Leveraging Quantum Explainable AI for Data-Driven Decision-Making and Worker Wellbeing in Sustainable Supply Chains

Sustainable supply chains often face challenges of operational uncertainty and institutional constraints as data-driven decision-making is complex process. Classical AI/ML focuses on efficiency and technical performance often overlooking employee well-being and transparent decision-making. The existing studies link explainable AI with transparency and interpretability, while scalability and optimizing efficiency are valuable implications of quantum computing. This demands to develop a conceptual framework for quantum XAI (QXAI) as a unique socio-technical resource exploring both technical and cognitive aspects. Therefore, this chapter situates QXAI as a dual-nature job resource within Job Demands Resources (JD-R) theory, through an integrative and semi-systematic review approach that can enhance quality decision making, employee well-being and sustainable performance in context of supply chain industry. The chapter further demonstrates that QXAI can concurrently improve operative competence, reinforce environmental authority, and endorse social sustainability in emerging economies.