Comparative significance of human resource management practices on banking financial performance with analytic hierarchy process
Bibliographic record
Abstract
Banking financial performance (BFP) has been recognized as a causation of human resource management (HRM).The causal linkages from HRM practices to BFP are different.Nevertheless, almost none of the studies has ranked and compared this difference among the practices of HRM in enhancing BFP.The current study applied an analytic hierarchy process to rank the relative importance of HRM practices.For the robustness of the results from the process of analytic hierarchy, the current work employed an analytic hierarchy process to reassess the relatively important levels of HRM practices on BFP as well.The findings are robust across both of the techniques.The practice of training and development (TT) plays the most critical part in BFP, followed by the practice of performance evaluation (PN) and the practice of reward system (RM) as the third most important.In contrast, the practice of recruitment and selection (RN) takes the least important position in BFP.Moreover, the findings also provide statistical evidence on the causal links from the practices of HRM to BFP.This work makes some contribution to how managers should decide on HRM practices in order to obtain the best possible BFP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".