moos, 1994) was tested for its ten factor structure

using scores on WES to test how well the data supports the existing structure of the

measure.

Goodness of Fit Indices. Following table displays goodness-of-fit indices for

10 factor model of WES.

Table 13

Goodness-of-fit statistics for ten-factor model of Work Environment (N = 426)

Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA = root mean square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; IFI = incremental fit index; NFI = normed fit index; CFI = comparative fit index; AIC = Akaike’s information criterion; and CAIC = Consistent Version of AIC.

Fit statistic Ten-factor Model

χ 2 8755.36 (p = 0.0)

df 3870

RMR .08

RMSEA .07

GFI .62

AGFI .60

IFI .78

NFI .66

CFI .78

AIC 12101.61

CAIC 13238.85

131

Findings as sown in Table 13 indicated that significant value of chi-square is

undesirable. To have better estimate of model fit, other indices will be evaluated. The

value of RMR should be below .05, which thereof id not good. The value of RMSEA

falls under reasonable fit. GFI and AGFI should closer to 1. Therefore, it somewhat

comes under mediocre level fit. In comparison of obtained values of GFI, AGFI, NFI,

the obtained values of IFI (.78) and CFI (.78) are better. The obtained value of Model

AIC (12101.61) is smaller than Independence AIC (26077.62) which is desirable.

However, the value of model AIC is greater than Saturated AIC (8190.00), which is

undesirable. For CAIC, as desirable, the obtained value of Model CAIC (13238.85) is

smaller than Independence CAIC (26532.52) and also with Saturated CAIC

(28887.93) as well. In comparison with AIC, the results of CAIC are representing

better model fit. The value of CN should be greater than 200; which in this case

(198.93) is of not up to the standard.

Overall, the values of CFI, IFI, RMSEA, CAIC are in good support of model

fit along with considering AIC as partial fit and GFI and AGFI as of mediocre fit.

Factor loadings of items with corresponding factors. Below is the detail of

factor loadings for ten factor model of WES.

132

Table 14

Factor loadings and Standard Errors for ten factor model of Work Environment (N =

426)

Item Nos.

Measure and Variable

Standardized Factor

Loading

SE

Involvement

1 The work is really challenging. .16 .97

11 There’s not much group spirit. .40 .84

21 A lot of people seem to be just putting in time. .15 .98

31 People seem to take pride in the organization. .36 .87

41 People put quite a lot of effort into what they do. .54 .71

51 Few people ever volunteer. .17 .97

61 It is quite a lively place. .48 .77

71 It’s hard to get people to do any extra work. .38 .85

81 The work is usually very interesting. .41 .83

Co-worker Cohesion

2 People go out of their way to help a new employee feel

comfortable. .14 .98

12 The atmosphere is somewhat impersonal. .33 .89

22 People take a personal interest in each other. .03 1.00

32 Employees rarely do things together after work. .21 .96

42 People are generally frank about how they feel. .48 .77

52 Employees often eat lunch together. .14 .98

62 Employees who differ greatly form the others in the

organization don’t get on well. .43 .82

72 Employees often talk to each other about their personal

problems. .07 .99

82 Often people make trouble by talking behind other’s back. .28 .92

Supervisor Support

3 Supervisors tend to talk down to employees. .08 .99

13 Supervisors usually compliment an employee who does

something well. .29 .92

Continued…

133

Item Nos.


Standardized Factor

Loading

SE

23 Supervisors tend to discourage criticism from employees. .32 .90

32 Supervisors usually give full credit to ideas contributed

by employees. .36 .87

42 Supervisors often criticize employees over minor things. .37 .86

52 Employees generally feel free to ask for a raise. .36 .87

62 Supervisors expects far too much from employees. .31 .91

72 Employees discuss their personal problems with

supervisors. .01 1.00

82 Supervisors really stand up for their people. .35 .88

Autonomy

4 Few employees have any important responsibilities. .08 .99

14 Employees have a great deal of freedom to do as they like. .48 .77

24 Employees are encouraged to make their own decisions. .48 .77

34 People can use their own initiative to do things. .53 .72

44 Supervisors encourage employees to rely on themselves

when a problem arises. .27 .93

54 Employees generally do not try to be unique and different. .13 .98

64 Employees are encouraged to learn things even if they

are not directly related to the job. .32 .90

74 Employees function fairly independently of supervisors. .37 .86

84 Supervisors meet with employees regularly to discuss

their future work goals. .25 .94

Task Orientation

5 People pay a lot of attention to getting work done. .45 .80

15 There’s lot of time wasted because of inefficiencies. .43 .82

25 Things rarely get “put off till tomorrow.” .05 1.00

35 This is a highly efficient, work-oriented place. .64 .59

45 Getting a lot of work done is important to people. .27 .93

55 There’s an emphasis on “work before play.” .27 .93

65 Employees work very hard. .55 .70

Continued…

134

Item Nos.


Standardized Factor

Loading

SE

75 People seem to be quite inefficient. .35 .88

85 There’s a tendency for people to come to work late. .36 .87

Work Pressure

6 There is constant pressure to keep working. .50 .75

16 There always seems to be an urgency about everything. .24 .94

26 People cannot afford to relax. .30 .91

36 Nobody works too hard. .26 .93

46 There is no time pressure. .37 .86

56 It is very hard to keep up with your workload. .27 .93

66 You can take it easy and still get your work done. .27 .93

76 There are always deadlines to be met. .32 .90

86 People often have to work overtime to get their work done. .07 .99

Clarity

7 Things are sometimes pretty disorganized. .34 .88

17 Activities are well-planned. .58 .67

27 Rules and regulations are somewhat vague and

ambiguous. .53 .72

37 The responsibilities of supervisors are clearly defined. .48 .77

47 The details of assigned jobs are generally explained to

employees. .54 .71

57 Employees are often confused about exactly what they

are supposed to do. .51 .74

67 Fringe benefits are fully explained to the employees. .40 .84

77 Rules and polices are constantly changing. .14 .98

87 Supervisors encourage employees to be neat and orderly. .38 .85

Managerial Control

8 There’s a strict emphasis on following policies and

regulations. .51 .74

18 People can wear wild looking clothing while on the job if

they want. .18 .97

Continued…

135

Item Nos.


Standardized Factor

Loading

SE

28 People are expected to follow set rules in doing their work. .34 .89

38 Supervisors keep a rather close watch on employees. .31 .91

48 Rules and regulations are pretty well enforced. .53 .72

58 Supervisors are always checking on employees and

supervise them very closely. .26 .93

68 Supervisors do not often give in to employee pressure. .09 .99

78 Employees are expected to conform rather strictly to the

rules and customs. .26 .93

88 If employee comes in late, he or she can make it up by

staying late. .04 1.00

Innovation

9 Doing things in a different way is valued. .42 .83

19 New and different ideas are always being tried out. .53 .72

29 This place would be one of the first to try out a new idea. .50 .75

39 Variety and change are not particularly important. .45 .80

49 The same methods have been used for quite a long time. .23 .95

59 New approaches to things are rarely tried. .31 .90

69 Things tend to stay just about the same. .31 .90

79 There is a fresh, novel atmosphere about the place. .56 .69

89 Things always seem to be changing. .08 .99

Physical Comfort

10 It sometimes gets too hot (room conditions). .12 .99

20 The lighting is extremely good (room conditions). .35 .88

30 Work place is awfully crowded. .08 .99

40 This place has a stylish and modern appearance. .41 .84

50 The place could stand some new interior decorations. .06 1.00

60 The colors and decorations make the place warm and

cheerful to work in. .30 .91

70 It is rather drafty (disorganized) at times. .33 .89

80 The furniture is usually well arranged. .54 .71

90 The rooms are well ventilated. .21 .96

Note. Factor loadings > .30 are in boldface.

136

Results of confirmatory factor analysis shown in Table 14 present the

standardised factor loadings along with residuals. For each factor, item with bold font

represent strong association of the item with its respective sub-scale. The initial

criterion to evaluate the factor loadings was set that values should be above .30.

According to the strength of factor loadings following items were considered for

further investigation to either to be retained or deleted form the measure: Involvement

(1, 21, & 51); Co-worker Cohesion (2, 22, 32, 52, 72, & 82); Supervisor Support (3,

13, & 73); Autonomy (4, 44, 54, & 84); Task Orientation (25, 45, & 55); Work

Pressure (16, 36, 56, 66, & 86); Clarity (77); Managerial Control (18, 58, 68, 78, &

88); Innovation (49 & 89); and physical Comfort (10, 30, 50, & 90).

Factor structure of Maslach Burnout Inventory-Educators Survey. The

present study has focused on examining the factorial structure of burnout measure

(Maslach, Jackson, & Leiter, 1996), to see whether it can be feasibly analyzed as a

unitary (one-factor) construct or whether it's a lot more complex than that with this

specific sample in this particular country. The complexity of multidimensional nature

of burnout measure was aimed to examine by testing three and five factor models

supported by the existing literature. CFA was computed using scores on burnout

measure to establish the most appropriate factor structure of burnout measure. Three

models of Maslach Burnout Inventory (MBI), as suggested by previous exploratory

factor analysis were tested; a one-factor, a three-factor, and a five-factor model. The

one-factor model tested to see the grouping of all items as one factor. Densten’s

research (2001) on factor structure of MBI reported to test original one factor model

compared with another one factor model with reduced items. However, Densten

reported that no previous test of burnout model as a unitary construct has been

137

reported in the literature. In comparison of models, his study also supported

multidimensional aspect of burnout compared to burnout as a unitary construct.

The second model to be tested contained three factors, as originally specified

by Maslach, Jackson, and Leiter (1996), namely emotional exhaustion (9 items),

depersonalization (five items), and personal accomplishment (eight items). Studies

using sample of teachers (Byrne, 1993; Evans & Fischer, 1993), elementary and high

school teachers of California (Gold, 1984), and cross validation on sample of 469

Massachusetts teachers (Iwanicki & Schwab, 1981), have demonstrated support for

three factor model. Using confirmatory factor analyses, studies have identified the

original three factor model ‘superior’ to other alternative models (Lee & Ashforth,

1990; Schaufeli & Van Dierendonck, 1993).

The third model to be tested was a five-factor model as suggested by previous

confirmatory factor analysis (Densten, 2001). This elaborated factor structure of

burnout with reduced items (19 items) was established using a sample of 480

Australian law enforcement managers. It suggested emotional exhaustion loaded on

two factors namely psychological (included items 6, 16, and 20) and somatic strain

(items 1, 2, 3, 8), depersonalization items loaded on a single factor (5, 10, 11, 15, and

22), personal accomplishment loaded on two factors namely self (4, 9, 18, and 19) and

others (7, 17, and 21).

Following is the description of findings obtained by LISREL output in form of

goodness of fit indices for each measurement model followed by factor loadings of

items along with values of residuals. The values arranged in descending order of the

strengths of the factor loadings so it’s clear as to which items are the most strongly

associated with each factor.

138

Goodness of fit indices obtained for testing measurement models of MBI.

Following table displays goodness-of-fit indices for each of the MBI models obtained

for unitary, three and five factor models of MBI

Table 15

Goodness-of-fit statistics for single, three and five-factor models of MBI (N = 426)

Fit statistic Single-factor Model Three-factor Model Five-factor Model

χ 2 718.24* 572.91* 347.84*

df 209 206 142

RMR 0.24 0.22 0.19

RMSEA 0.09 0.07 0.06

GFI 0.85 0.89 0.91

AGFI 0.81 0.86 0.89

IFI 0.88 0.93 0.95

NFI 0.85 0.90 0.91

CFI 0.88 0.93 0.94

AIC 937.22 680.31 474.84

CAIC 1159.62 917.87 717.45

*p = 0.0,

Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA =

root mean square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted

goodness-of-fit index; IFI = incremental fit index; NFI = normed fit index; CFI = comparative

fit index; AIC = Akaike’s information criterion; and CAIC = Consistent Version of AIC.

Findings shown in Table 15 display ten goodness-of-fit indices for each of the

models. Among absolute fit indices, the values of chi-square statistic (χ 2) denoted as

Minimum Fit Function Chi-Square for five-factor model is comparatively better in

terms of magnitude of obtained values. However, for each model, the value of chi-

square is significant which is undesirable to conclude about better fit of the models.

Kline (1993) mentioned that interpreting χ 2 test of significance especially in large

139

samples is more likely to fall under unacceptable range, even though the residual

matrix is small. Therefore, author mentioned that in such cases it’s recommended that

it’s best to ignore the test and focus on other indices. Comparing the magnitude of chi-

square values, the five factor model yielded a smaller value which is better.

For RMR, the five-factor model is more suitable. The RMSEA for one factor

model represents mediocre fit. The values for three-factor and five-factor model fall

under reasonable fit. The goodness-of-fit index (GFI) for five factor model is at

acceptable fit; whereas, its value for three factor model is also closer to 1. For AGFI,

five factor model is at better position. Among incremental fit indices, the value of IFI

and CFI comes under acceptable fit for three factor and five factor solutions.

However, the five-factor model is comparatively on edge due to slightly high values

of fit statistics closer to 1. Among parsimonious fit indices, the normed fit index NFI

is indicative of acceptable fit for three and five factor solutions. Since this value

should be closer to 1 and more desirably greater than .90; therefore, its value for one

factor model (.85) is also somewhat closer to 1. However, three and five factor

models are at better position. Evaluating PNFI for one factor (.77), three factors (.80),

and five factors (.76), values are lower than corresponding value of NFI which is

desirable for model fit.

Further, the values for Akaike’s information criterion (AIC) for one factor

model revealed value of hypothesized value (937.22) is acceptable as it’s less than

independence model (5735.29). However, it’s undesirable that model value is greater

than saturated model (506.00). Interpreting CAIC, value of model CAIC (1159.62) is

less than Independence CAIC (5846.49) and also less than Saturated CAIC (1784.77).

For three factor model, model value (680.31) is smaller than independence model

(5735.29) which is desirable. However, conflicting picture is portrayed due to greater

value in comparison with saturated model (506.00). Evaluating CAIC, similar picture

has been observed. For five factor model, value of Model AIC (474.84) is less than

140

Independence AIC (4491.63) but greater than Saturated AIC (380.00). In case of

CAIC, value of Model CAIC (717.45) is less compared to Independence CAIC

(4587.67) and Saturated CAIC (1340.34).

The statistics for three and five factor models are very closer in case of

RMSEA, IFI, NFI, CFI, and AIC with obtained values of fit-statistics slightly high or

comparatively at better position for five-factor model. The cutting edge for five factor

model has observed for CAIC and slightly on RMR, IFI, NFI, CFI, GFI and AGFI. In

conclusion, both three and five factor seems better solution as compared to unitary

model. However, five-factor solution comparatively is at better position.

Following is the detail of factor loadings for each model along with values of

standard errors.

Detail of factor loadings with corresponding factors. Below is the detail of

factor loadings for one factor, three factor, and five factor model solutions.

Table 16

Factor loadings and Standard Errors for one factor model of Maslach Burnout

Inventory (N = 426)

Item Nos.

Variables & Statements

Standardised Factor

Loadings SE

10 I have become more callous towards people since I took

this job. .68 .53

15 I don’t really care what happens to some recipients. .62 .62

Continued…

141

Item Nos.

Variables & Statements

Standardized Factor

Loadings SE

7 I deal very efficiently with the problems of my recipients. .58 .67

13 I feel frustrated by my job. .57 .67

8 I feel burned out from my work. .57 .68

17 I can easily create a relaxed atmosphere with my

recipients. .56 .69

5 I feel I treat some recipients as if they were impersonal

“objects”. .55 .70

11 I worry that this job is hardening me emotionally. .54 .71

12 I feel very energetic. .50 .75

20 I feel like I am at the end of my rope. .50 .75

18 I feel exhilarated after working closely with my

recipients. .49 .76

3 I feel fatigued when I get up in the morning and have to

face another day on the job. .48

.77

4 I can easily understand how my recipients feel about

things. .44 .81

16 Working directly with people puts too much stress on me. .43 .81

6 Working with people all day is really a strain for me. .41 .83

9 I feel I am positively influencing other people’s lives

through my work. .41 .83

21 In my work I deal with emotional problems very calmly. .41 .83

1 I feel emotionally drained. .38 .86

19 I have accomplished many worthwhile things in this job. .34 .88

22 I feel recipients blame me for some of their problems. .34 .88

2 I feel used up at the end of the day. .29 .91

14 I feel I am working too hard on my job. .00 1.00

Note: Factor loadings > .30 are in boldface.

Findings as shown in Table 16 revealed that item 14 is showing very weak

factor loading (.00) with total score of MBI. For item 2, factor loading is less than the

desirable criteria, e.g., .30.

142

Following table presents factor loadings and standard errors for five factor

model.

Table 17

Factor loadings and Standard Errors for three factor model of Maslach Burnout

Inventory (N = 426)

Item Nos.

Variables & Statements Standardised

Factor Loadings SE




face another day on the job. .57 .67

13 I feel frustrated by my job. .57 .67






14 I feel I am working too hard on my job. .10 .99

Depersonalization


this job. .71 .50




“objects”. .56 .69

22 I feel recipients blame me for some of their problems .35 .88


4 I can easily understand how my recipients feel about things .67 .55

17 I can easily create a relaxed atmosphere with my recipients .61 .62

Continued…

143

Item Nos.


Factor Loadings SE

18 I feel exhilarated after working closely with my

recipients. .55 .70

12 I feel very energetic. .52 .73







Findings in Table 17 indicated that item 14 is showing weak factor loading

(.10) against component of emotional exhaustion.

Following table presents factor loadings and standard errors for five factor

model with 19 items.

Table 18

Factor loadings and Standard Errors for five factor model of Maslach Burnout

Inventory (N = 426)

Item Nos. Variables & Statements

Standardised Factor Loadings

SE

Emotional Exhaustion (psychological strain)




Continued…

144

Item Nos.


Factor Loadings SE

Emotional Exhaustion (somatic strain)




face another day on the job. .64 .59


Depersonalization


“objects”. .57 .68


this job. .69 .52



22 I feel recipients blame me for some of their problems .36 .87

Personal Accomplishment (Self)



4 I can easily understand how my recipients feel about things. .52 .73

18 I feel exhilarated after working closely with my recipients. .57 .68


Personal Accomplishment (Others)


17 I can easily create a relaxed atmosphere with my recipients. .60 .64



Findings as shown in Table 18 indicated that factor loadings for each item is

acceptable in explaining five factor model of burnout.

145

Factor Structure of Organizational Commitment Questionnaire (OCQ)

CFA was computed using scores on OCQ measure to test how well the

hypothesized factor structure (Meyer & Allen, 1984) supported by data obtained on

sample of teachers. The unitary model of organizational commitment was also tested

as a comparison model to see support for multidimensional nature of the construct.

Goodness of fit indices obtained for testing measurement model of OCQ.

Following table displays goodness-of-fit indices for three factor model of OCQ.

Table 19

Goodness-of-fit statistics for a one-factor and three-factor model of OCQ (N = 426)

Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA =

root mean square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted

goodness-of-fit index; IFI = incremental fit index; NFI = normed fit index; CFI = comparative

fit index; AIC = Akaike’s information criterion; and CAIC = Consistent Version of AIC.

*p = 0.0

Fit statistic One-factor Model Three-factor Model

Χ 2 852.68* 636.76*

df 209 206

RMR .10 .10

RMSEA .09 .07

GFI .82 .87

AGFI .79 .84

IFI .83 .89

NFI .79 .85

CFI .82 .89

AIC 1079.87 783.97

CAIC 1302.26 1021.53

146

Findings shown in Table 19 highlighted goodness of fit indices which

indicated that significant value of chi-square has obtained for one factor and three

factor models, which is undesirable. RMR value is not below .05 for both models. The

value of RMSEA indicates reasonable fit for three factor model and represents

mediocre fit for one factor model. The values of GFI and AGFI are closer to 1; though

not greater than .90, but indicate reasonable fit. Both indices are comparatively

stronger for three factor model. The values of IFI, NFI, and CFI are reasonable as

ranged closer to 1 and are stronger for three factor model. To further compliment the

value of NFI (.85), the obtained value of Parsimony Normed Fit Index (PNFI) for

three factor model is smaller (.76) than NFI, which is desirable. Similarly, for three

factor model, the PNFI (.71) is smaller than obtained value of NFI (.79). For three

factor model, value of AIC is smaller than Independence model (4740.56), which is

desirable. However, conflicting picture is reflected as model AIC is greater than

Saturated AIC (506.00). In similar patter, one factor model showed desirable AIC

(1079.87) in comparison with independence model (4740.56) but conflicted when

compared against saturated model (506). To further evaluate AIC, value of CAIC for

one factor model (1302.26) and for three factor model (1021.53) is desirable as in

both cases it is smaller than Independence (4851.76) and Saturated (1784.77) models.

The CAIC is reflecting an adequate fit of the model with observed data for both

factors with somewhat more strong for three factor model. Overall, consistent with

theory, data found prominent support for three factor structure model of


147

Factor loadings of items with corresponding factors. Following tables

represents the factor loadings of each item along with values of residuals obtained on

scores of OCQ using one factor and three factor structure models.

Table 20

Factor loadings and Standard Errors for one factor model of Organizational

Commitment Questionnaire (N = 426)

Item Nos.

Variables & Item Statements Standardised Factor Loading

SE

4 I feel a strong sense of belonging to (name of

organization). .71 .50

2 I feel emotionally attached to (name of organization). .66 .56

7 I would be happy to work at (name of organization)

until I retire. .64 .59

3 Working at (name of organization) is a great deal of

personal interest to me. .61 .63

9 I enjoy discussing (name of organization) with people

outside of it. .57 .67

6 I am proud to tell others that I work at (name of


8 I really feel that many problems faced by (name of

organization) are also my problems. .54 .71

1 I do not feel like part of family (name of organization). .52 .73

21 It would be wrong to leave (name of organization) right

now because of my obligation to the people in it. .52 .73

5 (Name of organization) does not deserve my loyalty. .50 .75

20 (Name of organization) deserves my loyalty. .47 .78

12 Too much in my life would be disrupted if I decided I

wanted to leave (name of organization) now. .38 .86

Continued…

148

Item Nos.

Variables & Item Statements Standardised Factor Loading

SE

11 It would be very hard for me to leave (name of

organization) right now even if I wanted to. .36 .87

18 Even if it were to my advantage, I do not feel like it

would be right to leave (name of organization) now. .34 .88

19 I would feel guilty if I left (name of organization) now. .34 .89

22 I owe a great deal to (name of organization). .33 .89

17 I do not feel any obligation to remain with (name of


10 I am not concerned about what might happen if I left

(name of organization) without having another position

lined up.

.22 .95

14 Right now, staying with (name of organization) is a

matter of necessity as much as desire. .21 .96

16 One of the reasons I continue to work for (name of

organization) is that leaving would require considerable

sacrifices i.e., another organization may not match the

overall benefits I have here.

.16 .97

13 It wouldn’t be too costly for me to leave (name of

organization) now. .13 .98

15 One of the serious consequences of leaving (name of

organization) would be the scarcity of available

alternatives

.07 1.00

Note: Factor loadings ≥ .30 are in boldface.

Findings as shown in Table 20 pointed out items with factor loadings below

.30 including items 10, 14, 16, 13, and 15. These items will be examined further under

three factor model.

149

Table 21

Factor loadings and Standard Errors for three factor model of Organizational

Commitment Questionnaire (N = 426)

Item Nos.

Variables & Item Statements Standardised

Factor Loading

SE


4 I feel a strong sense of belonging to (name of


2 I feel emotionally attached to (name of organization). .69 .52

7 I would be happy to work at (name of organization)

until I retire. .64 .59

3 Working at (name of organization) is a great deal of

personal interest to me. .63 .60

6 I am proud to tell others that I work at (name of


9 I enjoy discussing (name of organization) with people

outside of it. .57 .67

8 I really feel that many problems faced by (name of

organization) are also my problems. .54 .71

1 I do not feel like part of family (name of organization). .53 .72

5 (Name of organization) does not deserve my loyalty. .49 .76


12 Too much in my life would be disrupted if I decided I

wanted to leave (name of organization) now. .67 .54

11 It would be very hard for me to leave (name of

organization) right now even if I wanted to. .51 .74

Continued…

150

Item Nos.

Variables & Item Statements Standardised

Factor Loading

SE

14 Right now, staying with (name of organization) is a

matter of necessity as much as desire. .50 .75

16 One of the reasons I continue to work for (name of

organization) is that leaving would require considerable

sacrifices i.e., another organization may not match the

overall benefits I have here.

.48 .77

15 One of the serious consequences of leaving (name of

organization) would be the scarcity of available

alternatives

.24 .94

13 It wouldn’t be too costly for me to leave (name of

organization) now. .15 .98

10 I am not concerned about what might happen if I left (name

of organization) without having another position lined up. .04 1.00


21 It would be wrong to leave (name of organization) right

now because of my obligation to the people in it. .70 .76

20 (Name of organization) deserves my loyalty. .53 .86

19 I would feel guilty if I left (name of organization) now. .49 .86

22 I owe a great deal to (name of organization). .45 .71

17 I do not feel any obligation to remain with (name of


18 Even if it were to my advantage, I do not feel like it

would be right to leave (name of organization) now. .38 .77


The findings as shown in Table 21 showed that items including 10, 13, and 15

are showing weak loadings for both one factor and three factor models. In

comparison, item nos. 14 and 16 showed stronger factor loadings when assessed

under three factor model.

151

Factor Structure of Mini-Markers Set (MM)

A recently emerged well-accepted personality structure comprising of Big

Five factors (Barrick & Mount, 1993) carries supporting evidence of its factorial

validity. John and Srivastava (1999) mentioned adequate model fit of Big Five

dimensions. Using data of university teachers on scores of MM measure, CFA was

computed to test how well the hypothesized factor structure fits the data obtained on

sample of teachers.

Goodness of fit indices obtained for CFA on scores of MM. Following table

displays goodness-of-fit indices for five factor model of MM.

Table 22

Goodness-of-fit statistics for five-factor models of MM (N = 426)

Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA = root mean

square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index;

IFI = incremental fit index; NFI = normed fit index; CFI = comparative fit index; AIC = Akaike’s

information criterion; and CAIC = Consistent Version of AIC.

*p = 0.0,

Fit statistic Five-factor Model

χ 2 2896.77*

df 730

RMR .45

RMSEA .09

GFI .72

AGFI

IFI

.68

.83

NFI .79

CFI .83

AIC 3516.70

CAIC 3971.60

152

The findings as shown in Table 22 highlighted that obtained value of chi-

square is significant which is not desirable. The value of RMR value is not below .05.

The value of RMSEA indicates mediocre fit. The value of GFI is somewhat closer to

1 and represents mediocre fit. The value of AGFI is less compared to GFI. IFI and

CFI are somewhat closer to 1 representing the reasonable fit.

NFI should also be closer to 1; although, its value is somewhat of mediocre fit.

Further evaluating the NFI on the basis of parsimony of the model, Parsimony

Normed Fit Index (PNFI) is lower than (.74) than NFI representing the better fit of the

model. The value of Model AIC (3516.70) is smaller than Independence AIC

(15949.32) which is desirable. However, it’s greater than Saturated AIC (1640.00)

which is undesirable. Further, complementing the value of AIC, the values of CAIC

are representing better fit of the model. The value of CAIC is smaller (3971.60) than

Independence CAIC (16151.49) and also smaller than Saturated CAIC (5784.64).

Factor loadings of items with corresponding sub-scales. Following table

presents the factor loadings along with residuals on scores of Mini Markers Set.

153

Table 23

Factor loadings and Standard Errors for five factor model of Mini Markers Set (N =

426)

Item

Nos.

Variables & Item

Statements Standardised Factor Loading SE

Extroversion

3. Bold .76 .41

4. Energetic .71 .50

2. Extroverted .60 .64

1. Talkative .57 .68

6. Quiet .41 .83

7. Bashful .40 .84

5. Shy .32 .90

8. Withdrawn .24 .94

Agreeableness

11. Kind .74 .46

9. Sympathetic .70 .51

12. Cooperative .68 .54

10. Warm .63 .60

15. Rude .57 .67

14. Unsympathetic .56 .68

16. Harsh .53 .72

13. Cold .19 .97

Conscientiousness

18. Efficient .76 .42

19. Systematic .74 .45

17. Organized .71 .50

23. Inefficient .62 .61

20. Practical .58 .66

24. Careless .56 .68

Continued…

154

Item

Nos.

Variables & Item

Statements Standardised Factor Loading SE

21. Disorganized .51 .74

22. Sloppy .43 .81

Emotional Stability

31. Touchy .63 .60

29. Temperamental .61 .63

30. Envious .61 .63

32. Fretful .48 .77

28. Jealous .47 .78

27. Moody .44 .81

26. Relaxed .07 .99

25. Un-envious .00 1.00

Openness

34. Imaginative .76 .43

33. Creative .74 .46

35. Philosophical .70 .50

36. Intellectual .62 .62

39. Uncreative .43 .82

38. Deep .36 .87

40. Un-intellectual .34 .88

37. Complex .16 .97


Findings as shown in Table 23 indicated that items including 8 (for

extroversion), 13 (for agreeableness), 25 and 26 (for emotional stability), and 37 (for

openness) are showing weak loadings with corresponding factors.

155

Discussion

The present study aimed to evaluate the psychometric issues of study

measures in terms of examining the cross-cultural transferability of tools developed in

English and testing their factor structure with working group in Pakistani cultural

context.

Factor Structure of Work Environment Model

Work Environment Scale (Moos, 1994) has been used in English language

with different samples of working groups in Pakistan (Imam, 1993; Maqsood &

Rehman, 2004; Rehman & Maqsood, 2008). However, no attempt was made to

examine the factor structure of the scale with respect to use with sample in cultural

context. Studies investigating the exploratory factor structure of the Work

Environment Scale have identified variations in findings. For example, studies have

identified seven factors (Booth, Norton, Webster, & Berry, 1976), and two factors

(Brookings, Chacos, Hightower, Howard, & Weiss, 1985). Moos (1994), however,

suggested that information about each three of underlying dimensions is desirable in

studies as it provides a comprehensive understanding of different facets of the work

environment. Further, he elaborated that the factor analytic dimensions identified in

different studies depend on conceptual considerations, characteristics of the specific

sample, decisions about statistical procedures, goodness of fit indices, and so on. The

scarcity of researches on confirmatory factor analysis of WES encouraged the focus

of present study to examine the items that may not well supported by the sample

156

responses. In that way, a more objective interpretation of further analysis may come

out.

For evaluating factor structure of WES, multiple solutions of CFA were tested,

e,g, testing with random sample as identified through SPSS procedure, examining

sub-sample, e.g., teachers of public sector employees, and also assessing

unstandardised solutions as well. The items were cross checked in obtained outputs of

multiple solutions before deleting from the measure.

For involvement, items including 1, 21, & 51 are showing weak factor

loadings; therefore, these items will be excluded from the inventory. For coworker

cohesion, items including 2, 22, 32, 52, & 72 will be deleted due to weak factor

loadings. For item 82 “often people make trouble by talking behind other’s back”, the

obtained factor loading (.28) is closer to the criteria. If we reset the criteria to retain

items as equivalent to factor loading of .25; there is a rationale to do so particular to

retain maximum items in WES. If we see the content of the item, it seems meaningful

to retain as it measures an important aspect which in turn may play very important

role to define how teachers see their relationships with colleagues. For supervisor

support items including 3 and 73 will be deleted. Item 13 “supervisors usually

compliment an employee who does something well” is showing factor loading of .29

which is considerable. Keeping in view the face validity of the item for assessing

supervisor support, the item will be retained.

For autonomy, item 4 “few employees have any important responsibilities”

will be deleted. There is possibility that interpretation of item may be done in

different context when responded by teachers of different hierarchical status. Item 54

“employees generally do not try to be unique and different” will be deleted due to

weak factor loadings. Item 44 “supervisors encourage employees to rely on

157

themselves when a problem arises” is showing factor loading of .27. However,

unstandardised solutions yielded factor loading of .33, which provided rational to

retain this item. Similarly, item 84 “supervisors meet with employees regularly to

discuss their future work goals” showed .25 factor loading and with unstandardised

solution, it was .27. Keeping in view the meaningfulness of the item to assess

autonomy of employees; there is rationale to retain the item by setting the criteria of

acceptable factor loading up to .25. For task orientation, item 25 “things rarely get put

off till tomorrow”, is showing very weak factor loading perhaps due to perception of

working culture of educational sector. Therefore, this item will be deleted. For item

45, the obtained factor loading is .27 which gets enhanced up to .30 for

unstandardised solution and .39 when examine don on sub sample of public sector

employees. Therefore, this item will be retained. For item 55, the factor loading .27 is

considerable in order to retain maximum items in the measure. For work pressure,

items including 16 and 86 will be deleted due to weak factor loadings. Whereas, item

36 (.26) yielded better factor loading equivalent to .34 with unstandardised solution.

Items including 56 and 66 with factor loadings equivalent to .27 will be considered to

retain due to importance of work pressure dimension as reflected through significant

findings obtained in pilot study results. Therefore it seems wise to decide to retain

maximum items in this dimension.

