Unravelling the dynamics of privacy, security, and trust in AI-powered self-service chatbots in the retail sector: a systematic literature review

The increasing deployment of artificial intelligence (AI)-powered self-service chatbots in the retail sector has transformed the nature of customer engagement, offering scalable, efficient, and personalized interactions. Despite these advancements, concerns surrounding privacy, security, and trust continue to shape user perceptions and influence adoption behaviour. This study presents a comprehensive systematic literature review (SLR) of scholarly work published between 2016 and 2026, with the aim of critically examining how these interrelated constructs affect the acceptance and use of chatbot technologies in retail environments. Drawing on the PRISMA framework, a total of 54 studies were selected from leading academic databases, including Scopus, Web of Science, ScienceDirect and IEEE Xplore. The findings indicate that privacy concerns particularly those related to data transparency, consent, and third-party data sharing remain a primary source of user resistance. Security vulnerabilities, including risks of data breaches and insufficient encryption mechanisms, further undermine confidence in chatbot systems. The findings further show that trust is an important variable that influences both behavioural intention and user satisfaction. This study contributes to the literature by integrating fragmented insights into a cohesive framework that explains the interconnectedness between privacy, security, and trust in retail chatbot contexts. It also highlights the need for organisations to adopt transparent data practices, strengthen cybersecurity infrastructures, and prioritise ethical AI design to foster sustainable user trust. The paper concludes by identifying key gaps for future research, particularly in relation to user experience and cross-cultural perspectives.