Wolfram Höpken

With the rapid development of the internet, social media platforms enable tourists to extensively research, compare, and evaluate potential destinations. Concurrently, these platforms have provided new channels for destinations to present themselves and engage with potential visitors. Therefore, the closer destinations’ self-presentation is to the actual experience of the tourists, the higher the likelihood that tourists’ expectations are met. This study presents an approach for analyzing customer preferences and comparing them with the self-representation of tourism destinations or providers, based on images uploaded to Instagram. Compared to existing approaches, the proposed methodology enables a more fine-grained analysis of preference profiles for individual customer segments by applying clustering techniques to the multidimensional preference profile and executing a cluster-specific congruence analysis. The results demonstrate that the data reveal distinct segments within the tourist groups of the studied destinations and differences between the external perception and self-presentation for specific customer segments could be identified, constituting a valuable input to a refinement of marketing strategies for tourism destinations.

Despite the Cultural Tourism experience phenomenon being far from understood, this chapter proposes an innovative approach that uses Google Maps data for real-time monitoring, which shows the capacity to estimate and visualize the geographical distribution of Google Places most relevant to the Cultural Tourism domain. By doing so, this approach uncovers insights about the geographical distribution of Cultural Tourism's offerings, thereby shedding light on the diversity and peculiarities of dispersion patterns of Cultural Tourism attractions throughout Sweden. The objectives of this chapter are: (a) to examine the state of the art of tourism research using big data gained from Google Maps; (b) to extract and use Google Maps-based supply-side data in the Cultural Tourism domain for interactive (near-) real-time tourism monitoring; (c) to extract and analyze visitors’ online feedback data gained from Google Maps in the Cultural Tourism domain; and (d) to unravel future veins of research in the domain of Google-Maps-based tourism monitoring and analytics. This chapter illustrates how user-generated content extracted from Google Maps can be used to automatically evaluate the complex experience outcomes of travelers’ Cultural Tourism encounters by applying a lexicon-based sentiment analysis technique as well as a machine-learning-based topic modeling approach, respectively. The proposed approach shows that the most popular places along with the most frequent opinion words can be exhibited on geographical maps. The potential of Google Maps data for reliably monitoring the fragmented Cultural Tourism supply is demonstrated, as well as for assessing travelers’ complex, worldview-dependent, and socially constructed Cultural Tourism experience outcomes.

User-generated content (UGC) created and distributed through social media and tourism-related websites provides potential travelers the opportunity to gain first-hand experiences about destination products and services. UGC is also of great value to tourism service providers. Since UGC represents customers’ opinions and experience outcomes, potential problems, but also drivers behind customer delight can be identified. In this regard, also temporal changes regarding customer requirements can be determined. The aim of this paper is to identify how certain topic areas mentioned in UGC affect customer satisfaction, exemplarily analyzed for the Lake of Constance Region. Furthermore, potential temporal changes regarding customer satisfaction since the outbreak of the COVID-19 pandemic will be examined. A sentiment analysis, topic detection and regression analysis are carried out on two datasets containing UGC before and after the outbreak of the pandemic. Findings show that the pandemic has changed customers’ attitudes towards certain topic areas.

Tourism managers are increasingly turning to the online sphere to gain relevant customer insights. However, current approaches to analyzing vast and rapidly changing user-generated content (UGC) face several limitations. Supervised approaches require significant effort to provide pre-tagged training data and cannot dynamically identify topics mentioned in UGC. On the other hand, unsupervised approaches typically do not support different abstraction levels or enable a successive refinement of analysis in a drill-down manner, which is often expected as a practical requirement of tourism and destination management. Our research objective is, therefore, to extend current supervised approaches for identifying predefined topics by adopting unsupervised approaches using cluster analysis. The results emphasize that unsupervised approaches can (1) detect non-predefined topics dynamically with an accuracy similar to supervised approaches, thus demonstrating the potential to replace them and avoid the necessity of providing pre-tagged training data. (2) To build a topic hierarchy, unsupervised approaches sense more fine-grained topics as an enhancement of predefined topics on a lower level of abstraction, enabling more powerful drill-down-like analyses. Overall, the proposed extended approach to topic detection promises to support tourism management by meaningfully analyzing the increasing mass of visitors’ online feedback.

In the area of industrial Internet of Things (IIoT), digital twins (DTs) are a powerful means for process improvement. In this paper the concept of a DT is explained and analysis possibilities throughout the life-cycle of a product and its production system are explored. The main part of this paper is focused on an approach to the analysis of manufacturing layouts and their parameters. The approach, which is based on a state of the art bottleneck detection method, allows an intelligent representation of the temporal process characteristics. The presented method is widely applicable for any type of manufacturing layout and time-span. The use of elementary heuristics leads to traceable results that can be used for further analysis or optimization. The results of this analysis method can be integrated in a DT and combined with machine learning and explainable artificial intelligence (XAI). The concept for a self-learning DT is explained and implementation possibilities are elucidated.

In extraordinary situations, like the Covid-19 pandemic, irregular demand fluctuations can hardly be predicted by traditional forecasting approaches. Even the current extent of decline of demand is typically unknown since tourism statistics are only available with a time delay. This study presents an approach to benefit from user generated content (UGC) in form of online reviews from TripAdvisor as input to estimate current tourism demand in near real-time. The approach builds on an additive time series component model and linear regression to estimate tourist arrivals. Results indicate that the proposed approach outperforms a traditional seasonal naïve forecasting approach when applied to a period of extraordinary demand fluctuations caused by a crisis, like Covid-19. The approach further enables a real-time monitoring of tourism demand and the benchmarking of tourism business in times of extraordinary demand fluctuations.

"Interoperability" published on Jul 2022 by Edward Elgar Publishing.