Who’s watching? Classifying sports viewers on social live streaming services

Liu, Haoyu; Tan, Kim Hua; Wu, Xianfeng

Abstract

The newly emergent social live streaming services (SLSSs) provide the sport consumers with a synchronised and more interactive viewing experience. In order to help the sport SLSSs firms understanding and engaging with the viewers effectively, this research aims to classify the sports SLSS viewers based on their engagement behaviour, and identify the perceived value and value contribution of each group of viewers. Firstly, 52,545 sports SLSSs viewers’ viewing duration time is predicted by a feedforward neural network. Second, the predicted viewing duration time and other extracted viewer behavioural data (number of messages, number of virtual gifts, and value of virtual gifts) are analysed through two-step clustering in SPSS, and classified viewers into four types. Semi-structured interviews were then conducted to understand how each type of viewer co-creates value. The results identified four groups of viewers, namely content consumers, super co-creators, co-creators, and tourists, and identified their distinct value co-creations and perceived value. This study sheds light on combining engagement behaviour and value co-creation literature to classify the sports viewers in the context of SLSSs. This understanding assists the decision-making processes of marketers and operators to promote viewers’ co-creation effectively.

Citation

Liu, H., Tan, K. H., & Wu, X. (2023). Who’s watching? Classifying sports viewers on social live streaming services. Annals of Operations Research, 325(1), 743–765. https://doi.org/10.1007/s10479-022-05062-y

• Journal Article Type: Article
• Acceptance Date: Nov 2, 2022
• Online Publication Date: Dec 3, 2022
• Publication Date: Jun 30, 2023
• Deposit Date: Oct 9, 2023
• Publicly Available Date: Dec 4, 2023
• Journal: Annals of Operations Research
• Print ISSN: 0254-5330
• Electronic ISSN: 1572-9338
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 325
• Issue: 1
• Pages: 743–765
• DOI: https://doi.org/10.1007/s10479-022-05062-y
• Keywords: Management Science and Operations Research; General Decision Sciences
• Public URL: https://nottingham-repository.worktribe.com/output/14602460
• Publisher URL: https://link.springer.com/article/10.1007/s10479-022-05062-y