A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction

Esterwood, Connor; Essenmacher, Kyle; Yang, Han; Zeng, Fanpan; Robert, Lionel

doi:10.1145/3411764.3445542

Cited by 43 publications

(23 citation statements)

References 71 publications

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“…Moreover, HRI research showed that the attribution of personality traits like extroversion or dominance to the robot can serve as predictors of their social acceptance (Woods et al, 2005 ; Mileounis et al, 2015 ; Dou et al, 2019 ; Tanevska et al, 2019 ; Mou et al, 2020 ). Based on the similarity-complementary hypothesis, it remains inconclusive whether humans do prefer robots with similar personality characteristics or not (Esterwood et al, 2021 ) and whether the personality preference is task-dependent (Joosse et al, 2013 ).…”

Section: Motivation and Related Workmentioning

confidence: 99%

Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task

et al. 2022

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A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. However, aspects like the uncanny valley and different technical affinity levels can impede the success of HRI scenarios, which has consequences on the establishment of long-term interaction qualities like trust and rapport. In the present study, we investigate the impact of a humanoid robot on human emotional responses during the performance of a cognitively demanding task. We set up three different conditions for the robot with increasing levels of social cue expressions in a between-group study design. For the analysis of emotions, we consider the eye gaze behavior, arousal-valence for affective states, and the detection of action units. Our analysis reveals that the participants display a high tendency toward positive emotions in presence of a robot with clear social skills compared to other conditions, where we show how emotions occur only at task onset. Our study also shows how different expression levels influence the analysis of the robots' role in HRI. Finally, we critically discuss the current trend of automatized emotion or affective state recognition in HRI and demonstrate issues that have direct consequences on the interpretation and, therefore, claims about human emotions in HRI studies.

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Section: Motivation and Related Workmentioning

confidence: 99%

Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task

et al. 2022

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“…The term "individual differences" includes traits such as demographics and personality (Harrison & Rainer, Jr., 1992;Robert, 2018;Robert et al, 2020). Prior research has examined the impact of individual differences on technology adoptions (Esterwood et al, 2021a;Ma et al, 2021). Using the technology acceptance model, Agarwal and Prasad (1999) found that individual differences influence technology acceptance via their effects on individual beliefs about technology.…”

Section: Introductionmentioning

confidence: 99%

Individual Differences and Expectations of Automated Vehicles

Zhang

Yang

Robert

2021

International Journal of Human–Computer Interaction

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Individual Differences and Expectations of Automated VehiclesDespite the benefits of automated vehicles (AVs), there are still barriers to their widespread adoption. Expectations about AVs have been identified as one of the most important factors in understanding AV adoption. Therefore, by understanding the public's expectations of AVs, we can better understand whether or when AVs are likely to be adopted on a wide scale.Individual differences, including demographics and personality, have been identified as factors that impact technology expectations and adoption. However, it is not clear whether and how individual differences can influence expectations of AVs. To examine this, we conducted an online survey with 443 U.S. drivers who were recruited and divided into subpopulations by age, gender, ethnicity, census region, educational level, marital status, income, driving frequency, driving experience, and personality traits. Results revealed that drivers' expectations of AVs differ significantly by age, gender, ethnicity, education levels, marital status, drive frequency, drive experience, and personality. More specifically, higher expectations are more often generated by drivers who are younger, men, White non-Hispanic, more highly educated, never married, with a higher frequency of driving, with less driving experience, and who are high in extraversion, agreeableness, conscientiousness, and emotional stability. The results of this study provide a foundation for future research related to expectations and have important implications on future design and development of AVs.

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“…Therefore, it cannot be overstated that to understand reactions to AI, including public support for regulating it, we must examine how society at large responds to AI. This means examining regulation of AI at a population level and not just using convenience samples of undergraduate students or online Mechanical Turk samples [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Personality and demographic correlates of support for regulating artificial intelligence

2023

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The arrival of artificial intelligence (AI) in our society has sparked many hopes and fears, with people having diverging views on the need to strictly regulate AI. The current study investigates how demographic and personality traits are associated with a desire to strictly regulate AI using a representative sample of adults from New Zealand (N = 47,951 participants). Data revealed that support for strict regulation of AI is positively related with agreeableness, neuroticism, and honesty–humility. However, it is negatively related to openness to experiences. A wide range of demographic factors including gender, age, ethnicity, religiosity, neighbourhood level economic deprivation, living rural, relationship status, and parental status were additionally related to support for regulation of AI. However, all these effects were fairly small suggesting that both personality and socio-demographic factors contribute to support for regulating AI, but other factors beyond these characteristics should also be considered for understanding people’s support for regulating AI.

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A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction

Cited by 43 publications

References 71 publications

Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task

Is It Me or the Robot? A Critical Evaluation of Human Affective State Recognition in a Cognitive Task

Individual Differences and Expectations of Automated Vehicles

Personality and demographic correlates of support for regulating artificial intelligence

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