“The Rise and Fall of Overtourism”

Sebastian Ferrari, MSc.

Modul University Vienna

PhD Proposal Manuscript

Doctoral Supervisors:

Prof. Dr. Josef A. Mazanec

Prof. Dr. Karl Wöber

Author Note:

The student´s PhD has been funded by the Vienna Tourist Board.

Table of Contents 1 Research Questions ...... 9

2 Overtourism ...... 13

2.1 Dictionary Entries ...... 13

2.2 Industry Reports ...... 14

2.3 Academic Sources ...... 14

2.4 TourMIS 2019 ...... 16

2.5 Final Remarks ...... 18

3 Literature ...... 20

3.1 Mass Media ...... 20

3.1.1 Agenda-Setting ...... 20

3.1.2 Framing ...... 25

3.1.3 Mass Media in Tourism Research ...... 28

3.2 Tourism and Leisure ...... 29

3.2.1 Historical Excursus ...... 30

3.2.2 Resident Attitudes / Tourism Impacts ...... 31

3.2.3 (Tourism) Carrying Capacity ...... 37

3.3 Overtourism Measurement...... 43

3.3.1 State of the Art ...... 43

4 Methodology ...... 49

5 Empirical Part ...... 51

5.1 Study I ...... 51

5.1.1 Rationale for this Study ...... 51

5.1.2 Methodology ...... 53

5.1.3 Data Collection ...... 61

5.1.4 Preliminary Results ...... 62

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5.1.5 Limitations ...... 67

5.2 Study II...... 68

5.2.1 Rationale for this Study ...... 68

5.2.2 Methodology ...... 71

5.2.3 Data Collection ...... 73

5.2.4 Preliminary Results ...... 76

5.2.5 Limitations ...... 80

5.3 Study III ...... 81

5.3.1 Rationale for this Study ...... 81

5.3.2 Methodology ...... 91

5.3.3 Data Collection ...... 94

5.3.4 Preliminary Results ...... 95

5.3.5 Limitations ...... 97

6 Conclusion ...... 98

7 References ...... 99

8 Appendices ...... 128

8.1 Appendix 1 ...... 128

8.2 Appendix 2 ...... 128

8.3 Appendix 3 ...... 129

8.4 Appendix 4 ...... 129

8.5 Appendix 5 ...... 130

8.6 Appendix 6 ...... 131

8.7 Appendix 7 ...... 132

8.8 Appendix 8 ...... 132

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List of Figures Figure 1: Dissertation Structure // Overtourism History...... 11 Figure 2: Knowledge throughout Education (Might, 2010) ...... 12 Figure 3: Word Cloud (n=24)...... 16 Figure 4: Respondents´ (Dis-)Agreement (n=22-23) ...... 17 Figure 5: Triangular Relationship Based on Lippmann (1922) ...... 21 Figure 6: Plato´s Cave ...... 22 Figure 7: Frame-Changing Model (Chyi & McCombs, 2004, p. 25) ...... 24 Figure 8: Framing - A Drawing from the Internet...... 25 Figure 9: Agenda-Setting and Framing Theory (McCombs & Ghanem, 2001, p. 71) ...... 27 Figure 10 : Publications about Resident Attitudes ...... 32 Figure 11: Four Phases (Deery et al., 2012, p. 65) ...... 32 Figure 12: Spectrum of Visitor-Resident Interaction (Sharpley, 2014, p. 39) ...... 34 Figure 13: Publications about Carrying Capacity ...... 37 Figure 14: (Non-)Linear Relations between Use and Impact (McCool & Lime, 2001, p. 375) .. 40 Figure 15: Crowding Scale (Neuts & Nijkamp, 2012, p. 2140) ...... 42 Figure 16: Publications about Overtourism Measurement ...... 44 Figure 17: Topic Modeling (Blei, 2012, p. 78) ...... 54 Figure 18: Structural Topic Modeling (Schmiedel et al., 2019, p. 945) ...... 56 Figure 19: Computational Grounded Theory Adapted from Nelson (2020, p. 14) ...... 60 Figure 20: News Coverage over Time 2017 - 2020 (n = 602) ...... 63 Figure 21: Most frequently mentioned locations (n = 602) ...... 65 Figure 22: PRISMA Flow Diagram (Moher et al., 2009) ...... 75 Figure 23: Co-Authorship Network ...... 76 Figure 24: Co-Occurrence Graph of Author Keywords ...... 77 Figure 25: Co-Citation Network ...... 79 Figure 26: Arrivals and Overnights by Month in 2019 ...... 84 Figure 27: Box Map - Airbnbs per km2 by District (14th Jan 2020) ...... 88 Figure 28: Box Map – Airbnbs per ha by Census District (14th January 2020) ...... 89 Figure 29: Number of Responses by District in 2019 (n=3.657) ...... 91 Figure 30: Analysis Workflow ...... 93

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Figure 31: Responses by Quarters and Years ...... 94 Figure 32: Average Liking of the City without Tourism ...... 95 Figure 33: Sample BTM ...... 96 Figure 34: Potential Relationships between Crowding and Satisfaction (Wagar, 1964, p. 7) .... 128 Figure 35: Postcard with Sample Text ...... 132 Figure 36: Postcard without Sample Text ...... 133

List of Tables Table 1: Dictionary Definitions ...... 13 Table 2: Seminal Works ...... 31 Table 3: Present vs Previous Studies ...... 52 Table 4: Selected Newspapers ...... 61 Table 5: Present vs Previous Studies ...... 70 Table 6: Indicators by Category ...... 81 Table 7: Indicators by District ...... 87 Table 8: Reviews about Residents Attitudes and Tourism Impacts, Aspects ...... 128 Table 9: Distribution of News Articles (n=602) ...... 129 Table 10: Junk Words ...... 129 Table 11: Indicators for the City of Vienna, 2009-2019 ...... 130 Table 12: Indicators on District Level ...... 131

List of Equations Equation 1: Nominalist Definition (Popper, 1945, p. 9) ...... 18

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List of Abbreviations

AIEST International Association of Scientific Experts in Tourism

APA American Psychological Association

CEO Chief Executive Officer

Covid-19 Coronavirus disease 2019

CTM Correlated Topic Model

DOI Digital Object Identifier

DTM Dynamic Topic Model

LAC Limits of Acceptable Change

LDA Latent Dirichlet Allocation

NAS Network Agenda-Setting Model

NUTS Nomenclature of Territorial Units for Statistics

SET Social Exchange Theory

STM Structural Topic Model

UNESCO United Nations Educational, Scientific and Cultural Organization

UNWTO United Nations World Tourism Organization

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“When there's an elephant in the room, introduce him.” (Randy Pausch)

ollowing the advice by Prof. Pausch, the author would like to address the elephant in the room right at the outset. As a matter of fact, two elephants need introduction F in this case. The first one relates to the content of this dissertation. Indeed, one may question the significance of a dissertation about “overtourism” in the middle of a pandemic. Last year, the Coronavirus disease 2019 (Covid-19) virtually annihilated the global tourism industry. According to the World Tourism Organization (UNWTO) (2021), international tourist arrivals decreased by 73.9% compared to the previous year. Yet, being at rock bottom can be either a blessing or a curse. As Martin Luther King once said, “Every crisis has both its dangers and its opportunities. Each can spell either salvation or doom.” On the one hand, there are several initiatives to rebuild tourism in a more sustainable manner. Indeed, next to top-down impulses from institutions in the public sector, such as the UNWTO, there have been bottom-up initiatives by companies in the private sector, such as 'Tourism Declares'. On the other hand, the behaviour of people tames one's hopes for a better tomorrow. For instance, Westcott and Culver (2020) reported huge crowds at various places in China. Similarly, Morris (2020) reported huge crowds at various beaches in the United Kingdom. Time will show which path tourism will take when rising from its ashes. Be that as it will, it is immaterial to the significance of this dissertation. To be sure, a return to business-as-usual would make the findings of this dissertation 'hotter'. However, a sustainable rebirth of tourism would by no means belittle knowledge of how a tourism-related phenomenon has been depicted in the news, of how useful the revival of old issues has been in academia, or of how locals experience their destination without tourism. In other words, the findings of the research conducted within the framework of this dissertation will be valuable either way. The second elephant in the room relates to the format of this dissertation. Indeed, the author´s decision to write a monograph, as opposed to publishing three articles, might be regarded as swimming against the stream. After all, the saying “publish or perish” is not without its reasons. Having said that, the author decided to write a monograph for two reasons. First, this format enables him to deal with the subject matter in depth. The detailed discussion of topic modeling will be an example thereof (pp. 54-61). Also, a monograph allows a few unrequired - yet insightful

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- digressions, such as the one about the “Allegory of the Cave” by Plato (pp. 22-23). Second, the flowery writing style of the author fits this format better, as it is not subject to some of the guidelines imposed by journals, such as word counts and the like. This, in turn, may come to the advantage of the reader too, as she or he may find the read more entertaining this way. This manuscript is structured as follows. The first section will introduce the three research questions addressed in this dissertation and highlight their significance. The second part will discuss the key concept of this research – that is, “overtourism”. The third section will review the scholarly literature that lays the foundation for the three empirical studies conducted within the framework of this project. The fifth part will introduce their methodology – albeit only briefly, as each study will then be discussed in depth in the sixth section. Here, the author will also present a few sample analyses to whet the reader´s appetite for the final dissertation. Finally, the author would like to point out that he cited all sources to the best of his knowledge according to the 7th edition of the manual of the American Psychological Association (APA) (2020). Specifically, the software “Citavi” (Swiss Academic Software GmbH, 2021) has been used for reference management.

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1 Research Questions

“The whole is greater than the sum of the parts” (Aristotle)

Overtourism has made the headlines in the last few years. Indeed, journalists have diligently reported about the occurrence of this phenomenon, particularly in Europe (e.g. Coldwell, 2017; Henley, 2020). In point of fact, news media is the birthplace of overtourism. Indeed, this word was accidently created by the Chief Executive Officer (CEO) of the news outlet 'Skift' (Ali, 2018), which later trademarked it (Skift, Inc., 2018). Considering the origins of overtourism, it comes as a surprise that only a handful of studies have dealt with its depiction in the news (Clark & Nyaupane, 2020; Pasquinelli & Trunfio, 2020; Phi, 2020). This, in turn, begs the question: “How has overtourism been portrayed in news media?” Using structural topic modeling (Roberts et al., 2014) as a point of departure rather than one of arrival in answering this question fits into the framework of Computational Grounded Theory proposed by Nelson (2020). In spite of being only a drop in the ocean, this approach makes a step forward in overcoming the long-standing "artificial dichotomy of inductive versus deductive reasoning" (Mazanec, 2009, p. 320). Moreover, given the role of news media in image formation (Gartner, 1994) and destination selection (Stepchenkova & Eales, 2011), knowledge of how a tourism-related phenomenon has been portrayed in newspapers might be of interest to Destination Management Organizations (DMOs). In fact, this retrospective analysis could provide them with knowledge that allows them to step in and mould the public discourse on other occasions in the future. Following its debut in the news, overtourism then transitioned to academia. Koens et al. (2018) ascribe the rapid uptake of this term by the scholarly community more to its currency than to its meaning. In fact, to date, there is no universally valid definition of “overtourism” (Avond et al., 2019; Capocchi et al., 2019; Capocchi et al., 2020; Nepal & Nepal, 2019). Interestingly, there has been a heated debate about this term in the academic community. By now, the general consensus seems to be that overtourism is indeed “old wine in new bottles” (Dredge, 2017). In this regard, Perkumienė and Pranskūnienė (2019) and Capocchi et al. (2020) differentiated between the word and the issue, and maintained that although the former has only appeared recently the latter actually dates back a long time. Koens et al. (2018) and Benner (2020) considered this word a hypernym for the unfavourable effects of tourism. Milano, Novelli, and Cheer (2019b) argued

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that this word denotes “a renewed interest in the adverse impacts of tourism” (Milano, Novelli, & Cheer, 2019b, p. 355). This, in turn, raises the question of what the added value of reigniting such long-standing discussion is. In other words: “How useful has research on overtourism been?” Establishing the usefulness of this research stream fulfils "the significant need for every mature field of knowledge to understand itself" (Tribe & Liburd, 2016, p. 44). Even though it would beyond doubt be premature to consider research on overtourism as such, an interim assessment of this interdisciplinary field of inquiry is worthwhile. In fact, the temporary timeout imposed by the outbreak of Covid-19 might just be the proverbial “calm before the storm” to be exploited for evaluating scholarship on overtourism. As this field of inquiry developed, scholars naturally shifted their attention from the conceptualisation to the measurement of overtourism. Regrettably, though, a review of this research shows that, for the most part, overtourism measurement still hinges on 'old' indicators. In fact, indicators like tourism density and tourism intensity, date back to research conducted by scholars of the International Association of Scientific Experts in Tourism (AIEST) in the mid-20th century (i.e. Markos, 1949; Sundt, 1950). This, in turn, suggests that – like in research on resident attitudes towards tourism – “more limited progress has been made than the volume of research might suggest (Sharpley, 2014, p. 39). Be that as it may, last year, the outbreak of Covid-19 razed the global tourism industry to the ground, thereby putting the measurement of tourism pressure on hold. Indeed, according to the UNWTO (2021), international tourist arrivals dropped by 73.9% in 2020. Thus, bearing in mind that “it is mostly loss that teaches us the worth of things” (Schopenhauer), one may wonder: How have residents experienced their destination without tourism? Insights into the experience of the complete absence of tourism on the part of residents is critical for DMOs, which are charged with the task of spearheading the rebuilt of tourism from scratch. To sum up, the author aims to answer the three following questions in this dissertation:

1. How has overtourism been portrayed in news media? 2. How useful has research on overtourism been? 3. How have residents experienced their destination without tourism?

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Interestingly, by addressing these three questions in that specific order, this dissertation follows the rise and fall of the phenomenon under scrutiny. Indeed, moving from the portrayal of overtourism in the newspapers (Study I) to an assessment of its usefulness in research (Study II) parallels the transition of “overtourism” from news to academia. In a similar vein, rounding off with the perception of the complete absence of tourism (Study III) parallels the recent fall of overtourism and, conversely, the emergence of its diametrical opposite – “undertourism”.

Academia • Coinage and • Occurrence of popularisation of opposite scenario: "overtourism" • Development "undertourism" of a pseudo-new research area

News Covid-19

Figure 1: Dissertation Structure // Overtourism History

Moreover, and perhaps more importantly, the findings of the three studies conducted to answer these three questions will achieve a balance between theoretical meaningfulness and practical relevance, overall1.

1 Since this dissertation is a monograph, its contribution shall be assessed as a whole.

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At this point, the author would like to make two last observations – one for the laymen and one for the experts. In fact, on one hand, the former may wonder what the significance of focusing on such a narrow topic is. Well, this is precisely what a PhD is about! Turning to the visual explanation of this degree by Might (2010) might help making this point (Figure 2). In short, assuming that the circle represents mankind's knowledge, a PhD essentially consists in advancing that frontier a tiny bit (Might, 2010).

→ This Dissertation!

Figure 2: Knowledge throughout Education (Might, 2010)

The experts, on the other hand, may criticise the one or the other aspect of this dissertation. Thus, it might be worth bearing in mind that the whole point of a PhD is essentially “to show that you’re able to do good enough research by yourself” (Rugg & Petre, 2004, p. 18). With this in mind, the reader is now welcome to proceed to the next section, in which the key concept of this dissertation – “overtourism” – will be thoroughly discussed.

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2 Overtourism

In this section, definitions of overtourism are critically reviewed. To this end, non- academic and academic sources were scoured for definitions. Mindful of the exhortation to “avoid becoming giants of definition while remaining midgets of explanation” (Mazanec, 2009, p. 320), the purpose of this section is to highlight the shortcomings of these definitions rather than to provide a comprehensive review thereof.

2.1 Dictionary Entries

Naturally, one would first look up the definition of overtourism in a dictionary. This, however, will be of little help. In fact, this term has only entered the MacMillan and the Cambridge dictionaries and is waiting to be accepted in the Collins dictionary (Table 1Error! Reference source not found.). While this dearth of definitions underscores the recent nature of overtourism, it comes as a surprise in light of the momentum this phenomenon has gained. Be that as it may, these dictionary definitions are volume-based in that they focus heavily on the number of tourists. However, as will be shown later, this is only one of the tip of the iceberg.

Dictionary Definition

Collins “the phenomenon of a popular destination or sight becoming overrun with tourists in an unsustainable way” (Dickinson, 2018)

MacMillan “a situation where the number of tourists visiting a place causes significant problems” (MacMillan Education Limited, 2019)

“the situation when too many people visit a place on holiday, Cambridge so that the place is spoiled and life is made difficult for the people who live there” (Cambridge University Press, n.d.)

Table 1: Dictionary Definitions

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2.2 Industry Reports

Dissatisfied with such reductionist definitions, one might then turn to industry reports for more nuanced explanations. Thus, for instance, the European Union defined overtourism as:

“the situation in which the impact of tourism, at certain times and in certain locations, exceeds physical, ecological, social, economic, psychological, and/or political capacity thresholds” (Peeters et al., 2018, p. 15)

On one hand, this definition acknowledges the specificity of overtourism – that is, the fact that it affects particular areas in specific moments. On the other hand, it refers to limits, which suggests the existence of numerical ceilings beyond which things go south. Alternatively, the World Tourism Organization defined overtourism as:

“the impact of tourism on a destination, or parts thereof, that excessively influences perceived quality of life of citizens and/or quality of visitors experiences in a negative way” (UNWTO et al., 2018, p. 6)

This definition is two-sided as well. In fact, while it recognizes the adverse effects of tourism on both residents and tourists, the use of the unquantifiable adverb 'excessively' implicitly hints at the existence of a tipping point.

