Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. (April 2020)

• Record Type: Journal Article
• Authors: Leong, Lai-Ying; Hew, Teck-Soon; Ooi, Keng-Boon; Wei, June
• Is Part Of: International journal of information management. Volume 51(2020)
• Publication Date: 2020-04
• Subjects: Mobile wallet resistance -- Innovation resistance theory -- Perceived novelty -- Socio-demographics -- Artificial neural network; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2019.102047
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: We extended IRT with perceived novelty and socio-demographic variables. A two-staged SEM-ANN approach was used to rank the normalized importance. The ANN model predicts m-wallet resistance with 76.4 % accuracy. Education and perceived novelty have negative effects on m-wallet resistance. Usage, risk, value & tradition barriers have positive effects on m-wallet resistance. Abstract: The advancement in mobile technology has enabled the application of the mobile wallet or m-wallet as an innovative payment method to substitute the traditional functions of the physical wallet. However, because of pro-innovation bias, scholars have a focus on the adoption of technology and very little attention has been given to the resistance of innovation, especially in the m-wallet context. This study addressed this absence by examining the inhibitors of m-wallet innovation adoption through the lens of innovation resistance theory (IRT). By applying a sophisticated two-staged structural equation modeling-artificial neural network (SEM-ANN) approach, we successfully extended the IRT by integrating socio-demographics and perceived novelty. The study has unveiled the noncompensatory and nonlinear relationships between the predictors and m-wallet resistance. Significant predictors from SEM analysis were taken as the ANN model's input neurons. According to the normalized importance obtained from the multilayer perceptrons of the feed-forward-back-propagation ANN algorithm, we found significant effects of education, income, usage barrier, risk barrier, value barrier, tradition barrier, and perceived novelty on m-wallet innovation resistance. The ANN model can predict m-wallet innovation resistance with an accuracy of 76.4 %. We also discussed several new and useful theoretical and practical implications for reducing m-wallet innovation resistance among consumers.