lofton oral defense
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FACTORS CONTRIBUTING TO JOB DISSATISFACTION AND ATTRITION
IN THE FEDERAL WORKPLACE
DENISE LOFTON, DOCTORAL LEARNER
Oral Defense PresentationFeb. 7, 2012
Presented in Partial Fulfillment of the Requirements for the Degree
Doctor of Management in Organizational Leadership
Dr. Alex Lazo, Chair
Dr. Betty Ahmed, Member
Dr. Gerald Nebeker, Member
Denise Lofton, Learner
The Committ
eeUniversity
of Phoenix
ABSTRACT
The purpose of the quantitative correlational study was to examine the relative influence of individual demographics (gender, age, tenure, supervisory status, location, and intent to leave) on job dissatisfaction (DV) with facets of employment (leadership and knowledge management, result orientation and performance, talent management, and job dissatisfaction index) in the Internal Revenue Service, and the Social Security Administration (n = 2,203).
The Study Goals…
The study used demographic profiling to look beyond the typical effect of independent variables on dependent variables, to create a picture of groups and organizations by like categories, and characteristics.
The study’s outcome is significant to leadership as it confirms the role of demographics in understanding the factors contributing to job dissatisfaction.
BACKGROUND
Congress mandates that all federal agencies assess employee perspectives and develop improvement plans to address key findings.
The bi-annual survey conducted by the Office of Personnel Management is the primary tool for data collection.
Employees provide their perceptions in two important workplace categories: (1.) leadership and management practices that contribute to agency performance, and (2.) employee satisfaction with aspects of employment (OPM, 2008).
Problem Statement
The general problem is dissatisfied employees withdraw, become disengaged, perceive employment to be less than desirable, and make the choice to leave (Bowling, Beehr & Lepisto, 2006; Ingersoll & Perda, 2006; Walker, 2007).
The specific problem is federal agencies lack the knowledge needed to: link job dissatisfaction to
attrition adequately predict causation reduce loss of talent
OPM predicts the federal civilian workforce will see 60 percent retirement eligibility by 2012 (OPM, 2009).
Purpose Statement
The purpose of the study was to investigate the relationship between job dissatisfaction with various facets of employment and individual demographic characteristics for respondents in the Federal Human Capital Survey in 2006 and 2008, in two Executive Agencies.
The study looked at the change in dissatisfaction among employee groups, by demographic characteristics: Age, Gender, Tenure, Supervisory Status, Location, and Intent to Leave
Research Questions & Hypotheses
RQ1: What are the relative influences of the respondent demographic characteristics (age, tenure, gender, supervisory status, location, intent to leave), on respondent dissatisfaction with facets of employment (leadership & knowledge management, results orientation & performance, talent management, and job satisfaction index)?
Research Questions & Hypotheses
H1o: The respondent demographic characteristics do not influence respondent dissatisfaction with facets of employment
H1a: Tenure and location of the respondent exert the greatest influence on dissatisfaction with facets of employment (leadership & knowledge management, results orientation & performance, talent management, job satisfaction index)
Research Questions & Hypotheses
RQ2: What are the differences in the influence of respondent demographic characteristics on dissatisfaction with facets of employment, between SSA and IRS agencies?
Research Questions & Hypotheses
H2o: For the IRS and SSA, there are no differences in the influences of respondent demographic characteristics on dissatisfaction with facets of employment
H2a: For the IRS and SSA, there are differences in the influences of respondent demographic characteristics on dissatisfaction with facets of employment
Research Questions & Hypotheses
RQ3: What are the differences in the influences of respondent demographic characteristics on dissatisfaction with facets of employment between 2006 and 2008?
Research Questions & Hypotheses
H3o: For 2006 and 2008, there are no differences in the influences of respondent demographic characteristics on dissatisfaction with facets of employment.
H3a: For 2006 and 2008, there are differences in the influences of respondent demographic characteristics on dissatisfaction with facets of employment
Study Assumptions
Study utilizes secondary analysis of existing data set and as such re-purposes data
Original respondents are examined solely by responses and demographic characteristics (age, tenure, gender, supervisory status, location, intent to leave)
The truthfulness of the responses is assumed reliable due to the privacy proffered and long-term acceptance of the survey in the federal community
Study Scope
Study utilizes data set derived from Office of Personnel Management (OPM) Federal Human Capital Survey in the years 2006 and 2008
Data used for the comparisons and analysis is limited to respondents from two Presidential Management Council member agencies, namely, the Internal Revenue Service, and the Social Security Administration.
