Reinterpreting the World Economic Forum's global tourism competitiveness index

Tourism Management Perspectives 20 (2016) 131–140

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Reinterpreting the World Economic Forum's global tourism competitiveness index Juan Ignacio Pulido-Fernández a,⁎, Beatriz Rodríguez-Díaz b a b

Laboratory of Analysis and Innovation in Tourism, University of Jaén, Department of Economics, Campus de Las Lagunillas, s/n. D3-273, 23071 Jaen, Spain University of Málaga, Department of Applied Economics (Mathematics), Campus Ejido, s/n, 29071 Málaga, Spain

a r t i c l e

i n f o

Article history: Received 27 October 2015 Received in revised form 8 August 2016 Accepted 13 August 2016 Available online xxxx Keywords: Competitiveness Tourism destinations Multicriteria methods Synthetic indicators Travel & Tourism Competitiveness Index World Economic Forum

a b s t r a c t The Travel & Tourism Competitiveness Index, published by the World Economic Forum since 2007 provides information about the relative position of each country in terms of tourism competitiveness, through a set of indicators, grouped into pillars. This index has been the subject of some methodological criticism, such as the arbitrary weighting of variables. This study uses an alternative methodology for calculating this index based on two points of reference to propose a new standardisation, which takes an aspiration and reservation level for each pillar; subsequently, a synthetic index that measures the state of the pillar in the worst position, as well as other alternative indices, is calculated. The results obtained enable the development of a ranking of countries substantially different from that obtained by the World Economic Forum, which permits further adjustment of the weighting of each pillar and the measurement of various degrees of tourism competitiveness. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction As the competition between destinations increased over the last two decades, there has been a growing need to acquire knowledge about a destination's competitive ability, as well as the strengths and weaknesses of its competitors. In fact, there is a whole body of scientific literature on tourism destination competitiveness (TDC), especially focused on the analysis of its determining factors. As will be seen later, during the last decade tourism researchers have had a particular interest in identifying, measuring and systematising the variables that determine the competitive position of host countries, which is of significant importance for making management decisions, by both policymakers and destination managers, as well as by the different tourism entrepreneurs and, even, by stakeholders in general. In addition to other proposals for measuring TDC (Croes, 2011; Croes & Kubickova, 2013; Gooroochurn & Sugiyarto, 2005; Leung & Baloglu, 2013), the World Economic Forum (WEF) started to produce an annual report in 2007 on tourism competitiveness in 124 countries around the world, known as The Travel & Tourism Competitiveness Report (TTCR), which aims to provide a comprehensive strategic tool for measuring the factors and policies that make it attractive to develop tourism in different countries, allowing all stakeholders to work jointly to improve

⁎ Corresponding author. E-mail addresses: [email protected] (J.I. Pulido-Fernández), [email protected] (B. Rodríguez-Díaz).

http://dx.doi.org/10.1016/j.tmp.2016.08.001 2211-9736/© 2016 Elsevier Ltd. All rights reserved.

the competitiveness of the tourism industry in their national economies, thereby contributing to national growth and prosperity. Among other issues, the TTCR measures tourism competitiveness at country level (which has been called Travel & Tourism Competitiveness Index, hereafter TTCI), which provides a global tourism competitiveness index (TCI) and four competitiveness sub-indices: the first one, related to enabling environment; the second one, to T&T policy and enabling conditions; the third one, to infrastructure; and the fourth one, to natural and cultural resources. In order to obtain these indices, the information available has been organised into 14 pillars of tourism competitiveness, which split, in turn, into 90 competitiveness variables or indicators. From the viewpoint of tourism management, a tool such as the TTCI is essential to explain and predict the tourism behaviour of host countries. In fact, as noted by Croes & Kubickova (2013: 146), “determining the level of competitiveness of destinations is important in measuring the performance of a destination compared to its competitors”. However, this index, which is the most used, is not perfect and has several criticisms. One of the main criticisms of this interesting tool has to do with the arbitrary weighting of the variables within each pillar. Also we consider a major shortcoming that this index allows a country to be considered competitive for tourism, although it has some very poorly valued indicators. Therefore, in order to analyse the tourism competitiveness of countries from what we understand to be competitive, we will use another index that we believe fits much better to the definition of competitiveness. We will use a multi-objective method of double reference point

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J.I. Pulido-Fernández, B. Rodríguez-Díaz / Tourism Management Perspectives 20 (2016) 131–140

(Luque, Miettinen, Eskelinen, & Ruiz, 2009), noting that there are significant differences with the WEF index, and thereby giving a new interpretation of tourism competitiveness. We have used the information provided in the latest report published by the WEF, and we compare the new index with the one presented by the WEF, and draw some conclusions. Furthermore, this new index can detect the particular pillar that is wrong in each country, so that policymakers and destination managers are given the opportunity to carry out the most appropriate actions in order to overcome these deficiencies. This methodology used opens up the possibility that a panel of experts may provide both the weightings of the different pillars and their aspiration and reservation levels, and therefore, these are not the result of the arbitrary will of the writers of this study. 2. Theoretical framework This section discusses, first, the main contributions of this study within the framework of TDC theory, paying particular attention to the various attempts to identify and systematise the factors determining TDC. One of these attempts has resulted in the TTCI, whose objectives, composition, calculation methodology and main criticisms are exposed in the second part of this section. 2.1. Competitiveness of tourism destinations Competitiveness is a broad, multidimensional and complex concept (Gomezelj & Mihalič, 2008; Gooroochurn & Sugiyarto, 2005; Hong, 2009; Mazanec, Wöber, & Zins, 2007; Ritchie & Crouch, 2003), which has led multiple definitions and analysis models. The difficulty to reach absolute consensus on such a complex concept is evidenced by the different perspective shown by the definitions provided by the OECD (1992), which focuses on the output of the country's achievement, and the WEF (2011), which focuses on the inputs that make a country's competitiveness. In the case of tourism destinations, competitiveness is understood as the role played by their stakeholders “in creating and integrating valueadded products to sustain resources while maintaining market position relative to other competitors” (Hassan, 2000: 239) or “their ability to maintain their market position and share and/or to improve upon them through time” (D'Hauteserre, 2000: 23). Therefore, TDC seems to be related exclusively to the relative position of destinations in tourism markets. Dwyer & Kim (2003) define TDC as the relative ability of a destination to meet the needs of visitors in different aspects of the tourism experience or to deliver products and services that perform better than other destinations on those aspects of the tourism experience considered to be important by tourists. Other authors, however, consider that the key issue in TDC relates to the economic prosperity of local population. Thus, Crouch & Ritchie (1999) argue that, since destinations compete mainly for economic reasons, trying to attract the highest possible tourism expenditure level, the analysis of tourism competitiveness should focus on economic prosperity in the long term, the criterion that should be used to determine whether a tourism destination is more or less competitive. These authors present an approach for analysing the ability of a tourism destination to be competitive in which comparative advantages, competitive advantages, tourism management activities and the environment are taken into consideration. Other authors, such as Craigwell & Worrell (2008), Dwyer, Forsyth, & Rao (2000) and Song & Witt (2000), emphasise tourism prices, considering that they play a decisive role in demand decisions. Similarly, researchers have focused the attention on specific aspects that affect TDC, such as sustainability (Pulido-Fernández, Andrades-Caldito, & Sánchez-Rivero, 2015), efficiency (Cracolici & Nijkamp, 2006; Cracolici, Nijkamp, & Rietveld, 2008), quality management (Go &

