influence of transformational leadership style on

INFLUENCE OF TRANSFORMATIONAL LEADERSHIP STYLE ON DECISION-MAKING STYLE AND TECHNOLOGY READINESS:

A CORRELATION STUDY

by

Crystal A. Mueller

A Dissertation Proposal Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Management in Organizational Leadership

UNIVERSITY OF PHOENIX

September 2009

UMI Number: 3399500

All rights reserved

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UMI 3399500

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ii

2009 by Crystal A. Mueller ALL RIGHTS RESERVED

iv

ABSTRACT

The research addressed the problem of technology initiatives failing to meet

organizational objectives. The purpose of the quantitative correlation study was to

determine the relationship between transformational leadership styles, decision-

making styles, and technology readiness. The findings of the study answered

research questions in three areas: transformation leadership styles in relationship

to decision-making styles, transformational leadership styles in relationship to

technology readiness, and decision-making styles in relationship to technology

readiness. The sample was a group of leaders at a large rural school district in

Wyoming. Findings indicated no evidence of transformational leadership styles as

related to the dependent variables. Evidence was found that decision-making

styles have a relationship to technology readiness. The significance of the study

was to increase knowledge in the areas of leadership, decision-making, and

technology implementation.

v

DEDICATION The study is dedicated in loving memory to my mother, Mary Katherine

Davis Sanwald. My mother taught me early the value of formal education.

Although she only completed the tenth grade, she continued to value knowledge

and completed her General Equivalency Diploma (GED) at 38 years old. She

lived a much different life than she dreamed but bore abundant fruit, which is

witnessed in my siblings and me.

vi

ACKNOWLEDGEMENTS

The doctoral process can only be completed with much assistance from

many. I would like to acknowledge the abundant support, guidance, and love so

many have given me. As in any endeavor, my Creator is my strength and light.

My sincere thanks to my mentor and chair Nancy S. Bostain, Ph.D. and

committee members Muhummad Betz, Ph.D. and Barbara Carnes, Ph.D. for their

direction, guidance, and patience through the dissertation process.

The monumental journey would not have been possible without the

support of my cohort and the facilitators who guided me along the way. I

remember one doctoral student describing how she was lying on the floor sick and

exhausted typing her responses to questions. There were days my fellow doctoral

students and I wondered if we would make it another step. We experienced and

celebrated new beginnings, births, marriages, new careers, and death. I have high

regard for the University of Phoenix staff that assisted and encouraged me,

especially, Denise Jenkins, my academic counselor. My deepest thanks are

extended to all who shared the academic journey.

Thanks to those who gave me birth, my mother (Mary Katherine) and

father (Richard Dale). I extend gratitude to my siblings (Tony, Monte Levi, and

Belinda) and their spouses who continued to communicate their belief in me. My

other families, the Potmesils, the Kidneighs, the Perrys, the Martins, and Dr. Mark

Humberson have taught me much about love and how good life can be. Thanks to

my stepson Michael, who left this life too soon but has been with me in spirit and

to the nieces and nephews who look to me as an example and testimony of the

vii

value of a formal education. My greatest blessings are my husband, Bill and my

daughter, Debbie who are my biggest fans and keep me going. They are truly the

wind beneath my wings.

Thank you to the leaders of Natrona County School District for sharing

their knowledge and opinions. I am eternally grateful to the great people I work

with in Human Resource Services who continued to encourage me each and every

day. I appreciate the wisdom and guidance from Dr. Jim Lowham and Dr. Joel

Dvorak. I am grateful to my mentor and guide Cheryl Quinlan who would not let

me stop no matter how rough it got. The guidance and knowledge of Dr. Anne

LaPlante and Dr. Michael Flicek were invaluable. I extend thanks to Dr. Mark

Mathern who was on a similar journey and was helpful to commiserate and

inspire. I appreciate my fellow cabinet members who kept me laughing and

encouraged me not to take life too seriously.

Finally, and by no means any less important than those aforementioned,

my gratitude is extended to the editors of the dissertation document (Jill

Eastwood, Mary Riis, and my daughter, Debbie), Dr. Michael Flicek, who helped

me through the statistics and research design, and Dr. Dennis Clodi who offered

wisdom and guidance. Many thanks for the work of Dr. Bernard M. Bass, Dr.

Bruce J. Avolio, Dr. Suzanne G. Scott, Dr. Reginald A. Bruce, Mr. Charles L.

Colby, and Dr. A. Parasuraman. If not for their work, my study would not exist.

The aforementioned researchers provided the survey tools: Multifactor Leadership

Questionnaire, General Decision-Making Styles Scale, and Technology Readiness

Index. Dr. Bruce and Mr. Colby were particularly helpful in the research. There

viii

were many people who continued to give me encouragement, and even though

their names are not listed does not mean they are less important. My greatest

learning from this journey is patience is key, and love is a necessity. Completing

the dissertation journey is more about believing that I can and not being

concerned so much about when it will happen. To those who may find this

dissertation, remember to live simply, love deeply, laugh loudly, and learn

continually.

ix

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................ xii

LIST OF FIGURES ......................................................................................................... xiii

CHAPTER 1: INTRODUCTION ........................................................................................1

Background of the Problem .................................................................................................2

Statement of the Problem .....................................................................................................6

Purpose of the Study ............................................................................................................7

Significance of the Study .....................................................................................................8

Nature of the Study ............................................................................................................11

Research Questions and Hypotheses .................................................................................13

Theoretical Framework ......................................................................................................15

Definition of Terms............................................................................................................17

Assumptions .......................................................................................................................18

Scope of the Study .............................................................................................................19

Limitations .........................................................................................................................19

Delimitations ......................................................................................................................20

Summary ............................................................................................................................20

CHAPTER 2: REVIEW OF LITERATURE .....................................................................22

Historical Overview ...........................................................................................................24

Leadership Styles ...................................................................................................24

Decision-Making Styles .........................................................................................27

Technology Implementation ..................................................................................30

Current Research ................................................................................................................34

x




Research Gap .....................................................................................................................50

Conclusion .........................................................................................................................51

Summary ............................................................................................................................52

CHAPTER 3: METHOD ...................................................................................................53

Research Method ...............................................................................................................53

Design Appropriateness .....................................................................................................54

Population Sample .............................................................................................................57

Data Collection ..................................................................................................................59

Instrumentation ..................................................................................................................61




Data Analysis .....................................................................................................................64

Summary ............................................................................................................................65

CHAPTER 4: PRESENTATION AND ANALYSIS OF DATA ......................................67

Population Sample .............................................................................................................67

Data Analysis Procedures ..................................................................................................73

Transformational Leadership Styles and Decision-Making Styles ........................77

Transformational Leadership Styles and Technology Readiness ..........................78

Decision-Making Styles and Technology Readiness .............................................79

xi

Findings..............................................................................................................................79

Conclusion .........................................................................................................................88

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS .....................................89

Findings and Analysis ........................................................................................................89

Transformational Leadership Styles and Decision-Making Styles ........................90

Transformational Leadership Styles and Technology Readiness ..........................91

Decision-Making Styles and Technology Readiness .............................................93

Recommendations ..............................................................................................................95

Suggestions for Future Research .......................................................................................97

Implications........................................................................................................................99

Summary ..........................................................................................................................100

Conclusion .......................................................................................................................101

REFERENCES ................................................................................................................103

APPENDIX A. INTRODUCTORY LETTER ................................................................122

APPENDIX B. INFORMED CONSENT ........................................................................124

APPENDIX C. PERMISSIONS ......................................................................................126

APPENDIX D. STUDY INSTRUMENTATION ..........................................................131

xii

LIST OF TABLES

Table 1 Descriptive Statistics and Normality Testing of Key Variables ............................80

Table 2 Correlation Between Transformational Leadership Styles and Decision-making

Styles ................................................................................................................................82

Table 3 Correlation Between Transformational Leadership Styles and Technology

Readiness ...........................................................................................................................83

Table 4 Correlation Between Decision-making Styles and Technology Readiness ..........84

Table 5 Null Hypotheses Testing .......................................................................................88

xiii

LIST OF FIGURES

Figure 1. Age distribution .................................................................................................69

Figure 2. Leader group distribution ...................................................................................70

Figure 3. Number of years in the organization distribution ..............................................71

Figure 4. Years in current position distribution .................................................................72

Figure 5. Technology readiness categories .......................................................................76

1

CHAPTER 1: INTRODUCTION

Between 1995 and 2001 turnover of chief executive officers (CEOs) within

corporations across the United States was 53% due to poor financial performance (Bass,

2007). The turnover rate represented a 130% increase over previous years. Bass cited

several studies concluding that CEOs commonly failed because they did not effectively

implement strategic change. Washington and Hacker (2005) estimated that nearly 75% of

American corporations have undergone some form of systemic change. The

implementation of technology epitomizes a significant systemic change for organizations

(Jasperson, Carter, & Zmud, 2005; Lavie, 2006; Lee & Xia, 2005).

Millions of dollars are typically invested in technology with objectives such as

increasing competitiveness, resolving complex internal system problems, and/or

providing tools to allow employees to effectuate efficient and timely customer service

(Bergmo & Johannessen, 2006; Kontoghiorghes, 2005; Woodall, Colby, & Parasuraman,

2007). Yet, a significant number of technology implementations fail to meet corporate

objectives (Clegg et al., 1997). Owen and Demb (2004) speculated that the manner in

which leaders implement technology influences the success of such endeavors. Van der

Merwe, Pretorius, and Cloete (2004) suggested that knowledge of the respective

technology, in terms of its complexity and the expected human reaction to its

implementation, determines success or failure.

The influence of organizational and social factors complicates technology

implementation; leader support is a pivotal factor (Adamson, 2004; Russell & Hoag,

2004; McAfee, 2006). Bagozzi (2007) advanced that a need exists to understand and

explain technology implementation and suggested that worldview influences leader

2

decisions toward goal achievement. The study examined variables with the potential to

collectively contribute to a deeper understanding of technology implementation.

Leadership style served as an independent variable. Two dependent variables were

decision-making styles and technology readiness. Using the same data set, decision-

making style served as an independent variable and technology readiness was the

dependent variable.

A brief background of the concepts encompassing leadership, decision making,

and technology implementation was provided in the study along with discussion defining

the problem and purpose of the research. The significance of the problem was explored

centered in leadership knowledge, human resources, and organizations in the midst of

innovative change. The nature of the study was described via a population profile and the

study instrumentation and methodology. The study hypotheses, research questions and

key terms were defined. Additionally, theoretical knowledge related to leadership,

decision-making theory, and change theory were explored as these aspects relate to

technology.

Background of the Problem

As noted earlier, organizations invest millions of dollars in new technology,

expending far beyond the purchase price for subsequent implementation

(Kontoghiorghes, 2005; Sherer, Kohli, & Baron, 2003). McAfee (2006) described a

pharmaceutical organization that allocated $100 million for an information technology

(IT) project, which ultimately sent the company into Chapter 11 bankruptcy. A major

aspect of IT implementation is organizational leaders who understand that their strategic

roles can increase the probability of success with change initiatives (Armitage, Brooks,

3

Carlen, & Schulz, 2006; Chahal & Kohli, 2006). Hirtz, Murray, and Riordan (2007)

asserted that an important component of transformational change is leader behavior.

Behavior a leader adopts upon initiation of a systemic change can define the tone of

project progress through both planned and unexpected situations, as well as ultimate

success (Adalbjarnardottir & Runarsdottir, 2006; Daghir & Al Zaydie, 2005; Russell &

Hoag, 2004). Leadership style can inspire others to consistent adherence to a chosen path

(Sun & Scott, 2005).

With U.S. organizations increasing in complexity and accountability, leaders can

become confused and frustrated, spurring the same reaction from employees

(Dervitsiotis, 2005; Mignonac & Herrbach, 2004). Extensive research (Ballou, Godwin,

& Shortridge, 2003; Ho, 2007; Kontoghiorghes, 2005; Lines, 2004; Schraeder, Swanides,

& Morrison, 2006; VanDam, 2005) has been conducted on the role of employees in

creating successful change. Employee contribution directly affects the success or failure

of innovative change within organizations and is as important as the leadership role

(Fang, Tsai, & Chang, 2005; Tellis, 2006). Leadership understanding of change

initiatives has the potential to affect employee commitment, which may in turn, affect

technology implementation (Cetin, 2006; Fedor, Caldwell, & Herold, 2006; Surie &

Hazy, 2006; Wong, Yik, & Kwong, 2006).

A plethora of research (Bass, 2007; Charbonneau, 2004; Clegg et al., 2006; De

Cremer, van Dijke, & Bos, 2004; Higgs, 2003) has examined leadership styles. Kao and

Kao (2007) investigated several leadership models developed over many decades such as

those grounded in trait, behavioral, contingency, and situational theory. A neglected area

in Kao and Kao’s research was transformational leadership styles, which was addressed

4

in this study with an examination of leadership characteristics reported by Avolio, Bass,

and Jung (1999). Spinelli (2006) suggested leaders who demonstrate competency in

various leadership styles manage workers more effectively within complex organizations.

In fact, his study indicated that subordinates who are influenced by leaders tend to

contribute extra effort toward their assigned work tasks. Such employees also typically

express greater satisfaction in their job roles than do employees not influenced by their

managers. Employees who are influenced by their leaders also tend to perceive their

managers as more effective leaders than do other employees (Spinelli, 2006).

Existing research that has specifically examined the relationship between

leadership style and leader choices during strategic change is minimal. With the barrage

of decisions made within brief time periods and uncertain environments, leaders must

develop a clear understanding of the effects of their decisions on the success of

technology implementation (Chahal & Kohli, 2006; Dawson & Buchanan, 2005). Where

leaders choose to spend their time during such activity is an important decision (Meyer &

Stensaker, 2006). Leaders tend to exhibit great energy at the onset of a change initiative

with subsequent depletion, as indicated by reduced involvement during the actual

implementation (Ford & Greer, 2005). Gill (2003) posited that leaders have the

responsibility to set the vision and develop the culture surrounding that vision. Once the

direction is clear, leaders must motivate and empower all process stakeholders to

accomplish the ultimate vision and organizational goals (Ilies, Judge, & Wagner, 2006;

Moody, Horton-Deutsch, & Pesut, 2007).

Although a great deal of research has investigated leadership, questions remain

concerning the alternatives leaders select during the implementation of change initiatives

5

(Ford & Greer, 2005; Higgs, 2003; Michalisin, Karau, & Tangpong, 2007). Cyert, Simon,

and Trow (1956), as well as Kahneman and Tversky (1979), contributed to the

establishment of a solid foundation for decision-making theories. Decision making was of

specific interest in many subsequent studies (Bakan, Suseno, Pinnington, & Money,

2004; McDevitt, Giapponi, & Tromley, 2007; Papenhausen, 2006; Vieth, 2007). Scott

and Bruce (1995) examined patterns of decision making, which they referred to as

decision-making styles. An understanding of these patterns may, in turn, assist in

developing a clearer understanding of associated behavior.

Existing evidence (Cowan, 1988; Menguc & Auh, 2005; Michel, 2007; Mintzberg

& Waters, 1985; Vonk, Geertman, & Schot, 2007) suggests that leaders influence the

successful formulation and implementation of innovative technology. The decision

making of organizational leadership is a key factor in the effective formulation of

strategic planning (Cowan, 1988). The framing of an initiative by an effective leader

during implementation of an innovative plan can drive progress toward ultimate success

(Ke & Wei, 2006; Rico, 2006). Technology readiness is one major component of

leadership involvement (Kontoghiorghes, 2005; Zhu, Kraemer, & Xu, 2006). Related

literature lacks research into how leadership styles influence decision-making styles and

technology readiness within the business environment.

The variables were examined in the study from the perspective of a public-school

setting. The Multifactor Leadership Questionnaire (MLQ) measured the leadership style

of each participating leader (Avolio et al., 1999). Two other surveys (i.e., the General

Decision-Making Style [GDMS] Scale and the Technology Readiness Index [TRI])

assessed the preferred decision-making style and technology readiness of the leaders

6

(Parasuraman, 2000; Scott & Bruce, 1995). The research was expected to contribute to a

clearer understanding of leadership styles, decision-making styles, and technology

readiness.

Statement of the Problem

Gratton and Ghoshal (2005) posited that managers have a vital role in creating

innovative change within organizations. Major innovation can be costly. Clegg et al.,

(1997) reported that initiatives successfully meeting objectives range from 10% to 20%.

O’Regan and Ghobandian (2004) postulated that leadership decisions significantly

contribute to the failure of change initiatives. Clegg et al., found that IT projects failed to

meet objectives at a rate of 90%, while 80% are late and over budget, and 40% are

completely abandoned. Higgs (2003) suggested that a need exists to identify leader

behavior conducive to successful technology implementation and sustained change.

The role of leadership is to ultimately accomplish outcomes for organizations

through influencing others (Chung & Lo, 2007). The problem is that technological

change initiatives often fail to meet organizational objectives (Luna-Reyes, Zhang,

Gil-García, & Cresswell, 2005). Leader decision making contributes to determining the

direction of initiatives, and leader knowledge and interest in technology remains a

contributing factor in implementation success or failure. Increased involvement of

top-level leaders and forward-focused middle managers may increase the success of such

innovative implementation (Bate & Johnston, 2005). Stakeholders find a new direction

easier to support when they are included in the decision-making process.

