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Record W4416097976 · doi:10.5539/ibr.v18n6p1

From Pleasure to Purchase: Impulse Buying and Financial Capability in Mobile Wallet Usage

2025· article· W4416097976 on OpenAlexvenueno aff
Giuseppe Granata, Giancarlo Scozzese

Bibliographic record

VenueInternational Business Research · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPleasureDatabase transactionFinancial literacyStructural equation modelingImpulse (physics)Context (archaeology)Financial transactionMobile paymentConsumer behaviour

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.050
GPT teacher head0.369
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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