Govers, 2000), demand satisfaction (Caber, Albayrak, & Matzler, 2012), economic globalisation (Namhyun, 2012), or the environment (Mihalič, 2000). It is worth highlighting the contribution of Crouch (2011), who states that a tourism product is, in fact, an experience delivered by a destination to its visitors. Therefore, TDC is strongly influenced by the quality of that experience, which will depend, in turn, on tourism businesses, other support organisations and institutions, organisations managing the destinations, the public sector, local residents and so on. In short, how competitive a territory can be in the market will depend on many circumstances, and thus, the degree of competitiveness of a destination may not be a significant indicator of the efficiency of its economy or of the level of welfare of its population. Indeed, a destination can base its competitiveness on low wages and few benefits, or on the availability natural resources that are unique in the world; or, alternatively, on the existence of high productivity that allows higher wages and excellent benefits, or on an improvement of the quality of services or, in general, of the tourism experience. In both cases, these tourism destinations would be competitive, but the meaning (and consequences) of that competitiveness would be radically different. It is along these lines that the index presented in this study is developed, as it challenges these forms of competitiveness; a destination cannot be considered competitive if some of its indicators are below a specified level. This conceptual debate has been accompanied by various attempts to identify and systematise the factors determining TDC. Different approaches explaining TDC can be distinguished in the literature, although, as Andrades-Caldito, Sánchez-Rivero, & Pulido-Fernández (2013) point out, the key reference framework for examining TDC is clearly that of Crouch & Ritchie (1999), known as the Calgary Model or Conceptual Model of Destination Competitiveness, which incorporates all the relevant factors that might typify a destination's tourism competitiveness. On the basis of this model, the Integrated Model of Destination Competitiveness (Dwyer & Kim, 2003; Dwyer, Livaic, & Mellor, 2003; Dwyer, Cvelbar, Mihalič, & Koman, 2014) has been developed, which has been empirically tested in the Republic of Korea and Australia in 2001 (Dwyer & Kim, 2003), Slovenia in 2004 (Gomezelj & Mihalič, 2008) and Serbia in 2009 (Armenski, Marković, Davidović, & Jovanović, 2011). Moving beyond the conceptual debate, during the last decade, researchers have directed their efforts towards measuring TDC. In this sense, it is possible to distinguish two types of approaches within the literature on tourism. On the one hand, it is necessary to consider approaches of a qualitative nature, or ‘soft measures’, among which two main groups can be identified: i) those measuring TDC using survey data of tourists' opinions and perceptions (Bahar & Kozak, 2007; Botha, Crompton, & Kim, 1999; Chen, Sok, & Sok, 2008; Cracolici & Nijkamp, 2008; Haahti & Yavas, 1983; Haahti and Yavas, 1983; Kozak & Rimmington, 1998, 1999) and ii) those based on the empirical evaluation of a number of subjective indicators of tourism competitiveness, using tourism stakeholder surveys (Bornhorst, Ritchie, & Sheehan, 2010; Chen, Sok, & Sok, 2008; Crouch, 2011; Dwyer, Mellor, Livaic, Edwards, & Kim, 2004; Dwyer, Cvelbar, Edwards, & Mihalič, 2012; Dwyer, Livaic, & Mellor, 2003; Enright & Newton, 2004, 2005; Faulkner, Opperman, & Fredline, 1999; Gomezelj & Mihalič, 2008; Kim & Dwyer, 2003; Lee & King, 2009; Sirše & Mihalič, 1999). However, critics of these approaches consider that they are too subjective, and prefer using quantitative data, as they lead to more precise and accurate results. In this regard, it is worth mentioning the research works by Cracolici & Nijkamp (2006), Cracolici, Nijkamp, & Rietveld (2008), Craigwell & Worrell (2008), Croes (2011), Das & DiRienzo (2010), Gooroochurn & Sugiyarto (2005), Mazanec, Wöber & Zins (2007), Zhang & Jensen (2007) and Zhang, Gu, Gu, & Zhang (2011), which use secondary data, published with the purpose of measuring TDC. Yet, there are also critics of this approach (Crouch, 2011). The third important issue, beyond the conceptual debate and the techniques for measuring TDC, is the importance of the factors affecting

J.I. Pulido-Fernández, B. Rodríguez-Díaz / Tourism Management Perspectives 20 (2016) 131–140

TDC. There has also been significant progress, and controversy, in this regard. Thus, through a survey of professionals in the tourism industry, Enright & Newton (2005) determine the relative importance of the attributes of TDC. Gooroochurn & Sugiyarto (2005) used data from the competitiveness monitor (CM) scale proposed by the World Travel and Tourism Council, which measures TDC through the development of eight key indicators (price, economic and social impact, human resources, infrastructure, environment, technology, openness and social development), calculating the weight for each main indicator by means of a confirmatory factor analysis, in order to calculate an aggregate index. Similarly, Mazanec, Wöber, & Zins (2007) used the CM database, which was reorganised, refined and completed, in order to propose a new explanatory model. In the same vein, Navickas & Malakauskaite (2009) propose an adaptation of the CM by modifying some indicators and adding new ones, in order to better respond to the changing needs of the tourism market. Moreover, Hong (2009) proposes a methodology for analysing the competitiveness of a tourism destination that takes into account i) Ricardo's comparative advantages, including the conditions of natural resources (exogenous comparative advantages) and the degree of technological change (endogenous comparative advantages); ii) Porter's competitive advantages, which explain the increase in trade among countries with similar factor endowments; iii) tourism management, providing high-quality education and training to improve comparative and competitive advantages and iv) environmental conditions, both domestic and global. The method allows the hierarchical organisation of these four dimensions, thus measuring the effect of each of them on the competitiveness of a tourism destination. Similarly, Daskalopoulou & Petrou (2009) have attempted to identify the factors contributing to TDC from the perspective of the competitive performance of tourism businesses. Bornhorst, Ritchie, & Sheehan (2010) have conducted interviews with tourism managers and stakeholders to identify the critical variables to define the success of a tourism destination. Crouch (2011) uses an analytical hierarchy process to evaluate 36 competitiveness factors through a survey of experts to rank the importance of the attributes that affect TDC. Focusing on the analysis of island destinations, Croes (2011) determined that the current measures of competitiveness do not respond to the needs of all destinations − and there are regions with heterogeneous characteristics − and therefore proposed a more accurate TDC index, using the most important factors affecting the competitiveness of island destinations. On the basis of the previously mentioned work, Croes & Kubickova (2013) proposed an alternative TCI, which they apply to the Central American region (Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua and Panama). Finally, it should be noted that Dwyer, Cvelbar, Mihalič, & Koman (2014) applied the Integrated Destination Competitiveness Model to the data available for a set of 139 countries in the period 2007–2011. The research work consisted of testing the 83 attributes of competitiveness of that model and the results made it possible to validate the appropriateness of the structure of the model, the validity of the groups of attributes of destination competitiveness and relevance of the different indicators used to measure the attributes of the destination. 2.2. Competitiveness index of the WEF The WEF has a strong international reputation for the various activities that it carries out, especially, for the annual assessment that, since 1979, is performed of the competitiveness of national economies, presented in the form of Global Competitiveness Report, and the Davos Conference of world leaders in business. Since 2007, it has also been producing an annual report on tourism competitiveness, which, in its latest edition (2015), analysed 141 countries worldwide, entitled The Travel & Tourism Competitiveness Report (TTCR). This

