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Record W4292959034 · doi:10.5267/j.ijdns.2022.7.009

The effects of operational, relational, and transformational e-HRM practices on HR service effectiveness: The mediating role of user training

2022· article· en· W4292959034 on OpenAlexvenueno aff
Fatima Lahcen Yachou Aityassine

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployer Branding and e-HRM
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipHuman resource managementKnowledge managementService (business)Sample (material)Human resourcesBusinessPsychologyComputer scienceMarketingManagementSocial psychology

Abstract

fetched live from OpenAlex

This study aims at investigating the effects of e-HRM (operational, relational, and transformational) practices on the effectiveness of human resource service through user training. A questionnaire was used to collect data from a sample consisting of HR staff in fifteen food firms. The results showed that the influence of operational e-HRM on the effectiveness of human resource service was fully mediated by user training, and the influence of relational e-HRM on the effectiveness of human resource service was partially mediated by user training. Further, it was found that transformation has an insignificant effect on HR service effectiveness. The study contributes to the literature through identifying the effects of operational, relational, and transformational e-HRM practices on the effectiveness of human resource service and therefore help filling such a gap in literature. In addition to providing HR managers with results, acknowledge the importance of user training to implement e-HRM effectively.

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)0.006
metaresearch head score (Gemma)0.029
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
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 head0.039
GPT teacher head0.300
Teacher spread0.261 · 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
Published2022
Admission routes1
Has abstractyes

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