Tuberculosis (TB) is a highly infectious disease and one of the top 10 causes of death worldwide, claiming approximately 4000 lives a day [1]. Each year, millions of people continue to fall ill with TB, a preventable and curable disease [2]. Among the member countries of the Organization for Economic Cooperation and Development, South Korea has the highest incidence of and mortality rates due to TB [2]. It remains a debilitating disease in the South Korean context in that the treatment is already generalized, but its prevalence and mortality rates are unevenly distributed among social classes [1,3]. Its eradication has been difficult owing to both stigmatization and insufficient understanding of the disease that cause delays in diagnosis and treatment [4]. Approximately one-quarter of the world’s population is estimated to be infected with TB, and approximately 5%-10% of those at risk of infection develop active TB in their lifetime [1,5].

In recent years, mobile health (mHealth) has rapidly emerged as a vehicle for delivering better health services at a lower cost, regardless of time and place [6]. It is used to treat a wide range of infectious diseases, including TB. An extensive investigation on the use of digital technologies for TB control reports various mobile technologies applied for treatment adherence, program management, and e-learning related to TB [7]. These technologies include video-observed treatment (VOT), SMS text messages, mobile apps, voice calls, and mobile phone 3D-printed induration. mHealth apps assist medical staff with patient adherence monitoring (eg, apps for direct observed treatment [DOT] and VOT), dosage adjustment based on patient conditions, and provision of information about diagnosis and management of TB [7,8]. They inform patients and people vulnerable to TB about the disease and its therapy, provide diagnostics based on data input, and evaluate treatment costs. They are also used to trace people who have been exposed to the disease, monitor and track patients, and create laboratory reports [7]. The number of mHealth apps has more than doubled since 2016, evidencing the increasing demand for a new approach to TB control. It is also noteworthy that 39 out of the total 55 apps (71%) are only provided in English, thereby limiting access to non–English-speaking countries, where the highest prevalence of TB cases is observed [7].

Chatbots are a conversational agent, a software program that interacts with natural language, and have emerged as a new form of mHealth service [9,10]. Chatbots are useful for providing information to users with low literacy: users interact with them through dialog, a universal form of interaction. Furthermore, they can provide information in formats that are accessible to people with low literacy, such as images, sounds, and videos [9]. Thus, they are relatively easy to learn and are also age-friendly. From the perspective of health care providers, chatbots can save time and labor [11], in addition to providing continuous treatment management plans, motivation for patients with chronic diseases, and access to real-time information [8,12].

However, despite the expansion of mHealth solutions for TB control and the potential of chatbots, little research has been conducted on applying these tools to the management of TB. To the best of our knowledge, few studies have attempted to develop chatbots and virtual agents to support information accessibility for patients with TB [8]. It is essential to understand the exact factors that predict the acceptance of chatbots by potential users before we introduce them to a TB eradication program, which indeed underscores that the success of digital interventions in health care will depend on how well users accept the technology [13]. Furthermore, understanding the factors that increase the use of chatbots would accelerate the acceptance of this technology among the people most at risk of contracting TB. For this reason, we developed an anti-TB chatbot to bridge the gap between technology and people and studied its acceptance based on the factors that predict potential users, using the Technology Acceptance Model (TAM).

In 2019, we developed an anti-TB chatbot that provides information about the disease, its treatment, and TB hospitals and facilities. It targets people vulnerable to TB, as well as those affected by it. Textbox 1 presents the features of the chatbot.

The chatbot was built on an open-source platform and operates within an instant messenger app called Kakao Talk. An advantage of using this platform is that the medium through which users interact with the chatbot, that is, the messenger app, is widely used in South Korea, with over 72% of the total population or roughly 36.6 million people using it [14]. This makes the chatbot highly accessible as most people already have experience in using the app. The open-source platform builder uses machine learning to respond and adapt to diverse conversation patterns. This allows for the accuracy and relevance of the chatbot responses to improve as more user data accumulate.

