Organizational antecedents and talent turnover: A relational analysis of credit card departments of banks
Bibliographic record
Abstract
The study is conducted on the management factors affecting talent turnover in the banks’ credit card centers. For this purpose, the primary data are gathered from 73 respondents of credit card departments of banks listed in Pakistan Stock Exchange (PSX). Reliability test of questionnaire items, chi-square test, cross-tab, relational and regression analyses are used to analyze the interactions among variables. The study finds that work pressure and industrial development prospectus have positive linkage with dimensions of employee loyalty and talent turnover while compensation and benefits, promotion opportunities, management communication and work responsibilities are negatively associated with dimensions of employee loyalty and talent turnover. Conclusively, the study finds the following aspects as the main causes of talent turnover; loyalty imbalance, small promotion space and unstable working conditions. The prime cause of talent turnover in banks’ credit card businesses is loyalty imbalance. The study suggests banking firms to focus on trainings, recruitment and employees’ professional development with active characteristics of work to reduce the talent turnover rate. The banking firms should also provide opportunities to their employees to show their abilities and expertise.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".