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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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              <text>Modeling the Intention to Use AI Healthcare Chabots in the Indian Context</text>
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              <text>AI Healthcare chatbots; Conceptual model; Indian Context; Intention to use</text>
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              <text>Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically.  2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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              <text>Nagarathinam A.; Chellasamy A.; Elangovan N.; Rengasamy S.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-573 LNNS, pp. 657-666.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-031-20429-6_59" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-20429-6_59&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144952612&amp;amp;doi=10.1007%2F978-3-031-20429-6_59&amp;amp;partnerID=40&amp;amp;md5=d496d2582d57f269c853e0b82582f049" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144952612&amp;amp;doi=10.1007%2f978-3-031-20429-6_59&amp;amp;partnerID=40&amp;amp;md5=d496d2582d57f269c853e0b82582f049&lt;/a&gt;</text>
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              <text>ISSN: 23673370; ISBN: 978-303120428-9</text>
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              <text>Online</text>
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              <text>Nagarathinam A., School of Business and Management, Christ University, Bengaluru, India; Chellasamy A., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India; Rengasamy S., School of Business and Management, Christ University, Bengaluru, India</text>
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