Change, we can believe in: Examining spillover effect of content switching on live streaming platform

Keran Zhao, Yingda Lu, Yuheng Hu, Yili Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

With the increasing popularity of live streaming services, an intensive competition emerges among streamers who are streaming during the same time period. In this competition, streamers frequently switch their streaming content which drives changes in audience flow. This content switching not only has a direct impact on the audience amount of entrant streamers but also would indirectly influence the incumbent streamers' audience size and thereby lead to disadvantage or advantages for streamers. Based on the dataset we collected from live streaming platform Twitch.tv, we empirically examine how content switching impacts the viewership of incumbent and entrant streamers from a spillover perspective. Our key results show that: 1) The entrant star streamer could benefit from the content switching due to the increasing amount of audience and further benefit the incumbent by a positive spillover. 2) Content switching may lower the audience amount of entrant streamer with limited popularity. This study contributes to literature in the live streaming community from a dynamic and empirical perspective. This study also gives actionable suggestions for streaming strategy and online advertising.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems 2018, ICIS 2018
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683173
StatePublished - 2018
Event39th International Conference on Information Systems, ICIS 2018 - San Francisco, United States
Duration: Dec 13 2018Dec 16 2018

Publication series

NameInternational Conference on Information Systems 2018, ICIS 2018

Conference

Conference39th International Conference on Information Systems, ICIS 2018
Country/TerritoryUnited States
CitySan Francisco
Period12/13/1812/16/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences
  • Applied Mathematics

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