Runhua Xu

Understanding the impact of personality traits on mobile app adoption - Insights from a large-scale field study

The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone users personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a machine-learning model is developed to automatically determine a users personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app. Personality has a significant impact on mobile app adoption.A novel approach is proposed to study mobile app adoption on a large scale.A machine-learning model is developed to predict a smartphone users personality.The predictive model can be integrated into any mobile app.

Mobile app adoption in different life stages: An empirical analysis

Abstract The analysis of individuals’ current life stages is a powerful approach for identifying und understanding patterns of human behavior. Different stages imply different preferences and consumer demands. Thus, life stages play an important role in marketing, economics, and sociology. However, such information is difficult to be obtained especially in the digital world. This work thus contributed to both theory and practice from two aspects. First, we conducted a large-scale empirical study with 1435 participants and showed that a person’s mobile app adoption pattern is strongly influenced by her current life stage. Second, we presented a data-driven, highly-scalable, and real-time approach of predicting an individual’s current life stage based on the apps she has installed on smartphone. Result showed that our predictive models were able to predict life stages with 241.0% higher precision and 148.2% higher recall than a random guess on average.

Mobile recommendations based on interest prediction from consumer's installed apps–insights from a large-scale field study

Abstract Recommender systems are essential in mobile commerce to benefit both companies and individuals by offering highly personalized products and services. One key pre-requirement of applying such systems is to gain decent knowledge about each individual consumer through user profiling. However, most existing profiling approaches on mobile suffer problems such as non-real-time, intrusive, cold-start, and non-scalable, which prevents them from being adopted in reality. To tackle the problems, this work developed real-time machine-learning models to predict user profiles of smartphone users from openly accessible data, i.e. app installation logs. Results from a study with 904 participants showed that the models are able to predict interests on average 48.81% better than a random guess in terms of precision and 13.80% better in terms of recall. Since the effectiveness of such predictive models is unknown in practice, the predictive models were evaluated in a large-scale field experiment with 73,244 participants. Results showed that by leveraging our models, personalized mobile recommendations can be enabled and the corresponding click-through-rate can be improved by up to 228.30%. Supplementary information, study data, and software can be found at https://www.autoidlabs.ch/mobile-analytics .

Measuring and Mitigating Product Data Inaccuracy in Online Retailing

Driven by the proliferation of Smartphones and e-Commerce, consumers rely more on online product information to make purchasing decisions. Beyond price comparisons, consumers want to know more about feature differences of similar products. However, these comparisons require rich and accurate product data. As one of the first studies, we quantify how accurate online product data is today and evaluate existing approaches of mitigating inaccuracy. The result shows that the accuracy varies a lot across different Web sites and can be as low as 20%. However, when aggregating product information across different Web pages, the accuracy can be improved on average by 11.3%. Based on the analysis, we propose an attribute-based authentication approach based on Semantic Web to further mitigate online data inaccuracy.