핀테크 앱 리뷰에서 주제-주관성 상호작용이 유용성에 미치는 영향: PPM 기반 ZINB 모형 분석

초록

This study investigates which types of online reviews are recognized as useful by users in the context of FinTech mobile applications. To this end, 65,323 user reviews posted on the Google Play Store for major FinTech services were analyzed. Using Latent Dirichlet Allocation (LDA), we identified 11 core reviewtopics and classified them into dissatisfaction (push), attractiveness (pull), and inertia (mooring) factors based on the Push–Pull–Mooring (PPM) framework. Review usefulness was measured using helpful votes, and a Zero-Inflated Negative Binomial (ZINB) regression model was estimated with review rating, sentiment polarity, subjectivity, review length, and topic proportions as independent variables. The results show that subjectivity has a negative main effect on review usefulness; however, its interaction effects with both review rating and review length are positive. This indicates that longer and more subjective reviews that provide detailed accounts of user experiences tend to be evaluated as more useful by other users. In particular, for topics involving complex service structures and high cognitive costs based descriptions are more likely to be interpreted as meaningful decision-relevant information, thereby increasing reviewusefulness. By contrast, reviews addressing topics such as convenience features, fraud, or financial losses exhibit a lower likelihood of receiving usefulness evaluations, suggesting that these topics are perceived as having relatively limited informational value from other users’ perspectives. Overall, this study provides empirical evidence that review usefulness in FinTech applications is not determined solely by surface-level characteristics such as sentiment or review length, but is differentially shaped by the interaction between topical context and subjective expression.

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