Influence of Demographic Profile On Acceptance of InternetBanking In A Non Metro City In Tamil Nadu, IndiaAn Empirical Study
Bibliographic record
Abstract
The study examines the influence of demographic variables age, gender, marital status, educational level, occupation, monthly income and type of account held by the clients in non metro cities of Tamil Nadu, India. The paper builds on existing literature in demographic variables towards acceptance of internet banking. Quantitative analysis was carried out by collecting response through a well designed questionnaire to clients who have a bank account in the private sector banks and living in non metro cities of Tamil Nadu. A sample of 200 customers from private sector banks was surveyed from Vellore city which is a non metro city. To analyze the data, chi square tests were used in testing the association of the demographic variables with internet bank acceptance. Out of seven demographic variables associated with intention to use and the study revealed that five variables like age, gender, educational qualification, monthly income and type of account held illustrate significance towards acceptance of internet banking services in Vellore city. The study was done on private bank customers in Vellore city only hence the results cannot be generalized.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".