For clarity, item 77 “rules and polices are constantly changing” is showing

weak factor loading (.14). The content of the item seems less applicable as in

educational sector, particularly the Government sector, the rules and regulations once

decided are not frequently subject to change. Therefore, it seems logical that this item

is not sufficiently contributing in assessing clarity of work procedures. Henceforth,

this item will be deleted. For managerial control, item 18, 68, and 88 are showing

158

weak factor loadings; therefore, will be deleted. Item 58 “supervisors are always

checking on employees and supervise them very closely” with obtained factor loading

.26 will be retained to keep maximum items in inventory. Similarly, item 78

“employees are expected to conform rather strictly to the rules and customs” with

factor loading .26 will also be retained. Moreover, the content of items seems very

meaningful to assess managerial control. For innovation, item 49 “the same methods

have been used for quite a long time”, is showing weak factor loading and therefore,

will be deleted. Item 89 “things always seem to be changing” is showing very weak

factor loading perhaps due to working culture of educational sector. Therefore, this

item will be deleted. For physical comfort, items including 10, 30, 50 will be deleted

due to weak factor loadings. Item 90 “the rooms are well ventilated”, is showing

factor loading equivalent to .29. However, when examined on subsample of public

sector, it yielded factor loading equivalent to .29 with error variance .92. Therefore,

to keep maximum items in scale, item 90 will be retained.

In conclusion, following is the detail of retained items leaving the scale with

total of 66 items: Involvement (11, 31, 41, 61, 71, & 81); Co-worker Cohesion (12,

42, 62, & 82); Supervisor Support (13, 23, 33, 43, 53, 63, & 83); Autonomy (14, 24,

34, 44, 64, 74, & 84); Task Orientation (5, 15, 35, 45, 55, 65, 75, & 85); Work

Pressure (6, 26, 36, 46, 56, 66, & 76); Clarity (7, 17, 27, 37, 47, 57, 67, & 87);

Managerial Control (8, 28, 38, 48, 58, & 78); Innovation (9, 19, 29, 39, 59, 69, & 79);

and physical Comfort (20, 40, 60, 70, 80, & 90).

159

Factor Structure of Burnout Models

Examining the factorial structure of burnout measure, one factor model

(Densten, 2001), three factor model (Maslach, Jackson, & Leiter, 1996), and an

extended five factor model was tested to see how well data may support the

competing models of burnout. Testing the unitary model of burnout indicated low

factor loadings for certain items including item 2 (.29) and 14 (.00) showing high

values of residuals. The factor loadings for item 2 which phrased “I feel used up at the

end of the day”, will be observed in three and five factor solutions as well. One of the

possible factors might be related to understandability of this item. However, swapping

back to unstandardised solution for this particular item exceeds factor loading up to

.57. Item 14 “I feel I’m working too hard on my job” is showing no association with

the construct. This item might be effected by element of social desirability. In work

settings, and especially in our collectivist culture (see Hofstede, 2001), sometimes

people are not much expressive in reporting about what exactly they are feeling.

Testing the three facet model of burnout, findings indicated that for item 2

corresponding to emotional exhaustion, there is considerable increase in magnitude of

factor loading compared to one factor solution. However, residual is still of high

values. Item 14 “I feel I’m working too hard on my job” which corresponds to

emotional exhaustion is showing poor association also in three factor model. Here, the

pressure of social desirability or difficulty in understandability of this particular item

seems very relevant. The standardised solution provided factor loadings of each item

pertaining to the relevant factors do not fall below 0.40.

The extent of variance that items of one factor can explain for another factor

can be examined to see the inter-correlations between subscales. This overlap of

160

explaining variance might be taken as one of the possible explanations while

examining the strength of factor loadings. The subscale of emotional exhaustion (by

retaining and excluding item no 14) with strength of factor loadings from .62 to .42

has shown same magnitude of correlation coefficients with subscales of

depersonalization (r = .60, p = .01) and personal accomplishment (r = -.40, p = .01).

The subscale of depersonalization with range of factor loadings from .71 to .35 has

showed association with subscale of personal accomplishment (r = -.59, p = .01).

Testing the five factor model of burnout indicated that in comparison with one

and three factor models, item 2 is showing high strength of factor loading in five

factor solution along with a slight decrease in corresponding residual value as well.

For three factor solution, the magnitude of standardised factor loadings obtained for

each item do not fall below 0.40. For five factor solution, the factor loadings do not

fall below .41. In fact the factor loadings for each item are very similar across three

and five factor models. This indicates that both the three and five factor models

account for a similar amount of variance in each item.

The reliability estimate indicated that Cronbach’s alpha reliability coefficient

for original three factor model with 22 items (α = .50) got affected due to deletion of

one item from the inventory and reached at .47. For five factor solution, the alpha

coefficient for total inventory with 19 items demonstrated an alpha coefficient

equivalent to .47. In three factor solution, an increase in alpha coefficient has

observed for subscale of emotional exhaustion from .72 (for 9 items) to .74 (for 8

items). In five factor solution, emotional exhaustion with dimension of psychological

strain obtained an alpha coefficient of .50. For emotional exhaustion- somatic strain,

an alpha coefficient of .64 has obtained. The subscale of depersonalization

maintaining its original item nos. maintained an alpha coefficient of .69 both in three

and five factor solutions. The subscale of personal accomplishment with 8 items

161

indicated an alpha coefficient of .76. In five factor solution, personal accomplishment

with dimension of self received an alpha coefficient of .59 and for personal

accomplishment-others an alpha coefficient of .60 has obtained.

In conclusion, findings supported that five factor model of burnout tested with

19 items found prominent support without excluding any item. For three factor model

of MBI and also in unitary model, item 14 of emotional exhaustion was identified to

be deleted from the inventory. In comparison of three and five factor solution, the

strength of factor loadings is also somewhat similar. There is not much variation in

strength of factor loadings. However, the comparison of fit indices put five-factor

solution at better position. A recent meta-analytic study (Worley et al., 2008) based on

45 factor analytic studies reported support for three factor structure of MBI measure.

The findings of present study may also be considered as supporting evidence for three

factor structure of MBI measure. However, present study contributed in establishing

the support for Densten’s (2001) extending model of MBI measure. Since, present

study found support for both original three-factor model and for its elaborated

structure; therefore, examining both three and five factor solutions for subsequent

analyses might yield a detailed picture of the role of burnout dimensions.

Factor Structure of Organizational Commitment Models

The present study focused to examine how well the dominant three factor

model of organizational commitment (Allen & Meyers, 1990) seems feasible to apply

on sample of university teachers in our cultural context. For comparison, a unitary

model was also tested. The values of fit indices found prominent support for three

factor structure model of organizational commitment compared to unitary model.

162

Result of confirmatory factor analysis revealed that sub dimension of

continuance commitment is showing weak association with items including 10, 13,

and 15 for both unitary and three factor models. Considering the dominant three factor

model, item 15 “one of the serious consequences of leaving (name of organization)

would be the scarcity of available alternatives”, is comparatively showing better

association, though beyond the acceptable criteria, with the dimension of continuance

commitment with factor loading .24 and marginal contribution in producing variance

in responses (R² = 0.06). According to definition given by scale’ authors, the

dimension of ‘Continuance Commitment’ represents perceived cost of leaving an

organization. Evaluating the content of item 15, it reflects non-availability of

alternatives. If responses related to the concern with lack of available alternatives are

no more contributing in explaining commitment based on perceived cost of leaving an

organization, it probably indicates the strong sense of loyalty among teachers in many

of universities.

Responses given to item 13 i.e., “It wouldn’t be too costly for me to leave

(name of organization) now”, does not seem to produce variance (R² = .02). The

content of item reflects probably the perceived cost of changing a job. This indicates

that perhaps fear of losing one’s job due to lack of alternatives is a strong underlying

motive behind teachers’ attitude. The item does not seem to produce variance in the

responses of teachers commitment based on perceived cost of leaving an organization.

One of the possibilities behind this fear of losing job might be the lack of available

job alternatives.

Similarly, the content of item 10 “I am not concerned about what might

happen if I left (name of organization) without having another position lined up” is

not contributing in producing any variance (R² = 0.00) in responses towards

continuance commitment dimension. Linking the content of item 10 with the main

163

concept of continuance commitment which represents the perceived cost of leaving an

organization, the item is representing the aspect of Job Security. This finding also

draws attention to comment on the current employment situation and limited

opportunities in job market in Pakistan, which is applicable for our sample of

university teachers. The possible fear of losing one’s job is perhaps a potential factor

due to which this particular item does not seem to contribute to the factor of

Continuance Commitment amongst the sample of Pakistani university teachers.

Furthermore, the authors of the Organizational Commitment Questionnaire

defined that ‘Continuance commitment’ represents a perceived cost of leaving an

organization. The items in this dimension mainly validated on Western sample are

showing contrasting differences in sample of Pakistani university teachers. Gelade,

Dobson, and Auer (2008) mentioned that potential sources of organizational

commitment may depend on cultural characteristics. In present findings, cross-

cultural variations with reference to costs of changing jobs and the job security are

clearly visible. The responses of our participants on this factor partly reflect on the

problematic job situation/job market, which seems to be directly linked with weak

Pakistani economy and political instability. Findings further reveal that the

participants of our sample are showing least concern with lack of available

alternatives in case of leaving the job. This indicates a sense of continuance

commitment irrespective of the concern which identifies the lack of available job

alternatives. This situation perhaps is carrying double loaded information. We may

interpret this as presence of strong sense of loyalty with their organizations. While; on

other hand, teachers are also showing concern with cost of leaving job especially with

reference to job security, indicating that it might be a case of tolerating problems at

work and keeping quiet for the sake of holding one’s job and associated benefits from

it.

164

While critical evaluation of contents of rest of the retained items of

Continuance Commitment revealed that if we decide to delete the items, it wouldn’t

cause any effect in assessing the indicators intended to be measured by the respective

dimension. Shifting LISREL output options to unstandardised solutions, item 10 (.05),

13 (.17), and 15 (.28) are showing weak factor loadings with dimension of

continuance commitment.

Based on these observations, it may be concluded that confirmatory factor

analyses on scores of Organizational Commitment Questionnaire (22 items) is overall

well supported by the data except for the dimension of Continuance Commitment.

Findings suggested reducing certain items (10, 13, and 15), which were not

contributing a considerable variance in assessing the ‘Continuance Commitment’.

Therefore, for present sample leaving the subscale with remaining four items and total

of 19 items will help to refine the measure. In this way, the use of measure for

drawing inferences about study hypotheses may lead to strong effect size of the

findings. The estimate of Cronbach’s alpha coefficient also supported the deletion of

items from OCQ. For instance, the original form of Continuance Commitment has

shown alpha reliability coefficient of .52. By deleting items (10, 13, & 15) after the

results of confirmatory factor analysis, alpha coefficient rises up to .61. There is slight

increase in overall reliability of the scale as well. The alpha reliability coefficient of

scale with 22 items was .82 which rises up to .84 for scale with total of 19 items.

Keeping in view the retained items in the scale, the strength of factor loadings

ranged from .73 to .38. The relatively moderate nature of strength of factor loadings

might be associated with the overlap between subscales up to a substantial extent.

This would help to know how some items naturally associate with one factor and

could also be explaining a sizeable amount of the variance in another factor as well.

For example, the subscale of affective commitment is strongly related with normative

165

commitment (r = .50, p = .01) and also with subscale of continuance commitment (r =

.29, p = .01). Continuance commitment is also showing association with normative

commitment (r = .33, p = .01).

In conclusion, findings of CFA recommended excluding items 10, 13, & 15 of

continuance commitment leaving the inventory with 19 items compared to original 22

items.

Factor Structure of Big- Five factor Model of Personality

The widely used Big Five factor structure of personality (Saucier, 1994) in

organizational behavior research was examined to see how well the data of the study

supports its applicability. Results of confirmatory factor analysis revealed that for

scale of Extroversion, i.e., adjective 8 “Withdrawn” is showing weak association with

factor loading of .24. Here, an interpretation may be directed towards understanding

of the concept itself. This might be relevant to sensitivities in admitting to certain

psychological experiences within the Pakistani culture. Even, if people do understand

it, they might not want to admit to it in a collectivist culture. Further, it might be an

aspect of some social desirability pressures at work with this item. However, looking

at LISREL output options, the unstandardised solution for this particular item yielded

better factor loading equivalent to .51. The item is capable of producing 6% variance

in responses towards extroversion tendencies. For an estimate of effect on internal

consistency of the scale, omitting item 8, Cronbach’s alpha coefficient slightly rises

from .76 to .77. The result of unstandardised solution provides a safer mean to retain

this item in the scale.

166

The scale of Agreeableness, i.e., adjective 13 ”Cold” is showing weak

association with factor loading of .19. This is another item that possibly could cause

confusion in terms of understandability of the concept and relating it to the concept of

agreeableness. It’s also important to understand here that how often this happens to be

used by participants to reflect how they usually feel. However, swapping back to

unstandardised solution yielded better factor loading equivalent to .45. The item is

capable of producing 3.4% variance in responses towards the trait of agreeableness.

Cronbach’s alpha coefficient of the scale rises from .79 to .82 in case of deleting this

item. Due to satisfactory estimate yielded by unstandardised solution, it was decided

to retain this item in the scale.

On scale of conscientiousness, an adequate range of factor loadings (.42 to

.81) have obtained which fully supports the factor structure of this scale. The high

magnitude of alpha coefficient (.83) on scores of the scale is a satisfactory estimate of

internal consistency of the scale items.

For the scale of emotional stability, adjectives 25 “un-envious” and 26

“relaxed” showed poor association with the scale in both standardised and

unstandardised solutions. By deleting both items, there is considerable increase in

alpha coefficient from .18 to .28. ‘Un-envious’ might be a confusing concept for

respondents in terms of what it means. But, interestingly, a high loading for adjective

of ‘envious’ (.61) has obtained. So this seems to overlap with people’s ups-and-downs

emotionally, but this item (un-envious) is probably not associated with participants’

levels of emotional stability. The item 26 ‘Relaxed’ is also not much surprising to

show low factor loading due to sensitivities in admitting to certain psychological

experiences within the Pakistani culture.

167

Looking at items with higher factor loadings including “Touchy” (item 31),

“Temperamental” (item 29), and “Envious” (Item 30), points out our attention to

cross-cultural differences. These experiences might explain well to reflect the

emotional experiences of those within a Pakistani collectivist culture, where generally

the expressions of extreme emotions such as being temperamental or touchy would be

acceptable. Whereas, for example in British culture, generally expressing emotions,

especially negative emotions would emphasize to avoid at all costs This reflects that

this would be even more pronounced in the workplace too.

For openness, i.e., adjective 37 “Complex” is showing weak association with

the scale with factor loading .16. Possibly, this may relate to somehow confusion

related to linking this concept with personality trait of openness to experience.

However, generally this concept seems simple to relate with the concept of openness.

Therefore, in case of swapping back to unstandardised solution, factor loading of the

item with scale rises up to .40, which is quiet satisfactory. Further, the item is

showing 2.5% variance in responses for openness tendencies. Evaluating the effect on

internal consistency of the scale, in case of deleting this item, there’s increase in

Cronbach’s alpha coefficient from .73 to .76. The situation reveled by unstandardised

solution provides a safer way to retain this item in the scale.

Data of the study well supports the factor structure of the Mini Marker scales

on sample of Pakistani teachers. However, it suggests that certain items including

item 25 (un-envious) and item 26 (relaxed) are showing weak association along with

high residuals with factor structure of emotional stability. Therefore, deleting these

items from the subscale will yield better representation of the trait structure in

subsequent analysis.

168

It is also useful to examine the correlation between personality traits to assess

the overlap between the factors. It is due to the reason that in case of using many

models of personality, there might be some overlap between personality traits. Some

behaviors that might be indicative of a particular trait could also overlap to some

extent with behaviors related to another trait. If they overlap to a substantial extent,

this would help to know how some items naturally associate with one factor and could

also be explaining a sizeable amount of the variance in another factor as well.

Evaluating the subscale of emotional stability, it does correlate with agreeableness (r

= .34, p = .01) and conscientiousness (r = .31, p = .01). Specifically the item

measuring the emotional stability including “un-envious” is showing considerable

correlation with subscale of conscientiousness (r = -.13, p = .01) other than the total

score on emotional stability (r = .43, p = .01). The item “relaxed” measuring the

emotional stability is showing correlation with subscales of agreeableness (r = .21, p

= .01) other than the emotional stability (r = .35, p = .01). This might be explaining

why weak factor loadings have obtained on both items (un-envious and relaxed) of

emotional stability. For subscale of extroversion, the range of strength of factor

loadings has observed from .76 to .32. This moderate range of strength of factor

loadings might be linked with overlap of extroversion items with subscale of

conscientiousness (r = .24, p = .05). Agreeableness with range of factor loadings from

.74 to .53 is showing strong association with conscientiousness (r = .51, p = .01).

Openness with range of factor loadings from .76 to .34 has showed association with

agreeableness (r = .41, p = .01) and conscientiousness (r = .37, p = .01) as well. In

conclusion, findings recommended excluding item 25 and 26 from subscale of

emotional stability leaving the 38 item inventory.

169

The study contributed to validate instruments with working group of teachers

of higher education level. This in turn helps to establish the validity of study measures

as a preliminary quality control test before performing the analysis related to

hypotheses testing.

Conclusion

The findings suggest that the factor loadings for most of the work

environment, burnout, commitment, and personality measures are within an

acceptable range and this seems to indicate that the concepts are still translating to the

Pakistani culture and within their working culture in Universities too. In Pakistan, no

noticeable earlier studies have tried to examine the factor structure of the measures

especially using sample of university teachers. For work environment scale, findings

suggested moderate support with recommendation of excluding certain items. For

burnout measure, there is prominent support for both three and five-factor model of

burnout with five factor model at better position. Incorporating original three and an

elaborative five-factor model in subsequent analyses will help to comprehensively

examine the role of burnout. However, in three factor model, only one item seems to

be might not translate that well to the Pakistani culture. The study is adding in

establishing the construct validity of five-factor model of the burnout, which

previously is relatively a less explored dimension in burnout research. The study also

added by demonstrating support for existing three factor structure of organizational

commitment. However, there are some discrepancies with reference to the

Commitment subscales (i.e., Continuance), and personality measures as well.

However, less supported items are culturally-bounded as especially in case of

170

commitment where job circumstances in our culture are very much important for

respondents. Hence, overall, it is satisfying to note that there still seems to be a clear

factor structure, in the most part, for all of the assessments tools of the study and the

findings have got a decent level of fit for these proposed models. The findings of the

study also support the cross-cultural validity of the instruments used in source

language i.e., in the English versions. This further supports to use these measures in

source language (English) for prospective research using the sample of university

teachers.

177

171

STEP II: MAIN STUDY

The Role of Work Environment in Predicting Burnout and Organizational

Commitment and the Moderating Role of Personal Variables

The step II of the main study aimed to test the following objectives.

Objectives of the Main Study

The phase II of the main study aimed to test the following objectives of the

study.

1. To establish the psychometric properties (i.e. reliability and validity) of

modified measures of study on the sample of main study.

2. To test the hypothesized predictive relationship of work environment with

burnout and organizational commitment.

3. To test the moderating role of personal variables in assessing the predictive

relationship with criterion variables.

Instruments

The modified versions of instruments; i.e. work environment, burnout,

organizational commitment, and personality variables are used, these are modified

based on the results of confirmatory factor analyses. These modifications were

utilized for the analyses of the main study- step II. Below are relevant details of each

instrument to be used in this phase of the study.

177

172

Work Environment Scale (WES). Perceived group consensus about the

psychosocial characteristics of the academic settings was measured using modified

63-item Work Environment Scale with ten subscales (Moos, 1994). The format of the

scale responses were modified in Likert format in which a score of “1” was given to

response options of Mostly True and True and a score of “0” was given to options of

Mostly False and False.

The Maslach Burnout Inventory-Educators Survey (MBI-ES). Maslach

Burnout Inventory – Educators Survey (Maslach, Jackson, & Leiter, 1996) was

modified leaving a 21-item measure to assess the burnout reported by teachers in

terms of frequency of experiences ranged from never (0) to always (6). The 3-factor

inventory measures emotional exhaustion, depersonalization, and reduced sense of

personal accomplishment aspects of the burnout. This modifies version incorporated

the change in emotional exhaustion component by excluding item 14 ‘I feel I am

working too hard on my job’ from this subscale. Burnout is conceptualized as ranging

from low to moderate to high degree of experienced feelings.

Organizational Commitment Questionnaire (OCQ). Three facets

Organizational Commitment Questionnaire (Allen & Meyer, 1990) was modified

leaving the 19-item measure anchored with 5-point Likert format. The component of

affective commitment as the emotion-based view of organizational commitment was

retained in terms of its individual items. Continuance component as a perceived cost

of leaving an organization was modified by omitting item nos. 10 “I am not concerned

about what might happen if I left (name of organization) without having another

position lined up”, 13 “It wouldn’t be too costly for me to leave (name of

177

173

organization) now”, and 15 “one of the serious consequences of leaving (name of

organization) would be the scarcity of available alternatives”. Normative component

as feelings of moral obligations or responsibilities was also retained in terms of its

indicators.

Mini-Marker Set (MM). The Mini-Marker Set (Saucier, 1994) as an

abbreviated version of Goldberg’s Big Five Personality Inventory was modified

leaving 38 items. Each item of subscales was retained in modified form except for the

subscale of emotional stability leaving 6 items by omitting adjectives 25 “un-envious”

and 26 “relaxed”.

Results

The data of main study (N = 426) collected from university teachers was

analyzed using SPSS 15.0.

Descriptive Analysis

Mean and standard deviation are computed (see Table 24) on the participants’

variations in (high, moderate, and low) the levels of work environment, burnout,

organizational commitment, and personality dimensions. The comparison of mean

values indicated that our sample of university teachers dominantly endorsed the work

environment on positive dimension. Teachers reported their academic settings as high

on clarity and task orientation and low on co-worker cohesion. Computing the levels

of burnout, teachers reported high on personal accomplishment and comparatively

low on depersonalization. In comparison with cut-off scores of normative sample of

177

174

teachers, the present sample showed high on personal accomplishment and low on

depersonalization. Teachers reported high on affective commitment and low on

continuance commitment. Moreover, scores indicated that teachers are high on

conscientiousness dimension of personality.

Following table 24 represents the levels (high, moderate, and low) computed

on scores of variables. The table 25 presents the Cronbach’s Alpha coefficients, inter-

correlations of variables using Pearson product moment correlation coefficients, and

the values of skewnees representing the distribution of scores.

177

Table 24 Mean & SD on scores representing Levels of Work Environment, Burnout, Organizational Commitment, and Personality Variables (N = 426) Levels of WES

Positive WE (n = 231)

Negative WE (n =

184)

Average WE (n =

11)

Overall WE (N = 426)

Levels of Burnout Levels of Organizational Commitment

Levels of Personality Factors

Mean S.D Mean S.D Mean S.D Mean S.D Mean S.D N Mean S.D N

Mean S.D

N

IN 5.43(.50) 2.25(.87) 4.00(.00) 4.18(1.46) High EE 27.99(31.47) 5.55(4.22) 199 High AC 38.51(3.36) 211 High EX 54.30(6.38) 208 CC 3.30(.46) .72(.45) 2.00(.00) 2.02(1.16) Mod. EE 10.28(21.35) 4.56(2.97) 206 Mod. AC 27.05(3.87) 185 Low EX 35.67(5.67) 200 SS 5.69(.76) 2.28(.94) 4.00(.00) 4.33(1.60) Low EE 17.99(9.74) .03(4.36) 21 Low AC 33.00(.00) 30 Mod. EX 44.02(.10) 18 AT 6.45(.50) 2.93(1.13) 5.00(.00) 4.71(1.69) Total EE 18.21 9.38 426 Total AC 33.15(6.52) 426 Total EX 45.12(10.87) 426 TO 6.86(.79) 2.97(1.18) 5.00(.00) 5.43(1.90) High DP 15.73(19.05) 4.30(2.77) 193 High CC 16.48(1.35) 184 High AG 61.83(4.45) 196 WP 5.72(.77) 2.27(.86) 4.00(.00) 3.99(1.64) Mod. DP 10.44(10.56) .04(1.47) 30 Mod. CC 10.82(2.03) 194 Mod. AG 43.01(7.91) 206 CL 7.01(.77) 2.80(1.25) 5.00(.00) 5.20(2.10) Low. DP 2.72(7.05) 2.12(2.65) 203 High CC 14.00(.00) 48 Low AG 53.97(.07) 24 MC 5.47(.50) 2.29(.81) 4.00(.00) 4.09(1.49) Total DP 8.07 6.32 426 Total CC 13.62(3.13) 426 Total AG 52.29(11.10) 426 INN 5.70(.75) 2.05(1.04) 4.00(.00) 4.18(1.82) High PA 41.50(41.87) 5.43(3.50) 204 High NC 24.91(1.81) 158 High CT 62.49(4.70) 204 PC 5.40(.49) 2.40(.79) 4.00(.00) 4.32(1.35) Mod. PA 35.00(33.86) .00(1.66) 19 Mod. NC 18.07(2.78) 204 Mod. CT 42.20(7.26) 212 WES 32.11(4.90) 14.85(7.04) 24.00(.00) 24.45(10.30) Low PA 25.92(24.15) 3.68(4.66) 203 Low NC 22.00(.00) 64 Low CT 54.0(6.15) 10 Total PA 33.78 8.87 426 Total NC 21.20(3.85) 426 Total CT 52.20(11.73) 426 Burnout 60.06 12.08 426 OCQ 67.96(10.56) 426 HighES 39.32(3.70) 199 Low ES 28.63(3.55) 194 Mod. ES 34.00(.00) 33 Total ES 34.04(6.20) 426 High OP 59.53(5.22) 203 Low OP 41.83(6.22) 204 Mod. OP 52.0(3.11) 19 Total OP 50.72(10.32) 426 Note. WES = work environment scale, RtD = relationship dimension, IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, PGD = personal growth dimension, AT = autonomy, TO = task orientation, WP = work pressure, SMD = system maintenance and change dimension, CL = clarity, MC = managerial control, INN = innovation, PC = physical comfort, MBI = burnout, EE = emotional exhaustion, Dp = depersonalization, PA = personal accomplishment, OCQ = organizational commitment, AC = affective commitment, CC = continuance commitment, NC = normative commitment, EX = extroverted, AG = agreeableness, CT = conscientiousness, ES = emotional stability, and OP = openness.

Table 25

177

Cronbach’s Alpha (on the diagonal), Pearson Product Moment Correlations for Predictive, Criterion, and Moderator Variables (N = 426) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Skew 1 WES .88 -.57 2 RtD .82** .74 -.12 3 IN .74** .80** .55 -.67 4 CC .57** .77** .45** .42 .01 5 SS .67** .85** .49** .53** .46 -.40 6 PGD .84** .55** .51** .40** .44** .68 -.65 7 AT .63** .48** .33** .31** .51** .67** .54 -.62 8 TO .78** .59** .59** .42** .43** .80** .34** .63 -.67 9 WP .33** .06 .12* -.08 -.04 .67** .09 .27** .47 -.09 10 SMD .90** .61** .61** .35** .49** .63** .49** .63** .18** .79 -.63 11 CL .77** .57** .53** .36** .49** .50** .41** .58** .02 .85** .69 -.52 12 MC .35** .02 .14** -.11* -.01 .31** .17** .22** .26** .50** .29** .53 -.48 13 INN .73** .61** .55** .43** .49** .52** .44** .48** .17** .73** .47** -.04 .61 -.46 14 PC .68** .46** .49** .26** .35** .47** .36** .50** .11* .76** .54** .21** .50** .44 -.70 15 MBI -.29** -.29** -.26** -.23** -.21** -.24** -.14** -.26** .09* -.23** -.21** -.11* -.19** -.13** .86 .20 16 MBI (five) -.28** -.28** -.25** -.23** -.20** -.23** -.12* -.26** .10* -.21** -.19** -.12 -.20** -.11* .99** .86 .22 17 EE -.30** -.31** -.29** -.21** -.24** -.21** -.20** -.27** .05 -.27** -.27** -.08 -.19** -.20** .83** .79** .74 .56 18 Dp -.23** -.23** -.22** -.18** -.16** -.19** -.08 -.22** -.11* -.17** -.16** -.11* -.12* -.08 .85** .86** .60** .69 .94 19 PA .17** .17** .13** .17** .12* .19** .04 .17** .18** .11* .08 .10* .11* .01 .81*** .81** -.40** -.59** .76 -.69 1 2 3 4 5 6 7 8 9 10 11 12 13 14 20 21 22 23 20 OCQ .40** .29** .30** .17** .22** .34** .29** .30** .13** .38** .34** .20** .27** .26** .84 -.66 21 AC .44** .36** .33** .27** .28** .39** .31** .34** .16** .39** .33** .18** .29** .29** .88** .83 -.49 22 CC .12* -.01 .11* -.07 -.04 .12* .07 .12* .05 .16** .18** .12* .06 .10* .59** .29** .61 .06 23 NC .24** .16** .17** .07 .15** .18** .20** .15** .03 .25** .22** .15** .20** .14** .77** .50** .33** .64 -.60

Continued…

177

1 2 3 4 5 6 7 8 9 10 11 12 13 14 24 25 26 27 28 Skew 24 EX .02 .01 .04 .01 .00 .03 .01 -.01 .09 .01 .01 .01 .06 .02 .76 .15 25 AG .08 .05 .05 .07 .02 .13** .01 .10* .17** .04 .00 .07 .04 .01 .41** .79 -.91 26 CT .17** .11* .16** .11* .01 .14* -.01 .19** .11* .17** .15** .13** .10* .11* .42** .55** .83 -.55 27 ES .12* .11* .17** .05 .04 .10* .10* .10* -.00 .10* .07 .09 .07 .07 .09 .18** .25** .28 .09 28 OP .05 .06 .04 .08 .04 .06 .00 -.05 .07 .01 -.04 .03 .05 .01 .35** .53** .52** .09 .73 -.39 Note. WES = work environment scale, RtD = relationship dimension, IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, PGD = personal growth dimension, AT = autonomy, TO = task orientation, WP = work pressure, SMD = system maintenance and change dimension, CL = clarity, MC = managerial control, INN = innovation, PC = physical comfort, MBI = burnout, EE = emotional exhaustion, Dp = depersonalization, PA = personal accomplishment, OCQ = organizational commitment, AC = affective commitment, CC = continuance commitment, NC = normative commitment, EX = extroverted, AG = agreeableness, CT = conscientiousness, ES = emotional stability, and OP = openness. *p = .05, **p = .01 .

177

Findings as shown in Table 25 highlighted the estimate about internal

consistency of the Work Environment Scale with reduced items. High magnitude of

alpha coefficient has obtained on total scores of WES (α = .88); whereas, the primary

scales of Relationship Dimension (α = .74), Personal Growth Dimension (α = .68),

and System Maintenance and Change Dimension (α = .79) have demonstrated

substantial evidence for concluding satisfactory estimate of internal consistency of the

measure. The reliability coefficients of secondary sub scales ranged from moderate (α

= .69) for the subscale of clarity to relatively low (α = .42) for co-worker cohesion.

For MBI, the refined 21-item measure yielded high magnitude of alpha

coefficient for total scores (α = .86). Examining the subscales’ internal consistency,

emotional exhaustion (α =.74) and personal accomplishment (α =.76) demonstrated

good internal consistency. The subscale of depersonalization also yielded satisfactory

estimate (α = .69) of the internal consistency. The five factor model of burnout with

19 items also yielded high magnitude of alpha coefficient (α = .86).

The magnitude of alpha coefficients for total scores on Organizational

Commitment Questionnaire (α = .84) and for subscales of affective commitment (α =

.83) is high. The moderate level of reliability indices have obtained for subscales of

continuance commitment (α = .61), and normative commitment (α = .64). Results

indicated that the scales of Mini Markers set including Extraversion (α = .76),

Agreeableness (α = .79), Conscientiousness (α = .83), and Openness (α = .73)

demonstrated high internal consistency. Whereas, the subscale of emotional stability

(α = .28) yielded low magnitude of alpha coefficient.

Results shown in Table 25 indicated bivariate correlations between study

variables. The inter-scale correlations of study constructs (see discussion section)

provides an estimate of construct validity. Examining the link between work

environment and burnout, work environment dimensions except work pressure are

177

showing significant relationship. For five factor model of burnout (with reduced 19

items), each of work environment dimensions are significantly associated. Emotional

exhaustion is found inversely linked to most of indicators of WES. Emotional

exhaustion has showed positive but non-significant relationship with work pressure.

Depersonalization has also found to be inversely associated with most of the

indicators of work environment. However, the subscale of autonomy has shown non-

significant relationship with depersonalization. Personal accomplishment has showed

positive relationship with most of the indicators of WES. However, the subscales of

autonomy, clarity, and physical comfort showed non-significant relationship with

personal accomplishment.

