PRE2023 3 Group4: Difference between revisions

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== Introduction to the course and project ==
=== Problem statement ===
Modern media is filled with images of highly sophisticated robots that speak, move and behave like humans would. The many movies, plays and books that are created speculate that these types of robots will be integrating into our daily lives in the near future. The idea of robots becoming increasingly more like humans is thus integrated into in our ideas. However, modern technology has not yet been able to catch up to this futuristic idea of what an artificial agent, like a robot, is able to do. This delay mainly comes from the lack of knowledge on how to replicate the behavior of humans in the hardware and programming of the artificial agents. One of the main areas that has been of growing interest is the implementation of emotions in robots and other artificial agents. Emotions of a human are not easy to replicate, as they consist of many different factors that make up the emotion. The research that will be presented in this wiki will also focus on emotions, but it will look at how these emotions have an effect on the acceptance of the robot. The question that will be answered is: <blockquote>“To what extent does a match between the displayed emotion of a social robot and the content of the robot’s spoken message influence the acceptance of this robot?"</blockquote>
=== Objectives ===
As a group, we outlined our objectives for our project. With our main objectives being contributing to knowledge about the role of emotions in social robot interactions and extending knowledge on the reliability of the acceptance measurement with the focus on young adults. As the Almere model is yet to be extensively tested on younger adults. In order to achieve these two main objectives, we have some smaller objectives that will guide us towards them. These concern conducting lab research and doing statistical and qualitative data analysis that are related to social and psychological research. Next to that, we are a multidisciplinary group, and are aiming towards working together in such a manner that every single group member is able to bring their own discipline to the table. And finally, properly programming and working with a robot is crucial to achieve our main objectives.  
=== Users ===
The users in this research are young adolescents. They have specific needs and require certain characteristics of the social robot in order to have a pleasant social interaction. In general, these users would like the robots to be authentic, imperfect, and to be active listeners. Active listening helps to build trust between the human and the robot. Also, by listening and showing that the robot understands the conversation and the emotional state of the person, the robot can adapt its interactions according to this, which will lead to a more personalized and meaningful interaction. The users require the robot to be easy to understand and it should have an intuitive interface. As already mentioned above a bit, the users like robots that can understand and respond to human emotions in order to have a meaningful interaction.


== Planning ==
== Planning ==
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== Literary review ==
=== Week 4 ===
{| class="wikitable"
|'''Name'''
|'''Total Hours'''
|'''Break-down'''
|-
|Danique Klomp
|11,5
|Tutor meeting (35min), group meeting (3h), review interview questions (1h), finding participants (0.5h), mail and contact (1h), reading and reviewing wiki (1.5h), group meeting (4h), lab preparations (1h)
|-
|Liandra Disse
|11
|Prepare and catch-up after missed meeting due to being sick (1,5h), find participants (0.5h), planning (1h), group meeting (4h), set-up final report and write introduction (4h)
|-
|Emma Pagen
|13
|Tutormeeting (35min), group meeting (3h), adding interview literature (2h), find participants (0,5h), group meeting (4h), reviewing interview questions (1h), going over introduction (1h), updating wiki (1h)
|-
|Isha Rakhan
|11,5
|Tutormeeting (35min), group meeting (3h), research and implement AI voices (2h), documentation choices Pepper behavior (2h), group meeting (4h)


=== State of the art   ===
|-
The use of social robots has increased rapidly over time. Social robots are being developed specifically for interacting with humans and other robots. They use artificial intelligence and are equipped with tools such as sensors, cameras and microphones. These tools enable the robot to interact with humans ​<ref name=":0">Biba, J. (2023, March 10). ''What is a social robot?'' Retrieved from Built In: <nowiki>https://www.nature.com/articles/s41598-020-66982-y#citeas</nowiki> </ref>. These robots come in all different kinds of shapes and sizes. For example, there are social robots such as Pepper, that look more humanlike, and there are robots such as PARO, which is seal-like ​<ref>Geva, N., Uzefovsky, F., & Levy-Tzedek, S. (2020, June 17). ''Touching the social robot PARO reduces pain perception and salivary oxytocin levels''. Retrieved from Scientific reports: <nowiki>https://www.nature.com/articles/s41598-020-66982-y#citeas</nowiki> </ref>. These types of robots are now mostly used in service settings ​<ref>Borghi, M., & Mariani, M. (2022, September). ''The role of emotions in the consumer meaning-making of interactions with social robots''. Retrieved from Science Direct: <nowiki>https://www.sciencedirect.com/science/article/pii/S0040162522003687</nowiki> </ref>.  
|Margit de Ruiter
|11
|Tutormeeting (35min), group meeting (1h), find participants (0.5h), testing interview questions (1h), group meeting (4h), start writing methods (4h)  


As human-robot interactions are growing more important, more research is being done around these interactions, such as acceptance, trust, responsibility and anthropomorphism. This can be done by investigating different properties of the robots, such as voice, appearance, and facial expressions. These properties can be programmed in the robot in such a way that people can recognize certain emotions in the robot ​<ref name=":1">Chuah, S. H. W., & Yu, J. (2021). The future of service: The power of emotion in human-robot interaction. ''Journal of Retailing and Consumer Services'', ''61'', 102551. <nowiki>https://doi.org/10.1016/J.JRETCONSER.2021.102551</nowiki> </ref>​. For example, a study has been done with the robot Sophia, who is developed to eventually work as a service robot in for example healthcare and education. She was given different emotional expressions, and pictures of Sophia were posted on Instagram. The comments on these posts were then analyzed to examine people’s responses to emotions on robots ​<ref name=":1" />​. This is only one example of many more similar studies.  
|}
 
While research is still being done on human reactions to social robots, many of these robots are already being used in real world settings. They are mainly used as companions and support tools for children, but they are also used for providing services such as cleaning ​<ref name=":0" />. Two examples of social robots that are applied in the real world will be given. The first is the robot Misty. This robot is capable of many different facial expressions and can move its arms and head. Moreover, it has face and speech recognition to remember people and recognize intents ​<ref name=":0" /> <ref>Misty Robotics. (n.d.). ''Misty Robotics''. Retrieved from Misty Robotics: <nowiki>https://www.mistyrobotics.com/</nowiki> </ref> . Another example is the robot Pepper. This robot has a more humanoid appearance than Misty and is more advanced in its movements. Pepper is also able of perceiving human emotions and adapting its behavior appropriately. The robot is mostly used in companies and schools, such as Palomar College, where the robot is used to highlight and promote programs and services at the college. The students are able to ask it questions, such as “How do I get to my class?” ​<ref>Becerra, T. (2017, October 3). ''Palomar College welcomes Pepper the robot''. Retrieved from The Telescope: <nowiki>https://www.palomar.edu/telescope/2017/10/03/palomar-robot-pepper-debut/</nowiki> </ref>​. 
 
