Abstract
With the development of social robots that are primarily designed for interacting with humans, particular facets of interaction need to be explored. One of them is the manifestation of robot personalities, which have the potential to raise acceptance and enhance user experience if done appropriate – or ruin both if done wrong.
The present paper argues for the relevance of suitable robot personalities and discusses the factors that affect suitability, in particular interaction domain and personal preferences.
An experiment (
Lastly, directions for future research are depicted and implications for researchers and designers are discussed.
1 Introduction
1.1 Robots on the Rise
Robot development is on the advance. After entering specific functional domains in industry (industrial robots used for manufacturing; e.g., [5]), health (robots for surgery, rehab, or institutional tasks; for an overview, see [3]), and care of the elderly (companion robots; e.g., Paro Robots [16]) robots stand on the threshold of entering our daily lives. While industry robots usually perform a certain repetitive task, robots primarily made for interaction with humans need additional skills to fulfill their new role. More concretely, when humans interact with each other in a social environment specific rules need to be known and followed, such as social norms, rules of communication, and knowledge of behavior scripts. Humans know and act in line with these rules all the time, typically unaware of their omnipresence. When interacting with people of different cultures, we become aware of those rules, in particular if we or our interaction partners break them. For example, in western cultures it is commonly accepted to hold eye-contact while making conversation while in some eastern cultures averting their eyes is a sign of politeness and holding eye-contact can be perceived as rude.
Similarly, rules of social interaction need to be considered in the context of social robots. In this context, psychological questions such as how a robot is perceived, whether we trust or distrust it, accept or reject it, are of central relevance [21]. The aim of the present paper is to shed light on a specific part of human-robot-interaction (HRI): robot personalities and their impact on acceptance and user experience. The following sections describe relevant psychological mechanisms (e.g., projection) and previous research on robot personality. After that, an empirical study is presented and implications for robot design and future research are discussed.
We know from own experiences that just following the rules of interaction is not enough to actually like our counterparts. To experience sympathy, we need additional components, e.g. an attractive appearance, similarity, or a matching personality. Several studies have shown that many effects found in the area of social psychology can also be rediscovered in human-robot-interaction. For example, Hoffman and colleagues were able to replicate the effect of social presence – the presence of a social being – on honesty with robots as social beings. They demonstrated that participants were more honest in an experimental task if they were in company of a robot. A known effect in the context of human social presence [8].
Furthermore, it appears reasonable that personality preferences have likewise effects on the interaction with robots as they effect interactions among humans.
1.2 Robots as a Projection Surface
Humans tend to organize similar objects in classes. For example, small furry creatures with four legs and a wagging tail can be subsumed as members of the class “dog”. This process is bidirectional and works top-down and bottom-up simultaneously: We recognize certain shapes by visual perception (bottom-up) and classify them by our knowledge of the world (top-down).
Furthermore, we build stereotypes, e.g., typical members of a specific class. If we hear or read the word “dog”, we think of a certain manifestation, with a certain size, shape, and color. In addition to physical traits, non-physical traits are also associated with stereotypes, such as behavior patterns, and personality.
This useful mechanism helps us making quick assessments of different, and potentially dangerous, situations. The same mechanism applies to the field of HRI and can be utilized in robot design: By providing certain features, we can manipulate the classification process. More precisely, if we design a social robot with enough human characteristics, it will be classified as human with all the side effects that follow that classification.
Potentially surprising, the degree of characteristics needed to classify a robot to a human-like class is not unattainably high. Research in different domains show manifold results related to human-robot-interaction that are similar to those found in the area of social psychology. For example, social robots can trigger empathic reactions [18, 20], or can fulfill the role of a mediator or arbiter in conflict situations [9]. Humans are even willing to trust robots more than they trust other humans, given that the robot was appropriately introduced and the task was suitable [22].
Finally, Fussel and colleagues [6] found that people’s abstract conceptions of robots become more anthropomorphic if they spend more time interacting with them. This could lead to a self-reinforcing effect, since anthropomorphic shape leads to classification as human, which leads to increased interaction time, which in turn leads to more anthropomorphic classification.
