Bibliographic record
Abstract
Purpose: To explore the demand and adoption of e-banking services in Bihar Demand/Methodology/Approach: The survey was conducted for 2 months using questionnaire made with four point Likert scale options with ten attributes tested. Every third bank customer that visited commercial banks to deal with any transactions is selected for the survey. Around 324 respondents were surveyed. The Demographic variables influence the usage of e-banking; the relationships between various demographic variables are tested with one-way analysis of variance (ANOVA). A reliability analysis is carried out to check for the underlying dimension of the success factors generated through factor analysis. Findings: Results indicate about the adoption of e-banking services in Bihar. Privacy and security are the major point of dissatisfaction of customers which have significantly impacted users’, for the time being customers are satisfied with the network availability and access to account. Rural areas are in much concern than the urban areas in terms of trust issues, lack of information and also the service availability. Lack of literacy rate is also the major reason for dissatisfaction in the adoption of e-banking services in rural areas. This paper also indicates the adoption rate of e-banking services with respect to different segmentation of income level, age group, education level. The results are expected to provide a concrete contribution in the area of retail banking and in understanding consumer behaviour in the state of Bihar using banking services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".