Knowledge sabotage as an extreme form of counterproductive knowledge behavior: conceptualization, typology, and empirical demonstration
Bibliographic record
Abstract
Purpose This paper introduces the concept of knowledge sabotage as an extreme form of counterproductive knowledge behavior, presents its typology, and empirically demonstrates its existence in the contemporary organization. Design/methodology/approach Through the application of the critical incident technique, this study analyzes 177 knowledge sabotage incidents when employees intentionally provided others with wrong knowledge or deliberately concealed critical knowledge while clearly realizing others’ need for this knowledge and others’ ability to apply it to important work-related tasks. Findings Over 40% of employees engaged in knowledge sabotage, and many did so repeatedly. Knowledge saboteurs usually acted against their fellow co-workers, and one-half of all incidents were caused by interpersonal issues resulting from the target’s hostile behavior, failure to provide assistance to others, and poor performance. Knowledge sabotage was often expressed in the form of revenge against a particular individual, who, as a result, may have been reprimanded, humiliated or terminated. Knowledge saboteurs rarely regretted their behavior, which further confirmed the maliciousness of their intentions. Practical implications Even though knowledge saboteurs only rarely acted against their organizations purposely, approximately one-half of all incidents produced negative, unintentional consequences to their organizations, such as time waste, failed or delayed projects, lost clients, unnecessary expenses, hiring costs, products being out-of-stock, understaffing, or poor quality of products or services. Organizations should develop comprehensive knowledge sabotage prevention policies. The best way to reduce knowledge sabotage is to improve inter-personal relationships among employees and to foster a friendly and collaborative environment. Originality/value This is the first well-documented attempt to understand the phenomenon of knowledge sabotage.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".