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The Doors of Social Robot Perception: The Influence of Implicit Self-theories

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Abstract

Understanding people’s perceptions and inferences about social robots and, thus, their responses toward them, constitutes one of the most pervasive research themes in the field of Human–Robot interaction today. We herein augment and extend this line of work by investigating, for the first time, the novel proposition that one’s implicit self-theory orientation (underlying beliefs about the malleability of self-attributes, such as one’s intelligence), can influence one’s perceptions of emerging social robots developed for everyday use. We show that those who view self-attributes as fixed (entity theorists) express greater robot anxiety than those who view self-attributes as malleable (incremental theorists). This result holds even when controlling for well-known covariate influences, like prior robot experience, media exposure to science fiction, technology commitment, and certain demographic factors. However, only marginal effects were obtained for both attitudinal and intentional robot acceptance, respectively. In addition, we show that incremental theorists respond more favorably to social robots, compared to entity theorists. Furthermore, we find evidence indicating that entity theorists exhibit more favorable responses to a social robot positioned as a servant. We conclude with a discussion about our findings.

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Notes

  1. Some have argued [22, 150, e.g.,], in particular, that individual difference research in HRI is, at present, only partly understood and warrants further investigation.

  2. The term “implicit self-theories” is used herein, rather than “mindset,” as the latter is an ambiguous term with numerous conceptualisations and diverse meanings (see, for example, [51, 54, 103, 131]). Accordingly, the terms “incremental theories” and “entity theories,” are used here, instead of “fixed” and “growth” mindsets (see also [147]).

  3. Following Bernotat and Eyssel [11], attitudinal and intentional robot acceptance were assessed as different dimensions of robot acceptance. According to Ezer [46], attitudinal robot acceptance refers to one’s positive beliefs regarding a robot more generally, whereas intentional acceptance refers to an individual’s intention to purchase or use a robot.

  4. For the purposes of this study, the role of “assistant” was used rather than “partner.” The rationale for this is the empirical evidence on robot role, which has repeatedly shown that people distinguish between the two roles (servant vs. assistant). In contrast, there appears to be much less evidence for partner role type perceptions in the HRI literature (e.g., [27]).

  5. As described in Sect. 2.3.1, higher (vs. lower) scores on the implicit self-theories measure indicate more (vs. less) of an incremental (vs. entity) theory. Therefore, positive (vs. negative) statistically significant effects indicate an association between incremental (vs. entity) theory and the dependant variable of interest.

References

  1. (2020) Conference Proceedings. Proceedings of the 2020 ACM/IEEE International Conference on Human–Robot Interaction, ISBN 9781450367462, Association for Computing Machinery, Cambridge, UK

  2. Aiken LS, West SG, Reno RR (1991) Multiple regression: testing and interpreting interactions. Sage, Thousand Oaks

    Google Scholar 

  3. Aldahdouh TZ, Nokelainen P, Korhonen V (2018) Innovativeness of staff in higher education: Do implicit theories and goal orientations matter? Int J Higher Edu 7(2):43–57

    Google Scholar 

  4. Andrist S, Mutlu B, Tapus A (2015) Look like me: matching robot personality via gaze to increase motivation. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, ACM, New York, NY, USA, CHI ’15, pp 3603–3612, https://doi.org/10.1145/2702123.2702592,

  5. Appel M, Izydorczyk D, Weber S, Mara M, Lischetzke T (2020) The uncanny of mind in a machine: Humanoid robots as tools, agents, and experiencers. Comput Hum Behav 102:274–286

    Google Scholar 

  6. Aruguete MS, Huynh H, Browne BL, Jurs B, Flint E, McCutcheon LE (2019) How serious is the carelessness problem on mechanical turk? Int J Soc Res Methodol 22(5):441–449. https://doi.org/10.1080/13645579.2018.1563966

    Article  Google Scholar 

  7. Bartneck C, Suzuki T, Kanda T, Nomura T (2007) The influence of peoples culture and prior experiences with aibo on their attitude towards robots. Ai Soc 21(1–2):217–230

    Google Scholar 

  8. Bartneck C, Duenser A, Moltchanova E, Zawieska K (2015) Comparing the similarity of responses received from experiments conducted in amazon’s mechanical turk to experiments conducted with traditional methods. PLoS ONE 10(4):e0121595. https://doi.org/10.1371/journal.pone.0121595

