Customer Perception of Mobile Banking: An Empirical Study inNational Capital Region Delhi
Bibliographic record
Abstract
Internet technology is regarded as the third wave of revolution after agricultural and industrial revolution. After phone and net banking, technology is heralding the era of mobile banking in India. The growth of Mobile banking is phenomenal compared to previous deliver channels. It took approximately twenty years for ATMs to become popular while online banking took a decade. More so, with India all set to emerge as the second largest mobile subscriber base in the world after China, the telecom operators and banks are raring to use this medium to offer banking services including fund transfers to all sorts of people. Mobile banking can be categorized as the latest advancement in electronic banking. This paper has examined the adoption and impact of mobile banking in on customer of different banks. The study surveys the opinion of 200 customers of banks located in Delhi. ANOVA and Factor Analysis have been used for having insights in the mobile banking services provided by the different banks. The population studied here is urban population which can be considered as representative of banking customers in Delhi.
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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 | 0.004 | 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 | 0.001 | 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".