Skip to main content
SpringerLink
Log in

The Social Perception of Robots Scale (SPRS): Developing and Testing a Scale for Successful Interaction Between Humans and Robots

  • Conference paper
  • First Online:
Collaborative Networks in Digitalization and Society 5.0 (PRO-VE 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 662))

Included in the following conference series:

Abstract

Robots are increasingly populating social settings. Social robots should elicit positive associations to be accepted and integrated into daily lives. Even though social perception is multi-dimensional, available scales do not adequately picture this complexity in the perception of robots. To develop a new scale, we aggregated data on social perception of robots, initially operationalized as competence, sociability, morality, and anthropomorphism from four prior studies. An exploratory factor analysis on a random sample revealed three factors: “anthropomorphism”, “morality/sociability”, and “activity/cooperation”. To validate these results, we performed confirmatory factor analysis (CFA) on the remaining sample and tested for validity and reliability. Reliability was appropriate. We found significant correlations between age, gender, educational level, and factors of the scale. However, missing values interfered with confirmatory and validating analyses. Despite these issues, the scale contributes to future research on social perception of robots.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardized Root Mean Square Residual.

  2. 2.

    Die Items sollten randomisiert vorgegeben werden.

References

  1. Demir, K.A., Döven, G., Sezen, B.: Industry 5.0 and human-robot co-working. Procedia Comput. Sci. 158, 688–695 (2019). https://doi.org/10.1016/j.procs.2019.09.104

    Article  Google Scholar 

  2. Brynjolfsson, E., McAfee, A.: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company Inc, New York, NY, USA (2014)

    Google Scholar 

  3. Savela, N., Turja, T., Oksanen, A.: Social acceptance of robots in different occupational fields: a systematic literature review. Int. J. Soc. Robot. 10(4), 493–502 (2017). https://doi.org/10.1007/s12369-017-0452-5

    Article  Google Scholar 

  4. Feil-Seifer, D., Mataric, M.J.: Socially assistive robotics. In: 2005 9th International Conference on Rehabilitation Robotics, ICORR 2005, pp. 465–468. IEEE, Chicago, IL, USA (2005). https://doi.org/10.1109/ICORR.2005.1501143

  5. Onnasch, L., Roesler, E.: A taxonomy to structure and analyze human–robot interaction. Int. J. Soc. Robot. 13(4), 833–849 (2020). https://doi.org/10.1007/s12369-020-00666-5

    Article  Google Scholar 

  6. Bishop, L., van Maris, A., Dogramadzi, S., Zook, N.: Social robots: the influence of human and robot characteristics on acceptance. Paladyn J. Behav. Robot. 10, 346–358 (2019). https://doi.org/10.1515/pjbr-2019-0028

    Article  Google Scholar 

  7. Zlotowski, J., Bartneck, C.: The inversion effect in HRI: are robots perceived more like humans or objects? In: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 365–372. IEEE, Tokyo, Japan (2013). https://doi.org/10.1109/HRI.2013.6483611

  8. Fiske, S.T., Cuddy, A.J.C., Glick, P., Xu, J.: A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition. J. Pers. Soc. Psychol. 82, 878–902 (2002). https://doi.org/10.1037/0022-3514.82.6.878

    Article  Google Scholar 

  9. Cuddy, A.J.C., Fiske, S.T., Glick, P.: Warmth and competence as universal dimensions of social perception: the stereotype content model and the BIAS map. Adv. Exp. Soc. Psychol. 40, 61–149 (2008). https://doi.org/10.1016/S0065-2601(07)00002-0

    Article  Google Scholar 

  10. Kervyn, N., Fiske, S., Yzerbyt, V.: Forecasting the primary dimension of social perception: symbolic and realistic threats together predict warmth in the stereotype content model. Soc. Psychol. 46, 36–45 (2015). https://doi.org/10.1027/1864-9335/a000219

    Article  Google Scholar 

  11. Abele, A.E., Ellemers, N., Fiske, S.T., Koch, A., Yzerbyt, V.: Navigating the social world: toward an integrated framework for evaluating self, individuals, and groups. Psychol. Rev. 128, 290–314 (2021). https://doi.org/10.1037/rev0000262

    Article  Google Scholar 

  12. Fiske, S.T.: Stereotype content: warmth and competence endure. Curr. Dir. Psychol. Sci. 27, 67–73 (2018). https://doi.org/10.1177/0963721417738825

