Determinants of tourism destination competitiveness in China

UNIVERSITÀ POLITECNICA DELLE MARCHE FACOLTÀ DI ECONOMIA “GIORGIO FUÀ” _______________________________________________________________ Dottorato di Ricerca in Economia Aziendale Ciclo X n.s. DETERMINANTS OF TOURISM DESTINATION COMPETITIVENESS: A THEORETICAL MODEL AND EMPIRICAL EVIDENCE Tutor: Tesi di dottorato di: Chiar.mo Prof. Marco Cucculelli Gianluca Goffi Coordinatore: Chiar.mo Prof. Luca Del Bene Ad Anna e Ottavio, i miei genitori RINGRAZIAMENTI (AKNOWLEGEMENTS) Si desidera ringraziare tutti coloro che con il loro aiuto e sostegno hanno consentito la realizzazione della presente tesi. Si ringrazia sentitamente il Supervisore, il Prof. Marco Cucculelli, per aver discusso le linee generali del progetto di ricerca, per la meticolosa e costante attenzione mostrata nel corso dello sviluppo del presente lavoro, per i preziosi suggerimenti e spunti di riflessione e per la grande disponibilità mostrata. Si ringrazia, inoltre, la Prof. Mariangela Paradisi per gli input iniziali forniti. Si ringraziano i Tutor delle Università estere presso cui sono stati svolti i periodi di visiting: il Prof. Luiz Gustavo Barbosa della Fundação Getúlio Vargas di Rio de Janeiro e la Prof. Francisca Paula Santos da Silva dell’Universidade do Estado da Bahia di Salvador (Brasile). A quest’ultima va un caloroso ringraziamento per la sua grande disponibilità e per avermi fatto conoscere la realtà turistica locale a 360 gradi. Si ringraziano il Preside della Facoltà di Economia dell’Università Politecnica delle Marche Prof. Gianluca Gregori, il Direttore del Dipartimento di Management e Organizzazione Industriale Prof. Stefano Marasca e il Responsabile del Curriculum Economia Aziendale del Dottorato in Economia Prof. Luca Del Bene. Un doveroso ringraziamento va al Dott. Amadio Sacripanti per l’importante supporto tecnico-informatico nella predisposizione e nella realizzazione delle survey on line. Un sentito ringraziamento va sia a tutti coloro che hanno risposto alle survey on line nei Comuni turistici di eccellenza italiani e nelle città di Rio de Janeiro e Salvador de Bahia, sia alle persone che hanno contribuito con preziosi consigli e rispondendo ai test iniziali. Un particolare ringraziamento va anche a tutti coloro che hanno promosso le indagini e contribuito alla loro diffusione. Si ringraziano, altresì, gli Osservatori Turistici e gli Uffici del Turismo delle Regioni Italiane (in alcune Regioni i singoli Osservatori e Uffici del Turismo Provinciali) per i dati forniti sui flussi turistici. Il ringraziamento più grande, infine, va a due persone speciali - Anna Maria Simonetti e Ottavio Goffi, i miei genitori - che mi hanno aiutato e supportato in questi anni. Dell’autore del suddetto lavoro la responsabilità di eventuali errori, imprecisioni o omissioni. ABSTRACT Competitiveness has been identified in the tourism literature as a critical factor for the success of tourism destinations. Many studies focus on the main factors affecting destination competitiveness. Nevertheless, there is still no evidence of a significant impact of these factors on the performance of a destination. This study aims at filling this gap, by adapting and extending the Richie & Crouch’s model and applying it on a unique dataset of 610 small and medium Italian Destinations of Excellence. To reduce the large set of variables, a principal component analysis (PCA) was performed. The dependency between the performance scores and the explanatory variables was analysed by an ordinary least square and a partial least square regression. The empirical findings show that a sustainable tourism policy and destination management is not only good for preserving the ecologic balance and for minimize negative cultural and social impacts, but has a great importance for improving the competitiveness of a tourism destination. The model has also been applied to measure the competitiveness of two leading tourism destinations in Latin America: Rio de Janeiro and Salvador de Bahia. Despite Brazil will host both the FIFA World Cup 2014 and the Olympic Games 2016, the competitiveness of its tourism destinations is still not adequately studied. Primary quantitative data were collected through experts’ judgement: 277 usable responses were received in the case of Rio de Janeiro, 164 in the case of Salvador. The findings provide tourism policy makers and stakeholders with a valuable and accurate body of data on which to base their future destination management strategies. CONTENTS Introduction 13 Part I Competitiveness of Tourism Destinations 21 1.1. Perspectives of Tourism Destination Competitiveness 1.2. Models of Destination Competitiveness 1.2.1. The Ritchie & Crouch’s Conceptual Model of Destination Competitiveness 1.2.2. The Dwyer & Kim’s Integrated Model of Destination Competitiveness 1.2.3. Theoretical and Applied Models 1.3. The Challenge of Measurement Competitiveness of Tourism Destinations 1.4. Tourism Sustainability and Competitiveness 21 24 24 27 30 35 41 Part II A Model of Tourism Destination Competitiveness 43 2.1. The Development of the Model 2.2. Insights into the model: Determinants and Indicators 2.2.1. Core Resources and Key Attractors 43 51 51 2.2.2. 2.2.3. 2.2.4. 2.2.5. 2.2.6. 2.2.7. Tourism Services General Infrastructures Conditioning and Supporting Factors Tourism Policy, Planning and Development Destination Management Demand Factor 53 54 55 57 59 62 Part III Methodology and Research Design 65 3.1. Methodology 3.1.1. Data collection 3.1.2. The Survey Instrument 3.1.3. Principal Component Analysis 3.1.4. OLS Regression 3.1.5. PLS Regression 3.2. The Italian Case 3.3. The Case Study: the Italian Destinations of Excellence 3.3.1 The Sample 3.3.2. The Destinations awarded with the “Blue Flag” 3.3.3. The Destinations awarded with the “Blue Sail” 3.3.4. The Destinations awarded with the “Orange Flag” 3.3.5. “The Most Beautiful Villages in Italy” 65 65 67 70 72 75 77 84 84 87 91 93 95 Part IV Empirical Analysis and Discussion of Findings 99 4.1. Principal Component Analysis Results 4.1.1. Component Solution 4.1.2. Description of the Components 4.2. Regression Results Part V The Competitiveness of two Leading Tourism Destinations in Latin America: the Cases of Rio de Janeiro and Salvador de Bahia 5.1. Introduction 99 99 101 110 115 115 5.2. 5.3 5.4. 5.5. Why Brazilian Tourism Destinations? Rio de Janeiro and Salvador de Bahia: the applied cases The adapted model Methodology 5.5.1. Data collection 5.5.2. The Survey Instrument 5.5.3. Moments of the Distribution: Mean-Variance-SkewnessKurtosis 5.6. Discussion of findings 5.6.1. The Case of Rio de Janeiro 5.6.2. The Case of Salvador de Bahia 116 121 126 130 130 133 137 139 139 154 Part VI Conclusion and Discussion 169 6.1. 6.2. 6.3. 6.4 6.5. 169 172 173 175 177 Introduction General Findings and Discussion Managerial Implications Limitations and Suggestion for Future Research Conclusions References 185 Appendix: the Italian Tourism Destinations Surveyed 201 INTRODUCTION Tourism is one of the fastest growing industries for many countries around the world. An increasing number of regions are turning to tourism as a crucial engine of economic growth; tourism is now the main source of foreign income for a significant number of developing countries. Therefore, the study of tourism destination competitiveness (TDC) has focused the attention of policy makers, public and private organizations, and tourism researchers. Competitiveness has been identified in the tourism literature as a critical factor for the success of tourism destinations (Pearce, 1997; Crouch & Ritchie, 1999; Kozak & Rimmington, 1999; Buhalis, 2000; Hassan, 2000; Dwyer & Kim, 2003; Enright & Newton, 2004). The great attention on this topic led to a proliferation of definitions of TDC, but there seems to be a consensus in the literature about the fact that achieving TDC is heavily dependent on the sustainability of a tourism destination. Ritchie & Crouch (2000) argue that “competitiveness is illusory without sustainability. To be competitive a destination’s devel- 13 opment of tourism must be sustainable, not just economically and not just ecologically, but socially, culturally and politically as well” (p. 5). Since tourism worldwide has become progressively more competitive, it is of critical importance to analyze strengths and weaknesses of tourism destinations (Richie & Crouch 2000). The majority of the studies in tourism literature concentrate on single aspects of destination competitiveness. Much less attention has been devoted to develop a comprehensive framework of the various components determining the competitive position of a tourism destination. Competition is not based on single features of the tourism product, but on an integrated set of characteristics, resources, facilities and services of a tourism destination (Buhalis, 2000); it is a multifaceted concept encompassing various elements. Many research studies progressively made more comprehensible the notion of TDC. Particular attention has been paid to develop a comprehensive framework of the various components determining the competitive position of a tourism destination. Ritchie & Crouch (2000, 2003) published the most important and detailed work in the analysis of TDC. Other theoretical models were also developed to explain destination competitiveness (De Keyser & Vanhove, 1994; Hassan, 2000; Heath, 2002; Dwyer & Kim, 2003). Many models were also applied with the aim of analyzing the competitive positions of tourism destinations (Sirše & Mihalič, 1999; Dwyer, Livaic, Mellor, 2003; Enright & Newton, 2004; Gomezeli & Mihalič, 2008). The majority of the studies were applied to measure competitiveness of specific countries or group of them. Some empirical TDC studies concentrate on islands (Croes, 2010; Mechinda et al., 2010), big cities (Enright & Newton, 2004, 2005, Minghetti, Montaguti, 2010), particular type of destinations (Botha, Crompton, Kim, 1999; d’Hautserre, 2000; Lee, King, 2009), famous resort destinations (Kozak, 2002), regions/provinces 14 (Faulkner, Oppermann, Fredline, 1999; Cracolici & Nijkamp, 2008; Zhang et al., 2011; Pestana et al., 2011). Very little empirical work has been done in small tourism destinations; TDC research applied to small towns or villages is almost inexistent. Many studies focus on the main factors affecting destination competitiveness (e.g. key attractors, supporting factors, destination management, tourism policy, and demand factor). Most of the work on TDC suggests, with different emphasis, that each one of these factors can improve destination competitiveness, but without a proper testing. There is still no evidence of a significant impact of these factors on the performance of a destination in the competitive market. This study aims at filling this gap, by extending the Richie & Crouch (2000) model and by applying it on a unique dataset of 610 small and medium Italian destinations of excellence. Italy is one of the world’s leading tourism destinations. Italy also has the most World Heritage sites (47) than any other country on the planet. In terms of its performance, Italy ranks 5th worldwide by the number of international tourist arrivals and also 5th by the amount of international tourism receipts (UNWTO, 2012). Italy also ranks 2nd worldwide for accommodation capacity after the United States. Nevertheless, Italy very rarely appears as a case study in international tourism literature (Formica, Uysal, 1996; Mazzocchi, Montini, 2001). It was only recently that a certain number of research studies started to concentrate on Italy/Italian destinations (Cracolici, Nijkamp, 2008; Cracolici, Cuffaro, Nijkamp, 2008; Guizzardi Mazzocchi, 2009; Massidda, Etzo, 2012). Italy can count on thousands of touristic sites, hundreds of medieval villages and historic churches, a great number of museums and archaeological sites which are spread all over the national territory. Nonetheless, tourist arrivals, especially international tourists, are concentrated in big cities, in the coastal areas or in ski resort destinations. This is also due to the 15 fact that Italian tourism faces many problems, including areas of management and marketing, policy and regulation, infrastructures, and quality of accommodation facilities (WEF, 2011; OECD, 2011). Many small destinations located on the mainland still have high growth potentials. Similar considerations can be extended to many small and medium seaside destinations rich in history and culture, where tourism is well developed but highly seasonal. These above are the main reasons why 610 Italian small and medium destinations of excellence both from the mainland and from the coast were chosen as case studies. Specifically, destinations of excellence that have been awarded with important International and National Certifications were selected: • Blue Flag, awarded by Foundation for Environmental Education – FEE (117 municipalities in the sample); • Blue Sail, by Legambiente/League for the environment (295 mu- nicipalities); • Orange Flag, by Italian Touring Club (181 municipalities); • The Most Beautiful Villages in Italy by National Association of Italian Municipalities – ANCI (199 municipalities). The main aim of these awards is the promotion of the diversity, value and authenticity of Italian destinations of excellence, both coastal (“Blue Flags” and “Blue Sails”) and non coastal (“The Most Beautiful Villages” and “Orange Flags”). They are also aimed at establishing a platform for encouraging tourism excellence in various forms. “Blue Flag” is an internationally recognized voluntary eco-label run by the Foundation for Environmental Education (FEE) that is awarded to beaches and marinas that satisfy stringent environmental quality standards and management (FEE, 2006); approximately 3850 beaches and marinas in 46 countries were awarded. In Italy, a roughly similar award, namely the “Blue Sail”, was introduced by Legambiente, the main environmental organization in the country. The awards “The Most Beautiful Villages” and “Orange Flags” 16 are directed to small towns and villages on the mainland not exceeding 15,000 inhabitants. Admission to the Club of “The most beautiful villages in Italy” requires the meeting of a number of prerequisites, both structural, such as the quality of the public and private building heritage, and general, regarding the quality of life in the villages in terms of activities and services for the people. The “Orange Flag” is an Italian recognized voluntary label that is awarded by Italian Touring Club to municipalities satisfying similar criteria to the above. The Dwyer, Livaic, Mellor (2003) approach is followed in defining a list of indicators, in order to operationalize the model. The indicators derive from the major empirical models of destination competitiveness and from the wider literature in tourism planning and management. With respect to previous empirical models, the indicators are particularly focused on the various dimensions of sustainability. In addition, they are tailored to fit the contest of small and medium Italian tourism destinations, as there is no universal set of competitiveness indicators applicable to all destinations at all times (Enright & Newton, 2004; Gomezeli & Mihalič, 2008). A total of 1.220 key tourist stakeholders from 610 Italian municipalities were contacted in the period from April to July 2011. For each destination two stakeholders, one from the public sector and one from the private sector, were chosen: the head of the tourism office and the head of the local hotel association (in small tourism destinations, in the absence of a hotel association, a hotel director was contacted). A total of 550 usable surveys were returned from 370 different municipalities. The response rate was very high, 45,1%, in line with the average response rates of similar studies (Baruch & Holtom, 2008). To reduce the large set of variables to a smaller set a principal component analysis (PCA) was performed, as in Dwyer et al (2004). The output is reasonably similar to the corresponding determinants of the original model; some differences were expected because of the aggregation issue. The study intends 17 to provide a more realistic display of the linkages between the various determinants of destination competitiveness and the performance of a tourist destination. The aim of the study is to test whether and to what extent the current set of indicators and determinants can help explaining the competitiveness of a tourism destination. In particular, the aim is to understand if two fundamental attributes as tourism policy and destination management have a positive role in the competitiveness of a tourist destination. A further objective is to test if a more sustainable tourism policy and destination management can improve TDC. In order to do this, the dependency between the performance scores given by various indicators (tourist arrivals, bed nights, gross occupancy rate of bed nights) and the explanatory variables given by the components resulting from the PCA, was studied by applying an ordinary least square (OLS). The robustness of the estimate was also tested performing a partial least square regression (PLS). The model of destination competitiveness was adapted and also applied to measure the competitiveness of two leading tourism destination in Latin America: Rio de Janeiro and Salvador de Bahia. These two destinations were chosen as applied cases for various reasons. Brazil is an emerging country undergoing through enormous economical and social changes. It is predicted to become the 5th largest global economy by 2025 and it will host both the FIFA World Cup 2014 and the Olympic Games 2016, which puts the country’s tourism and travel industry in the world spotlight. Nevertheless, there is still insufficient academic research on Brazil tourism, especially on the two of the leading tourist destinations in the country, Rio de Janeiro and Salvador de Bahia. Tourism literature on Brazil proves to be still scarce. There have not been enough studies on research, especially regarding destination competitiveness. 18 Brazilian tourism destinations are also interesting case studies because they still have vast growing potential. Moreover, as highlighted by the Travel and Tourism Competitiveness Report published by the World Economic Forum, they are also suffering from serious problems of tourism competitiveness (WEF, 2012). The relevance of the outcomes of this research is supported by the fact that tourism represents a primary resource to Brazil, a destination that has been going through enormous development for the economical and tourism perspective. By acknowledging the level of competitiveness of two leading tourism destinations in Brazil, this study intends to provide local stakeholders with a valuable and accurate body of information on which to base their destination management strategies. Therefore, the primary contribution of this study is essentially practical. Secondly, this study naturally contributes to the increasing literature on destination competitiveness, in which it discusses relevant factors of competitiveness, by measuring these factors from the perspective of tourism experts. The evidences of this research can provide tourism policy makers and practitioners with an overview of the competitiveness of two of the most important cities in Brazil, famous all over the world. Primary quantitative data were collected through experts’ judgement. The chosen participants were tourism experts, including incoming travel agents, tourist guides, hotel managers, travel consultants, tourism professors, tourism students, and tourism public servants and public managers. The data were collected with a web survey. The survey was submitted between December 2012 and January 2013; two weeks after the first invitation to participate, a second e-mail message (reminder) was sent to all the contacts that did not complete the survey in order to increase the participation rate. 19 277 usable responses were received in the case of Rio de Janeiro. 164 usable responses were received in the case of Salvador de Bahia. The high number of respondents was expected, given that in the periods spent by the researcher in Rio de Janeiro and Salvador de Bahia as a visiting student, many people were met in an effort to explain objectives, structure and relevance of the research, including hotel and travel guide associations, travel consultants, tourism professors and academic researchers, public managers and public servant working in the tourism sector. 20 PART I COMPETITIVENESS OF TOURISM DESTINATIONS 1.1. Perspectives of Tourism Destination Competitiveness 1.2. Models of Destination Competitiveness - 1.2.1. The Ritchie & Crouch (2000) model - 1.2.2. The Dwyer & Kim (2003) model - 1.2.3. Theoretical and Applied Models - 1.3. The Challenge of Measurement Competitiveness of Tourism Destinations - 1.4. Tourism Sustainability and Competitiveness 1.1. Perspectives of Tourism Destination Competitiveness The study of tourism destination competitiveness continues to achieve interest among researchers, policy makers and organizations. Competitiveness has been identified in the tourism literature as a critical factor for the success of tourism destinations (Kozak & Rimmington, 1999; Crouch & Ritchie, 1999; Hassan, 2000; Mihalic, 2000; Buhalis, 2000; Heath, 2002; Dwyer & Kim, 2003; Gooroochurn & Sugiyarto, 2005; Mazanec, Wöber & Zins, 2007). Some studies have defined competitiveness either explicitly or implicitly as having more of something such as market share, profits, success, etc. than that of another destination. For example, some destination specific studies have addressed the competitive position of the following countries and regions: the United States (Ahmed & Krohn, 1990); the Caribbean (De Keyser & Vanhove, 1994); South Africa (Kim, Crompton & Botha, 2000); Las Vegas (Chon & Mayer, 1995); Australia (Dwyer, 21 Liviac & Mellor, 2003); Spain and Turkey (Kozak & Rimmington, 1999); European cities (Mazanec, 2007); Mediterranean resorts ( Papatheodorou, 2002); Southeast Asia (Pearce, 1997); South Korea (Kim et al., 2001); Cambodia (Chen, 2008); Asia Pacific (Enright & Newton, 2004); and Cuba (Miller, Henthorne, & George, 2008). The above mentioned studies seem to imply that destinations that enjoy more arrivals and more spending from tourists, or have benefited from a higher market share in the global market than that of others, are considered to be competitive (Hassan, 2000; Sahli, 2004; Craigwell, 2007). There is a widely held view that competitiveness should be linked to high visitor numbers and increasing destination income. Poon (1993), for example, took issue with the assertion that more is better. She suggested four key principles supporting destination competitiveness: (i) put the environment first; (ii) make tourism a lead sector; (iii) strengthen the distribution channels in the market place; and, (iv) build a dynamic private sector. Others discussed topics relevant to destination competitiveness, such as destination positioning (Chacko, 1998); destination management systems (Baker, Hayzelden & Sussmann, 1996); the environment (Mihalic, 2000; Hassan, 2000); strategic management (Evan, 2002); quality management (Go & Govers, 2000); destination marketing (Buhalis, 2000); planning methods (Pearce, 1997) and price competitiveness (Dwyer, Forsyth, & Rao, 2000a, 2000b, 2000c, 2002). Ritchie & Crouch (2003) define competitiveness as the «ability to increase tourism expenditure, to increasingly attract visitors, while providing them with satisfying, memorable experiences and to do so in a profitable way, while enhancing the well-being of destination residents and preserving the natural capital of the destination for future generations». Buhalis (2000) and Hassan (2000) highlight the relationship between competitiveness and economic prosperity and the delivery of an experience that is more satisfying compared to other similar destinations. Buhalis (2000) defines competitiveness as «the effort and achievement of long-term profit- 22 ability, above the average of the particular industry within which they operate, as well as above alternative investment opportunities in other industries». Buhalis (2000) identifies four main objectives for a competitive destination: enhance the long-term prosperity of local people; maximise visitors’ satisfaction; maximize profitability of local businesses and generate multiplier effects; optimize tourism impacts. In Hassan’s view, competitiveness concerns «the destination’s ability to create and integrate valueadded products that sustain its resources while maintaining market position relative to competitors» (Hassan, 2000: 239). According to Dwyer & Kim (2003) destination competitiveness is «the ability of a destination to deliver goods and services that perform better than other destinations on those aspects of the tourism experience considered being important by tourists» (Dwyer & Kim, 2003: 375). D’Hartserre states that (2000, p.23) competitiveness is «the ability of a destination to maintain its market position and share and/or to improve upon them through time». While there seems to be a consensus in the literature about the main objectives of competitiveness, there are various ways of explaining and measuring competitiveness in tourism literature. Various models were developed to explain and/or measure destination competitiveness. Some of them are theoretical: De Keyser & Vanhove, 1994; Crouch & Ritchie, 1999; Ritchie & Crouch, 2000, 2003; Hassan, 2000; Heath, 2002; Dwyer & Kim, 2003. 23 1.2. Models of Destination Competitiveness 1.2.1. The Ritchie & Crouch’s Conceptual Model of Destination Competitiveness The general conceptual model of destination competitiveness developed by Crouch and Ritchie (1999), additionally refined in Ritchie & Crouch (2000) and amply detailed in Ritchie & Crouch (2003), is the most well-known conceptual model of destination competitiveness in tourism literature and has been the starting point for many other research studies about destination competitiveness. The model distinguishes 36 attributes of competitiveness classified into five key factors. It is based on the theories of comparative advantage (Smith, 1776; Ricardo, 1817) and competitive advantage (Porter 1990) adapted to the peculiar characteristics of destinations competition. The model is displayed schematically in Figure 1.1. Ritchie and Crouch (2003), by applying Porter’s (1990) core diamond theory of competitive advantage, provide a discerning framework distinguishing comparative from competitive advantages. Comparative advantage elements are: human and physical resources, availability of know how, capital, tourism infrastructure, and historical and cultural assets. Competitive advantage includes audits and inventories, maintenance, growth and development, efficiency and effectiveness. The micro (e.g., business) and macro (e.g., nature, technology, etc.) environments are impacted by four distinct domains - qualifying and amplifying determinants: destination policy, planning and development; core resources and attractors; and supporting factors and resources – which lend themselves to the design of a conceptual model for destination competitiveness. 24 Figure 1.1.The Ritchie & Crouch’s Conceptual Model of Destination Competitiveness Source: Ritchie & Crouch (2000) The model outlines a series of factors that play a determining role in the competitiveness of a tourist destination. It groups these factors as primary and secondary depending on their relevance. In total, the model identifies 36 destination competitiveness attributes and more than 250 factors. Macro environmental factors are categorized into six principal groups related to the economy, technology, ecology, political and legal developments, sociocultural issues, and the constantly evolving demographic environment. A destination’s competitive (micro) environment is made up of organizations, influences, and forces that lie within the destination’s immediate arena of tourism activities and competition. These elements of the micro environment tend to have a more direct and immediate impact than do elements of the global (macro) environment, as a general rule. The component “core resources and attractors” describes the primary elements of destination appeal. These factors are the key motivators for 25 visitation to a destination. While other components are essential for success and profitability, core resources and attractors are the fundamental reasons that prospective visitors choose one destination over another. Whereas the core resources and attractors of a destination constitute the primary motivations for inbound tourism, supporting factors and resources provide a foundation upon which a successful tourism industry can be established. A further component of the model is “destination policy, planning and development”. A strategic framework for the planning and development of the destination with particular economic and social goals can provide a guiding hand to the direction and structure of tourism development. The “destination management” component of the model focuses on those activities which implement, on a daily basis, the policy and planning framework established under destination policy, planning and development, enhance the appeal of the core resources and attractors, strengthen the quality and effectiveness of the supporting factors and resources, and adapt best to the constraints or opportunities imposed or presented by the qualifying and amplifying determinants. Finally, the potential competitiveness of a destination is conditioned or limited by a number of factors which fall outside the scope of the preceding groups of determinants. These qualifiers and amplifiers moderate or magnify destination competitiveness by filtering the influence of the other groups of factors. They may be so important as to represent a ceiling to tourism demand and potential, but are largely beyond the control or influence of the tourism sector alone to do anything about. 26 1.2.2. The Dwyer & Kim’s Integrated Model of Destination Competitiveness The Dwyer & Kim’s Integrated Model of Destination Competitiveness (Dwyer & Kim, 2003) displays a number of factors that are considered to impact tourism competitiveness, such as available resources (natural resources, cultural assets and heritage items), created resources (tourism infrastructure, available activities), supporting factors (infrastructure in general, quality of service, access to destination), and destination management factors. The model is displayed schematically in Figure 1.2. This model is substantially based on the Ritchie and Crouch model. The model brings together the main elements proposed in the wider literature on firm and national competitiveness and the main elements found in the tourism competitiveness literature. The model displays eight main themes: core resources (endowed and created resources); supporting factors and resource (general infrastructure, quality of services, accessibility); destination management factors (activities and functions); demand conditions (awareness, perception, and preferences); situational conditions (economic, social, cultural, demographic, environmental, political, etc.), and market performance indicators. The integrated model of destination competitiveness retains a good deal of the Crouch & Ritchie framework but there are some significant differences. The distinction between inherited (endowed) and created resources, explicitly drawn in the integrated model, but not in the CrouchRitchie model, seems to be a useful one, which has policy significance. The integrated model explicitly recognizes “demand conditions” as an important determinant of destination competitiveness. The CrouchRitchie model seems to neglect the demand side of competitiveness determination. 27 Figure 1.2. The Dwyer & Kim’s Integrated Model of Destination Competitiveness Source: Dwyer & Kim (2003) Additionally, the integrated model does not provide a separate box for “destination, policy and development” but subsumes this determinant type under “destination management”. Thus, in the integrated model “destination management” includes those factors that shape and influence a destination’s competitive strength, as well as those that create an environment within which tourism can flourish in an adaptive manner. In the integrated model, the competitive (micro) environment and the global (macro) environment are included among the “situational conditions”. Moreover, the integrated model groups some of the elementary determinants of destination competitiveness differently than does the CrouchRitchie model. The Crouch-Ritchie model lumps all infrastructure together under the label “superstructure” and includes this among the “core resources and attractors”. In contrast, the integrated model distinguishes 28 between tourism infrastructure and general infrastructure and allocates only the former to “created resources”. Dwyer & Kim (2003) introduce also a set of indicators of destination competitiveness that can be used to measure the competitiveness of a tourist destination. The main elements of each model are displayed in Table 1.1. Table 1.1 Differences between Ritchie & Crouch’s model and Dwyer & Kim’s model 29 1.2.3. Theoretical and Applied Models In addition to Ritchie & Crouch (2000) and Dwyer & Kim (2003), other theoretical models were developed to explain destination competitiveness: De Keyser & Vanhove (1994); Hassan (2000); Heath (2002). De Keyser & Vanhove (1994) develop a theoretical model underlining the macroeconomics factors influencing tourism industry. They argue that the analysis of a competitive position should take five groups of competitiveness factors into account: tourism policy, macroeconomic, supply, transport and demand factors. The model refers to the Caribbean area (De Keyser & Vanhove, 1994) and was applied in a competitiveness study of Slovenian tourism in 1998 (Sirse & Mihalic,1999). Hassan’s model (2000) highlights the importance of environmental sustainability, as one of the four determinants of tourism competitiveness. The model defines a destination’s commitment to the environment as one of the four determinants of tourism competitiveness; and includes also comparative advantage, industry structure and demand factors. The model is displayed schematically in Figure 1.3. Figure 1.3 Determinants of Market Competitiveness, Hassan’s Model of Destination Competitiveness Source: Hassan (2000) 30 The strategic framework shown in Figure 1.3 focuses attention on the four major determinants of market competitiveness. These four determinants are as follows: 1. Comparative advantage. The destination’s comparative advantage includes factors associated with both the macro and micro environments that are critical to market competitiveness. 2. Demand orientation. The destination’s ability to respond to the changing nature of market demand will influence its competitiveness. Figure 1.4 Detailed Depiction of Determinants of Market Competitiveness, Hassan’s Model Source: Hassan (2000) 31 3. Industry structure. The existence or absence of an organized tourism-related industry structure can be associated with the destination’s ability to compete. 4. Environmental commitment. The destination’s commitment to the environment will influence the potential for sustained market competitiveness. The detailed depiction of Determinants of Market Competitiveness is illustrated in Figure 1.4. Heath’s model (2002) presents an integrated consideration of the several issues involving the concept of competitiveness. Heath (2002) tailors a model that «… brings together the main elements of destination competitiveness as proposed in the wider literature and the main indicators of destination competitiveness as proposed by various tourism researchers such as Crouch et al. … and Dwyer (2001)» (p. 131). Heath’s model consists of components which he labels “foundations”. Another group of items in his model concerns “the cement” covering stakeholders, communication, partnerships and alliances, information and research, and performance measurement. The model also emphasizes various “key success drivers”, a “tourism script” in the form of a strategic framework, “building blocks” related to balancing development and marketing, a “sustainable development policy and framework”, and “strategic marketing framework and strategy”. The model is presented schematically in Figure 1.5. 32 Figure 1.5 Heath’s model of Destination Competitiveness Source: Heath (2002) 33 Other models of destination competitiveness were applied with the aim of analyzing the competitive position of particular destinations (Sirše & Mihalič, 1999; Dwyer, Livaic, Mellor, 2003; Enright & Newton, 2004; Omerzel Gomezelj & Mihalič, 2008). In particular, the Omerzel Gomezelj & Mihalič’s model is displayed schematically in Figure 1.6. Their model is adapted from the Dwyer & Kim’s model. Inherited (INHRES), Created (CRERES) and Supporting Resources (SUPRES) encompass the various characteristics of a destination that make it attractive to visit. Destination Management (DESTMNGM) covers factors that enhance the attractiveness of the inherited and created resources, and includes the activities of destination management organisations, destination marketing management, destination policy, planning and development, human resource development and environmental management. As in Dwyer and Kim (2003), the model also develops a separate box on demand conditions. These (DEMANDCON) comprise the three main elements of tourism demand: awareness, perception and preferences. The factors of situational conditions (SITCOM) can moderate, modify or even mitigate a destination’s competitiveness. 