Customers’ satisfaction with internet banking: Evidence from Bangladesh
Bibliographic record
Abstract
This study investigates the relationship of customers’ satisfaction with different internet banking services provided by Agrani Bank Limited using primary and secondary data. To investigate the relationship between customers’ satisfaction and the different internet banking services provided by Agrani Bank, a linear multiple regression model consisting of five independent variables – income of the customer (IC), maintaining the account (MA), using the ATM card (ATM), purchasing any product through the internet in the last 12 months (PP), and frequently visiting the bank (FV) – is used. These independent variables have been tested to evaluate internet banking structure, operations and to examine the customers’ satisfaction in Agrani Bank Limited. ANOVA and the coefficients techniques are also applied to examine the causal relationships between the variables. Results show that among five explanatory variables only IC and PP significantly affect the customers’ satisfaction, but the other three variables do not. Therefore, customers’ satisfaction (CS) with internet banking in Agrani bank depends on the income of the customers (IC) and purchasing a product through the internet in the last 12 months (PP). Keywords: Customers’ satisfaction, internet banking, multiple regression model, ANOVA, coefficient technique, Agrani Bank Limited
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".