Güven İnancı, Duygusal Tepkilerin Öncülü Olarak Otellerde Hizmet Robotu Kabulünü Nasıl Etkiler?

Abstract

Turizm ve konaklama endüstrisinde robot teknolojilerinin kullanımı her geçen gün daha da popüler hale gelmektedir. Robot-insan etkileşiminin yüksek seviyesi nedeniyle hem müşteri hem de hizmet sağlayıcı, robotların bu sektördeki adaptasyonunu disiplinler arası bir yaklaşımla değerlendirmek zorundadır. Bu çalışma, bilişim sistemleri perspektifinden bireylerin otel hizmetlerinde kullanılan robot teknolojilerini kabulünü, güven inancı faktörüne dayalı Teknoloji Kabul Modeli (TKM) çerçevesinde duygusal tepkilerin etkisiyle incelemektedir. Araştırma bulguları, güven inancının algılanan eğlence üzerinde olumlu, robot anksiyetesi üzerinde ise olumsuz yönde etkisi olduğu göstermektedir. Algılanan eğlencenin temel TKM değişkenleri olarak algılanan fayda ve algılanan kullanım kolaylığını olumlu yönde etkilediği, robot anksiyetesinin ise duygusal tepki olarak yalnızca kullanım kolaylığı üzerinde olumsuz bir etkiye sahip olduğu gözlemlenmiştir. Bu açıdan, otel hizmet robotları bağlamında, TKM ilkelerinin geçerliliği harici değişkenler kullanılarak test edilmiş ve araştırma kapsamında doğrulanmıştır. Bu çalışma, Türkiye’de otel hizmet robotu kabulü algısını müşteri perspektifinden anlamaya yönelik öncü çalışmalardandır. Çalışma bulgularının henüz gelişme aşamasında olan ilgili literatüre katkı sağlaması ve sektör yöneticilerine pratik öneriler sunması beklenmektedir. 

Keywords

• Hizmet Robotları
• Teknoloji Kabul Model
• İnançlara Güven
• Robot Anksiyetesi
• Eğlence
• Service Robots
• Technology Acceptance Model
• Trusting Belief
• Robot Anxiety
• Enjoyment

How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?

Abstract

The application of robot technology in the tourism and hospitality industries is becoming increasingly popular. Due to the high level of robot-human interaction, both the customer and the service provider must evaluate the adaptation of robots in this industry using an interdisciplinary approach. From the perspective of information systems, this study examines individuals’ acceptance of robots used in hotel services within the framework of a trusting belief-based technology acceptance model (TAM) that includes the effect of emotional reactions. According to the results, it was observed that trusting belief have positive effects in both enjoyment and negative robot anxiety, considering hotel service robots specifically. In terms of affective reactions, enjoyment was observed to positively affect the perceived usefulness and ease of use as core TAM variables, while robot anxiety has a negative effect only on ease of use. In the context of hotel service robots, the validity of the TAM principles has been tested and verified using external variables. To the best of our knowledge, this study is the first attempt to understand the perception of hotel service robot adaptation in Turkey from the customer perspective. The study findings are expected to contribute to the literature, which is still in the early development stage, and provide practical advice to sector managers. 

Keywords

• Service Robots
• Technology Acceptance Model
• Trusting Belief
• Robot Anxiety
• Enjoyment

References

1. Abou-Shouk, M., Gad, H. E., & Abdelhakim, A. (2021). Exploring customers’ attitudes to the adoption of robots in tourism and hospitality. Journal of Hospitality and Tourism Technology, 12(4), 762–776. https://doi.org/10.1108/JHTT-09-2020-0215 google scholar
2. Adhikari, R. (2017). Robots May Become Go-To Customer Service Reps. https://www.crmbuyer.com/story/robots-may-become-go-to-customer-service-reps-84765.html google scholar
3. Agarwal, R., & Karahanna, E. (1998). On the multi-dimensional nature of compatibility beliefs in technology acceptance. Digit, 1–22. https://pdfs. semanticscholar.org/0d67/5482ed99bfb243b442a923e5f92ef55183ef.pdf google scholar
4. Allied Market Research. (2021). Hospitality Robots Market by Type (Front Desk Robots, Delivery Robots, Cleaning Robots and Others) and end user (Hotels, Restaurants and Bars and Travel and Tourism Industry) Sales Channel (Online and Offline): Global Opportunity Analysis and Industry For. https://www.alliedmarketresearch.com/hospitality-robots-market-A13078 google scholar
5. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 google scholar
6. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi. org/10.1007/BF02723327 google scholar
7. Benbasat, I., & Wang, W. (2005). Trust In and Adoption of Online Recommendation Agents. Journal of the Association for Information Systems, 6(3), 72–101. https://doi.org/10.17705/1jais.00065 google scholar
8. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588 google scholar

