It’s mine but you took it: knowledge theft as a barrier to organizational knowledge management efforts
Bibliographic record
Abstract
Purpose Knowledge theft represents a significant barrier to knowledge management initiatives. Yet, despite recent attention in the popular press, little is known about the phenomenon overall. This study aims to fill this gap through the development of a reliable and valid measure of knowledge theft. Design/methodology/approach Using over 1,500 participants in seven separate samples, the authors engage in a process of item generation and establish the construct, convergent and discriminant validity of a knowledge theft scale. Findings The results demonstrate that knowledge theft is distinct from other forms of interpersonal deviance, such as social undermining and interpersonal aggression. Additionally, employees who have experienced knowledge theft report increased intentions to engage in knowledge hiding, defensive silence and other counterproductive work behaviors that might impede knowledge management efforts in organizations. Originality/value To the best of the authors’ knowledge, this research represents the first attempt to systematically study knowledge theft in organizations and demonstrates the ubiquity of the phenomenon. Further, the newly developed knowledge theft scale allows future research in this area to uncover the impact of knowledge theft on victims, witnesses, perpetrators, and organizations.
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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 | 0.015 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".