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Record W4417149665 · doi:10.1080/14778238.2025.2598823

The mediating role of perceived team member exchange in the relationship between work engagement and knowledge manipulation and knowledge hiding

2025· article· en· W4417149665 on OpenAlexaff
Jessica R. L. Good, You‐Ta Chuang, Mark Podolsky, Michael Halinski

Bibliographic record

VenueKnowledge Management Research & Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork UniversityToronto Metropolitan UniversityAthabasca University
Fundersnot available
KeywordsKnowledge sharingWork (physics)Social exchange theoryKnowledge workerWork engagementMediationTacit knowledge

Abstract

fetched live from OpenAlex

Knowledge provides employees with a strategic advantage. Thus, while organisations may encourage employees to share their knowledge, individuals may choose to manipulate or hide knowledge to maintain this advantage. By drawing from the broaden-and-build theory, this study examines the indirect effect of work engagement on knowledge manipulation and knowledge hiding via individual perceived team member exchange. In a time-separated field study (n = 128), results show that individual perceived team member exchange fully mediates the relationship between work engagement and knowledge manipulation and knowledge hiding, and that job tenure moderates the relationship between individual perceived team member exchange and knowledge manipulation, but not between individual perceived team member exchange and knowledge hiding. This paper contributes to the existing body of research on knowledge hiding and the growing literature on knowledge manipulation by uncovering affective and relational mechanisms as well as boundary conditions that impact these behaviours.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.217
GPT teacher head0.462
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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