133

report aims to provide a comprehensive strategic tool to measure the factors and policies that make the development of the tourism sector attractive in different countries, enabling all stakeholders to work jointly to improve the competitiveness of the tourism industry in their national economies, thereby contributing to growth and national prosperity. This report, which addresses each year a different problem of global tourism (tourism and economic development, environmental sustainability, overcoming the crisis, etc.), also includes a global TCI (WEF, 2015). The information provided by the TTCR is very useful in identifying the competitive advantages and disadvantages of a country as a tourism destination and allowing the private sector to create public policies and actions that boost tourism activity in that country. In fact, it is increasingly used by researchers as a source of data (either the indicators used, the pillars in which these indicators are grouped, or the final results) for the development of studies on the TDC (Dwyer, Cvelbar, Mihalič, & Koman, 2014; Ivanov & Webster, 2013; Kayar & Kozak, 2010; Kendall & Gursoy, 2007; Gursoy, Baloglu, & Chi, 2009; Leung & Baloglu, 2013; Pulido-Fernández, Andrades-Caldito, & Sánchez-Rivero, 2015; Webster & Ivanov, 2014, among others). However, the TTCI has faced extensive criticism, especially with regard to methodological issues (Crouch, 2007; Croes & Kubickova, 2013; Leung & Baloglu, 2013; Mazanec & Ring, 2011). Mazanec & Ring (2011: 729), who propose in their research paper different methodological alternatives to turn TTCI into a true indicator of the competitive ability of a tourism destination, summarise these criticisms in the following points: i) the composition of the index, especially the combination of hard data and survey data; ii) the use of weak theoretically justified; iii) the comparability of countries on different development levels; iv) the arbitrary weighting of the variables and v) the reliability and validity of the index and the statistical methods used to demonstrate the usefulness of the index. In fact, Croes & Kubickova (2013: 147) criticise that TTCI “seems more a systematic collection (comprehensive notwithstanding) of data than a model that reveals clear testable association among variables thereby facilitating inferential analysis”. These authors propose in their paper a TCI whose results are substantially different from those obtained in the TTCI. Croes & Kubickova (2013) point out that this divergence is a result of the different nature of the variables used in each indicator, and criticise that the TTCI uses variable inputs (instead of output variables) and that this may lead to misleading conclusions. In short, the TTCI has been increasingly used by researchers as a reference for determining the level of competitiveness of tourism destinations, but not without debate, both regarding the methodology used in its production as well as the results obtained and their interpretation. 3. Methodology As previously stated, one of the criticisms of the TTCI development process has to do with the arbitrary weighting of the variables within each pillar, which are not weighed to calculate the sub-indices. Moreover, these latter are not weighted to calculate the global index, although, in fact, there is an implicit weighting, as not all pillars comprise an equal number of indicators. However, this weighting has not been chosen on purpose and, therefore, it does not meet the criterion sought. Those sub-indices, which consist of a small number of pillars, lead to greater weight being placed on the latter when calculating the global index. Thus, a country showing high values in the fourth subindex (natural and cultural resources), which consists of two pillars instead of five as in the case of the first sub-index, would be more positively valued due to this grouping than if the 14 pillars were individually aggregated. Therefore, it is important to become aware of the implicit weights that are being given, and to consider whether these are appropriate according to the philosophy of tourism competitiveness. To do this, a new approach is used for calculating TTCI. It is based on a different standardisation and aggregation of the pillars (Luque,

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Miettinen, Eskelinen, & Ruiz, 2009), which enables, on the one hand, further adjustment of the weighting, and on the other hand, evaluation of the state of all other countries in relation to each pillar. This methodology has been applied in the HDI (Luque, Pérez-Moreno, & Rodríguez, 2016) and global competitiveness (Pérez-Moreno, Rodríguez, & Luque, 2016). It is based on two points of reference, using a piecewise linear achievement function for each pillar, which makes it possible to standardise the value of each country by means of reference values (aspiration level, understood as a desirable level for a certain pillar; and reservation value, understood as a desirable level). This methodology leads to a weak index, which measures the aggregate tourism competitiveness; a strong index, which measures the state of the pillar in the worst position; and a series of composite indices, which measure different degrees of tourism competitiveness (a composite index is a linear combination of the other two). With the current methodology, the results obtained from the TTCI for each country may lead to misleading conclusions, because there may be countries obtaining excellent results in some pillars which, due to the implicit weighting that occurs when calculating the TTCI, could offset poor results in other pillars. The application of the methodology proposed in this study avoids this problem, given that a country will remain in the last positions of the ranking as long as the poor results of its indicators do not improve. Moreover, the values of these indicators are weighted in a way that is more consistent with the objective of competitiveness. The tourism competitiveness of a country, as previously noted, is determined by multiple criteria. One approach that enables simultaneous evaluation of different alternatives based on several criteria is multicriteria decision making. In this case, countries will be the different alternatives to be evaluated, and pillars will be the criteria used. The methodology to be applied is a multi-criteria technique based on a double reference point approach. For each country, a weak index is obtained, which measures its aggregate competitiveness, enabling tradeoffs between the different pillars, as well as a strong index, which measures the situation of the lowest pillar, that is, not allowing trade-offs. In addition, composite indices for different possible levels of aggregation can be developed. This paper will study the strong index, since the authors subscribe to the philosophy of ‘strong’ competitiveness, according to which the weaknesses of certain indicators cannot be offset by the good results obtained in others. In any case, the comprehensive analysis of all these indices (weak, strong and composite) permits a more precise assessment of each country and the study of the factors explaining each situation. In this methodology, it is necessary to establish two reference values for each criterion (pillar): an aspiration value, considered as desirable, and a reservation value, below which the values will not be acceptable (Luque, Miettinen, Eskelinen, & Ruiz, 2009). In order to assess the tourism competitiveness of each country, an achievement function proposed by Wierzbicki, Makowski, & Wessels (2000), which is employed in both continuous and discrete programming, is used. Thus, the objective functions (pillars) are standardised, taking into account the reference levels. C is defined as the number of countries and M the number of pillars. The value for a country i in the pillar j is denoted by xij (i = 1 , … , C; j = 1 , … , M); and the value that is standardised by the achievement function is denoted by sij (i = 1 , … , C; j = 1 , … ,).


 sij xij ; xaj ; xrj ¼

8 xij −xaj > > > 1 þ max > > > x j −xaj > > > < xij −xr j xaj −xrj > > > > > x −xrj > ij > > > : xr −x min j

j

si xaj ≤xij ≤x jmax si xrj ≤xij ≤xaj si x jmin ≤xij ≤xrj

ð1Þ

and xmin are the higher and lower For each pillar j = 1 , … , M, xmax j j levels and xaj and xrj are the levels of aspiration and reservation, respectively. This type of achievement function allows standardisation of all the objective functions (pillars) in the range [−1, 2]. The piecewise linearity of the achievement function makes it possible to extract information that it would not be possible to obtain using classical standardisation (range between the maximum and minimum values). Given a country i and a pillar j, if sij = −1, it means that the value of the pillar is the minimum for that indicator; if sij = 0, it means that it is equal to the reservation level; if sij = 1, it is equal to the aspiration level and if sij = 2, it is equal to the maximum value. Thus, if sij lies between −1 and 0, it means that the value of the pillar for this country is below the reservation value; between 0 and 1, the value is between the aspiration and reservation values and between 1 and 2, it improves the aspiration value. For each country i, weak index ( Idi ), strong index ( Ifi) and composite index ( Im i ) are defined as M 1X s M j¼1 ij

ð2Þ

Iif ¼ min j¼1;…;M sij

ð3Þ

f d Im i ¼ α I i þ ð1−α Þ I i ; 0bαb1

ð4Þ

Idi ¼

The weak index is the arithmetic mean of the values of the M pillars; the strong index is the minimum values of the pillars and the composite index is a linear combination of the previous two. Tables 1 and 2 present a summary of the proposed methodologies and a comparison of our proposal with the WEF methodology. Consequently, the tourism competitiveness of 141 countries around the world will be assessed, based on the data published in the TTCR for 2015, through the previously presented methodology. The 14 pillars into which the WEF have grouped the 90 indicators used are the criteria chosen. For each pillar, standardised data on a scale [1–7], where 1 is the worst situation possible and 7 is the best, will be used. Therefore, all pillars are considered to maximise. Regarding the weights of each pillar, following the philosophy of the WEF, all weights are maintained the same. Aspiration and reservation levels are key to interpreting and analysing the results. These can be set statistically or according to the opinion of a group of experts. The use of reference levels provided by experts could lead to an absolute measure of tourism competitiveness of each region. However, given the difficulty in setting commonly agreed objective reference values for all countries, statistical reference values have been considered for each pillar, taking into account the real situation at a particular moment in time of the group of countries. These statistical criteria enable the measurement of the relative competitiveness of certain regions compared to others, which as outlined in the literature review, suggests that TDC may be related to the relative position of destinations in tourism markets. Reservation values are taken from the first quartile of the total number of countries for each pillar (the value below which 25% of countries are found); and aspiration values are taken from the third quartile (the value below which 75% of countries are found). Taking into consideration these reference values for the standardisation of the values makes the study of tourism competitiveness more realistic, as it provides an implicit weighting, which depends on these reference values, and these values are taken in relation to the general situation in the different countries. For instance, suppose we are examining a ‘pillar A’, in which all countries show very low values, and a country ‘x’ with a value that is one unit lower; similarly, we consider a ‘pillar B’ in which all countries show very high values, and this country ‘x’ shows a very low value (5 points below); then, if the value in ‘pillar A’ is lower than

J.I. Pulido-Fernández, B. Rodríguez-Díaz / Tourism Management Perspectives 20 (2016) 131–140 Table 1 Summary of methodologies. Source: Authors' own elaboration.

