A Conceptual Framework for Applying Artificial Intelligence in Shoplifting Prevention within Department Stores

Abstract

This study aims to examine the application of Artificial Intelligence (AI) technology for preventing merchandise theft in department stores. The research focuses on exploring AI technologies that can be applied to theft prevention, developing a conceptual framework for applying such technologies, and evaluating the suitability of the proposed framework through expert assessment. This research adopts a qualitative research approach using documentary research, emphasizing the analysis of relevant documents to develop a conceptual framework for this study in preventing theft in department stores. The proposed conceptual framework consists of theft risk management, the identification of risk-prone areas based on retail business formats to determine appropriate technological applications, budget and consideration of legal frameworks related to theft prevention technologies in department stores. The results from expert evaluation indicate that, overall, the proposed framework is appropriate for preventing merchandise theft in retail businesses. It can enhance the effectiveness of security systems and aligns with modern retail risk management practices, particularly through the integration of AI technologies with (Closed Circuit Television: CCTV) systems and behavioral analytics. Organizations can practically apply this conceptual framework for shoplifting prevention under a clear risk management approach by starting with high-risk areas and selecting technologies that align with the business model, budget, and existing organizational systems. In addition, legal requirements and cost-effectiveness should be carefully considered before implementation.