Diffusion of Internet Banking amongst educated consumers in a high income non-OECD country
Bibliographic record
Abstract
This study analyses the internet banking channels and service preferences of educated banking consumers in the UAE and examines the factors influencing the intention to adopt or to continue the use of internet banking among both users and non users of internet banking. It is shown that although the banking sector in the UAE is a regional leader, internet banking in the UAE is yet to be properly utilized as a real added value tool to improve customer relationship and to attain cost advantages. The Technology Acceptance Model (TAM) was used to identify factors influencing the intention to adopt and continued use of internet banking customers. Data was collected from internet banking users and potential users in the United Arab Emirates and factor analyses and multiple regression analyses were conducted to examine the data. Relative usefulness is introduced as one of the factors and is defined as the degree to which a new technology is better than exiting ones. There is a significant difference between users and non-users on six of the seven factors identified. Further, it was revealed that relative usefulness, perceived risk, computer efficacy and image had a significant impact on continued usage of internet banking for IB Users, while relative usefulness and result demonstrability were the only ones significant for Non-users of internet banking. The effects of age, gender, income, and e-commerce users also explored. Result demonstrability is significant for all categories of non-users except for those with income below AED 7,000. Implications of results were discussed, and future research directions outlined.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".