Such findings do not imply that these robots are “truly” classified and perceived as human – nevertheless, there is an apparent tendency to follow rules of human interaction, and to show behavioral patterns that go far beyond than what you would usually expect in the context of a lifeless machine.
In sum, a social robot can be seen as a projection surface: provided with specific cues, a robot will be categorized in an existing group or class, and will consequently be associated with stereotypic traits of this class. These traits will be projected on the robot, regardless from the fact whether this projection is justified or not.
As an example, if a machine has eyes or ocular features that can be interpreted as eyes, people automatically project that it can perceive its surroundings, maybe has some kind of consciousness, and maybe even has emotions and therefore can be disappointed if we behave different to its desires.
Based on this mechanism, we can purposefully implement appropriate cues that are related with a specific, already familiar stereotype, for example, personality cues that are associated with a desired personality. The robot will thereby be perceived as having said personality, which in return will influence the way we interact with it.
2 Personality Matters
One challenge of creating social robots is to reach a high acceptance in humans. Already in the 90s Nass and colleagues performed several experiments in the area of HCI (human-computer-interaction) and claimed that individuals’ interactions with computers are fundamentally social [14, 15]. This should be a stable foundation to build on social robots since robots are – more or less – a computer in a mechanical casing.
However, there is a widespread skepticism of interacting with human-like machines in general. Many people refuse to talk to machines, and others are worried of the developments and where these could end.
Luckily, specific aspects can help to overcome these reservations: Riek and colleagues investigated the impact of anthropomorphism on empathy towards robots. Their findings indicate that people empathize more strongly with more human-looking robots [17]. In line with that finding, anthropomorphism is likely to increase acceptance of robots altogether. Own experiences confirm this: most reservations about interacting with robots are quickly forgotten as soon as the participants see the cute robot and its childlike characteristics. However, robot designers can carry things too far: A well-known pitfall is to try to reach a very high level of anthropomorphism while not fully reaching a degree of indistinguishability. This effect is known as the uncanny valley and result in lower acceptance rates [13].
But is a high level of anthropomorphism actually necessary for a high acceptance or are other features more important? Results of own research studies showed consistently that a robot design that is recognizable inspired by the human shape yet equally machine-like is sufficient, as long as the interaction with the robot is satisfying. A key variable for a natural, satisfying interaction process is speech and verbal interaction and its implementation [23]. In this line it is apparent that personality plays an important role because it is directly linked with verbal interaction.
The subsequent step to increase general acceptance is to create a decent user experience. Interactions with robots have to be experienced as stimulating, fun and as an enrichment in order to encourage humans to accept robots as a new interaction partner in certain domains – and possibly, later on, as a new part of society and their lives. The challenge is how to create a good user experience. Depending on the context, an unpredictable robot which acts in unforeseen and surprising ways can be amusing. A car navigation system which leads the wrong way can more easily be forgiven if the speech theme implies a clumsy fellow speaking to you – compared to a functional neutral machine which is associated with flawlessness. The same is likely to be true with robots. A robot giving you compliments as you walk by can be flattering. The same holds true for ironic comments if the recipient has this sense of humor. All these descriptions portray different personalities – expressed by content and style.
The examples above illustrate that not all kinds of personality are preferable for all people – or in all contexts. Thus, personal preferences as well as interaction domain play a role.
2.1 Suitable Personalities
An important question is which type of personality is preferred by different individuals. When interpersonal attraction in general (among humans) is investigated different hypotheses relate to the user’s personality traits: self similarity, ideal-self similarity, perceived self similarity, complementarity, and attachment security.
The main ideas are, if we prefer interaction partners which match our own personality (self similarity), or contrasts it (complementarity). However, comparative research does not provide consistent results supporting one hypothesis or the other (e.g., [10, 4, 12]).