Although research (Ford & Greer, 2005; Higgs, 2003; Michalisin et al., 2007) has

produced an abundance of leadership theory, questions remain with regard to leadership

7

decisions during the implementation of change initiatives. This quantitative correlation

study examined the relationship between independent and dependent variables. In the

study, decision-making styles served a dichotomous role of both an independent and

dependent variable. Decision-making styles were examined as a dependent variable to

determine association of leadership styles influence on decision-making patterns. In

addition, decision-making styles functioned as an independent variable to examine

patterned decision-making attributes with the participants propensity to use technology or

technology readiness. A group of 160 leaders within a Wyoming school district were

selected to complete three questionnaires. One of the surveys determined the leadership

styles of the participants, another determined the decision-making style, and a third

measured the technology readiness of the respondents.

Purpose of the Study

The purpose of the cross-sectional, correlation quantitative research was to

determine the degree of association between independent variables and dependent

variables within an organizational environment experiencing an increase in technology

implementation. The independent variables of leadership styles and decision-making

styles were measured via administration of the MLQ and GDMS. The dependent

variables of decision-making styles and technology readiness were identified as factors in

the formulation and implementation of technological change initiatives. Technology

readiness was measured by TRI. An understanding of the relationship among the

variables could increase current knowledge surrounding leadership theory and the ability

of organizations to understand which factors influence the successful implementation of

change initiatives. The school district that participated in the research had experienced

8

significant change since 2000. The superintendent had increased leadership from 60

administrators to a diverse group of 160 leaders composed of directors, managers,

administrators, supervisors, coordinators, instructional facilitators, and cabinet members.

The district was located within central Wyoming with a main office in the city of Casper.

Technology use was encouraged throughout the school district that participated in

the research. The district had adopted a philosophy of paperless operation, which had

facilitated a move to computerized business systems. The U.S. Department of Education

(n.d.) had mandated technology as an essential component of educational success.

Significance of the Study

Organizational investments in technology initiatives are large, with an equally

large potential for failure. Leaders who understand critical factors during the

implementation of innovative change have a greater chance of successful outcomes

(Bagozzi, 2007). Leadership plays a significant role in the successful implementation of

systemic change (O'Regan & Ghobandian, 2004). Human resource professionals typically

understand their role in change initiatives, particularly in aligning the workforce to

various functions (Nybo, 2004). Once serving solely an administrative function, the

human resource professional’s role has evolved into a strategic partner among senior

executives (Karami, Analoui, & Cusworth, 2004; Sheehan, 2005). HR professionals are

expected to understand the strategic side of the organization and contribute to an increase

in the efficient operation and financial gain of the overall enterprise (Sheehan, 2005).

Recognized as a facilitator of change, leaders must also understand the professional

development of employees (Karami et al., 2004; Kontoghiorghes, 2005). As the human

resource function continues to be defined within the enterprise, a clear expectation is to

9

facilitate leader transition within the organization (Vloeberghs & Faes, 2003; Zaccaro &

Banks, 2004).

Kontoghiorghes (2005) examined technological transitions and found that less

than 10% of failed implementation is technical in nature. The human influence is the

factor inevitably limiting success (Harper & Utley, 2001; Russell & Hoag, 2004: Tellis,

2006). The emphasis of the study was on the human aspects of technology

implementation. The findings may assist human resource professionals and other

organizational leaders to gain a clearer understanding of their important roles during

major systemic change. Little substantial research has been conducted within the public

sector to examine major business operations (Wright, 2001). As state and federal

legislators demand increasingly greater accountability, public employers are encouraging

leaders to examine the business systems for which they are accountable (Finn & Hess,

2004; Sharma, 2005). Public entities could benefit from research that assists leaders in

determining how to strategically place and use the knowledge of managers within

organizations as the implementation of new technologies increases.

The research may contribute to the study of leadership theory through a focus on

leadership and decision-making styles. The school district that participated in the study

was in the midst of implementing several new technological initiatives within the

administrative area, professional development, and one-on-one computing. The study of

responses of leaders from an organization heavily involved in technology implementation

may contribute to the overall body of knowledge related to leadership through the

examination of the relationships between leadership styles, decision-making styles, and

technology readiness. Public-sector decision makers have limitations that may not be

10

readily apparent with leaders of the private sector (Nutt, 2006). As public managers

discuss organizational change, greater understanding of routine operations is needed,

because poor decisions can be costly to their organizations, both financially and

politically (Kee & Robbins, 2003).

The success of change initiatives depends upon leadership through coaching

(Hackman, 2003), the motivation of stakeholders (Owen & Demb, 2004), and the

provision of opportunities for member growth through work experiences (Ke & Wei,

2006). Armitage et al., (2006) suggested that a leader begins the process of good

leadership by exploring personal values (i.e., introspection), gradually understanding the

values of others within the organization through managing people and processes, and

becoming strategically competent at each level of leadership. The research may also

contribute to understanding the leadership role and assisting in leadership development.

The results could create a foundation for further study into other environments

implementing technology, especially within the public sector. Ultimately, the study could

increase rates of successful implementation of technology at the lowest level of the

organization.

Nature of the Study

The quantitative study sought to examine the relationship between

transformational leadership styles and the dependent variables (decision-making styles

and technology readiness). In addition, the study examined the association between

decision-making styles and technology readiness. The analysis was conducted through

correlation calculations. Correlation is a statistical test that determines “the tendency or

pattern for two (or more) variables or two sets of data to vary consistently” (Creswell,

11

2003, p. 325). Creswell posited that qualitative studies are designed to explore specific

problems or phenomena. The research questions lead to the examination of variables

rather than exploration. The quantitative correlation study evaluated the responses of

school-district leaders. The entire leadership group was asked to participate, because each

member had influence over a component of new technology implementation.

Kao and Kao (2007) studied leadership styles and the preferred decision-making

styles of Taiwanese executives working within mainland China. The Leader Behavior

Description Questionnaire (LBDQ) was used in data collection because Kao and Kao

based their research upon situational leadership. The measurement tool used to assess

leadership styles in the cross-sectional, correlation quantitative study was the MLQ, Form

5X, which measured transformational, transactional, and laissez-faire leadership styles.

Alimo-Metcalfe and Alban-Metcalfe (2006) posited that the situational leadership model

suggests an outdated paradigm, whereas the transformational model is demonstrative of

the leader characteristics needed during innovative change. The MLQ, Form 5X,

presented 45 items with a 5-point Likert-type response scale ranging from not at all to

frequently, if not always. The nine leadership styles assessed by the questionnaire were

(a) idealized influence-attributed, (b) idealized influence-behavior, (c) inspirational

motivation, (d) intellectual stimulation, (e) individualized consideration, (f) contingent

reward, (g) management by exception-active, (h) management by exception-passive, and

(i) laissez faire (Avolio et al., 1999). The first three leadership styles are types of

charisma/inspirational leadership style, which is transformational in nature.

The GDMS Scale measured decision-making styles in the study. The rigorous

quantitative method was valid and reliable and designed to solve real-world problems.

12

The GDMS Scale presented 25 items with a 5-point Likert-type response scale ranging

from strongly disagree to strongly agree. The five decision-making styles addressed in

the scale were (a) rational, (b) intuitive, (c) dependent, (d) avoidant, and (e) spontaneous

(Scott & Bruce, 1995). The third survey tool, the TRI, measured technology readiness.

The TRI presented 36 items with a 5-point Likert-type response scale ranging from

strongly agree to strongly disagree. The four technology-readiness dimensions measured

by this instrument were (a) optimism, (b) innovativeness, (c) discomfort, and (d)

insecurity (Parasuraman, 2000).

Each leader who participated in the study was contacted via a recruitment notice

and electronic reminder. Leaders who elected to participate completed and signed an

informed-consent form and were asked to complete all three surveys either prior to the

beginning of a regularly scheduled leadership in-service meeting or a breakfast. The

maximum time expected for completion of each survey was 20 minutes. Participants

were asked to schedule 60 minutes to complete the three surveys. The completion time

for all three surveys ranged from 20 minutes to 45 minutes. All survey responses were

held strictly confidential. Returned surveys were scored and subsequently analyzed using

both descriptive and inferential statistical techniques. The statistical analysis was

performed via the Pearson product-moment correlation coefficient. According to Cooper

and Schindler (2003), “Correlation coefficients reveal the magnitude and direction of

relationships” (p. 570).

Research Questions and Hypotheses

Higgs (2003) concluded that effective leadership is a combination of personality

and skill. Feinberg, Ostroff, and Burke (2005) suggested that the application of

13

transformational leadership behavior can create an environment where subordinates align

with a common mind-set. Senge, Lichtenstein, Kaeufer, Bradbury, and Carroll (2007)

advanced that leadership style contributes to diverse views of situations, which can

increase successful outcomes. The decision-making styles of leaders can be determined

through generational influences (Papenhausen, 2006); the manner in which a problem is

framed (Simon, Fagley, & Halleran, 2004); and the complexity of the problem (Vieth,

2007). Bass (2007) claimed that “effective strategies depend on effective

decision-making [sic]” (p. 43). Takala (2005) suggested that, to bring about effective

change, a leader must strategize in three areas: “environmental assessment, visioning and

responding to the complexities of the environment, and member integration and

empowerment” (p. 55). Kalaidjieva and Swanson (2004) posited that complex

information systems require a higher level of knowledge in decision makers.

Research question 1 of the study asked, “What is the relationship between

transformational leadership styles and decision-making styles?” Three null hypotheses

and three hypotheses relate to the first research question.

H01: Transformational leadership styles have no relationship with decision-

making styles.

HA1: Transformational leadership styles have a relationship with decision-

making styles.

H02: There is no relationship between transformational leadership styles and a

rational decision-making style.

HA2: There is a relationship between transformational leadership styles and a


14


dependent decision-making style.



Research question 2 asked, “What is the relationship between transformational

leadership styles and technology readiness?” Three null hypotheses and three hypotheses

are associated with this research question.

H04: There is no relationship between transformational leadership styles and

technology readiness.

HA4: There is a relationship between transformational leadership styles and


H05: There is no relationship between transformational leadership styles and the

technology-readiness dimension of optimism.

HA5: There is a relationship between transformational leadership styles and the



technology-readiness dimension of innovativeness


technology-readiness dimension of innovativeness.

Research question 3 asked, “What is the relationship between decision-making

styles and technology readiness?” Three null hypotheses and three hypotheses are

associated with this research question.

15

H07: There is no relationship between decision-making styles and technology

readiness.

HA7: There is a relationship between decision-making styles and technology

readiness.

H08: There is no relationship between rational decision-making style and the


HA8: There is a relationship between rational decision-making style and the


H09: There is no relationship between dependent decision-making style and the


HA9: There is a relationship between dependent decision-making style and the


Theoretical Framework

The theoretical framework underlying the research has a foundation in theory

related to leadership, decision making, and technology implementation. Kao and Kao

(2007) summarized the major leadership theories as trait, behavior, contingency, and the

new era (i.e., situational leadership). Kao and Kao disregard the contribution of Bass

(2007) who developed a leadership theory conducive to a complex 21st century society.

Higgs (2003) explored leadership over the 50 years preceding his study and determined

that leadership had dramatically changed. The evolution of leadership is driven by

societal values, changes in investor focus, challenges in change implementation, and the

impact of stress on employees. Alimo-Metcalfe and Alban-Metcalfe (2006) embraced

16

transformational or visionary leadership, particularly within the public sector, because the

characteristics of leaders determine long-term visions rather than current reality.

Drucker (1955) defined decision making as “the specific managerial process”

(p. 115). Although the activity of decision making involves a complex procedure,

Drucker described the following major steps: (a) define the situation, (b) determine what

is relevant, (c) determine the scope and validity of factual information, (d) develop

options, and (e) choose an option and put it into action. Cyert et al., (1956) conducted a

related qualitative study and also concluded that decision making is a process. The

Vroom (2003) model identified a continuum of decision making with the following five

components: (a) decide, (b) consult (i.e., individually), (c) consult (i.e., group), (d)

facilitate, and (e) delegate. Research related to decision making has also examined risk

(Kahneman & Tversky, 1979), styles (Scott & Bruce, 1995), and tolerance limits (Kazan,

2005).

The decision making of leaders is especially critical during periods of change

(McDevitt, Giapponi, & Tromley, 2007). Cowan (1988) posited that management styles

affect manager recognition and diagnosis of strategic problems. Nutt (1989) stated, “To

avoid implementation failure strategic managers should consider increasing involvement

or involvement of key stakeholders during implementation activities” (p. 146). Four

tactics include (a) intervention, (b) persuasion, (c) participation, and (d) edict. Nutt found

that these tactics, integrated at the appropriate time, led to successful implementation. Ke

and Wei (2006) concluded that the approach to leadership during the implementation of

new technology determines successful integration. Ke and Wei suggested leader

involvement as a key factor to successful implementation of innovation.

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Snow and Hambrick (1980) discussed the reluctance of leaders to abandon the

status quo during periods of change. Several studies (Clegg et al., 1997; Harper & Utley,

2001; Schneider & Sarker, 2005; Tellis, 2006) specifically addressed the failure of

innovative change including that involving technology. Dwivedi (2006) and Tellis

viewed the role of leadership as an important factor in the successful implementation of

technology. Parasuraman (2000) suggested that technology readiness is critical to

decision making regarding technology design and implementation, as well as in

managing related employee issues.

Definition of Terms

The following key terms will be used throughout the study and are defined for

purposes of the planned research:

Charisma/inspirational refers to a leadership style that provides a vision, a role

model for ethical standards, and clear direction (Avolio et al., 1999).

Decision making style is defined as “the learned, habitual response pattern

exhibited by an individual when confronted with a decision situation” (Scott and Bruce,

1995, p. 820)

Dependent decision making is characterized as “a search for advice and direction

from other[s]” (Scott & Bruce, 1995, p. 820).

Individualized consideration refers to a style of leadership that considers

employee needs and challenges each individual to reach his or her full potential (Avolio

et al., 1999).

Innovativeness refers to “a tendency to be a technology pioneer and thought

leader” (Parasuraman, 2000, p. 311).

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Intellectual stimulation is a leadership style conducive to creating an environment

that encourages others to question problem-solving methods toward task completion

(Avolio et al., 1999).

Optimism is “a positive view of technology and a belief that it offers people

increased control, flexibility, and efficiency in their lives” (Parasuraman, 2000, p. 311).

Rational decision making is characterized as “a thorough search for and logical

evaluation of alternatives” (Scott & Bruce, 1995, p. 820).

Technology readiness refers to “a propensity to embrace and use technologies for

accomplishing goals in home life and at work” (Parasuraman, 2000, p. 308).

Transformational leadership, as defined by Feinberg et al., (2005), is “leadership

that motivates followers to transcend self-interests for a collective purpose, vision, and/or

mission” (p. 471).

Assumptions

The research was based upon the following four assumptions:

1. The instruments selected to measure the study variables are reliable and valid.

2. Participating leaders would answer the questionnaire honestly and accurately.

3. The questionnaire would facilitate categorization of the participants by qualities

most similar to leadership characteristics found in other studies.

4. A leadership group internally identified by the respective organization would

affect technology implementation in a unique way, as opposed to other organizational

groups.

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Scope of the Study

The quantitative correlation study was designed to assess the relationship between

leadership style and decision-making style. The relationship between leadership style and

technology readiness was also assessed. To answer the third research question, the

relationship between decision-making styles and technology readiness was assessed. The

population sample was limited to 160 leaders within one Wyoming school district. The

senior leaders (i.e., superintendent and cabinet members) designated the members of the

leadership group anticipated to participate in the research. The district was selected

because the organization was in the midst of new technology implementation. Business

services, human resources, and student services, in cooperation with the schools were

implementing both software and hardware.

Limitations

A limitation to the research may be the narrow scope necessary to meet the time

frame of the study and the amount of time available to conduct the research. Another

limitation includes the planned study sample of leaders from one rural school district

within the state of Wyoming. This limits generalizability of the results to leaders within a

public-school environment. The study is also limited by its selection of solely volunteer

participants.

Delimitations

The study focused on leadership and decision-making styles, as well as

technology readiness. Delimitations included the selection of a diverse leadership group.

The sample was comprised of school administrators, ancillary supervisors and managers,

administrative interns, grant coordinators, program coordinators, professional

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development facilitators, and human resource professionals. The leadership groups varied

according to their respective senior leaders. The selection of survey tools also presented

delimitation.

The research design focused on the leadership styles, decision-making styles, and

technology readiness of school district leaders. The study was limited to a convenience

sample of such leaders. The scope of the study involved three questionnaires identifying

specified variables. Presumption that the results of the research were generalizable to

other groups of leaders external to the participating school district is discouraged. The

instrument used to determine leadership style has been administered within various

industries and countries. It is possible to generalize the study to leaders outside the public

school system. Although distinctions exist between public and private environments,

leader responsibility is often comparable (Nutt, 2006).

Summary

Implementation failures are costly for any organization (McAfee, 2006; Russell &

Hoag, 2004) and, as noted earlier, leadership is a key factor in the success or failure of

change initiatives (Adamson, 2004; Bass, 2007; Tellis, 2006). The purpose of the

quantitative correlation study was to determine the degree of association between

independent and dependent variables within an organizational environment experiencing

an increase in technology implementation. Leadership and decision-making research has

been conducted for decades, and the association between leadership and decision-making

styles throughout the literature is minimal. Technology research has evolved to

encompass various related facets; however, the association between leadership and

technology readiness is virtually nonexistent. The study may contribute to a deeper

21

understanding of these variables. Literature reviewed for the research in chapter 2 will

explore theory related to leadership, decision making, technology implementation, and

technology readiness. Chapter 3 will present the methodology and research design.

