Understanding the Determinants of Wearable Payment Adoption: An Empirical Study

Abstract

Aim/Purpose: The aim of this study is to determine the variables which affect the intention to use Near Field Communication (NFC)-enabled smart wearables (e.g., smartwatches, rings, wristbands) payments. Background: Despite the enormous potential of wearable payments, studies investigating the adoption of this technology are scarce. Methodology: This study extends the Technology Acceptance Model (TAM) with four additional variables (Perceived Security, Trust, Perceived Cost, and Attractiveness of Alternatives) to investigate behavioral intentions to adopt wearable payments. The moderating role of gender was also examined. Data collected from 311 Kuwaiti respondents were analyzed using Structural Equation Modeling (SEM) and multi-group analysis (MGA). Contribution: The research model provided in this study may be useful for academics and scholars conducting further research into m-payments adoption, specifically in the case of wearable payments where studies are scarce and still in the nascent stage; hence, addressing the gap in existing literature. Further, this study is the first to have specifically investigated wearable payments in the State of Kuwait; therefore, enriching Kuwaiti context literature. Findings: This study empirically demonstrated that behavioral intention to adopt wearable payments is mainly predicted by attractiveness of alternatives, perceived usefulness, perceived ease of use, perceived security and trust, while the role of perceived cost was found to be insignificant. Recommendations for Practitioners: This study draws attention to the importance of cognitive factors, such as perceived usefulness and ease of use, in inducing users’ behavioral intention to adopt wearable payments. As such, in the case of perceived usefulness, smart wearable devices manufacturers and banks enhance the functionalities and features of these devices, expand on the financial services provided through them, and maintain the availability, performance, effectiveness, and efficiency of these tools. In relation to ease of use, smart wearable devices should be designed with an easy to use, high quality and customizable user interface. The findings of this study demonstrated the influence of trust and perceived security in motivating users to adopt wearable payments, Hence, banks are advised to focus on a relationship based on trust, especially during the early stages of acceptance and adoption of wearable payments. Recommendation for Researchers: The current study validated the role of attractiveness of alternatives, which was never examined in the context of wearable payments. This, in turn, provides a new dimension about a determinant factor considered by customers in predicting their behavioral intention to adopt wearable payments. Impact on Society: This study could be used in other countries to compare and verify the results. Additionally, the research model of this study could also be used to investigate other m-payments methods, such as m-wallets and P2P payments. Future Research: Future studies should investigate the proposed model in a cross-country and cross-cultural perspective with additional economic, environmental, and technological factors. Also, future research may conduct a longitudinal study to explain how temporal changes and usage experience affect users’ behavioral intentions to adopt wearable payments. Finally, while this study included both influencing factors and inhibiting factors, other factors such as social influence, perceived compatibility, personal innovativeness, mobility, and customization could be considered in future research.

Students’ Continuance Intention to Use Moodle: An Expectation-Confirmation Model Approach

“…Hong et al (2006) and Dia et al (2020) reported that a user's perceived PE of a technology is a key determinant of satisfaction and continuance intention. Prior studies reported the significant positive effect of PE on satisfaction (e.g., Joo et al, 2016;Kim-Soon et al, 2017;Kumar et al, 2018;Lim et al, 2019), and continuous intention to use a technology (e.g., Foroughi et al, 2019;Gilani et al, 2017;Rabaai, 2015;Rabaai et al, 2015;Rabaa'i, 2016Rabaa'i, , 2017bRabaa'i and Zhu, 2021;Rabaa'i, in press;Shaw & Sergueeva, 2019;. Additionally, previous studies also confirmed the significant relationship between PE and users' attitude (e.g., Dai et al, 2020;Ding, 2019;Gasaymeh & Waswas, 2019;Ifinedo, 2018;Sabah, 2019).…”

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

“…As an emerging theme, DeFi intersects with blockchain, smart contracts, regtech, big data, digital wallets, peer‐to‐peer lending, e‐insurance, open banking, atomic swaps, autonomous liquidity pools, decentralized stablecoins, and flash loans (Aspris et al, 2021; Jiao et al, 2021; Zetzsche et al, 2020). DeFi's objective is to democratize finance by replacing centralized intermediaries, such as banks, exchanges, and insurers, with peer‐to‐peer relationships for greater efficiency and transparency on the full spectrum of BFSI products and services (Hamayel et al, 2021; Li et al, 2021; Nagar et al, 2021; Osakwe et al, 2021; Rabaa'i & Zhu, 2021). This democratization is particularly important to small and medium enterprises and family businesses, who have been dictated payment terms by larger entities in the supply chain (Crowe, 2020).…”

“…DeFi's objective is to democratize finance by replacing centralized intermediaries, such F I G U R E 1 2 Strategic diagram for the 2017-2018 period, sphere size denotes the total citations count (source: Authors, based on SciMAT visualization). as banks, exchanges, and insurers, with peer-to-peer relationships for greater efficiency and transparency on the full spectrum of BFSI products and services (Hamayel et al, 2021;Li et al, 2021;Nagar et al, 2021;Osakwe et al, 2021;Rabaa'i & Zhu, 2021). This democratization is particularly important to small and medium enterprises and family businesses, who have been dictated payment terms by larger entities in the supply chain (Crowe, 2020).…”

Predicting the intention and adoption of wearable payment devices using hybrid SEM-neural network analysis

“…They only focused on technological factors and overlooked external factors such as social influence. Rabaa'i and Zhu 13 conducted research on similar adoption factors in Kuwait, integrating trust, but they did not consider external factors and indirect relationships (mediation and moderation) in their research framework. However, few empirical studies based on mobile payment usage 14 , 15 and the adoption of wearable technologies 16 were conducted in Malaysia.…”