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Record W2921933504 · doi:10.5267/j.msl.2019.2.013

The mediating effect of knowledge sharing among intrinsic motivation, high-performance work system and authentic leadership on university faculty members’ creativity

2019· article· en· W2921933504 on OpenAlexvenueno aff
Syed Ibn Ul Hassan, Badariah Haji Din

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyAuthentic leadershipIntrinsic motivationWork (physics)Knowledge sharingWork motivationKnowledge managementSocial psychologyComputer science

Abstract

fetched live from OpenAlex

The aim of this research was to investigate the relationship among intrinsic motivation, authentic leadership and high-performance work system (HPWS) on university faculty members with the mediating role of knowledge sharing. A total of 286 full-time faculty members of public universities from 30 universities of Punjab, Pakistan were interviewed using a five-point Likert-scale questionnaire, adapted from the literature. The results of PLS-SEM indicate that authentic leadership and HPWS had a significant effect on faculty member's creativity whereas intrinsic motivation showed an insignificant relationship with creativity. Results further highlight that knowledge sharing mediated the relationship between HPWS and employee's creativity, however, no mediation effect was found for intrinsic motivation and authentic leadership with employee creativity.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.016
GPT teacher head0.200
Teacher spread0.184 · 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

Citations35
Published2019
Admission routes1
Has abstractyes

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