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

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 a significant contribution by adopting a holistic, multi-criteria framework to evaluate e-wallet adoption comprehensively