A SEM-neural network approach for predicting antecedents of m-commerce acceptance. Issue 2 (April 2017)

• Record Type: Journal Article
• Authors: Liébana-Cabanillas, Francisco; Marinković, Veljko; Kalinić, Zoran
• Is Part Of: International journal of information management. Volume 37:Issue 2(2017:Apr.)
• Page Start: 14
• Page End: 24
• Publication Date: 2017-04
• Subjects: m-Commerce -- Technology adoption -- Behavioral intention -- Neural network -- m-Service; 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.2016.10.008
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: The main goal is to analyze the individuals' use intention of m-commerce. The objective is to determine which variables are the most relevant. SEM was used to determine which variables had significant influence on m-commerce. Neural network model was used to rank the relative influence of predictors from SEM. This study provides a set of recommendations for companies. Abstract: Higher penetration of powerful mobile devices – especially smartphones – and high-speed mobile internet access are leading to better offer and higher levels of usage of these devices in commercial activities, especially among young generations. The purpose of this paper is to determine the key factors that influence consumers' adoption of mobile commerce. The extended model incorporates basic TAM predictors, such as perceived usefulness and perceived ease of use, but also several external variables, such as trust, mobility, customization and customer involvement. Data was collected from 224 m-commerce consumers. First, structural equation modeling (SEM) was used to determine which variables had significant influence on m-commerce adoption. In a second phase, the neural network model was used to rank the relative influence of significant predictors obtained from SEM. The results showed that customization and customer involvement are the strongest antecedents of the intention to use m-commerce. The study results will be useful for m-commerce providers in formulating optimal marketing strategies to attract new consumers.