MétaCan
Menu
Back to cohort

Examination of the Role of Competitive Work Environment in Enhancing Employees’ Openness to Share Knowledge

2024· article· en· W4400439754 on OpenAlexaff
Nour AlBuloushi, Noufou Ouédraogo, Mohammed Laid Ouakouak, Gertrude I. Hewapathirana

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsMacEwan University
Fundersnot available
KeywordsOpenness to experienceBusinessWork (physics)Work environmentKnowledge managementCompetitive advantagePsychologyMarketingWork performanceComputer scienceSocial psychologyEngineeringBusiness administration

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the effect of competitive work environment on openness to knowledge sharing. A conceptual model, drawing on the existing literature, is developed to analyze how a competitive work environment contributes to openness to sharing knowledge among employees in organizations. The conceptual model includes coworker desire to learn as a mediating variable and incentives to knowledge sharing and job security as moderating variables. Data collected from eight banks with a total of 237 employees is used to test the research hypotheses using structural equation modelling techniques. The results show that coworker desire to learn mediates the relationship between competitive work environment and openness to knowledge sharing, and both incentives to knowledge sharing and job security moderate the effect of competitive work environment on openness to knowledge sharing. We make theoretical and practical contributions in knowledge management by showing the mechanism through which competitive work environment contributes to openness to share knowledge in organizations.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicOrganizational and Employee PerformanceFrench-language works237,207