22

CHAPTER 2: REVIEW OF LITERATURE

The leadership approach to change initiatives can directly affect implementation

success (Ke & Wei, 2006). Systemic implementation deteriorates rapidly

(Kontoghiorghes, 2005; Russell & Hoag, 2004); failure rates range from 70% to 90%

(Axelrod, Axelrod, Jacobs, & Beedon, 2006; Kontoghiorghes, 2005; Luna-Reyes et al.,

2005). Markus and Robey (1988) reported a minimal amount of existing research focused

on both management and IT. Parasuraman (2000) expressed concern over the technology

readiness of organizational leaders and their employees. He reported that scholarly

research was nearly void of related study.

Clegg et al., (1997) found management involvement to be a determining factor in

the success of technology implementation. Russell and Hoag (2004) reported that

technological initiatives fail from issues that arise during implementation. Ke and Wei

(2006) posited that the top three factors of successful implementation are leadership, top-

management support, and change management. McAfee (2006) identified three

leadership roles pivotal to technology implementation: the selection of technologies, the

nurturing of technology adoption, and ensuring smooth integration. Organizations are led

through change by an accumulation of choices based upon the experience and knowledge

of the involved leaders (Bass, 2007).

As noted earlier, the purpose of the quantitative correlation study was to

determine the degree of association between the independent variables and the dependent

variables within an organizational environment experiencing an increase in technology

implementation. A major aspect of the leadership role is to influence organizational

stakeholders toward the achievement of goals established for the enterprise (Armitage

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et al., 2006; Hirtz et al., 2007). Within uncertain environments, the decision-making style

of the leader is a determining factor in remaining competitive (Michel, 2007; Wellman,

2007). Michel indicated that the type of decision making applied is difficult to duplicate

from one organization to another.

The study may provide further understanding of leadership dimensions

influencing innovative organizational change. The review of literature will provide a

summation of relevant literature supporting the research questions, leadership theory,

decision-making theory, and technological implementation research. A historical

overview is presented in addition to current research and noted gaps in theoretical

knowledge related to the formulation and implementation of technology. Theory

associated with changing environments, specifically involving technology

implementation; the leadership role in change; the construct of decision making; and

technology readiness are all illustrated within the literature reviewed.

A literature search facilitates the development of an understanding of a problem

under study through a summary of historical and current research (Creswell, 2003). The

literature reviewed for the research focused on issues associated with human resource

concepts, leadership interventions, technology implementation, and change management.

Keywords and terms searched separately and in combination were management,

leadership, decision, organizational behavior, change, strategic, information technology,

implementation, systems, technology readiness, innovation, public, human resource

management, success, and failure. Web-based searches, personal experience, and

background course work all contributed to the overall search. Sources included peer-

reviewed journal articles from numerous professions and specialties including business,

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organizational psychology, marketing, information technology, management, human

resources, finance, health care, education, and public administration.

The University of Phoenix Online Apollo Library search engines—EBSCOhost,

Infotrac, ProQuest, ERIC, Sage, and Digital Dissertation—provided a plethora of

information on the topic for the study. Personal contact with experts and colleagues

provided additional information and an opportunity to explore leadership issues such as

vision and continuous professional development from within organizations implementing

technology. Experts provided measurement tools that were used in the study, and

government Web sites (i.e., the U.S. Department of Education, the Wyoming Department

of Education, Natrona County School District Number One, and the Council for

Excellence in Government) provided both current and historical information, as well as

statistical data.

Historical Overview

Leadership Styles

Leaders who consider the organizational vision during decision making, rather

than simply short-term solutions to organizational problems, generally reap greater

success with change implementation (Drucker, 1955). Fiedler (1969) found that

leadership style is a result of the underlying needs of the respective leader. Leader

behavior can change, but only when the leader desires it to change. Leaders who

understand the importance of the connection between organizational system changes and

human choice typically experience a high degree of success with technology

implementation (Clegg et al., 1997). Fiedler posited that a leader is distinguishable from a

group by greater knowledge or task skill, although the knowledge or skill may not be

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transferable to other situations. Fiedler identified two leadership styles—the autocratic

and democratic. Autocratic leadership is task oriented, while democratic leadership is

group oriented. Although leaders adapt to a particular style, they will adjust that style to

accommodate specific situations.

Fiedler (1972) surveyed 454 separate organizational groups to determine the

influence of leadership styles during different situations. The three areas assessed were

leader-member relations, task structure, and position power. The results indicated a

pattern within which task-oriented leaders were successful. The group-oriented leaders

were successful when conditions were intermediately favorable. Fiedler concluded that a

leader must learn ways to modify the work environment to coincide with the management

style of the leader. Hersey and Blanchard (1982) partially disagreed with Fiedler by

concluding that managers must adapt to business environments.

Hersey and Blanchard (1982) found that leaders must vary their leadership styles

depending upon each situation. They discussed the manner in which various behaviors,

exhibited at different times, effectively influence change within organizations. They

concluded that the appropriate behavior and technology must be determined for each

situation. Fielder (1972) concluded that organizational leaders placed in positions that

best suit their management styles was optimal, noting that training increases the capacity

of a leader to lead. Hersey and Blanchard supported these findings, but also found

manager understanding of style in different situations increases leader effectiveness.

Nadler and Tushman (1990) suggested that a combination of managers with

various leadership styles leads to greater success within constantly changing

environments. They described incremental and reactive change as adaptations required

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for tactical decisions. They defined system-wide change as strategic change and referred

to strategic reaction change as re-creation and strategic anticipated change as

reorientation. Nadler and Tushman posited that both re-creation and reorientation are

risky endeavors; however, reorientation was generally associated with successful change.

The charismatic leadership style emerged as appropriate during strategic change. Nadler

and Tushman identified the following three behaviors of charismatic leadership:

envisioning, energizing, and enabling. Leaders who act as powerful role models inspire

others within organizations to perform in a similar manner.

Nadler and Tushman (1990) identified several limitations of the charismatic

leadership style such as unrealistic expectations, dependency and counterdependency,

employee reluctance to disagree with leaders, a need to continue “the magic,” the

potential to feel betrayed, disenfranchisement from the next level of management, and

human limitations. Even with limitations, Nadler and Tushman supported involved

leadership as vital during adoption and implementation of systemic organizational

change. Although the charismatic leadership style is important during systemic change,

Nadler and Tushman warned that this style is insufficient alone. Nadler and Tushman

introduced instrumental leadership as a component to sustained change. The instrumental

leader builds the processes, structures, and systems to sustain the desired direction.

Nadler and Tushman identified three components of instrumental

leadership—structuring, controlling, and rewarding—which are similar to the

transactional leadership style explored by Avolio et al., (1999).

Avolio et al., (1999) referred to three leadership styles—transformational,

transactional, and laissez-faire. They found that leaders display both transformational and

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transactional characteristics depending upon the situation. Avolio et al., suggested

differentiation of the components defined by Bass’s research that allow leaders to focus

on specific areas for training purposes. The six leadership styles defined by Avolio et al.,

were (a) charisma/inspirational, (b) intellectual stimulation, (c) individualized

consideration, (d) contingent reward, (e) active management by exception, and (f) passive

avoidant. The first three styles represent transformational leadership styles.

Decision-Making Styles

Cyert et al., (1956) described the act of decision making as the center of

leadership activity. Information gathering is a significant aspect of decision making.

Cyert et al., explored the decision-making construct and encouraged further development

of related theory. They questioned past economic theory concerning the process of leader

choice. They found four areas missing within the economic theory of decision making:

(a) discovering alternatives, (b) determining consequences, (c) decision-making

complexity with less than simple solutions, and (d) locating the right problem within

which to invest organizational time. Cyert et al., dissected the decision-making process

into subprograms, common processes, communication processes, and problem-solving

processes. Rather than one solution to problems, Cyert et al., hypothesized that effective

leaders develop multiple alternatives and ultimately select a feasible solution.

Snow and Hambrick (1980) defined strategic decision making as an art reserved

for those who lead. Strategic decision making promotes organizational alignment and

facilitates the systems management. Weick (1980) described organizational blind spots as

areas ignored by leaders because the problems were not necessarily solvable with

quantitative data. He further posited that blind spots receive minimal attention because

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system thinkers seek facts rather than theory (p. 182). Organizational complexity and

uncertainty necessitate complicated problem-solving mechanisms. Leaders tend to view

situations as comprised of negative or positive issues and approach such issues as a

conundrum requiring resolution before proceeding to the next “riddle” without

considering the myriad of possibilities. Weick viewed hypothesizing and problem solving

with equal value and suggested that leaders view uncertainty from a multifaceted

intellectual approach rather than as a one-sided issue. Leaders must invest additional

time, involving all stakeholders, when searching for solutions. Weick posited that the

organization would operate more intelligently if leaders expended time struggling with

complex issues and learning the process of solving problems rather than seeking solely

their own solutions.

Jago and Vroom (1978) reminded readers that different organizational questions

require different answers. They expanded organizational decision-making concepts by

providing the following leader attributes: (a) quality of the decision,

(b) extent of the knowledge and expertise of the leader, (c) problem structure,

(d) subordinate acceptance, (e) ability to accurately measure prior probability of

subordinate acceptance of autocratic decisions, (f) the degree to which subordinates share

organizational goals, and (g) the ability to discern the likelihood of subordinate conflict.

Jago and Vroom hypothesized that manager decisions could be determined in specific

situations. They found that leader decisions were predictable in situations with favorable

outcomes. Prediction was less successful when manager behavior was irrational. With

unfavorable decisions, managers tended to express emotional reactions during the

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decision-making process. Such irrational behavior made decision making difficult to

predict.

Depending upon the perspective of the leader and the type of decision (i.e.,

operational, strategic, human, or technical), Cowan (1988) determined that the reaction of

the leader to the process of decision making could influence problem formulation. Cowan

posited executive leaders interpret situations through a mental framework. Leader

perspectives influence decision making. Cowan examined the decision making of leaders

with various problem types such as operational versus strategic and human versus

technical. He found that the four problem types require different knowledge.

Management styles influence technical decisions less than the human formulation of

problems. Leaders tend to avoid problems related to human issues.

Kahneman and Tversky (1979) hypothesized that individuals make decisions

based upon the amount of risk. Guided by the prospect theory, Kahneman and Tversky

determined that their sample of university students made decisions based upon a

threshold of risk determined by each participant. As noted in the prospect theory, the

student participants edited and evaluated during the decision-making process. During the

evaluation phase, they assigned values, either negative or positive. March and Shapira

(1987) found that managers view a negative outcome as a risk and that their reaction to

risk differed from that originally accepted by management theorists. The decision making

of organizational leaders was contextual in nature. Managers adjusted to the level of risk

by adhering to the social norms of the organization. March and Shapira suggested further

research to examine how managers mentally process various aspects of their roles, rather

than attempt to change an entire belief system that grounds organizational operations.

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Driver, Svensson, Amato, and Pate (1996) defined decision-making styles as

patterns of thought influenced by the environment. The two factors that influence

decision-making styles are the information sought and the number of solutions generated.

Driver et al., referred to a manager who requires a great amount of information as a

maximizer, and a manager who requires key facts as a satisficer (p. 44). A manager with

solely one best option was labeled unifocus, and a manager who employed several

alternatives as multifocus (p. 44). Driver et al., identified five styles from a cross section

of the concepts described: (a) decisive, (b) flexible, (c) hierarchic, (d) integrative, and (e)

systemic. Managers applied a decisive style when employing combined satisficer and

unifocal decision-making behavior. Managers applied a flexible style when enlisting a

satisficer and multifocal behavior. Managers who adopted a hierarchic style employed

maximizing and unifocal behavior, and managers who exhibited an integrative style

enlisted maximizing and multifocal decision-making behavior. Driver et al., defined the

systemic style as a combination of the integrative and hierarchic styles. Managers enlist

an operating and a common-role decision-making style. Driver et al., found that, after

training, such styles often changed.

Technology Implementation

Morone (1989) hypothesized that organizational decision makers lack the

knowledge and skill to make strategic technological decisions. Organizations that did not

implement such strategies successfully were found to require a lengthy amount of time to

define problems. Morone posited leaders who lack technical knowledge avoid

implementing technological strategies. Organizations that can strategically plan

technology will efficiently send a product to market, receive feedback from consumers,

31

and make adjusts as needed. Organizational leaders who can approach technology with a

strategic mind-set often demonstrate success in other areas of strategic management.

As societal changes emerged in the manner in which work was performed within

organizations during the 1950s (i.e., from factory worker to knowledge worker), Leavitt

and Whisler (1958) projected that technology would dramatically change corporate

operations. Twenty years later, Miles, Snow, Meyer, and Coleman (1978) explored the

topic of information management with few contemporaries researching similar topics.

Organizational leaders continued to make decisions within increasingly complex

environments without a good understanding of how to adapt and achieve equilibrium.

Miles et al., developed the adaptive model with the following three components: (a) the

entrepreneur problem, (b) engineering problem, and (c) administrative problem. In the

case of a problem within the entrepreneur stage, the manager recognized when a change

was necessary. During the engineering phase, the manager committed resources to a

solution and selected appropriate supporting technology. During the final administrative

stage, the organization reverted to stability as quickly as possible after formulation and

implementation of the solution. Organizations also implemented new processes and

structures during this stage. Miles et al. identified three organization types (a) defenders,

(b) analyzers, and (c) prospectors. With further research, the reactor type surfaced as a

fourth type when strategic planning failed.

A defender organization demonstrates a desire to maintain the status quo (Miles

et al., 1978). The prospector seeks new opportunities within the market through

innovative products or services. The analyzer establishes consistent structures while

maintaining the flexibility to create new products and services. Reactor behavior results

32

from one or more of the following three situations: (a) unclear communication of

strategies, (b) strategies that do not align with current processes and the overall

organizational structure, and (c) maintenance of current structure and processes despite

environmental changes. The reactor equates to a failure in the process, structure, and/or

strategy. Progression from a reactor into one of the other three organization types must

ensue unless the industry is highly regulated. Miles et al., advanced that the role of

management is to envision the future, implement new structures, and direct/control

employees. Organizational strategy is complex and may not represent solely one

organization type, but rather, a combination of types (Snow & Hambrick, 1980). Snow

and Hambrick posited that the transition from a current mode of operation into a strategic

change manifests developing innovation while moving from the standard practices of the

current system.

Mintzberg and Waters (1985) explored various types of organizations and defined

the following eight types: (a) planned, (b) entrepreneurial, (c) ideological, (d) umbrella,

(e) process, (f) unconnected, (g) consensus, and (h) imposed. Mintzberg and Waters

presented a continuum of deliberate to emergent functions. The two factors driving the

continuum were found to be the degree of intention (i.e., formulation) and control over

bringing ideas to fruition (i.e., implementation). Mintzberg and Waters suggested that, as

strategic leaders emerge, organizational leaders seek to understand the concepts of

intention, choice, and pattern formation.

Markus and Robey (1988) explored causal structure through examining causal

agency, logical structure, and level of analysis surrounding change. Causal agency

identified the driver of change as (a) external forces, (b) individuals, or (c) the interaction

33

of people and events. Logical structure referred to the time span of the formulation and

the implementation of change. Level of analysis referred to a micro or macro view of the

change. Markus and Robey concentrated primarily on the impact of technology on

organizations. As they examined the causal-agency theory, three relationships

developed—the technology imperative, organizational imperative, and emergent

perspective (p. 585). Markus and Robey defined the technology imperative as the

technology driving the organizational change. They defined the organizational imperative

as the technology meeting the needs of the organization. The emergent perspective

supported the concept of an interaction between technology and humans creating

organizational change. The emergent perspective is the most complex and difficult to

simulate. The emergent theory suggested that managers make different choices and

behave differently when implementing technological strategies.

Nutt (1989) studied the decision making of managers during strategic

implementation and found that, when management decisions were made as recommended

through the decision algorithm, a 94% implementation success rate resulted. When

managers did not use recommended decision tactics, the initiative commonly failed.

Clegg et al., (1997) reported that an estimated 80% to 90% of new technology fails to

meet organizational objectives. These researchers found major reasons for

implementation failure such as poor management, poor articulation of user requirements,

inadequate attention to business needs and goals, and failure to involve users

appropriately. Clegg et al., concluded that complex organizational systems and decisions

shape the technology.

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Clegg et al., (1997) reported that senior management lacks the ability to integrate

technology and that managers often implement technology for the wrong reasons (i.e.,

cost reduction). Employees who can potentially be replaced or involved in job

restructuring typically implement new technology. Clegg et al., suggested that managers

have difficulty adopting long-range planning and abdicate to technology staff rather than

understanding the effects of change on employees and the function of the technology.

Clegg et al., posited that poor management crosses all industries.

Parasuraman (2000) suggested the need for organizations to manage the following

three interactions for effective technology implementation: (a) company-technology,

(b) technology-employee, and (c) technology-customer. He designed a marketing model

with three dimensions (i.e., company, employee, and customer) and three constructs of

marketing (i.e., external, internal, and interactive). Parasuraman advanced that, as

technology-based systems become germane to organizational operations, a greater

understanding of technology readiness will increase in those who use the systems.

