1. Background Employers around the industrialized world face challenges & needs posed by the pending retirements of Baby Boomers¹. Earlier research² examined job satisfaction & older workers. Results: Extrinsic factors such as pay and benefits mattered less. Employers seek to fill occupational niche with workers who are not well-suited for the type of work being offered. Workers want challenging, meaningful work. Few institutions have retention and innovative updating efforts in place for mature workers. Staffing shortages are creating stressful work environments. Many current workers need and want to continue to work. Fewer younger people will be available to fill traditional entry-level positions. ¹DeLong, D. (2004). Lost knowledge. New York: Oxford University Press. ²Eichar, D.M., Norland, S., Brady, E.M., Fortinsky, R.H. (December, 1991). Job Satisfaction and Older Workers. Journal of Organizational Behavior (1986- 1998); 12(7); ABI/INFORM Global, pg. 609-620. Purpose: Examine job characteristics that influence an older worker’s intention to either retire or leave the job. Research Questions: 1) What are the relationships between job characteristics, social support and organizational characteristics on quality of work life? 2) Collectively, what is the influence of job characteristics, social support, organizational characteristics, and quality of working life on turnover intention Purpose Methods Results Implications Teaching Employers and Corporate Trainers How to Retain Older Workers: Adaptation to Individual Differences Diane M. Spokus, Ph.D., C.H.E.S., The Pennsylvania State University, University Park Teaching Employers and Corporate Trainers How to Retain Older Workers: Adaptation to Individual Differences Diane M. Spokus, Ph.D., C.H.E.S., The Pennsylvania State University, University Park Results Older workers with ↑ quality of life index scores are less likely to leave Design, Measure & Analysis Independent variables: 1) Job characteristics 2) Social support 3) Organizational characteristics 4) Demographic characteristics Dependent variables: 1) Quality of work life 2) Turnover Intention Target population: Older workers > 50 years of age. N = 716; n = 155 respondents Web based survey: 63 closed-ended self-report response survey items supplemented with a few open- ended qualitative response items as well as Likert-type scale response scales about how organizations recruit older workers. 22% response rate Demographic and background information of older workers Primary variables: quality of work life, social support, job demand, role ambiguity, decision latitude, rewards, burnout, emotional exhaustion, training opportunities and career opportunities. JD = IT job demands RA = role ambiguity DL = decision latitude CH = challenge CS = support from colleagues SS = supervisory support TR = training opportunities CO = Career opportunities RE = rewards EEX= emotional exhaustion Figure1. Korunka, C., Hoonakker, P., & Carayon, P. (2005). Source: A Universal Turnover Model for the IT Work Force – A Replication Study. Human Factors in Organizational Design and Management – VIII, edited by Carayon, Kleiner, Robertson and Hoonakker. Santa Monica, CA: IEA Press, pp. 467-472. Used by permission of the author. Applications: Retention & recruitment strategies for older workers Succession planning Performance and evaluation Staff education, training & professional development Management strategies Future Research: Development of Older Worker Competencies Innovative strategies that influence older worker career path decisions Gender and minority status related to older worker retention Recreational employment for older workers Mentoring through intergenerational coaching Evaluation and performance systems for older workers Career lattice growth, rewards and job satisfaction of older workers First Analysis: Factors influencing older workers’ self-reported quality of life Six Quality of Life (proxy for job/work satisfaction) Significant Index Scores: Job Burnout (proxy for Emotional Exhaustion) (39%) Corporate Fit (proxy for Organizational Involvement) (12%) Desire to seek other employment (6%) Perceived extent of corporate fit (6%) Level of job tension (2%) Social support from supervisor (2%) Second Analysis: Three Collective Factors Influencing older workers’ intentions to leave current job within one year Quality of life subscale score (Exp B =.39) Self-reported corporate fit index ((Exp B – 2.91) Self-reported role ambiguity (Exp B = 2.44) 28% indicated their intention to leave Conversely, ↑ score on role ambiguity – older worker more likely to leave. Interesting finding – older workers with ↑ scores on corporate index also more likely to leave. Limitations of this Study The research is based on self-report, and therefore there is potential for response bias. The study sample was limited to respondents employed in two Central Pennsylvania hospitals. Majority of respondents were WHITE. This lack of diversity and geographic location may have influenced the results. The study sample was accessed through two third-party professional organizations and was limited to a criterion based sample and, therefore, may not be generalizable to the population of all older Hospital care workers in Central PA. FINDINGS: RQ #1 : What are the Relationships between Job Characteristics, Social Support and Organizational Characteristics on Quality of Work Life? Summary of Hierarchical Regression for Quality of Work Life Regressed on job Characteristics, Organizational Characteristics, and Social Support Indicators FINDINGS RQ#1: What are the Relationships between Job Characteristics, Social Support and Organizational Characteristics on Quality of Work Life? Summary of Hierarchical Regression for Corporate Fit Regressed on Job Characteristics, Organizational Characteristics and Social Support Indicators Summary of Hierarchical Regression for Job Burnout Regressed on Job Characteristics, Organizational Characteristics and Social Support Indicators FINDINGS RQ#2 : Collectively, what is the Influence of Job Characteristics, Social Support, Organizational Characteristics, and Quality of Working Life on Turnover Intention? Summary Descriptive Statistics for Variables Used in Regression Analysis for RQ#1 Predictor VariableB (SE B)WaldpExp (B) Quality of Life Score-1.42 (.33)18.34<.0010.241 Burnout Index Score-.15 (.20)0.530.4680.865 Corporate Fit Index Score.51 (.32)2.590.1081.66 Constant2.33(1.83)1.630.20110.3 Results of Binary Logistic Regression with Intention to Leave as the Dependent Variable (n=144) Note. Dependent variable coded as (0 = no intention to leave; 1= intention to leave) Model Chi Square =30.14; df = 3; p = <.001; Nagelkerke pseudo R square=.301