9. Bowen, J., & Morosan, C. (2018). Beware hospitality industry: the robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726–733. https:// doi.org/10.1108/WHATT-07-2018-0045 google scholar
10. Bröhl, C., Nelles, J., Brandl, C., Mertens, A., & Schlick, C. M. (2016). TAM reloaded: A technology acceptance model for human-robot cooperation in production systems. Communications in Computer and Information Science, 617, 97–103. https://doi.org/10.1007/978-3-319-40548-3_16 google scholar
11. Brosnan, M. J. (2002). Technophobia. In Technophobia. Routledge. https://doi.org/10.4324/9780203436707 google scholar
12. Cain, M. K., Zhang, Z., & Yuan, K. H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1261 google scholar
13. Califf, C. B., Brooks, S., & Longstreet, P. (2020). Human-like and system-like trust in the sharing economy: The role of context and humanness. Technological Forecasting and Social Change, 154(February). https://doi.org/10.1016/j.techfore.2020.119968 google scholar
14. Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating Greek employees’ intention to use web-based training. Computers and Education, 53(3), 877–889. https://doi.org/10.1016/j.compedu.2009.05.007 google scholar
15. Chen, M. F., & Tung, P. J. (2014). Developing an extended Theory of Planned Behavior model to predict consumers’ intention to visit green hotels. International Journal of Hospitality Management, 36, 221–230. https://doi.org/10.1016/j.ijhm.2013.09.006 google scholar
16. Choe, J. Y., Kim, J. J., & Hwang, J. (2021). Innovative robotic restaurants in Korea: merging a technology acceptance model and theory of planned behaviour. Asian Journal of Technology Innovation, 0(0), 1–24. https://doi.org/10.1080/19761597.2021.2005466 google scholar
17. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology. google scholar
18. Davis, F. D. (1989). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of. Management Information System Research Center, 13(3), 319–340. google scholar
19. 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 google scholar
20. de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E. (2020). Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots. Tourism Management, 78(April 2019), 104042. https:// doi.org/10.1016/j.tourman.2019.104042 google scholar
21. El-Said, O., & Al Hajri, S. (2022). Are customers happy with robot service? Investigating satisfaction with robot service restaurants during the COVID-19 pandemic. Heliyon, 8(3), e08986. https://doi.org/10.1016/j.heliyon.2022.e08986 google scholar
22. Engelberger, G. (1998). HelpMate, a service robot with experience. Industrial Robot, 25(2), 101–104. https://doi.org/10.1108/01439919810204667 google scholar
23. Etemad-Sajadi, R., & Sturman, M. C. (2021). How to Increase the Customer Experience by the Usage of Remote Control Robot Concierge Solutions. International Journal of Social Robotics, 14(2), 429–440. https://doi.org/10.1007/s12369-021-00800-x google scholar
24. Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2021). How do Consumers’ Gender and Rational Thinking Affect the Acceptance of Entertainment Social Robots? International Journal of Social Robotics. https://doi.org/10.1007/s12369-021-00845-y google scholar
25. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. American Marketing Association, 18(1), 39–50. https://doi.org/http://www.jstor.org google scholar
26. Fusté-Forné, F., & Jamal, T. (2021). Co-Creating New Directions for Service Robots in Hospitality and Tourism. Tourism and Hospitality, 2(1), 43–61. https://doi.org/10.3390/tourhosp2010003 google scholar
27. 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 google scholar
28. Hair, Joe F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https:// doi.org/10.2753/MTP1069-6679190202 google scholar
29. Hair, Joseph F., C. Black, W., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis, 7th Edition. In Decision Support Systems (Vol. 38, Issue 4). Pearson, London. google scholar
