1
|
Aşkın G, Saltık İ, Boz TE, Urgen BA. Gendered Actions with a Genderless Robot: Gender Attribution to Humanoid Robots in Action. Int J Soc Robot 2023; 15:1-17. [PMID: 36694634 PMCID: PMC9852799 DOI: 10.1007/s12369-022-00964-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2022] [Indexed: 01/22/2023]
Abstract
The present study aims to investigate how gender stereotypes affect people's gender attribution to social robots. To this end, we examined whether a robot can be assigned a gender depending on a performed action. The study consists of 3 stages. In the first stage, we determined masculine and feminine actions by a survey conducted with 54 participants. In the second stage, we selected a gender-neutral robot by having 76 participants rate several robot stimuli in the masculine-feminine spectrum. In the third stage, we created short animation videos in which the gender-neutral robot determined in stage two performed the masculine and feminine actions determined in stage one. We then asked 102 participants to evaluate the robot in the videos in the masculine-feminine spectrum. We asked them to rate the videos according to their own view (self-view) and how they thought society would evaluate them (society-view). We also used the Socialization of Gender Norms Scale (SGNS) to identify individual differences in gender attribution to social robots. We found the main effect of action category (feminine vs. masculine) on both self-view reports and society-view reports suggesting that a neutral robot was reported to be feminine if it performed feminine actions and masculine if it performed masculine actions. However, society-view reports were more pronounced than the self-view reports: when the neutral robot performed masculine actions, it was found to be more masculine in the society-view reports than the self-view reports; and when it performs feminine actions, it was found to be more feminine in the society-view reports than the self-view reports. In addition, the SGNS predicted the society-view reports (for feminine actions) but not the self-view reports. In sum, our study suggests that people can attribute gender to social robots depending on the task they perform.
Collapse
Affiliation(s)
- Gaye Aşkın
- Department of Psychology, Bilkent University, Ankara, Türkiye
| | - İmge Saltık
- Interdisciplinary Neuroscience Program, Bilkent University, Ankara, Türkiye
| | - Tuğçe Elver Boz
- Interior Architecture and Environmental Design, Bilkent University, Ankara, Türkiye
| | - Burcu A. Urgen
- Department of Psychology, Bilkent University, Ankara, Türkiye
- Interdisciplinary Neuroscience Program, Bilkent University, Ankara, Türkiye
- Aysel Sabuncu Brain Research Center, National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Türkiye
| |
Collapse
|
2
|
Fraune MR, Leite I, Karatas N, Amirova A, Legeleux A, Sandygulova A, Neerincx A, Dilip Tikas G, Gunes H, Mohan M, Abbasi NI, Shenoy S, Scassellati B, de Visser EJ, Komatsu T. Lessons Learned About Designing and Conducting Studies From HRI Experts. Front Robot AI 2022; 8:772141. [PMID: 35155588 PMCID: PMC8832512 DOI: 10.3389/frobt.2021.772141] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/18/2021] [Indexed: 01/04/2023] Open
Abstract
The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research.
Collapse
Affiliation(s)
- Marlena R. Fraune
- Intergroup Human-Robot Interaction (iHRI) Lab, Department of Psychology, New Mexico State University, Las Cruces, NM, United States
- *Correspondence: Marlena R. Fraune,
| | - Iolanda Leite
- Division of Robotics, Perception, and Learning (RPL), School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Nihan Karatas
- Human-Machine Interaction (HMI) and Human Characteristics Research Division, Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan
| | - Aida Amirova
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Amélie Legeleux
- Lab-STICC, University of South Brittany, CNRS UMR 6285, Brest, France
| | - Anara Sandygulova
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Anouk Neerincx
- Lab-STICC, University of South Brittany, CNRS UMR 6285, Brest, France
| | - Gaurav Dilip Tikas
- Strategy, Innovation and Entrepreneurship Area, Institute of Management Technology, Ghaziabad, India
| | - Hatice Gunes
- Affective Intelligence and Robotics Lab, Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Mayumi Mohan
- Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | - Nida Itrat Abbasi
- Affective Intelligence and Robotics Lab, Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Sudhir Shenoy
- Human-AI Technology Lab, Computer Engineering Program, University of Virginia, Charlottesville, VA, United States
| | - Brian Scassellati
- Social Robotics Lab, Department of Computer Science, Yale University, New Haven, CT, United States
| | - Ewart J. de Visser
- Warfighter Effectiveness Research Center, U.S. Air Force Academy, Colorado Springs, CO, United States
| | - Takanori Komatsu
- Department of Frontier Media Science, School of Interdisciplinary Mathematical Science, Meiji University, Tokyo, Japan
| |
Collapse
|
3
|
Zhexenova Z, Amirova A, Abdikarimova M, Kudaibergenov K, Baimakhan N, Tleubayev B, Asselborn T, Johal W, Dillenbourg P, CohenMiller A, Sandygulova A. A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context. Front Robot AI 2020; 7:99. [PMID: 33501266 PMCID: PMC7806116 DOI: 10.3389/frobt.2020.00099] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 06/17/2020] [Indexed: 11/20/2022] Open
Abstract
This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7-9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm-learning by teaching-the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches.
Collapse
Affiliation(s)
- Zhanel Zhexenova
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Aida Amirova
- Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Manshuk Abdikarimova
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Kuanysh Kudaibergenov
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Nurakhmet Baimakhan
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Bolat Tleubayev
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Thibault Asselborn
- CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Wafa Johal
- CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Pierre Dillenbourg
- CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Anna CohenMiller
- Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan
| | - Anara Sandygulova
- Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan
| |
Collapse
|
4
|
Henkel AP, Čaić M, Blaurock M, Okan M. Robotic transformative service research: deploying social robots for consumer well-being during COVID-19 and beyond. JOURNAL OF SERVICE MANAGEMENT 2020. [DOI: 10.1108/josm-05-2020-0145] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeBesides the direct physical health consequences, through social isolation COVID-19 affects a considerably larger share of consumers with deleterious effects for their psychological well-being. Two vulnerable consumer groups are particularly affected: older adults and children. The purpose of the underlying paper is to take a transformative research perspective on how social robots can be deployed for advancing the well-being of these vulnerable consumers and to spur robotic transformative service research (RTSR).Design/methodology/approachThis paper follows a conceptual approach that integrates findings from various domains: service research, social robotics, social psychology and medicine.FindingsTwo key findings advanced in this paper are (1) a typology of robotic transformative service (i.e. entertainer, social enabler, mentor and friend) as a function of consumers' state of social isolation, well-being focus and robot capabilities and (2) a future research agenda for RTSR.Practical implicationsThis paper guides service consumers and providers and robot developers in identifying and developing the most appropriate social robot type for advancing the well-being of vulnerable consumers in social isolation.Originality/valueThis study is the first to integrate social robotics and transformative service research by developing a typology of social robots as a guiding framework for assessing the status quo of transformative robotic service on the basis of which it advances a future research agenda for RTSR. It further complements the underdeveloped body of service research with a focus on eudaimonic consumer well-being.
Collapse
|
6
|
Ghazali AS, Ham J, Barakova EI, Markopoulos P. Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction. Front Robot AI 2018; 5:73. [PMID: 33500952 PMCID: PMC7805818 DOI: 10.3389/frobt.2018.00073] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 05/31/2018] [Indexed: 11/13/2022] Open
Abstract
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™, 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.
Collapse
Affiliation(s)
- Aimi S Ghazali
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands.,Department of Mechatronics Engineering, International Islamic University Malaysia, Selayang, Malaysia
| | - Jaap Ham
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Emilia I Barakova
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Panos Markopoulos
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|