=== How do students interact with robots? ===
Students are an important user group for robots, since robots can be helpful educational tools. They could help students to grasp difficult concepts. Especially they can be useful in providing language, science of technology education. A robot could take on the role of a peer, a tool or a tutor in the learning activity <ref>Mubin, O., Stevens, C. J., Shahid, S., Mahmud, A. A., & Dong, J. (2013). A REVIEW OF THE APPLICABILITY OF ROBOTS IN EDUCATION. ''Technology for Education and Learning'', ''1''(1). <nowiki>https://doi.org/10.2316/journal.209.2013.1.209-0015</nowiki> </ref>. Also, students can learn a lot from interacting with robots. Building teamwork and improving communication skills are just some examples of the multiple benefits of using robotics in education <ref>Center for Innovation and Learning. (2023, November 21). ''Explore the seven benefits of robotics in education for students''. Center for Innovation and Education. <nowiki>https://cie.spacefoundation.org/7-benefits-of-robotics-for-students/</nowiki> </ref>.  However, the implicit and multi-faceted impacts that this might bring into educational environments as a whole should be considered <ref>Shin, N., & Kim, S. (2007). Learning about, from, and with Robots: Students’ Perspectives. ''IEEE Xplore''. <nowiki>https://doi.org/10.1109/roman.2007.4415235</nowiki> </ref>. Another important aspect to stress is that exposure to robots at a relative young age prepares students for the future, it is likely that they will encounter robots in multiple industries. By familiarizing themselves with robots in an early spectrum, they will gain knowledge and benefit from this in their future careers.  
 
Students are an important target group for robots, because they represent future workforce and innovations. Understanding the needs of students is therefore important, since it can help developers design the robots so that they are engaging, user-friendly and educational. Teens have a desire for robots to be authentic, imperfect, and active listeners <ref name=":8">Björling, E. A., Thomas, K. A., Rose, E., & Çakmak, M. (2020). Exploring teens as robot operators, users and witnesses in the wild. ''Frontiers in Robotics and AI'', ''7''. <nowiki>https://doi.org/10.3389/frobt.2020.00005</nowiki> </ref>.  
 
In former research, a field study was conducted with qualitative interviews. The results showed a positive perception of the robot-supported learning environment, indicating a positive impact on the learning outcomes. Most students showed an additional value in the presence of the robot compared to traditional onscreen scenario or self-study and the robot increased their motivation, concentration and attention <ref>Donnermann, M., Schäper, P., & Lugrin, B. (2020). Integrating a Social Robot in Higher Education – A Field Study. ''IEEE Xplore''. <nowiki>https://doi.org/10.1109/ro-man47096.2020.9223602</nowiki> </ref>.  
 
Students also prefer robots that are easy to use and understand, it needs an intuitive interface and clear instructions. Apart from this, students like robots that can perform a wide range of activities and tasks and also, providing challenges and opportunities over time. They also like robots to be adaptable. Also, robots that can interact socially are interesting to students. They like robots that can understand and respond to human emotions, speech, gestures in order to have meaningful interactions and relationships.
 
===  The importance of social robots being able to display emotions ===
In many sectors, such as healthcare and education, social robots must be able to communicate with people in ways that are natural and easily understood. In order to make this human-robot interaction (HRI) feel natural and enjoyable for humans, robots must make use of human social norms <ref name=":2">Kirby, R., Forlizzi, J., & Simmons, R. (2010). Affective social robots. ''Robotics and Autonomous Systems'', ''58''(3), 322–332. <nowiki>https://doi.org/10.1016/J.ROBOT.2009.09.015</nowiki> </ref>. This requirement originates from humans anthropomorphizing robots, meaning that we attribute human characteristics to robots and engage and form relationships with them as if they are human <ref name=":2" /><ref name=":3">Breazeal, C. (2004). Designing Sociable Robots. ''Designing Sociable Robots''. <nowiki>https://doi.org/10.7551/MITPRESS/2376.001.0001</nowiki>
</ref>. We use this to make the robot’s behavior familiar, understandable and predictable to us, and infer the robot’s mental state. However, for this to be a correct as well as intuitive inference, the robot’s behavior must be aligned with our social expectations and interpretations for mental states <ref name=":3" />.
 
One very important integrated element in human communication is the use of nonverbal expressions of emotions, such as facial expressions, gaze, body posture, gestures, and actions <ref name=":2" /><ref name=":3" />. In human-to-human interaction as well as human-robot interaction, these nonverbal cues support and add meaning to verbal communication, and expressions of emotions specifically help build deeper and more meaningful relations, facilitate engagement and co-create experiences <ref name=":1" />. Besides adding conversational content, it is also shown that humans can unconsciously mimic the emotional expression of the conversational partner, known as emotional contagion, which helps to emphasize with others by simulating their feelings <ref name=":1" /><ref name=":2" />. Due to our tendency to anthropomorphize robots, it is possible that emotional contagion also occurs during HRI and can facilitate making users feel positive affect while interacting with a social robot <ref name=":1" />.
 
Artificial emotions can be used in social robots to facilitate believable HRI, but also provide feedback to the user about the robot’s internal state, goals and intentions <ref name=":4">Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. ''Robotics and Autonomous Systems'', ''42''(3–4), 143–166. <nowiki>https://doi.org/10.1016/S0921-8890(02)00372-X</nowiki> </ref>. Moreover, they can act as a control system through which we learn what drives the robots behavior and how he is affected by and adapts due to different factors over time <ref name=":4" />. Finally, the ability of social robots to display emotions is crucial in forming long-term social relationships, which is what people will naturally seek due to the anthropomorphic nature of social robots <ref name=":2" />.
 
===  Measurements in HCI research ===
In the past HCI (Human Computer Interaction) and HTI (Human Technology Interaction) research focused on improving the technological aspects of the interaction, but in more recent years increased interest has been developed into the aspects of user experience. User experience is still a broad area of research, yet in social robotics it has become increasingly relevant. User experience has many distinct aspects that are all a part of the overall experience, yet the basis of user experiences lies in the comfortable interaction with an agent. Making the interaction comfortable and likeable will create a sense of trust and eventually acceptance of the agent.  
 
The three factors mentioned above are all connected to each other. Specifically, trust and acceptance are linked. A paper by Wagner et al <ref>Wagner Ladeira, M. G. P., & Santini, F. (2023). Acceptance of service robots: a meta-analysis in the hospitality and tourism industry. ''Journal of Hospitality Marketing \& Management'', ''32''(6), 694–716. <nowiki>https://doi.org/10.1080/19368623.2023.2202168</nowiki> </ref> on a meta-analysis of acceptance in service robots described trust as a mediating factor between informational cues and acceptance of the service agent. However, there are also many contextual factors that play a role in this relationship. For example, acceptance and trust in agents seems to be smaller when the user is in a group than when the user uses the robot individually <ref>Martinez, J. E., VanLeeuwen, D., Stringam, B. B., & Fraune, M. R. (2023). Hey? ! What did you think about that Robot? Groups Polarize Users’ Acceptance and Trust of Food Delivery Robots. ''Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction'', 417–427. <nowiki>https://doi.org/10.1145/3568162.3576984</nowiki> </ref>.
 