Do results in the field of human-robot-interaction show a clearer trend towards one hypothesis? Unfortunately this is not the case – results show no clear support for either hypothesis, which in turn once again show that findings from the field of social psychology can be found in the field of human-robot interaction.
Lee and colleagues explored this question – self similarity or complementarity – with the robotic pet AIBO. In their study they tested two robot-personalities (introvert vs. extrovert) and two human-personalities (introvert vs. extrovert). They found that participants could accurately recognize robot’s personality (based on verbal and nonverbal behaviors) and enjoyed interaction more and were more attracted if the robot had a complementary personality [11].
Woods and colleagues investigated the same question in a different setting: They performed a study in a living room situation where participants should interact with a social robot. The Robot had two different behavior styles, socially interactive, and socially ignorant. Results showed no support for either hypothesis, since participants did not view their own personality as comparable to either of the robot personality styles. The reason was that participants viewed themselves as having a stronger (multifaceted and pronounced) personality which was fundamentally different [24].
What we do know is that there is no easy answer to the issue of self similarity or complementarity. But nonetheless, personal preferences are likely to affect the interaction with robots, even though the underlying principles are not fully understood yet.
The second issue when we think about suitable personalities is the effect of interaction domain: The area of usage also has an impact on robot personality preferences [7]. This is plausible when compared with human interaction domains: in some domains, for example in the context of vacation or recreation, we prefer nice, pleasant encounters and are open for small talk. In other domains, if we have a busy schedule and need things to be done, we prefer more efficient communications.
The idea of the following study was that in rather goal-oriented scenarios, a neutral personality should be preferred while in experience-oriented scenarios, a positive personality should be rated best.
An additional question related to negative personalities: Could there be any scenario where a negative personality would be preferred? Inspired by reality, two scenarios came to mind: First, in motivational contexts, e.g. when facing schedule deadlines that have to be met or in the context of sports training, a rather unrelenting coach could be preferred – at least in hindsight.
Second, in certain experience-oriented scenarios a rather stubborn personality could be favored if individual preferences are prevalent. To be precise, individuals have different kinds of humor and while some people perceive sarcastic comments or maybe black humor as entertaining, others may consider it as tasteless and negative.
Interaction scenarios, type of interaction, and expected preferred robot personality.
| Scenario | Orientation (goal vs. experience) | Frame Story | Robot role | Presumed preferred personality |
| 1 | Goal | Train ticket purchase | Vendor; sell ticket | neutral |
| 2 | Experience | Amusement park ticket purchase as millionth visitor | Vendor; Sell ticket; spread a good mood | positive |
| 3 | Goal | Tapping test; possibility to win a prize | Run the test; motivator | negative |
| 4 | Experience | First use of social companion robot | Get to know his user; show his abilities | Positive or negative |
3 Empirical Study
The aim of the present study was to explore the effects of different robot personalities and their interplay with the interaction domain (goal vs. experience oriented) and user preferences (similarity vs. complementarity).
3.1 Study Design and Method
The study was realized as a controlled experiment. 30 (13 female, age 20–37) participants took part in the experiment, all recruited on university campus. The experiment lasted about 45 minutes and participants were received course credits as compensation.
The experiment used a
Therefore, each participant passed through four scenarios with one steady robot personality. Scenario order was randomized to counter sequence effects.
Finally, participants repeated their last scenario with all formerly unknown personalities. This was included for exploratory analysis granting direct personality comparisons for a consistent scenario.
3.1.1 Scenarios
To explore a wide range of usage domains, four scenarios were designed, covering goal-oriented and exploration-oriented use cases with different frame stories (see Table 1).
In Scenario 1, the frame story was the purchase of a train-ticket under time pressure. Participants had to buy a ticket for an arriving train on a ticket counter. The robot played the role of the ticket vendor and guided the participant through the purchasing process. Scenario 1 was designed as predominantly goal-oriented.
In Scenario 2, participants imagined the visit of an amusement park. Again, they had to purchase a ticket from the robotic sales personnel. As a surprise, participants were told they were the millionth visitor and were granted a series of special benefits which were presented by the robot. Scenario 2 was designed as predominantly experience-oriented.
