The Feeling Economy in Healthcare: Satisfaction with Conversational AI [abstract]

Mariana Girão Carrilho; Diego Costa Pinto

The Feeling Economy in Healthcare: Satisfaction with Conversational AI [abstract]

Mariana Girão Carrilho, Diego Costa Pinto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

This study investigates the impact of Conversational AI on satisfaction in healthcare services. Drawing upon the Feeling Economy framework, we demonstrate that Conversational AI, as compared to human agents, negatively affects satisfaction primarily due to a perceived lack of empathy, particularly in scenarios involving physical health symptoms. Conversely, in mental health contexts, Conversational AI is preferred, indicating that its technological attributes provide unique benefits to consumers that extend beyond empathy. Across three experimental studies, the findings highlight a nuanced role of AI in healthcare: while it may negatively impact certain service encounters, it also offers enhancements in others, depending on the specific health issues involved. This research offers significant insights for healthcare providers and AI developers on optimizing AI deployment in healthcare settings.

Original language	English
Title of host publication	Proceedings of the European Marketing Academy
Publisher	European Marketing Academy (EMAC)
Pages	1-2
Number of pages	2
Publication status	Published - 25 Sept 2024
Event	EMAC Regional Conference 2024 - ISEG — Instituto Superior de Economia e Gestão, Lisboa, Portugal Duration: 25 Sept 2024 → 27 Sept 2024 Conference number: 15 https://www.iseg.ulisboa.pt/event/emac-2024-exploring-the-future-of-marketing-reshaping-marketings-role-in-the-society/

Conference

Conference	EMAC Regional Conference 2024
Country/Territory	Portugal
City	Lisboa
Period	25/09/24 → 27/09/24
Internet address	https://www.iseg.ulisboa.pt/event/emac-2024-exploring-the-future-of-marketing-reshaping-marketings-role-in-the-society/

Keywords

Consumer Behavior
Consumer Services
Customer Satisfaction and Delight
Services Marketing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

The_feeling_economy_in_healthcare_satisfaction_with_conversational_AI_abstractFinal published version, 226 KBLicence: Unspecified

https://proceedings.emac-online.org/pdfs/R2024-122683.pdfLicence: Unspecified

Cite this

@inproceedings{066285f1f6ec4ad48f4783aea3abf748,

title = "The Feeling Economy in Healthcare: Satisfaction with Conversational AI [abstract]",

abstract = "This study investigates the impact of Conversational AI on satisfaction in healthcare services. Drawing upon the Feeling Economy framework, we demonstrate that Conversational AI, as compared to human agents, negatively affects satisfaction primarily due to a perceived lack of empathy, particularly in scenarios involving physical health symptoms. Conversely, in mental health contexts, Conversational AI is preferred, indicating that its technological attributes provide unique benefits to consumers that extend beyond empathy. Across three experimental studies, the findings highlight a nuanced role of AI in healthcare: while it may negatively impact certain service encounters, it also offers enhancements in others, depending on the specific health issues involved. This research offers significant insights for healthcare providers and AI developers on optimizing AI deployment in healthcare settings.",

keywords = "Consumer Behavior, Consumer Services, Customer Satisfaction and Delight, Services Marketing",

author = "{Gir{\~a}o Carrilho}, Mariana and Pinto, {Diego Costa}",

note = "info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT# https://doi.org/10.54499/UIDB/04152/2020# Gir{\~a}o Carrilho, M., & Pinto, D. C. (2024). The Feeling Economy in Healthcare: Satisfaction with Conversational AI [abstract]. In Proceedings of the European Marketing Academy (pp. 1-2). Article 12268 European Marketing Academy (EMAC). https://proceedings.emac-online.org/pdfs/R2024-122683.pdf --- This work was supported by national funds through FCT (Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia), under the projects UIDB/04152/2020 and 2023.03729.BD, Centro de Investiga{\c c}{\~a}o em Gest{\~a}o de Informa{\c c}{\~a}o (MagIC)/NOVA IMS.; EMAC Regional Conference 2024 ; Conference date: 25-09-2024 Through 27-09-2024",

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N2 - This study investigates the impact of Conversational AI on satisfaction in healthcare services. Drawing upon the Feeling Economy framework, we demonstrate that Conversational AI, as compared to human agents, negatively affects satisfaction primarily due to a perceived lack of empathy, particularly in scenarios involving physical health symptoms. Conversely, in mental health contexts, Conversational AI is preferred, indicating that its technological attributes provide unique benefits to consumers that extend beyond empathy. Across three experimental studies, the findings highlight a nuanced role of AI in healthcare: while it may negatively impact certain service encounters, it also offers enhancements in others, depending on the specific health issues involved. This research offers significant insights for healthcare providers and AI developers on optimizing AI deployment in healthcare settings.

AB - This study investigates the impact of Conversational AI on satisfaction in healthcare services. Drawing upon the Feeling Economy framework, we demonstrate that Conversational AI, as compared to human agents, negatively affects satisfaction primarily due to a perceived lack of empathy, particularly in scenarios involving physical health symptoms. Conversely, in mental health contexts, Conversational AI is preferred, indicating that its technological attributes provide unique benefits to consumers that extend beyond empathy. Across three experimental studies, the findings highlight a nuanced role of AI in healthcare: while it may negatively impact certain service encounters, it also offers enhancements in others, depending on the specific health issues involved. This research offers significant insights for healthcare providers and AI developers on optimizing AI deployment in healthcare settings.

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