Küresel Konaklama Sektöründe Hizmet Robotlarının Kabulüne Duygusal Faktörlerin Etkisi: Karma Yöntemli Bir Analiz

Yıl 2025, Cilt: 18 Sayı: Uluslararası Girişimcilik Sosyal Bilimler Kongresi Özel Sayısı, 216 - 230, 30.10.2025

Ali Eren Balıkel

Öz

Bu çalışmanın amacı, tüketicilerin termal otel ortamlarında kullanılmak üzere özel olarak tasarlanmış servis robotlarını nasıl algıladıklarını ve kabul ettiklerini araştırmaktır. Bu amaca ulaşmak için araştırma, olumlu duygu, olumsuz duygu ve tüketicilerin servis robotlarını benimsemeye yönelik davranışsal niyetlerinin rollerine odaklanmaktadır. Karma yöntemli bir tasarım kullanan çalışma, duygusal ve bilişsel faktörler arasındaki karşılıklı ilişkileri incelemek için hem nitel içgörüleri hem de nicel titizliği entegre etmektedir. Veriler, Türkiye genelinde termal otellerde kalan 458 müşteriden toplanmıştır. Analiz, önerilen hipotezlerin sağlam ve çok boyutlu bir değerlendirmesine olanak sağlayan bulanık kümeli Nitel Karşılaştırmalı Analiz (fsQCA) ile birlikte Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi (PLS-SEM) kullanılarak yürütülmüştür. PLS-SEM’den elde edilen bulgular, yenilik ve memnuniyetin tüketicilerin servis robotlarını kabulü üzerinde istatistiksel olarak anlamlı pozitif bir etkiye sahip olduğunu, keyif ve olumsuz duygu etkilerinin ise anlamsız olduğunu göstermektedir. Ancak fsQCA sonuçları, tek başına bireysel faktörlerden ziyade belirli duygusal durum kombinasyonlarının daha yüksek kabul seviyelerine etkili bir şekilde yol açabileceğini ortaya koymuştur. Bu bulgular, konaklama ortamlarında hizmet robotu uygulama stratejilerini geliştirmek için değerli çıkarımlar sunmaktadır.

Anahtar Kelimeler

Hizmet robotları , Konaklama sektörü , Teknoloji kabulü , PLS-SEM , fsQCA

The Impact of Emotional Factors on Service Robot Acceptance in Global Hospitality: A Mixed-Method Analysis

Öz

The purpose of this study is to investigate how consumers perceive and accept service robots specifically designed for use in thermal hotel environments. To achieve this objective, the research focuses on the roles of positive emotions, negative emotions, and consumers’ behavioral intentions toward adopting service robots. Employing a mixed-methods design, the study integrates both qualitative insights and quantitative rigor to examine the interrelationships among emotional and cognitive factors. Data were collected from 458 guests staying at thermal hotels across Türkiye. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) alongside fuzzy-set Qualitative Comparative Analysis (fsQCA), enabling a robust and multidimensional evaluation of the proposed hypotheses. The findings from PLS-SEM indicate that novelty and satisfaction exert a statistically significant positive influence on consumers’ acceptance of service robots, whereas the effects of enjoyment and negative emotions were non-significant. However, fsQCA results revealed that specific combinations of emotional states rather than individual factors alone, can effectively lead to higher acceptance levels of acceptance. These findings provide valuable implications for developing strategies for implementing service robot in hospitality environments.

