The Impact of Electronic Human Resource Management (E-HRM) Practices on Human Resource Management Service Quality (HRMSQ): “An Applied Study on the Fast-Moving Consumer Goods Sector (FMCG) in Multinational Companies in Egypt”
Bibliographic record
Abstract
The purpose of this research is to examine the impact of electronic human resources management practices (E-HRM) across the dimensions represented in the (E-Recruitment, E-Selection, E-Training, E-Performance appraisal, E-Communication, and E-Compensation) on Human Resources Management Service Quality (HRMSQ) across the dimensions represented in (HR Strategic services, HR Supportive services, HR Executive (Administrative) services, and HR Change-related services). This research was conducted in the Fast-Moving Consumer Goods (FMCG) sector in the multinational companies in Egypt represented by all users of the E-HRM system at PepsiCo Egypt. Informal face-to-face structure interviews were first conducted in one of the companies of the FMCG sector that implement the E-HRM system. The research used questionnaire forms that consisted of 53 items and was distributed to 323 PepsiCo employees in Egypt who use the company E-HRM system. Descriptive analyses were then conducted to examine the correlation coefficients between variables of the research and testing hypotheses. The results revealed that there is a statistically significant impact of the dimensions of E-HRM practices on HRMSQ cross the dimensions represented in (HR Strategic services, HR Supportive services, HR Executive (Administrative) services, and HR Change-related services). It was also determined that there is a difference in employee’s perception towards the E-HRM Practices variable according to the demographic variables (age, educational qualifications, years of experiences, and job title). Finally, it revealed that there is a difference in employees’ perception towards the HRMSQ variable according to the demographic variables (age, educational qualifications, years of experiences, and job title).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".