Artificial Intelligence In Retail: Applications And Value Creation Logics

Purpose This paper seeks to answer three questions about how retailers can benefit from AI. (1) What are the main strategies for retailers to improve their AI-related data management? (2) How do retailers use AI to provide solutions in business processes? (3) What are the value creation logics of AI applications in retail? Design/methodology/approach Data- and solution-centric perspectives, as well as the concept of value creation logics, serve to build the analytical framework. The grounded theory multiple-case analysis of 54 representative retailers' adoptions and implementations of AI between 2008 and 2018 help to investigate the firm's AI applications and value creation logics. Findings This study identifies five main strategies for AI-related data management and reveals 28 AI-powered solutions, changing 14 business processes, with five management areas involved in AI applications to create value via four logics: automation, hyper-personalization, complementarity and innovation. Research limitations/implications This paper advances the research into AI applications in business and management by providing research propositions with an integrative framework to understand how firms can use and benefit from AI. However, secondary data and exploratory study still limit the findings. Practical implications The findings provide retail managers with an analytical framework that can help them to develop a rationale for their strategic choices and best practices relating to the adoption and implementation of AI. Originality/value The originality of this paper lies in its systematic examination of AI applications and value creations in retail. The findings provide managers with guidance, rational strategic choices and best practices to take action to embrace the great business opportunities created by AI technologies.