Is “Dr.” always good?: Effects of status-based expert source cues and contingency in healthcare chatbots

Yujie Dong; Yuheng Wu; Yiling Shao

Is “Dr.” always good? Effects of status-based expert source cues and contingency in healthcare chatbots

Yujie Dong, Yuheng Wu, Yiling Shao

Department of Media and Communication

Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review

Abstract

Healthcare chatbots are often endowed with status-based expert source cues (e.g., “Dr”). These status-based expert source cues may increase credibility, but sometimes generate negative cognition and affect in agents of low status in interpersonal relationships. Will these adverse effects exist in the human-chatbot relationship? A 2 (expert source cues: high- vs. low-status) x 2 (contingency: high vs. low) experiment will be conducted to probe the implications of status-based expert source cues in healthcare chatbots.

Original language	English
Publication status	Presented - 27 May 2023
Event	73rd Annual International Communication Association Conference (ICA 2023): Reclaiming Authenticity in Communication - Tonronto, Toronto, Canada Duration: 25 May 2023 → 29 May 2023 https://www.icahdq.org/page/annual-conference

Conference

Conference	73rd Annual International Communication Association Conference (ICA 2023)
Abbreviated title	ICA23
Place	Canada
City	Toronto
Period	25/05/23 → 29/05/23
Internet address	https://www.icahdq.org/page/annual-conference

Bibliographical note

Information for this record is supplemented by the author(s) concerned.

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title = "Is “Dr.” always good?: Effects of status-based expert source cues and contingency in healthcare chatbots",

abstract = "Healthcare chatbots are often endowed with status-based expert source cues (e.g., “Dr”). These status-based expert source cues may increase credibility, but sometimes generate negative cognition and affect in agents of low status in interpersonal relationships. Will these adverse effects exist in the human-chatbot relationship? A 2 (expert source cues: high- vs. low-status) x 2 (contingency: high vs. low) experiment will be conducted to probe the implications of status-based expert source cues in healthcare chatbots.",

author = "Yujie Dong and Yuheng Wu and Yiling Shao",

note = "Information for this record is supplemented by the author(s) concerned.; 73rd Annual International Communication Association Conference (ICA 2023) : Reclaiming Authenticity in Communication, ICA23 ; Conference date: 25-05-2023 Through 29-05-2023",

year = "2023",

month = may,

day = "27",

language = "English",

url = "https://www.icahdq.org/page/annual-conference",

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Is “Dr.” always good? Effects of status-based expert source cues and contingency in healthcare chatbots. / Dong, Yujie; Wu, Yuheng; Shao, Yiling.
2023. Paper presented at 73rd Annual International Communication Association Conference (ICA 2023), Toronto, Ontario, Canada.

Research output: Conference Papers › RGC 32 - Refereed conference paper (without host publication) › peer-review

TY - CONF

T1 - Is “Dr.” always good?

T2 - 73rd Annual International Communication Association Conference (ICA 2023)

AU - Dong, Yujie

AU - Wu, Yuheng

AU - Shao, Yiling

N1 - Information for this record is supplemented by the author(s) concerned.

PY - 2023/5/27

Y1 - 2023/5/27

N2 - Healthcare chatbots are often endowed with status-based expert source cues (e.g., “Dr”). These status-based expert source cues may increase credibility, but sometimes generate negative cognition and affect in agents of low status in interpersonal relationships. Will these adverse effects exist in the human-chatbot relationship? A 2 (expert source cues: high- vs. low-status) x 2 (contingency: high vs. low) experiment will be conducted to probe the implications of status-based expert source cues in healthcare chatbots.

AB - Healthcare chatbots are often endowed with status-based expert source cues (e.g., “Dr”). These status-based expert source cues may increase credibility, but sometimes generate negative cognition and affect in agents of low status in interpersonal relationships. Will these adverse effects exist in the human-chatbot relationship? A 2 (expert source cues: high- vs. low-status) x 2 (contingency: high vs. low) experiment will be conducted to probe the implications of status-based expert source cues in healthcare chatbots.

M3 - RGC 32 - Refereed conference paper (without host publication)

Y2 - 25 May 2023 through 29 May 2023