To find these relationships and correlations between the many varied factors influencing the overall user experience, there need to be reliable measures that can be used to measure the presence, extend and underlying principles of them. Yet, one of the main challenges when it comes to HCI research is creating a reliable measurement. This challenge is present in most HCI domains, for example speech interfaces <ref>Clark, L., Doyle, P., Garaialde, D., Gilmartin, E., Schlögl, S., Edlund, J., Aylett, M., Cabral, J., Munteanu, C., Edwards, J., & R Cowan, B. (2019). The State of Speech in HCI: Trends, Themes and Challenges. ''Interacting with Computers'', ''31''(4), 349–371. <nowiki>https://doi.org/10.1093/iwc/iwz016</nowiki> </ref>, user engagement <ref>Doherty, K., & Doherty, G. (2018). Engagement in HCI: Conception, Theory and Measurement. ''ACM Comput. Surv.'', ''51''(5). <nowiki>https://doi.org/10.1145/3234149</nowiki> </ref> and online trust <ref>Kim, Y., & Peterson, R. A. (2017). A Meta-analysis of Online Trust Relationships in E-commerce. ''Journal of Interactive Marketing'', ''38''(1), 44–54. <nowiki>https://doi.org/10.1016/j.intmar.2017.01.001</nowiki> </ref>. The main reason for the lack of reliable and valid measures in HCI research is that these measures are only needed in the user experience research, which is new, as stated before.  
 
Still there are several attempts to create reliable measures for artificial agent acceptance and trust. First of all, one of the more well-known measures of acceptance is the Almere model that was proposed by Marcel Heerink et al. <ref name=":5">Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model. ''International Journal of Social Robotics'', ''2''(4), 361–375. <nowiki>https://doi.org/10.1007/s12369-010-0068-5</nowiki></ref>. This measure consists of a questionnaire that covers twelve basic principles that range from induced anxiety to enjoyment and trust. The questionnaire itself consists of 41 questions. This model has an acceptable Cronbach’s alpha score of ~0.7 when it is used in the older adult and elderly population <ref name=":5" /><ref>Heerink, M. (2011). Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults. ''Proceedings of the 6th International Conference on Human-Robot Interaction'', 147–148. <nowiki>https://doi.org/10.1145/1957656.1957704</nowiki> </ref>. However, when the measure is used for young adults, the reliability stays around the same <ref>Guner, H., & Acarturk, C. (2020). The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. ''Universal Access in the Information Society'', ''19''(2), 311–330. <nowiki>https://doi.org/10.1007/s10209-018-0642-4</nowiki> </ref>. Although this is the measurement that was also proposed in the article that inspired this research, there are still many more measurements of acceptability and attitude towards robots <ref>Krägeloh, C. U., Bharatharaj, J., Sasthan Kutty, S. K., Nirmala, P. R., & Huang, L. (2019). Questionnaires to Measure Acceptability of Social Robots: A Critical Review. ''Robotics'', ''8''(4). <nowiki>https://doi.org/10.3390/robotics8040088</nowiki> </ref>.  
 
Measuring trust is harder as there is not one proposed overall method to measure trust, but this does not mean it cannot be measured. In a literary review of several papers measuring trust it was found that questionnaires are one of the most used methods to measure trust <ref>Bach, T. A., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2022). A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective. ''International Journal of Human–Computer Interaction'', 1–16. <nowiki>https://doi.org/10.1080/10447318.2022.2138826</nowiki> </ref>. Sadly, the questionnaires used are not one standard set of questions, but a variety. This creates the added problem that studies are hard to compare to each other. As stated before, trust is connected to acceptance. In the Almere model discussed in the previous paragraph, trust is included as a basic factor. It, however, it is only measured using 2 statements “I would trust the robot if it gave me advice” and “I would follow the advice the robot gives me”. When trust is being measured on its own, it will need to be extended. Luckily there have been some measures proposed. One of these measurements comes from a review of several measurements of trust that were combined by Madsen and Gregor <ref>Madsen, M., & Gregor, S. D. (2000). ''Measuring Human-Computer Trust''. <nowiki>https://api.semanticscholar.org/CorpusID:18821611</nowiki> </ref>. In the same paper they proposed a questionnaire that consists of 5 distinct factors that each have 5 questions related to them. The overall Cronbach’s alpha of this measurement was found to be 0.85, which is a good score.  
 
The constructs measured above are not the only constructs that can be measured in Human-Computer interactions, but they are still the most prevalent in recent HCI and HTI research. Given the circumstances, they do give a great overview of the overall concepts in HCI research, as they contain most of the different basic principles in their foundation. This means that these measures are a great starting point for more in-depth research. 
 
===  How to display emotions as a robot ===
A robot can display emotions when it combines body, facial and vocal expressions.  
 
The way such emotional reaction is expressed highly depends on the robot’s degree of anthropomorphism. For robots with a simple appearance, it may be sufficient to express emotions by means of e.g. lights or sounds. However, as the degree of anthropomorphism increases, it turns necessary to match the robot's behavior with the appearance to avoid falling into the uncanny valley <ref name=":6">Marcos-Pablos, S., & García‐Peñalvo, F. J. (2021). Emotional Intelligence in Robotics: A Scoping review. In Advances in intelligent systems and computing (pp. 66–75). <nowiki>https://doi.org/10.1007/978-3-030-87687-6_7</nowiki>  </ref>.
 
The idea behind the uncanny valley proposes that as robots keep approaching a more human-like appearance, people can experience a feeling of uneasiness / disturbance <ref>Wang, S., Lilienfeld, S. O., & Rochat, P. (2015). The Uncanny Valley: Existence and Explanations. Review Of General Psychology, 19(4), 393–407. <nowiki>https://doi.org/10.1037/gpr0000056</nowiki>  </ref>. These experiences also occur as the robot’s user perceives a mismatch between the robot’s appearance and behavior.  There are also differences in the way that the uncanny valley is perceived across different ages and cultures. As Eastern countries and children are less likely to be disturbed by this phenomenon <ref name=":6" />.
 
Developers of humanoid robots found that next to body posture, hands also play a role in conveying emotions, as human hands can contribute to the human ability of emotional expression. These developers then created the emotion expression humanoid robot WE-4RII, with the integration of robot hands. This humanoid robot was eventually able to express emotion using facial expression, arms, hands, waist and neck motion. They also concluded that motion velocity is equally as important as body posture. “WE-4RII quickly moves its body for surprise emotional expression, but it slowly moves its body for sadness emotional expression.” <ref>Effective emotional expressions with expression humanoid robot WE-4RII: integration of humanoid robot hand RCH-1. (2004). IEEE Conference Publication | IEEE Xplore. <nowiki>https://ieeexplore.ieee.org/abstract/document/1389736?casa_token=LP_352U3xbQAAAAA:Yugjlzs5aZ-KEfzz2UxVjNIZDKTyRkeEXNjyImWL_TXrR1NHVd75pi6-ZKfHd3Zd10c5xykvxQ</nowiki>  </ref>
 
Next to that, vocal prosody also contributes to the quality of the emotion that is being displayed. In human-to-human interaction, patterns of pitch, rhythm, intonation, timing, and loudness contribute to our emotional expression. A sudden change in volume or pitch could emphasize excitement or emphasis. Or when the pitch rises at the end of a sentence, it will be more clear that the robot is asking a question, this could indicate confusion and / or curiosity <ref name=":7">Crumpton, J., & Bethel, C. L. (2015). A Survey of Using Vocal Prosody to Convey Emotion in Robot Speech. International Journal Of Social Robotics, 8(2), 271–285. <nowiki>https://doi.org/10.1007/s12369-015-0329-4</nowiki>  </ref>.
 
