References

• Aksoy, L., Keiningham, T. L., Buoye, A., & Ball, J. (2017). Linking satisfaction to credit card decisions: an application of the wallet allocation rule. International Journal of Bank Marketing, 35(2), 205–219. https://doi.org/10.1108/IJBM-02-2016-0019
   Web of Science ®Google Scholar
• Ameen, N., Hosany, S., & Tarhini, A. (2021). Consumer interaction with cutting-edge technologies: Implications for future research. Computers in Human Behavior, 120, 106761. https://doi.org/10.1016/j.chb.2021.106761
   Web of Science ®Google Scholar
• Andriella, A., Huertas-García, R., Forgas-Coll, S., Torras, C., & Alenyà, G. (2020). Discovering SOCIABLE: Using a Conceptual Model to Evaluate the Legibility and Effectiveness of Backchannel Cues in an Entertainment Scenario. In RO-MAN 2020.
   Google Scholar
• Andriella, A., Huertas-García, R., Forgas-Coll, S., Torras, C., & Alenyà, G. (2022). “I Know how you feel”. The importance of interaction style on users’ acceptance in an entertainment scenario. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems, 23(1), 21–57. https://doi.org/10.1075/is.21019.and
   Web of Science ®Google Scholar
• Andriella, A., Torras, C., Abdelnour, C., & Alenyà, G. (2022). Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations. User Modeling and User-Adapted Interaction, 1–56. https://doi.org/10.1007/s11257-021-09316-5
   Web of Science ®Google Scholar
• Aymerich-Franch, L., & Ferrer, I. (2020). The implementation of social robots during the COVID-19 pandemic. arXiv preprint arXiv:2007.03941.
   Google Scholar
• Batra, R., & Ahtola, O. T. (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters, 2(2), 159–170. https://doi.org/10.1007/BF00436035
   Google Scholar
• Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: a review. International Journal of Research in Marketing, 13(2), 139–161. https://doi.org/10.1016/0167-8116(95)00038-0
   Google Scholar
• Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020a). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of Service Management, 31(2), 267–289. https://doi.org/10.1108/JOSM-05-2019-0156
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020b). Service robot implementation: a theoretical framework and research agenda. The Service Industries Journal, 40(3–4), 203-225. https://doi.org/10.1080/02642069.2019.1672666
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021). Examining the effects of robots’ physical appearance, warmth, and competence in frontline services: The Humanness-Value-Loyalty model. Psychology & Marketing, 38(12), 2357–2376. https://doi.org/10.1002/mar.21532
   Web of Science ®Google Scholar
• Bentler, P. (2006). Eqs structural equations program manual. Multivariate Software Inc.
   Google Scholar
• Bickmore, T. W., & Picard, R. W. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction, 12(2), 293–327. https://doi.org/10.1145/1067860.1067867
   Google Scholar
• Blaurock, M., Čaić, M., Okan, M., & Henkel, A. P. (2022). A transdisciplinary review and framework of consumer interactions with embodied social robots: Design, delegate, and deploy. International Journal of Consumer Studies, 46(5), 1877–1899. https://doi.org/10.1111/ijcs.12808
   Web of Science ®Google Scholar
• Bochmann, G., & Sunshine, C. (1980). Formal methods in communication protocol design. IEEE Transactions on Communications, 28(4), 624–631. https://doi.org/10.1109/TCOM.1980.1094685
   Web of Science ®Google Scholar
• Brackeen, B. (2017). How to humanize artificial intelligence with emotion. Medium, March 30. https://medium.com/@BrianBrackeen/how-to-humanize-artificial-intelligence-with-emotion-19f981b1314a.
   Google Scholar
• Breazeal, C., Dautenhahn, K., & Kanda, T. (2016). Social robotics. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (pp. 1935–1972). Springer Handbooks. Springer.
   Google Scholar
• Brennan, S. E., & Hanna, J. E. (2009). Partner-Specific adaptation in dialog. Topics in Cognitive Science, 1(2), 274–291. https://doi.org/10.1111/j.1756-8765.2009.01019.x
   PubMed Web of Science ®Google Scholar
• Broekens, J. (2007). Emotion and reinforcement: affective facial expressions facilitate robot learning. In T.S. Huang, A. Nijholt, M. Pantic, & A. Pentland (Eds.), Artificial intelligence for human computing (pp. 113–132). Springer.
