No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Digitales Besuchermanagement im Tourismus – Konzeptioneller Rahmen und Gestaltungsmöglichkeiten
Abstract
Digitales Besuchermanagement beschreibt die Beeinflussung von Reise- und Ausflugsentscheidungen durch digitale Verfahren zur Vermeidung von Überlastungen in Destinationen. Es umfasst die Bereitstellung von Daten, die Datenhaltung, das Erarbeiten von Empfehlungen und das Ausspielen der Ergebnisse über digitale Touchpoints. Dieser Beitrag beschreibt die Ziele und Bestandteile eines digitalen Besuchermanagementsystems im Tourismus und bewertet Möglichkeiten und Grenzen. Abschließend werden offene Forschungsfelder definiert.
... The United Nations already demanded in 2016 the transformation towards more sustainable tourism as part of the 17 sustainable development goals to address these issues (DESA 2016). Consequently, POIs must actively embrace transformation towards more sustainable and smart tourism (Tauber and Bausch 2022), focusing on implementing active visitor management strategies (Schmücker et al. 2022). Effective active visitor management entails the spatial distribution of visitors among nearby POIs and strategically times arrivals to prevent overcrowding at any single location (Hall and McArthur 1996;Mason 2005). ...
... Instead, preventive measures with close monitoring and active visitor management are required specifically at open-spaced natural POIs. Schmücker et al. (2022) distinguish active visitor management from general visitor guidance by emphasizing that it should rely on soft measures. Combined with digital technologies, these soft measures such as recommendation systems provide often untapped potential to effectively prevent overcrowding before it even occurs (Spenceley et al. 2015;Veiga et al. 2018). ...
After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience.
This paper contributes to the academic debate on tourism destination development in the COVID-19 crisis, by investigating the role of smart technology tools for managing tourism flows and shaping visitors’ behaviours. Considering tourism in the COVID-19 crisis as an emerging stream of research, the paper assumes a continuity with the overtourism research (pre-COVID-19) and builds on the cross-fertilization between the advances in this field and the smart destination literature. Based on an explorative online news media analysis, the paper provides fresh knowledge on the role of smart technologies in destination management, by proposing the Smart Technology Matrix. This frames the smart-tech tools and provides the conceptual background for opening future paths of inquiry on smart tourism destinations in the Covid-19 context.
Es wird untersucht, welchen Einfluss die Digitalisierung auf die nachhaltige Tourismusentwick-lung in der ökologischen und sozialen Dimension haben kann. Dazu werden im ersten Schritt aktuelle Entwicklungen in der Digitalisierung systematisch identifiziert und auf ihren aktuellen und zukünftigen Beitrag zu einer nachhaltigen Tourismusentwicklung hin untersucht. Es wird sowohl die Digitalisierung auf Seiten der Nachfrager (Touristen) als auch der Anbieter berück-sichtigt – mit dem Schwerpunkt auf Big Data-Analysen. Im Fokus steht die Nutzung digitaler Anwendungen während der Reise. Die Reisevor- und -nachbereitung steht nicht im Fokus.
Im zweiten Schritt werden die aus der Analyse erwachsenden Chancen und Risiken identifiziert und bewertet. Eine besondere Berücksichtigung finden dabei die Verhaltensweisen unterschied-licher Nutzergruppen mit den Einflüssen auf Ressourcennutzung, Umwelt und Klima. Es sollen sowohl mögliche Umweltbelastungen durch die Digitalisierung als auch Chancen für Klima-, Ressourcen- und Umweltschutz sowie soziale Nachhaltigkeitsaspekte (z. B. Vermeidung von Overtourism) analysiert werden.
Artificial intelligence currently counts among the most prominent digital technologies and promises to generate significant business value in the future. Despite a growing body of knowledge, research could further benefit from incorporating technological features, human actors, and organizational goals into the examination of artificial intelligence-enabled systems. This integrative perspective is crucial for effective implementation. Our study intends to fill this gap by introducing affordance-experimentation-actualization theory to artificial intelligence research. In doing so, we conduct a case study on the implementation of predictive maintenance using affordance-experimentation-actualization theory as our theoretical lens. From our study, we find further evidence for the existence of the experimentation phase during which organizations make new technologies ready for effective use. We propose extending the experimentation phase with the activity of 'conceptual exploration' in order to make affordance-experimentation-actualization theory applicable to a broader range of technologies and the domain of AI-enabled systems in particular.
