Comportamiento del consumidor en e-Turismo: Explorando nuevas aplicaciones del aprendizaje automático en los estudios de turismo

  1. Mendieta-Aragón, Adrián 1
  2. Garín-Muñoz, Teresa 1
  1. 1 Universidad Nacional de Educación a Distancia, España
Revista:
Investigaciones Turísticas

ISSN: 2174-5609

Año de publicación: 2023

Número: 26

Páginas: 350-374

Tipo: Artículo

DOI: 10.14198/INTURI.24629 DIALNET GOOGLE SCHOLAR lock_openRUA editor

Otras publicaciones en: Investigaciones Turísticas

Objetivos de desarrollo sostenible

Resumen

Los mercados digitales han alterado la forma en que interactúan los agentes económicos y han cambiado el comportamiento de los turistas. Además, la pandemia de COVID-19 ha demostrado que es necesario monitorear la evolución del comportamiento del consumidor digital y los factores que influyen en él, ya que son elementos dinámicos que evolucionan en el tiempo. Este artículo analiza las desigualdades digitales y valida los principales factores que influyen en los turistas para reservar servicios turísticos en línea. Esta investigación utiliza un conjunto de microdatos con 69.752 y 23.779 observaciones para analizar el modo de reserva de los servicios de alojamiento y transporte, respectivamente, obtenidos de la Encuesta de Turismo de Residentes del Instituto Nacional de Estadística durante el periodo 2016-2021. El artículo confirma variaciones en el perfil del consumidor online y en las características del viaje. Uno de los hallazgos más relevantes es la reducción de la brecha generacional en la contratación online de servicios turísticos. Sin embargo, subsisten desigualdades digitales, como las desigualdades regionales y otras basadas en el nivel de estudios y los ingresos de los turistas. También se destaca que diferentes tipos de viajes, dependiendo del destino, el tipo de alojamiento o transporte, tienen una propensión diferente a reservarse a través de canales de compra digitales. La accesibilidad a las fuentes de big data y los avances recientes en los modelos de aprendizaje automático también han hecho evolucionar las metodologías para analizar el comportamiento del consumidor digital y deben incorporarse a los estudios de turismo. Este estudio compara el rendimiento predictivo de diferentes metodologías en el contexto del turismo electrónico. En particular, evaluamos la potencial capacidad predictiva que podría obtenerse usando técnicas de aprendizaje automático para explicar el comportamiento del consumidor en e-Tourism y lo usamos como punto de referencia para compararlo con los resultados obtenidos usando métodos estadísticos tradicionales. Las métricas de evaluación predictivas seleccionadas muestran que el modelo estadístico de regresión logística mejora la capacidad predictiva de la red neuronal Multilayer Perceptron y presenta valores muy cercanos la máxima capacidad predictiva alcanzada por el algoritmo Random Forest.