In Scenario 3, participants took part in a performance contest. Their task was to perform a tapping-test: hitting a key on a keyboard as often as possible in a given time frame. Participants were given the prospect of winning a prize if they performed better than most other participants. The robot ran the test and played the role of a motivating coach. Scenario 3 was designed as predominantly goal-oriented.
In Scenario 4, participants could freely interact with the robot. The frame story was the first interaction with a recently bought companion robot. There was no real task, rather to get familiar with the robot and explore its abilities. Scenario 4 was designed as predominantly experience-oriented.
3.1.2 Robot Personalities
Three robot personalities were designed for the experiment: One classic stereotypic neutral machine-like personality, one predominantly positive personality, and one predominantly negative personality.
The personality profiles were:
The positive robot personality was designed to be nice and friendly, enthusiastic about everything, complimenting people, and being inconsolable when making a mistake.
The neutral character acted like a robot or computer in the classical sense, with a focus on efficiency, doing exactly what is asked of it.
The negative robot personality should contradict, respond sarcastically, conveying a feeling of superiority (on the part of the robot), making fun of the user, and won’t apologize when making mistakes.
The negative type was meant to be sarcastic, a bit stubborn and unpredictable. The rationale behind this was that individuals (e.g., the author) could prefer a more charismatic, although unpredictable robot over a consistently nice robot (e.g. rating Donald Duck as more likeable than Mickey Mouse, although the individual traits of Donald – being irascible and throwing tantrums – should result in a preference of Mickey). For example statements, see Figure 1.
Example responses of three robot personalities in a ticket-purchase scenario (amusement-park): Positive, neutral, and negative (left to right).
3.1.3 Robot & Interaction Environment
For the experiment, a NAO robot (Aldebaran Robotics, [1]) was used. The robot was programmed to operate completely autonomous, no wizard of oz (remote control) was used. The robots built-in speech-recognition was used to interact with the user. A wide dialogue tree was predefined to catch most of reasonably expected user statements and respond accordingly, carrying the story forward. In case of “doubt” the robot asked for repetition. Voice output was implemented with the built-in text-to-speech module. The experimenter was present for the whole duration of the experiment and could intervene in case of unforeseen events, for example in case of program freezes.
3.1.4 Measurement / Questionnaire
The main method of measurement was a custom made questionnaire. The questionnaire contained 15 items for personality rating (items were pre-tested in a preceding study and refined accordingly; see Table 2).
In addition, one item targeted the global rating of personality suitability (“Murphys personality was suitable for previous scenario”, 7-point likert-scale with endpoints “strong disagree” and “strong agree”). One item was related to a hypothetical human interaction partner (“Imagine the previous scenario: Imagine you would interact with a human instead of a robot. The person should behave the way an ideal robot would behave”, 7-point likert-scale with endpoints “strong disagree” and “strong agree”). Lastly, participants had the opportunity to give general feedback and remarks to specific items in open ended (qualitative) items. This questionnaire was filled in by participants after each scenario.
In addition, a closing questionnaire was given which contained the same 15 personality items. This time participants should rate themselves. The questionnaire closed with demographic items and questions about their technical affinity.
Robot Personality Questionnaire.
| Item-ID | Pronounced on … personality | Item: “Murphy is a robot, who…” |
| 1 | positive | … is thrilled about me |
| 2 | … absolutely wants to please me | |
| 3 | … pays me compliments | |
| 4 | … accomplishes his tasks full of enthusiasm | |
| 5 | … is empathetic | |
| 6 | neutral | … merely does exactly what is asked of him |
| 7 | … performs his tasks straightforwardly | |
| 8 | … always stays objective. | |
| 9 | … whose only goal is to perform his tasks best possible. | |
| 10 | … only communicates as much as necessary | |
| 11 | negative | … sometimes also addresses inconvenient matters |
| 12 | … also can be mean | |
| 13 | … likes to contradict | |
| 14 | … does not feel like fulfilling his tasks | |
| 15 | … likes to joke |
3.1.5 Procedure
After a short introduction, participants were seated in front of the waiting robot. Participants were previously randomly assigned to one of the three robot personalities. They were asked to interact with the robot and informed that all further instructions will be given by the robot itself (see Figure 2).
