RT Journal Article
T1 Understanding destination brand experience through data mining and machine learning
A1 Calderón Fajardo, Víctor
A1 Anaya-Sánchez, Rafael
A1 Molinillo-Jiménez, Sebastián
K1 Minería de datos
K1 Turismo
K1 Aprendizaje automático (Inteligencia artificial)
AB This research formalises a new methodology to measure and analyse Destination Brand Experience, improving upon traditional approaches by offering greater objectivity and rigour. Adopting a case study approach, five distinct and complementary types of analysis have been conducted: comprehensive sentiment analysis and topic modelling, an analysis using multiple thesauri, statistical analyses for hypothesis testing, and machine learning for classification. The methodological innovation, through the construction of thesauri, has enabled the measurement of sensory, affective, intellectual, and behavioural dimensions in unique and emblematic attractions, experiences, and transportation within a tourist destination, based on visitor reviews. This new approach allows tourism professionals and destination managers to identify areas for improvement and develop strategies to enhance tourist satisfaction. The findings suggest that there are significant differences in the relationships between specific dimensions and that gender and culture moderate or impact these relationships.
PB Elsevier
YR 2024
FD 2024-02-03
LK https://hdl.handle.net/10630/30440
UL https://hdl.handle.net/10630/30440
LA eng
NO Víctor Calderón-Fajardo, Rafael Anaya-Sánchez, Sebastian Molinillo, Understanding destination brand experience through data mining and machine learning, Journal of Destination Marketing & Management, Volume 31, 2024, 100862, ISSN 2212-571X, https://doi.org/10.1016/j.jdmm.2024.100862
NO Funding for open Access charge: Universidad de Málaga / CBUA.This study was supported by the European Regional Development Fund Operational Programme of Andalusia 2014–2020, through the Andalusian Research, Development and Innovation Plan (Plan Andaluz de Investigación, Desarrollo e Innovación) PAIDI 2020 (Grant: P20_00457), and by the Spanish Ministry of Education, Culture and Sport (Ministerio de Educación, Cultura y Deporte del Gobierno de España) (Grant: FPU20/00235).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 27 feb 2026