Abstract
Human robot interaction (HRI) offers potential in fulfilling the social needs of humans specifically, in the context of an interview setting. While human robot interaction has proven potentials to provide relatively measurable outcomes in experimentation, applications in real life are limited mainly due to primitive HRI design and implementation. Research in HRI is thus the necessary first step to the diffusion of robots and robotic technologies into social life. This study presents the results a case study on HRI design for a formal interview process. The findings are presented in a framework that elucidates the key expected robot affordances—action possibilities afforded by a humanoid robot—and their relationships with humans in the interactive interview context. This framework development has been informed by the Needs-Affordances-Features perspective.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B.: Conversational gaze aversion for humanlike robots. In: Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 25–32. University of Wisconsin-Madison Department of Computer Sciences (2014). https://doi.org/10.1145/2559636.2559666
Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S.: Human-Robot Interaction: An Introduction. Cambridge University Press, Cambridge (2019)
Deci, E., Vallerand, R., Pelletier, L., Ryan, R.: Motivation and education: the self-determination perspective. Educ. Psychol. 26(3–4), 325–346 (1991). https://doi.org/10.1080/00461520.1991.9653137
Edwards, A., Omilion-Hodges, L., Edwards, C.: How do patients in a medical interview perceive a robot versus human physician? In: Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017, Vienna, Austria, pp. 109–110. Companion (2017)
Elkins, A., Gupte, A., Cameron, L.: Humanoid Robots as Interviewers for Automated Credibility Assessment. Artificial Intelligence Lab. San Diego State University, USA (2018)
Evans, S.K., Pearce, K.E., Vitak, J., Treem, J.W.: Explicating affordances: a conceptual framework for understanding affordances in communication research. J. Comput.-Mediat. Commun. 22(1), 35–52 (2016)
Fallon, M., et al.: An architecture for online affordance-based perception and whole-body planning. J. Field Robot. 32(2), 229–254 (2015). https://doi.org/10.21236/ada602904
Fayard, A.-L., Weeks, J.: Affordances for practice. Inf. Organ. 24(4), 236–249 (2014). https://doi.org/10.1016/j.infoandorg.2014.10.001
Fox, J., Gambino, A.: Relationship development with humanoid social robots: applying interpersonal theories to human/robot interaction. Cyberpsychol. Behav. Soc. Netw. 1–5 (2021). https://doi.org/10.1089/cyber.2020.0181
Ghazali, A.S., Ham, J., Barakova, E., Markopoulos, P.: Assessing the effect of persuasive robots interactive social cues on users’ psychological reactance, liking, trusting beliefs and compliance. Adv. Robot. 33(7–8), 325–337 (2019). https://doi.org/10.1080/01691864.2019.1589570
Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., de Visser, E.J., Parasuraman, R.: A meta-analysis of factors affecting trust in human-robot interaction. Hum. Factors: J. Hum. Factors Ergon. Soc. 53(5), 517–527 (2011). https://doi.org/10.1177/0018720811417254
Harris, J., Sharlin, E.: Exploring the affect of abstract motion in social human-robot interaction. RO-MAN (2011). https://doi.org/10.1109/roman.2011.6005254
Jamone, L., et al.: Affordances in psychology, neuroscience, and robotics: a survey. IEEE Trans. Cogn. Dev. Syst. 4–25 (2018). https://doi.org/10.1109/tcds.2016.2594134
Kahn, P.H., et al.: Will people keep the secret of a humanoid robot? In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015, Portland, Oregon, pp. 173–180 (2015)
Kaptelinin, V., Nardi, B.: Affordances in HCI: toward a mediated action perspective. In: Proceedings on Human Factors in Computing Systems, CHI 2012, Austin, Texas, pp. 967–975 (2012)
Karahanna, E., Xin Xu, S., Xu, Y., Zhang, N.: The needs–affordances–features perspective for the use of social media. MIS Q. 42(3), 737–756 (2018). https://doi.org/10.25300/MISQ/2018/11492
Khan, A.N., Ihalage, A., Ma, Y., Liu, B., Liu, Y., et al.: Deep learning framework for subject-independent emotion detection using wireless signals. PLOS ONE 16(2), 1–16 (2021). https://doi.org/10.1371/journal.pone.0242946
Onnasch, L., Roesler, E.: A taxonomy to structure and analyze human–robot interaction. Int. J. Soc. Robot. (2020). https://doi.org/10.1007/s12369-020-00666-5
Onyeulo, E.B., Gandhi, V.: What makes a social robot good at interacting with humans? Information 11(43), 1–13 (2020). https://doi.org/10.3390/info11010043
Ötting, S.K., Masjutin, L., Steil, J.J., Maier, G.W.: Let’s work together: a meta-analysis on robot design features that enable successful human–robot (2020)
Interaction at Work. Hum. Factors: J. Hum. Factors Ergon. Soc. 1–24 (2020). https://doi.org/10.1177/0018720820966433
Pandey, A.K., Alami, R.: Affordance graph: a framework to encode perspective taking and effort based affordances for day-to-day human-robot interaction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IROS, Tokyo, Japan, pp. 2180–2187 (2013). https://doi.org/10.1109/iros.2013.6696661
Pandey, A.K., Gelin, R.: Pepper: the first machine of its kind a mass-produced sociable humanoid. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IROS, Tokyo, Japan, pp. 2180–2187 (2018). https://doi.org/10.1109/iros.2013.6696661
Shu, T., Ryoo, M.S., Zhu, S.-C.: Learning social affordance for human-robot interaction. In: 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, pp. 3454–3461 (2016)
Vallverdú, J., Trovato, G.: Emotional affordances for human–robot interaction. Adapt. Behav. 24(5), 320–334 (2016). https://doi.org/10.1177/1059712316668238
Vallverdú, J., Trovato, G., Jamone, L.: Allocentric emotional affordances in HRI: the multimodal binding. Multimodal Technol. Interact. 2(78), 1–20 (2018). https://doi.org/10.20944/preprints201808.0312.v1
Wang, S.M., Cheng, W.M.: Design thinking for developing a case-based reasoning emotion-sensing robot for interactive interview. In: Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, CHI 2020, Hawaii, pp. 13–16 (2020). https://doi.org/10.1145/3391203.3391205
Zheng, J., Jarvenpaa, S.L.: (PDF) [Internet]: Thinking Technology as Human: Affordances, Technology Features, and Egocentric Biases in Technology Anthropomorphism. ResearchGate (2020). https://www.researchgate.net/publication/347484661_Thinking_Technology_as_Human_Affordances_Technology_Features_and_Egocentric_Biases_in_Technology_Anthropomorphism. Accessed 21 Feb 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zaballa, K.N.H., Cameron, L.D., Lugo, A.S. (2021). Human-Robot Interactions Design for Interview Process: Needs-Affordances-Features Perspective. In: Nah, F.FH., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2021. Lecture Notes in Computer Science(), vol 12783. Springer, Cham. https://doi.org/10.1007/978-3-030-77750-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-77750-0_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77749-4
Online ISBN: 978-3-030-77750-0
eBook Packages: Computer ScienceComputer Science (R0)