2.3 Academic Sources

Eventually, one might look for a definition in academic publications. Some scholars have acknowledged that a universally accepted definition of overtourism does not exist to date (Avond et al., 2019; Capocchi et al., 2019; Capocchi et al., 2020; Nepal & Nepal, 2019). This comes as no surprise, considering that researchers do not even agree on an aspect as basic as its spelling. In fact, while the term is mostly spelt without hyphen (overtourism), it is occasionally also written with hyphen (over-tourism). For instance, in the publications examined in Study II, the former spelling

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variant has – on average2 – been used 92% of the time. The lack of agreement among scholars is also nicely illustrated by the use of separate interpretations in the recent book edited by Ribeiro de Almeida et al. (2020). For instance, in Chapter 5, overtourism is defined as "tourism impact that immensely and negatively affects residents’ life and/or tourists’ experience" (Lai et al., 2020, p. 93). However, in Chapter 12, it is defined as "the subjective belief of residents that there are too many visitors" (Rejón-Guardia et al., 2020, p. 236). An occasionally cited definition is the one by reporter Dave Richardson, who allegedly considered overtourism as “any destination suffering the strain of tourism” (Richardson, 2017; as cited in Huettermann et al., 2019; Koh & Fakfare, 2019; Séraphin et al., 2018). However, a careful reading of the original article raises doubts about the attribution of this interpretation. In any case, this alleged definition could neither be any more vague nor any less informative. Therefore, it will not be considered in this dissertation. Another occasionally quoted definition is the one published by Milano et al. (2018) in the semi-academic outlet “The Conversation”. They considered overtourism as:

"the excessive growth of visitors leading to crowding in areas where residents suffer the consequences of temporary and seasonal tourism peaks leading to permanent changes to their lifestyles with denied access to local amenities and a general loss of well-being" (Milano et al., 2018)

This definition acknowledges the dynamic component of overtourism – that is, the evolution of visitor numbers over time. At the same time, it ascribes the repercussions of overtourism to temporally limited circumstances. In reality, though, overtourism is also about the incessant presence of visitors. In proverbial words, constant dripping wears away the stone. Moreover, Nepal and Nepal (2019) defined overtourism as "a post-mass tourism phenomenon in which some destinations have transitioned from a state of ‘mass’ to a state of ‘over’" (Nepal & Nepal, 2019, p. 3), In spite of its appeal, this concise definition implies that overtourism is merely an extension of mass tourism. This, in turn, suggests the existence of a

2 Refers to the average of the spelling variant's relative usage in title, abstract and keywords.

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tipping point beyond which the phenomenon changes its name. In his editorial, Singh (2018) explicitly distinguished overtourism from mass tourism.

2.4 TourMIS 2019

Next to secondary sources, one could engage in primary data collection to define overtourism. Indeed, professionals might offer valuable input in this regard. Thus, the author conducted a survey at the 15th TourMIS Workshop, which was held at Modul University Vienna on September 12-13, 2019. The initial sample consisted of 47 questionnaires. Following the removal of ineligible and incomplete surveys (n=22), the final sample consisted of 25 questionnaires. Respondents were asked to formally define overtourism. Almost all of them answered this question (n=24). Four answers were corrected for minor mistakes (e.g. quaility → quality). The results are summarized by means of a word cloud (Figure 3). The mention of the terms ‘local’ (n=8) and ‘visitor’ (n=6) suggest that these are the two main parties involved. The occurrence of the stems ‘mani’ (n=6) and ‘overcrowd’ (n=5) lend support to the hypothesis that density and crowding play a major role in overtourism. Finally, the mention of the term ´quality´ (n=5) hints at the relationship between overcrowding and the nature of one´s experience.

Figure 3: Word Cloud (n=24)

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Thus, the answers suggest a linear mechanism of overtourism: the excessive concentration of people – both tourists and residents - in space (n=6) or time (n=6) negatively affects either party´s quality of experience. The behaviour of visitors, however, was hardly mentioned. In fact, only one respondent acknowledged the type of tourism as a potential problem. This comes as a surprise, as several cities in Europe suffer from misbehaving visitors (e.g. Amsterdam, Budapest, Prague), and indicates that respondents view overtourism from a quantitative rather than a qualitative perspective. Interestingly, respondents also mentioned destination management (n=4), which suggests that DMOs might become the scapegoats bearing the blame for the lamentable state of affairs. The respondents were further asked to express their dis(-)agreement with six statements about overtourism. The mean scores are summarized in the radar chart below (Figure 4). On one hand, they tend to disagree with the statement that overtourism is mainly a European phenomenon (Ø = 1.78). This shows that they are aware that destinations in other continents suffer from it, too (e.g. Boracay Island). On the other hand, they tend to agree with the statement that overtourism is not only about tourism (Ø = 4.05). This indicates that they understand that there is more to it than meets the eye.

2

.9 3 3

.3 .8

1

4 .8

Figure.0 4: Respondents´ (Dis-)Agreement (n=22-23) 3 Page 17 of 133 .8

2.5 Final Remarks

In sum, it seems that scholars have not yet agreed on one single definition and that professionals have a rather linear interpretation of overtourism. A few final consideration are in order. To begin, in the second volume of "The Open Society and its Enemies", Popper (1945) argues that the nominalist definition has to be viewed backwards. In other words, the 'defining formula' needs to precede the 'defined term' (Equation 1).

⏟푦표푢푛푔 푑표푔 = 푝푢푝푝푦⏟ 퐷푒푓푖푛푖푛푔 푓표푟푚푢푙푎 퐷푒푓푖푛푒푑 푡푒푟푚 Equation 1: Nominalist Definition (Popper, 1945, p. 9)

In the case of overtourism, scholars have devoted considerable attention to the 'defined term'. If one considers that, like “tourismphobia”, this term is only one of the "new arbitrary shorthand labels" (Popper, 1945, p. 13) in tourism research, the extent of the discussion surrounding its definition might seem exaggerated. Moreover, the suitability of the term itself could be called into question. According to Koens et al. (2018), this word originated from the news and made it into research more due to its currency than its meaning. It has grown to be an umbrella term for the adverse effects of tourism. However, it does not capture their heterogeneity. In fact, it wrongly implies that the effects of tourism are homogeneous. Hence, they recommend a more impartial expression, such as 'visitor pressure' (Koens et al., 2018, p. 9). Furthermore, the prefix over- is inappropriate for two reasons. First, it gives the whole word a negative connotation. The MacMillan Dictionary defines this prefix as “too much” (MacMillan Education Limited, n.d.). To be sure, one may argue that the sentiment of the whole word depends on the polarity of the term that follows the prefix, such as over-optimistic (positive) and over-pessimistic (negative). However, as Terence once said, “too much of anything is bad”. Thus, the connotation of words containing this prefix tends to be negative. Second, it creates the wrong impression that overtourism is solely about (too much) tourism. Koens et al. (2018) argue that occurrences in fields other than tourism are to some extent responsible for matters associated with it. For instance, the advent of online retail and the resulting fleets of delivery vans flooding the streets exacerbate the impression that the city is overcrowded (Koens et al., 2018). The

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emergence of “temporary migrants“ (Maitland & Newman, 2009a, p. 13), such as expats and students, complicates matters further. Indeed, blurring the lines between 'residents' and 'tourists', the rise of this group of people poses a challenge for DMOs. The Vienna Tourist Board, for instance, has responded to this trend by undertaking a paradigm shift from 'tourism' to 'visitor economy' in their most recent strategy (Kettner et al., 2021).

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3 Literature

As overtourism is a “multi-dimensional” (Koens et al., 2018) and “multi-faceted” (Capocchi et al., 2019) phenomenon, it shall come as no surprise that the literature review of this dissertation taps into different domains. The purpose of this section is to introduce the literature on which the three empirical studies will be based. In the first part (3.1), the author will introduce two theories from mass media research: agenda-setting and framing. In the second section (3.2), he will review the two fields of inquiry from which research on overtourism has allegedly emerged: tourism impacts and carrying capacity. In the third part (3.3), the author will survey the scarce body of literature on the measurement of this phenomenon.

3.1 Mass Media

3.1.1 Agenda-Setting

“It may not be successful much of the time in telling people what to think, but it is stunningly successful in telling its readers what to think about.” (Cohen, 1963, p. 13)

The origins of the agenda-setting theory can be traced back to the book "Public Opinion" by Lippmann (1922). In particular, in the introduction titled "The World Outside and the Pictures in our Heads", this author argued that people have indirect knowledge of their surroundings, which they consider to be veracious. He substantiated his claim with a few examples. For instance, the glorification and the condemnation of important figures are both based on the positive and negative perception the public has of them, respectively. Lippmann (1922) posited that people create mental representations of things ('fictions'), which they cannot sense or feel first-hand ('realities'). These conceptions, in turn, dictate people's actions in the real world (Figure 5). Thus, people operate in a setting, whose complexity requires them to rely on indirect knowledge. In sum, he asserted that any person “makes for himself a trustworthy picture inside his head of the world beyond his reach” (Lippmann, 1922, p. 29).

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Reality

Consequent Mental Action Representation

Figure 5: Triangular Relationship Based on Lippmann (1922)

Interestingly, Lippmann (1922) alluded to the allegory of the cave by Plato. In the seventh book of "The Republic", this philosopher wrote about prisoners in a cavern (Figure 6). They are bound with shackles and can only look forward (A). There is a barrier at their back (B) and farther behind it a fire is burning (C). Between the two, there is an elevated path (D). People move on it holding some items, whose shapes are then projected to the side of the cave in front of the prisoners (E) (Figure 6). Only being able to catch sight of the shades of the items, they mistake the former for the latter. In other words, for the prisoners "the truth would be literally nothing but the shadows of the images" (Plato, ca. 375 B.C./2012, p. 228)3.

3 The allegory does not finish here. In fact, Plato then writes about what happens when a prisoner is freed (Plato (ca. 375 B.C./2012). Since a detailed account of the whole allegory would exceed the scope of this review, the interested reader is directed to Book VII of "The Republic" for the whole story.

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C

B E

D

A

Figure 6: Plato´s Cave4

The parallel between the allegory of the cave and the agenda-setting theory is striking. The public (shackled prisoners) can only see the shadow of what the media (moving people) choose to publish (items held). At first sight, this analogy seems to capture the essence of the agenda-setting theory. On second thought, however, there is more to it than meets the eye. Thus, in what follows this framework will be concisely reviewed.

3.1.1.1 First Level Agenda-Setting (Objects)

Building on the work of Walter Lippman, McCombs and Shaw (1972) examined the role media played in the presidential elections of 1968. Specifically, they compared what citizen in Chapel Hill considered to be central matters with what the news they consumed actually reported. In short, they found a high degree of correspondence between the points highlighted by the media and the ones deemed critical by the people. Interestingly, this association holds irrespective of the

4 http://webspace.ship.edu/cgboer/PlatosCave.gif

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political orientation of citizen and of the political focus of news. This finding renders the alternative explanation of 'selective attention' improbable, and, conversely, lends weight to the agenda-setting role of news media. Far from intending to make any causal claims, these authors concluded that “the evidence that voters tend to share the media´s composite definition of what is important strongly suggests an agenda-setting function of mass media” (McCombs & Shaw, 1972, p. 184). Their study was the spark that ignited future research, as evidenced by the 13,800+ citations received to date, according to Google Scholar (29th March 2021), as well as by the recent creation of the 'The Agenda Setting Journal'.

3.1.1.2 Second Level Agenda-Setting (Attributes)

Two and a half decades later, McCombs et al. (1997) went a step further. They realised that agenda setting research need not be limited to items ('objects'). Rather, it could be extended to the features of items ('attributes'). Indeed, drawing on the notion of framing, they argued that the inclusion of some and the exclusion of other aspects determine people's mental representation of a given matter. Thus, this new avenue of inquiry shifts the focus of attention from the significance of items to that of their features. They referred to it as "the second level of agenda setting" (McCombs et al., 1997, p. 704). These authors put their amended framework to the test on two elections in Spain in 1995. In short, they investigated whether media's depiction of the contestants during the run moulded people's perception of them after the vote. Suffice it to say, they found some agreement between the two. Thus, far from meaning to make any causal claims, they built on Bernhard C. Cohen in concluding that "the news media not only tell us what to think about, they also tell us how to think about it" (McCombs et al., 1997, p. 716). Less than a decade later, Chyi and McCombs (2004) argued that news media perpetuates an item by stressing different aspects of it as time goes by ('frame-changing'). Drawing on framing literature, they noted a lack of generally applicable features ('frames'), which, in turn, restricts analysis possibilities. Hence, they set out to develop a framework that can be adopted irrespective of the item under scrutiny. In this undertaking, they focussed on the spatial (vertical axis) and temporal (horizontal axis) aspects of reporting (Figure 7).

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Figure 7: Frame-Changing Model (Chyi & McCombs, 2004, p. 25)

Chyi and McCombs (2004) put their framework through its paces. They investigated changes in reporting of the rampage at a high school by one news outlet (n=170) with regard to the two features of their framework – space and time. Concerning the former, they observed an expansion from a narrow (particulars) to a broad (criminality) scope. Regarding the latter, they noticed a switch from a backward-looking (circumstances) to a forward-oriented (precautions) perspective. Interestingly, these two frames are inter-related: while the narrow range goes hand in hand with the retrospective angle, the broad range goes arm in arm with the prospective angle (Chyi & McCombs, 2004).

3.1.1.3 Third Level Agenda-Setting (Networks)

Even though not used in this study, for the sake of completeness the existence of a third agenda-setting version ought to be mentioned. Vu et al. (2014) put forward the Network Agenda Setting (NAS) model, which posits that news media may impart the significance of inter- connections between items ('objects') and features ('attributes') together. In other words, they asserted that news media is “capable of telling us what and how to associate” (Vu et al., 2014, p. 669).

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3.1.2 Framing

The roots of framing theory can be traced back to the seminal essay by Goffman (1974) and the empirical work by Kahneman and Tversky (1984). However, for the purposes of this review, going back to the work of Entman (1993) may suffice. Lamenting the lack of a universal conceptualisation of 'framing', he defined this act as “to select some aspects of a perceived reality and make them more salient in a communicating text” (Entman, 1993, p. 52). In addition, he argued that frames have four roles. First, they establish an issue. Second, they find its origins. Third, they make appraisals. Fourth, they propose solutions (Entman, 1993). Since then, a plethora of publications have been published on this subject, as evidenced by the 18.700+ citations, according to Google Scholar (29th March 2021). A drawing frequently associated with news media on the Internet visualizes framing quite well (Figure 8). In reality, a victim is running away from a perpetrator. However, the news frames this situation in such a way that – in the eyes of the audience – the perpetrator is the victim, and vice versa. Another, more contemporary, example would be news coverage about COVID-19. Naturally, the reporting of absolute numbers (e.g. '500') captures more attention than that of relative percentages (e.g. '0,03%').

Figure 8: Framing - A Drawing from the Internet5

5 http://indiafacts.org/wp-content/uploads/2015/03/image0021.jpg

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Having reviewed the agenda-setting and framing theories separately, it is now time to connect these two conceptual frameworks. McCombs and Ghanem (2001) devoted an entire book chapter to this6. Thus, their work can be adduced to this end. They argue that second level agenda- setting and framing go hand in hand. In short, they claimed that:

“framing is the construction of an agenda with a restricted number of thematically

related attributes in order to create a coherent picture of a particular object”

(McCombs & Ghanem, 2001, p. 70)

McCombs and Ghanem (2001) considered frames as features of items and classified them according to their content as well as to their scope (Figure 9). Regarding the former, they differentiated between rational ('cognitive') and emotional ('affective') features. Concerning the latter, they distinguished between narrow ('micro') and broad ('macro') range. The authors argued that frames lie rather at the right end of the spectrum. They went a step further and drew a distinction between facets ('aspects') and main topics ('central themes'), and deemed the last interpretation to be more beneficial (McCombs & Ghanem, 2001).

6 The chapter is titled “The Convergence of Agenda Setting and Framing”.

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Figure 9: Agenda-Setting and Framing Theory (McCombs & Ghanem, 2001, p. 71)

Having said that, the lines between agenda-setting and framing remain quite blurred. In the author´s impression, there simply seem to be two separate school of thoughts. Be that as it may, 'framing' has undoubtedly become the more popular term. Weaver (2007) examined the evolution of these two concepts7 in the database 'Communication Abstracts' between 1971 and 2005. In short, he found that the usage of agenda-setting had modestly increased (n=+39), whereas that of framing had skyrocketed (n=+163). He suggested that the popularity of the latter term could be ascribed to its equivocality and inclusiveness (Weaver, 2007).

7 For the sake of completeness, it should be mentioned that Weaver's (2007) analysis included the concept of 'priming' as well. Not being of relevance to the purposes of this dissertation, the reader was spared this detail in the text.

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In sum, the author is well aware of the subtle difference between these two concepts. However, what is relevant to the purposes of this analysis is the common ground they share – that is, their preoccupation with the manner in which items are portrayed by the media (Takeshita, 2006; Weaver, 2007). Hence, even though the one or the other scholar will most certainly criticise this, for the purposes of this dissertation, framing will be interpreted rather narrowly, and will thus be used interchangeably with attribute agenda-setting in the remainder of this dissertation.

3.1.3 Mass Media in Tourism Research

Finally, it is worth spending a few words on the use of the agenda-setting and framing theories in tourism research. The number of studies adopting the former can be counted on the fingers of one hand. The article by Schweinsberg et al. (2017) is a notable example thereof. In contrast, the number of studies adopting the latter framework requires more than one hand to be counted. A few recent examples include the article by Hansen (2020) and that by Leung et al. (2019). Taken together, the combined use of second-level agenda setting and framing theory in tourism ventures into a little-known territory.

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3.2 Tourism and Leisure

An aspect that has provided for discussion is the question of whether the neologism "overtourism" actually represents something new or whether it simply rehashes old topics. The title of an article on LinkedIn, which was probably one of the sparks that ignited the debate, put it quite elegantly: "Overtourism: old wine in new bottles?" (Dredge, 2017). To begin, the origins of this term remain uncertain. Rafat Ali, the CEO of the news outlet Skift, proclaimed himself as the inventor of this term (Ali, 2018). However, an online search suggests otherwise. In fact, looking for "overtourism" in Nexis® Uni, reveals that this word was actually first used by Celia Herron on August 6, 1981 (Herron, 1981). To the best of the author´s knowledge, no scholar has remarked this to date. Some personalities deny the existence of this phenomenon. One, for instance, has equated "overtourism" with "overcrowding" (see e.g. Karantzavelou, 2020). In so doing he has mistaken a part for the whole. In fact, the quantitative aspect is only the tip of the iceberg. In a way, this reminds of the differentiation between density and crowding made by Stokols (1972). In his seminal work, he wrote:

"Density is viewed as a necessary antecedent, rather than a sufficient condition, for the experience of crowding" (Stokols, 1972, p. 275)

Most authors simply limit themselves to mentioning their stand on this debate. For instance, Koens et al. (2018) argued that the matters subsumed under this word go back a long way and then briefly review the literature on the effects of tourism. However, to the best of the author´s knowledge, only Capocchi et al. (2020) and Wall (2020) went a step further and actually devoted an entire publication to making their point. In their research letter, Capocchi et al. (2020) drew a distinction between overtourism as a word and as an occurrence. They examined the references of academic and non-academic documents about overtourism (n=29) and found that the authors of these documents cite research dating all the way back to the 1970s, such as Doxey (1975, as cited in Capocchi et al., 2020) (n=3) and Pizam (1978, as cited in Capocchi et al., 2020) (n=2). Accordingly, they concluded that while as a word overtourism is unheard of, as an occurrence it is well-known.

