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Record W2986492014 · doi:10.1142/s1363919620500693

EFFECTS OF EMPLOYEE CREATIVE PROBLEM-SOLVING ON INNOVATION OUTCOMES AND NON-FINANCIAL PERFORMANCE: THE MODERATING ROLE OF CULTURE AND COMMUNICATION

2019· article· en· W2986492014 on OpenAlexaff
Noufou Ouédraogo, Mohammed Laid Ouakouak, Tarek Salem

Bibliographic record

VenueInternational Journal of Innovation Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsMacEwan University
Fundersnot available
KeywordsStructural equation modelingSample (material)CreativityInnovation managementBusinessCreative problem-solvingKnowledge managementMarketingPsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

The purpose of this paper is to investigate the effects of employees’ creative problem-solving on organisations’ innovation outcomes, as well as the effects of innovation outcomes on organisations non-financial performance. Based on a sample of 320 participants from diverse organisations, using structural equation modelling techniques, we find that creative problem-solving does not have any effect on innovation outcomes, except with the moderating influence of an innovative culture or communication. We also find that innovation outcomes have a positive relationship with non-financial performance. We make theoretical contributions into the antecedents of innovation outcomes, while offering several practical insights for leading creative employees and managing innovation.

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)0.012
metaresearch head score (Gemma)0.065
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 score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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