The popularity of social networking sites (SNSs) has increased rapidly. SNSs are a key part of daily life for many people around the world. The use of SNSs is already a global phenomenon. Drawing on social capital theory, this research empirically explored how structural capital (social network ties), relational capital (trust in SNSs, trust in members of SNSs, social identification, and social norms), and cognitive capital (shared language and shared goals) influence stickiness, which in turn affects addiction. This study introduced key moderators, privacy concerns and perceived security, to the relationship between stickiness and addiction. The authors empirically evaluated the proposed model by using survey data collected from SNS users. Structural equation modeling was applied to test the model. This study can provide a deeper understanding of SNS users' addiction behavior by focusing on social capital theory, privacy concerns, and perceived security and therefore contribute to both research and practice.

We consider the problem of estimating the order of a stationary ergodic Markov chain. Our focus is on estimators which satisfy a generalized Neyman-Pearson criterion of optimality. Specifically, the optimal estimator minimizes the probability of underestimation among all estimators with probability of overestimation not exceeding a given value. Our main result identifies the best exponent of asymptotically exponential decay of the probability of underestimation. We further construct a consistent estimator, based on Kullback-Leibler divergences, which achieves the best exponent. We also present a consistent estimator involving a recursively computable statistic based on appropriate mixture distributions; this estimator also achieves the best exponent for underestimation probability.

We consider first the estimation of the order, i.e., the number of states, of a discrete-time finite-alphabet stationary ergodic hidden Markov source (HMS). Our estimator uses a description of the observed data in terms of a uniquely decodable code with respect to a mixture distribution, obtained by suitably mixing a parametric family of distributions on the observation space. This procedure avoids maximum likelihood calculations. The order estimator is shown to be strongly consistent with the probability of underestimation, decaying exponentially fast in the number n of observations, while the probability of overestimation does not exceed cn/sup -3/, where c is a constant. Next, we present a sequential algorithm for the uniquely decodable universal data compression of the HMS, which performs an on-line estimation of source order followed by arithmetic coding. This code asymptotically attains optimum average redundancy.>