The study organizations are representative of federal workforce large agencies, have similar missions, workforce composition, organizational structure, gender representation, customer base, and stakeholder alliances (BPTW, 2010).
Study Limitations
Each participating agency sample is based on employee population at the time of the original survey, without regard to gender, age, or tenure.
The study utilized the quantitative responses to the 206 and 2008 federal workforce survey without any subsequent qualitative aspects due to participant anonymity.
The unique demographics of each agency extends the possibility that an agency has an overrepresentation of males, versus females, or young versus older workers, in the study population
Literature Review
“There is a logical relation between our perceptual judgments of what is real (in the naïve-realist sense) and our perceptual judgment about our own experiences” (Murphey, 1994, p. 51).
The utility of job satisfaction measures seeks perception from a ‘real-world’ point of view and gives as much consideration to the consequences of job dissatisfaction, as to the causes (Seashore & Tabor, 1975).
Key Literary Findings
Federal agencies and their respective leaders seek to understand better the causes of employee disengagement and consider the trend an important leader challenge (Harter & Wagner, 2010)
Consideration of variables that may influence employee perceptions, like age, gender, and tenure, requires investigators to reevaluate past assumptions about workers as a cohort, and account for differences in employee traits (Dychtwald, Erickson, & Morrison, 2006).
Key Literary Findings
Job dissatisfaction also affects worker attitude, and the choice to remain or leave employment, even when the job or benefits may differ significantly (Berry, 2010; MSPB, 2008; Starks, 2007).
When aspects of employment once considered important to the decision to join an organization are no longer present, workers assess job search capabilities, and consider other options for employment where they may exist (Dooley, 2007; Judge & Klinger, 2008).
Research Methodology
Given the goals of the study, the large population, and multiple independent variables, the quantitative, correlational design, using hierarchical regression techniques was appropriate and fit for a reexamination of the Federal Human Capital Survey in 2006 and 2008.
The study utilizes a quantitative method with correlational design:
Useful in determining predictors Appropriate to test research
questions and study hypotheses Allows researchers to identify and
isolate behaviors within and between study variables
(Bryman, 2001; Creswell & Clark, 2007; Trochim and Donnelly, 2008)
Research Methodology
The inability to identity original respondents and the lack of access to original respondents supports use of a quantitative methodology and secondary data analysis (Gelman & Hill, 2007).
The study utilizes secondary analysis to re-purpose the original OPM survey:
Reduces research time and cost Supports use of large data sets with
proven reliability Provides a unique opportunity to
continue study of specific phenomenon, expand on prior knowledge, and ‘see’ the world differently
(Bedeian, Ferris, & Kacmar, 1992;Neuman 2003; Thomas & Heck, 2001)
Study Population
Study population consists of all respondents who answered each survey question in the High Impact Index and each demographic item included in the study
Overview of Study Population and Sample Frame
Study Agency/ Year Original Respondent Population
Sample Size
IRS 2006 1,147 1,147
SSA 2006 1,317 1,317
IRS 2008 1,153 1,153
SSA 2008 5,959* 1,140a
a modified sample size calculation to equalize sample groups
Study Population
Study population
was comprised of:
Sample Population (By Year, and Agency) 2006 2008
SSA IRS SSA IRS
650 484 690 411
• 741 males• 1,431 females• Average age 50 – 59 (both agencies, both
years)• 1,407 supervisors• 828 non-supervisors• Average tenure over 20 years (both
agencies)
High Impact Index
The High Impact Item Index questions comprised the dataset extracted from the original Office of Personnel survey archive and re-purposed for use in this study
High-Impact Item Index, 2006, 2008
Category Item # 2006 Item # 2008
Leadership & Knowledge Mgmt
Q9, Q17, Q36, Q55, Q57
Q9, Q17, Q37, Q56, Q58
Results Orientation & Performance
Q24, Q57 Q24, Q57
Talent Management Q2, Q18, Q59
Q2, Q18, Q60
Job Satisfaction Index
Q5, Q6, Q54, Q58, Q61
Q5, Q6, Q55, Q59, Q62
Note: High-Impact Item Index for 2006 and 2008 includes the same questions, but the numbers changed due to a new survey item in 2008
There were a total of (17) survey questions examined by demographic characteristic in the study. See Appendix C. for survey questions.
The survey questions were grouped into four facets of employment:Leadership and Knowledge Management
Results Orientation and Performance
Talent Management Job Dissatisfaction Index
DATA COLLECTION
Five questions covering all facets comprise the new index:
Q5, Q6, Q54, Q58, Q61*
* Renumbered as Q. 62 in 2008
Each response was coded to allow for quantitative analysis and results interpretation:
The scale was appropriate to each question asked, for example…
Q55 – How satisfied are you with your involvement in decisions that affect your work?
DATA COLLECTION
Response Scale
Responses coded as 1 = Dissatisfied2 = Very Dissatisfied0 = Neither, Satisfied, Very Satisfied
Each demographic characteristic was coded to allow for quantitative analysis and results interpretation:
The scale was appropriate to each question asked, for example…
X3: Tenure (in the agency, IRS, and
SSA) [under 1 year = 1; 1 to 5 years = 2; 6 to 10 years = 3; 11 to 20 years = 4; over 20 years = 5]
X6: Intent to Leave was coded: No = 1; Yes, = 2.
DATA COLLECTION
Response Scale
Each response option to the Demographic characteristics were grouped to facilitate interpretation of results
In the study, all categorical responses were coded in numeric format to facilitate regression and interpretation of results. Where a, b, c, d, e, f, are coefficients, the regression equation is:
The Y = a +bx1 + cx2+ dx3+ ex4+ fx5 + fx6 (1)
In Equation 1,
x1 = age of the respondent,
x2 = gender of the respondent,
x3 = tenure (in the agency, IRS, and SSA),
x4: = supervisory status,
x5 = organization,
x6 = intent to leave.
DATA ANALYSIS
Analysis Framework
Data was examined by Research Question, Hypotheses, Employment Facet and related demographic characteristic , using hierarchical regression analysis
The change in R2 was determined to see if there was a significant change when a new variable is added to the model. If the change in R2 was significant (indicating contribution to the model) the variable was retained in the next step.
The steps were repeated for each research question, to test hypotheses
DATA ANALYSIS – STEPWISE PROCESS
Regression Analysis
Step 1 – Gender & AgeStep 2 – TenureStep 3 – Supervisory Status and LocationStep 4 – Intent to Leave
See Appendix E for regression results for each facet of employment
The change in R2 was determined to see if there was a significant change when a new variable is added to the model. If the change in R2 was significant (indicating contribution to the model) the variable was retained in the next step.
The steps were repeated for each research question, to test hypotheses
DATA ANALYSIS – STEPWISE PROCESS
Regression AnalysisThe hierarchical regression analysis resulted in a total of (16) models:- 4 facets of employment
- 2 study agencies- 2 study years
See Appendix E for regression results for each facet of employment
Descriptive Statistics was used to : Examine the significant variable for
each facet of employment. For
example:
DATA ANALYSIS – DESCRIPTIVE STATISTICS
Descriptives
The variable(s) with highest level of dissatisfaction, by significant variable, was determined for each facet of employment
Variable Levels with Highest Dissatisfaction Score for the Significant Variables Demographic characteristic M SD n
Tenure 11-20 years 1.74 2.14 329
Non- Supervisor 1.70 2.25 813
Location – Field 1.48 2.05 1778
Intent to leave 2.24 2.55 470
Note: Results are for the Leadership and Knowledge Management facet of employment.
See Appendix F for descriptive statistics for all demographic variables, by facet of employment
Data Outcomes – RQ 1Relative influence of IV, Full Sample
The coefficients for each demographic characteristic was determined to assess the direction of the influence and the significance.
Leadership and Knowledge Management The coefficient for tenure was positive
and significant [ Beta .238, p < .01] The coefficients for supervisor [ Beta
-.525, p < .01 and location [ Beta -.320, p < .01] was negative and significant
The coefficient for intent to leave was positive and significant [ Beta 1.071, p < .01]
Key Study FindingsRQ 1
As tenure increased, respondent dissatisfaction with facets of employment increased
Non-supervisors expressed more dissatisfaction with facets of employment than supervisors
Employees indicating work in a Field location expressed more dissatisfaction with facets of employment than did Headquarters employees
Employees expressing an intent to leave was more dissatisfied than those intending to remain
Data Outcomes – RQ 2Relative Difference in influence of IV, Between IRS and SSA
The coefficients for each demographic characteristic was determined to assess the direction of the influence and the significance.
