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Record W2922524427 · doi:10.1002/hrdq.21344

Information sharing and innovative work behavior: The role of work‐based learning, challenging tasks, and organizational commitment

2019· article· en· W2922524427 on OpenAlexaff
Adalgisa Battistelli, Carlo Odoardi, Christian Vandenberghe, Gennaro Di Napoli, Luciano Piccione

Bibliographic record

VenueHuman Resource Development Quarterly · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsWork behaviorKnowledge managementOrganizational learningPsychologyTask (project management)Organizational commitmentInformation sharingStructural equation modelingWork (physics)Knowledge sharingSocial psychologyComputer scienceManagementEngineering

Abstract

fetched live from OpenAlex

The present study examines a model linking perceived information sharing as a Human Resource Management practice to employee innovative work behavior, using survey data collected from 756 employees of a military organization. Work‐based learning, challenging tasks, and organizational commitment were used as factors that could account for the relationship between information sharing and innovative behavior. Using structural equation modeling, findings indicated that information sharing had a positive relationship with task‐related and interactional dimensions of work‐based learning. Task‐related learning had a positive relationship with innovative behavior through challenging tasks while interactional learning had an indirect, positive relationship to innovative behavior via organizational commitment and challenging tasks. This article contributes to extend knowledge about the role of information sharing and work‐based learning in innovative work behavior. It also breaks new ground by uncovering potential antecedents of innovative behavior in military organizations.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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

Citations144
Published2019
Admission routes1
Has abstractyes

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