Testing Advice Response Theory in Interactions With Friends

MacGeorge, Erina L.; Guntzviller, Lisa M.; Hanasono, Lisa K.; Feng, Bo

doi:10.1177/0093650213510938

Cited by 88 publications

(110 citation statements)

References 39 publications

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“…Yet, advice from automation sources such as computers and robots has been largely overlooked by communication scholars. Advice response theory (ART) has emerged and been developed as the main theory of interpersonal advice (Feng & Feng, ; Feng & MacGeorge, ; MacGeorge et al, ; Van Swol, MacGeorge, & Prahl, ). Key aspects of ART include message‐related characteristics (such as the politeness of the message), advisor‐related characteristics (such as expertise), and receiver‐related characteristics (such as emotional state).…”

Section: Introductionmentioning

confidence: 99%

“…1.1 | Advice: The message and the advisor The interpersonal process of giving and receiving advice has received considerable attention from communication scholars over the past 20 years (for a review, see Bonaccio & Dalal, 2006;MacGeorge, Guntzviller, Hanasono, & Feng, 2013). Yet, advice from automation sources such as computers and robots has been largely overlooked by communication scholars.…”

Section: Introductionmentioning

confidence: 99%

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Understanding algorithm aversion: When is advice from automation discounted?

Prahl

Swol

2017

Journal of Forecasting

171

104

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Forecasting advice from human advisors is often utilized more than advice from automation. There is little understanding of why “algorithm aversion” occurs, or specific conditions that may exaggerate it. This paper first reviews literature from two fields—interpersonal advice and human–automation trust—that can inform our understanding of the underlying causes of the phenomenon. Then, an experiment is conducted to search for these underlying causes. We do not replicate the finding that human advice is generally utilized more than automated advice. However, after receiving bad advice, utilization of automated advice decreased significantly more than advice from humans. We also find that decision makers describe themselves as having much more in common with human than automated advisors despite there being no interpersonal relationship in our study. Results are discussed in relation to other findings from the forecasting and human–automation trust fields and provide a new perspective on what causes and exaggerates algorithm aversion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding algorithm aversion: When is advice from automation discounted?

Prahl

Swol

2017

Journal of Forecasting

171

104

View full text Add to dashboard Cite

show abstract

“…A growing body of research has documented that the effects of supportive messages are moderated by a number of factors, including characteristics of the source of support and the situation (see Bodie & Burleson, 2008 for a review). For example, gender moderates the relationship between message content and intent to implement advice, such that message content exerts a stronger influence on intent to implement advice for women than for men (MacGeorge, Guntzviller, Hanasono, & Feng, 2013). In another study, researchers found that HPC messages have a greater effect when support recipients are facing a severe stressor, as opposed to a less serious stressor (Bodie, Burleson, & Jones, 2012).…”

Section: Moderating the Effects Of Supportive Messagesmentioning

confidence: 99%

“…Scholars interested in supportive communication have called for increased research attention on the effects of a support provider on outcomes (Feng & MacGeorge, 2010). The source of a supportive message explains variations in decision-making processes regarding advice (Feng & MacGeorge, 2010;Jungermann & Fisher, 2005), perceptions of support quality and effectiveness (e.g., High & Solomon, 2014;High & Steuber, 2014;MacGeorge et al, 2013), and the extent to which a person copes effectively with a stressor (e.g., High & Solomon, 2014;MacGeorge et al, 2013). The results of this study indicate that different support providers can produce effects prior to an interaction even begins.…”

Section: Theoretical Significance Of Source Effectsmentioning

confidence: 99%

“…In this study, participants might have considered their interaction partner's college major and relationship to the news article to generate expectations for the upcoming interaction. Advice response theory (Feng & MacGeorge, 2010;MacGeorge et al, 2013) argues that people experience better outcomes when receiving support from more similar providers or providers with prior expertise with a stressor.…”

Section: Theoretical Significance Of Source Effectsmentioning

confidence: 99%

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Managing stigma through interpersonal interactions

Crowley¹

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Plain Language vs Standard Format for Youth Understanding of COVID-19 Recommendations

Stallwood,

Sammy,

Prebeg

et al. 2023

JAMA Pediatr

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ImportanceTo ensure that youths can make informed decisions about their health, it is important that health recommendations be presented for understanding by youths.ObjectiveTo compare understanding, accessibility, usability, satisfaction, intention to implement, and preference of youths provided with a digital plain language recommendation (PLR) format vs the original standard language version (SLV) of a health recommendation.Design, Setting, and ParticipantsThis pragmatic, allocation-concealed, blinded, superiority randomized clinical trial included individuals from any country who were 15 to 24 years of age, had internet access, and could read and understand English. The trial was conducted from May 27 to July 6, 2022, and included a qualitative component.InterventionsAn online platform was used to randomize youths in a 1:1 ratio to an optimized digital PLR or SLV format of 1 of 2 health recommendations related to the COVID-19 vaccine; youth-friendly PLRs were developed in collaboration with youth partners and advisors.Main Outcomes and MeasuresThe primary outcome was understanding, measured as the proportion of correct responses to 7 comprehension questions. Secondary outcomes were accessibility, usability, satisfaction, preference, and intended behavior. After completion of the survey, participants indicated their interest in completing a 1-on-1 semistructured interview to reflect on their preferred digital format (PLR or SLV) and their outcome assessment survey response.ResultsOf the 268 participants included in the final analysis, 137 were in the PLR group (48.4% female) and 131 were in the SLV group (53.4% female). Most participants (233 [86.9%]) were from North and South America. No significant difference was found in understanding scores between the PLR and SLV groups (mean difference, 5.2%; 95% CI, −1.2% to 11.6%; P = .11). Participants found the PLR to be more accessible and usable (mean difference, 0.34; 95% CI, 0.05-0.63) and satisfying (mean difference, 0.39; 95% CI, 0.06-0.73) and had a stronger preference toward the PLR (mean difference, 4.8; 95% CI, 4.5-5.1 [4.0 indicated a neutral response]) compared with the SLV. No significant difference was found in intended behavior (mean difference, 0.22 (95% CI, −0.20 to 0.74). Interviewees (n = 14) agreed that the PLR was easier to understand and generated constructive feedback to further improve the digital PLR.Conclusions and RelevanceIn this randomized clinical trial, compared with the SLV, the PLR did not produce statistically significant findings in terms of understanding scores. Youths ranked it higher in terms of accessibility, usability, and satisfaction, suggesting that the PLR may be preferred for communicating health recommendations to youths. The interviews provided suggestions for further improving PLR formats.Trial RegistrationClinicalTrials.gov Identifier: NCT05358990

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Testing Advice Response Theory in Interactions With Friends

Cited by 88 publications

References 39 publications

Understanding algorithm aversion: When is advice from automation discounted?

Understanding algorithm aversion: When is advice from automation discounted?

Managing stigma through interpersonal interactions

Plain Language vs Standard Format for Youth Understanding of COVID-19 Recommendations

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