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.
Authors
- T.C.E. Cheng
- Xiaoping Xu (ORCID: https://orcid.org/0000-0002-5930-3298)
- Samuel Shuai Liu
- Yan Zhou (ORCID: https://orcid.org/0000-0003-3381-357X)
Institutions
- Hong Kong Polytechnic University (HK)
- Anhui University (CN)
- Nanjing University of Science and Technology (CN)
- Nanjing University (CN)
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