Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico
Tourist Satisfaction Using Motivational Factors: Comparison of Statistical Learning Models
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El nivel de satisfacción de un turista con el destino visitado y su intención de volver a visitarlo se asumen como dependientes de su experiencia previa con el lugar. Para observar esta perspectiva relacional, se utilizó un conjunto de datos de 386 turistas que visitaron la ciudad de Medellín (Colombia) durante el año 2018. Para predecir la variable de volver a visitar la ciudad y la satisfacción con el destino, se usaron las variables consideradas de empuje (push) y aquellas que halan (pull) al turista. Se estimaron cuatro modelos de aprendizaje estadístico para la clasificación de los turistas: regresión logística, árboles aleatorios, máquinas de soporte vectorial y el conjunto de aumento de gradiente extremo. Las variables más importantes en las estimaciones de la satisfacción fueron ‘hablar sobre una experiencia de viaje en el futuro’ e ‘ir a lugares que mis amigos no han visitado’; y para volver a visitar la ciudad fueron ‘visitar lugares históricos’ y ‘viajar a bajos precios’.
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Abe, S. (2005). Support vector machines for pattern classification. Vol. 2. Springer. https://doi.org/10.1007/1-84628-219-5
Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52-77. https://doi.org/10.1016/j.ijhm.2019.01.003
Albayrak, T. & Caber, M. (2018). Examining the relationship between tourist motivation and satisfaction by two competing methods. Tourism Management, 69, 201-13. https://doi.org/10.1016/j. tourman.2018.06.015
Bloom, J. (2004). Tourist market segmentation with linear and non-linear techniques. Tourism Management, 25(6), 723-733. https://doi.org/10.1016/j.tourman.2003.07.004
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. ht tps://doi. org/10.1023/A:1010933404324
Chen, T. & Guestrin, C. (2016). xgboost: A scalable tree boosting system. En Association for Computing Machinery (Ed), Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining (pp. 785-794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
Chen, Y., Zhang, H., & Qiu, L. (2013). Review on tourist satisfaction of tourism destinations. Journal of System and Management Sciences, 3(1), 74-86. http://www.aasmr.org/jsms/Vol3/No1/ jsms_Vol3_No1_8.pdf
Chi, C. & Qu, H. (2008). Examining the structural relationships of destination image, tourist satis¬faction and destination loyalty: An integrated approach. Tourism Management, 29(4), 624-636. https://doi.org/10.1016/j.tourman.2007.06.007
Correia, A., Kozak, M., & Ferradeira, J. (2013). From tourist motivations to tourist satisfaction. International Journal of Culture, Tourism and Hospitality Research, 7(4), 411-424. https://doi. org/10.1108/ijcthr-05-2012-0022
Dean, D. & Suhartanto, D. (2019). The formation of visitor behavioral intention to creative tourism: The role of push–pull motivation. Asia Pacific Journal of Tourism Research, 24(5), 393-403. https:// doi.org/10.1080/10941665.2019.1572631
Deaton, A. (2013). The great escape. Princeton University Press.
Do Valle, P., Silva, J., Mendes, J., & Guerreiro, M. (2006). Tourist satisfaction and destination loyalty intention: A structural and categorical analysis. International Journal of Business Science & Applied Management, 1(1), 25-44. https://acortar.link/C2k3Jh
Egger, R. (2022). Machine learning in tourism: A brief overview. En R. Egger (Ed.), Applied data science in tourism: Interdisciplinary approaches, methodologies & applications (pp. 85-107). Springer. https://doi.org/10.1007/978-3-030-88389-8
Fodness, D. (1994). Measuring tourist motivation. Annals of Tourism Research, 21(3), 555-581. https://doi.org/10.1016/0160-7383(94)90120-1
Ghaderi, Z., Hatamifar, P., & Khalilzadeh, J. (2018). Analysis of tourist satisfaction in tourism supply chain management. Anatolia: An International Journal of Tourism and Hospitality Research, 29(3), 433-444. https://doi.org/10.1080/13032917.2018.1439074
Gil-León, J., Gutiérrez-Ayala, J., & Ramírez-Hernández, E. (2021). El papel del turismo patrimo¬nial en el índice de competitividad turística regional de Colombia: una evaluación de las relacio¬nes mediante PLS-PM. Revista Escuela de Administración de Negocios, (90), 169-192. https://doi.org/10.21158/01208160.n90.2021.2973
Guerra-Montenegro, J., Sánchez-Medina, J., Laña, I., Sánchez-Rodríguez, D. Alonso-González, I., & Del Ser, J. (2021). Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges. Applied Soft Computing, 102, 107082. https://doi.org/10.1016/j.asoc.2021.107082
Huang, Z., Kong, Y., & Zhou, C. (2018). A study on relationship between sports tourism motivation and tourists’ re-visiting intention: Based on Logistic Model. Advances in Social Science, Education and Humanities Research: Proceedings of the 2nd International Conference on Economics and Management, Education, Humanities and Social Sciences (EMEHSS 2018), 151, 54-61. https://doi.org/10.2991/emehss-18.2018.13
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer. https://www.stat.berkeley.edu/users/rabbee/s154/ISLR_First_Printing.pdf
Jang, S. & Cai, L. (2002). Travel motivations and destination choice: A study of British outbound mar¬ket. Journal of Travel & Tourism Marketing, 13(3), 111-133. https://doi.org/10.1080/10548400209511570
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Kahneman, D., Sibony, O., & Sunstein, C. (2021). Noise: A flaw in human judgment. Hachette Book Group.