Zusammenfassung
Im Zuge der vielfältigen Diskussionen um eine touristische Übernutzung von Städten liegen kaum Untersuchungen über den Tagestourismus vor. Insbesondere über intradestinationale Aktionsräume und die damit in Verbindung stehende Wahrnehmung von anderen Menschen im Stadtraum gibt es – im Gegenteil zu Studien über die Tourismusakzeptanz aufseiten der Einwohner – wenig empirische Befunde aus touristischer Perspektive. Vor diesem Hintergrund ist es Ziel des Beitrages, Erkenntnisse über die Bewegungsmuster und die Wahrnehmung von anderen Stadtnutzern von Tagestouristen in Hamburg zu gewinnen. Dafür wurden die Aktionsräume mithilfe eines GPS-Trackers gemessen und durch einen Fragebogen die Wahrnehmung von Crowding ermittelt. Die Ergebnisse zeigen eine deutliche Konzentration des tagestouristischen Raum-Zeit-Konsums im Hamburger Central Business District. Andere Stadtnutzer werden zwar wahrgenommen, haben jedoch kaum Einfluss auf das aktionsräumliche Handeln vor Ort. Das positive Crowding überwiegt deutlich. Durch die Kombination von Aktionsräumen und der Wahrnehmung von Crowding können zwei Kernzonen in Hamburg identifiziert werden, die im Rahmen einer sozial verträglichen Tourismus- und Stadtentwicklung bevorzugt behandelt werden sollten.
https://www.degruyter.com/downloadpdf/j/tw.2019.11.issue-2/tw-2019-0015/tw-2019-0015.pdf
There is growing interest in developing smart tourism beyond individual smart destinations, but research and practice currently do not supply the necessary conceptualizations that could inform smart tourism development at a regional level. This paper argues that this is the case because of smart tourism’s roots in smart city ideas and literature. It discusses the main pillars of smart tourism and smart destinations and then illustrates how pervasive urban biases are in the smart development arena. The paper then highlights the many ways in which these are problematic for reaching regional smart tourism understandings and designing appropriate regional-level strategies. It calls for smart tourism regions to be defined beyond clusters of individual smart destinations and across all domains of smartness.
With the development and popularity of social networks, an increasing number of consumers prefer to order tourism products online, and like to share their experiences on social networks. Searching for tourism destinations online is a difficult task on account of its more restrictive factors. Recommender system can help these users to dispose information overload. However, such a system is affected by the issue of low recommendation accuracy and the cold-start problem. In this paper, we propose a tourism destination recommender system that employs opinion-mining technology to refine user sentiment, and make use of temporal dynamics to represent user preference and destination popularity drifting over time. These elements are then fused with the SVD+ + method by combining user sentiment and temporal influence. Compared with several well-known recommendation approaches, our method achieves improved recommendation accuracy and quality. A series of experimental evaluations, using a publicly available dataset, demonstrates that the proposed recommender system outperforms the existing recommender systems.
Point of interest (POI) recommender systems for location-based social networks, such as Foursquare or Yelp, have gained tremendous popularity in the past few years. Much work has been dedicated to improving recommendation services in such systems by integrating different features (e.g., time or geographic location) that are assumed to have an impact on people’s choices for POIs. Yet, little effort has been made to incorporate or even understand the impact of weather on user decisions regarding certain POIs. In this paper, we contribute to this area of research by presenting the novel results of a study that aims to recommend POIs based on weather data. To this end, we have expanded the state-of-the-art Rank-GeoFM POI recommender algorithm to include additional weather-related features such as temperature, cloud cover, humidity and precipitation intensity. We show that using weather data not only significantly improves the recommendation accuracy in comparison to the original method, but also outperforms its time-based variant. Furthermore, we investigate the magnitude of the impact of each feature on the recommendation quality. Our research clearly shows the need to study weather context in more detail in light of POI recommendation systems. This study is relevant for researchers working on recommender systems in general, but in particular for researchers and system engineers working on POI recommender systems in the tourism domain.
Schema.org is an initiative by Bing, Google, Yahoo! and Yandex that publishes a vocabulary for creating structured data markup on web pages. The use of schema.org is necessary to increase the visibility of a website, making the content understandable to different automated agents (e.g. search engines, chatbots or personal assistant systems). The domain specifications are the subsets of types from the schema.org vocabulary, each associated with a set of properties. The challenge is to choose the right classes and properties for an annotation in a given domain. In this paper we address the problem of finding a subset of types and properties for complete and correct annotation of different tourism domains. The approach provides a collection of domain specifications that were built based on domain analysis and vocabulary selection.
With the rapid diffusion of new technologies in tourism, professionals face new challenges to use efficiently the vast amount of data created by tourists. Nowadays, these information come from multiple and varied sources, as cellular or social networks, touristic location attendance or dematerialized satisfaction surveys, and in huge amount. They are an important resource for the tourism industry, but their heterogeneity makes it difficult to aggregate and analyse them. The key issue for tourism actors, professionals or politics, is to manage and operate tourism information about their territory effectively. The purpose of this paper is to describe synthetically how tourism information are managed under the Tourinflux project. We describe in this paper an architecture named DataTourism for tourism data management which solve multiple technical locks encountered when working with tourism data: heterogeneity, quality, interoperability, reusability and standardisation.
Smart tourism is a new buzzword applied to describe the increasing reliance of tourism destinations, their industries and their tourists on emerging forms of ICT that allow for massive amounts of data to be transformed into value propositions. However, it remains ill-defined as a concept , which hinders its theoretical development. The paper defines smart tourism, sheds light on current smart tourism trends, and then lays out its technological and business foundations. This is followed by a brief discussion on the prospects and drawbacks of smart tourism. The paper further draws attention to the great need for research to inform smart tourism development and management.
