Antecedents and consequences of knowledge sabotage in the Turkish telecommunication and retail sectors
Bibliographic record
Abstract
Purpose This study aims to propose and test a model explicating the antecedents and consequences of knowledge sabotage. Design/methodology/approach Data obtained from 330 employees working in the Turkish retail and telecommunication sectors were analyzed by means of the Partial Least Squares Structural Equation Modeling technique. Findings Co-worker knowledge sabotage is the key factor driving knowledge sabotage behavior of individual employees, followed by co-worker incivility. Interactional justice suppresses individual knowledge sabotage, while supervisor incivility does not affect it. Co-worker knowledge sabotage reduces job satisfaction of other employees, which, in turn, triggers their voluntary turnover intention. Contrary to a popular belief that perpetrators generally benefit from their organizational misbehavior, the findings indicate that knowledge saboteurs suffer from the consequences of their action because they find it mentally difficult to stay in their current organization. Employees understate their own knowledge sabotage engagement and/or overstate that of others. Practical implications Managers should realize that interactional justice is an important mechanism that can thwart knowledge sabotage behavior, promote a civil organizational culture, develop proactive approaches to reduce co-worker incivility and strive towards a zero rate of knowledge sabotage incidents in their organizations. Co-worker incivility and co-worker knowledge sabotage in the workplace are possible inhibitors of intraorganizational knowledge flows and are starting points for job dissatisfaction, which may increase workers’ turnover intention. Originality/value This study is among the first to further our knowledge on the cognitive mechanisms linking interactional justice and uncivil organizational behavior with knowledge sabotage and employee outcomes.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".