30. Hair, Joseph F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). PLS-SEM Book: A Primer on PLS-SEM. In Sage (Second Edi). Springer International Publishing. google scholar
31. Hair, Joseph F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001 google scholar
32. Hair Jr., J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107. https://doi.org/10.1504/ijmda.2017.10008574 google scholar
33. Hayduk, L., Cummings, G., Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! testing! one, two, three - Testing the theory in structural equation models! Personality and Individual Differences, 42(5), 841–850. https://doi.org/10.1016/j.paid.2006.10.001 google scholar
34. 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 google scholar
35. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 google scholar
36. Huang, H. L., Cheng, L. K., Sun, P. C., & Chou, S. J. (2021). The Effects of Perceived Identity Threat and Realistic Threat on the Negative Attitudes and Usage Intentions Toward Hotel Service Robots: The Moderating Effect of the Robot’s Anthropomorphism. International Journal of Social Robotics, 13(7), 1599–1611. https://doi.org/10.1007/s12369-021-00752-2 google scholar
37. Huang, M. H., Rust, R., & Maksimovic, V. (2019). The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI). California Management Review, 43–65. https://doi.org/10.1177/0008125619863436 google scholar
38. 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 google scholar Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204. https://doi.org/10.1002/(sici)1097-0266(199902)20:2<195::aid-smj13>3.0.co;2-7 google scholar
39. Hwang, Y., & Kim, D. J. (2007). Customer self-service systems: The effects of perceived Web quality with service contents on enjoyment, anxiety, and e-trust. Decision Support Systems, 43(3), 746–760. https://doi.org/10.1016/j.dss.2006.12.008262 google scholar
40. Igbaria, M., & Chakrabarti, A. (1990). Computer anxiety and attitudes towards microcomputer use. Behaviour and Information Technology, 9(3), 229–241. https://doi.org/10.1080/01449299008924239 google scholar
41. Ivanov, S., Gretzel, U., Berezina, K., Sigala, M., & Webster, C. (2019). Progress on robotics in hospitality and tourism: a review of the literature. Journal of Hospitality and Tourism Technology, 10(4), 489–521. https://doi.org/10.1108/JHTT-08-2018-0087 google scholar
42. Ivanov, S., & Webster, C. (2019). Perceived Appropriateness and Intention to Use Service Robots in Tourism. In Information and Communication Technologies in Tourism 2019 (Vol. 1, pp. 237–248). Springer International Publishing. https://doi.org/10.1007/978-3-030-05940-8_19 google scholar
43. Ivanov, S., & Webster, C. (2020). Robots in tourism: A research agenda for tourism economics. Tourism Economics, 26(7), 1065–1085. https://doi. org/10.1177/1354816619879583 google scholar
44. Ivanov, S., Webster, C., & Garenko, A. (2018). Young Russian adults’ attitudes towards the potential use of robots in hotels. Technology in Society, 55(June), 24–32. https://doi.org/10.1016/j.techsoc.2018.06.004 google scholar
45. Jaruwachirathanakul, B., & Fink, D. (2005). Internet banking adoption strategies for a developing country: the case of Thailand. Internet Research, 15(3), 295–311. https://doi.org/10.1108/10662240510602708 google scholar Jia, J. W., Chung, N., & Hwang, J. (2021). Assessing the hotel service robot interaction on tourists’ behaviour: the role of anthropomorphism. Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-11-2020-0664 google scholar
46. Kim, S. (Sam), Kim, J., Badu-Baiden, F., Giroux, M., & Choi, Y. (2021). Preference for robot service or human service in hotels? Impacts of the COVID- 19 pandemic. International Journal of Hospitality Management, 93(November 2020), 102795. https://doi.org/10.1016/j.ijhm.2020.102795 google scholar
47. Kuo, C. M., Chen, L. C., & Tseng, C. Y. (2017). Investigating an innovative service with hospitality robots. International Journal of Contemporary Hospitality Management, 29(5), 1305–1321. https://doi.org/10.1108/IJCHM-08-2015-0414 google scholar
