Lofton oral defense

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)?


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)


RQ2: What are the differences in the influence of respondent demographic characteristics on dissatisfaction with facets of employment, between SSA and IRS agencies?


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


RQ3: What are the differences in the influences of respondent demographic characteristics on dissatisfaction with facets of employment between 2006 and 2008?


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


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]


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


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


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]


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


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


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.


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.


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.


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

://www.bus.lsu.edu/bedeian/articles/AgeTenureAndJobSatisfactionATale-1992.pdf

Berry, J. (2010). A message from John Berry. Retrieved from http://www.fedview.opm.gov/2010/

Bryman, A. (2001). Social research methods. (2nd ed.). Oxford, UK: Oxford Press

Cetin, M. O. (2006). The relationship between job satisfaction, occupational and organizational commitment of

academics. Journal of American Academy of Business, Cambridge, 8(1), 78-88.

Creswell, J.W., & Clark, V.P. (2007). Designing and conducting mixed methods research. Thousand Oaks, CA:

Sage Publications

http://www.bus.lsu.edu/bedeian/articles/AgeTenureAndJobSatisfactionATale-1992.pdf

http://www.bus.lsu.edu/bedeian/articles/AgeTenureAndJobSatisfactionATale-1992.pdf

http://www.fedview.opm.gov/2010/

References

DeConinck, J., & Johnson, J. (2009). The effects of perceived supervisor support, perceived organizational support, and

organizational justice on turnover among salespeople. Journal of Personal Selling & Sales Management, 29(14), 333-

350. doi:10.2753/PSS0885-3134290403

Detamore, J. A. (2008). An analysis of the relationships between job satisfaction, leadership, and intent to leave within an

engineering consulting firm. Dissertation Abstracts International, 68 (11), 225A. (UMI No. 3289494)

Dychtwald, K., Erickson, T., & Morrison, R. (2006). Workforce crisis: How to beat the coming shortage of skills and talent.

Boston, MA: Harvard Business School Press.

Gelman, A., & Hill, J. (2007). Data analysis using regression and multi-level hierarchical models. New York, NY: Cambridge

University Press.

Judge, T.A., Thoresen, C.J., Bono, J.E., & Patton, G.K. (2001). The job satisfaction–job performance relationship: A qualitative

and quantitative review. Psychological Bulletin, 127(3), 376-407. doi:10.1037/0033-2909.127.3.376

http://psycnet.apa.org/doi/10.1037/0033-2909.127.3.376

References

Kacmar, K. M.& Ferris, G.R. (1989).Theoretical and methodological considerations in the age-job satisfaction relationship. Journal of

Applied Psychology, 74(2), 201- 207. doi: 10.1037/0021-9010.74.2.201

Kalleberg, A. (1977). Work value and job rewards: A theory of job satisfaction. American Sociological Review, 42(2), 124-143. Retrieved

from

http://iscrat.org/soc-pol/wam-net/Launch-mini-conference/WAMSEM6/KALLEBERG%20Work%20values%20and%20job%20rewards.pdf

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

http://gmj.gallup.com/content/147653/Beware-Parochial-Managers.aspx

Seashore, S.E., & Taber, T.D. (1975). Job satisfaction indicators and their correlates. American Behavioral Scientist, 18(3), pp.

333-386. Retrieved from http://moodle.nmsu.edu/ocs/index.php/SWAM2010/Dallas/paper/viewFile/166/55



http://gmj.gallup.com/content/147653/Beware-Parochial-Managers.aspx

Questions and Answers

THANK YOU, ALL FOR YOUR PARTICIPATION AND CONSIDERATION OF MY

DISSERTATION AND DEFENSE.

Technology

Lofton oral defense