Studies have shown that humans will interpret both linguistic and non-linguistic emotion displaying sounds in an emotional way. But there is a preference towards the linguistic type of robot, as research has shown that people prefer human-like voices. In the example of a virtual car passenger, the driver appeared to be more attentive and less involved in accidents, as the virtual passenger’s speech matched the driver’s emotion. So it is not only beneficial to sound like a human being, but also the capability of matching the user’s emotions contributes to the emotion displaying quality of the robot <ref name=":7" />.


=== Pepper ===
=== Pepper ===
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Pepper can also use several gestures while responding to a human, like waving and nodding. It has 12 hours of battery life, and it can return to its charging station if necessary. It is 1.2 meter tall, has 3 omnidirectional wheels in order to move smoothly and 17 joints for body language <ref name=":9" />. Pepper is designed to make it appropriate and acceptable in daily life usage for interacting with human beings. Some design principles behind Pepper are; a pleasant appearance, safety, affordability, interactivity and good autonomy. The aim was to make it not too exact a human likeness robot, since the designers wanted to avoid the ‘uncanny valley’ <ref name=":9" />.   
Pepper can also use several gestures while responding to a human, like waving and nodding. It has 12 hours of battery life, and it can return to its charging station if necessary. It is 1.2 meter tall, has 3 omnidirectional wheels in order to move smoothly and 17 joints for body language <ref name=":9" />. Pepper is designed to make it appropriate and acceptable in daily life usage for interacting with human beings. Some design principles behind Pepper are; a pleasant appearance, safety, affordability, interactivity and good autonomy. The aim was to make it not too exact a human likeness robot, since the designers wanted to avoid the ‘uncanny valley’ <ref name=":9" />.   
== Introduction ==
The use of social robots, specifically designed for interacting with humans and other robots, has been rising for the past several years. These types of robots differ from the robots we have been getting used to over the past decades which often only perform on specific and dedicated tasks. Social robots are now mostly used in services settings, as companions and support tools<ref name=":0">Biba, J. (2023, March 10). ''What is a social robot?'' Retrieved from Built In: <nowiki>https://www.nature.com/articles/s41598-020-66982-y#citeas</nowiki> </ref><ref>Borghi, M., & Mariani, M. (2022, September). ''The role of emotions in the consumer meaning-making of interactions with social robots''. Retrieved from Science Direct: <nowiki>https://www.sciencedirect.com/science/article/pii/S0040162522003687</nowiki> </ref>;. In many promising sectors of application, such as healthcare and education, social robots must be able to communicate with people in ways that are natural and easily understood. To make this human-robot interaction (HRI) feel natural and enjoyable for humans, robots must make use of human social norms<ref name=":2">Kirby, R., Forlizzi, J., & Simmons, R. (2010). Affective social robots. ''Robotics and Autonomous Systems'', ''58''(3), 322–332. <nowiki>https://doi.org/10.1016/J.ROBOT.2009.09.015</nowiki> </ref>. This requirement originates from humans anthropomorphizing robots, meaning that we attribute human characteristics to robots and engage and form relationships with them as if they are human. We use this to make the robot’s behavior familiar, understandable and predictable to us, and infer the robot’s mental state. However, for this to be a correct as well as intuitive inference, the robot’s behavior must be aligned with our social expectations and interpretations for mental states <ref name=":3">Breazeal, C. (2004). Designing Sociable Robots. ''Designing Sociable Robots''. <nowiki>https://doi.org/10.7551/MITPRESS/2376.001.0001</nowiki>
</ref>.  
One very important integrated element in human communication is the use of nonverbal expressions of emotions, such as facial expressions, gaze, body posture, gestures, and actions<ref name=":2" /><ref name=":3" />. In human-to-human interaction as well as human-robot interaction, these nonverbal cues support and add meaning to verbal communication, and expressions of emotions specifically help build deeper and more meaningful relations, facilitate engagement and co-create experiences<ref name=":1">Chuah, S. H. W., & Yu, J. (2021). The future of service: The power of emotion in human-robot interaction. ''Journal of Retailing and Consumer Services'', ''61'', 102551. <nowiki>https://doi.org/10.1016/J.JRETCONSER.2021.102551</nowiki> </ref>. Besides adding conversational content, it is also shown that humans can unconsciously mimic the emotional expression of the conversational partner, known as emotional contagion, which helps to emphasize with others by simulating their feelings<ref name=":1" /><ref name=":2" />. Due to our tendency to anthropomorphize robots, it is possible that emotional contagion also occurs during HRI and can facilitate making users feel positive affect while interacting with a social robot<ref name=":1" />. Artificial emotions can be used in social robots to facilitate believable HRI, but also provide feedback to the user about the robot’s internal state, goals and intentions<ref name=":4">Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. ''Robotics and Autonomous Systems'', ''42''(3–4), 143–166. <nowiki>https://doi.org/10.1016/S0921-8890(02)00372-X</nowiki> </ref>. Moreover, they can act as a control system through which we learn what drives the robots’ behavior and how it is affected by and adapts due to different factors over time<ref name=":4" />. Finally, the ability of social robots to display emotions is crucial in forming long-term social relationships, which is what people will naturally seek due to the anthropomorphic nature of social robots<ref name=":2" />.
Altogether, the important role of emotions in human-robot interaction requires us to gather information about how robots can and should display emotions for them to be naturally recognized as the intended emotion by humans. A robot can display emotions when it combines body posture, motion velocity, facial expressions and vocal signs (e.g. prosody, pitch, loudness), highly depending on the possibilities considering the robot’s morphology and degree of anthropomorphism<ref name=":6">Marcos-Pablos, S., & García‐Peñalvo, F. J. (2021). Emotional Intelligence in Robotics: A Scoping review. In Advances in intelligent systems and computing (pp. 66–75). <nowiki>https://doi.org/10.1007/978-3-030-87687-6_7</nowiki>  </ref><ref name=":13">Miwa, H., Itoh, K., Matsumoto, M., Zecca, M., Takariobu, H., Roccella, S., Carrozza, M. C., Dario, P., & Takanishi, A. (n.d.). Effective emotional expressions with emotion expression humanoid robot WE-4RII. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 3, 2203–2208. <nowiki>https://doi.org/10.1109/IROS.2004.1389736</nowiki>   </ref><ref name=":7">Crumpton, J., & Bethel, C. L. (2015). A Survey of Using Vocal Prosody to Convey Emotion in Robot Speech. International Journal Of Social Robotics, 8(2), 271–285. <nowiki>https://doi.org/10.1007/s12369-015-0329-4</nowiki>  </ref>. Social robots are often more humanoid, increasing anthropomorphism, and therefore requiring to match the robot's behavior with the appearance to avoid falling into the uncanny valley which elicits a feeling of uneasiness or disturbance<ref name=":6" /><ref>Wang, S., Lilienfeld, S. O., & Rochat, P. (2015). The Uncanny Valley: Existence and Explanations. Review Of General Psychology, 19(4), 393–407. <nowiki>https://doi.org/10.1037/gpr0000056</nowiki>  </ref>. Some research has already been done on testing the capability of certain social robots to display emotions and resulted in robot-specific guidelines on how to program displaying certain emotions<ref name=":13" /><ref>Johnson, D. O., Cuijpers, R. H., & van der Pol, D. (2013). Imitating Human Emotions with Artificial Facial Expressions. International Journal of Social Robotics, 5(4), 503–513. <nowiki>https://doi.org/10.1007/S12369-013-0211-1/TABLES/8</nowiki> </ref><ref name=":15">Bishop, L., Van Maris, A., Dogramadzi, S., & Zook, N. (2019). Social robots: The influence of human and robot characteristics on acceptance. Paladyn, 10(1), 346–358. <nowiki>https://doi.org/10.1515/pjbr-2019-0028</nowiki> </ref>.
Based on these established guidelines for displaying emotions, we can look further into how humans are affected by the robot’s emotional cues during interaction with a robot. We will research this in a context where we would also expect a human to display emotions, namely during telling an emotional story. Our research takes inspiration from the study of Van Ottendijk et al<ref>Van Otterdijk, M., & Barakova, E. I., Torresen, J. & Neggers, M. E. M. (2021). Preferences of Seniors for Robots Delivering a Message With Congruent Approaching Behavior. 10.1109/ARSO51874.2021.9542833. </ref> and Bishop et al<ref name=":15" /> in which the robot Pepper was used to deliver either a positive or negative message accompanied by congruent or incongruent emotional behavior. We will extend on these studies by taking a different combination of context for application and research method: interaction with students as researching application in an educational setting rather than healthcare, and using interviews to gain a deep understanding rather than surveys. Moreover, students are an important target group for robots, because they represent future workforce and innovations. Understanding their needs can help developers design the robots so that they are engaging, user-friendly and educational<ref>Manzi, F., Sorgente, A., Massaro, D., Villani, D., Di Lernia, D., Malighetti, C., Gaggioli, A., Rossignoli, D., Sandini, G., Sciutti, A., Rea, F., Maggioni, M. A., Marchetti, A., & Riva, G. (2021). Emerging Adults’ Expectations about the Next Generation of Robots: Exploring Robotic Needs through a Latent Profile Analysis. Cyberpsychology, Behavior, and Social Networking, 24(5), 315–323. <nowiki>https://doi.org/10.1089/CYBER.2020.0161</nowiki> </ref>.
More specifically, the research question that will be studied in this paper is “To what extent does a match between the displayed emotion of a social robot and the content of the robot’s spoken message influence the acceptance of this robot?”. We expect that participants will prefer interacting with the robot while displaying the emotion that fits with the content of its message and to be open to more future interactions like this with the robot. On the other hand, we expect that a mismatch between the emotion displayed by robot and the story it is telling will make participants feel less comfortable and therefore less accepting of the robot. Moreover, we expect that the influence of congruent emotion displaying will be more prominent with a negative than a positive message. The main focus of this research is thus on how accepting the students are of the robot after interacting with it, but also gaining insights into potential underlying reasons, such as the amount of trust the students have in the robot and how comfortable they feel when interacting with them. The results could be used to provide insights into the importance of congruent emotion displaying and whether robots could be used on university campuses as assistant robots.


