Abstract
With the rapid development of technology, have humans come to regard robots as their competitors? If so, how has this perception affected human–robot interactions? This present study investigated three questions about this topic. First, do humans in social circumstances spontaneously perceive robots as competitors when there is no obvious conflict of interest? If so, what factors play a role? Finally, does this competitiveness hamper interactions between humans and robots? Experiment 1 assessed the sense of competitiveness by measuring the emotional responses of subjects to a job-seeking robot. As observers, individuals responded positively to the robot's failures and adversely to its successes, revealing a competitive drive of humans toward robots. Experiment 1 further identified that competitiveness increased as a function of the robot's human-like appearance, indicating that robot human-likeness is an influential element. Experiment 2 attempted to find if human awareness of competition with robots negatively impacted human–robot interaction and, more specifically, if humans in a directly competitive relationship intentionally sabotage the robot's performance. Results demonstrated that during competition, humans focused on improving their own performance rather than sabotaging the robot's. Comparing Experiment 1 (no direct competition) to Experiment 2 (direct competition) revealed that human preference for the robot decreased significantly, indicating that competition negatively impacts human–robot interaction. This study showed humans' competitive awareness toward robots in social settings and the factors that drive it. In addition, it provides preliminary empirical evidence on how competition affects human–robot interaction in social settings and how humans will behave in the future while competing directly with robots for jobs.
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References
Aspinwall LG, Taylor SE (1992) Modeling cognitive adaptation: a longitudinal investigation of the impact of individual differences and coping on college adjustment and performance. J Pers Soc Psychol 63(6):989–1003
Bartneck C, Nomura T, Kanda T, Suzuki T, & Kennsuke K (2005) A cross-cultural study on attitudes towards robots.
Bettencourt BA, Brewer MB, Croak MR, Miller N (1992) Cooperation and the reduction of intergroup bias: the role of reward structure and social orientation. J Exp Soc Psychol 28(4):301–319
Bornman, & Elirea. (1992) Factors influencing ethnic attitudes in south african work situations. J Soc Psychol 132(5):641–653
Brambilla M, Riva P (2017) Self-image and schadenfreude: Pleasure at others’ misfortune enhances satisfaction of basic human needs. Eur J Soc Psychol 47(4):399–411. https://doi.org/10.1002/ejsp.2229
Broadbent E (2017) Interactions with robots: the truths we reveal about ourselves. In S T Fiske (Ed.) Annual review of psychology, 68 pp. 627–652).
Campbell DT (1965) Ethnocentric and other altruistic motives. Nebr Symp Motiv 13:283–311
Cikara M, Fiske ST (2013) Their pain, our pleasure: stereotype content and schadenfreude. Annals of the New York Academy of Sciences, Newyork
Combs D, Powell C, Schurtz DR, Smith RH (2009) Politics, schadenfreude, and ingroup identification: the sometimes happy thing about a poor economy and death. J Exp Soc Psychol 45(4):635–646
Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 35(8):982–1003. https://doi.org/10.1287/mnsc.35.8.982
Epley N, Waytz A, Cacioppo JT (2007) On seeing human: a three-factor theory of anthropomorphism. Psychol Rev 114(4):864–886. https://doi.org/10.1037/0033-295x.114.4.864
Ferrari F, Paladino MP, Jetten J (2016) Blurring human-machine distinctions: anthropomorphic appearance in social robots as a threat to human distinctiveness. Int J Soc Robot 8(2):287–302. https://doi.org/10.1007/s12369-016-0338-y
Festinger L, Hutte HA (1954) An experimental investigation of the effect of unstable interpersonal relations in a group. J Abnormal Psychol 49(1):513–522. https://doi.org/10.1037/h0058604
Fiske & Susan T, (2018) Stereotype content: warmth and competence endure. Curr Direct Psychol Sci 27(2):67
Fraune M R, Sabanovic S, & Smith E R (2017) Teammates first: favoring ingroup robots over outgroup humans. 1432–1437.
