The nature of relationships within supply chains and understanding the dynamics of interactions between the supply chain members are key factors in today's globalised economy, where speed, efficiency and flexibility in the flow of goods and information are crucial. Improving the efficiency of supply chains and managing their inherent risks has become one of today's most pressing economic challenges, directly affecting not only the competitiveness of companies but also the achievement of sustainability goals. To better understand those relations among companies, a deeper understanding of the attitudes of the different supply chains is inevitable. This paper is the inaugural work towards this research. Analysing attitudes towards relationships within supply chains is a critical aspect of understanding interactions between companies and the performance of networks. Digital transformation, depth of customer integration, relationship engagement, and efficiency in joint problem-solving are factors that have a significant impact on supply chain flexibility and performance. To achieve sustainability and resilience, companies need to adopt adaptive and proactive strategies that can address environmental, social and economic challenges and anticipate and respond to future market changes. The projected aim is to understand the attitude of the organisation as an independent entity, not as an attitude generated by the individuals of the management.

Artificial intelligence (AI) is rapidly becoming a strategic driver of competitiveness in supply chain management (SCM), enhancing flexibility, efficiency, and responsiveness. However, successful adoption depends not only on technology but also on managerial attitudes and organizational culture. Understanding this behavioral dimension is essential in explaining how leadership influences AI-driven adaptability and resilience. This study develops a conceptual, interdisciplinary framework linking management theory, organizational psychology, and SCM to examine how leadership styles—particularly servant, transactional, and transformational—shape AI integration and adaptability. The proposed triadic model identifies managerial attitudes, AI-supported decision-making, and information sharing as key behavioral and technological enablers of supply chain flexibility. Findings highlight that innovation-oriented leadership and strong information-sharing cultures accelerate AI adoption and strengthen adaptive capacity, while hierarchical, risk-averse attitudes constrain its impact. The study contributes to a behavioral perspective of SCM, showing that flexibility in AI-enabled supply chains results from the interaction between human and artificial intelligence.

Background: Global supply chains operate in increasingly volatile and technology-intensive environments shaped by digital transformation and artificial intelligence integration. While prior research has emphasized structural and technological enablers of flexibility, the behavioral foundations of supply chain adaptability remain insufficiently explored. Methods: This study develops a conceptual integration of the Kano model and the Cobb–Douglas production function to position managerial attitude as a strategic “delighter” within supply chain systems. The proposed framework models supply chain flexibility as a function of capital, labor, artificial intelligence integration, and managerial attitude within an extended economic representation. Results: The model suggests that managerial attitude acts as a behavioral amplifier that strengthens the performance effects of technological and economic inputs, potentially generating nonlinear gains in responsiveness and adaptive capacity. By distinguishing human-driven, algorithmic, and hybrid attitudinal configurations, the framework clarifies how behavioral orientations influence artificial intelligence adoption and supply chain flexibility, particularly in small and medium-sized enterprise contexts. Conclusions: Although conceptual in nature, the framework provides a formal analytical foundation for future empirical testing and elasticity-based sensitivity analysis in supply chain research.