Antecedents of Behavioural Intention to Adopt Internet Banking Using Structural Equation Modelling
Why this work is in the frame
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Bibliographic record
Abstract
Technology is emerging as an as an important banking mode for customers, and although almost all the banks in India are offering Internet Banking, India faces problems related to the digital divide, e-frauds, and high rates of interest, amongst other things. This is causing concern in banks, which are trying to persuade people to adopt their online banking services. Therefore, the aim with this study is to determine the antecedents of behavioural intentions to adopt e-banking in an emerging economy such as India. We did this by administering a questionnaire with 34 questions and nine constructs to which participants responded using a Likert scale of 1 to 5, 1 being strongly disagree and 5 strongly agree. All constructs used in this questionnaire were adapted from literature related to the antecedents of behavioural intentions to adopt e-banking. We received 436 valid responses, which we analysed using Cronbach’s alpha, Confirmatory Factor Analysis, and Structural Equation Modelling. Results show that Performance Expectancy, Hedonic Motivation, Experience, Habit and Attitude, Perceived Website Usability, and Security and Reliability positively influence the intention to adopt Internet Banking, suggesting that policymakers and bankers should focus on improving website usability and hedonic enjoyment while focusing on Internet Banking performance, security, and dependability. In addition, Effort Expectations, Social Influence, Facilitating Conditions, and Trust resulted as not significant influencing factors of Internet Banking usage; Indians appear to find Internet Banking straightforward to use, perceive it as a breeze, and believe they are backed by solid support systems and organisational infrastructures. Moreover, trust is not a driving factor for Indians to adopt Internet Banking because they already perceive it as a trustworthy exercise.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it