Having the Right Attitude: How Attitude Impacts Trust Repair in Human-Robot Interaction

被引:11
作者
Esterwood, Connor [1 ]
Robert, Lionel P., Jr. [2 ]
机构
[1] Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Sch Informat, Inst Robot, Ann Arbor, MI 48109 USA
来源
PROCEEDINGS OF THE 2022 17TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI '22) | 2022年
关键词
Human-Robot Interaction; Trust Repair; Attitude; INDIVIDUAL-DIFFERENCES; AUTOMATION;
D O I
10.1109/HRI53351.2022.9889535
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Robot co-workers, like human co-workers, make mistakes that undermine trust. Yet, trust is just as important in promoting human-robot collaboration as it is in promoting human-human collaboration. In addition, individuals can significantly differ in their attitudes toward robots, which can also impact or hinder their trust in robots. To better understand how individual attitude can influence trust repair strategies, we propose a theoretical model that draws from the theory of cognitive dissonance. To empirically verify this model, we conducted a between-subjects experiment with 100 participants assigned to one of four repair strategies (apologies, denials, explanations, or promises) over three trust violations. Individual attitudes did moderate the efficacy of repair strategies and this effect differed over successive trust violations. Specifically, repair strategies were most effective relative to individual attitude during the second of the three trust violations, and promises were the trust repair strategy most impacted by an individual's attitude.
引用
收藏
页码:332 / 341
页数:10
相关论文
共 64 条
  • [1] Alarcon G M., 2020, 2020 IEEE INT C HUMA, P1, DOI [DOI 10.1109/ICHMS49158.2020.9209453, 10.1109/ICHMS49158.2020.9209453]
  • [2] Albayram Yusuf, 2020, HAI '20: Proceedings of the 8th International Conference on Human-Agent Interaction, P6, DOI 10.1145/3406499.3415064
  • [3] A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration
    Azevedo-Sa, Hebert
    Yang, X. Jessie
    Robert, Lionel P.
    Tilbury, Dawn M.
    [J]. IEEE ROBOTICS AND AUTOMATION LETTERS, 2021, 6 (03) : 5913 - 5920
  • [4] Toward an Understanding of Trust Repair in Human-Robot Interaction: Current Research and Future Directions
    Baker, Anthony L.
    Phillips, Elizabeth K.
    Ullman, Daniel
    Keebler, Joseph R.
    [J]. ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS, 2018, 8 (04)
  • [5] The effect of social-cognitive recovery strategies on likability, capability and trust in social robots
    Cameron, David
    de Saille, Stevienna
    Collins, Emily C.
    Aitken, Jonathan M.
    Cheung, Hugo
    Chua, Adriel
    Loh, Ee Jing
    Law, James
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2021, 114
  • [6] Chien SY, 2016, IEEE SYS MAN CYBERN, P2884, DOI 10.1109/SMC.2016.7844677
  • [7] Costa A., 2018, The SAGE handbook of industrial, work organizational psychology: Organizational psychology, P435, DOI [DOI 10.4135/9781473914957.N20, 10.4135/9781473914957.n, DOI 10.4135/9781473914957.N]
  • [8] Dai L., 2015, Psychology, V6, P767, DOI DOI 10.4236/PSYCH.2015.66075
  • [9] Exploring influencing variables for the acceptance of social robots
    de Graaf, Maartje M. A.
    Ben Allouch, Somaya
    [J]. ROBOTICS AND AUTONOMOUS SYSTEMS, 2013, 61 (12) : 1476 - 1486
  • [10] Towards a Theory of Longitudinal Trust Calibration in Human-Robot Teams
    de Visser, Ewart J.
    Peeters, Marieke M. M.
    Jung, Malte F.
    Kohn, Spencer
    Shaw, Tyler H.
    Pak, Richard
    Neerincx, Mark A.
    [J]. INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS, 2020, 12 (02) : 459 - 478