Investigating User Experience of HRI in the Context of a Realistic Retail Scenario - The Influence of Consumer Age on the Evaluation of a Humanoid Service Robot
Keywords:
Retail, Age, User Experience, Human Robot Interaction, Service Robots, Consumer SatisfactionAbstract
The fields of humanoid service robotics and human-robot interaction are interdisciplinary domains that are increasingly gaining momentum in practice and academia. However, as it is a relatively new area of interest in retail contexts, there are unanswered questions and challenges. The aim of this study is to (1) investigate and evaluate the user experience and satisfaction of consumers in human-robot interactions in a retail scenario and (2) analyze the interaction between consumers’ age and satisfaction with their user experience with humanoid service robots. For this purpose, quantitative data was collected using a questionnaire filled out by customers of a shopping mall after they had interacted with a social robot of the model ‘Pepper’. Collected data was analyzed by using descriptive statistical analysis. The results suggest that consumers tend to evaluate their interaction experience positively and satisfactorily. In addition, age was found to have a significant impact on consumers' satisfaction with the robot, with younger participants tending to be more satisfied with the interaction than more senior ones. These results may have implications for the design of service robots and how innovative customer journeys may improve the attractiveness and satisfaction of retail shopping for consumers in different age groups.
References
Alenljung, B., Lindblom, J., Andreasson, R., & Ziemke, T. (2017). User Experience in Social Human-Robot Interaction. International Journal of Ambient Computing and Intelligence, 8(2), 12–31. https://doi.org/10.4018/IJACI.2017040102
Bapat, D., & Kannadhasan, M. (2022). Satisfaction as a Mediator Between Brand Experience Dimensions and Word-of-Mouth for Digital Banking Services: Does Gender and Age Matter? Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 35. https://jcsdcb.com/index.php/JCSDCB/article/view/498
Barnett, W., Foos, A., Gruber, T., Keeling, D., Keeling, K., & Nasr, L. (2014). Consumer perceptions of Interactive Service Robots: A Value-Dominant Logic perspective. The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 1134–1139. https://doi.org/10.1109/ROMAN.2014.692640
Bartneck, C., Kulic, D., Croft, E., & Zoghbi, S. (2008). Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics, 1, 71–81. https://doi.org/10.1007/s12369-008-0001-3
Bobeth, J., Schrammel, J., Schwarz, S., Klein, M., Drobics, M., Hochleitner, C., & Tscheligi, M. (2014). Tablet, gestures, remote control? Influence of age on performance and user experience with iTV applications. Proceedings of the 2014 ACM International Conference on Interactive Expeirences for TV and Online Video-TVX 14,139–146. https://doi.org/10.1145/2602299.2602315
Bruna, M. T. (2011). The benefits of using high-level goal information for robot navigation [Master Thesis, Eindhoven University of Technology]. https://research.tue.nl/en/studentTheses/the-benefits-of-using-high-level-goal-information-for-robot-navig
Choi, S., Mattila, A. S., & Bolton, L. E. (2021). To Err Is Human(-oid): How Do Consumers React to Robot Service Failure and Recovery? Journal of Service Research, 24(3), 354–371. https://doi.org/10.1177/1094670520978798
Czaja, S. J., & Sharit, J. (1998). Age differences in attitudes toward computers. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 53(5), P329-340. https://doi.org/10.1093/geronb/53b.5.p329
Dautenhahn, K. (2007). Socially intelligent robots: Dimensions of human–robot interaction. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1480), 679–704. https://doi.org/10.1098/rstb.2006.2004
