Mum’s the Word: Leadership’s Role in Workplace Knowledge Hiding
Bibliographic record
Abstract
Mum’s the Word: Leadership’s Role in Workplace Knowledge Hiding Pouya Nikbakhsh Knowledge is a critical strategic asset, yet deliberate knowledge hiding poses significant challenges to collaboration and innovation. This thesis examines how supervisors’ rationalized knowledge hiding influences employees’ tendencies to hide knowledge, using social learning theory and perceived supervisor role modeling. Drawing on Leader–Member Exchange (LMX) theory, it also explores whether high-quality supervisor–subordinate relationships moderate these effects. Data collected via Prolific from full-time employees across various industries were analyzed using regression, mediation, and moderated mediation models. Findings show that supervisors’ rationalized knowledge hiding strongly influences employees’ similar behaviors, but not through explicit role modeling. Additionally, LMX quality did not significantly moderate these relationships. These results highlight the impact of leadership behaviors on workplace knowledge dynamics. While supervisors’ direct influence is notable, role modeling and LMX alone do not fully explain how knowledge-hiding norms spread. Organizations aiming to reduce knowledge hiding should address supervisory behaviors and foster a culture of transparency and collaboration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".