Robotic dining delight: Unravelling the key factors driving customer satisfaction in service robot restaurants using PLS-SEM and ML

References

• Agrawal, G., Sodhi, S., Mohapatra, A. K., & Bhandari, K. R. (2023). Can consumer citizenship behavior be created using service robots in luxury hospitality services? Indian Journal of Marketing, 53(4), 8. https://doi.org/10.17010/ijom/2023/v53/i4/172687
   Google Scholar
• Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37–21. https://doi.org/10.1007/BF00153759
   Web of Science ®Google Scholar
• Ahmed, S., Al Asheq, A., Ahmed, E., Chowdhury, U. Y., Sufi, T., & Mostofa, M. G. (2023). The intricate relationships between consumers’ loyalty and their perceptions of service quality, price and satisfaction in restaurant service. The TQM Journal, 35(2), 519–539. https://doi.org/10.1108/TQM-06-2021-0158
   Google Scholar
• Atulkar, S., & Kesari, B. (2017). Satisfaction, loyalty and patronage intentions: Role of hedonic shopping values. Journal of Retailing and Consumer Services, 39, 23–34. https://doi.org/10.1016/j.jretconser.2017.06.013
   Web of Science ®Google Scholar
• Becker, M., Mahr, D., & Odekerken-Schröder, G. (2023). Customer comfort during service robot interactions. Service Business, 17(1), 137–165. https://doi.org/10.1007/s11628-022-00499-4
   Web of Science ®Google Scholar
• Bello, D. C., & Etzel, M. J. (1985). The role of novelty in the pleasure travel experience. Journal of Travel Research, 24(1), 20–26. https://doi.org/10.1177/004728758502400104
   Google Scholar
• Bonfanti, A., Vigolo, V., Yfantidou, G., & Gutuleac, R. (2023). Customer experience management strategies in upscale restaurants: Lessons from the covid-19 pandemic. International Journal of Hospitality Management, 109, 103416. https://doi.org/10.1016/j.ijhm.2022.103416
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Cepeda-Carrion, G. Cegarra-Navarro, J.-G. & Cillo, V.(2019). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 23(1), 67–89. https://doi.org/10.1108/JKM-05-2018-0322
   Web of Science ®Google Scholar
• Chen, C., Nakayama, M., & Ractham, P. (2023). Increasing the intention of Gen zers to adopt drone delivery services based on a three-step decision-making process. Cogent Business & Management, 10(1), 2188987. https://doi.org/10.1080/23311975.2023.2188987
   Web of Science ®Google Scholar
• Chi, O. H., Chi, C. G., Gursoy, D., & Nunkoo, R. (2023). Customer’s acceptance of artificially intelligent service robots: The influence of trust and culture. International Journal of Information Management, 70, 102623. https://doi.org/10.1016/j.ijinfomgt.2023.102623
   Web of Science ®Google Scholar
• Chin, W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern business research methods (pp. 295–336). Lawrence Erlbaum Associates.
   Google Scholar
• Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a monte carlo simulation study and an electronic-mail Emotion/adoption study. Information Systems Research, 14(2), 189–217. https://doi.org/10.1287/isre.14.2.189.16018
   Web of Science ®Google Scholar
• Choi, M., Choi, Y., Kim, S., & Badu-Baiden, F. (2023). Human vs robot baristas during the COVID-19 pandemic: Effects of masks and vaccines on perceived safety and visit intention. International Journal of Contemporary Hospitality Management, 35(2), 469–491. https://doi.org/10.1108/IJCHM-02-2022-0157
   Web of Science ®Google Scholar
• Clarke, J., & Bowen, D. (2021). Repeat tourists and familiar place formation: Conversion, inheritance and discovery. Journal of Destination Marketing & Management, 20, 100605. https://doi.org/10.1016/j.jdmm.2021.100605
   Web of Science ®Google Scholar
• Correa, J. C., Dakduk, S., van der Woude, D., Sandoval-Escobar, M., & Lopez-Llamas, R. (2022). Low-income consumers’ disposition to use automated banking services. Cogent Business & Management, 9(1), 2071099. https://doi.org/10.1080/23311975.2022.2071099
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1023/A:1022627411411
   Web of Science ®Google Scholar
• Das, A. R., & Panja, S. (2022). An empirical investigation on the influence of behavioural factors on investment decision making. Vision: The Journal of Business Perspective, 09722629221131101. https://doi.org/10.1177/09722629221131101
