How to Make Robots' Optimal Anthropomorphism Level: Manipulating Social Cues and Spatial Context for an Improved User Experience

Hanna Chung, Sukho Lee, Soojin Jun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the growing interest in robot related research and industry, there is a demand to shape user experience more sophisticatedly in human-robot interaction. The purpose of this study is to define the elements for manipulating robot's verbal anthropomorphism and investigate the influence on user experience associated with spatial context. Based on the identified elements, we divided the robot's anthropomorphism into three levels (high, medium, low) and associated them with two spatial contexts (open, closed). The results revealed that a higher level of verbal anthropomorphism mostly induced positive user experiences; however, people sometimes tended to prefer a medium level, especially in terms of usefulness. Further, privacy concerns were significantly higher in open space. Consequently, we propose that designers and researchers deviate from the two levels of anthropomorphism (e.g., high or low, existing or not) generally used in prior studies to a new perspective that also considers the spatial context.

Original languageEnglish
Title of host publicationHRI 2022 - Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages731-736
Number of pages6
ISBN (Electronic)9781538685549
DOIs
Publication statusPublished - 2022
Event17th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2022 - Sapporo, Japan
Duration: 2022 Mar 72022 Mar 10

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
Volume2022-March
ISSN (Electronic)2167-2148

Conference

Conference17th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2022
Country/TerritoryJapan
CitySapporo
Period22/3/722/3/10

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A2A01045980).

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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