    Article  Google Scholar 

  9. Bartneck C, Belpaeme T, Eyssel F, Kanda T, Keijsers M, Abanovi S (2020) Human-Robot interaction: an introduction. Cambridge University Press, Cambridge

    Google Scholar 

  10. Beraldo G, Di Battista S, Badaloni S, Menegatti E, Pivetti M (2019) Sex differences in expectations and perception of a social robot. In: 2018 IEEE workshop on advanced robotics and its social impacts (ARSO), IEEE, pp 38–43

  11. Bernotat J, Eyssel F (2018) Can (t) wait to have a robot at home?-japanese and german users’ attitudes toward service robots in smart homes. In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN), IEEE, pp 15–22

  12. Blackwell LS, Trzesniewski KH, Dweck CS (2007) Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Dev 78(1):246–263

    Google Scholar 

  13. Buhrmester M, Kwang T, Gosling S (2011) Amazon’s mechanical turk: A new source of inexpensive, yet high-quality data? Perspect Psychol Sci 6(1):3–5. https://doi.org/10.1037/14805-009

    Article  Google Scholar 

  14. Burnette JL, O’Boyle EH, VanEpps EM, Pollack JM, Finkel EJ (2013) Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychol Bull 139(3):655

    Google Scholar 

  15. Cagiltay B, Ho HR, Michaelis JE, Mutlu B (2020) Investigating family perceptions and design preferences for an in-home robot. In: Proceedings of the interaction design and children conference, pp 229–242

  16. Chandler J, Paolacci G, Mueller P (2013) Risks and rewards of crowdsourcing marketplaces. Springer, Berlin, pp 377–392

    Google Scholar 

  17. Chiu Cy, Dweck CS, Tong JY, Fu JH (1997a) Implicit theories and conceptions of morality. J Personal Soc Psychol 73(5):923

    Google Scholar 

  18. Chiu Cy, Hong Yy, Dweck CS (1997b) Lay dispositionism and implicit theories of personality. J Personal Soc Psychol 73(1):19

    Google Scholar 

  19. Chmielewski M, Kucker SC (2020) An mturk crisis? shifts in data quality and the impact on study results. Soc Psychol Personal Sci 11(4):464–473

    Google Scholar 

  20. Clements WA (2000) From an implicit to an explicit theory of mind. Beyond Dissociation Int Dissociated Implicit Explicit Process 22:273

    Google Scholar 

  21. Clements WA, Perner J (1994) Implicit understanding of belief. Cognit Dev 9(4):377–395

    Google Scholar 

  22. Collins EC (2019) Drawing parallels in humanother interactions: a trans-disciplinary approach to developing humanrobot interaction methodologies. Philos Trans R Soc B 374(1771):20180433

    Google Scholar 

  23. Copleston S, Bugmann G (2008) Personal robot user expectations. Adv Commun Comput Netw Secur 5:230

    Google Scholar 

  24. Damholdt MF, Vestergaard C, Nrskov M, Hakli R, Larsen S, Seibt J (2020) Towards a new scale for assessing attitudes towards social robots: The attitudes towards social robots scale (asor). Interact Stud 21(1):24–56

    Google Scholar 

  25. Damiano L, Dumouchel P (2018) Anthropomorphism in humanrobot co-evolution. Frontiers Psychol 9(468), https://doi.org/10.3389/fpsyg.2018.00468,

  26. Dautenhahn K (2007) Methodology & themes of Human-Robot interaction: A growing research field. Int J Adv Robot Syst 4(1):15

    Google Scholar 

  27. Dautenhahn K, Woods S, Kaouri C, Walters ML, Koay KL, Werry I (2005) What is a robot companion-friend, assistant or butler? In: 2005 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 1192–1197

  28. De Castella K, Goldin P, Jazaieri H, Ziv M, Dweck CS, Gross JJ (2013) Beliefs about emotion: links to emotion regulation, well-being, and psychological distress. Basic Appl Soc Psychol 35(6):497–505

    Google Scholar 

  29. De Graaf MM, Allouch SB, Klamer T (2015) Sharing a life with harvey: exploring the acceptance of and relationship-building with a social robot. Comput Hum Behav 43:1–14

