Bagged Clustering and its application to tourism market segmentation

Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample.The fuzzy C-medoids Clustering (FCMdC) method is run on each bootstrap sam- ple, obtaining (B × C) medoids and the membership degrees of each unit to the different clusters.The sec- ond step consists in running a hierarchical clustering algorithm on the (B × C) medoids. The best partition of the medoids is obtained investigating properly the dendrogram.Then each unit is assigned to each cluster based on the membership degrees observed in the partitioning step.The effectiveness of the sug- gested procedure has been shown analyzing a suggestive tourism segmentation problem. Weanalyze two sample of tourists, each one attending adifferent cultural attraction, enlightening differences among clusters in socio-economic characteristics and in the motivational reasons behind visit behavior. © 2013 Elsevier Ltd. All rights reserved

What are the triggers of Asian visitor satisfaction and loyalty in the Korean heritage site?

Based on complexity theory, this study examines a configurational model that uses motivation antecedents and demographic configurations to explore the causal recipes that lead to high and low levels of Asian visitor satisfaction and loyalty. Data were collected from 183 Chinese and Japanese visitors to the Hanok heritage site in Seoul, South Korea. Asymmetrical modeling using a fuzzy-set qualitative comparative analysis was applied and a combination of desired behavioral outcomes identified. Hanok experience from the motivation configuration and gender from the demographic configuration appeared as necessary conditions to make visitors satisfied and loyal. Key tenets of complexity theory are supported by the study's findings

Segmenting visitors of cultural events: The case of Christmas Market

Market segmentation in tourism makes use of sets of powerful analytical tools for the sake of planning and managing demand-oriented policies. This paper contributes to this strand of literature by segmenting tourists visiting a cultural event. We utilize the Bagged Clustering method, a combination of traditional partitioning and hierarchical techniques, which is proven to be more effective. An ad hoc survey was conducted in 2011 among the Italian visitors of the Christmas Market in Merano, Northern Italy. A total of 802 questionnaires were collected. In discussing the results, marketing and managerial implications are stressed for both policymakers and local organizers. © 2014 Elsevier Ltd. All rights reserved

Visitors of two types of museums: A segmentation study

Market segmentation comprises a wide range of measurement tools that are useful for the sake of supporting marketing and promotional policies also in the sector of cultural economics. This paper aims to contribute to the literature on segmenting cultural visitors by using the Bagged Clustering method, as an alternative and effective strategy to conduct cluster analysis when binary variables are used. The technique is a combination of hierarchical and partitioning methods and presents several advantages with respect to more standard techniques, such as k-means and LVQ. For this purpose, two ad hoc surveys were conducted between June and September 2011 in the two principal museums of the two provinces of the Trentino-South Tyrol region (Bolzano and Trento), Northern Italy: the South Tyrol Museum of Archaeology in Bolzano (ÖTZI), hosting the permanent exhibition of the "Iceman" Ötzi, and the Museum of Modern and Contemporaneous Art of Trento and Rovereto (MART). The segmentation analysis was conducted separately for the two kinds of museums in order to find similarities and differences in behaviour patterns and characteristics of visitors. The analysis identified three and two cluster segments respectively for the MART and ÖTZI visitors, where two ÖTZI clusters presented similar characteristics to two out of three MART groups. Conclusions highlight marketing and managerial implications for a better direction of the museums. © 2012 Elsevier Ltd. All rights reserved

Development of Context-Aware Recommenders of Sequences of Touristic Activities

En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lícules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turístiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turístics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turístics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turístiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i períodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implícites entre els punts d'interès de cada perfil. Finalment, es fa un rànquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turística. També realitzem una segona fase d'anàlisi dels fluxos turístics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turística. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turística.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de películas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turísticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turísticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turísticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turísticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minería de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turística. También llevamos a cabo un análisis de los flujos turísticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turística. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turística.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems

Me, My Girls, and the Ideal Hotel: Segmenting Motivations of the Girlfriend Getaway Market Using Fuzzy C-Medoids for Fuzzy Data.

Segmenting the motivation of travelers using the push and pull framework remains ubiquitous in tourism. This study segments the girlfriend getaway (GGA) market on motivation (push) and accommodation (pull) attributes and identifies relationships between these factors. Using a relatively novel clustering algorithm, the Fuzzy C-Medoids clustering for fuzzy data (FCM-FD), on a sample of 749 women travelers, three segments (Socializers, Enjoyers, and Rejoicers) are uncovered. The results of a multinomial fractional model show relationships between the clusters of motivation and accommodation attributes as well as sociodemographic characteristics. The research highlights the importance of using a gendered perspective in applying well established motivation models such as the push and pull framework. The findings have implications for both destination and accommodation management

A digital response system to mitigate overtourism. The case of Dubrovnik.

In order to design effective responses to the complex phenomenon of overtourism, the tourism carrying capacity (TCC) of a destination is an essential reference point. This paper provides in-depth analysis of this correlation through the case study of Dubrovnik. The study applies a TCC calculation model that is able to quantitatively include the main effects of overtourism. The paper illustrates how these results can be used to automate specific decongestion policies by conceptualising a digital response system for real-time intervention to mitigate the undesirable effects of over-tourism

A digital response system to mitigate overtourism. The case of Dubrovnik

In order to design effective responses to the complex phenomenon of overtourism, the tourism carrying capacity (TCC) of a destination is an essential reference point. This paper provides in-depth analysis of this correlation through the case study of Dubrovnik. The study applies a TCC calculation model that is able to quantitatively include the main effects of overtourism. The paper illustrates how these results can be used to automate specific decongestion policies by conceptualising a digital response system for real-time intervention to mitigate the undesirable effects of overtourism

Creating a Tourism Destination through Local Heritage: The Stakeholders’ Priorities in the Canavese Area (Northwest Italy)

A specific region, characterized by a significant natural and cultural heritage, is not necessarily a tourist destination. However, it can become so if there is active participation of local stakeholders oriented towards local development. In this context, this study focuses on a specific area, the Canavese (northwest Italy), which needs to find new regional development alternatives to the industrial sector. In particular, the research focused on the level of integration of local stakeholders and on their ability to identify common guidelines for tourist enhancement of the region. From an operational point of view, a survey of public and private stakeholders was carried out through a mixed-method approach divided into three stages: a questionnaire developed by a group of experts and individual interviews carried out by the Delphi method, presentation of the results, and identification of local priorities by the nominal group technique. Findings show the opportunity to act on specific elements to enhance local tourism offerings: outdoor nature and landscape, culture, and food and wine. Moreover, the stakeholders underlined the need for coordination among the parties involved to strengthen the local system. This activity should be supported by a single third party capable of managing the various phases of local development