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Record W3212860276 · doi:10.5267/j.ijdns.2021.9.011

E-HRM practices and sustainable competitive advantage from HR practitioner’s perspective: A mediated moderation analysi

2021· article· en· W3212860276 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsContinuanceModerationKnowledge managementCompetitive advantageContext (archaeology)Human resource managementPath analysis (statistics)Structural equation modelingBusinessPerspective (graphical)PsychologyBusiness administrationMarketingComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

This paper seeks to investigate the impact of Electronic Human Resource Management (e-HRM) practices on attaining Sustainable Competitive Advantage (SCA) in the context of the Jordanian Industrial Sector (JIS) and identify the mediating role of e-HRM Perceived Usefulness (PU) and e-HRM Perceived Ease of Use (PEOU). Furthermore, it investigates the moderating role of User Satisfaction and e-HRM Continuance Usage Intention. To achieve the paper objectives, a Mediated-Moderation Model was designed. The researchers distributed (750) questionnaires, (615) questionnaires were returned and validated for analysis in HRM and development divisions and based on a Census method with the response rate was about (82%). The ‘Structural Equation Modeling’ (SEM) methodology was used, and for analysis, SPSS and Amos were applied. The results indicated that e-HRM practices had significant influence on SCA. The paper also demonstrated that there was a significant mediate effect of TAM constructs on the relationship between e-HRM practices and SCA. Finally, the findings indicated that the user satisfaction and e-HRM continuance usage intention did not moderate the relationship between e-HRM-PEOU and PU and SCA path.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.009
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.326
Teacher spread0.305 · 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