Referencias bibliográficas

  • Aeknarajindawat, N. (2019). The factors influencing tourists’ online hotel reservations in Thailand: An empirical study. International Journal of Innovation, Creativity and Change, 10(1), 121–136.
  • Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. In Action Control (pp. 11–39). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69746-3_2
  • Ali, B. J. (2020). Impact of COVID-19 on Consumer Buying Behavior Toward Online Shopping in Iraq. Economic Studies Journal, 18(42), 267–280. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3729323
  • Alsayat, A. (2023). Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca. Neural Computing and Applications, 35(6), 4701–4722. https://doi.org/10.1007/S00521-022-07992-X
  • Aluri, A., Price, B. S., & McIntyre, N. H. (2019). Using Machine Learning To Cocreate Value Through Dynamic Customer Engagement In A Brand Loyalty Program. Journal of Hospitality & Tourism Research, 43(1), 78–100. https://doi.org/10.1177/1096348017753521
  • Amaro, S., & Duarte, P. (2013). Online travel purchasing: A literature review. Journal of Travel and Tourism Marketing, 30(8), 755–785. https://doi.org/10.1080/10548408.2013.835227
  • Amaro, S., & Duarte, P. (2015). An integrative model of consumers’ intentions to purchase travel online. Tourism Management, 46, 64–79. https://doi.org/10.1016/j.tourman.2014.06.006
  • Arefieva, V., Egger, R., & Yu, J. (2021). A machine learning approach to cluster destination image on Instagram. Tourism Management, 85, 104318. https://doi.org/10.1016/j.tourman.2021.104318
  • Bigné, E., Oltra, E., & Andreu, L. (2019). Harnessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities. Tourism Management, 71, 490–503. https://doi.org/10.1016/j.tourman.2018.10.013
  • Boto-García, D., Zapico, E., Escalonilla, M., & Baños-Pino, J. F. (2021). Tourists’ preferences for hotel booking. International Journal of Hospitality Management, 92, 102726. https://doi.org/10.1016/j.ijhm.2020.102726
  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet-The state of eTourism research. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005
  • Buhalis, D., Leung, D., & Law, R. (2011). eTourism: Critical information and communication technologies for tourism destinations. Destination Marketing and Management: Theories and Applications, 205–224. https://doi.org/10.1079/9781845937621.0205
  • Chen, J. S., & Hsu, C. H. C. (1999). The Use of Logit Analysis to Enhance Market Segmentation Methodology. Journal of Hospitality & Tourism Research, 23(3), 268–283. https://doi.org/10.1177/109634809902300303
  • Chen, J., Zhang, Y., Zhu, S., & Liu, L. (2021). Does covid-19 affect the behavior of buying fresh food? Evidence from Wuhan, China. International Journal of Environmental Research and Public Health, 18(9), 4469. https://doi.org/10.3390/ijerph18094469
  • Chiang, W. Y. K., Zhang, D., & Zhou, L. (2006). Predicting and explaining patronage behavior toward web and traditional stores using neural networks: A comparative analysis with logistic regression. Decision Support Systems, 41(2), 514–531. https://doi.org/10.1016/j.dss.2004.08.016
  • CNMC. (2021). e-Commerce statistics in Spain. http://data.cnmc.es/datagraph/
  • Coenders, G., Ferrer-Rosell, B., & Martínez-García, E. (2016). Trip Characteristics and Dimensions of Internet Use for Transportation, Accommodation, and Activities Undertaken at Destination. Journal of Hospitality Marketing and Management, 25(4), 498–511. https://doi.org/10.1080/19368623.2015.1034827
  • Dang, H. D., & Pham, T. T. (2021). Predicting Contract Participation in the Mekong Delta, Vietnam: A Comparison between the Artificial Neural Network and the Multinomial Logit Model. Journal of Agricultural and Food Industrial Organization, 20(2), 135–147. https://doi.org/10.1515/jafio-2020-0023
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319– 339. https://doi.org/10.2307/249008
  • del Rio-Chanona, M. R., Mealy, P., Pichler, A., Lafond, F., & Doyne Farmer, J. (2020). Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective. Oxford Review of Economic Policy, 36(Supplement_1), S94–S137. https://doi.org/10.1093/oxrep/graa033
  • Desplas, N., & Mao, M. (2014). Parallel analysis between e-tourism and e-government: evolution and trends. Investigaciones Turísticas, 7, 1–22.
