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Development of a Causal Model of Post-Millennials’ Willingness to Disclose Information to Online Fashion Businesses (Thailand)
Author(s)
Kasisuwan, Jinnawat
Date Issued
January 1, 2024
Type
Article
Abstract
This research examines the causal factors influencing the willingness of Central Post-Millennials to disclose information to online fashion businesses by using privacy calculus theory as the basic principle for modeling. The study has three primary objectives: (1) to investigate the causal factors influencing willingness to disclose information, (2) to analyze both the direct and indirect effects of perceived risk, perceived benefit, perceived value, perceived control over the use of personalization data, and trust on the willingness to disclose information, and (3) to develop a causal factor model for understanding the determinants of willingness to disclose information among Central Post-Millennials in the context of online fashion businesses. The research sample consists of 385 individuals, and data were collected using a structured questionnaire. Descriptive and inferential statistical methods were employed for data analysis. The relationships between variables were assessed using Pearson's Correlation Coefficient. The model's fit to the empirical data was evaluated using goodness-of-fit measures, and the transmission of influence was tested through structural equation modeling (SEM). The findings reveal that demographic factors do not significantly affect the willingness to disclose information. However, the study identifies perceived risk, perceived benefit, perceived value, perceived control over the use of personalization data, and trust as key determinants of willingness to disclose information to online fashion businesses. Among these, perceived control exhibits the strongest influence, closely followed by trust. These results highlight the antecedent processes influencing the willingness to disclose information, as represented by a model developed from a comprehensive literature review and empirically tested for consistency with the data.
Citation
International Journal of Advanced Computer Science and Applications, 15(10), 456-463, 2024