48. Lankton, N. K., Harrison Mcknight, D., & Tripp, J. (2015). Technology, humanness, and trust: Rethinking trust in technology. Journal of the Association for Information Systems, 16(10), 880–918. https://doi.org/10.17705/1jais.00411 google scholar
49. Lee, L., Petter, S., Fayard, D., & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305–328. https://doi.org/10.1016/j.accinf.2011.05.002 google scholar
50. Lee, W. H., Lin, C. W., & 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. https://doi.org/10.1504/IJMC.2018.092666 google scholar
51. Li, Y., & Wang, C. (2021). Effect of customer’s perception on service robot acceptance. International Journal of Consumer Studies, August 2020, 1–21. https://doi.org/10.1111/ijcs.12755 google scholar
52. Lin, I. Y., & Mattila, A. S. (2021). The Value of Service Robots from the Hotel Guest’s Perspective: A Mixed-Method Approach. International Journal of Hospitality Management, 94(January), 102876. https://doi.org/10.1016/j.ijhm.2021.102876 google scholar
53. Luo, J. M., Vu, H. Q., Li, G., & Law, R. (2021). Understanding service attributes of robot hotels: A sentiment analysis of customer online reviews. International Journal of Hospitality Management, 98(April 2020), 103032. https://doi.org/10.1016/j.ijhm.2021.103032 google scholar
54. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530. https://doi.org/10.1093/biomet/57.3.519 google scholar
55. Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002 google scholar
56. Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2(2). https://doi.org/10.1145/1985347.1985353 google scholar
57. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81 google scholar
58. Miller, L., Kraus, J., Babel, F., & Baumann, M. (2021). More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety. Frontiers in Psychology, 12(April), 1–18. https://doi.org/10.3389/fpsyg.2021.592711 google scholar
59. Mori, M. (2012). The uncanny valley. IEEE Robotics and Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811 google scholar
60. Murphy, J., Gretzel, U., & Pesonen, J. (2019). Marketing robot services in hospitality and tourism: the role of anthropomorphism. Journal of Travel and Tourism Marketing, 36(7), 784–795. https://doi.org/10.1080/10548408.2019.1571983 google scholar
61. Murphy, J., Hofacker, C., & Gretzel, U. (2017). Dawning of the age of robots in hospitality and tourism: Challenges for teaching and research. European Journal of Tourism Research, 15(July 2018), 104–111. https://doi.org/10.54055/ejtr.v15i.265 google scholar
62. Naneva, S., Sarda Gou, M., Webb, T. L., & Prescott, T. J. (2020). A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots. International Journal of Social Robotics, 12(6), 1179–1201. https://doi.org/10.1007/s12369-020-00659-4 google scholar
63. Nomura, T., Kanda, T., Suzuki, T., & Kato, K. (2008). Prediction of human behavior in human - Robot interaction using psychological scales for anxiety and negative attitudes toward robots. IEEE Transactions on Robotics, 24(2), 442–451. https://doi.org/10.1109/TRO.2007.914004 google scholar
64. Nourbakhsh, I. (2000). Robots and Education in the classroom and in the museum: On the study of robots, …. … for Personal Robotics for Education. http://en.scientificcommons.org/42866175 google scholar
65. Park, E., & Kwon, S. J. (2016). The adoption of teaching assistant robots: a technology acceptance model approach. Program, 50(4), 354–366. https://doi. org/10.1108/PROG-02-2016-0017 google scholar
66. Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing and Management, 29(7), 739–756. https://doi.org/10.1080/19368623.2020.1722304 google scholar
67. Ringle, C. M., Wende, S., Becker, J.-M., & others. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. google scholar
68. Rouibah, K. (2012). Trust Factors Influencing Intention to Adopt Online Payment in Kuwait. Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA, 195–202.263 google scholar
69. Saari, U. A., Tossavainen, A., Kaipainen, K., & Mäkinen, S. J. (2022). Exploring factors influencing the acceptance of social robots among early adopters and mass market representatives. Robotics and Autonomous Systems, 151, 104033. https://doi.org/10.1016/j.robot.2022.104033 google scholar
