From Pleasure to Purchase: Impulse Buying and Financial Capability in Mobile Wallet Usage
Bibliographic record
Abstract
This study examines how perceived enjoyment influences impulsive buying in the context of mobile wallet usage, extending the Stimulus–Organism–Response (S–O–R) framework by introducing financial capability as a moderating construct. Survey data from 215 Generation Y consumers in a European digital market were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results show that perceived enjoyment significantly mediates the effects of interactivity, visual appeal, transaction convenience, and subjective norms on impulsive buying behavior. Among these antecedents, transaction convenience and visual appeal emerged as the strongest predictors of enjoyment. Moreover, financial capability strengthens the enjoyment–impulse link, suggesting that consumers with higher financial literacy and confidence are more likely to translate digital pleasure into spontaneous purchases. By integrating hedonic and financial factors, this research contributes to digital consumer behavior theory by clarifying the emotional mechanisms that drive impulse buying in fintech environments. The study also provides actionable insights for fintech providers and marketers seeking to balance engaging design with responsible financial behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".