Enhancing supply chain sustainability: the role of GPT-based AI in agency and boundary spanning

Research Backdrop Achieving sustainability-related targets has become a priority for most modern organisations. Boundary Spanning Theory highlights the importance of mechanisms that connect stakeholders across organisational and functional boundaries. These mechanisms enhance collaboration, information flow, and trust, all of which are essential for achieving sustainability goals in supply chains (Grawe et al., 2015). However, efforts often fail due to a lack of effective collective action across the supply chain. Existing literature suggests that factors such as information asymmetry, moral hazard, and misaligned incentives significantly contribute to these failures (Hung et al., 2025). In supply chains, Agency Theory identifies challenges arising from divergent interests and information gaps between principals (e.g., business owners, customers) and agents (e.g., suppliers, managers). These issues, such as information asymmetry and moral hazard, result in inefficiencies and conflicting objectives. Although various coordination mechanisms have been proposed to address these challenges, including contract design (Alom et al., 2024) and the use of technology (Huang et al., 2025), such problems persist in modern supply chains. This often leads to inefficiencies and other supply chain disruptions (Huang et al., 2025). AI-based tools like GPT, with their advanced natural language processing (NLP) capabilities, present innovative solutions to long-standing challenges in supply chains. GPT-based systems can analyse vast datasets, generate valuable insights, and enable seamless communication among stakeholders. These capabilities hold great potential for addressing agency problems and promoting boundary-spanning activities within supply chains. However, the existing supply chain literature offers limited exploration of such AI-based applications (Henderson, 2023). This research aims to address this gap by answering the following research question: "How can AI-based tools like GPT enhance the principles of Agency Theory to reduce information asymmetry and improve boundary spanning in complex supply chains?" Methodology This research utilises semi-structured interviews as a qualitative methodology to address the research question outlined in the introduction. Such techniques are well-suited for examining the intricate relationship between agency issues and boundary-spanning requirements, particularly when leveraging AI-based technologies to meet sustainability targets (Jamieson et al., 2023). The study aims to interview supply chain managers and information systems managers from a focal food sector buyer organisation in the UK, as well as managers from its supplier organisations who oversee the supply chain relationship with the buyer. Given that a significant portion of food in the UK is imported, rigorous quality checks are critical to ensure safety throughout the supply chain, safeguarding public health and well-being. This study will adopt a purposive sampling strategy, a non-probability method commonly used in qualitative research. This approach enables the selection of cases directly relevant to the research questions by facilitating interviews with key stakeholders. The research will adopt an abductive approach, which is particularly suitable for theory development (Dubois and Gadde, 2002). NVivo 14, a computer-assisted qualitative data analysis software, will be utilised for analysis. NVivo's capabilities support the open coding method, enabling the identification and emergence of relevant themes (Corbin and Strauss, 2014). Findings This research is expected to provide valuable insights into how contemporary disruptive technologies, such as GPT-based AI, can help supply chains address barriers like information asymmetry and misaligned incentives. By doing so, it aims to demonstrate how sustainability targets can be achieved not only at the individual organisational level but across entire supply chains. Relevance/Contribution A recent report published by the UK Parliament highlights the UK food sector's significant reliance on imports, noting that £58.1 billion worth of food and drink were imported in 2022 (Dillon and Wentworth, 2022). Many of these imports come from climate-vulnerable regions, further complicating sustainability efforts. While the report underscores the potential benefits of transitioning to more local food supply chains, such a shift is likely to be constrained by complex trade practices. This raises concerns about whether food sector organisations in the UK can achieve their sustainability targets. As a result, advancing theoretical frameworks that address boundary-spanning challenges across supply chains could provide valuable insights and contribute to the development of more efficient and sustainable practices.

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