The age of mobile social commerce: An Artificial Neural Network analysis on its resistances

Jun-Jie Hew, Lai-Ying Leong, Garry Wei-Han Tan, Keng-Boon Ooi and Voon-Hsien Lee

Technological Forecasting and Social Change, 2019, vol. 144, issue C, 311-324

Abstract: Drivers of social commerce usage has been the focus of scholars in recent years, but mobile social media users' resistance behavior towards mobile social commerce has been in the darkness and therefore worth torched lights on. With the data collected from mobile social media users who have no experience in mobile social commerce, Artificial Neural Network analysis was engaged to capture both linear and nonlinear relationships in a research model that consists of innovation barriers and privacy concern. Surprisingly, all resistances positively correlated with usage intention, except for image barrier, which appeared to be the most influencing resistance. Several explanations were offered for such outcomes. The possible coexistence of resistance behavior and usage intention resembles the fitting justification. Mobile social media users intend to embrace mobile social commerce; however, their intentions have been held up by their perceptions on innovation barriers and privacy concern. Based upon these outcomes, this study has reaffirmed the coexistence of resistances and usage intention, as well as the “privacy paradox” phenomenon. These discoveries are believed to have contributed to the existing literature. Practitioners are then advised to act accordingly to these findings, and several methods on catalyzing mobile social media users' adoption decision were suggested.

Keywords: Mobile social commerce; Mobile social media; Innovation resistance; Privacy concern; Artificial Neural Network; Privacy paradox (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (38)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:144:y:2019:i:c:p:311-324

DOI: 10.1016/j.techfore.2017.10.007

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