Current Research

Leadership Styles

Hirtz, Murray, and Riordan, (2007) defined leadership as “the process managers

use to influence subordinates to work toward organizational goals” (p. 22). Gill (2003)

suggested that effective leadership has specific requirements. A leader must possess

intellectual abilities and the ability to process information in not only a logical

assimilation, but also while intuitively considering the possibilities the information holds

for future outcomes. Knowledgeable leaders have the responsibility to set the vision and

develop a culture around that vision. Once the direction is clear, the leader must motivate

35

and empower all stakeholders to accomplish the vision and goals of the enterprise. The

influence of leaders on the work of an organization is a component that determines the

ultimate success or failure of the enterprise.

Hetland and Sandal (2003) hypothesized that subordinates follow

transformational leaders for reasons that extend beyond an exchange relationship. They

posited transformational leaders present unique qualities that do not exist within

transactional or passive-avoidant leadership styles. The leadership qualities that make

transformational leaders unique are the ability to connect with subordinates, agility within

changing environments, and logical thought processes during decision making. Hetland

and Sandal selected 100 middle managers from five organizations within Norway for

their study sample. The participants completed the Sixteen Personality Factors

Questionnaire and the MLQ. The manner in which personality relates to leadership styles

was subsequently analyzed and a weak association was found. Hetland and Sandal

indicated that personality had a stronger association with transformational leadership than

the transactional or passive-avoidant leadership styles. The findings question the theory

of “born leaders” and support the manifestation of leaders through management

development. Under the right circumstances, transformational leadership behavior was

also viewed as teachable.

Spinelli (2006) studied leadership behavior and subordinate perceptions. He

collected 101 surveys from managers in five hospitals within northeastern Pennsylvania.

The findings indicated a significant positive correlation between transformational

leadership and leader effectiveness. Regardless, Spinelli emphasized that

36

transformational leadership is only one style and works in concert with other leadership

styles toward successful organizational operation. The findings support three

concepts (a) transformational leadership motivates subordinates to perform, (b) a

combination of leadership styles is the most effective within complex and rapidly

changing environments, and (c) leaders can exhibit characteristics of several different

leadership styles.

Hirtz et al., (2007) examined the relationship between leadership styles and the

implementation of quality management. Employees of a Missouri university completed

the MLQ and a survey developed by the Excellence in Missouri Foundation. The sample

was comprised of 109 employees. Hirtz and colleagues found that transformational and

transactional factors positively correlated with the perception of participating employees

that quality initiatives had been fully implemented. The results also indicated a positive

correlation between the implementation of quality initiatives and characteristics of the

transformational and transactional leadership styles. A negative correlation was found

between employee perceptions of the implementation of quality management and passive

transactional leadership styles such as management by exception and laissez-faire. The

Hirtz et al., study supported the Deming and Juran theory of top-management

involvement in quality management implementation to increase success. The research

also contributed to past studies that supported an association between leadership style and

subordinate perception of change initiatives.

Nir and Kranot (2006) found that, although the transformational leadership style

assists in creating an environment conducive to satisfied employees, employee

motivation can also manifest from other sources. Nir and Kranot surveyed 755 Israeli

37

elementary teachers and 79 administrators from each of the participating schools. The

teachers completed a questionnaire measuring personal teacher efficacy and the

administrators completed the MLQ. Nir and Kranot found a minimal relationship

between transformational leadership and personal teacher efficacy. They posited that

self-efficacy develops through experiences external to the work environment and also

suggested that the ability of leaders to create a positive workplace enhances personal

teacher efficacy. Leadership characteristics may not suggest a particular personality type;

however, leadership styles do have some correlation to characteristics that influence

employee involvement and their perceptions of effective outcomes within rapidly

changing work environments.

Chung and Lo (2007) found that a combination of transformational and

transactional leadership styles can influence the effectiveness of management, internal

communications, and the financial structure of organizations. They surveyed employees

and volunteers of nonprofit organizations within Taiwan. Although the sample of 77

respondents was small, reliability was high (0.959). Chung and Lo found that gender

significantly influenced leader behavior within nonprofit organizations. Females tended

to relate leader behavior as either “high-transactional” or “high-transformational” (p. 87).

The behavior of male managers had a high probability of “high-transactional and high-

transformational” ratings or “low-transactional and high-transformational” (p. 87). Chung

and Lo found evidence of an association between leader behavior and the three areas

assessed in their study (i.e., management, internal communications, and financial

structure). In contrast, Barbuto, Fritz, Matkin, and Marx (2007) found gender is only one

variable in determining leadership style. Education and age may be contributing factors.

38

Feinberg et al., (2005) conducted research that included 68 managers, 285

subordinates, and 495 peers within a midsized U.S. financial organization. They

distributed a questionnaire designed specifically for their study that addressed the specific

needs of the organization and an additional survey to measure leadership attributes (i.e.,

the Leadership Assessment Inventory). Feinberg and colleagues found that the

transformational behavior of leaders must align with environmental factors to create a

collective mindset. They discovered that leader behavior and communication drives the

perceptions of subordinates.

Ben-Zur, Yagil, and Oz (2005) associated leadership style with the perceptions of

subordinates during organizational change. They surveyed nine kibbutz communities

within northern Israel and 270 questionnaires were returned. They found that

transformational leadership positively influenced the ability of employees to cope with

such change. Boerner, Eisenbeiss, and Griesser (2007) surveyed 91 leaders from 91

different German companies involved in various industries. They posited that leadership

behavior influences the behavior of subordinates. Boerner and colleagues also found

evidence that transformational leadership influenced employee innovation. Behavior may

be a viable indicator in determining factors that lead to successful organizations.

Understanding the association between leadership styles and various

organizational factors continues to be of research interest, particularly the manner in

which leadership styles influence decisions and the ultimate direction of the enterprise.

The environment created by leaders influences the level of employee contribution.

Although the research reviewed focused on leadership, the investigations did not include

analysis of the formulation and implementation of organizational change. Through this

39

research, a greater understanding of leadership may be revealed in terms of change

management, specifically as it relates to decision making and technology readiness.


Vroom (2003) speculated that behavior is a product of habit. He did not view a

particular pattern of behavior as a problem unless the environment in which the habit was

cultivated changed. Vroom conducted research in which management decision making

was matched with the problem being solved. Through his research he designed a

decision-making model. One end of the continuum identified the ability of individuals to

make independent decisions. The other end addressed individuals who included others in

decisions due to a manager’s lack of knowledge or a need for information. Vroom

suggested that the model was inappropriate for consideration due to management issues

that had multiple considerations and the complexity of the decision-making process,

including the magnitude of management responsibilities.

Simon et al., (2004) hypothesized that the manner in which individuals frame a

decision in conjunction with other variables determines whether thoughtful decisions are

manifested. They described the following three types of framing: (a) risky choice, (b)

attributes, and (c) goals. However, the research assessed solely risky-choice framing.

With this type of framing, an alternative is framed in either a positive or negative light.

Simon and colleagues expanded their previous studies that examined the need for

cognition (NC), which they defined as “an individual’s propensity to enjoy and engage in

thought” (p. 78). They conducted two independent studies that incorporated mathematic

variables. The first included 206 participants who completed all survey requirements. The

40

sample was composed of undergraduate psychology students who responded to three

decision-related problems, a questionnaire known as the NC Scale, and a

self-reported assessment of math ability. The second study included 257 participants who

completed four decision problems, the NC Scale, and four math assessments (i.e., the

Math Self-Efficacy Scale, the Math Anxiety Scale, a self-rated math ability test, and a

standard aptitude test).

Simon et al., (2004) found the same results as in previous studies. High NC did

not indicate a framing effect. One of the limitations of the studies is that participants were

asked to explain their thought process at the conclusion of the problem-solving exercise.

Simon and colleagues found that the participants continued to think through the process

while providing this explanation. Deep processing and high NC were evidenced to be less

susceptible to framing. Simon and colleagues struggled to find a way to assess the

decision-making process with consistency; however, measuring decision-making styles

did indicate a pattern of thought during decision making.

Spicer and Sadler-Smith (2005) posited that decision-making styles expose deeper

personality factors. They conducted two independent studies with a total of 200

undergraduates, who attended one of two business schools. They measured the decision-

making styles of students who participated in the study and used an assessment tool

designed by Scott and Bruce (1995). Although Spicer and Sadler-Smith confirmed the

validity and reliability of the GDMS Scale, they encouraged additional research with the

instrument. The scale was used in this study, which could increase validity and reliability

of the tool. The cross-sectional, correlation quantitative study assessed a sample of

organizational leaders rather than university students.

41

Nutt (2006) explored individual personality types in relation to public- and

private-sector organizations via a study of managers within both sectors. The 103

private-sector and 134 public-sector managers responded to questions related to specific

scenarios. Nutt developed one scenario for the private managers and another for the

public managers, all of whom subsequently completed a Meyers-Briggs questionnaire.

Categorization of decision-making types was a major focus and accomplished by analysis

of the dimensions of personality types. One set of dimensions—sensor and intuitor—

identified how individuals absorbed information. Another set of

dimensions—thinker and feeler—determined the manner in which individuals made

decisions. Nutt developed the following four types of decision preferences: ST (i.e.,

analytic), NT (i.e., speculative), SF (i.e., consultive), and NF (i.e., networked).

Nutt (2006) found a difference between private- and public-sector decision

making. He suggested that the risk public-sector managers were willing to take was not

valued. Tradition and the ability to work effectively with meager funding were the most

valued characteristics within the public sector. Nutt posited that private-sector managers

invest too much time on analysis and an insufficient amount of time on negotiation. He

viewed public-sector managers as overinvested in bargaining and in need of greater focus

on networking. Nutt suggested that additional variables increase the complexity of both

the public sector in general and successful decision making by leaders. Elected officials

make inquiries without following through on decision-making requests, and external

groups can “derail” decisions. Leaders within the public sector must include stakeholders

in the decision-making process more than is necessary within the private sector. The

organization that participated in the quantitative study is within the public sector and

42

practices a collaborative governance model. The study may contribute to a clearer

understanding of not only public-sector organizations, but also the decision-making styles

of managers within public entities.

Brousseau, Driver, Hourihan, and Larsson (2006) found that decision-making

styles appropriately change as leaders advance in their careers. If managers do not adjust

their decision-making styles, a lack of willingness to change is portrayed that can affect

their future careers. Executives, managers, and business professionals from Fortune 100

companies comprised the 120,000 participants of the Brousseau et al., study. The

researchers assessed behavior associated with decisive, flexible, integrative, and

hierarchic management types. Brousseau et al., found cultural differences in decision-

making styles at various management levels. They suggested that the styles are also

effective at varying levels of management. The knowledge gained through the Brousseau

et al., study may provide a mechanism for organizations to identify future leaders and

gain a clearer understanding of varying training needs at different management levels.

The research may contribute to the existing base of knowledge surrounding differing

levels of management and the relationship of decision-making styles.

Michel (2007) suggested that organizations need to understand decision making to

delegate decision-making activities to employees with success. As organizations increase

in complexity, employees make a greater amount of decisions at the customer level that

affect the overall enterprise. Michel emphasized the difficulty of measuring the decision-

making construct. He suggested that performance, culture, and standards of formal

decision making are the optimal measures. Michel designed a scorecard to monitor

multiple factors of organizational success. Brousseau et al., (2006) defined leaders who

43

maximize information, such as with a balanced scorecard, as either hierarchic or

integrative in their decision-making styles, depending upon the focus of the decision

maker. The Michel concepts do not address the unifocus or multifocus of the decision

maker.

Kao and Kao (2007) assessed 87 Taiwanese executives via administration of the

LBDQ and the GDMS Scale. Although the situational leadership model demonstrated a

positive correlation with the rational decision-making style, the total situational

leadership style demonstrated a negative correlation with the decision-making style

model. Dainty (1986) questioned the theory grounding the situational leadership model

and identified it as a training tool while questioning its use for research. The MLQ was

administered in this study, which measured the transformation, transactional, and laissez-

faire leadership styles. Transformational leadership styles have been associated with

effective leaders in changing organizations. Organizational environments in a state of

flux place great importance on the manner in which leaders formulate ideas and select

alternatives. Similar to the Kao and Kao research, this study administered the GDMS

Scale to measure the decision-making styles of the leaders. The research may contribute

to a better understanding of the relationship between leadership styles and decision-

making styles within public entities implementing various technologies.


Technology implementation has been studied with a focus on various industries

such as manufacturing (Karami et al., 2004; Laosirihongthong & Dangayach, 2005);

service (Genus, Rigakis, & Dickson, 2003; Owen & Demb, 2004); and health care

(Saleem, Jones, Tran, & Moses, 2006). Researchers ( Karami et al., 2004;

44

Laosirihongthong & Dangayach, 2005) have examined the ability of the manufacturing

industry to increase productivity. Study of service industries has explored the effects of

technology on the delivery of customer service (Genus et al., 2003; Owen & Demb,

2004). Champy (2003) suggested that technology implementation enhances the customer

experience, as well as the efficiency of organizational operations.

Yang, Ting, and Wei (2006) suggested that employees also contribute to

successful technology implementation. Schraeder and colleagues (2006) studied the

effects of technology on employee attitudes and found that, when employees understand

the nature of ongoing change, implementation reaps far more success. Tiong (2005)

suggested that, if leaders understood deep-rooted employee emotions, the likelihood of

implementation success would increase. Harper and Utley (2001) found that addressing

behavioral factors creates an environment conducive to successful IT implementation.

This study may contribute to the base of existing knowledge surrounding technology

implementation, specifically as it relates to technology readiness.

Harper and Utley (2001) found that cultural attributes contribute to successful

technology implementation. They conducted a 3-year study with 18 governmental and

commercial organizations. To measure culture influence, they used the Organizational

Culture Profile. Harper and Utley incorporated the Information Technology Profile to

assess successful information technology implementation. Employees responsible for

information systems (i.e., the information officer and information managers) completed

the surveys. Harper and Utley found a positive correlation between information

technology and organization culture. They identified the following indicators: autonomy,

trust, team orientation, flexibility, and information sharing. Other factors exhibited a

45

negative correlation greater than - 0.50 (i.e., rule orientation, compliance, carefulness,

preciseness, and predictability).

Leaders can increase the success of technology implementation with the

development of autonomy, trust, team orientation, flexibility, and information sharing

(Harper & Utley, 2001). The study differs from that conducted by Harper and Utley in

two respects—the measurement tools and the participants. Harper and Utley measured

the success of technology implementation, whereas the cross-sectional, correlation,

quantitative study measured technology readiness. Participants in the Harper and Utley

research were technology-specific leaders. A broader study of all organizational leaders

may provide a clearer understanding of the organizational perspective.

Kontoghiorghes (2005) explored organizational factors that could facilitate or

prohibit rapid technology implementation by administering a questionnaire to 198

employees within a U.S. auto-manufacturing company. The questions assessed work

environment, product quality, employee productivity, organization innovation, and rate of

change adaptation. The results evidenced that innovative risk taking typically equates to

organizational acceptance of new technology. Such organizations also tend to accept

participatory and open communication. Kontoghiorghes also found that leadership plays

an important role in facilitating the development and execution of implementation plans.

The inclusion of employees in this type of change initiative increases employee

satisfaction, which can increase the success of new-technology implementation.

Lines (2004) postulated that the ability of a manager to blend autocratic and

participative decision-making styles facilitates the successful implementation of strategic

change. Saleem et al., (2006) examined the relationship between employees with

46

knowledge of a particular function and the success of new-technology implementation.

Status congruence theory suggests a relationship exists between system users and the

influence of users on the respective system (Saleem et al., 2006). Through use of

employees who understood the systems of the organization, the Saleem et al., study-site

organization realized successful technology implementation. The research will not

include subordinates in the sample, but rather, focuses on organizational leaders. As

Kontoghiorghes (2005) emphasized, leaders have a significant influence on technology

implementation as their decisions guide others toward success.

Ke and Wei (2006) identified three critical factors in the success of technology

implementation: (a) leadership, (b) top-management support, and (c) change

management. They surveyed employees from two Chinese manufacturing firms with

relatively the same number of employees. Data collection involved multiple methods

(i.e., structured and semi-structured interviews, archival sources, and observations). Their

comparative case study investigated causal explanations for Enterprise System (ES)

implementation typically completed on time and within budget. Ke and Wei hypothesized

that a lack of understanding surrounding technology explains poor outcomes in

implementation. They explored the following four constructs: (a) implementation-

knowledge acquisition, (b) information distribution, (c) information interpretation, and

(d) organizational memory.

Ke and Wei (2006) interviewed 12 employees from a computer and

computer-peripheral manufacturing company of 800 total employees, which was located

within South China. The interviewees consisted of three senior managers, four middle

managers, and five line workers. The organization was undergoing a transformational

47

change due to corporate growth. A transformational vision had been formulated and

strong advocacy existed across the firm. A top-management team actively participated in

decision making. Committees were formed to work on various aspects of

implementation. Management instituted strict rules in terms of ES. The participants

surveyed rated the environment as trusting, cohesive, and enthusiastic.