    Article  Google Scholar 

  13. Ernst, C.-P.H., Herm-Stapelberg, N., Mainz, J.G.-U.: Gender stereotyping’s influence on the perceived competence of Siri and Co. In: Proceedings of the Americas Conference on Information Systems (AMCIS), AMCIS 2020, Salt Lake City, Utah, USA (2020). https://aisel.aisnet.org/amcis2020/cognitive_in_is/cognitive_in_is/4

  14. Schaefer, K.E., Sanders, T.L., Yordon, R.E., Billings, D.R., Hancock, P.A.: Classification of robot form: factors predicting perceived trustworthiness. Proc. Hum. Fact. Ergon. Soc. Ann. Meet. 56, 1548–1552 (2012). https://doi.org/10.1177/1071181312561308

    Article  Google Scholar 

  15. de Visser, E.J., et al.: Almost human: anthropomorphism increases trust resilience in cognitive agents. J. Exp. Psychol. Appl. 22, 331–349 (2016). https://doi.org/10.1037/xap0000092

    Article  Google Scholar 

  16. Mandl, S., et al.: Embodied digital technologies: first insights in the social and legal perception of robots and users of prostheses. Front. Robot. AI. 9, 787970 (2022). https://doi.org/10.3389/frobt.2022.787970

    Article  Google Scholar 

  17. Sauppé, A., Mutlu, B.: The social impact of a robot co-worker in industrial settings. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 3613–3622. ACM Press, Seoul, Republic of Korea (2015). https://doi.org/10.1145/2702123.2702181

  18. Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J.: The robotic social attributes scale (RoSAS): development and validation. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 254–262. ACM, Vienna Austria (2017). https://doi.org/10.1145/2909824.3020208

  19. Bartneck, C., Kulić, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Soc. Robot. 1, 71–81 (2009). https://doi.org/10.1007/s12369-008-0001-3

    Article  Google Scholar 

  20. Epley, N., Waytz, A., Cacioppo, J.T.: On seeing human: a three-factor theory of anthropomorphism. Psychol. Rev. 114, 864–886 (2007). https://doi.org/10.1037/0033-295X.114.4.864

    Article  Google Scholar 

  21. Broadbent, E., Stafford, R., MacDonald, B.: Acceptance of healthcare robots for the older population: review and future directions. Int. J. Soc. Robot. 1, 319–330 (2009). https://doi.org/10.1007/s12369-009-0030-6

    Article  Google Scholar 

  22. Scopelliti, M., Giuliani, M.V., Fornara, F.: Robots in a domestic setting: a psychological approach. Univ. Access Inf. Soc. 4, 146–155 (2005). https://doi.org/10.1007/s10209-005-0118-1

    Article  Google Scholar 

  23. Arras, K.O., Cerqui, D.: Do we want to share our lives and bodies with robots? A 2000-people survey. Swiss Federal Institute of Technology Lausanne, EPFL, Lausanne, Switzerland (2005)

    Google Scholar 

  24. Schermerhorn, P., Scheutz, M., Crowell, C.R.: Robot social presence and gender: do females view robots differently than males? In: Proceedings of the 3rd International Conference on Human Robot Interaction, HRI 2008. ACM Press, Amsterdam, The Netherlands (2008). https://doi.org/10.1145/1349822.1349857

  25. Kuo, I.H., et al.: Age and gender factors in user acceptance of healthcare robots. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2009, pp. 214–219. IEEE, Toyama, Japan (2009). https://doi.org/10.1109/ROMAN.2009.5326292

  26. Franke, T., Attig, C., Wessel, D.: A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) scale. Int. J. Hum.-Comput. Interact. 35, 456–467 (2019). https://doi.org/10.1080/10447318.2018.1456150

    Article  Google Scholar 

  27. de Graaf, M.M.A., Allouch, S.B.: Exploring influencing variables for the acceptance of social robots. Robot. Auton. Syst. 61(12), 1476–1486 (2013). https://doi.org/10.1016/j.robot.2013.07.007

    Article  Google Scholar 

  28. Heerink, M.: Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults. In: Proceedings of the 6th International Conference on Human Robot Interaction, Lausanne, Switzerland, pp. 147–148 (2011). https://doi.org/10.1145/1957656.1957704

  29. Cacioppo, J., Petty, R.E.: The need for cognition. J. Pers. Soc. Psychol. 42, 116–131 (1982). https://doi.org/10.1037/0022-3514.42.1.116

    Article  Google Scholar 

  30. Palan, S., Schitter, C.: Prolific.ac—a subject pool for online experiments. J. Behav. Exp. Finan. 17, 22–27 (2018). https://doi.org/10.1016/j.jbef.2017.12.004

