Measuring the Role of Website Design, Assurance, Customer Service and Brand Image Towards Customer Loyalty and Intention to Adopt Internet Banking
Bibliographic record
Abstract
The rapid growth in internet technology and electronic business has stimulated the banking sectors to encourage customers towards online banking (internet banking). Only in Pakistan there are 1.8 million internet banking users and millions more are expected to come online. Looking at the growth in banking technology this study explores the effect of e-service quality dimensions include: website design, Assurance, Customer Service on intention to adopt internet banking and customer loyalty. Next to this bank image is also incorporated to explore the customer loyalty. The data for this study is based on 500 internet banking users from commercial banks of Lahore, Pakistan. Researcher used the structural equation modeling to evaluate the hypothesized relationships. The results of this study revealed that the adoption of internet banking in Pakistan may be motivated by a set of specific factors (i.e., Website Design, Assurance, Customer service and Bank image). Further, these results are expected to help policy makers to understand critical factors that influence on internet banking usage. Finally limitation and future directions have been 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".