AI-Driven Conversational Interfaces in IoT Ecosystems: Systematic Review of User Acceptance Models for Chatbots

Abstract

Digital communication based on artificial intelligence and chatbots is rapidly expanding in Internet-of-Things (IoT) environments. User interaction with chatbots is being integrated into various systems, such as smart homes and connected devices. Adoption is determined by acceptance, influenced by psychological and technical factors that vary across different IoT use cases. While the Technology Acceptance Model (TAM) has been applied to diverse technologies and contexts, limited research addresses its application specifically to chatbots in IoT ecosystems. This study aims to systematically identify key acceptance factors and propose an integrated framework extending traditional TAM with IoT-specific variables. A systematic literature review (SLR) of peer-reviewed articles was conducted across six academic databases (October 2024 to December 2025), covering the last five years and yielding 11 studies meeting all inclusion criteria. Results reveal five critical acceptance factor categories: cognitive and affective factors, empathy and personalization, reliability and privacy, transparency and data management and social influence. The study's novel contribution is the integration of IoT-specific considerations including device interconnectivity, context-awareness, and data sensitivity with traditional TAM constructs, thereby addressing a significant gap in chatbot acceptance within connected device environments. Further research should validate this framework and explore long-term intentions.