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

By Benedict Tay Tiong Chee, Younbo Jung, Taezoon Park. Computers in Human Behavior, 2014

447 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, Social Psychology

View Paper: https://www.semanticscholar.org/paper/67b4d4bca180a91617835b2802d92b74c361e3c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Amit Kumar Pandey
2. Rodolphe Gelin
3. Daniel Belanche
4. Anthony Ventresque
5. Luis Vicente Casaló
6. Elayne Ruane
7. Jeroen J. L. Schepers
8. Carlos Flavián

In Safety Research

1. Thomas Llewelyn Webb
2. Abeba Birhane

In Social Psychology

1. Johannes Maria Kraus
2. Wolfgang Minker
3. Amit Kumar Pandey
4. Matthias Kraus
5. Rodolphe Gelin
6. Daniel Belanche
7. Luis Vicente Casaló
8. Thomas Llewelyn Webb

Citations in the corpus

• A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots (2020, methodology): “Subscales of questionnaires relating to specific models such as the Almere Model of robot acceptance [15] and Unified Theory of Acceptance and Use of Technology (UTAUT; [33]) were also used to measure cognitive attitudes [58–60].”
• Conversational AI: Social and Ethical Considerations (2019, background): “While female personas are often used in subservient contexts, male personas are often found in situations perceived as authoritative, such as an automatic interviewer [23] [45] [55].”
• Service robot implementation: a theoretical framework and research agenda (2019, background): “This signal of the robot ’ s personality and warmth thus can vary the formality of the interaction (Tay, Jung, & Park, 2014), and the kind of communication style might be either standardized or adapted to customer traits and preferences, leading to di ff erent reactions (as we discuss in the next…”
• A Mass-Produced Sociable Humanoid Robot: Pepper: The First Machine of Its Kind (2018, background): “Some studies already show that a person tends to rate a robot that looks like the opposite sex as more credible, trustworthy, and engaging [9] and that, if robots exhibit a gender, there is a stereotype-based bias in the expected services the robot should be providing [10].”
• Effects of Gender Stereotypes on Trust and Likability in Spoken Human-Robot Interaction (2018, background): “The two papers Tay et al. (2014) and Tay et al. (2013) investigated the impact of occupational roles (security - health care), personality (extroverted - introverted), and gender (male - female) of a robot on user acceptance.”