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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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.011
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

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