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Big data analytics application in multi-criteria decision making: the case of eWallet adoption

Author

Listed:
  • Babak Naysary

    (Monash University, School of Business, Selangor, Malaysia)

  • Mehdi Malekzadeh

    (Service Rocket Inc., Kuala Lumpur, Malaysia)

  • Ruth Tacneng

    (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges)

  • Amine Tarazi

    (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges)

Abstract

This multidisciplinary study aims to overcome the shortcomings of traditional data collection methods used in the literature to investigate drivers of e-wallet adoption. We apply big data analytics to gather and analyze real-world data from users' sentiments and opinions available on online platforms. We use a text analytics approach to identify and categorize principal themes of concern affecting user adoption. After, we use the Analytical Hierarchy Process (AHP) technique to weigh and rank these themes and subsequently construct a structural framework for choosing the optimal e-wallet alternative in the market. Our results identify 10 clusters of e-wallet adoption drivers that can be categorized into three groups. The first group includes factors such as usefulness, ease of use, trust, risk security, and associated costs, confirming existing findings in the literature. The second group reinforces the importance of more implicit factors which existing theories fail to integrate, such as customer service, user interface, and promotional rewards. And finally, the last group comprises interoperability, highlighting the importance of e-wallet connectivity and how conveniently it performs transactions with other platforms, systems, and applications. Based on the results of clustering and the AHP model, we provide several managerial recommendations that can guide decision-making and eventually optimize the performance of e-wallets. Our study makes significant contribution by adopting a holistic, multi-criteria framework to evaluate ewallet adoption comprehensively.

Suggested Citation

  • 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.
    • Handle: RePEc:hal:wpaper:hal-03632834
    Note: View the original document on HAL open archive server: https://hal.science/hal-03632834
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    References listed on IDEAS

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    1. Ajao Qasim & Emad Abu-Shanab, 2016. "Drivers of mobile payment acceptance: The impact of network externalities," Information Systems Frontiers, Springer, vol. 18(5), pages 1021-1034, October.
    2. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    3. 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).
    4. Lee, In & Shin, Yong Jae, 2018. "Fintech: Ecosystem, business models, investment decisions, and challenges," Business Horizons, Elsevier, vol. 61(1), pages 35-46.
    5. Jiabao Lin & Zhimei Luo & Jose Benitez & Xin (robert) Luo & Aleš Popovič, 2021. "Why do organizations leverage social media to create business value? An external factor-centric empirical investigation," Post-Print hal-03413223, HAL.
    6. Maryam Barkhordari & Zahra Nourollah & Hoda Mashayekhi & Yoosof Mashayekhi & Mohammad S. Ahangar, 2017. "Factors influencing adoption of e-payment systems: an empirical study on Iranian customers," Information Systems and e-Business Management, Springer, vol. 15(1), pages 89-116, February.
    7. Pham, Thanh-Thao T. & Ho, Jonathan C., 2015. "The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments," Technology in Society, Elsevier, vol. 43(C), pages 159-172.
    8. Lucini, Filipe R. & Tonetto, Leandro M. & Fogliatto, Flavio S. & Anzanello, Michel J., 2020. "Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews," Journal of Air Transport Management, Elsevier, vol. 83(C).
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    More about this item

    Keywords

    E-wallet adoption; big data analytics; AHP; mobile payment; text mining;
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