Fraune M R, Sherrin S, Abanovi S, & Smith E R (2019) Is human-robot interaction more competitive between groups than between individuals? paper presented at the 2019 14th ACM/IEEE International conference on human-robot interaction (HRI).
Gnambs T, Appel M (2019) Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe. Comput Human Behav 93:53–61
Greenier KD (2015) See you fall vs taking you down: the role of agency and liking in schadenfreude. Psychol Reports 116(3):941
Heider F (1958) The psychology of interpersonal relations. Wiley, New York
Jackson JW (1993) Realistic group conflict theory - a review and evaluation of the theoretical and empirical literature. Psychol Rec 43(3):395–414
Kim Y, Mutlu B (2014) How social distance shapes human–robot interaction. Int J Hum Comput Stud 72(12):783–795
Lefkeli D, Ozbay Y, Gürhan-Canli Z, Eskenazi T (2020) Competing with or against cozmo the robot: influence of interaction context and outcome on mind perception. Int J Soc Robot 13(8):715
Lehr SA, Ferreira ML, Banaji MR (2017) When outgroup negativity trumps ingroup positivity: fans of the boston red sox and New York Yankees place greater value on rival losses than own-team gains. Group Process Intergroup Relat 22(1):26–42. https://doi.org/10.1177/1368430217712834
Lehr SA, Ferreira ML, Banaji MR (2017) When outgroup negativity trumps ingroup positivity: fans of the boston red sox and New York Yankees place greater value on rival losses than own-team gains. Group Process Intergroup Relat 22(1):136843021771283
Li D, Rau P, Ye L (2010) A cross-cultural study: effect of robot appearance and task. Int J Soc Robot 2(2):175–186
Macdorman KF, Green RD, Ho CC, Koch CT (2009) Too real for comfort? Uncanny responses to computer generated faces. Comput Human Behav 25(3):695–710
Manyika J, Chui M, Miremadi M, Bughin J, George K, Willmott P, Dewhurst M (2017) A future that works: automation, employment, and productivity. McKinsey Glob Inst Res Tech Rep 60:1–135
Marco B, Paolo R (2016) Predicting pleasure at others’ misfortune: Morality trumps sociability and competence in driving deservingness and schadenfreude. Motiv Emot 40:1–11
Marker C, Gnambs T, Appel M (2022) Exploring the myth of the chubby gamer: a meta-analysis on sedentary video gaming and body mass. Soc Sci Med 301:112325. https://doi.org/10.1016/j.socscimed.2019.05.030
Müller B, Gao X, Nijssen S, Damen T (2021) I robot: how human appearance and mind attribution relate to the perceived danger of robots. Int J Soc Robot 13(7):691
Mutlu B, Osman S, Forlizzi J, Hodgins J, & Kiesler S (2006) Task structure and user attributes as elements of human-robot interaction design. paper presented at the IEEE international symposium on robot & human interactive communication.
Nass C, Moon Y (2000) Machines and mindlessness: social responses to computers. J Soc Issues 56(1):81
Oliveira R, Arriaga P, Correia F, Paiva A (2020) Looking beyond collaboration: socioemotional positive, negative and task-oriented behaviors in human-robot group interactions. Int J Soc Robot 12(2):505
Ozkara BY (2021) The neural substrate of schadenfreude: the effects of competition level changes on the processing of pain in others. New Ideas Psychol 62(2):100853
Pandey AK, Silva LD, Alami R (2016) A human-robot competition: towards evaluating robots’ reasoning abilities for HRI. Springer International Publishing, London
Phillips E, Xuan Z, Ullman D, & Malle B (2018) What is human-like?: Decomposing robots' human-like appearance using the anthropomorphic roBOT (ABOT) Database. paper presented at the the 2018 ACM/IEEE international conference.
