Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients

Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India

Anthropomorphism of AI-based Intelligent Customer Service, and Its Affective and Behavioral Consequences

Recently, as many users turn to social media to interact with service providers, organizations apply Artificial intelligence (AI) to improve the efficiency and effectiveness of the operation. This type of customer service system is called intelligent customer service (ICS) which one of the most commonly adopted tools is chatbot. Since chatbot is AI-empowered, whether this system can effectively interact with customers and solve their problems is critical. However, the quality of ICS has received significant attention recently, and a lack of systematic study on the outcomes of anthropomorphism leaves this question unanswered in an ICS context. Based on a cognitive-affective-behavioral framework, this study attempts to understand whether anthropomorphism can promote desired behaviors (including usage and citizen-ship behaviors) through enhancing affective out-comes, such as satisfaction and identity. Data collected from 183 chatbot-ICS users, this study illustrates how anthropomorphism can increase quality, enhance satisfaction and identity. Furthermore, we also show that satisfaction and identity lead to further usage and citizenship behaviors. This highlights the importance of increasing anthropomorphism for the chatbot-ICS

Considering the Context to Build Theory in HCI, HRI, and HMC: Explicating Differences in Processes of Communication and Socialization with Social Technologies

The proliferation and integration of social technologies has occurred quickly, and the specific technologies with which we engage are ever-changing. The dynamic nature of the development and use of social technologies is often acknowledged by researchers as a limitation. In this manuscript, however, we present a discussion on the implications of our modern technological context by focusing on processes of socialization and communication that are fundamentally different from their interpersonal corollary. These are presented and discussed with the goal of providing theoretical building blocks toward a more robust understanding of phenomena of human-computer interaction, human-robot interaction, human-machine communication, and interpersonal communication

THE RELATIONSHIP BETWEEN HUMAN AND VIRTUAL AGENTS: A LIFE CYCLE VIEW

Virtual agents powered by artificial intelligence (AI) have been implemented in different service contexts, which have brought some changes to our lives. Previous studies have examined individual users\u27 motivations to use virtual agents and the influences of virtual agents as social objects on individual users. There is a lack of knowledge on the relationship between humans and virtual agents, which could help understand the role of virtual agents in societies. In this work, we chose the mobile app Replika as our research context and utilized the big data analysis method to explore the major topics covered in online reviews about Replika on Twitter. Based on social penetration theory, we found four relationships between users and Replika, including relationship formation, exploration, maintenance, and destruction or termination. Our findings contribute to the literature by unrevealing a life circle of the relationship between human and virtual agents

Exploring The Design of Prompts For Applying GPT-3 based Chatbots: A Mental Wellbeing Case Study on Mechanical Turk

Large-Language Models like GPT-3 have the potential to enable HCI designers and researchers to create more human-like and helpful chatbots for specific applications. But evaluating the feasibility of these chatbots and designing prompts that optimize GPT-3 for a specific task is challenging. We present a case study in tackling these questions, applying GPT-3 to a brief 5-minute chatbot that anyone can talk to better manage their mood. We report a randomized factorial experiment with 945 participants on Mechanical Turk that tests three dimensions of prompt design to initialize the chatbot (identity, intent, and behaviour), and present both quantitative and qualitative analyses of conversations and user perceptions of the chatbot. We hope other HCI designers and researchers can build on this case study, for other applications of GPT-3 based chatbots to specific tasks, and build on and extend the methods we use for prompt design, and evaluation of the prompt design

Extending Chatbots to Probe Users: Enhancing Complex Decision-Making Through Probing Conversations

Chatbots have become commonplace-they can provide customer support, take orders, collect feedback, and even provide (mental) health support. Despite this diversity, the opportunities of designing chatbots for more complex decision-making tasks remain largely underexplored. Bearing this in mind leads us to ask: How can chatbots be embedded into software tools used for complex decision-making and designed to scaffold and probe human cognition' The goal of our research was to explore possible uses of such "probing bots". The domain we examined was stock investment where many complex decisions need to be made. In our study, different types of investors interacted with a prototype, which we called "ProberBot", and subsequently took part in in-depth interviews. They generally found our ProberBot was effective at supporting their thinking but when this is desirable depends on the type of task and activity. We discuss these and other findings as well as design considerations for developing ProberBots for similar types of decision-making tasks

How to Support Domestic Violence Survivors with Conversational Agents: Meta Requirements and Design Principles

Domestic violence is a prevalent and complicated issue that can have detrimental effects on the survivors, their families, and communities. Survivors are often reluctant to divulge their experiences to others in person for social, emotional, privacy, or cultural reasons. Consequently, many are not actively seeking support that meets their needs. Conversational agents, a form of technology support, hold great promise for facilitating counseling and support by promoting self-disclosure and enhancing user engagement. To address the knowledge gaps in design principles for conversational agents for DV survivors, we conducted in-depth interviews with 11 professionals working with domestic violence survivors. After analyzing the interview transcripts and related literature, we identified several meta-requirements and categorized them into four categories —conversation, language, support, and trust. We further grouped these meta-requirements into several design principles. Our work lays the foundation for design science research in designing and developing conversational agents to support domestic violence survivors

Be a Miracle - Designing Conversational Agents to Influence Users’ Intention Regarding Organ Donation

The increasing need for organ donations remains a worldwide challenge as transplant waiting lists grow and donation rates persist at constant levels. The increasing popularity of conversational agents (CAs) has prompted new strategies for educating and persuading individuals to adjust their cognitive and behavioral beliefs and become donors. However, how CAs should be designed to modify uninformed users’ intention to donate remains unclear. Against this background, we conducted an online experiment (N=134) to examine the impact of a human-like CA design on users\u27 intention to become organ donors. Based on the three-factor theory of anthropomorphism and the elaboration likelihood model, we derive three theoretical mechanisms to understand the influence of a CAs human-like design on users’ intention to donate. The findings show that perceived anthropomorphism does not directly impact persuasion and empathy but is mediated via perceived usefulness to influence the intention to donate