When stereotypes meet robots: The double-edge sword of robot gender and personality in human–robot interaction

Tay, Benedict Tiong Chee; Jung, Younbo; Park, Taezoon

doi:10.1016/j.chb.2014.05.014

Cited by 307 publications

(200 citation statements)

References 49 publications

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“…A potential explanation might be that the advisor's task was not associated with any explicit gender stereotypes, so participants held no expectations as to whether a male or female advisor should know the alien's taste better. Earlier research has indeed shown that gender stereotyping of tasks is manifested in the interaction among real humans (see Jeanquart-Barone and Sekaran, 1994;Eagly, 1997) and also in the interaction between a human and a robotic partner (see Kuchenbrandt et al, 2014;Tay et al, 2014). Thus, future research could investigate whether gender similarity (between user and robot) influences trusting beliefs and behavior when the task the robot has to perform is clearly related to gender stereotypes.…”

Section: Discussionmentioning

confidence: 99%

“…Another three Likert scales of 7 levels inquired agreement with statements that were adapted from the perceived trust questionnaires (Heerink et al, 2009;Tay et al, 2014) including: I will trust the advisor if the advisor gives me advice again in the future, I trust that the advisor can provide me correct answers to the game, and I will follow the advice that the advisor gives me. (ii) Nine semantic differential items with seven levels adapted from the individualized trust scale questionnaire (Wheeless and Grotz, 1977) with the following poles: untrustworthy-trustworthy, unreliable-reliable, insincere-sincere, dishonest-honest, distrustful-trustful, inconsiderate-considerate, divulging-confidential, deceitful-not deceitful, and disrespectful-respectful.…”

Section: Measure Trusting Beliefsmentioning

confidence: 99%

“…We measured trusting beliefs with a questionnaire using the scale developed by Jian et al (2000), perceived trust by using a scale by Tay et al (2014) and a scale by Heerink et al (2009), as well as individualized trust evaluations by using a scale developed by Wheeless and Grotz (1977). We combined the overlapping questions as appropriate.…”

Section: Measure Trusting Beliefsmentioning

confidence: 99%

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Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

et al. 2018

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The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBot TM , a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.

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Section: Discussionmentioning

confidence: 99%

Section: Measure Trusting Beliefsmentioning

confidence: 99%

Section: Measure Trusting Beliefsmentioning

confidence: 99%

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Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

et al. 2018

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“…The matching of robot's physical embodiment, perceived gender entity and gendered based role within humans' gender expectations is thought to improve HRI and elicit attitudes that are more positive. For example, Tay, Jung, and Park () found that participants rated robots more positively that matched stereotypical gender‐occupational role and personality‐occupational role. However, gender assignment relies heavily on roboticists' common‐sense assumptions about female and male gender roles (Robertson, ), contributing to the maintenance of gender‐based stereotypes.…”

Section: Drawbacks To the Humanization Of Robotsmentioning

confidence: 99%

Humanization of robots: Is it really such a good idea?

Giger

Piçarra²,

Alves‐Oliveira

et al. 2019

Human Behav and Emerg Tech

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The aim of this review was to examine the pros and cons of humanizing social robots following a psychological perspective. As such, we had six goals. First, we defined what social robots are. Second, we clarified the meaning of humanizing social robots. Third, we presented the theoretical backgrounds for promoting humanization. Fourth, we conducted a review of empirical results of the positive effects and the negative effects of humanization on human–robot interaction (HRI). Fifth, we presented some of the political and ethical problems raised by the humanization of social robots. Lastly, we discussed the overall effects of the humanization of robots in HRI and suggested new avenues of research and development.

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“…Moreover, recent research has demonstrated that social robots are more easily accepted when they conform with stereotypes that match their occupational role (e.g. healthcare, security) to the "gender" and "personality" that they are designed to possess [15].…”

Section: Introductionmentioning

confidence: 99%

Interacting with and via mobile devices and mobile robots in an assisted living setting

Dagioglou¹,

Lydakis²,

Kirstein³

et al. 2015

EAI Endorsed Transactions on Pervasive Health and Technology

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Using robotic home assistants as a platform for remote health monitoring offers several advantages, but also presents considerable challenges related to both the technical immaturity of home robotics and to user acceptance issues. In this paper we explore tablets and similar mobile devices as the medium of communication between robots and their users, presenting relevant current and planned research in humanrobot interaction that can help the telehealth community circumvent technical shortcomings, improve user acceptance, and maximize the quality of the data collected by robotic home assistants.

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When stereotypes meet robots: The double-edge sword of robot gender and personality in human–robot interaction

Cited by 307 publications

References 49 publications

Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction

Humanization of robots: Is it really such a good idea?

Interacting with and via mobile devices and mobile robots in an assisted living setting

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