Skip to main content
SpringerLink
Log in

One Size Does Not Fit All:

Developing Robot User Types for Office Robots

  • Conference paper
  • First Online:
Human-Computer Interaction (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14013))

Included in the following conference series:

  • 796 Accesses

Abstract

Office robots can be a solution to the shortage of skilled workers in certain areas. They perform tasks automatically and work around the clock. Examples of tasks performed by these robots include data processing, clerical work, and administrative tasks. We propose five types of robot users based on interviews after real-life use cases of an office robot. We investigate these types in an online study that shows relevant patterns associated with each type and first indications of type distribution. By using these individual robot user types, organizations can tailor robot implementation to their workforce and create ideal human-robot interactions in the workplace.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Glassman, J.: The labor shortage will outlast the pandemic (2022). https://www.jpmorgan.com/commercial-banking/insights/why-the-labor-shortage-persists

  2. Paluch, S., Tuzovic, S., Holz, H.F., Kies, A., Jörling, M.: “My colleague is a robot” – exploring frontline employees’ willingness to work with collaborative service robots. JOSM (2022). https://doi.org/10.1108/JOSM-11-2020-0406

    Article  Google Scholar 

  3. Asoh, H., et al.: Jijo-2: an office robot that communicates and learns. IEEE Intell. Syst. (2001). https://doi.org/10.1109/MIS.2001.956081

    Article  Google Scholar 

  4. Anagnoste, S.: Robotic automation process - the next major revolution in terms of back office operations improvement. In: Proceedings of the International Conference on Business Excellence (2017). https://doi.org/10.1515/picbe-2017-0072

  5. Garrell, A., Sanfeliu, A.: Cooperative social robots to accompany groups of people. Int. J. Robot. Res. (2012). https://doi.org/10.1177/0278364912459278

    Article  Google Scholar 

  6. Čaić, M., Mahr, D., Oderkerken-Schröder, G.: Value of social robots in services: social cognition perspective. JSM (2019). https://doi.org/10.1108/JSM-02-2018-0080

    Article  Google Scholar 

  7. Burke, J., Coovert, M., Murphy, R., Riley, J., Rogers, E.: Human-robot factors: robots in the workplace. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2006). https://doi.org/10.1177/154193120605000902

  8. Wolf, F.D., Stock-Homburg, R.M.: How and when can robots be team members? Three decades of research on human-robot teams. Group Org. Manag. (2022). https://doi.org/10.1177/10596011221076636

    Article  Google Scholar 

  9. Kim, S.: Working with robots: human resource development considerations in human-robot interaction. Hum. Resour. Dev. Rev. (2022). https://doi.org/10.1177/15344843211068810

    Article  Google Scholar 

  10. Gombolay, M., Bair, A., Huang, C., Shah, J.: Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences. Int. J. Robot. Res. (2017). https://doi.org/10.1177/0278364916688255

    Article  Google Scholar 

  11. Charalambous, G., Fletcher, S.R., Webb, P.: The development of a human factors readiness level tool for implementing industrial human-robot collaboration. Int. J. Adv. Manuf. Technol. 91(5–8), 2465–2475 (2017). https://doi.org/10.1007/s00170-016-9876-6

    Article  Google Scholar 

  12. Beer, J.M., Fisk, A.D., Rogers, W.A.: Toward a framework for levels of robot autonomy in human-robot interaction. J. Hum. Robot Interact. (2014). https://doi.org/10.5898/JHRI.3.2.Beer

    Article  Google Scholar 

  13. Bartneck, C., Suzuki, T., Kanda, T., Nomura, T.: The influence of people’s culture and prior experiences with Aibo on their attitude towards robots. AI Soc. (2006). https://doi.org/10.1007/s00146-006-0052-7

    Article  Google Scholar 

  14. Babel, F., Kraus, J., Baumann, M.: Findings from a qualitative field study with an autonomous robot in public: exploration of user reactions and conflicts. Int. J. Soc. Robot. (2022). https://doi.org/10.1007/s12369-022-00894-x

