Bibliographic record
Abstract
The main objective of the present study was to identify and prioritize the factors which were influential on bank customers' preferences in choosing Ayandeh Bank among the state and private banks in Tehran.To this end, an analysis of books, papers and researches in the literature revealed 37 factors as the primary factors that were influential.Population was all the customers of Ayandeh Bank in Tehran.The sample chosen from this population comprised of 267 customers via Cochran technique; random stratified sampling was used for the sampling purpose.A questionnaire was used as an instrument for the collection of the influential factors of choosing Ayandeh Bank.Results obtained from the factor analysis showed that all the 37 factors were considered to be affecting the issue under study and they were categorized into 6 categories.Results of Friedman test indicated that the item 'few number of bank' accusations had the highest frequency of being preferred among the other items of the first category.The most frequently preferred item in the second category was 'keeping customers' security.It was the 'low interest rate of loans' in the third category and 'having bank website' was the highest chosen item in the fourth category.Item 'appropriate location' was mostly chosen in the fifth category, and finally in the sixth category, 'good proportion of the number of bank locators and number of customers' had the highest preference of customers in choosing Ayandeh Bank among all the state and private banks by the customers.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.953 | 0.963 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".