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A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption

Chong, Alain Yee-Loong

Authors

Alain Yee-Loong Chong alain.chong@nottingham.edu.cn



Abstract

The advancement in wireless and mobile technologies has presented tremendous business opportunity for mobile-commerce (m-commerce). This research aims to examine the factors that influence consumers’ m-commerce adoption intention. Variables such as perceived usefulness, perceived ease of use, perceived enjoyment, trust, cost, network influence, and variety of services were used to examine the adoption intentions of consumers. Data was collected from 376 m-commerce users. A multi-analytic approach was proposed whereby the research model was tested using structural equation modeling (SEM), and the results from SEM were used as inputs for a neural network model to predict m-commerce adoption. The result showed that perceived usefulness, perceived enjoyment, trust, cost, network influence, and trust have significant influence on consumers’ m-commerce adoption intentions. However, the neural network model developed in this research showed that the best predictors of m-commerce adoption are network influence, trust, perceived usefulness, variety of service, and perceived enjoyment. This research proposed an innovative new approach to understand m-commerce adoption, and the result for this study will be useful for telecommunication and m-commerce companies in formulating strategies to attract more consumers.

Journal Article Type Article
Publication Date Mar 31, 2013
Journal Expert Systems with Applications
Print ISSN 0957-4174
Electronic ISSN 0957-4174
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 40
Issue 4
Institution Citation Chong, A. Y. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), doi:10.1016/j.eswa.2012.08.067
DOI https://doi.org/10.1016/j.eswa.2012.08.067
Keywords m-Commerce; Technology adoption; SEM; Neural network; Multi-analytic data analysis
Publisher URL https://doi.org/10.1016/j.eswa.2012.08.067

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