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In his perspective article, Wall (2020) traced the roots of overtourism back to carrying capacity. Initially, research on this subject was conducted by leisure scholars in rural areas in North America. Later, their work was picked up by tourism scholars, who extended it to urban destinations in Europe under the masquerade of overtourism. Thus, like Capocchi et al. (2020), Wall (2020) also differentiated between overtourism as a word and as an occurrence, albeit not as explicitly. However, unlike Capocchi et al. (2020), he did not substantiate his claims with empirical evidence – these are solely based on his expertise. In sum, these two studies suggest that one must tell apart the term and the phenomenon. The former is new, whereas the latter is old. In addition, they demonstrate that little attention has been paid to determining the roots of overtourism empirically.

3.2.1 Historical Excursus

Koens et al. (2018) argued that overtourism goes back a long way and urged other scholars “not to let this work go to waste” (Koens et al., 2018, p. 10). Not even two years later, Wall (2020) lamented that tourism scholars have taken up the concepts of carrying capacity “without full appreciation of their origin and history” (Wall, 2020, p. 213). As the author does not want to make himself guilty of the same mistake, in what follows the history of overtourism will be briefly reviewed. To this end, seminal works were identified by sifting through the bibliographies of selected publications. When inspecting the 'older' references of the chosen documents (i.e. 1900-2000), one comes across renowned classics, such as The Economics of Welfare (Pigou, 1930; as cited in Nepal & Nepal, 2019) and methodological milestones, such as A Paradigm for Developing Better Measures of Marketing Constructs (Churchill, 1979; as cited in Martín Martín et al., 2018). Most importantly, though, one stumbles upon influential works in tourism. Table 2 shows five frequently cited 'old' publications.

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Publication Topic Times Cited

Butler (1980) Tourism Area Life Cycle 37 Doxey (1975) Irridex Model 34 O'Reilly (1986) Tourism Carrying Capacity 14 Pizam (1978) Social Impacts of Tourism 9 Wagar (1964) Carrying Capacity 5

Table 2: Seminal Works

As can easily be seen from Table 2, overtourism seems to spring from research on resident attitudes / tourism impacts and (tourism) carrying capacity. The fact that both 'capacity' and 'resident' occurred over 200 times in the references lends further weight to this claim. Thus, these two streams of research will be concisely reviewed in the next subsections.

3.2.2 Resident Attitudes / Tourism Impacts

The presence of two distinct subjects in the heading of this section might irritate the one or the other reader and even raise the suspicion that the author will be mixing apples and oranges in what follows. Hence, it shall be pointed out that the decision to treat these topics together was made based on the assertation that the former subject has mainly been studied in tandem with the latter (Rasoolimanesh & Seyfi, 2020). Reviewing the scholarly literature on resident attitudes would require a strenuous effort. In fact, this is one of the most extensively investigated fields in tourism (McGehee & Andereck, 2004). Accordingly, conducting a comprehensive review of this subject "would be a difficult, if not impossible task" (Sharpley, 2014, p. 42). Hence, a slightly unconventional approach was adopted – that is, existing reviews and commentaries on this subject were analysed (Figure 10). To identify such publications, a backward reference search was performed starting from the recent viewpoint article by Rasoolimanesh and Seyfi (2020). The reader could now criticise that the quality of this review depends directly on the quality of the reviews selected. Thus, the recognition that existing reviews offer an outstanding insight into theories and methods adopted in this field (Hadinejad et al., 2019) might allay, or even clear, such doubt.

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Figure 10 : Publications about Resident Attitudes

Since it has just been mentioned that this topic has extensively been investigated, the reader might be surprised that the timeline above only includes reviews from the last decade. To be sure, earlier reviews were also identified, such as the ones by Yen and Kerstetter (2009), Harrill (2004), and Easterling (2004). However, Deery et al. (2012) argued that – save for the latter – these tend to have a specific emphasis. That said, the review by Easterling (2004) was excluded too, as it is relatively dated. There seems to be no agreement on exactly how long this topic has been investigated for. Almeida García et al. (2015) argued that it has been studied for over three decades, while Sharpley (2014) claimed that it goes back over three and a half decades. In any case, this subject has been investigated for a long time. Deery et al. (2012) divided this stream of research into four phases (Figure 11). In the first one, fundamental notions were explained. The second phase saw the emergence of conceptual frameworks, such as the Tourism Area Life Cycle by Butler (1980; as cited in Deery et al., 2012). The third one witnessed the appearance of measurement tools, such as the Tourism Impact Scale by Ap and Crompton (1998, as cited in Deery et al., 2012). In the fourth phase, these were put through their paces and fine-tuned.

Figure 11: Four Phases (Deery et al., 2012, p. 65)

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In retracing the history of this stream of research, Deery et al. (2012) cited almost only journal articles. It shall be mentioned, however, that the publication of articles was accompanied all along by that of books, such as The Golden Hordes (Turner & Ash, 1975), Tourism, the Good, the Bad, and the Ugly (Rosenow & Pulsipher, 1979), The Holidaymakers (Krippendorf, 1986), and Coping with Tourists (Boissevain, 1996). Overall, this stream of research paints a relatively coherent picture. Almeida García et al. (2015) claimed that while scholarly inquiry has generated consistent results about locals' appraisal of the economic effects of tourism, it has yielded mixed findings about their evaluation of the social and environmental ones. Generally, the former is positive (e.g. employment), whereas the latter are negative (e.g. congestion) (Almeida García et al., 2015). Thus, it seems that the adverse social and environmental effects offset, or even outweigh, the beneficial economic ones. Tourism scholarship seems to have followed that order. Indeed, Sharpley (2014) argued that the realization of the social and environmental disadvantages of tourism moderated the initial excitement for its economic advantages. A brief inspection of the reviews mentioned in Figure 10 suffices to identify three common limitations of research on resident attitudes and tourism impacts. In what follows these will be discussed. However, for the sake of readability, the author will not list all reviews for each aspect in an in-text citation parenthesis. Instead, a summary table of which publication mentions what point is provided in Table 8 in Appendix 1. First, some authors have noted the limited use of theory (Appendix 1Error! Reference source not found.). In their longitudinal review (n=140), Nunkoo et al. (2013) found that less than half of the publications (n=64; ≈46%) were 'theoretical'. At the same time, they observed that over time scholars have more and more embraced theory. Accordingly, they anticipated that this upward-rising tendency would persist in the future. Their bright prediction has recently been confirmed. In their systematic review (n=90), Hadinejad et al. (2019) found that more than half of the publications (n=50; ≈56%) were 'theoretical'. Moreover, there seems to be unanimous agreement on the fact that Social Exchange Theory (SET) is the most frequently used theoretical framework. In fact, without exception, in each review mentioned in Figure 10 the authors argued this to be case. The widespread adoption of this theory might be traced back to its advocacy by John Ap (e.g. Ap, 1992). Be that as it may, both the longitudinal and the systematic review mentioned above substantiate the currency of this theory.

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Nunkoo et al. (2013) found that a bit more than half (n=36; ≈56%) and Hadinejad et al. (2019) found that a little less than half (n=29; ≈45%) of the 'theoretical' publications adopted this framework, respectively. In spite of its widespread adoption, SET is not without its criticisms. Sharpley (2014) argued that it assumes a deliberate interaction between visitors and residents. This, however, need not always be the case. Inspired by the work of Krippendorf (1987; as cited in Sharpley, 2014), he put forward a spectrum of possible contacts between visitors and residents (Figure 12). At the one end, there is conscious trade. At the other end, there is inadvertent coexistence. The latter scenario reminds of the poetic description of overtourism by Singh (2018), who argued that it occurs “when local people cannot walk on the street without rubbing shoulders with crowd of tourists” (Singh, 2018, p. 415).

Figure 12: Spectrum of Visitor-Resident Interaction (Sharpley, 2014, p. 39)

In other words, Sharpley (2014) argued that SET is only applicable to the left half of the spectrum (Figure 12). In addition, he claimed that this model has been read in a superficial way - that is, disapproval of tourism by locals has been said to result from an unfavourable cost-benefit analysis on their part. Yet, in reality such straightforward logic is rarely adopted. Thus, he concluded that SET is ill-suited for this field of study (Sharpley, 2014). To be sure, one could find many more criticisms of this framework in the literature. However, this would turn what is meant to be a concise excursus into a tedious digression.

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Overall, SET seems to have been exhausted. Rasoolimanesh and Seyfi (2020) noted that in view of its shortcomings and its long-standing prevalence, scholars have attempted to integrate it with other frameworks and to amend it. The systematic review by Hadinejad et al. (2019) corroborates the adoption, albeit marginal, of alternative models. Indeed, they found that academics have employed, inter alia, social representation (n=4; ≈6%) and institutional theory (n=3; ≈5%). A variant of the latter has also been used in one of the publications identified in Study II (i.e. Taş Gürsoy, 2019). Be that as it may, Hadinejad et al. (2019) encouraged researchers to thoroughly deal with frameworks from other areas of inquiry, especially that of psychology. Second, some authors have observed the prevalence of quantitative methods (Appendix 1Error! Reference source not found.). Once again, the longitudinal and systematic reviews corroborate these claims. Nunkoo et al. (2013) found that over two thirds of the publications reviewed (n=101; ≈72%) employed a quantitative method. Hadinejad et al. (2019) went a step further and draw a distinction between data collection and data analysis. They found that a quantitative method was adopted in over four fifths of the publications (n=75, ≈83%) for both data collection and analysis8. Needless to say, the prevalence of quantitative methods is not without its consequences. In fact, some authors have ascribed the lamentable state of research to such preponderance. Most notably, Deery et al. (2012) claimed that scholarly inquiry has managed to discover numerous effects of tourism but has failed to unravel the rationale behind their (un-)favourable appraisal on the part of locals. They borrowed the onion model of culture from Rousseau (1990; as cited in Deery et al., 2012) – whose description would exceed the scope of this discussion – to show that the catalogue of effects generated by quantitative inquiry merely constitutes the outer skin. Accordingly, they advocated employing qualitative methods to scratch beneath the surface and transition from the description to the explanation of effects (Deery et al., 2012). Building on their work, Sharpley (2014) got to the heart of the matter, stating that academic inquiry “tends to describe what residents perceive, but not necessarily explain why” (Sharpley, 2014, p. 42). Third, some authors have noted the loose pertinence of research to reality (Appendix 1). Sharpley (2014) argued that the scholarly work does not mirror the rising importance of tourism

8 Figure to be verified with the article's lead author.

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in emerging states. In fact, he found that a great number of investigations has been conducted in the countryside in North America. Accordingly, he lamented that places whose economies hinge on tourism have considerably been neglected. Hadinejad et al. (2019) observed a chasm between academia and industry as well. At country level, they found that the lion's share of investigations has been performed in the United States (21%) and in China (10%). Inversely, at continent level, they found that most investigations have been carried out in Asia (30%) and in North America (26%). Having said that, they argued that official statistics indicate states in Europe, such as France, to be more appropriate study locations (Hadinejad et al., 2019). To be sure, the adoption of a one- dimensional figure, such as tourist arrivals, could be questioned. In fact, a two-dimensional indicator, such as tourism intensity, might be more telling. Nevertheless, the idea of using 'objective' figures to evaluate the need for studying the 'subjective' perception of locals sounds reasonable. Thus, overall, this review of reviews paints a tragic picture of research on resident attitudes and tourism impacts. In fact, it suggests that it has merely scratched the surface. Some authors put these disappointing findings quite elegantly. For instance, Deery et al. (2012) asserted that this field of study is stuck "in a state of arrested development" (Deery et al., 2012, p. 65). In addition, Sharpley (2014) maintained that “more limited progress has been made than the volume of research might suggest” (Sharpley, 2014, p. 39). However, on the bright side, this brief historical excursus offers two main take-home lessons. First, it demonstrates the value of systematic reviews. In fact, such investigations allow scholars to substantiate their claims with 'hard' evidence. This, in turn, lends weight to the work performed in this dissertation (Study II). Second, it raises several questions. In fact, this historical discussion makes one wonder whether research on overtourism has made itself guilty of the same 'mistakes' as that on resident attitudes and tourism impacts, or whether it has learnt from them. Specifically:

o Have scholars embedded their research in conceptual frameworks? o Have qualitative / mixed methods been used more than quantitative ones? o Has research been conducted in destinations in urgent need of solutions?

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3.2.3 (Tourism) Carrying Capacity

In a similar vein to the one about resident attitudes and tourism impacts, a review of carrying capacity in leisure and tourism would be a gargantuan undertaking. Hence, here too, a backward reference search was performed starting from the recent perspective articles by Wall (2020) and Butler (2019) to identify seminal works in the field (Figure 13). Thus, in what follows these works will be reviewed in chronological order, with a focus on the added content of each publication to avoid repetition.

Figure 13: Publications about Carrying Capacity

The roots of research on carrying capacity can be traced back to the monograph by Wagar (1964). He noted that even though often referred to in leisure research, carrying capacity lacks a recognized definition. For the aims of his publication, he interpreted it as “the level of recreational use an area can withstand while providing a sustained quality of recreation” (Wagar, 1964, p. 3). Yet, quality can be preserved at various degrees of use, as a balance is found at each point. Hence, carrying capacity has to be tied to administration goals, which serve to determine an acceptable grade of quality. In the case of leisure, the greater aim is to ensure visitor satisfaction, which stems from the achievement of wants. Thus, Wagar (1964) identified a set of motives for leisure and explores the influence of density9 on quality – not empirically, but conceptually – by sketching potential relations for the established purposes (Figure 34 in Appendix 2). For instance, if one longed for isolation, the presence of others would be detrimental (Figure 34-I). In contrast, if one yearned for company, the presence of others would be beneficial (Figure 34-K). In sum, he found

9 Wagar (1964) actually used the term 'crowding'. However, since he actually referred to 'density', the latter term is used. For thorough explanations of the difference between density and crowding, please read Stokols (1972) and Rapoport (1975).

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that the influence of density on quality hinges on the motives for leisure. Accordingly, carrying capacity is essentially about normative evaluations. A decade later, Wagar (1974) criticised the word 'capacity'. In fact, it implies that grounds for restricting use lie in the features of an area rather than in its influence on recreation quality. Also, this word conceals the fundamental difference between positive (is) and normative (should) matters. In this regard, he noted that determining degrees of use involves deciding what resulting alterations are admissible – and this is an evaluation of the latter kind, which has to be made by a person. Accordingly, Wagar (1974) recommended replacing 'carrying capacity' with expressions that help keep the attention on administration goals, such as 'use limits'. With regard to the latter, he advocated the adoption of a holistic perspective – that is, to assess their impacts on an entire territory rather than on solely part of it. Moreover, he argued that use restrictions are merely one of the measures people may put up with it in exchange for a satisfactory experience. Other trade- offs include admission charges and behavioural rules. Thus, administrative matters come with intricate challenges, which render “any search for an impersonal carrying capacity formula totally unrealistic” (Wagar, 1974, p. 278). Afterwards, Stankey and McCool (1984) performed a critical assessment of carrying capacity in leisure research drawing on the review article by Graefe et al. (1984, as cited in Stankey & McCool, 1984). They argued that this notion was not originally meant to explain the relation between contentment and number of people met, and identify six reasons for its feeble nature. First, visitors deliberately engage in leisure pursuits that yield enjoyment ('self-selection'). Second, when an area undergoes alteration, it gets visited by a different type of crowd ('displacement'). Third, visitors pursue leisure activities for several reasons. Thus, when confronted with circumstances unfavourable to the achievement of one objective, they give weight to another purpose attainable in that situation ('reducing dissonance'). Fourth, contentment is measured on a single-item scale, which requires respondents to summarise good and bad features into one figure. Such operationalization is at odds with the multi-faceted nature of events ('psychometric additivity'). Fifth, running into other people need not necessarily be unwelcome. In fact, visitors engage in leisure activities for reasons other than isolation, too. Thus, the influence of chance meetings on enjoyment hinges on the significance of one's purpose in a given situation ('saliency'). Sixth and last, contentment is influenced by visitors' liking for ('preferences') and anticipations of ('expectations') certain degrees of use. Moreover, and more importantly, Stankey and McCool

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(1984) claimed that the relation between number of people met and contentment is actually immaterial from an administrative point of view. Thus, they advocated moving from extent of use to aspired circumstances and put forward the 'Limits of Acceptable Change' (LAC) model10. In tourism research, the definition of carrying capacity by the UNWTO has grown to be the established one – in spite of falling under 'grey literature'. This institution defined carrying capacity as:

"the maximum number of people that may visit a tourist destination at the same time, without causing destruction of the physical, economic and sociocultural environment and an unacceptable decrease in the quality of visitors’ satisfaction" (UNWTO, 1981, p. 5)

The two underlined sets of words show that this interpretation of carrying capacity makes itself vulnerable to the same criticisms as that in leisure research. In fact, it implies that an excessive amount of people (extent of use) will lead to inadmissible reduction of enjoyment (normative evaluation). Be that as it may, the expression 'tourism carrying capacity' is also often associated with O'Reilly (1986). In his short article, this scholar acknowledged sub-dimensions of carrying capacity that had previously received little attention, such as the socio-cultural one. He argued that each has its own ceiling, and notes these may be at variance with one another. Finally, he stated that "capacity cannot be used as an absolute limit but as a means to identify critical thresholds which need attention" (O'Reilly, 1986, p. 258). Shortly before the turn of the millennium, Lindberg et al. (1997) discussed three shortcomings of carrying capacity. First, it offers limited direction for concrete application, as it hinges on normative standards that are either vague or impractical. Second, it gives the impression of being a value-less notion when yardsticks are by nature value-laden. Third, it places emphasis on degrees of use although administration goals are about aspired circumstances. Having said that, they identified prerequisites for carrying capacity to be valuable, such as accord on normative matters and authority to impose use restrictions. They found that these are seldom fulfilled,

10 For a detailed description of the LAC model, please read Stankey et al. (1985).

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wherefore they asserted that the “effective application of the traditional carrying capacity is difficult, if not impossible” (Lindberg et al., 1997, p. 463). Finally, they went a step further, and suggested that the expression itself ought to be dismissed, for it causes confusion. Shortly after the turn of the millennium, McCool and Lime (2001) published a critical review of carrying capacity. They noted that the relation between degree of use and extent of effect need not be linear (Figure 14). For instance, curve A shows a scenario in which little use suffices to swiftly bring about large effects. This would imply that adverse consequences can only be avoided by prohibiting use entirely, and that substantial cutbacks in use would be required to mitigate them. Alternatively, curve C shows a scenario in which large effects only occur after considerable use. This would suggest that sites have an inherent capacity, whose exceedance results in the drastic worsening of circumstances (McCool & Lime, 2001). The reasoning of scholars currently investigating the carrying capacity of urban destinations (e.g. Bertocchi et al., 2020; Tokarchuk et al., 2020) seems to be in keeping with the rationale underlying this last scenario.

