Generative artificial intelligence (GenAI) holds transformative potential for supply chains, yet its adoption in emerging markets is hindered by systemic complexities and ethical-institutional tensions. This study employs system dynamics and causal loop diagrams to model the feedback mechanisms governing GenAI integration, focusing on technical, human, and institutional interdependencies. Through the identification of reinforcing loops and balancing loops, the research reveals how factors such as infrastructure readiness, stakeholder trust, and regulatory pressures dynamically interact to accelerate or constrain adoption. Findings highlight the dual role of feedback processes: while reinforcing loops drive efficiency gains and innovation, balancing loops introduce critical trade-offs, such as workforce skill erosion and data privacy risks. The study proposes actionable strategies for harmonizing scalability with ethical governance, emphasizing context-sensitive policies and hybrid human-AI decision frameworks. By bridging gaps in feedback-aware adoption models, this work offers a roadmap for sustainable GenAI integration in low-digital-maturity environments, balancing technological advancement with socio-ethical imperatives.