Due to the many detrimental effects of counterproductive work behavior (CWB), it is important to measure the construct accurately. Despite this, there are some limitations inherent to current CWB measures that are somewhat problematic, including that they contain items that do not apply to all jobs while missing items that are important for other jobs (Bowling & Gruys, 2010). The current study tackles these issues by drawing on the benefits associated with open-ended response situational judgment tests (SJTs), such as them having the potential for more insight from respondents (Finch et al., 2018), to develop an open-ended response CWB SJT. To minimize the drawbacks currently associated with the manual analysis of open-ended response SJTs (e.g., being time-consuming and costly)—which is also a reason why they are rarely used— the study leverages natural language processing and machine learning to measure CWB. Using a two-dimensional conceptualization of CWB, including CWB against the organization (CWB-O) and individuals (CWB-I), the CWB SJT dimensions had a moderate to strong correlation with the popular CWB scale the Workplace Deviance scale (Bennett & Robinson, 2000). Findings further indicate the CWB SJT to be related to variables typically associated with CWB tendencies, such as neuroticism and trait self-control. By using topic modeling, it was also found that topic prevalence was largely consistent through time both for the full CWB SJT and for individual items, implying the test-retest reliability. The CWB SJT along with R code for analyzing the open-ended responses is provided. Implication of the CWB SJT for research and practice are discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2096 |
| Date | 01 January 2022 |
| Creators | Tavoosi, Saba |
| 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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