34 Figure 1.6 Omerzel Gomezelj & Mihalič’s Destination competitiveness model, the main determinants. Source: Omerzel Gomezelj & Mihalič (2008) 1.3. The Challenge of Measurement Competitiveness of Tourism Destinations Different approaches for explaining and measuring competitiveness of tourism destinations can be distinguished from the literature. Indicators of destination competitiveness can be classified in objectively or subjectively measured variables. For what concerns the first category, studies such as Gooroochurn & Sugiyarto (2005), Cracolici & Nijkamp (2006), Zhang & Jensen (2006),Mazanec, Wober, Zins (2007), Cracolici, Nijkamp, Rietveld (2008), Cracolici, Cuffaro, Nijkamp (2008), Das & Di Rienzo (2008), 35 Craigwell & Worrel (2008), Croes (2010), Das & Di Rienzo (2010), Zhang et al. (2011), Pestana et al. (2011), make use of published secondary data in order to measure TDC. Quantitative data have often been applied because these were seen as more precise and accurate. However, Crouch (2010) points out that using quantitative data could have some risks. First, the volume of indicators could be massive and discouraging. Second, finding available data for each measure of destination competitiveness would be very problematic. Third, many of the feature measures are multidimensional, abstract or inaccurate. Fourth, many indicators are not always quantifiable and may be necessary subjective. Concerning the second category – qualitative data or “soft measures” – two approaches could be found in tourism literature. In the first approach, competitiveness is measured using survey data of tourists’ opinions and perceptions (Haahti and Yavas, 1983; Haahti, 1986; Javalgi, Thomas, Rao, 1992; Driscoll, Lawson, Niven, 1994; Kozac & Rimmington, 1998, 1999; Botha, Crompton, Kim, 1999; Kozak, 2002; Bahar & Kozac, 2007; Cracolici & Nijkamp, 2008; Mechida et al., 2010). Enright & Newton (2004) claim that tourists could quite easily evaluate the standard components of destination attractiveness, but are less able to know the various factors that influence and determine the competitive position of a tourism destination. Thus, a second approach is based on the empirical evaluation of a number of subjective indicators of tourism competitiveness, surveyed on key tourism stakeholders (Sirše & Mihalič, 1999; Faulkner, Oppermann, Fredline, 1999; Dwyer, Livaic, Mellor, 2003; Dwyer et al., 2004; Kim, Dwyer, 2003; Enright & Newton, 2004, 2005; Omerzel Gomezelj, 2006; Chen, Sok, Sok, 2006; Kaynak & Marandu, 2007; Omerzel Gomezelj & Mihalič, 2008; Lee, King, 2009; Lee, Chen, 2010; Bornhorst, Ritchie, Sheehan, 2010; Crouch, 2010; Dwyer, Cvelbar, Edwards, Mihalič, 2012). Omerzel Gomezelj & Mihalič (2008) assert that the understanding of 36 people who have some significant knowledge of what makes a tourism destination competitive can supply a helpful point of departure for analyses such as this. In this study, a model of destination competitiveness that follows this approach is presented. Some studies focus on classifying the importance of the factors affecting destination competitiveness. Enright & Newton (2005) through survey data from tourism industry practitioners, aim at establishing the relative importance of TDC attributes. Crouch (2010) uses the analytic hierarchy process to evaluate 36 competitiveness factors through an expert survey, in order to classify the importance of attributes which affect TDC. Bornhorst, Ritchie, Sheehan (2010) conduct interviews from tourism managers and stakeholders to find out the critical variables to define the success of a tourist destination. Table 1.2 displays the main theoretical and applied studies on tourist destination competitiveness. In the cases of empirical studies, it is shown the different methodologies for explaining and measuring competitiveness of tourism destinations (secondary data, tourist interviews, stakeholder interviews), the sample and the destination surveyed. 37 Model of TDC WEF index Das, Di Rienzo (2008) De Keyser, Vanhove (1994) WEF index WEF index Das, Di Rienzo (2010) Crouch, Ritchie (1999) Crouch (2010) Croes (2010) Various Various Carribean Islands WTO data, IMF data Craigwell, Worrel (2008) Carribean Islands Carribean Islands Southern Italian Regions Italian Provinces Italian Provinces Sun/Lost City Other Applied Case Data available Model of TDC Turckey USA Applied Case: Country Cracolici, Nijkamp, Rietved (2008) 83 Stakeholders 84 Stakeholders Primary Data: Stakeholders Data available 302 tourists 1086 tourists Primary Data: Tourists Cracolici, Nijkamp (2008) Secondary Data Data available Focused on Marketing Competitiveness of TD Competitiveness of TD Descriptive Cracolici, Cuffaro, Nijkamp (2008) Buhalis (2000 Botha, Crompton, Kim (1999) Bornhorst, Ritchie, Sheehan (2010) Bordas (1994) Bahar, KozaK (2007) Ahmed, Khron (1990) Theorical Table 1.2 Theoretical and Applied Studies on Tourist Destination Competitiveness Primary Data: Tourists Botswana Australia / Korea 162 Korean, 132 Australian Stake. Kim, Dwyer (2003) Finland Finland Various Australia Slovenia Australia/Korea Applied Case: Country 68 Industry experts 9.000 Tourists 42 travel agents 183 Travel Managers 133 Various Stakeholders 210 Tourism Managers 162 Korean, 132 Australian Stake. 163 Tourism stakeholders Primary Data: Stakeholders Kaynak, Marandu (2006) Javalgi, Thomas, Rao (2008) Heath (2002) Hassan (2000) 681 Tourists WEF index Secondary Data Haahti, Yavas (1983) Model of TDC Competitiveness of TD Competitiveness of TD Descriptive 681 Tourists Model of TDC Model for Winter Sport D. Model of TDC Theorical Haahti (1986) Gooroochurn, Sugiyarto (2005) Go Govers (2000) Flagestad, Hope (2001) Faulkner, Oppermann, Fredline (1998) Enright, Newton (2004) Enright, Newton (2005) Dwyer, Livaic, Mellor (2003) Dwyer, Kim (2003) Dwyer, Cvelbar, Edwards, Mihalic (2012) Dwyer et al. (2004) d'Hautserre (2000 Table 1.2 Continued European Destinations 8 European Destinations South Australia Hong Kong H. Kong, Singapore, Bangkok Foxwood Casino Other Applied Case Primary Data: Tourists 1.033 Tour. Mugla, 467 Tour. Mallorca Competitiveness Measures Ritchie, Crouch, Hudson (2004) Zhang, Jensen (2006) Zhang, Gu, Gu, Zhang (2011) Sirse, Mihalic (1999) Model of TDC Ritchie, Crouch (2000) Pestana et al. (2011) Pearce (1987) Omerzel Gomezely, Mihalic (2008) Omerzel Gomezely (2006) Miller, Henthorne, George (2008) Mechinda et al. (2010) Competitiveness of TD WTO data Data available Data available 800 Tourists Various Slovenia Slovenia 187 Various Stakeholders 25 Tourism Experts Slovenia Cuba Various Taiwan - Cambodia Turkey Applied Case: Country 118 Stakeholders 25 Tourism experts Lee, King (2009) Mazanec, Wober, Zins (2007) 200 Stakeholders Primary Data: Stakeholders Lee, Chen (2010) 330 Tourists WTTC Secondary Data KozaK, Rimmington (1999) Competitiveness of TD Descriptive 1.190 Tourists Kozak (2002) Theorical KozaK (2002) Table 1.2 Continued Chinese Regions French Regions South East Asia Regions Koh Chang Taiwan Hot Spring Maiorca and Mugla Maiorca and Mugla Other Applied Case 1.4. Tourism Sustainability and Competitiveness Since the 1990s sustainability has been the focus of discussion and studies. Sustainability has become a prevailing issue in tourism literature (among others, Krippendorf 1987, Inskeep 1991, Müller 1994, Clarke 1997, Middleton & Hawkins 1998, Hassan 2000, Hall 2000, Ritchie & Crouch 2003, Wall & Mathieson 2006) and in many World (WTO 1998, 1999, 2004), or European reports (European Union 2006). Ritchie & Crouch (2000:5) argue that «to be competitive a destination’s development of tourism must be sustainable, not just economically and not just ecologically, but socially, culturally and politically as well (...). Competitiveness is illusory without sustainability». Sustainability is much more than only a function of the natural environment (Global Environmental Facility, 1998). Since the 1980s sustainability has been used more in the sense of human sustainability. This has resulted in the most widely quoted definition of sustainability as a part of the concept of sustainable development; the report of the World Commission on Environment and Development, known as the “Bruntland Report” propose this definition: «sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs» (WECD, 1987). The growing shift towards sustainable development has led to a renewed interest in the impacts of tourism on the environment, society, and culture. However, there has been difficulty in conceptualizing the ways that it produces various environmental, social, and cultural benefits as opposed to merely costs. Tourism is becoming, more than ever before, sensitive to and dependent on a high-quality sustainable environment (Eccles 1995; Ing 1995; Nelson, Butler, and Wells 1993). It is critical for future tourism development plans to be compatible with the environment for the industry to 41 maintain its market competitiveness. Therefore, tourism marketing must focus on forms of tourism that are sensitive to promoting and sustaining the environmental integrity of natural and cultural heritage resources (WTO 1994). Negative effects of tourism development and growth on the destination and its environment can decrease its long term comparative advantage and reduce tourist demand. According to the tourist demand cycle (Butler 1980), new and emerging destinations grow over time in their appeal to tourists. Initially, a new destination goes through an exploration phase during which it attracts few tourists who use existing resources. Then, as the appeal of the destination reaches broader groups through the diffusion of information, higher numbers of tourists visit the destination. As demand increases, new developments emerge as a “push” for growth. As the push for supply-side development increases, through heavy promotion the destination starts to reach critical ranges of carrying capacity and tourists start to become disenchanted with deterioration. Market information needs to be collected and analyzed to alert tourist destinations, particularly those in the maturation stage. Therefore, sustainable tourism development means planning attractions in such a manner that allows tourists to enjoy them, while also having minimal impacts on the host environment and culture. Sustainable development can occur only when the quality of the environment and community life can be preserved indefinitely. To achieve this goal, the local community needs to be included in all stages of development. 42 PART II A MODEL OF TOURISM DESTINATION COMPETITIVENESS 2.1. The Development of the Model - 2.2. Insights into the model: Determinants and Indicators - 2.2.1. Core Resources and Key Attractors - 2.2.2. Tourism Services - 2.2.3. General Infrastructures - 2.2.4. Conditioning and Supporting Factors 2.2.5. Tourism Policy, Planning and Development - 2.2.6. Destination Management - 2.2.7. Demand 2.1. The Development of the Model There are various issues that need to be considered when developing a comprehensive framework of sustainable tourism competitiveness. This model seeks to offer a detailed consideration of the various determinants of competitiveness of a tourism destination. The model recognizes seven key determinants of destination competitiveness, as shown in fig. 2: core resources and key attractors; tourism services; general infrastructures; conditioning and supporting factors; destination policy, planning and development; destination management; demand. As can be seen in the fig. 2, there is a separation between resources and services that transfer the value directly to the tourist and activities supporting or conditioning their performances. This is based on the “value fan” configuration by Flagestad & Hope (2001), which takes as a reference Porter’s (1985) value chain model and Stabell & Fjelstad (1996, 1998) studies. 43 “Core resources and key attractors” and “tourism services” are primary factors concerning the making of the product and the transferring of value to the tourists. They are closely and directly linked with the demand factor. They have the crucial role of central motivators for visiting a tourism destination. Over and above that, there is a complex system of essential prerequisites for destination competitiveness. These issues are related to “tourism policy, planning and development” (TPPD) and to “destination management”. Tourism policy provides the guidelines and directives for the long term developing of a tourism destination. Destination management handles its components in a short term; it is strictly connected with the creation of the tourism product. “Conditioning and supporting factors” can restrain or amplify a destination’s competitiveness. “General infrastructures” provide the foundations upon which a successful tourism industry can be built. “General infrastructures”, “conditioning and supporting factors”, “TPPD”, “destination management” are the activities and conditions which support the performance of primary activities. The one-directional arrows linking supporting and primary activities indicate this causal link. The boxes “TPPD” and “destination management” are grouped within a larger box; moreover “TPPD” is linked forwards to the box “destination management”. This indicates that while tourism policy set a framework within which a competitive destination can be developed on the long term, destination management deals with its various factors in a short time horizon, in order to ensure economic profitability, and avoiding degradation of the elements that forms the competitive position of a destination (Crouch & Ritchie, 1999). The one-directional arrows from this larger box indicates that it can influence both the conditioning and supporting factors and the availability and quality of general infrastructures; but they can also have an important role in the management of the core resources and can also influence the availability and quality of tourism services. As 44 in Dwyer and Kim (2003), the model contains a separate box for the “demand” factor. The arrow from the “key attractors” and “the tourism services” to the “demand” factor is consistent with the Porter’s framework, as appears in the Flagestad & Hope (2001) model. The study extends the Richie & Crouch model (2000) and groups some of the elementary determinants of destination competitiveness differently than does the Ritchie & Crouch model (2000). The elements “core resources and key attractors”, “tourism policy, planning and development” and “destination management” derive from the Richie & Crouch model (2000). They lump together under the label “supporting factors and resources” two subcomponents as “hospitality” and “infrastructure”. In the present model they are regarded as separate factors from “conditioning and supporting factors” and they are explicitly recognized. This is consistent with various studies in tourism literature (Pearce, 1981; Murphy, 1985; Inskeep, 1991; Gunn, 2002) which underline the importance of these components, regarding them as separate primary elements. Inskeep (1991) identifies “tourist attractions and activities, accommodations, other travel facilities and services, transportation, other infrastructures, institutional elements” as the basic components of the tourism developments. Pearce (1981) recognizes five elements of tourism supply: attractions, transport, accommodations, supporting facilities and infrastructure. Gunn (2002) indicates five components of the supply side: attraction, promotion, information, services and transportation. Murphy (1985) distinguishes between demand and supply side; in the supply side he includes: resources (attraction and hospitality), capital (facilities, accessibility and infrastructure) and experience (tourist product). These views underline the importance of these two elements. They represent two of the foundations upon which a successful tourism industry can be established. Moreover, respect to Ritchie & Crouch (2000), the model explicitly 45 recognizes the demand factor as a fundamental determinant, as illustrated by Dwyer & Kim (2003). Figure 2.1. Competitiveness and sustainability of a tourism destination: a model of evaluation 7. DEMAND Primary activities and resources 1. CORE RESOURCES AND KEY ATTRACTORS 4. CONDITIONING AND SUPPORTING FACTORS Support activities and resources 2. TOURISM SERVICES 6. DESTINATION MANAGEMENT 3. GENERAL INFRASTRUCTURES 5. TOURISM POLICY, PLANNING AND DEVELOPMENT Source: adapted from Ritchie & Crouch (2000) Various models were applied with the aim of analyzing the competitive position of particular destinations (Sirše & Mihalič, 1999; Dwyer, Livaic, Mellor, 2003; Enright & Newton, 2004; Gomezelj & Mihalič, 2008). Each one of these empirical models provides very useful insights into destination competitiveness. They focus on several issues and they consist of different determinants and various indicators. However, it is argued that none of the models above provides a comprehensive treatment of the various issues that regards each determinant of destination competitiveness. They do not appear to provide an integrated consideration of the various questions regarding tourism policy, planning and development. In addition, most of them recognize sustainability as a fundamental question, but do not seem to place enough emphasis on the several dimensions of sustainability. 46 It is suggested a set of indicators that is considered useful for analyzing the competitiveness of a tourism destination, as shown in tab. 2.1. The indicators derive from the major empirical models of destination competitiveness, further enriched with indicators that are inferred from the conceptual models of destination competitiveness and from the wider literature in tourism policy, planning and management. The choice is made considering the ability of each variable to identify the most important aspects that contribute to the competitiveness of a destination. The most important criterion for the selection of the indicators is that they are policy relevant, as suggested by Miller (2001). The majority of the indicators proposed refer to sustainability issues. A major frame of reference for the choice of the indicators, is represented by the “Tourism Development’s Magic Pentagon” (Müller, 1994). In Muller’s view, sustainable tourism means establishing the right balance on the following angles of the pentagon: unspoilt nature, healthy culture, subjective well being of the residents, optimum satisfaction of guest requirement, economic health. Pursuing these five objectives means moving to the path to sustainable tourism development. Since the 1990s sustainability has been the focus of discussion and studies. Sustainability has become a prevailing issue in tourism literature (among others, Krippendorf 1987, Inskeep 1991, Müller 1994, Clarke 1997, Middleton & Hawkins 1998, Hassan 2000, Hall 2000, Ritchie & Crouch 2003, Wall & Mathieson 2006) and in many World (WTO 1998, 1999, 2004), or European reports (European Union 2006). Nevertheless, in the previous empirical models, the various dimensions of sustainability have been partially neglected. Thus, the main elements of sustainability in their economic, social and environmental dimension (Swarbrooke, 1999) are translated into specific indicators. Sustainability is much more than only a function of the natural environment (Global Environmental Facility, 1998). Since the 1980s sustainability has been used 47 more in the sense of human sustainability. This has resulted in the most widely quoted definition of sustainability as a part of the concept of sustainable development; the report of the World Commission on Environment and Development, known as the “Bruntland Report” propose this definition: «sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs» (WECD, 1987). These findings support the view that «to be competitive a destination’s development of tourism must be sustainable, not just economically and not just ecologically, but socially, culturally and politically as well (...). Competitiveness is illusory without sustainability» (Ritchie & Crouch, 2000:5). 48 Table 2.1. Selected Indicators of Destination Competitiveness 1) CORE RESOURCES AND KEY ATTRACTORS Natural resources Historical and archaeological sites Artistic and architectural features Green areas Cultural attractors Events Leisure activities Evening entertainment and nightlife Gastronomy ant typical products Shopping opportunities 2) TOURISM SERVICES Quality of accommodations Quantity of accommodations Environmental friendliness of accommodations Food services quality Tourist oriented services 3) GENERAL INFRASTRUCTURES Environmental friendliness and quality of transportation services Quality of road system Communication system Accessibility of facilities by disabled persons Medical care facilities Sanitation, sewage and solid waste disposal 4) CONDITIONING AND SUPPORTING FACTORS Accessibility of destination Proximity to other tourist destinations Destination links with major origin markets Value for money in destination tourism experience Value for money in accommodations Presence of local businesses Management capabilities of tourism firms Use of IT by tourism firms Local supply of goods and services to tourists and tourism businesses Level of professional skills in tourism Hospitality of residents towards tourists Environmental quality Safety 49 Table 2.1. Continued 5) TOURISM POLICY, PLANNING AND DEVELOPMENT Political commitment to tourism Integrated approach to tourism planning Environmentally compatible approach to tourism development planning Public sector commitment to minimizing negative environmental impacts of tourism Public sector commitment to minimizing negative social impacts of tourism on local community Public sector commitment to maximising economic impacts of tourism on local community Clear policies in creating formal employment opportunities Emphasis on community empowerment Public sector commitment to tourism/hospitality education and training Collaboration among public sector units for local tourism development Cooperation between public and private sector for local tourism development Emphasis on community participatory process in tourism planning 6) DESTINATION MANAGEMENT Effectiveness of destination positioning Effective market segmentation Effectiveness in crafting tourism experiences Tourist destination communication Visitor satisfaction management Tourist guidance and information Stewardship of the natural environment Tourism impacts management and monitoring Effectiveness of destination management structure Promotion of partnerships between public and private stakeholders Promotion of partnerships among tourist businesses 7) DEMAND FACTOR Tourists' interests in natural and cultural local heritage Tourists' respect for local traditions and values Tourists' enviromental awareness Awareness of destination (Non) seasonality in tourist flows Level of repeat visitors "Fit" between destination products and visitor preferences 50 2.2. Insights into the model: Determinants and Indicators 2.2.1. Core Resources and Key Attractors Core resources and key attractors are the fundamental reasons why visitors choose one particular destination over another. There are various types of attractors (natural, cultural, events, activities, etc.); they provide the foundation for a memorable experience. Natural resources can be considered among the most important resources for a tourism destination. A natural resource is something that exists in nature which can be used by humans, also for tourism purposes, at current economic, social, cultural, and institutional conditions. In recent years, increasing awareness among tourism researchers of the relations between tourism and natural resource management has resulted in a significant body of academic literature examining this issue. Mihalič (2000) points out that a well-managed destination environment is the best destination advertiser. «A destination needs to protect the integrity and the attraction of its own product, plus guard against the action and rivalry of competitors» (Murphy 1995: 166). Cultural resources are represented by three indicators: “historical and archaeological sites”, “artistic and architectural features”, “cultural attractors”. «Culture, broadly defined, is a second very powerful dimension of destination attractiveness» (Ritchie & Crouch, 2003:115). In the last two decades, many texts were published about this subject of rising interest (Richards, 1996, 2007; Richards & Munsters, 2010; Boniface, 1995; Walle, 1998; McKercher & du Cros, 2002; Sigala & Leslie, 2005; Smith, 2003, 2009; Smith & Robinson, 2006). A high proportion of international travellers is now considered cultural tourists (Richards, 1996). The culture and heritage attractors of a destination provide a significant force for the potential visitor (Ritchie & Zins, 1978; Cohen, 1988; Prentice, 1993; Murphy, et al., 2000). 51 Events, leisure activities, nightlife and shopping are also primary motivations to visit a destination (Ritchie & Crouch, 2003). Events could extend the seasonal life, especially in tourism destination with an inbuilt seasonality (Getz, 1989, 1991; Hall, 1987; Faulkner, 2003). Hallmark events can generate high levels of interest in visitors and several advantages (Hall, 1992). This explains the high academic interest in events management and the publishing of several books on this subject (Getz, 1997; Shone & Parry, 2001; Van der Wagen, 2002; Yeoman et al., 2003; Raj et al., 2008; Allen et al., 2008; Bowdin et al., 2010; Robinson, 2010). The assortment of activities is of rising significance as the visitors ever increasing seek experiences that overtake the more inactive tourism of the past (Poon, 1993). Entertainment, also, can be a key supplier to the tourism sector (Hughes, 2000). It may occupy a major position in the destination competitive strategy, depending on its perceived uniqueness rather than on its quantity (Dwyer & Kim 2003). A research study demonstrates that while shopping is rarely mentioned as a primary reason for travel, it is perhaps the most universal of tourist activities (Kent, Shock, Snow, 1983); the study shows the economic importance for the local economy and reveals that shopping is for many tourists the most popular activity. Shopping tourism can also be seen as a vehicle to revitalize traditional urban centres, deteriorating resorts and even rural areas (Jansen-Verbeke, 1991). Timothy (2005) provides a comprehensive examination of the relationships between tourism, leisure and shopping. “Gastronomy and typical products” is also included among the key attractors. Systematic research on gastronomy and tourism has been neglected until recently. Gastronomy is one of the most important elements affecting the authenticity of a tourism destination (Sedmak & Mihalič, 2008). Hjalager & Richards (2002) explore the role of gastronomy as a 52 source of regional identity, and also a source of economic development related to tourism. 2.2.2. Tourism Services The core resources constitute the pull force of the destination, but if these are not accessible, if the private sector doesn’t create the business services which bring them to the market, if the infrastructures are not adequate, they will be considerably constraints in their ability to pull tourists (Ritchie & Crouch, 2003). «Under the pressure of increasing arrivals the business sector responds with the development of specialised services for visitors and so the area begins to take on the familiar characteristics of a tourist destination» (Laws, 1995: 9). This determinant includes “quality”, “quantity” and “environmental friendliness” of tourist accommodations, “food service quality” and “tourist oriented services”. Hospitality has been defined as “the very essence” of tourism (Page, 2003: 254) and has a very important role in the generation of economic benefits for the community (Cooper et. Al, 1998). A crucial issue related to hospitality is quality (Qu, Ryan & Chu, 2000); this question has been examined in a number of studies (among others, Sargeant & Mohamad, 1999; Tsang & Qu, 2000; Briggs, Sutherland, Drummond, 2007). Among the various forms of tourist accommodations, for many nations hotels are the more significant in terms of number of tourists and revenues (Page, 2003). According to Go, Pine, & Yu (1994), there is a mutual influence between destination’s economic growth and hotels performance. Nevertheless, many approaches in literature refer only to a limited number of elements of the hotel industry competitiveness; much less attention has been devoted to develop a comprehensive framework (Tsai, Song & Wong, 2009). Like the lodging industry, the food services are fundamental in order to guarantee the best possible experience to visitors. The food services consist of traditional restaurants, fast-food restaurants, cafeterias, travel 53 food services (in hotels, motels, airports). Over the past two decades the food business has grown at a exceptional rate, especially the fast-food segment. Although the fast food segment is the most rapidly growing segment, the high-quality segment and the local and traditional restaurants must not be overlooked; much of tourism business is based on customer seeking a special and authentic experience (Sedmak & Mihalič, 2008). 2.2.3. General Infrastructures General infrastructures provide the foundation upon which a tourism destination can be built and can be a particularly critical factor in less developed countries or regions, which often have limited infrastructures (Heraty, 1989). Even if a destination may possess a great quantity of resources and attractors, it is required the support of other elements in order to be adequate to receive tourists (Gunn, 2002). «The natural resources of tourism have no economic value in themselves. That is, for example, a scenic valley has no economic value in itself if the only creatures able to experience the scenery are the local fauna. Building a road into the valley, thus providing access to tourists does however provide value» (Crouch & Ritchie, 1999: 143). This determinant covers the road system and transportation, the communication system, the medical care facilities, sanitation and sewage. Kaul (1985), Prideaux (2000), Khadaroo & Seetanah (2007) shed light on the relevance of transport infrastructure as a critical component of successful tourism development. Passenger transportation relevance has been extensively recognized both in the tourism planning literature (Gunn, 2002; Hall, 2000; Inskeep, 2001), and in the wider tourism literature (Goeldner & Ritchie, 2003; Cooper et al., 1998; Page, 2003). Telecommunications are also fundamental for tourism, both for the operation of accommodation and touring services and for tourists, especially business travellers. This determinant is also composed by further attributes related to general infrastructures not-specific to tourism 54 (medical care facilities, sanitation, etc.). Maintaining minimum sanitation and hygiene standards is a prerequisite for tourism development: adequate medical care facilities are essential in any area, including tourism areas (Inskeep, 1991). 2.2.4. Conditioning and Supporting Factors Conditioning and supporting factors can strengthen or weaken the impact of all other determinants of destination competitiveness. This determinant incorporates measures related to the accessibility of a destination and to the links with other tourist areas. The “accessibility of destination” and the “proximity to other tourist destinations” are strictly connected to the infrastructure issue. Accessibility is concerned with the easiness to enter the destination, in part influenced by spatial issues, in part conditioned by transportation services. Proximity to other tourist areas can have an important role in the tourist development of a destination (Gunn, 2002); it is also influenced by transportation facilities. Destination links with major origin markets depend on the professional, organizational and personal ties that stimulate people to visit the destination; the challenge facing destination manager is to determine how to use these bonds to stimulate and facilitate travel to the area (Ritchie & Crouch, 2003). “Value for money in accommodation” and “value for money in destination tourist experience” are two more variables included in this determinant. A major element of attractiveness for a tourism destination is the cost of using tourist facilities and services within the destination compared to the costs within similar destinations (Inskeep, 1991). The price tourists pay to visit and enjoy a destination experience plays a key role in determining the choice travellers make (Crouch, 1992). Price competitiveness has been defined as the destination price differentials coupled with exchange rate movements, productivity levels of various components of 55 the tourist industry and qualitative factors affecting the attractiveness of a destination (Dwyer Forsyth, Rao, 2000). Various indicators refer to the conditions of the local businesses. Wall & Mathieson (2006: 138) claim that «it is essential that the tourist industry is serviced, as far as possible, by local producers if its full potential contribution to the local economy is to be realized». Page (2005) examines the questions affecting the management of the very fragmented nature of the businesses which may refer to tourism (accommodation and hospitality services, tour agencies, retailers, visitor attractions, transportation services, etc.). Moutinho (2000) widely analyzes the various aspects of the management of the tourism firms. On the question of the skill levels, Choy (1995) observes that the prevalence of hotels, restaurants and bars in tourism may induce to think that tourism industry is relatively low skilled. The great changes which have happened in tourism have made organizations more competitive and customers more demanding. Baum (1995) argues that skill levels and human resource management can play a strategic role in the challenge to improve the quality of the tourism product and enhance the market position of tourism destinations. Concerning the “use of IT by tourism firms”, Rimmington & Kozak in 1997 stated that IT could have created first and second class tourism destinations/organizations. Buhalis & Cooper in 1998 noted that the future competitiveness of tourism industry would have mostly depended on the range of telecommunication technology used. The forecasts have become reality; evidences show that operators and destinations with undeveloped telecommunication system are less suitable to reach potential tourists and to manage customers. This determinant is also associated with three more variables: “hospitality of residents”, “quality of the environment” and “safety”. “Hospitality of residents towards tourists” is an important element of the overall travel experience. The limit of tolerance for tourism may be described as 56 a social carrying capacity because exceeding this limit, will have negative effects on the industry, since an unfriendly atmosphere will reduce destination attractiveness» (Murphy, 1985:127). The quality of the environment is related to the attractiveness of the destination: tourism and environment are in a very complex relationship (Butler, 2000). In a progressively more competitive business situation, the environmental quality of the tourist destinations represents a vital ingredient. The attribute “safety” is also included in this determinant. During the vacation there is a possible risk of violence against tourist. Security problems are higher in particular destinations which are facing rapid development. Supposed risks and safety concerns were found to be stronger predictors of not choosing regions for vacation (Sonmez & Graefe, 1998). 