This study investigates how supply chain managers perceive the impact of artificial intelligence (AI) types on reverse logistics (RL) processes-network design, collection, warehousing and processing–and to identify the conditions under which these AI types are perceived to succeed or fall short.
A quantitative survey approach was adopted, with data collected from individual supply chain managers representing international manufacturing companies. A total of 228 valid responses were analysed using covariance-based structural equation modelling (CB-SEM).
The results highlight the perceived dominant role of Analytical AI, which managers reported as significantly enhancing all four RL processes–network design, collection, warehousing and processing. In contrast, Intuitive AI was perceived to influence only the warehousing stage, while Mechanical AI was not viewed as exerting a statistically significant effect on any RL process. These findings reflect managers' perceptions of how distinct AI types shape efficiency, responsiveness and adaptability in RL practices.
The study offers insights for practitioners by showing how managers interpret and experience the influence of AI tools across RL processes. Firms can use these perceptual insights to guide the strategic alignment between AI adoption and managers' operational priorities, thereby enhancing RL decision-making, agility, and resource use.
The originality of this study lies in its analysis of how managers differentiate among mechanical, analytical, and intuitive AI in RL contexts. While prior research has explored AI's general role in supply chain management, this study advances understanding by capturing managerial perceptions of the distinct contributions and limitations of different AI types across specific RL processes.
