The relationship of brand attachment and mobile banking service quality with positive word-of-mouth
Bibliographic record
Abstract
Purpose This study aims to examine the relationships between brand attachment, mobile service quality (MSQ), and positive word-of-mouth (WOM) for mobile banking applications (m-banking apps). Design/methodology/approach An online survey was conducted among 759 Canadian consumers who had used m-banking apps in the previous six months. To test the research hypotheses, a causal model using structural equation modeling was developed. Findings The results reveal that, in the m-banking context, brand attachment is associated with three MSQ dimensions – value-added features, security/privacy and interactivity – and positive WOM, with the usability dimension replacing interactivity in this case. Brand attachment is also associated with positive WOM. Practical implications To promote WOM, mobile banking managers should foster brand attachment and improve MSQ, mainly in terms of value-added features. Originality/value This is the first study to examine the relationships between brand attachment, mobile service quality and WOM in the context of m-banking apps. It also highlights the prominent role of value-added features available on m-banking apps to persuade customers.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".