The other corporation Ke and Wei (2006) surveyed was a multinational

electronics manufacturing company located within North China and employed 750

individuals. The leadership allowed 12 employees to interview for the study—three

senior managers, five middle managers, and four line workers. ES provided management

with a means to obtain information. However, a highly skilled team located in a different

location made decisions surrounding the system, management limited dissemination of

communication, and knowledge surrounding the system remained primarily within the

team. The team lacked formal structure and was not restricted by common rules

associated with ES. Participants described the environment as competitive, mistrusting,

resistant, and suspicious.

Ke and Wei (2006) found that both of the participating organizations met their

objectives; although, implementation was more difficult within the North China

organization than it was for the South China company. Employees of the South China

organization improved processes and made changes after implementation without the

additional cost of consultants due to the knowledge of leaders and employees surrounding

the system. In contrast, employees of the North China organization relied upon experts

external to their location; consequently, minimal knowledge remained within the

organization. Ke and Wei posited that the organizational mind-set of the South China

48

company contributed to the successful system implementation experienced by the study

site. Various approaches will arrive at successful system or technology implementation.

The cross-sectional, correlation quantitative study focused on one organization located

within the United States. The research examined one component of innovation

implementation, which was technology readiness.

Technology readiness has been defined as the existing human resource and

information technology infrastructure to implement a technological change with

efficiency (Zhu et al., 2006). Zhu et al., developed a conceptual model to illustrate the

process of innovation assimilation into the e-business industry. They conducted telephone

interviews with members of three different industries within 10 different countries. The

initial interviews resulted in a final data set of 1,857 participants. Zhu and colleagues

formulated questions from within three different contexts: (a) technological, (b)

organizational, and (c) environmental. Seven factors were addressed. The technology

context contained two factors. They were technology readiness and technology

integration. Firm size, global scope, and managerial obstacles defined the organizational

context. The environmental context was comprised of two factors—competition intensity

and regulatory environment. Zhu and colleagues also defined the following three stages

of assimilation: (a) initiation, (b) adoption, and (c) integration.

Technology readiness correlated positively with the three stages of assimilation

(i.e., initiation, adoption, integration) (Zhu et al., 2006). Adoption and integration

correlated to a higher degree than initiation (0.47 and 0.48 versus 0.20) (p. 1568).

Technology readiness also correlated positively and stronger with the total sample

compared to the other seven factors examined. In developing countries and newly

49

industrialized nations, technology readiness related positively with a significant

correlation. Zhu et al., suggested that managers develop the infrastructure to support

planned technology. Adapting processes, structures, and practices according to the stage

of technology assimilation is important. Zhu et al., also suggested that the environmental

context is a determining factor in innovation assimilation. The study was smaller in scope

than the Zhu et al., research that tested a comprehensive model and investigated the

relationships among multiple technology concepts. The research measured solely one

construct—technology readiness.

Matthing, Kristensson, Gustafsson, and Parasuraman (2006) conducted two

studies to examine consumer technology readiness. The first study consisted of 1,004

Swedish adult consumers who completed the TRI. The second study consisted of 52

university campus participants who completed an abbreviated TRI. In both studies,

mobile phones were the selected technology examined. Matthing et al., found that the

propensity of an individual to a given technology is a predictor of adoption of the

innovation. High technology readiness suggested innovative attitudes and the ability to

generate new ideas for services. The research implemented the same measurement tool

(i.e., the TRI); however, the study site was in a different geographical location and the

population sample was comprised of leaders in a specific location rather than random

consumers. The research met the need to examine technology readiness outside the

marketing industry (Matthing et al., 2006).

Research Gap

Higgs (2003) emphasized that exploring and examining the components of

effective leadership contributes to innovative management techniques. A wealth of

50

knowledge exists surrounding management and technology innovation. Orlikowski and

Barley (2001) expressed concern over the lack of study integration related to leadership

and information technology. Researchers within the information technology discipline

have recognized a connection between successful technology implementation and

leadership; the juxtaposition of technology and leadership has evolved gradually.

Studying the two constructs jointly may present a valuable contribution to the existing

knowledge base encompassing information technology, leadership, and organizational

theory.

Spicer and Sadler-Smith (2005) recommended further research into

decision-making theories. Bass (2007) indicated that leader understanding of decision

making could influence the success of strategic organizational change. As leaders gain

increased understanding of decision-making styles, they may institute processes that

better serve various levels of leadership (Brousseau et al., 2006). Nutt (2006) posited that

middle managers need education to understand the complexity of decisions at higher

levels. Within the public sector, it is critical that leaders understand effective leadership

methods. Littrell, Zagumny, and Zagumny (2005) reported that research focused on

public schools has centered on the introduction of technology to customers (i.e.,

students). Minimal attention has been given to the business and administrative operations

of the public-school arena as it seeks to meet the needs of a complex organization

through technology. As demands placed on leaders of public organizations increase,

technology formulation and implementation research will become increasingly valuable

to managers with the responsibility of leading such organizations (Finn & Hess, 2004).

The cross-sectional correlation quantitative study encapsulated the three concepts of

51

leadership, decision making, and technology. The results may assist organizational

leaders as they implement new technology and/or increase their understanding of the

capacity and capabilities needed to grow as leaders.

Conclusion

The leadership construct has evolved over the years (Kao & Kao, 2007). Further

examination of the construct is critical given changing operations within organizations

(Bass, 2007). Snow and Hamrick (1980) noted a competency reserved for leaders was

strategic decision making. Simon et al., (2004) found observing decision-making

processes was difficult due to its dynamic nature. By examining decision-making styles,

researchers (Cowan, 1988; Driver et al., 1996; Scott & Bruce, 1995; Weick, 1980) began

to understand those characteristics associated with the choices leaders make during the

formulation of a problem.

With the introduction of technology in the workplace the expectation of leaders to

make decisions and implement them strategically has changed (Kontoghiorghes, 2005;

Michel, 2007). Ke and Wei (2006) found evidence the role of leadership is a contributing

factor during innovative implementations such as new technology. The study examined

the relationship between leadership styles and the lesser-researched topics of decision-

making styles and technology readiness.

Summary

Bass (2007) suggested top executives formulate strategy and middle managers

implement strategic decisions. Jasperson et al., (2005) emphasized the need for manager

involvement in technology innovation and the importance of reflecting on

implementation toward improved processes. The transformational leadership style

52

delivers a concept or vision to others and influences subordinates toward its ultimate

achievement (Feinberg et al., 2005). A gap remains within the literature between leader

formulation and implementation of innovation. The study focused on technology and may

provide further knowledge into the factors associated with rapidly changing

environments.

The formulation process results in a decision (Cowan, 1988). The ability to bring

forward a decision and lead others to take action equates to implementation (Mintzberg &

Waters, 1985). Transforming an enterprise through formulation and implementation has

been difficult for a great number of organizations (Clegg et al., 1997). Understanding

how decisions are formulated may be valuable to leader development and ultimate

organizational success (Vroom, 2003). Michel (2007) posited leaders who understand the

formulation of ideas and the implementation of innovation can create a competitive edge

for organizations. Organization complexity necessitates a clear understanding of the

change process during innovation implementation to increase the likelihood of success. It

is important to establish an effective methodology to fulfill the purposes of research

addressing such a need.

53

CHAPTER 3: METHOD

The purpose of the correlation quantitative study was to determine the degree of

association between the independent variables and the dependent variables within an

organizational environment experiencing an increase in technology implementation. An

understanding of the relationship between the independent and dependent variables could

increase current knowledge surrounding leadership theory and the ability of organizations

to understand factors influencing successful change initiatives. Although decision-

making theory was the result of years of research, the construct of decision-making style

was a new concept (Scott & Bruce, 1995). As technology use increased, the need to

understand decisions related to technology implementation also increased (Parasuraman,

2000). The idea of technology readiness has also evolved as a construct requiring further

study.

Research Method

The areas of examination in the study are (a) leadership style,

(b) decision-making styles, and (c) technology readiness. Research using quantitative

methods defined predetermined variables as an aspect of the design (Creswell, 2003). The

variables are unknown in a qualitative research design (Cooper & Schindler, 2003); they

develop during exploration of a central phenomenon (Creswell, 2003). Qualitative

research provides a deeper understanding of a phenomenon. In the study, the identified

variables were examined to determine the degree to which a relationship exists. Study of

the relationship between variables supports a quantitative study (Cooper & Schindler,

2003; Creswell, 2003). A correlation quantitative study was cross-sectional research

within which data was collected at a given point in time rather than dynamic collection,

54

which is when a behavior is measured over intervals of time (Cooper & Schindler, 2003).

Correlation studies offer the opportunity to analyze the relationship between two or more

variables. It also provides a measurement of the degree of association between variables

(Creswell, 2003).

As noted earlier, Kao and Kao (2007) surveyed 87 Taiwanese executives to study

the relationship between an independent variable (i.e., leadership style) and a dependent

variable (i.e., decision-making styles). The research was a correlation study using the

Pearson product-moment correlation coefficient to measure the association between

variables. Kao and Kao assessed participating leaders via administration of two

questionnaires. Although the situational leadership model demonstrated a positive

correlation with rational decision-making style, the total situational leadership style

demonstrated a negative correlation with the decision-making style model. Thus, a

correlation study may provide evidence of an association between variables. The

variables in the study were examined to determine if a relationship exists and to what

degree. Scholarly research regarding decision-making styles and technology readiness is

non-existent. Spicer and Sadler-Smith (2005) recommended further research into

decision-making theories. Kontoghiorghes (2005) posited decision making influences

technology implementation.

Design Appropriateness

The research examined the correlation between leadership style and decision-

making styles in an organization implementing new technologies. Correlation between

leadership style and technology readiness was measured via a quantitative examination of

variables (Creswell, 2003). The data-collection tools were three questionnaires.

55

Leadership style was assessed through the MLQ, Form 5X, which measured

transformational, transactional, and laissez-faire leadership styles. Avolio et al., (1999)

conducted validity and reliability tests on the MLQ with 3,786 respondents. They

confirmed “a high degree of consistency in estimates of reliability, intercorrelations, and

factor loadings” (p. 458). Analysis for discriminate and convergent validity exceeded

desired levels.

Decision-making style was assessed with the GDMS Scale. Scott and Bruce

(1995) conducted validity and reliability tests with four study samples consisting of 1,943

respondents. They also conducted internal-consistency and factor-stability analyses on

three samples with excellent results. Scott and Bruce found evidence of face validity and

logical content validity. Technology readiness was assessed in the study with the TRI.

Parasuraman (2000) developed this tool to measure the technology readiness of

consumers and as a psychometric tool for the National Technology Readiness Survey.

The survey has been distributed annually to consumers since 2001 and provides a

statistically valid and reliable instrument for assessing technology readiness (Colby &

Parasuraman, 2003; Matthing et al., 2006; Parasuraman, 2000). The three questionnaires

were used in the study as tools to survey leaders of a large rural school district. The

process for data collection was

1. Leaders received a notice requesting their participation in the study.

2. Participating leaders were asked to complete the three questionnaires.

3. The researcher scored two questionnaires and an external firm scored the TRI.

Scores from administration of the MLQ and GDMS Scale were checked. The results were

analyzed through descriptive and inferential statistics. Descriptive calculations were

56

median, mean, and standard deviation. The inferential statistical analysis was calculated

by a correlation-coefficient computation. Upon return of the TRI from an outside agency,

the MLQ and TRI results were analyzed through descriptive and inferential statistics. In

addition, the GDMS and TRI were analyzed as prescribed by the corresponding research

question. Descriptive calculations were mean, minimum, maximum, and standard

deviation. A normality test was run to determine skewness and kurtosis. The inferential

statistical analysis was calculated by a correlation-coefficient computation.

The research was designed to measure the collective independent variable of

leadership style and the degree to which the independent variable influenced two separate

dependent variables—decision-making style and technology readiness. In addition, the

research design was structured to measure the association between decision-making

styles and technology readiness.

All three of the measurement tools used in the study have undergone extensive

validity testing in past research (Avolio et al., 1999; Parasuraman, 2000; Scott & Bruce,

1995). As described earlier, the MLQ has been used with numerous samples and tested

with various validity factors in differing cultures and industries (Avolio et al., 1999;

Hetland & Sandal, 2003; Hirtz et al., 2007; Nir & Kranot, 2006). The GDMS Scale has

not had the same extensive validity testing; however, evidence of high validity exists

(Kao & Kao, 2007; Scott & Bruce, 1995). The TRI has been administered to a large

number of consumers located in various regions of the United States with significant

evidence of instrument validity (Colby & Parasuraman, 2003; Matthing et al., 2006;

Parasuraman, 2000). In the study, the consideration of validity was assessed through

observation and the implementation of good research techniques. Cooper and Schindler

57

(2003) discussed two specific validity factors of experimentation models—internal and

external. Internal validity issues were minimized in the research, because the same

participants within two specific settings completed all of the surveys within

approximately one hour following a leadership breakfast or prior to a regularly scheduled

in-service meeting.

Population Sample

The population sample in the study was composed of approximately 160 leaders

within a large rural school district. Convenience sampling was used to form study groups

of leaders (i.e., cabinet members, coordinators, directors, facilitators, interns, managers,

principals, professional development facilitators, and supervisors). Teachers who

expressed an interest in leadership may have also comprised the leadership group.

Creswell (2003) suggested a sample size of 30 individuals as appropriate for a correlation

study. At least two data scores or observations were needed for each participant. In the

study, 109 participants completed at least three data scores, and the sample was

categorized into three leadership styles—transformational, transactional, and laissez-

faire. To address the hypotheses, those participants who scored high on the

transformational leadership style comprised an appropriate sample size of 34

respondents.

Given the innovative environment of the organization that served as the research

site, the study criteria were met. The school district is a public school system that has

adopted a collaborative approach to problem solving. During 2001, the board of trustees,

cabinet, and employee association groups approved a document known as the Compact.

The Compact defined a shared decision-making process. Stereotypically, public

58

employees are described as unmotivated and low risk-taking civil servants (Pattakos,

2004). Bureaucratic and hierarchical are common descriptors within these environments

(Park, Ribiere, & Schulte, 2004; Pattakos, 2004; Wright, 2001). Leaders of the large rural

school district who participated in the research changed their style of decision making

from a bureaucratic to a collaborative approach.

The Compact is a document defining the philosophy of the school district and the

shared governance model that includes decision making by consensus between employee

organization groups, administration, students, community, and the board of trustees. This

document is often referred to as the Compact of Trust, which is in reference to the

agreement that all involved parties can rely upon the understanding that all will follow

the terms of the Compact. The 11 philosophical components of the Compact are visionary

leadership, learning-centered education, organizational and personal learning, valuing

and trusting the relationship between staff and district partners, organizational agility, a

focus on the future, managing innovation, management by fact, public responsibility and

citizenship, a focus on results, and creating value. The guiding principles of the Compact

are a commitment to success for each student, honoring the Interest-Based Agreement

Process (IBAP), continual shareholder training, a system approach, and stakeholder

satisfaction. The Compact also defines the committees that will facilitate all work of the

Compact such as the Steering Committee, the Compact Issues Committee, and the

Problem Solvers.

Organizations that support broad participation in strategic change typically

demonstrate successful implementation (Lines, 2004; Marino, 2007; Vroom, 2003). The

administration in the school district study-site has created an environment wherein all

59

employees have the opportunity to participate in the decision-making process. Employee

inclusion in the problem-solving process increases the alternatives for solutions and

provides a diverse knowledge base (Meyer & Stensaker, 2006). The participatory

decision-making style of the school district complements the changing nature of the

organization.

Lavelle (2006) examined the gap between public and private sectors in

relationship to the role of the manager during change. Government regulations and

accountability expectations of the public have narrowed the differences between private

and public organizations (Wright, 2001). Change in public and private companies

continues to be of interest (Karl, Peluchette, Hall, & Harland, 2005; Lavelle, 2006). The

school district that participated in the research has received federal and state funds to

increase the technological capabilities of the organization. Leaders include employees in

technology implementation within business/human resource operations, virtual

classrooms, and digital classrooms. With the introduction of machines into a culture that

values human contact so highly, the success of implementing technology may be limited

by the willingness of humans to accept machines (Lin & Hsieh, 2006). The study may

enlighten organizational leaders as to the factors that increase technology acceptance.

Data Collection

All participants in the research were asked to complete an informed-consent form

prior to the onset of the study. They were reminded that participation was strictly

voluntary, and they could choose to withdraw their participation at any time. The

informed-consent form also communicated that all information provided by the

participant would remain confidential and that all materials would be maintained within a

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locked cabinet in the office of the researcher. After five years following study

completion, the surveys would be destroyed.

The research was limited to leadership at a large rural school district. The

administrative offices are located within one city with several smaller towns also under

the jurisdiction of the district, which serves approximately 12,000 students and employs

approximately 2,500 individuals. Leaders choosing to participate received a manila

envelope with a number assigned to the packet. The packet included six documents—a

demographics form, a letter of introduction (see Appendix A), an informed-consent form

(see Appendix B), an MLQ questionnaire, a GDMS Scale, and the TRI. A box was

placed on the bottom of the introductory letter where participants could request a

summary of the study upon completion of the research. In addition, a presidential coin

was placed in the envelope as a thank you for participating in the survey.