The pattern of relationship between work environment indicators and

organizational commitment revealed that each of the facets of work environment is

significantly linked in positive direction with organizational commitment and with

affective commitment. For subcomponent of continuance commitment, dimensions of

task orientation (r = .12, p < .05), clarity (r = .18, p < .01), managerial control (r =

.12, p < .05), and physical comfort (r = .10, p < .05) have found to be significantly

linked. For normative commitment, most of the dimensions are associated with work

environment factors except the dimensions of coworker cohesion and work pressure.

For moderator variables, extroversion is showing non-significant relationship

with work environment and each of its dimensions. Agreeableness is showing

significant positive relationship with task orientation (r = .10, p < .05) and with work

pressure (r = .17, p < .01). Relationship between conscientiousness and most of work

environment components have observed. Moreover, emotional stability and openness

have found significant association only with few indicators of work environment. The

values of skewness reported in t 25 are within acceptable range.

177

Predictive Relationship between Work Environment and Burnout

The predictive relationship of work environment with burnout was

investigated among University teachers using multiple regression analysis- enter

method. The tables below (26-30) presents the results for burnout and its components

including components of elaborated factor structure regressed against work

environment and its variables.

Table 26

Multiple Regression Analysis on scores of Emotional Exhaustion and its components

by Work Environment (N = 426)

Note. IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, AT = autonomy, TO =

task orientation, WP = work pressure, CL = clarity, MC = managerial control, INN = innovation, and

PC = physical comfort.

*p < .05, **p = .00

WES

EE Psy. Strain Somatic Strain

B

SE Β

Β

95% CI

LL UL

B

SE Β

Β

95% CI

LL UL

B

SE Β

β

95% CI

LL UL

IN -.97 .42 -.15* -1.79 -.16 -.50 .17 -.19** -.84 -.16 -.33 .24 -.09 -.79 .14

CC -.52 .47 -.07 -1.45 .41 -.35 .20 -.11 -.74 .04 -.39 .27 -.09 -.92 .14

SS -.15 .38 -.03 -.90 .61 -.06 .16 -.02 -.37 .25 .03 .22 .01 -.40 .46

A -.39 .32 -.07 -1.01 .23 .09 .13 .04 -.17 .35 -.32 .18 -.10 -.67 .04

TO -.56 .33 -.11

-1.20 .09-.21

.14-.11 -.49 .04 -.38

.19 -.14*

-.75 -

.02

WP .64 .29 .11* .07 1.22 .18 .12 .08 -.06 .42 .45 .17 .14** .12 .78

CT -.28 .30 -.06 -.87 .31 .10 .12 .06 -.14 .35 -.04 .17 -.02 -.38 .30

MC -.25 .33 -.04 -.90 .41 -.40 .14 -.15** -.67 -.13 -.05 .19 -.02 -.43 .32

INV .17 .33 .03 -.47 .81 -.13 .14 -.06 -.40 .13 .22 .19 .08 -.14 .58

PC -.03 .42 -.01 -.86 .79 .14 .17 .05 -.20 .49 -.23 .24 -.06 -.69 .24

R = .36, R2= .13,

F = 6.09**

R = .35, R2= .12,

F = 5.65**

R = .32, R2= .10,

F = 4.59**

177

Findings as shown in Table 26 revealed that work environment variables

including involvement and work pressure produced significant equation (F = 6.09, p =

.00) when regressed against scores of emotional exhaustion and accounts for 13%

variance (R2 = .13). Involvement as a negative predictor (B = -.97, t = 2.34, p < .02)

implies that one unit increase in employees’ involvement will result in a .97 decrease

in emotional exhaustion. Interpreting the beta value (β = -.15) implies that a change of

one standard deviation in involvement will result in a change of .15 standard deviation

in emotional exhaustion. Work pressure as a positive predictor accounts for .64 unit

increase in emotional exhaustion (B = .64, t = 2.19, p < .05). Beta value (β = .11)

implies that a change of one standard deviation in work pressure will result in .11

standard deviation change in emotional exhaustion The values of standardised betas

indicated that involvement is relatively a stronger predictor (β = -.15) compared to

work pressure (β = .11). Evaluating the elaborated structure of emotional exhaustion

namely psychological strain indicated that involvement (B = -.50, β = -.19, t = 2.91, p

= .00) and managerial control are negative predictors (B = -.40, β = -.15, t = 2.91, p =

.00). For somatic strain, task orientation is a negative predictor (B = -.38, β = -.14, t =

2.05, p < .05) and work pressure is a positive predictor (B = .45, β = .14, t = 2.68, p <

.05).

The obtained values of VIF (1.25 to 2.14) and tolerance (.47 to .80) are in

acceptable range, which ensures that multicollinearity is not likely a threat to the

substantive conclusions drawn from the parameter estimates.

177

Table 27

Multiple Regression Analysis on scores of Depersonalization by Work Environment

(N = 426)

Depersonalization

Work Environment Variables B SE Β Β 95% CI

LL UL

Involvement -.56 .29 -.13* -1.12 .01


Supervisor Support -.27 .27 -.07 -.79 .25

Autonomy .18 .22 .05 -.25 .61


Work Pressure -.22 .20 -.06 -.62 .18

Clarity -.08 .21 -.03 -.49 .32

Managerial Control -.36 .23 -.08 -.81 .10

Innovation .11 .23 .03 -.33 .55


R = .29, R2= .08, F = 3.67**

*p = .05, **p = .00

Findings as shown in Table 27 revealed involvement as a negative predictor (B

= -.45, β = -.13, t = 1.93, p = .05) explaining 8% variance in depersonalization (R2=

.08, F = 3.67, p = .00). Beta values indicated that one unit change in involvement

leads to .45 unit decrease in depersonalization.

177

Table 28

Multiple Regression Analysis on scores of Personal Accomplishment and its

components by Work Environment (N = 426)




*p ≤ .05, **p = .00

Findings as shown in table 28 indicated that together coworker cohesion, work

pressure, and physical comfort accounts for 8% variance in personal accomplishment

(R2= .08, F = 3.65, p = .00). Co-worker cohesion (B = .93, β = .12, t = 2.03, p < .05)

and work pressure (B = .76, β = .14, t = 2.67, p < .05) are positive predictors; whereas,

physical comfort is a negative predictor (B = -.79, β = -.12, t = 1.96, p = .05).

Standardised beta values indicated that comparatively work pressure is a stronger

predictor of personal accomplishment. Evaluating the elaborated structure namely the

sub-component of self related personal accomplishment, task orientation (B = .41, β =

WES

PA PA (Self) PA (others)

B

SE Β

Β

95% CI

LL UL

B

SE Β

Β

95% CI

LL UL

B

SE Β

β

95% CI

LL UL

IN .16 .40 .03 -.64 .95 .05 .22 .02 -.38 .48 .08 .19 .03 -.30 .45

CC .93 .46 .12* .03 1.84 .24 .25 .06 -.25 .73 .60 .22 .17* .17 1.03

SS .44 .37 .08 -.29 1.17 .34 .20 .11 -.05 .74 .13 .18 .05 -.22 .48

AT -.39 .31 -.08 -1.00 .21 -.24 .17 -.08 -.56 .09 -.09 .15 -.04 -.38 .20

TO .38 .32 .08 -.25 1.00 .41 .17 .16* .07 .75 -.02 .15 -.01 -.32 .28

WP .76 .28 .14* .20 1.32 .46 .15 .16** .16 .77 .24 .14 .09 -.03 .51

CT -.06 .29 -.02 -.64 .51 -.19 .16 -.08 -.50 .12 .10 .14 .05 -.17 .38

MC .55 .32 .09 -.08 1.19 .20 .18 .06 -.14 .54 .27 .15 .09 -.04 .57

INV .21 .32 .04 -.41 .83 -.00 .17 -.00 -.34 .34 .07 .15 .03 -.23 .36

PC -.79 .41 -.12* -1.59 .00 -.42 .22 -.12 -.85 .02 -.38 .19 -.12* -.76 .00

R = .28, R2= .08,

F = 3.65**

R = .29, R2 = .08,

F = 3.67**

R = .25, R2 = .06,

F = 2.81**

177

.16, t = 2.39, p < .05) and work pressure (B = .46, β = .16, t = 3.01, p = .00) are

positive predictors. For sub-component of personal accomplishment by others, co-

worker cohesion (B = .60, β = .17, t = 2.73, p < .05) is a positive predictor; whereas,

physical comfort (B = -.38, β = -.12, t = 1.96, p = .05) is a negative predictor.

Taking the burnout as a single composite construct, following analysis

highlighted the findings obtained for total scores on burnout (for three and five factor

models) regressed against the subscales of work environment.

Table 29

Multiple Regression Analysis on scores of Burnout by Work Environment (N = 426)




*p < .05, **p = .00

WES

Burnout (three

factor model)

Burnout (five factor

model)

B SE Β β 95 % CI

LL UL B SE Β β

95% CI

LL UL

IN -1.69 .91 -.12 -3.48 .09 -1.52 .82 -.12 -3.13 .10

CC -1.94 1.03 -.11 -3.97 .09 -2.08 .94 -.13* -3.92 -.24

SS -.88 .84 -.07 -2.52 .76 -.79 .76 -.07 -2.27 .70

AT .20 .69 .02 -1.15 1.55 .29 .62 .03 -.93 1.51

TO -1.22 .72 -.11 -2.63 .189 -1.29 .65 -.13* -2.56 -.02

WP -.33 .64 -.03 -1.58 .92 -.29 .58 -.03 -1.43 .85

CT -.31 .65 -.03 -1.60 .97 .06 .59 .01 -1.11 1.22

MC -1.17 .72 -.09 -2.59 .25 -1.29 .66 -.11* -2.57 .00

INV .06 .71 .01 -1.33 1.45 .12 .64 .01 -1.14 1.38

PC 1.11 .91 .07 -.68 2.91 1.07 .83 .08 -.55 2.70

R = .33, R2 = .11, F = 5.06** R = .33, R2 = .11, F = 5.11**

177

Results in Table 29 indicated that model predicting burnout (three factor

model) as composite factor indicated overall moderate fit (R = .33, F = 5.06, p = .00).

However, none of the variables of WES reaches at statistical significance. Evaluating

the five-factor model of burnout revealed that co-worker cohesion (B = -2.08, β = -

.13, t = 2.22, p = .03), task orientation (B = -1.29, β = -.13, t = 1.99, p = .05), and

managerial control (B = -1.29, β = -.11, t = 1.96, p = .05) are negative predictors.

Together, these values account for11% variability in five-factor structure of burnout

(R2 =.11 F = 5.11, p = .00).

Following bivariate regression analysis highlighted the extent of relationship

between burnout (total scores and subscales) and the work environment as a single

composite construct.

177

Table 30

Regression Analysis on Burnout and its components by total scores of Work


Work Environment B SE B Β 95% CI

LL UL Burnout (three-factor model)

-.57 .09 -.29** -.75 -.39

R = .28, R2 = .08, F = 38.49** Burnout (five-factor model)

-.49 .08 -.28** -.65 -.33

R = .28, R2 = .08, F = 34.65** Emotional Exhaustion

-.28 .04 -.30** -.36 -.19

R = .30, R2= .09, F = 42.71** Emotional Exhaustion-Psy. Strain

-.10 .02 -.28** -.14 -.07

R = .28, R2= .08, F = 34.77** Emotional Exhaustion- Somatic Strain

-.12 .02 -.24** -.17 -.07

R = .24, R2= .06, F = 25.17** Depersonalization

-.14 .03 -.23** -.20 -.08

R = .23, R2= .05, F = 22.93** Personal Accomplishment

.15 .04 .17** .07 .23

R = .17, R2 = .03, F = 13.23** Personal Accomplishment- Self

.06 .02 .13* .02 .10

R = .13, R2 = .02, F = 6.89* Personal Accomplishment- Others .06 .02 .16** .03 .10

R = .16, R2 = .03, F = 10.89** *p < .05, **p = .00

177

Findings in Table 30 indicate that work environment is a negative predictor

when regressed against the total scores of burnout three-factor and five-factor model

by accounting for small variance in burnout (R2 = .05). Work environment as a

negative predictor (B = -.28, β = -.30, t = 6.54, p = .00) accounts for 8% variance in

emotional exhaustion (R2 = .08, F = 34.77, p = .00). However, emotional exhaustion

as a whole is a stronger predictor (β = -.30) compared to its sub-components, e.g.,

psychological strain (β = -.28), and somatic strain (β = -.24).

In explaining depersonalization, work environment accounts for marginal

variation (R2 = .05) with a significant model fit (F = 22.93, p = .00). Model

parameters indicated work environment as a negative predictor of depersonalization

(B = -.14, β = -.23, t = 4.79, p = .00).

Work environment accounts for a very marginal (R2 = .03) variance in reduced

sense of personal accomplishment with significant model fit (F = 13.29, p = .00).

Model parameters indicated work environment as a positive predictor of personal

accomplishment (B = .15, β = .17, t = 3.64, p = .00). However, personal

accomplishment as a whole is a stronger predictor (β = .17) compared to its sub-

components, e..g, personal accomplishment related to self (β = .13), and others (β =

.16).

Predictive Relationship between Work Environment and Organizational

Commitment

The hypothesized predictive relationship of work environment with

organizational commitment was investigated using multiple regression analysis- enter

177

method. Below is the detail of analyses using scores of affective, continuance,

normative, and overall commitment regressed against the subscales of work

environment as well as the total scores.

Table 31

Multiple Regression Analysis on Affective Commitment by Work Environment (N =

426)


Work Environment Variables B SE BΒ

95% CI

LL UL

Involvement .43 .28 .10 -.11 .97



Autonomy .51 .21 .13* .10 .92


Work Pressure .33 .19 .08 -.05 .71

Clarity .23 .20 .08 -.16 .62


Innovation .06 .22 .02 -.36 .48


R = .46, R2 = .20, F = 10.64**

*p < .05, **p = .00

Results as shown in Table 31 indicated that autonomy as a positive predictor

produced significant equation when regressed against the scores of affective

commitment (F = 10.64, p = .00) and explains 20% variance (R2 = .20) in affective

commitment. The value of unstanderdized beta for autonomy (B = .51) indicated that

one unit increase in autonomy will lead to increase in affective commitment by .51

177

units. Whereas, standardised beta (β = .13) explains .13 standard deviation change in

affective commitment with significant t-statistics (t = 2.43, p = .02).

To check the issue of multicollinearity, the values of VIF are below 10 ranged

from 1.25 to 2.14, which is desirable. The values of tolerance should be greater than

.20 and the obtained values of tolerance ranged from .47 to .80.

Table 32

Multiple Regression Analysis on Continuance Commitment by Work Environment (N

= 426)


Work Environment Variables B SE Β

β 95% CI

LL UL

Involvement .20 .14 .09 -.08 .48

Coworker Cohesion -.37 .16 -.14* -.69 -.05

Supervisor Support -.32 .13 -.17* -.58 -.07

Autonomy .13 .11 .07 -.08 .34


Work Pressure .04 .10 .02 -.16 .24

Clarity .32 .10 .22** .12 .52


Innovation .01 .11 .01 -.21 .23


R = .27, R2= .08, F = 3.35**

*p < .05, **p = .00

Results as shown in Table 32 indicated that coworker-cohesion, supervisor

support, and clarity explains 8% variance (R2= .08) in continuance commitment (F =

3.35, p = .00). Standardised beta values indicated that clarity is the strongest predictor

177

compared to others. Findings revealed coworker-cohesion (B = -.37, β = -.14, t = 2.27,

p < .05) and supervisor support (B = -.32, β = -.17, t = 2.46, p < .05) as negative

predictors; whereas, the dimension of clarity is a positive predictor (B = .32, β = .22, t

= 3.13, p = .00).

Table 33

Multiple Regression Analysis on Normative Commitment by Work Environment (N =

426)


Work Environment Variables B SE Β β

95% CI

LL UL

Involvement .10 .18 .04 -.24 .45



Autonomy .25 .13 .11 -.01 .51


Work Pressure -.06 .12 -.02 -.30 .19

Clarity .22 .13 .12 -.03 .47


Innovation .25 .14 .12 -.02 .52


R = .29, R2 = .08, F = 3.69*

*p = .00

Results as shown in Table 33 highlighted overall model predicting normative

commitment by work environment variables is significant (F = 3.69, p = .00).

However, individual variables were not able to reach at significance level to interpret

as significant predictors. For example, autonomy and innovation showed p level

equivalent to .06.

177

Table 34

Multiple Regression Analysis on Organizational Commitment by Work Environment

(N = 426)


Work Environment Variables B SE Bβ

95% CI

LL UL

Involvement .74 .46 .10 -.16 1.63


Supervisor Support -.19 .42 -.03 -1.02 .63

Autonomy .89 .35 .14* .21 1.56


Work Pressure .32 .32 .05 -.32 .95

Clarity .78 .33 .16* .13 1.42

Managerial Control .65 .36 .09 -.07 1.36

Innovation .32 .36 .06 -.38 1.02


R = .42, R2= .17, F = 8.68**

*p < .05, **p = .00

Results as shown in Table 34 indicated that autonomy and clarity as positive

predictors produced significant equation when regressed against the scores of

affective commitment (F = 8.68, p = .00). Together, both variables are able to explain

17% variance (R2 = .17) in scores of affective commitment. Comparing the strength of

predictors using standardized Beta values, clarity is slightly on edge (β = .16)

compared to autonomy (β = .14). The value of unstanderdized beta for autonomy (B =

.89) indicated that one unit increase in autonomy will lead to increase in affective

commitment by .89 units. Whereas, standardised beta (β = .14) accounts for .14

standard deviation change with significant t-statistics (t = 2.56, p = .01). Similarly,

clarity accounts for .78 unit change, and .16 standard deviation change in affective

177

commitment (B = .78, β = .16, t = 2.37, p < .05). The values of VIF and tolerance are

desirable ensuring that multicollinearity is not likely a threat to the findings drawn

from the parameter estimates.

Work environment as a composite factor was regressed against total scores of

organizational commitment and its subscales. For this, following table explains the

bivariate regression analysis.

Table 35

Regression Analysis on Organizational Commitment and its components by total

scores on Work Environment (N = 426)

Work Environment B SE Β Β 95% CI

LL UL

Organizational Commitment (total scores)

.41 .05 .40** .32 .50

R =.40, R2 = .16, F = 79.29**


.28 .03 .44** .23 .34

R = .45, R2 = .20, F = 104.20**


.04 .02 .12* .01 .07

R = .12, R2 = .02, F = 6.24*


.09 .02 .24** .06 .12

R = .24, R2= .06, F = 25.74**

*p < .05, ** p = .00

Results as shown in Table 35 indicated that total scores of work environment

regressed against the total scores of organizational commitment accounts for a

177

significance model fit (F = 79.29, p = .00) and explains 16 % variation in

organizational commitment (R2 = .16). The model parameters (B = .41, β = .40, t =

8.90, p = .00) revealed that one unit increase in work environment results in increase

of organizational commitment by .41 units along with .40 standard deviation change

in organizational commitment. For affective commitment, work environment explains

20% variance in affective commitment (R2 = .20, F = 104.20, p = .00). Results

revealed that work environment predicts small variance in continuance commitment

(R2= .02 F = 6.24, p = .01) and in normative commitment (R2= .06, F = 25.74, p =

.00).

The Moderating Role of Personal Variable

For explaining predicting relationship of work environment with burnout and

organizational commitment, the moderating role of personal variables was

investigated by using Multiple Moderated Regression Analysis (MMR). The

methodology to test hypotheses regarding moderator variables with MMR involving

both dichotomous and continuous moderators relies upon the statistical test of the

unstandardized regression coefficient carrying information about the moderating (i.e.,

interaction) effect as mentioned by Aguinis and Stone-Romero (1997). In this way, a

regression model formed including predictor variables X, Z, and the X • Z product

term, which carries information regarding their interaction. The statistical significance

of the unstandardized regression coefficient of the product term (i.e., bx.z) indicates

the presence of the interaction.

In MMR process, the approach of hierarchical regression analysis was used in

which work environment (predictor) and each of the dimensions of personality (as

177

second predictor variables) were entered separately in first step (model 1) followed by

entering these variables again in next step (model 2) along with the interaction term

(predictor variable multiplied by the moderator variable). Hair, Anderson, Tatham and

Black (1998) suggested that due to multicollinearity among the variables, it is

recommended to first estimate the original (unmoderated) equation (model 1) and

then estimates the moderated relationships (model 2). Therefore, the incremental

effect assessed instead of individual values. In this case, a statistically significant

change in R2 indicates a significant moderator effect. For estimating moderator effect,

Aiken & West (1991) recommends using centering procedure, which involves

removing mean from raw score of variables leaving deviation scores. This may also

act as an advantage to reduce multicollinearity among predictor variables. Therefore,

for performing MMR, scores of independent and continuous moderator variables are

centered before deriving interaction term. Moreover, following the procedure

recommended by Jose (2008), the interaction plot along with significance test of

slopes was used for interpretation. The graphical display of interaction make use of

computed scores reflecting high, moderate, and low levels of the main effect of

independent variable (continuous variable) and of the moderator variable. Following

Aiken and West (1991), the levels are computed by using the mean as the medium

value, one standard deviation above the mean as high value, and one standard

deviation below the mean as the low value. In case of categorical variables, the

interaction plot will depict moderation through two lines representing the concerned

groups.

Following is the detail of moderator analysis computed on scores of

personality dimensions and considering composite scores of predictor and criterion

variables.

177

Table 36

Moderating Effects of Personality in predicting Burnout- three-factor model (N =

426)

Predictors B SE Β Β 95% CI LL UL

Step 1

Work Environment -.55 .08 -.28** -.72 -.39Extraversion -.70 .08 -.38** -.86 -.54R = .47, R2 = .22, F = 60.93**

Step 2

Work Environment -.60 .09 -.31** -.77 -.43Extraversion -.67 .08 -.36** -.83 -.51Work Environment × Extraversion .02 .01 .11* .00 .03R = .48, R2 = .24, F = 43.11**

Step 1

Work Environment -.48 .08 -.24** -.62 -.33Agreeableness -1.01 .07 -.56** -1.15 -.88R = .63, R2 = .39, F = 135.26**

Step 2

Work Environment -.62 .08 -.31** -.77 -.46Agreeableness -.99 .07 -.54** -1.13 -.86Work Environment × Agreeableness .04 .01 .19** .02 .05R = .65, R2 = .42, F = 101.57**

Step 1

Work Environment -.53 .09 -.27** -.71 -.35Emotional Stability -.48 .15 -.15** -.78 -.18

R = .32, R2 = .10, F = 24.63** Step 2

Work Environment -.54 .09 -.27** -.72 -.36Emotional Stability -.48 .15 -.15** -.77 -.18Work Environment × Emotional

Stability .01 .02 .04 -.02 .04

R = .33, R2 = .11, F = 16.64** Continued…

177

Predictors B SE Β Β 95% CI

LL UL

Step 1

Work Environment -.39 .08 -.20** -.55 -.24

Conscientiousness -.90 .07 -.52** -1.03 -.76

R = .59, R2 = .34, F = 109.98**

Step 2


Conscientiousness -.89 .07 -.51** -1.02 -.75

Work Environment ×

Conscientiousness .01 .01 .07 -.00 .03

R = .59, R2 = .35, F = 74.76**

Step 1


Openness -.84 .07 -.47** -.98 -.69

R = .55, R2 = .30, F = 89.24**

Step 2


Openness -.80 .07 -.45** -.94 -.66

Work Environment × Openness .03 .01 .19** .02 .05

R = .57, R2 = .33, F = 68.14**

*p < .05, **p = .00

For testing the statistical significance of moderating effects of extraversion,

agreeableness, and openness; the graphical display (Figure 3-5) and slope

computation (Table 37-39) using Jose’s procedure will help to see the statistical

significance of slopes.

177

Figure 3. Moderating Effects of Extraversion in predicting Burnout (three factor

model)

Table 37

Interaction Effects of Extraversion in predicting Work Environment and Burnout

Relationship

Moderator Slope SE t

High Agreeableness -.39 26.18 .02

Medium Agreeableness -.60 1.94 .31

Low Agreeableness -.81 26.18 .03

p = n.s

177

Figure 4. Moderating Effects of Agreeableness in predicting Burnout (three factor

model)

Table 38

Interaction Effects of Agreeableness in predicting Work Environment and Burnout

Relationship




Low Agreeableness -1.00 26.48 .04

p = n.s

177

Figure 5. Moderating Effects of Openness in predicting Burnout (three factor model)

Table 39

Interaction Effects of Openness in predicting Work Environment and Burnout

Relationship





p = n.s

Results as shown in Table 36 indicated the moderating role of personality for

relationship between work environment and burnout (three-factor model). Model 1

demonstrated significant predicting power of extraversion in model 1 (B = -.64, β = -

.38, t = 8.80, p = .00). After adding the interaction term, the value of R2 change was

.01, F(1, 422) = 5.88, p = .02. The significant change statistics further leads to

significant interaction term in model 2 indicated ‘extraversion’ as a significant

predictor (B = .02, β = .11, t = 2.43, p = .02). Further testing this statistically

significant interaction through Jose’ procedure (2008), the interaction plot (Figure 3)

177

and analysis of interaction (Table 37) indicated that slopes representing high,

medium, and low levels of agreeableness are not significantly different from zero.

Thus, the results show that when variations in agreeableness (i.e. high, medium, and

low levels) are observed, perceptions of work environment are not found having

potential influence on variations in burnout. This indicates that extraversion does not

account for moderation of the relationship between work environment and burnout.

For agreeableness, after adding the interaction term, the value of R2 change

was .03, F(1, 422) = 21.25, p = .00. The interaction term revealed significant

moderation effect of agreeableness (B = .04, β = .19, t = 4.61, p = .00). Interaction

plot (Figure 4) and analysis of interaction (Table 38) indicated that slopes

representing high, medium, and low levels of agreeableness are non-significant

showing that variations in agreeableness have not found potential influence to account

for variations in burnout. This indicates that agreeableness does not account for

moderation of the relationship between work environment and burnout.

For openness, addition of interaction term in model 2 leads to significant R2

change .03, F(1, 422) = 17.10, p = .00. Interaction effect revealed openness as a

significant moderator (B = .03, β = .18, t = 4.14, p = .00). Interaction plot (Figure 5)

and analysis of interaction (Table 39) indicated that slopes representing high,

medium, and low levels of openness are non-significant showing that variations in

openness have not found potential influence to account for variations in burnout. This

indicates that openness does not account for moderation of the relationship between

work environment and burnout. Interaction term indicates emotional stability (B =

.01, β = .02, t = .83, p = n.s) and conscientiousness (B = .01, β = .07, t = 1.67, p =

n.s).

The elaborated structure of burnout (five-factor model) is examined to have a

comprehensive understanding of the moderating role of the construct. Below table

represents findings obtained for total score of burnout five-factor model.

177

Table 40

Moderating Effects of Personality in predicting Burnout- five-factor model (N = 426)

Predictors B SE Β Β 95% CI LL UL

Step 1


Extraversion -.64 .07 -.38** -.78 -.50

R = .47, R2 = .22, F = 59.20**

Step 2


Extraversion -.61 .07 -.36** -.76 -.47

Work Environment × Extraversion .02 .01 .11* .00 .03

R = .48, R2 = .23, F = 41.89**

Step 1


Agreeableness -.92 .06 -.56** -1.05 -.80

R = .62, R2 = .39, F = 132.30**

Step 2


Agreeableness -.90 .06 -.55** -1.02 -.78

Work Environment × Agreeableness .03 .01 .18** .02 .05

R = .64, R2 = .41, F = 99.28**

Step 1


Emotional Stability -.48 .14 -.16** -.75 -.22

R = .32, R2 = .10, F = 24.03**

Step 2


Emotional Stability -.48 .14 -.16** -.75 -.21

Work Environment × Emotional Stability

.01 .01 .04 -.01 .04

R = .32, R2 = .10, F = 16.30**

Continued…

177

Predictors B SE Β Β 95% CI

LL UL

Step 1


Conscientiousness -.82 .06 -.52** -.94 -.70

R = .59, R2 = .34, F = 109.98**

Step 2


Conscientiousness -.81 .06 -.52** -.93 -.68

Work Environment ×

Conscientiousness .01 .01 .07 -.00 .03

R = .59, R2 = .35, F = 74.76**

Step 1


Openness -.84 .07 -.47** -.98 -.69

R = .55, R2 = .30, F = 89.24**

Step 2


Openness -.80 .07 -.45** -.94 -.66

Work Environment × Openness .03 .01 .19** .02 .05

R = .57, R2 = .33, F = 68.14**

*p < .05, **p = .00

Following is the graphical display of interactions (Figure 6-8) and slope

computation analysis (Table 41-43) for interpretation of Moderating Effects of slopes.

177

Figure 6. Moderating Effects of Extraversion in predicting Organizational

Commitment

Table 41

Interaction Effects of Extraversion in predicting Work Environment and Burnout

(five- factor model) Relationship





p = n.s

177

Figure 7. Moderating Effects of Agreeableness in predicting Organizational

Commitment

Table 42

Interaction Effects of Agreeableness in predicting Work Environment and Burnout

(five- factor model) Relationship





p = n.s

177

Figure 8. Moderating Effects of Conscientiousness in predicting Organizational

Commitment

Table 43

Interaction Effects of Openness in predicting Work Environment and Burnout (five-

factor model) Relationship


High Agreeableness -0.24 24.51 .01

Medium Agreeableness -0.57 2.15 .27


p = n.s

Results shown in Table 40 revealed that addition of interaction term in model

1 indicated significant R2 change i.e. 0.01, F(1, 422) = 5.88, p = .02. Interaction term

indicated the significant moderating effects of extraversion (B = .02, β = .11, t = 2.43,

177

p = .02) in predicting burnout (five-factor model). However, significant statistical

effects were not further supported when slopes representing high, medium, and low

levels of extraversion were observed (Table 41).

For agreeableness addition of interaction term (model 2) produced R square

change .03, F(1, 422) = 20.83, p = 00. Significant moderation effects were obtained

(B = .03, β = .18, t = 4.56, p = .00). However, these moderation effects were further

not supported through test of significance of slopes (Figure 7). Results presented in

Table 42 indicates that slopes representing high, medium, and low levels of

agreeableness do not significantly differ from zero. Thus, the results show that

relationship between work environment and burnout (five-factor structure) is

independent of moderating influence of agreeableness.

Addition of interaction term in model 2 showed significant R2 change .03, F(1,

422) = 18.53, p = .00. Openness demonstrated significant moderation effects (B = .03,

β = .19, t = 4.31, p = .00). However, moderation effects were not further supported

when high, medium, and low levels of openness were considered for slope test

(Figure 8, Table 43).

Results demonstrated non-significant moderation effects of conscientiousness

(B = .01, β = .07, t = 1.79, p = n.s) and emotional stability (B = .01, β = .04, t = .92, p

= n.s).

177

Table 44 Moderating Effects of Personality in predicting Organizational Commitment (N =

425)

Predictors B SE B Β 95% CI LL UL

Step 1

Work Environment .40 .05 .39** .32 .49

Extraversion .20 .04 .20** .11 .28

R = .45, R2 = .20, F = 50.09** Step 2


Extraversion .18 .04 .18** .10 .26

Work Environment × Extraversion -.01 .00 -.13** -.02 -.00

R = .46, R2 = .21, F = 38.11** Step 1


Agreeableness .29 .04 .31** .21 .37

R = .50, R2 = .25, F = 70.75** Step 2


Agreeableness .28 .04 .30** .20 .36

Work Environment × Agreeableness -.02 .00 -.15** -.02 -.01

R = .52, R2 = .27, F = 52.23** Step 1


Conscientiousness .25 .04 .28** .18 .33

R = .49, R2 = .24, F = 64.99** Step 2


Conscientiousness .24 .04 .27** .17 .32

Work Environment ×

Conscientiousness -.01 .00 -.12* -.02 -.00

R = .50, R2 = .25, F = 46.31** Continued…

177

Predictors B SE B Β 95% CI

LL UL Step 1


Emotional Stability .16 .08 .10* .01 .31

R = .41, R2 = .17, F = 42.26** Step 2


Emotional Stability .16 .08 .10* .01 .31

Work Environment × Emotional

Stability -.00 .01 -.02 -.02 .01

R = .41, R2 = .17, F = 28.22** Step 1


Openness .19 .05 .18** .10 .28

R = .44, R2 = .19, F = 49.97** Step 2


Openness .17 .05 .17** .08 .26

Work Environment × Openness -.01 .01 -.14** -.02 -.01

R = .46, R2 = .21, F = 36.90**

*p < .05, **p = .00

Following is the graphical display of obtained statistically significant

moderation effects (see Figures 9 to 12) followed by analysis of slopes (Tables 45 to

48) used for interpretation of moderating effects.

