Adopting generative AI for supply chain management decision-making

• The successful integration of generative AI technology into supply chain management (SCM) decision-making hinges on understanding managers’ behavioral intentions (BIs) towards using the technology. • Only performance expectancy, effort expectancy, and facilitating conditions of the UTAUT positively influence managers’ BIs to use ChatGPT for SCM decision-making. • Information quality and perceived privacy also exert a positive influence on managers’ BIs to use ChatGPT for SCM decision-making. • Performance expectancy, facilitating conditions, information quality, and perceived privacy are stronger determinants of BIs to use ChatGPT for SCM decision-making for managers with higher uncertainty avoidance. Supply chain management (SCM) decision-making is becoming a daunting challenge for managers as supply chains are growing in complexity. With the growing evidence for generative artificial intelligence (AI)’s capabilities in many areas, organizations see an opportunity to enhance the effectiveness of SCM decision-making. However, while the successful integration of generative AI technology into SCM decision-making hinges on understanding managers’ intentions towards using the technology, their behavioral intentions to use the technology for SCM decision-making remain underexplored. To address this, this study explores managers’ behavioral intentions to use generative AI for SCM decision making. In doing so, the study draws on the unified theory of acceptance and use of technology to explore, along its predictors (i.e., performance expectancy, effort expectancy, peer influence, and facilitating conditions), the impact of information quality and perceived privacy on managers’ behavioral intentions to use generative AI for SCM decision-making, focusing on ChatGPT as a representative generative AI tool. The study further examines the moderating role of individual uncertainty avoidance on the impact of the proposed factors on managers’ behavioral intentions. According to the analysis of 241 survey responses from managers of manufacturing firms, performance expectancy, effort expectancy, facilitating conditions, information quality, and perceived privacy exert a positive influence on managers’ behavioral intentions to use ChatGPT for SCM decision-making. However, only performance expectancy, facilitating conditions, information quality, and perceived privacy are stronger determinants of behavioral intentions for managers with higher uncertainty avoidance. The study puts forward important implications for the theory and practice of generative AI adoption.