70. Seo, K. H., & Lee, J. H. (2021). The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. Sustainability (Switzerland), 13(8). https://doi.org/10.3390/su13084431 google scholar
71. Sharma, A., Dwivedi, Y. K., Arya, V., & Siddiqui, M. Q. (2021). Does SMS advertising still have relevance to increase consumer purchase intention? A hybrid PLS-SEM-neural network modelling approach. Computers in Human Behavior, 124(June), 106919. https://doi.org/10.1016/j.chb.2021.106919 google scholar
72. Shead, S. (2019). World’s First Robot Hotel Fires Half Of Its Robots. Forbes.Com. https://www.forbes.com/sites/samshead/2019/01/16/ worlds-first-robot-hotel-fires-half-of-its-robots/?sh=6b91f648e1b1 google scholar
73. Song, S. Y. (2017). Modeling the Consumer Acceptance of Retail Service Robots [University of Tennessee, Knoxville]. In . http://trace.tennessee.edu/ utk_graddiss/4655/ google scholar
74. Thatcher, J. B., Loughry, M. L., Lim, J., & McKnight, D. H. (2007). Internet anxiety: An empirical study of the effects of personality, beliefs, and social support. Information and Management, 44(4), 353–363. https://doi.org/10.1016/j.im.2006.11.007 google scholar
75. Tung, V. W. S., & Law, R. (2017). The potential for tourism and hospitality experience research in human-robot interactions. International Journal of Contemporary Hospitality Management, 29(10), 2498–2513. https://doi.org/10.1108/IJCHM-09-2016-0520 google scholar
76. 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 and Management, 57(5), 103220. https://doi.org/10.1016/j.im.2019.103220 google scholar
77. Tussyadiah, I. (2020). A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research, 81(February), 102883. https://doi.org/10.1016/j.annals.2020.102883 google scholar
78. Tussyadiah, I. P., Zach, F. J., & Wang, J. (2020). Do travelers trust intelligent service robots? Annals of Tourism Research, 81(July 2019), 102886. https:// doi.org/10.1016/j.annals.2020.102886 google scholar
79. van Pinxteren, M. M. E., Wetzels, R. W. H., Rüger, J., Pluymaekers, M., & Wetzels, M. (2019). Trust in humanoid robots: implications for services marketing. In Journal of Services Marketing (Vol. 33, Issue 4, pp. 507–518). https://doi.org/10.1108/JSM-01-2018-0045 google scholar
80. Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi. org/10.2307/30036540 google scholar
81. Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412 google scholar
82. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi. org/10.1111/j.1540-5915.2008.00192.x google scholar
83. Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 google scholar
84. Wang, C., Teo, T. S. H., & Janssen, M. (2021). Public and private value creation using artificial intelligence: An empirical study of AI voice robot users in Chinese public sector. International Journal of Information Management, 61(July), 102401. https://doi.org/10.1016/j.ijinfomgt.2021.102401 google scholar
85. 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 google scholar
86. Wong, K. K.-K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS. Marketing Bulletin, 24(November), 1–32. google scholar
87. Yang, H., Song, H., Cheung, C., & Guan, J. (2021). How to enhance hotel guests’ acceptance and experience of smart hotel technology: An examination of visiting intentions. International Journal of Hospitality Management, 97(April), 103000. https://doi.org/10.1016/j.ijhm.2021.103000 google scholar
88. Zhong, L., Sun, S., Law, R., & Zhang, X. (2020). Impact of robot hotel service on consumers’ purchase intention: a control experiment. Asia Pacific Journal of Tourism Research, 25(7), 780–798. https://doi.org/10.1080/10941665.2020.1726421 google scholar
89. Zhong, L., Zhang, X., Rong, J., Chan, H. K., Xiao, J., & Kong, H. (2020). Construction and empirical research on acceptance model of service robots applied in hotel industry. Industrial Management and Data Systems, 121(6), 1325–1352. https://doi.org/10.1108/IMDS-11-2019-0603 google scholar