Author ORCID Identifier:
Date of Graduation
7-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Business Administration (PhD)
Degree Level
Graduate
Department
Supply Chain Management
Advisor/Mentor
Aloysius, John
Committee Member
Rossiter-Hofer, Adriana
Second Committee Member
Dobrzykowski, David
Third Committee Member
Brau, Rebekah
Keywords
artificial intelligence; behavior; decision making; demand forecasting; experiments
Abstract
Artificial Intelligence (AI) adoption continues to advance across supply chain management processes. A Gartner 2025 report shows that 84% of surveyed firms reported AI and machine learning investments for core supply chain tasks. However, both industry and academic literature highlight the importance of integrating AI and human judgment in decision making processes rather than fully automating with AI. The success of this integration depends heavily on the humans who use these AI tools. Therefore, this dissertation presents three essays that conceptually and empirical examine behavioral interactions between humans and AI in supply chain management decision making processes, primarily in the context of demand forecasting. The intention of these three essays is to provide insight into human behavior to inform ideal integration with AI and produce successful human-AI collaboration. Using a signaling theory perspective, Essay 1 examines how demand planner forecast adjustment behavior differs for AI-based forecasting algorithms compared to traditional model-based forecasting algorithms using a series of lab and natural field experiments. This essay highlights algorithm performance as a key signal of algorithm reliability and examines how positive and negative bias produce exacerbated adjustment behaviors toward AI-based forecasting algorithms. Essay 2 considers the effects of behavioral anthropomorphism in AI forecasting tools on demand planner performance by comparing standards for explainability in industry and academia, as well as incorporating elements of increased sociality and effectance motivations. This essay utilizes data from multiple lab experiments to test the effectiveness of behavioral anthropomorphism. Results show that sociality can improve demand forecaster performance in human-AI demand forecasting collaboration regardless of private information availability while effectance deteriorates performance when private information is available. Finally, Essay 3 presents a conceptual framework for integrating AI into OSCM theory development, identifying four core approaches at the intersection of theory work in academia and industry. Overall, the three essays tie specific attributes of AI to behavioral responses in OSCM practitioners and scholars and offer actionable insights into developing successful human-AI collaborations for OSCM decision making.
Citation
McKinley, F. (2026). Human-AI Collaboration for Operations and Supply Chain Management Decision Making. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/6361
