Are we in this together? Knowledge hiding in teams, collective prosocial motivation and leader-member exchange
Bibliographic record
Abstract
Purpose Although organizations expect employees to share knowledge with each other, knowledge hiding has been documented among coworker dyads. This paper aims to draw on social exchange theory to examine if and why knowledge hiding also occurs in teams. Design/methodology/approach Two studies, using experimental (115 student participants on 29 teams) and field (309 employees on 92 teams) data, explore the influence of leader-member exchange (LMX) on knowledge hiding in teams, as well as the moderating role of collective (team-level) prosocial motivation. Findings The results of experimental Study 1 showed that collective prosocial motivation and LMX reduce knowledge hiding in teams. Field Study 2 further examined LMX, through its distinctive economic and social facets, and revealed the interaction effect of team prosocial motivation and social LMX on knowledge hiding. Originality/value This study complements existing research on knowledge hiding by focusing specifically on the incidence of this phenomenon among members of the same team. This paper presents a multi-level model that explores collective prosocial motivation as a cross-level predictor of knowledge hiding in teams, and examines economic LMX and social LMX as two facets of LMX.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".