Knowledge sabotage as an extreme form of counterproductive knowledge behavior: the perspective of the target
Bibliographic record
Abstract
Purpose This study aims to explore the existence of knowledge sabotage in the contemporary organization from the perspective of the target. Design/methodology/approach This study collected and analyzed 172 critical incidents reported by 109 employees who were targets of knowledge sabotage in their organizations. Findings Over 50 per cent of employees experienced at least one knowledge sabotage incident. Knowledge sabotage is driven by three factors, namely, gratification, retaliation against other employees and one’s malevolent personality. Knowledge saboteurs are more likely to provide intangible than tangible knowledge. Knowledge sabotage results in extremely negative consequences for individuals, organizations and third parties. Organizations often indirectly facilitate knowledge sabotage among their employees. Both knowledge saboteurs and their targets believe in their innocence – saboteurs are certain that their action was a necessary response to targets’ inappropriate workplace behavior, whereas targets insist on their innocence and hold saboteurs solely responsible. Practical implications Organizations should recruit employees with compatible personalities and working styles, introduce inter-employee conflict prevention and resolution procedures, develop anti-knowledge sabotage policies, clearly articulate the individual and organizational consequences of knowledge sabotage and eliminate zero-sum game-based incentives and rewards. Originality/value This is the first study documenting knowledge sabotage from the target’s perspective.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".