Irem Önder

Tracing Tourists by Their Digital Footprints The Case of Austria

Traditional tourism data collection includes surveys, interviews and focus groups. However, these methods are both expensive and time consuming. Moreover, there is a lag between the time of data collection and the receipt of that data for analysis. Today, almost all individuals leave digital footprints on the Internet, which can also be used for tourism research. One type of digital footprint is the photos uploaded on websites such as Flickr. The aim of this study is to determine whether the digital footprints in Flickr provide a useful indicator for tourism demand. Photos tagged with “Austria” between 2007 and 2011 were collected using Flickr API. Residents were distinguished from tourists using the data, and spatial analyses were conducted of the tourist-generated data. The results indicate that geotagged photos in Austria are more representative of actual tourist numbers at the city level than at the regional level.

Forecasting tourism demand with Google Trends for a major European city destination.

The purpose of this study is to investigate whether using Google Trends indices for web and image search improves tourism demand forecast accuracy relative to a purely autoregressive baseline model. To this end, Vienna—one of the top-10 European city destinations—is chosen as a case example for which the predictive power of Google Trends is evaluated at the total demand and at the source market levels. The effect of the search query language on predictability of arrivals is considered, and differences between seasonal and seasonally adjusted data are investigated. The results confirm that the forecast accuracy is improved when Google Trends data are included across source markets and forecast horizons for seasonal and seasonally adjusted data, leaning toward native language searches. This outperformance not only holds relative to purely autoregressive baseline specifications but also relative to time-series models such as Holt–Winters and naive benchmarks, in which the latter are significantly outperformed on a regular basis.

Linked Data for Cross-Domain Decision-Making in Tourism

In today’s global economy, tourism managers need to consider a range of factors when making important decisions. Besides traditional tourism indicators (such as arrivals or bednights) they also need to take into account indicators from other domains, for example, economy and sustainability. From a technology perspective, building decision support systems that would allow inspecting indicators from different domains in order to understand their (potential) correlations, is a challenging task. Indeed, tourism (and other indicators), while mostly available as open data, are stored using database centric technologies that require tedious manual efforts for combining the data sets. In this paper we describe a Linked Data based solution to building an integrated dataset as a basis for a decision support system capable of enabling cross-domain decision-making. Concretely, we have exposed tourism statistics from TourMIS, a core source of European tourism statistics, as linked data and used it subsequently to connect to other sources of indicators. A visual dashboard explores this integrated data to offer cross-domain decision support to tourism managers.

Identifying Tourist Dispersion in Austria by Digital Footprints

Tourism data are important for destinations, especially for planning, forecasting tourism demand, marketing, measuring economic impacts and benchmarking. There are different ways to collect tourism data. Traditional methods include guest surveys and data from accommodation providers, which are time consuming and expensive. Today, everyone leaves digital footprints on the internet, which can be used as data. One such footprint is photos uploaded on photo sharing websites. The purpose of this study is to find out how representative Flickr data is in comparison to actual tourist numbers in Austria. Using Flickr API data were collected related to Austria. The tourists and residents were categorized based on their activity time span on Flickr. Polynomial regression was conducted to estimate actual tourist bed nights based on Flickr tourist numbers. The results show that Flickr data can be used as an estimation of actual tourist numbers in Austria.

Towards a sustainable urban tourism development in Europe

The development of indicators and metrics systems has been identified as being of paramount importance by many tourism boards and international tourism organizations. This article discusses the bottom-up, micro-level approach of TourMIS, which is a platform for exchanging tourism statistics among tourism organizations, for collecting measures of sustainable urban tourism development. The authors provide a synthesis of various frameworks for sustainable tourism indicators for subnational regions and cities, concluding that it is more feasible to analyse existing sustainable tourism indicators than to introduce new measures lacking in direct practical applicability for the organizations. The application of data envelopment analysis (DEA) for benchmarking urban tourism destinations is then demonstrated by assessing measures available in TourMIS. Findings include inefficiency scores that suggest both managerial and political implications. Furthermore, the concept of a virtual reference destination assisting managers and politicians to analyse their destination’s strengths and weaknesses is introduced.

Reframing the Image of a Destination: A Pre-Post Study on Social Media Exposure

The majority of the studies on destination image have so far mainly focused on the cognitive and affective components, and there is still a lack of research on the conative component of destination image (i.e., the declaration of a behavioral intention). Moreover, less research has been done on verbally reported self-perception on the baseline image (prior belief about a destination), and the enhanced image (after an exposure to online contents). This study shows the effect of social media exposure on the perceived image about tourism destinations. In particular, declarations of the intention to visit the destination (image conative component) were found on the reported perceived image about a destination. In general intention to visit the destination was influenced by the stimuli and in the same direction, if it was positive the results show an interest in visiting the city and vice a versa.

Determinants of Airbnb demand in Vienna and their implications for the traditional accommodation industry

This study identifies key determinants of Airbnb demand and quantifies their marginal contributions in terms of demand elasticities. A comprehensive cross-sectional data set of all Viennese Airbnb listings that were active between July 2015 and June 2016 is examined. Estimation results, which are obtained by cluster-robust ordinary least squares, show that Airbnb demand in Vienna is price-inelastic. Significant positive drivers include listing size, number of photos, and responsiveness of the host. Significant negative drivers include listing price, distance from the city center, and response time of the host. Implications for the traditional accommodation industry are that, on the one hand, it should better communicate its sought-after advantages (e.g. lower average minimum duration of stay). On the other hand, it should increase its offer of bigger and better equipped hotel rooms since hosting more than two guests at a time is one of the major benefits of Airbnb.

Destination Benchmarking with Geotagged Photographs

Benchmarking tourism destinations is essential to improve and also observe what others are doing right. This process has different steps and choosing the right partners is a crucial one. Although there are many studies about how to benchmark destinations, there are no clear steps that explain how to choose destination partners. Tourists who visit the same destinations can be an indication of destination benchmarking partners. This is an explorative study to identify benchmarking partners of Austrian regions using Flickr photos. First, the regions the tourists had visited in Europe and in Austria were located. Then the destinations that share the most tourists were chosen as benchmark partners. The results show that Vienna and Salzburg can be benchmarked with cities such as Paris and Prague. The smaller regions of Tyrolian Unterland and Traunviertel can be benchmarked with neighbouring regions, which offer similar outdoor activities like skiing and hiking.

Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria

Using data for the period 2010M06–2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organizations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error–trend–seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.

Spatial price dependencies between the traditional accommodation sector and the sharing economy

The emergence of peer-to-peer (P2P) accommodation (e.g. Airbnb) has steadily increased the pressure on the traditional accommodation sector. Although Airbnb listings are perceived as being more affordable than hotels, this has not yet been conclusively demonstrated. Therefore, the aim of this study is to investigate whether significant price dependencies exist between the Airbnb and traditional accommodation sectors and to analyze the underlying pricing strategies. For this purpose, the Estonian capital city of Tallinn is used as a case example. Airbnb data, prices and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest (POIs), and so on, are used in hedonic price regression models. The results show that Airbnb pricing positively depends on characteristics of the listing and the number of POIs within an optimal 650 m radius, which is obtained from a simulation study. Also, prices of hotels and of other Airbnb listings within the same radius positively impact Airbnb listing prices. Finally, Airbnb accommodations are shown to indeed be the more affordable alternative.