Advertisement

International Journal of Social Robotics

, Volume 9, Issue 3, pp 359–377 | Cite as

The Effects of Organism- Versus Object-Based Robot Design Approaches on the Consumer Acceptance of Domestic Robots

  • Sonya S. KwakEmail author
  • Jun San Kim
  • Jung Ju Choi
Article

Abstract

The current size of the market for domestic robots is smaller than expected, despite the rapid advance in robotic technologies. On the basis of the previous literature, we attempt to make a distinction between two design approaches for domestic robots: organism- versus object-based robot designs. This research investigates the effects of these domestic robot design approaches on consumer acceptance. Encompassing the theories of Human–Robot Interaction, design, and marketing, we predict that object-based robot design will be more effective than organism-based robot design for consumers’ evaluation of and intent to purchase domestic robots. We also predict that the categorization of robots will mediate the effects of robot design approaches on the evaluation. Two studies using two types of robots were conducted, and the results supported the hypotheses.

Keywords

Robot designs Organism-based robot Object-based robot Human–Robot interaction Consumer acceptance Categorization 

Notes

Acknowledgements

This work was supported by Industrial Technology Innovation Program (Design Technology Innovation Program) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (No. 10050008). This work was also supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2015-413-S1A5B8036983).

References

  1. 1.
    Alexander DL, Lynch JG, Wang Q (2008) As time goes by: do cold feet follow warm intentions for really new versus incrementally new products? J Mark Res (JMR) 45(3):307–319CrossRefGoogle Scholar
  2. 2.
    Andrist S, Spannan E, Mutlu B (2013) Rhetorical robots: making robots more effective speakers using linguistic cues of expertise. In: Proceedings of the ACM/IEEE International Conference on Human–Robot Interaction (HRI’13), pp 341–348Google Scholar
  3. 3.
    Ayesh A (2006) Structured sound based language for emotional robotic communicative interaction. In Proceedings of the 15th. In: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’06), pp 135–140Google Scholar
  4. 4.
    Ayesh A, Joseph C, Perril S, Thomas S (2014) Aesthetics of a robot: case study on AIBO dog robots for buddy-ing devices. J Intell Comput 5(1):1–15Google Scholar
  5. 5.
    Baron RB, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Personal Soc Psychol 51(6):1173–1182CrossRefGoogle Scholar
  6. 6.
    Bartneck C, Kanda T, Ishiguro H, Hagita N (2009) My robotic doppelgänger—a critical look at the uncanny valley theory. In: Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’09), pp 269–276Google Scholar
  7. 7.
    Bartneck C, Kulić D, Croft E, Zoghbi S (2009) Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robots 1:71–81CrossRefGoogle Scholar
  8. 8.
    Bartneck C, Bleeker T, Bun J, Fens P, Riet L (2010) The influence of robot anthropomorphism on the feelings of embarrassment when interacting with robots. Paladyn J Behav Robot 1(2):109–115Google Scholar
  9. 9.
    Bloch PH (1995) Seeking the ideal form: product design and consumer response. J Mark 59(3):16–29CrossRefGoogle Scholar
  10. 10.
    Breazeal C (1999) Robot in society: friend or appliance? In: Proceedings of Agents99 Workshop on Emotion Based Architectures, pp 18–26Google Scholar
  11. 11.
    Breazeal CL (2002) Designing sociable robots. MIT Press, CambridgezbMATHGoogle Scholar
  12. 12.
    Brenton H, Gillies M, Ballin D, Chattin D (2005) The uncanny valley: does it exist? In: Proceedings of Conference of Human Computer Interaction, Workshop on Human Animated Character InteractionGoogle Scholar
  13. 13.
    Carmon Z, Ariely D (2000) Focusing on the forgone: how value can appear so different to buyers and sellers. J Consum Res 27(3):360–370CrossRefGoogle Scholar
  14. 14.
    Cohen JB, Basu K (1987) Alternative models of categorization: toward a contingent processing framework. J Consum Res 13(4):455–472CrossRefGoogle Scholar
  15. 15.
    Consumer Reports (2013) Consumer reports web site. http://www.consumerreports.org. Accessed 31 Jan 2015
