Tripartite evolutionary game analysis of the adoption of AI delivery technology

english

2025

volume 20, issue 4

AI delivery   artificial intelligence   consumer behaviour   government incentives   logistics delivery services   logistics enterprise decision-making   technology adoption   tripartite evolutionary game mode

Univerza v Mariboru, Fakulteta za strojništvo
(Obvezni izvod spletne publikacije)

The decision of logistics enterprises to adopt AI delivery technology is influenced by multiple stakeholders, a fact that is largely ignored in existing research. A tripartite evolutionary game model incorporating the government, logistics enterprises, and consumers was established in this study. It analyzed the strategies adopted by the three actors in promoting AI delivery technology and examined the factors influencing their choices. Furthermore, the evolutionary equilibrium and stability of these strategies were explored and verified through simulation analysis. Results reveal several key insights: (1) Technology promotion is a collaborative process driven by multiple stakeholders. Government subsidies, enterprise costs and benefits, and consumer utility are the crucial variables determining system stability. (2) Government incentives not only reduce enterprises’ adoption costs but also increase consumer willingness to adopt through subsidies and publicity. These measures accelerate the system’s evolution toward the ideal stable state of “introduction, incentive, AI delivery”. (3) Enterprises’ investment decisions are highly sensitive to input costs and economic benefits. The threshold for technology adoption decreases when significant economic benefits are present or costs decline. (4) Consumers’ perceived utility and learning costs directly affect their usage intentions and subsequently influence the strategic choices of the government and enterprises through demand feedback. This study provides a novel perspective on the promotion of AI delivery and offers practical management insights for policy-making and user analysis in logistics enterprises.

06.07.2026