GLOBAL TRENDS IN WORKPLACE DEVIANCE RESEARCH: A BIBLIOMETRIC ANALYSIS (2015 – 2025)
Bibliographic record
Abstract
This bibliometric analysis examines global trends in workplace deviance research, a domain tied to organizational effectiveness through links to misconduct, incivility, and withdrawal behaviors. Despite rapid growth, the literature remains diffuse, which constrains cumulative theory development and application. The study maps key trends, influential contributors, themes, and collaboration structures. A Scopus advanced search using the terms workplace deviance and Counterproductive Work Behavior (CWB) identified 1,624 peer-reviewed records from 2015 to 2025. Descriptive statistics and graphs were produced with Scopus Analyzer, records were cleaned and harmonized in OpenRefine, and VOSviewer was used to model keyword co-occurrence and co-authorship by country networks. Output rose steadily from 2015 and accelerated after 2020, peaking in 2024, with 2025 showing strong partial-year activity. The United States and China lead by volume, followed by Pakistan, India, the United Kingdom, Canada, Germany, Australia, Malaysia, and the Netherlands. Themes centre on CWB, organizational justice, leadership styles, abusive supervision, knowledge hiding, cyberloafing, psychological contract breach, and crisis-related contexts. Collaboration networks show dense hubs in North America and Asia with growing ties across South and Southeast Asia. Implications point to strengthening ethical climate, fair procedures, leadership capability, and norms for digital conduct, with attention to knowledge hiding and related knowledge management behaviors associated with retaliation and withdrawal. Overall, the study delivers an integrated and current map of the field, clarifies intellectual anchors, highlights connectors across individual, relational, and organizational mechanisms, and outlines fronts for replication and intervention. It offers a robust reference point for future empirical and conceptual work and supports evidence-informed strategy in organizational settings.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.203 | 0.265 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".