Effects of robots’ appearance on guest service experiences

References

• Adem, K. (2020). Makine öğrenmesi. In H. Bakırtaş & S. Çavuşlu (Eds.), Yapay zeka disiplinleri dönüştürüyor: Değişime hazır mıyız? [Artificial intelligence is transforming disciplines: Are we ready for change?] (pp. 73–95). Ekin Yayınevi.
   Google Scholar
• Adewumi, T. P., Liwicki, F., & Liwicki, M. (2022). Word2vec: Optimal hyper-parameters and their impact on natural language processing downstream tasks. Open Computer Science 12, 134–141. https://doi.org/10.1515/comb-2022-0236.
   Web of Science ®Google Scholar
• Akdim, K., Belanche, D., & Flavián, M. (2023). Attitudes toward service robots: Analyses of explicit and implicit attitudes based on anthropomorphism and construal level theory. International Journal of Contemporary Hospitality Management, 35(8), 2816–2837. https://doi.org/10.1108/ijchm-12-2020-1406
   Web of Science ®Google Scholar
• Alsaad, A. (2023). The dual effect of anthropomorphism on customers’ decisions to use artificial intelligence devices in hotel services. Journal of Hospitality Marketing & Management, 32(8), 1048–1076. https://doi.org/10.1080/19368623.2023.2223584
   Web of Science ®Google Scholar
• Al-Saqqa, S., & Awajan, A. (2019). The use of word2vec model in sentiment analysis: A survey. In Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control (pp. 39–43). https://doi.org/10.1145/3388218.3388229
   Google Scholar
• An, T. K., & Kim, M. H. (2010). A new diverse AdaBoost classifier. In 2010 International Conference on Artificial Intelligence and Computational Intelligence, Sanya, China (pp. 359–363). IEEE.
   Google Scholar
• Atalay, M., & Çelik, E. (2017). Büyük veri analizinde yapay zeka ve makine öğrenmesi uygulamaları [Artificial intelligence and machine learning applications in big data analysis]. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Dergisi, 9(22), 155–172. https://doi.org/10.20875/makusobed.309727
   Google Scholar
• Aydoğan, M., & Karcı, A. (2019). Kelime temsil yöntemleri ile kelime benzerliklerinin incelenmesi [Investigation of word similarities with word embedding methods]. Çukurova Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(2), 181–196. https://doi.org/10.21605/cukurovaummfd.609119
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Belanche, D., Casaló, L., & Flavián, C. (2020). Frontline robots in tourism and hospitality: Service enhancement or cost reduction? Electronic Markets, 31(3), 477–492. https://doi.org/10.1007/s12525-020-00432-5
   Web of Science ®Google Scholar
• Blut, M., Wang, C., Wünderlich, N., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other ai. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y
   Web of Science ®Google Scholar
• Castelo, N., & Sarvary, M. (2022). Cross-cultural differences in comfort with humanlike robots. International Journal of Social Robotics, 14(8), 1865–1873. https://doi.org/10.1007/s12369-022-00920-y
   PubMed Web of Science ®Google Scholar
• Chang, C. Y., Lee, S. J., & Lai, C. C. (2017). Weighted word2vec based on the distance of words. In 2017 International Conference on Machine Learning and Cybernetics (ICMLC), Ningbo, China (pp. 563–568). IEEE.
