Identify and Ranking the Factors Affecting Recruitment and Retention of Corporate Customers within Banking System
Bibliographic record
Abstract
Customers are considered as key factor in banks’ activities. Recruitment and retention of corporate customers pave the way for continuous growth and development of banks. Hence, today recruitment and retention of corporate customers pave the way to realize all aims, strategies and resources at successful banks. This will not be possible without examination of customers’ demands and awareness from factors affecting recruitment and retention of corporate customers. The present paper considers this point which factors affect recruitment and retention of corporate customers in banking system and how is the priority given to these factors? The present paper is an applied research type, for which a descriptive survey has been used for data collection. The statistical population consists of corporate customers at branches of Bank Melli-city of Bushehr selected using simple random sampling method. The questionnaire has been used as data collection instrument, that the validity and reliability of it has been confirmed. Using exploratory factor analysis, eight factors have been recognized as factors affecting recruitment and retention of corporate customers. The results of this study indicated that reputation and security are the most important factors affecting recruitment and retention of corporate customers, yet diversity at banking services is the least important factor affecting recruitment and retention of corporate customers.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".