Kozak, M. (2001). Comparative assessment of tourist satisfaction with destinations across two nationalities. Tourism Management, 22(4), 391-401. https://doi.org/10.1016/S0261-5177(00)00064-9
Kuhn, M. & Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org
Kwon, W., Lee, M., & Back, K-J. (2020). Exploring the underlying factors of customer value in restaurants: A machine learning approach. International Journal of Hospitality Management, 91, 102643. https://doi.org/10.1016/j.ijhm.2020.102643
Lam-González, Y., León, C., & De León, J. (2019). Coopetition in maritime tourism: Assessing the effect of previous islands’ choice and experience in tourist satisfaction. Sustainability, 11(22), 6334. https://doi.org/10.3390/su11226334
Lee, T. (2009). A structural model to examine how destination image, attitude & moti¬vation affect the future behavior of tourists. Leisure Sciences, 31(3), 215-236. https://doi.org/10.1080/01490400902837787
Lee, G., O’Leary, J., Lee, S., & Morrison, A. (2002). Comparison and contrast of push and pull motivational effects on trip behavior: An application of a Multinomial Logistic Regression Model. Tourism Analysis, 7(2), 89-104. https://doi.org/10.3727/108354202108749970
Li, W., Xu, S., & Meng, W. (2009). A support vector machines method for tourist satisfaction degree evaluation. En IEEE Computer Society (Ed.), 2009 6th International Conference on Service Systems and Service Management (pp. 883-887). IEEE. https://doi.org/10.1109/icsssm.2009.5175007
Luna-Cortés, G. (2020). Análisis de la percepción de los estadounidenses que visitan Colombia. Un modelo de ecuaciones estructurales. Estudios y Perspectivas en Turismo, 29(1), 51-71. https://acortar.link/watpwr
Mansfeld, Y. (1992). From motivation to actual travel. Annals of Tourism Research, 19(3), 399-419. https://doi.org/10.1016/0160-7383(92)90127-B
Oh, H., Kim, B. Y., & Shin, J. H. (2004). Hospitality and tourism marketing: Recent developments in research and future directions. International Journal of Hospitality Management, 23(5), 425-447. https://doi.org/10.1016/j.ijhm.2004.10.004
Oh, H. & Lee, S. (2021). Evaluation and interpretation of tourist satisfaction for local Korean festivals using explainable AI. Sustainability, 13(19), 10901. https://doi.org/10.3390/su131910901
Olague de la Cruz, J. (2015). La imagen del destino y la motivación de viaje como determinantes de la satisfacción y lealtad del turismo urbano de ocio en Monterrey, México (Tesis doctoral, Universi¬dad Autónoma de Nuevo León). Repositorio Académico Digital UANL. http://eprints.uanl.mx/9248/
Prebensen, N., Skallerud, K., & Chen, J. (2010). Tourist motivation with sun and sand destinations: Satisfaction and the wom-effect. Journal of Travel & Tourism Marketing, 27(8), 858-873. https:// doi.org/10.1080/10548408.2010.527253
Salganik, M., Lundberg, I., Kindel, A., Ahearn, C., Al-Ghoneim, K., Almaatouq, A., Altschul, D., Brand, J., Bohme, N., Compton, R., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B., Jahani, E., Kashyap, R., Kirchner, A. ... & McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. PNAS: Proceedings of the National Academy of Sciences, 117(15), 8398-8403. https://doi.org/10.1073/pnas.2118703118
San Martín, H., Herrero, A., & García, M. (2019). An integrative model of destination brand equity and tourist satisfaction. Current Issues in Tourism, 22(16), 1992-2013. https://doi.org/10.1080/136 83500.2018.1428286
Schonlau, M. & Zou, R. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29. https://doi.org/10.1177%2F1536867X20909688
Song, Y., Wang, R., Fernández, J., & Li, D. (2021). Investigating sense of place of the Las Vegas Strip using online reviews and machine learning approaches. Landscape and Urban Planning, 205, 103956. https://doi.org/10.1016/j.landurbplan.2020.103956
Vapnik, V. (2000). The nature of statistical learning theory. Springer Science & Business Media.
Villamediana-Pedrosa, J, Vila-López, N., & Küster-Boluda, I. (2020). Predictors of tourist enga¬gement: Travel motives and tourism destination profiles. Journal of Destination Marketing & Management, 16, 100412. https://doi.org/10.1016/j.jdmm.2020.100412
Wickham, H., Averick, M., Bryan, J., Chang, W., D’Agostino, L., François R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Lin, T., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., ... Yutani, H. (2019). Welcome to the tidyverse. Journal of Open-Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Yoo, C., Yoon, D., & Park, E. (2018). Tourist motivation: An integral approach to destination choices. Tourism Review, 73(2), 169-185. https://doi.org/10.1108/TR-04-2017-0085
Yoon, Y. & Uysal, M. (2005). An examination of the effects of motivation and satisfaction on des¬tination loyalty: A structural model. Tourism Management, 26(1), 45-56. https://doi.org/10.1016/j. tourman.2003.08.016
Yu, L. & Goulden, M. (2006). A comparative analysis of international tourists’ satisfaction in Mongolia. Tourism Management, 27(6), 1331-1342. http://dx.doi.org/10.1016/j.tourman.2005.06.003
Żbikowski, K. & Antosiuk, P. (2021). A machine learning, bias-free approach for predicting business success using Crunchbase data. Information Processing & Management, 58(4), 102555. https://doi.org/10.1016/j.ipm.2021.102555