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Predicting the Effects of Extrinsic and Intrinsic Job Factors on Overall Job Satisfaction for Generation X and Baby Boomers in a Regional Healthcare Organization

Counseling And Psychological Services Dissertations (Georgia State University)Published 18 May 2022Open access
Cheryl J. Curry
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TL;DR

This review examines the correlation of job facets with overall job satisfaction and the problems with generations and theoretical implications of this research.

Abstract

This dissertation investigates the impact of intrinsic and extrinsic job factors on overall employee job satisfaction for two generation cohort groups, Baby Boomers and Generation X, in a small rural healthcare organization. Eight job factors were selected for the study, reflecting popular characteristics associated with the two groups. The job factors were classified as intrinsic or extrinsic using Hertzberg’s two-factor theory. Intrinsic factors studied were: work itself, promotion, and recognition. Extrinsic factors studied were: pay, supervision, people, technology, and work-family balance. The Job Descriptive Index (JDI) scale was used to assess employee satisfaction with certain job factors; work itself, promotion, pay, supervision, and people. Scales similar to the JDI were created and used to measure satisfaction with technology, work-family balance, and recognition. The Job In General (JIG) scale was used to assess overall job satisfaction for each generation group. Multiple regression analysis was used to determine which of the job factors predicted of overall job satisfaction for each group. Results of the study indicate that overall satisfaction is influenced a discreet combination of intrinsic and extrinsic factors for each group. Generation X’s overall job satisfaction is predicted by extrinsic job factors, (work-family balance, and supervision) as well as intrinsic job factors, (work itself). Baby Boomers’ overall job satisfaction is predicted by an intrinsic job factor, (recognition) as well as an extrinsic job factor (supervision). Smaller than optimal sample size reduces applicability of the results and implies the need for extended research in this area to confirm findings of this study.

Keywords

Computer ScienceNursingSocial Sciences