177

Figure 9. Moderating Effects of Openness in predicting Organizational Commitment

Table 45

Interaction Effects of Extraversion in predicting Work Environment and

Organizational Commitment Relationship (N = 426)


High Extraversion .30 16.75 0.02

Medium Extraversion .43 2.26 .19

Low Extraversion .56 10.11 0.06

p = n.s

177

Figure 10. Moderating Effects of Extraversion in predicting Burnout (five factor

model)

Table 46

Interaction Effects of Agreeableness in predicting Work Environment and



High Agreeableness .29 19.14 .02

Medium Agreeableness .44 3.16 .14

Low Agreeableness .60 6.73 .09

p = .00

177

Figure 11. Moderating Effects of Agreeableness in predicting Burnout (five-factor

model)

Table 47

Interaction Effects of Conscientiousness in predicting Work Environment and



High Conscientiousness .28 17.10 .02

Medium Conscientiousness .39 2.36 .17

Low Conscientiousness .50 9.12 .06

p = n.s

177

Figure 12. Moderating Effects of Openness in predicting Organizational Commitment

Table 48

Interaction Effects of Openness in predicting Work Environment and Organizational

Commitment Relationship (N = 426)


High Openness .31 20.18 .02

Medium Openness .45 3.27 .14

Low Openness .60 7.98 .08

p = n.s

Results in Table 44 present the moderation analysis on personality dimensions

in predicting work environment and organizational commitment relationship. For

extraversion, interaction term with significant overall model fit (R2 = .21, F(3, 422) =

177

38.11, p = .00) explains 21% variance in organizational commitment. This leads to

significant value of R2 change (.02, F(I, 422) = 8.34). Interaction effect indicated

extraversion as a significant moderator (B = -.01, β = -.13, t = 2.89, p = .00). Further

extending the analysis, the interaction plot (Figure 9) indicated that slope representing

low level of extraversion seems more strongly associated to predict organizational

commitment. However, associated t-value indicated that slopes representing high,

medium, and low levels of moderator do not significantly differ from zero. This

indicated that when levels of extraversion are considered, interaction terms did not

significantly predicted organizational commitment over and above the statistical main

effects of the work environment (see Table 45).

For agreeableness, the interaction term yielded the overall significance of the

model (R2 = .27, F(3, 422) = 52.23, p = .00) and explains 27% variance in the

organizational commitment. The addition of interaction term leads to significant value

of R2 change (.02, F(I, 422) = 11.63). Interaction effect indicated agreeableness as a

significant moderator (B = -.02, β = -.15, t = 3.41, p = .00). The interaction plot

(Figure 10) indicated that slope representing low level of agreeableness seems more

strongly associated to predict organizational commitment. However, analysis

indicated that slopes representing high, medium, and low levels of moderator do not

significantly differ from zero. This indicated the non-significance of interaction terms

in predicting organizational commitment over and above the statistical main effects of

the work environment (see Table 46).

For conscientiousness, addition of interaction term in model 2 leads to R2

change (.01, F(1, 422) = 7.09, p = .01). Interaction effect revealed conscientiousness

as a significant moderator (B = .01, β = -.12, t = 2.66, p < .05). Further analysis

177

indicated that high level of conscientiousness is comparatively better in predicting

organizational commitment. However, analysis indicated the non-significance of

interaction terms in predicting organizational commitment over and above the

statistical main effects of the work environment (Figure 11, Table 47).

For emotional stability, interaction effect revealed emotional stability as a

non-significant moderator (B = -.00, β = -.02, t = .52, p = n.s).

For openness, the interaction term indicated significance of the model (R2 =

.21, F(3, 422) = 36.90, p = .00) with significant value of R2 change (.02, F(1, 422) =

8.91, p = .00). The interaction term revealed openness as a significant moderator (B =

-.01, β = -.14, t = 2.98, p = .00). The interaction plot (Figure 12) indicated that low

level of openness is more explanatory. However, analysis indicated that slopes do not

significantly differ from zero. This indicates the non-significance of interaction terms

in predicting organizational commitment over and above the statistical main effects of

the work environment (see Table 48).

Moderating Role of Organizational and Demographic Related Personal Variables

The Multiple Moderator Regression analysis (MMR) was performed to

investigate the impact of demographic variables including organization related

(sector, rank, job duration, faculties, and side jobs) and demographic information

(age, gender, education and marital status). This analysis was done taking work

environment as a composite dimension.

Following tables present the details of findings in investigating the moderating

impact of employees’ organization related personal variables.

177

Table 49

Moderating Effects of Sector in predicting Burnout (N = 426)

Predictors B SE β β 95% CI LL UL

Burnout (three factor model)

Step 1

Work Environment -.58 .09 -.30* -.77 -.40

Sector -1.51 1.92 -.04 -5.29 2.26

R = .29, R2 = .09, F = 19.54*

Step 2

Work Environment -.95 .14 -.48* -1.23 -.67

Sector -1.73 1.90 -.04 -5.46 2.00

Work Environment × Sector .63 .19 .24* .26 .99

R = .33, R2 = .11, F = 17.05*

Burnout (five factor model)

Step 1


Sector -1.13 1.74 -.03 -4.56 2.30

R = .28, R2 = .08, F = 17.51*

Step 2

Work Environment -.83 .13 -.47* -1.08 -.57

Sector -1.33 1.73 -.04 -4.72 2.07

Work Environment × Sector .56 .17 .24* .22 .89

R = .32, R2 = .10, F = 15.48*

*p = .00

177

Figure 13. Moderating Effects of Sector in predicting Burnout (three factor model)

Table 50

Interaction Effects of Sector in predicting Work Environment and Burnout (three-

factor model) Relationship


Public Sector -.32 .12 2.74*

Private Sector -.95 .15 6.55**

*p < .01, **p = .00

177

Figure 14. Moderating Effects of Sector in predicting Burnout (five-factor model)

Table 51

Interaction Effects of Sector in predicting Work Environment Burnout (five-factor

model)


Public Sector -.53 .18 3.03**

Private Sector -.45 .10 4.75**

*p = .00

177

Table 52

Moderating Effects of Sector in predicting Organizational Commitment (N = 426)



Step 1


Sector 2.64 .95 .13* .77 4.51

R = .42, R2 = .17, F = 44.11**

Step 2


Sector 2.79 .93 .13* .96 4.62

Work Environment × Sector -.42 .09 -.31** -.60 -.24

R = .46, R2 = .21, F = 37.92**, R2 change = .04, F change (1, 422) = 21.29**

*p < .05, **p = .00

177

Figure 15. Moderating Effects of Sector in predicting Organizational Commitment

Table 53

Interaction Effects of Sector in predicting Work Environment and Organizational

Commitment (N = 426)


Public Sector .258 .05 4.71*

Private Sector .682 .07 9.65*

p = .00

177

Table 54 Moderating Effects of Rank in predicting Burnout (N = 426)


Burnout (three factor model)

Step 1

Work Environment -.57 .09 -.29* -.75 -.39Rank -.62 1.90 -.02 -4.36 3.11R = .29, R2 = .08, F = 19.26*

Step 2

Work Environment -.63 .19 -.32* -1.01 -.25Rank -.62 1.90 -.02 -4.35 3.12Work Environment × Rank .03 .09 .04 -.14 .20R = .29, R2 = .08, F = 12.86*

Burnout (five factor model)

Step 1

Work Environment -.49 .08 -.27* -.65 -.32Rank -1.20 1.73 -.03 -4.60 2.19R = .28, R2 = .08, F = 17.55*

Step 2

Work Environment -.56 .18 -.31* -.90 -.21Rank -1.20 1.73 -.03 -4.59 2.20Work Environment × Rank .04 .08 .05 -.12 .19R = .28, R2 = .08, F = 11.75*

*p = .00

177

Table 55

Moderating Effects of Rank in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI


Step 1 Work Environment .41 .05 .40* .32 .50Rank .22 .43 .02 -.63 1.07R = .40, R2 = .16, F = 39.70**

Step 2 Work Environment .40 .10 .39* .21 .59Rank .22 .43 .02 -.63 1.07Work Environment × Rank .01 .04 .01 -.08 .09R = .40, R2 = .16, F = 26.41**

*p = .00, p = n.s

Table 56

Moderating Effects of Employment Duration in predicting Burnout (N = 426) Predictors B SE B Β 95% CI

LL UL Burnout (three factor model)

Step 1 Work Environment -.60 .09 -.31* -.78 -.42Employment Duration -.70 .17 -.19* -1.04 -.36R = .34, R2 = .12, F = 28.10*

Step 2 Work Environment -.63 .09 -.32* -.81 -.45Employment Duration -.67 .17 -.18* -1.01 -.32Work Environment × Duration .02 .01 .08 -.00 .05R = .35, R2 = .12, F = 19.82*

Burnout (five factor model) Step 1

Work Environment -.52 .08 -.29* -.68 -.36Employment Duration -.60 .16 -.18* -.91 -.29R = .33, R2 = .11, F = 25.15*

Step 2 Work Environment -.55 .08 -.31* -.71 -.38Employment Duration -.57 .16 -.17* -.88 -.26Work Environment × Duration .02 .01 .09 -.00 .05R = .34, R2 = .11, F = 17.98*

*p = .00,

177

Table 57

Moderating Effects of Employment Duration in predicting Organizational Commitment (N = 426)

Predictors B SE B β 95% CI LL UL

Organizational Commitment Step 1

Work Environment .42 .05 .41* .33 .51Employment Duration .29 .09 .15* .12 .46R = .42, R2 = .18, F = 46.28*

Step 2 Work Environment .43 .05 .42* .34 .52Employment Duration .28 .09 .14* .11 .45Work Environment × Emp. Duration -.01 .01 -.05 -.02 .01R = .43, R2 = .18, F = 31.23*, R2 change = .00, F change (1, 422) = 1.11

*p = .00, p = n.s

Table 58

Moderating Effects of Faculties in predicting Burnout (N = 426) Predictors B SE B β 95% CI


Step 1 Work Environment -.57 .09 -.29** -.74 -.39Faculties -4.83 1.87 -.12* -8.51 -1.15R = .31, R2 = .10, F = 22.83**

Step 2 Work Environment -.66 .125 -.34** -.91 -.42Faculties -4.83 1.87 -.12* -8.51 -1.15Work Environment × Faculties .20 .18 .07 -.16 .56R = .32, R2 = .10, F = 15.64**


Work Environment -.49 .08 -.27** -.65 -.33 Faculties -4.58 1.70 -.13* -7.92 -1.24R = .30, R2 = .09, F = 21.21**

Step 2 Work Environment -.57 .11 -.32** -.79 -.34Faculties -4.58 1.70 -.13* -7.92 -1.24Work Environment × Faculties .17 .17 .07 -.16 .49R = .31, R2 = .09, F = 14.49**

*p < .05, **p = .00

177

Table 59

Moderating Effects of Faculties in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI


Step 1 Work Environment .41 .05 .38** .32 .51Faculties 2.62 1.00 .12* .66 4.58R = .40, R2 = .16, F = 39.76**

Step 2 Work Environment .42 .07 .39** .29 .55Faculties 2.62 1.00 .12* .66 4.58Work Environment × Faculties -.02 .10 -.01 -.21 .17R = .40, R2 = .16, F = 26.46**

*p < .05, **p = .00, p = n.s Table 60 Moderating Effects of Side Jobs in predicting Burnout (N = 426)


Burnout (three factor model) Step 1


Side jobs -18.62 4.28 -.20* -27.03

-10.21

R = .35, R2 = .12, F = 29.53* Step 2


Side jobs -16.34 4.48 -.18* -25.14 -7.54

Work Environment × Side jobs .52 .31 .09 -.08 1.12 R = .36, R2 = .13, F = 20.73*


Work Environment -.53 .08 -.30* -.69 -.37 Side jobs -16.54 3.89 -.20* -24.19 -8.89R = .34, R2 = .11, F = 27.06*

Step 2 Work Environment -.57 .09 -.32* -.74 -.40Side jobs -14.64 4.08 -.17* -22.65 -6.63Work Environment × Side jobs .43 .28 .08 -.12 .98R = .34, R2 = .12, F = 18.90*

*p = .00

177

Table 61

Moderating Effects of Side Jobs in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI


Step 1

Work Environment .42 .05 .41* .33 .51

Side jobs 3.62 2.18 .07 -.67 7.91

R = .40, R2 = .16, F = 41.19* Step 2


Side jobs 2.84 2.29 .06 -1.66 7.33

Work Environment × Side jobs -.18 .16 -.06 -.49 .13

R = .41, R2 = .17, F = 27.91* *p = .00

Table 62

Moderating Effects of Age in predicting Burnout (N = 426) Predictors B SE Β β 95% CI


Step 1 Work Environment -.57 .09 -.29* -.75 -.39Age -.18 .11 -.08 -.34 .02R = .30, R2 = .09, F = 20.85*

Step 2 Work Environment -.74 .10 -.38* -.93 -.55Age -.17 .10 -.08 -.37 .03Work Environment × Age .04 .01 .22* .02 .06R = .36, R2 = .13, F = 21.51*


Work Environment -.49 .08 -.28* -.66 -.33Age -.12 .10 -.06 -.30 .07 R = .28, R2 = .08, F = 18.09*

Step 2 Work Environment -.64 .09 -.36* -.82 -.47 Age -.11 .09 -.05 -.29 .08 Work Environment × Age .04 .01 .22 .02 .05 R = .35, R2 = .12, F = 19.48**

*p = .00

177

Figure 16. Moderating Effects of Age in predicting Burnout (three factor model)

Table 63

Interaction Effects of Age in predicting Work Environment and Burnout (three factor

model) (N = 426)


High Age -.36 26.06 .01

Medium Age -.74 .10 7.79**

Low Age -1.12 26.06 .04

p = .00

177

Figure 17. Moderating Effects of Age in predicting Burnout (five-factor model)

Table 64

Interaction Effects of Age in predicting Work Environment and Burnout (five-factor

model) (N = 426)


High Age -.29 23.73 .01

Medium Age -.63 .09 7.09**

Low Age -.98 23.73 .04

p = .00

177

Table 65

Moderating Effects of Age in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI


Step 1


Age .14 .05 .12* .04 .24

R = .42, R2 = .17, F = 43.93** Step 2


Age .14 .05 .12* .04 .24

Work Environment × Age -.01 .01 -.06 -.02 .00

R = .42, R2 = .18, F = 29.91**

*p < .05, **p = .00

Table 66 Moderating Effects of Gender in predicting Burnout (N = 426)


Burnout (three factor model) Step 1

Work Environment -.57 .09 -.29* -.75 -.39Gender -.68 1.95 -.02 -4.52 3.15R = .29, R2 = .08, F = 19.27*

Step 2 Work Environment -.72 .16 -.37* -1.04 -.40

Gender -.74 1.95 -.02 -4.58 3.09Work Environment × Gender .23 .20 .10 -.16 .61R = .29, R2 = .09, F = 13.30*


Work Environment -.49 .08 -.28* -.65 -.33 Gender -.13 1.77 -.00 -3.62 3.35R = .28, R2 = .08, F = 17.29*

Step 2 Work Environment -.62 .15 -.35* -.91 -.33Gender -.18 1.77 -.01 -3.67 3.30Work Environment × Gender .20 .18 .09 -.16 .55R = .28, R2 = .08, F = 11.93*

*p = .00

177

Table 67

Moderating Effects of Gender- Men (n = 268) vs. Women (n = 158) in predicting Organizational Commitment


Organizational Commitment Step 1

Work Environment .41 .05 .40* .32 .50Gender .06 .98 .00 -1.86 1.97R = .40, R2 = .16, F = 39.55*

Step 2 Work Environment .48 .08 .47* .32 .64Men .09 .98 .00 -1.83 2.00Work Environment × Gender -.11 .10 -.09 -.30 .08R = .40, R2 = .16, F = 26.80*

*p = .00

Table 68

Moderating Effects of Education in predicting Burnout (N = 426) Predictors B SE B β 95% CI


Step 1 Work Environment -.55 .09 -.28* -.73 -.37Education -2.68 2.16 -.06 -6.92 1.56R = .29, R2 = .09, F = 20.04*

Step 2 Work Environment -.53 .11 -.27* -.73 -.32Education -2.50 2.19 -.05 -6.80 1.80Work Environment × Education -.11 .22 -.03 -.54 .33R = .30, R2 = .09, F = 13.41*


Work Environment -.47 .08 -.26* -.63 -.31 Education -3.15 1.96 -.08 -6.99 .70R = .29, R2 = .08, F = 18.69*

Step 2 Work Environment -.45 .10 -.25* -.64 -.26Education -3.01 1.99 -.07 -6.91 .90Work Environment × Education -.08 .20 -.02 -.47 .31R = .29, R2 = .08, F = 12.49*

*p = .00

177

Table 69

Moderating Effects of Education in predicting Organizational Commitment (N = 426) Predictors B SE B Β 95% CI


Step 1


Education -.49 1.08 -.02 -2.61 1.63

R = .40, R2 = .16, F = 39.67* Step 2


Education -.18 1.09 -.01 -2.33 1.96

Work Environment × Education -.18 .11 -.09 -.40 .03

R = .40, R2 = .16, F = 27.50* *p = .00

Table 70

Moderating Effects of Marital Status in predicting Burnout (N = 426) Predictors B SE B Β 95% CI


Step 1 Work Environment -.55 .09 -.28** -.73 -.37Marital Status 3.02 2.01 .07 -.92 6.97R = .30, R2 = .09, F = 20.23**

Step 2 Work Environment -.54 .15 -.27** -.84 -.24Marital Status 3.04 2.01 .07 -.91 6.99Work Environment × Marital Status -.03 .19 -.01 -.40 .35R = .30, R2 = .09, F = 13.46**


Work Environment -.47 .08 -.27** -.64 -.31 Marital Status 3.43 1.82 .09 -.15 7.00R = .29, R2 = .08, F = 19.04**

Step 2 Work Environment -.45 .14 -.25** -.72 -.17Marital Status 3.45 1.83 .09 -.13 7.04Work Environment × Marital Status -.04 .17 -.02 -.39 .30R = .29, R2 = .08, F = 12.69**

*p < .05, **p = .00

177

Table 71

Moderating Effects of Marital Status in predicting Organizational Commitment (N = 426)



Step 1


Marital Status .91 1.00 .04 -1.05 2.87

R = .40, R2 = .16, F = 40.96* Step 2


Marital Status .94 1.00 .04 -1.02 2.91

Work Environment × Marital Status -.06 .10 -.05 -.25 .12

R = .41, R2 = .16, F = 27.42* *p = .00

Results in Table 49 present the moderation effects of sector (public and

private) using hierarchical multiple regression analysis. Adding interaction effect in

model 2 produced significant model fit (R2 = .11, F(3, 422) = 17.05, p = .00) and

gives rise to a significant change statistic (R2 change = .02, F(1, 422) = 11.14, p =

.00). Interaction term highlighted that in predicting burnout (three factor), sector does

serve as a significant moderator (B = .63, β = .24, t = 3.34, p = .00). The slope

computation when examined through analysis of interactions (see Table 50) indicated

that slopes representing public and private sector significantly differ from zero.

However, considering the moderating influence of sector as negative predictor (Figure

13), work environment more strongly relates to explain burnout within private sector

universities.

For elaborated structure of burnout- five factor model (Table 49), addition of

interaction term in model 2 leads to significant R2 change (.02, F(1, 422) = 10.61, p =

.00) and demonstrated significant moderation effect (B = .56, β = .24, t = 3.26, p =

177

.00). The interaction plot (Figure 14) when examined through analysis of interactions

(Table 51) indicated that slopes representing public and private sectors significantly

differ from zero. However, magnitude of slope representing public sector is stronger.

This indicated that for burnout five-factor structure, work environment more strongly

relates to predict burnout within private sector universities.

Results presented in Table 52 indicated that sector demonstrated significant

predictive power in model 1 (B = 2.64, β = .13, t = 2.77, p < .05) and in model 2 (B =

2.79, β = .13, t = 3.00, p = .00). Interaction term produced significant change

statistics, R2 change = .04, F(1, 422) = 21.29, p = .00. Interaction effect indicated

significant moderation effect (B = -.42, β = -.31, t = 4.61, p = .00) of sector in

predicting organizational commitment. Analysis of interactions (see Table 53)

indicated that slopes (see Figure 15) representing public and private sector

significantly differ from zero. However, slope representing private sector seems more

strongly relates to organizational commitment. This indicated the significance of

interaction terms for relationship between work environment and organizational

commitment.

Results presented in Table 54 indicated that with R2 change of .00, non-

significant interaction effect (B = .03, β = .04, t = .37, p = n.s) of rank in predicting

burnout (three factor model) was obtained. Similarly, for five factor model of burnout,

non-significant interaction effect was obtained (B = .04, β = .05, t = .46, p = n.s).

Results shown in Table 55 indicated that due to addition of interaction term in model

2, R2 change was .00, F(1, 422) = .01, p = n.s leading to non-significant interaction

effect (B = .01, β = .01, t = .12, p = n.s) of rank in predicting organizational

commitment.

177

Results presented in Table 56 indicated that with R2 change = .01, non-

significant interaction effect (B = .02, β = .08, t = 1.75, p = n.s) of employment

duration in predicting burnout- three factor model. Similarly, with R2 change = .01,

non-significant interaction effect was obtained for burnout-five factor model (B = .02,

β = .09, t = 1.83, p = n.s). Results presented in Table 57 indicated that with R2 change

= .00, non-significant moderation effect (B = -.01, β = -.05, t = 1.05, p = n.s) of

employment duration in predicting organizational commitment was obtained

Results presented in Table 58 indicate that after addition of interaction term in

model 2, R2 change was .00 (F(1, 422) = 1.23, p = n.s). Interaction term highlighted

the non-significant moderation effects (B = .20, β = .07, t = 1.11, p = n.s). Results

presented in Table 59 indicated that variable of faculties and work environment is

showing independent predictive power in model 1 with significant model fit F(2, 423)

= 39.76, p = .00 explaining 16% variance in organizational commitment. With

inclusion of interaction term in model 2, R2 change was .00, F(1, 422) = .04, p = n.s

leading to non-significant interaction effect (B = -.02, β = -.01, t = .20, p = n.s) of

faculties in predicting organizational commitment.

Results presented in Table 60 indicated that interaction term in model 2 with

non-significant change statistic (R2 change = .01, F(1, 422) = 2.87, p = n.s) leads to

non-significant interaction effect (B = .52, β = .09, t = 1.69, p = n.s) of side jobs in

predicting burnout (three factor model). For burnout five factor model, with R2 change

= .01, F(1, 422) = 2.40, p = n.s is showing non-significant moderation effect (B = .43,

β = .08, t = 1.55, p = n.s).

Results presented in Table 61 indicated that variable of side jobs is not

showing statistically significant predictive power in model 1 and in model 2. The

177

addition of interaction term in model 2 gives rise to non-significant R2 change

equivalent to .00, F(1, 422) = 1.30, p = n.s. leading to non-significant interaction

effect of side jobs in predicting organizational commitment (B = -.18, β = -.06, t =

1.14, p = n.s).

Results presented in Table 62 indicated that addition of interaction term leads

to non-significant R2 change (.04), F(1, 422) = 20.88. Interaction effect revealed age

as a significant moderator in predicting burnout (B = .04, β = .22, t = 4.57, p = .00).

Extending this analysis to Jose’s procedure indicated that age as positive moderator

(Figure 16) significantly explains moderation effects. Analysis of slope test (Table 63)

indicated that when medium level of age is considered, work environment influences

burnout (three factor model). Similarly, for elaborated structure of burnout, R2 change

was significant (.04, F(1, 422) = 20.58, p = .00) with significant interaction effects (B

= .04, β = .22, t = 4.53, p = .00). Age as positive moderator (Figure 17) indicated that

medium level of age seems more associated with predicting burnout five-factor model

(Table 64).

Results presented in Table 65 indicated age as a significant predictor in model

1 (B = .14, β = .12, t = 2.72, p = .01) and in model 2 (B = .14, β = .12, t = 2.67, p =

.01). With non-significant R2 change (.00), F(1, 422) = 1.71, age showed its non-

significant moderating effect (B = -.01, β = -.06, t = 1.31, p = n.s) in predicting the


Results in Table 66 indicates that with non-significant R2 change (.00), F(1,

422) = 1.33, interaction effect revealed gender as a non-significant moderator (B =

.23, β = .10, t = 1.16, p = n.s) in predicting burnout. Similar findings have obtained

for elaborated structure of burnout. Results presented in Table 67 indicated non-

significant predictive power of gender in model 1 (B = .06, β = .00, t = .06, p = n.s)

177

and in model 2 (B = .09, β = .00, t = .09, p = n.s). With non-significant R2 change

(.00), F(1, 422) = 2.22, interaction effect revealed gender as a non-significant

moderator (B = -.11, β = -.09, t = 1.11, p = n.s) in predicting organizational

commitment.

Results presented in Table 68 indicated that education with addition of

interaction term in model 2, R2 change was .00, F(1, 422) = .23 was non-significant.

Interaction effect highlighted education as a non-significant moderator in predicting

burnout (B = -.11, β = -.03, t = .48, p = n.s). Findings in Similar pattern of findings

have obtained for elaborated structure of the burnout. Results presented in Table 69

indicated that addition of interaction term produced non-significant R2 change (.01),

F(1, 422) = 2.81. Interaction effect highlighted education as a non-significant

moderator in predicting organizational commitment (B = -.18, β = -.09, t = 1.68).

Results presented in Table 70 indicated that marital status carries non-

significant independent predictive power both in model 1 and in model 2. Addition of

interaction term produced non-significant R2 change (.00), F(1, 422) = .02. Examining

interaction effect of marital status revealed marital status as a non-significant

moderator in predicting burnout (B = -.03, β = -.01, t = .14). Similar pattern of

findings have obtained for elaborated structure of the burnout. Results presented in

Table 71 indicated that with non-significant R2 change (.00), F(1, 422) = .45, the

interaction effect of marital status in predicting organizational commitment is also

non-significant (B = -.06, β = -.05, t = .67).

The proceeding section will thoroughly discuss the findings of the study.

177

Discussion

The propositions deduced from Moos’ model (1994) of the psychosocial work

environment concerning with impact of the work environment on employee and

organizational related outcomes are examined in the context of academic settings (i.e.

university level) of Pakistan. The burnout and organizational commitment of

University teachers as predicted by different facets of work environment were

investigated. Further, in explaining the work environment and outcome relationships,

the moderating role of employees’ personal system including personality,

organizational, and demographic variables were also examined. Considering a host of

organizational and demographic variables helped to control the possible influential

factors contributed in generalizing the study results across different groups of

teachers. One of the main objectives of present study was to establish the

psychometric properties of the measures used in the study. Testing the factor structure

of study measures highlighted few discrepancies related to cultural aspects. The

scrutiny of measures through confirmatory factor analyses and exclusion of certain

items contributed to qualify the measures i.e. Work Environment Scale, Maslach

Burnout Inventory, Organizational Commitment Questionnaire, and Min Markers to

be more useful for teachers (lecturers to professors) involved in offering services at

higher education level in Pakistan.

For comparison of scores of work environment, burnout, and organizational

commitment, cut-off scores, (i.e. high, moderate, & low) and estimated mean and

standard deviation of scores were used. Findings highlights that academic work

settings are highly oriented towards positive psychosocial work environment.

Teachers reported that overall universities’ work environmnet characterized with

177

clarity of work procedures along with high emphasis on getting the job done. Its also

encouraging that autonomy is being valued at universities. However, cohesion among

colleagues has found to be lacking. Greater variation of scores have obtained for

dimension of managerial control, work pressure, and clarity. Teachers also reported

high on reduced personal accomplishment and on emotional exhaustion, and

comparatively low on depersonalization. Dispersions in scores are evident in high

levels of emotional exhaustion and depersonalization. Variations in responses on low

personal accomplishment, is probably due to social desirability aspect of the

construct. For burnout, cut-off scores based on standard sample of teachers as

reported in MBI scoring sheet showed that present sample of teachers are

experiencing high level of emotional exhaustion and depersonalization and low level

of reduced sense of personal accomplishment. In this case, diverse responses have

obtained for low level of emotional exhaustion and high levels of depersonalization

and reduced sense of personal accomplishment.

Teachers reported high level of endorsement for affective commitment;

whereas mean scores are showing low on continuance commitment. Greater

dispersion in responses has observed in reporting low levels on affective, continuance,

and normative commitment. For measure of personality factors, teachers dominantly

endorsed for high levels of each trait factor. Teachers gave diverse responses in case

of high levels of extraversion and emotional stability and low levels of agreeableness,

conscientiousness, and openness. For emotional stability, having diverse responses

due to element of social desirability, seems a good explanation. After this initial level

of analysis, scores were subject to examine reliability and validity indices to assess

the psychometric soundness of the study measures.

177

Psychometric Issues

The psychometric properties of the measures of the study were established by

using estimates of the internal consistency and the construct validity and inter-scale

correlations. The findings of study have demonstrated that total score on Work

Environment Scale with reduced items yielded high magnitude of Cronbach’s alpha

coefficient (α = .88). For primary subscales of Relationship (α = .70), Personal

Growth (α = .66), and System Maintenance and Change Dimension (α = .75),

magnitude of alpha coefficients are providing substantial support for internal

consistency of the measure. Evaluating the secondary subscales yielded variation in

magnitude of alpha coefficients. For example, the subscale of clarity (α = .69) has

demonstrated comparatively high alpha coefficient compared to others. However,

subscale of coworker cohesion (α = .42) has shown relatively low alpha coefficient. In

comparison with results of pilot study (N = 102), coworker cohesion yielded better

estimate equivalent to .54. Moreover, in second pilot study (N = 40), the obtained

alpha coefficient was .49. The value of alpha got affected when computed on main

sample (N = 426) perhaps due to more restricted responses given by sample. This will

be supported if we compare the mean and standard deviation values on scores of work

environment obtained in pilot and main study (see Table 1 & 24).

Moreover, coworker cohesion comes under the primary dimension- the

relationship dimension (comprising involvement, coworker cohesion, and supervisor

support) which overall yields a satisfactory estimate of internal consistency equivalent

to .74 alpha coefficients. For subscale of managerial control, the value of alpha

coefficient in second pilot study was .38, which now in main study was enhanced up

177

to .53. However, collectively, managerial control as part of primary dimension of

system maintenance and change dimension (along with other subscales, e.g., clarity,

innovation, and physical comfort) yielded alpha coefficient of .79. Studies using

English version of WES also reported somewhat similar trends in magnitude of alpha

coefficients when examined on sample in context of Pakistan.

For example, in an earlier study (Rehman & Maqsood, 2008) using sample of

university teachers (N = 500) reported alpha coefficients of secondary sub scales

ranged from a lower for work pressure (α = .29) to substantial for task orientation (α

= .63). Study by Maqsood and Rehman (2004) using sample of service oriented

organization reported alpha coefficient ranged from .77 to .88 for primary subscales

and for secondary subscales alpha coefficients ranged from a lower for managerial

control to (α = .45) to moderate for innovation (α = .68). For measures of environment

assessment based on relatively broader concept (e.g., cohesion, areas of personal

growth etc.),

Moos (1990) commented that such measures may be less internally consistent

depending on length of a subscale or the type of response options used, or the

variability of responses. Moos further elaborated that relying too much on less diverse

items in a measure may end leaving the measure having a narrower construct.

Whereas, a measure with diverse items tends to assess the construct in a more real

context, which in turn may grade the measure high on validity.

For Maslach Burnout Inventory (21-item measure) with high obtained alpha

coefficient (α = .86) provided satisfactory evidence of psychometric soundness of the

revised measure to be used for sample of university teachers. Satisfactory estimate of

177

internal consistency has obtained for subscale of emotional exhaustion (α = .74),

depersonalization (α = .69), and personal accomplishment (α = .76). Pilot study

reported moderate magnitude of alpha coefficient for subscales of emotional

exhaustion (α = .60), depersonalization (α = .65), and for personal accomplishment (α

= .67). In comparison, findings of main study yielded increase in magnitude of

respective subscales. Due to exclusion of item 14 from subcomponent of emotional

exhaustion, the magnitude of alpha coefficient is increased in comparison with the

results of pilot study. Moreover, in main study, elaborated structure of burnout was

also examined. For psychological strain (α = .50) and somatic strain (α = .64)

components of emotional exhaustion, and self (α = .59) and others (α = .60) related

components of personal accomplishment yielded relatively moderate to low

magnitude of coefficients partly because of reduced number of items in respective

scales.

Organizational Commitment Questionnaire yielded high magnitude of alpha

coefficients on total scores (α = .84) and on dimension of affective commitment (α =

.83). The moderate level of reliability indices have obtained for subscales of

continuance commitment (α = .61), and normative commitment (α = .64). In

comparison with pilot results, there is an increase in magnitude of alpha coefficient

for subscale of continuance commitment from .55 to .61.

Mini Markers set has shown Cronbach’s alpha coefficients for subscales

ranges from high magnitude for Extroversion (α = .76), Agreeableness (α = .79),

Conscientiousness (α = .83), Intellect or openness (α = .73), to low on Emotional

Stability (α = .28). There is not much increase in magnitude of alpha coefficient in

comparison with pilot study. The sub-scale of emotional stability with excluded items

177

25 and 26 yielded slight increase in alpha coefficient from .27 to .28. The value of

Cronbach’s Alpha based on standardized items with reduced items is .30. Pallant

(2007) suggested that magnitude of alpha coefficient may not approach to an

appropriate value in case of small number of items e.g., less than 10. Saucier (1994)

noted consistently lower alpha coefficients typically by .05 to .10 for abbreviated

version of 40-item Mini-Marker subset in comparison to 100-item Markers.

