Artificial Intelligence in Nanostores: Enhancing Customer Service Efficiency, Customer Experience, Competitive Advantage, and Decision-Making

Abstract

This study explores the role of artificial intelligence (AI) in enhancing customer service in nanostores, addressing key aspects of AI adoption. First, it evaluates how AI-driven automation improves customer service efficiency by streamlining processes like inventory management and customer interactions, leading to faster service and reduced operational costs (Technology Acceptance Model). Second, it examines how AI-powered personalization enhances customer experience by offering tailored product recommendations and promotional offers, resulting in increased satisfaction and loyalty (Service-Dominant Logic). Third, it highlights how AI integration provides nanostores with a competitive advantage by enabling them to differentiate through superior service and operational efficiency (Resource-Based View). Fourth, the study shows how AI-driven insights support data-driven decision-making, optimizing inventory control and pricing strategies to improve overall performance (Data-Driven Decision-Making Theory). Lastly, it identifies the unique challenges nanostores face in adopting AI, including resource constraints, technological capabilities, and resistance to change, which affect successful implementation (Theory of Planned Behavior). A systematic literature review (SLR) was conducted using databases like Scopus, WoS, IEEE Xplore, and Google Scholar, focusing on recent and relevant contributions. The findings suggest that while AI adoption brings clear benefits, such as enhanced customer service, efficiency, and competitive advantage, successful implementation requires addressing barriers like limited resources and resistance to change. This research offers valuable insights for researchers and practitioners, providing a novel framework for AI adoption in small-scale retail contexts.