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Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach

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  • Liébana-Cabanillas, Francisco
  • Marinkovic, Veljko
  • Ramos de Luna, Iviane
  • Kalinic, Zoran

Abstract

As a modern alternative to cash, check or credit cards, the interest in mobile payments is growing in our society, from consumers to merchants. The present study develops a new research model used for the prediction of the most significant factors influencing the decision to use m-payment. To this end, the authors have carried out a study through an online survey of a national panel of Spanish users of smartphones. Two techniques were used: first, structural equation modeling (SEM) was used to determine which variables had significant influence on mobile payment adoption; in a second phase, the neural network model was used to rank the relative influence of significant predictors obtained by SEM. This research found that the most significant variables impacting the intention to use were perceived usefulness and perceived security variables. On the other side, the results of neural network analysis confirmed many SEM findings, but also gave slightly different order of influence of significant predictors. The conclusions and implications for management provide companies with alternatives to consolidate this new business opportunity under the new technological developments.

Suggested Citation

  • Liébana-Cabanillas, Francisco & Marinkovic, Veljko & Ramos de Luna, Iviane & Kalinic, Zoran, 2018. "Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 117-130.
    • Handle: RePEc:eee:tefoso:v:129:y:2018:i:c:p:117-130
    DOI: 10.1016/j.techfore.2017.12.015
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    References listed on IDEAS

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