2.2.5. Tourism Policy, Planning and Development There is an extensive literature on tourism planning with various emphases, including Gunn’s concentration on spatial planning (Gunn, 2002), Murphy’s work on a community approach (Murphy, 1985), Hall’s emphasis on the various levels of planning (Hall, 2000) and Inskeep’s comprehensive approach (Inskeep, 1991). «Tourism policy can be defined as a set of regulations, rules, guidelines, directives and development/promotion objectives and strategies that provide a framework within which the collective and individual decisions directly affecting long-term tourism development and daily activities within a destination are taken» (Goeldner & Ritchie, 2003: 413). Hall (2000) states that tourism planning needs a comprehensive and integrated approach, which recognizes that resources, services, facilities and infrastructures are interrelated with one another and with the social, cultural and natural environment. Planning for tourism is rarely exclusively devoted only to tourism and takes place in many forms (e. g. development, infrastructure, land and resource use, organization, human resource); structures (e.g. government, quasi-government, and 57 non-governmental organizations); scales (international, transnational, national, regional, local, site) and over different times scale (Hall, 2000). Getz (1986) reviews 150 models of tourism planning and classifies them into several categories. Getz (1987) also identifies four broad traditions in tourism planning, not mutually exclusive: boosterism, an economic/industry-oriented approach, a physical/spatial approach, and a community oriented approach. An important objective of tourism planning is to combine the tourism development with the social and economic life of a community (Gunn, 2002). Destination areas need to be planned with sensitivity to social, environmental, and economic impacts in order to minimize user conflicts and environmental stress. Insufficient attention to factors determining economic, social and environmental sustainability, have the potential to lead to undesirable consequences (Hall, 2000). That is the reason why various indicators comprised by this determinant refer to environment protection and minimization of negative social and cultural impacts. This determinant also refers to variables concerning the public sector commitment to maximizing economic impact of tourism on local community. Any tourism strategy must be able of meeting the economic needs of the residents over the long terms (Ritche & Crouch, 2003). In many authors’ view, economic benefits from tourism should be distributing among the population (among others, Müller, 1994; Ritchie & Crouch, 2003; Wall & Mathieson, 2006). Tourism industry must concentrate the efforts on increasing the utilization of local labour; this also depends on the public sector commitment to tourism and to hospitality education. The emphasis on community empowerment is also essential in order to increase the capacity and capability of the people working in the tourism industry, «it is an important way of affecting impacts in ways that are benign to destination communities» (Wall & Mathieson, 2006: 307). 58 “Collaboration among public sector units”, “cooperation between public and private sector” and “emphasis on community participatory process” are three more indicators comprised by this determinant. Wall & Mathieson (2006) claim that organizations at all levels should try to coordinate development and planning initiatives. Gunn (2002) points out that an important planning effort would be greater collaboration among public sector units: fragmentation of policy regulations and managerial practices tends to reduce greatly the competitiveness of a tourism destination. Gunn also asserts that antagonism and lack of collaboration between governmental agencies and private enterprises do not allow private investors adequate freedom. Hall (2000) argues that a destination needs to develop a series of positive inter-organisational relationships in which common goals should be established. Tourism generates changes which have serious consequences for residents in tourism areas. Because tourism affects the entire community, participatory planning is essential (Murphy, 1985). Since the publication of Murphy’s text on this issue, community-based tourism has become an area of extensive research in recent years. 2.2.6. Destination Management The success of tourism relies on a coordinated approach to the planning, development, management and marketing of the destination (Ritchie & Crouch, 2003). While tourism policy set a framework within which a competitive destination can be developed on the long term, destination management deals with its various factors in a short time horizon, in order to ensure economic profitability while avoiding degradation of the elements that forms the competitive position of a destination (Crouch & Ritchie, 1999). Swarbrooke (1999: 346) claims that «no one type of tourism is inherently more sustainable, or better than any other. Managed well, probably any kind of tourism can be highly sustainable, while managed badly all tourism is, perhaps, unsustainable». Destination management has 59 become a prevailing issue in tourism literature and many academic books were published (among others, Laws, 1995; Ritchie & Crouch, 2003; Weaver & Lawton, 2006; Buhalis & Costa, 2006; Wang & Pizam, 2011). This determinant incorporates various indicators related to the destination marketing. Destination marketing is a fundamental component of destination management. Buhalis (2000) asserts that destination marketing “facilitates the achievement of tourism policy”. Kozac & Baloglu (2010) point out that destination marketing is more challenging than other goods and services. A growing number of academic conferences featuring this theme have emerged; there have also been a number of papers related to destination marketing published in academic journals. In a highly competitive tourism market, segmentation, positioning and communication strategies are crucial to places aiming at develop or consolidate visitor interest and expenditure. Market segmentation has been defined as the process of dividing a potential market into different groups, and selecting one or more segments as a target to be reached with a distinct marketing mix (Wilkie, 1986). For what concerns destination positioning, many definitions exist in literature. Heath and Wall (1992:136) assert that positioning regards the development and the communication of significant differences between the offer of a region compared to competitors’ offer which address to the same market segment. Ahmed (1991) and Grabler (1997) also recognize that an accurate positioning strategy for a destination requires a comparison with the competitors. Richie & Crouch (2003: 200) define the destination’s position in the market «how a destination is perceived by potential and actual visitors in terms of the experience (and associated benefits) that it provides relative to competing destinations». Pike & Ryan (2004) list the key constructs to be considered to enhance destination position effectiveness. Many other variables are included in this determinant. “The effectiveness in crafting tourism experiences” is of rising significance as the visitor 60 ever increasing seeks experiences that overtake the more inactive tourism of the past (Poon, 1993). A progressively more important factor of the tourism system is the “traveller guidance and information”; nevertheless, Gunn (2002) notes that many public tourism agencies still confuse information with promotion. The “visitor satisfaction management” is also a fundamental issue. Evidence has shown that visitor satisfaction relates to product development and quality issues that can only be met through both improved training and cooperation between the public sector and the tourism industry (Baum, 1995). “Stewardship of the natural environment” and “tourism impacts monitoring” are also considered in this determinant. The management of the natural environment is one of the most important issues facing the world at the moment. Mihalic (2000) asserts that a well-managed destination environment is the best destination advertiser. Ritchie & Crouch (2000) use the word “stewardship” to give special emphasis on caring for the longterm well being of the natural resources. In order to protect the integrity of the attractions of a destination, it is fundamental to monitor tourism impacts. Monitoring tourism impacts implies systematic investigation of the changing effects of tourism (Laws, 1995). Tourism is a composite sector, including a network of interconnected stakeholders and organisations, both public and private, working together. Private-public sector configuration through partnership is difficult to achieve but would be highly desirable (Go & Govers, 2000). Tourism is a very fragmented and heterogeneous industry with many small businesses. A DMO (destination management organization) serves as a coordinating body for the many organizations involved in tourism. A primary aim of the DMOs is to promote partnerships among the various operators. DMOs, whose jurisdictions may cover a country, state/province, region, or a specific city/town, are a critical component of the tourism industry. DMOs can take various forms, may have low/high level of formalization, 61 can have various juridical status and type of organizations. DMO members may include governmental bodies, business associations, individuals or firms that directly or indirectly support tourism (hotels, restaurants, tour operators). The effectiveness of the DMO can play a critical role, helping local firms to build sustainable competitive advantage and to create competitive advantage for the entire destination (Sainaghi, 2006). 2.2.7. Demand Factor While the centre of the focus of the Ritchie & Crouch competitiveness model is the supply-side, Dwyer & Kim (2003) emphasize that focusing only on the supply-side factors gives an incomplete picture of destination competitiveness. The nature of demand for the industry’s product is regarded to have a significant influence also in the wider competitiveness literature (Porter, 1990). According to Dwyer & Kim (2003), this seems to be similar in the tourism contest. Three indicators - “tourists’ interest in local heritage”, “tourists’ respect for local culture” and “environmental awareness” - are connected with the concept of responsible tourist behaviour. Frequently, tourists forget many social norms that control their social life in their place of origin and «feel relatively free to indulge in a relaxed dress code, loose sexual morals, or heavy drinking, or over eating» (Laws, 1995: 74). Problems of crime, prostitution, drugs or alcohol may be aggravated by “non responsible” tourism. Sharpley (1994: 84) gives a description of the responsible tourist as the person who «seeks quality rather than value, is more adventurous, more flexible, more sensitive to the environment and searches for greater authenticity than the traditional, mass tourist». Swarbrooke (1999) lists the responsibilities of the tourists: obeying local laws, not offending cultural norms of behaviour, not harming physical environment, minimize the use of scarce resources. Tourist codes of behaviour have also been developed 62 to minimize negative impacts of tourists on the social and physical environment (e.g. Mason & Mowforth, 1996). Demand also involves seasonality. Seasonality is one of the main distinctive features of the tourism phenomenon. Strong seasonality causes difficulties for businesses and for destination managers, as facilities to meet peak demand has to be established, and at other time of the year reduced tourism activity cannot sustain the peak level of business (Laws, 1995). The special 1999 issue of Tourism Economics on this topic made an important contribution to the understanding of the problems related to seasonality; Baum (1999) summarizes the implications of seasonality in a destination by listing various disadvantages. The “awareness of a destination” is another important element of the demand factor. The effectiveness of the marketing effort depends on an understanding of potential visitors’ interests and attitudes toward the destination. Also the “fit between destination products and visitor preferences” is recognized to have a crucial role in satisfying visitor expectations (Dwyer, Livaic, Mellor, 2003). It is one of the main factors affecting the intentions to revisit a tourist destination. 63 64 PART III METHODOLOGY AND RESEARCH DESIGN 3.1. Methodology - 3.1.1 Data collection - 3.1.2. The Survey Instrument - 3.1.3. Principal Component Analysis - 3.1.4. OLS Regression - 3.1.5. PLS Regression - 3.2. The Italian Case 3.3. The Case Study: the Italian Destinations of Excellence 3.3.1 The Sample - 3.3.2. The Destinations awarded with the “Blue Flag” - 3.3.3. The Destinations awarded with the “Blue Sail” - 3.3.4. The Destinations awarded with the “Orange Flag” - 3.3.5. “The Most Beautiful Villages in Italy” 3.1. Methodology 3.1.1. Data Collection A survey instrument was prepared from the list of indicators of tourism destination competitiveness (see the next paragraph). The data were collected with a web survey. The web survey required respondents to rate their own tourism destination’s performance, on a 5-point Likert scale, on each of the 64 competitiveness indicators, against a reference group of destinations. «It would be meaningless to ask respondents to give absolute ratings for any destination on any given attribute of competitiveness» (Dwyer, Livaic, Mellor, 2003). This is motivated by the fact that a given location is not competitive in a vacuum, but against competing destinations (Kozac & Rimmington, 1999, Enright et. al., 1997; Enright & Newton, 2005; Bahar & Kozac, 2007; Gomezelj & Mihalič, 2008). As a conse- 65 quence, the web survey began by asking respondents the identification of the main competitive locations (maximum 5). The questionnaire was pretested on five hotel managers, on five tourism researchers and on five head of tourism public offices. On the basis of the pre-test, some indicators were simplified and/or rewritten. The final draft of the model was screened by a panel of both academics and practitioners. A total of 1.220 key tourist stakeholders from 610 Italian municipalities were contacted in the period from April to July 2011. For each destination two stakeholders, one from the public sector and one from the private sector, were chosen: the head of the tourism office and the head of the local hotel association (in small tourism destinations, in the absence of a hotel association, a hotel director was contacted). They were first contacted by phone to explain the objective of the study. A link to the websurvey was sent them after the first contact. Specifically, destinations of excellence that have been awarded with important International and National Certifications were selected: • Blue Flag, awarded by Foundation for Environmental Education – FEE (117 municipalities in the sample); • Blue Sail, by Legambiente/League for the environment (295 mu- nicipalities); • Orange Flag, by Italian Touring Club (181 municipalities); • The Most Beautiful Villages in Italy by National Association of Italian Municipalities – ANCI (199 municipalities). The main aim of these awards is the promotion of the diversity, value and authenticity of Italian destinations of excellence, both coastal (“Blue Flags” and “Blue Sails”) and non coastal (“The Most Beautiful Villages” and “Orange Flags”). They are also aimed at establishing a platform for encouraging tourism excellence in various forms. “Blue Flag” is an internationally recognized voluntary eco-label run by the Foundation for Envi- 66 ronmental Education (FEE) that is awarded to beaches and marinas that satisfy stringent environmental quality standards and management (FEE, 2006); approximately 3850 beaches and marinas in 46 countries were awarded. In Italy, a roughly similar award, namely the “Blue Sail”, was introduced by Legambiente, the main environmental organization in the country. The awards “The Most Beautiful Villages” and “Orange Flags” are directed to small towns and villages on the mainland not exceeding 15,000 inhabitants. Admission to the Club of “The most beautiful villages in Italy” requires the meeting of a number of prerequisites, both structural, such as the quality of the public and private building heritage, and general, regarding the quality of life in the villages in terms of activities and services for the people. The “Orange Flag” is an Italian recognized voluntary label that is awarded by Italian Touring Club to municipalities satisfying similar criteria to the above. A total of 550 usable surveys were returned from 370 different municipalities. The response rate was very high, 45,1%, in line with the average response rates of similar studies (Baruch & Holtom, 2008). 3.1.2. The Survey Instrument A self-administered on line questionnaire was used (LimeSurvey, www.limesurvey.org). LimeSurvey (formerly PHP Surveyor) is a free and open source online survey application written in PHP based on a MySQL,PostgreSQL or MSSQL database, distributed under the GNU General Public License. LimeSurvey is a web application that is installed to the user’s server. After installation, users can manage LimeSurvey from a web-interface. Designed for ease of use, it enables users to develop and publish surveys, and collect responses, without doing any programming. Surveys can include a variety of question types that take many response formats. 67 In the present survey, questions were added in groups, one for each determinant. The questions within each group (indicators) were organized on the same page. Figure 3.1 shows the front page of the web-survey, figure 3.2 illustrated the first page of the online questionnaire. Figure 3.1 Front page of the Web-Survey 68 Figure 3.2 Fisrt page of the Web-Survey 69 3.1.3. Principal Component Analysis To reduce the large set of variables to a smaller set a principal component analysis (PCA) was performed using STATA version 11.0 on the responses to the 64 questionnaire items measuring destination competitiveness. Principal component analysis (PCA) is used by the great majority of the scientific disciplines and it is one of the most well-known multivariate statistical techniques. It is also likely to be the oldest multivariate technique. In fact, as pointed out by Abdi & Williams (2010), its origin can be traced back to Pearson (1901) or even before (see Grattan-Guinness, 1997, p. 416 and also Stewart, 1993; Boyer and Merzbach, 1989), but its modern instantiation was formalized by Hotelling (1933) who also coined the term principal component. PCA analyzes a data table representing observations described by several dependent variables, which are, in general, inter-correlated. Its goal is to extract the important information from the data table and to express this information as a set of new orthogonal variables called principal components. PCA also represents the pattern of similarity of the observations and the variables by displaying them as points in maps. The goals of PCA are to: (a) Extract the most important information from the data table; (b) Reduce the size of the data set by keeping only this important information; (c) Simplify the description of the data set; (d) Analyze the structure of the observations and the variables. In order to achieve these objectives, PCA computes new variables named “principal components” which are obtained as linear combinations of the original variables. The first principal component is required to have the largest possible variance (i.e., inertia and therefore this component will “explain” or “ex- 70 tract” the largest part of the inertia of the data table). The second component is computed under the constraint of being orthogonal to the first component and to have the largest possible inertia. The other components are computed likewise. The values of these new variables for the observations are called factor scores, these factors scores can be interpreted geometrically as the projections of the observations onto the principal components. After the extraction the researcher must decide how many factors to retain for rotation. Both overextraction and underextraction of factors retained for rotation can have deleterious effects on the results. The default in most statistical software packages is to retain all factors with eigenvalues greater than 1.0. Alternate tests for factor retention include the scree test, Velicer’s MAP criteria, and parallel analysis (Velicer & Jackson, 1990). According to Costello and Osborne (2005) the best choice for researchers is the scree test. The scree test involves examining the graph of the eigenvalues (available via every software package) and looking for the natural bend or break point in the data where the curve flattens out. The number of datapoints above the “break” (i.e., not including the point at which the break occurs) is usually the number of factors to retain, although it can be unclear if there are data points clustered together near the bend. The next decision is rotation method. As with extraction method, there are a variety of choices. Varimax rotation is by far the most common choice. The Dwyer et al. (2004) approach is followed in this study. The PCA is a useful technique for multivariate statistics, and it was also used in the TDC research (Dwyer et al., 2004; Cracolici, Nijkamp, 2008). The most common approach is the Kaiser criterion that recommends to retain only components with a latent root or eigenvalue greater than 1. Scree test was 71 also performed. The final structure was based on the Varimax rotation method. Principal Component Analysis produced 13 components which significantly explain the variation in responses. They explain 69.66% of the total variance, which is reasonable for a dataset of this kind. Dwyer et al. (2004) obtained similar results after applying a PCA to a similar set of indicators. First, it has to be taken into account the heterogeneity of the 64 variables that constitute the model. Second, it has to be considered that the dataset is made up of a mixed group of different destinations: from small villages on the mainland, to well known coastal resort destinations with tens of hotels and thousands of tourist arrivals every year. 3.1.4. OLS Regression The dependency between the performance scores given by various indicators (tourist arrivals, bed nights, gross occupancy rate of bed nights), and explanatory variables which are the components of destination competitiveness resulting from the PCA, is studied by applying an ordinary least square (OLS). Yi , 2009   0  1Componentsi , 200409   2Controls i  i ,t Where Y = – i) (log) tourist arrivals – ii) (log) No. of bed-nights – iii) Gross occupancy rate of bed-places In statistics, ordinary least squares (OLS) or linear least squares is a method for estimating the unknown parameters in a linear regression 72 model. This method minimizes the sum of squared vertical distances between the observed responses in the dataset and the responses predicted by the linear approximation. The resulting estimator can be expressed by a simple formula, especially in the case of a single regressor on the righthand side. The OLS estimator is consistent when the regressors are exogenous and there is no perfect multicollinearity, and optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated. Under these conditions, the method of OLS provides minimumvariance mean-unbiased estimation when the errors have finite variances. Under the additional assumption that the errors be normally distributed, OLS is the maximum likelihood estimator. OLS is used in economics (econometrics) and electrical engineering (control theory and signal processing), among many areas of application. In this case, the general model has a dependent variable measured at the time t 2009 and the vectors of factors measured in a medium term period of time (2004-2009). As indexes of performance for the destinations, the logarithm of tourist arrivals, the logarithm of bed nights and the gross occupancy rate of bed nights, are chosen. This is motivated by the fact that tourist arrivals and bed nights have very positively skewed distributions (see the figures 3.6 and 3.7). As controls the population, the area (in square metres), the type of zone (as classified by Istat) and the location (coastal or not) were used. Data on number of bed places, population, area, elevation, type of municipality, etc. have been obtained from the National Institute of Statistics (Istat). Data on tourist arrivals and nights spent by tourists in each municipality in various years has been obtained from the various Regional and Provincial Tourism Observatories. Both tourist arrivals and number of bed nights can be considered as measures of the attractiveness of a tourist destination. Arrivals generate 73 bed nights and these two notions are similar, but it is interesting to separate them because the number of bed nights can better characterize the economic returns of tourism. Lim (1997) argues that the number of nights provides a better proxy for tourism demand. Bakkal (1991) suggests that this measure is more closely related to actual tourist expenditure. In an increasing number of studies overnight stays is chosen as a dependent variable versus tourist arrivals (see e.g. Song, Witt, & Jensen, 2003; Garin Munoz, 2007; Choyakh, 2008; Fernandez-Morales & Mayorga-Toledano, 2008; Guizzardi, Mazzocchi, 2010). Crouch (2010) points out that TDC is more concerned with a destination’s ability to reach a set of goals, some of which may be represented by measures of demand (arrivals, bed-nights, tourists expenditures), but more realistically they concern the achieving economic, social and environmental outcomes, as noted earlier. Croes (2010) suggests the implementation of strategies based on not overloading the carrying capacity of a destination in order to preserve the ecological and social balance. There are some limitations of indexes as arrivals and overnight stays, but in this specific case, the destinations selected were conferred with awards that request strict criteria dealing with environmental and also social carrying capacity. Of course, these awards do not, per se, guarantee the sustainability of tourist destinations, but they work towards their sustainable development. Crouch (2010) argues that measures as visitor nights and arrivals could be inappropriate measures of TDC, as they may be more suitable as measure of tourism demand. That is the reason why arrivals and bednights are considered as measures of the attractiveness of a tourism destination. An emphasis on measures of demand alone would be narrow. These drawbacks are overcome by considering a further measure of destination competitiveness, the gross occupancy rate of bed places (GORB). GORB is obtained by dividing total overnight stays (P) by the product of the bed-places and the number of days in the corresponding 74 year (Gp), multiplying the quotient by 100 to express the result as a percentage: GORB = (P/Gp) x 100 Where P = bed nights Gp = bed places x 365 GORB views competitiveness both from the tourist perspective or demand side (bed nights), and from the destination perspective or supply side (bed places). While the number of arrivals and bed-nights can be measures of the success of destination in attracting tourists, GORB is a measure of the destination’s existing capacity utilization. GORB can be used as a proxy of the effectiveness of the use of the resources, as it takes into account the amount of the available capacity used by the tourism activity. Because of the reasons above, GORB can also be considered a more sustainable measure of TDC. Analysis of these two concepts (attractiveness and effectiveness of the use of resources) can provide a more complete perspective on TDC. 3.1.4. PLS Regression To test the robustness of the estimate, a partial least square regression (PLS) is performed. Research often involves using controllable and/or easy-to-measure variables (factors) to explain, regulate, or predict the behaviour of other variables (responses). When the factors are few in number, are not significantly redundant (collinear), and have a well-understood relationship to the responses, then multiple linear regression (MLR) can be a good way to turn data into information (Tobias, 2002). However, if any of these three conditions breaks down, MLR can be inefficient or inappropriate. In such 75 so-called soft science applications, the researcher is faced with many variables and ill-understood relationships, and the object is merely to construct a good predictive model. Partial least squares (PLS) is a method for constructing predictive models when the factors are many and highly collinear (Geladi, Kowalski, 1986, Helland, 1988). The emphasis is on predicting the responses and not necessarily on trying to understand the underlying relationship between the variables. When prediction is the goal and there is no practical need to limit the number of measured factors, PLS can be a useful tool. PLS is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of mini- mum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares Discriminant Analysis (PLS-DA) is a variant used when the Y is binary. PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modelling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the Xspace that explains the maximum multidimensional variance direction in the Y space. PLS regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these cases. This technique is a form of structural equation modelling, distinguished from the classical method by being component-based rather than covariance-based. PLS was developed in the 1960’s by the Swedish statistician Herman Wold, who then developed it with his son, Svante Wold. 76 3.2. The Italian Case The Italian tourism system can be considered an interesting case study for many reasons. Italy is one of the world’s leading tourism destinations, with outstanding resources, historical exhibits and unique characteristics. Italy’s natural beauty offers magnificent beaches with 7,458 km of coastlines, 6,701 km of ski runs in the Alps and in the Apennine mountains. Moreover, it has an abundance of high quality cultural and natural heritage. Italy also has the most World Heritage Sites (47) than any other country on the planet1 (Figure 3.3). Figure 3.3 List of countries with more than 15 World Heritage Sites (2012) 0 10 20 30 40 50 Italy Spain China France Germany Mexico India United Kingdom Russia United States Australia Brasil Greece Japan Canada Source: WHC - UNESCO, 2012 In terms of its performance, Italy ranks 5th worldwide by the number of international tourist arrivals after France, USA, China and Spain (Table 1 As of 2012, there are a total of 962 World Heritage Sites located in 157 “state par- ties”. Of the 962 sites, 745 are cultural, 188 are natural and 29 are mixed properties (WHC - UNESCO, 2012). 77 3.1), and also 5th by the amount of international tourism receipts (Table 3.2, UNWTO, 2012). Table 3.1. International tourist arrivals by country of destination, 2011 Rank 1 2 3 4 5 6 7 8 9 10 Country France United States China Spain Italy Turkey United Kingdom Germany Malaysia Mexico International tourism arrivals 2010 79.5 million 62.3 million 57.6 million 56.7 million 46.1 million 29.3 million 29.2 million 28.4 million 24.7 million 23.4 million International tourism arrivals 2011 77.1 million 59.8 million 55.7 million 52.7 million 43.6 million 27.0 million 28.3 million 26.9 million 24.6 million 23.3 million Change 2010 to 2011 3.0% 4.2% 3.4% 7.6% 5.7% 8.7% 3.2% 5.5% 0.6% 0.5% Source: UNWTO, 2012 Table 3.2 International tourist receipts by country of destination, 2011 Rank 1 2 3 4 5 6 7 8 9 10 Country International tourism receipts United States Spain France China Italy Germany United Kingdom Australia Macao (China) Hong Kong $116.3 billion $59.9 billion $53.8 billion $48.5 billion $43.0 billion $38.8 billion $35.9 billion $31.4 billion $27.8 billion $27.2 billion Source: UNWTO, 2012 In the last fifteen years, international tourist arrivals have grown from 31.1 million to 43.6 million in Italy, less than in Spain (from 34.9 to 52.7, reaching a peak of 58.6 million in 2007), and more than in France (60 mil- 78 lion to 76.8 million), but in France the number of international tourists in 1995 were almost twice than in Italy (Figures 3.4 - 3.5). Figure 3.4. International tourist arrivals, Italy, France, Spain, 1995 - 2010 Source: Author’s elaboration from UNWTO (2004, 2008, 2012b) Spain (-6.5 million) and France (-2.5 million), two competitor tourism destinations, have shown a decline from 2007 to 2010, whereas in Italy the number of international tourist arrivals is almost stable (Figures 3.4 – 3.5). Adding the number of domestic tourists to foreigners, each year almost 95.5 million people travel around Italy (Istat, 2011). Tourism is one of Italy’s most significant economic sectors. While the direct contribution of tourism is 3.3% of GDP in 2011 (51.4 bn euros), the direct and indirect impacts are around 8.6% of GDP (136.1 bn euros) (WTTC, 2012). Italy has the highest share (5.1%) of people employed in the HORECA sector (hotels, restaurants, catering) in Europe after Spain (Eurostat, 2008). 79 Figure 3.5. Comparison of International tourist arrivals, Italy, France, Spain, 1995 - 2010 (all series Year 1995 = 100) Source: Author’s elaboration from UNWTO (2004, 2008, 2012b) Tourism generates 868,500 jobs directly (3.8% of total employment in 2011), but the total contribution of tourism to employment is estimated at 2,176,000 jobs (10.4% of total employment) (WTTC, 2012). Nonetheless, Italian tourism faces many problems, including areas of management (specifically marketing and promotion), policy and regulation, infrastructures, quality of accommodation facilities. Italy is the 27th ranked country in the World Economic Forum’s Travel & Tourism Competitiveness Index (compared to France’s 3rd, USA 6th and Spain’s 8th position) and is ranked 20th in Europe (WEF, 2012). Table 3.3 indicates that Italy is regarded as less competitive compared to France and Spain, two of the main Italian competitors, on the great majority of the indicators. Although various limitations have been identified by Crouch (2007) in the reliability and validity of this index, it can give a good starting point in order to identify the main problems and weaknesses of the Italian tourism system. These include policy rules and regulations, where Italy ranks 84th out of 139 countries, government prioritiza80 tion of the tourism industry (76th) and effectiveness of marketing and branding (108th). Table 3.3 Travel & Tourism Competitiveness Index, Italy, France, Spain 2011 Score (1-7 scale) 2011 Index 4.9 2009 Index 4.8 T&T regulatory framework 5 Policy rules and regulations 4.3 Environmental sustainability 4.7 Safety and security 5.2 Health and hygiene 6.2 Prioritization of Travel & Tourism 4.6 T&T business environment and infrastructure 4.8 Air transport infrastructure 4.4 Ground transport infrastructure 4.5 Tourism infrastructure 7 ICT infrastructure 4.5 Price competitiveness in the T&T industry 3.6 T&T human, cultural, and natural resources 4.8 Human resources 5.1 Education and training 5 Availability of qualified labor 5.3 Affinity for Travel & Tourism 4.4 Natural resources 3.7 Cultural resources 6.1 Italy 27 28 45 84 60 48 27 56 27 29 39 1 34 129 15 45 48 38 91 49 8 Rank (out of 139) France Spain 3 8 4 6 7 22 22 85 9 33 20 36 5 29 28 11 8 10 6 8 4 13 18 8 12 30 138 106 9 6 26 46 14 41 68 71 40 37 31 35 10 2 Source: WEF (2012) There is also insufficient focus on developing the sector in an environmentally sustainable way (Italy ranks 111th in the sustainability of tourism industry development). The country continues to suffer from a lack of price competitiveness (129th). In addition, WEF ranks its quality of air transport infrastructure 84th; Italy is ranked 89th in terms of international air transport network and 111th in terms of ground transportation network. The country, compared to some of the main competitors in Europe is lagging behind in terms of recent transport infrastructure development (OECD, 2011). OECD (2011) study on Italian tourism seems to confirm many of the indications emerging from the WEF report. 