    Article  Google Scholar 

  15. Mark, G., Czerwinski, M., Iqbal, S.T.: Effects of individual differences in blocking workplace distractions. In: Mandryk, R., Hancock, M., Perry, M., Cox, A. (eds.) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. CHI 2018: CHI Conference on Human Factors in Computing Systems, Montreal QC Canada, 21 April 2018–26 April 2018, pp. 1–12. ACM, New York (2018). https://doi.org/10.1145/3173574.3173666

  16. Babu, A.R., Rajavenkatanarayanan, A., Abujelala, M., Makedon, F.: VoTrE: a vocational training and evaluation system to compare training approaches for the workplace. In: Lackey, S., Chen, J. (eds.) VAMR 2017. LNCS, vol. 10280, pp. 203–214. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57987-0_16

    Chapter  Google Scholar 

  17. Lee, M.K., Forlizzi, J., Kiesler, S., Rybski, P., Antanitis, J., Savetsila, S.: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2012), Boston, Massachusetts, USA, 5–8 March 2012; Proceedings, Piscataway, NJ. IEEE (2012)

    Google Scholar 

  18. Ceja, L., Navarro, J.: Dynamic patterns of flow in the workplace: characterizing within-individual variability using a complexity science approach. J. Organ. Behav. (2011). https://doi.org/10.1002/job.747

    Article  Google Scholar 

  19. Baert, H., Govaerts, N.: Learning patterns of teams at the workplace. J. Work. Learn. (2012). https://doi.org/10.1108/13665621211261025

    Article  Google Scholar 

  20. Stock, R.M., Zacharias, N.A.: Patterns and performance outcomes of innovation orientation. J. Acad. Mark. Sci. (2011). https://doi.org/10.1007/s11747-010-0225-2

    Article  Google Scholar 

  21. 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 

  22. Heerink, M., Kröse, B., Evers, V., Wielinga, B.: The influence of social presence on acceptance of a companion robot by older people. jopha (2008). https://doi.org/10.14198/JoPha.2008.2.2.05

  23. Law, E., et al.: A Wizard-of-Oz study of curiosity in human-robot interaction. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, 28 August 2017–01 September 2017, pp. 607–614. IEEE (2017). https://doi.org/10.1109/ROMAN.2017.8172365

  24. High-Level Expert Group on Artificial Intelligence: A Definition of AI: Main Capabilities and Disciplines (2019). https://digital-strategy.ec.europa.eu/en/library/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines

  25. van Doorn, J., et al.: Domo arigato Mr. Roboto. J. Ser. Res. (2017). https://doi.org/10.1177/1094670516679272

  26. Wirtz, J., et al.: Brave new world: service robots in the frontline. JOSM (2018). https://doi.org/10.1108/JOSM-04-2018-0119

    Article  Google Scholar 

  27. Roesler, E., Naendrup-Poell, L., Manzey, D., Onnasch, L.: Why context matters: the influence of application domain on preferred degree of anthropomorphism and gender attribution in human–robot interaction. Int. J. Soc. Robot. (2022). https://doi.org/10.1007/s12369-021-00860-z

  28. Gockley, R., et al.: Designing robots for long-term social interaction. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alta., Canada, 02 August 2005–02 August 2005, pp. 1338–1343. IEEE (2005). https://doi.org/10.1109/IROS.2005.1545303

Download references

Acknowledgements

This research project is funded by the German Federal Ministry of Education and Research (BMBF) within the KompAKI project. The authors are responsible for the content of this publication.

Author information

Authors and Affiliations

  1. Technical University Darmstadt, Hochschulstraße 1, 64289, Darmstadt, Germany

    Ruth Stock-Homburg & Lea Heitlinger

Authors
  1. Ruth Stock-Homburg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lea Heitlinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruth Stock-Homburg .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Hosei University, Tokyo, Japan

    Ayako Hashizume

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stock-Homburg, R., Heitlinger, L. (2023). One Size Does Not Fit All:. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14013. Springer, Cham. https://doi.org/10.1007/978-3-031-35602-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35602-5_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35601-8

  • Online ISBN: 978-3-031-35602-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation