The Relationship Between Culture and Counterproductive Workplace Behaviors: A Meta-Analysis
Bibliographic record
Abstract
In the last few decades, there has been a growing interest in counterproductive work behaviors (CWBs) – behaviors that are deemed harmful to organizations and their employees (Bennett & Robinson, 2000). Unfortunately, little knowledge exists on the universality of well-established theories on CWBs across different cultures. Most theories have been developed using studies conducted in the United States and Canada, limiting our ability to confidently extrapolate these theories to other cultures. In this study, I examine the relationship between culture and CWBs. Specifically, two questions are addressed. First, does culture have a direct relationship with CWBs? Using GLOBE’s cultural dimensions, I proposed that these dimensions either control or motivate deviant behaviors. Second, does culture moderate antecedent-CWB relationships? Using Marcus and Schuler’s (2004) four antecedent categories (triggers, external controls, internal controls and propensities), I proposed that GLOBE’s cultural dimensions moderate the relationships between each of these four groups of antecedents and CWBs. I also examined the effects of two methodological moderators (publication bias and source of ratings) on the relationships between antecedent variables and CWBs and examined whether these two methodological moderators could have confounded any observed cultural moderation effects. To answer these questions, I conducted a meta-analytic study. For the analyses, 450 studies from 25 countries were used. These studies were obtained from peer-reviewed journals, dissertations, theses, conference papers, and unpublished manuscripts. The data were analyzed using weighted least squares regression and psychometric meta-analysis. Results indicate that high scores on the cultural dimensions (assertiveness and a composite construct consisting of future orientation, humane orientation, in-group collectivism, institutional collectivism and uncertainty avoidance) were associated with low scores on interpersonal and organizational deviance. Furthermore, these cultural dimensions strengthened or weakened the relationships between several of the antecedents in Marcus and Schuler’s (2004) four categories and CWBs. Finally, some support was found for the moderating effect of the source of CWB ratings, but not publication bias, on the bivariate relationships. However, these two methodological moderators did not completely confound any observed cultural moderation effects. These findings have implications for research and practice. These implications as well as the limitations of the study are discussed.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.036 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".