Figure 14: (Non-)Linear Relations between Use and Impact (McCool & Lime, 2001, p. 375)

Moreover, McCool and Lime (2001) commented on seven aspects of carrying capacity. First, any degree of use has some sort of effect. Second, carrying capacity is frequently interpreted as an upper limit above which things go south. However, as adverse impacts are inevitable (1st

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point), such arithmetic interpretation is impractical and ill-suited. Also, the buildout of tourism is about compromises. For instance, people may put up with environmental drawbacks (e.g. pollution) in exchange for economic benefits (e.g. employment). Third, the relation between degree of use and extent of effect is ambiguous, as a multitude of factors influences it (Figure 14). Fourth, the determination of a carrying capacity unavoidably leads to the imposition of restrictions – and this comes with considerable allocative implications. Fifth, carrying capacity confounds positive (is) with normative (should) matters – a criticism already voiced by Wagar (1974). Sixth, it implies that systems are steady when in reality the opposite is the case. Seventh, and last, carrying capacity is about setting the right focus, namely establishing reasonable circumstances, rather than excessive degrees of use, for a given place. Thus, they asserted that “the concept of a tourism and recreation carrying capacity maintains an illusion of control when it is a seductive fiction, a social trap, or a policy myth” (McCool & Lime, 2001, p. 386). Accordingly, they recommended abandoning it and adopting fundamentally different models, such as LAC, instead. Needless to say, this stream of research is not without its criticisms. In the author´s opinion, two of them are worth mentioning. To begin, leisure research in North America has been carried out by a relatively small group of researchers, which includes Jerri J. Vaske, Lori B. Shelby, Thomas A. Heberlein, and Alan R. Graefe. Having a second name is not the only characteristic these authors share. A brief look at the bibliographies of their publications suffices to suggest that they rarely cited sources outside their own field. Yet, crowding has been studied in disciplines other than leisure, too. For instance, in retail one can find conceptual (e.g. B. B. Anderson, 1976; Harrell & Hutt, 1976; Eroglu & Harrell, 1986) as well as empirical (e.g. Harrell et al., 1980; Eroglu & Machleit, 1990) articles on this subject. In addition, these leisure researchers have mostly employed the single-item scale by Heberlein and Vaske (1977, as cited in Shelby et al., 1989). This instrument is inherently flawed, as it allows for various shades of ‘crowded’ but precludes any nuances of ‘uncrowded’ (Figure 15). In spite of that, they abide by the old exhortation “to use this scale in other studies” (Shelby et al., 1989, p. 288).

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Figure 15: Crowding Scale (Neuts & Nijkamp, 2012, p. 2140)

The contribution of this historical excursus is twofold. First, it shows that the determination of a numerical carrying capacity in cities would raise several questions. For instance, who would decide on the threshold (e.g. scientists vs politicians)? Moreover, and more importantly, what would people do if they found it (e.g. restrict access, impose taxes)? Thus, the reduction of tourism carrying capacity to a single number seems to remain a mirage. This, in turn, begs the question of whether – in spite of the lessons this stream of research has taught – scholars investigating overtourism have fallen under the spell of the 'magic number'. Or, more formally:

o Do scholars further pursue the quest for a numerical carrying capacity?

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3.3 Overtourism Measurement

Like the phenomenon of overtourism, the measurement of tourism pressure is “old wine in new bottles” (Dredge, 2017). In fact, it may already have turned into vinegar. Around the middle of the past century, members of the International Association of Scientific Experts in Tourism (AIEST) were debating on the measurement of tourism. Markos (1949) noted the limited meaning of absolute figures in national tourism statistics. Hence, he suggested the adoption of a common denominator, namely each country's respective area. He named it “le degré d'intensité du tourisme“ (Markos, 1949, p. 132). Sundt (1950) argued that this measure is not well suited for countries with either a very small (e.g. Monaco) or a very large (e.g. Australia) area. Accordingly, he proposed using the resident population as common denominator. He referred to this measure as “l'intensité touristique” (Sundt, 1950, p. 4). Around seventy years later, scholars are still discussing the denominators of these two indicators, albeit under the masquerade of 'overtourism' (e.g. Amore et al., 2020; Weber et al., 2019). This seems to suggest that, with respect to the measurement of tourism pressure, the scholarly community may have remained 'none the wiser' (Ap, 1990, p. 615) after all.

3.3.1 State of the Art

The author finds that little attention has been paid to the measurement of 'overtourism' - an aspect noted by De La Calle-Vaquero et al. (2020), too. This comes as no surprise. In fact, it is natural for scholars to address the measurement of a phenomenon after having dealt with its conceptualisation. Deery et al. (2012), for instance, found this to be the case with research on the social effects of tourism. Since overtourism is a relatively recent phenomenon at the time of writing (2020-2021), only a handful of publications entirely dedicated to its measurement could be identified. In what follows, these will be reviewed in chronological order (Figure 16). Interestingly, this mirrors the evolution of overtourism. Indeed, this buzz word has transitioned from media to academia (Koens et al., 2018). In a similar way, research using this term has been published first in industry reports (2017-2019), then in book chapters (2019-2020), and finally in journal articles (2020-2021).

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Figure 16: Publications about Overtourism Measurement

Two words of caution are in order before delving into the literature review. First, the author would like to emphasize that only studies about 'overtourism' have been examined. Thus, publications in which scholars attempted to establish a destination's carrying capacity, such as the research note by Tokarchuk et al. (2020) or the journal article by Bertocchi et al. (2020), will only be acknowledged. Second, the author would like to point out that the latest research on this subject published in the 'Special Issue on Measuring and Monitoring Overtourism' in the Journal of Travel & Tourism Marketing (Wattanacharoensil & Weber, 2020) has not been reviewed, yet. It will, however, be integrated into the final dissertation.

3.3.1.1 Industry Reports

The first three seminal publications about overtourism measurement are non-academic (Figure 16). To begin, Guevara Manzo et al. (2017) came up with nine indicators. Of these, two are about the significance of tourism and seven about the implications of overcrowding. The authors computed these indicators for several cities (n=68), which they grouped into five percentiles for comparison11 (Guevara Manzo et al., 2017). Some of the measures calculated in this dissertation (Study III) will be compared against these percentiles, as done, for instance, by Alcalde Garcia et al. (2018). Next, Peeters et al. (2018) conducted a large-scale study at regional level (NUTS 2)12. They identified eight measures as indicative of overtourism hazard. Of these, five are about relative shares and three about proximity to certain infrastructure. Their study built in several respects on the one by Guevara Manzo et al. (2017). For instance, the authors adopted

11 1st → 5th percentile: risk = high → low (Guevara Manzo et al., 2017) 12 NUTS: Nomenclature of Territorial Units for Statistics

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two measures from that study, namely 'tourism share of GDP' and 'air transport seasonality' (Peeters et al., 2018, pp. 74–75). In addition, since the data is skewed and kurtic, the authors classified the regions into quintiles, too. To be exact, though, they reversed their polarity13 (Peeters et al., 2018, p. 44). That said, it cannot be emphasized enough, that in both reports the categories indicate varying degrees of potential hazard of overcrowding (Guevara Manzo et al., 2017) and overtourism (Peeters et al., 2018), respectively – not varying extents of actual occurrence of either phenomenon. In both publications, the authors pointed this out explicitly. These two studies are not without criticism. Peeters et al. (2018) argued that the approach by Guevara Manzo et al. (2017) has four shortcomings. First, the authors only considered urban destinations. Second, examining solely places where overtourism is felt hinders the determination of limits beyond which this phenomenon gets perceptible. Third, data for cities tends to lack completeness and comparability. Fourth and last, the authors mixed measures about antecedents with those about consequences of overtourism. In addition, Peeters et al. (2018) also acknowledged the limitations of their own study. Most notably, they could not identify critical thresholds for their measures, as these fluctuate a great deal across regions. Instead, they put forward a tentative catalogue of questions (n=10), whose answers indicate the extent to which a place is exposed to overtourism (Peeters et al., 2018, p. 79). Finally, Weber et al. (2019) brought it all together by calculating traditional statistics, measures from both reports, and alternative figures for a few destinations (n=9). Most importantly, though, they conveyed the importance of the denominator in two-dimensional indicators, such as tourism density and intensity (Weber et al., 2019). In this regard, Guevara Manzo et al. (2017) used the surface containing the twenty most popular sights as denominator for tourism density. Amore et al. (2020) were a little more conservative and used the surface of just the ten most popular sights to compute such measure. As for tourism intensity, the latter used the number of residents in the 'city' instead of in the 'larger urban agglomeration' as denominator (Amore et al., 2020, p. 122). This distinction brings to mind that made by Eurostat between 'city' 'functional urban area', and 'greater city' (Kotzeva et al., 2019, p. 14). It also evokes the differentiation made by TourMIS between 'city area only' and 'greater city area' (TourMIS, n.d.–a).

13 1st → 5th percentile: risk = low → high (Peeters et al., 2018)

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3.3.1.2 Academic Publications

In academia, four publications about overtourism measurement could be identified. Of these, two are book chapters (De La Calle-Vaquero et al., 2020; Visentin & Bertocchi, 2019) and two are journal articles (Amore et al., 2020; Perles-Ribes et al., 2020).

3.3.1.2.1 Book Chapters

Visentin and Bertocchi (2019) thoroughly examined the state of tourism in Venice. In particular, they analysed the infiltration of tourism into non-touristic areas of the city. To this end, they calculated the ratio of beds in vacation rentals (i.e. Airbnb) over beds in traditional lodging establishments (e.g. hostel), by district. Even though they found high values for less frequented districts, they argued that vacation rentals are only half the story. In fact, these are still reconvertible. Thus, they examined the commercial offering of the districts. To this end, they computed the ratio of alimentary over non-alimentary stores by district. Rather unsurprisingly, they found a high value for the city's most popular district: San Marco. Moreover, and perhaps more interestingly, they drew a connection between the two analyses and discovered that while traditional lodging establishments go hand in hand with non-alimentary stores, vacation rentals go arm in arm with alimentary stores. This result suggests different consumption patterns on the part of the visitors. In sum, they concluded that overtourism is “irreversibly changing the balance of the city´s economic landscape” (Visentin & Bertocchi, 2019, p. 19). Their conclusion echoes the stream of research on 'new tourism areas' (Maitland & Newman, 2009b), and also reminds of the words by Magnus Enzensberger, who stated that “the tourist destroys what he seeks by finding it". In fact, it is safe to say that, eventually, more and more visitation will turn the once authentic place into a touristic hotspot. This, in turn, will lead visitors to swarm to another 'off the beaten path' area, thereby furthering this vicious cycle. De La Calle-Vaquero et al. (2020) performed a comprehensive review of research on overtourism measurement. They drew a distinction between tourism activity and tourism specialisation indicators. The former merely measure the quantity of tourism (e.g. number of overnights). Hence, they are not well suited to reveal tourism pressure. Nevertheless, examining their evolution (growth) and distribution (seasonality) over time enhances their value. The latter

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link an element concerning tourism with one regarding the destination (e.g. tourism intensity). Moreover, and more importantly, these authors acknowledged that the measurement of overtourism faces the same challenges as the determination of carrying capacity – that is, “the inability to establish values based on which many visitors can be considered too many” (De La Calle-Vaquero et al., 2020, p. 319). On a side note, De La Calle-Vaquero et al. (2020) acknowledged big data as an additional data source. In spite of understanding its appeal, in the author's opinion this type of data is hard to 'institutionalise' for two reasons. First, using data from a company makes DMOs dependent on them. This, in turn, is not reliable in the long run. For instance, Kádár (2014) analysed tourists' consumption of Budapest with data from Flickr. The decline of this platform would have left DMOs relying on its data empty-handed. Hence, the author suggests enjoying the advent of big data with due caution.

3.3.1.2.2 Journal Articles

Amore et al. (2020) attempted to create a compound measure of overtourism. To begin, they computed traditional statistics in an innovative way. In the case of tourism density, they replaced the surface of the destination with that containing the ten most popular sights on TripAdvisor. In other words, they simply changed the denominator. In the case of tourism intensity, they were a little bolder and changed the numerator, too. That is, in addition to selecting the number of residents in the city instead of in the metropolitan area for the denominator, they also substituted the number of overnights with that of museum visitors in the numerator (Amore et al., 2020). While their choices are justified, they are not ground-breaking. In fact, other authors have already calculated tourism density using other areas (Guevara Manzo et al., 2017; Weber et al., 2019). Similarly, TourMIS gives users the possibility to select statistics for 'city area only' or for 'greater city area' (TourMIS, n.d.–a). Be that as it may, Amore et al. (2020) computed four measures for a few cities in Europe (n=15). They then standardised them14 and, finally, treated the simple mean as compound measure for overtourism.

14 Z-score standardisation by Amore et al. (2020): z = (x- x̅ ) / s

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Amore et al. (2020) adopted a purely quantitative – and thus allegedly objective – approach. Indeed, they explicitly stated to have favoured this over the subjective perspective (Amore et al., 2020, p. 121). In contrast, Perles-Ribes et al. (2020) prided themselves on appreciating the subjective nature of overtourism. In short, they operationalised overtourism as membership in the 'Network of Southern European Cities against Touristification'. The authors considered deliberately being part of this association a sign of a destination's concern for the adverse effects of tourism. Accordingly, they argued that the adoption of this measure differentiates theirs from other studies (Perles-Ribes et al., 2020). This line of reasoning seems a little far-fetched, though. In fact, the decision to join such association may be motivated by other factors, such as political reasons. In this regard, Perles-Ribes et al. (2020) only contemplated one alternative hypothesis, namely that of member destinations being particularly responsive to the phenomenon under scrutiny. Thus, they examined whether members of this network score higher on selected dimensions from Peeters et al. (2018). Otherwise, however, the authors did not attempt to rule out other surrogate options. Be that as it may, they modeled overtourism as a function of a destination's competitiveness as well as of its pressure from and dependence on tourism. They computed that model using different machine learning methods, first separately and then in conjunction15 (Perles- Ribes et al., 2020).

15 Methods adopted by Perles-Ribes et al. (2020): Logit Model, Naïve Bayes, Support Vector Machines, (Classification and Regression Tree).

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4 Methodology

This dissertation includes three studies. In this section, their methodologies will be presented, albeit only briefly. In-depth descriptions will be provided in the respective sections. In the first study, the author will analyse news articles about overtourism. To be more precise, content from the following five newspapers will be examined: Guardian, Independent, Financial Times, Times, and Telegraph. These are the main 'broadsheets' in the United Kingdom (Oxford Royale Academy, n.d.). Structural topic modeling (Roberts et al., 2014) will be performed to identify the 'attributes' (agenda-setting), or 'frames' (framing), used in the portrayal of this phenomenon. Most importantly, though, this method will only be a point of departure – not one of arrival. In line with Computational Grounded Theory (Nelson, 2020), topic modeling will solely be employed at the beginning of the analytical journey to let the words speak for themselves. The findings will then be fine-tuned and validated empirically (Nelson, 2020). While the successful application of this inductive-deductive framework will only be a drop in the ocean, it makes one step forward in overcoming the long-standing "artificial dichotomy of inductive versus deductive reasoning" (Mazanec, 2009, p. 320). In the second study, the author will examine academic publications about overtourism. More precisely, a bibliometric analysis will be conducted. This investigation will consist of two parts. In the first one (descriptive), the landscape of research on overtourism will be mapped. Then, bibliographic metadata will be summarised and topic modeling will be performed on the abstracts of selected documents. In the second part (inferential), a citation analysis will be carried out to ascertain the origins of overtourism research empirically. This knowledge, in turn, will enable the author to evaluate the “usefulness” of this stream of research. Here, this will be judged according to i) whether it has made itself vulnerable to the same criticisms of its precursors, and ii) its boldness. In other words, these analyses will establish whether overtourism research has advanced our knowledge or whether it has left us "none the wiser" (Ap, 1990, p. 615) after all. In the third study, selected indicators of tourism pressure will be computed for Vienna (Austria) to demonstrate the limited significance of allegedly objective measures. Accordingly, the author will undertake a leap to subjective indicators. In view of COVID-19, this will lead him to the diametrical opposite of overtourism – that is, “undertourism”. Specifically, the experience of the destination without tourism on the part of residents will be examined. Moreover, and perhaps more importantly, the author had already asked residents how they would imagine their destination

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without tourism before the outbreak of the pandemic. Back then, he posed this question out of a flash of inspiration, which, in turn, was motivated by the given that “mostly it is loss which teaches us about the worth of things” (Arthur Schopenhauer). In fact, never would he have thought that such apocalyptic scenario could become reality. Well, COVID-19 proved him wrong! Be that as it has been, in this study, residents´ hypothetical imagination of their destination without tourism (2018-2019) will be compared with their actual experience thereof (2020-2021). In the author´s opinion, this represents the unique contribution of this last study.

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5 Empirical Part

5.1 Study I

5.1.1 Rationale for this Study

In spite of the attention overtourism has received from the media, to the best of the author´s knowledge, in only three studies have researchers investigated how this phenomenon is depicted in the news. In what follows their shortcomings will be pinpointed. The author would like to emphasise that the aim is not to bash other researchers, but rather to highlight the added value of this study. To begin, Phi (2020) carried out content analysis on news articles in English. Her research suffers from three limitations. First, since the term "overtourism" was reportedly invented in 2016 (Ali, 2018), it is rather surprising that she found articles dating all the way back to 2008. This might be due to the fact that the platform she used disregards hyphens (LexisNexis, n.d.). If not get to the bottom of such odd results, she could have at least addressed them. Second, she did not provide concrete information about the sources of the articles. In fact, she only vaguely referred to them as "leading international/national newspaper outlets" (Phi, 2020, p. 2093). Such imprecise indication is of little help to anyone wishing to replicate her study. Third, her analytical effort was probably rather limited, as she fed the articles to a software, whose slogan used to be “text in insight out” (Leximancer, n.d.). Next, Pasquinelli and Trunfio (2020) carried out a thematic narrative analysis on news articles in English. Their study is not bulletproof either. First, they collected articles from pro- tourism outlets, such as Adventure Travel News, and from neutral outlets, such as The Independent. Thus, they mixed apples and oranges. If not considered, this should at least have been addressed in the analysis. Second, they inferred topics from the appearance of the most common terms together with "overtourism". This reminds of the approach adopted by Capocchi et al. (2020), who derived topics from the most common keywords of academic publications. Be that as it may, such inference seems a little far-fetched. In fact, there are more appropriate techniques to identify topics, such as Latent Dirichlet Allocation (Blei et al., 2003) and Structural Topic Modeling (Roberts et al., 2014). The latter will be used in this study. Finally, Clark and Nyaupane (2020) performed inductive and deductive content analysis on news articles in English. Before critically reviewing their article, a word of praise is indicated.

