Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption

Abstract

Technology has revolutionized various industries; notably, chatbots impact healthcare through the efficient streamlining of medical procedures, cost reductions, and improved accessibility to healthcare services. Consequently, understanding how to promote the adoption of healthcare chatbots has become crucial for enhancing the healthcare industry and medical service quality. Despite numerous studies identifying factors influencing healthcare chatbot adoption, there is a notable lack of empirical verification regarding their interrelationships, leading to a significant knowledge gap. Therefore, this study aims to address this gap by developing a decision-making model to analyze the relationships among key factors regarding three dimensions: technology, user, and society. The model begins by filtering out insignificant factors based on experts’ opinions. Subsequently, it employs DEMATEL (Decision Making Trial and Evaluation Laboratory) to construct a causal relationships graph and the ISM (interpretive structural modeling) method to categorize these factors into a hierarchical order. To mitigate uncertainties stemming from the topic’s complexity, this study utilizes fuzzy sets and Z-number theory in the assessment process. The findings reveal a predominance of causal factors within the technological dimension. Notably, the quality of information provided by chatbots stands out as the most influential causal factor. The insights from this study suggest implications for both enterprises and governments to boost chatbot adoption in society.