Supplement or replacement? Human and AI influencer strategies with attribute and quality information

Purpose Live streaming sales are currently experiencing a trend toward diversification. Human and AI influencers differ in information transmission efficiency across product attributes and quality. This transformation raises critical managerial questions regarding how firms should allocate human and AI influencer resources. Design/methodology/approach We construct a game-theoretic model with two competing firms that select among human-only (HO), AI-only (AO), and hybrid influencers (HI) strategies, and then make pricing decisions. An influencer conveys a signal through product presentation that matches or opposes the product's attribute proportion and true quality. Findings Introducing the AI influencer during off-peak periods by only one firm leads to a win-win outcome. For experience products, the AI influencer shifts from a supplement to a replacement as the cost advantage expands. For search products, early adoption during peak periods forces the other to adopt the AI influencer as well, making the Scenario AO-AO the unique equilibrium. The proportion of the human influencer is not the larger the better. It depends on the competitor's arrangement: profits may either first increase and then decrease, exhibit an interior optimum, decrease consistently, or remain unchanged. Originality/value Few studies have examined the role of AI influencers in the live-streaming supply chain. Our findings contribute to understanding the strategic value of AI influencers and the equilibrium structure of live-streaming markets in the AI era.

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Publication Details

Journal
Industrial Management & Data Systems
Published
2026-10-27
DOI
https://doi.org/10.1108/imds-02-2026-0202
Primary Topic
Supply Chain and Inventory Management
Type
article
Field-Weighted Citation Impact
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article

Supplement or replacement? Human and AI influencer strategies with attribute and quality information

Zheng Qi, Yongmei Liu, Liqun Wei
4 links
Industrial Management & Data Systems
Supply Chain and Inventory Management
article

Supplement or replacement? Human and AI influencer strategies with attribute and quality information

Zheng Qi, Yongmei Liu, Liqun Wei
article en

Abstract

Purpose Live streaming sales are currently experiencing a trend toward diversification. Human and AI influencers differ in information transmission efficiency across product attributes and quality. This transformation raises critical managerial questions regarding how firms should allocate human and AI influencer resources. Design/methodology/approach We construct a game-theoretic model with two competing firms that select among human-only (HO), AI-only (AO), and hybrid influencers (HI) strategies, and then make pricing decisions. An influencer conveys a signal through product presentation that matches or opposes the product's attribute proportion and true quality. Findings Introducing the AI influencer during off-peak periods by only one firm leads to a win-win outcome. For experience products, the AI influencer shifts from a supplement to a replacement as the cost advantage expands. For search products, early adoption during peak periods forces the other to adopt the AI influencer as well, making the Scenario AO-AO the unique equilibrium. The proportion of the human influencer is not the larger the better. It depends on the competitor's arrangement: profits may either first increase and then decrease, exhibit an interior optimum, decrease consistently, or remain unchanged. Originality/value Few studies have examined the role of AI influencers in the live-streaming supply chain. Our findings contribute to understanding the strategic value of AI influencers and the equilibrium structure of live-streaming markets in the AI era.

Industrial Management & Data Systems
Central South University (CN), Intelligent Systems Research (United States) (US)
Openalex Percentile: Top 7%
Supply Chain and Inventory Management
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Supplement or replacement? Human and AI influencer strategies with attribute and quality information — Zheng Qi, Yongmei Liu, et al. · Industrial Management & Data Systems (2026) | TGRS Research Map | TGRS