The rise of multidrug-resistant tuberculosis (MDR-TB), defined as tuberculosis showing resistance to at least isoniazid and rifampicin, is a serious threat to tuberculosis control in some high prevalence countries and may have some impact on low prevalence regions as well.The World Health Organization estimates that 50 million people worldwide are infected with MDR-TB, and that, in the year 2000, 273,000 (3.1%) MDR-TB cases were among the 8.7 million new tuberculosis cases. In 1998, the highest MDR-TB rates among new cases and the highest combined (new and previously treated cases) MDR-TB rates were found in Estonia (14.1 and 18.1%), Henan province in China (10.8 and 15.1%), Latvia (9.0 and 12.0%), and Ivanovo Oblast (9.0 and 12.3%) and Tomsk Oblast (6.5 and 13.7%) in the Russian Federation. The risk factors for MDR-TB are previous treatment or relapse, originating from “hot spot” areas, a history of imprisonment, homelessness and possibly also human immunodeficiency virus.The treatment of multidrug-resistant tuberculosis is difficult due to side-effects and a treatment duration of up to 3 yrs, which is expensive and often unsuccessful. Therefore, strategies for the treatment and prevention of multidrug-resistant tuberculosis are urgently required. This requires functioning tuberculosis control programmes (directly observed treatment short course), and, in some high prevalence countries, the introduction of second-line drugs on the basis of appropriate susceptibility testing (directly observed treatment short course-Plus). Only the future will show whether this “ticking time bomb” can be defused.

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Conversational agents (CAs), described as software with which humans interact through natural language, have increasingly attracted interest in both academia and practice because of improved capabilities driven by advances in artificial intelligence and, specifically, natural language processing. CAs are used in contexts such as peoples private lives, education, and healthcare, as well as in organizations to innovate or automate tasks for example, in marketing, sales, or customer service. In addition to these application contexts, CAs take on different forms in terms of their embodiment, the communication mode, and their (often human-like) design. Despite their popularity, many CAs are unable to fulfill expectations, and fostering a positive user experience is challenging. To better understand how CAs can be designed to fulfill their intended purpose and how humans interact with them, a number of studies focusing on human-computer interaction have been carried out in recent years, which have contributed to our understanding of this technology. However, currently, a structured overview of this research is lacking, thus impeding the systematic identification of research gaps and knowledge on which future studies can build. To address this issue, we conducted an organizing and assessing review of 262 studies, applying a sociotechnical lens to analyze CA research regarding user interaction, context, agent design, as well as CA perceptions and outcomes. This study contributes an overview of the status quo of CA research, identifies four research streams through cluster analysis, and proposes a research agenda comprising six avenues and sixteen directions to move the field forward

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The increasing capabilities of conversational agents (CAs) offer manifold opportunities to assist users in a variety of tasks. In an organizational context, particularly their potential to simulate a human-like interaction via natural language currently attracts attention both at the customer interface as well as for internal purposes, often in the form of chatbots. Emerging experimental studies on CAs look into the impact of anthropomorphic design elements, so-called social cues, on user perception. However, while these studies provide valuable prescriptive knowledge of selected social cues, they neglect the potential detrimental influence of the limited responsiveness of present-day conversational agents. In practice, many CAs fail to continuously provide meaningful responses in a conversation due to the open nature of natural language interaction, which negatively influences user perception and often led to CAs being discontinued in the past. Thus, designing a CA that provides a human-like interaction experience while minimizing the risks associated with limited conversational capabilities represents a substantial design problem. This study addresses the aforementioned problem by proposing and evaluating a design for a CA that offers a human-like interaction experience while mitigating negative effects due to limited responsiveness. Through the presentation of the artifact and the synthesis of prescriptive knowledge in the form of a nascent design theory for anthropomorphic enterprise CAs, this research adds to the growing knowledge base for designing humanlike assistants and supports practitioners seeking to introduce them into their organizations.