Leadership and Knowledge Management Tenure was significant for SSA only. The coefficient
for tenure was positive and significant [ Beta .244, p < .01]
Supervisory status was significant for both agencies. The coefficients for supervisor IRS [ Beta -.484, p < .01] and SSA [ Beta -.523, p < .01] were negative and significant
Location was significant for IRS only. The coefficient for location was negative [ Beta -.002, p < .01 ]
The coefficient for intent to leave was positive and significant for both agencies: IRS [ Beta 1.123, p < .01], SSA [ Beta 1.104, p < .01]
Key Study FindingsRQ 2
Location was significant, and negative for IRS only, in all facets of employment Field employees expressed
more dissatisfaction with facets of employment than Headquarters
Tenure continued to influence employee dissatisfaction with facets, when examined by Agency: SSA employees expressed
more dissatisfaction as tenure increased
IRS employees expressed less dissatisfaction as tenure increased
Key Study FindingsRQ 2
Intent to Leave was significant, and negative for both agencies, in all facets of employment Employees expressing an intent
to leave demonstrated more dissatisfaction with facets of employment
NOTE: Future research is needed to determine if, and how often, the intent to leave was acted upon, and the related demographics
Supervisory status was significant for SSA only. Non-supervisors expressed more
dissatisfaction with facets of employment than did supervisors
The Job Dissatisfaction Index facet of employment was affected by employees dissatisfaction for: Tenure (SSA =more years of
service, more dissatisfaction; IRS more years of service, less dissatisfaction
Data Outcomes – RQ 3Relative Difference in influence of IV, for 2006 and 2008
The coefficients for each demographic characteristic was determined to assess the direction of the influence and the significance.
Leadership and Knowledge Management Tenure was significant for both years. The coefficient
for tenure was positive 2006, [ Beta .265, p < .01]; 2008 [ Beta .243, p < .01]
Supervisory status was significant for both agencies. The coefficients for supervisor IRS [ Beta -.484, p < .01] and SSA [ Beta -.523, p < .01] were negative and significant
Location was significant for IRS only. The coefficient for location was negative [ Beta -.002, p < .01]
The coefficient for intent to leave was positive and significant for both agencies: IRS [ Beta 1.123, p < .01], SSA [ Beta 1.104, p < .01]
Key Study FindingsRQ 3
Location was significant in each facet of employment, but not in the same years: Leadership and Knowledge
Management in 2008 only. Results Orientation and
Performance, 2006 and 2008
Talent Management and Job Dissatisfaction Index in 2006 only
Tenure continued to influence employee dissatisfaction with facets, when examined by Year: As SSA and IRS
employees tenure increased, the expressed dissatisfaction with leadership and knowledge management increased
Key Study FindingsRQ 3
Intent to Leave was significant, and negative for both years, in all facets of employment Employees expressing an intent
to leave demonstrated more dissatisfaction with facets of employment
NOTE: Future research is needed to determine if, and how often, the intent to leave was acted upon, and the related demographics
Supervisory status was significant in each facet of employment, but not in each year: Leadership and Knowledge
Management in 2006 only. Results Orientation and
Performance,
Talent Management and Job Dissatisfaction Index in 2006 and 2008
( significance at p< .01 level )
Significance of Study Findings
The purpose of the research study was to examine the relative influence of demographic characteristics on respondent dissatisfaction with facets of employment, in the 2006 and 2008 Federal Human Capital Survey, for the Internal Revenue Service and the Social Security Administration.
The present study addresses the factors that may contribute to job dissatisfaction and intent to leave in the federal workplace. The range of available responses was
provided, instead of collapsing them by group, which allows the findings to be more specific and informative
Findings support previous research indicating age, in the presence of gender, is insignificant as a predictor of dissatisfaction
(Cetin, 2006; Kacmar & Ferris, 1989)
Significance of Study Findings
The Judge study (2001) found a relationship between organizational placement, individual performance, and perceptions of supervisor performance (employee ratings, communications, policy, and practices).