    Article  Google Scholar 

  31. SoftBank Robotics: Pepper. https://www.softbankrobotics.com/emea/en/pepper. Accessed 11 Nov 2021

  32. Arlamovsky, M.: ROBOLOVE. NGF - Nikolaus Geyrhalter Filmproduktion (2019)

    Google Scholar 

  33. Chita-Tegmark, M., Law, T., Rabb, N., Scheutz, M.: Can you trust your trust measure? In: Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021, Boulder, Colorado, USA (2021). https://doi.org/10.1145/3434073.3444677

  34. Aquino, K., Reed, A.: The self-importance of moral identity. J. Pers. Soc. Psychol. 83, 1423–1440 (2002). https://doi.org/10.1037//0022-3514.83.6.1423

    Article  Google Scholar 

  35. Meyer, B., Asbrock, F.: Disabled or Cyborg? How bionics affect stereotypes toward people with physical disabilities. Front. Psychol. 9, 2251 (2018). https://doi.org/10.3389/fpsyg.2018.02251

    Article  Google Scholar 

  36. Bless, H., Wänke, M., Bohner, G., Fellhauer, R.F., Schwarz, N.: Need for cognition: a scale measuring engagement and happiness in cognitive tasks. Zeitschrift für Sozialpsychologie. 25, 147–154 (1994)

    Google Scholar 

  37. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021)

    Google Scholar 

  38. Watkins, M.W.: Exploratory factor analysis: a guide to best practice. J. Black Psychol. 44, 219–246 (2018). https://doi.org/10.1177/0095798418771807

    Article  Google Scholar 

  39. Horn, J.L.: A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179–185 (1965). https://doi.org/10.1007/BF02289447

    Article  MATH  Google Scholar 

  40. Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297–334 (1951)

    Article  Google Scholar 

  41. Buckell, J., et al.: Hypothetical Bias (2020). https://catalogofbias.org/biases/hypothetical-bias/

  42. Müller, B.C.N., Gao, X., Nijssen, S.R.R., Damen, T.G.E.: I, robot: how human appearance and mind attribution relate to the perceived danger of robots. Int. J. Soc. Robot. 13(4), 691–701 (2020). https://doi.org/10.1007/s12369-020-00663-8

    Article  Google Scholar 

  43. Turja, T., Oksanen, A.: Robot acceptance at work: a multilevel analysis based on 27 EU countries. Int. J. Soc. Robot. 11(4), 679–689 (2019). https://doi.org/10.1007/s12369-019-00526-x

    Article  Google Scholar 

Download references

Acknowledgments

The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation – [Project-ID 416228727 – SFB 1410]).

Author information

Authors and Affiliations

  1. Professorship of Personality Psychology and Assessment, Chemnitz University of Technology, Wilhelm-Raabe-Str. 43, 09120, Chemnitz, Germany

    Sarah Mandl & Anja Strobel

  2. Professorship of Social Psychology, Chemnitz University of Technology, Wilhelm-Raabe-Str. 43, 09120, Chemnitz, Germany

    Maximilian Bretschneider & Frank Asbrock

  3. Professorship of Work, Organizational, and Economic Psychology, Chemnitz University of Technology, Wilhelm-Raabe-Str. 43, 09120, Chemnitz, Germany

    Maximilian Bretschneider & Bertolt Meyer

Authors
  1. Sarah Mandl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maximilian Bretschneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frank Asbrock
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bertolt Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anja Strobel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarah Mandl .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Monte da Caparica, Portugal

    Luis M. Camarinha-Matos

  2. Polytechnic University of Valencia, Valencia, Spain

    Angel Ortiz

  3. École des Mines de Saint-Étienne, Saint-Étienne Cedex, France

    Xavier Boucher

  4. Instituto Politécnico de Lisboa, Lisbon, Portugal

    A. Luís Osório

Appendix

Appendix

Table A. Percentage of missing values per item, sorted by factors
Full size table

The Social Perception of Robots Scale (SPRS) – German Version.Footnote 2

Wie schätzen Sie den Roboter in Hinblick auf die angegebenen Merkmale ein? Wie handelt/denkt/wirkt der Roboter aus Ihrer Sicht?

figure a

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mandl, S., Bretschneider, M., Asbrock, F., Meyer, B., Strobel, A. (2022). The Social Perception of Robots Scale (SPRS): Developing and Testing a Scale for Successful Interaction Between Humans and Robots. In: Camarinha-Matos, L.M., Ortiz, A., Boucher, X., Osório, A.L. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2022. IFIP Advances in Information and Communication Technology, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-031-14844-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-14844-6_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14843-9

  • Online ISBN: 978-3-031-14844-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation