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Record W2945429283 · doi:10.5267/j.msl.2019.5.014

The effect of high involvement work systems on organizational performance: The mediating role of knowledge-based capital

2019· article· en· W2945429283 on OpenAlexvenueno aff
Yousef Ahmed Hussein, Dilber Çağlar

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingHuman capitalModerationSocial capitalPsychologyWork (physics)Organizational performanceKnowledge managementHuman resourcesWork systemsSocial psychologyLeadership styleBusinessManagementComputer sciencePolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

This study investigate the effects of high involvement human resources on organizational performance.The study is accomplished through hypothesizing the effect of high involvement work systems on performance as the main relationship.This effect is theorized to be mediated through knowledge-based capital.Additionally, leadership style is considered as the potential moderating force in the theorized model.Data is analyzed from 380 collected questionnaires distributed among hotel employees in Jordan using structural equation modelling techniques.The results show that high involvement work systems had a positive effect on performance, social and human capital.Human and social capital mediating role is also observed.Finally, leadership style is emerged as a moderating variable affecting the relationship between high involvement work systems and performance.The findings of this study show the importance of the role played by human and knowledgebased capital in achieving human resources performance goals.

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)0.009
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
Metaresearch0.0020.009
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 integrity0.0000.001
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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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