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Record W2611517192 · doi:10.5465/ambpp.2016.39

Explaining Employee Creativity: The Roles of Knowledge-sharing Efforts and Organizational Context

2016· article· en· W2611517192 on OpenAlexaff
Zahid Rahman, Dirk De Clercq, Barry Wright, Dave Bouckenooghe

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsBrock University
Fundersnot available
KeywordsCreativityKnowledge managementKnowledge sharingContext (archaeology)BusinessOrganizational learningEmployee researchPsychologyPublic relationsOrganizational commitmentComputer scienceSocial psychologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

This study contributes to creativity research by investigating the link between employees’ knowledge-sharing efforts and creativity and how this link is moderated by two aspects of relationship quality (informality and emotional openness) and by the belief that organizational decision making is marked by destructive political maneuvers. It theorizes that the usefulness of knowledge-sharing efforts for stimulating creativity is higher when employees maintain informal relationships with their colleagues and feel comfortable in expressing a diverse range of emotions with them. In addition, extensive knowledge-sharing efforts are less likely to enhance creativity when employees believe that organizational decision making is guided by destructive political games. Finally, the harmful effect of perceived organizational politics on the knowledge-sharing efforts-creativity relationship is mitigated when employees can rely on high levels of relationship quality. The results provide empirical support for these predictions, informing organizations regarding the circumstances in which the application of employees’ knowledge to the generation of novel solutions to problem situations is most effective.

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)0.022
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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.296
Teacher spread0.262 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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