Blessing or curse? Strategic adoption of platform artificial intelligence in competitive platform supply chains

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Authors: Jun Zheng, Tao Li, Qi Tan, Xiang Wang, Bin Liu
Cited by: 1
References: 1

Abstract

As platform economies become increasingly intelligent, platform provided artificial intelligence (AI) services have emerged as an important tool for brand manufacturers to improve competitiveness. This study examines whether platform AI serves as a blessing or a curse for brand manufacturers. We develop a benchmark model with a single manufacturer and extend it to two Stackelberg game settings featuring either similar market positions or a strong and a weak manufacturer. We analyze how strategic AI adoption shapes pricing decisions, firm profits, consumer utility and social welfare. The results show that without a competition platform AI increases the manufacturer's profits, thus constitutes a clear blessing for the brand manufacturer. When competition exists brand manufacturers always have incentives to adopt platform AI and unilateral adoption benefits the adopter. However, under Nash equilibrium where both manufacturers adopt AI outcomes differ by market structure. With symmetric competition equilibrium adoption leads to a prisoner's dilemma that harms both manufacturers. With asymmetric competition the prisoner's dilemma affects only the weak manufacturer while the strong manufacturer continues to benefit making platform AI a curse for the weak party. We further show that under symmetric competition equilibrium AI adoption always improves platform profits, consumer utility and social welfare. Under asymmetric competition consumer utility still rises but the effects on platform profits and social welfare depend on manufacturers costs and the platform commission rate. Extensions indicate that asymmetry in base demand shares does not alter equilibrium adoption. Overall, the study offers managerial insights into AI adoption in competitive platform markets.

Fields of Study & Subjects

Digital Platforms and Economics Supply Chain and Inventory Management Innovation Diffusion and Forecasting

Publication details

Journal / source
International Journal of Industrial Engineering Computations
Publisher
Growing Science
Volume
17
Issue
3
Pages
1249–1266
Publication date
1 January 2026