   Google Scholar
• Chang, W., Li, Y., & Du, Q. (2023). Microblog emotion analysis using improved dbn under spark platform. International Journal of Information Technologies and Systems Approach, 16(2), 1–16. https://doi.org/10.4018/ijitsa.318141
   Web of Science ®Google Scholar
• Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: Understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613–615. https://doi.org/10.1080/19368623.2020.1703871
   Web of Science ®Google Scholar
• Choi, Y., Oh, M., Choi, M., & Kim, S. (2021). Exploring the influence of culture on tourist experiences with robots in service delivery environment. Current Issues in Tourism, 24(5), 717–733. https://doi.org/10.1080/13683500.2020.1735318
   Web of Science ®Google Scholar
• Christou, P., Simillidou, A., & Stylianou, M. C. (2020). Tourists’ perceptions regarding the use of anthropomorphic robots in tourism and hospitality. International Journal of Contemporary Hospitality Management, 32(11), 3665–3683. https://doi.org/10.1108/IJCHM-05-2020-0423
   Web of Science ®Google Scholar
• Darapaneni, N., Muthuraj, S., Prabakar, K., & Sridhar, M. (2019). Demand and revenue forecasting through machine learning. In 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India (pp. 0328–0331). IEEE.
   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
• Demšar, J., Zupan, B., Leban, G., & Curk, T. (2004). Orange: From experimental machine learning to interactive data mining. In European conference on principles of data mining and knowledge discovery, Pisa, Italy (pp. 537–539). Springer.
   Google Scholar
• DiGennaro, G., Buonanno, A., DiGirolamo, A., Palmieri, F. A., & Fedele, G. (2021). An analysis of Word2Vec for the Italian language. In A. Esposito, M. Faundez-Zanuy, F. C. Morabito, & E. Pasero (Eds.), Progresses in artificial intelligence and neural systems (pp. 137–146). Springer.
   Google Scholar
• Du, G., Chen, M., Liu, C., Zhang, B., & Zhang, P. (2018). Online robot teaching with natural human–robot interaction. IEEE Transactions on Industrial Electronics, 65(12), 9571–9581. https://doi.org/10.1109/TIE.2018.2823667
   Web of Science ®Google Scholar
• Duffy, B. R. (2003). Anthropomorphism and the social robot. Robotics and Autonomous Systems, 42(3–4), 177–190. https://doi.org/10.1016/S0921-8890(02)00374-3
   Web of Science ®Google Scholar
• Eisenstein, J. (2019). Introduction to natural language processing. MIT Press.
   Google Scholar
• Erlandson, D. A., Harris, E. L., Skipper, B. L., & Allen, S. D. (1993). Doing naturalistic inquiry: A guide to methods. Sage Publications.
   Google Scholar
• Fang, S., Han, X., & Chen, S. (2022). The impact of tourist–robot interaction on tourist engagement in the hospitality industry: A mixed-method study. Cornell Hospitality Quarterly, 64(2), 246–266. https://doi.org/10.1177/19389655221102383
   Web of Science ®Google Scholar
• Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3/4), 143–166. https://doi.org/10.1016/S0921-8890(02)00372-X
   Web of Science ®Google Scholar
• Fox, J., & Gambino, A. (2021). Relationship development with humanoid social robots: Applying interpersonal theories to human–robot interaction. Cyberpsychology, Behavior and Social Networking, 24(5), 294–299. https://doi.org/10.1089/cyber.2020.0181
   PubMed Web of Science ®Google Scholar
• Frick, W. (2020). Patronunuz metal pantolon giydiğinde [when your boss wears metal pants], (N.Ö. Translator). Yapay Zeka (199–209). Optimist. (Original worked published in 2019).
   Google Scholar
• Fuentes-Moraleda, L., Díaz-Pérez, P., Orea-Giner, A., Muñoz- Mazón, A., & Villacé-Molinero, T. (2020). Interaction between hotel service robots and humans: A hotel-specific service robot acceptance Model (sRAM. Tourism Management Perspectives, 36, 100751. https://doi.org/10.1016/j.tmp.2020.100751
   Web of Science ®Google Scholar
• 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
• Future Market Insights. (2023, May). Robotic market, https://www.futuremarketinsights.com/reports/robotics-market
   Google Scholar
• Ganga, R. S., Reddy, P. C. P., & Mohan, B. C. (2018). System for intelligent tourist information using machine learning techniques. International Journal of Applied Engineering Research, 13(7), 5321–5327.