   Google Scholar
• Davis, F. D. (1985). Perceived usefulness, perceived ease of use, and user acceptance of Information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
   Google Scholar
• Davis, F. D.(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• de Graaf, M. M. A. (2016). An ethical evaluation of human–robot relationships. International Journal of Social Robotics, 8(4), 589–598. https://doi.org/10.1007/s12369-016-0368-5
   Web of Science ®Google Scholar
• De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline service technology infusion: Conceptual archetypes and future research directions. Journal of Service Management, 30(1), 156–183. https://doi.org/10.1108/JOSM-03-2018-0082
   Web of Science ®Google Scholar
• Dong, P., & Siu, N. Y.-M. (2013). Servicescape elements, customer predispositions and service experience: The case of theme park visitors. Tourism Management, 36, 541–551. https://doi.org/10.1016/j.tourman.2012.09.004
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• El-Said, O. A., Smith, M., & Al Ghafri, W. (2021). Antecedents and outcomes of dining experience satisfaction in ethnic restaurants: The moderating role of food neophobia. Journal of Hospitality Marketing & Management, 30(7), 799–824. https://doi.org/10.1080/19368623.2021.1888368
   Web of Science ®Google Scholar
• Feng, X., Xie, R., Sheng, J., & Zhang, S. (2019). Population statistics algorithm based on MobileNet. Journal of Physics: Conference Series, 1237(2), 22045. https://doi.org/10.1088/1742-6596/1237/2/022045
   Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382. https://doi.org/10.2307/3150980
   Web of Science ®Google Scholar
• Friedman, J. H. (2001a). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
   Web of Science ®Google Scholar
• Friedman, J. H. (2001b). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
   Web of Science ®Google Scholar
• Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01
   PubMed Web of Science ®Google Scholar
• Fritsch, S., Guenther, F., & Wright, M. N. (2019). Neuralnet: Training of neural networks. https://cran.r-project.org/package=neuralnet
   Google Scholar
• Gaber, H. R., Wright, L. T., Kooli, K., & Kostadinova, E. (2019). Consumer attitudes towards Instagram advertisements in Egypt: The role of the perceived advertising value and personalization. Cogent Business & Management, 6(1), 1618431. https://doi.org/10.1080/23311975.2019.1618431
   Web of Science ®Google Scholar
• Go, H., Kang, M., & Suh, S. C. (2020). Machine learning of robots in tourism and hospitality: Interactive Technology Acceptance Model (iTAM) – cutting edge. Tourism Review, 75(4), 625–636. https://doi.org/10.1108/TR-02-2019-0062
   Web of Science ®Google Scholar
• Gupta, K. P., & Pande, S. (2023). Understanding generation Z consumers’ revisit intentions to robotic service restaurants. Young Consumers, 24(3), 331–351. https://doi.org/10.1108/YC-09-2022-1598
   Web of Science ®Google Scholar
• Hair, J. F., Gabriel, L. D. S., da Silva, M., & Braga Junior, S. (2019). Development and validation of attitudes measurement scales: Fundamental and practical aspects. RAUSP Management Journal, 54(4), 490–507. https://doi.org/10.1108/RAUSP-05-2019-0098
   Web of Science ®Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®Google Scholar
• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques. Morgan Kaufmann Publishers.
   Google Scholar
• Hare, C., & Kutsuris, M. (n.d.). Measuring swing voters with a supervised machine learning ensemble. POLITICAL ANALYSIS. https://doi.org/10.1017/pan.2022.24
   Google Scholar
• Harman, G. H., & Chomsky, N. (1967). Psychological aspects of the theory of syntax. The Journal of Philosophy, 64(2), 75. https://doi.org/10.2307/2023773
   Web of Science ®Google Scholar
• Hastie, T. J., & Tibshirani, R. J. (1986). Generalized additive models. Chapman and Hall.
   Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009a). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.
   Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009b). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.
   Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hwang, J., Joo, K., & Moon, J. (2023). A study on behavioral intentions in the field of eco-friendly drone food delivery services: Focusing on demographic characteristics and past experiences. Sustainability, 15(7), 6253. https://doi.org/10.3390/su15076253
   Web of Science ®Google Scholar
• Hwang, J., Kim, H., & Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality & Tourism Management, 38, 102–110. https://doi.org/10.1016/j.jhtm.2019.01.004
   Web of Science ®Google Scholar
• Jang, H.-W., & Lee, S.-B. (2020). Serving robots: Management and applications for restaurant business sustainability. Sustainability, 12(10), 3998. https://doi.org/10.3390/su12103998
   Web of Science ®Google Scholar
• Kamran, Q., Topp, S., Henseler, J., & Gupta, M. (2021). Towards the co-evolution of food experience search spaces based on the design weltanschauung model in food Marketing. Cogent Business & Management, 8(1), 1901643. https://doi.org/10.1080/23311975.2021.1901643
   Web of Science ®Google Scholar
• Kim, Y. (2023). Examining the impact of frontline service robots service competence on hotel frontline employees from a collaboration perspective. Sustainability, 15(9), 7563. https://doi.org/10.3390/su15097563
   Web of Science ®Google Scholar
• Kim, T., Lee, O.-K. D., & Kang, J. (2023). Is it the best for barista robots to serve like humans? A multidimensional anthropomorphism perspective. International Journal of Hospitality Management, 108, 103358. https://doi.org/10.1016/j.ijhm.2022.103358
   Web of Science ®Google Scholar
• Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1jais.00302
   Web of Science ®Google Scholar
• Kuhn, M. (2021). Caret: Classification and regression training. https://cran.r-project.org/package=caret
   Google Scholar
• Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., & De Keyser, A. (2017). “Service encounter 2.0”: An investigation into the roles of technology, employees and customers. Journal of Business Research, 79, 238–246. https://doi.org/10.1016/j.jbusres.2017.03.008
   Web of Science ®Google Scholar
• Lee, C. J. (2011). Understanding bank service quality in customers’ terms: An exploratory analysis of top-of-mind definition. International Journal of Business & Social Science, 2(21), 1–8. http://www.ijbssnet.com/journals/Vol_2_No_21_Special_Issue_November_2011/1.pdf
   Google Scholar
• Leong, L. Y., Hew, T. S., Ooi, K. B., & Wei, J. (2020a). Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. International Journal of Information Management, 51(April 2019), 102047. https://doi.org/10.1016/j.ijinfomgt.2019.102047
   Google Scholar
• Leong, L. Y., Hew, T. S., Ooi, K. B., & Wei, J. (2020b). Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. International Journal of Information Management, 51(April), 102047. https://doi.org/10.1016/j.ijinfomgt.2019.102047
   Google Scholar
• Leung, X. Y., Zhang, H., Lyu, J., & Bai, B. (2023). Why do hotel frontline employees use service robots in the workplace? A technology affordance theory perspective. International Journal of Hospitality Management, 108, 103380. https://doi.org/10.1016/j.ijhm.2022.103380
   Web of Science ®Google Scholar
• Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
   Google Scholar
• Lu, V. N., Wirtz, J., Kunz, W. H., Paluch, S., Gruber, T., Martins, A., & Patterson, P. G. (2020). Service robots, customers and service employees: What can we learn from the academic literature and where are the gaps? Journal of Service Theory & Practice, 30(3), 361–391. https://doi.org/10.1108/JSTP-04-2019-0088
   Web of Science ®Google Scholar
• Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology. ACM Transactions on Management Information Systems, 2(2), 1–25. https://doi.org/10.1145/1985347.1985353
   Google Scholar
• Meyer, P., Roth, A., & Gutknecht, K. (2023). Service robots in organisational frontlines—A retail managers’ perspective. Journal of Retailing and Consumer Services, 70, 103173. https://doi.org/10.1016/j.jretconser.2022.103173
   Web of Science ®Google Scholar
• Moon, H. Y., & Lee, B. Y. (2022). Self-service technologies (SSTs) in airline services: Multimediating effects of flow experience and SST evaluation. International Journal of Contemporary Hospitality Management, 34(6), 2176–2198. https://doi.org/10.1108/IJCHM-09-2021-1151
   Web of Science ®Google Scholar
• Negnevitsky, M. (2011). Artificial intelligence 3e e-book a guide to intelligent Systems [Internet. 73. http://ecite.utas.edu.au/75263