Outdoor recreation, particularly in winter, causes pressure on wildlife. While many species seem to adjust well to predictable on-trail recreation activities, unpredictable off-trail activities are considered harmful. Measures to minimise human disturbance require the identification of 'conflict-sites' where human activities are likely to interfere with the requirements of wildlife. We used winter recreation data combined with spatial modelling to predict where recreationists move from marked trails into wildlife habitats in winter and to determine the environmental factors that trigger this off-trail behaviour. We surveyed marked winter trails in the southern Black Forest, Germany, by foot or ski for tracks of people leaving the trail, with three types of recreationists distinguished: hikers, snowshoe users and cross-country skiers. Using a maximum entropy approach, the probability of leaving the trail was modelled as a function of topographic, forest structure and tourism infrastructure variables. By combining the results with previously mapped habitat information of two disturbance sensitive species, the capercaillie Tetrao urogallus and the red deer Cervus elaphus, we identified conflict sites where mitigation measures would be most effective. All models were effective in predicting the locations where people left the trails and the three types of recreationists showed a similar pattern: the presence of closed summer trails and signposts along these trails proved to be the factors most strongly affecting the probability of leaving marked trails, followed by slope, which was negatively correlated with the probability of going off-trail. People leaving directly into the forest, not using a summer trail, were most positively influenced by the successional stages 'regeneration' and 'old forest', whereas increasing canopy cover decreased the probability of leaving the trail. The models were extrapolated to all marked trails in the study area. Locations with a high probability of people leaving the trails were identified and intersected with the previously mapped key habitats of the two wildlife species, thereby showing the locations where leaving the trail would be linked with a high potential of human-wildlife conflict. By indicating what triggers people to leave the trails, and identifying the critical locations, our results contribute to the determination of adequate management measures.
Identifies a distinction between the physical condition, density, defined purely in terms of spatial parameters, and the experience of crowding, a motivational state aroused through the interaction of spatial, social, and personal factors, and directed toward the alleviation of perceived spatial restriction. Future studies pertaining to augmentation of space, subjective reports of restriction, observational indexes of tension, and physiological indicators of strain are suggested. (19 ref.) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The governance of natural resources used by many individuals in common is an issue of increasing concern to policy analysts. Both state control and privatization of resources have been advocated, but neither the state nor the market have been uniformly successful in solving common pool resource problems. After critiquing the foundations of policy analysis as applied to natural resources, Elinor Ostrom here provides a unique body of empirical data to explore conditions under which common pool resource problems have been satisfactorily or unsatisfactorily solved. Dr Ostrom uses institutional analysis to explore different ways - both successful and unsuccessful - of governing the commons. In contrast to the proposition of the 'tragedy of the commons' argument, common pool problems sometimes are solved by voluntary organizations rather than by a coercive state. Among the cases considered are communal tenure in meadows and forests, irrigation communities and other water rights, and fisheries.
https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/9496
Das Wissen, wie sich Touristen zum und im Reiseziel bewegen, ist für eine nachhaltige Destinationsentwicklung unerlässlich geworden und hat durch die Covid-19-Pandemie und die damit zusammenhängende Notwendigkeit der Kenntnis über (touristische) Besucherhotspots nochmal deutlich an Bedeutung gewonnen. Bei der Messung von touristischen Bewegungsmustern zeigt sich seit einigen Jahren eine Hinwendung zum Digitalen, was sich in einer verstärkten Nutzung digitaler Datenquellen in Forschung und Praxis äußert. Eine besondere Rolle nehmen bei dieser Entwicklung so genannte Big Data ein. Die im Rahmen von verschiedenen Prozessen anfallenden digitalen Datenspuren wie passive Mobilfunkdaten, Twitter-Posts, Kreditkartentransaktionen usw. sind mit geographischen Koordinaten versehen und werden verstärkt für eine Analyse des raumzeitlichen Verhaltens von Personen genutzt. Die Forschung zu diesen Datenquellen – insbesondere in der deutschsprachigen Tourismusgeographie – steht noch am Anfang. Auf der anderen Seite entwickeln sich gleichzeitig eine Vielzahl von Small Data Studies, bei denen im Rahmen interdisziplinärer Forschung Menschen mit Sensorik ausgestattet werden, um mehr über die Mensch-Umwelt-Beziehungen zu erfahren. Diese Ansätze, bspw. durch die Nutzung von Körpermesswerten wie Herzrate und Hautleitfähigkeit, werden für die tourismusgeographische Forschung adaptiert und für die Identifikation von Emotionen beim touristischen Erleben in der Destination genutzt. Auch hier zeigen sich noch vielfältige Forschungsbedarfe bezüglich des Methodeneinsatzes und der Analysemethodik.