   Google Scholar
• Butler, J. (2006). Gender trouble. Routledge.
   Google Scholar
• Byrd, K., Fan, A., Her, E., Liu, Y., Almanza, B., & Leitch, S. (2021). Robot vs human: expectations, performances and gaps in off-premise restaurant service modes. International Journal of Contemporary Hospitality Management, 33(11), 3996–4016. https://doi.org/10.1108/IJCHM-07-2020-0721
   Web of Science ®Google Scholar
• Čaić, M., Odekerken-Schröder, G., & Mahr, D. (2018). Service robots: value co-creation and co-destruction in elderly care networks. Journal of Service Management, 29(2), 178–205. https://doi.org/10.1108/JOSM-07-2017-0179
   Web of Science ®Google Scholar
• Casula, M., Rangarajan, N., & Shields, P. (2021). The potential of working hypotheses for deductive exploratory research. Quality & Quantity, 55(5), 1703–1725. https://doi.org/10.1007/s11135-020-01072-9
   PubMedGoogle Scholar
• Cesta, A., Cortellessa, G., Orlandini, A., & Tiberio, L. (2016). Long-term evaluation of a telepresence robot for the elderly: methodology and ecological case study. International Journal of Social Robotics, 8(3), 421–441. https://doi.org/10.1007/s12369-016-0337-z
   Web of Science ®Google Scholar
• Chawla, D., & Joshi, H. (2020). The moderating role of gender and age in the adoption of mobile wallet. Foresight (los Angeles, Calif ), 22(4), 483–504. https://doi.org/10.1108/FS-11-2019-0094
   Web of Science ®Google Scholar
• Chiang, A. H., & Trimi, S. (2020). Impacts of service robots on service quality. Service Business, 14(3), 439–459. https://doi.org/10.1007/s11628-020-00423-8
   Web of Science ®Google Scholar
• Chidambaram, V., Chiang, Y. H., & Mutlu, B. (2012). Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues. In HRI’12 - Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 293–300).
   Google Scholar
• Chita-Tegmark, M., Ackerman, J. M., & Scheutz, M. (2019). Effects of assistive robot behavior on impressions of patient psychological attributes: Vignette-based human-robot interaction study. Journal of Medical Internet Research, 21(6), e13729. https://doi.org/10.2196/13729
   PubMed Web of Science ®Google Scholar
• Conti, D., Commodari, E., & 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.
   Web of Science ®Google Scholar
• Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the Bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132–148. https://doi.org/10.1177/00222429211045687
   Web of Science ®Google Scholar
• Darley, W. K., & Smith, R. E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of Advertising, 24(1), 41–56. https://doi.org/10.1080/00913367.1995.10673467
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• De Gauquier, L., Brengman, M., Willems, K., Cao, H. L., & Vanderborght, B. (2021). In or out? A field observational study on the placement of entertaining robots in retailing. International Journal of Retail & Distribution Management, 49(7), 846–874. https://doi.org/10.1108/IJRDM-10-2020-0413
   Web of Science ®Google Scholar
• De Graaf, M. M., & Allouch, S. B. (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
   Web of Science ®Google Scholar
• De Graaf, M. M., Allouch, S. B., & Van Dijk, J. A. (2019). Why would I use this in my home? A model of domestic social robot acceptance. Human–Computer Interaction, 34(2), 115–173. https://doi.org/10.1080/07370024.2017.1312406
   Web of Science ®Google Scholar
• De Keyser, A., Lemon, K. N., Klaus, P., & Keiningham, T. L. (2015). A framework for understanding and managing the customer experience. Marketing Science Institute Working Paper Series, 85(1), 15–121.
