Push-Pull-Mooring approach to investigate the word-of-mouth intention to use AI-Driven mental health care platforms: insights from CDT, ELM and IRT

Purpose In today's competitive climate, AI-driven mental health care services (such as, “wysa,” “healthGPT”) can significantly reduce medical costs, improve operational efficiency and effectiveness, improve resource equity and better match consumer needs. However, the success of AI-driven mental health treatment requires consistent contributions. As a result, this study aims to address an important research question: what elements drive users word-of-mouth (WOM) intentions toward AI-driven mental health platforms as a form of knowledge diffusion behavior? Design/methodology/approach The survey data were collected from the respondents applying the purposive sampling technique, and partial least squares structural equation modeling (PLS-SEM) was conducted to analyze the result. Findings The findings revealed that push factors, for instance, perceived cognitive dissonance with physical health care services, and pull factors, such as perceived platform compatibility, perceived interaction quality, perceived reliability and perceived anthropomorphism, were key drivers of WOM intention to use AI-driven mental health care platforms. On the other hand, anchoring factors, such as perceived traditional resistance, perceived risk resistance and perceived usage resistance, were found as the primary impediments to users’ WOM desire to use the platform. Practical implications These insights offer critical implications for business owners, platform developers, managers and policymakers aiming to enhance user retention and WOM intention to use the platforms. Originality/value To achieve the research goal, this study used the “Push-Pull-Mooring” model to build the conceptual framework by combining multiple theories such as “Cognitive Dissonance Theory,” “Elaboration Likelihood Model," and “Innovation Resistance Theory.”