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Affective Processes in Human–Automation Interactions

Affective Processes in Human–Automation Interactions Objective: This study contributes to the literature on automation reliance by illuminating the influences of user moods and emotions on reliance on automated systems.Background: Past work has focused predominantly on cognitive and attitudinal variables, such as perceived machine reliability and trust. However, recent work on human decision making suggests that affective variables (i.e., moods and emotions) are also important. Drawing from the affect infusion model, significant effects of affect are hypothesized. Furthermore, a new affectively laden attitude termed liking is introduced.Method: Participants watched video clips selected to induce positive or negative moods, then interacted with a fictitious automated system on an X-ray screening task. At five time points, important variables were assessed including trust, liking, perceived machine accuracy, user self-perceived accuracy, and reliance. These variables, along with propensity to trust machines and state affect, were integrated in a structural equation model.Results: Happiness significantly increased trust and liking for the system throughout the task. Liking was the only variable that significantly predicted reliance early in the task. Trust predicted reliance later in the task, whereas perceived machine accuracy and user self-perceived accuracy had no significant direct effects on reliance at any time.Conclusion: Affective influences on automation reliance are demonstrated, suggesting that this decision-making process may be less rational and more emotional than previously acknowledged.Application: Liking for a new system may be key to appropriate reliance, particularly early in the task. Positive affect can be easily induced and may be a lever for increasing liking. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Human Factors SAGE

Affective Processes in Human–Automation Interactions

Merritt, Stephanie M.
Human Factors , Volume 53 (4): 15 – Aug 1, 2011
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References (92)

Publisher
SAGE
Copyright
© 2011, Human Factors and Ergonomics Society
ISSN
0018-7208
eISSN
1547-8181
DOI
10.1177/0018720811411912
pmid
21901933
Publisher site
See Article on Publisher Site

Abstract

Objective: This study contributes to the literature on automation reliance by illuminating the influences of user moods and emotions on reliance on automated systems.Background: Past work has focused predominantly on cognitive and attitudinal variables, such as perceived machine reliability and trust. However, recent work on human decision making suggests that affective variables (i.e., moods and emotions) are also important. Drawing from the affect infusion model, significant effects of affect are hypothesized. Furthermore, a new affectively laden attitude termed liking is introduced.Method: Participants watched video clips selected to induce positive or negative moods, then interacted with a fictitious automated system on an X-ray screening task. At five time points, important variables were assessed including trust, liking, perceived machine accuracy, user self-perceived accuracy, and reliance. These variables, along with propensity to trust machines and state affect, were integrated in a structural equation model.Results: Happiness significantly increased trust and liking for the system throughout the task. Liking was the only variable that significantly predicted reliance early in the task. Trust predicted reliance later in the task, whereas perceived machine accuracy and user self-perceived accuracy had no significant direct effects on reliance at any time.Conclusion: Affective influences on automation reliance are demonstrated, suggesting that this decision-making process may be less rational and more emotional than previously acknowledged.Application: Liking for a new system may be key to appropriate reliance, particularly early in the task. Positive affect can be easily induced and may be a lever for increasing liking.

Journal

Human Factors – SAGE

Published: Aug 1, 2011

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