   Google Scholar
• Echterhoff, G., Higgins, E. T., & Levine, J. M. (2009). Shared reality: Experiencing commonality with others’ inner states about the world. Perspectives on Psychological Science, 4(5), 496–521. https://doi.org/10.1111/j.1745-6924.2009.01161.x
   Web of Science ®Google Scholar
• Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: a three-factor theory of anthropomorphism. Psychological Review, 114(4), 864. https://doi.org/10.1037/0033-295X.114.4.864
   PubMed Web of Science ®Google Scholar
• Eyssel, F., Kuchenbrandt, D., & Bobinger, S. (2011). Effects of anticipated human-robot interaction and predictability of robot behavior on perceptions of anthropomorphism. In Proceedings of the 6th International Conference on Human-Robot Interaction (pp. 61–68).
   Google Scholar
• Fausto-Sterling, A. (2001). Myths of gender: Biological theories about women and men. Basic Books.
   Google Scholar
• Flavián, C., & Casaló, L.V. (2021). Artificial intelligence in services: current trends, benefits and challenges. The Service Industries Journal, 41(13–14), 853–859. https://doi.org/10.1080/02642069.2021.1989177
   Web of Science ®Google Scholar
• Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293–320. https://doi.org/10.1108/JOSM-10-2020-0378
   Web of Science ®Google Scholar
• Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2022a). Does the personality of consumers influence the assessment of the experience of interaction with social robots?. International Journal of Social Robotics. In press. https://doi.org/10.1007/s12369-022-00935-5
   PubMed Web of Science ®Google Scholar
• Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2022b). The effects of gender and personality of robot assistants on customers’ acceptance of their service. Service Business, 16(2), 359–389. https://doi.org/10.1007/s11628-022-00492-x
   Web of Science ®Google Scholar
• Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2022c). How do consumers’ gender and rational thinking affect the acceptance of entertainment social robots? International Journal of Social Robotics, 14(4), 973–994. https://doi.org/10.1007/s12369-021-00845-y
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.1177/002224378101800313
   Web of Science ®Google Scholar
• Gamecho, B., Silva, H., Guerreiro, J., Gardeazabal, L., & Abascal, J. (2015). A context-aware application to increase elderly users compliance with physical rehabilitation exercises at home via animatronic biofeedback. Journal of Medical Systems, 39(11), 135. https://doi.org/10.1007/s10916-015-0296-1
   PubMed Web of Science ®Google Scholar
• Ghazali, A. S., Ham, J., Barakova, E., & 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. https://doi.org/10.1007/s12369-019-00611-1
   Web of Science ®Google Scholar
• Goertzel, B., Mossbridge, J., Monroe, E., Hanson, D., & Yu, G. (2017). Humanoid robots as agents of human consciousness expansion. arXiv preprint arXiv:1709.07791.
   Google Scholar
• Gupta, A. (2016). Redefining service quality scale with customer experience quality scale: a critical review. International Journal of Services and Operations Management, 25(1), 48–64. https://doi.org/10.1504/IJSOM.2016.078070
   Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. A global perspective. Pearson Education Limited Harlow.
   Google Scholar
• Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (7nd ed.). The Guilford Press.
   Google Scholar
• He, J., & Freeman, L. A. (2019). Are men more technology-oriented than women? The role of gender on the development of general computer self-efficacy of college students. Journal of Information Systems Education, 21(2), 203–212.
