The role of information technology (IT) performance in the relationship between high-performance work systems and competitive advantage
Bibliographic record
Abstract
Human resources that are of high quality create a competitive advantage for companies, thus Human Resource Management (HRM) and High-Performance Work Systems (HPWS) that are good become key success factors that need to be considered by every company. This study aims to investigate the influence of Human Resource Management (HRM) and High-Performance Work Systems (HPWS) on Information Technology (IT) Performance and Competitive Advantage of a company. The method used in this study is a quantitative approach with a questionnaire as the data collection method. The research sample consisted of 191 supervisors, managers, and executives of manufacturing companies located in Medan, Indonesia. Data analysis was conducted using SmartPLS 4.0 software. The results showed that Human Resource Management significantly affects IT Performance but does not directly affect Competitive Advantage. Meanwhile, HPWS Capability does not affect IT Performance but significantly affects Competitive Advantage. IT Performance significantly affects Competitive Advantage. IT Performance also mediates the relationship between Human Resource Management and Competitive Advantage. However, it is not significant in mediating the relationship between HPWS Capability and Competitive Advantage. These findings underscore that Human Resource Management (HRM) and High-Performance Work Systems (HPWS) play pivotal roles in shaping Information Technology (IT) performance and competitive advantage within companies, thus impacting the overall success and sustainability of companies.
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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 | 0.003 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".