Table 3 Results (from highest to lowest strong index). Source: Authors' own elaboration.

WEF

SIi ðdÞ ¼

∑ Pij ðdÞ j¼1

md n

Ii ðdÞ ¼ ∑ SIi ðdÞ

Index: the average of sub-indices

d¼1

Proposal (Luque et al., 2009) M

I di ¼ M1 ∑sij

Weak index: the average of all pillars

j¼1

Strong index: the normalised value of the worst pillar Mixed index: linear combination of weak and strong indices

Ifi = minj= 1, … ,Msij d Im i = α Ii + (1 − α) Ifi , 0 b α b 1

the value in ‘pillar B’, the standardisation used takes into account the situation of other countries, increasing the value of ‘pillar A’. Regarding aggregation, this study considers a strong index, which does not allow compensation between the pillars, and therefore, leaves no room for tourism competitiveness that is not representative of the efficiency of the economy or the welfare level of its population. In order to test the robustness of the model, a sensitivity analysis has been performed by modifying the weights of each pillar (~10%), concluding that the variations are not significant. Correlation coefficients are high (Pearson's correlation coefficient: weak index (1), strong index (0.988) and composite index (0.997). Moreover, regarding the position of each country in the ranking, a high correlation is shown (Spearman's correlation coefficient: 0.999, 0.993 and 0.997, respectively). 4. Analysis and discussion of results While implying a relative difference with respect to the results presented in the TTCI, as a result of the different standardisation used, considering the weak index maintains the philosophy of an aggregation that allows for trade-offs between pillars. In this study, it is considered that analysing the tourism competitiveness of different countries by applying a strong index is more interesting, as it is based on the pillar of each country showing the worst result, which is very useful for each country to detect its main weaknesses and generate policies and actions that help enhance tourism competitiveness. When working with standardised values, as indicated in the previous section, the pillar with the lowest value may not broadly coincide with the pillar with the lowest value once standardised. Thus, the latter

Table 2 Comparison of our proposal and WEF. Source: Authors' own elaboration.

Equation

Aggregation Methodology

WEF

Proposal (Luque et al., 2009)

(0–1)

(−1, 2)

xij −min Þ 6ðmax−min

sij ðxij ; xaj ; xrj Þ ¼

þ1

Weighted sum md

Equation

SIi ðdÞ ¼ Ii

ðdÞ

∑ Pij md

¼ ∑ SIi d¼1

8 xij −xaj > > −xaj > 1 þ xmax j > < x −x r > > > > :

Strong index

ðdÞ

j¼1

n

ðdÞ

Country

Ranking

Taiwan, China Hungary Chile Panama Czech Republic Bulgaria Estonia United Arab Emirates Romania Poland Sri Lanka Lithuania Costa Rica Portugal Tunisia Spain Slovenia Latvia Uruguay Morocco Luxembourg The United States Republic of Korea Cyprus Greece Malta Malaysia Lao PDR Turkey Peru Singapore Croatia New Zealand South Africa Macedonia, FYR Japan Belgium Mexico Cambodia Finland Trinidad and Tobago Ireland Bhutan Nicaragua Dominican Republic Canada Germany Vietnam Russian Federation Hong Kong SAR Iceland Oman Kyrgyz Republic Georgia The Netherlands Indonesia Jordan Iran, Islamic Rep. Rwanda Jamaica Philippines Italy Austria Bolivia Mauritius Azerbaijan Botswana Brazil Thailand Sweden Slovak Republic Honduras China

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73

Ranking WEF 2015

md

Sub-indices: the average of the pillars in this sub-index

Standardisation Scale

135

Ifi = minj=1 , … , Msij

ij j xaj −xrj

xij −xrj

xrj −xmin j

si xaj ≤ xij ≤ xmax j si xrj ≤ xij ≤ xaj si xmin ≤ xij ≤ xrj j

0.388 0.273 0.267 0.217 0.180 0.167 0.161 0.122 0.102 0.091 0.084 0.072 0.036 0.023 0.014 0.012 0.006 0.004 −0.003 −0.003 −0.034 −0.043 −0.047 −0.075 −0.087 −0.090 −0.095 −0.108 −0.116 −0.117 −0.121 −0.124 −0.137 −0.138 −0.142 −0.143 −0.150 −0.152 −0.154 −0.156 −0.161 −0.162 −0.164 −0.164 −0.166 −0.181 −0.184 −0.193 −0.193 −0.193 −0.193 −0.199 −0.200 −0.201 −0.202 −0.211 −0.216 −0.218 −0.219 −0.220 −0.223 −0.224 −0.224 −0.228 −0.231 −0.231 −0.244 −0.244 −0.248 −0.259 −0.274 −0.278 −0.279

32 41 51 34 37 49 38 24 66 47 63 59 42 15 79 1 39 53 73 62 26 4 28 36 31 40 25 96 44 58 11 33 16 48 82 9 21 30 105 22 69 19 87 92 81 10 3 75 45 13 18 65 116 71 14 50 77 97 98 76 74 8 12 100 56 84 88 29 35 23 61 90 17

4.35 4.14 4.04 4.28 4.22 4.05 4.22 4.43 3.78 4.08 3.80 3.88 4.10 4.64 3.54 5.31 4.18 4.01 3.65 3.81 4.38 5.12 4.37 4.25 4.36 4.16 4.41 3.33 4.09 3.88 4.86 4.30 4.64 4.08 3.50 4.94 4.51 4.36 3.24 4.47 3.72 4.53 3.44 3.38 3.50 4.92 5.22 3.60 4.08 4.68 4.54 3.79 3.08 3.67 4.67 4.05 3.59 3.32 3.31 3.59 3.63 4.98 4.82 3.29 3.90 3.48 3.42 4.37 4.26 4.45 3.85 3.41 4.54

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Table 3 (continued) Country

Ranking

Denmark El Salvador Kazakhstan India Guatemala Israel Qatar Norway Kenya Bahrain Bangladesh Serbia Nepal Madagascar Armenia Egypt Senegal Australia Ghana Mali France Mongolia Zimbabwe Tajikistan Côte d'Ivoire Pakistan Argentina The United Kingdom Lebanon Kuwait Paraguay Uganda Tanzania Colombia Gambia Switzerland Sierra Leone Guinea Nigeria Venezuela Puerto Rico Zambia Malawi Albania Burundi Cameroon Ethiopia Barbados Guyana Algeria Namibia Saudi Arabia Burkina Faso Haití Mauritania Mozambique Myanmar Montenegro Gabon Yemen Moldova Angola Suriname Cape Verde Seychelles Chad Swaziland Lesotho