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In fact, these two authors are the only ones to have adopted a conceptual framework from mass media – that is, framing theory by Entman (1993, as cited in Clark & Nyaupane, 2020). Having said that, their study differs in a few fundamental respects from the present one. To begin, they identified destinations linked with overtourism first, and then looked for articles about them. In this study, the reverse approach will be adopted. In addition, they applied ex ante defined frames. In this study, the frames, or rather the topics, will be determined ex post – thereby letting 'the text speak for itself'. Finally, like Pasquinelli and Trunfio (2020), the authors analysed articles from different types of outlets. However, unlike these two scholars, they took this into consideration during the analysis. In contrast, in this study, only articles from broadsheet newspapers will be considered. In sum, this study differs in several respects from the ones mentioned above. In a nutshell, it boasts a larger sample (n>300), covers the whole lifecycle of overtourism (2016-2020), adopts a more sophisticated method (structural topic modeling), and, most importantly, borrows theory from another discipline. Hence, the added value of this research. Similarities and differences between the present and the three aforementioned studies are summarized in Table 3.

Pasquinelli and Trunfio Clark and Nyaupane Criterion Phi (2018) Present Study (2020) (2020)

overtourism, Keyword(s) overtourism > overtourism over-tourism

Sample Size n = 202 n = 56 n = 85 n > 300

Article Language English English English English

Time Period 2008 - 2018 05/2018 - 08/2018 - 06/2016 - 06/2020

news media outlets, Article Source established newspapers online news broadsheet news credible publications

thematic narrative inductive and deductive structural topic Method content analysis analysis content analysis model

agenda-setting and Theory - - framing theory framing theory

Software Leximancer NVivo - R (Studio)

Table 3: Present vs Previous Studies

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5.1.2 Methodology

5.1.2.1 (Structural) Topic Modeling

Much of the research conducted on topic models builds on the work by Blei et al. (2003). In fact, in this publication the authors put forward the Latent Dirichlet Allocation (LDA). Their work has been a catalyst for further research, as evidenced by the 36.800+ citations received thus far, according to Google Scholar (30th March 2021). Following the example by Blei et al. (2003), the meaning of central terms is explained right at the outset. Thus, in an effort to bring the description of this method closer to the subject of this examination, what they named 'document' and 'corpus' will here be denoted as 'article' (news) and 'collection' (dataset), respectively. In contrast to Blei et al. (2003), LDA will only be explained conceptually, though. Hence, the curious reader is redirected to their original paper for technical details, such as the calculation of posterior distributions and the like. LDA is defined as "a generative probabilistic topic model" (Blei et al., 2003, p. 997). Its fundamental proposition is that articles display several topics (Blei, 2012). This sounds reasonable. Indeed, an article about overtourism may primarily deal with resident attitudes, but could also touch upon other subjects, such as gentrification and degrowth. Specifically, LDA posits that a collection of articles is formed by a fixed set of topics, each of which comprises a given set of words. Thus, the creation of an article consists of two steps. In the first one, the constellation of topics is determined (1). In the second one, every word is allocated to a topic (2A) and selected from that topic's pool of words (2B) (Blei, 2012). This creation process is shown in Figure 17.

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(1)

(2B) (2A)

(2B) Figure 17: Topic Modeling (Blei, 2012, p. 78)

Blei (2012) made a very interesting observation in this regard – that is, the articles are revealed, whereas the constellation of topics is concealed. Accordingly, topic models aim to derive the invisible from the visible, thus "reversing the generative process" (Blei, 2012, p. 79). As mentioned above, LDA has been a springboard for further research. Blei et al. (2003) praised it for its versatility. Almost a decade later, Blei (2012) observed that several modifications have been developed. For instance, Blei and Lafferty (2006a) acknowledged one shortcoming of LDA, namely its inability to account for associations between topics. Therefore, they developed the Correlated Topic Model (CTM), which, as the name suggests, takes such inter-relations into account. Specifically, this shortcoming is ascribed to the independence postulate of the Dirichlet distribution. The CTM circumvents this impractical restriction by working with a logit-normal distribution instead. They put this topic model through its paces and found that it outperforms LDA in both exploration and prediction (Blei & Lafferty, 2006a). In addition, Blei and Lafferty (2006b) noted that the exchangeability postulate of LDA is often ill-suited. In fact, in various collections

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of records16, topics change with the passing of time (e.g. academic publications). Therefore, these authors advanced the Dynamic Topic Model (DTM). As the name suggests, this variant takes into consideration the temporal development of topics. In short, it does so by splitting a collection of records into time spans and deriving the topics of an interval from those of the previous one. The resulting topics are then used to create the records of that interval. The authors put the DTM to the test and found that it yields good results (Blei & Lafferty, 2006b). To be sure, there are countless other alterations of LDA. However, reviewing them would exceed the scope of this section. The CTM could be quite useful to identify overtourism themes. Indeed, several overtourism-related issues are interconnected, such as vacation rentals and urban gentrification. The DTM, in contrast, would be less helpful. In fact, as overtourism has only been studied for a few years, the temporal sequence is only of marginal relevance. Nevertheless, it might become more important in the future. For instance, if scholars were to analyse news coverage about overtourism in a few years, they would be well advised to divide their datasets into before, during, and after COVID-19. Be that as it may, the method of choice for this study is the Structural Topic Model (Roberts et al., 2014). Even though their publication has 'only' received 900+ citations, according to Google Scholar (30th March 2021), their work is a gateway to further research, too17. Roberts et al. (2016) emphasize that they have created STM as a generic topic model for social science researchers, because the development of ad hoc ones would otherwise be an extremely onerous task for most such scholars. According to Roberts et al. (2014), the value of STM lies in its ability to integrate relevant variables into the determination of the popularity and the composition of topics. Broadly speaking, STM belongs to the family of 'unsupervised models'. As such, it lets topics emerge from words a posteriori. More specifically, STM falls into the sub-family of 'mixed- membership models'. As such, it lets articles display several topics. Thus, in short, STM can be defined as “a mixed membership model for the analysis of documents with meta-information” (Roberts et al., 2016, p. 1000). STM is only described theoretically here. For technical details, the reader is redirected to Roberts et al. (2016). Thus, according to Roberts et al. (2014), like every

16 The more general term ´record´ is deliberately used here to remind the reader that topic modeling need not be restricted to text – an aspect stressed by its inventors, too (Blei et al. (2003). 17 To be precise, this method had already been introduced by Roberts et al. (2013).

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other topic model, STM consists of two building blocks (Figure 18). In the first one, the share of words that can be allocated to any one topic is established for every single article ('topical prevalence'). In the second one, the sets of words that have the highest probability of arising from any one topic are determined ('topical content'). Unlike in every other topic model, though, in STM, both can be influenced by characteristics of the articles ('covariates') (Roberts et al., 2014).

Figure 18: Structural Topic Modeling (Schmiedel et al., 2019, p. 945)

Finally, the author is concerned about the interpretability of the topics. In fact, it has previously been noted that “latent topics are not amenable to substantive interpretation” (Dickinger et al., 2017, p. 812). Hence, an attempt will be made to perform topic modeling with 'word embeddings'. This being only an aspiration of the author, a thorough discussion of that technique would be premature. In short, words are placed in a vector, which contains information about its context (Žižka et al., 2019). The linguistic concept underpinning this approach – that is, the distributional hypothesis by Harris (1954) – will be discussed in the dissertation. For the time being, it shall suffice to put it in Firthian terms: “You shall know an object by the company it keeps!” (Firth, 1962, p. 179). Even though, there are some relevant packages in Python, such as lda2Vec (Moody, 2016) and Top2Vec (Angelov, 2020), this attempt will be performed in R. The reason for

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this is quite simple: the author does not master the other programming language. Having said that, the word2vec (Wijffels, 2020c) package in R will be used to create the word embeddings. This relies on the homonymous algorithm by Mikolov et al. (2013).

5.1.2.2 Suitability of the Method

The reader may wonder why the author chose to use 'topic modeling' to analyse the 'frames' (framing), or 'attributes' (agenda-setting), of overtourism in news media. After all, these seem to be two conceptually distinct entities. In computer science, a topic is "a distribution over a fixed vocabulary" (Blei, 2012, p. 78). In communication research, a frame is "a central organizing idea or story line that provides meaning to an unfolding strip of events" (Gamson & Modigliani, 1987, p. 143). Turning to the field of linguistics helps unveil the common ground between these two entities. According to the Cambridge Dictionary of Linguistics, topics have an effect on the register used, which includes – inter alia – words (Brown & Miller, 2013). Put simply, if newspapers were to emphasize the conflict between residents and tourists, they would probably use words like 'angry' and 'backlash', whereas if they were to stress the sharing economy they would probably use words like 'ban' and 'rentals'. Hence, the connection between the two entities. Some scholars have explicitly addressed the suitability of topic modeling to the identification of frames. DiMaggio et al. (2013) argued that topics could be regarded as frames. Ylä-Anttila et al. (2018) went a step further and claimed that three conditions have to be fulfilled for this to be the case. First, framing needs to be considered as inter-concept associations. Second, the text to be placed under scrutiny needs to be about one sole matter. Third, the resulting topic model needs to be validated (Ylä-Anttila et al., 2018). In this study, McCombs and Ghanem's (2001) conceptualisation of framing is adopted (1st condition). In addition, the corpus to be examined consists only of news articles about 'overtourism' (2nd condition). Finally, the identified topic model will be subject to validation (3rd condition). Furthermore, even though the use of topic modeling for the identification of frames is a relatively new ground, a few applications can be found. Scholars have for instance done this on the subject of the Refugee Crisis (e.g. Heidenreich et al., 2019) and Covid-19 (e.g. Yu et al., 2020). Thus, overall, this brief discussion suggests that topic modeling is suitable for identifying frames, or attributes, in thematically restricted news articles.

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5.1.2.3 Some Thoughts

The reader may observe that the author could have opted for supervised machine learning, as done by Burscher et al. (2014) for instance. However, Nicholls and Culpepper (2020) noted that such approach comes with the risk of succumbing to a particular bias – that is:

“there is a danger of falling into the drunk under the streetlight syndrome, in which we focus on the points we can see under the streetlight of supervised methods, and failing to explore the darker terrain of unconsidered and unexpected potential frames.” (Nicholls & Culpepper, 2020, p. 3)

In addition, these authors put k-means clustering, exploratory factor analysis, and structural topic modeling to the test and found that the latter one outperforms the former two. In more detail, they concluded that this method works well, albeit only for thematically restricted content (Nicholls & Culpepper, 2020). Since this study only examines articles about one subject – overtourism – such constraint should not pose a problem. The reader may also question the role of the researcher in such approach. According to Grimmer and Stewart (2013), by no means does automated text analysis make scholars redundant. Rather, they continue to play an important role throughout the entire investigation (Grimmer & Stewart, 2013). This point of view is shared by other scholars, too. For instance, DiMaggio et al. (2013) argued that since the software has no knowledge of the text it processes, it is their own reading of the topics that imbues them with meaning. Accordingly, they consider topic models a point of departure rather than one of arrival (DiMaggio et al., 2013). In sum, the literature seems to suggest that, at last, "the burden of making sense of the results is still on the researcher" (Jacobi et al., 2016, p. 103). In addition to the role of the scholar, the reader may also question the role of theory in such approach. Indeed, the advent of big data has raised doubts about the need for theory. Most notably, the CEO of Wired, Chris Anderson, published a provocative article titled “The End of Theory” (C. Anderson, 2008). His piece of writing was the spark that ignited the debate about the role of theory in science, as evidenced by the plethora of scholars citing his work (e.g. Boyd & Crawford, 2012). In this regard, Mazanec (2020) argued that research is never free from theory, as “we are bound to hypothesize if we like it or not” (Mazanec, 2020, p. 5).

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The 'Computational Grounded Theory' framework introduced by Nelson (2020) nicely rounds up the two previous paragraphs. In short, this is a methodological workflow that consists of three phases (Figure 19). In the first one ('Pattern Detection'), the analyst uses software tools to bring the text into a more intelligible format. This might unveil aspects that had not been thought of. In addition, the analysis is documented and can therefore easily be replicated. In the second phase ('Pattern Refinement'), the analyst can simply inspect the most relevant documents of each topic instead of having to browse through all of them. This selective engagement with the data guarantees the trustworthiness of its reading as well as the completeness of its coverage. In the third stage ('Pattern Confirmation'), the analyst examines the generalisability of his or her results with other text mining tools. This empirically validates – or better said, 'corroborates'18 – the findings from the two previous phases. In sum, this framework is "a three-step approach that combines inductive grounded theory with deductive quantitative tests" (Nelson, 2020, p. 5). Interestingly, the clever combination of rivalling research strategies (inductive vs deductive) and approaches (qualitative vs quantitative) calls into question the usefulness of these long-standing dichotomies. This, however, shall by no means overshadow another noteworthy aspect of this framework, namely its focus on replicability. The latter is of utmost importance, especially considering the heatedly debated 'reproducibility crisis' of the scientific realm19.

18 See Popper (1934/1959). 19 See e.g. Baker (2016).

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2. Pattern Refinement

• Inductive • Deductive

• e.g. STM • Guided deep reading • e.g. NLP

1. Pattern Detection 3. Pattern Confirmation

Figure 19: Computational Grounded Theory Adapted from Nelson (2020, p. 14)

Thus, the author will perform structural topic modeling on news articles to detect patterns, fine-tune these by carefully reading the most representative ones for each topic, and then corroborate these empirically by means of other text mining tools (Figure 19).

5.1.2.4 Applications in Tourism

A search for prior applications of structural topic models in tourism research was performed by sifting through the publications in tourism journals that cited Roberts et al. (2014). The underlying assumption was that if someone used this method, she or he would also reference its developers. Relatively few publications were identified. Nevertheless, a few comments can be made. To begin, STM has mainly been used to analyse reviews – be those of restaurants (e.g. Wen et al., 2020), airlines (e.g. Stamolampros et al., 2019) or hotels (e.g. Hu et al., 2019). To the best of the author´s knowledge, this method has not been used to examine news media in tourism research to date. Thus, the adoption of STM for the analysis of news articles represents a methodological contribution to tourism research.

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5.1.3 Data Collection

A search was performed in Nexis® Uni for news articles in English containing the term "overtourism" published between 1st January 2016 and 31st December 2020. The variant "over- tourism" was deliberately not used, as this platform disregards hyphens (LexisNexis, n.d.). The search was also limited to the following five sources: Guardian, Independent, Financial Times, Times, and Telegraph (n=602). These include different editions (e.g. The Sunday Times) as well as sister outlets (e.g. The Observer). The detailed breakdown is provided in Table 9 in Appendix 3. The list of articles was exported as .xlsx file from this platform for preliminary analysis. For the dissertation, duplicates and irrelevant articles will be removed. A few words about the choice of newspaper is in order. According to the Oxford Royale Academy (n.d.), the five selected outlets belong to the so-called “broadsheets” and each has its own political inclination. As can be seen from Table 4, these five papers cover the whole political spectrum. At the same time, the distribution of articles leans toward the right (≈ 51%). Having said that, since political orientation is not of central interest to this investigation, this slight imbalance shall not be a cause for concern.

Political Source Articles21 Orientation20 Guardian left-wing 46

Independent centre-left 113

Financial Times centrist 33

Times centre-right 104

Telegraph right-wing 306

Total 602 Table 4: Selected Newspapers

20 According to Oxford Royale Academy (n.d.). 21 Approximate number of articles before removal of duplicates.

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From the list exported from Nexis® Uni only the following information was kept: headline, publisher and date. Even though topic modeling could be performed on the titles of the articles, doing so on their bodies allows a much 'richer' analysis. Therefore, for the dissertation, the full text of each relevant article will be manually copy-pasted into Excel. At first glance, this may seem like an incredibly tedious task. On closer examination, though, the two alternatives – setting up a web crawler and using an Application Programming Interface (API) – are not appreciably less demanding, at least for a social scientists. Hence, the author´s decision.

5.1.4 Preliminary Results

Even though the first phase of the analysis consists in letting the data speak for itself, the mind of the author is no blank slate. In fact, as Prof. Mazanec asserted over a decade ago and corroborated last year, human beings "cannot not hypothesize" (Mazanec, 2009, p. 320, 2020, p. 5). Having read the articles by Clark and Nyaupane (2020), Pasquinelli and Trunfio (2020) and Phi (2020), this is even more so the case. Thus, the author will embed his a priori expectations, along with their underlying rationale, in the subsequent presentation of preliminary results.

5.1.4.1 Descriptive Results

In this subsection, basic descriptive aspects are presented. One such feature will be the temporal distribution of news coverage about overtourism. The three studies reviewed earlier (Clark & Nyaupane, 2020; Pasquinelli & Trunfio, 2020; Phi, 2020) offer little insight into the temporal distribution of news articles. Understandably22, none of them has considered the entire lifecycle of news coverage about overtourism. Therefore, it comes as no surprise that they have not resorted to any theory for the temporal distribution of news articles. In contrast, this study

22 Since the articles by Clark and Nyaupane (2020), Pasquinelli and Trunfio (2020) and Phi (2020) have been published online on 4th August 2020, 7th May 2020 and 21st May 2019, respectively, their data collection must have taken place before those dates. Hence, the rationale for 'Understandably'.

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examines news coverage about overtourism from its start (2016) to its end (2020)23, wherefore a reference to a conceptual framework is indicated. The temporal distribution of the unfiltered headlines retrieved from Nexis® Uni (n=602) was summarised by quarter and plotted by means of a line graph in Excel. The visualisation suggests that news coverage about overtourism might have followed the issue-attention cycle by Downs (1972) (Figure 20). Another aspect worth noting is the fact that the peaks in 2018 and 2019 occurred before (Q2) and during (Q3) the summer, respectively. This mirrors the findings of Guizi et al. (2020), who observed a similar pattern. They suggested that the adverse effects of tourism might become more visible in this time (Guizi et al., 2020). Finally, in the dissertation, an attempt will be made to identify potential explanations for the observed peaks (e.g. notable events).

90

80

70

60

50

40

30

20

10

0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2017 2018 2019 2020

Figure 20: News Coverage over Time 2017 - 2020 (n = 602)

23 By no means does the author claim that overtourism has ended. On the contrary, he believes that the current paralysis of the tourism industry due to Covid-19 – and thus its absence in news media – is only temporary.

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The other descriptive aspect will be the geographic spread of news coverage about overtourism. Scholars seem to agree on the reported geography and nature of overtourism. Phi (2020) claimed that this phenomenon tends to occur in urban destinations. Pasquinelli and Trunfio (2020) substantiated this claim empirically. They examined the concomitance of the most common terms with 'overtourism' and found a value of 82% for 'city'. In addition, Phi (2020) found that the most frequently cited cities are Barcelona (n=65) and Venice (n=44). Pasquinelli and Trunfio (2020) also identified 'Barcelona' (74%) and 'Venice' (56%) as destinations often connected to 'overtourism'. Clark and Nyaupane (2020) adopted an entirely different approach. They established which places are most often linked to overtourism through an online search first, and then gathered news articles about them. Nevertheless, 6 of the 17 locations identified are cities (≈35%). A 'quick and dirty ' way to identify the most popular destinations is to check the list of headlines against a geographic database. In this case, the titles of the articles were compared against the basic database from SimpleMaps.com (n.d.) using the COUNTIF function in Excel. While this approach is unable to detect some non-urban destinations, such as islands (e.g. Boracay, Santorini, Skye, etc), it provides a 'good enough' overview of the spatial distribution of news coverage about overtourism. The results of this exercise (Figure 21) show the bias inherent in restricting the dataset to newspapers in English. In fact, Edinburgh occupying the fifth place might more likely be due to the language of the articles than to actual tourism pressure. This, in turn, raises the question of whether the most frequently mentioned destinations are also the ones suffering the most from overtourism. This will be verified in the dissertation by taking tourism intensity as a proxy for tourism pressure24.