  • Din, A. U., Han, H., Ariza-Montes, A., Vega-Muñoz, A., Raposo, A., & Mohapatra, S. (2022). The Impact of COVID-19 on the Food Supply Chain and the Role of E-Commerce for Food Purchasing. Sustainability, 14(5). https://doi.org/10.3390/SU14053074
  • Dsouza, D., & Sharma, D. (2021). Online food delivery portals during COVID-19 times: An analysis of changing consumer behavior and expectations. International Journal of Innovation Science, 13(2), 218–232. https://doi.org/10.1108/IJIS-10-2020-0184
  • Ellies-Oury, M. P., Chavent, M., Conanec, A., Bonnet, M., Picard, B., & Saracco, J. (2019). Statistical model choice including variable selection based on variable importance: A relevant way for biomarkers selection to predict meat tenderness. Scientific Reports, 9(1). https://doi.org/10.1038/S41598-019-46202-y
  • Femenia-Serra, F., Perles-Ribes, J. F., & Ivars-Baidal, J. A. (2019). Smart destinations and techsavvy millennial tourists: hype versus reality. Tourism Review, 74(1), 63–81. https://doi.org/10.1108/TR-02-2018-0018
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
  • Fudenberg, D., Kleinberg, J., Liang, A., & Mullainathan, S. (2022). Measuring the Completeness of Economic Models. Journal of Political Economy, 130(4), 956–990. https://doi.org/10.1086/718371
  • Gardella, N. B., Díaz, A. P. O., & Hernández, J. C. R. (2021). Tourist preferences in hotels and destinations: An approach based on content analysis and decision trees. Investigaciones Turísticas, 22, 121–147. https://doi.org/10.14198/INTURI2021.22.6
  • Garín-Muñoz, T., & Pérez-Amaral, T. (2011). Internet usage for travel and tourism. The case of Spain. Tourism Economics, 17(5), 1071–1085. https://doi.org/10.5367/te.2011.0080
  • Garín-Muñoz, T., Pérez-Amaral, T., & López, R. (2020). Consumer engagement in e-Tourism: Micro-panel data models for the case of Spain. Tourism Economics, 26(6), 853–872. https://doi.org/10.1177/1354816619852880
  • Ghaedi, M. (2022). Tourists’ Booking Behavior: Online Social Media Perspectives. Journal of Promotion Management, 28(6), 702–728. https://doi.org/10.1080/10496491.2021.2015509
  • Gratzer, M., Werthner, H., & Winiwarter, W. (2004). Electronic business in tourism. International Journal of Electronic Business, 2(5), 450. https://doi.org/10.1504/IJEB.2004.005878
  • Gratzer, M., Winiwarter, W., & Werthner, H. (2002). State of the Art in eTourism. 3rd South Eastern European Conference on E-Commerce 2002.
  • Greene, M. N., Morgan, P. H., & Foxall, G. R. (2017). Neural Networks and Consumer Behavior: Neural Models, Logistic Regression, and the Behavioral Perspective Model. The Behavior Analyst, 40(2), 393. https://doi.org/10.1007/S40614-017-0105-x
  • Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., Pesonen, J., Zanker, M., & Xiang, Z. (2020). e-Tourism beyond COVID-19: a call for transformative research. Information Technology and Tourism, 22(2), 187–203. https://doi.org/10.1007/s40558- 020-00181-3
  • Guthrie, C., Fosso-Wamba, S., & Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570. https://doi.org/10.1016/j.jretconser.2021.102570
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer New York. https://doi.org/10.1007/978-0-387-84858-7
  • Hernández, A. M. M., & Hernández, C. F. (2019). Access of women to economic autonomy through rural tourism on the island of la Palma. Investigaciones Turísticas, 18, 22–41. https://doi.org/10.14198/INTURI2019.18.02
  • Hu, M., & Song, H. (2020). Data source combination for tourism demand forecasting. Tourism Economics, 26(7), 1248–1265. https://doi.org/10.1177/1354816619872592
  • Inversini, A., & Masiero, L. (2014). Selling rooms online: The use of social media and online travel agents. International Journal of Contemporary Hospitality Management, 26(2), 272–292. https://doi.org/10.1108/IJCHM-03-2013-0140
  • Jun, S. H., Vogt, C. A., & Mackay, K. J. (2010). Online Information Search Strategies: A Focus On Flights and Accommodations. Journal of Travel & Tourism Marketing, 27(6), 579–595. https://doi.org/10.1080/10548408.2010.507151
  • Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324. https://doi.org/10.1016/S0004-3702(97)00043-X