The knowledge base was obtained from the information provided by the Korea Disease Control and Prevention Agency. We acquired the content with permission and then reorganized it in a dialog format. In addition to the text information, multimedia content was actively adopted, considering the tendency of low health literacy level of the poor and older people [15], who are characterized by a higher-than-average incidence of TB [16]. The curated content was examined by medical staff at a Seoul municipal hospital before publication. Gamification elements, including quizzes and prizes, were also adopted to motivate learners to engage with the chatbot [17].

We gave the chatbot the personality of a doctor. A chatbot with identity cues, such as a name, profile, and language style, is perceived as more empathetic, friendly, and personal [18,19]. Dr Colochman, the personality of the chatbot, is a retired doctor with a long record of treating patients with TB at a municipal hospital and is now working voluntarily for TB hospitals and support facilities. Its identity is conveyed through portraits, names, and intonation. Users encounter Dr Colochman for the first time during the tutorial that provides information on the chatbot and the instructions on how to use it in (what is supposedly) Dr Colochman’s voice.

The chatbot provides graphic and text information on the disease, its treatment, and neighboring TB facilities. Users navigate the content by scrolling the page vertically and horizontally. They communicate with the chatbot by selecting menus at the bottom of the screen, pushing buttons, or typing texts (Figure 1).

Davis et al [20] developed the TAM to investigate users’ intent to accept various technologies, including chatbots, and the factors that predict their decisions [20,21]. The key determinants used to study the acceptance of new technologies with the TAM are perceived usefulness, perceived ease of use, attitude, and behavioral intention. In this study, perceived usefulness is defined as the extent to which users think using anti-TB chatbots is helpful for TB management, and perceived ease of use is the extent to which users think using anti-TB chatbots is convenient and low-effort. According to the TAM, the adoption of a particular technology is governed by individual perceptions of usefulness and ease of use [21].

The TAM is widely used in technology acceptance research; however, it can predict only approximately 40% of the overall explanatory power [22]. A number of extended TAMs have been proposed to overcome the limitations of the original model. Venkatesh and Davis [23] developed the TAM2, adding social influence processes such as subjective norms, voluntariness, and image as external constructs of perceived usefulness. Social influence in our context is the extent to which users think that important others believe in using the anti-TB chatbot. Venkatesh and Davis [24] developed the Unified Theory of Acceptance and Use of Technology, which is the latest derivative of the TAM, adding facilitating conditions as a determinant of behavioral intention. Facilitating conditions here refer to the extent to which users think organizational and technical infrastructure exists to support the use of anti-TB chatbots.

Among the studies that have validated the TAM, some extended the model to address different contexts and populations, including the acceptance and continuous use of chatbots. For example, Huang and Chueh [25] reported that perceived accuracy and ease of use increased pet owners' satisfaction with veterinary consultation chatbots. Ashfaq et al [26] found that perceived enjoyment, usefulness, and ease of use are significant predictors of the continuance intention of chatbot-based customer service. In a study that investigated the acceptance of the health chatbot, Softić et al [27] identified the lack of users’ trust and qualified medical opinion as barriers; data confidentiality, speed of access to information, information security, and ease of use as facilitators; and reduced time spent on visiting doctors, increased access and care of patients, and enhanced protection of patient data as motivators for using a chatbot.

In the absence of studies that explain the acceptance of chatbots in the context of TB control, we aim to explore the benefits and concerns regarding accepting an anti-TB chatbot as perceived by potential users, to provide an extended TAM that can better predict the acceptance of anti-TB chatbots. Thus, we present studies 1 and 2. Study 1 aims to identify the factors that predict the acceptance of anti-TB chatbots through interviews with patients with TB and homeless facility personnel. On the basis of the interview results, we derived an operational definition of the questionnaire items and identified the factors for the extended TAM. Study 2 aims to verify the proposed theoretical model and identify the factors predicting the acceptance of an anti-TB chatbot.