== Survey and interview questions ==
== Survey and interview questions ==

Revision as of 19:09, 9 March 2024

This study was approved by the ERB on Sunday 03/03/2024 (number ERB2024IEIS22).

Below a few links are listed to important documents that were used for this study:

  • ERB form [1]
  • Research proposal [2]
  • Consent form [3]
  • Research protocol [4]

Group members

Name Student Number Current Study program Role or responsibility
Margit de Ruiter 1627805 BPT Note-taker
Danique Klomp 1575740 BPT Contact person
Emma Pagen 1889907 BAP End responsible Wiki update
Liandra Disse 1529641 BPT Planner
Isha Rakhan 1653997 BPT Programming responsible

Planning

Each week, there will be a mentor meeting on Monday morning followed by a group meeting. Another group meeting will be held on Thursday afternoon and by Sunday afternoon the wiki will be updated for work done that week (weekly deliverable).

Week 1

  • Introduction to the course and team
  • Brainstorm to come up with ideas for the project and select one (inform course coordinator)
  • Conduct literature review
  • Specify problem statement, user group and requirements, objectives, approach, milestones, deliverables and planning for the project

Week 2

  • Get confirmation for using a robot lab, and which robot  
  • Ask/get approval for conducting this study
  • Create research proposal (methods section of research paper)
  • If approval is already given, start creating survey, programming the robot or creating video of robot

Week 3

  • If needed, discuss final study specifics, including planning the session for conducting the study
  • If possible, finalize creating survey, programming the robot or creating video of robot
  • Make consent form
  • Start finding and informing participants

Week 4

  • Final arrangements for study set-up (milestone 1)
  • Try to start with conducting the study  

Week 5

  • Finish conducting the study (milestone 2)

Week 6

  • Conduct data analysis
  • Finalize methods section, such as including participant demographics and incorporate feedback
  • If possible, start writing results, discussion and conclusion sections

Week 7

  • Finalize writing results, discussion and conclusion sections and incorporate feedback, all required research paper sections are written (milestone 3)
  • Prepare final presentation

Week 8

  • Give final presentation (milestone 4)
  • Finalize wiki (final deliverable)
  • Fill in peer review form (final deliverable)

Individual effort per week

Week 1

Name Total Hours Break-down
Danique Klomp 13.5 Intro lecture (2h), Group meeting (2h), Group meeting (2h), Literary search (4h), Writing summary LS (2h), Writing problem statement first draft (1,5h)
Liandra Disse 13.5 Intro lecture (2h), group meeting (2h), Searching and reading literature (4h), writing summary (2h), group meeting (2h), updating project and meeting planning (1,5h)
Emma Pagen 12 Intro lecture (2h), group meeting (2h), literary search (4h), writing a summary of the literature (2h), writing the approach for the project (1h), updating the wiki (1h)
Isha Rakhan 11 Intro lecture (2h), group meeting (2h), group meeting (2h), Collecting Literature and summarizing (5h)
Margit de Ruiter 13 Intro lecture (2h), group meeting (2h), literature research (4h), writing summary literature (3h) group meeting (2h)

Week 2

Name Total Hours Break-down
Danique Klomp 16,5 Tutormeeting (35min), groupmeeting 1(2.5h),  groupmeeting 2 (3h), send/respond to mail (1h), literature interview protocols and summarize (3h), literature on interview questions (6.5h),  
Liandra Disse 12 Tutormeeting (35min), groupmeeting (3h), write research proposal (3h), groupmeeting (3h), finalize research proposal and create consent form (2,5h)
Emma Pagen 11,5 Tutormeeting (35min), groupmeeting (3h), write research proposal (2h), groupmeeting (3h), finalize research proposal and create consent form (1,5h), updating wiki (1,5h)
Isha Rakhan 10 Research on programming (7h), groupmeeting (3h)
Margit de Ruiter 11,5 Tutormeeting (35min), groupmeeting (3h), read literature Pepper and summarize (3h), groupmeeting (3h), research comfort question interview (2h)

