PRE2023 3 Group4

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

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:

“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?"

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

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*

Literary review

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 ​. 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 ​. These types of robots are now mostly used in service settings ​.  

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 ​​. 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 ​​. 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 ​. 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 ​ . 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?” ​​.

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 . 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 .  However, the implicit and multi-faceted impacts that this might bring into educational environments as a whole should be considered . 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 .  

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 .  

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 . 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 .

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 . 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 . 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 . 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 .

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 . 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 . 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 .

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 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 .

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 , user engagement and online trust . 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. . 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 . However, when the measure is used for young adults, the reliability stays around the same . 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 .  

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 . 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 . 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 .

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 . 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 .

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.”

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 .

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 .

Pepper

Pepper, the Humanoid and Programmable Robot

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 .  

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 .

Pepper has a lot of capabilities, among which mapping and navigation, object detection, hearing, speech, and face detection . 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 .

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 . 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 . Several applications of this robot are answering questions, greeting guests and playing with kids in Japanese homes .  

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 . 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’ .

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 .

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?

Appendix

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