Customer’s Acceptance of Humanoid Robots in Services: The Moderating Role of Risk Aversion

  • Daniel BelancheEmail author
  • Luis V. Casaló
  • Carlos Flavián
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 167)


The emerging introduction of humanoid robots in service encounters is becoming a reality in the present and the short-term. Owing to this unstoppable advance, there is a need to better understand customers’ perceptions and reactions toward humanoid agents in service encounters. To shed some light on this underexplored phenomenon, this research investigates how the interaction between robot and customer’s features may contribute to a successful introduction of this disruptive innovation. Results of an empirical study with a sample of 168 US customers reveal that customer’s perceptions of robot’s human-likeness increase the intentions to use humanoid service robots. Interestingly, customers’ risk aversion moderates this relationship. Specifically, the study found that highly risk-averse customers tend to avoid using humanoids when they are perceived as highly mechanical-like. The discussion highlights the main contributions of the research, which combine previous knowledge on human–robot interaction and risk aversion from a marketing approach. Managerial implications derived from the research findings and the avenues opened for further research are described at the end.


Humanoid Robot Risk aversion 


  1. 1.
    Mende, M., Scott, M.L., van Doorn, J., Grewal, D., Shanks, I.: Service robots rising: how humanoid robots influence service experiences and elicit compensatory consumer responses. J. Mark. Res. 56(4), 1–22 (2019)CrossRefGoogle Scholar
  2. 2.
    Negahban, A., Smith, J.S.: Simulation for manufacturing system design and operation: literature review and analysis. J. Manuf. Syst. 33, 241–261 (2014)CrossRefGoogle Scholar
  3. 3.
    Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A.: Brave new world: service robots in the frontline. J. Serv. Manag. 29(5), 907–931 (2018)CrossRefGoogle Scholar
  4. 4.
    International Federation of Robotics, Robot density rises globally, (2018). Accessed 03 June 2019
  5. 5.
    Huang, M.H., Rust, R.T.: Artificial intelligence in service. J. Serv. Res. 21(2), 155–172 (2018)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Van Doorn, J., Mende, M., Noble, S.M., Hulland, J., Ostrom, A.L., Grewal, D., Petersen, J.A.: Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. J. Serv. Res. 20(1), 43–58 (2017)Google Scholar
  8. 8.
    Rosenthal-von der Püthen, A.M., Kramer, N.C.: How design characteristics of robots determine evaluation and uncanny valley related responses. Computers in Human Behavior 36, 422–439 (2014)CrossRefGoogle Scholar
  9. 9.
    Allen, D.G., Weeks, K.P., Moffitt, K.R.: Turnover intentions and voluntary turnover: the moderating roles of self-monitoring, locus of control, proactive personality, and risk aversion. J. Appl. Psychol. 90(5), 980–996 (2005)CrossRefGoogle Scholar
  10. 10.
    Walters, M.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Koay, K.L.: Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion. Auton. Robots 24(2), 159–178 (2008)CrossRefGoogle Scholar
  11. 11.
    Nass, C., Moon, Y., Fogg, B.J., Reeves, B., Dryer, D.C.: Can computer personalities be human personalities? Int. J. Hum Comput Stud. 43, 223–239 (1995)CrossRefGoogle Scholar
  12. 12.
    Luczak, H., Roetting, M., Schmidt, L.: Let’s talk: anthropomorphization as means to cope with stress of interacting with technical devices. Ergonomics 46(13–14), 1361–1374 (2003)CrossRefGoogle Scholar
  13. 13.
    Mori, M.: The uncanny valley. Energy 7(4), 33–35 (1970)Google Scholar
