DOI QR코드

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Investigation of Demand-Control-Support Model and Effort-Reward Imbalance Model as Predictor of Counterproductive Work Behaviors

  • Mohammad Babamiri (Department of Ergonomics, Hamadan University of Medical Sciences) ;
  • Bahareh Heydari (Department of Ergonomics, Hamadan University of Medical Sciences) ;
  • Alireza Mortezapour (Department of Ergonomics, Hamadan University of Medical Sciences) ;
  • Tahmineh M. Tamadon (Department of Ergonomics, Hamadan University of Medical Sciences)
  • 투고 : 2022.01.14
  • 심사 : 2022.08.16
  • 발행 : 2022.12.30

초록

Background: Nowadays, counter-productive work behaviors (CWBs) have turned into a common and costly position for many organizations and especially health centers. Therefore, the study was carried out to examine and compare the demand-control-support (DCS) and effort-reward imbalance (ERI) models as predictors of CWBs. Methods: The study was cross-sectional. The population was all nurses working in public hospitals in Hamadan, Iran of whom 320 were selected as the sample based on simple random sampling method. The instruments used were Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, and Counterproductivity Work Behavior Questionnaire. Data were analyzed using correlation and regression analysis in SPSS18. Results: The findings indicated that both ERI and DCS models could predict CWB (p ≤ 0.05); however, the DCS model variables can explain the variance of CWB-I and CWB-O approximately 8% more than the ERI model variables and have more power in predicting these behaviors in the nursing community. Conclusion: According to the results, job stress is a key factor in the incidence of CWBs among nurses. Considering the importance and impact of each component of ERI and DCS models in the occurrence of CWBs, corrective actions can be taken to reduce their incidence in nurses.

키워드

과제정보

The authors of this article would like to thank all the participants in this study. The authors also thank the Vice Chancellor for Research and Technology of Hamadan University of Medical Sciences for financial support of this research (grant number: 14000117204).

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