Supervisor support, control over work methods and employee well-being: new insights into nonlinearity from artificial neural networks

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Abstract

The purpose of this study was to test a nonlinear model of psychological well-being at work. Specifically, artificial neural networks (ANNs) were used to identify and map nonlinearities among supervisor support, control over work methods and employee well-being. Our findings confirmed results from prior studies in that ANNs explained significantly more variance in well-being than did OLS regression. Visualization of nonlinear relationships extended prior research, demonstrating strong patterns of nonlinearity between two dimensions of supervisor support, direct support and trust, and well-being. Discussion was focused on the implications of observed nonlinearities for theory development and on the value of ANNs in building more accurate predictive models of employee well-being.

Original languageEnglish (US)
JournalInternational Journal of Human Resource Management
DOIs
StateAccepted/In press - Jan 1 2018

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Organizational Behavior and Human Resource Management
  • Management of Technology and Innovation

Keywords

  • Artificial neural networks
  • employee well-being
  • nonlinear models
  • support and control

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