Human-Technology Relationship: AI Adoption in Manufacturing

This study examined key organizational and individual factors that shape trust in artificial intelligence (AI) adoption within the manufacturing sector. Due to the growing strategic importance of AI and the fragmented conceptualization of adoption approaches, we conducted a systematic literature review with 51 publications dealing with AI adoption in the manufacturing industry. By using a combination of the technology acceptances model (TAM) and the technology-organization-environment (TOE) framework, the findings shed light on individual and organizational drivers that capture user perceptions and organizational readiness for example. The results indicated that trust in AI adoption is driven by system features such as transparency and explainability as well as digital literacy, structural fit, external partnerships, and leadership support. This study builds upon existing acceptance models and incorporates trust-specific and normative dimensions and provides a practical framework for trust-centered AI adoption. Based on the findings, implications for theory and practice are derived. Consequently, we propose avenues for future research to strengthen trust as a main factor in the human-technology relationship. By investigating the importance of trust while adopting AI, we contributed to the recent literature streams regarding human-centric vision in AI adoption.