    Google Scholar 

  30. Doron J, Stephan Y, Boich J, Scanff CL (2009) Coping with examinations: exploring relationships between students’ coping strategies, implicit theories of ability, and perceived control. Br J Edu Psychol 79(3):515–528

    Google Scholar 

  31. Duffy BR (2003) Anthropomorphism and the social robot. Robot Autonom Syst 42(3–4):177–190

    MATH  Google Scholar 

  32. Dupeyrat C, Marin C (2005) Implicit theories of intelligence, goal orientation, cognitive engagement, and achievement: A test of dwecks model with returning to school adults. Contemp Edu Psychol 30(1):43–59

    Google Scholar 

  33. Dweck CS (2008) Mindset: the new psychology of success. Random House Digital, Inc

  34. Dweck CS (2013) Self-theories: Their role in motivation, personality, and development. Psychology Press, Hove

    Google Scholar 

  35. Dweck CS, Leggett EL (1988) A social-cognitive approach to motivation and personality. Psychol Rev 95(2):256

    Google Scholar 

  36. Dweck CS, Sorich L (1999) Mastery-oriented thinking. Coping 11:232–251

    Google Scholar 

  37. Dweck CS, Yeager DS (2019) Mindsets: a view from two eras. Perspect Psychol Sci 14(3):481–496. https://doi.org/10.1177/1745691618804166

    Article  Google Scholar 

  38. Dweck CS, Chiu Cy, Hong Yy (1995) Implicit theories: elaboration and extension of the model. Psychol Inq 6(4):322–333

    Google Scholar 

  39. Elliott ES, Dweck CS (1988) Goals: an approach to motivation and achievement. J Pers Soc Psychol 54(1):5

    Google Scholar 

  40. Enz S, Diruf M, Spielhagen C, Zoll C, Vargas PA (2011) The social role of robots in the futureexplorative measurement of hopes and fears. Int J Social Robot 3(3):263

    Google Scholar 

  41. Erdley CA, Loomis CC, Cain KM, Dumas-Hines F (1997) Relations among children’s social goals, implicit personality theories, and responses to social failure. Dev Psychol 33(2):263

    Google Scholar 

  42. Eurobarometer S (2012) Public attitudes towards robots. Special Eurobarometer 382 European Commission

  43. Eyssel F (2017) An experimental psychological perspective on social robotics. Robot Autonom Syst 87:363–371

    Google Scholar 

  44. Eyssel F, Hegel F (2012) (s) he’s got the look: gender stereotyping of robots 1. J Appl Soc Psychol 42(9):2213–2230

    Google Scholar 

  45. Eyssel F, De Ruiter L, Kuchenbrandt D, Bobinger S, Hegel F (2012) if you sound like me, you must be more human: on the interplay of robot and user features on Human–Robot acceptance and anthropomorphism. In: 2012 7th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 125–126

  46. Ezer N (2008) Is a robot an appliance, teammate, or friend? age-related differences in expectations of and attitudes toward personal home-based robots. Thesis, Georgia Institute of Technology

  47. Ferrari F, Paladino MP, Jetten J (2016) Blurring humanmachine distinctions: anthropomorphic appearance in social robots as a threat to human distinctiveness. Int J Social Robot 8(2):287–302

    Google Scholar 

  48. Floridi L (2017) Robots, jobs, taxes, and responsibilities. Philos Technol 30(1):1–4

    Google Scholar 

  49. Fong LHN, Chan ICC, Law R, Ly TP (2018) The Mechanism that Links the Implicit Theories of Intelligence and Continuance of Information Technology: Evidence from the Use of Mobile Apps to Make Hotel Reservations, Springer, pp 323–335

  50. Ford JB (2017) Amazon’s mechanical turk: a comment. J Advert 46(1):156–158

    Google Scholar 

  51. Freitas AL, Gollwitzer P, Trope Y (2004) The influence of abstract and concrete mindsets on anticipating and guiding others’ self-regulatory efforts. J Exp Soc Psychol 40(6):739–752

    Google Scholar 

  52. Gnambs T, Appel M (2019) Are robots becoming unpopular? changes in attitudes towards autonomous robotic systems in europe. Comput Hum Behav 93:53–61

