Revolutionising retail: A review of Intelligent Unmanned Convenience Stores and their market implications

Intelligent Unmanned Convenience Stores (IUCVSs, hereafter) introduce a fresh, intelligent service providing customers with an innovative shopping experience making use of technologies such as computer vision, Artificial Intelligence, automated payment systems, smart entry and exit systems, and more that eliminate lines and direct interaction with retail staff. This integrated suite of digital technologies functions as a silicon shopkeeper, automating the responsibilities typically carried out by human personnel in traditional convenience store operations. Although there has been a growing emphasis on research over the past decade, the literature concerning IUCVSs still lacks coherence, being dispersed across diverse contexts. Therefore, the current moment offers a favourable opportunity to evaluate the field by establishing the foundation for forthcoming research to delineate the extent of IUCVSs. The present research provides a glimpse into the existing body of literature examining consumer receptiveness to technological interfaces in retail as opposed to human interfaces by empirically investigating the emergence and growth of IUCVSs. Searching on SCOPUS and the Web of Science has yielded a final set of 106 articles relevant to IUCVSs. Therefore, by utilising the TCCM review framework, this study offers an in-depth understanding of the prevailing theories, countries shaping the contexts, the crucial variables as typical characteristics, and the various research approaches employed in IUCVSs research from 1996 to 2024. This study contributes to the existing literature by conducting a comprehensive review of these articles, a task previously unexplored by scholars, and presents an integrated conceptual framework for IUCVSs by amalgamating the insights gleaned from such studies. This study concludes by discussing various theoretical and managerial implications and suggesting potential avenues for future research directions using the TCCM framework.