Is Smart Working Beneficial for Workers’ Wellbeing? A Longitudinal Investigation of Smart Working, Workload, and Hair Cortisol/Dehydroepiandrosterone Sulfate during the COVID-19 Pandemic

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Abstract

Building on the job demands–resources (JD-R) and allostatic load (AL) models, in the present study we examined the role of smart working (SW) in the longitudinal association between workload/job autonomy (JA) and a possible biomarker of work-related stress (WRS) in the hair—namely, the cortisol–dehydroepiandrosterone sulfate (DHEA(S)) ratio—during the COVID-19 pandemic. Overall, 124 workers completed a self-report questionnaire (i.e., psychological data) at Time 1 (T1) and provided a strand of hair (i.e., biological data) three months later (Time 2, T2). Results from moderated multiple regression analysis showed that SW at T1 was negatively associated with the hair cortisol/DHEA(S) ratio at T2. Additionally, the interaction between workload and SW was significant, with workload at T1 being positively associated with the hair cortisol/DHEA(S) ratio at T2 among smart workers. Overall, this study indicates that SW is a double-edged sword, with both positive and negative consequences on employee wellbeing. Furthermore, our findings suggest that the hair cortisol/DHEA(S) ratio is a promising biomarker of WRS. Practical implications that organizations and practitioners can adopt to prevent WRS and promote organizational wellbeing are discussed.

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One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

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Sources of method bias in social science research and recommendations on how to control it.

Philip M. Podsakoff, Scott B. MacKenzie, Nathan P. Podsakoff (2012)

Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.