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Predicting viewer gifting behavior in sports live streaming platforms: The impact of viewer perception and satisfaction

Tan, Kim Hua; Pawar, Kulwant; Liu, Haoyu

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Authors

Professor Kim Tan kim.tan@nottingham.ac.uk
PROFESSOR OF OPERATIONS AND INNOVATION MANAGEMENT

Haoyu Liu



Abstract

Sports viewers have been offered an unprecedented viewing experience to share their passion with their team and communicate in real-time with a streamer and other viewers by sending messages and virtual gifts on sport live streaming platforms (SLSPs). These activities can reflect viewers' underlying perceptions and levels of satisfaction towards the viewing experience. Using actual viewers' behavioral big data, comprising of 16,204 real-time messages and 5,540 virtual gifts, this study combines machine learning techniques and structural equation modelling (SEM) to examine the influence of viewer value perception on gifting behavior through the mediation effect of satisfaction. The results suggest that satisfaction fully mediates the effects of viewer value perception on gifting amount and partially mediates the effects of value perception on gifting number. Important theoretical and managerial implications of this study for social live streaming service (SLSSs) researchers and practitioners are also discussed.

Citation

Tan, K. H., Pawar, K., & Liu, H. (2022). Predicting viewer gifting behavior in sports live streaming platforms: The impact of viewer perception and satisfaction. Journal of Business Research, 144, 599-613. https://doi.org/10.1016/j.jbusres.2022.02.045

Journal Article Type Article
Acceptance Date Feb 12, 2022
Online Publication Date Feb 16, 2022
Publication Date 2022-05
Deposit Date Feb 24, 2022
Publicly Available Date Aug 17, 2023
Journal Journal of Business Research
Print ISSN 0148-2963
Electronic ISSN 1873-7978
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 144
Pages 599-613
DOI https://doi.org/10.1016/j.jbusres.2022.02.045
Keywords Engagement behavior; Value perception; Social live streaming services; Sports live steaming platforms; Machine learning
Public URL https://nottingham-repository.worktribe.com/output/7505774
Publisher URL https://www.sciencedirect.com/science/article/pii/S0148296322001655

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