Predicting Personal Mobility Data Disclosure

Abstract

Personal data has multiple categories, such as demographic, firmographic (organizational), behavioral and psychographic data. Mobility data is one of the behavioral pattern information, for example, location tracking. Disclosing mobility data is a privacy concern of individuals as their location and physical moving pattern can be uncovered and potentially be misused by third parties. Despite the privacy concern, exposing mobility data can be beneficial for individuals to gain advantages of getting customized services from service providers. On the other hand, companies get benefit from mobility data by providing value added services to individuals. Several influential factors of individual's intention in personal data disclosure have been identified based on existing literature. However, we argue that contextual aspects play an important role in effecting the intention. Therefore, in this research we seek to investigate if the identified factors by prior studies are significantly influential in Malaysian context. Additionally, from a different perspective, our research focuses on mobility data instead of personal data in general using Structural Equation Modelling analysis method. Based on our research finding, we identified an unexplored factor that can be used to predict the intention to disclose mobility data, and our result also confirmed that context aspects such as demographics and different personal data categories (e.g. mobility data) do have impact on the disclosure attention.