Die vorliegende kumulative Dissertation setzt an diesem Punkt an, indem sie die digitale Neu-Vermessung touristischer Aktionsräume diskutiert. Der Fokus der Arbeit liegt auf der Darstellung, Erprobung und Erörterung digitaler Datenquellen zur Ermittlung des raumzeitlichen Verhaltens von Touristen. Dies erfolgt anhand von drei in nationalen und internationalen Fachzeitschriften veröffentlichen Forschungsbeiträgen (double-blind-Review). Dabei werden neue Datenquellen (passive Mobilfunkdaten) hinsichtlich ihrer Nutzbarkeit zur Messung touristischer Aktionsräume geprüft (Artikel 1), eine bereits etablierte Forschungsmethode (aktives GPS-Tracking in Kombination mit Befragung) inhaltlich und analytisch weiterentwickelt (Artikel 2) sowie ein neuer Ansatz präsentiert, der mit Hilfe von aktivem GPS-Tracking und Biosensing hilft, das emotionale Erleben von Touristen in der Destination zu verstehen (Artikel 3). In dem vorliegenden Rahmenwerk werden die drei Artikel in einen Kontext eingeordnet und vor diesem Hintergrund diskutiert. Klassische Konzepte der Aktionsraumforschung werden für die Tourismusgeographie übertragen und somit ein konzeptioneller Rahmen für die Spezifika touristischer Aktionsräume erarbeitet. Die im Rahmen eines vielschichtigen digital turns aufkommenden neuen Datenquellen zur Messung touristischer Aktionsräume werden beschrieben und anhand ihrer räumlichen Abdeckung klassifiziert. Der Klassifikationsvorschlag digitaler Datenquellen auf der Makro-, Meso- und Mikro-Ebene dient gleichsam als Schema zur Einordnung der drei veröffentlichen Forschungsarbeiten. Im Ergebnis leistet die kumulative Dissertation einen Beitrag in der Anwendung neuer sowie der Weiterentwicklung bestehender Methoden der digitalen Erfassung touristischer Aktionsräume und zeigt so Möglichkeiten, aber auch Grenzen einer digitalen Neu-Vermessung touristischer Aktionsräume auf.
The digital re-measurement of tourist spatio-temporal behaviour
Understanding how tourists move to and within a destination has become essential for sustainable destination management. In the light of the Covid-19 pandemic and the related awareness of (tourist) visitor hotspots, the relevance of understanding tourist movements has even grown. In measuring tourist spatio-temporal behaviour, a turn towards the digital has been evident for some years, which is expressed in an increased use of digital data sources in research and practice. Big data plays a particularly important role in this development. Digital data traces that occur in the course of various processes, such as passive mobile data, Twitter posts, credit card transactions, etc., are tagged with geographical information and are increasingly used to analyse the spatio-temporal behaviour of people. Re-search on these data sources–especially in the field of German-speaking tourism geography–is still in its infancies. On the other hand, a large number of small data studies are developing at the same time, in which people are equipped with sensors to learn more about human-environment interrelationships. These approaches, e.g. by using body measurements such as heart rate and skin conductivity, are adapted for tourism geography and used to identify emotions during the tourist experience in the destination. However, there are also still many research gaps regarding the use of methods and analytical procedures here.
This cumulative dissertation takes this as a starting-point to discuss the digital re-measurement of tourist spatio-temporal behaviour. The focus of the thesis is on the presentation, testing and discussion of digital data sources for determining the spatio-temporal behaviour of tourists. This is done based on three research papers published in national and international journals (double-blind review). New data sources (passive mobile data) are examined with regard to their usability for measuring tourist spatio-temporal behaviour (paper 1), an already established research method (active GPS tracking in combination with surveys) is further developed in terms of content and analytical perspective (paper 2), and a new approach is presented that helps to understand the emotional experience of tourists in the destination combining active GPS tracking and biosensing (paper 3). In this framework, the three papers are contextualised and discussed against this background. Classical concepts of action space research are transferred for tourism geography and thus a conceptual framework for the specifics of tourist action spaces is elaborated. New data sources for measuring tourists spatio-temporal behaviour, which are emerging in the context of a multi-layered digital turn, are described and classified with regard to their spatial coverage. The proposed classification of digital data sources on the macro, meso and micro levels serves as a scheme for classifying the three published research papers. As a result, this cumulative dissertation contributes to the application of new methods as well as the further development of existing methods of a digital measurement of tourist action spaces and thus shows possibilities but also limits of a digital re-measurement of tourist spatio-temporal behaviour.
This book considers visitor management (VM) as a component of destination management at all levels of a destination and involving a wide range of stakeholders. It aims to demonstrate current knowledge on VM and to provide insights into conceptual issues rather than providing merely descriptive case studies. This book is primarily aimed at postgraduate students and researchers. The introduction and foundation chapters in Part I provide the context for the book as well as the broader topic of VM. Part II considers critical concepts in VM (indicators and standards-based VM frameworks, VM and service quality and visitor affinity) while Part III illustrates current issues in VM (visitor monitoring, tourist behaviour and vandalism, and the use of technology). Part IV of the book addresses guiding and interpretation, and their roles and relevance in VM. The book has 15 chapters and a subject index.
This book considers visitor management (VM) as a component of destination management at all levels of a destination and involving a wide range of stakeholders. It aims to demonstrate current knowledge on VM and to provide insights into conceptual issues rather than providing merely descriptive case studies. This book is primarily aimed at postgraduate students and researchers. The introduction and foundation chapters in Part I provide the context for the book as well as the broader topic of VM. Part II considers critical concepts in VM (indicators and standards-based VM frameworks, VM and service quality and visitor affinity) while Part III illustrates current issues in VM (visitor monitoring, tourist behaviour and vandalism, and the use of technology). Part IV of the book addresses guiding and interpretation, and their roles and relevance in VM. The book has 15 chapters and a subject index.
