E-HRM practices and sustainable competitive advantage from HR practitioner’s perspective: A mediated moderation analysi
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".