81 Italy also ranks 2nd worldwide for accommodation capacity after the United States and 1st among European countries. The general picture regarding the number of accommodation facilities shows that Italy has 145,358 accommodation facilities and 4.598 million bed spaces (total accommodation). It can count on 33,967 hotels (from five-star luxury to one star) and 2.227 million bed spaces (Istat, 2011). The Italian hotel market is the second biggest in the world; nevertheless it appears extremely fragmented and relatively low quality: 32% of the hotels are one or two stars (Istat, 2011). The accommodation supply is constituted by 23.4% hotels and 76.6% of other accommodation facilities. However, other accommodation facilities account for 51.6% of total beds, suggesting that they are on average smaller in size than hotels. Chain penetration is minimal in Italy accounting for 6% of the room stock (Mintel, 2004). The Italian tourism supply is dominated by companies which are family-owned. Italy’s company structure in this industry has one of the highest proportion of micro (one to nine employees) and small companies (<50 employees) in the EU: 62,3% under 20 employees compared to the 54,8% of France, to the 24,7% of Spain, and 20,4% of the UK (OECD, 2010). There are advantages to such an industry structure as market niche advantages, flexibility, personalized services, but on the negative side, small family-owned and managed hotels often suffer from limited marketing skills, lack of planning, gaps in human resource management and difficulties in financing (Buhalis, 1994; Buhalis & Main, 1998; Weiermair, 2000). Most of the tourism activity in Italy is generated by the domestic demand which weights, on average, 57% for arrivals during the period 19982007 (Massidda & Etzo, 2012). Domestic travel spending generates 67.5% of direct tourism GDP in 2011 (WTTC, 2012). Short domestic trips in Italy represented 46% of total holiday trips in 2010 (Eurostat, 2010). Italy can count on thousands of touristic sites, 4,739 museums, 393 archaeological sites, hundreds of medieval villages and historic churches, 82 which are distributed all over the national territory (FareAmbiente 2011). Nevertheless, tourist arrivals, especially international tourists, are concentrated in big cities, in the coastal areas or in ski resort destinations. This is also due to the fact that many Italian regions have identified tourism as a major industry for their economic development, but «regional structures for developing and promoting tourism products are often too dispersed and they sometimes lack the capacity to operate effectively on foreign markets. (…) Evidence indicates that there is a lack of clarity and coordination on promotion activities between the government, regions, provinces and municipalities» (OECD, 2011: 17). Due to the problems and weakness described above, combined with the increasing competition of new destinations, Italy lost the top position in the ranking of the most visited countries in the world that it held in 1970 (table 3.4). Table 3.4. International tourist arrivals, top ten world destinations, 1950 - 2011 1950 1970 1990 2004 2011 United States Italy France France France Canada Canada United States Spain United States Italy France Spain United States China France Spain Italy China Spain Switzerland United States Hungary Italy Italy Ireland Austria Austria United Kingdom Turkey Austria Germany United Kingdom Hong Kong United Kingdom Spain Switzerland Mexico Mexico Germany Germany Jugoslavia Germany Germany Malaysia United Kingdom United Kingdom Canada Austria Mexico Source: UNWTO, 2012 Formica & Uysal in 1996 stated that «the life-cycle analysis reveals one important trend that can lead Italy out of decline, a movement towards a ‘high-qualitative learning’ type of tourism. The tendency toward this type of tourism is demonstrated by the growing interest in green, rural and his- 83 torically appealing places. Italy’s landscapes and cultural places are in an excellent position to benefit from this trend» (Formica & Uysal, 1996). Many small destinations located on the mainland still have high growth potentials in Italy. Similar considerations can be extended to many small and medium seaside destinations rich in history and culture, where tourism is well developed but highly seasonal. 3.3. The Case Study: the Italian Destinations of Excellence 3.3.1. The Sample In the last paragraph it is shown why 610 Italian small and medium destinations of excellence were chosen as case study. Specifically, destinations of excellence that have been awarded with important International and National Certifications were selected: • “Blue Flag”, awarded by Foundation for Environmental Education - FEE (117 Municipalities in the sample); • “Blue Sail”, awarded by Legambiente/Italian League for the Environment (295 Municipalities); • “Orange Flag”, awarded by Italian Touring Club (181 Municipalities); • “The Most Beautiful Villages in Italy”, awarded by National Association of Italian Municipalities – ANCI (199 Municipalities). The main aim of these awards is the promotion of the diversity, value and authenticity of Italian destinations of excellence, both coastal (“Blue Flags” and “Blue Sails”) and non coastal (“The Most Beautiful Villages” and “Orange Flags”). They are also aimed at establishing a platform for encouraging tourism excellence in various forms. Municipalities awarded with “Blue flags” and “Blue Sails” are visited by a higher number of tourists than non-coastal destinations (table 3.5): the mean value is 764 thousand bed-nights for the destinations awarded with 84 the “Blue Flags” and 459 thousand for the “Blue Sail” destinations. The mean value is 58 thousand for the “Most Beautiful Villages” and 68 thousand for the “Orange Flag” destinations. The number of bed nights in the “Blue Flag” destinations increased by 10.6% from 2004 to 2009, it grew by 5.5% in the Municipalities awarded with the “Blue Sails”, by 1.6% in the “Most Beautiful Villages” and by 8.8% in the “Orange Flag” destinations. Table 3.5. Descriptive statistics: no. of respondents, mean and median number of bed Table. Municipalities awarded with Blue Flags, Blue Sails, Beautiful Villages, Orange Flags: nights (Blue Flags, Sails, Most Beautiful Villages, Orange Flags) No. ofBlue respondents, mean and median number of bed-nights Blue Flags 2004 No. Of municipalities Mean p50 Sum No. Mean p50 Sum Blue Sails 2009 76 690861 324841 5.25E+07 76 764030 299152 5.81E+07 Beautiful Villages 2004 2009 103 103 58057 58982 16683 21518 5979969 6075153 2004 170 435652 147837 7.41E+07 2009 170 459746 157088 7.82E+07 Orange Flags 2004 2009 94 94 63372 68976 16265 15433 5956988 6483772 Figure 3.6 shows the distribution of coastal Municipalities (“Blue Flag” and “Blue Sail” destinations) by tourist arrivals. As can be seen from the histogram, the shape of the distribution of the Municipalities reveals a very positive skewness. 85 Figure 3.6. Descriptive statistics: distribution of coastal municipalities (Blue Flags + 6.0e-06 4.0e-06 0 2.0e-06 Density 8.0e-06 1.0e-05 Blue Sails) by number of tourist arrivals 0 200000 400000 600000 Arr2num 800000 1000000 A very positively skewed distribution can also be seen in Figure 3.7, which shows the distribution of non-coastal Municipalities (“Orange Flag” destinations and “Most Beautiful Villages”) by tourist arrivals. 86 Figure 3.7. Descriptive statistics: distribution of non-coastal municipalities (Orange 0 2.0e-05 Density 4.0e-05 6.0e-05 Flags + Most Beautiful Villages) by number of tourist arrivals 0 50000 100000 Arr2num 150000 200000 3.3.2. The Destinations awarded with the “Blue Flag” “Blue Flag” is an internationally recognized voluntary eco-label that is awarded to beaches and marinas that satisfy stringent environmental quality standards and management (FEE, 2006); approximately 3850 beaches and marinas in 46 countries were awarded. The “Blue Flag” programme for beaches and marinas is run by the international, non-governmental, non-profit organisation FEE (the Foundation for Environmental Education). It was started in France in 1985. It has been operating in Europe since 1987 and in areas outside of Europe since 2001, when South Africa joined. Today, “Blue Flag” has become a truly global programme with an ever-increasing number of countries par- 87 ticipating in the programme. The internet site - www.blueflag.org - is shown in figure 3.8. The Blue Flag programme promotes sustainable development in freshwater and marine areas. It challenges local authorities and beach operators to achieve high standards in the four categories of: water quality, environmental management, environmental education and safety. Over the years, the Blue Flag has become a highly respected and recognised eco-label working to bring together the tourism and environmental sectors at local, regional and national levels. It has become a symbol of quality recognised by tourists and tour operators and can be used for the promotion of the awarded beach or marina. The programme is designed to raise environmental awareness and increase good environmental practices among tourists, local populations and beach and marina management and staff. The programme criteria are also designed to work with the national, regional and local legislation of each country, thereby assuring that the legislation is being followed. It can also be used to set a benchmark higher than what already exists. The criteria for the Blue Flag Beach Programme have developed over the years to become more holistic and to address the various issues of sustainability. The main criteria are: 1) Environmental Education and Information • Information about coastal zone eco-systems and nearby sensitive areas must be displayed • Information about bathing water quality must be posted • A code of conduct for the beach area must be displayed • A minimum of 5 environmental education activities must be offered 2) Water Quality • Compliance with standards for excellent bathing water quality 88 • No industrial or sewage related discharges may affect the beach area • Compliance of the community with requirements for sewage treatment and effluent quality 3) Environmental Management • The beach must be clean • Camping and driving must be regulated • A beach management committee must be established to be in charge of the environmental management system 4) Safety and Services • Lifeguards and/or lifesaving equipment must be available at the beach • A map of the beach indicating different facilities must be displayed • An emergency plan to cope with pollution safety risks must be in place 89 Figure 3.8. Blue Flag, interet site Blue Flag, Foundation for Environmental Education – FEE; 117 Municipalities in the sample Source: www.blueflag.org 90 3.3.3. The Destinations awarded with the “Blue Sail” In Italy, a roughly similar award, namely “Blue Sail”, was introduced by Legambiente, the main environmental organization in the country, a few years ago. The programme of Legambiente considers a wider spectrum of factors than FEE’s. The “Blue Sail” is being awarded to areas which combine adherence to environmental standards of water quality, safety, education, information and management, with policies that seeks to preserve and protect sites and landscapes with historic significance, and promote local productions. The internet site - www.legambiente.it - is shown in figure 3.9. Like “Blue Flags”, “Blue Sails” are awarded yearly following the publication of the “Guida Blu/Blue Guide”, the official “Blue Sail” ranking issued by Legambiente and Italian Touring Club. In addition to the complete list of Blue Sail areas, the “Blue Guide” includes a recommended list of boating tour itineraries. Legambiente shares with Italian Touring Club the commitment to protect the environment in a wider meaning, including aesthetic and cultural senses. The “Blue Guide” gives a short introduction of heritage, landscape and character for each region and also has short profiles of the localities. There is also a shortlist of culture events, food and wines itineraries, excursions, and specialties. The section of the marine coves describes main natural aspects and accessibility levels. 367 sea and lake resorts and 50 marine caves have been classified based on destination indicators and listed. There is also a section listing all Legambiente Turismo ecolabelled tourist businesses: over 400 places to stay totalling an estimate of about 6,000,000 overnights per year. 91 Figure 3.9. Blue Sail, internet site Blue Sail, Legambiente/Italian League for the Environment; 295 Municipalities in the sample Source: www.legambiente.it 92 3.3.4. The Destinations awarded with the “Orange Flag” The Orange Flag Program is an initiative started in 1998 and implemented through Italian Touring Club’s M.A.T. (National Analysis Model) application process. This process makes it possible to identify and promote small Municipalities that are located far from Italy’s urban centres but that enjoy a rich historical, cultural and environmental heritage. These communities also stand out for the excellence of what they have to offer and the high quality of their hospitality. The “smaller” Italy, which is mostly hidden and less known, represents the unfolding of the millenary history that has left its indelible marks, especially in these places. The Orange Flag program (the internet site - www.bandierearancioni.it - is shown in figure 3.10), geared as it is towards promoting sustainable tourism, is in accord with the values, and the history, of the Italian Touring Club. In 1894, a group of young entrepreneurs created a private, financially self-supporting association for bicyclists called the “Touring Club Ciclistico Italiano”. The Club’s purpose was to offer members a network of contacts and a range of services to learn about, discover and travel in Italy. The Italian Touring Club, is credited with having make Italy more accessible and known in the early 1900s, thanks to maps, guide books, signage, and magazines on tourism. Today, Italian Touring Club is a 350,000member private association that pursues goals in the public interest. The Club’s projects include Italy-oriented tools, services, initiatives, and festivals, as well as conferences and events. The Italian Touring Club collaborates side-by-side with organizations that work to protect the bounty of artistic and environmental riches that our country offers. The association is organized into different divisions, each with specific areas of responsibility, but with a single objective: the 93 protection and promotion of our cultural heritage and natural environment for the benefit of residents and tourists. Figure 3.10. Orange Flag, internet site Orange Flag, Italian Touring Club; 117 Municipalities in the sample Source: www.bandierearancioni.it 94 3.3.5. “The Most Beautiful Villages in Italy” The Club of “The Most Beautiful Villages in Italy” intends to promote the great heritage of history, art, culture, environment and traditions found in small Italian towns which are, for the most part, cut off from the flow of visitors and tourists. There are hundreds of small villages in Italy that risk depopulation and a consequent decline caused by a situation of marginalization as regards the economic interests that gravitate toward tourism and commercial trends. Thus, in March 2001 the Tourism Council of the ANCI (National Association of Italian Municipalities) decided to establish a Product Club that brings together the needs of those administrators who are sensitive to the protection and promotion of the Village. Admission to the Club requires the meeting of a number of prerequisites, both structural, such as the architectonic harmony of the urban fabric and the quality of the public and private building heritage, and general, regarding the quality of life in the village in terms of activities and services for the people. It is also necessary to be committed to continuously improving these prerequisites, as admission to the Club does not guarantee being allowed to stay in the Club, if the village does not show its commitment to increasing its qualities through concrete actions and practices. One of the goals of the Club is to guarantee - through protection, restoration, promotion and utilization - the preservation of a great heritage of monuments and memories that would otherwise be irretrievably lost. Another main objective is to increase the numbers of people who return to live in these small historic villages. The internet site - www.borghitalia.it - is shown in figure 3.11. The Club also publishes an annual guide of “The Most Beautiful Villages in Italy”. 95 The guide is divided into four parts. The first part gives the general information on each village (population, elevation, tourist information, Internet sites, patron saint, distances from major towns, and how to get there). The second part drafts a portrait of the village, taken from its most deeply rooted and characteristic features: the most significant dates in its history, the atmosphere that has been created over the centuries that make it unique and instantly recognizable; etc. The third section gives a brief description of the most important architectural structures in the village and surrounding area: churches, fortresses, public buildings, residences, streets, squares, views, etc. The last part describes the typical dishes, the main events taking place in the village, the museums and all else that should be seen or experienced, with the listing of local craft workshops, shops where one can purchase typical products, and also restaurants, agritourism establishments, hotels and other possibilities for accommodation. 96 Figure 3.11. The Most Beautiful Villages in Italy, Internet site The Most Beautiful Villages in Italy, National Association of Italian Municipalities – ANCI; 119 municipalities in the sample Source: www.borghitalia.it 97 98 PART IV EMPIRICAL ANALYSIS AND DISCUSSION OF FINDINGS 4.1. Principal Component Analysis Results – 4.1.1. Component Solution - 4.1.2 Description of the Components - 4.2. Regression Results 4.1. Principal Component Analysis Results 4.1.1. Component Solution PCA yielded 13 components which significantly explain the variation in responses. They are analyzed separately in the next paragraph. They explain 69.66% of the total variance, which is reasonable for a dataset of this kind. First, it has to be taken into account the heterogeneity of the 64 variables that constitute the model. Second, it has to be considered that the dataset is made up of a mixed group of different destinations: from small villages on the mainland, to well known coastal resort destinations with tens of hotels and thousands of tourist arrivals every year. Dwyer et al. (2004) obtained similar results after applying a PCA to a similar set of indicators. The quantity of the variance explained by each component is specified in parentheses. The component solution for the destination competitiveness indicators is shown in table 4.1. 99 Table 4.1 Component Solution for Destination Competitiveness Indicators Component Solution Component 1: Sustainable Tourism Policy and Destination Management (35.94%) Public sector commitment to minimizing negative environmental impacts of tourism Integrated approach to tourism planning Political commitment to tourism Public sector commitment to minimizing negative social impacts of tourism on local community Environmentally compatible approach to tourism development planning Emphasis on community participatory process in tourism planning Cooperation between public and private sector for local tourism development Collaboration among public sector units for local tourism development Stewardship of the natural environment Promotion of partnerships between public and private stakeholders Tourist destination communication Effectiveness of destination management structure Public sector commitment to tourism/hospitality education and training Effectiveness in crafting tourism experiences Tourism impacts management and monitoring Tourist guidance and information Promotion of partnerships among tourist businesses Component 2: General Infrastructures (5.06%) Quality of road system Accessibility of destination Communication system Accessibility of facilities by disabled persons Medical care facilities Environmental friendliness and quality of transportation services Proximity to other tourist destinations Sanitation, sewage and solid waste disposal Component 3: Events and Activities (3.56%) Evening entertainment and nightlife Leisure activities Events Shopping opportunities Component 4: Responsible Tourist Behaviour (3.56%) Tourists' interests in natural and cultural local heritage Tourists' respect for local traditions and values Tourists' enviromental awareness Component 5: Managerial Competencies of Local Tourism Firms (3.28%) Use of IT by tourism firms Management capabilities of tourism firms Level of professional skills in tourism Presence of local businesses Component 6: Destination Marketing (2.99%) Awareness of destination Effectiveness of destination positioning Effective market segmentation Component 7: Quality of natural resources (2.73%) Safety Environmental quality Natural resources Component 8: Gastronomy (2.29%) Gastronomy ant typical products Food services quality Local supply of goods and services to tourists and tourism businesses 100 Model Component determinant Loadings 5 0.8173 5 0.8022 5 0.7606 5 0.7529 5 0.7391 5 0.7299 5 0.7264 5 0.7118 6 0.6484 6 0.6475 6 0.6245 6 0.6107 5 0.6019 6 0.6002 6 0.5928 6 0.5739 6 0.5642 3 0.6896 4 0.6442 3 0.6298 3 0.6029 3 0.6019 3 0.5877 4 0.5107 3 0.4245 1 0.7406 1 0.7062 1 0.6528 1 0.5586 7 0.8539 7 0.8224 7 0.7919 4 0.7194 4 0.7027 4 0.5899 4 0,5500 7 0.6784 6 0.6701 6 0.5495 4 0.7231 4 0.7077 1 0.6359 1 0.6498 2 0.6108 4 0.5165 Model Component determinant Loadings Tab. 2. Continued Component 9: Historical and Artistic Features (1.93%) Historical and archaeological sites Artistic and architectural features Cultural attractors Component 10: Price Competitiveness (1.88%) Value for money in accommodations Value for money in destination tourism experience Destination links with major origin markets Component 11: Visitor Satisfaction Management (1.83%) Visitor satisfaction management Level of repeat visitors Component 12: Tourist Accommodations (1.69%) Quantity of accommodations Quality of accommodations Environmental friendliness of accommodations Component 13: Emphasis on maximising local economic development (1.65%) Emphasis on community empowerment Clear policies in creating formal employment opportunities Public sector commitment to maximising economic impacts of tourism on local community 1 0,8079 1 0.7133 1 0.5023 4 0.7778 4 0.6655 4 0.4639 6 0.4756 7 0.4486 2 0.6326 2 0.4869 2 0.4406 5 0.4693 5 0.4648 5 0.4144 Model Elements: 1) CORE RESOURCES AND KEY ATTRACTORS 2) TOURISM SERVICES 3) GENERAL INFRASTRUCTURES 4) CONDITIONING AND SUPPORTING FACTORS 5) TOURISM POLICY, PLANNING AND DEVELOPMENT 6) DESTINATION MANAGEMENT 7) DEMAND 4.1.2. Description of the Components Component 1: Sustainable Tourism Policy and Destination Management (35.94%) As it is frequently encountered in PCA, the first component consists of many variables, 17 in this case, accounting by itself for a high percentage of the variance (35,94%). This mixed grouping encompasses the notion of “sustainable tourism policy and destination management”. It includes indicators regarding an integrated and sustainable approach to tourism planning. They refer to the collaboration in the decision-making process of tourism development and to the emphasis on minimizing negative impacts of tourism on natural, cultural and social resources. It also involves 101 1 2 3 4 5 6 7 some destination management variables related to the stewardship of the natural environment and to the monitoring of the tourism impacts. All these indicators are included in the 5th and 6th determinants of the model presented in this study. There is an extensive literature on tourism planning with various emphases, including Gunn’s concentration on spatial planning (Gunn, 2002), Murphy’s work on a community approach (Murphy, 1985), Hall’s emphasis on the various levels of planning (Hall, 2000) and Inskeep’s comprehensive approach (Inskeep, 1991). In contrast to tourism planning, management has a shorter time horizon. Destination management has become a prevailing issue in tourism literature and many academic books were published (among others, Laws, 1995; Ritchie & Crouch, 2003; Weaver & Lawton, 2006; Buhalis & Costa, 2006; Wang & Pizam, 2011). Tourism is a composite sector, including a network of interconnected stakeholders and organisations, both public and private, working together. The success of a destination relies on a coordinated approach to the planning, development, management and marketing of a destination (Ritchie & Crouch, 2003). Component 2: General Infrastructures (5.06%) The second component comprises 8 items, six of which lend themselves to the infrastructure label (3rd determinant). This component covers the road system and transportation, the communication system and the medical care facilities. Kaul (1985), Prideaux (2000), Khadaroo & Seetanah (2007) shed light on the relevance of transport infrastructure as a critical component of successful tourism development. Passenger transportation relevance has been extensively recognized both in the tourism planning literature (Gunn, 2002; Hall, 2000; Inskeep, 2001), and in the wider tourism literature (Goeldner & Ritchie, 2003; Cooper et al., 1998; 102 Page, 2003). This component is also composed by further attributes related to general infrastructures; they are extensively described by Inskeep (1991). Moreover, there are two indicators from the 4th determinant: “accessibility of destination” and “proximity to other tourist destinations”. They are strictly connected to the infrastructure issue. Accessibility is concerned with the easiness to enter the destination, in part influenced by spatial issues, in part conditioned by transportation services. Proximity to other tourist areas can have an important role in the tourist development of a destination (Gunn, 2002); it is also influenced by transportation facilities. Component 3: Events and Activities (3.56%) The third component contains 4 items, and has been named “events and activities”. Events, leisure activities, nightlife and shopping are often the primary motivations to visit a destination (Ritchie & Crouch 2003). This element can be considerably influenced by the destination management. Events could extend the seasonal life especially in tourism destinations with an inbuilt seasonality (Getz, 1989, 1991; Hall, 1987; Faulkner, 2003). Hallmark events can generate high levels of interest in visitors and several advantages (Hall, 1992). This explains the high academic interest in events management and the publishing of several books on this subject (Getz, 1997; Shone & Parry, 2001; Van der Wagen, 2002; Yeoman et al., 2003; Raj et al., 2008; Allen et al., 2008; Bowdin et al., 2010; Robinson, 2010). The assortment of activities is of rising significance as the visitors ever increasing seek experiences that overtake the more inactive tourism of the past (Poon, 1993). Entertainment, also, can be a key supplier to the tourism sector (Hughes, 2000). It may occupy a major position in the destina- 103 tion competitive strategy, depending on its perceived uniqueness rather than on its quantity (Dwyer & Kim 2003). A research study demonstrates that while shopping is rarely mentioned as a primary reason for travel, it is perhaps the most universal of tourist activities (Kent, Shock, Snow, 1983). The study shows the economic importance for the local economy and reveals that shopping is for many tourists the most popular activity. Shopping tourism can also be seen as a vehicle to revitalize traditional urban centres, deteriorating resorts and even rural areas (Jansen-Verbeke, 1991). Timothy (2005) provides a comprehensive examination of the relationships between tourism, leisure and shopping. Component 4: Responsible Tourist Behaviour (3.56%) “Tourists’ interest in local heritage”, “tourists’ respect for local culture” and “environmental awareness” are the indicators included in this component. These three characteristics are connected with the concept of responsible tourist behaviour. Frequently, tourists forget many social norms that control their social life in their place of origin and «feel relatively free to indulge in a relaxed dress code, loose sexual morals, or heavy drinking, or over eating» (Laws, 1995: 74). Problems of crime, prostitution, drugs or alcohol may be aggravated by “non responsible” tourism. Sharpley (1994: 84) gives a description of the responsible tourist as the person who «seeks quality rather than value, is more adventurous, more flexible, more sensitive to the environment and searches for greater authenticity than the traditional, mass tourist». Swarbrooke (1999) lists the responsibilities of the tourists: obeying local laws, not offending cultural norms of behaviour, not harming physical environment, minimize the use of scarce resources. Tourist codes of behaviour have also been developed 104 to minimize negative impacts of tourists on the social and physical environment (e.g. Mason & Mowforth, 1996). Component 5: Managerial Competencies of Local Tourism Firms (3.28%) This component is represented by 4 variables comprising the management capabilities and professional skills of the business operators, the use of IT and the presence of local tourism firms. Page (2005) examines the questions affecting the management of the very fragmented nature of the businesses which may refer to tourism (accommodation and hospitality services, tour agencies, retailers, visitor attractions, transportation services, etc.). Moutinho (2000) widely analyzes the various aspects of the management of the tourism firms. On the question of the skill levels, Choy (1995) observes that the prevalence of hotels, restaurants and bars in tourism may induce to think that tourism industry is relatively low skilled. The great changes which have happened in tourism have made organizations more competitive and customers more demanding. Baum (1995) argues that skill levels and human resource management can play a strategic role in the challenge to improve the quality of the tourism product and enhance the market position of tourism destinations. Concerning the “use of IT by tourism firms”, Rimmington & Kozak in 1997 stated that IT could have created first and second class tourism destinations/organizations. Buhalis & Cooper in 1998 noted that the future competitiveness of tourism industry would have mostly depended on the range of telecommunication technology used. The forecasts have become reality; evidences show that operators and destinations with undeveloped telecommunication system are less suitable to reach potential tourists and to manage customers. 105 Component 6: Destination Marketing (2.99%) The three indicators referring to the destination marketing components are: “effectiveness of destination positioning”, “market segmentation” and “awareness of the destination”. For what concerns the destination positioning, many definitions exist in literature. Heath and Wall (1992:136) assert that positioning regards the development and the communication of significant differences between the offer of a region compared to competitors’ offer which address to the same market segment. Ahmed (1991) and Grabler (1997) also recognize that an accurate positioning strategy for a destination requires a comparison with the competitors. Richie & Crouch (2003: 200) define the destination’s position in the market «how a destination is perceived by potential and actual visitors in terms of the experience (and associated benefits) that it provides relative to competing destinations». Pike & Ryan (2004) list the key constructs to be considered to enhance destination position effectiveness. Market segmentation has been defined as the process of dividing a potential market into different groups, and selecting one or more segments as a target to be reached with a distinct marketing mix (Wilkie, 1986). Tourism market segmentation has been similarly defined. The last variable “awareness of the tourist destination” is included in the 7th determinant (demand) of the model, but also depends on the success of the marketing strategy. A growing number of academic conferences featuring the destination marketing theme have also emerged. There have also been a number of papers related to destination marketing published in academic journals. 106 Component 7: Quality of natural resources (2.73%) “Natural resources”, “environmental quality” and “safety” are the three variables comprised by this component. Natural resources can be considered among the most important resources for a tourism destination. A natural resource is something that exists in nature which can be used by humans, also for tourism purposes, at current economic, social, cultural, and institutional conditions. In recent years, increasing awareness among tourism researchers of the relations between tourism and natural resource management has resulted in a significant body of academic literature examining this issue. The quality of the environment is related to the attractiveness of the destination: tourism and environment are in a very complex relationship (Butler, 2000). Mihalič (2000) points out that a wellmanaged destination environment is the best destination advertiser. «A destination needs to protect the integrity and the attraction of its own product, plus guard against the action and rivalry of competitors» (Murphy 1995: 166). The attribute “safety” is also included in this component. A possible explanation is that this element is probably interpreted as the absence of natural calamities. This is most likely related to the fact that security is not a problem in most of the destinations surveyed. Component 8: Gastronomy (2.29%) “Gastronomy and typical products” (1st determinant) and “food services quality” (2nd determinant) are two of the three variables incorporated in this component. Systematic research on gastronomy and tourism has been neglected until recently. Gastronomy is one of the most important elements affecting the authenticity of a tourism destination (Sedmak & Mihalič, 2008). Hjalager & Richards (2002) explore the role of gastronomy 107 as a source of regional identity, and also a source of economic development related to tourism. The third variable included in this component, “local supply of goods” (4th determinant), highlights the association between authenticity of a tourism destination and local products and producers. Component 9: Historical and Artistic Features (1.93%) This component is associated with 3 variables: “historical and archaeological sites”, “artistic and architectural features”, “cultural attractors”. In the last two decades, many texts were published about this subject of rising interest (Richards, 1996, 2007; Richards & Munsters, 2010; Boniface, 1995; Walle, 1998; McKercher & du Cros, 2002; Sigala & Leslie, 2005; Smith, 2003, 2009; Smith & Robinson, 2006). «Culture, broadly defined, is a second very powerful dimension of destination attractiveness» (Ritchie & Crouch, 2003:115). A high proportion of international travellers is now considered cultural tourists (Richards, 1996). The culture and heritage attractors of a destination provide a significant force for the potential visitor (Ritchie & Zins, 1978; Cohen, 1988; Prentice, 1993; Murphy, et al. 2000). Component 10: Price Competitiveness (1.88%) A major element of attractiveness for a tourism destination is the cost of using tourist facilities and services within the destination, compared to the costs within similar destinations (Inskeep, 1991). The price tourists pay to visit and enjoy a destination experience plays a key role in determining the choice travellers make (Crouch, 1992). Price competitiveness has been defined as the destination price differentials coupled with exchange rate movements, productivity levels of various components of the 108 tourist industry and qualitative factors affecting the attractiveness of a destination (Dwyer, Forsyth, Rao, 2000). Component 11: Visitor Satisfaction Management (1.83%) It is interesting to note that the two variables “visitor satisfaction management” and “level of repeat visitors” are included into this single component. It seems to confirm that the first element can influence the decision to revisit a destination. Evidence has shown that visitor satisfaction relates to product development and quality issues that can only be met through both improved training and cooperation between the public sector and the tourism industry (Baum, 1995). Component 12: Tourist Accommodations (1.69%) This component includes “quality”, “quantity” and “environmental friendliness” of tourist accommodations. Even if a destination may possess a great quantity of resources and attractors, it is required the support of other elements in order to be adequate to receive tourists (Gunn, 2002). Hospitality has been defined as “the very essence” of tourism (Page, 2003: 254) and has a very important role in the generation of economic benefits for the community (Cooper et. Al, 1998). A crucial issue related to hospitality is quality (Qu, Ryan & Chu, 2000); this question has been examined in a number of studies (among others, Sargeant & Mohamad, 1999; Tsang & Qu, 2000; Briggs, Sutherland, Drummond, 2007). Among the various forms of tourist accommodations, for many nations hotels are the more significant in terms of number of tourists and revenues (Page, 2003). According to Go, Pine, & Yu (1994), there is a mutual influence between destination’s economic growth and hotels performance. Nevertheless, many approaches in literature refer only to a lim- 109 ited number of elements of the hotel industry competitiveness; much less attention has been devoted to develop a comprehensive framework (Tsai, Song & Wong, 2009). Component 13: Emphasis on maximising local economic development (1.65%) This component refers to three variables concerning the public sector commitment to maximizing economic impact of tourism on local community. Any tourism strategy must be able of meeting the economic needs of the residents over the long terms (Ritche & Crouch, 2003). In many authors’ view, economic benefits from tourism should be distributing among the population (among others, Müller, 1994; Ritchie & Crouch, 2003; Wall & Mathieson, 2006). Tourism industry must concentrate the effort on increasing the utilization of local labour; this also depends on the public sector commitment to tourism and hospitality education. The emphasis on community empowerment is essential in order to increase the capacity and capability of the people working in the tourism industry: «it is an important way of affecting impacts in ways that are benign to destination communities» (Wall & Mathieson, 2006: 307). 