Bibliographic record
Abstract
In this first quarter of the 21st century, the fact that digital technology applications are becoming more involved in people's lives has caused more changes in the banking ecosystem than ever before. This change has directed banking institutions to use the possibilities of digital technology channels as much as possible to deliver and offer their services to their customers. Formerly, a bank customer had to go to a branch of that bank in order to receive services from the bank, but now, customers can make the banking transactions they need using the various service channels offered by the bank without having to go to a physical bank branch. As a result of all these developments, it is believed that the technological and digital service channels that currently used by banks have become an important factor in determining the bank where their customers will work / receive services. This new banking ecosystem in the bank individual customers of our study today's preference for measuring the factors in order to develop an appropriate scale developments in the field of banking, the online survey, the data obtained from the Explanatory Factor Analysis (AFA) and Confirmatory Factor Analysis (DFA) are reviewed. 1,163 participants who are individual bank customers participated in the survey conducted throughout Turkey. Participants were asked 32 questionnaires to find out their bank preferences. With the data obtained, AFA analysis was performed in SPSS 26 program and DFA analysis was performed in AMOS 24 program. The scale developed as a result of both analyzes is appropriate and the factors that affect the bank preference of individual customers; It has been concluded that "Caring for Customer Satisfaction, Offering Digital Services, Offering Extra Advantages, Being Strong and Having Domestic Capital, Providing Quality Service at Low Cost, Providing High Profit, Having an Accessible Service Network".
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".