| Summary: | 碩士 === 國立臺灣大學 === 國際企業學研究所 === 103 === This thesis takes Youtube, a social networking and video sharing website, as an example, conducting research by using online browser history of household users in Comscore database. We define states of user stickiness in each period by two variables, which are frequency of entering the website and pages viewed each time. We apply Markov chain theory and hierarchical Bayesian model to build up transition probability matrix of each individual user. Therefore, we can utilize the matrix to predict the states of each user in the following periods. Furthermore, this thesis sets six possible migration paths of user stickiness, and conduct stepwise regression analysis to identify the relationship between demographic variables and possibility of the migration paths, which considers both heterogeneity and dynamic of users. In the end, we use transition probability matrixes to calculate the convergence states of each individual user, and thus we can know what would be the ultimate static states of users. Also, we find that there are significant attributes of users within segments between different ultimate states. Through the research conducted, managers of social networking website can recognize future stickiness of their users, and therefore could plan appropriate management strategies in advance.
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