Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context
Bibliographic record
Abstract
Several studies have made known that internet banking (IB) implementation is not only advantageous for banks, but also by perception and experience of IB users. Therefore, little is known about factors propelling user's intention to adopt internet banking in Pakistan. Thus, the purpose of this research is to investigate the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. A quantitative approach based survey was conducted to collect the data from 398 internet banking users. For statistical analysis structural equation model (SEM) approach was used. The result of this study indicates that, UTAUT model provided a good theoretical foundation in technology adoption investigation. Findings confirmed that all four predictors (performance expectancy, effort expectancy, social influence and facilitating condition) were significant and had significant amount of variance in predicting user's intention to adopt internet banking. Additionally, the IPMA test revealed that performance expectancy was the most important factor among all other variables to predict user's intention towards adoption of internet banking. Lastly, managerial implications, limitations and future recommendations are discussed.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".