The Structure of Counterproductive Work Behavior
Bibliographic record
Abstract
Although counterproductive work behavior (CWB) has long been established as a broad domain of job behaviors, little agreement exists about its internal structure. The present research addressed alternative models of broadly defined CWB according to which specific behaviors can be grouped into (a) one general factor, or into (b) two, (c) five, or (d) eleven narrower facets, and a number of possible integrations of these models. First, conceptual differences between these models (including the nature of overall CWB as implying a reflective or formative model, boundaries of the domain, and relations among specific facets) are reviewed with regard to theoretical and practical implications. In Study 1, structural meta-analysis was then used to test whether a reflective higher-order factor underlies meta-analytically constructed correlation matrices of five CWB facets. Analyses supported a general factor model. For Study 2, a primary data set (N = 1,237 employees) was collected in order to test alternative structural models and possible integrations of these models. Confirmatory factor analyses revealed that the best fit was for a bimodal (nonhierarchical) model in which individual CWBs simultaneously load on one of the eleven facets describing their content (e.g., theft, absenteeism) and on one of three factors describing the target primarily harmed (organization, other persons, self). Less support was found for hierarchical models and for models involving fewer content factors. These findings suggest that CWB is best described by a reflective higher-order factor at the general level and by a complex set of bimodal facets at the more specific level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".