Human and Artificial Intelligence collaborative decision-making in operations and supply chain management

Hao, Xinyue 2025. Human and Artificial Intelligence collaborative decision-making in operations and supply chain management. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

This doctoral thesis aims to advance the understanding of how Human Intelligence (HI) and Artificial Intelligence (AI) can be effectively integrated to enhance decision-making processes within supply chain and operations management. In an era marked by rapid technological advancements, organizations face increasing complexity in decision-making, where the integration of AI and HI offers a promising avenue for optimizing outcomes. The research is driven by the central objective of developing a comprehensive framework that leverages the strengths of both AI and HI, facilitating informed, ethical, and strategic decisions in dynamic and complex environments. The study is structured around four key research questions. The first research question explores the underlying motivations for integrating HI, AI, and collaborative decision-making paradigms, examining how these integrations can enhance decision quality and innovation. The second research question identifies the enablers and constraints of AI implementation within supply chain and operations management, focusing on the Technological, Organizational, and Environmental (TOE) factors that impact successful AI integration. The third research question proposes strategies to amplify enablers and mitigate constraints in the AI-HI collaboration paradigm, aiming to optimize the synergy between AI and HI. The final research question centers on the development and validation of a conceptual framework that supports effective and responsible decision-making in environments where HI and AI collaborate. Methodologically, this research employs a mixed approach, combining qualitative and quantitative methods to achieve a comprehensive understanding of the research questions. The study incorporates case studies, expert interviews, and a quasi-experimental design, ensuring a robust analysis of AI and HI integration across different industries. Additionally, knowledge graphs and SWOT analysis are utilized to explore the complex interrelations among factors influencing AI integration, while predictive modeling offers insights into the potential outcomes of various factor interactions. The contributions of this research are significant both theoretically and practically. Theoretically, it advances the discourse on AI and HI collaboration by proposing a structured framework that highlights the distinct roles of AI and HI in decision-making processes. This framework emphasizes the importance of ethical governance, continuous learning, and strategic leadership in ensuring responsible AI-HI integration. Practically, the research provides actionable strategies for organizations to enhance AI-HI collaboration, focusing on critical areas such as data quality, technological infrastructure, and responsible ethical practices. These insights are particularly relevant for industries where decision-making is complex and requires a balance between automated processes and human judgment. To conclude, this thesis addresses the challenges of integrating AI and HI and also offers a practical roadmap for organizations to navigate these challenges effectively. The findings underscore the potential of AI-HI collaboration to transform decision-making processes, leading to more informed, ethical, and strategic outcomes that benefit both organizations and society. As the field continues to evolve, this research provides a solid foundation for future exploration and practical application, ensuring that AI and HI are harnessed to their full potential in shaping the future of decision making.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Schools > Business (Including Economics)
Funders:	China Scholarship Council
Date of First Compliant Deposit:	16 May 2025
Last Modified:	16 May 2025 14:56
URI:	https://orca.cardiff.ac.uk/id/eprint/178333

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