Factors Influencing Older Worker Quality of Life and Intent to Continue to Work

Open Access
- Author:
- Spokus, Diane M
- Graduate Program:
- Workforce Education and Development
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 23, 2008
- Committee Members:
- William J Rothwell, Committee Chair/Co-Chair
Edgar Paul Yoder, Committee Member
Edgar I Farmer Sr., Committee Member
Wesley Edward Donahue, Committee Member - Keywords:
- quality of life
human resource management
supervisory support
nursing
retention
older workers
exhaustion
corporate fit
healthcare management - Abstract:
- High turnover has been a major problem in healthcare organizations. The purpose of this study was to examine the relationship among job characteristics, social support, and organizational characteristics on quality of the working life. Subsequently, the intent was to examine how those factors collectively influence turnover intention. A conceptual model (Korunka, Hoonakker & Caragon, 2005) provided the conceptual basis for the research. Their model is appropriate for older workers and highly educated workers, which makes it applicable to the Baby Boomer generation. The target population was workers age 50 or older in two healthcare organizations, with a target population of 716. Using a web-based survey consisting of closed-ended response survey items supplemented with open-ended qualitative response items, a return rate of 22% was obtained. The survey also collected information on the personal demographics of the older workers in addition to employment background information. The investigator utilized several instruments from prior research to measure the primary variables of interest—quality of work life, social support, job demand, role ambiguity, decision latitude, rewards, burnout, emotional exhaustion, training opportunities, and career opportunities. The first analysis examined factors influencing older workers’ self-reported quality-of-life. Six variables were found to statistically influence older workers’ self-reported quality-of-life. Overall, these six variables explained 66% of the variance in the quality-of-life index scores. The six variables and the relative contribution of each to explaining the variance in quality of life were level of job burnout (39%), level of organizational involvement (12%), desire to seek other employment (6%), perceived extent of corporate fit (6%), level of job tension (2%), and social support received from the supervisor (2%). The overall regression model with the six statistically significant (p < .05) variables collectively explained 66% of the variance (F= 35.29, model p <.001). The second analysis examined collective influence on the older workers’ intentions to leave their current job within the next year. For this analysis, binary logistic regression was used to examine the variables that significantly influenced the worker’s decision to seek other employment. Of the study participants, 28% indicated their intention to leave. Three variables were found to significantly (p < .05) influence the older workers’ intentions to leave. The three factors were: reported quality-of-life subscale score (Exp B = .39), self-reported corporate fit index score (Exp B = 2.91), and self-reported role ambiguity score (Exp B = 2.44). The overall logistic regression model had a Nagelkerke pseudo R square value of 34.8% (Model Chi square = 30.17, p <.001). The results indicated that older workers with higher quality-of-life index scores were less likely to leave. Conversely, a higher score on the role ambiguity index indicated that a healthcare worker above the age of 50 was more likely to leave. An interesting finding was that older workers with higher scores on the corporate fit index also were more likely to leave.