24 The measurement of overtourism will be discussed in more detail in Study III.

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50 45 40 35 30 25 20 15 10 5 0

Figure 21: Most frequently mentioned locations (n = 602)

5.1.4.2 Topics / Frames

In this subsection, the portrayal of overtourism in the news will be examined. As such, it constitutes the core of this exploratory analysis. The findings of the previous research yield insights into the actual content of news articles. To begin, Phi (2020) found that the lion's share of coverage deals with the volume of visitors. Hence, she argued that “the current public media discourse on this issue remains rather simplistic” (Phi, 2020, p. 2095). Moreover, Pasquinelli and Trunfio (2020) found that news media places special emphasis on the amount of visitors. Contrasting this portrayal with scholarly research, they realised that the numerical dimension is only half the story, though (Pasquinelli & Trunfio, 2020). Thus, the author expects news coverage about overtourism to mainly revolve around the volume of tourism. This, in turn, would be a simplistic as well as reductionist depiction of this 'multidimensional' (Koens et al., 2018) and 'multi-faceted' (Capocchi et al., 2019) phenomenon. If this were the case, news media would make itself vulnerable to the criticism of spreading “false knowledge”. Moreover, previous studies have portrayed overtourism as a clash between visitors and residents. Phi (2020) found that news media depicts the former as the ones creating the problem and the latter as the ones suffering from it. Pasquinelli and Trunfio (2020) found that news media neglects the contribution of locals to the occurrence of this phenomenon. These findings suggest

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the adoption of the 'conflict' frame (Semetko & Valkenburg, 2000). On a side note, the contemporariness of the guest-host dichotomy is questionable. In fact, Maitland and Newman (2009a) asserted that local inhabitants “increasingly behave like tourists in their own city” (Maitland & Newman, 2009a, p. 14). Tourism scholars – particularly those focusing on overtourism as an urban phenomenon – have acknowledged this (e.g. Koens et al., 2018; Novy, 2019). Thus, the author expects the news coverage under scrutiny to portray overtourism as a conflict in which residents are victims and visitors perpetrators. Furthermore, it would be surprising if news media had not found a whipping boy. After all, as Eisenhower said, “the search for a scapegoat is the easiest of all hunting expeditions”. Phi (2020) found that news media puts the blame on local stakeholders rather than on the sector's growth. In a similar vein, Pasquinelli and Trunfio (2020) found that news media holds public administrations accountable. Together, these findings suggest the adoption of the 'attribution of responsibility' frame (Semetko & Valkenburg, 2000). On another side note, this ascription of guilt can be traced back to a statement by the now former Secretary-General of the UNWTO, Taleb Rifai. Indeed, he declared that “growth is not the enemy; it´s how we manage it that counts” (World Tourism Organization, 2017). This statement indirectly puts the blame for the state of affairs on local stakeholders while pledging allegiance to the growth paradigm (Novy & Colomb, 2019). Thus, the author expects news coverage to put the blame on local authorities. As the three paragraphs above nicely demonstrate, the scarce body of knowledge on the portrayal of overtourism in the news suffices to generate a few expectations a priori. Thus, the analysis will be carried out with these in mind. Apropos, a thumbnail sketch thereof is in order. In the dissertation, text pre-processing will be performed with the tm package (Feinerer et al., 2008) in R. More precisely, the filtered Excel file will be converted into a corpus. Then, upper-case characters will be converted into lower-case ones, and non-alphabetic characters (numbers and punctuation) as well as stop words (English) and junk words (custom) will be removed. Next, a Document-Term Matrix (DTM) will be created. As mentioned earlier, the Structural Topic Model (Roberts et al., 2014) will be preferred over the Latent Dirichlet Allocation (Blei et al., 2003). Accordingly, the analysis will be performed with the stm package (Roberts et al., 2019) in R. The topics will be chosen according to the criteria of 'cohesion' and 'exclusivity', as recommended by Roberts et al. (2014). Moreover, since these authors emphasize the importance of personal evaluation, selected articles will be attentively read by the researcher. Finally, the identified topics

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will be ratified by a senior scholar familiar with topic modeling (Prof. Mazanec), and validated empirically. Overall, this procedure will be in line with the tripartite 'Computational Grounded Theory' framework by Nelson (2020) presented earlier.

5.1.5 Limitations

This study is not without its limitations. To begin, the author will only consider news from the United Kingdom. Future studies could also consider news from the United States, such as the New York Times and the Washington Post. Indeed, it would be interesting to make a comparison between English and American news coverage of overtourism. In addition, the author will only consider news articles published in English. As noted in the preliminary analysis, this restriction might cause some bias. Hence, future research could consider news in other languages, such as Italian and Dutch. Indeed, the preliminary analysis showed that Venice and Amsterdam are two destinations frequently mentioned in connection with overtourism. Finally, even though the author will attempt performing structural topic modeling with word embeddings, there is still room for improvement as regards the methodology.

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5.2 Study II

5.2.1 Rationale for this Study

The second study of this dissertation is a bibliometric analysis of research on overtourism. At first glance, only one such study seems to have been published on this subject (Barbieri da Rosa et al., 2020). On closer examination, though, three systematic reviews bearing some resemblance to a bibliometric analysis can be identified (Agyeiwaah, 2019; Carvalho et al., 2020; Veríssimo et al., 2020). Thus, one may wonder what the benefit of this study could possibly be. Hence, in what follows the methodologies of the aforementioned publications will be critically reviewed to show how this study overcomes their shortcomings. The bibliometric analysis conducted by Barbieri da Rosa et al. (2020) is the only investigation that is labelled as such. Their work has a few shortcomings. To start with, there is an inconsistency between the different sections of their book chapter. In fact, in the methodology, they used the plural form two times when claiming to have looked for “the terms of overtourism” (Barbieri da Rosa et al., 2020, p. 46) ; which suggests the adoption of multiple keywords. However, in the conclusion, the use of the singular form in their statement to have looked for “only the search term overtourism” (Barbieri da Rosa et al., 2020, p. 46) indicates the adoption of a single keyword. Moreover, and more importantly, considering that their data collection took place in March 2019, the small sample size (n=24) seems rather improbable. The systematic literature review by Veríssimo et al. (2020) is introduced as a systematic review, yet the analysis is described as bibliometric. At first sight, their study seems to bear a striking resemblance to the present one. In fact, they, too, performed a bibliometric analysis and visualised results with VOSViewer (van Eck & Waltman, 2010). However, on second thought, one realises that their study differs in one fundamental respect from the present investigation – that is, they performed a review of studies about 'tourismphobia' AND 'overtourism' (Veríssimo et al., 2020). In the author's opinion, in spite of being closely related, these are two distinct concepts. The former indicates “the fear, aversion or social rejection that local citizens in a destination feel towards tourists”, whereas the latter denotes “the subjective belief of residents that there are too many visitors” (Rejón-Guardia et al., 2020, p. 236). Thus, one may conclude that looking for both terms in their systematic review, Veríssimo et al. (2020) ended up mixing apples and oranges.

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The mapping literature review by Carvalho et al. (2020) is not without its flaws. The description of their literature search is somewhat confusing. In the text, they claimed to have looked for words other than 'overtourism', which they vaguely referred to as “synonyms, singular, plural and derivative words” (Carvalho et al., 2020, p. 18). Expecting to find these words in the table, the reader is then disappointed to discover that none is actually mentioned there. Moreover, Carvalho et al. (2020) only retained heavily cited publications. This criterion is not particularly sensible, as more recent publications (e.g. 2019) will naturally have less citations than older ones (e.g. 2018). In other words, the adoption of this criterion leads to the a priori exclusion of potentially valuable studies. Furthermore, Carvalho et al. (2020) reviewed grey literature, too (e.g. magazines). In contrast, in this study, only peer-reviewed articles are considered, as this format of knowledge dissemination is deemed to be the 'gold standard' in academia. The systematic literature review by Agyeiwaah (2019) has a few limitations as well. The description of her methodology is imprecise. She used the abbreviation "e.g." when listing the keywords and platforms used (Agyeiwaah, 2019, p. 101). Since this expression, by definition, indicates a non-exhaustive set of instances, the reader cannot be sure that the keywords and platforms mentioned are the only ones actually used. Moreover, as she used the abbreviation "i.e." earlier on the same page (Agyeiwaah, 2019, p. 101), the reader would assume that she is aware of the difference between these two expressions. The description of her methodology is also incomplete. Agyeiwaah (2019) specified neither in which field(s) she looked for the keywords nor whether she looked for them together or separately. Finally, the set of publications resulting from her search seems implausible. In fact, it is virtually impossible that using a "list of broad key search terms" (Agyeiwaah, 2019, p. 101), she obtained so few journal articles (n=17). Overall, this study differs in three respects from the ones reviewed above. First, they only covered scientific inquiry about overtourism until 2019. Accordingly, the entire the body of research produced on this subject in 2020 has not been considered in their analysis. This, in turn, explains the difference in sample size. In fact, while in the four studies it ranged from 15 to 84, in this one it is expected to lie around 150. Second, the four studies relied on relatively simple methods. Barbieri da Rosa et al. (2020) conducted a bibliometric analysis, Carvalho et al. (2020) performed a content analysis, and Veríssimo et al. (2020) carried out both. Building on the latter, in this study the author will perform a bibliometric analysis and topic modeling on peer-reviewed publications about overtourism. Third, and most importantly, the objective of the four reviews was

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– albeit each with a different focus – to provide an overview of research on overtourism. In contrast, this study aims to assess the added value of “overtourism”. The differences between the four studies reviewed in this section and the present one are summarised in Table 5.

Barbieri da Rosa Verissimo et Carvalho et Agyeiwaah Present et al. (2020) al. (2020) al. (2020) (2019) Study25

Review Bibliometric Systematic literature Mapping literature Systematic literature Bibliometric Type analysis review review review analysis

Tague-Sutckiffe Method Tranfield et al. (1992) and - > > by (2003) Garousi (2015)

Keywords "Overtourism" "over-?tourism", "overtourism" and 'list of broad key "over-?tourism" searched (Boolean operators) "touris(-)?mphobia" variations thereof search terms (e.g.)'

Data March 2019 June 2019 May-Oct 2019 - May 2021 collected

'e.g. ScienceDirect, B-on, ProQuest, Scopus, Databases Scopus, Web of WOS, Scopus WOS, Scopus Scopus, Web of Science, searched Science, Google WOS, Emerald Dimensions scholar'

Period 'no year limit 1998-2018 → 2019 1997-2019 2016-2020 considered was used'

Articles n = 24 n = 53 n = 84 n = 15 n ≈ 150 reviewed

Bibliometric Bibliometric Method Bibliometric Content analysis, Synthesis analysis, Topic used analysis analysis Content analysis modeling

VOSviewer, Software - Nvivo 12 Plus - VOSviewer, R Histcite

Table 5: Present vs Previous Studies

25 At the moment (Q1-2021). Elements may be subject to change.

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5.2.2 Methodology

The analysis conducted in this study consists of two parts. In the first one (descriptive), bibliographic metadata (e.g. authorship) will be summarised to map the scientific landscape of research on overtourism. In addition, topic modeling (Blei, 2012) will be performed on the abstracts of the identified publications to establish the facets of this kaleidoscopic phenomenon. The combination of these two methods is not unprecedented, as evidenced – for instance – by the work of De Battisti et al. (2015) and that of Figuerola et al. (2017). In the second part (inferential), a citation analysis will be conducted to determine whether overtourism is in fact “old wine in new bottles” (Dredge, 2017). Knowledge of its origins in academia, in turn, will enable an informed judgement of whether overtourism research has overcome the shortcomings of its predecessors. And, what´s more, it will provide a baseline against which to evaluate the “boldness” of this stream of research.

5.2.2.1 Bibliometrics

In the author´s opinion, the history of bibliometrics is quite intriguing. This term is generally attributed to Pritchard (1969). Dissatisfied with the term 'statistical bibliography', this scholar advanced the term 'bibliometrics' - which he defined as "the application of mathematics and statistical methods to books and other media of communication" (Pritchard, 1969, p. 349). A few years later, Da Fonseca (1973) remarked that the latter had neglected literature in other languages. In fact, Otlet (1934) titled one of his sections "Le Livre et la Mesure. Bibliométrie." (Otlet, 1934, p. 13). Be that as it may, the roots of bibliometrics go back even further. In fact, such type of analysis can be traced back – albeit under a different name – to the work by Cole and Eales (1917), who examined documents about animal anatomy between 1543 and 1860 (n=6,436). The terminological debate does not end here, though. In fact, the two related terms "scientometrics" and "informetrics" cause further confusion. Suffice it to say, at least for the moment, that while these terms share some common ground, they are not one and the same (Hood & Wilson, 2001). A search for bibliometric studies in tourism journals shows that in this field such type of investigation has only gained momentum in the last few years (2018-2020). Some researchers have focused on a specific journal, such as Travel and Tourism Marketing (Mulet-Forteza et al., 2018) and Tourism Geographies (Merigó et al., 2019). Other scholars have concentrated on a particular

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topic, such as sustainable tourism (Moyle et al., 2021; Ruhanen et al., 2015) and forecasting (Liu et al., 2019; Zhang et al., 2020). The former investigations are 'journal-focused', whereas the latter ones are 'theme-focused' (Koseoglu et al., 2016). The bibliometric analysis performed in this study falls under the last-mentioned category, as it focuses on academic publications about overtourism.

5.2.2.2 Descriptive Part

In the first part of the analysis, the author will examine the identified publications to map the body of research on overtourism. To the best of the authors knowledge, only Barbieri da Rosa et al. (2020) and Veríssimo et al. (2020) have conducted a bibliometric analysis about overtourism, so far. However, they examined the metadata of a relatively small number of papers (n=24 and n=53, respectively). In this study, both bibliographic data (e.g. authors) and raw text (e.g. abstracts) will be inspected. Concerning the latter, Capocchi et al. (2019) inferred topics from keywords. In the author's opinion, this is a bit of a stretch. Therefore, topic modeling (Blei, 2012) will be performed on the abstracts of the selected publications instead. The reader is redirected to Section 5.1.2.1 for the description of this method. It might be worth mentioning, though, that terms belonging to the academic jargon, such as 'methodology' and 'findings', will be treated as “junk words” (Table 10 in Appendix 4). The choice of limiting the analysis to the abstract of the articles might be questioned. Hence, it only seems fair to spend a few words on its rationale. In short, the author´s decision was motivated by the significance of this section. Landes (1951) asserted that “in terms of the market reached, the abstract is the most important part of the paper” (Landes, 1951, p. 1660). In view of the plethora of papers published, scholars use it as a screening tool to decide which ones to dedicate their attention to (Pitkiiz, 1987). Previous research backs this choice, too. For instance, Mazanec (2017) analysed 858 abstracts published in Annals of Tourism Research between 1975 and 2015. In addition, Kirilenko and Stepchenkova (2018) examined 8,890 abstracts published in Annals of Tourism Research, Journal of Travel Research, and Tourism Management between 1974 and 2017.

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5.2.2.3 Inferential Part

In the second part, the author will conduct a citation analysis of the selected publications to ascertain whether overtourism is in fact “old wine in new bottles” (Dredge, 2017). While several scholars have expressed their view on the age of this phenomenon, to the best of the author´s knowledge, Capocchi et al. (2020) are the only ones who have pursued an empirical investigation of this matter. However, they examined the bibliographies of a rather small number of papers (n=29) (Capocchi et al., 2020). Veríssimo et al. (2020) also inspected the bibliographies of selected documents. While their sample was almost twice as large (n=53), it included articles about both 'over(-)tourism' and 'tourism(-)phobia' (Veríssimo et al., 2020). The present analysis builds on these two studies, but boasts a larger sample (n=149) and covers a longer period (2017-2020). On a more technical note, the author will conduct the citation analysis with a dedicated software. Specifically, the following ones will be considered:

A. Bibexcel (Persson et al., 2009) B. bibliometrix (Aria & Cuccurullo, 2017)26 C. SciMAT (Cobo et al., 2011) D. VOSViewer (van Eck & Waltman, 2010)

An up-to-date comparison of these, and other, software can be found in Moral-Muñoz et al. (2020).

5.2.3 Data Collection

The keywords selected for this study are "overtourism" and "over-tourism". Synonyms were consciously not used for two reasons. First, in the author´s opinion, there is no synonym for overtourism. In fact, terms such as 'mass tourism' or 'tourism-phobia' have, albeit only slightly, different meanings. Second, one of the objectives of this study is to determine whether overtourism

26 Package in R.

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is "old wine in new bottles" (Dredge, 2017). This can only be achieved by analysing solely the literature that uses this specific term. These two keywords were searched for in Scopus and Web of Science. In view of the high number of duplicates, performing the search in other databases was deemed redundant. The identified documents were filtered. As the term itself was invented in June 2016 (Ali, 2018), documents with an earlier publication date came as somewhat of a surprise. However, a brief inspection revealed the reason for the appearance of such results – that is, the platforms also looked for the search term without a hyphen, thereby generating irrelevant results. Hence, only documents published between 2017 and 2020 were kept (Scopus=247, Web of Science=225). Next, the search was restricted by document type: only articles, reviews, editorials, and other short formats – such as research notes and letters – were maintained (Scopus=208, Web of Science=189). After that, the search was limited to documents published in English (Scopus=201, Web of Science=180). The refined results lists were then exported from Scopus and Web of Science as comma- and tab- delimited files, respectively. Next, the two files were imported into Microsoft Excel. Only non-duplicate records were kept (n=209). Publications from journals that publish in a language other than English were deleted (n=15), as were those without a Digital Object Identifier (DOI) (n=4). Further, publications that were not directly related to overtourism were removed (n=41). These included articles that were completely unrelated to overtourism, such as those in which the expression "over tourism" had been used. They also included papers that were only partly related to overtourism, such as those in which one of the two search terms had been marginally mentioned. Thus, the final dataset consists of 149 documents. The steps of this filtering process are visualised in Figure 22.

