Family caregivers’ acceptance of Artificial Intelligence-enabled technologies for providing care to older adults

Abstract

Artificial intelligence (AI)-enabled technologies hold promise for assisting in the care of an aging population. Few studies have focused on exploring family caregivers’ (FCGs) behavioural intention of using such innovation, and even fewer have employed a technology acceptance framework. This study examined FCGs of older adults’ behavioural intention of using AI-enabled technologies for caregiving. We conducted a theory-based cross-sectional quantitative survey. Eligible FCGs for this study were: (1) aged 45–64; (2) residing in Quebec, Canada; (3) providing care for at least one older adult (65+); (4) having access to a computer or smartphone with internet connectivity; and, (5) having proficiency in reading and comprehending English or French. We adapted and expanded the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to measure their behavioural intention of using AI-enabled technologies for caregiving. We used descriptive statistics and a random forest model to assess the most important predictive factors across nine variables and their direction of association with behavioural intention. The Consensus-Based Checklist for Reporting of Survey Studies (CROSS) guidelines was used for reporting the study’s results. Among the polling firm’s 100,000 panelists, 2740 eligible individuals were randomly chosen to receive an email invitation to the study. Of 465 panelists who opened the survey (i.e., unique visitors),199 were eligible and completed the online survey. The random forest model explained between 56% and 86% of the behavioural intention variance of using AI, with social influence demonstrating the highest predictive relevance as indicated by a 35% increase in mean-squared error once removed from the model. Among the nine variables considered, six demonstrated a positive association with behavioural intention. These variables included social influence, effort expectancy, performance expectancy, perceived trust, confidence in healthcare professionals’ advice for the use of AI-enabled technologies, and facilitating connditions. The variables perceived cost and technology anxiety indicated a negative association with behavioural intention. Our extended UTAUT model identified factors associated with FCGs' intention to use AI. While all nine variables contributed, attitudes toward AI within caregivers’ social circles was the strongest predictor. Stakeholders from industry, government, and healthcare can enhance the adoption of AI-enabled technologies in older adult care by leveraging facilitators and addressing barriers experienced by caregivers.

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