Prewett MS, Johnson RC, Saboe KN, Elliott LR, Coovert MD (2010) Managing workload in human–robot interaction: a review of empirical studies. Comput Hum Behav 26(5):840–856
Reeves B, Nass C (1996) The media equation: how people treat computers, television, and new media like real people and places. Cambridge University Press
Reysen, & Stephen. (2005) Construction of a new scale: the reysen likability scale. Soc Behav Personal Int J 33(2):201–208
Riek BM, Mania EW, Gaertner SL (2006) Intergroup threat and outgroup attitudes: a meta-analytic review. Personal Soc Psychol Rev 10(4):336
Safeea M, Neto P, Béarée R (2019) The third hand. Springer, Cham, Cobots Assisted Precise Assembly
Shah J, Wiken J, Williams B, Breazeal C (2011) Improved human-robot team performance using chaski, a human-inspired plan execution system. In: Proceedings of the 6th International Conference on Human-Robot Interaction. ACM, Lausanne, pp 29–36
Short E, Hart J W, Vu M, & Scassellati B (2010) No fair!! an interaction with a cheating robot. paper presented at the acm/ieee international conference on human robot interaction.
Smith RH, Turner TJ, Garonzik R, Leach CW, Urch-Druskat V, Weston CM (1996) Envy and schadenfreude. Pers Soc Psychol Bull 22(2):158–168
Stein J-P, Liebold B, Ohler P (2019) Stay back, clever thing! Linking situational control and human uniqueness concerns to the aversion against autonomous technology. Comput Hum Behav 95:73–82. https://doi.org/10.1016/j.chb.2019.01.021
Stein JP, Appel M, Jost A, Ohler P (2020) Matter over mind? How the acceptance of digital entities depends on their appearance, mental prowess, and the interaction between both. Int J Hum Comput Stud 142:102463
Stephan WG, Ybarra O, Bachman G (1999) Prejudice toward immigrants. J Appl Soc Psychol 29(11):2221–2237
Ullman D, Leite I, Phillips J, Kim-Cohen J, & Scassellati B (2014) Smart human, smarter robot: how cheating affects perceptions of social agency. paper presented at the proceedings of the 36th annual conference of the cognitive science society (CogSci2014).
van Dijk WW, Ouwerkerk JW, Wesseling YM, van Koningsbruggen GM (2011) Towards understanding pleasure at the misfortunes of others: the impact of self-evaluation threat on schadenfreude. Cogn Emot 25(2):360–368. https://doi.org/10.1080/02699931.2010.487365
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Appendices
Appendix 1
Human-likeness |
|---|
Please rate the intelligence level of the robot in the video |
Please rate how much humanity you feel in the robot? |
To what extent do you think the robot in the video has realistic feelings? |
Please rate the level of human-likeness of the robots |
To what extent do you think the robot design in the video resembles a real human? |
Please rate the robot in the video, and its ability to understand accept as well as express human emotions |
Uncanny valley-eeriness |
Please rate whether the robot in the video is bland or scary… |
Please rate whether the robots in the video are bland or easy to attract attention… |
Please rate whether the robot in the video is clichéd or exciting… |
Do you see the robot in the video with no emotion or with hair standing on end… |
Please rate whether the robot in the video is boring or shocking… |
Please evaluate whether the robot in the video is boring and stagnant or weird and perverse… |
Is the robot in the video ordinary or unbelievable… |
Please rate the robot in the video as bland or weird… |
Please evaluate whether the robot in the video is predictable or weird unconventional… |
Uncanny valley-likeability |
Please rate the level of warmth of the robots in the video on a scale from 1 to 7… |
Please rate the friendliness level of the robot in the video from 1 to 7… |
Please rate the likability level of the robots in the video on a scale from 1 to 7… |
Please rate the accessibility of the robots in the video on a scale from 1 to 7… |
Utilization |
How willing are you to work with the robot in the video? On a scale from 1 to 7… |
How willing are you to talk to the robot in the video about personally relevant issues and listen to its advice? |
Schadenfreude-negative affect |
Please tell me how much the result of this interview made you feel frustrated… |
Please tell me to what extent the results of this interview in the video made you feel angry… |
To what extent did the results of this interview make you feel down… |
How much did the interview result make you feel irritated? |
Schadenfreude-positive affect |
How satisfied are you with the results of this interview… |
How satisfied are you with this interview… |
Please rate your level of excitement after knowing the results of the interview… |
Please rate how happy you are to know the result of the interview… |
Schadenfreude-Envy |
Have you felt anger at the presence of artificial intelligence in the video and the seizure of work resources… |
Have you felt envious of the AI robots in the video, on a scale from 1 to 7… |
Have you been frustrated and upset by the presence of AI robots in the video… |
Appendix 2
To make the experiment more rigorous, we further explored whether the strong correlation between the sense of competition and schadenfreude holds in the human–robot context.
