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Cooperation or Confrontation? An Evolutionary Game Study on Content Clipping Authorization in Live Streaming E-Commerce Under Platform Regulation

Feng Luo et al.

Games2026https://doi.org/10.3390/g17020017article
AJG 1ABDC B
Weight
0.50

Abstract

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration.

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https://doi.org/https://doi.org/10.3390/g17020017

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@article{feng2026,
  title        = {{Cooperation or Confrontation? An Evolutionary Game Study on Content Clipping Authorization in Live Streaming E-Commerce Under Platform Regulation}},
  author       = {Feng Luo et al.},
  journal      = {Games},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3390/g17020017},
}

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Cooperation or Confrontation? An Evolutionary Game Study on Content Clipping Authorization in Live Streaming E-Commerce Under Platform Regulation

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.