Evasive knowledge hiding in academia: when competitive individuals are asked to collaborate
Bibliographic record
Abstract
Purpose Academic knowledge work often presumes collaboration among interdependent individuals. However, this work also involves competitive pressures to perform and even outperform others. While knowledge hiding has not yet been extensively examined in the academic environment, this study aims to deepen the understanding of the personal (individual-level) and situational (job-related) factors that affect evasive knowledge hiding (EKH) within academia. Design/methodology/approach A field study was conducted on a nation-wide sample of 210 scholars from both public and private business schools in a European Union member state. A series of paired samplet-tests were followed by hierarchical regression analyses to test moderation using the PROCESS macro. Findings The results suggest that scholars hide more tacit than explicit knowledge. The findings also indicate a consistent pattern of positive and significant relationships between trait competitiveness and EKH. Furthermore, task interdependence and social support buffer the detrimental relationship between personal competitiveness and evasive hiding of explicit knowledge, but not tacit knowledge. Originality/value The research provides insights into several important antecedents of EKH that have not been previously examined. It contributes to research on knowledge transfer in academia by focusing on situations where colleagues respond to explicit requests by hiding knowledge. The moderating role of collaborative job design offers practical solutions on how to improve knowledge transfer between mistrusted and competitive scholars. The collaboration–competition framework is extended by introducing personal competitiveness and relational job design, and suggesting how to manage the cross-level tension of differing collaborative and competitive motivations within academia.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".