Design aesthetics as drivers of value in mobile banking: does customer happiness matter?
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how customers derive value (functional, emotional, social and epistemic value) from the design aesthetics of mobile banking applications and then form intention to adopt mobile banking. Furthermore, this research investigates the moderating effect of happiness, which is predicted – and showed – to strengthen the effects of design aesthetics on value. Design/methodology/approach A survey using screenshots of mobile banking applications was administered to a sample of 281 bank customers. Data were analysed using SmartPLS. Findings The results show that design aesthetics have a positive effect on functional, emotional, social and epistemic value. In turn, these value dimensions positively affect intention to adopt mobile banking. The findings also demonstrate that happiness moderates the effects of design aesthetics on these value dimensions. Practical implications This work can be useful to designers of banking applications and other practitioners to improve their policies and strategies related to mobile applications. Originality/value This research represents an initial attempt to examine how customers derive functional, emotional, social and epistemic value from design aesthetics in mobile banking. In addition, this research demonstrates that happiness moderates – and more specifically strengthens – the effects of design aesthetics on customer value. The results provide a theoretical contribution to the importance of value in customer decision making, and in the current case, in the seldom-researched area of mobile 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 | 0.003 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".