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Promoting Usage of Location-based Services, an Approach Based on Intimacy Theory and Data Mining Techniques 1. Introduction • With the development of technology, nowadays, smartphones have become an integral part of people daily lives. • Accordingly, variously kinds of applications that provides location-based services (LBS) have been developed in todays’ mobile service market. • On the contrary, people’s interests towards LBS did not increase correspondently, even shows a continuous decease instead [ Trends of people’s interests on LBS 2. Materials and Methods 2.1 Data collection • The data was collected from 220 participants in three universities locate in Seoul, Korea. • The data which contains 165 sample that finally used for data analysis in this study includes 35 variables, including demographic information, user mobile use behavior feature, actual LBS use situation and attitudes regarding LBS usage 2.2 Test method • Three steps were used to analysis the data: first, the Kmean clustering analysis was used to segment the mass Smart-phone users into different groups based on their smartphone usage characteristics and attitude variables. • Second, decision tree analysis was conducted using SAS in to identify the characteristics of users belong to different clusters regarding the selected clustering criteria. • Next, an inter & intra-clustering analysis were applied to explore the latent antecedent factors associated with the clustering criteria: Intimate Behavior and Intimate Experience, as well as to generate specific association rules, which can be used for designing suitable marketing packages. 3. Results 3.1 Test result • Based on the factors regarding users’ intimacy level to service provider-intimate behavior intention and expectation to receive the intimate experience, as well as the real usage situation of LBS applications • We segmented the 165 smart phone users using a clustering algorithm. K-mean with a Newton algorithm was used for clustering purpose, and the clustering number was be selected as 5 in order to distinguish different clusters. Distance between 5 clusters in terms of selected criteria Decision Tree Analysis-Intimacy Level for Each Cluster User Group Characteristics Willingness to discl Intention to use User groups ose personal inform LBS ation Cluster Cluster Cluster Cluster Cluster 1 2 3 4 5 Low High Low Medium High Low Low High Medium High One example of Association rule analysis in cluster 2 Lift Support Confidence( Rule: Antecedent ==> Cons (%) %) equent 1.31 58.33 87.5 1.29 50 85.71 1.25 41.67 83.33 1.13 50 75 1.07 41.67 71.43 1.5 41.67 100 kaoka1 ==> Use intention AV E2 Sns2 ==> Use intention AVE2 Used1 ==> Use intention AV E2 recomm3 ==> Use intention AVE2 facility2 ==> Use intention A VE2 Heard3 ==> Use intention AV E2 4. Discussion • In this study, three main contributions are provided: first, a user segmentation process was proposed from a CRM perspective based on users intimacy level with service provide: intimate behavior intention- users willingness to disclose personal information and intimate experience expectationusers’ expectation to receive personalized service by using location-based service. • Second, regarding user’ personalized needs for locationbased services functions, different types of LBS application were suggested to different user groups in terms of intimacy level.
