Counterproductive work behavior (CWB) represents a significant threat to organizations and organizational stakeholders, such that understanding the processes driving employee engagement in deviant workplace behaviors is for essential for ensuring well-being at work. Although past evidence suggests that a variety of work stressors predict CWB, the application of this research has been constrained by a lack of longitudinal studies that investigate the relationship between job stressors and CWB over time and a failure to examine the mechanisms driving dynamic CWB processes. I sought to address these limitations by investigating the dynamic relationships between job stressors, negative affect, and CWB, via the stressor-emotion model of CWB, with the goal of enhancing our understanding of "why" and "when" employees are motivated to engage in CWB in response to changing work experiences. Utilizing Bayes slope estimates and a random coefficient modeling (RCM) approach, this study explored how the strength and direction of changing job stressors and negative affect impacts both CWB frequency and trajectory (i.e., static and dynamic CWB). The moderating effects of temporal focus (past, present, and future), job preservation motivation, and self-control were also investigated. The results suggest that job stressor trajectories predict CWB trajectory and static CWB, but only when specific timeframes are utilized to estimate these relationships. Additionally, evidence suggests that both negative affect trajectory and self-control are crucial for understanding: (1) change in CWBs overtime and (2) the dynamic relationships between job stressors and CWBs. Implications regarding the importance of establishing appropriate timeframes for studying dynamic relationships and the distinctiveness of specific job stressor effects are discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2365 |
| Date | 01 January 2022 |
| Creators | Ciarlante, Katherine |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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