Mid Sweden University

In the face of the present ecological crisis, a relational understanding of human-nature relationships is increasingly critical, especially in protected nature areas. This understanding encompasses not only the values assigned to nature but also the environmental values that individuals hold and their impact on sense-making. We apply the Two Major Environmental Value scale, which allows for the classification of individuals into four environmental value groups along a broader spectrum. For the first time, we examine the scales’ explanatory power in the context of nature-based tourism in a protected area. Specifically, we explore the dynamic between different environmental value groups and their sense-making of the restrictions limiting access to nature in a Swedish nature reserve. Findings reveal significant differences in how visitors with varying environmental values perceive these restrictions. We introduce a newly identified value-based visitor group, i.e. the dualcentric environmental value group. Its perception of restrictions is found to be located between those of the biocentric and the anthropocentric group. Implications for how management should work towards creating transformative nature-based tourist experiences based on human-nature relationships are discussed along with an agenda for future research.

As an alternative to contemporary linear-extractive models of the economy, the concept of the circular economy has gained increasing popularity. It has been suggested that small-scale tourism and creative businesses’ circular socio-relational activities facilitate regenerative processes for places, natural assets, and all living creatures by prompting a multiplicity of transformative and creative interactions among regional stakeholders. The tourism literature has adopted the concept of the circular economy only recently; however, it has remained mostly conceptual. Against this background, this chapter presents an advanced input–output methodology to assess how regional cultural tourism and major creative subsectors favour the formation of a regional circular economy. Through a brief literature review, the chapter first discusses the relationships between cultural tourism and the creative sectors that are responsible for fostering regenerative processes and are thus crucial in building a regional circular economy. Second, the input–output methodology employed for the Swedish county of Jämtland Härjedalen is introduced. The gained findings demonstrate the crucial role of the cultural tourism and creative sectors in transforming current linear-extractive economies into regional circular economies. The conclusion outlines limitations and sketches an agenda for future research.circular economy, cultural tourism, creative sectors, input–output methodology, regionalization. 

This contribution elaborates on the theoretical and practical implications of the circular economy in tourism and hospitality through an inter-disciplinary approach advancing novel possibilities for future research. Acknowledging the literature gap on circular economy in tourism and hospitality as an under-researched and under-theorised area of research, this contribution identifies a set of theoretical lenses that can help to elaborate the notion of circular economy and unpack it through an inter-disciplinary approach for future research. It does so by discussing the notion of circular economy through a micro-meso-macro framework combining practice theory, network theory, complexity theory, and the spatial and mobilities turn in social sciences. The originality of this work lies in its inter-disciplinary approach based on a micro-meso-macro theoretical framework offering novel opportunities to discuss, envision, and operationalize circular regenerative processes in tourism futures in terms of multidimensional, networked, complex, practice-based, and localised processes and operations. 

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. 

Place attachment and other place-related concepts are typically utilised to explain park visitors' pro-environmental behaviours in nature-based tourism. However, these concepts mostly pertain to unobservable psychological aspects, restricting their practical application for park management. To rectify this shortcoming, our study suggests the inclusion of two antecedent factors activity participation and place value. These factors could provide more quantifiable interventions by monitoring park visitors' activities and the values they perceive during their visits. Accordingly, we offer a unique application by incorporating these factors in existing attachment-behaviour frameworks and empirically validating this extended model in a cross-country research setting. Analyses use PLS-SEM to evaluate the model and to independently test 11 hypotheses across two data sets among domestic park visitors in natural and national parks in Italy and Vietnam. The partially successful validation of the proposed model in a multi-country setting supports its applicability in future studies. The study contributes theoretically by exploring a serial sequence, i.e. behaviour - attitude - behaviour, instead of typical singular aspects between attitude - behaviour or behaviour - attitude. Finally, it recommends tangible strategies for pro-environmental behaviours beyond place attachment, focusing on promoting participation in nature-based tourism activities and effectively communicating the place's values to park visitors.

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. 

Traditional measurements of tourism’s economic impact refer to primary and secondary effects that are typically quantified through input–output (IO) methodology. From a sustainable regional development perspective, however, economic impact analyses are criticised for their one-dimensional analysis focussing mainly on growth-oriented effects represented by aggregates for output, employment, income or tax. Although existing literature comprises various extensions of IO models, the focus of these models is restricted to indicators at a high aggregate level. Thus, distributional or other socio-economically important aspects related to the tourism workforce are seldom discussed. In our approach to study tourism’s impacts over a nine-year period, we consider macro-and meso-level perspectives and disaggregate tourism’s impact on regional employment and income for particular occupational areas in the Swedish region of Jämtland. Results indicate weakening employment effects; relatively low but increasing income-inequalities; and increasing shares of elementary positions with precarious working conditions despite para-industrial initiatives from tourism institutions to develop the industry. By enhancing traditional tourism economic impact methodology, we hope that our approach is supportive in putting the tourism workforce at the heart of the regional development and tourism sustainability discourse.

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.