Counterproductive work behaviors (CWBs) have been identified as pervasive employee behaviors with the potential to cause significant harm in the workplace (e.g., Sackett & DeVore, 2001). Because of the considerable threat CWBs pose to organizational and employee well-being, a literature has emerged to better understand the structure of these behaviors and identify the factors and conditions that effect employee engagement in counterproductive acts. While past research has distinguished between types of CWBs, i.e., theft, sabotage, withdrawal, less attention has been paid to the specific forms these behaviors take. For example, being two hours late to work is more serious and harmful than being five minutes late, and traditional frequency-based measures fail to distinguish between these behaviors. In order to understand and account for the full range of variation in employee CWBs, research must advance in ways that incorporates severity. The current study introduces a novel conceptualization of CWB severity that distinguishes between intra-behavioral differences and develops modified versions of the CWB-C (Spector et al., 2006; Bennett & Robinson, 2000) which assess engagement in low and high severity versions of each CWB. These new measures are utilized to test a hypothesized model of CWB severity that predicts how individual (negative affect) and contextual factors (self-control & perceived consequences) interact to predict low and high severity CWBs. This research seeks to expand our understanding of the diverse ways employees respond to stressful work conditions and represents an important first step in identifying the types of employees and work environments that are associated with the most harmful, high severity, CWBs. Implications for future CWB research are discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7468 |
| Date | 01 January 2019 |
| Creators | Ciarlante, Katherine |
| Publisher | University of Central Florida |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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