Yıl: 2022 Cilt: 6 Sayı: 2 Sayfa Aralığı: 245 - 263 Metin Dili: İngilizce DOI: 10.26650/acin.1117238 İndeks Tarihi: 24-05-2023

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

Öz:
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.
Anahtar Kelime:

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

Öz:
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.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Brosnan, M. J. (2002). Technophobia. In Technophobia. Routledge. https://doi.org/10.4324/9780203436707
  • 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/s13428-016-0814-1
  • 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
  • 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
  • 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
  • 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
  • Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
  • Davis, F. D. (1989). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of. Management Information System Research Center, 13(3), 319–340.
  • 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
  • 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
  • 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
  • Engelberger, G. (1998). HelpMate, a service robot with experience. Industrial Robot, 25(2), 101–104. https://doi.org/10.1108/01439919810204667
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.2006.12.008
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Mori, M. (2012). The uncanny valley. IEEE Robotics and Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Ringle, C. M., Wende, S., Becker, J.-M., & others. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH.
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Song, S. Y. (2017). Modeling the Consumer Acceptance of Retail Service Robots [University of Tennessee, Knoxville]. In . http://trace.tennessee.edu/ utk_graddiss/4655/
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Wong, K. K.-K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS. Marketing Bulletin, 24(November), 1–32.
  • 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
  • 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
  • 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
APA ÇALLI L (2022). How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. , 245 - 263. 10.26650/acin.1117238
Chicago ÇALLI Levent How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. (2022): 245 - 263. 10.26650/acin.1117238
MLA ÇALLI Levent How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. , 2022, ss.245 - 263. 10.26650/acin.1117238
AMA ÇALLI L How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. . 2022; 245 - 263. 10.26650/acin.1117238
Vancouver ÇALLI L How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. . 2022; 245 - 263. 10.26650/acin.1117238
IEEE ÇALLI L "How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?." , ss.245 - 263, 2022. 10.26650/acin.1117238
ISNAD ÇALLI, Levent. "How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?". (2022), 245-263. https://doi.org/10.26650/acin.1117238
APA ÇALLI L (2022). How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. Acta Infologica, 6(2), 245 - 263. 10.26650/acin.1117238
Chicago ÇALLI Levent How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. Acta Infologica 6, no.2 (2022): 245 - 263. 10.26650/acin.1117238
MLA ÇALLI Levent How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. Acta Infologica, vol.6, no.2, 2022, ss.245 - 263. 10.26650/acin.1117238
AMA ÇALLI L How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. Acta Infologica. 2022; 6(2): 245 - 263. 10.26650/acin.1117238
Vancouver ÇALLI L How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?. Acta Infologica. 2022; 6(2): 245 - 263. 10.26650/acin.1117238
IEEE ÇALLI L "How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?." Acta Infologica, 6, ss.245 - 263, 2022. 10.26650/acin.1117238
ISNAD ÇALLI, Levent. "How Does Trusting Belief Affect Service Robot Adoption in Hotels as an Antecedent of Affective Reaction?". Acta Infologica 6/2 (2022), 245-263. https://doi.org/10.26650/acin.1117238