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Article published In:
Interaction Studies
Vol. 23:1 (2022) ► pp.21–57

“I know how you feel”

The importance of interaction style on users’ acceptance in an entertainment scenario

| Institut de Robòtica i Informàtica Industrial
| Universitat de Barcelona
| Universitat de Barcelona
| Institut de Robòtica i Informàtica Industrial
| Institut de Robòtica i Informàtica Industrial
In this article, we aim to evaluate the role of robots’ personality-driven behavioural patterns on users’ intention to use in an entertainment scenario. Toward such a goal, we designed two personalities: one introverted with an empathic and self-comparative interaction style, and the other extroverted with a provocative and other-comparative interaction style. To evaluate the proposed technology acceptance model, we conducted an experiment (N = 209) at a public venue where users were requested to play a game with the support of the TIAGo robot. Our findings show that the robot personality affects the acceptance model and three relevant drivers: perceived enjoyment, perceived usefulness, and social influence. The extroverted robot was perceived as more useful than the introverted, and participants who interacted with it were faster at solving the game. On the other hand, the introverted robot was perceived as more enjoyable but less useful than the extroverted, and participants who interacted with it made fewer mistakes. Taken together, these findings support the importance of designing proper robot personalities in influencing users’ acceptance, featuring that a given style can elicit a different driver of acceptance.
Keywords: robot personality, robot communication style, robot acceptance, technology acceptance model
Article outline
  • 1.Introduction
    • 1.1Research questions
    • 1.2Hypotheses
    • 1.3Contributions
  • 2.Related work
    • 2.1Robot communication style
    • 2.2Robot personality
    • 2.3Technology acceptance model
  • 3.The proposed model of acceptance
  • 4.The “guessing the Nobel prize winner” game
  • 5.Modelling robot personality-driven behaviour patterns
    • 5.1Modelling robot personality
    • 5.2Modelling robot assistive communication style
  • 6.Experimental design
    • 6.1Metrics
    • 6.2Apparatus
    • 6.3Pre-test: Validating robot personality
    • 6.4Procedure and sample
  • 7.Results
    • 7.1Psychometric characteristics
    • 7.2General structure model of the modified UTAUT
    • 7.3Acceptance model based on robot personality
    • 7.4Participants performance
  • 8.Discussion and conclusion
  • 9.Limitation and future work
  • Notes
  • Acronyms
  • References
Published online: 20 October 2022
DOI logo
https://doi.org/10.1075/is.21019.and
References (65)
References
Akalin, N., Kristoffersson, A., and Loutfi, A. (2019). The Influence of Feedback Type in Robot-Assisted Training. Multimodal Technologies and Interaction, 3(4):67. DOI logoGoogle Scholar
Aly, A. and Tapus, A. (2013). A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction. Proceedings of the 13th ACM/IEEE International Conference on Human-Robot Interaction, pages 325–332. DOI logoGoogle Scholar
Andriella, A., Huertas-Garcia, R., Forgas-Coll, S., Torras, C., and Alenyà, G. (2020a). Discovering SOCIABLE: Using a Conceptual Model to Evaluate the Legibility and Effectiveness of Backchannel Cues in an Entertainment Scenario. In Proceedings of the 29th IEEE International Conference on Robot and Human Interactive Communication, pages 752–759. DOI logoGoogle Scholar
Andriella, A., Siqueira, H., Fu, D., Magg, S., Barros, P., Wermter, S., Torras, C., and Alenyà, G. (2021). Do I Have a Personality? Endowing Care Robots with Context-Dependent Personality Traits. International Journal of Social Robotics, 13(8):2081–2102. DOI logoGoogle Scholar