2.1 Subjects
Subjects (N = 40; 31 female; 9 male; mean age = 22.13) were recruited through the internet. Each subject gave informed consent at the beginning of the study after receiving comprehensive information about the voluntary nature of the study and the anonymity of his or her participation. To compensate them for their time and effort, all subjects received a 5RMB payment.
2.2 Procedure
The experimental procedure was largely the same as in Experiment 1, with the only difference being that we measured subjects' sense of competition directly after the presentation of the robot's self-introduction (See the figure below).
One of the three experimental rounds insert
The specific sequence for each robot was as follows: subjects first watched a video of the robot conducting a self-introduction, then rated the level of sense of competition, and then learned the results of the robot interview (subjects were randomly assigned to either the success condition or the failure condition), and then completed the schadenfreude scale. This process was repeated three times (all subjects watched three human-like level robots introduce themselves), once for each robot.
2.3 Measure
To measure the sense of competition, we referred to existing studies and made some modifications. The purpose of the measure was to directly measure whether subjects had a sense of competition with the robot, so a total of six items were included, "Do you think the robot in the video is your competitor" "Do you think the robot in the video will compete with you for a job" ……, from 1 to 7, with 1 representing not at all and 7 representing very much, the questionnaire showed acceptable reliability with Cronbach's α = 0.85; Schadenfreude was measured the same as in Experiments 1 and 2 [18, 49].
Result
We performed a Pearson correlation analysis for each of the two conditions, robot success and failure, as a way to examine the correlation between subjects’ sense of competition and schadenfreude toward the robot. In the robot failure condition, the correlation between competitive awareness and schadenfreude-envy scores was significant, r = 0.51, p < 0.001, indicating that the stronger the subjects' competitive awareness of the robot the higher the level of envy; the strong correlation between schadenfreude and competitive awareness was more pronounced in the robot success condition, and the correlation between sense of competition and schadenfreude-negative affect was significant, r = 0.63, p < 0.001, indicating that the stronger the sense of competition, the more pronounced the negative affect of the subjects when they saw the success of the robots, and the negative correlation was significant between sense of competition and schadenfreude—positive affect, r = − 0.36, p < 0.05, indicating that the stronger the sense of competition the lower the positive affect of the subjects when they saw the success of the robots, the correlation between the sense of competition and schadenfreude—envy was also significant, r = 0.82, p < 0.000, indicating that the more competitive subjects were, the higher the level of envy towards the robot. The above results demonstrated that the strong correlation between the sense of competition and schadenfreude persisted when the objects were humans and robots. In the human–robot situation, schadenfreude is a valid indirect measure of competitive awareness.
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Yang, D., He, X. The Transition of Robot Identity from Partner to Competitor and Its Implications for Human–Robot Interaction. Int J of Soc Robotics 14, 2029–2044 (2022). https://doi.org/10.1007/s12369-022-00932-8
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DOI: https://doi.org/10.1007/s12369-022-00932-8