Do interactions matter? Assessing the role of job characteristics interactions on employee well-being.
Journal
Journal of Applied Psychology
Type
journal article
Date Issued
2026-09-14
Author(s)
;
Landers, Richard N.
;
Schade, Hannah M.
;
Lincke, Hans-Joachim
;
Nuebling, Matthias
;
;
Rinkenauer, Gerhard
;
Metzler, Yannick A.
Abstract
The job demands–resources model suggests that job demands (e.g., workload) and resources (e.g., autonomy) relate to employee well-being through both main and interactive effects. Although the predictive validity of main effects is well established, the validity of the proposed interaction effects, buffering and boosting, remains unclear. Prior research has examined only selected combinations of job characteristics, leaving open the possibility that unexplored interactions may still be informative. In this study, we conducted a comprehensive analysis of 24 job characteristics and all possible nonlinear and higher order interactions among them using machine learning methods in a large, quota-representative sample of the German working population (N = 100,000) stratified by industry, gender, age, and employment status. We compared the out-of-sample predictive performance of models capturing main effects (linear mixed-effects models) with models capable of modeling complex interactions (boosting algorithms). The inclusion of interaction effects did not improve predictive accuracy beyond models based solely on main effects. These findings challenge the validity of buffer and boost effects as global mechanisms across jobs and organizations, as interactions among job characteristics did not meaningfully contribute to predicting either burnout or work engagement. Instead, job characteristics appear to exert only additive effects.
Keywords
employee well-being
job demands
job resources
interactions
machine learning
HSG Classification
contribution to scientific community
Refereed
No
Publisher
American Psychological Association (APA)
Subject(s)
Division(s)
Additional Information
©American Psychological Association, 2026. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available, upon publication, at: 10.1037/apl0001414
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