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Employee creativity: antecedents and outcomes

2017· article· en· W2766223241 on OpenAlexaboutno aff
Mohammed Laid Ouakouak, Noufou Ouédraogo

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyEmpirical researchKnowledge managementValue (mathematics)Sample (material)Organizational cultureConceptual frameworkPublic relationsSocial psychologySociologyPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

As an important source of organizational innovation, employee creativity is considered by most scholars and practitioners as a must for organizations. In this vein, organizational members should and must engage actively in generating new and valuable ideas. Various scholars have emphasized the value of identifying and understanding the factors encouraging employees to develop creative ideas. The purpose of this research is to investigate the factors fostering individual creativity and organizational innovation. To this end, we have developed a conceptual model and tested it with an empirical study based on a sample of 307 participants from Canadian organizations. The findings generally support the hypothesized relationships. Knowledge sharing and person–organization fit have positive impacts on individual creativity. Furthermore, personal trust moderates the relationship between business ethics and individual creativity. The results also show that an initiative-friendly culture moderates the impact of individual creativity on organizational innovation. These findings offer a new framework for developing further creativity studies, as well as important practical implications for managers.

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)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.120
GPT teacher head0.413
Teacher spread0.293 · 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 designQualitative
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
Published2017
Admission routes1
Has abstractyes

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