   Google Scholar
• Nicodemus, K. K., Malley, J. D., Strobl, C., & Ziegler, A. (2010). The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics, 11(1), 110. https://doi.org/10.1186/1471-2105-11-110
   PubMed Web of Science ®Google Scholar
• Pai, C.-K., Wu, Z.-T., Lee, S., Lee, J., & Kang, S. (2022). Service quality of Social media-based self-service technology in the food service context. Sustainability, 14(20), 13483. https://doi.org/10.3390/su142013483
   Web of Science ®Google Scholar
• Podsakoff, P. M. MacKenzie, S. B. & Podsakoff, N. P.(2012). Sources of method bias in Social Science Research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
   PubMed Web of Science ®Google Scholar
• Rasheed, H. M. W., He, Y., Khizar, H. M. U., & Abbas, H. S. M. (2023). Exploring consumer-robot interaction in the hospitality sector: Unpacking the reasons for adoption (or resistance) to artificial intelligence. Technological Forecasting and Social Change, 192, 122555. https://doi.org/10.1016/j.techfore.2023.122555
   Web of Science ®Google Scholar
• Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. SmartPLS GmbH. http://www.smartpls.com
   Google Scholar
• Rodrigues, F., Hair, J. F., Neiva, H. P., Teixeira, D. S., Cid, L., & Monteiro, D. (2019). The basic psychological need satisfaction and frustration scale in exercise (BPNSFS-E): Validity, reliability, and Gender Invariance in Portuguese exercisers. Perceptual and Motor Skills, 126(5), 949–972. https://doi.org/10.1177/0031512519863188
   PubMed Web of Science ®Google Scholar
• Roozen, I., Raedts, M., & Yanycheva, A. (2023). Are retail customers ready for service robot assistants? International Journal of Social Robotics, 15(1), 15–25. https://doi.org/10.1007/s12369-022-00949-z
   PubMed Web of Science ®Google Scholar
• Ruiz-Palomino, P., Gutierrez-Broncano, S., Jimenez-Estevez, P., & Hernandez-Perlines, F. (2021). CEO servant leadership and strategic service differentiation: The role of high-performance work systems and innovativeness. Tourism Management Perspectives, 40, 100891. https://doi.org/10.1016/j.tmp.2021.100891
   Web of Science ®Google Scholar
• Ruiz-Palomino, P., Linuesa-Langreo, J., Rincón-Ornelas, R. M., & Martinez-Ruiz, M. P. (2023). Putting the customer at the center: Does store managers’ ethical leadership make a difference in authentic customer orientation?. Academia Latin American Journal of Administration, 36(2), 269–288. https://doi.org/10.1108/ARLA-11-2022-0201
   Google Scholar
• Ryan, R. M. & Deci, E. L.(2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166.
   PubMed Web of Science ®Google Scholar
• Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1249
   Web of Science ®Google Scholar
• Schölkopf, B., & Smola, A. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press.
   Google Scholar
• Seo, W., Kim, W., & Oh, J. (2022). The effects of kiosk service quality on value, Satisfaction, and reuse intention. The Journal of Internet Electronic Commerce Resarch, 22(5), 25–43. https://doi.org/10.37272/JIECR.2022.10.22.5.25
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Shah, T. R., Kautish, P., & Mehmood, K. (2023). Influence of robots service quality on customers’ acceptance in restaurants. Asia Pacific Journal of Marketing & Logistics. https://doi.org/10.1108/APJML-09-2022-0780
   Web of Science ®Google Scholar
• Skandrani, H., Triki, A., & Baratli, B. (2011). Trust in supply chains, meanings, determinants and demonstrations: A qualitative study in an emerging market context. Qualitative Market Research: An International Journal, 14(4), 391–409. https://doi.org/10.1108/13522751111163227
   Google Scholar
• Song, B., Zhang, M., & Wu, P. (2022). Driven by technology or sociality? Use intention of service robots in hospitality from the human–robot interaction perspective. International Journal of Hospitality Management, 106, 103278. https://doi.org/10.1016/j.ijhm.2022.103278
   Web of Science ®Google Scholar
• Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307. https://doi.org/10.1186/1471-2105-9-307
   PubMed Web of Science ®Google Scholar
• Tiwari, P. (2022). Transparency as a predictor of loyalty in the banking sector: A SEM-ANN approach. FIIB Business Review, 23197145221113376, 231971452211133. https://doi.org/10.1177/23197145221113377
   Google Scholar
• Tiwari, P. (2023). Effect of innovation practices of banks on customer loyalty: An SEM-ANN approach. BENCHMARKING-AN INTERNATIONAL JOURNAL. https://doi.org/10.1108/BIJ-06-2022-0392
