Bibliographic record
Abstract
This paper seeks to identify empirically the factors underlying the decision to adopt online banking in Tehran.The sample used in this study is based on 385 interactive questionnaires completed by Tehran internet users.Data were analyzed by employing correlation and multiple linear regression analysis.The results showed that perceived usefulness, perceived ease of use, trust and use of other banking products positively associated with the intention to use online banking in Tehran.This study was conducted in Tehran and future research can use this model to study the adoption of online banking in other cities.The results allow banks' decision makers to develop strategies that can increase the adoption of online banking.Banks should improve the security and privacy of the websites, which will increase the trust of users.Banks should also create features which are useful to users, try to make the process of using the services easy for consumers, teach customers how to use the online services and use a package deal, such as an account with online access, debit or credit card and a SMS banking service.The findings allow the factors that can influence the adoption of online banking in Tehran to be understood.Unlike existing studies based on Technology Acceptance Model (TAM), this study includes, trust and use of other banking products on top of the existing variables used in TAM.Most studies on adoption of online banking are focused on developed countries.By focusing on Iran, this model can also be applied to other countries which are relatively new to ecommerce and online banking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.890 | 0.838 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".