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Do I Fit? Differential Predictors of Organizational Vs. Professional Turnover

Do I Fit? Differential Predictors of Organizational Vs. Professional Turnover. Sara L. Curtis Lillian T. Eby Katie M. Kincade The University of Georgia. Current State. Recognition that turnover is a costly problem, especially in regards to knowledge workers

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Do I Fit? Differential Predictors of Organizational Vs. Professional Turnover

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  1. Do I Fit?Differential Predictors of Organizational Vs. Professional Turnover Sara L. Curtis Lillian T. Eby Katie M. Kincade The University of Georgia

  2. Current State • Recognition that turnover is a costly problem, especially in regards to knowledge workers • Identification of numerous predictors of turnover • Inconsistent findings, unexplained variance • Development of more and more complex models of turnover

  3. Complications • Turnover can be a tricky construct to study. • What can we measure? • Intent to turnover • Actual turnover behavior • Pre- and post-turnover attitudes • How do we define it? • Departure • Voluntary • Functional • Avoidable

  4. A different Angle “Though overlooked, more accurate turnover classifications may well boost the explanatory power of predictor variables as much as the expansion of predictor batteries is expected to.” -Hom & Griffeth, 1994, p. 6

  5. Is All Turnover Equal? • Are the processes of leaving an organization the same as leaving a profession? • Professional change can have greater costs, more change, more risk. • Some things will change across organizations, some will stay the same.

  6. Current approach • Focus on refining the OUTCOME rather than expanding the PREDICTORS • Breaking apart organizational and professional turnover • Distinguishing between context-specific and context-generalizable antecedents • Examining the differential relationship between predictor types and turnover destination • Based in Social Exchange Theory

  7. Turnover Prediction

  8. Differential Relationships

  9. Hypotheses • Hypothesis 1a: POS, supervisor relationship quality, and coworker support will be significantly lower and role overload will be significantly higher for those who leave the organization compared to those who stay. • Hypothesis 1b: POS, supervisor relationship quality, and coworker support will be significantly lower and role overload will be significantly higher for those who leave the organization compared to those who leave the profession. • Hypothesis 2a: Affective professional commitment, intrinsic job satisfaction, earned credentials, and professional tenure will be significantly lower and emotional exhaustion will be higher for those who leave the profession compared to those who stay. • Hypothesis 2b: Affective professional commitment, intrinsic job satisfaction, earned credentials, and professional tenure will be significantly lower and emotional exhaustion will be higher for those who leave the profession compared to those who leave the organization.

  10. The Study • 591 substance abuse treatment professionals • 372 remained employed • 120 changed organizations voluntarily • 99changed professions voluntarily • Time 1: Survey data on antecedents for all participants • All scales had acceptable reliability • Time 2: Interviews with those who turned over to determine type of turnover (i.e. destination)

  11. Descriptive results

  12. ANOVA RESULTS * p < .025 ** p < .01

  13. ANOVA RESULTS * p < .025 ** p < .01

  14. ANOVA RESULTS * p < .025 ** p < .01

  15. ANOVA RESULTS * p < .025 ** p < .01

  16. Results • The context-generalizable predictors differentiated between organizational and professional turnover. • Intrinsic job satisfaction, emotional exhaustion, affective professional commitment, earned credentials • Dual prediction • POS, Supervisor relationship quality, coworker support, intrinsic job satisfaction, emotional exhaustion, professional tenure

  17. Interpretations & Implications • Organizational and professional turnover not mutually exclusive • Everyone who leaves the profession also left their organization. • Turnover as a 2-part process • Departure: most of the traditional predictors predicted both types of departure • Destination: Context-generalizable predictors separated professional turnover from organizational turnover • Taking turnover destination into account in future research may help account for additional variance

  18. Thanks! This project described was supported by Award Number R01DA019460 from the National Institutes on Drug Abuse awarded to Lillian T. Eby. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health.

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