  14. 14.
    Tussyadiah, I.P., Park, S.: Consumer evaluation of hotel service robots. In: Stangl, B., Pesonen, J. (eds.) Information and Communication Technologies in Tourism 2018, pp. 308–320. Springer (2018)Google Scholar
  15. 15.
    Gray, K., Wegner, D.M.: Feeling robots and human zombies: mind perception and the uncanny valley. Cognition 125(1), 125–130 (2012)CrossRefGoogle Scholar
  16. 16.
    Pratt, J.W.: Risk aversion in the small and in the large. In: Uncertainty in Economics, pp. 59–79. Academic Press (1978)Google Scholar
  17. 17.
    Hofstede, G., Bond, M.H.: Hofstede’s culture dimensions: an independent validation using Rokeach’s value survey. J. Cross Cult. Psychol. 15, 417–433 (1984)CrossRefGoogle Scholar
  18. 18.
    Bao, Y., Zhou, K.Z., Su, C.: Face consciousness and risk aversion: do they affect consumer decision-making? Psychol. Market. 20(8), 733–755 (2003)CrossRefGoogle Scholar
  19. 19.
    Sun, J.: How risky are services? An empirical investigation on the antecedents and consequences of perceived risk for hotel service. Int. J. Hosp. Manag. 37, 171–179 (2014)CrossRefGoogle Scholar
  20. 20.
    Meroño-Cerdán, A.L., López-Nicolás, C., Molina-Castillo, F.J.: Risk aversion, innovation and performance in family firms. Econ. Innov. New Technol. 27(2), 189–203 (2017)CrossRefGoogle Scholar
  21. 21.
    Wu, C.H.J., Liao, H.C., Hung, K.P., Ho, Y.H.: Service guarantees in the hotel industry: their effects on consumer risk and service quality perceptions. Int. J. Hosp. Manag. 31(3), 757–763 (2012)CrossRefGoogle Scholar
  22. 22.
    Bauer, K., Hein, S.E.: The effect of heterogeneous risk on the early adoption of Internet banking technologies. J. Bank. Finance 30(6), 1713–1725 (2006)CrossRefGoogle Scholar
  23. 23.
    Tan, S.J.: Strategies for reducing consumers’ risk aversion in Internet shopping. J. Consum. Market. 16(2), 163–180 (1999)CrossRefGoogle Scholar
  24. 24.
    Schleich, J., Gassmann, X., Meissner, T., Faure, C.: A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies. Energy Econ. 80, 377–393 (2019)CrossRefGoogle Scholar
  25. 25.
    Dedeke, A.: Service quality: a fulfilment-oriented and interactions-centered approach. Manag. Serv. Qual. Int. J. 13(4), 276–289 (2003)CrossRefGoogle Scholar
  26. 26.
    De Jong, A., De Ruyter, K., Lemmink, J.: The adoption of information technology by self-managing service teams. J. Serv. Res. 6(2), 162–179 (2003)CrossRefGoogle Scholar
  27. 27.
    Cauberghe, V., De Pelsmacker, P.: The impact of banners on digital television: the role of program interactivity and product involvement. CyberPsychol. Behav. 11(1), 91–94 (2008)CrossRefGoogle Scholar
  28. 28.
    Steenkamp, J.B., Geyskens, I.: How country characteristics affect the perceived value of a Website. J. Market. 70(3), 136–150 (2006)CrossRefGoogle Scholar
  29. 29.
    Wu, L., Chen, J.L.: An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. Int. J. Hum Comput Stud. 62(6), 784–808 (2005)CrossRefGoogle Scholar
  30. 30.
    Jaccard, J., Turrisi, R.: Interaction effects in multiple regression, 72. Sage (2003)Google Scholar
  31. 31.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)CrossRefGoogle Scholar
  32. 32.
    Jaccard, J., Wan, C.: LISREL approaches to interaction effects in multiple regressions. Sage Publications, Thousand Oaks, CA (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Daniel Belanche
    • 1
    Email author
  • Luis V. Casaló
    • 2
  • Carlos Flavián
    • 1
  1. 1.Faculty of Economy and BusinessUniversity of Zaragoza (Spain)SaragossaSpain
  2. 2.Faculty of Business and Public AdministrationUniversity of Zaragoza (Spain)HuescaSpain

Personalised recommendations