    Google Scholar 

  53. Goetz J, Kiesler S, Powers A (2003) Matching robot appearance and behavior to tasks to improve Human–Robot cooperation. In: The 12th IEEE international workshop on robot and human interactive communication, 2003. Proceedings. ROMAN 2003., IEEE, pp 55–60

  54. Gollwitzer PM (2012) Mindset theory of action phases. Sage Publications Ltd, Thousand Oaks, pp 526–545. https://doi.org/10.4135/9781446249215.n26

  55. Gouaillier D, Hugel V, Blazevic P, Kilner C, Monceaux J, Lafourcade P, Marnier B, Serre J, Maisonnier B (2009) Mechatronic design of nao humanoid. In: 2009 IEEE international conference on robotics and automation, IEEE, pp 769–774

  56. de Graaf MM, Allouch SB, van Dijk JA (2016) Long-term evaluation of a social robot in real homes. Interact stud 17(3):462–491

    Google Scholar 

  57. de Graaf MMA, Ben Allouch S, van Dijk JAGM (2019) Why would i use this in my home? A model of domestic social robot acceptance. Hum Comput Interact 34(2):115–173. https://doi.org/10.1080/07370024.2017.1312406

    Article  Google Scholar 

  58. Gray K, Wegner DM (2012) Feeling robots and human zombies: mind perception and the uncanny valley. Cognition 125(1):125–130

    Google Scholar 

  59. Greenwald AG, Banaji MR, Rudman LA, Farnham SD, Nosek BA, Mellott DS (2002) A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychol Rev 109(1):3

    Google Scholar 

  60. Haegele M (2016) World Robotics Service Robots, 2016. IFR Statistical Department, VDMA

  61. Hafeez A (2019) Promoting upskilling: How a situational growth mindset increases consumers adoption of really new products. Unpublished doctoral dissertation, University of South-Eastern Norway

  62. Haimovitz K, Dweck CS (2017) The origins of children’s growth and fixed mindsets: new research and a new proposal. Child Dev 88(6):1849–1859

    Google Scholar 

  63. Halperin E, Russell AG, Trzesniewski KH, Gross JJ, Dweck CS (2011) Promoting the middle east peace process by changing beliefs about group malleability. Science 333(6050):1767–1769

    Google Scholar 

  64. Han B, Wang L, Li X (2019) To collaborate or serve? effects of anthropomorphized brand roles and implicit theories on consumer responses. Cornell Hospitality Quarterly p 1938965519874879

  65. Haring KS, Silvera-Tawil D, Matsumoto Y, Velonaki M, Watanabe K (2014) Perception of an android robot in japan and australia: A cross-cultural comparison. In: International conference on social robotics, Springer, pp 166–175

  66. Haring KS, Watanabe K, Velonaki M, Tossell CC, Finomore V (2018) Ffabthe form function attribution bias in humanrobot interaction. IEEE Trans Cognit Dev Syst 10(4):843–851

    Google Scholar 

  67. Haselhuhn MP, Schweitzer ME, Wood AM (2010) How implicit beliefs influence trust recovery. Psychol Sci 21(5):645–648

    Google Scholar 

  68. Haselhuhn MP, Schweitzer ME, Kray LJ, Kennedy JA (2017) Perceptions of high integrity can persist after deception: How implicit beliefs moderate trust erosion. J Bus Ethics 145(1):215–225

    Google Scholar 

  69. Hauser D, Paolacci G, Chandler JJ (2018) Common concerns with mturk as a participant pool: Evidence and solutions

  70. Heslin PA, Latham GP, VandeWalle D (2005) The effect of implicit person theory on performance appraisals. J Appl Psychol 90(5):842

    Google Scholar 

  71. Hinks T (2020) Fear of robots and life satisfaction. Int J Soc Robot. https://doi.org/10.1007/s12369-020-00640-1

    Article  Google Scholar 

  72. Hong Yy, Chiu Cy, Dweck CS, Sacks R (1997) Implicit theories and evaluative processes in person cognition. J Exp Soc Psychol 33(3):296–323

    Google Scholar 

  73. Hong Yy, Chiu Cy, Dweck CS, Lin DMS, Wan W (1999) Implicit theories, attributions, and coping: a meaning system approach. J Pers Soc Psychol 77(3):588

    Google Scholar 

  74. Horowitz MC (2016) Public opinion and the politics of the killer robots debate. Res Polit 3(1):2053168015627183

    Google Scholar 

  75. Horstmann AC, Bock N, Linhuber E, Szczuka JM, Stramann C, Krmer NC (2018) Do a robots social skills and its objection discourage interactants from switching the robot off? PLoS ONE 13(7):e0201581

    Google Scholar 

  76. Huang N, Zuo S, Wang F, Cai P, Wang F (2017) The dark side of malleability: incremental theory promotes immoral behaviors. Frontiers Psychol 8:1341

    Google Scholar 

  77. Huff C, Tingley D (2015) who are these people? Evaluating the demographic characteristics and political preferences of mturk survey respondents. Res Polit 2(3):2053168015604648

    Google Scholar 

  78. Jain SP, Weiten TJ (2020) Consumer psychology of implicit theories: a review and agenda. Consumer Psychol Rev 3(1):60–75

    Google Scholar 

  79. Jin CH (2013) The effects of individual innovativeness on users adoption of internet content filtering software and attitudes toward childrens internet use. Comput Hum Behav 29(5):1904–1916

    Google Scholar 

  80. Kahn PH, Reichert AL, Gary HE, Kanda T, Ishiguro H, Shen S, Ruckert JH, Gill B (2011) The new ontological category hypothesis in Human–Robot interaction. In: 2011 6th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 159–160

  81. Kam TK (2011) Implicit theories and the trust repair process. In: 22nd annual IACM conference paper

  82. Kees J, Berry C, Burton S, Sheehan K (2017) An analysis of data quality: professional panels, student subject pools, and amazon’s mechanical turk. J Advert 46(1):141–155

    Google Scholar 

  83. Kim HC, Kramer T (2015) Do materialists prefer the brand-as-servant? The interactive effect of anthropomorphized brand roles and materialism on consumer responses. J Consumer Res 42(2):284–299

    Google Scholar 

  84. King RB, dela Rosa ED (2019) Are your emotions under your control or not? Implicit theories of emotion predict well-being via cognitive reappraisal. Personal Individ Differ 138:177–182

    Google Scholar 

  85. Knee CR (1998) Implicit theories of relationships: assessment and prediction of romantic relationship initiation, coping, and longevity. J Pers Soc Psychol 74(2):360

    Google Scholar 

  86. Knee CR, Patrick H, Lonsbary C (2003) Implicit theories of relationships: orientations toward evaluation and cultivation. Personal Soc Psychol Rev 7(1):41–55

    Google Scholar 

  87. Kok BC, Soh H (2020) Trust in robots: challenges and opportunities. Current Robot Rep 1(4):297–309. https://doi.org/10.1007/s43154-020-00029-y

    Article  Google Scholar 

  88. Kuo IH, Rabindran JM, Broadbent E, Lee YI, Kerse N, Stafford R, MacDonald BA (2009) Age and gender factors in user acceptance of healthcare robots. In: RO-MAN 2009-The 18th IEEE international symposium on robot and human interactive communication, IEEE, pp 214–219

  89. Levy SR, Dweck CS (1998) Trait-versus process-focused social judgment. Soc Cognit 16(1):151–172

    Google Scholar 

  90. Levy SR, Stroessner SJ, Dweck CS (1998) Stereotype formation and endorsement: the role of implicit theories. J Pers Soc Psychol 74(6):1421

    Google Scholar 

  91. Lewis M, Sycara K, Walker P (2018) The role of trust in Human-Robot interaction. Springer, Cham, pp 135–159

    Google Scholar 

  92. Li F, Harmer P, Duncan TE, Duncan SC, Acock A, Boles S (1998) Approaches to testing interaction effects using structural equation modeling methodology. Multivar Behav Res 33(1):1–39

    Google Scholar 

  93. Liang Y, Lee SA (2017) Fear of autonomous robots and artificial intelligence: evidence from national representative data with probability sampling. Int J Social Robot 9(3):379–384

    Google Scholar 

  94. Mangels JA, Butterfield B, Lamb J, Good C, Dweck CS (2006) Why do beliefs about intelligence influence learning success? A social cognitive neuroscience model. Soc Cognit Affect Neurosci 1(2):75–86

    Google Scholar 

  95. Mathur P, Jain SP, Hsieh MH, Lindsey CD, Maheswaran D (2013) The influence of implicit theories and message frame on the persuasiveness of disease prevention and detection advocacies. Organ Behav Hum Decis Process 122(2):141–151

    Google Scholar 

  96. Mathur P, Block L, YucelAybat O (2014) The effects of goal progress cues: an implicit theory perspective. J Consumer Psychol 24(4):484–496

    Google Scholar 

  97. Mathur P, Chun HH, Maheswaran D (2016) Consumer mindsets and self-enhancement: signaling versus learning. J Consumer Psychol 26(1):142–152. https://doi.org/10.1016/j.jcps.2015.06.007

    Article  Google Scholar 

  98. Matthews G, Hancock PA, Lin J, Panganiban AR, Reinerman-Jones LE, Szalma JL, Wohleber RW (2020) Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems. Personality and Individual Differences p 109969

  99. Molden DC, Dweck CS (2006) Finding “meaning” in psychology: a lay theories approach to self-regulation, social perception, and social development. Am Psychol 61(3):192

    Google Scholar 

  100. Montford WJ, Leary RB, Nagel DM (2019) The impact of implicit self-theories and loss salience on financial risk. J Bus Res 99:1–11

    Google Scholar 

  101. Morsunbul U (2019) Human-Robot interaction: How do personality traits affect attitudes towards robot? J Human Sci 16(2):499–504

    Google Scholar 

  102. Munger K, Luca M, Nagler J, Tucker J (2018) Everyone on mechanical turk is above a threshold of digital literacy: Sampling strategies for studying digital media effects. Report, Working Paper. https://csdp.princeton.edu/sites/csdp/files/media/munger

  103. Murphy MC, Dweck CS (2016a) Mindsets and consumer psychology: a response. J Consumer Psychol 26(1):165–166. https://doi.org/10.1016/j.jcps.2015.06.006

    Article  Google Scholar 

  104. Murphy MC, Dweck CS (2016b) Mindsets shape consumer behavior. J Consumer Psychol 26(1):127–136

    Google Scholar 

  105. Mutlu B, Osman S, Forlizzi J, Hodgins J, Kiesler S (2006) Task structure and user attributes as elements of Human–Robot interaction design. In: ROMAN 2006-The 15th IEEE international symposium on robot and human interactive communication, IEEE, pp 74–79

  106. Neyer FJ, Felber J, Gebhardt C (2012) Entwicklung und validierung einer kurzskala zur erfassung von technikbereitschaft. Diagnostica

  107. Ng AS, Tong EM (2013) The relation between implicit theories of personality and forgiveness. Personal Relation 20(3):479–494

    Google Scholar 

  108. Ninomiya T, Fujita A, Suzuki D, Umemuro H (2015) Development of the multi-dimensional robot attitude scale: Constructs of peoples attitudes towards domestic robots. In: International conference on social robotics, Springer, pp 482–491

  109. Nomura T, Kanda T, Suzuki T, Kato K (2004) Psychology in Human–Robot communication: An attempt through investigation of negative attitudes and anxiety toward robots. In: RO-MAN 2004. 13th IEEE International workshop on robot and human interactive communication (IEEE Catalog No. 04TH8759), IEEE, pp 35–40

  110. Nomura T, Kanda T, Suzuki T, Kato K (2009) Age differences and images of robots: social survey in japan. Interact Stud 10(3):374–391

    Google Scholar 

  111. Park JK, John DR (2010) Got to get you into my life: Do brand personalities rub off on consumers? J Consumer Res 37(4):655–669

    Google Scholar 

  112. Park JK, John DR (2012) Capitalizing on brand personalities in advertising: The influence of implicit self-theories on ad appeal effectiveness. J Consumer Psychol 22(3):424–432

    Google Scholar 

  113. Park JK, John DR (2018) Developing brand relationships after a brand transgression: the role of implicit theories of relationships. J Assoc Consumer Res 3(2):175–187

    Google Scholar 

  114. Plaks JE, Stecher K (2007) Unexpected improvement, decline, and stasis: a prediction confidence perspective on achievement success and failure. J Pers Soc Psychol 93(4):667

