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Previous studies on tourism input-output (IO) primarily focus on a single year’s snapshot or utilize outdated IO coefficients. The purpose of this paper is to analyze the multi-period development of regional tourism capacities and its influence on the magnitude of the industry’s regional economic contribution. The paper highlights the importance of applying up-to-date IO coefficients to avoid estimation bias typically found in previous studies on tourism’s economic contribution.

For the period 2008-2014, national IO tables are regionalized to estimate direct and indirect economic effects for output, employment, income and other value-added deffects. A comparison of Leontief inverse matrices is conducted to quantify estimation bias when using outdated models for analyzing tourism’s economic contribution.

On the one hand, economic linkages strengthened, especially for labour-intensive sectors. On the other hand, sectoral recessions in 2012 and 2014 led to an economy-wide decline of indirect effects, although tourists’ consumption was still increasing. Finally, estimation bias observed after applying an outdated IO model is quantified by approximately US$4.1m output, 986 jobs full-time equivalents, US$24.8m income and US$14.8m other value-added effects.

Prevailing assumptions on IO modelling and regionalization techniques aim for more precise survey-based approaches and computable general equilibrium models to incorporate net changes in economic output. Results should be cross-validated by means of qualitative interviews with industry representatives.

Additional costs for generating IO tables on an annual base clearly pay off when considering the improved accuracy of estimates on tourism’s economic contribution.

This study shows that tourism IO studies should apply up-to-date IO models when estimating the industry’s economic contribution. It provides evidence that applying outdated models involve the risk of estimation biases, because annual changes of multipliers substantially influence the magnitude of effects.

This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.

The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.

Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.

This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.

This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

Probably no aspect of librarianship presents such variations of practice in individual libraries as does the provision of subject catalogues. The author catalogue, which tells the user whether a given work of which he knows the author and title is in the library, must necessarily take a similar form everywhere, and such variations as do exist in the treatment of certain types of heading—that of academies is a case in point—are quickly assimilated by the reader as he moves from library to library. The same cannot be said of the catalogue which tells the user what works are to be found in the library on a given topic. In the Anglo‐Saxon countries subject catalogues may be arranged, if indeed they exist at all, according to a variety of systems, and even where one of the accepted classification schemes or lists of subject headings is used the local modifications are often legion. Many university and research libraries find that no existing scheme offers an arrangement of the whole field of knowledge which reflects the approach to which their readers are accustomed; and certainly no ready‐made scheme is entirely suitable for a university library in the United Kingdom, although many libraries do attempt to provide a useful arrangement both of the books on the shelves and of the entries in the subject catalogue by adapting Dewey, the Brussels decimal classification, or the Library of Congress classification. Bliss, when his full scheme has been published, will probably be found to provide the arrangement most suitable for use in academic libraries, but even his admirable classification fails to provide a scheme which can be identified at all points with the approach which is required in a library which serves first and foremost the teaching of a university.

The paper presents important measurement approaches in the field of costumer satisfaction with services and applies those empirically for service bundles at the level of the tourism destination. After working out the most prominent characteristics of existing satisfaction concepts according to the American and the Scandinavian school of thought, the latter will be critically evaluated for its potential practical use in measuring guest satisfaction. Based on this preparatory work, the Importance‐Performance Analysis, the Implicit Importance Analysis and the Penalty‐Reward‐Contrast Analysis are implemented and show that differing satisfaction models will lead to varying results and hence, ambiguous implications for destination management. However, due to its model parsimony and methodical stringency the Penalty‐Reward‐Contrast Analysis will be retained as the most valuable instrument for measuring tourist satisfaction. The paper concludes with implications for the management of destinations and a brief outlook for further research.

A missunderstanding by the publishing house has lead to a number of mistakes in table 4 of the article of Matthias Fuchs. For a better understanding of the corresponding article, the corrected table is re‐published in this edition. Please excuse that mistake. Thank you for your understanding.

The paper emphasises the increasing importance and the role of destination benchmarking for the tourism industry. The first part of the paper critically discusses an existing benchmarking concept for destinations, namely “The Tourism Barometer” developed in the new German Federal States. In analysing the main weaknesses of this barometer approach an alternative way is shown towards a potential methodological re‐development of benchmarking exercises which can now include aspects of value generation for tourists and at the same time sharpen the analytical measurement of factors of production underlying the process of value creation in tourism. Thus, the proposed destination benchmarking model attempts to simultaneously integrate the related tourism supply and demand forces in the form of both, the destination specific resource use as well as the perceived customer value measured in terms of destination specific customer satisfaction. The proposed benchmarking system works with a battery of indicators which are being further employed in a data envelopment analysis (DEA). Based on empirical destination data from Austrian winter resorts a multivari‐ate DEA‐Model is presented, thus allowing an expost calculation of efficiency pattern in the creation of customer value. The concluding part of the paper explains the usefulness of data envelopment analysis for the field of destination management.

This chapter offers an experience-based report about the development of the first Scandinavian PhD program in tourism studies at Mid-Sweden University. This process is documented through a framework which, rather than having the coherence of a single clearly bounded discipline, focuses on tourism as a study area encompassing multiple disciplines. Tourism knowledge is derived through a synthesis of fact-oriented positivist methodologies and critical theory. The theoretical framework employed to develop the graduate program in tourism studies is presented by critically discussing its multidisciplinary base and briefly outlining future veins of further development.

This chapter explains the background of the book and begins with an introduction of Jafar Jafari’s tremendous contribution to tourism knowledge creation and education. This is followed by a report on the content analysis of 573 tourism education related articles published in the past 10 years. Results indicated the need for philosophical discussion about the nature of tourism education and the popularity of teaching and learning approaches as a research topic. The two main sections of this book, namely philosophical issues in tourism education and experiential/active learning in tourism education, fit into these two identified issues. A synopsis of each chapter is provided next; and future directions for tourism education research are suggested.