Benefit–Risk Perceptions of FinTech Adoption for Sustainability from Bank Consumers’ Perspective: The Moderating Role of Fear of COVID-19

Abstract

Industry 4.0 technologies, designed to optimize efficiencies, are indisputable change agents for sustainability. In the context of financial technology (FinTech), the burgeoning question concerns how to create FinTech natives from the COVID-19-pandemic-induced adoption and realize FinTech’s impact on sustainability? Thus, this study had the following purposes: (1) to examine whether perceived benefits and risks affect FinTech services adoption; (2) to test the role of fear of COVID-19 in FinTech adoption; and (3) to investigate whether FinTech adoption contributes to sustainability. The hypotheses derived from the net valence framework, sustainable information society theory, and protection motivation theory were tested using structural equation modeling (SEM). Our online survey of bank consumers in Malaysia between December 2021 and February 2022 yielded 1279 usable questionnaires, randomly selected to generate 400 respondents. The results revealed that: (1) the perceived benefits significantly influence FinTech adoption, whereas perceived risk does not; (2) fear of COVID-19 moderates the perceived benefits–FinTech adoption relationship and fully mediates the perceived risk–FinTech adoption relationship; and (3) FinTech adoption significantly affects sustainability. This study demonstrates that FinTech adoption models must exploit consumer sentiment (e.g., fear) to optimize FinTech’s benefits and risks, thereby creating FinTech natives to realize its impacts on economic, environmental, and social sustainability.

The Mediating Effect of Information Adoption on The Association between Social Media Influencer Information Credibility and Purchase Intention

“…Convergent validity was investigated by computing the factor loading, which must be more than or equal to .60 (Awang, 2015;Hair et al, 2010). The next step was determining composite reliability (CR) and average variance extracted (AVE), which must be at least .60 and more than .50, respectively (Abdul- Rahim et al, 2022;Awang, 2015). The data in Table 2 shows that this investigation effectively met all of the requirements.…”

“…An analysis was carried out using AMOS version 24 to evaluate whether the data were normal. For a sample size that is more than 200, it is advisable to use skewness values that fall between -1.5 and +1.5 (Abdul-Rahim et al, 2022;Awang et al, 2018). The kurtosis's crucial region value is lower than 7.0 (Awang, 2015).…”

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“…By comparing the average variance extracted (AVE) and composite reliability (CR) values, the convergent validity of the constructs can be assessed. The CR values are more than .6 and the value for AVE is set at .5 (Abdul-Rahim et al, 2022;Awang, 2015;Hair et al, 2014). Table 5 presents the factor loading, AVE, and CR.…”

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