Andriella, A., Suárez-Hernández, A., Segovia-Aguas, J., Torras, C., and Alenyà, G. (2019a). Natural Teaching of Robot-Assisted Rearranging Exercises for Cognitive Training. In Proceedings of the 11th International Conference on Social Robotics, volume 118761 LNAI, pages 611–621. Springer, Cham. DOI logoGoogle Scholar
Andriella, A., Torras, C., Albedenour, C., and Alenyà, G. (2022). Introducing CARESSER: a Framework for in Situ Learning Robot Social Assistance from Expert Knowledge and Demonstrations. User Model User-Adap Inter. DOI logoGoogle Scholar
Andriella, A., Torras, C., and Alenyà, G. (2019b). Short-Term Human-Robot Interaction Adaptability in Real-World Environments. International Journal of Social Robotics.Google Scholar
(2020b). Cognitive System Framework for Brain-Training Exercise Based on Human-Robot Interaction. Cognitive Computation, 12(4):793–810. DOI logoGoogle Scholar
Anzalone, S. M., Varni, G., Ivaldi, S., and Chetouani, M. (2017). Automated Prediction of Extraversion During Human-Humanoid Interaction. International Journal of Social Robotics, 9(3):385–399. DOI logoGoogle Scholar
Bandura, A. (1986). The Explanatory and Predictive Scope of Self-Efficacy Theory. Journal of Social and Clinical Psychology, 4(3):359–373. DOI logoGoogle Scholar
Bentler, P. M. (1989). EQS 6 Structural Equations Program Manual. Technical report.Google Scholar
Bochmann, G. V. and Sunshine, C. A. (1980). Formal Methods in Communication Protocol Design. IEEE Transactions on Communications, 28(4):624–631. DOI logoGoogle Scholar
Brennan, S. E. and Hanna, J. E. (2009). Partner-Specific Adaptation in Dialog. Topics in Cognitive Science, 1(2):274–291. DOI logoGoogle Scholar
Chidambaram, V., Chiang, Y. H., and Mutlu, B. (2012). Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues. In Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction, pages 293–300. DOI logoGoogle Scholar
Clabaugh, C., Mahajan, K., Jain, S., Pakkar, R., Becerra, D., Shi, Z., Deng, E., Lee, R., Ragusa, G., and Matarić, M. (2019). Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders. Frontiers in Robotics and AI, 61:611–621. DOI logoGoogle Scholar
Conti, D., Commodari, E., and Buono, S. (2017). Personality factors and acceptability of socially assistive robotics in teachers with and without specialized training for children with disability. Life Span and Disability, 20(2):251–272.Google Scholar
Cutrona, C. E. and Suhr, J. A. (1992). Controllability of Stressful Events and Satisfaction With Spouse Support Behaviors. Communication Research, 19(2):154–174. DOI logoGoogle Scholar
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems, 13(3):319–339. DOI logoGoogle Scholar
De Graaf, M. M. A. and Ben Allouch, S. (2014). Expectation setting and personality attribution in HRI. In Proceedings of the 9th ACM/IEEE International Conference on Human-Robot Interaction, pages 144–145. IEEE Computer Society. DOI logoGoogle Scholar
De Ruyter, B., Saini, P., Markopoulos, P., and Van Breemen, A. (2005). Assessing the effects of building social intelligence in a robotic interface for the home. Interacting with Computers, 17(5):522–541. DOI logoGoogle Scholar
Dryer, D. C. (1999). Getting personal with computers: how to design personalities for agents. Applied Artificial Intelligence, 13(3):273–295. DOI logoGoogle Scholar
Esterwood, C., Essenmacher, K., Yang, H., Zeng, F., and Robert, L. (2021). A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction. SSRN Electronic Journal. DOI logoGoogle Scholar
Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., and Alenyà, G. (2021). How do Consumers’ Gender and Rational Thinking Affect the Acceptance of Entertainment Social Robots? International Journal of Social Robotics, pages 1–22.Google Scholar
Fornell, C. and Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3):382–388. DOI logoGoogle Scholar
Gerrig, R. J. (2014). Psychology and life. New International Edition, Essex.Google Scholar