The data-collection tools were distributed at two different sessions. One session

was following a leadership breakfast. The second session was prior to the beginning of a

leadership in-service meeting. Participants at each event were asked to complete the

MLQ and to answer the questions honestly and with their best understanding. The MLQ

survey completion was expected to consume approximately 20 minutes. Following a 5-

minute break, the participants were asked to complete the GDMS Scale and the TRI;

again, they were reminded to answer the questions honestly and with their best

understanding. The surveys were expected to consume approximately 35 minutes. The

times varied by participant. The total time to complete all three surveys ranged from 20

minutes to 45 minutes.

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An explanation was provided regarding internal scoring of the MLQ and GDMS

Scale. In addition, participants were told the TRI surveys would be sent to an external

association for scoring. The survey respondents were asked to place the demographics

form, informed-consent form, the MLQ, GDMS Scale, and the TRI in the manila

envelope. If an executive summary of the study was desired, the respondent also placed

the introductory letter back into the envelope. The manila envelopes were collected and

all participants were thanked for their participation and reminded to keep the coin.

The survey tools used for the research provided tested psychometric instruments

that measured the constructs of leadership styles, decision-making styles, and technology

readiness. The data-collection method allowed for an efficient collection process with a

brief turnaround period to gather data. All variables were measured within a short time

from collection of the data and analysis began immediately following collection. A

minimal delay resulted from the scoring of the TRI.

Instrumentation

Leadership Styles

Authorization to use the MLQ, Form5X (see Appendix C), was purchased for the

research. The MLQ, Form 5X instrument presented 45 items with a 5-point Likert-type

response scale ranging from not at all to frequently, if not always. The six leadership

styles addressed were (a) charisma/inspirational, (b) intellectual stimulation, (c)

individualized consideration, (d) contingent reward, (e) active management by exception,

and (f) passive avoidant (Avolio et al., 1999). Charisma/inspirational leadership involves

the provision of a vision, role models, ethical standards, and a clear direction.

Charisma/inspirational leadership was identified on the survey by idealized influence

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(attribute and behavior) and inspirational motivation. Intellectual stimulation creates an

environment that encourages others to question problem-solving methods and explore

alternate methods for task completion. Individualized consideration understands

employee needs and challenges each individual to reach his or her full potential.

Contingent reward clarifies performance expectations and rewards. Active management

by exception focuses on task completion and addresses problems to maintain

performance levels. Passive management by exception and laissez faire leadership reacts

to problems after they become serious and avoids decision making.

Since inception of the MLQ during 1978, researchers have continued to evaluate

the validity and reliability of the instrument, which has several versions (Avolio et al.,

1999). Avolio et al., analyzed the MLQ, Form 5X, with a confirmatory factor analysis

and found a significant goodness-of-fit ratio for the six-factor model. These researchers

also confirmed earlier findings that supported reliability of the instrument. The MLQ

(Appendix D) was appropriate for the research, because it measured transformational,

transactional, and laissez-faire leadership styles. Research (Ben-Zur et al., 2005; Hetlund

& Sandal, 2003; Ilies et al., 2006; Pearce et al., 2003; Spinelli, 2006; Woods, 2007) had

evidenced that transformational leadership styles demonstrate effective behavior within

innovative and changing environments.


Authorization for use of the GDMS Scale (see Appendix C) was obtained from

one of the creators of the instrument (see Appendix D). This scale presented 25 items

with a 5-point Likert-type response scale ranging from strongly disagree to strongly

agree. Scott and Bruce (1995) identified the following five decision-making styles

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addressed by the tool: (a) rational, (b) intuitive, (c) dependent, (d) avoidant, and (e)

spontaneous. Rational decision making is characterized by “a thorough search for and

logical evaluation of alternatives” (p. 820). Intuitive decision making is characterized by

“a reliance on hunches and feeling” (p. 820). Dependent decision making is characterized

by “a search for advice and direction from others” (p. 820). Avoidant decision making is

characterized by “attempts to avoid decision-making [sic]” (p. 820). A spontaneity

decision-maker “has a sense of immediacy and a desire to get through the decision-

making process as soon as possible” (p. 823).

Scott and Bruce (1995) tested the GDMS Scale for validity and reliability. They

analyzed validity with correlation measure and an analysis of variance using scale

dependence, concurrent analysis, and construct assessment. Reliability was analyzed by

comparing independent sample groups. Spicer and Sadler-Smith (2005) confirmed the

validity documented in previous research. The scale was appropriate for use in the study,

given the dual purpose of the variable of decision-making style. Such styles demonstrate

a pattern of choice in alternatives, which allows researchers to understand interval data

that categorizes decision-making styles in a manner conducive to drawing conclusions

related to how leaders formulate decisions.


The TRI was the third tool distributed in the research and measured technology

readiness. Authorization was obtained from the developer (see Appendix C). The TRI

(see Appendix D) presents 36 items with a 5-point Likert-type response scale ranging

from strongly agree to strongly disagree. The four technology readiness dimensions

addressed by the instrument are (a) optimism, (b) innovativeness, (c) discomfort, and (d)

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insecurity (Parasuraman, 2000). According to Parasuraman, optimism is “a positive view

of technology and a belief that it offers people increased control, flexibility, and

efficiency in their lives” (p. 311). Innovativeness is the “tendency to be a technology

pioneer and thought leader” (p. 311). Discomfort is “a perceived lack of control over

technology and a feeling of being overwhelmed by it. . . . Insecurity is distrust for

technology and skepticism about its ability to work properly” (p. 311).

Parasuraman (2000) conducted reliability and validity testing on the TRI during a

qualitative study, and common themes arose from interviews with focus groups. With

3,000 participants (i.e., students and young professionals) who responded either by mail

or online, the scale items were evaluated. Adjustments to the 28 items within four

categories of the survey tool resulted. A factor analysis supported the four categories, and

further analysis supported high reliability of the instrument. Extensive construct-validity

analysis was also conducted, which further solidified confidence in the measurement tool.

Tsikriktsis (2004) also analyzed the TRI and supported the Parasuraman findings. The

TRI is a measurement tool used exclusively within the customer-service industry.

Implementation of the instrument in the study may further research focused on the

propensity of individuals to use technology within various disciplines. Examining the

degree of association between transformational leadership style and technology readiness

may also further existing knowledge on leadership theory.

Data Analysis

Leadership styles and decision-making styles were analyzed in the study via

application of the Pearson product-moment correlation coefficient. In addition, leadership

styles and technology readiness were analyzed with the same correlation coefficient. The

65

research was designed to determine the relationship between the independent variables

(i.e., leadership styles and decision-making styles) and the dependent variables (i.e.,

decision-making style and technology readiness). Decision-making styles and technology

readiness were analyzed with the same inferential analysis. The research design

determined a relationship between variables rather than a causal condition. Kao and Kao

(2007) researched a similar question and examined the relationship between leadership

styles and the decision-making styles of leaders. They calculated the Pearson product-

moment correlation coefficient with the use of a statistical software package and studied

executive leaders within an Asian county. The LBDQ was administered for data

collection. The same analysis with a similar software package was used in this study, but

with a different population and instrument in the measurement of leadership styles.

The hypotheses of this study were tested using the Pearson product-moment

correlation coefficient, and examined both the leadership styles (i.e.,

charisma/inspirational, intellectual stimulation, and individualized consideration) and

decision-making styles (i.e., rational and dependent) of interest. The dependent variable

of technology readiness was also tested with the Pearson product-moment correlation

coefficient. This test determined any association between variables with a continuously

normal distribution (Cooper & Schindler, 2003). The degree of relationship—whether

negative or positive in direction—can be reflected with the measure.

Summary

The described research methodology was appropriate for the correlation study and

addressed the problem of interest. A quantitative correlation study efficiently examined

the leadership styles, decision-making styles, and technology readiness of leaders within

66

a large rural school district. The six factors of the MLQ, the five factors of the GDMS

Scale, and the four factors of the TRI presented instrumentation conducive to the goals of

the data collection. Participating leaders followed a defined process during completion of

the questionnaires and were given the opportunity to request a subsequent summary of

the study. In addition, all participants received a coin for participating in the study. The

Pearson product-moment correlation coefficient was used to examine the relationship

between the variables. A thorough description of the data collection and analysis, along

with a discussion of the findings, is important.

67

CHAPTER 4: PRESENTATION AND ANALYSIS OF DATA

The purpose of the correlation quantitative study was to determine the degree of

association between independent variables and dependent variables within an

organizational environment experiencing an increase in technology implementation.

Chapter 1 provided an overview of the study. In chapter 2, a historical overview of

research and current knowledge of independent variables (transformational leadership

styles and decision-making styles) and the dependent variables (decision-making styles

and technology readiness) was explored. In addition, a gap analysis in the literature was

examined and the subsequent void the research study would fill in relationship to current

knowledge in the areas of leadership, decision making, and technology.

Chapter 3 provided an explanation of the methodology and design of the study.

The population sample in the study was comprised of approximately 160 leaders within a

large rural school district. The sample was surveyed using three different instruments:

Multifactor Leadership Questionnaire (MLQ), General Decision-Making Style Scale

(GDMS), and the Technology Readiness Index (TRI). The data analysis consisted of

calculating descriptive and inferential statistics. The inferential analysis was Pearson

correlation coefficient two-tail test. The data collection varied from the original design

due to management changes in the organization. The changes to data collection will be

explained within the chapter. In chapter 4, the analysis of the data and findings of the

study will be discussed.

Population Sample

The population sample in the study was comprised of approximately 160 leaders

within a large rural school district. The school district is a public school system that has

68

adopted a collaborative approach to problem solving. During 2001, the board of trustees,

cabinet, and employee association groups approved a document known as the Compact.

The Compact defined a shared decision-making process. Stereotypically, public

employees are described as unmotivated and low risk-taking civil servants (Pattakos,

2004). Bureaucratic and hierarchical are common descriptors within these environments

(Park, Ribiere, & Schulte, 2004; Pattakos, 2004; Wright, 2001). Leaders in the large rural

school district who participated in the research have been encouraged to change their

style of decision making from a bureaucratic to a collaborative approach between 2001

and 2008.

Convenience sampling was used to form the study population group. The group of

leaders consisted of cabinet members, coordinators, directors, facilitators, interns,

managers, principals, and supervisors. At least two data scores or observations were

needed for each participant. In the study, each participant had at least three data scores to

answer each research question. In order to answer the first two research questions, the

participants with a cumulative score of 4.0 were determined to have a transformational

leadership style. The sample of transformational leadership style was 34 participants.

The gender distribution of participants was primarily female (76%). Male

participation was 24%. A wide variation in age was demonstrated. The largest group

ranged from 45 – 55 years (a 46% distribution rate). The next two groups ranged from 55

– 65 (28%) and between 36 – 45 years of age (24%). The final group that was represented

was 5% in the 25 – 35 age range. No one was represented in the 18 to 24 age group nor in

the 65 + age group (see Figure 1).

69

05

10

1520253035

404550

18-24 25-35 35-45 45-55 55-65 65+

Age Groups

Figure 1. The sample population ranged from 25 years old to 65 years old with 45 – 55

years old having the highest distribution at 46 %.

Two leadership groups almost equally represented were the administrator group

(23%) and the supervisor/manager/coordinator group (25%) for a total of 48% of the

population. The largest represented leadership group was the other group with 46% of the

participants. The smallest leadership group was the cabinet or senior management group

(6%) (see Figure 2). Conversely, nearly 100% of the senior management group

completed the surveys and were included in the study.

70

0

10

20

30

40

50

60

Cabinet Administrator Manager Other

Leadership Groups

Figure 2. The largest leader group was in the category of other (46%). Cabinet

represented the smallest proportion (6%) but had the highest percentage represented

(100%).

Two additional areas of demographic information were collected: years of service

with the employer and number of years the employee had been in his or her current

position. Two groups of participants were the largest with over 66% of the sample

ranging in employment years with the school district from 1 to 10 years (33 %) and 11 to

20 years (34%). Participants with 21 to 30 years ranked third at 27%. Participants with

less than 1 year of service were 5% of the sample population. Those with over 30 years

with the organization represented 9% of the sample (see Figure 3).

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0

5

10

15

20

25

<1 1 - 5 6 - 10 11 - 15 16 - 20 21 - 25 26 - 30 >30Years Employed at Organization

Figure 3. The greatest span of years of employment with the organization range from one

to twenty years and represented over 66% of the sample group.

The category with the largest number of participants in their positions was 1 to 5

years (49%). Participants who had less than one year in their position represented 22% of

the population and those with 6 to 10 years was 20%. Participants who had been in their

position 11 to 20 years were 9% of the population. Twenty-one years or greater were 9%

of the sample distribution (see Figure 4).

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0

10

20

30

40

50

60

< 1 1 - 5 6 - 10 11 - 15 16 - 20 21 - 25 26 - 20 > 30Years in Current Position

Figure 4. Almost half (49%) of the participants in the study had been in their current

position for one to five years.

Data Analysis Procedures

Of the 160 possible participants, 120 participants attended one of two sessions at

which the three surveys were distributed. Of the 120 participants, results of 109 were

included in the data analysis. The 10 surveys not included were incomplete or the

participant did not give permission for the data to be used in the study. The percentage of

acceptable surveys was 78%. Participants completed three surveys to measure

independent variables (leadership style and decision-making styles) and dependent

variables (decision-making styles and technology readiness).

Leadership styles were assessed with the MLQ. The first research question,

“What is the relationship between transformational leadership styles and decision-making

styles?” and the second research question, “What is the relationship between

transformational leadership styles and technology readiness?” focused on

73

transformational leadership style. Transformational leadership styles are leader attributes

that motivate subordinates to embrace a vision identified by the leader (Feinberg et al.,

2005). The leadership styles measured by the MLQ were (a) idealized influence-

attributed, (b) idealized influence-behavior, (c) inspirational motivation, (d) intellectual

stimulation, (e) individualized consideration, (f) contingent reward, (g) active

management by exception, (h) passive management-by-exception, and (i) laissez-faire

(Avolio et al., 1999). Charisma/inspirational leadership types include idealized influence

(attributed and behavior) and inspirational motivation. Charisma/inspirational,

intellectual stimulation, and individualized consideration are transformational leadership

styles (Avolio et al., 1999).

Nine null and alternative hypotheses were tested using the Pearson correlation

coefficient. Transformational leadership styles influence on decision-making styles were

the focus of three hypotheses. An additional three hypotheses were analyzed with

decisions-making styles as a dependent variable and the final three hypotheses were

examined with decision-making styles as an independent variable. Decision-making

styles are patterns of behavior attributed to an individual when confronted with decision

making (Scott and Bruce, 1995). Leaders’ attributes influence on decision-making

patterns had equal value in the research as decision-making patterns relationship to an

individual’s propensity to use technology. Both associations addressed the questions

examined in this research. Participants completed the GDMS to determine decision-

making styles. The five decision-making styles measured by the scale are (a) rational, (b)

intuitive, (c) dependent, (d) avoidant, and (e) spontaneous (Scott & Bruce, 1995).

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An additional goal of this study was to examine how technology was influenced

by the independent variables (i.e. transformational leadership styles and decision-making

styles). Technology readiness was identified as a dependent variable. Participants

completed the TRI to measure technology readiness. Technology readiness refers to the

tendency an individual has toward technology use (Parasuraman, 2000). The four

technology-readiness dimensions measured by this instrument are (a) optimism, (b)

innovativeness, (c) discomfort, and (d) insecurity (Parasuraman, 2000).

The MLQ and GDMS surveys were scored on-site. The TRI was emailed to a

designated scoring site as required by the survey originators. The MLQ was scored as

directed. In addition, a calculation was conducted with the transformational leadership

style questions that varied from the instructions.

Avolio et al., (1999) identified the three different types of leadership styles that

were measured in the MLQ. Five leadership styles were indicative of transformational

leadership, two were indicative of transactional leadership styles, and two were indicative

of laissez-faire leadership style (Avolio et al., 1999). The survey had 45 items with a 5-

point Likert-type response scale. Scoring of the MLQ ranged from 0 to 4.00. Bass and

Avolio (2004) designed a table which scores were based on percentiles ranging from 5 to

95. Idealized influence – attributed (IIA) ranged from 2.00 to 3.75. Idealized influence-

behavior (IIB) ranged from 2.00 to 4.00. Inspirational motivation (IM) ranged from 2.00

to 4.00. Intellectual stimulation (IS) ranged from 2.00 to 3.75. Individual consideration

(IC) ranged from 2.25 to 4.00. Contingent reward (CR) ranged from 2.00 to 3.75.

Management-by-exception active (MBEA) ranged from .25 to 3.00 and passive (MBEP)

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ranged from .25 to 2.25. Laissez faire (LF) ranged from .00 to 1.50 (Bass & Avolio,

2004).

The MLQ scoring was modified for this study in order to address the research

questions. Initially, the surveys were scored according to the directions, then all questions

designated as transformational were summed and divided by 20 to determine an average

score. The scoring instructions indicated the survey questions associated with each

dimension of transformational leadership style were to be added together and divided by

the number of questions for that dimension. The result of the calculation would produce

an average. For the purpose of the study, the 20 questions designated as transformational

leadership were added together and divided by 20 to determine the average. The surveys

with a mean score of 4.0 were defined as transformational leadership style.

The GDMS and TRI scoring followed established scoring guidelines. Decision-

making styles scores ranged from five to 25 per decision-making style. The score of 25

would indicate a high propensity toward the decision-making style. The lower score of

five indicated a low tendency toward the decision-making style. The GDMS highest

score indicated the preferred decision-making style of the participant. Scott & Bruce

(1995) posited people are complex and have more than one decision-making style. Thus,

the second highest score would indicate the next preference of the participant.

