Should start-up firms shift to employee or AI livestreaming? Mode selection after influencer-driven customer acquisition

Guoxuan Huang et al.

Asia-Pacific Journal of Operational Research2026https://doi.org/10.1142/s021759592640004xarticle
AJG 1
Weight
0.50

What the paper says

College of Business Administration, Hunan University of Finance and Economics, Changsha, Hunan, 410006, China With the boom in livestreaming e-commerce, numerous start-up firms heavily rely on inuencer livestreaming to drive customer acquisition, yet high inuencer commission rates threaten their long-term operational sustainability. Addressing this dilemma, this study develops a two-stage game-theoretic model to examine whether start-ups should continue with inuencer livestreaming or switch to self-run livestreaming (employee/AI) post initial inuencer-driven customer acquisition. The research shows that the optimal livestreaming model depends on the commission rate, the network externality from inuencer-driven customer acquisition (customer retention rate), and the AI streamer technology maturity. Counter-intuitively, firms may prefer inuencer livestreaming at a high commission rate, but favor self-run models even at a low commission rate, due to the network externality and the AI streamer technology maturity. Moreover, we derive interesting two-stage pricing strategies for these three livestreaming models: firms in the inuencer livestreaming model consistently employ low-to-high (L-H) pricing, whereas firms in employee and AI models adopt high-to-low (H-L) pricing when the customer retention rate is low. These findings provide actionable guidance for start-ups to optimize livestreaming strategies and pricing decisions.

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https://doi.org/https://doi.org/10.1142/s021759592640004x

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@article{guoxuan2026,
  title        = {{Should start-up firms shift to employee or AI livestreaming? Mode selection after influencer-driven customer acquisition}},
  author       = {Guoxuan Huang et al.},
  journal      = {Asia-Pacific Journal of Operational Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s021759592640004x},
}

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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.