   Google Scholar
• Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: The almere model. International Journal of Social Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5
   Web of Science ®Google Scholar
• Hofmeyr, J., Goodall, V., Bongers, M., & Holtzman, P. (2008). A new measure of brand attitudinal equity based on the Zipf distribution. International Journal of Market Research, 50(2), 181–202. https://doi.org/10.1177/147078530805000204
   Web of Science ®Google Scholar
• Hoy, M. B. (2018). Alexa, siri, cortana, and more: An introduction to voice assistants. Medical Reference Services Quarterly, 37(1), 81–88. https://doi.org/10.1080/02763869.2018.1404391
   PubMedGoogle Scholar
• Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687
   PubMed Web of Science ®Google Scholar
• Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
   Web of Science ®Google Scholar
• Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https://doi.org/10.1177/1094670520902266
   Web of Science ®Google Scholar
• Hyun, Y., Hlee, S., Park, J., & Chang, Y. (2022). Discovering meaningful engagement through interaction between customers and service robots. The Service Industries Journal, 42(13–14), 973–1000. https://doi.org/10.1080/02642069.2022.2088738
   Web of Science ®Google Scholar
• Kim, S., Chen, R. P., & Zhang, K. (2016). Anthropomorphized helpers undermine autonomy and enjoyment in computer games. Journal of Consumer Research, 43(2), 282–302. https://doi.org/10.1093/jcr/ucw016
   Web of Science ®Google Scholar
• Klaus, P. P., & Maklan, S. (2013). Towards a better measure of customer experience. International Journal of Market Research, 55(2), 227–246. https://doi.org/10.2501/IJMR-2013-021
   Web of Science ®Google Scholar
• Kuehnl, C., Jozic, D., & Homburg, C. (2019). Effective customer journey design: consumers’ conception, measurement, and consequences. Journal of the Academy of Marketing Science, 47(3), 551–568. https://doi.org/10.1007/s11747-018-00625-7
   Web of Science ®Google Scholar
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
   PubMed Web of Science ®Google Scholar
• Lee, N., Kim, J., Kim, E., & 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. https://doi.org/10.1007/s12369-017-0420-0
   Web of Science ®Google Scholar
• Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
   Web of Science ®Google Scholar
• Lien, C. H., Hsu, M. K., Shang, J. Z., & Wang, S. W. (2021). Self-service technology adoption by air passengers: a case study of fast air travel services in Taiwan. The Service Industries Journal, 41(9–10), 671–695. https://doi.org/10.1080/02642069.2019.1569634
   Web of Science ®Google Scholar
• Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
   Google Scholar
• Lu, J., Wang, L., & Hayes, L.A. (2012). How do technology readiness, platform functionality and trust influence C2C user satisfaction? Journal of Electronic Commerce Research, 13(1), 50–69.
   Web of Science ®Google Scholar
• Lu, V. N., Wirtz, J., Kunz, W. H., Paluch, S., Gruber, T., Martins, A., & Patterson, P. G. (2020). Service robots, customers and service employees: what can we learn from the academic literature and where are the gaps? Journal of Service Theory and Practice, 30(3), 361–391. https://doi.org/10.1108/JSTP-04-2019-0088
   Web of Science ®Google Scholar
• Luff, P., Frohlich, D., & Gilbert, N. G. (2014). Computers and conversation. Elsevier Science.
   Google Scholar
• Lv, L., Huang, M., & Huang, R. (2022). Anthropomorphize service robots: the role of human nature traits. The Service Industries Journal, 1–25. https://doi.org/10.1080/02642069.2022.2048821
   Web of Science ®Google Scholar
• Lyons, J. B., Vo, T., Wynne, K. T., Mahoney, S., Nam, C. S., & Gallimore, D. (2021). Trusting autonomous security robots: the role of reliability and stated social intent. Human Factors: The Journal of the Human Factors and Ergonomics Society, 63(4), 603–618. https://doi.org/10.1177/0018720820901629
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of Marketing, 69(2), 61–83. https://doi.org/10.1509/jmkg.69.2.61.60759
   Web of Science ®Google Scholar
• Meyers-Levy, J., & Loken, B. (2015). Revisiting gender differences: What we know and what lies ahead. Journal of Consumer Psychology, 25(1): 129-149. https://doi.org/10.1016/j.jcps.2014.06.003
   Web of Science ®Google Scholar
• MMR. (2022). Social robots market: Global industry analysis and forecast (2021-2027) by end user, application, Industry. https://www.maximizemarketresearch.com/market-report/global-social-robots-market/78802/
   Google Scholar
• Moss, G. (2009). Gender, design, and marketing. Gower.
   Google Scholar
• Nickel, K., Orth, U. R., & Kumar, M. (2020). Designing for the genders: The role of visual harmony. International Journal of Research in Marketing, 37(4), 697–713. https://doi.org/10.1016/j.ijresmar.2020.02.006
   Web of Science ®Google Scholar
• Oh, K., Lee, J. S., Kim, S. K., Jung, J. Y., & Kim, B. (2013). Service prototyping for service testing in virtual reality. International Journal of Information and Electronics Engineering, 3(3), 304–308.
   Google Scholar
• PAL Robotics. (2022). TIAGo specifications. https://pal-robotics.com/es/robots/tiago/
   Google Scholar
• Parasuraman, A. (2000). Technology readiness index (Tri). Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
   Google Scholar
• Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
   Web of Science ®Google Scholar
• Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Sage.
