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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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    Cited by:

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    6. Al-Okaily, Manaf & Lutfi, Abdalwali & Alsaad, Abdallah & Taamneh, Abdallah & Alsyouf, Adi, 2020. "The Determinants of Digital Payment Systems’ Acceptance under Cultural Orientation Differences: The Case of Uncertainty Avoidance," Technology in Society, Elsevier, vol. 63(C).
    7. Lew, Susan & Tan, Garry Wei-Han & Loh, Xiu-Ming & Hew, Jun-Jie & Ooi, Keng-Boon, 2020. "The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model," Technology in Society, Elsevier, vol. 63(C).
    8. 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.
    9. Singh, Nidhi & Sinha, Neena, 2020. "How perceived trust mediates merchant's intention to use a mobile wallet technology," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    10. Tu, Gengyang & Faure, Corinne & Schleich, Joachim & Guetlein, Marie-Charlotte, 2021. "The heat is off! The role of technology attributes and individual attitudes in the diffusion of Smart thermostats – findings from a multi-country survey," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
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    12. Komlan Gbongli & Yongan Xu & Komi Mawugbe Amedjonekou, 2019. "Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach," Sustainability, MDPI, Open Access Journal, vol. 11(13), pages 1-33, July.
    13. Kamble, Sachin S. & Gunasekaran, Angappa & Kumar, Vikas & Belhadi, Amine & Foropon, Cyril, 2021. "A machine learning based approach for predicting blockchain adoption in supply Chain," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    14. Naeem, Muhammad & Ozuem, Wilson, 2021. "The role of social media in internet banking transition during COVID-19 pandemic: Using multiple methods and sources in qualitative research," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    15. Liébana-Cabanillas, Francisco & Japutra, Arnold & Molinillo, Sebastián & Singh, Nidhi & Sinha, Neena, 2020. "Assessment of mobile technology use in the emerging market: Analyzing intention to use m-payment services in India," Telecommunications Policy, Elsevier, vol. 44(9).
    16. Yosaka Eka Putranta & Rusli Alamsyah & Lisan Tan & Dewi Tamara, 2020. "The Determinant Factors of Mobile Payment Adoption," Eurasian Journal of Social Sciences, Eurasian Publications, vol. 8(3), pages 134-147.
    17. Dastane, Omkar & Goi, Chai Lee & Rabbanee, Fazlul, 2020. "A synthesis of constructs for modelling consumers’ perception of value from mobile-commerce (M-VAL)," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    18. Higueras-Castillo, Elena & Kalinic, Zoran & Marinkovic, Veljko & Liébana-Cabanillas, Francisco J., 2020. "A mixed analysis of perceptions of electric and hybrid vehicles," Energy Policy, Elsevier, vol. 136(C).
    19. Yoonyoung Hwang & Sangwook Park & Nina Shin, 2021. "Sustainable Development of a Mobile Payment Security Environment Using Fintech Solutions," Sustainability, MDPI, Open Access Journal, vol. 13(15), pages 1-15, July.
    20. Balakrishnan, Vimala & Shuib, Nor Liyana Mohd, 2021. "Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia," Technology in Society, Elsevier, vol. 65(C).
    21. Ameen, Nisreen & Shah, Mahmood Hussain & Sims, Julian & Choudrie, Jyoti & Willis, Robert, 2020. "Are there peas in a pod when considering mobile phone and mobile applications use: A quantitative study," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    22. Kalinic, Zoran & Marinkovic, Veljko & Molinillo, Sebastián & Liébana-Cabanillas, Francisco, 2019. "A multi-analytical approach to peer-to-peer mobile payment acceptance prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 143-153.

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