Enhancing Competitive Advantage Through AI-Driven Live Streaming Sales

Artificial intelligence (AI)-supported live streaming sales have emerged as a popular marketing strategy. Faced with competition from bricks-and-mortar (B&M) retailers, online retailers are considering how to utilize live streaming to enhance their competitive advantage. We develop a duopoly competition game model in which a B&M retailer sells a product through an offline channel, while an online retailer utilizes human-hosted or AI-supported live streaming to sell the product through an online channel. Our findings reveal that the B&M retailer can sustain a higher price and more profits unless the online retailer adopts live streaming sales. Human-hosted live streaming enables the online retailer to gain a competitive advantage, especially in the situation of low online acceptance with notable or minimal live streaming advantage, or high online acceptance with limited live streaming advantage. AI-supported live streaming proves beneficial when consumers encounter high hassle costs. Furthermore, we underscore the advantage of live streaming sales for the online retailer, noting that it can benefit from utilizing either human-hosted or AI-supported live streaming when consumers' hassle costs are between moderately low and not too high. Moreover, our study offers insights into the online retailer's optimal live streaming selection strategy that the choice between human-hosted and AI-supported live streaming depends on the cost of human-hosted live streaming and consumers' hassle costs.

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Publication Details

Journal
IEEE Transactions on Engineering Management
Published
2025-01-01
DOI
https://doi.org/10.1109/tem.2025.3550400
Citations
14
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
31.98
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article

Enhancing Competitive Advantage Through AI-Driven Live Streaming Sales

T.C.E. Cheng, Xiaoping Xu, Samuel Shuai Liu, Yan Zhou
14 citations
IEEE Transactions on Engineering Management
Big Data and Business Intelligence
31.98
article

Enhancing Competitive Advantage Through AI-Driven Live Streaming Sales

T.C.E. Cheng, Xiaoping Xu, Samuel Shuai Liu, Yan Zhou
article en
14 citations

Abstract

Artificial intelligence (AI)-supported live streaming sales have emerged as a popular marketing strategy. Faced with competition from bricks-and-mortar (B&M) retailers, online retailers are considering how to utilize live streaming to enhance their competitive advantage. We develop a duopoly competition game model in which a B&M retailer sells a product through an offline channel, while an online retailer utilizes human-hosted or AI-supported live streaming to sell the product through an online channel. Our findings reveal that the B&M retailer can sustain a higher price and more profits unless the online retailer adopts live streaming sales. Human-hosted live streaming enables the online retailer to gain a competitive advantage, especially in the situation of low online acceptance with notable or minimal live streaming advantage, or high online acceptance with limited live streaming advantage. AI-supported live streaming proves beneficial when consumers encounter high hassle costs. Furthermore, we underscore the advantage of live streaming sales for the online retailer, noting that it can benefit from utilizing either human-hosted or AI-supported live streaming when consumers' hassle costs are between moderately low and not too high. Moreover, our study offers insights into the online retailer's optimal live streaming selection strategy that the choice between human-hosted and AI-supported live streaming depends on the cost of human-hosted live streaming and consumers' hassle costs.

IEEE Transactions on Engineering ManagementVol. 72
Hong Kong Polytechnic University (HK), Anhui University (CN), Nanjing University of Science and Technology (CN), Nanjing University (CN)
Openalex Percentile: Top 0%
Big Data and Business Intelligence
31.98
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Enhancing Competitive Advantage Through AI-Driven Live Streaming Sales — T.C.E. Cheng, Xiaoping Xu, et al. · IEEE Transactions on Engineering Management (2025) | TGRS Research Map | TGRS