   Google Scholar
• Pine, B. J., & Gilmore, J. H. (1998). Welcome to the experience economy. Harvard Business Review, 76(July-August), 97–105.
   PubMed Web of Science ®Google Scholar
• Polaine, A., Løvlie, L., & Reason, B. (2013). Service design: From insight to implementation. Rosenfeld Media, LLC.
   Google Scholar
• Pollio, H. R., Henley, T. B., Thompson, C. J., & Thompson, C. B. (1997). The phenomenology of everyday life: Empirical investigations of human experience. Cambridge University Press.
   Google Scholar
• Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of Interactive Marketing, 18(3), 5–14. https://doi.org/10.1002/dir.20015
   Google Scholar
• Rancati, G., & Maggioni, I. (2022). Neurophysiological responses to robot–human interactions in retail stores. Journal of Services Marketing. In press. https://doi.org/10.1108/JSM-04-2021-0126
   Web of Science ®Google Scholar
• Razek, A. R. A., Van Husen, C., Pallot, M., & Richir, S. (2018). A proposed research framework and model for service prototyping. In 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 1–8.
   Google Scholar
• Robert, L. P. Jr, Alahmad, R., Esterwood, C., Kim, S., You, S., & Zhang, Q. (2019). A review of personality in human–robot interactions. Foundations and Trends® in Information Systems, 4(2), 107–212. https://doi.org/10.1561/2900000018
   Google Scholar
• Rogers, Y., Sharp, H., & Preece, J. (2011). Interaction design: Beyond human-computer interaction (3rd ed.). John Wiley & Sons, Ltd.
   Google Scholar
• Sabelli, A. M., & Kanda, T. (2016). Robovie as a mascot: a qualitative study for long-term presence of robots in a shopping mall. International Journal of Social Robotics, 8(2), 211–221. https://doi.org/10.1007/s12369-015-0332-9
   Web of Science ®Google Scholar
• Schepers, J., Belanche, D., Casaló, L. V., & Flavián, C. (2022). How smart should a service robot Be? Journal of Service Research, 25(4), 565–582. https://doi.org/10.1177/10946705221107704
   Web of Science ®Google Scholar
• Schrepp, M., Hinderks, A., & Thomaschewski, J. (2017). Design and evaluation of a short version of the user experience questionnaire (UEQ-S). International Journal of Interactive Multimedia and Artificial Intelligence, 4(6), 103–108. https://doi.org/10.9781/ijimai.2017.09.001
   Web of Science ®Google Scholar
• Shin, D-H., & Choo, H. (2011). Modeling the acceptance of socially interactive robotics. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems, 12(3), 430–460. https://doi.org/10.1075/is.12.3.04shi
   Web of Science ®Google Scholar
• Shourmasti, E. S., Colomo-Palacios, R., Holone, H., & Demi, S. (2021). User experience in social robots. Sensors, 21(15), 5052. https://doi.org/10.3390/s21155052
   PubMed Web of Science ®Google Scholar
• Shridhar, K. (2017). How close are chatbots to passing the turing test? Chatbots Magazine, May 2. https://chatbotsmagazine.com/how-close-are-chatbots-to-pass-turing-test-33f27b18305e
   Google Scholar
• Solichin, A., Jayaun, J., & Purabaya, R. (2019). Mobile-based In-room check-in system for optimizing check-in process at the sultan hotel & residence Jakarta. In 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) (pp. 255–258). https://doi.org/10.1109/ICIMCIS48181.2019.8985218
   Google Scholar
• Speer, N. K., Zacks, J. M., & Reynolds, J. R. (2007). Human brain activity time-locked to narrative event boundaries. Psychological Science, 18(5), 449–455. https://doi.org/10.1111/j.1467-9280.2007.01920.x
   PubMed Web of Science ®Google Scholar
• Stets, J. E., & Burke, P. J. (2000). Identity theory and social identity theory. Social Psychology Quarterly, 63(3), 224–237. https://doi.org/10.2307/2695870
   Web of Science ®Google Scholar