   Google Scholar
• Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of mind perception. Science, 315(5812), 619–619. https://doi.org/10.1126/science.1134475
   PubMed Web of Science ®Google Scholar
• Hegel, F., Krach, S., Kircher, T., Wrede, B., & Sagerer, G. (2008). Understanding social robots: A user study on anthropomorphism. In RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication, Munich, Germany (pp. 574–579). IEEE.
   Google Scholar
• Hirst, G., & Feiguina, O. G. (2007). Bigrams of syntactic labels for authorship discrimination of short texts. Literary and Linguistic Computing, 22(4), 405–417. https://doi.org/10.1093/llc/fqm023
   Google Scholar
• Huang, H., & Liu, S. Q. (2022). Are consumers more attracted to restaurants featuring humanoid or non-humanoid service robots? International Journal of Hospitality Management, 107. https://doi.org/10.1016/j.ijhm.2022.103310
   PubMed Web of Science ®Google Scholar
• Hu, Q., Pan, X., Luo, J., & Yu, Y. (2022). The effect of service robot occupational gender stereotypes on customers’ willingness to use them. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.985501
   Web of Science ®Google Scholar
• Husain, R., Ratna, V. V., & Saxena, A. (2023). Past, present, and future of anthropomorphism in hospitality & tourism: Conceptualization and systematic review. Journal of Hospitality Marketing & Management, 32(8), 1077–1125. https://doi.org/10.1080/19368623.2023.2232382
   Google Scholar
• Ivanov, S., & Webster, C. (2021). Willingness-to-pay for robot-delivered tourism and hospitality services–an exploratory study. International Journal of Contemporary Hospitality Management, 33(11), 3926–3955. https://doi.org/10.1108/IJCHM-09-2020-1078
   Web of Science ®Google Scholar
• Ivanov, S., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Journal of Tourism and Development, 27(28), 1501–1517.
   Google Scholar
• Ivanov, S., Webster, C., & Seyitoğlu, F. (2023). Humans and/or robots? tourists’ preferences towards the humans–robots mix in the service delivery system. Service Business, 17(1), 195–231. https://doi.org/10.1007/s11628-022-00517-5
   Web of Science ®Google Scholar
• Jiang, L., Zhang, L., Li, C., & Wu, J. (2018). A correlation-based feature weighting filter for naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 31(2), 201–213. https://doi.org/10.1109/TKDE.2018.2836440
   Web of Science ®Google Scholar
• Kala, D. (2022). Tourism & hospitality students’ perception towards the use of robots in service organizations: A qualitative study in India. Advances in Hospitality and Tourism Research (AHTR), 10(2), 306–326. https://doi.org/10.30519/ahtr.969999
   Google Scholar
• Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses: Support for perceptual mismatch as one road to the valley of eeriness. Frontiers in Psychology, 6, 6. https://doi.org/10.3389/fpsyg.2015.00390
   PubMed Web of Science ®Google Scholar
• Khder, M. (2021). Web scraping or web crawling: State of art, techniques, approaches and application. International Journal of Advances in Soft Computing and Its Applications, 13(3), 145–168. https://doi.org/10.15849/ijasca.211128.11
   Google Scholar
• Kim, H., & Lim, P. (2021). A tensor space model-based deep neural network for text classification. Applied Sciences, 11(20), 9703. https://doi.org/10.3390/app11209703
   Google Scholar
• King, B. (2016). Augmented [Artırılmış Gerçeklik]. Kapital Medya Hizmetleri A.Ş.