  • Li, Z. F., Zhou, Q., Chen, M., & Liu, Q. (2021). The impact of COVID-19 on industry-related characteristics and risk contagion. Finance Research Letters, 39, 101931. https://doi.org/10.1016/j.frl.2021.101931
  • Liébana-Cabanillas, F., & Lara-Rubio, J. (2017). Predictive and explanatory modeling regarding adoption of mobile payment systems. Technological Forecasting and Social Change, 120, 32–40. https://doi.org/10.1016/j.techfore.2017.04.002
  • Liu, Y., Li, Q., Edu, T., & Negricea, I. C. (2023). Exploring the Continuance Usage Intention of Travel Applications In the Case of Chinese Tourists. Journal of Hospitality & Tourism Research, 47(1), 6–32. https://doi.org/10.1177/1096348020962553
  • Lyu, S. O., & Hwang, J. (2021). A Discrete Choice Experimental Approach to Understand Sports Event Tourists’ In-Stadium Beer Consumption Preferences. Journal of Hospitality & Tourism Research, 45(7), 1324–1345. https://doi.org/10.1177/1096348021992099
  • Mahmood, H., Rehman, A. U., Sabir, I., Rauf, A., Afthanorhan, A., & Nawal, A. (2022). Restaurant Diners’ Switching Behavior During the COVID-19 Pandemic: Protection Motivation Theory. Frontiers in Psychology, 13. https://doi.org/10.3389/FPSYG.2022.833627
  • Mariani, M. M., & Baggio, R. (2022). Big data and analytics in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 34(1), 231–278. https://doi.org/10.1108/ijchm-03-2021-0301
  • Martín-Baos, J. Á., García-Ródenas, R., & Rodriguez-Benitez, L. (2021). Revisiting kernel logistic regression under the random utility models perspective. An interpretable machinelearning approach. Transportation Letters, 13(3), 151–162. https://doi.org/10.1080/19427867.2020.1861504
  • Mehmood, T. (2021). Regularized Feature Selection in Categorical PLS for Multicollinear Data. Mathematical Problems in Engineering, 2021, 1–8. https://doi.org/10.1155/2021/5561752
  • Mendieta-Aragón, A. (2022). Cambios en el comportamiento turístico tras la COVID-19: hacia un nuevo perfil del turista y del viaje de ocio en España. Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA, 23(1), 37–51. https://doi.org/10.24309/recta.2022.23.1.03
  • Mendieta-Aragón, A. (2023). Efectos de la COVID-19 en el sector turístico de la Unión Europea: Análisis económico para el caso de España. Revista Universitaria Europea, 38(EneroJunio), 41–70.
  • Miranda, D. D., & Briley, D. (2021). Digital tourist: Variables that define their purchasing behaviour. Investigaciones Turísticas, 21, 1–21. https://doi.org/10.14198/INTURI2021.21.1
  • Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(6), 275–285. https://doi.org/10.1002/cem.873
  • Navío-Marco, J., Ruiz-Gómez, L. M., & Sevilla-Sevilla, C. (2018). Progress in information technology and tourism management: 30 years on and 20 years after the internet Revisiting Buhalis & Law’s landmark study about eTourism. Tourism Management, 69, 460–470. https://doi.org/10.1016/j.tourman.2018.06.002
  • Neidhardt, J., & Werthner, H. (2018). IT and tourism: still a hot topic, but do not forget IT. Information Technology and Tourism, 20(1–4). https://doi.org/10.1007/s40558-018- 0115-x
  • Nguyen, H. V., Tran, H. X., Van Huy, L., Nguyen, X. N., Do, M. T., & Nguyen, N. (2020). Online Book Shopping in Vietnam: The Impact of the COVID-19 Pandemic Situation. Publishing Research Quarterly, 36(3), 437. https://doi.org/10.1007/S12109-020-09732-2
  • Ong, A. K. S., Cleofas, M. A., Prasetyo, Y. T., Chuenyindee, T., Young, M. N., Diaz, J. F. T., Nadlifatin, R., & Redi, A. A. N. P. (2021). Consumer Behavior in Clothing Industry and Its Relationship with Open Innovation Dynamics during the COVID-19 Pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 211. https://doi.org/10.3390/joitmc7040211
  • Pejić-Bach, M. (2021). Editorial: Electronic Commerce in the Time of Covid-19 Perspectives and Challenges. Journal of Theoretical and Applied Electronic Commerce Research, 16(1), i–i. https://doi.org/10.4067/S0718-18762021000100101
  • Phillips, P., Zigan, K., Santos Silva, M. M., & Schegg, R. (2015). The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis. Tourism Management, 50, 130–141. https://doi.org/10.1016/j.tourman.2015.01.028
  • Pollak, F., Markovic, P., Vachal, J., & Vavrek, R. (2022). Analysis of E-Consumer Behavior During the COVID-19 Pandemic. In Intelligent Processing Practices and Tools for E-Commerce Data, Information, and Knowledge (pp. 95–114). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78303-7_6
