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Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach

Author

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  • Leong, Lai-Ying
  • Hew, Teck-Soon
  • Ooi, Keng-Boon
  • Wei, June

Abstract

The advancement in mobile technology has enabled the application of the mobile wallet or m-wallet as an innovative payment method to substitute the traditional functions of the physical wallet. However, because of pro-innovation bias, scholars have a focus on the adoption of technology and very little attention has been given to the resistance of innovation, especially in the m-wallet context. This study addressed this absence by examining the inhibitors of m-wallet innovation adoption through the lens of innovation resistance theory (IRT). By applying a sophisticated two-staged structural equation modeling-artificial neural network (SEM-ANN) approach, we successfully extended the IRT by integrating socio-demographics and perceived novelty. The study has unveiled the noncompensatory and nonlinear relationships between the predictors and m-wallet resistance. Significant predictors from SEM analysis were taken as the ANN model’s input neurons. According to the normalized importance obtained from the multilayer perceptrons of the feed-forward-back-propagation ANN algorithm, we found significant effects of education, income, usage barrier, risk barrier, value barrier, tradition barrier, and perceived novelty on m-wallet innovation resistance. The ANN model can predict m-wallet innovation resistance with an accuracy of 76.4 %. We also discussed several new and useful theoretical and practical implications for reducing m-wallet innovation resistance among consumers.

Suggested Citation

  • Leong, Lai-Ying & Hew, Teck-Soon & Ooi, Keng-Boon & Wei, June, 2020. "Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach," International Journal of Information Management, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ininma:v:51:y:2020:i:c:s0268401219306012
    DOI: 10.1016/j.ijinfomgt.2019.102047
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