[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125854-en":3,"doc-seo-125854-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125854,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Understanding destination brand experience through data mining and machine learning","This research formalises a new methodology to measure and analyse Destination Brand Experience, improving upon traditional approaches by offering greater objectivity and rigour. Using a case study design, five complementary analyses are applied: comprehensive sentiment analysis and topic modelling, work with multiple thesauri, statistical hypothesis testing, and machine learning classification. Thesauri construction enables assessment of sensory, affective, intellectual and behavioural dimensions across emblematic attractions, experiences and transport based on visitor reviews, supporting destination improvement and tourist satisfaction.","Journal of Destination Marketing & Management 31 (2024) 100862  \nContents lists available at ScienceDirect  \nJournal of Destination Marketing & Management  \njournal [homepage:](homepage: www.elsevier.com/locate/jdmm)[ www.elsevier.com/locate/jdmm](homepage: www.elsevier.com/locate/jdmm)  \nUnderstanding destination brand experience through data mining and machine learning  \nVíctor Calder´on-Fajardo a, b, *, Rafael Anaya-S´anchez c, Sebastian Molinilloc  \na Department of Business Management, Faculty of Tourism, University of Malaga, Teatinos Campus, Le´on Tolstoi 4, 29010, Malaga, Spain b Inter-University PhD Programme in Tourism, Faculty of Tourism, University of Malaga, Malaga, Spain  \nc Department of Business Management, Faculty of Economics and Business, University of Malaga, Malaga, Spain  \n\n| A R T I C L E I N F O\u003Cbr>Keywords:\u003Cbr>Destination brand experience\u003Cbr>Data mining\u003Cbr>Machine learning\u003Cbr>Destination experience, Destination branding | A B S T R A C T |\n| --- | --- |\n|  | 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. |\n\n1. Introduction  \nLiterature has demonstrated that destination branding provides a competitive advantage (Boo et al., 2009), and in this regard, understanding tourists’ experience with a destination brand is crucial (Berrozpe et al., 2019; Rather et al., 2021; Kumar & Kaushik, 2020), as it encompasses aspects such as sensory, affective, intellectual, and behavioural (Barnes et al., 2014). The construct of Destination Brand Experience (DBE), first introduced by (Barnes et al., 2014) and derived from the general Brand Experience (BE) construct of Brakus et al.(2009), is a comprehensive model that captures these four essential dimensions of destination brand experience. DBE refers to the cumulative perception and emotional response that a tourist forms through direct or indirect interaction with a destination’s brand identity. This encompasses all touchpoints, including cultural attributes, natural scenery, amenities, customer service, marketing communication, and personal experiences. As such, it plays a significant role in destination selection, satisfaction, and loyalty, profoundly influencing the overall success of tourism marketing strategies (Barnes et al., 2014; Boo et al., 2009; Kumar & Kaushik, 2018)  \nDestinations are complex products, not only because they combine various tourism-related offerings, but also due to consumers’ subjective  \ninterpretation of them (Buhalis, 2000). The multisensory, fantasy, and emotional aspects of consumer behaviour in relation to products are particularly significant in the tourism industry (Govers et al., 2007; Hirschman & Holbrook, 1982). The application of the BE construct to tourist destinations presents a formal, rigorous, and systematic approach for assessing destination brand experiences. This comprehensive approach makes the DBE construct a valuable tool in the tourism industry for evaluating and enhancing travellers’ experiences with d","cbCaiuZckQg6q4Hk","https://ap.wps.com/l/cbCaiuZckQg6q4Hk","pdf",4455484,5,1,13,"English","en",105,"# Introduction\n## Destination brand experience and its dimensions\n## Limits of SEM and PLS with abundant digital data\n## Motivation for data mining and machine learning\n# Methodology (overview)\n## Sentiment analysis and topic modelling\n## Multi-thesauri analysis and statistical testing\n## Machine learning classification and implications","[{\"question\":\"What does Destination Brand Experience (DBE) include in this study?\",\"answer\":\"DBE captures sensory, affective, intellectual and behavioural dimensions formed through direct or indirect interaction with a destination’s brand identity across all touchpoints.\"},{\"question\":\"Why do the authors propose data mining and machine learning instead of traditional methods?\",\"answer\":\"Traditional approaches like SEM and PLS may struggle with large volumes of digital data and may be less capable of detecting non-linear patterns and complex interactions in online reviews and social media.\"},{\"question\":\"How is the new methodology implemented and what are its main analysis components?\",\"answer\":\"The case study applies sentiment analysis and topic modelling, analysis using multiple thesauri, statistical analyses for hypothesis testing, and machine learning for classification.\"}]","Understanding destination brand experience through data mining and machine learning | 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