4.2. Regression Results A number of point emerge from the results of the OLS applied to study the dependency between the performance scores given by various indicators (tourist arrivals, bed nights, gross occupancy rate of bed nights) and explanatory variables which are the components of destination competitiveness resulting from the PCA. As can be seen in tab. 4.2, if indicators that look more at the attractiveness of the destinations (tourist arrivals and bed-nights) are used, can be observed a positive relationship of a number of different variables: 110  “general infrastructures”;  “tourist accommodations”;  “managerial competencies of local tourism firms”;  “destination marketing”;  “emphasis on maximising local economic development”. In particular, it implies that both variable related to the “general infrastructures”, and to the private sector, as “tourist accommodations” and “managerial competencies of local tourism firms” have a positive role in the performances of a destination. But also the public sector seems to play a very important role, because the variables “destination marketing” and “emphasis on maximising local economic development” are also significant in determining the destination performance. When the analysis move from measures of attractiveness to GORB, which can be considered a proxy of the effectiveness of the use of the resources of a destination, the variable “sustainable tourism policy and destination management” becomes very significant. This demonstrates that a sustainable tourism policy and destination management not only is good for preserving the ecologic balance and for minimize negative cultural and social impacts, but has a great importance in improving the competitiveness of a tourism destination. If GORB is used as a dependent variable, “tourist accommodations” also plays a positive role. This component is composed by the indicators “quantity” and “quality” of tourist accommodation, and by an indicator of sustainability: “environmental friendliness of tourist accommodation”; as a consequence, a certain role of the sustainability of the tourist accommodations has been also detected in determining TDC. In both cases there are some factors that play a negative role, in the sense that their association with the dependent variable is negative. For each of them there is an explanation, but the general explanation of the 111 negative association of factors as “responsible tourist behaviour” and “historical and artistic features” is that maybe the growth of many destinations, especially coastal, is probably associated with less educated and less responsible tourists, and also with tourists which are less interested in historical and cultural aspects. Table 4.2. Regression results – dept. var. Log. arrivals, Log. Bed-nights, GORB, by municipality - OLS estimates Log Tourist arrivals Log No. of bednights Gross occupancy rate F1 Sustainable Tourism Policy and Destination Management F2 General Infrastructures F3 Events and Activities F4 Responsible Tourist Behaviour 0.112399 0.1980659 ** 0.0032904 -0.3540656 *** 0.1314763 0.2274139 ** 0.0533405 -0.3522891 *** 1.380709 *** 0.5967373 -1.220822 ** -1.177515 ** F5 Managerial Competencies of Local Tourism Firms F6 Destination Marketing F7 Quality of Natural Resources F8 Gastronomy F9 Historical and Artistic Features F10 Price Competitiveness F11 Visitor Satisfaction Management F 12 Tourist Accommodations F13 Emphasis on maximising local economic development 0.2034402 0.2092575 -0.0467754 0.0959415 -0.2765544 -0.3026803 0.0811003 0.3331341 0.3295595 0.1893995 0.1634374 -0.0289817 0.1318736 -0.385605 -0.2650652 0.0746041 0.3773587 0.3525727 Zone Location Area Population _cons -0.0641557 1.189277 *** 0.0023484 ** 0.0000358 *** 8.318384 -0.0600656 1.648385 *** 0.0031208 ** 0.0000304 *** 9.488443 0.2890472 3.676503 ** -0.016561 ** 0.0001199 ** 12.6571 No. of Obs. F (17, 177) Prob > F R - Squared 195 14.72 0.000 0.5858 195 16.81 0.000 0.6175 195 5.08 0.000 0.328 ** ** *** *** *** *** * * *** *** *** *** 0.6023456 0.817226 0.3579168 0.7644885 -0.6529676 -0.7761689 -0.0644369 1.604883 *** 0.632089 To test the robustness of the estimate, a partial least square regression is performed, that is a method for constructing predictive models when the factors are many and highly collinear. The results are similar to the plain OLS regression. 112 Table. 4.3. Partial Least Square Regression results – dept. var. Log. arrivals, Log. Bed-nights, GORB, by municipality - PLS estimates Log Tourist arrivals Log No. of bednights Gross occupancy rate 0.1117653 0.1890755 -0.0605349 -0.3103728 0.0418359 0.0618321 0.0248313 0.1430665 -0.341087 -0.2226117 0.0839721 0.4015089 0.332387 1.047061 1.057689 -1.354793 -1.349495 1.144126 0.6803208 0.3698302 0.5366294 -0.3150243 -1.14816 -0.3816157 1.566566 0.3170485 F1 Sustainable Tourism Policy and Destination Management F2 General Infrastructures F3 Events and Activities F4 Responsible Tourist Behaviour F5 Managerial Competencies of Local Tourism Firms F6 Destination Marketing F7 Quality of Natural Resources F8 Gastronomy F9 Historical and Artistic Features F10 Price Competitiveness F11 Visitor Satisfaction Management F 12 Tourist Accommodations F13 Emphasis on maximising local economic development 0.1012157 0.1869708 -0.1171785 -0.3449516 0.0886278 0.1459929 0.0063665 0.0937211 -0.279362 -0.2791212 0.094542 0.3447921 0.320664 Zone Location Area Population _cons -0.2150702 * 1.495064 *** 0.0005347 0.0000209 -0.2041113 * 2.015958 *** 0.0006651 7.56E-06 0.183906 4.529361 *** -0.0196881 ** 0.0001012 No. of Obs. F (17, 177) Prob > F R - Squared 194 10.063 0.000 0.4915 194 12.09 0.000 0.5373 194 6.0634 0.000 0.368 113 * *** ** ** ** *** *** *** *** *** ** *** *** ** * ** ** ** ** *** 114 PART V THE COMPETITIVENESS OF TWO LEADING TOURISM DESTINATIONS IN LATIN AMERICA: THE CASES OF RIO DE JANEIRO AND SALVADOR DE BAHIA 5.1. Introduction - 5.2 Why Brazilian Tourism Destinations? 5.3 Rio de Janeiro and Salvador de Bahia - 5.4. The adapted model - 5.5. Methodology - 5.5.1. Data collection - 5.5.2. The Survey Instrument - 5.5.3. Moments of the Distribution: Mean, Variance, Skewness, Kurtosis - 5.6 Discussion of findings 5.6.1 The case of Rio de Janeiro - 5.6.2 The case of Salvador de Bahia 5.1. Introduction In this chapter, the model of destination competitiveness described in the Second Chapter has been adapted and applied to measure the competitiveness of two leading tourism destination in Latin America: Rio de Janeiro and Salvador de Bahia. The original model has been adapted as for any given destination, different indicators of competitiveness will be relevant (Gomezelj & Mihalič, 2008). Primary quantitative data were collected through experts’ judgement. The chosen participants were tourism stakeholders, including incoming travel agents, tourist guides, hotel managers, travel consultants, tourism professors, tourism students, and tourism public servants. 115 The data were collected with a web survey. The survey was submitted between December 2012 and January 2013; two weeks after the first invitation to participate, a second e-mail message (reminder) was sent to all the contacts that did not complete the survey in order to increase the participation rate. 277 usable responses were received in the case of Rio de Janeiro. 164 usable responses were received in the case of Salvador de Bahia. The high number of respondents was expected, given that in the periods spent by the researcher in Rio de Janeiro and Salvador de Bahia as a visiting student, many people were met in an effort to explain objectives, structure and relevance of the research, including hotel and travel guide associations, travel consultants, tourism professors and academic researchers, public managers and public servant working in the tourism sector. 5.2. Why Brazilian Tourism Destinations? Brazil is an emerging country undergoing through enormous economical and social changes. It is predicted to become the 5th largest global economy by 2025 and it will host both the FIFA World Cup 2014 and the Olympic Games 2016, which puts the country’s tourism and travel industry in the world spotlight. Nevertheless, there is still insufficient academic research on Brazil tourism, especially on the two of the leading tourist destinations in the country, Rio de Janeiro and Salvador de Bahia. Tourism literature on Brazil proves to be still scarce. There have not been enough studies on research, especially regarding destination competitiveness. Brazilian tourism destinations are also interesting case studies because they still have vast growing potential. Moreover, as highlighted by the Travel and Tourism Competitiveness Report published by the World 116 Economic Forum, they are also suffering from serious problems of tourism competitiveness (WEF, 2012). Brazil is the 52nd ranked country in the World Economic Forum’s Travel & Tourism Competitiveness Index, which is a measurement of the factors that make it attractive to develop business in the travel and tourism industry of individual countries. Brazil ranks 3rd among Latin American Countries after Mexico and Costa Rica, and 7th in the Americas (WEF, 2012). Although various limitations have been identified by Crouch (2007) in the reliability and validity of this index, it can give a good starting point in order to identify the main problems and weaknesses of the Brazilian tourism system. These include policy rules and regulations, where Brazil ranks 114th out of 139 countries (table 5.1 and figure 5.1), and government prioritization of the tourism industry (108th). Its ground transport infrastructure remains underdeveloped (116th), with the quality of roads ranking in the 105th place; moreover, the country continues to suffer from a lack of price competitiveness (114th), due in part to high ticket taxes and airport charges in the country, as well as high prices and high taxation in general. Safety and security have improved significantly, ranking in the 75th place in 2011, up from the 128th position in 2008. 117 Table 5.1. Travel & Tourism Competitiveness Index, Brazil 2011, Rank out of 139 Countries Rank (out of 139) 2011 Index 52 2009 Index 45 T&T regulatory framework 80 Policy rules and regulations 114 Environmental sustainability 29 Safety and security 75 Health and hygiene 73 Prioritization of Travel & Tourism 108 T&T business environment and infrastructure 75 Air transport infrastructure 42 Ground transport infrastructure 116 Tourism infrastructure 76 ICT infrastructure 56 Price competitiveness in the T&T industry 114 T&T human, cultural, and natural resources 11 Human resources 70 Education and training 44 Availability of qualified labor 106 Affinity for Travel & Tourism 97 Natural resources 1 Cultural resources 23 Source: WEF (2012) The relevance of the outcomes of this research is supported by the fact that tourism represents a primary resource to Brazil, a destination that has been going through enormous development. It is a country with continental proportions, and tourism has been playing a major role, especially since the creation of the Ministry of tourism in 2003. Its tourism revenue generated 5.8 billion dollars in 2008, and the country is placed 7th worldwide in number of international events. 118 Figure 5.1. Travel & Tourism Competitiveness Index, Brazil 2011, Score (1-7 scale) 0 1 2 3 4 5 6 T&T regulatory framework Policy rules and regulations Environmental sustainability Safety and security Health and hygiene Prioritization of Travel & Tourism T&T business environment and infrastructure Air transport infrastructure Ground transport infrastructure Tourism infrastructure ICT infrastructure Price competitiveness in the T&T industry T&T human, cultural, and natural resources Human resources Education and training Availability of qualified labor Affinity for Travel & Tourism Natural resources Cultural resources Source: WEF (2012) Tourism in Brazil is a growing sector and key to the economy of several regions of the country. The country had 5.1 million visitors in 2010 (figure 5.2), ranking in terms of the international tourist arrivals as the second main destination in South America, and third in Latin America after Mexico and Argentina (UNWTO, 2012). Revenues from international tourists reached US$5.9 billion in 2010, showing a recovery from the 2008-2009 economic crisis (UNWTO, 2012). Brazil offers for both domestic and international tourists, an ample game of options, with natural areas being its most popular tourism product, a combination of ecotourism with leisure and recreation, mainly sun and beach, and adventure travel, as well as historic and cultural tourism. 119 7 Figure 5.1. International tourist arrivals, Brazil, 1995 - 2010 Source: UNWTO (2004, 2008, 2012a) Brazil main competitive advantages are its natural resources, which are ranked 1st out of all countries and its cultural resources (23rd), with many World Heritage sites, a great proportion of protected land area, and the richest fauna in the world. Brazil ranks 12th on the number of World Heritage Sites2 (Figure 5.3) with 19 sites, second among Latin American countries after Mexico. Of the 19 sites, 12 are cultural and 7 are natural3. As of 2012, there are a total of 962 World Heritage Sites located in 157 “state parties”. Of the 962 sites, 745 are cultural, 188 are natural and 29 are mixed properties (WHC - UNESCO, 2012). 3 Cultural Sites: Rio de Janeiro Carioca Landscapes between the Mountain and the Sea, Historic Centre of Salvador de Bahia, Historic Town of Ouro Preto, Historic Centre of the Town of Olinda, Jesuit Missions of the Guaranis (San Ignacio Mini, Santa Ana, Nuestra Señora de Loreto and Santa Maria Mayor Argentina, Ruins of Sao Miguel das Missoes Brazil), Sanctuary of Bom Jesus do Congonhas, Brasilia, Serra da Capivara National Park, Historic Centre of São Luís, Historic Centre of the Town of Diamantina, Historic Centre of the Town of Goiás, São Francisco Square in the Town of São Cristóvão. Natural Sites: Iguaçu National Park, Atlantic Forest South-East Reserves, Discovery Coast Atlantic Forest Reserves, Central Amazon Conservation Complex , Pantanal Conservation Area, Brazilian Atlantic Islands: Fernando de Noronha and Atol das Ro2 120 Figure 5.3. List of countries with more than 15 World Heritage Sites 2012 0 10 20 30 40 50 Italy Spain China France Germany Mexico India United Kingdom Russia United States Australia Brasil Greece Japan Canada Source: WHC - UNESCO, 2012a 5.3 Rio de Janeiro and Salvador de Bahia: the applied cases Rio de Janeiro is the capital city of the State of Rio de Janeiro, the second largest city of Brazil, and the third largest metropolitan area and agglomeration in South America, boasting approximately 6.3 million people. Rio de Janeiro represents the second largest GDP in the country. The city has extensive areas of attractive and unique beauty – an endless coast with marvellous beaches and rocky hills above the sea with incredibly panoramic views – that are recognized worldwide and a climate that is conductive to experiencing these resources throughout much of the year. cas Reserves, Cerrado Protected Areas: Chapada dos Veadeiros and Emas National Parks. 121 The pulsating nightlife is a major attraction of RJ, which is also the place of the biggest Carnival in the world - with hundreds of street parties (“Carnaval da Rua”) and the typical RJ carnival parade in the Sambodromo - and hundreds of thousands of people flocking to RJ each year to celebrate the famous festival. Rio de Janeiro has become a home of a World Heritage Site named "Rio de Janeiro: Carioca Landscapes between the Mountain and the Sea", as granted by UNESCO on 1 July 2012 in the category Cultural Landscape. Rio de Janeiro is one the most visited city in Latin America and is known for its natural settings, carnival celebrations, music, beaches such as Copacabana, Ipanema, and Leblon. Some of the most famous landmarks in addition to the beaches include the giant statue of Christ the Redeemer ("Cristo Redentor") atop Corcovado mountain; Sugarloaf Mountain (Pão de Açúcar) with its cable car; the Sambódromo, a permanent grandstand-lined parade avenue which is used during Carnival; and Maracanã Stadium, one of the world's largest football stadiums. Salvador de Bahia is the largest city on the northeast coast of Brazil and the capital of the Northeastern Brazilian state of Bahia. Salvador, with 3.5 million of people, is the third most populous Brazilian city, after São Paulo and Rio de Janeiro. The density of monuments makes Salvador de Bahia the colonial city par excellence in the Brazilian Northeast. The Pelourinho, the ‘old town’ of Salvador, was designated a World Heritage Site by UNESCO in 1985 and has been largely restored during the 1990s. Salvador was also one of the major points of convergence of European, African and American Indian cultures in the 16th-18th centuries and was the first historic capital of Brazil, from 1549 to 1763. The city of Salvador is notable in Brazil for its cuisine, music and architecture, and its metropolitan area is the wealthiest in Brazil’s Northeast. The African influence in many cultural aspects of the city makes it the centre of Afro-Brazilian culture. Salvador is also 122 known as Brazil’s capital of happiness due to its easygoing population and countless popular outdoor parties, including its street carnival. Rio de Janeiro and Salvador de Bahia were chosen as applied cases as they are two of the leading tourism destinations in Brazil and in the whole Latin America. Rio de Janeiro is the most visited city by international tourists for leisure trips (26,7%) in Brazil, Salvador de Bahia ranks 5th (6,8%) (table 5.2). Table 5.2 Most visited tourism destinations by international leisure tourists Brazil, 2005 – 2011, data as percentage of total international tourist flows Rio de Janeiro - RJ Foz do Iguaçu - PR Florianópolis - SC São Paulo - SP Salvador - BA 2005 31.5 17 12.1 13.6 11.5 2006 30.2 17.1 15.1 12.6 11.4 2007 30.2 16.1 15.3 13.7 10.2 2008 29.1 19 16.9 14.9 8.7 2009 30 21.4 16.7 11.5 7.2 2010 27.3 23.4 19.3 9.9 7.4 2011 26.7 19.8 19.7 11 6.8 Source: Ministério do Turismo Brazil – Fipe (2012a) Rio de Janeiro also ranks 1st on the number of international events organized in Brazil (69 events), whereas Salvador de Bahia ranks 3rd (17 events) (table 5.3). Table 5.3 Number of International Events organized in Brazil, 2005 – 2011 Brazil Rio de Janeiro - RJ São Paulo - SP Salvador - BA Florianópolis - SC Brasília - DF 2004 114 36 21 9 6 4 2005 146 39 29 18 3 6 2006 207 48 54 17 8 11 Source: Ministério do Turismo Brazil – Fipe (2012a) 123 2007 209 37 61 27 9 1 2008 254 41 75 13 7 11 2009 283 62 79 15 13 8 2010 278 62 75 9 12 12 2011 309 69 60 17 13 13 Rio de Janeiro is the second most visited city by domestic leisure tourists (3,6%) in Brasil in 2011, followed by Salvador (2,2%) (figure 5.4). Figure 5.4 Most visited tourism destinations by domestic leisure tourists, Brazil 2011, data as percentage of total tourist flows Source: Ministério do Turismo Brazil – Fipe (2012b) Rio de Janeiro is the third most desired tourism destination by domestic leisure tourists (10,7%), also followed by Salvador de Bahia (7%) (figure 5.5). 124 Figure 5.5 Most desired tourism destinations by domestic leisure tourists, Brazil 2011, data as percentage of total Source: Ministério do Turismo Brazil – Fipe (2012b) Despite Rio de Janeiro and Salvador de Bahia will host the FIFA World Cup 2014 and Rio de Janeiro will also host the Olympic Games 2016, their competitiveness as tourism destinations is still not adequately studied. By acknowledging the level of competitiveness of two leading tourism destinations in Brazil, this research intends to provide local stakeholders with a valuable and accurate body of data on which to base their destination management strategies. Therefore, the primary contribution of this study is essentially practical. Secondly, this study naturally contributes to the increasing literature on destination competitiveness, in which it discusses relevant factors of competitiveness, by measuring these factors from the perspective of local tourism experts. The evidences of this research can provide tourism policy makers and practitioners with an overview of the competitiveness of two of the most important cities in Brazil, famous all over the world. 125 5.4. The Adapted Model The model developed in this study and applied on a dataset of 610 small and medium Italian destinations of excellence recognizes seven key determinants of destination competitiveness: core resources and key attractors; tourism services; general infrastructures; conditioning and supporting factors; destination policy, planning and development; destination management; demand. In order to operationalize the model, it was defined a list of indicators. The indicators derive from the major empirical models of destination competitiveness and from the wider literature in tourism planning and management. The indicators are particularly focused on the various dimensions of sustainability. In addition, they are tailored to fit the contest of small and medium Italian tourism destinations. The size of the destinations and the local contest of Rio de Janeiro and Salvador de Bahia are very different, compared to the small and medium Italian Municipalities. In this Chapter the model is adapted in order to fit to these two tourism destinations: new factors are added and some indicators are changed. As Enright & Newton (2004) and Omerzel Gomezeli & Mihalič (2008) pointed out, there is no universal set of competitiveness indicators applicable to all destinations at all times. In particular, a new determinant has been introduced: “General conditions”. Whereas “Conditional and supporting factors” represent factors that are directly related to tourism activities, “General conditions” are forces in the wider environment that can define the limit, or influence the potential of destination competitiveness. They include: banking and financial system; overall economic condition; entrepreneurship; exchange rate; political stability; safety; environmental quality; overall cleanliness of the destination; cleanliness of government; development of modern public administration. Moreover, it is introduced the category “Tourism Output and Impacts”. The respondent has to indicate how much agree or disagree with 126 each of the Tourism Development Impact statements (1= Strongly Disagree 2= Disagree 3 = Neither Disagree nor Agree 4= Agree 5 = Strongly Agree). Monitoring tourism outcomes and impacts is a very important issue in the tourism destination management because it provides a systematic feedback about the current situation of a tourism system. It can help to introduce management actions that allow achieving not only conservationist objectives, but also sustainable goals for a tourism destination. The variables in this group involve different issues concerning social/environmental impacts of tourism, cultural exchange opportunities, formal jobs creation and economic benefits from tourism, standards of living of the residents and of the poorest communities, investments, infrastructures and tourists satisfaction. This is in line with the Müller’s view. According to Müller (1994), sustainable tourism means establishing the right balance on the following angles of the pentagon: unspoilt nature, healthy culture, subjective well being of the residents, optimum satisfaction of guest requirement, economic health. Pursuing these five objectives means moving to the path to sustainable tourism development. Ritchie & Crouch (2000:5) also argue that «to be competitive a destination’s development of tourism must be sustainable, not just economically and not just ecologically, but socially, culturally and politically as well (...). Competitiveness is illusory without sustainability». 127 Table 5.3 Selected Indicators of Destination Competitiveness 1) Core Resources and Key Attractors Natural resources Historical and archaeological sites Artistic and architectural features Green areas Cultural attractors Events Leisure activities Evening entertainment and nightlife Gastronomy ant typical products Shopping opportunities 2) Tourism Services Quality of accommodations Quantity of accommodations Environmental friendliness of accommodations Food services quality Tourist oriented services 3) General Infrastructures Environmental friendliness and quality of transportation services Quality of transport infrastuctures Communication system Public medical care facilities Sanitation, sewage and solid waste disposal Accessibility of facilities by disabled persons 4) Conditioning and Supporting Factors Accessibility of destination Value for money in destination tourism experience Local supply of goods and services to tourists and tourism businesses Presence of local businesses in the tourism sector Management capabilities of tourism firms Use of IT by tourism firms Level of professional skills in tourism Hospitality of residents towards tourists 128 (tab. 5.3 continued) 5) General Conditions Banking and financial system Overall economic condition Entrepreneurship Exchange rate Political stability Safety Environmental quality Overall cleanliness of the destination Cleanliness of government Development of modern public administration 6) Tourism Policy, Planning and Development Political commitment to tourism Integrated approach to tourism planning Public sector commitment to minimizing negative environmental impacts of tourism Public sector commitment to minimizing negative social impacts of tourism on local community Public sector commitment to maximising economic impacts of tourism on local community Public sector commitment to tourism/hospitality education and training Collaboration among public sector units for local tourism development Cooperation between public and private sector for local tourism development Emphasis on community participatory process in tourism planning 7) Destination Management Tourist destination communication Effectiveness of destination positioning Effective market segmentation Effectiveness of destination management structure Tourist guidance and information Stewardship of the natural environment Tourism impacts management and monitoring Promotion of partnerships among tourist businesses Promotion of partnerships between public and private stakeholders 8) Demand factor Tourists' respect for local traditions and values Tourists' enviromental awareness Awareness of destination Level of repeat visitors (Non) seasonality in tourist flows 129 (tab. 5.3 continued) Tourism outcomes and impacts Tourists are satisfied with their holiday because of an enriching tourism experience The economic growth has improved increasingly because of tourism The standard of living of poor communities has increased considerably because of tourism The local standard of living has increased considerably because of tourism Tourism has created formal employment opportunities Tourism has given economic benefits to local businesses Tourism has resulted in positive impacts on the cultural identity of the local community Tourism has resulted in positive environmental impacts Tourism has attracted important investments The public transportation system has increased considerably because of tourism 5.5. Methodology 5.5.1. Data Collection A survey instrument was prepared from the list of indicators of tourism destination competitiveness (see the next paragraph). Primary quantitative data were collected through experts’ judgement, the most appropriate method identified in order to reach the expect results, especially if one considers the large number of tourism destination competitiveness attributes to be analysed. Not only this method proved to be more feasible, but the judgement of individuals is a valuable source of information, because it is based on their experiences, expertise and the capacity of their minds in absorbimg and sorting large amounts of evidence, information, experience and data (Crouch, 2010). In tourism literature, competitiveness is also measured using survey data of tourists’ opinions and perceptions (Haahti and Yavas, 1983; Haahti, 1986; Javalgi, Thomas, Rao, 1992; Driscoll, Lawson, Niven, 1994; Kozac & Rimmington, 1998, 1999; Botha, Crompton, Kim, 1999; Kozak, 2002; Bahar & Kozac, 2007; Cracolici & Nijkamp, 2008; Mechida et al., 130 2010). Enright & Newton (2004) claim that tourists could quite easily evaluate the standard components of destination attractiveness, but are less able to know the various factors that influence and determine the competitive position of a tourism destination. Thus, a second approach is based on the empirical evaluation of a number of subjective indicators of tourism competitiveness, surveyed on key tourism stakeholders (Sirše & Mihalič, 1999; Faulkner, Oppermann, Fredline, 1999; Dwyer, Livaic, Mellor, 2003; Dwyer et al., 2004; Kim, Dwyer, 2003; Enright & Newton, 2004, 2005; Omerzel Gomezelj, 2006; Chen, Sok, Sok, 2006; Kaynak & Marandu, 2007; Omerzel Gomezelj & Mihalič, 2008; Lee, King, 2009; Lee, Chen, 2010; Bornhorst, Ritchie, Sheehan, 2010; Crouch, 2010; Dwyer, Cvelbar, Edwards, Mihalič, 2012). Omerzel Gomezelj & Mihalič (2008) assert that the understanding of people who have some significant knowledge of what makes a tourism destination competitive can supply a helpful point of departure for analyses such as this. The chosen participants were tourism experts, including incoming travel agents, tourist guides, hotel managers, travel consultants, tourism professors, tourism students, and tourism public managers. The main reason is that they are supposed to possess a solid knowledge of the destination. Various sources - personal contacts, the websites of the public tourism offices, the websites of the local Universities, etc. - provided the researcher with a reasonable mailing list, quantity and quality wise. Research was also conducted through search tools (Google) and listing websites. Many websites related to tourism were checked in order to identify the most appropriate potential respondents. The data were collected with a web survey. The web survey required respondents to rate their own tourism destination’s performance, on a 5point Likert scale, on each of the competitiveness indicators, against a ref- 131 erence group of destinations. «It would be meaningless to ask respondents to give absolute ratings for any destination on any given attribute of competitiveness» (Dwyer, Livaic, Mellor, 2003). This is motivated by the fact that a given location is not competitive in a vacuum, but against competing destinations (Kozac & Rimmington, 1999, Enright et. al., 1997; Enright & Newton, 2005; Bahar & Kozac, 2007; Gomezelj & Mihalič, 2008). As a consequence, the web survey began by asking respondents the identification of the main competitive locations (maximum 5). The questionnaire was pretested on five Brazilian hotel managers, on five tourism researchers and on five tourism professionals. On the basis of the pre-test, some indicators were simplified and/or rewritten. The final draft of the model was screened by a panel of both academics and practitioners. An online survey was distributed electronically, available in portuguese. The survey were submitted in December 2012; two weeks after the first invitation to participate, a second e-mail message (reminder) was sent to all the contacts that did not complete the survey in order to increase the participation rate. 277 usable responses were received in the case of Rio de Janeiro. 164 usable responses were received in the case of Salvador de Bahia. 132 5.5.2. The Survey Instrument A self-administered on line questionnaire was used (LimeSurvey, www.limesurvey.org). LimeSurvey (formerly PHP Surveyor) is a free and open source online survey application written in PHP based on a MySQL,PostgreSQL or MSSQL database, distributed under the GNU General Public License. LimeSurvey is a web application that is installed to the user’s server. After installation, users can manage LimeSurvey from a web-interface. Designed for ease of use, it enables users to develop and publish surveys, and collect responses, without doing any programming. Surveys can include a variety of question types that take many response formats. In the present survey, questions were added in groups, one for each determinant. The questions within each group (indicators) were organized on the same page. Figure 5.6 shows the front page of the Rio de Janeiro web-survey, figure 5.7 illustrates the first page of the online questionnaire. Figure 5.6. Front page of the Rio de Janeiro Web-Survey 133 Figure 5.7. First page of the Rio de Janeiro Web-Survey 134 Figure 5.8 shows the front page of the Salvador de Bahia web-survey, figure 5.9 illustrates the second page of the online questionnaire (the first determinant, “Core Resources and Key Attractors”). Figure 5.8 Front page of the Salvador de Bahia Web-Survey 135 Figure 5.9 Second page of the Salvador de Bahia Web-Survey: Core Resources and Key Attractors 136 5.5.3. Moments of the Distribution: Mean, Variance, Skewness, Kurtosis For each indicator of destination competitiveness, the first four moments of the distribution were determined and discussed: mean value, variance, skewness, kurtosis. When a set of values has a sufficiently strong central tendency, that is, a tendency to cluster around some particular value, then it may be useful to characterize the set by a few numbers that are related to its moments, the sums of integer powers of the values. Best known is the mean of the values, which estimates the value around which central clustering occurs. The arithmetic mean of a set of values is the quantity commonly called “the” mean or the average. Given a set of samples , the arithmetic mean is: Having characterized a distribution’s central value, one conventionally next characterizes its “width” or “variability” around that value. More than one measure is available: most common is the variance. For a single variate having a distribution the population variance where is with known population mean , , commonly also written the population mean and value of . For a discrete distribution with denotes , is defined as the expectation possible values of , the population variance is therefore whereas for a continuous distribution, it is given by The variance is therefore equal to the second central moment 137 . When a set of values has a sufficiently strong central tendency, that is a tendency to cluster around some particular value, then it may be useful to characterize the set by a few numbers that are related to its moments, the sums of integer powers of the values. Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable. The skewness value can be positive or negative, or even undefined. Qualitatively, a negative skew indicates that the tail on the left side of the probability density function is longer than the right side and the bulk of the values (possibly including the median) lie to the right of the mean. A positive skew indicates that the tail on the right side is longer than the left side and the bulk of the values lie to the left of the mean. A zero value indicates that the values are relatively evenly distributed on both sides of the mean, typically (but not necessarily) implying a symmetric distribution. The skewness of a random variable X is the third standardized moment, denoted γ1 and defined as where μ3 is the third moment about the mean μ, σ is the standard deviation, and E is the expectation operator. The last equality expresses skewness in terms of the ratio of the third cumulant κ3 and the 1.5th power of the second cumulant κ2. Kurtosis is any measure of the “peakedness” of the probability distribution of a real-valued random variable. In a similar way to the concept of skewness, kurtosis is a descriptor of the shape of a probability distribution and, just as for skewness, there are different ways of quantifying it for a theoretical distribution and corresponding ways of estimating it from a sample from a population. The fourth standardized moment is defined as 138 where μ4 is the fourth moment about the mean and σ is the standard deviation. 5.6. Discussion of findings 5.6.1. The Case of Rio de Janeiro Core Resources and Key Attractors “Core resources and key attractors” received the highest rating among the eight determinants of destination competitiveness (4.07). Figure 5.10 Survey Results, calculated means 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 4.07 Core Resources and Key Attractors 3.42 Tourism Services 3.32 Conditioning and Supporting Factors 2.79 Tourism Policy, Planning and Development 2.90 Destination Management 3.49 Demand factor 2.26 General Infrastructures 2.86 General conditions 3.39 Tourism outcomes and impacts 139 Among them the experts gave the highest value to “natural resources” (4.71), followed by “events” (4.21) and “evening entertainment and nightlife” (4.20). Based also on the variance of each item, respondents tended to strongly agree that these three elements represent the core of the Rio de Janeiro (RJ) tourist supply. The very high rating (4.71) given to RJ natural resources is to be expected. This element received the highest rating in the whole set of competitiveness indicators. Moreover, “natural resources” has the smallest variance in the whole set of variables in the questionnaire and also the highest skewness and kurtosis. This indicates high agreement between respondents. After all, it is well known that the city has extensive areas of attractive and unique beauty – an endless coast with marvellous beaches and rocky hills above the sea with incredibly panoramic views – that are recognized worldwide and a climate that is conductive to experiencing these resources throughout much of the year. The high rating should not be a cause of complacency, however. Maintenance of Rio de Janeiro’s competitive advantage will require constant environmental monitoring of the impacts of development. The very elevated rating given to RJ events and entertainment is to be expected also. The pulsating nightlife is a major attraction of RJ, which is also the place of the biggest Carnival in the world - with hundreds of street parties (“Carnaval da Rua”) and the typical RJ carnival parade in the Sambodromo - and hundreds of thousands of people flocking to RJ each year to celebrate the famous festival. RJ also ranked high in shopping opportunities (4.11), but the variability in responses to this question is the highest in the group (0.915), demonstrating that respondents have more variable perceptions of this elements compared to other components. The perception is also that RJ rates relatively high in terms of green areas (4.07) and leisure activities (4.06). In comparison, smaller mean values were found in cultural, historical and artistic features. 