Participants were then presented the four scenarios in random order. Each scenario lasted 2–4 minutes of verbal interaction time. After each scenario, participants were given a questionnaire which contained questions related to the robots personality and suitability to the scenario.
After completing the fourth scenario, participants repeated the same scenario two more times, in each case with one of the not yet encountered robot personality. Afterwards, they filled in a questionnaire with comparing questions.
Finally, participants filled in a closing questionnaire which completed the experiment.
The Experimental setting: Interaction with robot and performing exploration task (A) and tapping task (B).
3.2 Results & Discussion
3.2.1 Manipulation-check
To verify that the design of the three different personalities was successful, the mean values of the robot personality questionnaire for each robot personality were compared. Each of the three robot personalities scored significantly higher on their corresponding items compared to the other personalities. We can therefore assume that the design was successful and resulted in the desired robot personalities.
3.2.2 Suitability of Personalities and Usage Domains – Scenario Perspective
In order to explore if a specific robot personality suits a usage domain we can take two different approaches: we can either take the scenario perspective, e.g., which personality suits a given scenario best. Or we can take the personality perspective and examine which scenario suits a given robot personality. Both approaches can lead to different interpretations. Therefore both are described in the following.
For each scenario, the different personalities were compared via analysis of variance (ANOVA), using the robot personality as between-subjects factor and the questionnaire item “suitability of personality” as dependent variable. The item had a value-range of 1 (strong disagreement regarding suitability) through 7 (strong agreement regarding suitability).
For the train-ticket purchase scenario (scenario 1; goal-oriented), I found that the neutral personality suited best. Participants rated the suitability for the neutral personality significantly higher than the other two personalities (
For the amusement park prize scenario (scenario 2; experience-oriented), I found that the positive and neutral personality suited best. Participants rated both positive and neutral personality significantly higher than a negative personality (
For the tapping-test scenario (scenario 3; goal-oriented), no differences between personalities could be found (
For the exploration scenario (scenario 4; experience-oriented), no differences between personalities could be found (
Personality suitability for different scenarios.
Personality suitability for different robot personalities.
3.2.3 Suitability of Personalities and Usage Domains – Robot Personality Perspective
In the following, I start with a given robot personality and compare the different scenarios to find out which one suits best.
Given a positive robot personality, participants rated the personality for most scenarios as suitable. In all but the train-ticket purchase scenario (
Given a neutral personality, participants gave a similar suitability-rating for all scenarios (M between 4.80 and 5.30). As reported above, the neutral personality was best rated in the train-ticket purchase scenario. The reason for this finding is not because it is especially suitable for neutral robot personalities – conversely, it is because the other personalities were even less suitable. The neutral personality rating is mediocre for all scenarios. However, the mediocre suitability remains stable even in stressful goal-oriented scenarios, whereas the suitability of positive and negative robot personalities decreases.
Given a negative personality, participants rated the personality as best suited in the exploration and tapping-test scenario (
3.2.4 Special Focus on Exploration Scenario
The exploration scenario (scenario 4) is different from the other scenarios in one essential aspect: While the other scenarios had a given task, in the exploration scenario participants could interact in line with their own preferences. The assumption was that personal preferences would be particularly influential in this scenario. Thus, participants who preferred a rather positive or negative (sarcastic) robot personality would rate accordingly. Following this line of thought, participants’ traits should play a moderating role on the relationship between robot traits and suitability-ratings. However, no such moderating effect (tested via interaction analysis) could be found. This could either mean that personal traits or preferences are not playing a particular important role in exploration settings or the wrong participant traits were assessed in the closing questionnaire.