A Recommendation or Suggestion System (RSS) helps on-demand digital content and social media platforms identify associations amongst large amounts of transaction data, which are then used to provide personalised viewing and shopping recommendations to consumers. This preface introduces how RSSs are used in the marketplace and various purposes it serves. This paper is a contribution to the ongoing research beyond content-based recommender system. It presents an examination of how the Collective Intelligence Social Tagging System makes a fundamental difference to content-based recommender systems and a suggested hybrid approach to RSS architecture which uses crowdsourcing and tagging to increase the accuracy of content-based RSSs. The lack of a huge data repository for online and e-commerce enterprises restricts the effectiveness of the content-based approach, so RSS research must aim to address these issues and propose a novel approach which incorporates the best of both methods. This preface also introduces three articles which present alternative approaches to effective recommendation approaches. On the social dimension, the use of invasive methods which capture user profile in order to influence behaviour, have opened a pandora’s box of legal and ethical considerations. The design of future RSS cannot ignore these constraints.
Unlike previous texts that have focussed on migratory patterns of tourists and new mobilities in tourism, Tracking Tourists: Movement and mobility is the first text to address tourist movement in from a methodological angle in the post-digital era. It assesses how movement and migration has been recorded in the past, how it may be recorded and assessed now and the possibilities for exploring movement in the future. Using international case studies that are both current and historical, it explores the range of options that exist for assessing tourists’ movement, along with the relative merits of each method. It will give a special focus to new technologies that facilitate our understanding of movement, such as the use of big data, hashtag scraping, Wi-Fi tracking, farming data from mobile phone towers and cutting-edge GPS tracking. It discusses the positive and negative consequences of the use of these new technologies and tackles issues such as ethical dilemmas and future trends and technology needs.
The rapidly developing concept of carsharing is an essential and scalable part of sustainable, multimodal mobility in urban environments. There is a clear need for carsharing operators to understand their users and how they use different transportation modes to intensify the development of carsharing and its positive impacts on the environment and urban cohabitation. We foster this understanding by analyzing usage data of carsharing in a medium-sized German city. We compare user groups based on individual characteristics and their carsharing usage behavior. We focus on a station-based two-way carsharing scheme and its relation to free-floating carsharing. Based on different clustering and segmentation approaches, we defined 20 particularly interesting user groups among the carsharing users and analyzed noticeable usage patterns. Additionally, we examined these partially overlapping user groups in the spatial dimension. With these results, we support research and operators in understanding carsharing customers and assessing users’ individual behavior.
Creating efficiency in AI research will decrease its carbon footprint and increase its inclusivity as deep learning study should not require the deepest pockets.
Passive mobile data (PMD) are event data recorded by mobile network operators (MNOs) in the course of a consumer's use of mobile phones connected to public voice and data networks. Increasingly, MNOs provide such data for research and applications in tourism, anonymised according to national regulations and aggregated based on the technical and economic interests of the MNO. Alongside mobility research, it is evident that tourism research has been one of the early adopters of this data source. Possible applications of PMD in tourism research include the identification of tourists, the detection of temporal and spatial distribution patterns, and the analysis of spatial and temporal relations. However, a number of drawbacks have been identified. These include the results of anonymisation and aggregation procedures, and, most of all, the inability to identify tourist activities properly, as opposed to everyday or other non-tourist types of mobility. This paper analyses and aggregates the results of different research projects on different spatial levels in Germany in order to build a conceptual framework for the specific strengths and weaknesses of the use of PMD in tourism research. The study found that, at the current state of research, PMD can measure the mobility of people in space and time but are not suitable for correctly identifying tourists and distinguishing them from non-tourists. Destination management organisations (DMOs) that are working with PMD should be aware of these barriers and adapt their research questions accordingly. However, PMD can be a powerful instrument, particularly because of its high temporal and spatial granularity.
Online-to-offline (O2O) commerce, e.g., the internet celebrity economy, provides a seamless service experience between online commerce and offline bricks-and-mortar commerce. This type of commerce model is closely related to location-based social networks (LBSNs), which incorporate mobility patterns and human social ties. Personalized point-of-interest (POI) recommendations are crucial for O2O commerce in LBSNs; such recommendations not only help users explore new venues but also enable many location-based services, e.g., the targeting of mobile advertisements to users. However, producing personalized POI recommendations for O2O commerce is highly challenging, since LBSNs involve heterogeneous types of data and the user-POI matrix is very sparse. LBSNs have substantially altered how people interact by sharing a wide range of user information, such as the products and services that users use and the places and events that users visit. To address these challenges in O2O commerce LBSNs, we analyze users’ check-in behaviors in detail and introduce the concept of a heterogeneous information network (HIN). Then, we propose a HIN-based POI recommendation system, which consists of two components: an improved singular value decomposition (SVD++) and factorization machines (FMs). The results of experiments on two real-world O2O commerce websites, namely, Gowalla and Foursquare, demonstrate that our method is more accurate than baseline methods. Additionally, a case study of the bricks-and-mortar brand of internet celebrity indicates that our proposed POI recommendation system can be used to conduct online promotion and purchasing to drive offline marketing and consumption.