    Google Scholar 

  115. Plaks JE, Stroessner SJ, Dweck CS, Sherman JW (2001) Person theories and attention allocation: preferences for stereotypic versus counterstereotypic information. J Pers Soc Psychol 80(6):876

    Google Scholar 

  116. Price LL, Coulter RA, Strizhakova Y, Schultz AE (2017) The fresh start mindset: transforming consumers lives. J Consumer Res 45(1):21–48

    Google Scholar 

  117. Priester JR, Petty RE (2016) A research dialogue on mindsets. J Consumer Psychol 26(1):125–126. https://doi.org/10.1016/j.jcps.2015.06.016

    Article  Google Scholar 

  118. Quintanilla VD (2011) Judicial mindsets: the social psychology of implicit theories and the law. Neb L Rev 90:611

    Google Scholar 

  119. Rai D, Lin CWW (2019) The influence of implicit self-theories on consumer financial decision making. J Bus Res 95:316–325

    Google Scholar 

  120. Ray C, Mondada F, Siegwart R (2008) What do people expect from robots? In: 2008 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 3816–3821

  121. Reich-Stiebert N, Eyssel F (2015) Learning with educational companion robots? toward attitudes on education robots, predictors of attitudes, and application potentials for education robots. Int J Social Robot 7(5):875–888. https://doi.org/10.1007/s12369-015-0308-9

    Article  Google Scholar 

  122. Reich-Stiebert N, Eyssel F, Hohnemann C (2019) Involve the user! changing attitudes toward robots by user participation in a robot prototyping process. Comput Hum Behav 91:290–296

    Google Scholar 

  123. Robb DA, Ahmad MI, Tiseo C, Aracri S, McConnell AC, Page V, Dondrup C, Chiyah Garcia FJ, Nguyen HN, Pairet E (2020) Robots in the danger zone: exploring public perception through engagement. In: Proceedings of the 2020 ACM/IEEE international conference on human–robot interaction, pp 93–102

  124. Robert L, Alahmad R, Esterwood C, Kim S, You S, Zhang Q (2020) A review of personality in humanrobot interactions. Available at SSRN 3528496

  125. Robinette P, Howard AM, Wagner AR (2017) Effect of robot performance on humanrobot trust in time-critical situations. IEEE Trans Hum Mach Syst 47(4):425–436. https://doi.org/10.1109/THMS.2017.2648849

    Article  Google Scholar 

  126. Robins RW, Pals JL (2002) Implicit self-theories in the academic domain: implications for goal orientation, attributions, affect, and self-esteem change. Self Identity 1(4):313–336

    Google Scholar 

  127. Robinson J, Rosenzweig C, Moss AJ, Litman L (2019) Tapped out or barely tapped? recommendations for how to harness the vast and largely unused potential of the mechanical turk participant pool. PLoS ONE 14(12)

  128. Rossi A, Dautenhahn K, Koay KL, Saunders J (2017) Investigating human perceptions of trust in robots for safe hri in home environments. In: Proceedings of the companion of the 2017 ACM/IEEE international conference on human–robot interaction, pp 375–376

  129. Rossi A, Dautenhahn K, Koay KL, Walters ML (2018) The impact of peoples personal dispositions and personalities on their trust of robots in an emergency scenario. Paladyn J Behav Robot 9(1):137–154

    Google Scholar 

  130. Rossi A, Dautenhahn K, Koay KL, Walters ML, Holthaus P (2020) Evaluating peoples perceptions of trust in a robot in a repeated interactions study. In: International conference on social robotics, Springer, pp 453–465

  131. Rucker DD, Galinsky AD (2016) Growing beyond growth: Why multiple mindsets matter for consumer behavior. J Consum Psychol 26(1):161–164

    Google Scholar 

  132. Rueben M, Nikolaidis S, de Graaf M, Phillips E, Robert L, Sirkin D, Kwon M, Thellman S (2020) Half day workshop on mental models of robots. In: Companion of the 2020 ACM/IEEE international conference on human–robot interaction, pp 658–659

  133. Sandoval EB, Mubin O, Obaid M (2014) Human robot interaction and fiction: a contradiction. In: International conference on social robotics, Springer, pp 54–63

