Consumer Acceptance of Conversational Bots: Systematic Literature Review and Meta‐Analysis

ABSTRACT As consumers increasingly rely on conversational bots for daily tasks, evidence surrounding motivations for acceptance remains scattered. A systematic literature review (SLR) was conducted on 64 journal articles published between 2008 and 2024, of which 48 provided sufficient quantitative data for inclusion in a meta‐analysis. The meta‐analysis synthesizes 117 independent study‐level effect sizes across 17 distinct relationships, of which 14 are statistically significant, based on an aggregate sample of 34,302 respondents. Further, an extended search identified an additional 38 recent studies (2024–2025), which were synthesized narratively to capture emerging technological and conceptual developments. We propose a renewed definition of conversational bots that encompasses text‐based, voice‐driven, and multimodal interactions. The SLR analyses reveal five central themes that emerge: (a) service quality and performance, (b) human‐like interaction and trust, (c) context‐specific applications, (d) user experience, and (e) privacy and security concerns. Meta‐analysis results reveal that perceived usefulness, ease of use, trust, and satisfaction consistently drive acceptance. Anthropomorphic features enhance enjoyment but do not necessarily foster trust. This study integrates evidence from prior research into a unified acceptance model that clarifies the relative roles of cognitive, relational, and experiential drivers of conversational bot acceptance. Building on this synthesis, the study outlines directions for future research using the TCCM framework to guide theory development and empirical research on motivations for acceptance of conversational bots.