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Record W4409517893 · doi:10.1002/cjas.70006

Examining the Impact of Role Overload on Knowledge Hiding and Knowledge Manipulation

2025· article· en· W4409517893 on OpenAlexaffvenue
Jessica R. L. Good, You‐Ta Chuang, Mark Podolsky, Michael Halinski

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork UniversityToronto Metropolitan UniversityAthabasca University
Fundersnot available
KeywordsInformation overloadKnowledge managementComputer scienceBusinessPsychologyWorld Wide Web

Abstract

fetched live from OpenAlex

ABSTRACT Role overload was rated as one of the top workplace stressors by the American Psychological Association in 2015. Drawing from the stressor‐emotion model of Counterproductive Work Behaviour, we examine the indirect effect of role overload on knowledge hiding and manipulation, via negative affect. In a 3‐wave field study ( n = 161), our analysis found that negative affect fully mediates the relationship between role overload and knowledge hiding, and partially mediates the relationship between role overload and knowledge manipulation. This study extends research on the antecedents of knowledge hiding and knowledge manipulation by adopting an emotional lens, and suggests that organizations should take steps to decrease role overload in order to reduce the costs associated with knowledge hiding and knowledge manipulation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
GPT teacher head0.386
Teacher spread0.229 · 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; both teacher heads agree on what is shown here.

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 routes2
Has abstractyes

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