Human-Centric AI Forecasting Models for Enhancing Product Availability Perception in Seasonal Retail Microenterprises

Problem Statement: This study indicates towards the questions like How do awareness of AI and trust in the predictions of components of AI affect consumer perceptions regarding product availability in small-scale sessional e-commerce retail store? And how perceived product availability mediates the relationship between AI trust and customer loyalty Methodology: Using Structural Equation Modelling (SEM) to test the proposed framework, this study looks at how usefulness, efficiency, service responsiveness, and loyalty are related to each other when people shop online. Findings: The final model shows that service responsiveness has a strong positive effect on loyalty (β = 0.864, p < 0.001), while efficiency (β = 0.748, p < 0.001) and usefulness (β = 0.200, p = 0.01) have a strong negative effect on service responsiveness. Also, usefulness is a strong predictor of efficiency (β = 0.816, p < 0.001), which means that perceived usefulness leads to higher efficiency, which leads to higher service responsiveness, which leads to higher customer loyalty. These results are in line with earlier studies on the Technology Acceptance Model ([14]; [56]) and service quality frameworks ([48]). They show how important functional and experiential features are for building long-term customer relationships. Implications: The model gives e-commerce managers useful information, showing that spending money to make things run more smoothly and provide better service can lead to big increases in customer loyalty, no matter what demographic group they belong to.