Uncovering post-adoption usage of AI-based voice assistants: a technology affordance lens using a mixed-methods approach

Zhen Shao, Jing Zhang, Lin Zhang and Jose Benitez

European Journal of Information Systems, 2025, vol. 34, issue 3, 475-501

Abstract: Despite the growing proliferation of AI-based voice assistants in our daily lives, how different functions of AI-based voice assistants affect users’ post-adoption usage behaviours remains an under-investigated research question. This study explores the nature and causation of post-adoption usage behaviours (effective utilisation after initial adoption or diffusion) within the context of AI-based voice assistants. Using a sequential mixed-methods research design, we first identify the post-adoption usage behaviours of AI-based voice assistants as a multidimensional concept comprised of routine use and extended use, then develop a contextualised model by revealing technology-specific antecedents, cognitive beliefs, and boundary conditions. By integrating results from the quantitative study and qualitative study, we find that three technology affordances (i.e., anthropomorphism affordance, interactivity affordance, and personalisation affordance) are salient antecedents of two cognitive beliefs, which further affect users’ routine use and extended use of AI-based voice assistants. Additionally, we uncover use frequency as a boundary condition and obtain a complementary view of post-adoption usage of AI-based voice assistants. The empirical research findings can extend the post-adoption IS usage literature and provide practical implications for crafting user-centred functionalities to facilitate effective human-AI interactions.

Date: 2025
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DOI: 10.1080/0960085X.2024.2363322

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