This study aimed to propose a chatbot that provides information for the prevention and treatment of TB and identify factors that predict the acceptance of the chatbot. We conducted interviews with 16 patients with TB and 10 experts in TB and identified the factors that predict the acceptance of the anti-TB chatbot in study 1. From the results, we found social influence and facilitating conditions as additional factors in the extended TAM model. In study 2, we proposed an extended TAM model capable of predicting the acceptance of the anti-TB chatbot and evaluated it. We found that social influence was a strong predictor of perceived usefulness, regardless of history of TB. Study 1 suggests that social influence can arise from both health care experts and peers. Regarding users' behavioral intention, the predictive factor varied in the participants’ history of TB. Overall, our findings were consistent with those of other researchers [18,19,21], indicating that (1) perceived usefulness was predicted by social influence, (2) attitude was predicted by perceived usefulness, and (3) attitude toward the system and facilitating conditions predicted behavioral intention.

Our study confirmed that people needed information about the disease, as well as TB hospitals and support facilities. It also suggested that the reliability of the information provided by the chatbot is crucial to perceived usefulness and eventually the acceptance of the chatbot. Although this may sound rather obvious, existing mHealth apps that provide information on TB have been found to contain errors such as spelling and grammatical mistakes, outdated information, and wrong and potentially harmful content, according to a recent study that investigated 29 e-learning and information apps on TB [7]. The reliability of the information can be achieved by using trusted sources, having the content examined by experts before publication, and keeping it up to date through continuous maintenance.

Perceived usefulness was significantly predictive of people’s attitude toward the anti-TB chatbot if they have experienced TB. When people seek information about TB, stigmatization and its consequences (eg, social isolation and reduced economic opportunities) can be barriers to active information seeking and timely access to necessary services [32]. We expect that the anti-TB chatbot can contribute to lowering this barrier by facilitating access to information and reducing the risk of stigmatization (see Figure 4 for a glimpse of the chatbot experience). For patients with TB, the primary channel through which they receive information related to the disease is the medical staff. However, due to limited time at hand, medical staff provide selective information. The anti-TB chatbot can reduce staff workload while providing patients with the necessary information when needed. In other words, it bridges the distance between patients and medical staff by acting as a virtual assistant [33]. It also mitigates the information asymmetry between the 2 parties by empowering patients with the ability to access the information they need.

Study 2 confirmed that perceived ease of use was predictive of perceived usefulness but not predictive of the attitude toward technology. The latter result has been observed in studies where participants were proficient in using the technology (eg, responses of experienced mobile phone users to a mobile app or a chatbot) [34,35]. The same trend was observed in study 2, where there was a roughly even distribution in age of the survey participants (22 to 85 years). Does this imply that participants of different ages, and possibly varied levels of digital literacy, were proficient in using the chatbot that they were introduced for the first time? If so, what aspects of the chatbot are associated with proficiency? We speculate that this may be due to the popularity of the platform on which the anti-TB chatbot runs, that is, the messenger app widely used by people. The familiar user interface of the chatbot may have been transferred to the perceived proficiency in the use of the chatbot and a positive opinion of its utility. Thus, we conclude that the perceived usefulness of a chatbot can increase when its user interface is familiar to the target users.

In study 1, we observed social influence acting on the interviewees when a staff member in the hospital or TB treatment facility recommended the use of the anti-TB chatbot or when a peer introduced them. Thus, social influence can have a positive impact on the perceived usefulness of the chatbot. In a study that investigated the acceptance of conversational agents for disease diagnosis, social influence was identified as a factor influencing users’ intention to adopt or use a chatbot [9]. It has also been reported that users’ trust in providers and chatbots predicts performance expectancy. Performance expectancy refers to the degree to which using a chatbot will provide benefits to users in improving their health conditions [24].

Social influence can be derived from the authority and credibility of the service provider (ie, hospital) and those who have (expert or user) knowledge about the disease and technology. Among the different types of social influence was the peer pressure from other people who use a smartphone and a chatbot. For example, a patient with TB whom we met in study 1 was among several patients who did not have a smartphone and were eager to learn to use the smartphone and the anti-TB chatbot (Figure 6). Considering that there is still a large population who cannot access mHealth solutions, our findings reiterate a barrier to these technologies and simultaneously a strong demand for them that remains to be met. It is beyond the scope of this study to discuss how to meet this demand, but we introduce some of the existing efforts and emphasize the need for facilitating conditions in the Facilitation Condition section.

