This paper explores how managers perceive AI-driven decision support systems (AI-DSS) within supply chain management (SCM), offering insights into managerial perceptions and trust in AI tools. As AI becomes increasingly integrated into business operations, decision-makers face trust, transparency, and explainability challenges.
Employing an exploratory multiple case study approach, this paper focuses on different decision-makers, chosen for their ability to provide a deep and contextualized understanding of a phenomenon within its real-world environment. Primary data is collected through semi-structured interviews with management-level decision-makers, while secondary data and a developed ML model predicting early contract terminations provide additional insights. The paper captures the socio-technical dynamics of AI adoption.
Findings highlight the interplay between rational analysis and intuitive decision-making, revealing that while AI enhances decision-making efficiency, its opaque nature can hinder adoption due to trust issues. This paper contributes to a socio-technical framework for effective AI adoption, providing insights that can help organizations address trust-related challenges and optimize decision-support systems, aligning with business needs. By understanding managerial perceptions, this research offers foundational guidance for improving AI adoption and reshaping decision-making approaches in an increasingly automated landscape.