   Google Scholar
• Tussyadiah, I. P., Zach, F. J., & Wang, J. (2020). Do travelers trust intelligent service robots? Annals of Tourism Research, 81, 102886. https://doi.org/10.1016/j.annals.2020.102886
   Web of Science ®Google Scholar
• Tuv, E. (2002). Ensemble Learning. In M. Marinaro & R. Tagliaferri (Eds.), Feature Extraction (Vol. 2486, pp. 187–204). Springer. https://doi.org/10.1007/978-3-540-35488-8_8
   Google Scholar
• Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S (fourth). Springer. https://www.stats.ox.ac.uk/pub/MASS4/
   Google Scholar
• Wali, A. F., Uduma, I. A., Wright, L. T., & Liu, S. (2016). Customer Relationship Management (CRM) experiences of Business-to-Business (B2B) marketing firms: A qualitative study. Cogent Business & Management, 3(1), 1183555. https://doi.org/10.1080/23311975.2016.1183555
   Google Scholar
• Wang, Y., Kang, Q., Zhou, S., Dong, Y., & Liu, J. (2022). The impact of service robots in retail: Exploring the effect of novelty priming on consumer behaviour. Journal of Retailing and Consumer Services, 68, 103002. https://doi.org/10.1016/j.jretconser.2022.103002
   Web of Science ®Google Scholar
• Wang, X. V., & Wang, L. (2021). A literature survey of the robotic technologies during the COVID-19 pandemic. Journal of Manufacturing Systems, 60, 823–836. https://doi.org/10.1016/j.jmsy.2021.02.005
   PubMed Web of Science ®Google Scholar
• Wang, Q., Zhu, X., Wang, M., Zhou, F., Cheng, S., & Al-Adwan, A. S. (2023). A theoretical model of factors influencing online consumer purchasing behavior through electronic word of mouth data mining and analysis. PLoS One, 18(5), e0286034. https://doi.org/10.1371/journal.pone.0286034
   PubMed Web of Science ®Google Scholar
• Wijesekera, A. T., & Fernando, R. L. S. (2023). Service climate and service quality of Sri Lankan public service: A Partial Least Squares Structural Equation Modeling approach (PLS-SEM). Journal of Business & Management, 1(2), 174–187. https://doi.org/10.47747/jbm.v1i2.1115
   Google Scholar
• Wirtz, J. (2020). Organizational ambidexterity: Cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 49(3), 100719. https://doi.org/10.1016/j.orgdyn.2019.04.005
   Web of Science ®Google Scholar
• Wirtz, J., & Pitardi, V. (2023). How intelligent automation, service robots, and AI will reshape service products and their delivery. Italian Journal of Marketing, 2023(3), 289–300. https://doi.org/10.1007/s43039-023-00076-1
   Google Scholar
• 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 & Logistics, 35(5), 1112–1133. https://doi.org/10.1108/APJML-09-2021-0709
   Web of Science ®Google Scholar
• Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467), 673–686. https://doi.org/10.1198/016214504000000980
   Web of Science ®Google Scholar
• Xie, L., Liu, C., Li, Y., & Zhu, T. (2023). How to inspire users in virtual travel communities: The effect of activity novelty on users’ willingness to co-create. Journal of Retailing and Consumer Services, 75, 103448. https://doi.org/10.1016/j.jretconser.2023.103448
   Web of Science ®Google Scholar
• Xu, J., Hsiao, A., Reid, S., & Ma, E. (2023). Working with service robots? A systematic literature review of hospitality employees’ perspectives. International Journal of Hospitality Management, 113, 103523. https://doi.org/10.1016/j.ijhm.2023.103523
   Web of Science ®Google Scholar
• Yang, C., Sun, Y., Wang, N., & Shen, X.-L. (2023). Disentangling the antecedents of rational versus emotional negative electronic word of mouth on a peer-to-peer accommodation platform. Internet Research. https://doi.org/10.1108/INTR-02-2022-0120
   Web of Science ®Google Scholar
• Yani de Soriano, M., Hanel, P. H. P., Vazquez-Carrasco, R., Cambra-Fierro, J., Wilson, A., & Centeno, E. (2019). Investigating the role of customers’ perceptions of employee effort and justice in service recovery. European Journal of Marketing, 53(4), 708–732. https://doi.org/10.1108/EJM-09-2017-0570
   Web of Science ®Google Scholar
• Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1988). Communication and control processes in the delivery of service quality. Journal of Marketing, 52(2), 35–48. https://doi.org/10.1177/002224298805200203
   Web of Science ®Google Scholar
• Zou, H., & Hastie, T. (2005). Regularization and variable selection via the Elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
   Web of Science ®Google Scholar