   Google Scholar
• Ladeira, W., Perin, M. G., & Santini, F. (2023). Acceptance of service robots: A meta-analysis in the hospitality and tourism industry. Journal of Hospitality Marketing & Management, 32(6), 694–716. https://doi.org/10.1080/19368623.2023.2202168
   Web of Science ®Google Scholar
• Law, T., de Leeuw, J., & Long, J. H. (2021). How movements of a non-humanoid robot affect emotional perceptions and trust. International Journal of Social Robotics, 13(8), 1967–1978. https://doi.org/10.1007/s12369-020-00711-3
   Web of Science ®Google Scholar
• Lee, T. R., Wood, W. T., & Phrampus, B. J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. Global Biogeochemical Cycles, 33(1), 37–46. https://doi.org/10.1029/2018GB005992
   Web of Science ®Google Scholar
• Lin, M., Cui, X., Wang, J., Wu, G., & Lin, J. (2022). Promotors or inhibitors? Role of task type on the effect of humanoid service robots on consumers’ use intention. Journal of Hospitality Marketing & Management, 31(6), 710–729. https://doi.org/10.1080/19368623.2022.2062693
   Web of Science ®Google Scholar
• Li, D., Rau, P. P., & Li, Y. (2010). A cross-cultural study: Effect of robot appearance and task. International Journal of Social Robotics, 2(2), 175–186. https://doi.org/10.1007/s12369-010-0056-9
   Web of Science ®Google Scholar
• Liu, X. S., Wan, L. C., & Yi, X. S. (2022). Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic? Annals of Tourism Research, 94, 103383. https://doi.org/10.1016/j.annals.2022.103383
   Web of Science ®Google Scholar
• Ma, B. (2014). A new kind of parallel K_NN network public opinion classification algorithm based on hadoop platform. In Applied mechanics and materials (Vol. 644, pp. 2018–2021). https://doi.org/10.4028/www.scıentıfıc.net/AMM.644-650.2018.
   Google Scholar
• MacDorman, K. F. (2006). Subjective ratings of robot video clips for human likeness, familiarity, and eeriness: An exploration of the uncanny valley. In ICCS/CogSci-2006 Long Symposium: Toward Social Mechanisms of Android Science, Vancouver, Canada (pp. 26–29).
   Google Scholar
• Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research, 9(1), 381–386. https://doi.org/10.21275/ART20203995
   Google Scholar
• Manthiou, A., Klaus, P., Kuppelwieser, V. G., & Reeves, W. (2021). Man vs machine: Examining the three themes of service robotics in tourism and hospitality. Electronic Markets, 31(3), 511–527. https://doi.org/10.1007/s12525-020-00434-3
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Mingotto, E., Montaguti, F., & Tamma, M. (2021). Challenges in re-designing operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry. Electronic Markets, 31(3), 493–510. https://doi.org/10.1007/s12525-020-00439-y
   Web of Science ®Google Scholar
• Moens, M. (2018). Argumentation mining: How can a machine acquire common sense and world knowledge? Argument & Computation, 9(1), 1–14. https://doi.org/10.3233/aac-170025
   Google Scholar
• Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
   Web of Science ®Google Scholar
• Murphy, R. R., Nomura, T., Billard, A., & Burke, J. L. (2010). Human–robot interaction. Robotics & Automation Magazine, IEEE, 17(2), 85–89. https://doi.org/10.1109/MRA.2010.936953
   Web of Science ®Google Scholar
• Nakanishi, J., Kuramoto, I., Baba, J., Kohei, O., Yoshikawa, Y., & Ishiguro, H. (2018). Can a humanoid robot engage in heartwarming interaction service at a hotel? In Proceedings of the 6th International Conference on Human-Agent Interaction (pp. 45–53). https://doi.org/10.1145/3284432.3284448
   Google Scholar
• Narkhede, S. (2018). Understanding AUC-ROC curve. Towards Data Science, 26(1), 220–227
   Google Scholar
• Nguyen, T. T. S., Do, P. M. T., & Nguyen, T. T. (2018). Improving customer behaviour prediction with the item2item model in recommender systems. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 5(17), e4. https://doi.org/10.4108/eai.19-12-2018.156079
   Google Scholar
• Nugrahaeni, R. A., & Mutijarsa, K. (2016). Comparative analysis of machine learning KNN, SVM, and random forests algorithm for facial expression classification. In 2016 International Seminar on Application for Technology of Information and Communication (ISemantic), Semarang, Indonesia (pp. 163–168). IEEE.