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
de Graaf, M. M. A., & Ben Allouch, S. (2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12), 1476–1486. https://doi.org/10.1016/j.robot.2013.07.007
Feil-Seifer, D., Haring, K. S., Rossi, S., Wagner, A. R., & Williams, T. (2020). Where to Next? The Impact of COVID-19 on Human-Robot Interaction Research. ACM Transactions on Human-Robot Interaction, 10(1), 1–7. https://doi.org/10.1145/3405450
Golchinfar, D., Vaziri, D. D., Stevens, G., & Schreiber, D. (2022). Let’s Go to the Mall: Investigating the Role of User Experience in Customers’ Intention to Use Social Robots in a Shopping Mall. Designing Interactive Systems Conference, 377–386. https://doi.org/10.1145/3532106.3533490
Ham, J., Bokhorst, R., Cuijpers, R., van der Pol, D., & Cabibihan, J.-J. (2011). Making Robots Persuasive: The Influence of Combining Persuasive Strategies (Gazing and Gestures) by a Storytelling Robot on Its Persuasive Power. In B. Mutlu, C. Bartneck, J. Ham, V. Evers, & T. Kanda (Hrsg.), Social Robotics (S. 71–83). Springer. https://doi.org/10.1007/978-3-642-25504-5_8
Hassenzahl, M. (2008). User experience (UX): Towards an experiential perspective on product quality. Proceedings of the 20th International of the Association Francophone d’Interactione Homme-Machine-IHM ’08,339, 11–15. https://doi.org/10.1145/1512714.1512717
Hassenzahl, M., Platz, A., Burmester, M., & Lehner, K. (2000). Hedonic and ergonomic quality aspect determine a software’s appeal. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2, 201–208. https://doi.org/10.1145/332040.33243 2
Hassenzahl, M., & Tractinsky, N. (2006). User experience—A research agenda. Behaviour and Information Technology, 25, 91–97. https://doi.org/10.1080/01449290500330331
Hauk, N., Hüffmeier, J., & Krumm, S. (2018). Ready to be a Silver Surfer? A Meta-analysis on the Relationship Between Chronological Age and Technology Acceptance. Computers in Human Behavior, 84, 304–319. https://doi.org/10.1016/j.chb.2018.01.020
Heikkilä, P., Lammi, H., Niemelä, M., Belhassein, K., Sarthou, G., Tammela, A., Clodic, A., & Alami, R. (2019). Should a Robot Guide Like a Human? A Qualitative Four-Phase Study of a Shopping Mall Robot. In M. A. Salichs, S. S. Ge, E. I. Barakova, J.-J. Cabibihan, A. R. Wagner, Á. Castro-González, & H. He (eds.), Social Robotics (S. 548–557). Springer International Publishing. https://doi.org/10.1007/978-3-030-35888-4_51
Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–262. https://doi.org/10.1177/1094670517752459
Hunt, H. K. (1991). Consumer Satisfaction, Dissatisfaction, and Complaining Behavior. Journal of Social Issues, 47(1), 107–117. https://doi.org/10.1111/j.1540-4560.1991.tb01814.x
Joosse, M., Lohse, M., Pérez, J. G., & Evers, V. (2013). What you do is who you are: The role of task context in perceived social robot personality. 2013 IEEE International Conference on Robotics and Automation, 2134–2139. https://doi.org/10.1109/ICRA.2013.6630863
Kelley, J. F. (1984). An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Information Systems, 2(1), 26–41. https://doi.org/10.1145/357417.357420
Kim, K. J., Park, E., & Shyam Sundar, S. (2013). Caregiving role in human–robot interaction: A study of the mediating effects of perceived benefit and social presence. Computers in Human Behavior, 29(4), 1799–1806. https://doi.org/10.1016/j.chb.2013.02.009
Krogsager, A., Segato, N., & Rehm, M. (2014). Backchannel Head Nods in Danish First Meeting Encounters with a Humanoid Robot: The Role of Physical Embodiment. In M. Kurosu (eds.), Human-Computer Interaction. Advanced Interaction Modalities and Techniques (S. 651–662). Springer International Publishing. https://doi.org/10.1007/978-3-319-07230-2_62
