The effects of the human-like features of generative AI on usage intention and the moderating role of information overload

Abstract

With the rapid adoption of generative artificial intelligence (GenAI) chatbots on e-commerce platforms, users’ expectations for anthropomorphic service experiences have risen significantly. Despite the growing presence of GenAI, little is known about how different types of anthropomorphic users’ self-efficacy and the intention to adopt as a decision aid through distinct cognitive pathways. Addressing this research gap, this study draws on the elaboration likelihood model (ELM) to develop a comprehensive framework that integrates central and peripheral cues. Using large-scale survey data from e-commerce users and structural equation modeling, the research empirically examines the mediating role of self-efficacy and the moderating effect of information overload. Results indicate that human-like empathy and perceived warmth (peripheral cues) and perceived competence (central cue) all significantly enhance self-efficacy, which in turn positively influences the intention to adopt as a decision aid. Moreover, information overload intensifies the effect of peripheral cues on self-efficacy but has a limited impact on central cues. These findings advance the theoretical understanding of GenAI–human interaction by clarifying the mechanisms through which anthropomorphic features operate, and provide actionable insights for designing user-centric GenAI recommendation services to optimize user experience and encourage the intention to adopt as a decision aid in e-commerce.