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<p>This study analyzed the key factors influencing user behavioural intention and usage behaviour regarding wearable robot technology grounded in the extended unified theory of acceptance and use of technology (UTAUT2). To achieve this, a methodological framework integrating partial least squares based structural equation modeling (PLS-SEM) with an artificial neural network (ANN) was proposed to comprehensively analyze both the direct and nonlinear interactions among variables. Using survey data collected from 200 adults, the findings highlight habit as the most critical predictor of behavioural intention, followed by performance expectancy, price value, and hedonic motivation. In contrast, effort expectancy and social influence were not found to significantly affect behavioural intention. Moreover, user experience was found to act as a key moderator, amplifying the influence of performance expectancy and effort expectancy on behavioural intention. These findings underscore the importance of user experience in the development and usage strategies of wearable robots. The study provides actionable insights to foster the behavioural intention and actual usage of wearable robot technology and offers strategic design guidance for diverse industries and user groups.</p>

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Modeling user behavioural intention and usage behaviour for wearable robots: a UTAUT2-integrated SEM and ANN approach

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Sanghun Shin,
  • Junseok Hwang,
  • Soo-Min Lee

摘要

This study analyzed the key factors influencing user behavioural intention and usage behaviour regarding wearable robot technology grounded in the extended unified theory of acceptance and use of technology (UTAUT2). To achieve this, a methodological framework integrating partial least squares based structural equation modeling (PLS-SEM) with an artificial neural network (ANN) was proposed to comprehensively analyze both the direct and nonlinear interactions among variables. Using survey data collected from 200 adults, the findings highlight habit as the most critical predictor of behavioural intention, followed by performance expectancy, price value, and hedonic motivation. In contrast, effort expectancy and social influence were not found to significantly affect behavioural intention. Moreover, user experience was found to act as a key moderator, amplifying the influence of performance expectancy and effort expectancy on behavioural intention. These findings underscore the importance of user experience in the development and usage strategies of wearable robots. The study provides actionable insights to foster the behavioural intention and actual usage of wearable robot technology and offers strategic design guidance for diverse industries and user groups.