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Predicting the antecedents of trust in social commerce – A hybrid structural equation modeling with neural network approach

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  • Leong, Lai-Ying
  • Hew, Teck-Soon
  • Ooi, Keng-Boon
  • Chong, Alain Yee-Loong

Abstract

Trust is an essential concern in s-commerce. Though existing research has studied the association between trust and purchasing intention; the determinants of the formation of trust in s-commerce remain largely unexplored. This study examines the determinants of trust in s-commerce based on social presence and social support. Unlike most business research, we applied a hybrid SEM-ANN approach that can detect non-linear and non-compensatory relationships. Linear and compensatory models assume that a shortfall in one factor may be compensated by other factors. However, consumer decision-making processes are complicated and non-compensatory and linear models tend to oversimplify these processes. Criterion sampling was used to gather 462 datasets of social commerce users using a mall intercept technique. Information support has the strongest effect followed by the social presence of interaction with the sellers, income and social presence of others. The integrated model predicts 76.9% trust in s-commerce. Theoretical and managerial contributions are discussed.

Suggested Citation

  • Leong, Lai-Ying & Hew, Teck-Soon & Ooi, Keng-Boon & Chong, Alain Yee-Loong, 2020. "Predicting the antecedents of trust in social commerce – A hybrid structural equation modeling with neural network approach," Journal of Business Research, Elsevier, vol. 110(C), pages 24-40.
    • Handle: RePEc:eee:jbrese:v:110:y:2020:i:c:p:24-40
    DOI: 10.1016/j.jbusres.2019.11.056
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