Modeling Users’ Online Participation : Evidence from Online Weight-Loss Communities

Users’ participation in virtual communities is a complex process. On the one hand, there exist more than one dynamic sources, such as sequential dynamics and temporal dynamics. On the other hand, users may develop different participation focuses through the participation process. These aspects are underexplored in the extant literature. This research gap motivates us to develop an effective approach to calibrate users’ online behaviors. Specifically, we develop a multi-dimensional continuous-time hidden Markov model (HMM). This model is able to control both sequential and temporal dynamics of users’ online behaviors. In addition, it allows us to explore users’ multiple correlated behavioral dimensions at the same time. To gauge the effectiveness of our approach, we root our research in the context of weight management. The data is collected from a leading online weight-loss community in the US. Our main findings include 1). Timing information can bring valuable insights on users’ participation pattern in virtual communities, and it improves our model forecast performance. 2). Users may shift their participation focuses during the participation process. 3). Different social-support exchange strategies have statistically different effects on users’ weight management, and these effects are mediated by users’ community commitment. Together, these findings suggest that overlooking information imbedded in observed behaviors of users may result in inaccurate or even falsified conclusions on participation pattern. In addition, personalized health care strategy need to be implemented when individual’s condition is complicated and varying with time, such as chronic diseases like obesity and diabetes. Further, we provide several application examples to illustrate the practical values of our study, in which we combine our empirical model with machine learning techniques. These examples can be readily applied to a variety of business settings such as personalized recommendation, precision marketing and user characterization