  • Pourfakhimi, S., Duncan, T., & Coetzee, W. (2019). A critique of the progress of eTourism technology acceptance research: time for a hike? Journal of Hospitality and Tourism Technology, 10(4), 689–746. https://doi.org/10.1108/JHTT-08-2018-0077
  • Pourfakhimi, S., Duncan, T., Ould, L., Allan, K., & Coetzee, W. (2020). Acceptance and Adoption of eTourism Technologies. In Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken (Eds.), Handbook of e-Tourism (pp. 1–31). https://doi.org/10.1007/978-3-030-05324-6_58-1
  • Purwanto, H., Sidanti, H., & Kadi, D. C. A. (2021). Traditional Market Transformation Into Digital Market (Indonesian Traditional Market Research Library). International Journal of Science, Technology & Management, 2(6), 1980–1988. https://doi.org/10.46729/ijstm.v2i6.384
  • Rahmanov, F., Mursalov, M., & Rosokhata, A. (2021). Consumer behavior in digital era: impact of COVID 19. Marketing and Management of Innovations, 5(2), 243–251. https://doi.org/10.21272/mmi.2021.2-20
  • Reverte, F. G., & Luque, P. D. (2021). Digital Divide in E-Tourism. In Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken (Eds.), Handbook of e-Tourism (pp. 1–21). Springer, Cham. https://doi.org/10.1007/978-3-030-05324-6_109-1
  • Rosenblatt, F. (1961). Principles of neurodynamics. Perceptrons and the theory of brain mechanisms. https://apps.dtic.mil/sti/citations/AD0256582
  • Sharma, S., Singh, G., Pratt, S., & Narayan, J. (2020). Exploring consumer behavior to purchase travel online in Fiji and Solomon Islands? An extension of the UTAUT framework. International Journal of Culture, Tourism, and Hospitality Research, 15(2), 227–247. https://doi.org/10.1108/IJCTHR-03-2020-0064
  • Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059
  • Solano Sánchez, M. Á., Nuñez Tabales, J. M., & Caridad y López del Río, L. (2022). Nuevos tipos de alojamiento: apartamentos turísticos y determinantes de valoración en el precio de la estancia. Investigaciones Turísticas, 23, 94–111. https://doi.org/10.14198/INTURI2022.23.5
  • Timotius, E., & Octavius, G. S. (2021). Global Changing of Consumer Behavior to Retail Distribution due to Pandemic of COVID-19: A Systematic Review. Journal of Distribution Science, 19(11), 69–80. https://doi.org/10.15722/JDS.19.11.202111.69
  • Ukpabi, D. C., & Karjaluoto, H. (2017). Consumers’ acceptance of information and communications technology in tourism: A review. Telematics and Informatics, 34(5), 618–644. https://doi.org/10.1016/j.tele.2016.12.002
  • van Nuenen, T., & Scarles, C. (2021). Advancements in technology and digital media in tourism. Tourist Studies, 21(1), 119–132. https://doi.org/10.1177/1468797621990410
  • Vollero, A., Sardanelli, D., & Siano, A. (2021). Exploring the role of the Amazon effect on customer expectations: An analysis of user-generated content in consumer electronics retailing. Journal of Consumer Behaviour, 1–12. https://doi.org/10.1002/cb.1969
  • Vu, H. L., Ng, K. T. W., Richter, A., & An, C. (2022). Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation. Journal of Environmental Management, 311, 114869. https://doi.org/10.1016/j.jenvman.2022.114869
  • Walker, K. W., & Jiang, Z. (2019). Application of adaptive boosting (AdaBoost) in demanddriven acquisition (DDA) prediction: A machine-learning approach. The Journal of Academic Librarianship, 45(3), 203–212. https://doi.org/10.1016/j.acalib.2019.02.013
  • Wen, L., Liu, C., & Song, H. (2019). Forecasting tourism demand using search query data: A hybrid modelling approach. Tourism Economics, 25(3), 309–329. https://doi.org/10.1177/1354816618768317
  • Xu, X., Law, R., Chen, W., & Tang, L. (2016). Forecasting tourism demand by extracting fuzzy Takagi–Sugeno rules from trained SVMs. CAAI Transactions on Intelligence Technology, 1(1), 30–42. https://doi.org/10.1016/j.trit.2016.03.004
  • Zhang, J., Cui, X., Cai, W., & Shao, X. (2018). A variable importance criterion for variable selection in near-infrared spectral analysis. Science China Chemistry, 62(2), 271–279. https://doi.org/10.1007/S11426-018-9368-9
  • Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society, 20, 22–35. https://doi.org/10.1016/j.tbs.2020.02.003
Fuente de los datos: Dialnet