Moreover, the decision to retain the subscale also relates to the estimate of

confirmatory factor analysis which leads to deletion of two items of the subscale.

Evaluation of reliability estimates may also be made in comparison of mean

and variance of subscales in comparison of rest of subscales in a particular sample

(see Moos, 1990). For example, if we refer back to Table 24 in result section, mean

scores on low, moderate, and high levels of emotional stability are less in comparison

of other dimensions. In connection with this, comparatively low values of variance are

showing restricted range of responses for dimension of emotional stability.

Inter-scale correlations presented in Table 25 are showing that each subscale

of WES is significantly related with total score on WES. This indicates the

satisfactory estimate of construct validity of the measure. The primary dimensions of

WES, relationship dimension (r = .82, p < .01), personal growth dimension (r = .84, p

< .01), and system maintenance and change dimension (r = .90, p < .01) are showing

highly significant correlation with total scores of WES. Rehman and Maqsood (2008)

reported construct validity of WES by correlating the scores of secondary subscales

with score of its primary dimension. For instance, involvement (r = .85, p < .01),

coworker cohesion (r = .73, p < .01), and supervisor support (r = .75, p < .01) showed

high correlation with relationship dimension. Autonomy (r = .63, p < .01), task

177

Orientation (r = .76, p < .01), and work pressure (r = .44, p < .01) showed substantial

correlation with personal growth dimension. Clarity (r = .78, p < .01), managerial

control (r = .55, p < .01), innovation (r = .84, p < .01), and physical comfort (r = .71,

p < .01) showed significant correlation with system maintenance and change

dimension. With regard to psychometric issues of WES, Flarey (1991) claims that

repeated use of the Work Environment Scale has accounted in establishing its content

reliability and validity. Moos and Billings (1991) suggested that items of WES are

easy to understand and have high face validity.

For burnout, emotional exhaustion (r = .80, p < .01), depersonalization (r =

.56, p < .01), and personal accomplishment (r = -.81, p < .01) are showing significant

relationship with the total score of MBI. The similar patter of findings has obtained

for elaborated structure of burnout as well. This suggests the satisfactory estimate of

construct validity of MBI. Moreover, affective commitment (r = .44, p < .01),

continuance commitment (r = .12, p < .05), and normative commitment (r = .24, p <

.01) are significant related with total score of OCQ.

The aforesaid discussion provided satisfactory estimates to conclude about

substantial support for psychometric soundness of the measures of the present study.

The possible explanation of low Cronbach’s alpha coefficients for subscales (in case

of Work Environment Scale) seems to be at modest in context of evaluating the

overall alpha for total score as discussed above. Low Cronbach’s alpha in case of

personality measure obtained for subscale of emotional stability was justified by a

referring note by original author of the measure along with the possible explanation

attributed to cultural differences in responses for subscale items emerged out of

examining the theoretical factor structure. In conclusion, the detailed psychometric

177

examination through confirmatory factor analysis provides a safer mode to retain the

respective subscales with low alpha coefficients.

Following is the description of findings related to hypothesized relationships

of study variables.

Predictive Impact of Work Environment on Burnout and Organizational

Commitment

Teachers reported that they were experiencing burnout particularly high on

personal accomplishment and emotional exhaustion. Findings of regression analysis

found that work environment variables are non-significant predictors of burnout.

Findings suggested support in favor of five factor model of burnout. Findings

revealed that coworker cohesion, task orientation, and managerial control are negative

predictors of burnout five-factor structure. Overall, together these dimensions account

for 11% variability in burnout. Coworker cohesion has thought to influence burnout

(Kim, Lee, & Kim, 2009; Savicki & Cooley, 1987; Turnipseed, 1994). The findings

of our study suggested the negative relationship. This is an important dimension to

consider the importance of relationship dimensions in influencing burnout. In

predicting burnout, task orientation was found as a negative predictor. Overall, task

orientation is linked with burnout and its components (Constable & Russell, 1986;

Savicki, 2002). Our findings suggested negative relationship between task orientation

and burnout. Therefore, it may be expected that too much emphasis on planning and

efficiency to get the job is helpful in getting the job done, which in turn lessens the

177

feeling of burnout. It may be interpreted that high emphasis on task orientation may

lead to a characteristic managerial style, which may influences teachers’ sense of

personal accomplishment. Findings highlighted managerial control as a negative

predictor of burnout. This may important to relate that managerial control is expected

to reflect in workplace dynamics, e.g. maintaining equity, feedback and evaluation of

employees, etc., which in turn is expected to influence the experience of burnout.

Emotional Exhaustion as feelings of emotionally overextended and exhausted

by one's work has found to be prevalent high among university teachers compared to

depersonalization. The work environment facets including involvement and work

pressure is found linked with explaining variance in emotional exhaustion. Findings

highlighted that together these facets accounts for 13% change in emotional

exhaustion. With empirical support (Adali et al., 2003; Robinson et al., 1991), current

findings revealed involvement as a negative predictor of emotional exhaustion.

Teachers reporting high emotional exhaustion are considered exerting an impact on

teachers’ involvement in job tasks.

Management of universities needs to plan strategies to enhance employees’

involvement. Moreover, the careful monitoring and maintaining an optimal level of

workload is desirable to manage emotional exhaustion among employees. Since, work

pressure has received empirical support as a potential contributor in experiencing

emotional exhaustion (Chan & Huak, 2004; De Croone, Sluiter, & Blonk, 2004;

Goddard, O’Brien, & Goddard, 2006; Levert, Lucas, & Ortlepp, 2000; Robinson et

al., 1991); therefore, this is essential to recognize the nature of teaching job itself and

the potential harm that work pressure may exert of teachers. For instance, teachers’

contribution in outside work assignments and contribution in community related

177

issues are considered as important criteria of their performance evaluation. If teachers

are reporting work pressure in performing their routine office job, then concerning

question about their expected outstanding contribution in the field of education may

arise. Turnipseed (1994) suggested that work pressure might affect the cognitive

abilities of the individual. Teachers’ attempt to reduce work stressors, consciously and

unconsciously, may results in influencing the level of emotional exhaustion. This

might be of the reason that work pressure may directly affect the emotional

exhaustion or may act indirectly by influencing the individual’s ability to cope with

stress. This is also encouraging that teachers’ feeling of depersonalization is not being

influenced by work pressure.

Evaluating the elaborated structure of emotional exhaustion in terms of its

underline constructs namely psychological strain and somatic strain demonstrated

some significant differences. This in turn added in empirical support for five-factor

model of burnout. It may be concluded that future researches need to examine the

emotional exhaustion more extensively. Current findings highlighted that involvement

and managerial control were explaining 12% variance in psychological strain. In line

with expected direction, involvement and managerial control are found as negative

predictors, with involvement as more powerful predictor.

While testing the elaborated structure, managerial control is found as an

important predictor explaining the psychological strain. Previous studies have

supported that managerial control is associated to explain variance in burnout (Adali

et al., 2003). Current findings highlighted the need for careful monitoring and further

in depth investigation using group interviews or focus groups of teachers. This will

help to probe their prevailing perceptions and knowing their preference for a desirable

177

shape of managerial control in educational settings. Perhaps, the diagnostic efforts

need to explore the management style in various directions, e.g., subjective views

about the negative aspects of working conditions, equity in work place, feedback

mechanisms, control procedures, involvement of teachers, etc., if implemented at

management level, may contribute well in understanding the role of managerial

control in developing impersonal and negative attitude among employees.

For somatic strain component of emotional exhaustion, work pressure as

positive predictor and task orientation as negative predictor is explaining 10%

variance. Task orientation may be observed related with high efficiency and emphasis

on proper planning to complete the job. Since, employees have reported that work

settings are dominantly high on task orientation; therefore, it seems logical that high

emphasis on getting the work done in desirable manner may put teachers in a

condition to effect through emotional exhaustion. Overall, studies have found that

task orientation has association with burnout (Constable & Russell, 1986; Munir,

2005). Specifically, emotional exhaustion has found to be linked with task orientation

(Chan & Huak, 2004; Savicki, 2002).

Findings highlighted that among different psychosocial factors, involvement-

an important variable of relationship dimension has found to be a negative predictor

explaining low variation up to 8% in depersonalization. Since, teachers are reporting

relatively low on depersonalization; therefore, a negative relationship with

involvement was expected. The influence of involvement on depersonalization as well

as on emotional exhaustion can be explained to effect due to mental preoccupation

due to demanding nature of the profession as the causal stressor. The pattern of

interpersonal relationships at work has taken as a potential contributor to exert impact

177

on depersonalization (Leiter & Maslach, 1988). Previous studies also reported that

depersonalization is inversely linked with involvement (Adali et al., 2003; Robinson

et al., 1991).

In our study, teachers have reported high on personal accomplishment.

Extending its causal factors with workplace characteristics revealed that certain

aspects of relationship, personal growth, and system maintenance and change

dimensions are explaining considerable variance in personal accomplishment.

Workplace characteristics including coworker cohesion and work pressure as positive

predictors and physical comfort as negative predictor are explaining marginal

variance up to 8% in personal accomplishment. Generally, cohesiveness with

coworkers (Savicki & Cooley, 1987; Turnipseed, 1994) is linked to explain burnout

experience and particularly the sense of personal accomplishment (Savicki, 2002).

The enhanced feelings of personal accomplishment might be a logical result of

working in such a work environment where element of support from coworkers is

present. However, in present study, coworker cohesion is adding in expalining the

reduced sense of persoanl accomplishmnet. The positive association between

coworker cohesion and reduced perosnal accomplishment implies that propbably

teachers are self sufficient and an increase in cohesion may not likely to enhance their

sense of personal accomplihsment. Moreover, personal accomplishment related to

self, is the one independent of the level of cohesion.

Work pressure stands out as a stronger predictor compared to dimensions of

co-worker cohesiveness and physical comfort. Previous studies have pointed out work

pressure as an important predictor of personal accomplishment (Goddard, O’Brien, &

Goddard, 2006; Robinson et al., 1991). Similarly, work pressure is explaining

177

variance in personal accomplishment related to self. Whereas, in predicting personal

accomplishment related to others, work pressure has found to be a non-significant

predictor. Findings of the study revealed physical comfort as negative predictor for

personal accomplishment and for its sub-component related to others. Previous

studies have shown that physical comfort is associated with experience of burnout

(Constable & Russell, 1986) and depersonalization (Salyers & Bond, 2001). Since,

teachers of our study have reported high level of personal accomplishment; therefore,

it may interpret that physical conditions of work environment including comfort

associated with physical surroundings and its pleasantness, the proper ventilation and

lighting system, privacy and proper office space, etc., may not lead to decrease in

personal accomplishment. In predicting personal accomplishment related to self, task

orientation found as a positive predictor. In line with previous finding, Robinson et

al., (1991) noted that personal accomplishment was predicted by task orientation.

Overall, task orientation is linked with burnout and its components (Constable &

Russell, 1986; Savicki, 2002). Since, teachers are reporting their work settings high

on task orientation and also reporting high on personal accomplishment; therefore, it

may be concluded that emphasis on structured job will positively influence the

feelings of personal accomplishment.

In support of effective role of work environment in predicting burnout, current

findings highlights that work environment as a composite factor (both three and five

factor models) is explaining significant variance in burnout and each of its

components. Inverse relationship is demonstrated with burnout and its components,

except for reduced sense of personal accomplishment. Work environment as a

composite factor did account for marginal variance in burnout components.

177

Comparing the direction and magnitude of difference in pilot and main study,

different findings are obtained. For instance, the findings of our pilot study (N = 102)

showed stronger influence of work pressure as positive predictor and clarity as

negative predictor for explaining 26% variance in emotional exhaustion which was

highly significant. Whereas, findings of our main study highlights involvement as

negative and work pressure as positive predictor contributing 13% variance in

emotional exhaustion. For elaborated structure, task orientation and managerial

control also provided significant explanations. In the pilot study, work pressure as

positive predictor accounts for 10% variance in depersonalization; whereas, in main

study, involvement accounts for 8% variance. Pilot study did not demonstrate the

significant contribution of work environment variables in predicting personal

accomplishment. In comparison, main study highlights that majority of factors of

work environment are explaining variance in personal accomplishment and for its

elaborated structure. Based on findings of present study, the present study contributes

well in establishing empirical support for Densten’s (2001) elaborated model of

burnout.

While reporting organizational commitment, teachers reported high on

affective commitment; whereas, normative commitment was on second level priority.

Teachers reported low on continuance commitment. This is somewhat consistent with

findings of a validity study (Cheng & Stockdale, 2003) of Meyer and Allen’s model

of organizational commitment; the findings mentioned Chinese sample as being the

collectivistic culture reported low on continuance commitment. However, authors also

mentioned that reporting high on affective commitment is considered to be linked

177

with individualistic cultures and being low on continuance commitment seems

contradictory particularly in context of masculine cultures.

In the context of Pakistani culture, the participants of study endorsed highly

for affective based commitment and lowest on continuance based commitment may

lead towards understanding the similarities and difference in broader national and

particular organizational culture, e.g., academic settings. At this point, the study is

points towards a new direction for future research that cross-cultural studies may need

to consider that organization environment as being a unique culture may be in

contradiction with broader national culture and thus leading to characteristic

differences in employees’ attitudes. Future researches may need to consider the

differences in national and particular organizational cultures while interpreting

findings in context of broader national cultural classifications or cross-cultural

comparisons.

Various researchers have identified existing gaps in research on work

environment and organizational commitment. Present study provided empirical

evidence suggesting significant relationship of two, particularly in academic settings.

Teachers’ organizational commitment is found to enhance by work environment

characteristics including autonomy and clarity. Findings revealed that autonomy and

clarity are positive predictors and contribute 17% variance in organizational

commitment. Some researchers have although mentioned that workplaces with clarity

of rules and procedures may exert profound influence on employees’ commitment

(Mauser, as cited in Moos, 2008). Clarke and Iles (2000) suggested that workplace-

promoting support for diversity could enhance employees’ commitment.

177

With respect to present findings, this may be inferred that diversity in

academic settings may foster through certain strategies i.e. by letting employees be

autonomous in certain job related decisions. For maintaining the performance of

teachers affiliated with higher education; this is important to acknowledge that

teachers require having strong attachment with the organization. Maintaining a sense

of shared values and unity among teachers should be of utmost importance for the

management system of universities. In this regard, the findings of present study help

to suggest that management needs to explore for suitable/adequate mechanism, which

help promote personal growth of teachers. For example, academic settings should

design strategies to promote the autonomous environment. In academic settings,

preferred environment should be the autonomous culture. This particular is logical to

understand especially within group of those teachers who are involved in teaching at

higher education level. Since, effective teaching is a demanding task and requires one

to be innovative while managing it. This is important to understand that an innovative

environment should be the autonomous one. The autonomous environment may

contribute well in performing the job responsibilities. Moreover, if teachers are

autonomous to contribute in decision making, especially those which are related to

them, it will positively add in maintain healthy work environment by satisfying

personal growth needs. Along with this, an important consideration pointed out by

present study is that management requires explicit rules and procedures of the

organization. This focus on clarity of rules and procedures particularly comes under

system maintenance of the academic settings, which in turn positively contributes in

developing emotional bonding of employees with their organization.

177

Affective commitment is linked with work environment characteristics

(Dramstad, 2004). The findings of present study suggest that autonomy (personal

growth dimension) accounts for 21% variance in affective commitment. This is in line

with previous findings of Stewart et al. (2007), the researchers supported that

autonomy and fairness in workplace environment is linked with affective

commitment. This is reasonable to believe that autonomy should be valued in

workplace because of the nature of teaching profession, which demands continuous

input for effective performance. The current findings point out towards implications

for the respective management staff of these academic institutions for access weather

teachers are fully involved in decision-making process, especially those decisions

which can directly affect them, or exert positive influence in maintaining their

affective based commitment to the organization.

Continuance commitment, which reflects in the form of investments and

associated perceived cost, has shown association with relationship dimension and

system maintenance of the workplace. Stewart et al. (2007) supported that work

places characterized with cohesion, trust, support, and fairness contributes in

strengthening the continuance-based commitment. Certain other studies also provided

support for association of supervision and cohesion with organizational commitment

(Brooks & Seers, 1991; Chughtai & Zafar, 2006). Findings of present study

highlighted that supervisor support and coworker cohesion as negative predictors

explain variability in continuance commitment. Moreover, the dimension of clarity

(system maintenance and change dimension) as positive predictor has found to be

contributing in explaining continuance-based commitment. Together, coworker-

cohesion, supervisor support and clarity accounts for relatively low variance (8%) in

177

continuance commitment. The present study suggested that clarity of workplace rules

and procedures is the strongest predictor of continuance commitment. Work settings

characterized with clear job procedures positively add in predicting teachers’

investments in their job, this may results in their tenure or continuation to work in the

organization. This seems imperative for the management of these institutions to

carefully manage the relationship dynamics of work settings as well as the aspects of

system maintenance and change dimension of the work setting.

Consistent with findings of pilot study, results of main study highlight that

normative based commitment to the organization has non-significant influence on

work environment factors. The participants of our study have reported good emphasis

on normative based experience of commitment. However, none of workplace

dimension reached at statistical significance to infer association with normative

commitment. Study of Cheng and Stockdale (2003) mentioned that individual within

collectivistic culture report better on normative commitment, as being high in group

emphasis.

This further may lead to understand that workplace characteristics may not

influence the normative commitment if considered that within collectivist cultures, the

development of normative commitment seems a product of individuals’ internalized

values within cognitive schema of group preference. Our Findings highlighted that

work environment as a composite factor explains 15 % variance in organizational

commitment, 20% in affective commitment, and 6% in normative commitment. This

also explains that affective commitment stands out as the most important dimension

of work environment, which may exert significantly effect in combination with work

environment characteristics. Comparing the direction and magnitude of difference in

177

results of pilot and main study, considerable differences are observed for continuance

commitment.

In the pilot study, non-significant findings were obtained for continuance

commitment. However, the findings of main study point towards small variance in

continuance commitment produced by cohesiveness, supervisor support, and clarity of

work procedures. For affective commitment, pilot study pointed out considerable

variance (24%) produced by managerial control and innovation; whereas, main study

highlighted that only autonomy has found to explain considerable variance in

affective commitment.

This is worth mentioning that the extent of variance which work environment

characteristics are significant but it’s not large in effect size especially when we see

that co-worker cohesion, supervisor support, and clarity contributes in explaining 8%

variance in continuance commitment. This aspect leads towards various possible

explanatory factors. This indicates the influential role of certain other variables as

well in predicting work environment and commitment relationship. It further adds in

complexity of understanding employees’ workplace behavior with its sensitivity

towards getting influence from host of certain other organizational, situational,

contextual, and personal factors.

This may be associated with degree of strength of relationship between work

environment and organizational commitment. For instance, meta-analytic review have

reported 0.24 average absolute value correlation between perceptions of work

environment and job attitudes including involvement and commitment (see Parker et

al., 2003). Moreover, since employees are showing dominant endorsement for

positive aspects for work environment (involvement, coworker cohesion, supervisor

177

support, autonomy, task orientation, clarity, innovation, and physical comfort) as

compared to negative aspects (work pressure and managerial control); this may be

attributed as one of the reasons explaining low magnitude of variance in burnout and

organizational commitment produced by work environment.

Moderating Effects of Personality

Based on Moos’s (1986) model of psychosocial environment, it may be

assumed that personal factors moderate the link between work environment and its

outcomes. Present study focused to explore the moderating role of Big Five factor

model to see how does personality influences perceptions regarding work

environment in predicting burnout and organizational commitment. The moderator

analysis was performed using composite scores of criterion variables regressed

against total scores of work environment. The subscales were excluded keeping in

view the page limit of report writing. A recent meta-analysis (Alarcon, Eschleman, &

Bowling, 2009) supports that burnout is associated with Big-Five dimensions of

personality. The current findings highlighted that extraversion, agreeableness, and

openness are predictors of burnout. Extraversion is producing 24% variance in

burnout. Similar pattern of finding has obtained for burnout five factor model; with

extraversion explaining 23%varaince. Agreeableness, demonstrates as the strongest

moderator explains 42% variance in burnout; whereas, accounts for 41% variance in

five factor model of burnout. Openness is explaining 32% variance in burnout three

factor model and 33% variance in burnout five factor model. Findings suggested

emotional stability and conscientiousness as non-significant moderators of burnout.

177

These findings are in line with previous studies. For example, extroverts are reported

more prone for maintaining interpersonal relationships and are considered

demonstrating relationship with burnout (Maslach, Schaufeli, & Leiter, 2001).

Similarly, individuals’ disposition to openness reflects through flexibility of thoughts

and readiness to new ideas (Barrick & Mount, 1991) is related with the experience of

burnout (Swider & Zimmerman, 2010). Findings of studies are in direction to indicate

that burnout is associated with extraversion and openness to experience (Bakker, Van

Der Zee, Lewig, & Dollard, 2006; Rothman & Storm, 2003).

The significant moderation effect of personality dimensions (extraversion,

agreeableness, and openness) suggested through multiple moderation analysis (MMR)

was further tested at an advance level, to see the significance of slopes reflecting

levels (high, medium, & low) of predictor and moderator variables. Based on Aiken

and West (1991) methodology, Jose (2008) recommended computing significance of

slopes for interpreting moderating effects. This analysis further revealed that

evaluating extraversion as moderator revealed it as a negative moderator of the

relationship between work environment and burnout (three factor model).

Comparatively slope representing low level of extraversion seems to exert stronger

influence compared to medium and low levels. However, slopes representing level

(i.e. high, medium, and low) do not reach at statistical significance (Table 37). This

helps to interpret that interaction term does not significantly predicted burnout over

and above the statistical main effects of work environment. Similar pattern of findings

have obtained for agreeableness and openness. In evaluating moderating effects of

personality dimensions for relationship between work environment and burnout (five

factor model), similar pattern of findings have obtained. The statistically significant

177

moderation effects of extraversion, agreeableness, and openness were not further

supported through Jose’s procedure.

Our findings highlighted non-significant moderating influence of

conscientiousness and emotional stability. Findings suggested that conscientiousness

and emotional stability have shown their independent predictive power.

Conscientiousness characteristics reflect as one being careful, thorough, and

responsible includes typical behavior like hard working, achievement oriented, and

consistent (Barrick & Mount, 1991). Since, our participants (university teachers) are

dominantly scoring high on conscientiousness; therefore being over emphasis on

conscientiousness thought to influence their experiences of burnout. Present findings

suggest that conscientiousness as a negative predictor. Emotional stability, which

reflects individual differences in explaining tendency towards distress (McCrae &

John, 1992), has found to be a negative independent predictor.

Findings revealed that teachers’ workplace perceptions and their personality

factors or dispositional factors contribute to predict their organizational commitment.

Findings revealed that extraversion as moderator explains 21% variance in overall

organizational commitment; agreeableness as strongest moderator explains 26%

variance; conscientiousness explains 24% variance; and openness explains 20%

variance in the organizational commitment. Emotional stability stands as carrying

predictor power but is regarded as a non-significant moderator. Each of personality

factors demonstrated significant direct predictive effects as well.

Meyer and Allen (2006) cited rationale in explaining the reason for relating

commitment with extraversion tendencies by considering emotionality as core

extraversion, which may suggest that these individuals would show more emotional

177

attachments with their organization. This is supported, if we see that in moderator

analysis, extraversion is showing independent positive predictive power to predict

organizational commitment. Interaction effect revealed extraversion as negative

predictor which implies that the work environment of universities when interacts with

extraversion personalities, it might influence their commitment. Individuals high on

agreeableness are viewed as more trustworthy, which can aid them to maintain good

interpersonal relationships at work setting (Costa & McCrae, 1995) helping them to

build strong emotional affiliation with the organization. However, current findings

suggest that workplace perceptions through agreeableness have inverse relationship

with organizational commitment. Moderator analysis revealed agreeableness as

positive predictor, whereas, interaction effect revealed its negative moderating power.

This implies that workplace characteristics are carrying powerful effects; therefore,

individuals with certain personality traits interact with workplace characteristics to

influence work outcomes.

Similar pattern of findings obtained for conscientiousness and openness.

Conscientious individuals are highly oriented towards good managing ability in work

behavior; for instance to be more orderly and good emotionally; controls the positive

work related outcomes (Thoreson et al., 2004). Meta-analytical study of Salgado

(2003) provides empirical support in building rationale to test conscientiousness as

strongest predictor of job outcomes. Employees’ predisposition to openness to

experience may help them to adapt and change considering requirements of their work

environments, which may enhance positive outcomes (Westerman, Simmons, & Bret,

2007). Current findings supported that conscientiousness influences workplace

perceptions to explain variance in organizational commitment. Openness as negative

177

moderator is explaining variance in organizational commitment. The pattern of

current findings have supported the theoretical assumptions of work environment

model of Moos (1994) which implies that workplace environment is unique to each

organization requiring its employees to sense the environment of the organization to

adapt or behave accordingly.

It is important to note that although moderation effect of certain personality

dimensions reached at statistical significance level. However, extending moderation

effect to complex testing, e.g., examining the significance of slope tests highlighted

that high, medium, and low levels of personality dimensions were not significantly

explaining moderation effects over and above statistical main effects of the work

environment. The pattern of findings in initial moderation analysis is significant.

However, the extended analysis is presenting a different picture. At this stage, this is

to acknowledge that findings provide us direction for future research.

The findings of the study suggested that five-factor model appeared as an

informative framework in examining the dispositional sources of burnout and


Moderating Effects of Organizational and Demographic related Personal

Variables

The study focused to explore how personal variables e.g., organizational

(public or private sector universities, hierarchical status, job duration, faculties

differences, involvement in other paid jobs) and demographic variables (age, gender,

education, marital status) influence work environment perceptions to predict burnout

177

and organizational commitment. Moderator analysis on sector based differences

highlighted that belongingness to public and private sector universities, moderates the

work environment and burnout relationship. This moderating influence accounts for

11% variance in predicting burnout. Similar pattern of findings have obtained for

five-factor model of burnout and 10% variance in burnout is being explained by

interaction term.

In Pakistan, studies very few studies are conducted which aimed at assessment

of work environment and its outcomes within public and private sector universities

(Rehman & Maqsood, 2008). It also has been reported in a recent systematic literature

review that burnout studies on university teachers are needed to explain by means of

comparison across sectors (Watts & Robertson, 2011). The findings of present study

contributed in establishing empirical support for comparative studies explaining

differences in work environment for university academics of public and private sector.

Present study extended the moderation analysis using Jose’s (2008) procedure,

which helped to understand the interpretation of graphical display of moderation

effects. The findings of significance of slope indicated that slopes representing public

and private sector significantly differ from zero. This indicated that sector is a

significant moderator for relationship between work environment and burnout.

However, findings of the study suggested that private sector is more strongly

associated to predict burnout (three factor model); whereas, for burnout (five factor

model), slope representing public sector is more strongly associated. This finding

helps to deduce that public and private sector is an important variable of comparison

to manage the relationship between work environment and burnout.

177

As regards relationship of work environment and commitment findings

suggests significant moderation effect of sector. The model explaining moderation

effect highlights that belongingness to public and private sector contributes 21%

variance in organizational commitment. Previous studies have provided support that

affiliation with private vs. public sector serves as an important variable to influence

organizational commitment (Boardman, Bozeman, & Ponomariov, 2010; Shirbagi,

2007). Further extending moderation analysis through Jose’s procedure, the statistical

significance of moderation impact appeared strengthened. The findings demonstrated

that both public and private sector are contributing significant moderating influence.

However, magnitude of slopes highlighted that private sector more strongly relates to

explain work environment and commitment relationship compared to public sector.

This helps us to infer that perhaps characteristics of work environment operating in

public sector universities might have been a potential reason, to explain differences in

level of commitment of our participants. The findings of the study highlights the need

of ‘intervention planning’ that the management of public sector universities need to

design suitable strategies, based upon the feedback of their perceptions of work

environment and commitment with their organizations. In this respect, this is to

consider important for the personnel management of public sector universities to

improve the work environment facets; i.e. making work environment conducive to

their professional growth and preferences.

Investigating the moderating role of hierarchical status /job position, revealed

that teachers’ hierarchical status served as a non-significant moderator in predicting

burnout and for organizational commitment. However, teachers’ belongingness to

basic and high rank is showing direct predictive relationship with burnout variable. If

177

we see, hierarchical status as independent predictor in model 1 and in model 2 is

showing negative relationship in predicting burnout (for both three and five factor

models). This is in line with previous finding where academic rank was negatively

associated with burnout (Haque & Khan, 2001). Previously, job status was found

positively related to aspects of organiztaional commitment (Meyer et al., 1993).

Researches have shown that the duration of job appears as an independent

predictive power. For example, organizational tenure is a potential factor to enhance

employees’ well being (Long, 1993) and particularly their organizational commitment

(Kushman, 1992; Meyer & Allen, 1997). However, the findings of present study

indicated that in predicting organizational commitment, employment position/rank is

not showing independent predictive power. However, current findings suggest that

employment duration is not significantly moderating work place perceptions to

predict burnout (three and five factor models) and organizational commitment. This

helps to interpret that tecahers percpetons and outocme variables are indepndnet of

their job status. This refelcts the explicit importance of workplace charcateristics as

mor important influential factors comapred to tecahers’ perosnal variables.

Some researchers have reported that faculty/departmental affiliation does

relate to employees’ perception of work environment (Avallone & Gibbon, 1998;

Maloney et. al., 1996; Straker, 1989). Affiliation with natural and social sciences

disciplines might be an influential variable to explain differences in burnout and

commitment; mainly due to the nature of their job and work environment. Current

findings suggests that affiliation with natural or social sciences departments does not

serve as a moderating factor in predicting teachers’ burnout and their commitment.

177

However, its direct relationship was evident for burnout and also for organizational

commitment.

Present study reported that only a small portion of teachers have reported

about their involvement in side jobs. Moderator analysis revealed that teachers’

involvement and non-involvement in side jobs does not serve as a moderate to

influence workplace perceptions in predicting burnout and organizational

commitment. Although, the direct predictive power of this variable was evident; the

variable accounts for 13% variance in burnout (three factor model) and 12% in

burnout five factor model.

Moderator analysis performed for age highlighted that association between

work environment and its outcomes is being moderated by teachers’ age. In

explaining work environment and burnout (three factor model), age as moderator

explains 13% variance; whereas for predicting burnout five factor model, age as

moderator accounts for 12% variance. A recent research (Luk, Chan, Cheong & Ko ,

2010) suggests that age is associated with burnout. This finding is an important

consideration within work settings that management should consider the possible

impact of employees’ age in designing and implementing strategies. Further,

extending this statistically significant moderation analysis to test the significance of

slope tests, results revealed that slope representing moderate level of age is more

stronger to predict burnout (both three and four factor models). This implies that when

moderate level of age is considered, work environment is significantly associated with

burnout.

Some earlier empirical studies (Grau et al., 1991; Mathieu & Zajac, 1990)

have suggested positive and stronger relationship of age with organizational

177

commitment. The findings of our study are in line with these studies, suggesting that

age is positively related with organizational commitment explaining 17% variance.

However, these findings highlight the non-significant moderating influence of age in

predicting organizational commitment.

Moderating impact of gender is revealed as a non-significant moderator in

predicting burnout. Phelan et al. (1993) also suggested that non-significant gender

difference in the perceptions of work environment. However, substantial body of

research suggests that gender does influence work environment and work subsequent

work related attitudes, e.g., organizational commitment (Clarke & Iles, 2000; Stewart

et al., 2007; Witt, 1989). Findings of present study added in arguments in favor of non-

significant impact of gender, which otherwise is also well supported by previous

studies (Phelan et al., 1993; Sahu & Misra, 2004). However, we do acknowledge that

evaluating gender as independent predictor may add to some valuable information.

Findings highlighted that gender is not a significant independent predictor for burnout.

The findings of present study indicated that in predicting organizational

commitment, gender does not serve as a significant moderator. Current findings also

aid in supporting (see Meyer et al., 2002; Riketta, 2005; Thorsteinson, 2003) that

teachers’ commitment to their organizations is independent of their gender perhaps

because of the dominant influence of workplace practices and management on work

outcomes instead of gender. Substantial body of research suggests that gender does

influence work environment and work subsequent work related attitudes, e.g.,

organizational commitment (Clarke & Iles, 2000; Stewart et al., 2007; Witt, 1989).

The findings of present study indicated that in predicting organizational commitment,

gender does not serve as a significant moderator. Current findings also aid in

177

supporting (see Meyer et al., 2002; Riketta, 2005; Thorsteinson, 2003) that teachers’

commitment to their organizations is independent of their gender perhaps because of

the dominant influence of workplace practices and management on work outcomes

instead of gender.

Previously, Maloney et al. (1996) reported that employees’ education level

relates to the aspect of work environment. Investigating the impact of teachers’

education highlighted that educational level (masters degree vs. higher research

degrees i.e., Master of Philosophy or Doctorate of Philosophy: MPhil or PhD) does

not act as moderators in predicting teachers’ burnout and organizational commitment.

