Ethical reputation and retail bank selection: a sequential exploratory mixed-methods study in an emerging economy
Bibliographic record
Abstract
Purpose In this study, the authors examine how a retail bank's positive, neutral, and negative prior ethical reputations influence customers' perceptions and attitudes, leading to their bank selection decisions and also analyze whether there is a trade-off between a bank's negative prior ethical reputation and its functional benefits to customers. Design/methodology/approach The authors followed a sequential exploratory mixed-methods research design with two studies. The authors’ first study was qualitative, in which the authors conducted interviews and focus groups with banking customers in Pakistan. The results of this study were used to generate hypotheses that were tested in the second study using random choice experiments. Findings The results indicate that positive and neutral prior ethical reputations do not significantly impact customers' choices; however, a negative reputation does affect selection. The results also show that customers punished negative reputations, even when the associated functional benefits were higher than the alternatives. Originality/value This is one of the first mixed-methods studies in an emerging economy context to consider the impact of ethical reputation on consumer orientation and bank selection decisions.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".