Assessing the Impact of Electronic Human Resource Management on Creation of Organizational Agility: A Study in the Bushehr Banks, Iran
Bibliographic record
Abstract
Agility is a series of capabilities and competencies, which can cause survival and growth of the organization in business environment. One of the approaches that help organizations to enhance agility is electronic human resource management (E-HRM) approach. The main objective of the present study is finding an answer for this issue that how one can use E-HRM approach to develop organizational agility. In this study that has been conducted using survey method, after review of foreign and domestic research literature and construction of conceptual model, a questionnaire would be provided and distributed among employees of bank branches of Bushehr City in Iran and finally, collected data from 315 questionnaires would be analyzed using structural equation modeling method. Obtained results from the study confirm the presented conceptual model and indicate that using electronic learning of human resources, electronic payment of human resources, human resources maintenance and electronic performance appraisal of human resources and also electronic human resource management (E-HRM) can affect organizational agility significantly in confidence level of 99%. In addition, effect of variables of Electronic employment of human resources and human resource communications on organizational agility has not been confirmed.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".