Anahtar Kelimeler

Service robots , Hospitality , Technology acceptance , PLS-SEM , fsQCA

Kaynakça

• Ajina, A. S., Joudeh, J. M., Ali, N. N., Zamil, A. M., & Hashem, T. N. (2023). The effect of mobile-wallet service dimensions on customer satisfaction and loyalty: An empirical study. Cogent Business and Management, 10(2), 2229544. https://doi.org/10.1080/23311975.2023.2229544
• Asatryan, V. S., & Oh, H. (2008). Psychological ownership theory: An exploratory application in the restaurant industry. Journal of Hospitality and Tourism Research, 32(3), 363-386. https://doi.org/10.1177/1096348008317391
• Ayyildiz, A. Y., Baykal, M., & Koc, E. (2022). Attitudes of hotel customers towards the use of service robots in hospitality service encounters. Technology in Society, 70, 101995. https://doi.org/10.1016/j.techsoc.2022.101995
• Canton, E., Hedley, D., & Spoor, J. R. (2023). The stereotype content model and disabilities. The Journal of Social Psychology, 163(4), 480-500. https://doi.org/10.1080/00224545.2021.2017253
• Chi, O. H., Gursoy, D., & Chi, C. G. (2020). Tourists’ attitudes toward the use of artificially intelligent (AI) devices in tourism service delivery: moderating role of service value seeking. Journal of Travel Research, 61(1), 170-185. http://dx.doi.org/10.1177/0047287520971054
• Chirico, A., & Gaggioli, A. (2023). How real are virtual emotions?. Cyberpsychology, Behavior, and Social Networking, 26(4), 227-228. https://doi.org/10.1089/cyber.2023.29272.editorial
• Cohen, J. B., Pham, M. T., & Andrade, E. B. (2018). The nature and role of affect in consumer behavior. In Handbook of Consumer Psychology (pp. 306-357). New York: Routledge.
• Cook, K. S., Cheshire, C., Rice, E. R., & Nakagawa, S. (2013). Social exchange theory. In Handbook of social psychology (pp. 61-88). https://doi.org/10.1007/978-94-007-6772-0_3
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008
• Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption. Journal of Business Research, 122, 180-191. http://dx.doi.org/10.1016/j.jbusres.2020.08.058
• Fu, S., Zheng, X., & Wong, I. A. (2022). The perils of hotel technology: The robot usage resistance model. International Journal of Hospitality Management, 102, 103174. https://doi.org/10.1016/j.ijhm.2022.103174
• Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
• Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
• Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424. https://psycnet.apa.org/doi/10.1037/1082-989X.3.4.424
• Huang, D., Chen, Q., Huang, S., & Liu, X. (2023). Consumer intention to use service robots: a cognitive–affective–conative framework. International Journal of Contemporary Hospitality Management, 36(6), 1893-1913. http://dx.doi.org/10.1108/IJCHM-12-2022-1528
• Jung, H. S., & Yoon, H. H. (2011). The effects of nonverbal communication of employees in the family restaurant upon customers’ emotional responses and customer satisfaction. International Journal of Hospitality Management, 30(3), 542-550. http://dx.doi.org/10.1016/j.ijhm.2010.09.005
• Kelley, H. H., & Michela, J. L. (1980). Attribution theory and research. Annual Review of Psychology, 31(1), 457-501. https://doi.org/10.1146/annurev.ps.31.020180.002325
• Khoa, D. T., Gip, H. Q., Guchait, P., & Wang, C. Y. (2023). Competition or collaboration for human–robot relationship: a critical reflection on future cobotics in hospitality. International Journal of Contemporary Hospitality Management, 35(6), 2202-2215. http://dx.doi.org/10.1108/IJCHM-04-2022-0434
• Kim, H., So, K. K. F., & Wirtz, J. (2022). Service robots: Applying social exchange theory to better understand human–robot interactions. Tourism Management, 92, 104537. https://doi.org/10.1016/j.tourman.2022.104537
• Kumar, S., Sahoo, S., Ali, F., & Cobanoglu, C. (2023). Rise of fsQCA in tourism and hospitality research: a systematic literature review. International Journal of Contemporary Hospitality Management, 36(7), 2165-2193. https://doi.org/10.1108/IJCHM-03-2023-0288
• Lim, W. M., & Ting, D. H. (2012). E-shopping: an analysis of the technology acceptance model. Modern Applied Science, 6(4), 49. http://dx.doi.org/10.5539/mas.v6n4p49
• Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36-51. https://doi.org/10.1016/j.ijhm.2019.01.005
• 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
• Maggetti, M., & Levi-Faur, D. (2013). Dealing with errors in QCA. Political Research Quarterly, 198-204. https://www.jstor.org/stable/23563603
• McCartney, G., & McCartney, A. (2020). Rise of the machines: towards a conceptual service-robot research framework for the hospitality and tourism industry. International Journal of Contemporary Hospitality Management, 32(12), 3835-3851. https://doi.org/10.1108/IJCHM-05-2020-0450
• Mori, M., MacDorman, K.F., & Kageki, N. (2012). The uncanny valley. IEEE Robotics and Automation Magazine, 19(2), 98-100. http://dx.doi.org/10.1109/MRA.2012.2192811
• Ou, Y. C., & Verhoef, P. C. (2017). The impact of positive and negative emotions on loyalty intentions and their interactions with customer equity drivers. Journal of Business Research, 80, 106-115. https://doi.org/10.1016/j.jbusres.2017.07.011
• Ragin, C. C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. Univ of California Press.
• Rasoolimanesh, S. M., Valaei, N., & Rezaei, S. (2023). Guideline for application of fuzzy-set qualitative comparative analysis (fsQCA) in tourism and hospitality studies. In Cutting Edge Research Methods in Hospitality and Tourism (pp. 137-156). Emerald Publishing Limited. https://doi.org/10.1108/978-1-80455-063-220231009
• Rosete, A., Soares, B., Salvadorinho, J., Reis, J., & Amorim, M. (2020). Service robots in the hospitality industry: An exploratory literature review. In Exploring Service Science: 10th International Conference, IESS 2020, Porto, Portugal, February 5–7, 2020, Proceedings 10, pp. 174-186, Springer International Publishing. https://doi.org/10.1007/978-3-030-38724-2_13
• 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
• Seo, K. H., & Lee, J. H. (2021). The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. Sustainability, 13(8), 4431. https://doi.org/10.3390/su13084431
• Shu, X., & Ye, Y. (2023). Knowledge discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2022.102817
• Trope, Y., & Liberman, N. (2012). Construal level theory. Handbook of Theories of Social Psychology, 1, 118-134.
• Tuomi, A., Tussyadiah, I. P., & Stienmetz, J. (2021). Applications and implications of service robots in hospitality. Cornell Hospitality Quarterly, 62(2),232-247. https://doi.org/10.1177/1938965520923961
• Tussyadiah, I. P., & Park, S. (2018). Consumer evaluation of hotel service robots. In Information and Communication Technologies in Tourism 2018: Proceedings of the International Conference in Jönköping, Sweden, January 24-26, 2018, pp. 308-320, Springer International Publishing. https://doi.org/10.1007/978-3-319-72923-7_24
• Tziolas, E., Karapatzak, E., Kalathas, I., Karampatea, A., Grigoropoulos, A., Bajoub, A., ... & Kaburlasos, V. G. (2023). Assessing the economic performance of multipurpose collaborative robots toward skillful and sustainable viticultural practices. Sustainability, 15(4), 3866. https://doi.org/10.3390/su15043866
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
• 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.
• Won, D., Chiu, W., & Byun, H. (2023). Factors influencing consumer use of a sport-branded app: The technology acceptance model integrating app quality and perceived enjoyment. Asia Pacific Journal of Marketing and Logistics, 35(5), 1112-1133. https://doi.org/10.1108/APJML-09-2021-0709
• Zhang, J. & Zhang, Y. (2021). A qualitative comparative analysis of tourism and gender equality in emerging economies. Journal of Hospitality and Tourism Management, 46, 284-292. https://doi.org/10.1016/j.jhtm.2021.01.009