140 Table 5.4 Core Resources and Key Attractors, descriptive statistics Core Resources and Key Attractors Natural resources Historical and archaeological sites Artistic and architectural features Green areas Cultural attractors Events Leisure activities Evening entertainment and nightlife Gastronomy ant typical products Shopping opportunities Mean Variance Skewness Kurtosis 4.71 0.421 -3.202 13.343 3.94 0.844 -0.538 -0.374 3.83 0.795 -0.364 -0.424 4.07 0.869 -0.784 0.198 3.99 0.716 -0.379 -0.440 4.21 0.650 -0.762 -0.035 4.06 0.869 -0.870 0.287 4.20 0.759 -0.967 0.487 3.63 0.874 -0.375 -0.272 4.11 0.915 -1.068 0.985 Tourism Services “Tourism services” received the third highest rating among the eight determinants of destination competitiveness (3.42). The core resources constitute the pull force of the destination, but if these are not accessible, if the private sector doesn’t create the business services which bring them to the market, if the infrastructures are not adequate, they will be considerably constraints in their ability to pull tourists (Ritchie & Crouch, 2003). Among them the experts gave the highest value to the “food services quality” (3.77), followed by “quality of accommodations” (3.54), “tourist oriented services” (3.51) and “quantity of accommodations” (3.47). Compared to other items, “quality of accommodations” has a low variance (0.682), indicating general agreement among the respondents. The perception is that RJ rates very low in terms of “environmental friendliness of accommodations” (2.80). In this case the low skewness and kurtosis indicates a symmetric distribution of responses and a mesokurtic or normal distribution. 141 Table 5.5 Tourism Services, descriptive statistics Tourism Services Quality of accommodations Quantity of accommodations Environmental friendliness of accommodations Food services quality Tourist oriented services Mean Variance Skewness Kurtosis 3.54 0.672 -0.277 0.548 3.47 0.899 -0.190 -0.217 2.80 0.837 0.057 -0.053 3.77 0.763 -0.401 -0.056 3.51 0.924 -0.316 0.042 General Infrastructures “General infrastructures” had the lowest rating among the eight determinants of destination competitiveness (2.26). These are the weaker point of RJ tourism product. According to the experts none of these attributes supports the competitiveness of the RJ tourism. Many models of destination competitiveness do not raise the question of general infrastructures, which is an important factor of destination competitiveness and influences the choice of a vacation destination. Even if a destination may possess a great quantity of resources and attractors, it is required the support of other elements in order to be adequate to receive tourists (Gunn, 2002). Crouch & Ritchie (1999) argue that the key attractors have no economic value in themselves. «That is for example, a scenic valley has no economic value in itself if the only creatures able to experience the scenery are the local fauna. Building a road into the valley, thus providing access to tourists does however provide value». Promoting the treasures of Rio de Janeiro necessitates more investments in the form of tourism infrastructure. Therefore, although the government is working on this issue with consistent investments due to the FIFA World Cup 2014 and to the Olympic Games 2016, it is necessary to place more emphasis on sufficiently preparing the infrastructures. This is demonstrated by the very low rating given to all the infrastructure indicators. “Public medical care facilities” had the second smallest rating in the whole set of variables (1.85). Quality of transport infrastructures (2.12), 142 “sanitation, sewage and solid waste disposal” (2.32) had very low rating also; “communication system” was rated slightly higher (2.91). This is consistent with the results reported by the Travel and Tourism Competitiveness Report published by the World Economic Forum (WEF, 2012). The Report underlines that Brazilian tourism destinations are suffering from serious problems of competitiveness in infrastructures: its ground transport infrastructure ranked 116th out of 139 countries examined and the quality of roads ranked in the 105th place. Table 5.6 General Infrastructures, descriptive statistics General Infrastructures Environmental friendliness and quality of transportation services Quality of transport infrastuctures Communication system Public medical care facilities Sanitation, sewage and solid waste disposal Mean Accessibility of facilities by disabled persons Variance Skewness Kurtosis 2.31 2.12 2.91 1.85 1.069 1.021 1.061 0.875 0.511 0.543 0.080 0.761 -0.233 -0.552 -0.666 -0.357 2.32 0.988 0.312 -0.669 2.04 0.918 0.746 0.140 Conditioning and Supporting Factors “Conditioning and Supporting Factors” ranked in fifth position (rating 3.32) among the eight determinants of destination competitiveness. Conditioning and supporting factors can strengthen or weaken the impact of all other determinants of destination competitiveness. “Hospitality of residents towards tourists” scored the highest rating (4.30) among the group and second highest rating among the whole set of indicators of destination competitiveness; the low variance (0.6) demonstrate general agreement. After all, Brazilians are reputedly one of the most hospitable people in the world; friendship and hospitality are highly praised traits in the Brazilian society. The rest of the indicators were rated between 3.09 and 3.47, with one exception. It is quite worrying the low rating given to “value for money in destination tourism experience” 143 (2.76). Cost is one of many factors which affect consumer’s choice of a holiday destination. What is perhaps, more important than cost is value for money. Visitors want to go home feeling that the price they paid was fair for the quality or standard of the goods or services they bought, or for how good or unique an experience was. Value for money is important to all visitors, whether they are from the luxury or budget ends of the market. Positioning and promoting RJ as a destination that provides value for money is a key to achieving the tourism policy’s objective of bringing RJ to one of the most attractive tourist destination in the world. Accessibility of destination had the second highest rating (3.47) in the group, but also the highest variance, indicating much higher degree of dispersion in the responses. A relative low rating was given to key indicators “management capabilities of tourism firms” (3.09) and “level of professional skills in tourism” (3.16). The great changes which have happened in tourism have made organizations more competitive and customers more demanding. Baum (1995) argues that skill levels and human resource management can play a strategic role in the challenge to improve the quality of the tourism product and enhance the market position of tourism destinations. Table 5.7 Conditioning and Supporting Factors, descriptive statistics Conditioning and Supporting Factors Accessibility of destination Value for money in destination tourism experience Local supply of goods and services to tourists and tourism businesses Presence of local businesses in the tourism sector Management capabilities of tourism firms Use of IT by tourism firms Level of professional skills in tourism Hospitality of residents towards tourists 144 Mean Variance Skewness Kurtosis 3.47 1.101 -0.422 -0.192 2.76 1.063 -0.011 -0.547 3.16 0.880 -0.139 -0.097 3.38 0.931 -0.168 -0.376 3.09 3.22 3.16 4.30 0.829 0.898 0.950 0.600 -0.051 -0.157 -0.135 -0.890 0.163 -0.154 -0.148 0.197 General conditions “General conditions” ranked in sixth position (rating 2.86) among the eight determinants of destination competitiveness. “Political stability” (3.65), “banking and financial system” (3.44), “entrepreneurship” (3.31), “overall economic conditions” (3.25) and “exchange rate” (3.22) scored the highest rating among the group. The rest of the indicators received a low rating, between 1.98 and 2.70. Safety had a score of 2.64. RJ suffers from crime and violence that emanates out from hundreds of shantytowns, also known as “favelas” ruled by drug traffickers. Favelas appeared in the 1970s due to rural exodus, when many people left rural areas of Brazil and moved to cities. Without finding a place to live, many people ended up in the favelas, areas of irregular occupation definable by lack of public services and urbanization. Drug trafficking started in Rio in 1980s when some favela residents began to sell cocaine. Over the years, drug trafficking has increased dramatically and has become a common routine. Today, the lack of security and fear is the consequence of this situation. Supposed risks and safety concerns were found to be stronger predictors of not choosing RJ and other Brazilian destinations for vacation. But the situation is rapidly improving in this sense. Few years ago, in preparation for hosting the World Cup and Olympic Games, the RJ authorities got their act together. Rather than merely patrol the favelas, which had done little good over the decades, the police started occupying them. The police set up stations at the heart of the favelas, and disarm the locals street by street. The consequence is that now the tourist heart of RJ is turned into a safer place. This has happened also in the main Brazilian cities. Therefore, safety and security have improved significantly. This is also showed by the World Economic Forum’s Travel & Tourism Competitiveness Index (WEF, 2012), 145 ranking in the 75th place in 2011 out of 139 countries, up from the 128th position in 2008. “Cleanliness of government” (1.79) received the lowest rating in the whole set of competitiveness indicators and “development of modern public administration” had the third lowest rating (1.98). This may be due to the fact that the political and ethical context usually influence the functioning of the public administration. Tourism industry may benefits from a high-quality and efficient public administration. Reforming the public administration to make its work transparent and free of unnecessary administrative barriers should be a priority in the agenda of both local and national government, so that services will be accessible and administration effective. The high level of corruption in Brazil is also shown by Transparency International, which ranks countries based on the transparency of public institutions. The Corruption Perceptions Index scores countries on a scale from 0 (highly corrupt), to 100 (very clean): it gives Brazil a 43 out of 100 (http://cpi.transparency.org/cpi2012/results). This indicates that central and local governments need to integrate anti-corruption actions into all aspects of decision-making. The perception is that RJ also rates relatively low in terms of “overall cleanliness of the destination” (2.65) and “environmental quality” (2.70). Table 5.8 General conditions, descriptive statistics General conditions Banking and financial system Overall economic condition Entrepreneurship Exchange rate Political stability Safety Environmental quality Overall cleanliness of the destination Cleanliness of government Development of modern public administration Mean Variance Skewness Kurtosis 3.44 0.924 -0.113 -0.399 3.25 0.779 -0.160 -0.075 3.31 0.758 -0.096 -0.431 3.22 1.154 -0.148 -0.182 3.65 1.005 -0.502 -0.195 2.64 1.066 0.020 -0.793 2.70 0.847 0.238 -0.155 2.65 1.085 0.199 -0.489 1.79 0.987 1.179 0.837 1.98 146 1.302 0.940 -0.054 Tourism Policy, Planning and Development “Tourism Policy, Planning and Development” received the second lowest rating among the eight determinants of destination competitiveness (2.86). This group also had a higher variance which may be an indication that the differences in awareness respondents have to these area could be due to asymmetric information. Political commitment to tourism scored 3.73. This item received a relative high rating compared to the other elements in this category, indicating the efforts in prioritizing tourism as a vital component of a community’s economic development strategy. “Integrated approach to tourism planning” recorded the second highest score in the group (3.01) and the lowest variance; this indicates high agreement between respondents. Planning for tourism is rarely exclusively devoted only to tourism and takes place in many forms, structures and scales. Hall (2000) states that tourism planning needs a comprehensive and integrated approach, which recognizes that resources, services, facilities and infrastructures are interrelated with one another and with the social, cultural and natural environment. The rest of the indicators were rated similarly, they received a low rating, between 2.45 and 2.79. Public sector commitment to minimizing negative social and environmental impacts and to maximizing economic impacts of tourism on local community was not judged to be satisfactory. “Collaboration among public sector units” and “cooperation between public and private sector” also recorded very low rating. Wall & Mathieson (2006) claim that organizations at all levels should try to coordinate development and planning initiatives. Emphasis on community participatory process in tourism planning scored 2.45. Because tourism affects the entire community, participatory planning is essential (Murphy, 1985). 147 Respondents seem to highlight the need for serious changes in the priorities of future tourism policies. They express the need for better planning by state and local governments to improve (or mitigate the negative) social and environmental impacts associated with tourism growth. The results point toward a need for a collaborative approach and an emphasis on community participatory process in tourism planning. Table 5.9 Tourism Policy, Planning and Development, descriptive statistics Tourism Policy, Planning and Development Political commitment to tourism Integrated approach to tourism planning Mean Variance Skewness Kurtosis 3.73 1.274 -0.591 -0.479 Public sector commitment to minimizing negative environmental impacts of tourism Public sector commitment to minimizing negative social impacts of tourism on local community Public sector commitment to maximising economic impacts of tourism on local community Public sector commitment to tourism/hospitality education and training Collaboration among public sector units for local tourism development Cooperation between public and private sector for local tourism development Emphasis on community participatory process in tourism planning 3.01 0.866 0.018 -0.240 2.63 1.180 0.108 -0.700 2.58 1.118 0.189 -0.507 2.79 1.011 -0.137 -0.363 2.57 1.097 0.231 -0.403 2.67 1.074 0.030 -0.515 2.64 1.064 0.007 -0.769 2.45 1.165 0.187 -0.968 Destination Management “Destination Management” ranked in fifth position (rating 2.90) among the eight determinants of destination competitiveness, slightly higher than “Tourism Policy, Planning and Development” (2.79). While tourism policy set a framework within which a competitive destination can be developed on the long term, destination management deals with its various factors in a short time horizon. 148 “Effectiveness of destination positioning” scored the highest rating (3.97) among the group; the low variance and the positive kurtosis express general agreement and a fair clustering of responses. It is followed by “effective market segmentation” (3.52) and “tourist destination communication” (3.29). It should be noted that the three variables are related to the destination marketing. This is a consequence of the big changes in the Brazilian and RJ marketing campaigns over the last decade. In 2004, the Brazilian government, through the efforts of Embratur (Brazilian Tourism Institute), implemented “Plano Aquarela” (The Watercolor Plan) which was a suitable name for the operation as its intention was to show the many colours of Brazil to the world. Embratur underwent the first ever research study into how to improve the image of Brazil abroad in order to increase foreign investment, primarily in the form of tourism. The first study of foreign tourists leaving Brazil highlighted various areas where the Brazilian government could focus its marketing efforts. Topping the list for the best that Brazil has to offer were its natural beauty and the friendliness of the people. Brazil’s federal government and RJ government moved in this direction in terms of tourism marketing, after years of marketing campaigns based on the 3 S’s stereotype (Sun, Sand and Sea, and implicitly, Sex). The results are an increasing awareness of the different tourism options in RJ, as pointed out by the respondents of this survey. The rest of the items, including “tourist guidance and information”, “stewardship of the natural environment”, “tourism impacts management and monitoring”, “promotion of partnerships among tourist businesses” and “between public and private stakeholders” received a low rating (from 2.38 to 2.77) suggesting that specific actions need to be implemented to improve the management of RJ as a tourist destination. Respondents seem to highlight the need for serious changes in the destination management, in particular due to the apparent lack of attention toward sustainability issues. 149 Table 5.10 Destination Management, descriptive statistics Destination Management Tourist destination communication Effectiveness of destination positioning Effective market segmentation Effectiveness of destination management structure Tourist guidance and information Stewardship of the natural environment Tourism impacts management and monitoring Promotion of partnerships among tourist businesses Promotion of partnerships between public and private stakeholders Mean Variance Skewness Kurtosis 3.29 0.727 -0.345 0.079 3.97 0.871 -0.850 0.493 3.52 1.009 -0.215 -0.435 2.77 2.47 2.47 2.38 0.933 1.120 0.964 0.979 -0.007 0.187 0.229 0.238 -0.222 -0.701 -0.439 -0.685 2.59 0.924 0.061 -0.685 2.64 0.967 0.121 -0.428 Demand factor The nature of demand for the industry’s product is regarded to have a significant influence also in the wider competitiveness literature (Porter, 1990). According to Dwyer & Kim (2003), this seems to be similar in the tourism contest. “Demand factor” received the second highest rating among the eight determinants of destination competitiveness (3.49). Among them the experts gave the highest value to the “level of repeat visitors” (3.70) and to the “awareness of destination” (3.67). They also had the smallest variance in the group indicating high agreement between respondents. “Tourists’ respect for local traditions and values” (3.57) and “Tourists’ environmental awareness” (3.35) also had a relative high score. These characteristics are connected with the concept of responsible tourist behaviour. Sharpley (1994: 84) gives a description of the responsible tourist as the person who «seeks quality rather than value, is more adventurous, more flexible, more sensitive to the environment and searches for greater authenticity than the traditional, mass tourist». 150 Table 5.11 Demand factor, descriptive statistics Demand factor Tourists' respect for local traditions and values Tourists' enviromental awareness Awareness of destination Level of repeat visitors (Non) seasonality in tourist flows Mean Variance Skewness Kurtosis 3.57 0.806 -0.428 0.136 3.35 0.955 -0.448 0.011 3.67 0.777 -0.251 -0.182 3.70 0.686 -0.448 0.321 3.18 1.154 -0.289 -0.366 “Level of repeat visitors” scored the highest rating (3.97) among the group; the low variance (0.686) and the positive kurtosis express general agreement and a fair clustering of responses. Is is followed by “awareness of destination” (3.70). “(Non) seasonality in tourist flows” scored the worst rating among the category (3.18) and the higher variance (1.154) indicating lesser levels of agreement between respondents than the other items in terms of dispersion. Tourism outcomes and impacts Monitoring tourism outcomes and impacts is a very important issue in the tourism destination management because it provides a systematic feedback about the current situation of a tourism system. It can help to introduce management actions that allow achieving not only conservationist objectives, but also sustainable goals for a tourism destination. The respondent has to indicate how much agree or disagree with each of the Tourism Development Impact statements (1= Strongly Disagree 2= Disagree 3 = Neither Disagree nor Agree 4= Agree 5 = Strongly Agree). The highest mean score was “the economic growth has improved increasingly because of tourism” (4.04). Respondents also expressed a somewhat highly favourable response to another economic item as “tourism has attracted important investments” (3.93). 151 “The local standard of living has increased considerably because of tourism” (3.18), “the standard of living of poor communities has increased considerably because of tourism” (3.20), “tourism has given economic benefits to local businesses” (3.25) and “tourism has created formal employment opportunities” (2.99) had a lower mean value. These results seem to show a positive role of tourism in RJ’s economic development, but the central question is who benefits from economic growth? Foreign investors or local population? Big hotel chains or local entrepreneurs? To answer these questions more research need to be undertaken. However, the results seem to point toward a need for better targeting and planning of tourism investments in order to improve the local standards of living and to enhance poverty-reducing impacts. RJ is known to have one of the sharpest contrasts between rich and poor and this is evident everywhere. Targeting and planning may be easier now that the essential physical infrastructure is in place. Without such infrastructure, tourism growth, whether poverty reducing or not, would not have occurred. Respondents were likely to disagree that “tourism has resulted in positive environmental impacts” (2.85). These findings point out the need for better planning by state and local governments to mitigate the negative environmental impacts associated with tourism growth. Respondents tend to strongly agree that tourist satisfaction is high in RJ. “Tourists are satisfied with their holiday because of an enriching tourism experience” (4.02) was the second higher mean score and the lower variance score in this group (0.621). Visitor satisfaction is determined by a combination of perceived value and quality, consumer expectations and actual experience. A visitor’s overall satisfaction will influence the likelihood of repeat visitation, extended length of stay, increased expenditure, enhanced yield and word-of-mouth referrals. The low rating given to tourism policy/destination management attributes and to general conditions and infrastructures, and on the contrary the high rating received by 152 the core resources, seems to point out that the high tourist satisfaction is not a result of the effectiveness of destination management/tourist policy actions in crafting tourism experiences, but is likely to be due to the extensive areas of unique beauty and to RJ big events and entertainment. Table 5.12 Tourism outcomes and impacts, descriptive statistics Tourism outcomes and impacts Tourists are satisfied with their holiday because of an enriching tourism experience The economic growth has improved increasingly because of tourism The standard of living of poor communities has increased considerably because of tourism The local standard of living has increased considerably because of tourism Tourism has created formal employment opportunities Tourism has given economic benefits to local businesses Tourism has resulted in positive impacts on the cultural identity of the local community Tourism has resulted in positive environmental impacts Tourism has attracted important investments The public transportation system has increased considerably because of tourism 153 Mean Variance Skewness Kurtosis 4.02 0.621 -0.840 1.108 4.04 0.697 -0.851 0.743 3.20 1.469 -0.310 -0.853 3.18 1.147 -0.235 -0.551 2.99 1.538 -0.127 -1.067 3.25 1.267 -0.380 -0.721 3.43 1.119 -0.566 -0.278 2.85 3.93 1.065 0.803 0.086 -0.897 -0.658 0.867 2.99 1.467 -0.084 -1.019 5.6.2. The Case of Salvador de Bahia Core Resources and Key Attractors “Core resources and key attractors” received the highest rating among the eight determinants of destination competitiveness (3.64). Figure 5.11 Survey Results, calculated means 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 3.64 Core Resources and Key Attractors 3.23 Tourism Services 3.14 Conditioning and Supporting Factors Tourism Policy, Planning and Development 2.44 Destination Management 2.47 3.19 Demand factor 2.06 General Infrastructures 2.58 General conditions 2.76 Tourism outcomes and impacts Among them the experts gave the highest value to “historical and archaeological sites” (4.34), followed by “artistic and architectural features” (4.19) and “natural resources” (4.01). Based also on the variance of each item, respondents tended to strongly agree that these three elements represent the core of the Salvador de Bahia (SA) tourist supply. These elements received the highest rating in the whole set of competitiveness indicators. Moreover, “historical and archaeological sites” and “artistic and architectural features” have the smallest variance in the whole 154 set of variables in the questionnaire and also the highest skewness and kurtosis. This indicates high agreement between respondents. The very high rating given to SA historical and artistic resources is to be expected. After all, the density of monuments makes SA the colonial city par excellence in the Brazilian Northeast. The Pelourinho, the ‘old town’ of Salvador, was designated a World Heritage Site by UNESCO in 1985 and has been largely restored during the 1990s. SA was also one of the major points of convergence of European, African and American Indian cultures in the 16th-18th centuries and was the first historic capital of Brazil, from 1549 to 1763. Natural resources also received a very high rating, the third highest rating among the whole set of indicators. The city has an endless coast with marvellous beaches and a climate that is conductive to experiencing these resources throughout much of the year. The elevated rating given to historical, artistic and natural features should not be a cause of complacency, however. Maintenance of Salvador de Bahia’s competitive advantage will require constant environmental monitoring of the impacts of development. The relatively high rating given to SA events (3.53) and entertainment (3.23) is to be expected also. SA is the place of one of the biggest Carnival in the world, with hundreds of thousands of people flocking to SA each year to celebrate the famous festival. SA also ranked high in shopping opportunities (3.92) and gastronomy and typical products (3.87). The perception is that SA rates very low in terms of green areas (2.58). This is due to the fact that the city has grown particularly fast since 1966 owing to the industrial development of the region. This has resulted in the historic city being enclosed by a very dense urban zone, without a planned presence of green areas. 155 Table 5.13 Core Resources and Key Attractors, descriptive statistics Core Resources and Key Attractors Natural resources Historical and archaeological sites Artistic and architectural features Green areas Cultural attractors Events Leisure activities Evening entertainment and nightlife Gastronomy ant typical products Shopping opportunities Mean Variance Skewness Kurtosis 4.01 0.901 -0.901 0.820 4.34 0.688 -1.231 1.465 4.19 0.728 -1.124 1.368 2.58 0.952 0.227 -0.132 3.62 1.146 -0.470 -0.432 3.53 1.061 -0.297 -0.538 3.17 0.947 0.159 -0.694 3.23 1.177 0.021 -0.741 3.87 0.899 -0.685 0.199 3.92 0.910 -0.887 0.771 Tourism Services “Tourism services” received the second highest rating among the eight determinants of destination competitiveness (3.23). Among them the experts gave the highest value to the “quantity of accommodations” (3.55), followed by “food services quality” (3.48), “quality of accommodations” (3.39) and “tourist oriented services” (3.22). Compared to other items, “quantity of accommodations” has a low variance (0.687), indicating general agreement among the respondents. The perception is that SA rates very low in terms of “environmental friendliness of accommodations” (2.53). In this case the low skewness indicates a symmetric distribution of responses. 156 Table 5.14 Tourism Services, descriptive statistics Tourism Services Quality of accommodations Quantity of accommodations Environmental friendliness of accommodations Food services quality Tourist oriented services Mean Variance Skewness Kurtosis 3.39 0.795 -0.209 0.480 3.55 0.687 -0.252 0.441 2.53 0.895 0.018 -0.613 3.48 0.752 -0.404 0.068 3.22 0.950 -0.121 0.042 General Infrastructures “General infrastructures” had the lowest rating among the eight determinants of destination competitiveness (2.06). “Quality of transport infrastructures” (1.73), “accessibility of facilities by disabled persons (1.77), “public medical care facilities” (1.79), “environmental friendliness and quality of transportation services” (1.87) received the smallest rating in the whole set of variables, “communication system” was rated slightly higher (2.95). This component is an important factor of destination competitiveness and influences the choice of a vacation destination. Promoting the beauties of Salvador de Bahia necessitates more investments in the form of infrastructure. These are the weaker point of SA tourism product. According to the experts none of the factors supports the competitiveness of the SA tourism. Therefore, although the government is working on this issue with consistent investments due to the FIFA World Cup 2014, it is necessary to place more emphasis on sufficiently preparing the infrastructures. This is also demonstrated by the very low rating given to all the infrastructure indicators, as emerged from the Travel and Tourism Competitiveness Report published by the World Economic Forum (WEF, 2012). The Report also underlines that Brazilian tourism destinations are suffering from serious problems of competitiveness in infrastructures: its ground transport infrastructure ranked 116th out of 139 countries examined and the quality of roads ranked in the 105th place. 157 Table 5.15 General Infrastructures, descriptive statistics General Infrastructures Environmental friendliness and quality of transportation services Quality of transport infrastuctures Communication system Public medical care facilities Sanitation, sewage and solid waste disposal Accessibility of facilities by disabled persons Mean 1.87 1.73 2.95 1.79 2.26 1.77 Variance Skewness Kurtosis 0.820 0.752 0.963 0.833 0.854 0.913 0.683 1.129 -0.063 1.203 0.199 1.145 -0.522 1.030 -0.251 1.454 -0.523 0.899 Conditioning and Supporting Factors “Conditioning and Supporting Factors” ranked in fourth position (rating 3.14) among the eight determinants of destination competitiveness. Conditioning and supporting factors underpin destination competitiveness. “Hospitality of residents towards tourists” scored the highest rating (3.91) among the group and fifth highest rating among the whole set of indicators of destination competitiveness. After all, the personality and hospitality of Brazilians are among the most striking national characteristics. It is quite worrying the relatively low rating given to “management capabilities of tourism firms” (2.83), “level of professional skills in tourism” (2.85), and to “value for money in destination tourism experience” (2.75). The prevalence of hotels, restaurants and bars in tourism may induce to think that tourism industry is relatively low skilled. Due to the great changes which have happened in tourism, management capabilities and skill levels play a strategic role in the challenge to improve the quality of the tourism product and consequently enhance the market position of tourism destinations. Accessibility of destination had the second highest rating (3.39) in the group. “Local supply of goods and services to tourists and to tourism business” received a rating of 3.16. Wall & Mathieson (2006: 138) claim 158 that «it is essential that the tourist industry is serviced, as far as possible, by local producers if its full potential contribution to the local economy is to be realized». Table 5.16 Conditioning and Supporting Factors, descriptive statistics Conditioning and Supporting Factors Accessibility of destination Mean Variance Skewness Kurtosis 3.34 0.893 -0.006 -0.191 Value for money in destination tourism experience Local supply of goods and services to tourists and tourism businesses Presence of local businesses in the tourism sector Management capabilities of tourism firms Use of IT by tourism firms Level of professional skills in tourism Hospitality of residents towards tourists 2.75 0.956 -0.156 -0.111 3.11 0.772 -0.065 0.067 3.28 2.83 3.08 2.85 3.91 0.787 0.735 0.985 0.994 1.000 -0.149 0.173 -0.049 -0.055 -0.847 -0.017 0.332 -0.456 -0.117 0.603 General conditions “General conditions” ranked in fifth position (rating 2.58) among the eight determinants of destination competitiveness. “Banking and financial system” (3.25), “exchange rate” (3.24) and “political stability” (3.21) scored the highest rating among the group, followed by “entrepreneurship” (2.98) and “overall economic conditions” (2.80). The rest of the indicators received a very low rating, between 1.95 and 2.20. It is particularly worrying the low rating given to “cleanliness of government” (2.05). The high level of corruption in Brazil is also shown by Transparency International, which ranks countries based on the transparency of public institutions. The Corruption Perceptions Index scores countries on a scale from 0 (highly corrupt), to 100 (very clean): it gives Brazil a 43 out of 100 (http://cpi.transparency.org/cpi2012/results). This indicates that central and local governments need to integrate anti-corruption actions into all aspects of decision-making. 159 “Development of modern public administration” also received a low rating (2.20). This is not surprising considering that the political and ethical context usually influence the functioning of the public administration. Tourism industry may benefits from a high-quality and efficient public administration. Reforming the public administration to make its work transparent and free of unnecessary administrative barriers should be a priority in the agenda of both local and national government, so that services will be accessible and administration effective. The variability in responses to these indicators (“cleanliness of government” and “development of modern public administration”) was considerably higher than for the other variables. In these cases, it is likely that less consistent agreement results from differences in experience than differences in awareness that stems from information asymmetry. Safety had a low score of 2.00. SA suffers from a grim underbelly of poverty, inequality, crime, and violence that emanates out from hundreds of shantytowns, also known as “favelas” ruled by drug traffickers. Supposed risks and safety concerns were found to be stronger predictors of not choosing regions for vacation (Sonmez & Graefe, 1998). The perception is that SA also rates very low in terms of “overall cleanliness of the destination” (1.95) and “environmental quality” (2.13). 