Facilitating condition is strongly associated with the acceptance of chatbots by patients with TB and thus should be considered when designing an anti-TB chatbot. TB occurs more commonly in older adults and low-income groups. It is also these groups who find it most challenging to access and use mHealth solutions. They are often reluctant to accept new solutions, such as chatbots, due to a lack of internal resources (eg, information on and capabilities to use mHealth solutions) according to study 1. However, this lack can be compensated by the provision of external support, such as a peer who can help them learn how to use a chatbot or smartphone tutorials. Existing chatbot-related studies tend to focus on the efficiency and usefulness of these technologies [11,36-38]. However, our findings suggest that it is equally important to design facilitating conditions from the perspective of users to encourage and accelerate their acceptance of chatbots. In other words, both services and an environment that empowers people to use the chatbots should be designed to eradicate TB.

Trans-sectoral efforts have been made to disseminate smartphones among homeless people as a strategy to reinforce self-sufficiency and mitigate poverty. Organizations, such as the Community Technology Alliance, Seoul Municipality, and Underheard in New York, have implemented smartphone giveaway projects in which donated smartphones were delivered to homeless people and used to find accommodation, economic opportunities, and fulfill other basic needs [39,40]. These examples demonstrate the possibility of making mHealth solutions accessible to the bottom of the pyramid, although they do not report any integration with mHealth. On the basis of our empirical study, we cautiously argue that there is sufficient demand for mHealth solutions, including the anti-TB chatbot, among poor and older people. The question is how to deliver them in a scaled-up and sustained manner. As we witness the rapid growth of the mHealth industry and anticipate a variety of solutions for TB control, including chatbots, the facilitating conditions are all the more important for the democratization of these technologies, that is, the development of technologies for people who are most affected by TB should be concurrent with sustained efforts to empower them.

This study has several limitations. First, our hypotheses were evaluated using correlation methods; therefore, the derived model did not explain causal relationships among the identified constructs. Second, this study was conducted using a convenience sample, which limits the generalizability of our findings. Thus, future studies should conduct a more comprehensive inspection of how these individual differences are associated with the acceptance of technology using representative and larger samples [41]. With larger samples, we may also be able to identify additional external factors that are predictive of the acceptance of the anti-TB chatbot. Potential candidates include social support and stigma, which have been identified as relevant for treating TB [42-44]. Third, our sample with a history of TB is relatively small and homogeneous due to the invisibility caused by the fear of stigmatization. Although this study showed that the predictor of anti-TB chatbot acceptance depends on history of TB, the number of patients was not sufficient to obtain results with greater statistical and conceptual strengths. Finally, the impact of this study remains limited in the current environment, where technological advances are not accessible to many homeless people who can benefit from them. At the same time, we are reminded that technology alone cannot solve complex societal problems. We also need to invest in scaling up the ongoing efforts to empower these people (eg, digital literacy education) and build the necessary infrastructure (eg, provide mobile devices and services that they can afford and expand public Wi-Fi zones in low-income residential areas).

Despite the expansion of mHealth solutions for TB control and the potential of chatbots to save costs and reduce the risk of stigma associated with the diagnosis and treatment of TB, few studies have sought to investigate the determinants of their adoption. In this context, we conducted 2 studies to develop an extended TAM that incorporates additional variables obtained from an empirical study with patients with TB and explain the intention to use a chatbot for TB control. The results showed that the intention to use the anti-TB chatbot was predicted by attitude toward the chatbot and facilitating conditions. Attitude toward the chatbot was positively predicted by its perceived usefulness but was not significantly predicted by perceived ease of use. The results also suggested that the perceived usefulness of the anti-TB chatbot was positively predicted by perceived ease of use and social influence. The importance of this study is to identify the underlying factors associated with the intention to use an anti-TB chatbot. These findings can be used to inform future design of anti-TB chatbots. For future work, it will be necessary to integrate the proposed model with other theories and factors that can help explain greater acceptance.