   Google Scholar
• Ocnarescu, I., & Cossin, I. (2020). Discovery report following 5 years of research project on socially assistive robotics. Current Robotics Reports, 1(4), 269–278. https://doi.org/10.1007/s43154-020-00031-4
   Google Scholar
• Padmavaty, V., Geetha, C., & Priya, N. (2020). Analysis of data mining tool Orange. International Journal of Modern Agriculture, 9(4), 1146–1150
   Google Scholar
• Papadopoulos, I., & Koulouglioti, C. (2018). The influence of culture on attitudes towards humanoid and animal‐like robots: An integrative review. Journal of Nursing Scholarship, 50(6), 653–665. https://doi.org/10.1111/jnu.12422
   PubMed Web of Science ®Google Scholar
• Pranckevicius, T., & Marcinkevičius, V. (2017). Comparison of Naive Bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Baltic Journal of Modern Computing, 5(2), 221–232. https://doi.org/10.22364/bjmc.2017.5.2.05
   Google Scholar
• Pranoto, S. (2022). Web scraping on forecast report form of geomagnet. [Cepat] Journal of Computer Engineering Progress Application and Technology, 1(2), 16–23. https://doi.org/10.25124/cepat.v1i02.5226
   Google Scholar
• Qi, X., Chen, G., Li, Y., Cheng, X., & Li, C. (2019). Applying neural-networkbased machine learning to additive manufacturing: Current applications, challenges, and future perspectives. Engineering, 5(4), 721–729. https://doi.org/10.1016/j.eng.2019.04.012
   Web of Science ®Google Scholar
• Qiu, G., Su, F., Le, G., & Chen, C. (2021). Research on translation style in machine learning based on linguistic quantitative characteristics perception. Sensors and Materials, 33(6), 2031. https://doi.org/10.18494/sam.2021.3286
   Web of Science ®Google Scholar
• Rajan, K., & Saffiotti, A. (2017). Towards a science of integrated AI and robotics. Artificial Intelligence, 247, 1–9. https://doi.org/10.1016/j.artint.2017.03.003
   Web of Science ®Google Scholar
• Reis, J., Melão, N., Salvadorinho, J., Soares, B., & Rosete, A. (2020). Service robots in the hospitality industry: The case of Henn-Na hotel, Japan. Technology in Society, 63, 101423. https://doi.org/10.1016/j.techsoc.2020.101423
   Web of Science ®Google Scholar
• Roesler, E., Naendrup-Poell, L., Manzey, D., & Onnasch, L. (2022). Why context matters: The influence of application domain on preferred degree of anthropomorphism and gender attribution in human–robot interaction. International Journal of Social Robotics, 14(5), 1155–1166. https://doi.org/10.1007/s12369-021-00860-z
   Web of Science ®Google Scholar
• Rong, X. (2014). Word2vec parameter learning explained. ArXiv, abs/1411.2738.
   Google Scholar
• 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.
   Google Scholar
• Saeedvand, S., Jafari, M., Aghdasi, H. S., & Baltes, J. (2019). A comprehensive survey on humanoid robot development. The Knowledge Engineering Review, 34, e20. https://doi.org/10.1017/S0269888919000158
   Web of Science ®Google Scholar
• Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., & Joublin, F. (2013). To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability. International Journal of Social Robotics, 5(3), 313–323. https://doi.org/10.1007/s12369-013-0196-9
   Web of Science ®Google Scholar
• Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., & Saunders, J. (2015). Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 141–148). https://doi.org/10.1145/2696454.2696497
   Google Scholar
• Saygın, A., Chaminade, T., Ishiguro, H., Driver, J., & Frith, C. (2011). The thing that should not be: Predictive coding and the uncanny valley in perceiving human and humanoid robot actions. Social Cognitive and Affective Neuroscience, 7(4), 413–422. https://doi.org/10.1093/scan/nsr025
   PubMed Web of Science ®Google Scholar
• Schwab, K., & Davis, N. (2019). Dördüncü sanayi devrimini şekillendirmek [Shaping the fourth industrial revolution], (N.Ö. Trans). Optimist. Original work published 2018.