Recently, recommender systems are getting popular in the e-commerce industry for retrieving and recommending most relevant information about items for users from large amounts of data. Different stochastic gradient descent (SGD) based adaptive strategies have been proposed to make recommendations more precise and efficient. In this paper, we propose a fractional variant of the standard SGD, named as fractional stochastic gradient descent (FSGD), for recommender systems. We compare its convergence and estimated accuracy with standard SGD against a number of features with different learning rates and fractional orders. The performance of our proposed method is evaluated using the root mean square error (RMSE) as a quantitative evaluation measure. We examine that the proposed strategy is more accurate in terms of RMSE than the standard SGD for all values of fractional orders and different numbers of features. The contribution of fractional calculus has not been explored yet to solve the recommender systems problem; therefore, we exploit FSGD for solving this problem. The results show that our proposed method performs significantly well in terms of estimated accuracy and convergence as compared to the standard SGD.
Crowding studies have a long history in urban geography and environmental psychology, although results are mixed at best. While crowding is found to influence residential quality and visitor experience and crowding perception models show some agreement on influential determinants, research is typically limited to a single setting, therefore minimising generalisability. To account for contextual effects, this paper tested a crowding perception model in two study areas: Amsterdam, the Netherlands, and Antwerp, Belgium. The results confirm that while most variables from earlier studies are significant, their effect on crowding perception depends largely on environmental context, with elements such as street layout, primary functionality of the area, and local culture affecting strength and direction of the relationship. The study reveals the importance of a case based approach to crowding perception, while demonstrating the overall applicability of the methodology and the possibilities of comparative analyses to suggest policy recommendations for specific areas.
Recommender systems have emerged in the e-commerce domain and are developed to actively recommend the right items to online users. Traditional Collaborative Filtering (CF) recommender systems recommend the items to users based on their single-rating feedback which are used to match similar users. In multi-criteria CF recommender systems, however, multi-criteria ratings are used instead of single-rating feedback which can significantly improve the accuracy of traditional CF algorithms. These systems have been successfully implemented in Tourism domain. In this paper, we propose a new recommendation method based on multi-criteria CF to enhance the predictive accuracy of recommender systems in tourism domain using clustering, dimensionality reduction and prediction methods. We use Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Support Vector Regression (SVR) as prediction techniques, Principal Component Analysis (PCA) as a dimensionality reduction technique and Self-Organizing Map (SOM) and Expectation Maximization (EM) as two well-known clustering techniques. To improve the recommendation accuracy of proposed multi-criteria CF, a cluster ensembles approach, Hypergraph Partitioning Algorithm (HGPA), is applied on SOM and EM clustering results. We evaluate the accuracy of recommendation method on TripAdvisior dataset. Our experiments confirm that cluster ensembles can provide better predictive accuracy for the proposed recommendation method in relation to the methods which solely rely on single clustering techniques.
This book presents cutting edge research on the development of analytics in travel and tourism. It introduces new conceptual frameworks and measurement tools, as well as applications and case studies for destination marketing and management. It is divided into five parts: Part one on travel demand analytics focuses on conceptualizing and implementing travel demand modeling using big data. It illustrates new ways to identify, generate and utilize large quantities of data in tourism demand forecasting and modeling. Part two focuses on analytics in travel and everyday life, presenting recent developments in wearable computers and physiological measurement devices, and the implications for our understanding of on-the-go travelers and tourism design. Part three embraces tourism geoanalytics, correlating social media and geo-based data with tourism statistics. Part four discusses web-based and social media analytics and presents the latest developments in utilizing user-generated content on the Internet to understand a number of managerial problems. The final part is a collection of case studies using web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging online reviews in the hotel industry, and evaluating destination communications and market intelligence with online hotel reviews. The chapters in this section collectively describe a range of different approaches to understanding market dynamics in tourism and hospitality.
It is important to understand tourist movements to gain a better idea of tourist behavior, the operationalization of motives, and how to better manage destinations. The study of movements, historically, has been inhibited by a number of pragmatic method challenges. As such, most research focused on modeling interdestination movement patterns. Recently, though, the development of Global Positioning System tracking devices and Geographic Information System software have overcome these limitations, providing the opportunity to examine movements at a much finer scale. As a result, the focus has shifted to intradestination movements or movements with in destination. This chapter reviews the historical development of movement studies, presents the core inter- and intradestination movement models and also discusses a range of intervening factors that can affect movements.
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Context-aware recommender systems have been developed to consider users’ preferences in various contextual situations. While designing such systems, one immediate concern, is to preserve the integrity of the recommender and minimize the attack probability of biased users who may indirectly influence the outcome of the system. Several algorithms have been developed to identify malicious users in contextual environments. In this paper, we propose a reputation-controlled fish school (RCFS) algorithm to identify trustable users and utilize them in recommendations. In addition, we propose a recommendation algorithm that replicates the behavior of social insects using a hybrid artificial bee colony (ABC) and simulated annealing (SA) technique. Finally, we demonstrate that the resulting feedback strategies can increase the effectiveness of the recommenders’ decisions.
