An Empirical Study of Rural CustomerâÂÂs Satisfaction from EBankingin India
Bibliographic record
Abstract
In India there are 6, 40,867 villages and 68.84% of population resides in rural areas that offer a huge potential to the economy (Census 2011). Banking sector being the forefront of the economy has ventured into many innovative services to cater the need of these non-urban residents and e-banking is one of the most splendid offers in this context. E-banking has alchemized the conventional way of banking through providing countless benefits to its users. But the adaptability of e-banking in rural areas is not in consensus with the proliferate growth of e-banking observed in other areas. In this context the present paper attempts to explore different factors that might be interrupting the burgeoning development of e-banking in rural areas. The study is based upon the primary data collected from 520 rural respondents regarding 17 variables which are expected to affect the satisfaction level of e-banking users. The data has been tested through Cronbach Alpha, Kaiser-Meyer-Olkin measure, Bartlett’s test and correlation among different variables. It has further been analyzed though factor analysis, regression analysis and ANOVA. The study attempts to submit some suggestions to enhance the level of overall satisfaction of rural customers and resultant rise in the propensity to use e-banking as a primary channel of banking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".