74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141

Ranking WEF 2015 −0.280 −0.283 −0.288 −0.293 −0.297 −0.302 −0.303 −0.305 −0.307 −0.313 −0.314 −0.328 −0.343 −0.343 −0.343 −0.343 −0.356 −0.358 −0.368 −0.389 −0.393 −0.394 −0.395 −0.411 −0.431 −0.442 −0.448 −0.461 −0.470 −0.477 −0.479 −0.480 −0.492 −0.502 −0.508 −0.511 −0.527 −0.546 −0.549 −0.550 −0.551 −0.564 −0.564 −0.569 −0.569 −0.570 −0.575 −0.600 −0.600 −0.618 −0.631 −0.633 −0.638 −0.657 −0.662 −0.707 −0.710 −0.723 −0.745 −0.745 −0.754 −0.783 −0.815 −0.846 −0.908 −0.908 −0.938 −0.938

27 91 85 52 80 72 43 20 78 60 127 94 102 121 89 83 112 7 120 128 2 99 115 119 117 125 57 5 95 103 113 114 93 68 109 6 132 140 131 110 55 107 126 106 135 122 118 46 104 123 70 64 136 133 137 130 134 67 124 138 111 139 101 86 54 141 108 129

4.38 3.41 3.48 4.02 3.51 3.66 4.09 4.52 3.58 3.85 2.90 3.35 3.27 2.99 3.42 3.49 3.14 4.98 3.01 2.87 5.24 3.31 3.08 3.03 3.05 2.92 3.90 5.12 3.34 3.27 3.11 3.11 3.35 3.73 3.20 4.99 2.78 2.58 2.79 3.18 3.91 3.22 2.90 3.22 2.70 2.95 3.03 4.08 3.26 2.93 3.69 3.80 2.67 2.75 2.64 2.81 2.72 3.75 2.92 2.62 3.16 2.60 3.28 3.46 4.00 2.43 3.21 2.82

will be considered to be the strong index, as it will take into account the aspiration and reservation levels. The strong index indicates that if the value is lower than 0, at least one pillar is lower than 0 too (at least one pillar does not reach its

corresponding reservation value). If the strong index is higher than 1, all pillars increase their respective reservation values. According to this scheme, the analysis in Table 3 supports the conclusion that only 18, out of the 141 countries analysed, show a higher value than the reservation level in all pillars (which make up what has been called top 18). Moreover, most of these countries are not the same as those ranked in the top 18 positions according to the WEF. Those countries which, by using this new way of calculating the index, are ranked in the top 18, occupied the following positions in the WEF ranking: 32, 41, 51, 34, 37, 49, 38, 24, 66, 47, 63, 59, 42, 15, 79, 1, 39 and 53. This means that only two countries included in the WEF ranking remain in the current top 18, which are listed in Table 3 (Portugal and Spain). Although all countries included in the top 18 show values above the reservation level, the highest value obtained has been 0.388, which means that no country meets the aspiration level (1) in all pillars. This is understandable, given that, among the 14 pillars, any of them could be lower; and with only one being lower, the strong index will be below 1. Table 4 shows the standardised values for each pillar of the countries ranked in the top 18 list of countries with higher values in the strong index. It should be highlighted that all values are between 0 and 2, with pillar 12 being the best ranked (Tourist Service Infrastructure), and pillar 14 (Cultural Resources and Business Travel) the worst ranked. In short, from highest to lowest value, the pillars are ordered as follows: 12, 3, 2, 9, 5, 7, 4, 11, 6, 1, 8, 13, 10 and 14. The lowest value (0.004) is shown by Latvia in pillar 14; while the highest value (1.928) is shown by Spain in the same pillar 14. This top 18 list does not include 16 of the countries having these same positions in the ranking of the WEF: France, Germany, the United States, the United Kingdom, Switzerland, Australia, Italy, Japan, Canada, Singapore, Austria, Hong Kong SAR, the Netherlands, New Zealand, China and Iceland. It is therefore interesting to analyse which countries, out of the 16 countries that have been left out from the top 18 once the new index has been calculated, are the best and worst ranked. Table 5 includes four of the countries with the highest stronger index that appear in the WEF ranking and are left out from the current top18, which takes into account the strong index. It should be noted that pillars 8 (price competitiveness) and 9 (environmental sustainability) show the lowest values, with pillar 11 (ground and port infrastructure) showing the highest values. By country, it can be seen that, in the case of Singapore, New Zealand and Japan, it is the poor performance of pillar 8 that has prevented both countries from being among the top-ranked; whereas, in the case of the United States, not appearing at the top of the ranking has been the result of the poor performance of pillar 9. Table 6 shows the four countries, out of those included in the WEF ranking, with the lowest strong index that have been left out from the current top 18. What these countries have in common is the fact that pillar 8 (price competitiveness) shows the lowest values, noting that the values of the other pillars are considerably high, being, in general, above the aspiration level. Therefore, by improving that pillar, these countries might be at the top of the ranking of countries presented in this study. We are aware that the strong index is very demanding. Indeed, if a country had all indicators at the maximum level (2) and just one indicator below the aspiration level (1), for instance 0.9, and another country had all values within the aspiration level (1), on the basis of the strong index, one would choose the second country as the best. However, by considering other pillars (allowing a certain degree of offset), the former country could improve its position. The composite index (Eq. (4)), which is a linear combination of the strong index and the weak index, can be applied in order to relax this requirement. When calculating this index, the problem lies in deciding the weight of the strong index and the weak index; that is, the criteria under which the weight of each one of them is decided.

J.I. Pulido-Fernández, B. Rodríguez-Díaz / Tourism Management Perspectives 20 (2016) 131–140

137

Table 4 Top18 list of the index elaborated (standardised values). Source: Authors' own elaboration. Country

Pillar 1

Pillar 2

Pillar 3

Pillar 4

Pillar 5

Pillar 6

Pillar 7

Pillar 8

Pillar 9

Pillar 10

Pillar 11

Pillar 12

Pillar 13

Pillar 14

Taiwan, China Hungary Chile Panama Czech Republic Bulgaria Estonia United Arab Emirates Romania Poland Sri Lanka Lithuania Costa Rica Portugal Tunisia Spain Slovenia Latvia

1.192 0.305 1.079 1.009 0.389 0.234 1.126 1.486 0.102 0.389 0.677 0.545 0.737 0.617 0.473 0.078 0.006 0.677

1.160 0.954 1.117 0.315 0.887 0.492 1.169 1.654 0.643 1.013 0.777 0.761 0.777 1.420 0.172 1.108 1.307 0.954

1.074 1.585 0.499 0.447 1.713 1.681 1.202 0.556 0.932 1.160 0.533 1.798 0.396 1.000 0.487 1.053 0.994 1.117

1.118 0.930 0.764 0.217 0.879 0.841 1.128 1.142 0.637 0.943 0.344 1.053 0.930 1.155 0.318 1.012 0.803 1.155

1.161 0.914 0.762 0.604 1.045 0.830 1.319 1.346 0.634 0.899 0.206 1.098 0.536 0.934 0.428 1.082 0.983 1.261

0.388 1.041 0.837 1.113 0.579 0.167 1.364 0.923 0.321 0.091 1.062 0.349 1.164 1.210 0.866 1.431 0.885 0.608

1.031 1.058 1.223 1.041 1.058 0.936 0.802 0.265 0.960 1.035 0.534 1.005 1.002 1.078 0.027 0.973 0.832 0.997

0.965 0.453 0.267 1.041 0.302 1.005 0.477 0.860 0.791 0.849 0.535 0.767 0.221 0.023 1.280 0.012 0.151 0.733

0.539 1.280 0.916 0.747 1.178 1.068 1.112 0.929 0.877 1.068 0.084 0.968 0.565 0.968 0.708 1.065 1.115 1.057

0.736 0.353 0.414 1.213 0.586 0.214 0.497 1.651 0.147 0.275 0.314 0.175 0.492 1.011 0.253 1.331 0.203 0.581

1.366 0.985 0.501 0.812 1.267 0.275 0.949 1.232 0.179 0.764 0.860 1.030 0.036 1.026 0.131 1.422 1.259 0.848

0.514 0.957 0.870 1.183 1.172 1.501 1.401 1.183 0.952 0.692 0.559 0.600 1.082 1.533 0.737 1.777 1.321 0.984

