How Does Workplace Helping Behavior Step Up or Slack Off? Integrating Enrichment-Based and Depletion-Based Perspectives

Abstract

Although helping behavior at work is widely studied, little is known about the processes via which help providers increase or decrease their helping behavior. In the current research, we integrated both enrichment-based and depletion-based perspectives on helping with Kahn’s psychological conditions for engagement to offer more comprehensive understanding of how helping behavior may change. Specifically, based on Kahn’s model, we simultaneously consider the beneficial effects of helping on help providers’ psychological meaningfulness and psychological safety along with the detrimental effects of helping on help providers’ psychological resource availability in order to uncover the differential processes through which helping behavior may change. To test our theoretical model, we collected data from a sample of 375 employees using a three-wave time-lagged design. Supporting the enrichment-based perspective, our results demonstrated that employees’ helping behavior was positively related to increases in their psychological meaningfulness and psychological safety. Supporting the depletion-based perspective, results showed that helping behavior was also positively related to increases in emotional exhaustion, an indicator of psychological resource availability. Whereas psychological meaningfulness and psychological safety were, in turn, positively related to increases in job involvement, emotional exhaustion was negatively related to increases in job involvement. Finally, job involvement was positively related to subsequent increases in employee helping behavior. We discuss the implications of our findings for both theories and practices.

“Hey everyone, look at me helping you!”: A contingency view of the relationship between exhibitionism and peer-oriented helping behaviors

“…Because these items appeared in the second survey, employees assessed them 6 months after they had rated the other constructs. Our reliance on a self-rated measure of peer-oriented helping behavior is consistent with previous studies (Lin et al, 2020;Rubenstein et al, 2019) and with the argument that other raters may have only a partial view of the range of extra-role helping activities that employees might exhibit toward all their peers (Chan, 2009;Organ et al, 2006).…”

Curvilinear Effects of Extraversion on Socialization Outcomes Among Chinese College Students

“…Means, SDs, and inter-correlations among all study variables are presented in Table 1 . Before testing hypotheses, we followed previous studies ( Li et al, 2018 ; Lin et al, 2020 , 2021 ) and conducted model comparisons using a series of confirmatory factor analyses to examine the distinctiveness of our focal variables. All analyses were conducted with Mplus 8.3 ( Muthén and Muthén, 2017 ).…”

When Moral Tension Begets Cognitive Dissonance: An Investigation of Responses to Unethical Pro-Organizational Behavior and the Contingent Effect of Construal Level

“…Although our proposed model operated at the individual level, our data had a nested structure (i.e., an average of 2.76 subordinates were nested within the same supervisor), which created the potential for non-independence in the data, downwardly biasing the standard errors (Bliese, 2000). Following Muthén and Muthén's (2015) recommendation, we first clustered employees by supervisors and then adopted maximum likelihood parameter estimates with standard errors (MLR) using a sandwich estimator to account for the non-independence of observations due to cluster sampling, which can provide a more conservative and accurate test (Bliese, 2000;Huber, 1967;Lin et al, 2020;Muthén & Muthén, 2015;Welsh et al, 2020). We conducted all the analyses using a latent variable structural equation model in Mplus 8 (Muthén & Muthén, 2015), which can provide a more robust approach than path analysis by mitigating potential measurement bias (Cole & Preacher, 2014;Cortina et al, 2021;Preacher et al, 2010).…”

“…Enders (2010) and Newman (2014) have suggested that maximum likelihood method is a direct estimation technique to yield unbiased parameter estimates and accurate SEs under MCAR and MAR. The similar approach has also been used by Lin et al (2020) and Welsh et al (2020). Additionally, we conducted a set of tests to identify proper auxiliary variables that were correlated with the incomplete CWB (because most missingness were in CWB).…”