Dataset for: The More You Know, the Less You Want to Rely on It –Consumer preferences for AI-based health monitoring in smart home systems

Smart home technology, which uses artificial intelligence (AI) to perform tasks such as detecting anomalies and making emergency calls, enables residents to live safely and independently in their own homes. However, its adoption for health monitoring remains limited. The objective of this study was to describe consumer preferences for AI-based smart home technology for health monitoring and to identify sociodemographic variables, AI literacy and technology affinity that might predict these preferences. A sample of 300 participants (57% female) aged 18 to 69 completed a choice-based conjoint analysis (CBCA). The participants in the CBCA evaluated fifteen choice sets, each of which described three smart home variants in terms of cost, location, emergency detection rate, type of sensor and data-processing. Cost was the most important attribute (Relative Importance [RI] = 41 %), followed by emergency detection rate (RI = 19 %), data-processing (RI = 14 %) and location (RI = 14 %). The attribute with the lowest importance for participants was the type of sensor (RI = 9 %). While most expected correlations between sociodemographic variables and importances were not observed, the expected correlation between self-reported health status and emergency detection rate was identified (p < .01). The majority of participants (61 %) expressed a preference for AI over human involvement in data-processing. However, logistic regression revealed that participants with higher AI literacy are less likely to prefer AI in data processing over human involvement (p < .05). The findings indicate that it is crucial to align smart home system development with user preferences and prioritize cost-effective solutions. The results suggest that in the context of AI-based smart home technology for health monitoring, AI literacy plays a significant role in technology acceptance. Further research is needed to elucidate other factors and to address the potential reluctance of users to adopt new technologies.