Talent management model: How to boost the central bank’s performance in the disruptive era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it