Bivariate relationship between education and burnout has been reported in earlier

studies (Kabadayi, 2010; Moghadam & Tabatabaei, 2006; Wilber & Specht, 1994).

However, our study does not support the direct predictive relationship between

education and burnout. Literature has provided support for stronger bivariate

relationship between level of education and organizational commitment (Grau et al.,

1991; Mishra & Srivastava, 2001). However, education showed no direct relationship

with organizational commitment.

Findings highlight that teachers’ marital status does not moderates the

relationship of work environment with burnout and organizational commitment.

Earlier some studies have found out that marital status is linked to explain burnout

(Kim-Wan, 1991). However, present study did not suggest the direct as well as

moderating influence of marital status in predicting burnout. Moreover, previous

findings (John & Taylor, 1999; Tsui et al., 1994) have demonstrated that married

people were more committed to their organization than unmarried people were.

177

However, in present study, marital status did not showed independent power to

predict organizational commitment.

In support of present findings, Patterniti, Niedhammer, Lang, and Consoli

(2002) had pointed out the non-significant moderating influence demographic and

organizational variables, e.g. age, education, marital status, and hierarchical status in

examining the link between work environment and health outcomes. Similarly,

Fejgin, Ephraty, and Ben-Sira (1995) suggested that burnout experience is

independent of influence of personal and job related variable. For variables of

hierarchical status, further analysis (ANOVA) was also computed to see how each

level of rank (e.g., lecturers, assistant professors, associate professors, and

professors), separately tend to influence the reporting about burnout and commitment.

However, non-significant findings were obtained for each case. Keeping in view the

possible interaction of age and hierarchical status, One-Way Between Group analyses

showed non- significant associations. These further added to conclude that overall, the

non-significant impact of most of personal variables compliment the point raised by

Moos (1994), who posited that actual characteristics of the work settings are the major

determinants of employees’ perceptions of workplace rather than the demographic

and organizational related personal variables.

177

Implications of the Study

The present study contributed to evaluate the psychometric feasibility of using

instruments of study variables for university teachers in Pakistan. This aided in

making this study as indigenized one to further use the validated instruments (i.e.

Work Environment Scale, Maslach Burnout Inventory, Organizational Commitment

Questionnaire, & Mini Markers) for teachers’ population. Moreover, this research

provided cross cultural empirical evidence for establishing the relationship between

work environment and outcomes. The study has pointed out that management need to

consider teachers’ personality and age while improving or managing the workplace

environment. The study provided a comparison for future researches for the

evaluation of work environment of academic settings for designing possible

interventions in context of the organizational development. The findings of the study

are important to realize and encourage the management to understand and manage

their work settings on basis of desirable psychosocial characteristics which in turn

influences teachers’ burnout and commitment to their organizations.

Limitations and Future Research

To understand the applicability of research findings within the framework of

research design, it’s important to acknowledge the limitations of the study. Keeping in

view the response rate of the participants, implementation of random sampling was

not feasible in present study. The present study selected the instruments on basis of

demonstrated use in researches in context of Pakistan. However, the procedure of

177

examining the face validity in perspective of intended use within academic settings

was not done. This in turn might be one of the potential reasons for obtaining a

moderate model fit especially in case of Work Environment Scale. It was also

observed that for studies reporting perceptions about work environment should also

focus on qualitative methodology as well. This will help to understand the unique

characteristic features of the environment, which otherwise may missed out while

using pure quantitative approaches.

The present research raises certain questions for direction of the future

research. Keeping in view the gaps in literature, it is needed further exploration of

moderating as well as mediating role of personal variables. Future studies need to

focus on nationwide data, so that generalizability of the findings may be enhanced.

Since, quite low reliability coefficient has obtained in case of emotional stability;

which thereof, got unaffected even using large sample during main study phase.

Future research need to explore the possible reasons explaining how individuals

respond to the dimension of emotional stability in our culture. For future research,

intervention based studies are needed to plan; this will help in systematic diagnose of

workplace concerns leading to design appropriate planning for its effective

management. Future researches may focus ‘case study’ research design for thorough

description, diagnosis, and interventions of work environment related concerns. This

will add in expanding the field of occupational psychology in Pakistan.

Conclusion

The theoretical contribution of present study tested Moos’s (1994) model of

work environment in the context of work related outcomes (for the teaching faculty

177

members of Universities of Punjab, Pakistan) and elaborated the moderating role of

personal variables in this process. The findings provided empirical support for

existing measurement models of study variables. Given that work environment

influences burnout and organizational commitment, requires attention of policy maker

i.e. management of the universities, requires re evaluating the existing dynamics of

their work environment, especially from the feedback of findings of the present study.

The study emphasizes that interventions and organizational development programs of

universities’ faculty require emphasis on aspects such as; enhancing teachers’

involvement and coworker cohesion. An optimal emphasis on work pressure,

managerial control and task orientation is needed. This in turn will lead to healthy

environment potentially carrying less tendencies of burnout experience among

teachers. Managing high commitment among teachers found to have association with

autonomous environment where clarity of job procedures was highly considered for

effective performance of university teachers. The study suggests that extraversion,

agreeableness, and openness may combine to interact with operating working

conditions leading to experience of burnout. Personality has also found to be

contributing in effecting employees’ commitment except for the dimension of

emotional stability. The potential moderating influence of public and private sector

suggests the evaluation of existing employees’ policies for planning the training and

interventions for the effective management of teachers’ burnout and organizational

commitment. Moreover, across different subgroup of teachers, age is an important

consideration in managing teachers’ burnout. The study highlights that situational

context of working conditions is needed to consider for understanding the impact of

personality and other personal variables on environment and outcome relationship.

269

REFERENCES

Abdulla, M., & Shaw, D. (1999). Personal factors and organizational commitment:

Main and interactive effects in the United Arab Emirates. Journal of

Managerial Issues, 11(1), 77-93.

Abdullah. (2011). Evaluation of Allen and Meyer’s organizational commitment scale:

a cross cultural application in Pakistan. Journal of Education and Vocational

Research, 1(3), 82-86. Retrieved from http://www.ifrnd.org/JEVR/

1(3)%20June%202011/Evaluation%20of_Allen%20and%20Meyer%E2%80%

99s%20Organizational%20Commitment%20Scale. pdf

Abraham, I., & Foley, T. (1984). The Work Environment Scale and the Ward

Atmosphere Scale (Short Forms): Psychometric data. Perceptual and Motor

Skills, 58, 319-322.

Adali, E., Priami, M., Evagelou, H., Mouglia, V., Ifanti, M., & Alevizopoulos, G.

(2003). Burnout in psychiatric nursing personnel in Greek hospitals. European

Journal of Psychiatry, 17(3), 173-181.

Aguinis, H. A., & Stone-Romero, E. F. (1997). Methodological artifacts in moderated

multiple regression and their effects on statistical power. Journal of Applied

Psychology, 82, 192-206.

Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and Interpreting

Interactions. Newbury Park, CA: Sage.

Akram, S. (2003). Relationship between self-esteem and burnout among women

primary school teachers (Unpublished M.Sc Research Report). National

Institute of Psychology, Quaid-e-Azam University, Islamabad, Pakistan.

270

Alarcon, G., Eschleman, K. J. & Bowling, N. A. (2009). Relationship between

personality variables and burnout: A meta-analysis. Work & Stress, 23(3),

244-263. doi: 10.1080/02678370903282600

Allen, N. J., & Meyer, J. P. (1990). The measurement and antecedents of affective,

continuance and normative commitment to the organization. Journal of

Occupational Psychology, 63, 1-18.

Allen, N. J., & Meyer, J. P. (1996). Affective, continuance, and normative

commitment to the organization: An examination of construct validity.

Journal of Vocational Behavior, 49(3), 252-276.

Aluja, A., Blauch, A. S., Garcia, L. F. (2005). Dimensionality of The Maslach

Burnout Inventory in school teachers: A study of several proposals. European

Journal of Psychological Assessment, 21(1), 67-76.

Anastasi, Anne. Urbina, Susana. (1997). Psychological Testing (7th ed.). Upper

Saddle River (NJ): Prentice Hall.

Angle, H. L., & Lawson, B. L. (1993). Changes in affective and continence

commitment in times of relocation. Journal of Business Research, 26, 3-15.

Angle, H. L., & Perry, J. L., (1981). An empirical assessment of organizational

commitment and organizational effectiveness. Administrative Science

Quarterly, 26(1), 296-319.

Armelius, K., & Jeanneau, M. (2000). Self-image and burnout in psychiatric staff.

Journal of Psychiatric and Mental Health Nursing, 7, 399-406.

Armstrong, M. (1996). Human management: Strategy and action. London: Kogan

Page.

271

Ashforth, B. (1985). Climate formation: Issues and extensions. Academy of

Management Review, 10, 837-847.

Avallone, A., & Gibbon, B. (1998). Nurses’ perceptions of their work environment in

a Nursing Development Unit. Journal of Advanced Nursing, 27, 1193-1201.

Aven, F. F., Parker, B., & McEvoy, G. M. (1993). Gender and attitudinal commitment

to organizations: A meta-analysis. Journal of Business Research, 26, 63-73.

Bakker, A. B., Schaufeli, W. B., Sixma, H. J., Bosveld, W., & Van Dierendonck, D.

(2000). Patient demands, lack of reciprocity, and burnout: a five year

longitudinal study among general practitioners. Journal of Organizational

Behavior, 21, 425-441.

Bakker, A. B., Van Der Zee, K. I., Lewig, K. A., & Dollard, M. F. (2006). The

relationship between the Big Five personality factors and burnout: a study

among volunteer counselors. The Journal of Social Psychology, 146, 31 – 50.

Barker, R. G. (1965). Explorations in ecological psychology. American Psychologist,

20, 1-14.

Barnett, R. C., Gareis, K. C., & Brennan, R. T. (1999). Fit as a mediator of the

relationship between work hours and burnout. Journal of Occupational Health

Psychology, 4, 307 – 317.

Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job

performance: A meta-analysis. Personnel Psychology, 44, 1-26.

Barrick, M. R., & Mount, M. K. (1993). Autonomy as a moderator of the relationships

between the Big Five personality dimensions and job performance. Journal of

Applied Psychology, 78(1), 111-118.

272

Basir, A. (2006). Personality traits and burnout among public sector university

teachers (Unpublished M.Sc Research Report). National Institute Of

Psychology, Quaid-e-Azam University, Islamabad, Pakistan.

Becker, H. S. (1960). Notes on the concept of commitment. American Journal of

Sociology, 66, 32-42.

Belicki, K., & Woolcott, R. (1996). Employee and patient designed study of burnout

and job satisfaction in a chronic care hospital. Employee Assistance Quarterly,

12(1), 37-45.

Benjamin, L. (1987). Understanding and managing stress in the academic world.

Retrieved from http://www.ericdigests.org/pre-927/stress.htm.

Berry, M. A. (1994) Protecting the built environment: Cleaning for health. USA:

Chapel Hill.

Blegan, M. A. (1993). Nurses’ job satisfaction: a meta-analysis of related variables.

Nursing Research, 42, 36-41.

Blum, M. L., & Naylor, C. J. (2004). Industrial Psychology: It’s Theoretical and

Social Foundations. India: Nazia Printers.

Boardman, C., Bozeman, B., & Ponomariov, B., (2010). Private sector imprinting: an

examination of the impacts of private sector job experience on public manager

work attitudes. Public Administration Review, 70(1), 50-59.

Bogler, R., & Somech, A. (2004). Influence of teacher empowerment on teachers'

organizational commitment, professional commitment and organizational

citizenship behavior in schools. Teaching-and-Teacher-Education, 20(3), 277-

289.

273

Boles, J. S., Dean, D. H., Ricks, J. M., Short, J. C., & Wang, G (2000). The

dimensionality of the Maslach Burnout Inventory across small business

owners and educators. Journal of Vocational Behavior, 56, 12-34.

Booth, R., Norton, R., Webster, E., & Berry, N. (1976). Assessing the psychosocial

characteristics of occupational training environments. Journal of Occupational


Boyas, J., & Wind, L. H. (2010). Employment-based social capital, job stress, and

employee burnout: a public child welfare employee structural model. Children

and Youth Services Review, 32(3), 380-388. doi: 10. 1016/j. childyouth. 2009.

10. 009

Brierley, J. A. (2000). An analysis of the impact of the work environment on

chartered accountants professional examination performance. The Journal of

Social Psychology, 140(3), 397-408.

Brimeyer, T., M., Perrucci, R., & Wadsworth, S., M. (2010). Age, tenure, resources

for control and organizational commitment. Social Science Quarterly, 91(2),

511-530.

Brookings, J. B., Chacos, K. M., Hightower, S. E., Howard, M. E., & Weiss, C. S.

(1985). Work environment and burnout in two social service agencies. Journal

of Health and Human Resources Administration, 7, 311-320.

Brooks, J., & Seers, A. (1991). Predictors of organizational commitment: Variations

across career stages, Journal of Vocational Behavior, 38, 53-64.

Brown, G. T., & Pranger, T. (1992). Predictors of burnout for psychiatric

occupational therapy personnel. Canadian Journal of Occupational Therapy,

59, 258-267

274

Brown, S. P., & Leigh, T. W. (1996). A new look at psychological climate and its

relationship to job involvement, effort, and performance. Journal of Applied


Brown, S., Cron, W., & Slocum, J. (1998). Effects of trait competitiveness and

perceived intraorganizational competition on salesperson goal setting and

performance. Journal of Marketing, 62, 88-98.

Buchanan, B. (1974). Building organizational commitment: The socialization of

managers in work organizations. Administrative Science Quarterly, 19, 553-

546.

Buhler, K., & Land, T. (2004). Burnout and personality in extreme nursing: an

empirical study. Schweiz. Archive for Neurology and Psychiatry, 155, 35-42.

Retrieved from http://cat.inist. fr/?aModele=afficheN&cpsidt=15482763

Byrne, B. M. (1991). Burnout: Investigating the impact of background variables for

elementary, intermediate, secondary and university educators. Teaching And

Teacher Education, 7(2), 197-209. Abstract retrieved from http://www.

sciencedirect. com/science/article/pii/0742051X9190027M

Byrne, B. M. (1993). The Maslach Burnout Inventory: Testing for factorial validity

and invariance across elementary, intermediate and secondary teachers.

Journal of Occupational and Organizational Psychology, 66, 197-212.

Byrne, B. M. (1994). Burnout: Testing for the validity, replication, and invariance of

causal structure across elementary, intermediate, and secondary teachers.

American Education Research Journal, 31(3), 645-673. Abstract retrieved

from http://aer. sagepub. com/content/31/3/645. short

Cabrera-Nguyen, P. (2010). Author guidelines for reporting scale development and

validation results in the journal of the society for social work and research.

275

Journal of the Society for Social Work and Research, 1(2), 99-103. doi: 10.

5243/jsswr. 2010. 8.

Camman, C., Fichman, M., Jenkins, G. D., & Klesh, J. (1983). Assessing the attitudes

and perceptions of organizational members. In S. Seashore, E. Lawler, P.

Mirvis & C. Camman (Eds.), Assessing organizational changes: A guide to

methods, measure and practices (pp. 72-138). New York: John Wiley & Sons.

Cano-Garcia, F. J., Padillo, Munoz, E. M., & Carrasco-Ortiz, M. A. (2004).

Personality and contextual variables in burnout. Personality and Individual

Differences, 38, 929-940.

Carr, J. Z., Schmidt, A. M., Ford, J. K., & DeShon, R. P. (2003). Climate perceptions

matter: A meta-analytic path analysis relating molar climate, cognitive and

affective states, and individual level work outcomes. Journal of Applied

Psychology, 88(4), 605–619

Casper, W. J., Martin, J. A., Buffardi, L. C., & Erdwins, C. J. (2002). Work family

conflict perceived organizational support and organizational commitment

among employed mothers. Journal of Occupational Health Psychology, 7, 99–

108.

Chan, A. O. M., & Huak, C. Y. (2004). Influence of work environment on emotional

health in a health care setting. Occupational Medicine, 54(3), 207-212.

Cheng, Y., & Stockdale, M. S. (2003). The validity of the three component model of

organizational commitment in a Chinese context. Journal of Vocational

Behavior. 62, 465-489.

Cherniss, C. (1980). Professional burnout in human service organizations. New York:

Praeger.

276

Chesney, M. A., & Rosenman, R. H. (1980). Type A behavior in the work setting. In

C. L. Copper & R. Payne (Eds.), Current concepts in occupational stress (pp.

186-212). New York: Wiley.

Chesney, M., Sevelius, G., Black, G., Ward, M., Swan, G., & Rosenman, R. (1981).

Work environment, Type A behavior, and coronary heart disease risk factors.

Journal of Occupation Medicine, 23, 551-555.

Chughtai, A., & Zafar, S. (2006). Antecedents and consequences of organizational

commitment among Pakistani university teachers. Applied Human Resource

Management Research, 11(1), 39-64

Clarke, D. H., & Iles, P. (2000). Climate for diversity and its effects on career and

organizational attitudes and perceptions. Personnel Review, 29(3), Retrieved

from http://www. emeraldinsight. com/journals. htm?articleid=879245

&show=abstract

Constable, J. F., & Russell, D. W. (1986). The effect of social support and the work

environment upon burnout among nurses. Journal of Human Stress, 12(1),

20-26. doi: 10. 1080/0097840X. 1986. 9936762

Cook, J., & Wall, T. (1980). New work attitude measures of trust, organizational

commitment and personal need non-fulfillment. Journal of Occupational


Cooper, C. L., & Cartwright, S. (1994). Healthy mind; Healthy organization- A

proactive approach to occupational stress. Human Relations, 47(4), 455.

Cooper, C. L., Dewe, P. J. & O'Driscoll, M. (2001) Stress and Work Organizations: a

review and critique of theory, research and applications. California: Sage.

Cooper-Hamik, A., & Viswesvaran, C. (2005). The construct of work commitment:

Testing an integrative framework. Psychological Bulletin, 131, 241-258.

277

Cordes, C. L., & Dougherty, T. W. (1993). A review and an integration of research on

job burnout. Academy of Management Review, 18, 321-656.

Corley, E., A. (2005). How do career strategies, gender, and work environment affect

faculty productivity levels in university – based science centers? The review of

Policy Research, 22(5), 637- 655.

Costa, P. T. Jr., McCrae R. R. (1995). Domains and facets: Hierarchical personality

assessment using the Revised NEO Personality. Journal of Personality

Assessment, 64, 21-50.

Cotton, S. J., Dollard, M. F., & de Jonge, J. (2002). Stress and student job design:

Satisfaction, well-being, and performance in university students. International

Journal of Stress Management, 9(3), 147-162.

Culpepper, R. (2011). A test of Revised Scales for the Meyer and Allen (1991) Three-

Component Commitment Construct. Abstract retrieved from http://epm.

sagepub. com/content/60/4/604

Dalal, R. S. (2005). A meta-analysis of the relationship between organizational

citizenship behavior and counterproductive work behavior. Journal of Applied

Psychology, 90 (6), 1241-1255.

Damanpour, F. (1991). Organization innovation: A meta-analysis of effects of

determinants and moderators. Academy of Management Journal, 557-568.

Davis, S. F. & Smith, R. A. (2005). An introduction to statistics and research

methods: Becoming a psychological deductive. New Jersey: Prentice Hall.

Day, G., Minichiello, V., & Madison, J. (2007). Self-reported perceptions of

registered nurses working in Australian hospitals. Journal of Nursing

Management, 15(4), 403-413.

De Croone, E., Sluiter, J. K., & Blonk, R. W. B. (2004). Stressful work, psychological

278

job strain, and turnover: a 2-year prospective cohort study of truck drivers.

Journal of Applied Psychology, 89, 442-454.

Deer, C. E. (1980). Measuring organizational climate in secondary schools.

Australian Journal of Education, 24, 26-43.

Demerouti, E., Bakker, A. B., de Jonge, J., Janssen, P. P., & Schaufeli, W. B. (2001).

Burnout and engagement at work as a function of demands and control.

Scandinavian Journal of Work Environment & Health, 27(4), 279-286.

Densten, I. L. (2001). Re-Thinking Burnout. Journal of Organizational Behavior,

22(8), 833-847.

Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing Liseral: A guide for

uninitiated. London: Sage Publications.

Dick, M. J. (1986). Burnout in nursing faculty: Relationships with management style,

collegial support, and workload in collegiate programs. Journal of

Professional Nursing, 2, 252-260.

Dick, M. J. (1992). Burnout in doctorally prepared nurse faculty. Journal of Nursing

Education, 31, 341-346.

Dickens, G., Sugarman, P., & Rogers, G. (2005). Nurses’ perceptions of the working

environment: A United Kingdom independent sector study. Journal of

Psychiatric and Mental Health Nursing, 12, 297-302.

Digman, J. M. (1997). Higher Order Factor of Big Five. Journal of Personality and

Social Psychology. 73, 1246-1256.

Digman, J. M. (1990). Personality Structure: Emergence of the Five-Factor Model.

Annual Review of Psychology. 41,417– 440.

279

Dignam, J. T., Barrera, M., & West, S. G. (1986). Occupational stress, social support

and burnout among correctional officers. American Journal of Community

Psychology, 14, 177 – 193.

Docker, J. G., Fisher, J. L., & Fraser, B. J. (1989). Differences in the psychosocial

work environment of different types of schools. Journal of Research in

Childhood Education, 4, 5-17.

Dodd, D. K., & Jacobs, S. R. (2003). Student burnout as a function of personality,

social support, and workload. Journal of College Student Development, 44(3),

291-303.

Dodd-McCue, D., & Wright, G. B. (1996). Men, women, and attitudinal commitment:

The effects of workplace experiences and socialization. Human Relations,

49(8), 1065-1091.

Dorman, J. P. (2003). Relationship between school and class room environment &

teacher burnout: A lisrel analysis. Social Psychology of Education, 6, 107-127.

Retrieved from http://www.springerlink.com/content/

u5157t85n6417370/fulltext. pdf

Downey, R. G., Hemenover, S., & Rapopport, L. (2000). Personality and job

burnout: Can coping skills reduce job burnout. Personnel management and

labor relations. Retrieved from http://stinet.dtic.

mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA406375

Dramstad, S. A. (2004). Job satisfaction and organizational commitment among

teachers in Norway: A comparative study of selected schools from public and

private educational systems (Unpublished Dissertation), Andrews University.

Retrieved from circle. adventist. org/files/CD2010/bibliographies/

EducatorStudies. rtf

280

Dunham, R. B., Grube, J. A., & Castaneda, M. B. (1994). Organizational

commitment: the utility of an integrative definition. Journal of Applied


Eastburg, M. C., Williamson, M., Gorsuch, R., & Ridley, C. (1994). Social support,

Personality, and Burnout in Nurses. Journal of Applied Social Psychology,

24(14), 1233-1250.

Edelwich, J., & Brodsky, A. (1980). Burnout: Stages of disillusionment in the helping

professions. New York: Human Sciences Press.

Ellemers, N., Gilder, D., & Heuvel, H. (1998). Career oriented versus team-oriented

commitment and behavior at work. Journal of Applied Psychology, 83(5),

717-730.

Erdheim, J., Wang, Mo., Zickar, M. (2006). Linking the Big Five personality

constructs to organizational commitment Personality and Individual

Differences, 41(5), 959-970.

Ervin, K. S., & Langkamer, K. L., (2008). Psychological climate, organizational

commitment and morale: implications for Army Captains Career intent.

Military Psychology, 20, 219 – 236. doi: 10. 1080/08995600802345113

Escribà-Agüir, V., Martín-Beena, D., & Pérez-Hoyos, S. (2006). Psychosocial work

environment and burnout among emergency medical and nursing staff.

International Archives of Occupational and Environmental Health, 80(2),

127-133. doi: 10. 1007/s00420-006-0110-y

Etzioni, A. (1975). A comparative analysis of complex organizations. New York: The

Free Press.

281

Evans, B. K., & Fischer, D. G. (1993). The nature of burnout: A study of the three

factor model of burnout in human service and non- human service samples.


Evans, C. R., & Dion, K. L. (1991). Group cohesion and performance: A meta-

analysis. Small Group Research, 22, 175-186.

Ever, W., J., Tomic, W., Brouwers, A. (2004). Burnout among teachers: students' and

teachers' perceptions compared. School Psychology International, 25(2), 131-

148.

Farid, S. (2001). Perception of work environment: A comparison of private and public

sector (Unpublished M.Sc Research Report). National Institute Of


Fejgin, N., Ephraty, N., & Ben Sira, D. (1995). Work environment and burnout of

physical education teachers. Journal of Teaching in Physical Education, 15(1),

64-78. Abstract retrieved from http://psycnet. apa. org/psycinfo/1996-21267-

001

Field, A. (2005). Discovering statistics using SPSS (2nd ed.). London: Sage

Publications.

Finegan, H. (2000). The impact of person and organizational values on organizational

commitment. Journal of Occupational and Organizational Psychology, 73,

149- 169.

Fisher, D. L., & Fraser, B. J. (1991). Validity and use of school environment

instruments. Journal of Classroom Interaction, 26, 13-18.

Fisher, D., & Fraser, B. (1983). Use of Work Environment Scale to assess science

teachers’ perceptions of school environment. European Journal of Science

Education, 5, 231-233.

282

Flarey, D. L. (1991). The social climate scale: A tool for organizational change and

development. Journal of Nursing Administration, 21(4), 37-44.

Fletcher, T. D., & Nusbaum, D. N. (2010). Development of the Competitive Work

Environment Scale: A multidimensional climate constructs. Educational and

Psychological Measurement, 70, 105 – 124.

Fletcher, T. D., Major, D. A., & Davis, D. D. (2008). The interactive relationship of

competitive climate and trait competitiveness with work place attitudes, stress,

and performance. Journal of Organizational Behavior, 29, 899-922.

Fong, C. M. (1993). A longitudinal study of the relationships between overload, social

support, and burnout among nursing educators. Journal of Nursing Education,

32, 24-29.

Fraser, B. J., Docker, J. G., & Fisher, D. L. (1989). Assessing and improving school

social climate. Evaluation and Research in Education, 2, 1-13.

Fresko, B., Kfir, D., & Nasser, F. (1997). Predicting teacher commitment. Teaching

and Teacher Education, 13(4), 429-438.

Freudenberger, H. J. (1974a). Staff burnout. Journal of Social Issues, 30(1), 159-165.

Freudenberger, H. J. (1974b). The staff burnout syndrome in alternative institutions.

Psychotherapy: Theory, Research; and Practice, 12(1), 73-82.

Friedman, I. A. (1995). Student behavior patterns contributing to teacher burnout.

Journal of Educational Research, 88 (5). 281-289.

Gautam, T., Dick, R. V., & Wagner, U. (2001). Organizational commitment in

Nepalese settings. Asian Journal of Social Psychology. 4, 239-248: doi/10.

1111/1467-839X. 00088

Gavin, J., & Howe, J. (1975). Psychological Climate. Some theoretical and empirical

considerations. Behavioural Science, 20, 228-240.

283

Gaynor, S. E., Verdin, J. A., & Bucko, J. P. (1995). Peer social support: A key to

caregiver morale and satisfaction. Journal of Nursing Administration, 25, 23-

28.

Gelade, G. A. Dobson, P., & Auer, K. (2008). Individualism, masculinity and the

sources of organizational commitment. Journal of Cross-Cultural Psychology,

39(5), 599-617.

Goddard, R., O’Brien, P., & Goddard, M. (2006). Work environment predictors of

beginning teacher burnout. British Educational Research Journal, 32(6), 857-

874.

Gold, Y. (1984). The factorial validity of the Maslach Burnout Inventory in a sample

of California elementary and junior high school class room teachers.

Educational and Psychological Measurement, 44, 545 – 560.

Gold, Y., Roth, R. A., & Wright C. R., & Michael, W. B. (1991). The relationship of

scores on the educators survey, a modified version of the Maslach Burnout

Inventory, to three teaching related variables for a sample of 132 beginning

teachers. Educational and Psychological Measurement, 51, 429 – 438.

Goldberg, L. R. (1992). Development of markers for the big five factor structure.

Psychological Assessment, 4, 26 – 42.

Goldenberg, D., & Waddel, J. (1990). Occupational stress and coping strategies

among female baccalaureate nursing faculty. Journal of Advanced Nursing,

15, 531-543.

Golembiewski, R. T., Ninzenrider, R. F., & Stevenson, J. G. (1986). Stress in

Organizations: Towards a phase model of burnout. New York: Praeger.

284

Golembiewski, R. T., Scherb, K., & Munzenrider, R. F. (1994). Burnout among

Florida teachers. Journal of Health and Human Resources Administration, 16,

393-421.

Golembiewski, Robert. T., Ninzenrider, Robert. F., & Stevenson, Jerry. G. (1986).

Stress in organizations: Toward a phase model of burnout. New York:

Praeger.

Grau, L., Chandler, B., Burton, B., & Kolditz, D. (1991). Institutional loyalty and job

satisfaction among nurse aides in nursing homes. Journal of Aging and

Health, 3, 47-65.

Green, D. E., Walkey, F. H., & Taylor, A. J. W. (1991). The three factor structure of

the Maslach Burnout Inventory. Journal of Social Behavior and Personality,

6, 33–39.

Greenberg, J., & Baron, R. A. (1993). Behavior in organization: Understanding and

managing the human side of work (4th ed.). U. S. A: Prentice Hall.

Greenglass, E. R., Fiksenbaum, L., & Burke, R. J. (1994). The relationship between

social support and burnout over time in teachers. Journal of Social Behavior

and Personality, 9, 219-230.

Grusky, D. (1966). Career mobility and organizational commitment. Administrative

Science Quarterly, 10, 488-503.

Guion, R. (1973). A note on organizational climate. Organizational Behavior and

Human Performance, 9, 120-125.

Gummer, B. (2001). Abusive supervisors, competent workers, and (white) friends in

high places: current perspectives on the work environment. Administration in

Social Work, 25(1), 87-106. doi: 10. 1300/J147v25n01_08.

285

Haines, J., Williams, C. L., & Carson, J. (2004). Workers’ compensation for

psychological injury: Personal and environmental correlates. Work, 22(3),

183- 194.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data

analysis (5th ed.). London: Prentice Hall.

Halbesleben, J. R. B., & Bowler, W. M. (2007). Emotional exhaustion and job

performance: the mediating role of motivation. Journal of Applied

Psychology, 92, 93–106.

Hamann, D., & Gordon, D. (2000). Burnout: An occupational hazard. Music

Educators Journal, 87 (3), 34-39.

Haq, A., & Sheikh, H. (1992). Employees’ perceptions of work environment in

certain setting of Hyderabad city: Pakistan Journal of Psychological

Research, 7(3-4), 53-59.

Haque, A., & Sohail, T. (1997). Stress, social support and Burnout in nurses. Pakistan

Journal of Psychological Research, 12(3-4), 77-86.

Haque, M. A., & Khan, S. (2001). Burnout and organizational sources of social

support in human services professionals: A comparison of woman, doctors and

nurses. Journal of the Indian Academy of Applied Psychology, 27, 57 – 66.

Harris, K. J., & Lee, R. E. (2004). The Customer, Co-worker and Management

Burnout Distinction in Service Settings. Personality Influencers and

Outcomes. Retrieved from www.haworthpress. com/store/E-

Text/View_EText. asp?a=3&fn=J396v25n04_02&i=4&s=J396&v=25.

Hayat, S. (2004). Relationship between work environment and organizational

commitment among employees of government banks (Unpublished M.Sc

286

Research Report). National Institute of Psychology, Quaid-i-Azam University,

Islamabad, Pakistan.

Hayhurst, A., Saylor, C., & Stuenkel, D. (2005). Work environmental factors and

retention of nurses. Journal of Nursing Care Quality, 20(3), 283-288.

Hellriegel, D., & Slocum, J. W. (1974). Organizational climate: Measures, research

and contingencies. Academy of Management Journal, 17, 255-280.

Hemingway, M. A., & Smith, C. S. (1999). Organizational climate and occupational

stressors as predictors of withdrawal behaviors and injuries in nurses. Journal

of Occupational and Organizational Psychology, 72, 285-299.

Herscovitch, L., & Meyer, J. P. (2002). Commitment to organizational change:

Extension of a three-component model. Journal of Applied Psychology, 87,

474- 487.

Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing

stress. American Psychologist, 44, 513-524.

Hochwalder, J. (2007). The psychosocial work environment and burnout among

Swedish registered and assistant nurses: the main, mediating, and moderating

role of empowerment. Nursing and Health Sciences, 9, 205-211. doi: 10.

1111/j. 1442-2018. 2007. 00323. x

Hochwarter, W. A., Zellars, K. L., Perrewé, P. L., Hoffman, N., & Ford, E. W. (2004).

Experiencing job burnout: The roles of positive and negative traits and states.

Journal of Applied Social Psychology, 34(5), 887-911.

287

Hochw lder, J. (2007). The psychosocial work environment and burnout among

Swedish registered and assistant nurses: The main, mediating, and moderating

role of empowerment. Nursing & Health Sciences, 9(3), 205-211. doi: 10.

