JOURNAL ARTICLE
The Value of Virtual Engagement: Evidence from a Running Platform.
Published In: Management Science (INFORMS), 2024, v. 70, n. 9. P. 6179 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Liu, Che-Wei; Wang, Weiguang; Gao, Guodong; Agarwal, Ritu 3 of 3
Abstract
This article investigates the impact of virtual races—remote running events hosted on digital fitness platforms—on user engagement and physical activity within the Connected Fitness industry, which integrates mobile, wearable technologies, and social platforms. Using data from a leading Taiwanese running platform, the study finds that new users participating in virtual races increase their platform engagement by approximately 42.8 days and boost related e-commerce spending by 48.4%. The research employs rigorous methods, including propensity score matching, instrumental variables based on zodiac sign-themed races, and fuzzy regression discontinuity design, to address self-selection and endogeneity concerns. Additionally, virtual race participation enhances physical activity among existing users, who run on average 4.41 km more in the three weeks following the event, and fosters social connections even for users with initially low social ties. The findings suggest that virtual races can effectively overcome initial behavioral resistance, promote sustained healthy habits, and increase user loyalty, with implications for platform design and public health promotion.
Additional Information
- Source:Management Science (INFORMS). 2024/09, Vol. 70, Issue 9, p6179
- Document Type:Article
- Subject Area:Sports and Leisure
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4945
- Accession Number:179339496
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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