Cashless Japan: Unlocking Influential Risk on Mobile Payment Service

Abstract

In Japan, cashless is not yet popular but government and companies are devoted to the development of mobile payment methods. This research collected 241 Japanese users and applied decision trees algorithm. Six types of perceived risks (financial, privacy, performance, psychological, security, and time) were used and the categorized class is intention to use mobile payment (low, medium, and high). We also compared different competitive models to examine the performance, including decision trees, kNN, Naïve Bayes, SVM, and logistic regression and decision trees outperformed among all models. The findings indicated that privacy and performance risks are import to Japanese users. Safe, secured, reliable, and fast mobile payment environment are more important to low intention users (less concerns about financial risk). Financial loss, safe, secured, reliable, and fast mobile payment environment are more important to medium intention users (less concerns about time and security risk). Monetary loss, safe, reliable, and fast mobile payment environment are more important to high intention users (less concerns about security risk and psychological risk). The results can help Japanese companies unlock the perceived risk on mobile payment and furnish appropriate strategies to improve usage.