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

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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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    Citations

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    Cited by:

    1. Nguyen, Nguyen-Hong & Nguyen, Luan-Thanh, 2023. "The impact of online shopping motivation on customer loyalty in Mobile Applications," MPRA Paper 119657, University Library of Munich, Germany, revised 02 Jan 2024.
    2. Fu, Shihui & Xue, Kunkun & Yang, Mengya & Wang, Xiaona, 2023. "An exploratory study on users' resistance to mobile app updates: Using netnography and fsQCA," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    3. Hew, Jun-Jie & Lee, Voon-Hsien & Leong, Lai-Ying, 2023. "Why do mobile consumers resist mobile commerce applications? A hybrid fsQCA-ANN analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    4. Jacques Nel & Christo Boshoff, 2023. "Unraveling the link between status quo satisfaction and the rejection of digital-only banks," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 189-207, March.
    5. Talwar, Shalini & Srivastava, Shalini & Sakashita, Mototaka & Islam, Nazrul & Dhir, Amandeep, 2022. "Personality and travel intentions during and after the COVID-19 pandemic: An artificial neural network (ANN) approach," Journal of Business Research, Elsevier, vol. 142(C), pages 400-411.
    6. Khondaker Sazzadul Karim & Mohammad Ekramol Islam & Abdullah Mohammed Ibrahim & Shin-Hung Pan & Md. Mominur Rahman, 2023. "Online Marketing Trends and Purchasing Intent Advances in Customer Satisfaction through PLS-SEM and ANN Approach," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(4), pages 24-54, December.
    7. Lai-Ying Leong & Teck-Soon Hew & Keng-Boon Ooi & Bhimaraya Metri & Yogesh K. Dwivedi, 2023. "Extending the Theory of Planned Behavior in the Social Commerce Context: A Meta-Analytic SEM (MASEM) Approach," Information Systems Frontiers, Springer, vol. 25(5), pages 1847-1879, October.
    8. Fahad Javed Baig & Rana Shahid Yaqub, 2022. "Exploring the Usage Behavior of Mobile Wallet: An Empirical Study in Pakistan," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 8(3), pages 11-16, December.
    9. Abbasi, Ghazanfar Ali & Sandran, Thiviya & Ganesan, Yuvaraj & Iranmanesh, Mohammad, 2022. "Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    10. Zhong, Junying & Chen, Tiao, 2023. "Antecedents of mobile payment loyalty: An extended perspective of perceived value and information system success model," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    11. Nurul-Ain Abdul-Halim & Ali Vafaei-Zadeh & Haniruzila Hanifah & Ai Ping Teoh & Khaled Nawaser, 2022. "Understanding the determinants of e-wallet continuance usage intention in Malaysia," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3413-3439, October.
    12. Liu, Aiping & Urquía-Grande, Elena & López-Sánchez, Pilar & Rodríguez-López, Ángel, 2022. "How technology paradoxes and self-efficacy affect the resistance of facial recognition technology in online microfinance platforms: Evidence from China," Technology in Society, Elsevier, vol. 70(C).
    13. Babak Naysary & Mehdi Malekzadeh & Ruth Tacneng & Amine Tarazi, 2022. "Big data analytics application in multi-criteria decision making: the case of eWallet adoption," Working Papers hal-03632834, HAL.
    14. Yubo Peng & LingWu Wang & Shuiqing Yang, 2021. "Explaining mobile government social media continuance from the valence perspective: A SEM-NN approach," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-13, February.
    15. de Blanes Sebastián, María García & Antonovica, Arta & Sarmiento Guede, José Ramón, 2023. "What are the leading factors for using Spanish peer-to-peer mobile payment platform Bizum? The applied analysis of the UTAUT2 model," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    16. Hemant Kumar Sah & Gyanendra Singh Sisodia & Gouher Ahmed & Aqila Rafiuddin & Naseem Abidi, 2023. "The Role of Energy Consumption and Economic Growth on Carbon Emission- Application of Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 591-596, November.
    17. Giacomo Migliore & Ralf Wagner & Felipe Schneider Cechella & Francisco Liébana-Cabanillas, 2022. "Antecedents to the Adoption of Mobile Payment in China and Italy: an Integration of UTAUT2 and Innovation Resistance Theory," Information Systems Frontiers, Springer, vol. 24(6), pages 2099-2122, December.
    18. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    19. Sutticherchart Juthatip & Rakthin Sirisuhk, 2023. "Determinants of digital wallet adoption and super app: A review and research model," Management & Marketing, Sciendo, vol. 18(3), pages 270-289, September.
    20. Phan Cong Thao Tien & Tran Thien Phuc & Nguyen Thi Hai Binh, 2023. "A hybrid SEM/ANN analysis to understand youtube video content's influence on university students' eLearning acceptance behavior," EconStor Conference Papers 279146, ZBW - Leibniz Information Centre for Economics.
    21. Izquierdo-Yusta, Alicia & Martínez–Ruiz, María Pilar & Pérez–Villarreal, Héctor Hugo, 2022. "Studying the impact of food values, subjective norm and brand love on behavioral loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    22. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    23. Jaiswal, Deepak & Mohan, Ashutosh & Deshmukh, Arun Kumar, 2023. "Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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