Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Jan 8, 2021
Date Accepted: Sep 3, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A Technology Acceptance Model to Increase the Acceptance of Anti-tuberculosis Chatbots: Qualitative and Quantitative Study
ABSTRACT
Background:
Tuberculosis (TB) is a highly infectious disease. However, its eradication has been difficult owing to negative perceptions and insufficient knowledge. Recently, mobile-based healthcare interventions such as chatbots have emerged as a support for TB eradication programs. However, prior to introducing anti-TB chatbots, it is important to understand the factors that influence its acceptance by the population.
Objective:
This study aims to explore the acceptance of an anti-TB chatbot in South Korea.
Methods:
We conducted user interviews to extract the factors influencing user acceptance and constructed a conceptual framework based on a technology acceptance model (TAM). In this extended TAM, social influence and perceived resources were identified as the major influencing factors. 123 study participants were divided into two groups based on their TB history.
Results:
The effect of social influence on perceived usefulness was identified to be strong in both groups. However, when it comes to behavioral intention of the user, perceived resources have the most important effect for the group with TB history, while attitude toward the chatbot is more influential for the group with no TB history.
Conclusions:
In conclusion, chatbots can help prevent TB and support its treatment by providing useful information to users without stigmatization.
Citation
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Copyright
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