Smart Cities are paving the way for the development of new services in the field of tourism. The “smart” concept is based on the intensive deployment of Information and Communication Technology infrastructures, as well as on the proliferation of mobile technology and its apps. However, a destination is not smart because it makes intensive use of technology. It is smart because it also uses technology in order to seek a deeper understanding about the characteristics and meaning of human mobility. It uses latent knowledge and capacities to empower local institutions and industries to create knowledge-based policies and advanced mobile services for visitors. This paper presents a new approach to the Smart Destination concept and a cloud-based infrastructure designed to reach that vision. This infrastructure promotes the creation of advanced mobile tourism applications by tourism stakeholders with tools adapted to people with no programming skills.
With increased visitation to protected natural areas over the last four decades, there is a need for implementation of effective visitor management strategies at these sites to mitigate visitor impacts. This study explores the application of mobile learning (mLearning) in environmental interpretation and visitor education within the context of conservation and sustainable tourism. Specifically, it proposes a conceptual framework for mLearning as a visitor management tool for sustainable tourism. Current developments and innovations in mobile broadband networks, smartphone technology, and mobile software applications present opportunities for the utilization of such mobile-driven applications in interpretive programs to encourage free-choice learning and mindful visitor experiences. If effectively implemented, such interpretive programs and mLearning applications can affect visitor perceptions, attitudes, and future intentions toward conservation and environmental protection.
The concept of Smart City embraces several definitions depending on the meanings of the word “smart”: intelligent city , knowledge city , ubiquitous city , sustainable city , digital city , etc. Many definitions of Smart City exist, but no one has been universally acknowledged yet. From literature analysis it emerges that Smart City and Digital City are the most used terminologies in literature to indicate the smartness of a city. This Chapter explores the literature about Smart City and Digital City from 1993 to the end of 2012 in order to investigate how these two concepts were born, how they have developed, which are the shared features and differences between them. To accomplish with these goals, three steps were followed: (1) to set up a search strategy for systematic literature review to collect a representative subset of papers about Smart City and Digital City using Google Scholar; (2) to store the selected subset in an ad-doc database to synthesize the literature review; (3) to organize the literature review subset to extract quantitative and qualitative data and information about Smart City and Digital City evolution. The author proposes a literature review taxonomy through five specific analysis: (1) time analysis, to explore the causes of the trend of Smart City and Digital City literature in the latest twenty years; (2) terminology analysis, to examine how and where these two ideas were born and what have been the main events influenced their development; (3) definitions analysis, to select and compare the most cited and validated definitions of Smart City and Digital City trying to identify similarities, differences or overlaps between these two concepts; (4) typology analysis, to investigate if Smart City and Digital City are included into a specific urban strategy pursued by government or if they face specific urban problems without a comprehensive framework; (5) geographic analysis, to understand where are the largest concentrations of Smart Cities and Digital Cities in the world and which are their main characteristics and best practices.
This paper proposes the use of micro-mobility patterns and service blueprints in visitor management planning. It argues that such planning approaches can improve management outcomes as well as visitor experiences whilst adding efficiency to the relevant management processes. The paper is based on the findings of visitor research on visitor flows and perceptions of visitor management in a nature-based tourism attraction in Wellington, New Zealand. These findings are used to adapt a service blueprint for the overall attraction to separately reflect visitor experiences of international visitors and New Zealanders. The paper posits that it is thus possible to identify and subsequently address the visitor management requirements of different visitor groups. Implications are discussed at three levels; first, for the case study attraction; second, for tourism attractions more broadly; third, conceptual implications for visitor management research are considered. Specific findings include the differences in micro-mobilities found across different market sectors, the need to improve signposting to offer distance and time guidance, the importance of topography, the potential to spread usage pressures across sites and the future potential to use mobile GPS units to obtain more detailed information.
The governance of natural resources used by many individuals in common is an issue of increasing concern to policy analysts. Both state control and privatization of resources have been advocated, but neither the state nor the market have been uniformly successful in solving common pool resource problems. After critiquing the foundations of policy analysis as applied to natural resources, Elinor Ostrom here provides a unique body of empirical data to explore conditions under which common pool resource problems have been satisfactorily or unsatisfactorily solved. Dr Ostrom uses institutional analysis to explore different ways - both successful and unsuccessful - of governing the commons. In contrast to the proposition of the 'tragedy of the commons' argument, common pool problems sometimes are solved by voluntary organizations rather than by a coercive state. Among the cases considered are communal tenure in meadows and forests, irrigation communities and other water rights, and fisheries.
Urban tourism is booming and overcrowding is recognized as a major problem in many tourist cities. However, the way tourists experience high tourist densities is still a neglected topic in urban tourism research, whereas it is one of the most frequently studied subjects in outdoor recreation. In this article the crowding concept is transferred to urban tourism. The study is based on qualitative in situ interviews using Florence, Italy as an example. The interviews reveal that negative crowding (i.e. a feeling of stress) is a major problem, but good crowding (i.e. a positive feeling where the crowd adds to the experience) is also important for the urban tourists’ experience, albeit difficult to achieve in the case of Florence. Furthermore, coping mechanisms to best experience a city of mass tourism have been identified, with spatial and temporal strategies playing a major role.
