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Record W3136088698 · doi:10.1108/ejim-05-2020-0199

Creative leadership, innovation climate and innovation behaviour: the moderating role of knowledge sharing in management

2021· article· en· W3136088698 on OpenAlexaff
Pinghao Ye, Liqiong Liu, Joseph Tan

Bibliographic record

VenueEuropean Journal of Innovation Management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsKnowledge sharingIncentiveKnowledge managementBusinessAntecedent (behavioral psychology)Organisation climateMarketingStructural equation modelingPsychologyEconomicsSocial psychology

Abstract

fetched live from OpenAlex

Purpose Innovation, in most enterprises, originates from employees. In this study, how organizational climate, creative leadership ability and emotional reaction to imposed change impact on innovative behaviour of employees vis-à-vis knowledge sharing within the workplace is explored. Design/methodology/approach Adopting a social cognitive perspective, a model is constructed to explain factors influencing the innovation behaviour of employees along two key aspects, that is, organizational climate (innovation vs risk-taking climate) and creative leadership ability (leadership skills, vision incentive) vis-à-vis other moderating factors. A survey questionnaire, administered to a total of 311 manufacturing employees in China, was used to verify the proposed research model via Smart PLS. Findings Results unveil several key factors impacting positively on creative leadership in organizations. Specifically, creative leadership ability, emotional reaction to imposed change, innovation climate and knowledge sharing are found to impact positively on innovation behaviour while supportive versus risk-taking climate as well as emotional reaction are found to impact positively on innovation climate. Additionally, knowledge sharing is found to regulate the relationship between innovation climate and innovation behaviour. Research limitations/implications While offering insights into the antecedent factors of innovation behaviour, the study extends research on the intermediary role of innovation climate and employees' innovation behaviour. Additionally, it improves one's understanding on the moderating role between knowledge sharing and innovation behaviour. Practical implications The study findings will assist enterprises in diagnosing the implementation environment of innovation strategy, thereby providing a reference for training enterprise leadership while improving the employees' understanding of innovation and reform in the workplace. Originality/value The study contributes both theoretical and managerial thinking on the extent in which organizational climate and creative leadership ability may and/or should be evolved appropriately to support, encourage and nurture employees' innovation behaviour in the workplace.

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 score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.085
GPT teacher head0.318
Teacher spread0.233 · 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

Citations135
Published2021
Admission routes1
Has abstractyes

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