TRI scores ranged from 1.00 to 5.00. Individuals would have a score for each

technology readiness dimension. Individual scores in this study for optimism ranged from

2.70 to 5.00, innovation ranged from 1.43 to 5.00, discomfort ranged from 1.50 to 4.20,

and insecurity ranged from 1.44 to 4.67. Once the technology readiness dimensions are

determined, the four areas are assessed according Figure 5 (Colby & Parasuraman, 2003).

76

The explorers are those who most likely would be the first to adopt new technologies.

Pioneers are excited to adopt new technology but foster insecurities and discomfort with

the new technology. Skeptics are not motivated to use technology but do not necessarily

have inhibitors. Paranoids are optimistic about technology and have high inhibitors.

Laggards are not motivated to use technology and have high inhibitors in using

technology.

Figure 5. Technology readiness categories are contributors (optimism and

innovativeness) and inhibitors (discomfort and insecurity).

The Pearson product-moment coefficient correlation was applied to the survey

results as had been planned from the beginning of the study. The cross-sectional

correlation quantitative study was designed to determine the relationship between

leadership styles decision-making styles, and technology readiness. The research design

chosen determined a relationship between variables rather than a causal condition. The

SPSS 16.0 software package provided the calculations to test each hypothesis.

77

Three research questions provided focus for the study. Each question had three

null hypotheses and three alternative hypotheses for a total of nine null hypotheses and

nine alternative hypotheses. The hypotheses of the study were tested with the use of the

Pearson product-moment correlation coefficient and examined both the leadership styles

(i.e., idealized influence, inspirational motivation, intellectual stimulation, and

individualized consideration) and decision-making styles (i.e., rational and dependent) of

interest. The additional dependent variable of technology readiness (i.e. optimism and

innovativeness) was measured by the Pearson product-moment correlation coefficient

two-tail test analysis. The calculation provides a means to determine an association

between variables with a continuously normal distribution (Cooper & Schindler, 2003).

The degree of relationship, whether negative or positive in direction, can be reflected

with the measure.

Transformational Leadership Styles and Decision-Making Styles

The first question was “What is the relationship between transformational

leadership styles and decision-making styles?” All participants completed the MLQ to

determine their transformational leadership style. All participants completed the GDMS

to determine their decision-making style. The three null hypotheses and three alternative

hypotheses were associated with the first research question.

H01: Transformational leadership styles have no relationship with decision-

making styles.

HA1: Transformational leadership styles have a relationship with decision-

making styles.

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Transformational Leadership Styles and Technology Readiness

The second research question was, “What is the relationship between

transformational leadership styles and technology readiness?” MLQ was used to

determine transformational leadership style, and all participants completed the TRI to

determine technology readiness dimension. The three null hypotheses and three

alternative hypotheses were associated with the second research question.

H04: There is no relationship between transformational leadership styles and


HA4: There is a relationship between transformational leadership styles and






79





Decision-Making Styles and Technology Readiness

The third research question was “What is the relationship between decision-

making styles and technology readiness?” All participants in the study completed the

GDMS and TRI to determine decision-making style and technology readiness dimension,

respectfully. Three null hypotheses and three alternative hypotheses were associated with

the third research question.

H07: There is no relationship between decision-making styles and technology

readiness.

HA7: There is a relationship between decision-making styles and technology

readiness.

H08: There is no relationship between rational decision-making style and the


HA8: There is a relationship between rational decision-making style and the


H09: There is no relationship between dependent decision-making style and the


HA9: There is a relationship between dependent decision-making style and the


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Findings

The survey data sets were calculated to determine the descriptive statistics for

each of the key variables. In addition, a calculation was completed to determine the

degree, which the data conformed to a normal-shaped curve (skewness and kurtosis).

Table 1 reflects the summation of the descriptive and normality calculations.

Table 1

Descriptive Statistics and Normality Tests of Key Variables

M SD Min Max Skewness Kurtosis

Transformational Leadership Styles (N=110)

IIA 3.05 .54 1.50 4.00 -.23 -.43

IIB 3.28 .49 2.00 4.00 -.43 -.28

IM 3.35 .46 2.25 4.00 -.44 -.86

IS 3.20 .45 2.00 4.00 -.29 -.53

IC 3.42 .47 2.00 4.00 -.76 -.01

Decision-making Styles (N=110)

Rational 22.35 2.15 15.00 25.00 -.72 .22

Intuitive 22.65 3.34 11.00 29.00 -.44 .45

Dependent 18.33 3.74 8.00 25.00 -.57 .53

Avoidant 8.82 4.10 5.00 20.00 1.23 .80

Spontaneous 10.67 3.89 5.00 20.00 .48 -.59

Technology Readiness (N=109)

Optimism 4.07 .50 2.70 5.00 -.27 -3.85

Discomfort 2.63 .65 1.50 4.20 .45 -.59

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Table 1 (continued)

M SD Min Max Skewness Kurtosis

Innovative 3.31 .83 1.43 5.00 -.03 -.71

Insecurity 3.06 .70 1.44 4.67 .23 -.54

Of the 110 participants who completed the MLQ in the study, 34 (31%) of the

participants scored a mean of 4.0 on the MLQ. The 4.0 mean was determined to have a

transformational leadership style. The analysis of the scores of the 34 participants with a

transformational leadership style indicated no correlation between transformational

leadership style and decision-making style or transformational leadership style and

technology readiness. The data analysis with the sample studied suggested a relationship

does not exist between the variables. The evidence may indicate transformational

leadership does not influence a leader’s decision-making style or propensity to

technology.

Next, data from participants survey scores were analyzed to answer the research

questions. The first research question answered by the study was “What was the

relationship between transformational leadership styles and decision-making styles?” The

first null hypothesis (H01) was not rejected. No relationship between transformational

leadership styles and decision-making styles was indicated (see Table 2).

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Table 2

Correlations Between Transformational Leadership Styles and Decision-making Styles

Transformational Leadership Styles

N = 110 IIA IIB IM IS IC

Rational Pearson Correlation .14 -.02 .10 .01 -.02

Sig. (2-tailed) .15 .85 .31 .91 .81

Intuitive Pearson Correlation -.07 .07 .09 .08 .18

Sig. (2-tailed) .49 .49 .37 .44 .06

Dependent Pearson Correlation -.19 -.12 -.06 .02 -.04

Sig. (2-tailed) .05 .37 .56 .88 .70

Avoidant Pearson Correlation -.09 .07 -.07 -.02 .06

Sig. (2-tailed) .36 .50 .44 .88 .52

Spontaneous Pearson Correlation -.05 .12 -.05 .13 .06

Sig. (2-tailed) .58 .22 .61 .19 .55

The next null hypothesis (H02) was not rejected. No relationship between

transformational leadership styles and rational decision-making style was indicated.

Participants’ scores for rational decision-making style ranged from a minimum of 15.00

to a maximum score of 25.00. The mean was 22.35 with a standard deviation of 2.15. No

relationship between transformational leadership styles and rational decision-making

style was indicated.

The null hypothesis (H03) was not rejected, as a relationship between the

independent variable (transformational leadership styles) and the dependent variable

83

(decision-making style) was not found. Participants’ scores for dependent decision-

making style ranged from a minimum of 8.00 and a maximum of score of 25.00. The

mean was 18.33 with a standard deviation of 3.74.

Of the 110 participants, 109 TRI surveys were accepted for analysis. The second

research question answered by the study was “What is the relationship between

transformational leadership styles and technology readiness?” Three null hypotheses and

three hypotheses related to the second research question. The first null hypothesis (H04)

for the question was: transformational leadership styles have no relationship with

technology readiness. The null hypothesis was not rejected. No relationship between

transformational leadership styles and technology readiness was indicated (see Table 3).

Table 3

Correlation Between Transformational Leadership Styles and Technology Readiness

Transformational Leadership Styles

N=109 IIA IIB IM IS IC

Optimism Pearson Correlation -.04 .01 .05 .04 -.04

Sig. (2-tailed) .67 .90 .58 .71 .65

Innovative Pearson Correlation -.08 .08 .10 .02 .10

Sig. (2-tailed) .38 .41 .29 .81 .31

Discomfort Pearson Correlation -.03 -.08 .02 .01 -.03

Sig. (2-tailed) .77 .40 .81 .90 .75

Insecure Pearson Correlation .07 -.08 -.01 .06 -.06

Sig. (2-tailed) .49 .39 .96 .52 .53

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The null hypothesis (H05) was not rejected, as no relationship was found between

transformational leadership styles and the technology-readiness dimension of optimism.

Participants’ scores for the technology-readiness dimension of optimism ranged from a

minimum of 2.70 to a maximum score of 5.00. The mean was 4.06 with a standard

deviation of .50. No relationship between transformational leadership styles and the

technology-readiness dimension of optimism was indicated. The null hypothesis (H06)

was not rejected, as evidence of a relationship between transformational leadership styles

and the technology-readiness dimension of innovativeness was not found. Participants’

scores for the technology-readiness dimension of innovativeness ranged from a minimum

of 1.43 to a maximum score of 5.00. The mean was 3.31 with a standard deviation of .83.

The third research question was “What is the relationship between decision-

making styles and technology readiness?” Three null hypotheses and three hypotheses

related to the third research question. No relationship between decision-making styles and

technology readiness was indicated. The first null hypothesis (H07) for question three was

rejected. A significant positive correlation resulted between the two decision-making

styles and two technology-readiness dimensions. One significant negative correlation was

indicated (see Table 4).

Table 4

Correlation Between Decision-making Styles and Technology Readiness

Decision-making styles

N = 109 Rat Int Dep Avo Spo

Optimism Pearson Correlation -.03 -.01 -.16 .26** -.03

Sig. (2-tailed) .73 .90 .11 .01 .74

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Table 4 (continued)

Rat Int Dep Avo Spo

Innovative Pearson Correlation .01 -.04 -.10 -.14 -.00

Sig. (2-tailed) .92 .71 .32 .14 .98

Discomfort Pearson Correlation .04 .02 .17 .24** .04

Sig. (2-tailed) .71 .82 .78 .01 .65

Insecure Pearson Correlation .20* .01 .07 .17 -.03

Sig. (2-tailed) .03 .92 .49 .07 .72

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

The leaders in the study were primarily explorers and pioneers (67%) regarding

technology. The majority of the participants were comfortable with technology given the

high ratings in the explorer and pioneer categories (Colby & Parasuraman, 2003). The

descriptive statistics for the technology-readiness dimensions illustrated the tendency of

the participants regarding optimism (M = 4.06, skewness = -.27, kurtosis = -.39),

discomfort (M = 2.63, skewness = .45, kurtosis = -.59), and insecurity (M = 3.06,

skewness = .23, kurtosis = -.54). The analysis indicates the sample in the study has a

propensity to technology, which may affect the positive outcome of the results. The

participants tended to be comfortable with using technology.

The participants’ results regarding decision-making style demonstrated similar

variation from a normal distribution particularly in the categories emphasized in the

research, rational (M = 22.34, skewness = -.72, kurtosis = .221) and avoidant (M = 8.82,

skewness = 1.23, kurtosis = .80). The participants tended toward rational decision-

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making styles. The sample was less likely to tend toward the avoidant decision-making

style.

The analysis indicated a correlation between rational decision-making style and

technology-readiness dimension of insecurity with a significance level of .05 and a

coefficient of r = 0.20. Leaders who scored high on the rational decision-making style

were significantly more likely to score high on the technology-readiness dimension of

insecurity. The findings would indicate someone with a rational decision-making style

would be more likely to be insecure with technology.

The avoidant decision-making style demonstrated evidence of two correlations

between technology-readiness dimensions of discomfort was r = 0.24 (p < 0.01) and a

negative correlation with optimistic was r = - 0.26 (p < 0.05). Leaders who score high on

the avoidant decision-making style were significantly more likely to score high on the

technology-readiness dimension of discomfort. In addition, leaders who scored high on

the avoidant decision-making style were likely to score low on the technology-readiness

dimension of optimism. The findings would indicate someone with an avoidant decision-

making style would have a discomfort with technology and be less likely to be optimistic

concerning technology.

The positive correlation indicates a variable (x axis) is influenced by another

variable (y axis). A leader with a high score on rational decision-making style will result

in a high score on the technology-readiness dimension of insecurity. A negative

correlation indicates if a variable (x axis) exists another variable will be present on the

opposite end of the spectrum (y axis). A high score on the decision-making style will

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result in a low score for the technology-readiness dimension of optimism. No correlation

indicates one variable does not influence or have a relationship with another variable.

The null hypothesis (H08) was not rejected as no evidence of a relationship

between rational decision-making style and the technology-readiness dimension of

optimism was indicated. The null hypothesis (H09) was not rejected. No evidence of a

relationship between dependent decision-making style and the technology-readiness

dimension of innovativeness was indicated (see Table 4).

Table 5 illustrates the null hypothesis testing outcomes for the study. An

additional finding was a statistically significant correlation coefficient between the

transactional leadership style of contingent reward and the rational decision-making style

(r = 0.26; p < 0.01). Leaders who had high scores on the transactional leadership style of

contingent reward were significantly more likely to score high on the rational decision-

making style. Transactional leadership style of contingent reward correlated with the

technology readiness dimension insecurity (r = 0.21; p < 0.05). Leaders who scored high

on transactional leadership style of contingent reward were significantly more likely to

score high on the technology readiness dimension of insecurity. Although the research

questions did not address other leadership styles, the data analysis indicated a relationship

between the transactional leadership styles and both dependent variables (decision-

making styles and technology readiness).

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Table 5

Null Hypotheses Testing

Null

Hypothesis

Hypotheses Testing

H1 Transformational leadership styles: Decision-making styles Not rejected

H2 Transformational leadership styles: Rational decision-making Not rejected

styles

H3 Transformational leadership styles: Dependent decision-making Not rejected

styles

H4 Transformational leadership styles: Technology readiness Not rejected

H5 Transformational leadership styles: Optimism Not rejected

H6 Transformational leadership styles: Innovativeness Not rejected

H7 Decision-making styles: Technology readiness Rejected

H8 Rational decision-making style: Optimism Not rejected

H9 Dependent decision-making style: Innovativeness Not rejected

Conclusion

Participants were primarily female between 45 to 55 years of age in middle

management positions. The longevity with the organization ranged from 1 to 20 years.

The majority of the participants held their current position from one to five years. Besides

summarizing the demographics, chapter 4 described the research design, the data

analysis, and results of the three research questions and nine hypotheses, which is the

89

basis of the research design. Chapter 5 will summarize the many months of research

design, analysis, and findings. In addition, chapter 5 will examine the implications of the

research and offer recommendations for further study.

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CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

As business leaders come under public scrutiny for questionable business

practices and inability to lead organizations through rapid change, a better understanding

of why leadership struggles with successful implementation of initiatives continues to be

of interest. An area with minimal research is the examination of the relationship between

leadership styles, decision-making styles and technology readiness. The purpose of the

quantitative correlation study was to determine the degree of association between

independent variables (leadership style and decision-making styles) and dependent

variables (decision-making styles and technology readiness) within an organizational

environment experiencing an increase in technology implementation.

Three surveys (Multifactor Leadership Questionnaire, General Decision-Making

Style Scale, and Technology Readiness Index) were distributed to leaders who

volunteered to participate in the survey at a large rural school district. The groups had the

choice to attend one of two sessions to complete all three surveys. After reviewing the

surveys for each participant, 110 participants completed two surveys and had authorized

use of their data in the research. The third survey had 109 participants who completed all

the requirements. In chapter 4, the three research questions were answered and nine null

hypotheses and nine alternate hypotheses were tested. Chapter 5 will present the findings

and analysis of the study, recommendations for further research, and implications of the

research. The findings and analysis section is organized by the three research questions.

Findings and Analysis

The research addressed the view of Orlikowski and Barley (2001) concerning the

lack of integration in research between leadership and technology. Nutt (2006)

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encouraged a better understanding of leadership decision making, especially in the public

sector. He hypothesized leadership decision making was different in public and private

sectors. Nutt found there are differences and theorized culture had a part in the difference.

The population surveyed was in the public sector and decision-making styles were

analyzed. By examining public sector leadership that had implemented multiple

technology interventions, the research addressed Orlikowski and Barley’s concerns about

integration of leadership and technology concepts and furthered Nutt’s theories involving

leader decision making in the public sector.

Bass (2007) supported the importance of leaders’ understanding of decision

making and leaderships’ influence toward the success of strategic organizational change.

Higgs (2003) expressed a need to explore how effective leadership contributed to

innovative management techniques. This research contributes to an increased

understanding of leadership style in relationship to decision-making styles and

technology readiness. In addition, the study addressed the minimal knowledge between

decision-making styles and technology readiness.

Transformational Leadership Styles and Decision-Making Styles

The results of analysis indicated that transformational leadership styles have no

correlation in relationship to decision-making styles. The analysis between

transformational leadership styles and decision-making styles answered the first research

question, “What is the relationship between transformational leadership styles and

decision-making styles?” In comparison, Kao and Kao (2007) found evidence of a

correlation between situational leadership characteristics and decision-making styles. The

rational decision-making style correlated positively to the executives’ total leadership

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style (r = .48). Kao and Kao found a negative correlation between the executives’ total

leadership style and the decision-making style dimensions of dependent (r = -.42) and

avoidant (r = -.49).

