Investigating the role digital transformation and human resource management on the performance of the universitie
Bibliographic record
Abstract
This study aims to analyze the effect of transformational leadership (TL) on organizational citizenship behavior (OCB), the relationship between TL and performance, TL and IWB, Leader member exchange (LMX) and OCB, LMX and performance, LMX and innovative work behavior ( IWB), organizational commitment (OC) and OCB, OC and performance, OC and IWB, digital transformation (DT) and OCB, DT and performance, DT and IWB, QWL and OCB, QWL and performance, QWL and IWB, OCB and performance, and the relationship between IWB and performance. The method of this research is quantitative and the population in this study were 341 private universities while the number of samples used in this study were 181 private universities. The sampling technique in this study used multistage random sampling. In this study the study used a seven-point Likert scale. The study uses SmartPLS software as a data processing tool. Validity testing is applied to all question items in each variable and there are several stages of testing that will be carried out, namely through convergent validity testing, average variance extracted (AVE) testing, and discriminant validity testing. There is a positive and significant relationship between TL and OCB, a positive relationship between TL and the performance of private universities. There is also a positive and significant relationship between TL and IWB, a positive and significant relationship between LMX and OCB. However, there is no significant relationship between LMX and university performance. There is a positive and significant relationship between LMX and IWB, a positive and significant relationship between OC and OCB, a positive and significant relationship between OC and university performance, and a positive and significant relationship between OC and IWB. There is also a positive and significant relationship between DT and OCB, an insignificant relationship between DT and university performance. This means that DT cannot directly affect performance. There is an insignificant relationship between DT and IWB, a positive and significant relationship between QWL and OCB. There is a non-significant relationship between QWL and university performance. There is a positive and significant relationship between QWL and IWB as well as between OCB and university performance and also between IWB and university performance.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".