Investigating electronic human resource management systems, sustainable innovation, and organizational agility on sustainable competitive advantage in the manufacturing industries
Bibliographic record
Abstract
This research aimed to analyze the relationship between electronic human resources (e-HRM) and sustainable competitive advantage, organizational agility and sustainable competitive advantage, and sustainable innovation and sustainable competitive advantage in the manufacturing industry in Indonesia using a quantitative approach. The population of this research was managers of manufacturing companies in Indonesia. A total of 800 online questionnaires were sent using a simple random sampling method and 540 valid questionnaires were received. The questionnaire contains statement items using a Likert scale from 1 to 7. To measure the structural model and to test research hypotheses, the research used the PLS-SEM method with WarPLS 7.0 software. The stages of data analysis in this research were reliability and validity tests, significance tests and hypothesis testing. This research concludes that e-HRM had a positive and significant relationship with sustainable competitive advantage, Organizational Agility had a positive and significant relationship with sustainable competitive advantage and sustainable innovation has a positive and significant relationship with sustainable competitive advantage. The research emphasizes that e-HRM practices encourage sustainable innovation and organizational agility to achieve competitive advantage. The study also provides a more comprehensive understanding that e-HRM practices contribute to sustainable competitive advantage by driving continuous innovation and strengthening organizational agility. The research emphasizes the importance of utilizing digital technology for human resource management (HRM) processes, organizations must implement digital transformation by adopting e-HRM practices to increase manufacturing efficiency and effectiveness thereby increasing performance and competitiveness. This research encourages companies to increase continuous innovation to increase sustainable competitiveness.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".