  134. Santamaria T, Nathan-Roberts D (2017) Personality measurement and design in Human-Robot interaction: a systematic and critical review. In: Proceedings of the human factors and ergonomics society annual meeting, SAGE Publications Sage CA, Los Angeles, CA 61:853–857

  135. Schaefer KE (2016) Measuring trust in human robot interactions: development of the trust perception scale-HRI. Springer, pp 191–218

  136. Schermerhorn P, Scheutz M, Crowell CR (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, ACM, pp 263–270

  137. Severson RL, Carlson SM (2010) Behaving as or behaving as if? Childrens conceptions of personified robots and the emergence of a new ontological category. Neural Netw 23(8–9):1099–1103

    Google Scholar 

  138. Sevincer AT, Kluge L, Oettingen G (2014) Implicit theories and motivational focus: desired future versus present reality. Motiv Emot 38(1):36–46

    Google Scholar 

  139. Sharifi SS, Palmeira M (2017) Customers’ reactions to technological products: The impact of implicit theories of intelligence. Comput Hum Behav 77:309–316

    Google Scholar 

  140. Solberg E, Traavik LE, Wong SI (2020) Digital mindsets: recognizing and leveraging individual beliefs for digital transformation. California Management Review p 0008125620931839

  141. Song YA, Lee SY, Kim Y (2019) Does mindset matter for using social networking sites?: understanding motivations for and uses of instagram with growth versus fixed mindset. International Journal of Advertising pp 1–19

  142. Strait M, Urry HL, Muentener P (2019) Children’s responding to humanlike agents reflects an uncanny valley. In: 2019 14th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 506–515

  143. Strait MK, Aguillon C, Contreras V, Garcia N (2017) The public’s perception of humanlike robots: Online social commentary reflects an appearance-based uncanny valley, a general fear of a technology takeover and the unabashed sexualization of female-gendered robots. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), IEEE, pp 1418–1423

  144. Takayama L, Ju W, Nass C (2008) Beyond dirty, dangerous and dull: what everyday people think robots should do. In: 2008 3rd ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 25–32

  145. Tamir M, John OP, Srivastava S, Gross JJ (2007) Implicit theories of emotion: Affective and social outcomes across a major life transition. J Pers Soc Psychol 92(4):731

    Google Scholar 

  146. Walters ML, Koay KL, Syrdal DS, Dautenhahn K, Te Boekhorst R (2009) Preferences and perceptions of robot appearance and embodiment in Human–Robot interaction trials. Procs of New Frontiers in Human–Robot Interaction

  147. Wheeler SC, Omair A (2016) Potential growth areas for implicit theories research. J Consumer Psychol 26(1):137–141

    Google Scholar 

  148. Wullenkord R, Eyssel F (2020) The influence of robot number on robot group perceptiona call for action. ACM THRI 9(4):1–14

    Google Scholar 

  149. Xia Y, LeTendre G (2020) Robots for future classrooms: A cross-cultural validation study of negative attitudes toward robots scale in the us context. Int J Soc Robot pp 1–12

  150. Xu K (2019) First encounter with robot alpha: How individual differences interact with vocal and kinetic cues in users social responses. New Media Society p 1461444819851479

  151. Yogeeswaran K, Zotowski J, Livingstone M, Bartneck C, Sumioka H, Ishiguro H (2016) The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research. J Hum Robot Interact 5(2):29–47

    Google Scholar 

  152. Yorkston EA, Nunes JC, Matta S (2010) The malleable brand: the role of implicit theories in evaluating brand extensions. J Market 74(1):80–93

    Google Scholar 

  153. Zotowski J, Yogeeswaran K, Bartneck C (2017) Can we control it? autonomous robots threaten human identity, uniqueness, safety, and resources. Int J Hum Comput Stud 100:48–54

    Google Scholar 

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Acknowledgements

This research was supported by the University of Canterbury, New Zealand.

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Authors and Affiliations

  1. Human Interface Technology Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand

    D. D. Allan & Christoph Bartneck

  2. Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand

    Andrew J. Vonasch

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  1. D. D. Allan
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Allan, D.D., Vonasch, A.J. & Bartneck, C. The Doors of Social Robot Perception: The Influence of Implicit Self-theories. Int J of Soc Robotics 14, 127–140 (2022). https://doi.org/10.1007/s12369-021-00767-9

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