Bibliographic record
Abstract
Backgrounds/Objectives: The present study attempts to identify the effectiveness of CRM and to determine the lacunae in the process of CRM by establishing an empirically tested CRM model. Methods/Analysis: Analytical and descriptive types of research have been carried out for the purpose of the study. The majority of the study is conducted using primary data. Simple Random Sampling Method is used to gather the primary data. The sample for the research study is selected scientifically. Two sets of questionnaires have been used for the study to collect information from Customers and Bank Employees. Findings: The average mean scores of six elements of CRM of customers and employees are 21.23 and 24.53 respectively. This parametric yield is a perfect projection of customers and employees perception. Since the services/facilities are offered by the banks, it is considered to be 100% for CRM effectiveness. The total mean scores of the employees are considered as the effectiveness of CRM in customers’ perceptions. The percentage difference would reveal the effectiveness side of CRM as well as the lacunae in the process. The model concludes that the customers’ perception of the CRM elements is effective at 86.55% (21.23/24.53*100) level and the lacuna is 13.45%. The banks have to employ specific strategies to fulfill the lacunae in the process of CRM and to obtain the full effectiveness of CRM. The study has given a clear message that the real challenge before the banks is to translate sentiments into dealings, and a dealings-based relationship into a psychologically linked and dedicated one within a time period. Novelty: The study has developed an empirically tested CRM model for the banks to acquire new customers and retain the existing ones.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".