A SEM-neural network approach for predicting antecedents of m-commerce acceptance |
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| Institution: | 1. Faculty of Business & Information Science, UCSI University, Kuala Lumpur, Malaysia;2. Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia;1. Operations and Supply Chain Management, Room No.211, Administration building, 2nd Floor, National Institute of Industrial Engineering (NITIE), Vihar Lake, Mumbai 87, India;2. PGDM-Reasearch & Business Analytics, Prin. L. N. Welingkar Institute of Management Development & Research, Lakhamshi Napoo Road, Near Matunga (Central Rly.), Mumbai 400 019, India;3. Mechanical Engineering Department, 8, Saboo Siddik Polytechnic Road, New Nagpada, Byculla, Mumbai, Maharashtra 400008, India;4. Marketing Management, National Institute of Industrial Engineering (NITIE), Mumbai 87, India |
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| 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. |
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| Keywords: | m-Commerce Technology adoption Behavioral intention Neural network m-Service |
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