The role of the supervisor and how well supervisory performance is perceived influences employee perception of dissatisfaction (Judge, Thoresen, Bono, & Patton, 2001) The present study indicates supervisory
status as a negative and significant demographic characteristic in the study agencies and study years
Employees in non-supervisory (authoritative positions) express more dissatisfaction in every facet of employment Note: Judge et al study included 312 research
samples, and over 54,000 respondents
Overview of Study Recommendations
The federal workplace is a unique employer, with many internal and external stakeholders. With a projected 60% attrition, via voluntary and normal retirement, engaging the workforce and increasing productivity is key to mission accomplishment (Berry, 2011) )
The level of significance of employee surveys increases when combined with specific information related to experiences and individual achievement in the organization (Joshi, 2010).
Several recommendations are formed, based on study analysis and results.
Study Recommendations
Chapter 5 provides specific recommendations and supporting
theoretical framework for each demographic characteristic
addressed.
Recommendations for leadership consideration are offered for each demographic characteristic included in the study: Gender Age Tenure Supervisory Status Location Intent to Leave
Study Recommendations
New studies indicate females more likely to act on thoughts of leaving when dissatisfied with career advancement, particularly when they believe that the organization does not offer a chance to apply a broader set of skills (Cech, Rubineau, Silbey, & Seron, 2011)..
While age and gender, when present together, were not found significant in the study, the presence of over 1000 females in the population warrant future examination of employee perception, by gender.
Study Recommendations
Studies examining the role of tenure in job dissatisfaction reflect greater significance in the presence of age (Kalleberg & Loscocco, 1983).
High unemployment generally means the marketplace is flooded with talent, though alignment between what is required and what is available may mean the number of unemployed will continue to rise (BLS, 2011).
Agencies may consider stratifying responses to annual surveys by age and tenure, and compare the results to efforts to recruit and retain high performing individual to assess the gap.
Study Recommendations
Making critical decisions based on location may create new silos and support negative competition for scarce resources (Rieger, 2011).
Location of employee influenced employee dissatisfaction with facets of employment, for both study agencies.
Leaders should consider re-examine policies established based on location, to ensure that when taken as a whole they still support goals of the organization.
Study Recommendations
The reasons people leave jobs, careers, organizations, and industries vary with age and tenure, and often reflect the relationship and interactions with managers and supervisors (DeConinck & Johnson, 2009; Robinson, 2008).
Intent to Leave was a significant demographic characteristic in the study agencies for each study year.
The findings for the influence of intent to leave in each facet of employee supports further investigation to ascertain how long the employee thought about leaving and whether or not the employee experienced a triggering event.
Conclusion
Investigating dissatisfaction is an important construct in our efforts to understand employee perceptions, affective mood and reasons for disillusion (ME, 2012).
The Federal Human Capital Survey is a rich data source for the federal community and for organizations who seek comparisons between the private and public sector.
It has been a rewarding experience to conduct this investigation and add to the conversation about employee dissatisfaction.
References
Bedeian, A.G., Ferris, G. R., & Kacmar, K. M. (1992). Age, tenure and job satisfaction: A tale of two perspectives.
Journal of Vocational Behavior, 40, 33-48. Retrieved from http
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Berry, J. (2010). A message from John Berry. Retrieved from http://www.fedview.opm.gov/2010/
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Creswell, J.W., & Clark, V.P. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA:
Sage Publications
References
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Dychtwald, K., Erickson, T., & Morrison, R. (2006). Workforce crisis: How to beat the coming shortage of skills and talent.
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References
Kacmar, K. M.& Ferris, G.R. (1989).Theoretical and methodological considerations in the age-job satisfaction relationship. Journal of
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Kalleberg, A. (1977). Work value and job rewards: A theory of job satisfaction. American Sociological Review, 42(2), 124-143. Retrieved
from
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Murphey, M.G. (1994). Philosophical foundations of historical knowledge. New York, NY: SUNY Press
Rieger, T. (2011). Beware of parochial managers. Gallup Management Journal Online. Retrieved from
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333-386. Retrieved from http://moodle.nmsu.edu/ocs/index.php/SWAM2010/Dallas/paper/viewFile/166/55
Questions and Answers
THANK YOU, ALL FOR YOUR PARTICIPATION AND CONSIDERATION OF MY
DISSERTATION AND DEFENSE.