Transformational leadership styles are not necessarily associated with the leaders’

pattern of decision-making (decision-making style). Spinelli (2007) suggested

transformational leaders create a feeling with employees to motivate them. Motivating

employees may not be enough. Employees will need to put the motivation into action.

Hirtz, et al., (2007) posited leaders’ presence during initiatives increases the likelihood of

success of quality initiatives. Nir and Kranot (2006) suggested leaders create an

environment in which employees can work, which leads to successful outcomes. Chung

and Lo (2007) suggested a combination of transformational and transactional leadership

styles creates effective leadership.

Although a relationship was not found between transformational leadership styles,

a correlation was found between transactional leadership styles and decision-making

styles. Transactional leadership styles are contingent reward and management-by-

exception (Avolio et al., 1999) Transactional leadership is based on an exchange

relationship between supervisor and the employees. The correlation between transactional

leadership styles and a rational decision-making style may be explained by the local locus

of control by the leader. The expectations developed by the manager and the results of the

expectations monitored by the manager, allow the control to remain with the manager.

Transformational Leadership Styles and Technology Readiness

The examination between transformational leadership styles and technology

readiness answered the second research question, “What is the relationship between

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transformational leadership styles and technology readiness?” Zhu et al., (2006) posited

technology change is a complex infrastructure that includes human resources and

information technology. The information technology context has two factors–technology

readiness and technology integration. Two additional components of technology change

were organizational and environmental. Zhu et al., suggested leadership’s role was to

develop the infrastructure to address all the components of technology change. Zhu et al.,

found a strong positive correlation between the three stages of technology assimilation

and technology readiness.

This study may offer a better understanding of the influence of technology

readiness on successful technology implementation. Harper and Utley (2001) found that

cultural attributes contribute to successful technology implementation. The behaviors

valued in a culture that accepts technology were autonomy, trust, team orientation,

flexibility, and information sharing. Leaders can increase technology implementation by

fostering the behaviors identified in Harper and Utley’s research. The results of the

analysis indicated no significant relationship between transformational leadership styles

and technology readiness. The results may suggest the behaviors that inspire followers

are independent from the desire to use a particular technology or innovation. Tiong

(2005) posited if leaders understood the deep-rooted emotions of employees, the

likelihood of implementation would increase.

The sample had a high propensity to use technology. The comfort a majority of

the participants demonstrated may have influenced the results. As a population sample

that has a high comfort level with technology, the leadership style may not be influenced

by the mindset. Saleem et al., (2006) found a similar result in their research with

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employees that had a comfort with technology. Implementation was more efficient

regardless of the employees’ roles. Matthing et al., (2007) posited individuals with a high

propensity to technology demonstrate high adoption rate with innovation.

Decision-Making Styles and Technology Readiness

The analysis of the data for the third research question, “What is the relationship

between decision-making styles and technology readiness?” indicated a correlation

between two decision-making styles and two technology readiness dimensions. Using the

Pearson two-tail correlation coefficient, a significant correlation existed between rational

decision-making style and insecure technology readiness dimension (r = 0.20) and

avoidant decision-making style and two technology readiness dimensions [optimism (r =

-0.26) and discomfort (r = 0.24)]. Leaders with a rational decision-making style are likely

to have insecurity with technology. Leaders with an avoidant decision-making style are

likely to be uncomfortable with technology and less likely to be optimistic about

technology.

Scott and Bruce (1995) defined the rational decision maker as one who attempts

to control destiny and has an internal control orientation whereas the avoidant decision-

making style lacks confidence in one’s decision-making ability and a lack of control over

life events. Rational decision makers desire to have control of situations. A leader with a

rational decision-making style may have a perception of loss of control with the increased

use of technology. The leader with an avoidant decision-making style may feel pressed

into making decisions due to technology. The leader would become uncomfortable with

the technology due to the pressure to make decisions. An avoidant decision maker may

feel the availability of information by the leader and employees may force him or her into

95

making decision when he or she would rather avoid making decisions. The leader would

not be optimistic toward technology.

Colby and Parasuraman (2003) defined the technology readiness index as a scale,

which ranged from strongly positive to strongly negative in reference to the propensity

for an individual’s use of technology in life or work. Strong feelings toward technology

impel an individual toward technology, and a negative sense holds an individual back.

The optimism dimension is a positive view of technology and a belief that technology

offers control, flexibility and efficiency in their lives (Parasuraman, 2000). Leaders who

score high on the discomfort dimension perceive technology as lack of control over

technology and feeling of being overwhelmed by it. Leaders who score high on insecurity

are distrustful of technology and are skeptical about its ability to work properly

(Parasuraman, 2000).

When leaders use a rational decision-making style and locus of control is

supported internally, the insecurity with technology may indicate why such initiatives fail

to meet objectives. The leaders may not value the function technology serves in an

organization. The analysis of data indicated a negative correlation between a rational

decision-making style and the sense of optimism regarding using technology and feeling

insecure about the function technology serves in the organization. With the complexity of

organizations, legislators and elected officials are looking for people to make decisions.

Public sector leaders are typically better at including more people in the decision-making

process. The research indicated a large number of dependent decision makers but no

correlation with the technology-readiness dimensions. Vonk, Geertman, and Schot (2007)

demonstrated the high failure factor of attitude of management in the failure of projects

96

(p. 749). Leaders need to understand the influence their attitude has in regard to the

propensity to technology because leaders make decisions to purchase and plan for

technology. Leaders’ attitudes toward technology may be a hindrance to successful

implementation.

Nutt (2006) suggested public sector leaders do not typically support decisions

based on analysis due to the more anecdotal decision-making culture. The uncertainty of

funds and continuing support limits taking substantial risk on innovative initiatives.

Nutt’s research is important to understand the value of the avoidant decision-making style

correlation. The avoidant decision-making style lacks confidence in one’s decision-

making ability and lacks control over life events (Scott & Bruce, 1995). The findings

between the avoidant decision-making style and the technology readiness dimensions of

optimism and discomfort were significant in the study. Having the right decision maker

with the appropriate technology readiness dimension could be an important factor in the

formulation and implementation of technology in organizations. Vonk et al., (2007)

suggested that for an organization to become successful with innovation, it must first

become a learning organization. The leaders of the organization in the study may find

reflecting on the research results can be a tool to improve their approach to

technologically innovative projects.

Recommendations

Nutt (2006) suggested certain characteristics draw managers into the public sector

as opposed to the private sector. In the public sector, operating funds are allocated

through oversight authorities. The amount of funding follows historical precedence.

Networking and connections with political parties can determine the addition or

97

elimination of funding. In the private sector, revenue is based on the how well the

organization competes in the target market. In addition, success is determined by how

well the leaders identify changes in the market. In the public sector, leaders are motivated

to enhance collaboration and cooperation with a variety of stakeholder groups. In the

private sector, information is protected and not shared, and in order to decrease coveted

information from being exposed, only select leaders are informed. Minimal data is

available for review in the public sector. In the private sector, most decisions are

dependent on good data (Nutt, 2006).

Meyer and Stensaker (2006) concluded different organizations vary in their

capacity for change. They suggested leaders have control of where they will spend time

during a given day. If leaders fail to demonstrate a commitment to an initiative or

program, employees will spend less time on the initiative or program. Employees will

spend time on the projects leaders endorse and abandon projects to which leaders do not

demonstrate a commitment. The cost of technology implementations is particularly

expensive and time consuming. If leaders do not commit to the change, the employees

will not commit to the implementation. The technology implementation becomes a waste

of money and time.

Recommendations for further research include:

1. The correlation between decision-making style and technology readiness

showed evidence that leaders with an awareness of their decision-making styles can

understand the significance of the decisions made during change.

98

2. The awareness and subsequent education to adjust leaders’ decision-making

styles would allow leaders to guide the organization in a direction of successful

formulation and implementation of technology initiatives.

3. Leaders should receive differentiated instruction with consideration given to

decision-making style and technology readiness. In addition, the type of training may

vary according to the project the leader is undertaking.

Suggestions for Future Research

Transformation leadership styles did not correlate with the dependent variables

(decision-making styles and technology readiness), which could suggest that certain

leadership characteristics do not influence the patterns of an individual’s choice or

propensity to use technology. The outcome may have resulted from other factors such as

sample size. Additional research with the variables would be worthy of examination. Kao

and Kao’s (2007) results showed evidence of correlations between leadership

characteristics and decision-making styles, which would indicate more research is needed

in the leadership styles. Harper and Utley’s (2001) research indicated a correlation

between culture and decision-making styles. Including the TRI in a research design

similar to Harper and Utley’s design may demonstrate a correlation not seen in previous

studies.

In the public school environment conducting research at the beginning or end of

an academic year is not conducive to getting full participation. Due to the timing of the

research, the survey was distributed after leaders returned from summer vacation. They

were getting schools ready for returning students. Further research should survey public

99

school personnel a month or two after the start of the academic year or a month after

returning from a break. Leaders may be more receptive to participating in a study.

The study should be repeated with different organizations in the public sector,

including other public school districts. Finn and Hess (2004) suggested minimal research

has been completed regarding operational functions of public schools. Additional

research in public schools would enhance the body of knowledge.

Given Nutt’s (2006) research, the examination of the relationship between the

independent variables (leadership styles and decision-making styles) and dependent

variables (decision-making styles and technology readiness) should be repeated in the

private sector. Leadership style relationships can vary from the public sector to the

private sector. Continued research in the public and private sectors will be of interest.

Zhu, Kraemer, and Xu’s (2006) study demonstrated a strong propensity toward

technology with professionals who practice in the technology field. Research outside the

technology field, including leadership, may offer a deeper understanding of the

technology readiness of varying professional groups. In addition, including populations

from countries outside the United States in further studies may provide a perspective of

how the cultural aspects change the results of the study. Michel (2007) posited measuring

the decision-making construct is difficult. Including variables such as performance,

culture, and formal decision making are optimal measures. With more research, leaders in

organizations that are experiencing an increase in technology implementation may want

to have leaders complete the GMDS and the TRI. With a better understanding of

correlations between decision-making styles and technology readiness, human resource

100

professionals can develop programs for leaders that improve decision-making skills in

innovative environments.

Implications

Leaders have a great deal of responsibility for the success of an organization. In

order to be competitive in the marketplace, understanding the influence of technology is

imperative. The quantitative correlation study indicated the skill to bring forth a vision

might not necessarily correlate with the pattern of decision making or propensity to

technology. Spinelli (2006) emphasized transformational leadership is only one style and

works in concert with other leadership styles toward successful organizational operations.

The study would indicate that the decision-making style and technology readiness are

more closely related with each other than either is to the transformational leadership

style. Nir and Kranot (2006) indicated leaders’ contribution to employee involvement and

the creation of the perception of effective outcomes correlate with a positive work

environment. Kontoghiorghes (2005) posited leaders have an important role in the

development and implementation of rapid change. The complexity of an organization

challenges leaders’ understanding of the multiple variables that can affect successful

change. Other leadership characteristics besides style or other models of leadership style

may offer greater insight into what contributes to successful leadership.

The quantitative correlation study expanded the understanding of decision-making

styles and technology readiness. Minimal research exists. The correlations between

decision-making styles and technology readiness in the study were encouraging. The

continued research between additional variables and populations may further the

understanding of how to develop leaders and employees and increase successful

101

implementation of technology initiatives. Human resource professionals would find the

research valuable to develop professional development programs for leaders who

implement technological changes. As leaders search for successful approaches, the

decision making and technology research continues to be important for their success. As

leaders work in the rapidly changing global enterprises where unexplored and unexpected

situations arise, the continued study of decision-making styles and technology readiness

is essential.

Summary

The quantitative correlation study examined the relationship between independent

variables (leadership styles and decision-making styles) and dependent variables

(decision-making styles and technology readiness). In the first chapter and expanded in

literature review, the theoretical framework outlined the concepts pertaining leadership,

decision making and technology implementation. The findings of the research contribute

to previous studies in three areas: leadership styles, decision-making styles, and

technology readiness dimensions.

Chapter 5 was a summary of the findings and analysis of the three research

questions. Although the first and second research questions did not reject the null

hypothesis, additional findings beyond the hypotheses were examined. The third research

question was answered with valuable information that may assist leaders when designing

and implementing technology. Recommendations included a suggestion to differentiate

instruction when working with leaders. Future researchers could use the research design

in different environments (public and private), including additional leadership styles, and

a more appropriate timeframe. The implications of the research are an increased

102

understanding of leadership needs in order for human resource professionals to design

education concerning change initiatives. In addition, the study may help leaders to select

the right people for technology implementations in the public sector.

Conclusion

As indicated by the research, transformational leadership styles do not correlate

with decision-making styles or technology readiness; leaders’ decision-making styles

appear to have a correlation with the propensity to utilize technology. The confusion and

frustration leaders demonstrate during the formulation and implementation of technology

leads to employee confusion and frustration (Dervitsiotis, 2005; Mignonac & Herrbach,

2004). Millions of dollars are typically invested in technology with objectives such as

increasing competitiveness, resolving complex internal system problems, and/or

providing tools allowing employees to effectuate efficient and timely customer service

(Bergmo & Johannessen, 2006; Kontoghiorghes, 2005; Woodall, Colby, & Parasuraman,

2007). Yet, a significant number of technology implementations fail to meet corporate

objectives (Clegg et al., 1997). Owen and Demb (2004) speculated the manner in which

leaders implement technology influences the success of such endeavors. Van der Merwe,

Pretorius, and Cloete (2004) suggested that knowledge of the respective technology, in

terms of its complexity and the expected human reaction to its implementation,

determines success or failure.

Organizations are continuing to struggle with how to bring innovative initiatives

to fruition. The knowledge leaders will need to develop concerning changing

organizations relies on researchers continuing to understand the variables that influence

good formulation and implementation of innovative initiatives. The quantitative

103

correlation study provided no correlation evidence with the transformational leadership

style. Given the results of the study, more research is warranted concerning decision-

making styles and technology readiness. The continued investigation of factors that

influence change will facilitate successful implementation of technology initiatives.

104

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APPENDIX A: INTRODUCTORY LETTER

124

Dear Leader,

I am a student at the University of Phoenix working on a Doctorate of Management in Organizational Leadership. I am conducting a research study entitled The Relationship Between Leadership Styles, Decision-Making Styles, and Technology Readiness: A Correlation Study. The purpose of the research study is to examine the association of independent variables (leadership styles and decision-making styles) and two dependent variables (decision-making style and technology readiness) within an organizational environment experiencing an increase of technology implementation.

Your participation will involve completing three surveys (Multifactor Leadership Questionnaire, General Decision-Making Scale and the Technology Readiness Index). Your completion of the surveys should be approximately 60 minutes. Your participation in this study is voluntary. If you choose not to participate or to withdraw from the study at any time, you can do so without penalty or loss of benefit to yourself. The results of the research study may be published but your name will not be used and your results will be maintained in confidence.

In this research, there are no foreseeable risks to you. Although there may be no direct benefit to you, the possible benefit of your participation is a greater understanding for you of your leadership style, decision-making style, and technology readiness. In addition, the study may contribute to a better understanding of leadership concepts.

If you have any questions concerning the research study, please call me at 307-577-4414. If you would like a copy of the research summary, please indicate below and include your name and address.

Sincerely,

Crystal A. Mueller

□ I would like to receive a copy of the research summary.

Name of Participant: ______________________________________________

Address: _______________________________________________

125

APPENDIX B: INFORMED CONSENT

127

APPENDIX C: PERMISSIONS

128

Permission to Use Facility

129

Permission to Use Multifactor Leadership Questionnaire

130

Permission to Use General Decision-Making Style Scale

131

Permission to Use the Technology Readiness Index

132

APPENDIX D: STUDY INSTRUMENTATION

135

General Decision-Making Style Scale

Listed below are statements describing how individuals go about making important decisions. Please indicate the extent to which you agree or disagree with each statement.

Neither Strongly Somewhat Agree Nor Somewhat Strongly Disagree Disagree Disagree Agree Agree 1 2 3 4 5

a. I plan my important decisions carefully. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

b. I double-check my information sources to be sure I have the right facts before making decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

c. I make decisions in a logical and systematic way. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

d. My decision making requires careful thought. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

e. When making a decision, I consider various options in terms of a specific goal. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

f. When making decisions, I rely upon my instincts. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

g. When I make decisions, I tend to rely on my intuition. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

h. I generally make decisions which feel right to me. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

. When I make a decision, it is more important for me to feel the decision is right than to have a rational reason for it. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

. When I make a decision, I trust my inner feelings and reactions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

k. I often need the assistance of other people when making important decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

. I rarely make important decisions without consulting other people. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

m. If I have the support of others, it is easier for me to make important decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

n. I use the advice of other people in making my important decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

o. I like to have someone to steer me in the right direction when I am faced with important decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

p. I avoid making important decisions until the pressure is on. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

q. I postpone decision making whenever possible. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

r. I often procrastinate when it comes to making important decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

s. I generally make important decisions at the last minute. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

t. I put off making many decisions because thinking about them makes me uneasy. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

u. I generally make snap decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

v. I often make decisions on the spur of the moment. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

w. I make quick decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

x. I often make impulsive decisions. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

y. When making decisions, I do what seems natural at the moment. [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

influence of transformational leadership style on

Documents