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Scopus Web of Science (n = 201) (n = 180)

Records after duplicates removed (n = 209)

Records screened Records excluded (n = 209) (n = 19)

Full-text articles Full-text articles assessed for eligibility excluded, with reason (n = 190) (n = 41)

Studies included in synthesis (n = 149)

Figure 22: PRISMA Flow Diagram (Moher et al., 2009)

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5.2.4 Preliminary Results

5.2.4.1 Descriptive Part

An example of the descriptive bibliometric analysis might be the co-authorship network shown in Figure 23, which was created with VOSviewer (van Eck & Waltman, 2010). This visualisation allows to rapidly identify teams of scholars researching overtourism. For instance, Joseph Cheer, Claudio Milano and Marina Novelli constitute such a group. They edited the special issue 'Overtourism and Tourismphobia' (Milano, Novelli, & Cheer, 2019b) as well as the book 'Overtourism: Excesses Discontents and Measures in Travel and Tourism' (Milano, Cheer, & Novelli, 2019). In addition, they published an article about degrowth (Milano, Novelli, & Cheer, 2019a). Be that as it may, this is only one example of how such an exploratory visualisation may help the analyst detect bibliographic patterns. Further analysis would for instance demonstrate that Hugues Séraphin is the most prolific author (n=10). At the same time, it would show that his publications are mostly co-authored (Ø = 3,7 authors) and rather short (Ø = 8,3 pages).

Figure 23: Co-Authorship Network27

27 Specifications: network visualization / co-authorship: authors → fractional counting / min docs = 2 / weights = links.

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An example of the descriptive text analysis could the co-occurrence graph of author keywords shown in Figure 24, which was also created using VOSviewer (van Eck & Waltman, 2010). The presence of both spelling variants of 'over(-)tourism' highlights the need of specifying synonyms prior to the analysis. This can easily be done using the thesaurus function in VOSviewer (see van Eck & Waltman, 2020). For instance, "social carrying capacity" and "tourism carrying capacity" could be subsumed under "carrying capacity"28.

Figure 24: Co-Occurrence Graph of Author Keywords29

28 Strictly speaking, these terms are not interchangeable. However, if one is only interested in the relations between overarching concepts, this methodological step is in order. 29 Specifications: overlay visualization / co-occurrence: author keywords → fractional counting / min. occ. key word: 2 / weights: occurrences & scores: avg. pub. year.

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The colouring in Figure 24 shows the timing of the terms. Thus, this visualisation suggests that the academic discourse on overtourism has evolved over time. To give an example: both spelling variants of 'tourism(-)phobia' are shown in violet, whereas 'tourism intensity' is shown in lime (Figure 24)30. This suggests that scholars dealt first with the conceptualisation and then with the measurement of overtourism. Such sequence would mirror the evolution of research on tourism impacts as outlined by Deery et al. (2012). Be that as it may, the nature of the text under scrutiny calls for the adoption of DTM (Blei & Lafferty, 2006b) or, alternatively, the use of time as covariate in STM (Roberts et al., 2014). Finally, while no sample analysis of topic modeling is presented here, a preliminary reading of the literature suggests the presence of the following topics31:

➢ Carrying capacity (e.g. Bertocchi et al., 2020; Tokarchuk et al., 2020) ➢ Degrowth (e.g. Higgins-Desbiolles et al., 2019; Milano, Novelli, & Cheer, 2019a) ➢ Resident attitudes (e.g. Kuščer & Mihalič, 2019; Muler Gonzalez et al., 2018) ➢ Social media (e.g. Alonso-Almeida et al., 2019; Bourliataux-Lajoinie et al., 2019) ➢ Urban tourism (e.g. Novy, 2019; Novy & Colomb, 2019) ➢ Vacation rentals (e.g. Celata & Romano, 2020; Sarantakou & Terkenli, 2019)

5.2.4.2 Inferential Part

An example of the second part of the analysis might be the co-citation network shown in Figure 25, which was created with Bibexel (Persson et al., 2009) and Pajek (Batagelj & Mrvar, 2020). This visualisation allows for two comments. First, the presence of older references, such as Emerson (1976) (Social Exchange Theory), Butler (1980) (Tourism Area Life Cycle), and O'Reilly (1986) (Tourism Carrying Capacity), lends some weight to the claim that overtourism is indeed

30 The software allows to interactively browse through the graph (van Eck and Waltman, 2020), which considerably facilitates its interpretation. 31 The expected topics are listed in alphabetical order.

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"old wine in new bottles" (Dredge, 2017). This would be in line with the results of the empirical investigation by Capocchi et al. (2020) and of the bibliometric analysis by Veríssimo et al. (2020).

Figure 25: Co-Citation Network32

Second, it shows the challenge of dealing with bibliographic data. The presence of duplicates, such as the literature review by Capocchi et al. (2019), shows that tiny differences in referencing suffice for the same work to be counted as two separate records. In this case, the discrepancy lay in the inclusion versus omission of the publication's issue number. Thus, it seems that after all, “the devil is in the detail”. Since the author expects to analyse 6,000+ references, 'harmonising' them will be a resource-intensive task. Cobo et al. (2011) noted the inaccuracy of data extracted from bibliographic platforms. Accordingly, they considered the preparation of the data to be a task of paramount importance (Cobo et al., 2011).

32 Co-citation network: n > 4, Layout: Kamada-Kawai (Free).

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5.2.5 Limitations

Having critically reviewed several studies, it only seems fair to acknowledge the own imperfections. It is probably best to start by addressing "the elephant in the room" – that is, the fact that the author only searched for 'over(-)tourism'. While the adoption of only one keyword may raise some eyebrows, this was consciously done to identify the body of research using this particular term. Future research could collect academic articles employing related – yet distinct – terms, such as 'tourismphobia' or 'touristification', and compare them to the ones using 'over(-)tourism'. The filtering process was relatively strict, too. In fact, publications in other languages, such as Spanish and German, were excluded. Future research could make a comparison between scholarly research in different languages. Finally, as suggested by Mazanec (2017), it would be really intriguing to compare rejected manuscripts against published articles about overtourism. What could the body of knowledge on this subjects have looked like? Would it have advanced our understanding further or would it have left us "none the wiser" (Ap, 1990, p. 615)?

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5.3 Study III

5.3.1 Rationale for this Study

The purpose of this section is to show the limitations of allegedly 'objective' and 'granular' indicators. To this end, a thorough analysis of (over-)tourism in Vienna will be conducted. Bearing in mind that “les chiffres absolus ne disent pas grande chose” (Markos, 1949, p. 132), one- dimensional measures, such as overnights, will not be discussed at length. However, for the sake of completeness, they are mentioned in Table 11 in Appendix 5Error! Reference source not found.. That said, this section is structured according to the classification by De La Calle-Vaquero et al. (2020) (Table 6).

Category (Sample) Indicators

Magnitude absolute number of arrivals and overnights

(average) growth rate of arrivals and overnights, Dynamics population decline at destination

Seasonality highest / lowest month, length of high season

Intensity tourism intensity

Density tourism density

Table 6: Indicators by Category

5.3.1.1 Magnitude

The first two indicators are arrivals and overnights. In 2019, Vienna recorded 7.926.768 arrivals33 and 17.604.573 overnights34 (TourMIS, n.d.–b). By themselves, these one-dimensional statistics are not particularly meaningful. Or better said, they “have only a comparative value” (De

33 Arrivals in all forms of paid accommodation in city area only 34 Bednights in all forms of paid accommodation in city area only

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La Calle-Vaquero et al., 2020, p. 309). According to the latest benchmarking report by European Cities Marketing (2020), Vienna ranks eighth in terms of total overnights. A direct comparison with the cities preceding it would be misleading, though. In fact, it would be like comparing apples and oranges – or, rather, watermelons and cherries. For instance, with a population of 3.664.371 inhabitants and an area of 891,1 km2 (Amt für Statistik Berlin-Brandenburg, n.d.), Berlin is roughly twice as big as Vienna. Hence, two-dimensional measures ought to be considered.

5.3.1.2 Dynamics

In this subsection, the temporal evolution of one-dimensional indicators is mainly presented. The underlying reason is to follow the logic of De La Calle-Vaquero et al. (2020), who argue that taking into consideration this aspect enhances the informativeness of elementary measures. Accordingly, the temporal evolution of two-dimensional indicators is presented in their respective subsections. Concerning demand, the number of arrivals has grown by 81% and that of overnights by 79% between 2009 and 2019 (TourMIS, n.d.–b). The average yearly growth rate stands at roughly 6% for both statistics (TourMIS, n.d.–b). Interestingly, this figure is higher than the one Visentin and Bertocchi (2019) found for Venice between 2009 and 2017 (4,4%). Another indicator is the growth of air transport (Peeters et al., 2018), which is computed as the yearly percentage increase of air passengers. In the present study, the “passengers carried (arrival)” figure35 (Eurostat, n.d.) was used to calculate this measure. The number of people transported to Vienna International Airport by plane in 2019 (15.776.153) increased by 75,1% with respect to 2009 (9.009.250). The average yearly growth rate over this period is 5,9%. Regarding supply, the number of hotels and pensions grew from 400 in 2009 to 422 in 2019 (MA 23, n.d.–d). This corresponds to an increase of 5,5%. The number of beds rose from 50.911 in 2009 to 68.200 in 2019 (MA 23, n.d.–b), which represent an increase of 34%. These two different growth rates suggest the opening of large properties. This is also evidenced by the average number of beds per hotel, which went from 127,3 in 2009 and to 161,6 in 2019. Since several authors have associated overtourism with Airbnb (e.g. Martín Martín et al., 2018; Sarantakou &

35 Figure excludes direct transit passengers.

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Terkenli, 2019), examining the evolution of vacation rentals would be worthwhile, too. Having said that, since this type of accommodation is relatively recent, the analysis possibilities are rather limited. Indeed, for Vienna, data is only available as of 2015 (Inside Airbnb, n.d.). Finally, Visentin and Bertocchi (2019) identified population shrinkage as a symptom of overtourism. More precisely, they noted that the centre's population has decreased by 74% between 1950 and 201536. In Vienna, the population increased from 1.627.566 in 1961 to 1.714.227 in 2011 (MA 23, n.d.–a). This represents an overall increase of 5,3%. At first sight, this figure seems more reassuring than that for Venice. At a closer look, however, considerable differences between the districts can be detected. For instance, the population of the 1st district – the city’s touristic hotspot – dropped from 32.243 in 1961 to 16.374 in 2011 (MA 23, n.d.–a). This corresponds to a decrease of 49%. In contrast, the population of the 22nd district – an area of expansion – rose from 57.137 in in 1961 to 161.419 in 2011 (MA 23, n.d.–a). This marks an increase of 183%.

5.3.1.3 Seasonality

Seasonality has been given a great deal of attention by tourism researchers. Guevara Manzo et al. (2017) and Peeters et al. (2018) both adopted air transport statistics to this end. Specifically, they computed the ratio of the peak over the trough month in terms of 'arriving seats' (Guevara Manzo et al., 2017) and 'passengers' (Peeters et al., 2018). As mentioned above, in the present study, the “passengers carried (arrival)” figure (Eurostat, n.d.) has been used instead. For 2019, dividing the top (August) over the bottom (January) month yields a value of 1,76. In the author´s opinion, however, it is not so much the severity of the temporal concentration but rather its duration that matters in urban destinations. For instance, Visentin and Bertocchi (2019) observed that 11 of 12 months are regarded as high season in Venice. Accordingly, they argued that the adverse effects of tourism “affect the entire historical city unrelentingly throughout the year” (Visentin & Bertocchi, 2019, p. 34). In the case of Vienna, arrivals and overnights were above average in 8 of 12 months in 2019 (Figure 26). Such unabated tourism

36 To be exact, they only mention absolute numbers. The percentage change has been calculated by the author on that basis.

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pressure most likely generates a feeling of restlessness and, consequently, a sense of impotence in the local population.

2.000.000

1.750.000

1.500.000

1.250.000 n - Arrivals 1.000.000 n - Overnights 750.000 Ø - Arrivals 500.000 Ø - Overnights

250.000

0

Figure 26: Arrivals and Overnights by Month in 2019

5.3.1.4 Intensity

One of the two most popular indicators of tourism pressure is tourism intensity. According to Eurostat, it is computed as the number of overnights with respect to the destination’s population (Kotzeva et al., 2019, p. 151). However, some variations do exist. For instance, Guevara Manzo et al. (2017) replaced overnights with arrivals for this measure. In the present study, the conventional calculation of this indicators has been adopted. In 2019, Vienna had a high intensity (9,3) (Appendix 5). A comparison of this value with the results by Peeters et al. (2018) highlights its extreme. The intensity figure lies slightly below the upper threshold of the fourth quintile (9,58) (Peeters et al., 2018). Given the importance of temporal evolution, it is worth noting that tourism intensity rose from 5,9 in 2009 to 9,3 in 2019. This represents an increase of 58% (Appendix 5). Furthermore, several authors have linked overtourism to budget airlines (e.g. Dodds & Butler, 2019; Visentin & Bertocchi, 2019). Thus, it might be worthwhile determining the intensity of low-cost travel. The share of passengers traveling with budget airlines to and from Vienna International Airport grew from 23,7% in 2018 to 31,6% in 2019 (Flughafen Wien AG, 2020,

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p. 35). This constitutes an increase of 33,3%. Thus, a qualitative analysis of air transport seems to be desirable.

5.3.1.5 Density

The other most popular indicator of tourism pressure is tourism density. According to Eurostat, it is computed as the number of overnights with respect to the destination’s surface (Kotzeva et al., 2019, p. 148). Here, too some variations do exist. For instance, Guevara Manzo et al. (2017) restricted the surface of the destination to the one containing the twenty most popular sights for this measure. In the present study, the conventional calculation of this indicator has been adopted. In 2019, Vienna had a very high density (42.434) (Appendix 5). Here, too, contrasting this value with the results by Peeters et al. (2018) highlights its extreme. The density figure lies somewhat above the upper threshold of the fifth quintile (37.290) (Peeters et al., 2018). Moreover, tourism intensity rose from 23.725 in 2009 to 42.434 in 2019. This represents an increase of 79% (Appendix 5). The fact that this growth rate is much larger than that of tourism intensity (58%) can easily be explained by the fact that – unlike the population – the surface of a destination remains constant.

5.3.1.6 Others

Finally, Peeters et al. (2018) also considered the proximity to cruise harbours and airports as well as World Heritage Sites (WHS) as potential catalysts for overtourism. Vienna has one harbour (Schiffstation Wien) and is close to two airports (Vienna International Airport; Bratislava Airport)37. Technically, there are only two WHSs in a radius of 30 km: the historic centre and the complex of Schönbrunn. However, three other sites are relatively near: the cultural landscapes of Neusiedlersee (≈50km) and Wachau (≈70km) as well as the railway in Semmering (≈80km)38 (UNESCO, n.d.). Furthermore, it shall be mentioned that the city centre was placed on the 'List of World Heritage in Danger' in July 2017 because of the renovation project of the Hotel

37 Strictly speaking, both airports lie beyond the boundaries of the city. 38 Approximate beeline distances estimated using luftlinie.org.

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InterContinental 39 (UNESCO, 2017). Drawing on the opinion of the former director of the UNESCO World Heritage Centre, Francesco Bandarin, the director of the Vienna Tourist Board, Norbert Kettner, argued that the revocation of such status would not have major repercussions on a mature destination like Vienna (Wien Tourismus, 2016). To be sure, his statement made some waves. However, the 6% (n= 970.767) and 7% (n= 1.121.076) increase in overnights in 2018 and 2019, respectively, suggest that he may have been right after all. In conclusion, the author would like to make a final remark. As mentioned in the theoretical part earlier (Section 3.3), the measurement of overtourism is "old wine in new bottles". Having said that, in his research, the author uncovered a few rather aged indices – such as Defert’s tourist function index (Defert, 1954). Rather surprisingly, though, these have not been mentioned in any of the publications reviewed earlier (Figure 16). At the same time, they seem to have found some adoption by scholars in South-Eastern Europe (e.g. Marković et al., 2017; Štefko et al., 2018). The reasons for this remain unclear.

5.3.1.7 Granularity: Fool's Gold

Towards the end of their chapter on overtourism measurement, Peeters et al. (2018) call for more granular data. Specifically, they assert that:

“…it is advisable to develop a guideline for tourism statistics to be gathered at the NUTS 3 level because the NUTS 2 level is too coarse to successfully develop such an early warning tool…” (Peeters et al., 2018, p. 78)

In the particular case of Vienna, the surface (414,82 km2) and population (1.911.191) of NUTS-2 (AT13) and NUTS-3 (AT130) are the same (Statistik Austria, 2020). Hence, a shift from one level to the other yields no additional information. Furthermore, Weber et al. (2019) affirm that “disaggregated data is needed that includes the spatial and temporal distribution of visitors”

39 More about this here: www.heumarkt-neu.at.

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(Weber et al., 2019, p. 94). Following these calls for finer-grained analyses, in this subsection the author would like to offer a word of caution to scholars wishing to act on these exhortations.

5.3.1.7.1 District Level ('Bezirk)

Visentin and Bertocchi (2019) analysed the state of tourism in Venice on district level. Following their example, selected indicators have been computed on district level ('Bezirk') for Vienna. Table 7 shows the three districts with the highest values for tourism density and intensity. The scores of each district (n=23) can be found in Table 12 in Appendix 6Error! Reference source not found.. Rather unsurprisingly, the 1st district ranks first. The rest of the podium is not jaw- dropping either. In fact, the 7th and 6th districts occupy the second and third place, respectively.

Area Tourism Density Tourism Intensity

Vienna (overall) 42.434 9,3

1st district 1.087.441 191,3

7th district 752.353 37,5

6th district 498.779 22,8

Vienna (median) 55.064 6,1 Table 7: Indicators by District

Indicators could be mapped for better interpretation. For instance, the density of vacation rentals by district can be shown with a 'box map'40 (Figure 27). The 6th district has the highest number of listings per square kilometre (≈181), closely followed by the 6th (≈172) and the 5th (≈168) district. The 4th (≈149), 1st (≈135) and the 8th (≈133) district follow at some distance. The scores of each district are provided in Appendix 6 and a methodological note on the calculation of these figures is offered in Appendix 7. Bearing in mind how this density figure is computed, it comes as no surprise that its upper quartile is dominated by rather small districts (Ø = 1,8 km2). At

40 i.e. spatial equivalent of a box plot (Anselin, 2020).

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the same time, this leaves one wondering what may lie behind the seemingly unremarkable state of larger district (e.g. 2nd district). This will be examined in the next subsection.

