Tell me if you can: time pressure, prosocial motivation, perspective taking, and knowledge hiding
Bibliographic record
Abstract
Purpose The belief that knowledge actually expands when it is shared has been deeply rooted in the mainstream knowledge management literature. Although many organizations and managers expect employees to share their knowledge with their colleagues, this does not always occur. This study aims to use the conservation of resources theory to explain why employees who experience greater time pressure are more likely to engage in knowledge hiding; it further considers how this behavior may be moderated by these employees’ prosocial motivation and perspective taking. Design/methodology/approach The paper uses quantitative multi-study research design as a combination of two-wave field study among 313 employees at an insurance company and a lab experimental study. Findings In the field study (Study 1), the authors find that perceived time pressure is positively related to knowledge hiding. Furthermore, this relationship is moderated by prosocial motivation: employees who perceive greater time pressure hide knowledge only when they are low in prosocial motivation. An experiment (Study 2) replicates these findings, and finds that perspective taking mediates the moderating effect of prosocial motivation on the relationship between time pressure and knowledge hiding. Research limitations/implications Despite its many contributions, the present research is also not without limitations. Study 1 was a cross-lagged sectional field study with self-reported data (although the two-wave design does help alleviate common-method-bias concerns). Causality concerns were further alleviated by using additional experimental study. Practical implications The paper highlights important reasons why people hide knowledge at work (because of experienced time pressure) as well as identifies two interlinked potential remedies (prosocial motivation and perspective taking) to reduce knowledge hiding. Originality/value This paper contributes to expanding nomological network of knowledge hiding construct by extending the set of known antecedents and contingencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".