Factors affecting customerâÂÂs satisfaction of Debit card: A comparative study between Islamic banks and conventional banks in Bangladesh
Bibliographic record
Abstract
Purpose The paper aims at making comparative analysis about debit card user’s satisfaction of Islamic and conventional banks of Bangladesh. Furthermore, this paper also tries to point out the key factors of customer satisfaction in study area. Methodology A well-planned questionnaire was distributed to 300 debit card user from which 150 from Islamic banks and rest 150 is from conventional bank. These customers were selected through random sampling. For analysis questionnaire responses were coded, summarized, and analyzed using the Statistical Package for Social Sciences (SPSS windows version 20). Also bi-variate& regression model has been used to find out the important factors that influence most the customer’s satisfaction regarding debit card of both type of banks. Findings The study found the users of conventional banks more satisfied than that of Islamic banks. Result indicates that, security and responsiveness and different value denominated notes are most important factors in conventional banks customers’ satisfaction whereas in Islamic banks, availability of taka and transaction cost are comparatively less important. Originality/value Multiple electronic payment method has been emerged, but scant attention paid to measure the customer satisfaction in the study area. So, this paper will add value in this field of study.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".