0.482 0.273 0.424 1.187 0.180 0.791 0.252 0.122 0.259 0.576 1.009 0.072 1.416 0.978 0.014 1.263 1.043 0.151

1.124 0.642 0.821 0.269 0.699 0.455 0.161 0.462 0.534 1.012 0.197 0.125 0.254 1.231 0.183 1.928 0.054 0.004

Table 5 Countries of the WEF ranking with the highest strong index, but outside the top18. Source: Authors' own elaboration. Country

Pillar 1

Pillar 2

Pillar 3

Pillar 4

Pillar 5

Pillar 6

Pillar 7

Pillar 8

Pillar 9

Pillar 10

Pillar 11

Pillar 12

Pillar 13

Pillar 14

The United States Singapore New Zealand Japan

1.196 1.593 1.388 1.121

0.559 1.481 1.489 1.177

0.795 0.647 0.806 1.394

1.202 1.299 1.230 1.165

1.346 1.462 1.245 1.472

1.215 1.462 1.292 1.200

1.015 1.421 1.190 1.061

0.070 −0.121 −0.137 −0.143

−0.043 0.825 1.194 0.812

1.664 1.443 1.270 1.213

1.077 1.778 0.752 1.323

1.629 1.029 1.401 0.541

1.471 0.381 1.125 1.064

1.484 1.136 0.677 1.748

A valid criterion for determining this weight is not provided by the authors of this paper, so any decision made would be subjective and arbitrary. It is considered, therefore, that this decision should be made by a panel of experts. Furthermore, it is considered that the weight that is given to the strong index and the weak index in order to obtain a composite index will depend on the characteristics of the destination, so the composite index should be calculated for each specific destination. The same is true for reservation and aspiration values, which should also be determined by a group of experts and for each destination analysed. Therefore, in order to show the variations that would occur if the requirement of the strong index would be relaxed, Table 7 presents the ranking for the weak index (0% strong–100% weak), composite index at 50 (50% strong–50% weak), composite index at 60 (60% strong–40% weak), composite index at 70 (70% strong–30% weak), composite index at 80 (80% strong–20% weak), composite index at 90 (90% strong–10% weak) and the strong index (100% strong–0% weak). It was decided not to further reduce the weight of the strong index since this study is committed to the philosophy of not allowing full tradeoffs between pillars. Table 7 shows the results obtained in each case for the countries currently ranked in top 18 positions in the WEF ranking. It can be seen how, as offset decreases, ranking positions are lost; with the exception of Spain, the United States and Portugal, which

have improved their positions in the ranking Composite 50 or Composite 60 with respect to the weak index. In general, as can be inferred from Table 8, the position of a country decreases as the pillars cannot be offset, in cases in which it is unbalanced; that is, there are enough pillars performing well while one or more pillars perform very poorly. Moreover, when balanced, a country is not ranked lower, but even higher. Accordingly, two groups of countries can be distinguished: - Those whose position has declined as trade-off are no longer observed. In general, these are countries performing very good in all pillars (above the aspiration level in almost all of them) except for one pillar, which performs very poorly. This group includes: France, Germany, the United Kingdom, Switzerland, Australia, Italy, Japan, Canada, Singapore, Austria, Hong Kong SAR, the Netherlands, New Zealand, China and Iceland. In this case, pillar 8 (price competitiveness) performs poorly in all cases (except China, which is in pillar 9), being below the reservation level. - Those whose position has declined, but not as much as in the previous case; and whose position is even sometimes higher during the transition. In general, all pillars of these countries perform well, except for one, but even the performance of that pillar is not very poor. This group of countries include Spain, the United States and Portugal.

Table 6 Countries of the WEF ranking with the lowest strong index. Source: Authors' own elaboration. Country

Pillar 1

Pillar 2

Pillar 3

Pillar 4

Pillar 5

Pillar 6

Pillar 7

Pillar 8

Pillar 9

Pillar 10

Pillar 11

Pillar 12

Pillar 13

Pillar 14

Switzerland The United Kingdom France Australia

1.421 1.393 0.593 1.084

1.411 0.660 0.660 1.342

1.468 0.869 1.489 1.085

1.369 1.206 1.053 0.841

1.488 1.520 1.235 1.330

1.303 1.026 1.056 0.770

1.045 1.088 1.081 1.021

−0.511 −0.461 −0.393 −0.358

1.464 1.135 1.088 1.076

1.370 1.398 1.354 1.616

1.604 1.410 1.517 0.531

1.655 0.984 1.549 1.172

1.180 1.324 1.327 1.483

1.049 1.743 1.897 1.563

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Table 7 Ranking based on the different levels of tourism competitiveness in the 18 top countries of the WEF ranking. Source: Authors' own elaboration. Ranking WEF 2015

Country

Weak index

Composite 50

Composite 60

Composite 70

Composite 80

Composite 90

Strong index

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Spain France Germany The United States The United Kingdom Switzerland Australia Italy Japan Canada Singapore Austria Hong Kong SAR The Netherlands Portugal New Zealand China Iceland

5 6 3 12 7 1 15 26 8 13 4 2 9 14 19 10 46 11

2 36 11 12 45 33 40 39 15 20 10 13 18 24 6 16 57 21

6 43 14 11 50 46 45 39 16 24 12 18 23 29 9 17 60 26

9 49 20 12 64 62 48 41 21 33 15 24 31 36 10 23 61 34

9 69 31 15 81 84 65 43 27 35 20 38 37 40 11 25 66 39

14 85 35 19 93 96 78 50 30 37 26 45 41 44 13 29 71 42

16 94 47 22 101 109 91 62 36 46 31 62 50 55 14 33 73 50

Portugal shows good results in all situations. It is the largest example of homogeneity, winning positions as it incorporates the strong index, as this index penalises countries that are good because they compensate fair or poor indicators with other good indicators. Spain also improved when the strong index of 50% is incorporated, but then it gets somewhat worse, with increasing the weight of the strong index, due to the variation of other countries. The United States behaves similarly.

5. Conclusions As noted in the theoretical framework of this study, a territory can be competitive in the market due to different circumstances, so the degree of competitiveness of a destination may not be indicative of the efficient production and the proper use of the resources of that territory. Competitiveness is a relative variable; a country may or may not be regarded as competitive in relation to other countries. Therefore, the fact that a country shows higher values than all other countries in all indicators measuring competitiveness will mean that this country is more competitive, although this does not necessarily mean that this country is doing well; it just means that it is doing better than others.

The proposal presented in this paper defines other way of calculating the TTCI of the WEF, more demanding than the TTCI. It requires a high level in all the pillars that compose the index, in order to consider that a country is competitive, and therefore avoiding the current pillar compensation mechanism. Moreover, our proposal does not only take into account the position of each country in relation to others, but also previously requires that countries maintain a certain level in all pillars which is, at least, above a reservation value previously defined. With respect to methodological implications, the use of strong index implies that countries classified as strongly competitive in the WEF ranking may be relegated significantly in the new ranking; and, conversely, countries considered as less competitive in the WEF ranking, now, may improve their position. Those countries in need to improve some of the pillars more urgently are the bottom of this new ranking (based on the calculation of a strong index); and they cannot improve their position in the ranking until they have not improved that pillar, no matter how much the other pillars improve. This is important because, in this way, a situation in which a country appears to be very competitive due to the fact that it shows certain aspects that are very good, while presenting some other really bad aspects, is avoided. It is noteworthy that only two countries,

Table 8 Top18 countries of the WEF ranking. Source: Authors' own elaboration. Country

Pillar 1

Pillar 2

Pillar 3

Pillar 4

Pillar 5

Pillar 6

Pillar 7

Pillar 8

Pillar 9

Pillar 10

Pillar 11

Pillar 12

Pillar 13

Pillar 14

Spain France Germany The United States The United Kingdom Switzerland Australia Italy Japan Canada Singapore Austria Hong Kong SAR Netherlands Portugal New Zealand China Iceland

