Does social banking matter in times of crisis? Evidence from the COVID-19 pandemic: a combined SEM-neural network approach

Nattaporn Thongsri and Orawan Tripak

International Journal of Social Economics, 2023, vol. 51, issue 2, 227-247

Abstract: Purpose - The purpose of this study was to investigate the factors that would influence the intention to use social banking during the coronavirus disease 2019 (COVID-19) pandemic. This study integrated two theories, namely the integrated technology acceptance model (TAM), which focused on the acceptance of technology by consumers, and electronic word of mouth (eWOM), which focused on consumer behavior. This study also applied the significant variables in the context of Thailand, which were trust and perceived risk. Design/methodology/approach - A quantitative research method was applied by collecting data from 411 consumers during the COVID-19 pandemic in Thailand. A combined multi-analytic approach of a structural equation model (SEM)-neural network was used to analyze the data. In the first step, the SEM was used to determine the important factors that affected the adoption of social banking. In the second step, a neural network model was used to prioritize the important factors to confirm the results of the SEM method in step 1. Findings - The empirical results of the data analysis using the SEM method showed that the perceived ease of use, perceived usefulness and trust were the most significant determinants of adopting social banking. This was consistent with the neural network method of the important factors. Practical implications - The results of this research could initiate issues that should be developed for the continued use of online banking among consumers in the context of developing countries, such as Thailand. Originality/value - This research model provided guidelines for the effective development of mobile banking applications for use on mobile devices. The results of this research made strong theoretical contributions to the existing literature on online banking and offered procedures and information to the relevant sectors. Peer review - The peer review history for this article is available at:https://publons.com/publon/10.1108/IJSE-10-2022-0709

Keywords: Social banking; Structural equation modeling (SEM); Neural network model; COVID-19; Thailand (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijsepp:ijse-10-2022-0709

DOI: 10.1108/IJSE-10-2022-0709

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