Publication
Artificial Intelligence-Enabled Conversational Agents in Healthcare: Acceptance, Engagement, and Implementation
| Summary: | Artificial intelligence (AI)-enabled conversational agents (CA) have increasingly been applied in health and healthcare contexts to support well-being, mental health, and access to digital services. Despite their growing adoption, limited understanding remains regarding how users accept, engage with, and experience these systems across different cultural and healthcare settings. Existing research is fragmented, with insufficient integration of theoretical models, limited cross-country evidence, and a lack of insight into real-world implementation challenges. In response to these gaps, the objective of this doctoral thesis is to systematically examine user acceptance, engagement, and implementation of AI-enabled well-being and healthcare chatbots. Specifically, this research aims to (1) review and synthesize existing research on the acceptance and use of well-being CAs; (2) develop and validate theoretical models that explain user engagement with well-being CAs; (3) examine cross-country differences in user acceptance and engagement; and (4) explore implementation experiences, as well as the challenges, of AI CAs as digital healthcare solutions across different national contexts. To achieve these objectives, this study employed both quantitative and qualitative methods. An integrative literature review was conducted to synthesize prior research on the acceptance of well-being CAs. Quantitative survey studies were conducted to test new extended research models and examine user engagement across countries, including a comparative analysis between the United States and China. Additionally, qualitative methods were used to explore perceptions, experiences, and implementation challenges of AI-driven digital healthcare solutions. The findings demonstrate that user acceptance and engagement with AI-based chatbots are influenced by technological, psychological, and contextual factors, with notable cross-country differences. The results also highlight key challenges related to accessibility, trust, and implementation in healthcare contexts. This thesis contributes to the field by advancing theoretical understanding of user engagement with AI-based well-being chatbots, providing cross-national empirical evidence, and offering practical insights to inform the design, implementation, and governance of digital health technologies. |
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| Main Authors: | Yang, Yanrong |
| Subject: | AI conversational agent Well-being Healthcare Digital intervention Engagement Cross culture |
| Year: | 2026 |
| Country: | Portugal |
| Document type: | doctoral thesis |
| Access type: | embargoed access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | Artificial intelligence (AI)-enabled conversational agents (CA) have increasingly been applied in health and healthcare contexts to support well-being, mental health, and access to digital services. Despite their growing adoption, limited understanding remains regarding how users accept, engage with, and experience these systems across different cultural and healthcare settings. Existing research is fragmented, with insufficient integration of theoretical models, limited cross-country evidence, and a lack of insight into real-world implementation challenges. In response to these gaps, the objective of this doctoral thesis is to systematically examine user acceptance, engagement, and implementation of AI-enabled well-being and healthcare chatbots. Specifically, this research aims to (1) review and synthesize existing research on the acceptance and use of well-being CAs; (2) develop and validate theoretical models that explain user engagement with well-being CAs; (3) examine cross-country differences in user acceptance and engagement; and (4) explore implementation experiences, as well as the challenges, of AI CAs as digital healthcare solutions across different national contexts. To achieve these objectives, this study employed both quantitative and qualitative methods. An integrative literature review was conducted to synthesize prior research on the acceptance of well-being CAs. Quantitative survey studies were conducted to test new extended research models and examine user engagement across countries, including a comparative analysis between the United States and China. Additionally, qualitative methods were used to explore perceptions, experiences, and implementation challenges of AI-driven digital healthcare solutions. The findings demonstrate that user acceptance and engagement with AI-based chatbots are influenced by technological, psychological, and contextual factors, with notable cross-country differences. The results also highlight key challenges related to accessibility, trust, and implementation in healthcare contexts. This thesis contributes to the field by advancing theoretical understanding of user engagement with AI-based well-being chatbots, providing cross-national empirical evidence, and offering practical insights to inform the design, implementation, and governance of digital health technologies. |
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