   Google Scholar
• Seyama, J., & Nagayama, R. (2007). The uncanny valley: Effect of realism on the impression of artificial human faces. Presence: Virtual and Augmented Reality, 16(4), 337–351. https://doi.org/10.1162/pres.16.4.337
   Google Scholar
• Seyitoğlu, F., & Ivanov, С. (2020). Service robots as a tool for physical distancing in tourism. Current Issues in Tourism, 24(12), 1631–1634. https://doi.org/10.1080/13683500.2020.1774518
   Web of Science ®Google Scholar
• Seyitoğlu, F., & Ivanov, С. (2023). Service robots and perceived discrimination in tourism and hospitality. Tourism Management, 96(104710), 1–7. https://doi.org/10.1016/j.tourman.2022.104710
   Google Scholar
• Shaik, A. B., & Srinivasan, S. (2019). A brief survey on random forest ensembles in classification model. In International Conference on Innovative Computing and Communications, Singapore (pp. 253–260). Springer.
   Google Scholar
• Shan, M., Zhu, Z., Chen, H. A., & Sun, S. (2024). Service robot’s responses in service recovery and service evaluation: The moderating role of robots’ social perception. Journal of Hospitality Marketing & Management, 33(2), 145–168. https://doi.org/10.1080/19368623.2023.2246456
   Web of Science ®Google Scholar
• Singh, P., & Mukerjee, A. (2015). Words are not equal: Graded weighting model for building composite document vectors. arXiv preprint. https://doi.org/10.48550/arxiv.1512.03549
   Google Scholar
• Somisetti, K., Tripathi, K., & Verma, J. K. (2020). Design, implementation, and controlling of a humanoid robot. In 2020 International Conference on Computational Performance Evaluation (ComPE), Shillong, India (pp. 831–836). IEEE.
   Google Scholar
• Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica, 24(1), 12–18. https://doi.org/10.11613/BM.2014.003
   PubMed Web of Science ®Google Scholar
• Tamilarasi, P., & Rani, R. U. (2020). Diagnosis of crime rate against women using k-fold cross validation through machine learning. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India (pp. 1034–1038). IEEE.
   Google Scholar
• Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126. https://doi.org/10.1016/j.eswa.2016.03.028
   Web of Science ®Google Scholar
• Tung, V. W. S., & Au, N. (2018). Exploring customer experiences with robotics in hospitality. International Journal of Contemporary Hospitality Management, 30(7), 2680–2697. https://doi.org/10.1108/IJCHM-06-2017-0322
   Web of Science ®Google Scholar
• Tuomi, A., Tussyadiah, I. 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
• 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
   Web of Science ®Google Scholar
• Tutar, H. (2022). Nitel araştırmalarda geçerlilik ve güvenilirlik: Bir model önerisi [Validity and reliability in qualitative research: A model proposal]. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(Özel Sayı 2), 117–140. https://doi.org/10.18037/ausbd.1227323
   Google Scholar
• Wang, X., Hwang, Y., & Guchait, P. (2023). When robot (vs. human) employees say “sorry” following service failure. International Journal of Hospitality & Tourism Administration, 24(4), 540–562. https://doi.org/10.1080/15256480.2021.2017812
   Web of Science ®Google Scholar
• Wang, S., Lilienfeld, S., & Rochat, P. (2015). The uncanny valley: Existence and explanations. Review of General Psychology, 19(4), 393–407. https://doi.org/10.1037/gpr0000056
   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
• 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
• Wongkar, M., & Angdresey, A. (2019). Sentiment analysis using Naive Bayes algorithm of the data crawler: Twitter. In 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia (pp. 1–5). IEEE.
