A Hybrid SEM and Neural Network Approach to Understand and Predict the Determinants of Consumers’ Acceptance and Usage of Mobile-Commerce Application
Keywords:Privacy, security, Neural Network Approach, mobile commerce, mobile application, acceptance
The growth of mobile commerce marketplaces worldwide has been boosted by modern advances in digital technology. However, Privacy and security are still concern in m-commerce application. Since the previous study has researched the link between security and privacy and purpose to use, the factors that influence the formation of privacy and security in m-commerce are mostly unidentified. On the basis of UTAUT2, this study investigates the factors of security and privacy effecting mobile commerce acceptance. A hybrid SEM-ANN method was utilized to identify non-linear and compensatory interactions. Compensatory and Linear models are based on the idea that a deficiency in one component might also be compensated for by other variables. Linear and Non-compensatory models, on the other hand, seem to overcomplicate buyer decision mechanisms. Survey criteria have been conducted to obtain 890 mobile commerce consumer’s datasets utilizing an application on m-commerce. The following are the results. (1) M-commerce acceptability and use were positively influenced by five determinants (Security, performance expectancy, effort expectancy, habit, and price value). (2) Un-authorization, Error, secondary usage, collection, control, and awareness were all shown to directly and significantly negatively impact M-COMMERCE acceptance and use. (3) Three additional variables (social influence, hedonic motivation, and facilitating conditions) did not affect customers' intentions to use m-commerce applications in Jordan. In m-commerce, the integrated model expects a 45% percent increase in security and privacy.
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Copyright (c) 2022 Ashraf Saleh, Odai Enaizan , Bilal Eneizan , Lu’ay Al-Mu’ani, Ahmad Al-Radaideh
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