0.078 0.593 1.215 1.196 1.393 1.421 1.084 −0.144 1.121 1.224 1.593 1.037 1.570 1.271 0.617 1.388 0.269 1.047

1.108 0.660 1.186 0.559 0.660 1.411 1.342 0.861 1.177 1.177 1.481 1.541 1.403 1.273 1.420 1.489 0.685 1.602

1.053 1.489 1.840 0.795 0.869 1.468 1.085 1.223 1.394 0.738 0.647 1.968 1.372 1.191 1.000 0.806 0.561 1.011

1.012 1.053 1.155 1.202 1.206 1.369 0.841 0.497 1.165 1.225 1.299 1.114 1.211 1.132 1.155 1.230 1.165 1.299

1.082 1.235 1.214 1.346 1.520 1.488 1.330 1.018 1.472 1.145 1.462 1.314 1.588 1.451 0.934 1.245 0.531 1.409

1.431 1.056 0.799 1.215 1.026 1.303 0.770 0.589 1.200 0.952 1.462 1.241 1.297 0.627 1.210 1.292 0.694 1.431

0.973 1.081 1.088 1.015 1.088 1.045 1.021 1.038 1.061 0.521 1.421 1.005 0.881 1.091 1.078 1.190 0.113 1.134

0.012 −0.393 −0.184 0.070 −0.461 −0.511 −0.358 −0.224 −0.143 −0.181 −0.121 −0.224 −0.193 −0.202 0.023 −0.137 1.016 −0.193

1.065 1.088 1.178 −0.043 1.135 1.464 1.076 0.864 0.812 1.072 0.825 1.268 0.370 1.143 0.968 1.194 −0.279 1.186

1.331 1.354 1.338 1.664 1.398 1.370 1.616 1.123 1.213 1.920 1.443 1.043 1.494 1.325 1.011 1.270 1.126 1.254

1.422 1.517 1.600 1.077 1.410 1.604 0.531 1.069 1.323 0.782 1.778 1.374 1.782 1.687 1.026 0.752 0.663 0.925

1.777 1.549 1.263 1.629 0.984 1.655 1.172 1.820 0.541 1.454 1.029 1.910 0.514 0.774 1.533 1.401 0.053 1.618

1.263 1.327 1.208 1.471 1.324 1.180 1.483 1.266 1.064 1.321 0.381 1.104 0.928 0.612 0.978 1.125 1.404 0.928

1.928 1.897 1.766 1.484 1.743 1.049 1.563 1.886 1.748 1.304 1.136 1.047 0.900 1.185 1.231 0.677 1.869 0.147

J.I. Pulido-Fernández, B. Rodríguez-Díaz / Tourism Management Perspectives 20 (2016) 131–140

Portugal and Spain, included in the WEF ranking remain in the new top 18. We can conclude that the most competitive countries are not a homogenous group. With respect to the countries with the highest stronger index that appear in the WEF ranking and are left out from the new top 18 proposed, Singapore, New Zealand and Japan, match that the poor performance of price competitiveness has prevented both countries from being among the top-ranked, whereas, in the case of the United States, not appearing at the top of the ranking has been the result of the poor performance of environmental sustainability. The countries out of those included in the WEF ranking with the lowest strong index that have been left out from the new top 18 proposed (Switzerland, the United Kingdom, France and Australia) have in common that price competitiveness shows the lowest values, noting that the values of the other pillars are considerably high. Therefore, by improving that pillar, these countries might be at the top of the ranking of countries presented in this study. With respect to the management and policies that can be carried out, it is particularly striking that in most of the top 18 countries in the WEF ranking, the specific pillar which does not meet this goal (reaching values above the reservation value) is pillar 8, which measures the price competitiveness of travel and tourism industry and includes, among other indicators, national purchasing power parity prices. It should be noted that many of these countries are members of the European Union, and that the strong appreciation of euro against dollar has remained largely unchanged since the creation of this single European currency. Therefore, there is evidence of how having a strong euro affects the tourism competitiveness of the countries of the European Union, as clearly shown in the case of Spain and Portugal. The situation of the pillar considered to be the best one is just the opposite, with a particular emphasis on cultural resources, as a result of the quantity and quality of the resources available in both countries. Stefan (2014) studied the influence of the different pillars with respect to global competitiveness, and he revealed a very strong correlation between the overall competitiveness and the business environment and infrastructure, respectively human, cultural and natural resources, as well as the strong correlation between the overall competitiveness and the specific regulatory framework. He also noted that price competitiveness has an inverse association and almost negligible compared with the overall competitiveness. However, price competitiveness is a pillar of significant importance in the tourism sector, and it should be taken into account, which is achieved with the application of the proposed methodology. The fact that no country achieve the aspiration level in all pillars, although is comprensible given the high number of pillars, shows that much improvement is needed. Consequently, the application of this type of analysis to each one of the pillars, and each one of the indicators that make up each pillar, would allow policymakers and destination managers to develop specific actions that improve deficient aspects. Detecting the pillars with low levels, given this consideration, can help improve them, which most likely will improve the situation of other pillars indirectly. If we allow governments to have this information on what pillars are failing, they can focus on improving these and be really competitive. If reference levels were determined by a panel of experts, not only could a ranking of countries be developed, but it would also be possible to identify which countries are competitive and which are not (those above or below the reservation level). It lets us measure competitiveness in each pillar not in relative terms, but in relation to the reservation value. That is, it is not only important the position relative to other countries, but if a country really is competitive on a pillar. This entails great difficulty, as countries have very diverse characteristics and will be difficult to agree to the experts, so this is considered a limitation of the work, but also a future line of research.

