Promoting Usage of Location-based Services, an Approach Based on Intimacy Theory and Data Mining Techniques

Objectives: To explore factors pertain to Smartphone users’ resistance to location-based services using data mining techniques, and to develop strategies to promote the usage of LBS. Methods: To analysis Smartphone users’ characteristics and conduct user segmentation, we used K-mean clustering to segment 165 Smartphone users based on their intimacy with service providers. Then, decision tree analysis was used to explore the characteristics for each user group. At last, the association rule analysis was used to figure out the relationship among users’ Smartphone usage patterns and their willingness to use LBS and willingness to provide location information. Findings: The result shows that Smartphone users can be clearly categorized into five groups based their intimacy towards LBS providers. The user group that showed the highest intimacy to service providers is also characterized as high willingness to use LBS and also high willingness to share their location information. Also, we explored the factors that prevent users from disclosing their location information and factors that facilitate users to use LBS for each users group respectively. The results suggest that in addition to the privacy concern, the concerns of battery life, data usage and perceived low usefulness are also accounted for users’ hesitation to share their location information to use LBS. The high needs of life information (traffic, weather and restaurant), high frequency of outdoor activities, friends’ recommendation as well as unlimited data plan were associated with users’ high intention to use LBS. Based on the association analysis result, the promotion strategies were developed for each user group respectively. Applications/Improvements: This study extends the knowledge of factors hinder users from using LBS, and also provides LBS providers with practical insights about how to promote users to a higher intimacy group.