Kuo, I. H., Rabindran, J. M., Broadbent, E., Lee, Y. I., Kerse, N., Stafford, R. M. Q., & MacDonald, B. A. (2009). Age and gender factors in user acceptance of healthcare robots. RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 214–219. https://doi.org/10.1109/ROMAN.2009.5326292
Larsen, V., & Wright, N. D. (2021). Aggregate Consumer Satisfaction: The Telos of Marketing. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. https://jcsdcb.com/index.php/JCSDCB/article/view/361
Lu, L., Zhang, P., & Zhang, T. (Christina). (2021). Leveraging “human-likeness” of robotic service at restaurants. International Journal of Hospitality Management, 94, 102823. https://doi.org/10.1016/j.ijhm.2020.102823
Manner, C. K., & Lane, W. C. (2018). Who Posts Online Customer Reviews? The Role of Sociodemographics and Personality Traits. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 30. https://jcsdcb.com/index.php/JCSDCB/article/view/226
Manzi, F., Massaro, D., Di Lernia, D., Maggioni, M. A., Riva, G., & Marchetti, A. (2021). Robots Are Not All the Same: Young Adults’ Expectations, Attitudes, and Mental Attribution to Two Humanoid Social Robots. Cyberpsychology, Behavior and Social Networking, 24(5), 307–314. https://doi.org/10.1089/cyber.2020.0162
Meiners, N., Reucher, E., Kahn, H. T. A., & Spille, L. (2021). Consumer (Non) Complaint Behavior: An Empirical Analysis of Senior Consumers in Germany. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 34. https://jcsdcb.com/index.php/JCSDCB/article/view/419
Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & 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. https://doi.org/10.1177/0022243718822827
Mkpojiogu, E. O. C., LailyHashim, N., Hussain, A., & Tan, K. (2019). The Impact of User Demographics on the Perceived Satisfaction and Comfort of use of M- Banking Apps. International Journal of Innovative Technology and Exploring Engineering, 8(8), 7.
Mohammad, Y., & Nishida, T. (2014). Human-like motion of a humanoid in a shadowing task. 2014 International Conference on Collaboration Technologies and Systems (CTS), 123–130. https://doi.org/10.1109/CTS.2014.6867553
Nasir, J., Oppliger, P., Bruno, B., & Dillenbourg, P. (2022). Questioning Wizard of Oz: Effects of Revealing the Wizard behind the Robot. 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1385–1392. https://doi.org/10.1109/RO-MAN53752.2022.9900718
Niemelä, M., Heikkilä, P., Lammi, H., & Oksman, V. (2019). A Social Robot in a Shopping Mall: Studies on Acceptance and Stakeholder Expectations. In O. Korn (eds.), Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction (S. 119–144). Springer International Publishing. https://doi.org/10.1007/978-3-030-17107-0_7
Nowak, D. P., Dahl, A. J., & Peltier, J. W. (2023). An Updated Historical Review of the Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. https://jcsdcb.com/index.php/JCSDCB/article/view/869
OECD. (2020). COVID-19 and the retail sector: Impact and policy responses—OECD. OECD. https://read.oecd-ilibrary.org/view/?ref=134_134473-kuqn636n26&title=COVID-19-and-the-retail-sector-impact-and-policy-responses
Priluck, R. (2023). Online Shopping Pre and Post Vaccine and the Role of Trust and Commitment on Satisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 36 (2). https://jcsdcb.com/index.php/JCSDCB/article/view/858
Riek, L. D. (2012). Wizard of Oz studies in HRI: A systematic review and new reporting guidelines. Journal of Human-Robot Interaction, 1(1), 119–136. https://doi.org/10.5898/JHRI.1.1.Riek
Roozen, I., Raedts, M., & Yanycheva, A. (2023). Are Retail Customers Ready for Service Robot Assistants? International Journal of Social Robotics, 15(1), 15–25. https://doi.org/10.1007/s12369-022-00949-z
Salem, M., Weiss, A., Baxter, P., & Dautenhahn, K. (2015). Fourth International Symposium on “New Frontiers in Human-Robot Interaction”. AISB 2015, 2.