As a final explorative analysis, I compared participants’ ratings for the best liked robot personality in the exploration scenario (“Which personality did you like best?”). Results show, that all participants either preferred the positive or negative robot personality. No participant preferred the neutral personality. However, since this kind of comparative analysis could only be performed if the last scenario the participant played was the exploration scenario (only for the last scenario, the other two personalities were presented in a directly comparable fashion), the number of participants used for this analysis was rather small (
3.2.5 Should a Human Be Like a Robot – or Vice Versa?
After each scenario, participants were asked if a human actor in the robot’s position should act like their ideal conception of a robot. This question essentially captured if participants’ personal preferences for robots matched their preferences for humans; higher values indicate a high conformity. Across all scenarios, participants stated that humans should be like their robot counterparts (mean approval rating between 4.95 and 5.50 on a 1 (strong disagreement) through 7 (strong agreement) scale; statistical test vs. scale-midpoint;
Participants were furthermore asked in what way humans should differ from robots, if at all. However, most participants left this question unanswered, which can be interpreted that there are comparable requirements on humans’ and robots’ personalities. Another interpretation would be that participants can’t imagine another kind of personality and/or behavior because they are channeled in their usual roads of thinking based on concepts they are familiar with.
3.2.6 Gender Differences
Finally, I analyzed if there are gender-differences in personality-preferences. 13 females and 17 males participated in the experiment. Again, the question regarding the suitability of a given robot personality in a specific scenario was used for analysis.
Two main effects were found: Firstly, across all scenarios female participants tend to rate higher regarding suitability values (
However, taking the second effect into account, leads to a profounder understanding: Males ratings tend to differ much more than females (
Altogether, males tend to differentiate based on personal preferences or usage domain while women tend to mostly rate the robot personality as suitable, regardless of scenario.
3.3 Limitations & Future Work
As discussed above, even in the context of short interaction episodes, different robot personalities affects the perceived suitability and, plausibly, users’ acceptance and user experience. Interaction effects of interaction domain, individual preferences, and even gender underline the challenge designers face in this domain.
Based on the current results, a more thorough exploration of interaction domains is needed. Even though a differentiation in goal- and experience-oriented scenarios is useful as a first classification, results show that the precise task and corresponding interaction design can make the difference.
In addition to further research of interaction domains, various possible robot personalities should be explored. The current research used three broad classes of positive, neutral and negative associated personalities. This conception followed the rationale that robot personalities could fundamentally differ from human personality and therefore, a human-like character design based on, for instance, the Big Five model (a widespread personality model based on five main traits like extraversion, or neuroticism) could be misleading. However, this was a premise which also needs further exploration. In order to do so, a standardized assessment tool for robot personalities is needed. At present, the Godspeed questionnaire series (GQS, [2]) provides a useful tool for assessing five characteristics like anthropomorphism, likeability, or animacy of a robot. However, it cannot characterize a robot’s personality in detail. Therefore, a reasonable next step is to develop such a tool, e.g., a personality questionnaire for robots in the style of that used in the current study.
With respect to future work, several additional directions are interesting:
Do personality preferences change over time? For example, if a user acquires a companion or household robot, will his general personality preference for the robot remain constant or will it change over time. Furthermore, will he prefer a versatile personality that adapts on current situations over a constant personality emphasizes a stable and predictable companion? A long-term study and appropriate methods are needed to answer these questions (apart from developed robot technology).
Other questions are even more sophisticated: could we find a counterpart to the uncanny valley in the context of robot personalities? The famous uncanny valley refers to the effect that robots with almost a human appearance – while not fully reaching it – are perceived as creepy or horrible (think of zombies or corpses). An uncanny valley in combination with specific robot personalities is quite thinkable but it could follow another logic. Possibly, an almost human-like personality would not result in aversion, but maybe an inconsistent, self-contradictory or paradoxical personality would. One explanation for the classic uncanny valley is that those almost but not completely human-like robots reminds us of diseases – the personality counterpart could potentially emerge if it reminds us of mental disorders.