1111/j. 1442-2018. 2007. 00323. x

Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviours,

institutions, and organizations across nations. (2nd ed.). Thousand Oaks, CA:

Sage.

Holland, J. L. (1985). Making vocational choices: A theory of vocational personalities

and work environments. Englewood Cliffs, NJ: Prentice Hall.

Holland, P. J., Michael, W. B., & Kim, S. (1994). Construct validity of the educator

survey for a sample of middle school teachers. Educational and Psychological

Measurement, 54, 822-829.

Holmgren, K., Hensing, G., & Dellve, L. (2010). The association between poor

organizational climate and high work commitments, and sickness absence in a

general population of women and men. Journal of Occupational &

Environmental Medicine, 52(12), 1179-1185.

Hovekamp, T. M. (1994). Organizational commitment of professional employees in

union and non-union research libraries. College & Research Libraries, 43,

297-307.

Hox, J. J., & Bechger, T. M. (1998). An introduction to Structural Equation Modeling.

Family Science Review, 11, 354-373.

Hrebiniak, L. G., & Alutto, J. (1972). Personal and role-related factors in the

development of organizational commitment. Administrative Science

Quarterly, 17, 555-572.

288

Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Eds.),

Structural equation modeling: Concepts, issues and applications. Thousand

Oaks: Sage.

Huberty, T. J., & Huebner, E. S. (1988). A national survey of burnout among school

psychologists. Psychology in the Schools, 25, 54-61.

Huebner, E. S., & Mills, L. B. (1994). Burnout in school psychology: The

contribution of personality characteristics and role expectations. Special

Services in the Schools, 8, 53-67.

Hussain, S. (2004). Relationship between perceptions of organizational politics and

organizational commitment (Unpublished M. Sc Research Report). National

Institute Of Psychology, Quaid-e-Azam University, Islamabad, Pakistan.

Hussan, M. (2008). Relationship between psychological contract and organizational

commitment in private sector banks (Unpublished M.Sc Research Report).

National Institute Of Psychology, Quaid-e-Azam University, Islamabad,

Pakistan.

Hyvonen, K., Feldt, T., Tolvenen, A., & Kinnunen, U. (2010). The role of goal pursuit

in the interaction between psychosocial work environment and occupational

well being. Journal of Vocational Behavior, 76, 406-418.

Imam, S. (1993). Work environment of college teachers: Pakistan Journal of

Psychological Research, 8(3-4), 77-88.

Insel, P. M., & Moos, R. H. (1974). Work Environment Scale Form R. Palo Alto, Ca:

Consulting Psychologists Press.

Iwanicki, E. (1982). Toward understanding and alleviating teacher burnout. Theory

into Practice, 22, 27-32.

289

Iwanicki, E. F., & Schwab, R. L. (1981). A cross- sectional study of the Maslach

Burnout Inventory. Educational and Psychological Measurement, 41, 1167 –

1174.

Jackson, S. E., & Maslach, C. (1982). After-effects of job related stress: Families as

victims. Journal of Occupational Behavior, 3, 63-77.

Jaffe, D. T. (1995). The healthy company: research paradigms for personal and

organizational health. In S. L. Sauter & L. R. Murphy (Eds.), Organizational

Risk factors for job stress (pp. 13-37). Washington, D.C: American

Psychological Association.

James, L. A., & James, L. R. (1989). Integrating work environment perceptions:

explorations into the measurement of meaning. Journal of Applied

Psychology, 74 (5), 739-751.

James, L. R., James, L. A., & Ashe, D. K. (1990). The meaning of organizations: the

role of cognition and values. In B. Schneider (Ed.), Organizational climate

and culture (pp. 282–318). San Francisco: Jossey-Bass.

James, L., & Jones, A. (1974). Organizational climate: A review of theory and

research. Psychological Bulletin, 18, 1096-1112.

James, L., & Sells, S. (1981). Psychological Climate: Theoretical perceptive and

empirical research. In D. Magnusson (Ed), Toward a psychology of situations:

An Interactional perceptive (pp. 275-295). Hillsdale, NJ: Erlbaum.

Jaros, S. (2007). Meyer and Allen model of organizational commitment: measurement

issues. Journal of Organizational Behavior, 6(4), 7-25.

Jaros, S. J., Jermier, J. M., Koehler, J. W., & Sincich, T. (1993). Effects of

continuance, affective, and moral commitment on the withdrawal process: An

290

evaluation of eight structural equation models. Academy of Management

Journal, 36, 951-995.

Joffres, C., & Haughey, M. (2001, March). Elementary teachers' commitment

declines: Antecedents, processes, and outcomes. The Qualitative Report, 6(1).

Retrieved from http://www. nova. edu/ssss/QR/QR6-1/joffres. html.

John, M. C., & Taylor, W. T (1999). Leadership style, school climate and the

institutional commitment of teachers. International Forum, 2(1), 25-57.

John, Olive P., & Srivastava, Sanjay. (1999). The Big-Five trait taxonomy: history,

measurement, and theoretical perspectives. In L. Pervin & O. P. John (Eds.),

Handbook of personality: Theory & research (2nd ed.). New York: Guilford.

Johnson, R. E., & Chang, C. H. (2008). Relationship between organizational

commitment and its antecedents: Employee self concept matters. Journal of

Applied Social Psychology, 38, 513 – 541.

Jones, B., Flynn, D. M., & Kelloway, E. K. (1995). Perception of support from the

organization in relation to work stress, satisfaction, and commitment. In S. L.

Sauter & L. R. Murphy (Eds.), Organizational risk factors for job stress (pp.

41-51). Washington, D. C.: American Psychological Association.

Jose, P. E. (2008). ModGraph-I: A programme to compute cell means for the

graphical display of moderational analyses: The internet version, Version 2.

0. Victoria University of Wellington, Wellington, New Zealand. Retrieved

from http://www. victoria. ac. nz/psyc/staff/paul-jose-

files/modgraph/modgraph. php

Kabadayi, A. (2010, July). Investigating demographic characteristics and teaching

perceptions of turkish preschool teachers. Early Child Development and Care,

180(6), 809-822. Retrieved from http://www.eric.ed.

291

gov/ERICWebPortal/search/recordDetails.jsp?ERICExtSearch_SearchValue

_0=EJ690523&ERICExtSearch_SearchType_0=no&_pageLabel=RecordDeta

ils&accno=EJ887214&_nfls=false

Kalliath, T. J., O’Driscoll, M. P., Gillespie, D. F., & Bluedorn, A. C. (2000). A test of

the Maslach Burnout Inventory in three samples of healthcare professionals.

Work and Stress, 14, 94-100.

Kanter, R. M. (1968). Commitment and social organization: A study of commitment

mechanisms in utopian communities. American Sociological Review, 33, 499-

517.

Karasek, R. A. (1979). Job demands, job decision latitude and mental strain:

implication for job redesign. Administrative Science Quarterly, 24I, 285 – 308.

Karasek, R. A., & Theorell, T. (1990). Healthy work: Stress, productivity, and the

reconstruction of working life. New York: Basic Books.

Karim, N. H., & Noor, N. H. (2006). Evaluating the psychometric properties of Allen

and Meyer’s Organizational Commitment Scale: A cross cultural application

among Malaysian academic librarians. Malaysian Journal of Library &

Information Science, 11(1), 89-101.

Karrasch, A. I. (2003). Antecedents and consequences of organizational commitment.

Military Psychology, 15, 225-236.

Karsh, B., Booske, B. C., & Sainfort, F. (2005). Job and organizational determinants

of nursing home employee commitment, job satisfaction, and intent to

turnover. Ergonomics, 48(10), 1260-1281.

Khan, S. (1999), Differences between teachers’ perceptions of work environment in

private and government schools (Unpublished M.Sc Research Report).

National Institute of Psychology, Islamabad, Pakistan.

292

Khan. Z. N. (2000). Factor analysis cum factorial study of stress and burnout

variables related to the teachers of deaf and dumb schools. Disabilities and

Impairments, 14, 23 – 34.

Khurshid, F., Butt, Z., & Malik, S. (2011). Language in India. 11. Retrieved from

http://www.languageinindia.com/aug2011/stressmanagementpakistanfinalpaid

.pdf.

Kim, H. (2011). Job conditions, unmet expectations, and burnout in public child

welfare workers: How different from other social workers? Children & Youth

Services Review,33(20), 358-67. doi: 10. 1016/j. childyouth. 2010. 10. 001

Kim, H., & Ji, J. (2009). Factor structure and longitudinal invariance of the Maslach

Burnout Inventory. Research on Social Work Practice, 19(3), 325-339. doi:

10. 1177/1049731508318550.

Kim, M., Lee, J., & Kim, J. (2009). Relationships among Burnout, Social Support,

and Negative Mood Regulation Expectancies of Elementary School Teachers

in Korea. Asia Pacific Education Review, 10(4), 475-482. Retrieved from

http://www.eric.ed.gov/ERICWebPortal/search/recordDetails.jsp?ERICExtSea

rch_SearchValue_0=EJ690523&ERICExtSearch_SearchType_0=no&_pageL

abel=RecordDetails&accno=EJ863585&_nfls=false

Kim Wan, M. O. (1991). Teacher Burnout: Relations with stress, personality and

social support. Chinese University of Hong Kong Educational Journal, 19(1),

3-11. Retrieved from http://sunzi.lib.hku.hk/hkjo/view/33/3300476.pdf

Kirschenbaum, A. (1991). The corporate transfer: Origin and destination factors in the

decision to change jobs. Journal of Vocational Behavior, 38, 101-123.

Kline, P. (1993). An easy guide to factor analysis. London: Routledge.

293

Kobasa, S., & Puccetti, M. (1983). Personality and social resources in stress

resistance. Journal of Personality and Social Psychology, 45, 839-850.

Kokkinos, C. M. (2006). Factor structure and psychometric properties of the Maslach

Burnout Inventory- educator survey among elementary and secondary school

teachers in Cyprus. Stress and Health, 22, 25-33.

Kokkins, C. M. (2007). Job stressors, personality and burnout in primary school

teachers. British Journal of Educational Psychology. 77(1), 229-43. Abstract

retrieved from http://www.ncbi.nlm.nih.gov/pubmed/17411497

Kompier, M. (2005). Assessing the psychosocial work environment- “subjective”

versus “objective” measurement. Scandinavian Journal of Work Environment

& Health, 31(6), 405-408.

Koniarek, J., & Dudek, B. (1996). Social support as a buffer in the stress-burnout

relationship. International Journal of Stress Management, 3, 99-106.

Kopelman, R. E., Brief, A. P., & Guzzo, R. A. (1990). The role of climate and culture

in productivity. In B. Schneider (Ed.), Organizational climate and culture (pp.

282–318). San Francisco: Jossey-Bass.

Koran, L. M., Moos, R., H., Moos, B., & Zasslow, M. (1983). Changing hospital

work environment : An example of burn unit. General Hospital Psychiatry, 5,

7-13.

Kottkamp, R., & Mansfield, J. (1985). Role conflict, role ambiguity, powerlessness

and burnout among high school supervisors. Journal of Research and

Development, 18(4), 29-38.

Kotzer, A. M., Koepping, D. M., & LeDuc, K. (2006). Perceived nursing work

environment of acute care pediatric nurses. Pediatric Nursing, 32(4), 327-332.

294

Kouvonen, A. Kivimäki, M., Virtanen M., Heponiemi T., Elovainio M., Pentti,

J…Vahtera J. (2006). Effort-reward imbalance at work and the co-occurrence

of lifestyle risk factors: Cross-sectional survey in a sample of 36,127 public

sector employees. BMC Public Health, 6(24), Retrieved from http://www.

biomedcentral. com/content/pdf/1471-2458-6-24. pdf

Koys, D. J., & DeCotiis, T. A. (1991). Inductive measures of psychological climate.

Human Relations, 44(3), 265-285.

Kumar, S., Hatcher, S., Dutu, G., Fischer, J., & Ma’u, E. (2011). Stresses experienced

by psychiatrists and their role in burnout: a national follow-up study. The

International Journal of Social Psychiatry, 57(2), 166-179. doi: 10.

1177/0020764009341211

Kushman, J. W. (1992). The organizational dynamics of teacher workplace

commitment: A study of urban elementary and middle schools. Educational

Administration Quarterly, 28, 5-42.

Langballe, E. M., Innstrand, S. T., Aasland, O. G., & Falkum, E. (2011). The

predictive value of individual factors, work-related factors, and work–home

interaction on burnout in female and male physicians: a longitudinal study.

Stress and Health, 27(1), 73-87.

Lee, R. T., & Ashforth, B. E. (1990). On the meaning of Maslach’s three dimensions

of burnout. Journal of Applied Psychology, 75, 743-747.

Lee, R. T., & Ashforth, B. E. (1993). A further examination of Managerial Burnout:

Toward an integrated model. Journal of Organizational Behaviour, 14, 3-20.

Lee, R. T., & Ashforth, B. E. (1996). A meta- analytic examination of the correlates

of the three dimensions of job burnout. Journal of Applied Psychology, 81,

123 – 133.

295

Leiter, M. P., & Maslach, C. (1988). The impact of interpersonal environment on

burnout and organizational commitment. Journal of Organizational Behavior,

9, 297-308.

Leka, S., & Houdmont, J. (2010). Occupational Health Psychology. Chichester: John

Wiley & Sons.

Levert, T., Lucas, M., & Ortlepp, K. (2000). Burnout in psychiatric nurses.

Contribution of the work environment and a sense of coherence. South African

Journal of Psychology, 30(2), 36-43. Abstract retrieved form http://psycnet.

apa. org/psycinfo/2000-05552-004

Lewin, K. (1951). Field theory in social science. New York: Harper & Row.

Lieter, M. P., & Maslach, C. (1988). The impact of interpersonal environment on

burnout and organizational commitment. Journal of Organizational Behavior,

9, 297-308.

Lindblom, K. M., Linton, S. J., Fedeli, C., & Bryngelsson, I, L. (2006). Burnout in the

working population: relations to psychosocial work factors. International

Journal of Behavioral medicine, 13(1), 51–59.

Littler, C. (1985). The experience of work. Adershot: Gower.

Litwin, G., & Stringer, R. (1968). Motivation and organizational climate. Cambridge,

MA: Harward University Press.

Long, B. C. (1993). Coping strategies of male managers: a prospective analysis of

predictors of psychosomatic symptoms and job satisfaction. Journal of

Vocational Behavior, 42, 184-199.

Lubbert, V. M. (1995). Structure and faculty perception of climate in schools of

nursing. Western Journal of Nursing Research, 17, 317-327.

296

Luk, A., Chan, B., Cheong, S., & Ko, S. K. (2010). An exploration of the burnout

situation on teachers in two schools. Macau Social Indicators Research, 95(3),

489-502. doi: org/10.1007/s11205-009-9533-7

Lusk, E., Diserens, D., Cormier, P., Geranmayeh, A., & Neves, J. (1983). The Work

Environment Scale: Baseline data for dental schools. Psychological Reports,

53, 1160-1162.

MacGuffie, R., & Henderson, H. (1977). A practicum-internship model for counselor

training. Counselor Education and Supervision, 16, 233-236.

Maloney, J. P., Anderson, F. D., Gladd, D. L., Brown, D. L., & Hardy, M. A. (1996).

Evaluation and comparison of Health Care Work Environment Scale in

military settings. Military Medicine, 161, 284-289.

Maqsood, A., & Rehman, G. (2004). Employees’ perceptions of real and ideal work

environment. Bangladesh Psychological Studies, 14, 1-18.

Margall, M. A., & Duquette, A. (2000). Working environment in a university

hospital: Nurses’ perceptions. Enferm Intensiva, 11(4), 161-169. Retrieved

from http://www. seeiuc. com/revista/res1143. htm

Maruyama, G. M. (1998). Basics of structural equation modeling. London: Sage

Maslach, C. (1982a). Burnout, the cost of caring. Englewood Cliffs, N. J: Prentice-

Hall.

Maslach, C. (1982b). Understanding burnout: Definitional issues in analyzing a

complex phenomenon. In W. S. Paine (Ed.), Job stress and burnout:

Research, theory and intervention perspectives (pp. 29-40). Beverly Hills, CA:

Sage.

297

Maslach, C. (1993). Burnout: A multidimensional perspective. In W. B. Schaufeli, C.

Maslach, & T. Marek (Eds.), Professional burnout: Recent developments in

theory and research (pp. 19-32). New York: Hemisphere.

Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout.

Journal of Occupational Behavior, 2, 99-113.

Maslach, C., & Jackson, S. E. (1984). Burnout in organizational settings. Applied

Social Psychology Annual, 5, 133 – 153.

Maslach, C., & Leiter, M. P. (1997). The truth about burnout. San Francisco: Jossey-

Bass.

Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach Burnout Inventory

Manual (3rd ed.). Palo Alto, Calif: Consulting Psychologists Press.

Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job burnout. Annual Review of

Psychology, 52, 397 – 422.

Mathieu, I., & Zajac, D. (1990). A review and meta-analysis of the antecedents,

correlates, and consequences of organizational commitment. Psychological

Bulletin, 108, 171-194.

Mayer, R. C., & Schoorman, F. D. (1992). Predicting participation and production

outcomes through a two-dimensional model of organizational commitment.

Academy of Management Journal. 35, 671-684.

Mayer, R. C., & Schoorman, F. D. (1998). Differentiating antecedents of

organizational commitment: a test of March and Simon's model. Journal of

Organizational Behavior, 19(1), 15 – 28.

McClure, L. F., & De Piano, L. G. (1983). School Advisory Council participation and

effectiveness. American Journal of Community Psychology, 11, 68-704.

298

McCrae, N., Prior, S., Silverman, M., & Banerjee, S. (2007). Workplace satisfaction

in a mental health service for older adults: An analysis of the effects of setting

and professional status. Archives of Psychiatric Nursing, 21(1), 17-24.

McCrae, R. R., & John, O. P. (1992). Introduction to the five factor model and its

application. Journal of personality, 60, 175-215.

McGee, G. M., & Ford, R. C. (1987). Two (or more?) dimensions of organizational

commitment: Reexamination of the affective and continuance scales. Journal

of Applied Psychology, 74, 424-432.

McKenna, E. (2000). Business psychology and organizational behavior: A student’s

handbook (3rd ed.). New York: Psychology Press.

McManus, I. C., Keeling, A., & Paice, E. (2004). Stress, burnout and doctors' attitudes

to work are determined by personality and learning style: A twelve year

longitudinal study of UK medical graduates. BMC Medicine, 2, 29. doi: 10.

1186/1741-7015-2-29

McShane, S. L., & Glinow, M. A. V. (2003). Organizational behaviour. New York:

McGraw-Hill.

Mehwash, W. (2006). Work environment and alienation among university teachers

(Unpublished M.Sc Research Report). Fatima Jinnah Women University,

Islamabad, Pakistan, Pakistan.

Meyer, J. P., & Allen, N. J. (1984). Testing the side-bet theory of organizational

commitment: Some methodological considerations. Journal of Applied


Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of

Organizational Commitment. Human Resource Management Review, 1, 61-98.

299

Meyer, J. P., & Allen, N. J. (1997). Commitment in the work place: theory, research

and application. Thousand Oaks, CA: Sage.

Meyer, J. P., & Allen, N. J. (2004). TCM employee commitment survey academic

users guide. Ontario: University of Western Ontario.

Meyer, J. P., & Allen, N. J. (2006). Linking the Big Five personality construct to

organizational commitment. Personality and Individual Differences, 41, 959 –

970.

Meyer, J. P., & Herscovitch, L. (2001). Commitment in the workplace: Toward the

general model. Human Resource Management Review, 11, 299-326.

Meyer, J. P., Allen, N. J., & Smith, C. A. (1993). Commitment to organizations and

occupations: Extension and test of a three component model. Journal of

Applied Psychology, 78, 538-551.

Meyer, J. P., Stanely, D. J., Herscovitch, L., & Topolnytsky, L. (2002). Affective,

Continuance, and normative committement to organization: A meta-analysis

of antecedents, correlates, and consequences. Journal of Vocational Behavior,

61, 20-52.

Meyer, J. P., Stanley, D. J., Herscovitch, L., & Topolnytsky, L. (2002). Affective,

continuance, and normative commitment to the organization: A meta-analysis

of antecedents, correlates, and consequences. Journal of Vocational Behavior,

61(1), 20-52.

Miller, K., Birkholt, M., Scott, C., & Stage, C. (1995). Empathy and burnout in

human service work: an extension of a communication model. Communication

Research, 22(2), 123-147.

300

Mills, L. B., & Huebner, E. S. (1998). A prospective study of personality

characteristics, occupational Stressors, and burnout among school psychology

practitioners. Journal of School Psychology, 36, 103-120.

Miner, J. B. (1992). Individual and organizational psychology. New York: McGraw

Hill.

Mishra, P. C., & Srivastava, S. (2001). Job satisfaction as a moderator variable of the

organizational commitment and job satisfaction relationship. Journal of the

Indian Academy of Applied Psychology, 27, 45 – 49.

Mitchell, T. R. (1979). Commitment in organizational behavior. Annual Review of


M kikangas, A., H tinen, M., Kinnunen, U., & Pekkonen, M. (2010). Longitudinal

factorial invariance of the Maslach Burnout Inventory- General Survey among

employees with job related psychological health problems. Stress and Health.

doi: 10.1002/smi.1381

Mobley, W. H., Griffeth, R. W., Hand, H. H., & Meglino, B. M. (1979). Review and

conceptual analysis of the employee turnover process. Psychological Bulletin,

86, 493-522.

Moghadam, M. M. G., & Tabatabaei, F. H. (2006). Prevalence of Burnout Syndrome

and its relationship with gender, education level, job satisfaction and

geographical location among teachers and employee of the education

organization. Psychological Research, 9, 56 – 73.

Mooradian, T., & Nezlek, J. (1995). Comparing the NEO-FFI and Saucier’s mini

makers as measures of the big five. Journal of Personality and Individual

Differences, 21, 213-215. Retrieved from http://www. sciencedirect. com/

science/article/pii/0191886996000578

301

Moos, R. (2001). ---------------------------------------

Moos, R. (2008). Work Environment Scale: annotated bibliography- first and second

editions. California: Mind Gardens, Inc.

Moos, R. H. (1986). Work as a human context. In M. S. Pallack & R. Perloff (Eds.),

Psychology and work: Productivity, change, and employment: Vol 5. Master

lecture series (pp. 9-52). Washington, DC: American Psychological

Association.

Moos, R. H. (1988). Psychosocial factors in the workplace. In S. Fisher & J. Reason

(Eds.), Handbook of life stress, cognition, and health (pp. 193-209). New

York: Wiley.

Moos, R. H. (1990). Conceptual and empirical approaches to developing family-based

assessment procedures: Resolving the case of the Family Environment Scale.

Family Process, 29, 199-208.

Moos, R. H. (1991). Connection among school, family and work settings. In B. Fraser

& H. Welburg (Eds.), Educational environments: Evaluation, antecedents,

and consequences (pp. 29-53). New York: Pergamon.

Moos, R. H. (1994). Work Environment Scale Manual (3rd ed.). Palo Alto, CA:

Consulting Psychologists Press.

Moos, R. H, & Billings, A. (1991). Understanding and improving work climates. In J.

W. Jones, B. D. Steffy, & D. W. Bray (Eds.), Applying psychology in

business: The handbook for managers and human resource professionals (pp.

552-562). Lexington, MA: Lexington Books.

Morrow, P. C. (1983). Concept redundancy in organizational research: The case of

work commitment. Academy of Management Review, 8, 486-500.

302

Morrow, P. C. (1993). The theory and measurement of work commitment. Greenwich,

CT: JAI Press.

Mowday, R., Porter, L., & Steers, R. (1982). Employee- organization linkages. In P.

Warr (Ed.), Organizational and occupational psychology (pp. 219-229). New

York: Academic Press.

Mowday, R., Steers, R., & Porter, L. (1979). The measurement of organizational

commitment. Journal of Vocational Behavior, 14, 224-227.

Muchinsky, P. M. (2007). Psychology applied at work. (8th ed.). New Delhi:

Thomson.

Munir, A. (2005). Relationship between work environment and burnout among

employees (Unpublished M.Sc Research Report). National Institute of


Nasir, S. J., & Haque, M. A. (1996). Job Stress, organizational commitment and job

satisfaction among government officials. Pakistan Journal of Psychological

Research, 11(3-4), 49-58.

Newman, J. E. (1977). Development of a measure of Perceived Work Environment

(PWE). The Academy of Management Journal, 20(4), 520-534.

Ngo, H., & Tsang, W. (1998). Employment practices and organizational commitment:

Differential effects for men and women? International Journal of

Organizational Analysis, 6(3), 251-266.

Nicola, B., Richard, K., & Rosemerg, S. (2006). Multiple Regression: An introduction

to multiple regression performing a multiple regression on SPSS. London:

Lawrence Erlbaum Associates.

303

Nye, C., Roberts, B., Saucier, G., & Zhou, X. (2008). Testing the measurement

equivalence of personality adjective items across cultures. Journal of Research

in Personality, 42, 1524-1536. doi:10. 1016/j. jrp. 2008. 07. 004

O’Driscoll, M. P. & Evans, R. (1988). Organizational factors and perceptions of

climate in three psychiatric units. Human Relations, 41(5), 371-388.

Okoh, B. A. (2007). Support staff perceptions of work environment and psychological

ambiance: A study of universities and research institutes in the mid-Atlantic

region (Doctoral dissertation, University of Maryland Eastern Shore, Princess

Anne, MD). Retrieved from http://www. scribd. com/doc/19091246/Support-

staff-perceptions-of-work-environment-and-psychological-ambiance-A-study-

of-universities-and-research-institutes-in-the-Mid-Atlantic-Region

O'Reilly, C., & Chatman, J. (1986). Organizational commitment and psychological

attachment: The effect of compliance, identification, and internalization on

prosocial behavior. Journal of Applied Psychology, 71, 492-499.

Ostroff, C. (1993). The effects of climate and personal influences on individual

behavior and attitudes in organizations. Organizational Behavior and Human

Decision Processes, 56, 56–90.

Ostroff, C., Kinicki, A. J., & Tamkins, M. M. (2003). Organizational climate and

culture. In W. C. Borman, D. R. Ilgen, & R. J. Klimoski (Eds.),

Comprehensive handbook of psychology, Volume 12: Industrial and

organizational psychology (pp. 365–402). Mahwah, NJ: Erlbaum.

Otero-Lόpez, J. M., Mariño, M. J. S., & Bolaño, C. C. (2008). An integrating

approach to the study of burnout in university professors. Psicothema.

20(4):766-72. Retrieved from http://www.psicothema.com/PDF/3553.pdf

304

Owens, R. G. (1998). Organizational behavior in education (6th ed.). Boston: Allyn

& Bacon.

Oxenstierna, G., Wildmark, M., Finnholm, K., & Elofsson, S. (2008). A new

questionnaire and model for research into the impact of work and the work

environment on employee health. Scandinavian Journal of Work Environment

& Health, 6, 150 – 162.

Paine, W. S. (1981). The burnout syndrome in context. In J. Jones (Ed.), The burnout

syndrome: Current research, theory, interventions (pp. 1-29). Park Ridge, IL:

London House Press.

Painter, J., & Akroyd, D. (1998). Predictors of organizational commitment among

occupational therapists. Occupational Therapy in Health Care, 11(2), 1-15.

Pallant, J. (2007). SPSS Survival Manual: A Step by Step Guide to Data Analysis

using SPSS for Windows (3rd ed.). Open University Press.

Pandey, R., & Tripathi, S. (2001). Occupational stress and burnout in engineering

college teachers. Journal of the Indian Academy of Applied Psychology, 27,

67 – 73.

Parker, C. P., Baltes, B. B., Young, S. A., Huff, J. W., Altmann, R. A., Lacost, H. A.,

et al. (2003). Relationship between psychological climate perceptions and

work outcomes: A meta- analytic review. Journal of Organizational Behavior,

24, 389 – 416.

Parkes, K. R., & Von Rabenau, C. (1993). Work characteristics and well being among

psychiatric health care staff. Journal of Community and Applied Social

psychology, 3, 243-259.

305

Patterniti, S., Niedhammer, I., Lang, T., & Consoli, S. M. (2002). Psychosocial

factors at work, personality traits, and depressive symptoms. British Journal of

Psychiatry, 181, 111-117.

Peeters, M. A. G., & Rutte, C. G. (2005). Time Management Behavior as a Moderator

for the Job Demand-Control Interaction. Journal of Occupational Health

Psychology, 10(1), 64-75. Abstract retrieved from http://psycnet. apa.

org/journals/ocp/10/1/64/

Pervin, L. A. (1968). Performance and satisfaction as a function of individual-

environment fit. Psychological Bulletin, 69, 56-68.

Peter, R., Siegrist, J., Hallqvist, J., Reuterwall, C., Theorell, T., & the SHEEP Study

Group. (2002). Theory and Methods: Psychosocial work environment and

myocardial infarction: improving risk estimation by combining two

complementary job stress models in the SHEEP Study, Journal of

Epidemiology & Community Health, 56, 294–300. Retrieved from http://www.

ncbi. nlm. nih. gov/pmc/articles/PMC1732130/pdf/v056p00294. pdf

Phelan, L., Bromet, E. J., Schwartz, J. E., Dew, M. A., Parkinson, D., & Curtis, E. C.

(1993). The work environment of males and females professionals: Objective

and subjective characteristics. Work and Occupations, 20, 999-1012.

Pines, A. M., Aronson, E., & Kafry, D. (1981). Burnout: From tedium to personal

growth. New York: The Free Press.

Popper, M., & Lipshitz, R. (1992). Ask not what your county can do for you: The

normative basis of organizational commitment. Journal of Vocational

Behavior, 41, 1-12.

306

Portello, J. Y., & Long, B. C. (2001). Appraisals and coping with workplace

interpersonal stress: A model for women managers. Journal of Counseling

Psychology, 48(2), 144-156.

Porter, L. W., Steers, R. M. Mowday, R. T. & Boulian, P. V. (1974) Organizational

commitment, job satisfaction, and turnover among psychiatric technicians.

Journal of Applied Psychology, 59, 603-609.

Posig, M., & Kickul, J. (2003). Extending our understanding of burnout: test of an

integrated model in non-service occupations. Journal of Occupational Health


Powers, S., & Gose, K. F. (1986). Reliability and cinstruct validity of the Maslach

Burnout Inventory in a sample of university students. Educational and


Pretty, G. H., McCarthy, M., & Catano, V. (1992). Psychological environments and

burnout: Gender considerations in the corporation. Journal of Organizational

Behavior, 13, 701 – 711.

Quick, J. C., Simmons, B., & Nelson, D. L. (2000). Working condition. In A. E.

Kazdin. Encyclopedia of psychology (Vol. 8, pp 269-274). New York: Oxford

University Press.

Qureshi M. T., & Hijazi T. S. (2006). “Testing Teachers Burnout Level In Pakistan”

Pakistan Journal of Education, xx111(1), 95-103.

Rashid, S. (2000). Relationship of perceived organizational support with

organizational commitment among female school teachers (Unpublished M.Sc

Research Report), National Institute of Psychology, Quaid-i-Azam University,

Islamabad, Pakistan.

307

Ratliff, N. (1988). Stress and burnout in the helping professions. Journal of

Contemporary Social Work, 69, 147-154.

Rehman, G., & Maqsood, A. (2008). Impact of work environment on organizational

and personal outcomes among private and public sector employees. Research

Report. Higher Education Commission, Social Sciences and Humanities

Research Council, Pakistan.

Reichers, A. E. (1985). A review of re-conceptualization of organizational

commitment. Academy of Management Review, 10, 465-476.

Reilly, P. N., & Orsak, C. L. (1991). A career stage analysis of career and

organizational commitment in nursing. Journal of Vocational Behavior, 39,

311-330.

Repetti, R. L., Matthews, K. A., & Waldron, I. (1989). Employment and women’s

health. American Psychologist, 44, 1394-1401.

Richards, B., O’Brien, T., & Akroyd, D. (1994). Predicting the organizational

commitment of marketing education and health occupations education

teachers by Work Related Rewards. Journal of Industrial Teacher Education,

32(1), 49-64.

Richardsen, A. M., & Martinussen, M. (2004). The Maslach Burnout Inventory:

Factorial validity and consistency across occupational groups in Norway.


Riggar, T. F. (1985). Stress burnout: An annotated bibliography. Carbondale, IL:

Southern Illinois University Press.

Riketta, M. (2002). Attitudinal organizational commitment and job performance: A

meta-analysis. Journal of Organizational Behavior, 23, 257-266.

308

Riketta, M. (2005). Organizational identification: A meta-analysis. Journal of

Vocational Behavior, 66(2), 358-384.

Ritzer, G., & Trice, H. M. (1969). An empirical study of Howard Becker’s side-bet

theory. Social Forces, 47, 475-479.

Robbins, S. P., & Coulter, M. (1999). Management (6th ed.). New Jersey: Prentice

Hall International, Inc.

