Trust and Acceptance of Social Robots: References

cover
12 Jun 2024

Author:

(1) Katrin Fischer, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: katrinfi@usc.edu);

(2) Donggyu Kim, Annenberg School for Communication at the University of Southern California, Los Angeles (Email: donggyuk@usc.edu);

(3) Joo-Wha Hong, Marshall School of Business at the University of Southern California, Los Angeles (Email: joowhaho@marshall.usc.edu).

Abstract Introduction & Related Work

Method

Analysis & Results

Discussion & Conclusion

References

REFERENCES

[1] M. Deutsch, The resolution of conflict: Constructive and destructive processes. Yale University Press, 1973.

[2] J. Riegelsberger, M. A. Sasse, and J. D. McCarthy, “The researcher’s dilemma: Evaluating trust in computer-mediated communication,” International Journal of Human Computer Studies, vol. 58, no. 6, pp. 759–781, 2003.

[3] J. K. Rempel, J. G. Holmes, and M. P. Zanna, “Trust in close relationships,” Journal of Personality and Social Psychology, vol. 49, no. 1, pp. 95–112, 1985.

[4] J. D. Lee and K. A. See, “Trust in automation: Designing for appropriate reliance,” Human Factors, vol. 46, no. 1, pp. 50–80, 2004.

[5] D. Ullman and B. F. Malle, “Measuring gains and losses in humanrobot trust: Evidence for differentiable components of trust,” in Proceedings of the 14th ACM/IEEE International Conference on HumanRobot Interaction, ser. HRI ’19. IEEE Press, 2019, pp. 618–619.

[6] R. C. Mayer, J. H. Davis, and F. D. Schoorman, “An integrative model of organizational trust,” Academy of management review, vol. 20, no. 3, pp. 709–734, 1995.

[7] W. Kim, N. Kim, J. B. Lyons, and C. S. Nam, “Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach,” Applied Ergonomics, vol. 85, may 2020.

[8] C. M. Carpinella, A. B. Wyman, M. A. Perez, and S. J. Stroessner, “The robotic social attributes scale (RoSAS) development and validation,” in ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: IEEE Computer Society, 2017, pp. 254–262.

[9] S. T. Fiske, A. J. Cuddy, and P. Glick, “Universal dimensions of social cognition: Warmth and competence,” Trends in Cognitive Sciences, vol. 11, no. 2, pp. 77–83, 2007.

[10] A. J. Cuddy, S. T. Fiske, and P. Glick, “The BIAS Map: Behaviors From Intergroup Affect and Stereotypes,” Journal of Personality and Social Psychology, vol. 92, no. 4, pp. 631–648, 2007.

[11] B. Reeves, J. Hancock, and X. Liu, “Social robots are like real people: First impressions, attributes, and stereotyping of social robots,” Technology, Mind, and Behavior, vol. 1, no. 1, 2020.

[12] C. Nam and J. Lyons, Eds., Trust in human-robot interaction: Research and applications. Elsevier, 2020.

[13] P. A. Hancock, D. R. Billings, K. E. Schaefer, J. Y. Chen, E. J. De Visser, and R. Parasuraman, “A meta-analysis of factors affecting trust in human-robot interaction,” Human Factors, vol. 53, no. 5, pp. 517– 527, 2011.

[14] S. Naneva, M. Sarda Gou, T. L. Webb, and T. J. Prescott, “A systematic review of attitudes, anxiety, acceptance, and trust towards social robots,” International Journal of Social Robotics, 2020.

[15] M. Salem and K. Dautenhahn, “Evaluating trust and safety in HRI: Practical issues and ethical challenges,” Emerging Policy and Ethics of Human-Robot Interaction, 2015.

[16] M. Salem, G. Lakatos, F. Amirabdollahian, and K. Dautenhahn, “Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust,” in Proceedings of the Tenth Annual ACM/IEEE International Conference on HumanRobot Interaction. ACM, 2015, pp. 141–148.

[17] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly: Management Information Systems, vol. 13, no. 3, pp. 319–339, 1989.

[18] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly: Management Information Systems, vol. 27, no. 3, pp. 425– 478, 2003.

[19] M. J. Mataric and B. Scassellati, “Socially assistive robotics,” in ´ Springer Handbook of Robotics, K. O. Siciliano B., Ed. Springer, 2016, pp. 1973–1994.

[20] M. S. Fritz and D. P. MacKinnon, “Required sample size to detect the mediated effect,” Psychological Science, vol. 18, no. 3, pp. 233–239, 2007.

[21] A. J. Fairchild, D. P. MacKinnon, M. P. Taborga, and A. B. Taylor, “R2 effect-size measures for mediation analysis,” Behavior research methods, vol. 41, no. 2, pp. 486–498, 2009.

[22] D. L. Streiner, “Finding our way: An introduction to path analysis,” Canadian Journal of Psychiatry, vol. 50, no. 2, pp. 115–122, 2005.

[23] A. F. Hayes, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 3rd ed. The Guilford Press, 2022.

This paper is available on arxiv under CC 4.0 license.