Dataset Information

Human versus chatbot: Understanding the role of emotion in health marketing communication for vaccines.

ABSTRACT: Based on the theoretical framework of agency effect, this study examined the role of affect in influencing the effects of chatbot versus human brand representatives in the context of health marketing communication about HPV vaccines. We conducted a 2 (perceived agency: chatbot vs. human) × 3 (affect elicitation: embarrassment, anger, neutral) between-subject lab experiment with 142 participants, who were randomly assigned to interact with either a perceived chatbot or a human representative. Key findings from self-reported and behavioral data highlight the complexity of consumer-chatbot communication. Specifically, participants reported lower interaction satisfaction with the chatbot than with the human representative when anger was evoked. However, participants were more likely to disclose concerns of HPV risks and provide more elaborate answers to the perceived human representative when embarrassment was elicited. Overall, the chatbot performed comparably to the human representative in terms of perceived usefulness and influence over participants' compliance intention in all emotional contexts. The findings complement the Computers as Social Actors paradigm and offer strategic guidelines to capitalize on the relative advantages of chatbot versus human representatives.

SUBMITTER: Tsai WS

PROVIDER: S-EPMC8441681 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Human versus chatbot: Understanding the role of emotion in health marketing communication for vaccines.

Tsai Wan-Hsiu Sunny WS Lun Di D Carcioppolo Nicholas N Chuan Ching-Hua CH

Psychology & marketing 20210727 12

Based on the theoretical framework of agency effect, this study examined the role of affect in influencing the effects of chatbot versus human brand representatives in the context of health marketing communication about HPV vaccines. We conducted a 2 (perceived agency: chatbot vs. human) × 3 (affect elicitation: embarrassment, anger, neutral) between-subject lab experiment with 142 participants, who were randomly assigned to interact with either a perceived chatbot or a human representative. Key ...[more]

PMID: 34539051

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Project description:BackgroundEmpathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.ObjectiveWe aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy.MethodsWe conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers.ResultsWe found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=-5.49, P<.001, Cohen d=0.36).ConclusionsOur work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

Project description:Background and significanceIt is increasingly recognized that some patients self-manage in the context of social networks rather than alone. Consumer health information technology (IT) designed to support socially embedded self-management must be responsive to patients' everyday communication practices. There is an opportunity to improve consumer health IT design by explicating how patients currently leverage social media to support health information communication.ObjectiveThe objective of this study was to determine types of health information communication patterns that typify Facebook users with chronic health conditions to guide consumer health IT design.Materials and methodsSeven hundred participants with type 2 diabetes were recruited through a commercial survey access panel. Cluster analysis was used to identify distinct approaches to health information communication both on and off Facebook. Analysis of variance (ANOVA) methods were used to identify demographic and behavioral differences among profiles. Secondary analysis of qualitative interviews ( n = 25) and analysis of open-ended survey questions were conducted to understand participant rationales for each profile.ResultsOur analysis yielded 7 distinct health information communication profiles. Five of 7 profiles had consistent patterns both on and off Facebook, while the remaining 2 demonstrated distinct practices, with no health information communication on Facebook but some off Facebook. One profile was distinct from all others in both health information communication practices and demographic composition. Rationales for following specific health information communication practices were categorized under 6 themes: altruism, instrumental support, social support, privacy and stigma, convenience, and Facebook knowledge.ConclusionFacebook has been widely adopted for health information communication; This study demonstrates that Facebook has been widely adopted for health information communication. It also shows that the ways in which patients communicate health information on and off Facebook are diverse.

Project description:BackgroundAutomated conversational agents, or chatbots, have a role in reinforcing evidence-based guidance delivered through other media and offer an accessible, individually tailored channel for public engagement. In early-to-mid 2021, young adults and minority populations disproportionately affected by COVID-19 in the United States were more likely to be hesitant toward COVID-19 vaccines, citing concerns regarding vaccine safety and effectiveness. Successful chatbot communication requires purposive understanding of user needs.ObjectiveWe aimed to review the acceptability of messages to be delivered by a chatbot named VIRA from Johns Hopkins University. The study investigated which message styles were preferred by young, urban-dwelling Americans as well as public health workers, since we anticipated that the chatbot would be used by the latter as a job aid.MethodsWe conducted 4 web-based focus groups with 20 racially and ethnically diverse young adults aged 18-28 years and public health workers aged 25-61 years living in or near eastern-US cities. We tested 6 message styles, asking participants to select a preferred response style for a chatbot answering common questions about COVID-19 vaccines. We transcribed, coded, and categorized emerging themes within the discussions of message content, style, and framing.ResultsParticipants preferred messages that began with an empathetic reflection of a user concern and concluded with a straightforward, fact-supported response. Most participants disapproved of moralistic or reasoning-based appeals to get vaccinated, although public health workers felt that such strong statements appealing to communal responsibility were warranted. Responses tested with humor and testimonials did not appeal to the participants.ConclusionsTo foster credibility, chatbots targeting young people with vaccine-related messaging should aim to build rapport with users by deploying empathic, reflective statements, followed by direct and comprehensive responses to user queries. Further studies are needed to inform the appropriate use of user-customized testimonials and humor in the context of chatbot communication.

Dataset Information

Human versus chatbot: Understanding the role of emotion in health marketing communication for vaccines.

Publications

Human versus chatbot: Understanding the role of emotion in health marketing communication for vaccines.

Similar Datasets

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