Developing a Model for Understanding the Factors that Affect Individuals' Intentions to Share Personal Information

Abstract

With the outbreak of COVID-19, digital contact tracing (DCT) applications have been implemented to track the transmission of the highly contagious disease. However, there has been significant resistance to using these apps due to individuals' concerns about disclosing personal information. This study aimed to examine individuals' intentions to reveal their details on DCT applications. A conceptual framework was developed incorporating theories of dual calculus, Hofstede's cultural theory, information boundary theory, and individual factors. A quantitative approach was employed, using a random sampling methodology to gather data from 533 respondents. The proposed framework was validated through partial least squares path modeling. The findings revealed that COVID-19-related stress, lack of transparency, and uncertainty avoidance negatively predicted intentions to disclose personal information. Conversely, collectivism, expected community-related outcomes of sharing information, expected personal outcomes of sharing information, and information privacy concerns positively predicted intentions to disclose personal information. This research provides insights into the factors that influence the widespread acceptance of DCT applications and can assist related authorities in ensuring their successful implementation.