Gerrig, R. J., Zimbardo, P. G., Campbell, A. J., Cumming, S. R., and Wilkes, F. J. (2011). Psychology and life. Pearson Higher Education AU., 2nd edition.Google Scholar
Ghazali, A. S., Ham, J., Barakova, E., and Markopoulos, P. (2020). Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots. International Journal of Social Robotics, 12(5):1075–1092. DOI logoGoogle Scholar
Hair, J. F., Black, B., Babin Barry, J., and Anderson, R. E. (2010). Multivariate Data Analysis. Pearson Education.Google Scholar
Hall, C. S. and Lindzey, G. (1957). Theories of personality. John Wiley & Sons Inc.Google Scholar
Hampton, G. J. (2015). Imagining Slaves and Robots in Literature, Film, and Popular Culture. Lexington Books.Google Scholar
Hayes, A. F. (2014). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, volume 511. The Guilford Press.Google Scholar
Heerink, M., Kröse, B., Evers, V., and Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: The almere model. Int J Soc Robot, 2(4):361–375. DOI logoGoogle Scholar
Icek Ajzen, M. F. (1980). Understanding attitudes and predicting social behavior.Google Scholar
Joosse, M., Lohse, M., Perez, J. G., and Evers, V. (2013). What you do is who you are: The role of task context in perceived social robot personality. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 2134–2139. DOI logoGoogle Scholar
Lee, K. M., Peng, W., Jin, S. A., and Yan, C. (2006). Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction. Journal of Communication, 56(4):754–772. DOI logoGoogle Scholar
Lee, N., Kim, J., Kim, E., and Kwon, O. (2017). The Influence of Politeness Behavior on User Compliance with Social Robots in a Healthcare Service Setting. International Journal of Social Robotics, 9(5):727–743. DOI logoGoogle Scholar
Lee, W. H., Lin, C. W., and Shih, K. H. (2018). A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality. International Journal of Mobile Communications, 16(4):361–376. DOI logoGoogle Scholar
Leite, I., Castellano, G., Pereira, A., Martinho, C., and Paiva, A. (2014). Empathic Robots for Long-term Interaction: Evaluating Social Presence, Engagement and Perceived Support in Children. International Journal of Social Robotics, 6(3):329–341. DOI logoGoogle Scholar
Lin, H., Chi, O. H., and Gursoy, D. (2020). Antecedents of customers’ acceptance of artificially intelligent robotic device use in hospitality services. Journal of Hospitality Marketing and Management, 29(5):530–549. DOI logoGoogle Scholar
Maggi, G., Dell’Aquila, E., Cucciniello, I., and Rossi, S. (2020). “Don’t Get Distracted!”: The Role of Social Robots’ Interaction Style on Users’ Cognitive Performance, Acceptance, and Non-Compliant Behavior. International Journal of Social Robotics, pages 1–13.Google Scholar
McCrae, R. R. and John, O. P. (1992). An Introduction to the Five-Factor Model and Its Applications. Journal of Personality, 60(2):175–215. DOI logoGoogle Scholar
Meerbeek, B., Hoonhout, J., Bingley, P., and Terken, J. M. (2008). The influence of robot personality on perceived and preferred level of user control. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems, 9(2):204–229. DOI logoGoogle Scholar
Mende, M., Scott, M. L., van Doorn, J., Grewal, D., and Shanks, I. (2019). Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses. Journal of Marketing Research, 56(4):535–556. DOI logoGoogle Scholar
Mota, P., Paetzel, M., Fox, A., Amini, A., Srinivasan, S., and Kennedy, J. (2018). Expressing Coherent Personality with Incremental Acquisition of Multimodal Behaviors. In Proceedings of the 27th IEEE International Symposium on Robot and Human Interactive Communication, pages 396–403. Institute of Electrical and Electronics Engineers Inc. DOI logoGoogle Scholar
Netemeyer, R., Bearden, W., and Sharma, S. (2003). Scaling Procedures. SAGE Publications, Inc. DOI logoGoogle Scholar
Paetzel-Prüsmann, M., Perugia, G., and Castellano, G. (2021). The Influence of robot personality on the development of uncanny feelings. Computers in Human Behavior, 1201:106756. DOI logoGoogle Scholar
Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., and Robres, E. (2019). User Acceptance of Mobile Apps for Restaurants: An Expanded and Extended UTAUT-2. Sustainability, 11(4):1210. DOI logoGoogle Scholar
Petty, R. E. and Cacioppo, J. T. (1986). Communication and Persuasion. Springer New York. DOI logoGoogle Scholar
Robert, L., Alahmad, R., Esterwood, C., Kim, S., You, S., and Zhang, Q. (2020). A Review of Personality in Human-Robot Interactions. SSRN Electronic Journal. DOI logoGoogle Scholar
Robert, L. P. (2018). Personality in the Human Robot Interaction Literature : A Review and Brief Critique. In Proceedings of the 24th Americas Conference on Information Systems, (May).Google Scholar
Rossi, S., Conti, D., Garramone, F., Santangelo, G., Staffa, M., Varrasi, S., and Di Nuovo, A. (2020). The role of personality factors and empathy in the acceptance and performance of a social robot for psychometric evaluations. Robotics, 9(2):39. DOI logoGoogle Scholar
Savela, N., Turja, T., and Oksanen, A. (2018). Social Acceptance of Robots in Different Occupational Fields: A Systematic Literature Review. International Journal of Social Robotics, 10(4):493–502. DOI logoGoogle Scholar
Schneider, S. and Kummert, F. (2016). Motivational effects of acknowledging feedback from a socially assistive robot. In Proceedings of the 8th International Conference on Social Robotics, volume 99791 LNAI, pages 870–879. DOI logoGoogle Scholar
Soldz, S. and Vaillant, G. E. (1999). The Big Five Personality Traits and the Life Course: A 45-Year Longitudinal Study. Journal of Research in Personality, 331:208–232. DOI logoGoogle Scholar
Staffa, M., Rossi, A., Bucci, B., Russo, D., and Rossi, S. (2021). Shall I Be Like You? Investigating Robot’s Personalities and Occupational Roles for Personalised HRI. In Li, H., Ge, S. S., Wu, Y., Wykowska, A., He, H., Liu, X., Li, D., and Perez-Osorio, J., editors, Proceedings of the 13th International Conference on Social Robotics, pages 718–728. Springer International Publishing. DOI logoGoogle Scholar
Sverre Syrdal, D., Dautenhahn, K., Woods, S. N., Walters, M. L., and Lee Koay, K. (2006). Looking Good? Appearance Preferences and Robot Personality Inferences at Zero Acquaintance. Technical report.Google Scholar
Swift-Spong, K., Short, E., Wade, E., and Mataric, M. J. (2015). Effects of comparative feedback from a Socially Assistive Robot on self-efficacy in post-stroke rehabilitation. In IProceedings of the EEE International Conference on Rehabilitation Robotics, volume 2015- Septe, pages 764–769. DOI logoGoogle Scholar
Tapus, A., Tapus, C., Mataric, M., and Matari, M. J. (2008). User-Robot Personality Matching and Robot Behavior Adaptation for Post-Stroke Rehabilitation Therapy. Therapy. Intelligent Service Robotics, 1(2):169–183. DOI logoGoogle Scholar
Tay, B., Jung, Y., and Park, T. (2014). When stereotypes meet robots: The double-edge sword of robot gender and personality in human-robot interaction. Computers in Human Behavior, 381:75–84. DOI logoGoogle Scholar
Turja, T., Aaltonen, I., Taipale, S., and Oksanen, A. (2019). Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots. Information & Management, 57(5):103–220.Google Scholar
Venkatesh, V. and Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2):186–204. DOI logoGoogle Scholar
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3):425–478. DOI logoGoogle Scholar
Venkatesh, V., Thong, J. Y., and Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5):328–376. DOI logo
Whittlesea, B. W. (1993). Illusions of Familiarity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19(6):1235–1253.Google Scholar
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., and Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5):907–931. DOI logoGoogle Scholar
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