Week 3

Name Total Hours Break-down
Danique Klomp 14 Tutormeeting (35min), groupmeeting 1(3h), meeting Task (3h), preparation Thematic analysis & protocol (2h), mail and contact (1,5h), meeting Zoe (1h), group meeting (3h)
Liandra Disse 12 Tutormeeting (35min), groupmeeting 1(3h), meeting Task (3h), update (meeting) planning (1h), prepare meeting (1h),  group meeting (3h), find participant (30min)
Emma Pagen 12 Tutormeeting (35min), groupmeeting 1(3h), finish ERB form (1h), create lime survey (1,5h), make an overview of the content sections of final wiki page (1h), group meeting 2 (3h), updating the wiki (2h)
Isha Rakhan 12,5 Tutormeeting (35min), groupmeeting 1(3h), meeting zoe (1h), group meeting (3h), programming (5h)
Margit de Ruiter *was not present this week, but told the group in advance and had a good reason*

Week 4

Name Total Hours Break-down
Danique Klomp 11,5 Tutor meeting (35min), group meeting (3h), review interview questions (1h), finding participants (0.5h), mail and contact (1h), reading and reviewing wiki (1.5h), group meeting (4h), lab preparations (1h)
Liandra Disse 11 Prepare and catch-up after missed meeting due to being sick (1,5h), find participants (0.5h), planning (1h), group meeting (4h), set-up final report and write introduction (4h)
Emma Pagen 13 Tutormeeting (35min), group meeting (3h), adding interview literature (2h), find participants (0,5h), group meeting (4h), reviewing interview questions (1h), going over introduction (1h), updating wiki (1h)
Isha Rakhan 11,5 Tutormeeting (35min), group meeting (3h), research and implement AI voices (2h), documentation choices Pepper behavior (2h), group meeting (4h)
Margit de Ruiter 11 Tutormeeting (35min), group meeting (1h), find participants (0.5h), testing interview questions (1h), group meeting (4h), start writing methods (4h)

Pepper

Pepper, the Humanoid and Programmable Robot[1]

Currently, Pepper is deployed in thousands of homes and schools. However, Pepper was initially designed for an application of business-to-business. It was launched in June 2014. Then, Pepper became of interest all over the world for multiple other applications. For example, in business-to-consumer, business-to-academics and business-to-developers fields. So, in the end, it was adapted for business-to-consumer purposes [2].  

Pepper is capable of exhibiting body language, perceiving and interacting with its environment and it is able to move itself around. The robot can also analyse other people's expressions and their voice tones, using emotion and voice recognition algorithms in order to create interaction. It is equipped with high-level interfaces and features for multimodal communication with humans surrounding Pepper [2].

Pepper has a lot of capabilities, among which mapping and navigation, object detection, hearing, speech, and face detection [3]. Pepper is a humanoid robot, meaning it is designed to have a physical human appearance. It's sound and speech recognition capabilities yield good results, even with several accents. However, it's built-in navigation system is unreliable, which makes it hard to get to destinations accurately. Sometimes, object and face detection of Pepper gives inconsistent results. So, Pepper can be improved in those fields [3].

Pepper uses facial recognition to pick up emotions on human faces, like sadness or hostility and it uses voice recognition to hear concern. It has age tools like age detection and basic emotions embedded intro its framework [3]. It bases the recognition mostly on eye contact, the central part of the face and distance. It can not only detect emotions, but also knows how to respond and react to them appropriately. For example, it will detect sadness based on a person’s expression and voice tone and by using sensors that are built-in and pre-programmed algorithms, the robot will react properly [1]. Several applications of this robot are answering questions, greeting guests and playing with kids in Japanese homes [4].  

Pepper can also use several gestures while responding to a human, like waving and nodding. It has 12 hours of battery life, and it can return to its charging station if necessary. It is 1.2 meter tall, has 3 omnidirectional wheels in order to move smoothly and 17 joints for body language [2]. Pepper is designed to make it appropriate and acceptable in daily life usage for interacting with human beings. Some design principles behind Pepper are; a pleasant appearance, safety, affordability, interactivity and good autonomy. The aim was to make it not too exact a human likeness robot, since the designers wanted to avoid the ‘uncanny valley’ [2].

Introduction

The use of social robots, specifically designed for interacting with humans and other robots, has been rising for the past several years. These types of robots differ from the robots we have been getting used to over the past decades which often only perform on specific and dedicated tasks. Social robots are now mostly used in services settings, as companions and support tools[5][6];. In many promising sectors of application, such as healthcare and education, social robots must be able to communicate with people in ways that are natural and easily understood. To make this human-robot interaction (HRI) feel natural and enjoyable for humans, robots must make use of human social norms[7]. This requirement originates from humans anthropomorphizing robots, meaning that we attribute human characteristics to robots and engage and form relationships with them as if they are human. We use this to make the robot’s behavior familiar, understandable and predictable to us, and infer the robot’s mental state. However, for this to be a correct as well as intuitive inference, the robot’s behavior must be aligned with our social expectations and interpretations for mental states [8].  

One very important integrated element in human communication is the use of nonverbal expressions of emotions, such as facial expressions, gaze, body posture, gestures, and actions[7][8]. In human-to-human interaction as well as human-robot interaction, these nonverbal cues support and add meaning to verbal communication, and expressions of emotions specifically help build deeper and more meaningful relations, facilitate engagement and co-create experiences[9]. Besides adding conversational content, it is also shown that humans can unconsciously mimic the emotional expression of the conversational partner, known as emotional contagion, which helps to emphasize with others by simulating their feelings[9][7]. Due to our tendency to anthropomorphize robots, it is possible that emotional contagion also occurs during HRI and can facilitate making users feel positive affect while interacting with a social robot[9]. Artificial emotions can be used in social robots to facilitate believable HRI, but also provide feedback to the user about the robot’s internal state, goals and intentions[10]. Moreover, they can act as a control system through which we learn what drives the robots’ behavior and how it is affected by and adapts due to different factors over time[10]. Finally, the ability of social robots to display emotions is crucial in forming long-term social relationships, which is what people will naturally seek due to the anthropomorphic nature of social robots[7].

Altogether, the important role of emotions in human-robot interaction requires us to gather information about how robots can and should display emotions for them to be naturally recognized as the intended emotion by humans. A robot can display emotions when it combines body posture, motion velocity, facial expressions and vocal signs (e.g. prosody, pitch, loudness), highly depending on the possibilities considering the robot’s morphology and degree of anthropomorphism[11][12][13]. Social robots are often more humanoid, increasing anthropomorphism, and therefore requiring to match the robot's behavior with the appearance to avoid falling into the uncanny valley which elicits a feeling of uneasiness or disturbance[11][14]. Some research has already been done on testing the capability of certain social robots to display emotions and resulted in robot-specific guidelines on how to program displaying certain emotions[12][15][16].

Based on these established guidelines for displaying emotions, we can look further into how humans are affected by the robot’s emotional cues during interaction with a robot. We will research this in a context where we would also expect a human to display emotions, namely during telling an emotional story. Our research takes inspiration from the study of Van Ottendijk et al[17] and Bishop et al[16] in which the robot Pepper was used to deliver either a positive or negative message accompanied by congruent or incongruent emotional behavior. We will extend on these studies by taking a different combination of context for application and research method: interaction with students as researching application in an educational setting rather than healthcare, and using interviews to gain a deep understanding rather than surveys. Moreover, students are an important target group for robots, because they represent future workforce and innovations. Understanding their needs can help developers design the robots so that they are engaging, user-friendly and educational[18].

