The effect of e-HRM and digital orientation on MEs' performance in Amman: The moderating role of government support
Bibliographic record
Abstract
Human resource management is essential to the success of the organization based on the concept that an organization achieves a competitive edge by effectively and efficiently utilizing its people. However, HR professionals require organizational support to increase employee commitment and passion for their work. In this study, the goal of the study is to explore the moderating impact of government support in the relation between electronic human management (E-HRM), digital orientation, and performance of medium enterprises. To examine the relationships, the researcher collected data from 309 managers from Amman's medium-sized businesses via a survey questionnaire. Partial least squares-structural equation modelling (PLS-SEM) is used in statistical analysis to evaluate the data as well as test hypotheses. The data showed that E-HRM and digital orientation have a good and substantial impact on ME performance. Furthermore, government support has a positive and significantly moderating effect between digital orientation and ME performance. On the other hand, government support has insignificant moderating influence on E-HRM and ME performance. This research extends to the literature on digital services and electronic human resource management practices in the sector of local medium enterprises. This research also discusses the implications and the future directions. One of these is that the study framework gives guidelines to HR practitioners on what competences they should focus on to improve in digital and electronic human resource management. According to the results of this research, HR professionals in medium-sized businesses should be involved in digital services, strategy planning and implementation in their organizations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".