Robinson, S. E., Roth, S. L., Keim, J., Levenson, M., Flentje, J. R., & Bashor, K.

(1991). Nurse burnout: Work related and demographic factors as culprits.

Research in Nursing and Health, 14, 223 – 228.

Rolland, J. P. (2002). Cross-cultural generalizability of the Five Factor model of

Personality, in McCrae, R. R., & Allik, J. (eds). The Five-Factor Model of

personality across cultures. New York: Kluwer Academic. Retrieved from,

http://books.google.com.pk/books?id=y3xJuUzNLg8C&printsec=frontcover#v

=onepage&q&f=true

Rothman, S., & Storm, K. (2003). Burnout in the South African police service.

European Congress on Work and Organizational Psychology. Retrieved from

http://www. workwellness. co. za/pages/publications. aspx

Rubin, R., & Butllar, L. (1992). A study of the organizational commitment of high

school library media specialists in Ohio. Library Quarterly, 62(3), 306-324.

Russell, D. W., Altmaier, E., & Dawn, V. V. (1987). Job related stress, social support

and burnout among classroom teachers. Journal of Applied Psychology, 72(2),

269-274. Abstract retrieved from http://psycnet.apa.org/journals/apl/72/2/269/

Russell, D. W., Ataimer, E., & Van Zelen, D. (1987). Job related stress, social support

and Burnout among classroom teachers. Journal of Applied Psychology, 72,

269-274.

309

Sahu, K., & Misra, N. (1996). Stress and Burnout in teachers. Personality study and

Group Behavior, 16, 39-48.

Sahu, K., & Misra, N. (2004). Personality Hardiness and Coping as Correlations of

Burnout. Personality study and Group Behavior, 24, 143-154.

Salancik, G. (1977). Commitment and the control of organizational behavior. In Staw,

B. M. & Salancik, G. (Eds.). New directions in organizational behavior (pp. 1-

53). Chicago: St. Claire Press.

Salgado, J. F. (2003). Predicting job performance using FFM and non-FFM

personality measures. Journal of Occupational And Organizational


Salgado, J. F., Remeseiro, C., & Iglesias, M. (1996). Organizational climate and job

satisfaction in a PYME. Psicothema, 8(2), 329-335. Abstract obtained from

http://www. citeulike. org/user/marychuy/article/8848311

Salyers, M. P., & Bond, G. R. (2001). An exploratory analysis of racial factors in staff

burnout among assertive community treatment workers. Community Mental

Health Journal, 37(5), 393-404.

Sand, G., & Miyazaki, A. D. (2000). The impact of social support on salesperson

burnout and burnout components. Psychology and Marketing, 17, 13-26.

Sandoval, J. (1993). Personality and burnout among school psychologists. Psychology

in the Schools, 30, 321-326.

Saucier, G. (1994). Mini Marker: A brief version of Goldberg’s unipolar big-five

markers. Journal of Personality Assessment, 63(3), 506-516.

Savicki, V. (2002). Burnout across thirteen cultures: Stress and coping in child and

youth care workers. Westport, CT: Praeger/Greenwood.

310

Savicki, V., & Cooley, E. (1987). The relationship of work environment and client

contact to burnout in mental health professionals. Journal of Counseling and

Development, 65(5), 249-252. Abstract retrieved from http://psycnet. apa.

org/psycinfo/1987-20011-001

Schaefer, J., & Moos, R. (1996). Effects of work stressors and work climate on long-

term care staff’s job morale and functioning. Research in Nursing and Health,

19, 63-73.

Schaufeli, B., & Van Dierendonck, D. (1993). The construct validity of two burnout

measures. Journal of Organizational Behavior, 14, 631-647.

Schaufeli, W. B. & Van Dierendonck, D. (1993). The construct validity of two

burnout measures. Journal of Organizational Behavior, 14, 631-647.

Schaufeli, W. B. (2003). Past performance and future perspectives of burnout

research. South African Journal of Industrial Psychology, 29, 1 – 15.

Schaufeli, W. B., & Enzmann, D. (1998). The burnout comparison to study and

practice: A critical analysis. London: Taylor & Francis.

Schaufeli, W. B., Daamen, J., & Van Mierlo, H. (1994). Burnout among Dutch

teachers: An MBI- validity study. Educational and Psychological


Schneider, B. (1990). Organizational climate and culture. San Francisco: Jossey-

Bass.

Schneider, B. (2000). The psychological life of organizations. In N. M. Ashkanasy, C.

P. M. Wilderom, & M. F. Peterson (Eds.), Handbook of organizational culture

and climate (pp. xvii–xxi). Thousand Oaks, CA: Sage.

Scholl, R. W. (1981). Differentiating organizational commitment from expectancy as

a motivating force. Academy of Management Review, 6, 589-599.

311

Seltzer, J., & Numerof, R. E. (1986). Supervisory leadership and subordinate burnout.

Academy of Management Journal, 31, 567-586.

Seras, S. F., Urizar, G. G., & Evans, G. D. (2000). Examining a stress coping model

of burnout and depression in extension agents. Journal of Occupational

Health Psychology, 5, 56 – 62.

Shah, A. A., Kaur, R., & Haque, A. (1992). Work values and organizational

commitment in public and private sector industries. Pakistan Journal of

Psychological Research, 7, 41-51.

Shahid, H. (2006). Impact of organizational and personality traits on organizational

identification. (Unpublished M.Sc Research Report). National Institute of


Shaw, J., & Reyes, P. (1992). School cultures: Organizational value, orientation, and

commitment. Journal of Educational Research, 85(5), 295-303.

Shechtman, Z., Levy, M., & Leichtentritt, J. (2005). Impact of life skills training on

teachers’ perceived environment and self-efficacy. Journal of Educational

Research, 98(3), 144-154.

Sheesley, D. F. (2001). Burnout and the academic teaching librarian. Journal of

Academic Librarianship, 27.

Shirbagi, N. (2007). Exploring organizational commitment and leadership frames

within Indian and Iranian higher education institutions. Bulletin of Education

& Research, 29, 17-32.

Shirom, A. (2003). Job-related burnout. In J. C. Quick & L. E. Tetrick (Eds.),

Handbook of occupational health psychology (pp. 245–265). Washington, DC:

American Psychological Association.

312

Siegrist, J. (1996). Adverse health effects of high-effort/low-reward conditions.

Journal of Occupational Health Psychology, 1(1):27-41.

Singh, K., & Billingsley, B. S. (1998). Professional support and its effects on teachers'

commitment. Journal of Educational Research, 91(4), 229-239.

Stansfeld, S., & Candy, B. (2006). Psychosocial work environment and mental health-

a meta analytic review. Scandinavian Journal of Work Environment & Health,

32, 443- 462.

Starker, S. (1989). Working up to a better workplace. VA Practitioner, 6, 33-44.

Stern, G. (1970). People in context: Measuring person-environment congruence in

education and industry. New York : John Wiley & Sons.

Stewart, S. M., Bing, M. N., Gruys, M. L., & Helford, M. C. (2007). Men, women,

and perceptions of work environments, organizational commitment, and

turnover intentions. Journal of Business and Public Affairs, 1(1). Retrieved

from http://www. scientificjournals. org/journals2007/articles/1035. htm

Strange, C. C., & Banning, J. H. (2001). Educating by design: Creating campus

learning environments that work. San Francisco: Jossey-Bass,

Sulsky, L., & Smith, C. S. (2005). Work Stress. USA: Thomson/Wadsworth.

Sverke, M. (2008). The importance of the psychosocial work environment for

employee well-being and work motivation, Scandinavian Journal of Work

Environment & Health, 35(4), 241-243.

Swider, B. W., & Zimmerman, R. D. (2010). Born to burnout: A meta-analytic path

model of personality, job burnout, and work outcomes. Journal of Vocational

Behavior, 76, 487 – 506.

Taylor, S., & Gryskiewicz, N. (1993). A test of the validity of the Work Environment

Inventory. Educational and Psychological Measurement, 53, 557 – 563.

313

Tayyab, S., & Riaz, M. N. (2004). Validation of three component model of

organizational commitment in Pakistan. Pakistan Journal of Psychological

Research, 19(3-4), 123-149.

Tayyab, S., & Tariq, P. N. (2001). Work values and organizational commitment in

public and private sector executives. Pakistan Journal of Psychological

Research, 16, 95-112.

Thomson, W. C., & Wendt, J. C. (1995). Contribution of hardiness and school climate

to alienation experienced by student teachers. Journal of Educational

Research, 88, 269-274.

Thoresen, C. J., Bradley, J. C., Bliese, P. D., & Thoresen, J. D. (2004). The Big Five

personality traits and individual performance growth trajectories in

maintenance and transitional job stages. Journal of Applied Psychology, 89,

835-853.

Thorsteinson, T. J. (2003). Job attitudes of part-time vs. full-time workers: A meta-

analytic review. Journal of Occupational and Organizational Psychology,

76(2), 151-177.

Toppinen-Tanner, S., Kalimo, R., & Mutanen, P. (2002). The process of burnout in

whit- color and blue-collar jobs: Eight-year prospective study of exhaustion.

Journal of Organizational Behavior, 23, 555-570.

Tsui, K., Leung, T., Cheung, Y., Mok, H., & Ho, W., (1994). The relationship of

teacher’s organizational commitment to their perceived organizational health

and personal characteristics in primary schools. CUHK Journal of Primary

Education, 4(2), 27-41. Retrieved from http://sunzi.hku.hk/

hkjo/view/48/48000/2. pdf

Tumulty, G., Jernigan, I. E., & Kohut, G. (1994). The impact of perceived work

314

environment on job satisfaction of hospital staff nurses. Applied Nursing

Research, 7, 84-90.

Turnipseed, D. L. (1994). An analysis of the influence of work environment variables

and Moderators on the Burnout Syndrome. Journal of Applied Social

Psychology, 24(9), 782- 800. Abstract Retrieved from http://onlinelibrary.

wiley.com/doi/10. 1111/j. 1559-1816. 1994. tb00612. x/abstract

Turnipseed, D. L. (1998). Anxiety and burnout in the health care work environment.

Psychological Reports, 82(2), 627-642. doi: 10. 2466/PR0. 82. 2. 627-642

Van Dierendonck, D., Garssen, B., & Visser, A. (2005). Burnout prevention through

personal growth. International Journal of Stress Management, 12(1), 62-77.

Van Dierendonck, D., Schaufeli, W. B., & Buunk, B. P. (2001). Burnout of inequity

among human service professionals: A longitudinal study. Journal of

Occupational Health Psychology, 6, 43 – 52.

Vander, V. M. E. G., Bossink, C. J. H., & Jansen, P. G. W. (2003). Gender differences

in the influence of professional tenure on work attitudes. Sex Roles, 49(3-4),

153-162.

Vanderberg, R. J., Self, R. M., & Seo, J. H. (1994). A critical examination of the

internalization, identification, and compliance commitment measures. Journal

of Management, 20(1), 123-140.

Vanroelen, C., Levecque, K., Moors, G., Gadeyne, S., & Louckx, F. (2009). The

structuring of occupational stressors in a Post-Fordist work environment.

Moving beyond traditional accounts of demand, control and support. Social

Science & Medicine, 68(6), 1082-1090. doi:10. 1016/j. socsimed. 2009. 01.

012

315

Vermeulen, M., & Mustard, C. (2000). Gender differences in job strain, social support

at work, and psychological distress. Journal of Occupational Health


Wadsworth, E. J. K., Chaplin, K. S., Allen, P. H., & Smith, A. P. (2010). What is a

good job? Current perspectives on work and improved health and well being.

The Open Occupational Health & Safety Journal, 2, 9-15.

Wahn, J. C. (1998). Sex differences in the continuance component of organizational

commitment. Group & Organization Management, 23(3), 256-266.

Walker, J. (2007). Service climate in New Zealand English language centres. Journal

of Educational Administration, 45(3), 315–337. doi: 10.

1108\09578230710747839

Walkey, F. H., & Green, D. E. (1992). An exhaustive examination of the replicate

factor structure of Maslach Burnout Inventory. Education and Psychological


Walsh, B., & Betz, N. (1994). Tests and Assessment (4th ed.). New York: Prentice

Hall.

Waryszak, R. Z. (1997). Student perceptions of the cooperative education work

environment in service industries. Progress in Tourism and Hospitality

Research, 3, 249-256.

Waryszak, R. Z. (1999). Students’ expectations from their cooperative education

Placements in the hospitality industry: An international perspective. Education

and Training, 41, 33-40.

Wasti, S. (2002). Affective and continuance commitment to the organization: Test of

an integrated model in the Turkish Context. International Journal of

Intercultural Relations, 26, 525-550.

316

Watts, J., & Robertson, N. (2011). Burnout in university teaching staff: A systematic

literature review Educational Research, 53(1), 33-50. doi:

10.1080/00131881.2011.552235

Weisberg, J., & Kirschenbaum, A. (1993). Gender and turnover: A re-examination of

the impact of sex on intent and actual job changes. Human Relations, 46(8),

987-1006.

Westerman, J. W., & Cyr, L. A. (2004). An integrative analysis of person-

organization fit theories. International Journal of Selection and Assessment,

12(3), 252-261.

Westerman, J. W., & Simmons, B. I. (2007). The effects of work environment on the

personality-performance relationship: An exploratory study. Journal of

Managerial Issues, 19(2), 288-305.

Westerman, J. W., & Yamamura, J. H. (2007). Generational preferences for work

environment fit: Effects on employee outcomes. Career Development

International, 12(2), 150-161.

Wetzel, J. (1978). Depression and dependence upon unsustaining environments.

Clinical Social Work Journal, 6(2), 75-89.

Wetzel, J., & Redmond, F. (1980). A person-environment study of depression. Social

Service Review, 54, 363-375.

Whitener, E. M., & Walz, P. M. (1993). Exchange theory determinants of affective

and continuance commitment and turnover. Journal of Vocational Behavior,

42, 265-281.

Wiener, Y. (1982). Commitment in organizations: A normative view. Academy of

Management Review, 7(3), 418-428.

317

Wiener, Y., & Vardi, Y. (1980). Relationships between job, organization, and career

commitments and work outcomes-an integrative approach. Organizational

Behavior and Human Performance, 26, 81-96.

Wilber, K. H. & Specht, C. V. (1994). Prevalence and Predictors of Burnout among

Adult Day Care Providers. The Journal of Applied Gerontology, 13(3), 282-

298.

Wilber, K. H., & Specht, C. V. (1994). Prevalence and predictors of burnout among

adult day care providers. Journal of Applied Gerontology, 13, 282-298.

Wilk, L. A., & Redmon, W. K. (1998). The effects of feedback and goal setting on the

productivity and satisfaction of university admissions staff. Journal of

Organizational Behavior Management, 18, 45-68.

Wilkerson, K. (2009). An examination of burnout among school counsellors guided

by stress strain coping theory. Journal of Counseling and Development, 87(4),

428- 437.

Wilkes, B., Stammerjohn, L., & Lalich, N. (1981). Job demands and worker help in

machine paced poultry inspection. Scandinavian Journal of Work

Environment & Health, 7(4), 12-19.

Winnubst, J. (1993). Organizational Structure, Social Support, and Burnout. In W. B.

Schaufeli, C. Maslach, & T. Marek (Eds.), Professional burnout: Recent

developments in theory and research (pp. 253-260). Washington, DC: Taylor

& Francis.

Witt, L. A. (1989). Sex differences among bank employees in the relationships of

commitment with psychological climate and job satisfaction. Journal of

General Psychology, 116(4), 419-426.

318

Wood, T. & McCarthy, C. (2002). Understanding and Preventing Teacher Burnout.

Retrieved from http://www.ericdigests.org/2004-1/burnout.htm.

Worley, J. A., Vassar, M., Wheeler, D. L., & Barnes, L. L. (2008). Factor structure of

scores from the Maslach Burnout Inventory: A review and Meta analysis of 45

exploratory and confirmatory factor analytic studies. Educational and


Wu, J. (1998). School work environment and its impact on the professional

competence of newly qualified teachers. Journal of In-service Education,

24(2), 213-225.

Yavuz, M. (2009). An investigation of burnout levels of teachers working in

elementary and secondary educational institutions and their attitudes to

classroom management. Educational Research and Reviews, 4(12), 642-649.

Retrieved from http://www. academicjournals. org/ERR2

WeiBo, Z., Kaur, S., & Jun, W. New development of Organizational Commitmnet: A

critical review (1960-2009). (2010). African Journal of Bussiness

Management.4(1). 012-020. Retrieved from

http://www.academicjournals.org/ajbm/PDF/pdf2010/Jan/WeiBo%20et%20al.

pdf

Zadeh, Z. F., & Ghani, H. (2012). The emerging status of Organizational Psychology

in Pakistan. International Journal of Business and Social Science, 3(4),

Retrievedfromhttp://www.ijbssnet.com/journals/Vol_3_No_4_Special_Issue_

February_2012/29.pdf.

Zhong, J., You, J., Gan, Y., Zhang, Y., Lu, C., & Wang, H. (2009). Job stress,

burnout, depression symptoms, and physical health among Chinese university

teachers. Psychological Reports, 105(3), 1248-1254.

319

Zimmerman, R. D. (2008). Understanding the impact of personality traits on

individuals, turnover decisions: A Meta analytic path model. Personal

Psychology, 61, 309 – 348.

319

Appendix A

Consent Letter to Participate in the Research Study

National Institute of Psychology (NIP), Quaid-i-Azam University, Islamabad, is an

educational/research institute. NIP conducts researches on different educational,

organizational, and social issues. Present research/study is a part of study program.

This research is related with employees’ views about their work place, experiences at

work place, and their personal characteristics.

This letter concerns to seek out your consent to participate in the study. Your true

responses are very much important and serve as facilitation in this academic activity.

It is assured that your responses will be kept confidential and will used only for the

research purpose.

Please clearly your consent to participate in this study by responding to the

undertaking below.

I hereby agree to participate in this study. I also have right to decline to participate in

this study as well.

Signature of Participant Signature of Researcher

Thank you for participation in this study and for your time.

320

Appendix B

General Instructions & Demographic Information Sheet

To participate in this study, you are required to fill out this attached Performa. This

Performa is composed of 04 questionnaires along with instructions. Kindly read

instructions carefully mentioned on each questionnaire and respond to all questions.

Please do not leave any question unattempted.

You are required to respond to all questions keeping in mind the views related to your

current organization (university), where you are employed in the capacity of

permanent employee. It is desirable to fully ignore your views related to your

involvement to any side jobs or work settings.

Demographic Information Sheet

Before proceeding, kindly provide the detail of your demographic information cited

below.

Gender --------------------------

Age --------------------------

Education --------------------------

Marital Status --------------------------

Duration of Employment in the present organization -------------------------------

Rank/ Job Title/ Grade ---------------------------

Department -----------------------------

Private/ Public Sector (tick any one option) ------------------------------

University Name (optional): --------------------------

Are you involved in any paid side-jobs? (tick relevant one) YES NO

If “yes”, please specify in what capacity you are working. ---------------------------------

-------------------------------------------------------------------------------------------------------

321

Appendix C

Work Environment Scale Instructions: These are 90 statements about the place in which you work. The

statements are intended to apply to all work environments. However, some words may

not be quite suitable for your work environment. For example, the term supervisor is

meant to refer to the boss, manager, department head, or the person or persons to

whom all employees report. You are to decide which statements are true of your work

environment and which are false.

1. The work is really challenging. True False

2. People go out of their way to help a new employee feel comfortable. True False

3. Supervisors tend to talk down to employees. True False

4. Few employees have any important responsibilities True False

5. People pay a lot of attention to getting work done. True False

6. There is constant pressure to keep working. True False

7. Things are sometimes pretty disorganized. True False

8. There’s a strict emphasis on following policies and regulations. True False

9. Doing things in a different way is valued. True False

10. It sometimes gets too hot (room conditions). True False

11. There’s not much group spirit. True False

12. The atmosphere is somewhat impersonal. True False

13. Supervisors usually compliment an employee who does something well. True False

14. Employees have a great deal of freedom to do as they like. True False

15. There’s lot of time wasted because of inefficiencies. True False

16. There always seems to be an urgency about everything. True False

17. Activities are well-planned. True False

18. People can wear wild looking clothing while on the job if they want. True False

19. New and different ideas are always being tried out. True False

20. The lighting is extremely good (room conditions). True False

21. A lot of people seem to be just putting in time. True False

22. People take a personal interest in each other. True False

23. Supervisors tend to discourage criticism from employees. True False

322

24. Employees are encouraged to make their own decisions. True False

25. Things rarely get “put off till tomorrow.” True False

26. People cannot afford to relax. True False

27. Rules and regulations are somewhat vague and ambiguous. True False

28. People are expected to follow set rules in doing their work. True False

29. This place would be one of the first to try out a new idea. True False

30. Work place is awfully crowded. True False

31. People seem to take pride in the organization. True False

32. Employees rarely do things together after work. True False

33. Supervisors usually give full credit to ideas contributed by employees. True False

34. People can use their own initiative to do things. True False

35. This is a highly efficient, work-oriented place. True False

36. Nobody works too hard. True False

37. The responsibilities of supervisors are clearly defined. True False

38. Supervisors keep a rather close watch on employees. True False

39. Variety and change are not particularly important. True False

40. This place has a stylish and modern appearance. True False

41. People put quite a lot of effort into what they do. True False

42. People are generally frank about how they feel. True False

43. Supervisors often criticize employees over minor things. True False

44. Supervisors encourage employees to rely on themselves when a

problem arises.

True False

45. Getting a lot of work done is important to people. True False

46. There is no time pressure. True False

47. The details of assigned jobs are generally explained to employees. True False

48. Rules and regulations are pretty well enforced. True False

49. The same methods have been used for quite a long time. True False

50. The place could stand some new interior decorations. True False

51. Few people ever volunteer. True False

52. Employees often eat lunch together. True False

53. Employees generally feel free to ask for a raise. True False

54. Employees generally do not try to be unique and different. True False

55. There’s an emphasis on “work before play.” True False

323

56. It is very hard to keep up with your workload. True False

57. Employees are often confused about exactly what they are supposed to

do.

True False

58. Supervisors are always checking on employees and supervise them very

closely.

True False

59. New approaches to things are rarely tried. True False

60. The colors and decorations make the place warm and cheerful to work

in.

True False

61. It is quite a lively place. True False

62. Employees who differ greatly form the others in the organization don’t

get on well.

True False

63. Supervisors expects far too much from employees. True False

64. Employees are encouraged to learn things even if they are not directly

related to the job.

True False

65. Employees work very hard. True False

66. You can take it easy and still get your work done. True False

67. Fringe benefits are fully explained to the employees. True False

68. Supervisors do not often give in to employee pressure. True False

69. Things tend to stay just about the same. True False

70. It is rather drafty (disorganized) at times. True False

71. It’s hard to get people to do any extra work. True False

72. Employees often talk to each other about their personal problems. True False

73. Employees discuss their personal problems with supervisors. True False

74. Employees function fairly independently of supervisors. True False

75. People seem to be quite inefficient. True False

76. There are always deadlines to be met. True False

77. Rules and polices are constantly changing. True False

78. Employees are expected to conform rather strictly to the rules and

customs.

True False

79. There is a fresh, novel atmosphere about the place. True False

80. The furniture is usually well arranged. True False

81. The work is usually very interesting. True False

82. Often people make trouble by talking behind other’s back. True False

324

83. Supervisors really stand up for their people. True False

84. Supervisors meet with employees regularly to discuss their future work

goals.

True False

85. There’s a tendency for people to come to work late. True False

86. People often have to work overtime to get their work done. True False

87. Supervisors encourage employees to be neat and orderly. True False

88. If employee comes in late, he or she can make it up by staying late. True False

89. Things always seem to be changing. True False

90. The rooms are well ventilated. True False

325

Appendix D

Work Environment Scale Scoring Key The following list contains the scoring key for the Work Environment Scale (WES) - Form (Real). The scale has 90 items listed below in form of item numbers. An item number listed as true (T) is scored 1 point if marked “true” and an item listed as false (F) is scored 1 point if marked “false”. The total subscale score is the number of items answered in the scored direction. Involvement Item # 1 11 21 31 41 51 61 71 81

Scoring T F F T T F T F T

Coworker Cohesion

Item # 2 12 22 32 42 52 62 72 82

Scoring T F T F T T F T F

Supervisor Support Item # 3 13 23 33 43 53 63 73 83

Scoring F T F T F T F T T

Autonomy Item # 4 14 24 34 44 54 64 74 84

Scoring F T T T T F T T T

Task Orientation Item # 5 15 25 35 45 55 65 75 85

Scoring T F T T T T T F F

326

Work Pressure Item # 6 16 26 36 46 56 66 76 86

Scoring T T T F F T F T T

Clarity

Item # 7 17 27 37 47 57 67 77 87

Scoring F T F T T F T F T

Managerial Control Item # 8 18 28 38 48 58 68 78 88

Scoring T F T T T T T T F

Innovation Item # 9 19 29 39 49 59 69 79 89

Scoring T T T F F F F T T

Physical Comfort Item # 10 20 30 40 50 60 70 80 90

Scoring F T F T F T F T T

327

Appendix E

MBI-Educators Survey The purpose of this survey is to discover how educators view their job and the people

with whom they work closely.

Instructions: There are 22 statements of job-related feelings. Please read each

statement carefully and decide if you ever feel this way about your job. If you have

never had this feeling, write a “0” (zero) in the space provided for options right after

the statement. If you have had this feeling, indicate how often you feel it by writing

the number (from 1-6) that best describes how frequently you feel that way.

S# Statements 0 1 2 3 4 5 61. I feel emotionally drained.

2. I feel used up at the end of the day.

3. I feel fatigued when I get up in the morning and have to face another

day on the job.

4. I can easily understand how my recipients feel about things.

5. I feel I treat some recipients as if they were impersonal “objects”.

6. Working with people all day is really a strain for me.

7. I deal very efficiently with the problems of my recipients.

8. I feel burned out from my work.

9. I feel I am positively influencing other people’s lives through my

work.

How often:

0 1 2 3 4 5 6

Never A few times a year or less

Once a month or less

A few times a month

Once a week

A few times a week

Every day

328

10. I have become more callous towards people since I took this job.

11. I worry that this job is hardening me emotionally.

12. I feel very energetic.

13. I feel frustrated by my job.

14. I feel I am working too hard on my job.

15. I don’t really care what happens to some recipients.

16. Working directly with people puts too much stress on me.

17. I can easily create a relaxed atmosphere with my recipients.

18. I feel exhilarated after working closely with my recipients.

19. I have accomplished many worthwhile things in this job.

20. I feel like I am at the end of my rope.

21. In my work I deal with emotional problems very calmly.

22. I feel recipients blame me for some of their problems.

329

Appendix F

Organizational Commitment Questionnaire Instructions: These are the statements about how you feel towards your organization.

Read each statement carefully and indicate the extent (strongly agree, agree, neutral,

disagree, and strongly disagree) to which you agree or disagree with the statement.

1. I do not feel like part of family (name of

organization). SA Ag N DA SD

2. I feel emotionally attached to (name of organization).

SA Ag N DA SD

3. Working at (name of organization) is a great deal of personal interest to me.

SA Ag N DA SD

4. I feel a strong sense of belonging to (name of organization).

SA Ag N DA SD

5. (Name of organization) does not deserve my loyalty.

SA Ag N DA SD

6. I am proud to tell others that I work at (name of organization).

SA Ag N DA SD

7. I would be happy to work at (name of organization) until I retire.

SA Ag N DA SD

8. I really feel that many problems faced by (name of organization) are also my problems.

SA Ag N DA SD

9. I enjoy discussing (name of organization) with people outside of it.

SA Ag N DA SD

10. I am not concerned about what might happen if I left (name of organization) without having another position lined up.

SA Ag N DA SD

11. It would be very hard for me to leave (name of organization) right now even if I wanted to.

SA Ag N DA SD

12. Too much in my life would be disrupted if I decided I wanted to leave (name of organization) now.

SA Ag N DA SD

13. It wouldn’t be too costly for me to leave (name of organization) now.

SA Ag N DA SD

14. Right now, staying with (name of organization) is a matter of necessity as much as desire.

SA Ag N DA SD

15. One of the serious consequences of leaving (name of organization) would be the scarcity of available alternatives.

SA Ag N DA SD

16. One of the reasons I continue to work for (name of organization) is that leaving would require considerable sacrifices i.e., another organization may not match the overall benefits I have here.

SA Ag N DA SD

330

17. I do not feel any obligation to remain with (name of organization).

SA Ag N DA SD

18. Even if it were to my advantage, I do not feel like it would be right to leave (name of organization) now.

SA Ag N DA SD

19. I would feel guilty if I left (name of organization) now.

SA Ag N DA SD

20. (Name of organization) deserves my loyalty. SA Ag N DA SD 21. It would be wrong to leave (name of

organization) right now because of my obligation to the people in it.

SA Ag N DA SD

22. I owe a great deal to (name of organization). SA Ag N DA SD

331

Appendix G MINI-MARKERS

How Accurately Can You Describe Yourself?

Instructions: Please use this list of common human traits to describe yourself as

accurately as possible. Describe yourself as you see yourself at the present time, not

as you wish to be in the future. Describe yourself as you are generally or typically, as

compared with other persons you know of the same sex and of roughly your same

age.

Before each trait, please write a number indicating how accurately that trait describes

you, using the following rating scale:

______________________________________________________________

INACCURATE.......................................................................................... ACCURATE

------------------------------------------------------------------------------------------------------------ Extremely...Very...Moderately...Slightly........Slightly...Moderately...Very...Extremely

_______ _______ ________ ______ ? ________ ________ ______ _______

......1..............2.............3...............4..............5........6...............7...............8..............9

332

Personality traits INACCURATE ACCURATE

Extre

mel

y

Ver

y

Mod

erat

ely

Slig

htly

Nei

ther

in

accu

rate

no

r t

Slig

htly

Mod

erat

ely

Ver

y

Extre

mel

y

1 2 3 4 5 6 7 8 9 1.Talkative 2. Extroverted (expressive) 3. Bold 4. Energetic 5. Shy 6. Quiet 7. Bashful (reserved) 8. Withdrawn 9. Sympathetic 10. Warm 11. Kind 12.Cooperative 13. Cold 14.Unsympathetic 15. Rude 16. Harsh 17. Organized 18. Efficient 19. Systematic 20. Practical (realistic) 21. Disorganized 22. Sloppy (casual) 23. Inefficient 24. Careless 25. Un-envious 26. Relaxed 27. Moody 28. Jealous 29. Temperamental 30. Envious (resentful) 31. Touchy 32. Fretful (anxious) 33. Creative 34. Imaginative 35. Philosophical 36. Intellectual 37. Complex 38. Deep 39. Uncreative 40. Un-intellectual

333

Appendix H

MINI-MARKERS- Scoring Key Extraversion: It consists of 1-8 items in the inventory (see annexure F) with

negatively phrased items including 5, 6, 7 and 8.

Agreeableness: It consists of 9-16 items with negative item nos. 13, 14, 15 and 16.

Conscientiousness: It consists of 17-24 items and negative items are 21, 22, 23 and

24.

Emotional Stability: It consists of 25-32 items with negative item nos. 27, 28, 29, 30

and 32.

Intellect or Openness: It consists of 33-40 items with item nos. 39 and 40 as negative

items.

The inventory is scored by adding up the numbers that have been circled for

each of these traits, so that it ends up with 5 scores, one for each of the traits.

334

Appendix I

Descriptive Profile of Pilot Sample

Mean, SD, Percent, Range, & Frequency of Personal Variables of University

Teachers (N = 102)

Organizational & Demographic

Variables M SD Percent Range N

Age 31.08 7.6 43 102

Duration of Employment 2.48 3.17 21.90 102

Sector

Public Sector Universities 60.8 62

Private Sector Universities 39.2 40

Departments

Natural Sciences 27.5 28

Social Science 71.6 73

Hierarchical Status

Basic Rank 63.7 65

High Rank 36.3 37

Side Paid Jobs

Involvement in side jobs 21.6 22

Non-Involvement in side jobs 77.5 80

Gender

Men 49 50

Women 51 52

Education

Master Level 46.1 47

Research Degree or Doctorate 53.9 55

Marital Status

Married 48 49

Single 52 53

335

Appendix J

Descriptive Profile of Sample of Main Study

Mean, SD, Percent, Range, & Frequency of Personal Variables of University

Teachers (N = 426)

Organizational & Demographic

Variables Mean S.D Percent Range Frequency

Age 36.57 8.96 52 426

Duration of Employment 5.06 5.20 31.98 426

Sector

Public Sector Universities 49.8 212

Private Sector Universities 50.2 214

Departments

Natural Sciences 53.5 228

Social Science 46.5 198

Hierarchical Status

Basic Rank 43.4 185

High Rank 56.6 241

Side Paid Jobs

Involvement in side jobs 4.9 21

Non-Involvement in side jobs 95.1 405

Gender

Men 62.9 268

Women 37.1 158

Education

Master Level 26.3 112

Research Degree or Doctorate 53.9 314

Marital Status

Married 65.7 280

Single 33.6 143

aneela maqsood - higher education...

Documents