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

Document Type

Article

Publication Date

1-1-2021

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.

Identifier

85057331129 (Scopus)

Publication Title

International Journal of Human Resource Management

External Full Text Location

https://doi.org/10.1080/09585192.2018.1540442

e-ISSN

14664399

ISSN

09585192

First Page

1620

Last Page

1642

Issue

7

Volume

32

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