More specifically, the research question that will be studied in this paper is “To what extent does a match between the displayed emotion of a social robot and the content of the robot’s spoken message influence the acceptance of this robot?”. We expect that participants will prefer interacting with the robot while displaying the emotion that fits with the content of its message and to be open to more future interactions like this with the robot. On the other hand, we expect that a mismatch between the emotion displayed by robot and the story it is telling will make participants feel less comfortable and therefore less accepting of the robot. Moreover, we expect that the influence of congruent emotion displaying will be more prominent with a negative than a positive message. The main focus of this research is thus on how accepting the students are of the robot after interacting with it, but also gaining insights into potential underlying reasons, such as the amount of trust the students have in the robot and how comfortable they feel when interacting with them. The results could be used to provide insights into the importance of congruent emotion displaying and whether robots could be used on university campuses as assistant robots.

Survey and interview questions

Survey

At the start of the experiment, the participants are asked to fill in a survey on limesurvey. The questions are listed below.

  1. What is your age is years?
  2. What is your gender?
    • Male
    • Female
    • Non-binary
    • Other
    • Do not want to say
  3. What study program are you enrolled in currently?
  4. In general, what do you think about robots?
  5. Did you have contact with a robot before? Where and when?

Interview

In the study that we propose, the participants are subjected to the experiments which are followed with an interview. This interview is done face-to-face with the participant in a quiet room. The interview will be a (semi)-structured interview, as this allows us to prepare questions in advance to the interview, but also allows follow up questions that are not scripted. This will give a slightly more complete way of answering questions, however it will also be slightly harder to code the interviews as some participants might be asked follow up questions.  The semi-structured design will allow for further conversation where necessary, and it will seek the fine line between a too structured interview that is avoid of free conversation, which makes it difficult to collect enough information, and an unstructured interview that has only free conversation, which makes it hard to compare the answers that are given by the participants [19].

In general there are several important steps to preparing and doing an interview. These are the steps that are normally taken:

  1. Design interview questions.
    • Think about who you will interview: In this case we will interview peer students. These students are similar to us, but not all students might understand the same jargon, as not all students will be familiar with robots and psychological terms. This is something to keep in mind when deciding upon questions. In addition to this, the student population is, on average, more intelligent than the general public, allowing for more advanced questions. In addition to this, the students are all from a technical university, creating the expectation that these students will have a positive attitude towards the robot, as they are often familiar with the workings and the ideas.
    • Think about what kind of information you want to obtain from interviews: The research questions aims to look at three constructs. First of all, we want to look at the acceptance of robots by the students. Second, we want to focus on whether students trust the robot and what it says. Third, we would like to focus on how comfortable students are in interacting with the robot. These three constructs need to be included in the interview.
    • Think about why you want to pursue in-depth information around your research topic : The results could be used in robot design for different purposes. As the sample population consists of students, the results will only be applicable to them. This leads to applications like student counselor, assistive robots on campus and classroom bots. The study does not focus on the generalization of results, which might be work for future research, as elderly and children might react in different ways to the robot than students would.
  2. Develop an interview guide (what you do during the actual interview, the protocol).
  3. Plan and manage logistics
    • The interviews will be audio-recorded and transcribed using name program. The recordings will be destroyed July 7th 2024 for privacy reasons.
    • The interviews are done one-on-one, where the interviewer has a printed page of the questions with space to make notes.
    • Each interview will be about 10 minutes.


The interview questions are divided into three subjects: attitude, trust and comfort.

Attitude

There are little interview questions that regard the attitude towards robots. However, attitude is divided into several different aspects. For example, we have general attitude that extends over the general ideas that participants have on robots. The second is more specific attitudes towards one application, as people can have different experiences with different robots and thus have different attitudes.

Concerning general attitude, one article found focussed on the expectations that people have with robots and their expectations when confronted with other social robots concepts (Horstmann & Krämer, 2019). The interview included some great baseline questions, these are great to use as the general attitude questions in the demographics survey:

  • In general, what is your attitude towards new technologies?
  • Did you have contact with a robot before? Where and where?

Specific attitude tries to measure the attitude of students towards the robot application tested in the experiment. This is often measure in how accepting they are of the robot specifically. Acceptance, like general attitude, is often measured in survey data not interviews. Still there is research that tests the user experience and the attitude of the participant using interview questions. One of the found studies applied a semi-structured interview to explore the interaction with the robot more deeply. This was part of a larger research and there were only 5 questions included in the interview (Wu et al., 2014). This research had as an application assistive technologies for in the house, which does not align with the context of school and education. However the questions are general enough that only a few have to be adjusted to fit the context. The questions included in the research were:

  • What do you think about this experiment?
  • What do you think about the appearance of the robot?
  • What do you think about the interaction with the robot?
  • What do you think about having this type of robot one day?
  • Would you use this kind of robot one day?

The only question that falls a bit out of tone is the first question. In our research the robot does a general task, which is telling a story. In the study of Wu et al. the robot performed multiple tasks and interacted with the participant more. This would result in more attitudes towards the experiment itself. In addition to this, question 1 asks about the general attitude towards the experiment. As our study is under some time contraints, it might be a good idea to leave this question out. Apart from that question 2 is not as relevant to our research. it could give some idea on whether the manipulation worked (people need to perceive the robot as happy/positive or sad/negative and neutral). This could thus be a good question to ask. Yet, when we look at time containts it might be another question to remove. Last but not least, question 4 needs to be adjusted as the robot application that we intend is not about domestic use. So the participants will not have the robot, only interact. For this reason and the reason of time contraints, question 4 will be removed. When applying these questions, we could phrase them as a comparative question, as we want to compare the different conditions (happy/sad/neutral).

Attitude is a big part of our research and it often includes aspects of the other concepts (trust and comfort) that we would like to measure. There are multiple questionnaires that used attitude as a basis for their research. On of these studies, researched the effect of emotions on virtual character design (del Valle-Canencia et al., 2022). As this research was directed at students, it is applicable to our target group as well. The study did a mix of user experience centered questions and more attitude-based questions. A selection of these questions has been made, and can be found below. The full interview list can be found in the article itself.

Questions from del Valle-Canencia et al.:

  • Briefly describe, in your own words, your emotion (regarding the robot).
  • Do you think the character seen above would be suitable to be ... ?
  • In a virtual assistant, would you prefer ... ?
  • What did you like about your character?


Trust

Trust is a common measure in the evaluation of robot designs. It is divided into trust in the robot itself and trust in the message that the robot tries to convey (Jung et al., 2021). This division can be used here as well. In contrast to attitude, the same paper suggests that trust is best measured through in-depth interviews, instead of biometric measurements (Jung et al., 2021). However, the in-depth interviews are never standardized and differ a lot between the studies. There are also a lot of studies that measure trust through scales and standardized pronciples. One of the most well-known scales to measure human-computer trust is the measurement scale developed by Madsen and Gregor. In their study they found that affective measures are the most reliable measurements of trust (Madsen & Gregor, 2000). They developed a scale that covers nine different factors. Each of these factors was than classified to fit to affective measures, cognitive measures or a combination of both. The affective measures are faith, personal attachment and perceived reliability. As the scale includes relatively many items, a selection was made to include in our research. After selection, four items were left. These items are again put in the table and rephrased like open questions for the interview. These rephrased questions are listed below:

  • Which robot character did you find the most reliable? And which one the least? Why?
  • Which robot character do you think told the story in the most trustworthy way? Why?
  • Which robot character did you feel the most attracted to?


