Does COVID-19 Pandemic Motivate Privacy Self-Disclosure in Mobile Fintech Transactions? A Privacy-Calculus-Based Dual-Stage SEM-ANN Analysis

• Published in: IEEE transactions on engineering management Vol. 71; pp. 1 - 15
• Main Authors: Yuan, Yun-Peng, Tan, Garry Wei-Han, Ooi, Keng-Boon
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agency theory; Appraisal; Artificial neural network (ANN); Artificial neural networks; Bank technology; Calculus; Consumer behavior; Consumers; control agency theory; coronavirus disease 2019 (COVID-19); Coronaviruses; COVID-19; Decision making; Decision theory; Mathematical models; mobile commerce; mobile financial technology; Pandemics; partial least squares structural equation modeling; Privacy; privacy calculus; Psychology; Public health; Structural equation modeling; Technology Acceptance Model; Viral diseases
• ISSN: 0018-9391; 1558-0040
• DOI: 10.1109/TEM.2022.3204285

The emergence of mobile financial technology (mobile fintech) services raises numerous public concerns regarding privacy issues; consequently, researchers in mobile technology acceptance have focused on consumers' privacy self-disclosure behaviors under the usual scenario. However, there is still a lack of understanding on how external influences, such as a public health crisis, affect consumers' privacy decision-making process. Therefore, in this article, we examine the effects of privacy- and pandemic-related antecedents on mobile fintech users' information self-disclosure behavior during the coronavirus disease 2019 pandemic. The present research adopts a self-administered questionnaire with 712 effective responses for data collection and a two-stage partial least squares-structural equation modeling-artificial neural network (PLS-SEM-ANN) approach to test the theoretical lens proposed. The results indicate that the significant structural paths in the model are consistent with the proposed hypotheses and existing literature. Surprisingly, face-to-face avoidance (FFA) does not significantly influence consumers' self-disclosure willingness. Infection severity and infection susceptibility were insignificant with FFA. The present research is the first to investigate consumers' privacy-related behavior via integrating the privacy-calculus framework with control agency theory. This research focuses on consumers' decision-making during the pandemic, explicitly highlighting the macroenvironment's role in influencing an individual's behavior.