Human-AI interaction in remanufacturing: exploring shop floor workers' behavioural patterns within a specific human-AI system

Artificial intelligence (AI) is increasingly discussed as an innovation enabler for the enhancement of circular economy (CE) approaches in industries. The further deployment of intelligent technologies is considered to be very promising particularly in remanufacturing, which can be regarded as an implementation approach of CE at a firm level. AI's potential to contribute to advancements in remanufacturing can be traced back to these modern technologies' extended capacities of supporting and assisting humans during rather manual processes which are regarded as more common in remanufacturing than in traditional linear production. As a result, we argue that in future application scenarios, humans are going to interact more often with AI agents who may direct and assist humans' behaviour and decision-making processes. We assume that a better understanding of the specific dynamics and novel aspects of these kind of newly emerging human-AI systems is a key prerequisite for sustainable process innovation, particularly in remanufacturing organisations. However, empirical-based contributions about humans' behavioural changes in interaction with AI agents have so far been rather rare and limited, especially in the field of remanufacturing and CE. In this article, we seek to contribute to this gap in research by exploring the interaction between shop floor workers and an AI agent based on a case study research approach at a plant of a German automotive supplier that is remanufacturing used parts. We conducted semi-structured interviews among the shop floor workers who are involved in a joint decision-making task with an AI agent. We interpret the findings of our qualitative data in the light of related research in the field of AI in CE, AI implementation in organisation and human-AI interaction literature. In summary, our analysis reveals 13 behavioural patterns that shop floor workers reported on referring to their interaction with the AI agent. The behavioural patterns are systemised into a cognitive, emotional and social dimension of a competence framework. These findings shall contribute to a more specific understanding about how humans interact with AI agents at work, while considering the specific context variables of the interaction paradigm and the AI agent's role during joint decision-making in a human-AI system. Implications for literature in the field of human-AI interaction as well as AI implementation in organisations with a particular focus on CE are discussed.