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

5 Pages Posted: 11 Dec 2018

See all articles by Tongxin Zhou

Tongxin Zhou

University of Washington - Michael G. Foster School of Business

Lu (Lucy) Yan

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Yingfei Wang

University of Washington - Michael G. Foster School of Business

Yong Tan

University of Washington - Michael G. Foster School of Business

Date Written: November 16, 2018

Abstract

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.

Keywords: temporal dynamics (timing information), multiple behavioral dimensions, community commitment, multi-dimensional continuous-time hidden Markov model

Suggested Citation

Zhou, Tongxin and Yan, Lu (Lucy) and Wang, Yingfei and Tan, Yong, Modeling Users’ Online Participation: Evidence from Online Weight-Loss Communities (November 16, 2018). Kelley School of Business Research Paper No. 19-9. Available at SSRN: https://ssrn.com/abstract=3285599 or http://dx.doi.org/10.2139/ssrn.3285599

Tongxin Zhou (Contact Author)

University of Washington - Michael G. Foster School of Business ( email )

WA 98195-3200
United States

Lu (Lucy) Yan

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Department of Operations and Decision Technologies
1309 E. Tenth Street
Bloomington, IN 47401
United States

Yingfei Wang

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

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