160 Table 5.17 General conditions, descriptive statistics General conditions Banking and financial system Overall economic condition Entrepreneurship Exchange rate Political stability Safety Environmental quality Overall cleanliness of the destination Cleanliness of government Development of modern public administration Mean Variance Skewness Kurtosis 3.25 0.892 -0.261 0.317 2.80 0.729 0.055 -0.169 2.98 0.732 0.034 0.049 3.24 1.003 -0.009 -0.047 3.21 1.233 -0.235 -0.517 2.00 0.842 0.485 -0.734 2.13 1.045 0.597 -0.309 1.95 0.910 0.661 -0.312 2.05 1.270 0.940 0.192 2.20 1.403 0.704 -0.474 Tourism Policy, Planning and Development “Tourism Policy, Planning and Development” received the second lowest rating among the eight determinants of destination competitiveness (2.44). This group also had a higher variance which may be an indication that the differences in awareness respondents have to these area could be due to asymmetric information. Political commitment to tourism scored 3.25. This item received a relative high rating compared to the elements in this category, indicating the efforts in prioritizing tourism as a vital component of a community’s economic development strategy. It also had the higher variance (1.464), indicating that respondents share very different views about their perception of this attribute. The rest of the indicators were rated similarly, they received a low rating, between 2.16 and 2.58. Public sector commitment to minimizing negative social and environmental impacts and to maximizing economic impacts of tourism on local community was not judged to be satisfactory. Respondents seem to highlight the need for serious changes in the priorities of future tourism policies. They express the need for better planning by state and local governments to improve (or mitigate the negative) social and environmental impacts associated with tourism growth. 161 “Collaboration among public sector units” and “cooperation between public and private sector” also recorded very low rating. Wall & Mathieson (2006) claim that organizations at all levels should try to coordinate development and planning initiatives. “Emphasis on community participatory process in tourism planning” had the lowest rating in this group (1.92) and also the lowest variance indicating high agreement between respondents. It is particularly worrying because this item received the fifth lowest rating in the whole set of variables. Participatory process is fundamental to the development of sustainability strategies. Because tourism affects the entire community, participatory planning is indispensable (Murphy, 1985). Increasing participation in tourism planning and development is essential to creating a shared vision for a sustainable future of the SA tourism sector as a whole. This is particularly the case of SA where the pace of change as a result converging environmental, economic and social trends threatens sustainability. Table 5.18 Tourism Policy, Planning and Development, descriptive statistics Tourism Policy, Planning and Development Political commitment to tourism Integrated approach to tourism planning Public sector commitment to minimizing negative environmental impacts of tourism Public sector commitment to minimizing negative social impacts of tourism on local community Public sector commitment to maximising economic impacts of tourism on local community Public sector commitment to tourism/hospitality education and training Collaboration among public sector units for local tourism development Cooperation between public and private sector for local tourism development Emphasis on community participatory process in tourism planning 162 Mean Variance Skewness Kurtosis 3.25 1.464 -0.164 -0.882 2.58 1.063 0.151 -0.617 2.26 1.067 0.398 -0.584 2.16 1.076 0.651 -0.132 2.54 1.285 0.337 -0.653 2.32 1.071 0.569 -0.082 2.45 0.969 0.500 0.039 2.44 1.161 0.320 -0.538 1.92 0.916 0.816 -0.006 Destination Management “Destination Management” ranked in sixth position (rating 2.47) among the eight determinants of destination competitiveness, slightly higher than “Tourism Policy, Planning and Development” (2.44). The success of tourism relies on a coordinated approach to the planning, development, management and marketing of the destination (Ritchie & Crouch, 2003). “Effective market segmentation” (3.05),“tourist destination communication” (2.92) and “effectiveness of destination positioning” (2.75) scored the highest rating among the group. The three variables are related to the destination marketing. This is a consequence of the big changes in the Brazilian and SA marketing campaigns over the last decade. In 2004, the Brazilian government, through the efforts of Embratur (Brazilian Tourism Institute), implemented “Plano Aquarela” (The Watercolor Plan). Since January 2003, upon the establishment of the Ministry of Tourism, Embratur’s actions were concentrated in the promotion, marketing am support to the trading of products, services and tourism destinations. Embratur focussed its marketing efforts in promoting natural beauties, local cultures and the friendliness of the people. Brazil’s federal government moved in this direction in terms of tourism marketing, after years of marketing campaigns based on the 3 S’s stereotype (Sun, Sand and Sea, and implicitly, Sex). The results are an increasing awareness of the different tourism options in SA, as pointed out by the respondents of this survey. The rest of the items, including “tourist guidance and information”, “stewardship of the natural environment”, “tourism impacts management and monitoring”, “promotion of partnerships among tourist businesses” and “between public and private stakeholders” received a low rating (from 1.94 to 2.46) suggesting that specific actions need to be implemented to improve the management of SA as a tourist destination. Respondents 163 seem to highlight the need for serious changes in the destination management, in particular due to the apparent lack of attention toward sustainability issues. Table 5.19 Destination Management, descriptive statistics Destination Management Tourist destination communication Effectiveness of destination positioning Effective market segmentation Effectiveness of destination management structure Tourist guidance and information Stewardship of the natural environment Tourism impacts management and monitoring Promotion of partnerships among tourist businesses Promotion of partnerships between public and private stakeholders Mean Variance Skewness Kurtosis 2.92 1.011 -0.158 -0.371 2.75 1.277 0.126 -0.745 3.05 1.078 0.186 -0.391 2.12 2.46 2.10 1.94 0.923 0.998 0.920 0.878 0.388 0.360 0.539 0.803 -0.588 -0.321 -0.094 0.115 2.39 0.931 0.544 0.095 2.46 0.765 0.281 -0.214 Demand factor The nature of demand is regarded to have a significant influence in the competitiveness of a tourism destination (Dwyer & Kim, 2003). “Demand factor” received the third highest rating among the eight determinants of destination competitiveness (3.19). Among this group of indicators the experts gave the highest value to the “tourists' respect for local traditions and values” (3.50). Tourists’ environmental awareness scored 3.04. These characteristics are connected with the concept of responsible tourist behaviour. “Awareness of destination” also received a relatively high rating (3.48) and the lower variance in the group. This indicates high agreement between respondents. Awareness closely depends on the success of the marketing strategy. “(Non) seasonality in tourist flows” had the lowest score in the group 2.81. Experts seem to confirm that tourism in SA is well developed but highly seasonal. Seasonality is one of the main distinctive features of 164 the tourism phenomenon. Strong seasonality causes difficulties for businesses and for destination managers, as facilities to meet peak demand has to be established, and at other time of the year reduced tourism activity cannot sustain the peak level of business. SA has a climate that is conductive to experiencing these resources throughout much of the year, consequently to reduce seasonality a better tourism planning approach should be implemented. Events could extend the seasonal life and should be concentrated out of the peak season. Table 5.20 Demand factor, descriptive statistics Demand factor Tourists' respect for local traditions and values Tourists' enviromental awareness Awareness of destination Level of repeat visitors (Non) seasonality in tourist flows Mean Variance Skewness Kurtosis 3.50 0.895 -0.631 0.234 3.04 1.016 -0.198 -0.470 3.48 0.720 -0.123 -0.146 3.14 1.093 -0.384 -0.412 2.81 1.105 -0.352 -0.544 Tourism outcomes and impacts The variables in this group involve different issues concerning social/environmental impacts of tourism, cultural exchange opportunities, formal jobs creation and economic benefits from tourism, standards of living of the residents and of the poorest communities, investments, infrastructures and tourists satisfaction. Monitoring tourism outcomes and impacts is a very important issue in the tourism destination management. Respondents were asked to indicate their agreement on each item measured by five point Likert-type scales ranging from 1 being “Strongly Disagree” to 5 being “Strongly Agree”. Based on the mean score of each item, respondents were likely to agree that tourist “tourists are satisfied with their holiday because of an enriching tourism experience” (3.51). The low rating given to tourism policy/destination management attributes and to general conditions and in- 165 frastructures, and on the contrary the high rating received by the core resources, seems to point out that the high tourist satisfaction is not a result of the effectiveness of destination management/tourist policy actions in crafting tourism experiences, but is likely to be due to the SA key attractors. Further, respondents were likely to agree that “tourism has attracted important investments” (3.51) and that “the economic growth has improved increasingly because of tourism” (3.40). Experts were likely to disagree that “the local standard of living has increased considerably because of tourism” (2.45), and also that “the standard of living of poor communities has increased considerably because of tourism” (2.26), and that “tourism has created formal employment opportunities” (2.33). These results seem to show a positive role of tourism in SA economic growth and development, but do not prove consistent beneficial to the local population and to poor communities. There is a need for better targeting and planning of tourism investments and attention to improve the local standards of living and to enhance poverty-reducing impacts. Respondents were also likely to disagree that “tourism has resulted in positive environmental impacts” (2.33). These results indicate that tourism development has not clearly provided economic benefits in terms of formal jobs creation and increase of local standards of living, and has also resulted in negative environmental impacts. These findings point out the need for better planning by state and local governments to improve economic impacts and mitigate the negative environmental impacts associated with tourism growth. 166 Table 5.21 Tourism outcomes and impacts, descriptive statistics Tourism outcomes and impacts Tourists are satisfied with their holiday because of an enriching tourism experience The economic growth has improved increasingly because of tourism The standard of living of poor communities has increased considerably because of tourism The local standard of living has increased considerably because of tourism Tourism has created formal employment opportunities Tourism has given economic benefits to local businesses Tourism has resulted in positive impacts on the cultural identity of the local community Tourism has resulted in positive environmental impacts Tourism has attracted important investments The public transportation system has increased considerably because of tourism 167 Mean Variance Skewness Kurtosis 3.51 1.031 -0.592 -0.223 3.40 1.067 -0.480 -0.389 2.26 1.612 0.705 -0.691 2.45 1.232 0.344 -0.834 2.35 1.292 0.487 -0.671 2.80 1.199 0.000 -0.977 2.87 1.250 -0.025 -0.907 2.33 3.51 1.042 0.889 0.385 -0.477 -0.519 -0.006 2.13 1.106 0.568 -0.494 168 PART VI CONCLUSION AND DISCUSSION 6.1. Introduction - 6.2 General Findings and Discussion - 6.3. Managerial Implications - 6.4. Limitations and Suggestion for Future Research - 6.5. Conclusions 6.1. Introduction Competitiveness has been identified in the tourism literature as a critical factor for the success of tourism destinations (Pearce, 1997; Crouch & Ritchie, 1999; Kozak & Rimmington, 1999; Buhalis, 2000; Hassan, 2000; Dwyer & Kim, 2003; Enright & Newton, 2004). Many studies focus on the main factors affecting destination competitiveness. Nevertheless, there is still no evidence of a significant impact of these factors on the performance of a destination. This study aims at filling this gap, by adapting and extending the Richie & Crouch’s model and applying it on a unique dataset of 610 small and medium Italian Destinations of Excellence. The Dwyer, Livaic, Mellor (2003) approach is followed in defining a list of indicators, in order to operationalize the model. The indicators derive from the major empirical models of destination competitiveness and from the wider literature in tourism planning and management. With respect to previous empirical models, the indicators are particularly focused on the various dimensions of sustainability. In addition, they are tailored to fit the contest of small and medium Italian tourism destinations, as there is 169 no universal set of competitiveness indicators applicable to all destinations at all times (Enright & Newton, 2004; Gomezeli & Mihalič, 2008). Specifically, destinations of excellence that have been awarded with important International and National Certifications were selected: • Blue Flag, awarded by Foundation for Environmental Education – FEE (117 municipalities in the sample); • Blue Sail, by Legambiente/League for the Environment (295 mu- nicipalities); • Orange Flag, by Italian Touring Club (181 municipalities); • The Most Beautiful Villages in Italy by National Association of Italian Municipalities – ANCI (199 municipalities). A total of 1.220 key tourist stakeholders from 610 Italian municipalities were contacted in the period from April to July 2011. For each destination two stakeholders, one from the public sector and one from the private sector, were chosen: the head of the tourism office and the head of the local hotel association (in small tourism destinations, in the absence of a hotel association, a hotel director was contacted). A total of 550 usable surveys were returned from 370 different municipalities. The response rate was very high, 45,1%, in line with the average response rates of similar studies (Baruch & Holtom, 2008). To reduce the large set of variables, a Principal Component Analysis (PCA) has been performed. The dependency between the performance scores and the explanatory variables has been then analysed by an ordinary least square and a partial least square regression. The empirical findings show that a sustainable tourism policy and destination management is not only good for preserving the ecologic balance and for minimize negative cultural and social impacts, but has a great importance for improving the competitiveness of a tourism destination. The model has also been applied to measure the competitiveness of two leading tourism destination in Latin America: Rio de Janeiro and Sal- 170 vador de Bahia. These two destinations were chosen as applied cases for various reasons. Despite Brazil will host the FIFA World Cup 2014 and the Olympic Games 2016, the competitiveness of its tourism destinations is still not adequately studied. There is still insufficient academic research on Brazil tourism, especially on the two of the leading tourist destinations in the country, Rio de Janeiro and Salvador de Bahia. Tourism literature on Brazil proves to be still scarce. There have not been enough studies on research, especially regarding destination competitiveness. Brazilian tourism destinations are also interesting case studies because they still have vast growing potential. Primary quantitative data were collected through experts’ judgement. The chosen participants were tourism experts, including incoming travel agents, tourist guides, hotel managers, travel consultants, tourism professors, tourism students, and tourism public servants and public managers. The data were collected with a web survey. The survey was submitted between December 2012 and January 2013. 277 usable responses were received in the case of Rio de Janeiro, 164 in the case of Salvador. The evidences provide tourism policy makers and stakeholders with a valuable and accurate body of data on which to base their future destination management strategies. The relevance of the outcomes of this research is supported by the fact that tourism represents a primary resource to Brazil, a destination that has been going through enormous development for the economical and tourism perspective. The major focus of this final chapter is to present the summary, discussion and managerial implications of the findings of the analyses. Then, the limitations of the study are discussed; the Chapter also presents the suggestions for future research. The Chapter ends with the conclusions. 171 6.2. General Findings and Discussion The objective of the study is to develop a model about tourism destination competitiveness and to empirically test the factors that are likely to explain the competitiveness of a tourism destination. The study of tourism destination competitiveness (TDC) has focused the attention of policy makers, public and private organizations, and tourism researchers. Since tourism worldwide has become progressively more competitive, it is of critical importance to analyze strengths and weaknesses of tourism destinations (Richie & Crouch 2000). There seems to be a consensus in the literature about the fact that achieving TDC is heavily dependent on the sustainability of a tourism destination. Many research studies progressively made more comprehensible the notion of TDC. Particular attention has been paid to develop a comprehensive framework of the various components determining the competitive position of a tourism destination. Ritchie & Crouch (2000, 2003) published the most important and detailed work in the analysis of TDC. Other theoretical models were also developed to explain destination competitiveness (De Keyser & Vanhove, 1994; Hassan, 2000; Heath, 2002; Dwyer & Kim, 2003). Many models were also applied with the aim of analyzing the competitive positions of tourism destinations (Sirše & Mihalič, 1999; Dwyer, Livaic, Mellor, 2003; Enright & Newton, 2004; Gomezeli & Mihalič, 2008). Most of the studies on the main factors affecting TDC (e.g. key attractors, supporting factors, destination management, tourism policy, demand factor) suggest, with different emphasis, that each one of these factors can improve destination competitiveness. Different approaches for explaining and measuring competitiveness of tourism destinations can be distinguished from the literature. Indicators of destination competitiveness can be classified in objectively or subjectively measured variables. Enright & Newton (2004) claim that it is necessary to 172 survey persons who could answer to questions on both the attractiveness and the management features. Many authors assert that the understanding of people who have some significant knowledge of what makes a tourism destination competitive can supply a helpful point of departure for analyses such as this (Evans & Chon, 1989; Faulkner, Opperman & Fredline E. 1999; Omerzel Gomezelj & Mihalič, 2008). In this study, a model of destination competitiveness that follows this approach was developed and applied. 6.3. Managerial Implications In an increasingly competitive market, an understanding of how tourism destination competitiveness can be enhanced and sustained is a fundamental issue in successful destination management and planning. Since tourism destinations involve multi-faceted components of natural and cultural tourism resources and a multiplicity of man-made tourism businesses, a systematic analysis and framework for destination competitiveness is required. This model may contribute to creating and integrating value added tourism attractions/resources to achieve greater destination competitiveness. These research findings may help tourism planners, developers, and policy-makers to understand the key tourism factors. This study was focused on an investigation of the impact of the factors of tourism destination competitiveness on the performance of a destination in the competitive market. The most critical research finding from this study was the strong relationship of a number of critical variables - “general infrastructures”, “tourist accommodations”, “managerial competencies of local tourism firms”, “destination marketing” – with the performance of a tourist destination. The public sector seems to play a very important role, as the vari173 ables “destination marketing” and “emphasis on maximising local economic development” are significant in determining the destination performance. The analysis demonstrates that a sustainable tourism policy and destination management not only is good for preserving the ecologic balance and for minimize negative cultural and social impacts, but has a great importance in improving the competitiveness of a tourism destination. An important finding for destination competitive strategies from this research was related to destination management organizations’ roles. Especially, in order to effectively use tourism resources over the long term, destination management organizations’ roles could be emphasized and systematically established, because their functions and roles within the tourism destination may be critical in terms of their responsibility to the well-being of all aspects of destination management and operation. As Mihalič (2000) have discussed, destination competitiveness could be enhanced through management organizations’ capabilities and efforts. According to Crouch & Ritchie (1999), destination management organizations’ roles should be understood as total “management” rather than “marketing.” Therefore, destination managers and planners may need to understand what combinations or matches of tourism attrac- tions/resources and destination competitive strategies can be achieved to create more competitive tourism destinations. More specific implications in this study were that tourism destination management organizations may play an important role as facilitators between local government and agencies for tourism planning and development. The establishment of effective linkages between local government and agencies could improve destination competitiveness in the long run. 174 6.4. Limitations and Suggestion for Future Research This study investigats the structural relationships of tourism destination competitiveness from tourism stakeholders’ perspectives. In the first applied case presented in this study, the surveyed data were collected in 610 Italian small and medium Destination of Excellence. This geographically limited survey may produce different results and conclusions in terms of the impacts of the determinants on tourism destination competitiveness. Tourism stakeholders in other destinations and countries may have different perceptions concerning tourism development and destination competitive strategies. Other geographic boundaries and research scopes should be explored to see if similar findings and results could be addressed. And also, future research may collect data from other competitive states and countries so that comparison studies can be conducted. This study is somewhat limited in its selection of determinants and indicators. Even if those determinants and indicators were selected based on the literature review, other critical variables and constructs may exist to achieve further insights of destination competitiveness. For example, more specific variables and constructs that address competitive strategies are limited. The various variables that are related to tourism infrastructures and services, tourism policy or destination management were abbreviated. Therefore, future studies may address destination competitiveness that includes further variables. Another limitation to this study is related to the respondents. Generally, in the tourism literature, tourism stakeholders may include residents, tourists, and tourism experts, such as people who are involved in organizations, associations, and attractions. This study does not include residents’ and tourists’ opinions of destination competitive strategies. Accordingly, compared with the respondents (tourism stakeholders) surveyed in this study, residents and tourists may express different perceptions, attitudes, and behaviours concerning the issues and topics presented 175 in this study. As a result, for more comprehensive and thorough investigations of destination competitive strategies supported by all tourism stakeholders, future research is recommended to include both residents and tourists. Conducting studies that include comparisons and differences among tourism stakeholders in terms of tourism destination competitiveness may be possible. In the Italian case, the performance scores were given by various indicators (tourist arrivals, bed nights, gross occupancy rate of bed nights). There is much debate among researcher about the appropriate measures of TDC. In recent years researchers have developed measures of TDC which include economic as well as quality of life variables, and variables relating to environmental quality. The development of indicators of destination competitiveness can benefit from the ongoing research in this area. There is a need to explore the different types of indicators relevant to the different contexts in which the model can be applied. A major problem, underlying all attempts to establish indices of competitiveness, involves the integration of objective and subjective attributes of competitiveness. An important issue for further research is to explore the possibility to incorporating qualitative factors into the construction of a competitiveness index. More research needs to be undertaken on the relative importance of the different dimensions of competitiveness. There is also a need for further research on the importance of different attributes of destination competitiveness in determining tourism flows for visitors in different market segments, and for travel decisions made in different buying situations or contexts. 176 6.5. Conclusions This study extends the Richie & Crouch (2000) competitiveness model and applies it on a unique dataset of 610 small and medium Italian destinations of excellence, both from the mainland and from the coast. Specifically, destinations of excellence that have been awarded with important International and National Certifications were selected. A total of 1.220 key tourist stakeholders from 610 Italian municipalities were contacted in the period from April to July 2011. For each destination two stakeholders, one from the public sector and one from the private sector, were chosen: the head of the tourism office and the head of the local hotel association (in small tourism destinations, in the absence of a hotel association, a hotel director was contacted). A total of 550 usable surveys were returned from 370 different municipalities. The response rate was very high, 45,1%. To reduce the large set of variables to a smaller set a principal component analysis (PCA) was performed. The results of the principal component analysis confirm the validity of the model. It seems to display a coherent structure of the interrelations among the competitiveness indicators, which is capable of being analyzed. However, there are also some differences compared to the determinants of the model. It was not expected that the PCA would precisely reproduce the same aggregation of the assumed model. In particular, the results show that 13 components can be extracted from the variables defined above. Their structure is reasonably similar to the 7 determinants of the model. Specifically, four components – “quality of natural resources”, “historical and artistic features”, “gastronomy” and “events and activities” – refer to the first determinant of the model named “core resources and key attractors”. Even if some relationship may exist among these elements, this signifies that in the respondents’ minds there is a clear distinction of the 177 types of primary resources. It could imply that not only they need to be separately characterized and promoted, but that different marketing strategies may also be implemented to reach each target consumer group. As is common in PCA, the first component comprises a large number of variables and is fairly general. However, it strongly encompasses the tourism policy and destination management variables. It explains 36% of the variance of the data. Dwyer et al. (2004) obtained similar results from the PCA based on their model. This denotes that respondents display a distinction between tourism policy/destination management variables and other measures of destination competitiveness. It is interesting to notice that respondents do not clearly distinguish between destination management activities and tourism policy issues. They associate in their minds those elements related to sustainability which are affecting tourism policy-making and management processes. They distinguish them from attributes closely linked to the marketing and to the visitor satisfaction management: these dimensions are included under two separate headings. The “emphasis on maximising local economic development” is regarded as a distinct component from other tourism policy issues. This support the view that public sector commitment on generating economic benefits for locals is fundamental in order to increase the well-being of the residents. It implies that both the optimal satisfaction of visitor needs and economic wealth of the community have great importance. These determinants are those over which public sector has a high degree of control. The respondents also recognize the key role played by “tourist accommodations” and “general infrastructures”. They correspond to the 2nd and 3rd determinants of the model. “Tourist accommodation” is a primary factor concerning the transferring of the value to the tourists, while “general infrastructures” provide the foundations upon which a successful tourism 178 industry can be built. This last component is one of the essential prerequisites for a successful tourism destination. It is a supporting resource, along with the “managerial competencies of local tourism firms” and the “price competitiveness” of the destination. They are also seen as distinctive components. These two components can enhance the market position of a tourism destination. The demand factor (7th determinant of the model) is represented by the component “responsible tourist behaviour”. It implies that respondents clearly distinguish demand condition, along with other components, as a key determinant of strategic decision making. This is consistent with the structure of the model, which brings together Ritchie & Crouch (2000) model and Dwyer et al. (2003) model. PCA seems to confirm many of the considerations emerged in previous tourism literature. The model developed here can constitute the starting point for additional empirical research. A number of points emerge from the results of the OLS applied to study the dependency between the performance scores given by various indicators (tourist arrivals, bed nights, gross occupancy rate of bed nights) and explanatory variables which are the components of destination competitiveness resulting from the PCA. If indicators that look more at the attractiveness of the destinations (tourist arrivals and bed-nights) are used, can be observed a positive relationship of a number of different variables:  “general infrastructures”;  “tourist accommodations”;  “managerial competencies of local tourism firms”;  “destination marketing”;  “emphasis on maximising local economic development”. In particular, it implies that both variable related to the “general infrastructures”, and to the private sector, as “tourist accommodations” and 179 “managerial competencies of local tourism firms” have a positive role in the performances of a destination. But also the public sector seems to play a very important role, because the variables “destination marketing” and “emphasis on maximising local economic development” are also significant in determining the destination performance. When the analysis moves from measures of attractiveness to GORB, which can be considered a proxy of the effectiveness of the use of the resources of a destination, the variable “sustainable tourism policy and destination management” becomes very significant. This demonstrates that a sustainable tourism policy and destination management not only is good for preserving the ecologic balance and for minimize negative cultural and social impacts, but has a great importance in improving the competitiveness of a tourism destination. If GORB is used as a dependent variable, the variable “tourist accommodations” also plays a positive role. This component is composed by the indicators “quantity” and “quality” of tourist accommodation, and by an indicator of sustainability: “environmental friendliness of tourist accommodation”; as a consequence, a certain role of the sustainability of the tourist accommodations has been also detected in determining TDC. In both cases there are some factors that play a negative role, in the sense that their association with the dependent variable is negative. For each of them there is an explanation, but the general explanation of the negative association of factors as “responsible tourist behaviour” and “historical and artistic features” is that maybe the growth of many destinations, especially coastal, is probably associated with less educated and less responsible tourists, and also with tourists which are less interested in historical and cultural aspects. To test the robustness of the estimate, a partial least square regression is performed, that is a method for constructing predictive models when 180 the factors are many and highly collinear. The results are similar to the plain OLS regression. The model of destination competitiveness has been adapted and applied to measure the competitiveness of two leading tourism destinations in Latin America: Rio de Janeiro and Salvador de Bahia. These two destinations were chosen as applied cases for various reasons. Brazil is an emerging country undergoing through enormous economical and social changes. It is predicted to become the 5th largest global economy by 2025 and it will host both the FIFA World Cup 2014 and the Olympic Games 2016, which puts the country’s tourism and travel industry in the world spotlight. Nevertheless, there is still insufficient academic research on Brazil tourism, especially on the two of the leading tourist destinations in the country, Rio de Janeiro and Salvador de Bahia. Tourism literature on Brazil proves to be still scarce. There have not been enough studies on research, especially regarding destination competitiveness. Brazilian tourism destinations are also interesting case studies because they still have vast growing potential. By acknowledging the level of competitiveness of two leading tourism destinations in Brazil, this study intends to provide local stakeholders with a valuable and accurate body of information on which to base their destination management strategies. The evidences of this research can provide tourism policy makers and practitioners with an overview of the competitiveness of two of the most important cities in Brazil, famous all over the world. Primary quantitative data were collected through experts’ judgement. The chosen participants were tourism experts, including incoming travel agents, tourist guides, hotel managers, travel consultants, tourism professors, tourism students, and tourism public servants and public managers. The data were collected with a web survey. 181 277 usable responses were received in the case of Rio de Janeiro. 164 usable responses were received in the case of Salvador de Bahia. The results are similar in the case of Rio de Janeiro (RJ) and Salvador de Bahia (SA), even if according to the experts’ judgment the situation of SA is relatively worse than RJ. “Core resources and key attractors” received the highest rating among the eight determinants of destination competitiveness. The very high rating given to RJ and SA natural resources is to be expected. This element received the highest rating in the whole set of competitiveness indicators. After all, it is well known that the two cities have extensive areas of attractive beauty, marvellous beaches and a climate that is conductive to experiencing these resources throughout much of the year. The high rating should not be a cause of complacency, however. Maintenance of RJ and SA competitive advantage will require constant monitoring of the impacts of development. The very elevated rating given especially to RJ events and entertainment is to be expected also. The pulsating nightlife is a major attraction of RJ, which is also the place of the biggest Carnival in the world. “Tourism services” had a relatively high rating in the two Brazilian destinations; on the contrary, “general infrastructures” had the lowest rating among the eight determinants of destination competitiveness. This is one of the weakest point of RJ and SA tourism product. “Public medical care facilities”, “sanitation”, “quality of transport infrastructures had three of the smallest rating in the whole set of variables in RJ and SA. According to the experts none of the infrastructure attributes supports the competitiveness of the local tourism. Promoting the beauties of RJ and BA necessitates more investments in the form of tourism infrastructures. Therefore, although the government is working on this issue with consistent investments due to the FIFA World Cup 2014 and to the Olympic Games 2016, it is necessary to place more emphasis on sufficiently preparing the infrastructures. 