JMIR Publications

ABSTRACT

Background:

Tuberculosis (TB) is a highly infectious disease. Negative perceptions and insufficient knowledge have made it difficult to eradicate. Recently, mobile healthcare interventions, such as an anti-TB chatbot developed by the research team, have emerged in support of TB eradication programs. Before the anti-TB chatbot is deployed, however, it is important to understand the factors that predict its acceptance by the population.

Objective:

This study aims to explore the acceptance of the anti-TB chatbot that provides information about the disease and its treatment, to people vulnerable to TB in South Korea. To this aim, we investigated the factors that predict technology acceptance through qualitative research based on the interviews of TB patients and homeless facility personnel. We then verified the extended technology acceptance model (TAM) and predicted the factors associated with the acceptance of the chatbot.

Methods:

In study 1, we conducted interviews with potential chatbot users to extract the factors that predict user acceptance, and construct a conceptual framework based on TAM. Sixteen interviews with TB patients and one focus group interview with ten TB experts were conducted. In study 2, we conducted surveys of potential chatbot users to validate the extended TAM. Survey participants were recruited among late-stage patients in TB facilities and members of online communities sharing TB information. A total of 123 responses were collected.

Results:

The results indicate that perceived ease of use and social influence were significantly predictive of the perceived usefulness (P=.04, P<.001 respectively). Perceived usefulness was predictive of the attitude toward the chatbot (P<.001), while perceived ease of use (P=.88) was not. Behavioral intention was positively predicted by attitude toward the chatbot and facilitating conditions (P<.001, P=.03 respectively). The research model explained 55.4% of the variance in using anti-TB chatbots. The moderating effect of TB history was found in the relationships between attitude toward the chatbot and behavioral intention (P=.01) and between facilitating conditions and behavioral intention (P=.02).

Conclusions:

This study can be used to inform future design of anti-TB chatbots and to claim the importance of services and environment that empower people to use the technology.