Enhancing Technology Acceptance through User Experience Evaluation: Comparative Analysis of Banking Website Versus Mobile Application
Bibliographic record
Abstract
Despite the high rate of technology adoption in banking services, there is still a void in the literature on technology adoption and diffusion in this area, and hence, the purpose of this paper is to analyze the comparative user behavior of two different banking platforms, namely Banking Websites and Mobile Applications in India.The theoretical underpinning for the empirical validation is the Technology Acceptance Model (TAM).The research is based on the quantitative analysis with a sample size of 304 and 411 customers in the aforementioned banking platforms respectively.Structural Equation Modelling (SEM) has been adopted as the technique of analysis.Results indicate that among the ten variables of the study behavioral intention, dependability, efficiency, and perspicuity have a similar effect on technology acceptance, and the rest of the variables differ in their impact with respect to the two platforms under comparison.The implication of the study is that both platforms need to focus on the aforementioned variables during the system design as they are crucial in connection to the actual usage of the system.The outcome of this research could be of use to academicians, system designers, and application developers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".