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Turn Your Online Weight Management from Zero to Hero: A Multidimensional, Continuous-Time Evaluation

Author

Listed:
  • Tongxin Zhou

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

  • Lu (Lucy) Yan

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Yingfei Wang

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Yong Tan

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

Abstract

Online weight-loss communities (OWCs) provide individuals with various tools to support their weight management, such as weight recorders and weight-loss journals. These tools enable individuals to focus on different aspects of their self-regulation, including weight-loss outcomes and behavioral routines. Prior research, however, has not fully incorporated individuals’ self-regulation focuses; thus, there is limited understanding of individuals’ online weight-management dynamics as well as the operating mechanisms of OWCs. This gap in the literature motivates us to develop a framework that is able to account for individuals’ multiple self-regulation focuses, termed self-regulatory dimensions in this study. We propose a multidimensional, continuous-time hidden Markov model, which can not only capture individuals’ self-regulatory dimensions jointly as a multidimensional vector, but also can incorporate a hidden layer of dynamics that depicts individuals’ cognitive states in producing weight-management behaviors. By investigating a leading noncommercial OWC in the United States, we find that individuals tend to increase their journal-recording behaviors while decreasing self-weighing behaviors after they have participated in online social activities. Given that individuals usually expend limited effort toward weight management, this result suggests that individuals may shift their focus from weight-loss outcomes (i.e., changes in weight) to weight-management behavioral routines. Therefore, neglecting either self-regulatory dimension would result in an underestimation of individuals’ engagement in conducting self-management in OWCs. Our results also provide insight into social influence on individuals’ weight-management behaviors. This study contributes to the extant literature on individuals’ engagement in online healthcare communities and the functionality of OWCs.

Suggested Citation

  • Tongxin Zhou & Lu (Lucy) Yan & Yingfei Wang & Yong Tan, 2022. "Turn Your Online Weight Management from Zero to Hero: A Multidimensional, Continuous-Time Evaluation," Management Science, INFORMS, vol. 68(5), pages 3507-3527, May.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:5:p:3507-3527
    DOI: 10.1287/mnsc.2021.4046
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    References listed on IDEAS

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    1. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    3. Lu (Lucy) Yan, 2018. "Good Intentions, Bad Outcomes: The Effects of Mismatches between Social Support and Health Outcomes in an Online Weight Loss Community," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 9-27, January.
    4. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    5. Lu Yan & Yong Tan, 2014. "Feeling Blue? Go Online: An Empirical Study of Social Support Among Patients," Information Systems Research, INFORMS, vol. 25(4), pages 690-709, December.
    6. Buunk, Abraham P. & Gibbons, Frederick X., 2007. "Social comparison: The end of a theory and the emergence of a field," Organizational Behavior and Human Decision Processes, Elsevier, vol. 102(1), pages 3-21, January.
    7. Bandura, Albert, 1991. "Social cognitive theory of self-regulation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 248-287, December.
    8. Youngsoo Kim & Ramayya Krishnan, 2019. "The Dynamics of Online Consumers’ Response to Price Promotion," Service Science, INFORMS, vol. 30(1), pages 175-190, March.
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