Comfort


The interview questions that will be asked in between the interactions with Pepper are the following.

Manipulation check {see whether the story did come across as positive}

  1. What was your impression of the story that you heard
    • Briefly describe, in your own words, the emotions that you felt when listening to the three stories?

Attitude towards the robot:

  1. What do you think about the appearance of the robot during the three stories that you heard?
    • How was the robot feeling when it told the story?
    • How did the robot convey this feeling?
    • Did the robot do something unexpected?
  2. What did you like/dislike about each of the three robot characters?
    • What are concrete examples of this (dis)liking?
    • How did these aspects/example influence your feelings about the robot?
    • What effect did the other characters have compared to each other?
      • What was the most noticeable difference?
  3. Which of the three robot interactions do you prefer to see in the robots that you will use?
    • Why do you prefer this character of the robot?

Trust:

  1. Which robot character did you find the most trustworthy? And which one the least trustworthy?
    • Why was this character the most/least trustworthy?
      • What did the robot do to convey this?
    • What did the other characters do to be less trustworthy?

Comfort:

  1. Which of the three robot characters made you feel the most comfortable in the interaction?
    • Why did this characters made you feel comfortable?
    • What effect did the other characters have?

General:

  1. Do you think it would be suitable to use Pepper (with your preferred character) in real life settings?
    • In what setting would you think it would be suitable to use Pepper?
    • Thinking about your daily life, where would you (not) like to encounter Pepper?
  2. Are there any other remarks that you would like to leave, that were not touched upon during the interview, but that you feel are important?

Sources

  1. ↑ 1.0 1.1 Mlot, S. (2014, June 5). “Pepper” robot reads, reacts to emotions. PCMAG. https://www.pcmag.com/news/pepper-robot-reads-reacts-to-emotions
  2. ↑ 2.0 2.1 2.2 2.3 Pandey, A. K., & Gelin, R. (2018). A Mass-Produced sociable humanoid robot: Pepper: the first machine of its kind. IEEE Robotics & Automation Magazine, 25(3), 40–48. https://doi.org/10.1109/mra.2018.2833157
  3. ↑ 3.0 3.1 3.2 Mishra, D., Romero, G., Pande, A., Bhuthegowda, B. N., Chaskopoulos, D., & Shrestha, B. (2023). An exploration of the Pepper robot’s capabilities: unveiling its potential. Applied Sciences, 14(1), 110. https://doi.org/10.3390/app14010110
  4. ↑ Glaser, A. (2016, June 7). Pepper, the emotional robot, learns how to feel like an American. WIRED. https://www.wired.com/2016/06/pepper-emotional-robot-learns-feel-like-american/
  5. ↑ Biba, J. (2023, March 10). What is a social robot? Retrieved from Built In: https://www.nature.com/articles/s41598-020-66982-y#citeas
  6. ↑ Borghi, M., & Mariani, M. (2022, September). The role of emotions in the consumer meaning-making of interactions with social robots. Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S0040162522003687
  7. ↑ 7.0 7.1 7.2 7.3 Kirby, R., Forlizzi, J., & Simmons, R. (2010). Affective social robots. Robotics and Autonomous Systems, 58(3), 322–332. https://doi.org/10.1016/J.ROBOT.2009.09.015
  8. ↑ 8.0 8.1 Breazeal, C. (2004). Designing Sociable Robots. Designing Sociable Robots. https://doi.org/10.7551/MITPRESS/2376.001.0001
  9. ↑ 9.0 9.1 9.2 Chuah, S. H. W., & Yu, J. (2021). The future of service: The power of emotion in human-robot interaction. Journal of Retailing and Consumer Services, 61, 102551. https://doi.org/10.1016/J.JRETCONSER.2021.102551
  10. ↑ 10.0 10.1 Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3–4), 143–166. https://doi.org/10.1016/S0921-8890(02)00372-X
  11. ↑ 11.0 11.1 Marcos-Pablos, S., & García‐Peñalvo, F. J. (2021). Emotional Intelligence in Robotics: A Scoping review. In Advances in intelligent systems and computing (pp. 66–75). https://doi.org/10.1007/978-3-030-87687-6_7  
  12. ↑ 12.0 12.1 Miwa, H., Itoh, K., Matsumoto, M., Zecca, M., Takariobu, H., Roccella, S., Carrozza, M. C., Dario, P., & Takanishi, A. (n.d.). Effective emotional expressions with emotion expression humanoid robot WE-4RII. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 3, 2203–2208. https://doi.org/10.1109/IROS.2004.1389736  
  13. ↑ Crumpton, J., & Bethel, C. L. (2015). A Survey of Using Vocal Prosody to Convey Emotion in Robot Speech. International Journal Of Social Robotics, 8(2), 271–285. https://doi.org/10.1007/s12369-015-0329-4  
  14. ↑ Wang, S., Lilienfeld, S. O., & Rochat, P. (2015). The Uncanny Valley: Existence and Explanations. Review Of General Psychology, 19(4), 393–407. https://doi.org/10.1037/gpr0000056  
  15. ↑ Johnson, D. O., Cuijpers, R. H., & van der Pol, D. (2013). Imitating Human Emotions with Artificial Facial Expressions. International Journal of Social Robotics, 5(4), 503–513. https://doi.org/10.1007/S12369-013-0211-1/TABLES/8
  16. ↑ 16.0 16.1 Bishop, L., Van Maris, A., Dogramadzi, S., & Zook, N. (2019). Social robots: The influence of human and robot characteristics on acceptance. Paladyn, 10(1), 346–358. https://doi.org/10.1515/pjbr-2019-0028
  17. ↑ Van Otterdijk, M., & Barakova, E. I., Torresen, J. & Neggers, M. E. M. (2021). Preferences of Seniors for Robots Delivering a Message With Congruent Approaching Behavior. 10.1109/ARSO51874.2021.9542833.
  18. ↑ Manzi, F., Sorgente, A., Massaro, D., Villani, D., Di Lernia, D., Malighetti, C., Gaggioli, A., Rossignoli, D., Sandini, G., Sciutti, A., Rea, F., Maggioni, M. A., Marchetti, A., & Riva, G. (2021). Emerging Adults’ Expectations about the Next Generation of Robots: Exploring Robotic Needs through a Latent Profile Analysis. Cyberpsychology, Behavior, and Social Networking, 24(5), 315–323. https://doi.org/10.1089/CYBER.2020.0161
  19. ↑ Pollock, T. (2022, June 14). The Difference Between Structured, Unstructured & Semi-Structured Interviews — Oliver Parks - Search Based Recruitment Experts. Oliver Parks - Search Based Recruitment Experts. https://www.oliverparks.com/blog-news/the-difference-between-structured-unstructured-amp-semi-structured-interviews

Appendix

The complete research protocol can be found via the following link:

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