Acceptance of an Informational Antituberculosis Chatbot Among Korean Adults: Mixed Methods Research

Area of Science:

• Digital Health
• Public Health Interventions
• Health Technology Acceptance

Background:

• Tuberculosis (TB) eradication is hindered by negative perceptions and lack of knowledge.
• Mobile health interventions, like anti-TB chatbots, offer potential solutions.
• Understanding user acceptance factors is crucial before deploying anti-TB chatbots.

Purpose of the Study:

• To explore the acceptance of an anti-TB chatbot in South Korea.
• To identify factors predicting technology acceptance among vulnerable populations.
• To validate an extended Technology Acceptance Model (TAM) for anti-TB chatbot use.

Main Methods:

• Qualitative interviews with TB patients and homeless facility personnel.
• Focus group interviews with TB experts.
• Quantitative surveys with late-stage TB patients and online TB communities (n=123).
• Validation of an extended TAM framework.

Main Results:

• Perceived ease of use and social influence predicted perceived usefulness (P=.04, P<.001).
• Perceived usefulness predicted attitude toward the chatbot (P<.001).
• Attitude and facilitating conditions predicted behavioral intention (P<.001, P=.03).
• The model explained 55.4% of variance in chatbot use.
• TB history moderated attitude and facilitating conditions' effects on behavioral intention.

Conclusions:

• Findings inform the design of effective anti-TB chatbots.
• Emphasizes the importance of user-centered services and supportive environments for technology adoption.
• Highlights the role of social influence and perceived usefulness in driving acceptance.