Towards a Model for Building Trust and Acceptance of Artificial Intelligence Aided Medical Assessment Systems
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Proceedings of the 49th EMAC 2020 Annual Conference, Budapest, May 26-29, 2020. Brüssel: European Marketing Academy, 2020. 64418 (Proceedings of the European Marketing Academy ; Band 2020).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Towards a Model for Building Trust and Acceptance of Artificial Intelligence Aided Medical Assessment Systems
AU - Seitz, Lennart
AU - Bekmeier-Feuerhahn, Sigrid
AU - Bontrup, Florian
AU - Wildt, Justus
AU - Gohil, Krutika
N1 - Conference code: 49
PY - 2020/5/27
Y1 - 2020/5/27
N2 - This study aims to identify determinants for the emergence of trust in AI-based medical assessment systems consisting of chatbots and telemedicine. Existing studies have been failing to create a holistic understanding due to focusing on single trust antecedents. Our study closes this research gap by conducting semi-structured interviews and standardized questionnaires to identify relevant variables and their relationship to each other. Participants (n = 40) take part in a laboratory experiment interacting with a chatbot (vs. chatbot + human agent) for initial medical assessment. The first results indicate the importance of the chatbot’s purpose and the transparency of underlying data base. Furthermore, communication patterns conveying uncertainty reduction are found to be more important than chatbot’s social skills. The additional human expert complements the chatbot due to the possibility of more specific and detailed questioning and patients’ wish of having a responsible person.
AB - This study aims to identify determinants for the emergence of trust in AI-based medical assessment systems consisting of chatbots and telemedicine. Existing studies have been failing to create a holistic understanding due to focusing on single trust antecedents. Our study closes this research gap by conducting semi-structured interviews and standardized questionnaires to identify relevant variables and their relationship to each other. Participants (n = 40) take part in a laboratory experiment interacting with a chatbot (vs. chatbot + human agent) for initial medical assessment. The first results indicate the importance of the chatbot’s purpose and the transparency of underlying data base. Furthermore, communication patterns conveying uncertainty reduction are found to be more important than chatbot’s social skills. The additional human expert complements the chatbot due to the possibility of more specific and detailed questioning and patients’ wish of having a responsible person.
KW - Management studies
KW - Chatbots
KW - Medibility
KW - Trust Building
M3 - Article in conference proceedings
T3 - Proceedings of the European Marketing Academy
BT - Proceedings of the 49th EMAC 2020 Annual Conference, Budapest, May 26-29, 2020
PB - European Marketing Academy
CY - Brüssel
T2 - 49th Annual of the European Marketing Academy - EMAC 2020
Y2 - 26 May 2020 through 29 May 2020
ER -