Trust Recipes for Enhancing the Intention to Adopt Conversational Agents for Disease Diagnosis: An fsQCA Approach

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dc.contributor.authorAnton, Eduard
dc.contributor.authorOesterreich, Thuy Duong
dc.contributor.authorFitte, Christian
dc.contributor.authorTeuteberg, Frank
dc.date.accessioned2021-12-24T17:54:25Z
dc.date.available2021-12-24T17:54:25Z
dc.date.issued2022-01-04
dc.description.abstractIn this study, we examine the configurations of trust-enhancing factors that determine the intention to adopt conversational agents (CAs) for disease diagnosis. After identifying trust factors influencing the behavioral intent to adopt CAs based on the information systems acceptance research field, we assigned 201 participants to use the mobile Ada application and surveyed them about their experience. Ada is a medical diagnostic CA that combines patients’ symptoms with their medical history and provides diagnostic suggestions. The collected data was analyzed using a fuzzy set qualitative comparative analysis to capture the causal complexity of trust. We identified several configurations of trust-enhancing factors affecting the intention to adopt the CA. In particular, our results show that the adoption intentions are strongly determined by trust factors associated with the performance dimension. Furthermore, we derived two propositions for the development of CAs for healthcare purposes and elaborated implications for research and practice.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.481
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79817
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIT Adoption, Diffusion, and Evaluation in Healthcare
dc.subjectconversational agents
dc.subjectfsqca
dc.subjecthealthcare
dc.subjecttrust
dc.titleTrust Recipes for Enhancing the Intention to Adopt Conversational Agents for Disease Diagnosis: An fsQCA Approach
dc.type.dcmitext

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