Figure 27: Box Map - Airbnbs per km2 by District (14th Jan 2020)

5.3.1.7.2 Census District ('Zählbezirk')

At first sight, the results between tourism density and tourism intensity are rather similar (Appendix 6). However, on closer examination, a few dissimilarities spring to the eye. For instance, the 2nd district ranks fifth in terms of intensity (20,9) but only tenth in terms of density (113.836) (Appendix 6). This can easily be explained by its relatively large area (19,2km2) (MA 18, n.d.). Intrigued by such disparities, one could go a step further and try to compute indicators per census district ('Zählbezirk'). The curious researcher will soon be disappointed, though. In fact, traditional tourism statistics are not available at such level of granularity in the case of Vienna. The reason for this is that in some census districts there are so few properties that one could theoretically figure out which one the statistics refer to – and this, in turn, would violate the Federal Statistics Act for confidentiality reasons (J. Urlesberger, personal communication, March 23, 2020). However, all is not lost. In fact, it is still possible to calculate 'non-traditional' indicators on such level, albeit with some effort. The number of vacation rentals per census district is an example thereof (Figure 28). The step-by-step description of how these figures were calculated can be found in Appendix 7. This analysis yields interesting results. For instance, the 2nd district has the highest

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absolute number of listings (n≈569). With a surface of approximately 19,2 km2, it only has a density of 29,6 Airbnbs per km2, though. Consequently, it did not stand out in the analysis conducted on district level earlier (Figure 27). In contrast, the analysis on census district level shows considerable variations within districts. For instance, in the 2nd district, the density of Airbnbs ranges from 0 (Zählbezirk 210) to 2,3 (Zählbezirk 207) listings per hectare41. A look at the map helps explain this intra-district variation. The former area is the port ('Hafen'), whereas the latter one is an area that has been developing recently ('Stuwerviertel').

89 listings ≈ 2,3 38 hectares

Figure 28: Box Map – Airbnbs per ha by Census District (14th January 2020)

In sum, this small exercise demonstrates that reportedly 'granular' analyses may be fool's gold. Accordingly, tourism professionals are advised not let themselves be blinded by the apparent lustre of such findings. Rather, bearing in mind that "all that glitters is not gold", they are encouraged to scratch beneath the surface. In fact, since overtourism occurs in specific places, spatial analyses have to be conducted at the most detailed level possible – as permitted by law. To the best of the author's knowledge, a state-of-the-art example in this regard is the 'tourism observatory' by the city of Buenos Aires in Argentina.

41 The scale was shifted to hectare, as square kilometre would not be very meaningful at such level of analysis.

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5.3.1.8 Shifting the Focus

To be sure, one could now attempt to combine some of the above-mentioned measures into a compound index, as attempted by Amore et al. (2020). However, such focus on numbers might cause one to not see the wood for the trees – that is, one might easily get lost in details and become oblivious to the fact that, after all, these are only numbers. More than half a century ago, the founding father of market psychology, Bernt Spiegel, asserted that market reality ultimately boils down to consumer perception. To be more precise, he wrote:

“Die Realität im sozialen Feld ist alleine das Phänomenale, das unmittelbar Angetroffene; nicht aber, jedenfalls nicht allein, die nackte objektive Beschaffenheit, die uns im Leben wohl kaum einmal völlig gesondert begegnet.“ (Spiegel, 1961, p. 30)42

It follows that these allegedly 'objective' indicators are only half the story. Accordingly, the next logical step would be to relate these indicators to the subjective perception of tourism by residents. After all, overtourism has been defined as “the subjective belief of residents that there are too many visitors” (Rejón-Guardia et al., 2020, p. 236). Thus, one could for instance compare the density of vacation rentals in a particular district with the perception thereof by its inhabitants43. This would allow bridging the gap between the objective and the subjective dimensions of overtourism. However, in the case of the resident survey of the Vienna Tourist Board, quotas have only been set with respect to age and gender (Wien Tourismus, 2020), wherefore the number of responses fluctuates a great deal across districts. For example, in 2019, 12,6% of the respondents (n=459) were living in the 22nd district and only 0,9% (n=32) of them were living in the 1st district (Figure 29). Such variation makes statistical – let alone meaningful – comparisons difficult.

42 Quoting Spiegel (1961) has been an arbitrary choice. In fact, for the distinction between perception and reality, the author could also have cited other 'authors', such as Plato (Allegory of the Cave) and Kant (Noumenal vs Phenomenal). 43 e.g. with data from the resident survey of the Vienna Tourist Board.

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459

363 348

270 215 185 192 198 169 165 137 110 114 109 95 109 78 81 70 63 48 47 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Figure 29: Number of Responses by District in 2019 (n=3.657)

5.3.2 Methodology

5.3.2.1 The Origins

In 2018, the term overtourism was on everyone's lips. It was even in the running for that year's “Word of the Year” (Oxford University Press, n.d.). Having learnt that “mostly it is loss that teaches us the worth of things”(Schopenhauer), when seeing the extensive news coverage of protests against tourism, the author wondered what residents would actually do without it. Thus, upon his recommendation, the Vienna Tourist Board included the following two questions in their ongoing survey of resident attitudes towards tourism:

- How would you like the city without tourists? (0-100) - And how would you imagine life and the city without tourists? (text)

At that time, the idea of a deserted city was an unimaginable scenario. This year, the outbreak of Covid-19 turned this dystopian vision into reality. Nevertheless, since “in the middle of difficulty lies opportunity” (Einstein), upon the author's recommendation, the Vienna Tourist Board then asked residents about their experience of the city (and the life) without tourism. Specifically, the two questions above were altered as follows:

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- How did you like the city without tourists? (0-100) - How did you experience life and the city without tourists? (text)

This way, a comparison between the hypothetical and actual loss of tourism can be made. Moreover, upon recommendation of a senior employee of the Vienna Tourist Board, respondents had to answer the last question in form of a post card. That person, in turn, had been inspired by the work of Tussyadiah and Miller (2020), who adopted the 'letters from the future' method. In short, the idea behind the adoption of this method was to elicit richer responses. Finally, the post card format was adopted both with and without sample text in the third and fourth quarter of 2020, respectively (Appendix 8).

5.3.2.2 Hypotheses

In this section, the research framework and its underlying hypotheses are presented. The analysis will consist of two 'blocks'. In the first one, a comparison is made between residents' imagination of the city without tourists in the second quarters of 2018 and 2019 (Figure 30-A). Tourism has continued to grow between these two periods (July 2018 - March 2019), as evidenced, for instance, by the average monthly increase of 7,7% in the number of overnights44 with respect to the previous year (TourMIS, n.d.–b). This, in turn, is then reflected in measures of tourism pressure. Tourism intensity, for example, was 8,6% higher compared to the previous year (TourMIS, n.d.–b). Hence, it seems reasonable to assume that as 'objective' indicators of tourism pressure increase, the outlook of a city without tourists becomes more appealing. Thus, the first hypothesis reads as follows:

H1: As 'objective' measures of overtourism increase, the prospect of a city without tourism becomes more appealing.

44 Bednights in all forms of paid accommodation in city area only.

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In the second block, the imagination of the city without tourists by respondents in the third and fourth quarters of 2019 is compared with the experience thereof by respondents in the third and fourth quarters of 2020 (Figure 30-B). Here, the author assumes that once residents realise what they have lost, they appreciate what they had. More formally:

H2: Residents' hypothetical liking is higher than their actual liking of the city without tourism.

The analysis of the numerical indications by the respondents (0 →100) will be complemented by an examination of their answers to the open-ended questions (text). Specifically, these will be examined by means of topic modeling. However, unlike in Study I, it would not be prudent to use STM here. In fact, Yan et al. (2013) identified sparsity as an obstacle to the application of traditional topic models (e.g. LDA) to brief documents (e.g. tweets). They ascribed this to the fact that the traditional algorithms operate at the document level. Accordingly, they developed an alternative model that generates topics from pairs of words in the entire corpus. They named this the 'Biterm Topic Model' (BTM) (Yan et al., 2013). This algorithm is implemented in the BTM package (Wijffels, 2020a) in R. The presence vs absence of the sample text in the postcard will be taken into consideration during this analysis. Figure 30 visualizes the two 'blocks' of the analysis. The answers given last year (2020) need to be taken with a grain of salt, though. In fact, external circumstances, such as the 'hard' lockdown in November 2020, might have distorted the results. To the extent possible, aspects like this will be considered in the analysis.

Figure 30: Analysis Workflow

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5.3.3 Data Collection

The data under scrutiny was collected in form of an online survey by Manova GmbH on behalf of Wien Tourismus. The ‘accessible population’ (Trochim & Donnelly, 2006) consisted of people residing in Vienna aged between 18 and 70. ‘Proportional quota sampling’ (Trochim & Donnelly, 2006) was adopted by age and gender. The collected data was cleaned and weighted by age, gender, district, and education (Wien Tourismus, 2020). Figure 31 provides an overview of the responses by quarters and years.

1000 912 915 910 918 920 873 900 851 871 865

800 709 700 605 600 511 500 Closed 400 Open 300

200

100

0 Q2 Q2 Q3 Q4 Q3 Q4 2018 2019 2019 2020 Imagination vs Imagination Imagination vs Perception

Figure 31: Responses by Quarters and Years45

45 Q3-2020 is about a third smaller than the other quarters due to a mishap on the part of the market research company.

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5.3.4 Preliminary Results

5.3.4.1 Closed Questions

For the purposes of this sample analysis, responses to the closed question were averaged by quarter. The comparison between the imagined liking of the city without tourism in the second quarters of 2018 and 2019 is rather unspectacular. In fact, this value only increased by 11% (Figure 32). In contrast, the comparison between the imagined and experienced liking of the city without tourism in the third and fourth quarters of 2019 and 2020 is striking. This value increased by 76% and 65%, respectively (Figure 32). This preliminary result suggests a rejection of H2.

70 63,2 59 60

50

40 36 35,8 34,1 30,8 30

20

10

0 Q2 Q2 Q3 Q4 Q3 Q4 2018 2019 2019 2020 Imagination vs Imagination Imagination vs Experience

Figure 32: Average Liking of the City without Tourism

5.3.4.2 Open Questions

For the purposes of this sample analysis, the responses to the open-ended question in the second quarter of 2018 (n=851) were examined. The data was pre-processed with the tm package (Feinerer et al., 2008). Words that indicate a negation were consciously retained (e.g. 'nicht'), as Covid-19 is about the absence of 'things' (e.g. 'nicht überlaufen'). The text was then annotated with

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the udpipe package (Wijffels, 2020b). A pre-trained model was used for this (i.e. german-hdt)46. Finally, a Biterm Topic Model was performed with five topics using the BTM package (Wijffels, 2020a). This being a demonstrative exercise, special attention was paid neither to the number nor to the evaluation of topics.

Figure 33: Sample BTM

A brief look at the graph (Figure 33) suggests that the BTM delivers what it promises. Indeed, it is safe to say that the topics are more interpretable than they would have been if generated with traditional algorithms – even more so considering the limited length of the answers (Ø ≈ 7 words). Topic 1 includes three frequently used adjectives: empty ('leer'), quiet ('ruhig'), and boring ('langweilig')47. Respondents seem to be well aware of what would go missing without tourism. Topic 2 mentions the economic aspect ('fehlen', 'einnahmequellen') and topic 3 mentions the reduced offering ('weniger', 'Angebot'). Interestingly, topic 4 could be interpreted as almost suggesting that tourism is part of the Viennese DNA. A closer examination of this topic is desirable.

46 Information about this model can be found here.

47 This is because the author set background=TRUE when building the model.

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Nevertheless, respondents also appear to see some benefit in the absence of tourism. Topic 5 mentions more housing space ('mehr', 'Wohnraum'). Thus, overall, Figure 33 suggests that while residents see the advantages of tourism, they are not completely blind to its disadvantages, either. At this point, the reader might be perplexed by the dissonance between the scores of the closed-ended and the text of the open-ended questions. In fact, the high liking of the city without tourism in the third and fourth quarters of 2020 (Figure 32) stands in stark contrast to the awareness of this sector's benefits displayed in the second quarter of 2018 (Figure 33). This incongruence cannot be left uncommented. On one hand, the results in Figure 32 suggest that residents enjoy having the city to themselves. This seems plausible. In fact, after a decade of unabated growth48, tourism in Vienna was at its record high in 201949. It only seems fair that locals breathe a sigh of relief when getting a break from such 'tour de force'. On the other hand, the results in Figure 33 indicate that the downsides of tourism's success have made themselves felt. This, in turn, begs the question of how the attitude of residents will develop in the long run. Will rising unemployment rates change their mind? Perhaps not right away. Yet, in the author's opinion, at the latest when the façades of the city's iconic buildings start crumbling, their enthusiasm will fade in favour of regret.

5.3.5 Limitations

Like most studies, this one is not without its limitations either. To begin, the respondents' answers might have been influenced by a myriad of other factors. This could especially have been the case in 2020. In fact, the consequences of the pandemic (e.g. sudden unemployment) as well as the measures adopted to counter them (e.g. 'hard' lockdown) may, to some extent, have distorted the results. In addition, the critical reader may view the open-ended questions as “double-barreled” (Babbie, 2008, pp. 273–275).

48 Average yearly growth rate of arrivals and overnights ≈ 6%. 49 Arrivals = 7.926.768; Overnights = 17.604.573.

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6 Conclusion

To sum up, in this manuscript, the author has offered the reader a comprehensive glimpse of what their journey into the rise and fall of overtourism will look like. In Study I, the analysis of newspaper articles will show how overtourism has been portrayed in the news. The use topic modeling will allow traveling beyond the light of the known into the darkness of the yet unknown themes of this discourse. Moreover, the corroboration of the discoveries with other text mining techniques will take one more brick off the iron curtain separating inductive and deductive approaches. In Study II, the analysis of journal articles will map the landscape of research on overtourism. The citation analysis will then determine the roots of this phenomenon, thereby contributing to the debate about its novelty with empirical evidence. In addition, evaluating the usefulness of overtourism research will allow a critical reflection on tourism scholarship. In Study III, the Pindaric flight to its diametrical opposite – “undertourism” – will bring the reader back to the present. Understanding the limitations of the objective and granular indicators of tourism pressure gives some food for thought on what is worth measuring. Most importantly, though, insights into the residents´ experience of their destination without tourism will yield information that is essential for shaping travel and tourism in a sustainable manner.

Page 98 of 133

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8 Appendices

8.1 Appendix 1

Limited Use Prevalence of Loose Geo. Publication of Theory Quant. Methods Relevance

Deery et al., 2012 ✓

Nunkoo et al., 2013 ✓ ✓

Sharpley, 2014 ✓ ✓ ✓

Almeida García et al., 2015 ✓ ✓

Hadinejad et al., 2019 ✓ ✓ Table 8: Reviews about Residents Attitudes and Tourism Impacts, Aspects

8.2 Appendix 2

Figure 34: Potential Relationships between Crowding and Satisfaction (Wagar, 1964, p. 7)

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8.3 Appendix 3

Category Outlet Quality Level Articles

The Guardian Morning Quality 44 Guardian The Observer Sunday Quality 2

The Independent - 93 Independent i-Independent Print Morning Quality 20

Financial Times Financial Times Morning Quality 33

The Times Morning Quality 28

Times The Sunday Times Sunday Quality 33

thetimes.co.uk - 43

The Daily Telegraph Morning Quality 48

Telegraph The Sunday Telegraph Sunday Quality 17

telegraph.co.uk - 241 Table 9: Distribution of News Articles (n=602)

8.4 Appendix 4

Junk Words

Paper, Article, Study, Purpose, Design, Methodology, Approach, Findings, Research, Limitations, Implications, Originality, Value

Table 10: Junk Words

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8.5 Appendix 5

Dynamics Category Indicator Change 2019 2009

Arrivals 7.926.768 4.385.529 +81%

Overnights 17.604.573 17.604.573 +79% Magnitude Hotels 422 400 +6%

Beds 68.200 50.911 +34%

highest / lowest month 1,7 (1,9) 2,0 (2,2) - Seasonality months above average 8 (8) 6 (6) -

Tourism intensity 9,3 5,9 +58%

Intensity Share of budget travel 31,6% - -

Airbnbs per hotel 17,3 - -

Tourism density 42.434 23.725 +79% Density Airbnbs per km2 17,6 - -

World heritage sites 2 2 -

Others Airports 1 1 -

Cruise harbour 1 1 -

Table 11: Indicators for the City of Vienna, 2009-201950

50 Figures and calculations available upon request as separate Excel file.

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8.6 Appendix 6

Tourism Tourism Airbnbs Airbnbs District Density Intensity per km2 per Hotel (2019)51 (2019)52 (2020) (2019-2020) 1. Innere Stadt 1.087.441 191,3 134,9 4,9 2. Leopoldstadt 113.836 20,9 29,6 13,5 3. Landstraße 237.836 19,2 68,4 16,9 4. Wieden 442.878 23,6 149,3 13,9 5. Margareten 303.480 11,0 167,5 33,7 6. Mariahilf 498.779 22,8 181,4 13,2 7. Neubau 752.353 37,5 172,9 9,6 8. Josefstadt 487.177 20,9 133,0 6,3 9. Alsergrund 201.391 14,2 125,0 13,3 10. Favoriten 57.613 9,0 9,8 10,4 11. Simmering 13.010 2,9 2,8 8,3 12. Meidling 28.473 2,4 25,7 26,0 13. Hietzing 7.682 5,4 1,2 5,0 14. Penzing 14.255 5,2 3,4 10,4 15. Rudolfsheim-F. 249.114 12,6 103,4 22,5 16. Ottakring 26.838 2,2 26,7 33,1 17. Hernals 30.717 6,1 15,6 25,4 18. Währing 4.145 0,5 22,1 140,0 19. Döbling 8.248 2,8 3,8 11,9 20. Brigittenau 55.064 3,6 42,2 48,2 21. Floridsdorf 1.254 0,3 0,8 9,3 22. Donaustadt 6.842 3,7 1,2 7,4 23. Liesing 2.344 0,7 0,7 2,6 Table 12: Indicators on District Level

51 = Overnights (MA 23 (n.d.–c) / Area (MA 18 (n.d.) 52 = Overnights (MA 23 (n.d.–c) / Population (Statistik Austria and MA 23 (n.d.)

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8.7 Appendix 7

The initial dataset consisted of 13.157 observations. First, the dataset was restricted to listings of entire apartments (8.743). Second, it was limited to listings with at least one review in the last year (6.632). Third, and last, only listings with a precise location were kept (5.343). This last step was taken, as Airbnb data is not entirely accurate. Indeed, its anonymisation entails a degree of inaccuracy of up to 150 metres as well as the dispersal of rentals in the same edifice to the nearby environment (Inside Airbnb, n.d.). In sum, the dataset was reduced by 59%. The number of listings per district was calculated in R (R Core Team, 2020) and that per census district in QGIS (QGIS Development Team, 2020). These figures were then attached to the shapefile of the district (MA 41, n.d.) and that of the census district (MA 21, n.d.) boundaries, respectively. Finally, the calculation of density figures (raw rate) and the corresponding visualisations (box map) were performed directly in GeoDa (Anselin et al., 2006).

8.8 Appendix 8

Figure 35: Postcard with Sample Text

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Figure 36: Postcard without Sample Text

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