  16. 16.
    Creusen M, Schoormans J (2005) The different roles of product appearance in consumer choice. J Prod Innov Manag 22(1):63–81CrossRefGoogle Scholar
  17. 17.
    Crilly N, Good D, Matravers D, Clarkson PJ (2008) Design as communication: exploring the validity and utility of relating intention to interpretation. Des Stud 29(5):425–457CrossRefGoogle Scholar
  18. 18.
    Crilly N, Moultrie J, Clarkson PJ (2009) Shaping things: intended consumer response and the other determinants of product form. Des Stud 30(3):224–254CrossRefGoogle Scholar
  19. 19.
    DiSalvo CF, Gemperle F, Forlizzi J, Kiesler S (2002) All robots are not created equal: The design and perception of organism-based robot heads. In: Proceedings of the 4th Conference on Designing Interactive Systems (DIS’02), pp 321–326Google Scholar
  20. 20.
    Ferber D (2003) The man who mistook his girlfriend for a robot. Pop Sci 236:1–7Google Scholar
  21. 21.
    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):287–302CrossRefGoogle Scholar
  22. 22.
    Fong T, Nourbakhsh I, Dautenhahn K (2003) A survey of socially interactive robots. Robot Auton Syst 42(4–3):143–166CrossRefzbMATHGoogle Scholar
  23. 23.
    Forlizzi J, DiSalvo C (2006) Service robots in the domestic environment: a study of the roomba vacuum in the home. In: Proceedings of the ACM/IEEE International Conference on Human–Robot Interaction (HRI’06), pp 258–265Google Scholar
  24. 24.
    Forlizzi J, DiSalvo C, Zimmerman J, Mutlu B, Hurst A (2005) The sensechair: the lounge chair as an intelligent assistive device for elders. In: Proceedings of the Conference on Designing for User Experiences (DUX’05), pp 1–13Google Scholar
  25. 25.
    Friedman B, Khan PH, Hagman J (2003) Hardware companions?—What online AIBO discussion forums reveal about the human–robotic relationship. In: Proceedings of the CHI 2003 Conference on Human Factors in Computing Systems (CHI’03), pp 273–279Google Scholar
  26. 26.
    Gouaillier D, Hugel V, Blazevic P, Kilner C, Monceaux J, Lafourcade P, Marnier B, Serre J, Maisonnier B (2008) The nao humanoid: a combination of performance and affordability. In: Proceedings of the IEEE International Conference on Robotics and AutomationGoogle Scholar
  27. 27.
    Graaf MMA, Allouch SB (2016) The influence of prior expectations of a robot’s lifelikeness on users’ intentions to treat a zoomorphic robot as a companion. Int J Soc Robot 1–16Google Scholar
  28. 28.
    Gregan-Paxton J, Hoeffler S, Zhao M (2005) When categorization is ambiguous: factors that facilitate the use of a multiple category inference strategy. J Consum Psychol (Lawrence Erlbaum Associates) 15(2):127–140CrossRefGoogle Scholar
  29. 29.
    Hanool Robotics (2003) Ottoro. http://robotics.co.kr/english/sub_b11.html. Accessed 31 Jan 2015
  30. 30.
    Hanool Robotics (2006) Nettoro. http://www.robotics.co.kr/home/kor/product08.html. Accessed 31 Jan 2015
  31. 31.
    Hanson D, Olney A, Pereira IA, Zielke M (2005) Upending the uncanny valley. In: Proceedings of the American association for artificial intelligence (AAAI), pp 24–31Google Scholar
  32. 32.
    Hegel F, Krach S, Kircher T, Wrede B, Sagerer G (2008) Understanding social robots: a user study on anthropomorphism. In: Proceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’08), pp 574–579Google Scholar
  33. 33.
    Herzenstein M, Posavac SS, Brakus JJ (2007) Adoption of new and really new products: the effects of self-regulation systems and risk salience. J Mark Res 44(2):251–260CrossRefGoogle Scholar
  34. 34.
    Hoffman G (2007) Ensemble: fluency and embodiment for robots acting with humans. Ph.D. dissertation, Massachusetts Institute of Technology, CambridgeGoogle Scholar
  35. 35.
    Hornyak T (2012) Roomba turns 10, still the best baby chariot around. http://news.cnet.com/8301-17938_105-57514087-1/roomba-turns-10-still-the-best-baby-chariot-around/. Accessed 31 Jan 2015
  36. 36.
    IFR (2014) World robotics 2014 service robots. Service Robot Statistics. http://www.ifr.org/service-robots/statistics/. Accessed 25 Sep 2015
  37. 37.
    IFR (2014) World robotics 2014 industrial robots. Industrial Robot Statistics. http://www.ifr.org/industrial-robots/statistics/. Accessed 25 Sep 2015
  38. 38.
    Jhang JH, Grant SJ, Campbell MC (2012) Get it? Got it. Good! Enhancing new product acceptance by facilitating resolution of extreme incongruity. J Mark Res 49(2):247–259CrossRefGoogle Scholar
  39. 39.
    Kanda T, Ishiguro H, Ishida T (2001) Psychological analysis on human–robot interaction. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2001), pp 4166–4171Google Scholar
  40. 40.
    Kennedy J, Baxter P, Belpaeme T (2015) Comparing robot embodiments in a guided discovery learning interaction with children. Int J Soc Robot 7(2):293–308CrossRefGoogle Scholar
  41. 41.
    Kurt T (2006) Hacking roomba: extremetech. Wiley, New YorkGoogle Scholar
  42. 42.
    Kwak SS, Kim EH, Kim J, Son Y, Kwak I, Park J, Lee EW (2012) Field trials of the block-shaped edutainment robot HangulBot. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI’12), pp 40Google Scholar
  43. 43.
    Kwak SS, Kim JS, Choi JJ (2014) Can robots be sold?: The effects of robot designs on the consumers’ acceptance of robots. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI’14), pp 220–221Google Scholar
  44. 44.
    Lajos J, Zsolt K, Amitava C, Miklos S (2009) Category activation model: a spreading activation network model of subcategory positioning when categorization uncertainty is high. J Consum Res 36(1):122–136CrossRefGoogle Scholar
  45. 45.
    Leite I, Martinho C, Paiva A (2013) Social robots for long-term interaction: a survey. Int J Soc Robot 5(2):291–308CrossRefGoogle Scholar
  46. 46.
    Li D, Rau PLP, Li Y (2010) A cross-cultural study: effect of robot appearance and task. Int J Soc Robot 2(2):175–186CrossRefGoogle Scholar
  47. 47.
    Minato T, Shimada M, Ishiguro H, Itakura S (2004) Development of an android robot for studying human–robot interaction, innovations in applied artificial intelligence. In: Proceedings of the 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE’04), pp 424–434Google Scholar
  48. 48.
    Moravec H (2003) Robots, after all. Commun ACM 46(10):90–97CrossRefGoogle Scholar
  49. 49.
    Moreau CP, Markman AB, Lehmann DR (2001) What is it? Categorization flexibility and consumers’ responses to really new products. J Consum Res 27(4):489–498CrossRefGoogle Scholar
  50. 50.
    Mori M (1970) Bukimi no tani (The uncanny valley). Energy 7(4):33–35Google Scholar
  51. 51.
    Nasar JL, Stamps AE III, Hanyu K (2005) Form and function in public buildings. J Environ Psychol 25(2):159–165CrossRefGoogle Scholar
  52. 52.
    Neuberg SL, Newsom JT (1993) Personal need for structure: individual differences in the desire for simple structure. J Person Soc Psychol 65(1):113–131CrossRefGoogle Scholar
  53. 53.
    Noseworthy TJ, Cotte J, Lee SH (2011) The effects of ad context and gender on the identification of visually incongruent products. J Consum Res 38(2):358–375CrossRefGoogle Scholar
  54. 54.
    Nowak K, Biocca F (2001) The influence of agency and the virtual body on presence, social presence and copresence in a computer mediated interaction. Presence 12(5):481–494CrossRefGoogle Scholar
  55. 55.
    Oliver RL (1980) A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res 17(4):460–469CrossRefGoogle Scholar
  56. 56.
    Paauwe RA, Hoorn JF, Konijn EA, Keyson DV (2015) Designing robot embodiments for social interaction: affordances topple realism and aesthetics. Int J Soc Robot 7(5):697–708CrossRefGoogle Scholar
  57. 57.
    Pepe A, Ellis LU, Sims VK, Chin MG (2008) Go, dog, go: maze training AIBO vs. a live dog, an exploratory study. Anthrozoos Multidiscip J Interact People Anim 21(1):71–83CrossRefGoogle Scholar
  58. 58.
    Personal Robots Group (2008) MDS overview. Personal Robots Group. http://robotic.media.mit.edu/portfolio/nexi/. Accessed 31 Jan 2015
  59. 59.
    Powers A, Kiesler S (2006) The advisor robot: Tracing people’s mental model from a robot’s physical attributes. In: Proceedings of the ACM/IEEE International Conference on Human–Robot Interaction (HRI’06), pp 218–225Google Scholar
  60. 60.
    Ray C, Mondada F, Siegwart R (2008) What do people expect from robots? In: Proceedings of the IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems (IROS’08), pp 3816–3821Google Scholar
  61. 61.
    Reber R, Schwarz N, Winkielman P (2004) Processing fluency and aesthetic pleasure: is beauty in the perceiver’s processing experience? Person Soc Psychol Rev 8(4):364–382CrossRefGoogle Scholar
  62. 62.
    Reeves B, Nass C (1996) The media equation: how people treat computers, television, and new media like real people and places. Cambridge University Press, CambridgeGoogle Scholar
  63. 63.
    Rey F, Leidi M, Mondada F (2009) Interactive mobile robotic drinking glasses. Proc Distrib Auton Robot Syst 8:543–551Google Scholar
  64. 64.
    Rosch E (1978) Principles of categorization. In: Rosch E, Lloyd BB (eds) Cognition and categorization. Lawrence Erlbaum Associates, HillsdaleGoogle Scholar
  65. 65.
    Sakamoto D, Kanda T, Ono T, Ishiguro H, Hagita N (2007) Android as a telecommunication medium with a human-like presence. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI’07), pp 193–200Google Scholar
  66. 66.
    Sandry E (2015) Re-evaluating the form and communication of social robots. Int J Soc Robot 7(1):1–12CrossRefGoogle Scholar
  67. 67.
    Sirkin D, Mok B, Yang S, Ju W (2015) Mechanical ottoman: how robotic furniture offers and withdraws support. In: Proceedings of the ACM/IEEE International Conference on Human–Robot Interaction (HRI’15), pp 11–18Google Scholar
  68. 68.
    Softbank (2015) Pepper. http://www.softbank.jp/en/robot/ Accessed 22 Mar 2016
  69. 69.
    Sphero (2014) Sphero 2B. http://www.sphero.com/sphero. Accessed 22 Mar 2016
  70. 70.
    Sujan M (1985) Consumer knowledge: effects on evaluation strategies mediating consumer judgments. J Consum Res 12(1):31–46MathSciNetCrossRefGoogle Scholar
  71. 71.
    Sujan M, Dekleva C (1987) Product categorization and inference making: some implications for comparative advertising. J Consum Res 14(3):372–378CrossRefGoogle Scholar
  72. 72.
    Sullivan L (1896) The tall office building artistically considered. In: Isabella A (ed) Louis Sullivan, kindergarten chats and other writings. George Wittenborn, New YorkGoogle Scholar
  73. 73.
    Takayama L, Ju W, Nass C (2008) Beyond dirty, dangerous and dull: What everyday people think robots should do. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI’08), pp 25–32Google Scholar
  74. 74.
    UPI (2006) Sony to stop producing Aibo, the robot dog. Science News. http://www.upi.com/Science_News/2006/01/26/Sony-to-stop-producing-Aibo-the-robot-dog/UPI-63471138289317/. Accessed 31 Jan 2015
  75. 75.
    Van Rompay TJL, De Vries PW, Van Venrooij XG (2010) More than words: on the importance of picture-text congruence in the online environment. J Interact Mark 24(1):22–30CrossRefGoogle Scholar
  76. 76.
    Walters M, Syrdal D, Dautenhahn K, Boekhorst R, Koay K (2008) Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion. Auton Robot 24(2):159–178CrossRefGoogle Scholar
  77. 77.
    Walters M, Koay K, Syrdal D, Dautenhahn K, Boekhorst R (2009) Preferences and perceptions of robot appearance and embodiment in human-robot interaction trials. In: Proceedings of the New Frontiers in Human-Robot Interaction, Symposium at the AISB09 Convention, pp 136–143Google Scholar
  78. 78.
    Woods S, Walters M, Koay K, Dautenhahn K (2006) Methodological issues in HRI: A comparison of live and video based methods in robot to human approach direction trials. In: Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’06), pp 51–58Google Scholar
  79. 79.
    Zhao M, Hoeffler S, Dahl DW (2009) The role of imagination-focused visualization on new product evaluation. J Mark Res 46(1):46–55CrossRefGoogle Scholar
  80. 80.
    Zhao M, Hoeffler S, Dahl DW (2011) Mental simulation and product evaluation: the affective and cognitive dimensions of process versus outcome simulation. J Mark Res 48(5):827–839CrossRefGoogle Scholar
  81. 81.
    Złotowski J, Proudfoot D, Yogeeswaran K, Bartneck C (2014) Anthropomorphism: opportunities and challenges in human–robot interaction. Int J Soc Robot 6:1–14CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Industrial DesignEwha Womans UniversitySeoulKorea
  2. 2.Data Analytics DepartmentKB Financial GroupSeoulKorea

Personalised recommendations