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References Andrades-Caldito, L., Sánchez-Rivero, M., & Pulido-Fernández, J. I. (2013). Differentiating competitiveness through tourism image assessment: An application to Andalusia (Spain). Journal of Travel Research, 52(1), 68–81. Armenski, T., Marković, V., Davidović, N., & Jovanović, T. (2011). Integrated model of destination competitiveness. Geographica Pannonica, 15(2), 58–69. Bahar, O., & Kozak, M. (2007). Advancing destination competitiveness research: Comparison between tourists and service providers. Journal of Travel and Tourism Marketing, 22(2), 61–71. 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. Botha, C., Crompton, J. L., & Kim, S. S. (1999). Developing a revised competitive position for Sun/Lost City, South Africa. Journal of Travel Research, 37(4), 341–352. Caber, M., Albayrak, T., & Matzler, K. (2012). Classification of the destination attributes in the content of competitiveness. Journal of Vacation Marketing, 18(1), 43–56. Chen, C. Y., Sok, P., & Sok, K. (2008). Evaluating the competitiveness of the tourism industry in Cambodia: Self-assessment from professionals. Asia Pacific Journal of Tourism Research, 13(1), 41–66. Cracolici, M. F., & Nijkamp, P. (2006). Competition among tourist destination. An application of data envelopment analysis to Italian provinces. In M. F. Giaoutzi, & P. Nijkamp (Eds.), Tourism and regional development: New pathways (pp. 133–152). Aldershot, UK: Ashgate. Cracolici, M. F., & Nijkamp, P. (2008). The attractiveness and competitiveness of tourist destinations: A study of southern Italian regions. Tourism Management, 30(3), 336–344. Cracolici, M. F., Nijkamp, P., & Rietveld, P. (2008). Assessment of tourism competitiveness by analysing destination efficiency. Tourism Economics, 14(2), 325–342. Craigwell, R., & Worrell, D. (2008). The competitiveness of selected Caribbean tourism markets. Social and Economic Studies, 57(1), 72–107. Croes, R. (2011). Measuring and explaining competitiveness in the context of Small Island destinations. Journal of Travel Research, 50(4), 431–442. Croes, R., & Kubickova, M. (2013). From potential to ability to compete: Towards a performance-based tourism competitiveness index. Journal of Destination Marketing & Management, 2(3), 146–154. Crouch, G. I. (2007). Measuring tourism competitiveness. Research, theory and the WEF Index. Proceedings of the ANZMAC 2007, 3–5 December. New Zealand: Dunedin. Crouch, G. I. (2011). Destination competitiveness: An analysis of determinant attributes. Journal of Travel Research, 50(1), 27–45. Crouch, G. I., & Ritchie, J. R. B. (1999). Tourism, competitiveness and societal prosperity. Journal of Business Research, 44(3), 137–152. Das, J., & DiRienzo, C. (2010). Tourism competitiveness and corruption: Across-country analysis. Tourism Economics, 16(3), 477–492. Daskalopoulou, I., & Petrou, A. (2009). Urban tourism competitiveness: Networks and the Regional Asset Base. Urban Studies, 46(4), 779–801. D'Hauteserre, A. M. (2000). Lessons in managed destination competitiveness: The case of Foxwoods Casino Resort. Tourism Management, 21(1), 23–32. Dwyer, L., & Kim, C. (2003). Destination competitiveness: Determinants and indicators. Current Issues in Tourism, 6(5), 369–414. Dwyer, L., Forsyth, R., & Rao, P. (2000). The price competitiveness of travel and tourism: A comparison of 19 destinations. Tourism Management, 21(1), 9–21. 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. Dwyer, L., Cvelbar, L. K., Edwards, D., & Mihalič, T. (2012). Fashioning a destination tourism future: The case of Slovenia. Tourism Management, 33(2), 305–316. Dwyer, L., Cvelbar, L. K., Mihalič, T., & Koman, M. (2014). Integrated destination competitiveness model: Testing its validity and data accessibility. Tourism Analysis, 19(1), 1–17. 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, 43(1), 339–350. 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. Go, F., & Govers, R. (2000). Integrated quality management for tourist destinations: A European perspective on achieving competitiveness. Tourism Management, 21(1), 79–88. Gomezelj, D. O., & Mihalič, T. (2008). Destination competitiveness. Applying different models, the case of Slovenia. Tourism Management, 29(2), 294–307. Gooroochurn, N., & Sugiyarto, G. (2005). Competitiveness indicators in the travel and tourism industry. Tourism Economics, 11(1), 25–43. Gursoy, D., Baloglu, S., & Chi, C. G. (2009). Destination competitiveness of Middle Eastern countries: An examination of relative positioning. Anatolia, 20(1), 151–163. Haahti, A., & Yavas, U. (1983). Tourists' perceptions of Finland and selected European countries as travel destinations. European Journal of Marketing, 17(2), 34–42. Hassan, S. S. (2000). Determinants of market competitiveness in an environmentally sustainable tourism industry. Journal of Travel Research, 38(3), 239–245. Hong, W. (2009). Global competitiveness measurement for the tourism sector. Current Issues in Tourism, 12(2), 105–132. Ivanov, S., & Webster, C. (2013). Globalisation as a driver of destination competitiveness. Annals of Tourism Research, 43, 624–650.

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J.I. Pulido-Fernández, B. Rodríguez-Díaz / Tourism Management Perspectives 20 (2016) 131–140

Kayar, C. H., & Kozak, N. (2010). Measuring destination competitiveness: An application of the travel and tourism competitiveness index (2007). Journal of Hospitality Marketing and Management, 19(3), 203–216. Kendall, K. W., & Gursoy, D. (2007). A managerial approach to positioning and branding: Eponymous or efficient. Tourism Analysis, 12(5–6), 473–483. Kim, C., & Dwyer, L. (2003). Destination competitiveness and bilateral flows between Australia and Korea. Journal of Tourism Studies, 14(2), 54–67. Kozak, M., & Rimmington, M. (1998). Benchmarking: Destination attractiveness and small hospitality business performance. International Journal of Contemporary Hospitality Management, 10(5), 184–188. Kozak, M., & Rimmington, M. (1999). Measuring tourist destination competitiveness: Conceptual considerations and empirical findings. International Journal of Hospitality Management, 18(3), 273–283. Lee, C. F., & King, B. (2009). A determination of destination competitiveness for Taiwan's hot springs tourism sector using the Delphi technique. Journal of Vacation Marketing, 15(3), 243–257. Leung, X. Y., & Baloglu, S. (2013). Tourism competitiveness of Asia Pacific destinations. Tourism Analysis, 18(4), 371–384. Luque, M., Miettinen, K., Eskelinen, P., & Ruiz, F. (2009). Incorporating preference information in interactive reference point methods for multiobjective optimization. OMEGA - International Journal of Management Science, 37(2), 450–462. Luque, M., Pérez-Moreno, S., & Rodríguez, B. (2016). Measuring human development: A multi-criteria approach. Social Indicator Research, 125(3), 713–733. Mazanec, J. A., & Ring, A. (2011). Tourism destination competitiveness: Second thoughts on the world economic forum reports. Tourism Economics, 17(4), 725–751. Mazanec, J. A., Wöber, K., & Zins, A. H. (2007). Tourism destination competitiveness: From definition to explanation? Journal of Travel Research, 46(1), 86–95. Mihalič, T. (2000). Environmental management of a tourist destination: A factor of tourism competitiveness. Tourism Management, 21(1), 65–78. Namhyun, K. (2012). Tourism destination competitiveness, globalization, and strategic development from a development economics perspective. PhD dissertation Illinois: University of at Urbana-Champaign. Navickas, V., & Malakauskaite, A. (2009). The possibilities for the identification and evaluation of tourism sector competitiveness factors. Engineering Economics, 1(61), 37–44. Organization for Economic Co-operation and Development - OECD (1992E). Technology and the economy the key relationships. Paris: OECD. Pérez-Moreno, S., Rodríguez, B., & Luque, M. (2016). Assessing global competitiveness under multi-criteria perspective. Economic Modelling, 53, 398–408. Pulido-Fernández, J. I., Andrades-Caldito, L., & Sánchez-Rivero, M. (2015). Is sustainable tourism an obstacle to the economic performance of the tourism industry? Evidence from an international empirical study. Journal of Sustainable Tourism, 23(1), 47–64. Ritchie, J. R. B., & Crouch, G. I. (2003). The competitive destination. A sustainable tourism perspective. Wallingford, UK: CABI. Sirše, J., & Mihalič, T. (1999). Slovenian tourism and tourism policy. A case study. 3. (pp. 34–47). Revue de Tourisme, 34–47. Song, H., & Witt, S. F. (2000). Tourism demand Modelling and forecasting, modern economic approaches. Amsterdam: Pergamon.

Stefan, C. (2014). Travel & tourism competitiveness: A study of World's top economic competitive countries. Procedia Economics and Finance, 15, 1273–1280. Webster, C., & Ivanov, S. (2014). Transforming competitiveness into economic benefits: Does tourism stimulate economic growth in more competitive destinations? Tourism Management, 40, 137–140. Wierzbicki, A. P., Makowski, M., & Wessels, J. (Eds.). (2000). Model-based decision support methodology with environmental applications. Dordrecht: Kluwer Academic Publishers. World Economic Forum (2011). The travel & tourism competitiveness report 2011. Geneva: WEF. World Economic Forum (2015). The travel & tourism competitiveness report 2015. Geneva: WEF. Zhang, J., & Jensen, C. (2007). Comparative advantage: Explaining tourism flows. Annals of Tourism Research, 34(1), 223–243. Zhang, H., Gu, C. L., Gu, L. W., & 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(2), 443–451.

Juan Ignacio Pulido-Fernández is Associate Professor in the Department of Economics and Head of the Laboratory of Analysis and Innovation in Tourism (LAInnTUR) at the University of Jaén in Spain. His main research interests focus on tourism economics, destination management, sustainability of tourism, tourism impacts, and social network analysis. Email: , [email protected]

Beatriz Rodríguez Díaz is Lecturer in the Department of Applied Economics (Mathematics) at the University of Málaga, Spain. His research interests include applications of multiobjective techniques in several fields such as tourism. Email: , [email protected]