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Record W4417299555 · doi:10.63329/av3nz1231

Knowledge Hiding and Occupational Stress: A Systematic Review and Sectoral Analysis

2025· article· en· W4417299555 on OpenAlexaff
Harmandeep Kaur, Irfan ul Haq

Bibliographic record

VenueScientific Societal & Behavioral Research Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsConcordia UniversityYorkville University
Fundersnot available
KeywordsOccupational stressKnowledge sharingKnowledge workerStress (linguistics)Work (physics)Job stressBody of knowledgeOrganizational structure

Abstract

fetched live from OpenAlex

Employee performance and organizational outcomes are influenced by critical factors such as knowledge hiding and occupational stress across various sectors. A systematic review is conducted, and findings from 130 studies are synthesized to explore the interplay between knowledge hiding, occupational stress, and their impact on employees. Key antecedents of knowledge hiding, such as ethical leadership, workplace incivility, and organizational politics, are identified, and the role of occupational stress in mediating or moderating these relationships is examined. It is revealed that stress levels are exacerbated by knowledge hiding, resulting in reduced job satisfaction, organizational commitment, and performance. The negative effects are mitigated by ethical leadership and supportive work environments. Differences across sectors are highlighted, with knowledge hiding and stress being found to be more prevalent in high-pressure industries like IT, restaurant, and healthcare. A comprehensive framework for understanding these phenomena is provided, and practical recommendations are offered to organizations for fostering knowledge sharing and reducing stress.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.219
GPT teacher head0.532
Teacher spread0.313 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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