Single or hybrid? manufacturers' live-streaming mode selection considering virtual streamers

The emergence of AI-generated virtual streamers in live-streaming commerce has complicated manufacturers' mode selection. In response, this study develops a game-theoretic decision-aid framework to analyze manufacturers' optimal strategies across both single live-streaming modes (self-streaming, streamer, and virtual streamer) and hybrid live-streaming modes (‘self−streaming+streamer’ and ‘self−streaming+virtual streamer’). It further incorporates different slotting fee structures and the platform's drainage effect to evaluate how these factors shape the manufacturer's mode choice. The findings show that: (i) consumer distrust may reduce demand for virtual streamers and the return on AI customisation, but a sufficiently strong drainage effect can offset this disadvantage; (ii) as streamer traffic effects strengthen, or drainage effects rise when traffic effects are still weak, the optimal strategy shifts from self-streaming to external or virtual streamer live-streaming, and eventually to hybrid modes; (iii) manufacturers prefer a single mode when both traffic and drainage effects are weak, but hybrid modes when these effects become stronger; and (iv) these results remain robust under different slotting fee structures and platform drainage strategies, while the manufacturer–platform equilibrium depends on drainage costs and off-peak incremental gains. Overall, the threshold-based framework guides manufacturers and platforms in allocating human and AI-enabled live-streaming resources across channels and periods.

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Publication Details

Journal
International Journal of Production Research
Published
2026-08-14
DOI
https://doi.org/10.1080/00207543.2026.2718501
Primary Topic
Image and Video Quality Assessment
Type
article
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article

Single or hybrid? manufacturers' live-streaming mode selection considering virtual streamers

Xinru Hou, Gaoke Wu, Mengze Chen, Wensheng Yang
International Journal of Production Research
Image and Video Quality Assessment
article

Single or hybrid? manufacturers' live-streaming mode selection considering virtual streamers

Xinru Hou, Gaoke Wu, Mengze Chen, Wensheng Yang
article en

Abstract

The emergence of AI-generated virtual streamers in live-streaming commerce has complicated manufacturers' mode selection. In response, this study develops a game-theoretic decision-aid framework to analyze manufacturers' optimal strategies across both single live-streaming modes (self-streaming, streamer, and virtual streamer) and hybrid live-streaming modes (‘self−streaming+streamer’ and ‘self−streaming+virtual streamer’). It further incorporates different slotting fee structures and the platform's drainage effect to evaluate how these factors shape the manufacturer's mode choice. The findings show that: (i) consumer distrust may reduce demand for virtual streamers and the return on AI customisation, but a sufficiently strong drainage effect can offset this disadvantage; (ii) as streamer traffic effects strengthen, or drainage effects rise when traffic effects are still weak, the optimal strategy shifts from self-streaming to external or virtual streamer live-streaming, and eventually to hybrid modes; (iii) manufacturers prefer a single mode when both traffic and drainage effects are weak, but hybrid modes when these effects become stronger; and (iv) these results remain robust under different slotting fee structures and platform drainage strategies, while the manufacturer–platform equilibrium depends on drainage costs and off-peak incremental gains. Overall, the threshold-based framework guides manufacturers and platforms in allocating human and AI-enabled live-streaming resources across channels and periods.

International Journal of Production Research
Nanjing University of Science and Technology (CN)
Openalex Percentile: Top 11%
Image and Video Quality Assessment
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Single or hybrid? manufacturers' live-streaming mode selection considering virtual streamers — Xinru Hou, Gaoke Wu, et al. · International Journal of Production Research (2026) | TGRS Research Map | TGRS