Talent management model: How to boost the central bank’s performance in the disruptive era
Bibliographic record
Abstract
Organizations are increasingly acknowledging the vital impact of talent management on boosting their performance. Effective talent management within the central bank is crucial, as it plays an indispensable role in maintaining economic stability and advancing the nation’s financial well-being. The study aims to examine the role of talent management, transformational leadership, organizational climate, employee engagement, employee performance, and organizational commitment in increasing the central bank’s performance. The study uses a quantitative approach by collecting data from 600 sample employees of Bank Indonesia in 30 divisions of departments at the head office, 45 domestic representative offices, and 5 foreign representative offices. The data was analyzed using Structural Equation Modelling-LISREL. The finding shows that transformational leadership has a positive impact on talent management. Talent management has a positive impact on organizational climate, employee engagement, and organizational commitment. Organizational climate has a positive impact on employee engagement. Employee engagement has a positive impact on organizational commitment. Organizational climate, employee engagement, and organizational commitment have a positive impact on employee performance, while talent management does not have a positive impact on employee performance. Employee performance, organizational commitment, and talent management have a positive impact on organizational performance. The study offers valuable insights into talent management practices within central banks. It serves as a guide for central bank management and human capital professionals in formulating policies to enhance performance amidst disruptive times. Additionally, educators can leverage these findings to develop curricula that align more closely with industry demands and produce competent graduates ready to excel on the global stage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".