• Subero-Navarro, Á, Pelegrín-Borondo, J., Reinares-Lara, E., & Olarte-Pascual, C. (2022). Proposal for modeling social robot acceptance by retail customers: CAN model+ technophobia. Journal of Retailing and Consumer Services, 64, 102813. https://doi.org/10.1016/j.jretconser.2021.102813
   Web of Science ®Google Scholar
• Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78. https://doi.org/10.1016/j.ijhcs.2005.04.013
   Web of Science ®Google Scholar
• Sundar, S. S., Jung, E. H., Waddell, T. F., & Kim, K. J. (2017). Cheery companions or serious assistants? Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens. International Journal of Human-Computer Studies, 97, 88–97. https://doi.org/10.1016/j.ijhcs.2016.08.006
   Web of Science ®Google Scholar
• Tkachenko, Y., Kochenderfer, M. J., & Kluza, K. (2016). Customer simulation for direct marketing experiments. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 478–487). https://doi.org/10.1109/DSAA.2016.59
   Google Scholar
• Tuomi, A., Tussyadiah, L. P., & Hanna, P. (2021). Spicing up hospitality service encounters: the case of Pepper™. International Journal of Contemporary Hospitality Management, 33(11), 3906–3925. https://doi.org/10.1108/IJCHM-07-2020-0739
   Web of Science ®Google Scholar
• Turja, T., Aaltonen, I., Taipale, S., & Oksanen, A. (2020). Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots. Information & Management, 57(5), 103220. https://doi.org/10.1016/j.im.2019.103220
   Web of Science ®Google Scholar
• Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/1094670516679272
   Web of Science ®Google Scholar
• Van Pinxteren, M. M., Wetzels, R. W., Rüger, J., Pluymaekers, M., & Wetzels, M. (2019). Trust in humanoid robots: implications for services marketing. Journal of Services Marketing, 33(4), 507–518. https://doi.org/10.1108/JSM-01-2018-0045
   Web of Science ®Google Scholar
• Veling, L., & McGinn, C. (2021). Qualitative research in HRI: A review and taxonomy. International Journal of Social Robotics, 13(7), 1689–1709. https://doi.org/10.1007/s12369-020-00723-z
   Web of Science ®Google Scholar
• Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001
   Web of Science ®Google Scholar
• Voss, K. E., Spangenberg, E. R., & Grohmann, B. (2003). Measuring the hedonic and utilitarian dimensions of consumer attitude. Journal of Marketing Research, 40(3), 310–320. https://doi.org/10.1509/jmkr.40.3.310.19238
   Web of Science ®Google Scholar
• Walters, M. L., Syrdal, D. S., Dautenhahn, K., Te Boekhorst, R., & Koay, K. L. (2008). Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion. Autonomous Robots, 24(2), 159–178. https://doi.org/10.1007/s10514-007-9058-3
   Web of Science ®Google Scholar
• Waytz, A., & Norton, M. I. (2014). Botsourcing and outsourcing: Robot, British, Chinese, and German workers are for thinking—not feeling—jobs. Emotion, 14(2), 434–444. https://doi.org/10.1037/a0036054
   PubMed Web of Science ®Google Scholar
• Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Springer Science & Business Media.
   Google Scholar
• Williams, L., Buoye, A., Keiningham, T. L., & Aksoy, L. (2020). The practitioners’ path to customer loyalty: Memorable experiences or frictionless experiences? Journal of Retailing and Consumer Services, 57, 102215. https://doi.org/10.1016/j.jretconser.2020.102215
   Web of Science ®Google Scholar
• Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://doi.org/10.1108/JOSM-04-2018-0119
   Web of Science ®Google Scholar
• Wolfe, J., & Roberts, C. R. (1993). A further study of the external validity of business games: Five-year peer group indicators. Simulation & Gaming, 24(1), 21–33. https://doi.org/10.1177/1046878193241004
   Web of Science ®Google Scholar
• Złotowski, J., Proudfoot, D., Yogeeswaran, K., & Bartneck, C. (2015). Anthropomorphism: opportunities and challenges in human–robot interaction. International Journal of Social Robotics, 7(3), 347–360. doi:10.1007/s12369-014-0267-6
   Web of Science ®Google Scholar