Background Tuberculosis (TB) is a highly infectious disease. Negative perceptions and insufficient knowledge have made its eradication difficult. Recently, mobile health care interventions, such as an anti-TB chatbot developed by the research team, have emerged in support of TB eradication programs. However, before the anti-TB chatbot is deployed, it is important to understand the factors that predict its acceptance by the population. Objective This study aims to explore the acceptance of an anti-TB chatbot that provides information about the disease and its treatment to people vulnerable to TB in South Korea. Thus, we are investigating the factors that predict technology acceptance through qualitative research based on the interviews of patients with TB and homeless facility personnel. We are then verifying the extended Technology Acceptance Model (TAM) and predicting 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 constructed a conceptual framework based on the TAM. In total, 16 interviews with patients with TB and one focus group interview with 10 experts on TB 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 web-based 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 perceived usefulness (P=.04 and P<.001, respectively). Perceived usefulness was predictive of the attitude toward the chatbot (P<.001), whereas perceived ease of use (P=.88) was not. Behavioral intention was positively predicted by attitude toward the chatbot and facilitating conditions (P<.001 and P=.03, respectively). The research model explained 55.4% of the variance in the use of anti-TB chatbots. The moderating effect of TB history was found in the relationship 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 highlight the importance of services and the environment that empower people to use the technology.
In human–computer interaction (HCI) research, relational agents (RAs) are increasingly used to improve social support for vulnerable groups including people exposed to stigmas, alienation, and isolation. However, technical support for tuberculosis (TB) patients, one such vulnerable group, remains insufficient due to the nature of the infectious disease and difficulties in accessing the homeless community. To derive design considerations for developing RAs targeting homeless TB patients, we conducted an empirical study on the patients. Data were collected through participatory observations and interviews and were processed using deductive thematic analysis. The patients’ environmental and behavioral characteristics were classified, which showed that understanding these factors in the design of an RA is important because the patients’ perception, attitudes, and expectations towards the agent are shaped by (and also shape) their environmental and behavioral characteristics, which consequently affect the nature of relationships formed between them. Therefore, we drew the following design considerations: (1) protection of privacy is a prerequisite to the use of an RA for homeless TB patients and can be addressed from both short-term (technical) and long-term (sociotechnical) perspectives; (2) the homeless group emphasized affective support from the agent, suggesting that relationships per se are already valuable to people who have been socially isolated and stigmatized; (3) consideration of the past memories in selecting social cues can facilitate the exchange of affective expressions in user–agent interaction; and (4) an RA should clarify to its interlocuters its identity as a machine to avoid confusing people with low technological literacy.
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. To achieve this aim, we investigated the factors that influence 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) quantitatively, and made predictions regarding the factors influencing the acceptance of an anti-TB chatbot. 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.
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