Abstract
Smartphones make it easier for brands and manufacturers to provide services in a digital and ubiquitous way. Consumers' adoption of different mobile services could be influenced by their individual differences in demographics and personality traits. Therefore, we developed a mobile app and conducted an empirical study with 2043 Android users to understand the impact of individual differences on their mobile service adoption behavior. Our contributions are two-fold. First, we find that age, gender, salary, and personality traits have significant impact on the adoption of 16 different mobile services. Second, we propose a data-mining approach to automatically determine a user's personal profile based on her installed apps. The prediction precision and recall of our models are 70% and 35% higher than random, respectively. Our approach can be deployed in a non-intrusive and highly scalable manner as part of any mobile app thereby enabling better business intelligence and decision-making.