Community participation in the tourism planning process is advocated as a way of implementing sustainable tourism. There are, however, few studies that detail tangible and practical ways to promote or measure participation. This paper reviews the principal theories used to discuss community participation, including the ‘ladder of citizen participation’, power redistribution, collaboration processes and social capital creation. These theories form the basis for defining a community-based tourism (CBT) model. The paper shows how this model can be used to assess participation levels in a study site, and suggests further actions required. The model is applied in a case study in Palawan, the Philippines, where an indigenous community previously initiated a community-based ecotourism project. The project resulted in a number of problems, including conflicts with non-indigenous stakeholders. The model identifies the current situation of the project and provides suggestions for improvement.
This paper introduces the applicability of passive mobile positioning data in studying tourism. Passive mobile positioning data is automatically stored in the memory files of mobile operators for call activities or movements of handsets in the network. For tourism studies we use database of the locations of roaming (foreign phones) call activities in network cells: the location, time, random ID and country of origin of the called phone. We describe the peculiarities of data, data gathering, sampling, the handling of the spatial database and some analysis methods, using examples from Estonia. The results proved that mobile positioning data has valuable applications for geographical studies. Correlations with conventional accommodation statistics in Estonia were up to 0.99 in the most commonly visited tourist regions. Correlations of positioning data with accommodation statistics were lower in regions with a high number of transit tourists and less tourism infrastructure. The results show that positioning data has advantages: data can be collected for larger spatial units and in less visited areas; spatial and temporal preciseness is higher than for regular tourism statistics. Random IDs allow one to study tourists’ movements, for example to study typical routes of tourists of certain nationalities. The weaknesses of data are related to problems with accessing data, as operators do not wish to share data and because of privacy and surveillance concerns. Problem is also that positioning data is another quantitative dataset with limited features.
Ausflugsticker Bayern. Bayern Tourismus Marketing GmbH o
- Bayern Tourismus Marketing Gmbh
Bayern Tourismus Marketing GmbH. (o. J.). Ausflugsticker Bayern. Bayern Tourismus Marketing
GmbH o. J. Zugegriffen am 11.04.2022.
Bericht über eine europäische Datenstrategie
- Europäisches Parlament
Europäisches Parlament. (2020). Bericht über eine europäische Datenstrategie. https://www.europarl.europa.eu/doceo/document/A-9-2021-0027_DE.html. Zugegriffen am 22.09.2021.
BayernCloud im Tourismus
- Fortiss
Fortiss (Hrsg.). (2021). BayernCloud im Tourismus, Abschlussbericht des Forschungsprojekts.
https://www.fortiss.org/fileadmin/user_upload/05_Veroeffentlichungen/Studien_und_Road-maps/fortiss_Bericht_Bayerncloud_Tourismus_web.pdf. Zugegriffen am 24.09.2021.
Besuchermanagement aus internationaler Sicht -Ein Überblick über Forschungen und Anwendungen
- A Arnberger
Arnberger, A. (2013). Besuchermanagement aus internationaler Sicht -Ein Überblick über Forschungen und Anwendungen. In C. Clivaz, R. Rupf & D. Siegrist (Hrsg.), Beiträge zu Besuchermonitoring und Besuchermanagement in Pärken und naturnahen Erholungsgebieten (S. 15-26)
NatursportPlaner -integratives Wegemanagement und rechtliche Aspekte des Natursports
- E Magut
Magut, E. (2019). NatursportPlaner -integratives Wegemanagement und rechtliche Aspekte des
Natursports. In R. Forst, V. Scherfose & M. Porzelt (Hrsg.), Konflikte durch Erholungsnutzung in
Großschutzgebieten und deren Entschärfung durch innovatives Besuchermanagement (S. 65-76,
BfN-Skripten 520). Bundesamt für Naturschutz.
Smart Destination in den Großschutzgebieten NRWs. NIT Institut für Tourismus-und Bäderforschung in Nordeuropa GmbH
- D Schmücker
Schmücker, D. (2021). Smart Destination in den Großschutzgebieten NRWs. NIT Institut für Tourismus-und Bäderforschung in Nordeuropa GmbH. https://tourismusverband.nrw/_Resources/Pers
istent/0/5/9/3/0593f686d9bf4e06f56b7abe6ca7920cf9652e46/20210628-Gutachten-Smart-Destination-final.pdf. Zugegriffen am 11.04.2022.
Die zweifache digitale Transformation: Ausgewählte Datenquellen für ein digitales Besuchermanagement. Beitrag zur Jahrestagung der Deutschen Gesellschaft für Tourismuswissenschaft
- D Schmücker
- J Reif
Schmücker, D., & Reif, J. (2021b). Die zweifache digitale Transformation: Ausgewählte Datenquellen für ein digitales Besuchermanagement. Beitrag zur Jahrestagung der Deutschen Gesellschaft für Tourismuswissenschaft, 18.-20.11.2021. https://doi.org/10.5281/zenodo.5710231