Saxby, C., Celuch, K., & Walz, A. (2015). How Employee Trustworthy Behaviors Interact to Emotionally Bond Service Customers. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior. 28. https://jcsdcb.com/index.php/JCSDCB/article/view/213
Schillaci, G., Bodiroža, S., & Hafner, V. V. (2013). Evaluating the Effect of Saliency Detection and Attention Manipulation in Human-Robot Interaction. International Journal of Social Robotics, 5(1), 139–152. https://doi.org/10.1007/s12369-012-0174-7
Schram, A., & Ule, A. (2019). Introduction to the Handbook of Research Methods and Applications in Experimental Economics. In Handbook of Research Methods and Applications in Experimental Economics (S. 1–7). Edward Elgar Publishing. https://www.elgaronline.com/view/edcoll/9781788110556/9781788110556.00006.xml
Strait, M., Briggs, P., & Scheutz, M. (2015, April 20). Gender, more so than age, modulates positive perceptions of language-based human-robot interactions. In Salem M., Weiss A., Baxter, P, and Dautenhahn, K. (eds.), 4th International Symposium on New Frontiers in Human Robet Interaction, 21-22. AISB Convention 2015.
Torta, E., Oberzaucher, J., Werner, F., Cuijpers, R., & Juola, J. (2013). Attitudes Towards Socially Assistive Robots in Intelligent Homes: Results From Laboratory Studies and Field Trials. Journal of Human-Robot Interaction, 1, 76–99. https://doi.org/10.5898/JHRI.1.2.Torta
Tuomi, A., Tussyadiah, I. P., & Hanna, P. (2021). Spicing up hospitality service encounters: The case of PepperTM. International Journal of Contemporary Hospitality Management, 33(11), 3906–3925. https://doi.org/10.1108/IJCHM-07-2020-0739
van Dijk, E. T., Torta, E., & Cuijpers, R. H. (2013). Effects of Eye Contact and Iconic Gestures on Message Retention in Human-Robot Interaction. International Journal of Social Robotics, 5(4), 491–501. https://doi.org/10.1007/s12369-013-0214-y
Vaziri, D., Golchinfar, D., Stevens, G., & Schreiber, D. (2020). Exploring Future Work—Co-Designing a Human-robot Collaboration Environment for Service Domains. Proceedings of the 2020 ACM Designing Interactive Systems Conference, 153–164. https://doi.org/10.1145/3357236.3395483
Vogels, E. a. (2019). Millennials stand out for their technology use, but older generations also embrace digital life. Pew Research Center. https://www.pewresearch.org/short-reads/2019/09/09/us-generations-technology-use/
Weiss, A., & Bartneck, C. (2015). Meta analysis of the usage of the Godspeed Questionnaire Series. 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 381–388. https://doi.org/10.1109/ROMAN.2015.7333568
Weiss, A., Bernhaupt, R., & Tscheligi, M. (2011). The USUS evaluation framework for user-centered HRI. In K. Dautenhahn & J. Saunders (eds.), New Frontiers in Human-Robot Interaction (S. 89–110). John Benjamins Publishing Company. https://doi.org/10.1075/ais.2.07wei
Young, J. E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H. I., & Grinter, R. E. (2011). Evaluating Human-Robot Interaction. International Journal of Social Robotics, 3(1), 53–67. https://doi.org/10.1007/s12369-010-0081-8
Zlotowski, J., & Bartneck, C. (2013). The inversion effect in HRI: Are robots perceived more like humans or objects? 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 365–372. https://doi.org/10.1109/HRI.2013.6483611
Złotowski, J., Strasser, E., & Bartneck, C. (2014). Dimensions of Anthropomorphism: From Humanness to Humanlikeness. 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 66–73. https://doi.org/10.1145/2559636.2559679
Downloads
Published
Issue
Section
License
Each volume is copyrighted by Consumer Satisfaction, Dissatisfaction and Complaining Behavior. We encourage authors to submit published articles to research aggregators such as researchgate.net or academia.edu. You may use the PDF files from the published journal for submission to these aggregators.