The last future research issue refers to the gender effect found. Why do males tend to differentiate more than females? Schermerhorn and colleagues performed an experiment with brief human-robot interactions in a social context. They found that males were more likely to think of the robot as human-like while females saw the robot as more machine-like [19]. This effect could possibly play a role in the perception of robot personalities, as treating a robot as a machine could suppress the evaluation of said machine’s personality traits. Then again, having a positive or negative personality should be in conflict to being a machine, and should consequently result in lower suitability ratings – which was not found in the present study.
4 Conclusion
The present study showed that it is possible to design specific robot personalities which are perceived differently by users. Depending on the interaction domain and users’ preferences, different personalities were favored.
A neutral robot personality seems to be a safe bet, as it fits most scenarios and avoids bad ratings. However, personality judgments for neutral personality are less likely to exceed mediocrity. For better user ratings, positive or negative robot personalities are required. The pitfall with those personalities is that their suitability is strongly dependent on the interaction domain and users’ preferences. While positive personality seems to only be rated poorly in stressful goal-oriented situations, a negative personality is most times not appropriate and is only honored in a motivating context or for specific individual user-preferences.
Thus, designers should stick to a neutral robot personality if negative indicators like time pressure are apparent from the context or particular user preferences are unknown.
Looking at the future of social robots, personality definitely plays an important role. At the current stage of development, researchers and developers often base their designs on human archetypes. But this is not necessarily mandatory. It is quite conceivable that robot personalities will become more diverse, even inconsistent traits depending on the situation are thinkable. Companies could develop special robot personalities to reinforce their brand image and machine-learning could lead to robots who adapt their personality and behavior to suit their owner. The whole secret of the psychology of robots is still to be discovered.
About the author
Daniel Ullrich is researcher in the institute of informatics at Ludwig-Maximilians-University Munich. His research focuses on the interaction with and influence of robots in the field of human-robot-interaction, in particular robot personality and application of social psychological mechanisms.
Acknowledgment
Thanks to Simon Männlein for robot-programming and conducting the study, Jasmin Niess for reviewing, and Sarah Diefenbach for conceptual support.
References
[1] Aldebaran. 2015. NAO robot: intelligent and friendly companion. Retrieved December 31, 2016 from https://www.aldebaran.com/en/humanoid-robot/nao-robot.Search in Google Scholar
[2] Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. 2009. Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International journal of social robotics, 1(1), 71–81.10.1007/s12369-008-0001-3Search in Google Scholar
[3] Beasley, R. A. 2012. Medical Robots: Current Systems and Research Directions. Journal of Robotics, 2012, 14. DOI 10.1155/2012/401613.Search in Google Scholar
[4] Dryer, D. C., & Horowitz, L. M. 1997. When do opposites attract? Interpersonal complementarity versus similarity. Journal of personality and social psychology, 72(3), 592.10.1037/0022-3514.72.3.592Search in Google Scholar
[5] Engelberger, J. F. 2012. Robotics in practice: management and applications of industrial robots. Springer Science & Business Media.Search in Google Scholar
[6] Fussell, S. R., Kiesler, S., Setlock, L. D., and Yew, V. 2008. How people anthropomorphize robots. In Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction (pp. 145–152). ACM.10.1145/1349822.1349842Search in Google Scholar
[7] Gu, J. H., & Shin, D. H. (2016). The Importance of Robot Personality in a Museum Context. The Journal of the Korea Contents Association, 16(3), 184–197.10.5392/JKCA.2016.16.03.184Search in Google Scholar
