Consumer behaviour in e-Tourism: Exploring new applications of machine learning in tourism studies

Adrián Mendieta-Aragón; Teresa Garín-Muñoz

doi:10.14198/INTURI.24629

Autores/as

Adrián Mendieta-Aragón Universidad Nacional de Educación a Distancia, España https://orcid.org/0000-0001-8250-9182
Teresa Garín-Muñoz Departamento de Análisis Económico (UNED), España https://orcid.org/0000-0001-6300-1375

DOI:

https://doi.org/10.14198/INTURI.24629

Palabras clave:

turismo electrónico, reservas en línea, comportamiento del consumidor, big data, regresión logística, aprendizaje automático, comportamiento dinámico

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.

Financiación

National University of Distance Education (Spain) under Grant [BICI N.3, October 21, 2019].

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