Voluntary turnover and job performance: Curvilinearity and the moderating influences of salary growth and promotions.
Abstract
[Excerpt] The relationship between job performance and voluntary employee turnover was investigated for 5,143 exempt employees in a single firm. As hypothesized, support was found for E. F Jackofsky's (1984) curvilinear hypothesis, as turnover was higher for low and high performers than it was for average performers. Two potential moderators of the curvilinearity were examined in an attempt to explain conflicting results in the performance-turnover literature. As predicted, low salary growth and high promotions each produced a more pronounced curvilinear performance-turnover relationship. Most notably, salary growth effects on turnover were greatest for high performers, with high salary growth predicting rather low turnover for these employees, whereas low salary growth predicted extremely high turnover. Additionally, once salary growth was controlled, promotions positively predicted turnover; with poor performer turnover most strongly affected.
Cited by (9)
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- The relationship of age to ten dimensions of job performance.Ng, Thomas W. H., Feldman, Daniel C. · 2008 · Journal of Applied Psychology
- The relationship between pay and job satisfaction: A meta-analysis of the literatureJudge, Timothy A., Piccolo, Ronald F., Podsakoff, Nathan P., Shaw, John C., Rich, Bruce L. · 2010 · Journal of Vocational Behavior
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Voluntary turnover and job performance: Curvilinearity and the moderating influences of salary growth and promotions. https://app.cochat.ai/papers/10.1037//0021-9010.82.1.44
- The paper
- Trevor, C. O., Gerhart, B., & Boudreau, J. W. (1997). Voluntary turnover and job performance: Curvilinearity and the moderating influences of salary growth and promotions. Journal of Applied Psychology. https://doi.org/10.1037//0021-9010.82.1.44
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.