What influences consumer AI chatbot use intention? An application of the extended technology acceptance model

Abstract

Purpose As information technology advances, the prevalence of AI chatbot products is on the rise. Despite optimistic market projections, consumer skepticism towards these agents persists. This paper aims to expand the scope of the technology acceptance model by integrating the aspect of appearance. It examines the influence of different attributes of AI chatbot, such as usefulness, ease of use and appearance, individually and in combination, on consumers' intentions to share and purchase. Design/methodology/approach Using an exploratory study of Web Texts, a 2 (usefulness: high vs low) × 2 (ease of use: high vs low) mixed design and a 2 (usefulness: high vs low) × 2 (ease of use: high vs low) × 2 (anthropomorphism appearance: humanoid vs cartoon) for between-subjects designs and the price level (high vs low) for within-subjects designs. The hypotheses were tested by Octoparse and SPSS 22.0. Findings The research highlights the significant role of usefulness, ease of use and anthropomorphic appearance in shaping consumer attitudes towards AI chatbots, thus influencing their intentions to share information and make purchases. Grouped regression analysis reveals that lower prices exert a more pronounced positive influence on consumers' inclinations to both share and purchase, compared to higher prices. Moreover, novelty-seeking behavior moderates the effect of perceived usefulness or ease of use on attitude. Specifically, heightened novelty-seeking tendencies mitigate the impact of low perceived usefulness or ease of use, leading to sustained positive attitudes towards AI chatbots among consumers. Originality/value This study innovatively incorporates product appearance into the Technology Acceptance Model (TAM), considering both the functional attributes and appearance of AI chatbot and their impact on consumers. It offers valuable insights for marketing strategies, extends the scope of TAM application and holds significant practical implications for enhancing enterprise product planning.