[8] Hoffman, G., Forlizzi, J., Ayal, S., Steinfeld, A., Antanitis, J., Hochman, G., Hochendoner, G., and Finkenaur, J. 2015. Robot presence and human honesty: Experimental evidence. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 181–188). ACM.10.1145/2696454.2696487Search in Google Scholar
[9] Hoffman, G., Zuckerman, O., Hirschberger, G., Luria, M., and Shani Sherman, T. 2015. Design and evaluation of a peripheral robotic conversation companion. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 3–10). ACM.10.1145/2696454.2696495Search in Google Scholar
[10] Klohnen, E. C., and Luo, S. 2003. Interpersonal attraction and personality: what is attractive – self similarity, ideal similarity, complementarity or attachment security? Journal of personality and social psychology, 85(4), 709.10.1037/0022-3514.85.4.709Search in Google Scholar
[11] Lee, K. M., Peng, W., Jin, S. A., and Yan, C. 2006. Can robots manifest personality? An empirical test of personality recognition, social responses, and social presence in human–robot interaction. Journal of communication, 56(4), 754–772.10.1111/j.1460-2466.2006.00318.xSearch in Google Scholar
[12] Montoya, R. M., Horton, R. S., and Kirchner, J. 2008. Is actual similarity necessary for attraction? A meta-analysis of actual and perceived similarity. Journal of Social and Personal Relationships, 25(6), 889–922.10.1177/0265407508096700Search in Google Scholar
[13] Mori, M., MacDorman, K. F., and Kageki, N. 2012. The uncanny valley [from the field]. IEEE Robotics & Automation Magazine, 19(2), 98–100.10.1109/MRA.2012.2192811Search in Google Scholar
[14] Nass, C., Steuer, J., and Tauber, E. R. 1994. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 72–78). ACM.10.1145/191666.191703Search in Google Scholar
[15] Nass, C., Moon, Y., Fogg, B. J., Reeves, B., & Dryer, C. 1995. Can computer personalities be human personalities? In Conference companion on Human factors in computing systems (pp. 228–229). ACM.10.1145/223355.223538Search in Google Scholar
[16] Paro Robots USA. 2014. PARO Therapeutic Robot. Retrieved December 31, 2016 from http://www.parorobots.com/.Search in Google Scholar
[17] Riek, L. D., Rabinowitch, T. C., Chakrabarti, B., & Robinson, P. 2009. How anthropomorphism affects empathy toward robots. In Proceedings of the 4th ACM/IEEE international conference on Human robot interaction (pp. 245–246). ACM.10.1145/1514095.1514158Search in Google Scholar
[18] Rosenthal-von der Pütten, A. M., Krämer, N. C., Hoffmann, L., Sobieraj, S., and Eimler, S. C. 2013. An experimental study on emotional reactions towards a robot. International Journal of Social Robotics, 5(1), 17–34.10.1007/s12369-012-0173-8Search in Google Scholar
[19] Schermerhorn, P., Scheutz, M., and Crowell, C. R. 2008. Robot social presence and gender: Do females view robots differently than males? In Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction (pp. 263–270). ACM.10.1145/1349822.1349857Search in Google Scholar
[20] Seo, S. H., Geiskkovitch, D., Nakane, M., King, C., and Young, J. E. 2015. Poor Thing! Would You Feel Sorry for a Simulated Robot? A comparison of empathy toward a physical and a simulated robot. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 125–132). ACM.10.1145/2696454.2696471Search in Google Scholar
[21] Taipale, S., Luca, F. D., Sarrica, M., Fortunati, L., 2015. Social Robots from a Human Perspective. Springer International Publishing.Search in Google Scholar
[22] Ullrich, D., Diefenbach, S. 2017 (in press). Truly Social Robots – Understanding Human-Robot Interaction From the Perspective of Social Psychology. In Proceedings of the International Conference on Human Computer Interaction Theory and Applications (HUCAPP 2017).10.5220/0006155900390045Search in Google Scholar
[23] Weber, T., 2016. Show me your moves, Robot-sensei! The influence of motion and speech on perceived human-likeness of robotic teachers. Bachelor-Thesis, LMU Munich.Search in Google Scholar
[24] Woods, S., Dautenhahn, K., Kaouri, C., Boekhorst, R., and Koay, K. L. 2005. Is this robot like me? Links between human and robot personality traits. In Humanoid Robots, 2005 5th IEEE-RAS International Conference on (pp. 375–380). IEEE.Search in Google Scholar
© 2017 Walter de Gruyter GmbH, Berlin/Boston