182 It is particularly worrying the low rating given to “general conditions”, in particular to “cleanliness of government” and to “development of modern public administration”. Tourism industry may benefits from a high-quality and efficient public administration. Reforming the public administration to make its work transparent and free of unnecessary administrative barriers should be a priority in the agenda of both local and national government. It is also quite worrying the low rating given to “value for money in destination tourism experience”. Among the “conditioning and supporting factors”, “hospitality of residents towards tourists” recorded the highest rating; after all, the personality and hospitality of Brazilians are among the most striking national characteristics. Since January 2003, upon the establishment of the Ministry of Tourism, Embratur (Brazilian Tourism Institute) focussed its marketing efforts in promoting the friendliness of the people, joint with the natural beauties and the local cultures and traditions. Brazil’s federal government moved in this direction in terms of tourism marketing, after years of marketing campaigns based on the 3 S’s stereotype (Sun, Sand and Sea, and implicitly, Sex). The results are an increasing awareness of the different tourism options in Brazil, as pointed out by the respondents of this survey (destination marketing attributes scored the highest rating among the group). The rest of the destination management items received a low rating. Experts seem to highlight the need for serious changes in the destination management, in particular due to the apparent lack of attention toward sustainability issues. “Tourism Policy, Planning and Development” had a very low rating. Public sector commitment to minimizing negative social and environmental impacts and to maximizing economic impacts of tourism on local community was not judged to be satisfactory. The results also point to- 183 ward a need for a collaborative approach and an emphasis on community participatory process in tourism planning. For what concern the “tourism outcomes and impacts”, the results seem to point toward a need for better targeting and planning of tourism investments and attention to improve the local standards of living and to enhance poverty-reducing impacts. Experts in RJ and SA were likely to disagree that “tourism has resulted in positive environmental impacts”. These findings point out the need for better planning by state and local governments to mitigate the negative environmental impacts associated with tourism growth. Respondents tend to strongly agree that tourist satisfaction is high, but this seems to be due to the extensive areas of unique beauty and to RJ and BA big events and entertainment, more than a result of the effectiveness of destination management/tourist policy actions in crafting tourism experiences. 184 REFERENCES Abdi, H., & Williams L. J. (2010), “Principal Component Analysis”, Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433-459. Ahmed, Z.U. (1991) “The Influence of the Components of a State’s Tourist Image on Product Positioning Strategy”. Tourism Management, December: 331-340. Allen, J., McDonnell, I., O'Toole, W., Harris, R. (2008), Festival and Special Event Management, Wiley. Bahar O. & Kozak M. (2007), “Advancing Destination Com-petitiveness Research: Comparison Between Tourists and Service Providers”, Journal of Travel Marketing, 22 (2), 61-71. Baruch, Y., & Holtom, B. C. (2008). “Survey response rate levels and trends in organizational research”. Human Relations, 61(8): 1139-1160. Baum, T. (1995), Managing Human Resources in the European Tourism and Hospitality Industry: A Strategic Approach, Chapman and Hall, London. Baum, T. (1999). “Seasonality in tourism: understanding the challenges.” Tourism Economics, Special Edition on Seasonality in Tourism, 5 (1): 5-8. Boniface, P. (1995). Managing quality cultural tourism. London; New York: Routledge. 185 Bornhorst, T., Ritchie, J.R.B., & Sheehan, L., (2010), “Determinants of tourism success for DMOs & destinations: An empirical examination of stakeholders' perspectives”, Tourism Management, 31(5), 572–589. Bowdin, G., McDonnell, I., Allen, J., O’Toole, W. (2010) Events Management 3rd edition. Oxford: Butterworth-Heinemann. Boyer, C., and Merzbach, U. (1989). A history of mathematics (2nd Edition). New York: Wiley. Briggs S., Sutherland J, Drummond S. (2007), “Are hotel serving quality? An explanatory study of service quality in the Scottish hotel sector”, Tourism Management 28: 1006-1019. Buhalis, D. & Main, H. (1998), “Information Technology in small and medium hospitality enterprises: Strategic analysis and critical factors”. International Journal of Contemporary Hospitality Management, Vol. 10, No. 5, 198-202. Buhalis, D. (1994), “Information and Telecommunications Technologies as a strategic tool for small and medium tourism enterprises in the contemporary business environment”. In Seaton, A. (Ed.) Tourism-The State of the Art: The Strathclyde Symposium, Wiley and Sons, London, 254-275. Buhalis, D. (2000) “Marketing the Competitive Destination of the Future”, Tourism Management, 21, 97-116. Buhalis, D. & Costa, C. (2006), Tourism Management Dynamics: Trends, Management, Tools, Oxford: Elsevier. Buhalis, D., & Cooper, C. (1998). “Competition or Cooperation?”, in E. Laws, B. Faulkner & G. Moscardo, (Eds.), Embracing and Managing Change in Tourism, Routledge, London. Butler, R.W. (2000), “Tourism and the environment: A geographical perspective”, Tourism Geographies, 2(3): 337-58. Choy, D. (1995), “The quality of tourism employment”, Tourism Management, 16(2), 129-137. 186 Clarke, J. (1997). “A framework of Approaches to Sustainable Tourism”, Journal of Sustainable Tourism, 5:3, 224-233. Cohen (1988), “Authenticity and commodification in tourism”. Annals of Tourism Research 15(2), 371-86. Cooper C., Fletcher J., Gilbert D., Shepherd R., Wanhill S. (1998), Tourism Principles and Practice. Addison, Wesley Longman Ltd, 2nd ed. Costello A. B. & Osborne J. W. (2005), “Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis”. Practical Assessment Research & Evaluation, 10(7). Cracolici M. F., Nijkamp P., Rietveld P. (2008), “Assessment of Tourism Competitiveness by Analysing Destination Efficiency”, Tourism Economics, 14 (2), 325-342. Cracolici, M. F., Nijkamp, P. (2006) “Competition among Tourist Destination. An Application of Data Envelopment Analysis to Italian Provinces”, in Giaoutzi M. and Nijkamp P. (eds.), Tourism and Regional Development: New Pathways, Ashgate, Aldershot, UK. Cracolici, M. F., Nijkamp, P. (2008), “The attractiveness and competitiveness of tourist destinations: A study of Southern Italian regions”, Tourism Management, 30, 336-344. Croes R. (2010), “Measuring and Explaining Competitiveness in the Context of Small Island Destinations”, Journal of Travel Research, XX(X) 1– 12. Crouch, G. I. (1992), “Effect of Income and Price on international tourism”, Annals of Tourism Research, 19 (4), 643-64. Crouch, G. I. (2007). “Measuring Tourism Competitiveness: Research, Theory and the WEF Index”. paper presented at Anzmac, Dudedin, New Zealand, 3-5 December. Crouch, G. I. (2010). “Destination Competitiveness: An Analysis of Determinant Attributes”, Journal of Travel Research, XX(X) 1 –19. 187 Crouch, G. I., & Ritchie, J. R. B. (1999). “Tourism, Competitiveness and Societal Prosperity”. Journal of Business Research 44 (3), 137-152. D’Harteserre, A. (2000), “Lessons in Managerial Destination Competitiveness in the case of Foxwoods Casino Resort”, Tourism Management, 21(1), 23-32. De Keyser, R., & Vanhove, N. (1994), “The competitive situation of tourism in the Caribbean area—Methodological approach”, Revue de Tourisme, 3, 19–22. Dwyer, L., & Kim, C. (2003). “Destination competitiveness: Determinants and Indicators”, Current Issues in Tourism, 6(5), 369–413. Dwyer, L., Forsyth P., Rao, P. (2000), “The Price competitiveness of travel and tourism: a comparison of 19 destinations”, Tourism Management, 21, 9-22. Dwyer, L., Livaic, Z., & Mellor, R. (2003). “Competitiveness of Australia as a tourist destination”, Journal of Hospitality and Tourism Management, 10(1), 60–78. Dwyer, L., Mellor, R., Livaic, Z., Edwards, D. & Kim, C. (2004). “Attributes of Destination Competitiveness: A Factor Analysis”. Tourism Analysis, 9 (1-2), 91-101. Enright, M. J., & Newton, J. (2004). “Tourism Destination Competitiveness: A Quantitative Approach”. Tourism Management, 25 (6), 777-788. Enright, M. J., & Newton, J. (2005). “Determinants of Tourism Destination Competitiveness in Asia Pacific: Comprehensiveness and Universality”. Journal of Travel Research, 45 (4), 339-350. European Union (2006). Sustainable tourism as a factor of cohesion among European regions. Committee of the Regions, Brussels. Eurostat (2008), Labour Force Survey, Eurostat, Luxembourg. Eurostat (2010), Tourism Statistics in the European Statistical System, 2008 data, Methodologies and Working Papers, Eurostat, Luxembourg. 188 Evans, M. R., & Chon, K. S. (1989), “Formulating and evaluating tourism policy using importance-performance analysis”. Hospitality Education and Research Journal, 13(2), 203–213. FareAmbiente (2011), Rapporto Beni Culturali 2011, Roma. Faulkner B. (2003), Progressing Tourism Research. Channel View Publications, Clevedon. Faulkner B., Opperman M., Fredline E. (1999), “Destination Competitiveness: An Exploratory Examination of South Australia’s Core Attractions”, Journal of Vacation Marketing, 5 (2), 125-139. Federalberghi (2010), Sesto Rapporto sul Sistema Alberghiero in Italia 2010, Federalberghi, Rome. FEE (2006), Awards for Improving the Coastal Environment: The example of the Blue Flag, Foundation for Environmental Education, Copenhagen. Flagestad, A. & Hope, C.A. (2001), “Strategic success in winter sports destinations: a sustainable value creation perspective”, Tourism Management, vol. 22, p 445-461. Formica, S., & Uysal, M. (1996). “The revitalization of Italy as a tourist destination”. Tourism Management, 17(5), 323–331. Geladi, P, and Kowalski, B. (1986), ‘‘Partial leastsquares regression: A tutorial’’ Analytica Chimica Acta, 185, 1-17. Getz, D. (1986), “Models in tourism planning: Towards integration of theory and practice”. Tourism Management 7 (1), 21–32. Getz, D. (1987), “Tourism Planning and Research: Traditions, Models and Futures”, paper presented at The Australian Travel Research Workshop, Bunbury, Western Australia, 5-6 November. Getz, D. (1989), “Special events: Defining the product”. Tourism Management: 125-137. Getz, D. (1991), Festival, special events and tourism, Virginian Nostrand Reinhold, NY. 189 Getz, D. (1997), Event Management and Event Tourism, Cognizant. Communication Corporation, New York. Global Environment Facility (1998), Valuing the Global Environment: Actions and Investment for a 21st Century. Washington, DC: World Bank Group. Go, F. M., Pine, R., & Yu, R. (1994), “Hong Kong: Sustaining competitive advantage in Asia’s hotel industry”. Cornell Ho-tel and Restaurant Administration Quarterly, 35(5), 50-61. Goeldner, C.R., and Ritchie, J.R.B. (2003), Tourism: Principles, Practices, Philosophies, 9th ed. NY: John Wiley & Sons, Inc. Gomezelj, D. O., & Mihalič, T. (2008). “Destination Competitiveness Applying different models, the case of Slovenia”. Tourism Management, 29 (6), 294-307. Gooroochurn, N. and Sugiyarto, G. (2005). “Competitiveness indicators in the travel and tourism industry”. Tourism Economics 11:(1), pp. 25-43. Grabler, K. (1997) “Perceptual Mapping and Positioning of Tourist Cities”, in J. A. Mazanec (ed.) International City Tourism: Analysis and Strategy, London: Pinter. Grattan-Guinness, I. (1997). The rainbow of mathematics. New York: Norton. Guizzardi A. and Mazzocchi M. (2009), “Tourism demand for Italy and the business cycle”, Tourism Management, 30, 1-11. Gunn, C. (2002), Tourism planning—Basics, concepts, cases (4th ed.), New York: Taylor & Francis Books. Haahti, A. & Yavas, U. (1983) “Tourists’ perceptions of Finland and selected European countries as travel destinations”. European Journal of Marketing 17, 34–42. Hall, C. M. (1987) “The Effects of Hallmark Events on Cities.” Journal of Travel Research. 26(2): 44-5. Hall, C. M. (1992). Hallmark Tourist Events, Impact, Management, Planning. London: Belhaven Press. 190 Hall, C.M. (2000), Tourism Planning: Policies, Processes and Relationships, Prentice Hall, Harlow. Hassan, Salah S. (2000), “Determinants of Market Competitiveness in an Environmentally Sustainable Tourism Industry”, Journal of Travel Research, 38 (3), 239-245. Heath, E. & Wall, G. (1992) Marketing Tourism Destinations: A Strategic Planning Approach. Canada: John Wiley and Sons. Heath, E. (2002). “Towards a Model to Enhance Destination Competitiveness: A Southern African Perspective”. Journal of Hospitality and Tourism Management, 10 (2), 124-141. Helland, I. (1988), ‘‘On the structure of partial least squares regression,’’ Communications in Statistics, Simulation and Computation, 17(2), 581-607. Heraty, M.J. (1989) “Tourism transport: Implications for developing countries”, Tourism Management, 10(4): 288-92. Hjalager A.-M., Richards G. (2002), Tourism and Gastronomy, London and NY Routledge. Hotelling, H. (1933). “Analysis of a complex of statistical variables into principal components”. Journal of Educational Psychology, 25, 417-441. Hughes H (2000). Arts, entertainment and tourism. Oxford: ButterworthHeineman. Inskeep, E. (1991). Tourism planning: An integrated and sustainable development approach. New York: Wiley. Istat (2011), Capacity of accommodation facilities, National Institute of Statistics, Rome. Jansen-Verbeke, M. (1991) “Leisure shopping: A magic concept for the tourism industry?”, Tourism Management , Vol. 12, No. 1, pp. 9-14 . Kaul, R., ed. (1985), Dynamics of Tourism: A Trilogy. Transportation and Marketing (Vol. 3). New Delhi: Sterling Publishers. 191 Kaynak, E., & Marandu, E. E. (2007), “Tourism Market Potential Analysis in Botswana: A Delphi Study”, Journal of Travel Research November 45: 227-237. Kent, W. E., Shock, P. J., Snow, R. E. (1983) “Shopping: Tourism's unsung hero(ine)”, Journal of Travel Research , Vol. 21, No. 4, pp. 2-4. Khadaroo, J., & Seetanah, B. (2007), “Transport Infrastructure and Tourism Development”, Annals of Tourism Research, Vol. 34, No. 4, pp. 1021–1032. Kline P (1993), The Handbook of Psychological Testing: London, Routledge. Kozak, M. & Rimmington, M. (1998), “Benchmarking: Destination attractiveness and small hospitality business performance”. International Journal of Contemporary Hospitality Management 10:(5), pp. 184-188. Kozak, M. & Rimmington, M. (1999), “Measuring tourist destination competitiveness: Conceptual considerations and empirical findings”. International Journal of Hospitality Management 18:(3), pp. 273-283. Kozak, M., Baloglu, S. (2010) Managing and marketing tourist destinations. Strategies to gain a competitive edge, Routledge Publishing House, NY. Krippendorf, J. (1987) The Holiday Makers: Understanding the Impact of Leisure and Travel. London: William Heinemann. Laws, E. (1995), Tourist Destination Management. Issues, Analysis and Policies. London New York: Routledge. Mason, P. & Mowforth, M. (1996), “Codes of conducts in tourism”, Progress in Tourism and hospitality Research, 2 (2): 151-67. Massidda C., Etzo I. (2012), “The determinants of Italian domestic tourism: a panel data analysis”, in Tourism Management, 33: 603-610. Mazanec, J.A., Crouch, G.I., Ritchie, J.R.B., & Woodside, A.G. (Eds.) (2001), Consumer Psychology of Tourism, Hospitality and Leisure, Vol. 2. CAB International. 192 Mazanec, J.A., Wober, K. & Zins, A.H.(2007). “Tourism destination competitiveness: From definition to explanation?”. Journal of Travel Research 46:(1), pp. 86-95. Mazzocchi M. and Montini A. (2001), “Earthquake effects of Tourism in central Italy”, Annals of Tourism Research, 28 (4), 1031–1046. McKercher, B., du Cros, H., (2002), Cultural Tourism: The Partnership Between Tourism and Cultural Heritage Management. New York: Hayworth Hospitality Press. Middleton, V.T.C., & Hawkins, R. (1998), Sustainable Tourism: a Marketing Perspective, Butterworth Heinemann, Oxford. Mihalič, T. (2000), “Environmental management of a tourist destination: a factor of tourism competitiveness”. Tourism management, vol. 21, (1) 6578. Miller G. (2001), “The Development of Indicators for Sustainable Tourism: Results of a Delphi Survey of Tourism Researchers”. Tourism Management, 22 (4), pp. 351-362. Ministério do Turismo Brazil (2012), Anuário Estatístico de Turismo – 2012, Brasilia. Ministério do Turismo Brazil - Fipe (2012a), Estudo da Demanda Turística Internacional - 2010-2011, Brasilia. Ministério do Turismo Brazil – Fipe (2012b) Caracterização e Dimensionamento do Turismo Doméstico no Brasil – 2010/2011, Fundação Instituto de Pesquisas Econômicas, São Paulo. Mintel (2004), European Hotel Chain Expansion, Travel & Tourism Analyst, May 2004, London. Moutinho L. (ed.) (2000), Strategic Management in Tourism. Wallingford: CABI Publishing. Müller, H (1994). “The thorny path to sustainable tourism development”. Journal of Sustainable Tourism, 2:3, 131-136. Murphy, P. (1985), Tourism: A Community Approach. New York: Methuen. 193 Murphy, P., Pritchard, M. P., Smith, B. (2000). “The destination product and its impact on traveller perceptions”. Tourism Management 21 (1) 43– 52. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGrawHill. OECD (2010), OECD Tourism Trends and Policies 2010, OECD Publishing. OECD (2011), OECD Studies on Tourism: Italy, Review of Issues and Policies, OECD Publishing. Page, S.J. (2003), Tourism Management: Managing for Change, Oxford: Elsevier. Butterworth – Heinemann. Pallant, J. (2001), SPSS Survival Manual : a step by step guide to data analysis using SPSS (Version 10). Open University Press: Buchingam. Pearce, D. G. (1981), Tourist development. Topics in applied geography. Harlow: Longman. Pearson, K. (1901). “On lines and planes of closest fit to systems of points in space”. Philosophical Magazine, 6, 559-572. Pike, S., & Ryan, C. (2004). “Destination positioning analysis through a comparison of cognitive, affective, and conative perceptions”, Journal of Travel Research, 42(4), 333-342. Poon, A. (1993). Tourism, Technology and Competitive Strategies, CAB International, Wallingford, UK. Porter, M. E. (1985). Competitive advantage. New York: The Free Press. Porter, M. E. (1990). The Competitive advantage of Nations. New York: The Free Press. Prentice, R. (1993). Heritage consumers in the leisure market: An application of the Manning-Haas demand hierarchy. Leisure Sciences, 273-290. Prideaux, B. (2000), “The Role of the Transport System in Destination Development”. Tourism Management 21:53–63. 194 Qu, H., Ryan, B., & Chu, R. (2000), “The importance of hotel attributes in contributing to travellers’ satisfaction in the Hong Kong hotel industry”. Journal of Quality Assurance in Hospitality & Tourism, 1(3), 65–83. Raj, R., Walters, P., Rashid, T. (2008) Events Management: An Integrated and Practical Approach. London: Sage. Ricardo, D. (1817), On the Principles of Political Economy and Taxation, London: John Murray (3rd ed., 1821). Richards, G. (ed.) (1996) Cultural Tourism in Europe. CABI, Wallingford. Richards, G. (ed) (2007), Cultural Tourism: Global and Local Perspectives. The Haworth Press, Inc., New York. Richards, G., Munsters, W., (2010), Cultural Tourism Research Methods, CABI, Wallingford. Rimmington, M., & Kozak, M. (1997), “Developments in information technology: Implication for the tourism industry and tourism marketing”. Anatolia, an International Journal of Tourism and Hospitality Research, 8 (3), 59-80). Ritchie, J. R. B., & Crouch, G. I. (2000). “The competitive destination, a sustainable perspective”. Tourism Management, 21(1), 1–7. Ritchie, J. R. B., & Crouch, G. I. (2003). The competitive destination, a sustainable tourism perspective. Cambridge: Cabi Publishing. Ritchie, J.R.B., & Zins, M.(1978), “Culture as determinant of attractiveness of a tourism region”. Annals of Tourism Research 5, 252-267). Ritchie, J.R.B., Crouch, G.I., & Hudson, S. (2001), “Developing operational measures for the components of a destination competitiveness/sustainability model: Consumer versus managerial perspectives”, in Mazanec J.A. et. al, Tourism, Hospitality and Leisure, 2. Ed. CAB International, Wallingford. Robinson, P., Wale, D., Dickson, G. (2010) Events Management. CABI: Wallingford. 195 Sainaghi R. (2006), “From Contents to Processes: Versus a Dynamic Destination Management Model (DDMM)”, Tourism Management, 27: 10531063. Sargeant, A., & Mohamad, M. (1999), “Business performance in the UK hotel sector: Does it pay to be market-oriented?”, The Services Industry Journal, 19(3), 42–59. Schmitt, M. (1996). “Uses and abuses of coefficient alpha”. Psychological Assessment, 8(4), 350-353. Sedmak, G., & Mihalič, T. (2008). “Authenticity in mature seaside resorts”. Annals of Tourism Research (35), 1007-1031. Sharpley, R. (1994), Tourism, tourists and society. Huntingdon, ELM Publications. Shone, A., & Parry, B. (2001), Successful Event Management. London: Continuum. Sigala, M., & D. Leslie (2005), International Cultural Tourism: Management, Implications and Cases, Elsevier Butterworth-Heinemann, Oxford. Sirše, J., & Mihalič, T. (1999). “Slovenian tourism and tourism policy — a case study”. Revue de Tourisme, 3, 34–47. Smith M. (2003), Issues in Cultural Tourism Studies, London, Routledge. Smith, M. (2009). Issues in Cultural Tourism Studies. 2. rev. edition. Routledge, London. Smith, M., & Robinson, M., (eds) (2006), Cultural Tourism in a Changing World. Clevedon, U.K. Sonmez, S. F. and A. R. Graefe (1998), “Determining Future Travel Behavior from Past Travel Experience and Perceptions of Risk and Safety”. Journal of Travel Research, 37: 171-177. Stabell, C. B., & Fjeldstad, D. (1996). “Value configuring for competitive advantage: On chains, shops and networks” (discussion paper 1996/9). Sandvika, Norway: Norwegian School of Management. 196 Stabell, C. B., & Fjeldstad, D. (1998). “Configuring value for competitive advantage: On chains, shops, and networks”. Strategic Management Journal, 19(5), 413-437. Stewart, G.W. (1993). “On the early history of the singular value decomposition”. SIAM Review, 35, 551-566. Swarbrooke, J. (1999), Sustainable Tourism Management. CAB International, Wallingford, UK. Timothy, D. (2005), Shopping Tourism, Retailing And Leisure, Channel View Publications, Clevedon, UK. Tobias R. D. (2002), An Introduction to Partial Least Squares Regression, SAS Institute Inc. Cary, NC. Tsai H., Song H. Wong (2009), “Tourism and Hotel Competitiveness Research”, Journal of Travel & Tourism Marketing, 26: 5, 522 — 546. Tsang, N., & Qu, H. (2000), “Service quality in China’s hotel industry: A perspective from tourists and hotel managers”. International Journal of Contemporary Hospitality Management, 12(5), 316–335. UNWTO (2004), Compendium of Tourism Statistics Data 1998-2002, United Nations World Tourism Organization. 2012 Edition. UNWTO (2008), Compendium of Tourism Statistics Data 2002-2006, United Nations World Tourism Organization. 2012 Edition. UNWTO (2012a), Compendium of Tourism Statistics Data 2006-2010, United Nations World Tourism Organization. 2012 Edition. UNWTO (2012b), Tourism Highlights, United Nations World Tourism Organization. 2012 Edition. Van der Wagen, L. (2002) Event Management: For Tourism, Cultural, Business and Sporting Events. Melbourne: Hospitality Press. Velicer, W. F., & Jackson, D. N. (1990). “Component Analysis Versus Common Factor-Analysis – SomeFurther Observations”. Multivariate Behavioral Research, 25(1), 97-114. 197 Wall, G., & Mathieson, A. (2006). Tourism: change, impacts and opportunities. Pearson. Education Limited, England. Walle, A. (1998). Cultural tourism: A strategic focus. Boulder, CO: Westview Press Wang, Y. & Pizam, A. (Eds.) (2011), Destination Marketing and Management. Theories and Applications, Cambridge: Cabi Publishing. Weaver, D.B. & Lawton, L.J. (2006) Tourism Management. Brisbane, Australia: John Wiley & Sons Australia. WECD (1987), Report of the World Commission on Environ-ment and Development: Our Common Future, Oxford University Press. WEF (2011), The Travel & Tourism Competitiveness Report 2011, World Economic Forum, 2011, Geneva. Weiermair, K. (2000), “Know-how and qualification gaps in the tourism industry: the case of alpine tourism in Austria”. The Tourist Review, Vol. 2, No. 45-53. WHC - UNESCO (2012), World Heritage 2012-2013, United Nations Educational, Scientific and Cultural Organization. Wilkie, W.L. (1986), Consumer Behaviour, John Wiley, New York. WTO (1998). Guide for Local Authorities on Developing Sustainable Tourism. Madrid: World Tourism Organization. WTO (1999). Sustainable development of tourism: an annotated bibliography. Madrid: World Tourism Organization WTO (2004). Indicators of sustainable development for tourism destinations: a guidebook. Madrid: World Tourism Organization. WTTC (2012), Travel & Tourism, Economic Impact 2012, Italy, World Travel and Tourism Council, London. Yeoman, I., Robertson, M., Ali-Knight, J., Drummond, S., McMahonBeattie, U. (eds.) (2003) Festival and Events Management: An International Arts and Culture Perspective. Oxford: Butterworth-Heinemann. 198 Zhang H., Gu Chao-lin, Gu Lu-wen, Zhang Y (2011), “The evaluation of tourism destination competitiveness by Topsis & information entropy. A case in the Yangtze River Delta of China”, Tourism Management 32, 443-451. 199 200 APPENDIX: DESTINATION SURVEYED MOST BEAUTIFUL VILLAGES IN ITALY Val d'Aosta Val d'Aosta Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Veneto Veneto Veneto Bard Etroubles Apricale Borgio Verezzi Brugnato Campo Ligure Castelbianco Castelvecchio Cervo Finalborgo Laigueglia Lingueglietta(borgo di Cipressa) Manarola Millesimo Montemarcello Noli Tellaro Triora Varese Ligure Vernazza Zuccarello Chianale Garessio Macugnaga Mombaldone Neive Orta San Giulio Ostana Ricetto di Candelo Usseaux Vogogna Volpedo Bienno Castellaro Lagusello Cornello dei Tasso Fortunago Gradella Gromo Lovere Monte Isola Pizzale (borgo di Porana) San Benedetto Po Soncino Tremezzo Tremosine Zavattarello Canale di Tenno Chiusa Glorenza Mezzano Rango San Lorenzo in Banale Vipiteno Arquà Petrarca Asolo Borghetto ORANGE FLAGS Val d'Aosta Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Piemonte Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Lombardia Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Trentino Alto Adige Veneto Veneto Veneto Veneto Veneto Veneto Veneto Veneto 201 Etroubles (AO) Apricale (IM) Brugnato (SP) Castelnuovo Magra (SP) Castelvecchio di Rocca Barbena (SV) Dolceacqua (IM) Pigna (IM) Pignone (SP) Santo Stefano d'Aveto (GE) Sassello (SV) Seborga (IM) Toirano (SV) Triora (IM) Varese Ligure (SP) Alagna Valsesia (VC) Avigliana (TO) Barolo (CN) Bene Vagienna (CN) Bergolo (CN) Candelo (BI) Cannero Riviera (VB) Cherasco (CN) Cocconato (AT) Fenestrelle (TO) Grinzane Cavour (CN) La Morra (CN) Macugnaga (VB) Malesco (VB) Mergozzo (VB) Monforte d'Alba (CN) Neive (CN) Orta San Giulio (NO) Santa Maria Maggiore (VB) Varallo (VC) Bienno (BS) Castellaro Lagusello (MN) Chiavenna (SO) Clusone (BG) Gardone Riviera (BS) Gromo (BG) Menaggio (CO) Sabbioneta (MN) Tignale (BS) Torno (CO) Ala (TN) Caderzone Terme (TN) Molveno (TN) Tenno (TN) Arquà Petrarca (PD) Asolo (TV) Malcesine (VR) Marostica (VI) Mel (BL) Montagnana (PD) Portobuffolè (TV) Sappada (BL) Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Umbria Dozza Montefiore Conca Montegridolfo San Giovanni in Marignano San Leo Vigoleno Anghiari Barga Buonconvento Castelfranco di Sopra Cetona Coreglia Antelminelli Giglio Castello Loro Ciuffenna Montefioralle Montescudaio Pitigliano Poppi San Casciano dei Bagni Scarperia Sovana Suvereto Cingoli Corinaldo Esanatoglia Gradara Grottammare Matelica Montecassiano Montecosaro Montefabbri Montefiore dell'Aso Montelupone Moresco Offagna Offida San Ginesio Sarnano Treia Visso Arrone Bettona Bevagna Castiglione del Lago Citerna Corciano Deruta Giove Lugnano in teverina Massa Martana Monte Castello di Vibio Montefalco Montone Norcia Paciano Panicale Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche 202 Fontanellato (PR) Longiano (FC) Montefiore Conca (RN) Monteleone (FC) Pennabilli (RN) Portico e San Benedetto (FC) Premilcuore (FC) San Leo (RN) Sestola (MO) Verucchio (RN) Vigoleno (PC) Anghiari (AR) Barberino Val d'Elsa (FI) Barga (LU) Casale Marittimo (PI) Casole d'Elsa (SI) Castelnuovo Berardenga (SI) Castelnuovo di Val di Cecina (PI) Castiglion Fiorentino (AR) Certaldo (FI) Cetona (SI) Collodi (PT) Cutigliano (PT) Lari (PI) Lucignano (AR) Massa Marittima (GR) Montalcino (SI) Montecarlo (LU) Montefollonico (SI) Montepulciano (SI) Monteriggioni (SI) Murlo (SI) Peccioli (PI) Pienza (SI) Pitigliano (GR) Pomarance (PI) Radda in Chianti (SI) Radicofani (SI) San Casciano dei Bagni (SI) San Gimignano (SI) Sarteano (SI) Sorano (GR) Suvereto (LI) Trequanda (SI) Vinci (FI) Volterra (PI) Acquaviva Picena (AP) Camerino (MC) Corinaldo (AN) Genga (AN) Gradara (PU) Mercatello sul Metauro (PU) Mondavio (PU) Montecassiano (MC) Montelupone (MC) Monterubbiano (FM) Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Molise Campania Campania Campania Campania Campania Campania Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Basilicata Basilicata Basilicata Basilicata Basilicata Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Sardegna Sardegna Sardegna Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Pescocostanzo Pettorano sul Gizio Pietracamela Rocca San Giovanni S. Stefano di Sessanio Scanno Tagliacozzo Villalago Oratino Àlbori Atrani Castellabate Cusano Mutri Furore Nusco Alberona Bovino Cisternino Locorotondo Otranto Pietramontecorvino Roseto Valfortore Specchia Vico del Gargano Acerenza Castelmezzano Guardia Perticara Pietrapertosa Venosa Altomonte Bova Chianalea di Scilla Fiumefreddo Bruzio Gerace Morano Calabro Santa Severina Stilo Bosa Carloforte Castelsardo Brolo Castelmola Cefalù Geraci Siculo Montalbano Elicona Novara di Sicilia San Marco d'Alunzio Savoca Lazio Lazio Lazio Lazio Lazio Lazio Lazio Abruzzo Abruzzo Molise Molise Molise Campania Campania Puglia Puglia Puglia Puglia Puglia Puglia Basilicata Calabria Calabria Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sicilia 203 Nemi (RM) San Donato Val di Comino (FR) Sermoneta (LT) Sutri (VT) Trevignano Romano (RM) Tuscania (VT) Vitorchiano (VT) Palena (CH) Roccascalegna (CH) Agnone (IS) Frosolone (IS) Scapoli (IS) Cerreto Sannita (BN) Sant'Agata de' Goti (BN) Alberobello (BA) Alberona (FG) Cisternino (BR) Orsara di Puglia (FG) Pietramontecorvino (FG) Sant'Agata di Puglia (FG) Valsinni (MT) Civita (CS) Morano Calabro (CS) Aggius (OT) Galtellì (NU) Gavoi (NU) Laconi (OR) Oliena (NU) Sardara (VS) Sutera (CL) BLUE SAILS Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Molise Molise Molise Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Calabria Basilicata (no. of sails) Rocca San Giovanni Ortona Pineto Roseto degli Abruzzi San Vito Chetino Vasto Giulianova Martinsicuro Silvi Torino di Sangro Tortoreto Alba Adriatica Campomarino Petacciato Termoli Roccella Jonica Scilla Amendolara Badolato Bagnara Calabra Belvedere Marinttimo Bova Marina Brancaleone Cirò Isola di Caporizzuto Marina di Giocosa Jonica Melito di Porto Salvo Monasterace Palmi Tropea Cirò Marina Cittadella del Capo Cropani Crucoli Cutro Gizzeria Locri Marina di Schiavonea Montebello Jonico Pietrapaola Pizzo Ricaldi Rocca Imperiale Roseto Capo Spulico Rossano Calabro Siderno Soverato Staletti Zambrone Amantea Cannitello-Villa San Giovanni Diamante Longobardi Parghelia Maratea Bernalda 4 3 3 3 3 3 2 2 2 2 2 1 2 2 1 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 4 2 204 BLUE FLAGS Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Abruzzo Basilicata Calabria Calabria Calabria Calabria Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Friuli-Venezia Giulia Friuli-Venezia Giulia Lazio Lazio Lazio Lazio Lazio Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Rocca San Giovanni Ortona San Salvo San Vito Chietino Vasto Fossacesia Alba Adriatica - Sospesa Pineto Roseto degli Abruzzi Martinsicuro - Sospesa spiaggia Villa Rosa Silvi Marina Tortoreto Giulianova Maratea Cariati - Marina di Cariati Cirò Marina - Punta Alice Roccella Jonica Marina di Gioiosa Jonica Massa Lubrense Positano Vibonati - Villammare Sapri Montecorice-Agnone - Agnone e Capitello Pisciotta Ascea Velia Agropoli Casal Velino Centola - Palinuro Castellabate Pollica - Acciaroli e Pioppi Comacchio - Lidi comacchiesi Cesenatico San Mauro Pascoli - San Mauro Mare Ravenna - Lidi Ravennati Cervia Bellaria Igea Marina Rimini Cattolica Grado Lignano Sabbiadoro Gaeta Sabaudia San Felice Circeo Sperlonga Anzio Moneglia Chiavari Lavagna Bordighera Camporosso Lerici Ameglia - Fiumaretta Noli Savona - Fornaci Loano Bergeggi Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Campania Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Sardegna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Emilia Romagna Catellabate Cetara Massa Lubrense Positano San Giovanni a Piroscario Agropoli Amalfi Camerota Casal Velino Ischia Praiano Procida Sapri Sorrento Vico Equense Vietri sul Mare Baunei Bosa Posada Alghero Arbus Arzachena Cabras Domus de Maria Orosei Pula Santa Teresa di Gallura Sant'Anna Arresi Siniscola Villasimius Aglientu Barì Sardo Budoni Buggerru Calasetta Carloforte Castelsardo Castiadas Dorgali Golfo Aranci La Maddalena Muravera Olbia Quartu Sant'Elena San Teodoro Sant'Antioco Stintino Teulada Tortolì Villaputzu Palau Cervia Bellaria-Igea Marina Cattolica Cesenatico Comacchio 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 5 5 5 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 205 Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Marche Molise Piemonte Piemonte Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Sardegna Sardegna Sicilia Sicilia Sicilia Sicilia Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Veneto Veneto Veneto Veneto Veneto Veneto Grottammare San Benedetto del Tronto Cupra Marittima Porto San Giorgio Porto S.Elpidio Porto Recanati Civitanova Marche - Lungomare Nord e Centro Potenza Picena - Porto Potenza Picena Gabicce Mare Fano Pesaro Mondolfo Termoli Cannobio Cannero Riviera Ostuni Polignano a Mare Rodi Garganico Salve Castro Melendugno Castellaneta Ginosa La Maddalena - Punta Tegge; Spalmatore Santa Teresa Gallura - La Rena Bianca Menfi Fiumefreddo di Sicilia - Marina di Cottone Ragusa - Marina di Ragusa Pozzallo Monte Argentario Castiglione della Pescaia Grosseto - Marina di Grosseto e Principina a Mare Follonica Cecina Piombino - Riotorto - Parco Naturale della Sterpaia Livorno - Antignano e Quercianella San Vincenzo Rosignano Marittimo - Castiglioncello e Vada Bibbona - Marina di Bibbona Castagneto Carducci Forte dei Marmi Pietrasanta Camaiore Viareggio Pisa - Marina di Pisa, Tirrenia, Calambrone Caorle San Michele al Tagliamento - Bibione Eraclea Venezia - Lido di Venezia Jesolo Cavallino Treporti Marche Marche Marche Marche Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Liguria Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Puglia Porto Recanati Porto San Giorgio Porto Sant'Elpidio San Benedetto del Tronto Cinque Terre Levanto Portovenere Bergeggi Camogli Deiva Marina Lerici Moneglia Noli Portofino Taggia Ameglia Andora Camporosso Mare Celle Ligure Cervo Framura Lavagna Marinella di Sarzana Rapallo Sestri Levante Spotorno Varazze Alassio Bordighera Borgio Verezzi Ceriale Diano Marina Finale Ligure Laigueglia Loano Ospedaletti Pietra Ligure Riva Ligure Sanremo Santa Margherita Ligure Santo Stefano al Mare Sori Nardò Ostuni Otranto Andrano Castro Chieuti Diso Gallipoli Manduria Monopoli Polignano a Mare Rodi Garganico Salve Carovigno 2 2 2 2 5 4 4 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 5 5 4 4 4 4 4 4 4 4 4 4 3 206 Puglia Puglia Puglia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Sicilia Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Toscana Trani Ugento Castellaneta Noto Salina San Vito lo Capo Cefalù Favignana Menfi Pantelleria Brolo Capo D'Orlando Custonaci Lampedusa e Linosa Lipari Marsala Modica Portopalo di Capo Passero Sant'Agata di Militello Taormina Ustica Campobello di Mazara Erice Gioiosa Marea Patti Piraino Sciacca Selinunte Acireale Cinisi Ispica Pozzallo Santa Croce Camerina Scicli Termini Imerese Capalbio Capraia Castiglione della Pescaia Follonica Isola del Giglio Marina di Grosseto Orbetello Scarlino Bibbona Campo Nell'Elba Capoliveri Castagneto Carducci Marciana Marciana Marina Monte Argentario Porto Azzurro Portoferraio Rio Nell'Elba Camaiore Cecina Forte dei Marmi 2 2 1 5 5 5 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 1 1 1 1 1 1 5 5 5 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 207 View publication stats