   Google Scholar
• Xing, W., & Bei, Y. (2019). Medical health big data classification based on KNN classification algorithm. Institute of Electrical and Electronics Engineers Access, 8, 28808–28819. https://doi.org/10.1109/ACCESS.2019.2955754
   Google Scholar
• Yamada, Y., Kawabe, T., & Ihaya, K. (2012). Categorization difficulty is associated with negative evaluation in the “uncanny valley” phenomenon. Japanese Psychological Research, 55(1), 20–32. https://doi.org/10.1111/j.1468-5884.2012.00538.x
   Web of Science ®Google Scholar
• Yang, H., Song, H., Xia, L., & Yang, A. (2024). Understanding the consequences of robotic interaction quality and outcome quality: A three-phased affordance theory-based approach. Journal of Hospitality Marketing & Management, 1–21. https://doi.org/10.1080/19368623.2024.2308531
   Web of Science ®Google Scholar
• Yiğit, C. B. (2018). İnsansı robotlarda birleşik kuvvet-konum kontrolü için yenilikçi mekanizma ve kontrol tasarımı [Novel mechanism and controller design for hybrid force-position control of humanoid robots] [ Unpublished doctoral dissertation]. Istanbul Teknik University
   Google Scholar
• Yılmaz, Ş. (2020). Word2vec temsillerini kullanarak Türkçe’de soru sınıflandırmasında derin öğrenme analizi [A deep learning analysis on Turkish question classification task using word2vec representations] [ Unpublished doctoral dissertation]. Düzce University
   Google Scholar
• Yu, C. E. (2020). Humanlike robots as employees in the hotel industry: Thematic content analysis of online reviews. Journal of Hospitality Marketing & Management, 29(1), 22–38. https://doi.org/10.1080/19368623.2019.1592733
   Web of Science ®Google Scholar
• Yu, C. H., Jannasch-Pennell, A., & DiGangi, S. (2011). Compatibility between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. The Qualitative Report, 16(3), 730–744
   Google Scholar
• Zad, S., Heidari, M., Jones, J. H., & Uzuner, O. (2021). A survey on concept-level sentiment analysis techniques of textual data. In 2021 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA (pp. 0285–0291). IEEE.
   Google Scholar
• Zhang, M., Cui, J., & Zhong, J. (2023). How consumers react differently toward humanoid vs. nonhumanoid robots after service failures: A moderated chain mediation model. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-06-2022-1023
   Web of Science ®Google Scholar
• Zhang, M., Gürsoy, D., Zhu, Z., & Shi, S. (2021). Impact of anthropomorphic features of artificially intelligent service robots on consumer acceptance: Moderating role of sense of humor. International Journal of Contemporary Hospitality Management, 33(11), 3883–3905. https://doi.org/10.1108/ijchm-11-2020-1256
   Web of Science ®Google Scholar
• Zhang, Y., Jin, Z., & Chen, Y. (2020). Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems. Knowledge-Based Systems, 187, 104836. https://doi.org/10.1016/j.knosys.2019.07.007
   Web of Science ®Google Scholar
• Zhang, D., Wang, J., & Zhao, X. (2015). Estimating the uncertainty of average F1 scores. In Proceedings of the 2015 International conference on the theory of information retrieval (pp. 317–320). https://doi.org/10.1145/2808194.2809488
   Google Scholar
• Zhu, D. H., & Chang, Y. P. (2020). Robot with humanoid hands cooks food better? Effect of robotic chef anthropomorphism on food quality prediction. International Journal of Contemporary Hospitality Management, 32(3), 1367–1383. https://doi.org/10.1108/IJCHM-10-2019-0904
   Web of Science ®Google Scholar
• Коромыслова, А., Semenkina, M., & Sergienko, R. (2017). Feature selection for natural language call routing based on self-adaptive genetic algorithm. Iop Conference Series Materials Science and Engineering, 173, 012008. https://doi.org/10.1088/1757-899x/173/1/012008
   Google Scholar