This is the peer reviewed version of the following article: [Durán-Román, J. L., Vena-Oya, J., Núñez-Tabales, J. M., & Rey-Carmona, F. J. (2023). How to achieve economic development through tourism? Different ways for different economies: A new approach through fuzzy set qualitative comparative analysis. Tourism Economics, 0(0).], which has been published in final form at [https://doi.org/10.1177/13548166231222668]. This article may be used for non-commercial purposes in accordance with SAGE Journals terms and conditions for use of self-archived versions. this article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from SAGE Journals or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. the article must be linked to SAGE Journal´s version of record on SAGE Journals online library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than SAGE Journals online library must be prohibited.

HOW TO ACHIEVE ECONOMIC DEVELOPMENT THROUGH TOURISM? DIFFERENT WAYS FOR DIFFERENT ECONOMIES: A NEW APPROACH THROUGH FSQCA

Abstract

Policy makers and tourism investors have a limited vision of how a certain level of tourism development is possible depending on country-specific conditions such as incomes and competitiveness factors. In this context, the aim of this research is to identify different strategies or solutions, based on different drivers of tourism competitiveness, that lead to tourism development in countries with different levels of per capita income. To this end, the Travel & Tourism Development Index will be used to answer the research questions raised. This paper proposes the use of an alternative methodology such as fuzzy set Qualitative Comparative Analysis (fsQCA). As a result, a total of eight different strategies which lead to tourism development have been identified for the different categories considered, identifying the key drivers to achieve such development for the different economies.

Keywords

Tourism development, tourism competitiveness, competitiveness drivers, economic development, fsQCA.

1. INTRODUCCIÓN

Beyond the conceptual debate in the literature that tourism development leads to economic development (Brida et al., 2020; Nunkoo et al., 2020), that it is economic development that expands the tourism sector (Lin et al., 2019; Suryandaru, 2020), the reciprocity between the two (Pulido-Fernández and Cárdenas-García, 2020) or the absence of a relationship (Paramati et al., 2016), there is no doubt about the importance of tourism activity in the economy as a whole.  Over 2022, the travel and tourism industry (including its direct, indirect, and induced impacts) were responsible for 7.6% of global GDP and the creation of 295.92 million jobs (WTTC, 2023). However, these figures are still far from those achieved in the years prior to the COVID-19 pandemic, where they stood at 10.4% and 10.3% worldwide, respectively, in 2019 (WTTC, 2023).

It is therefore not surprising that in recent decades a large number of countries have opted for the expansion of tourism as an instrument of economic growth and development (Pulido-Fernández and Cárdenas-García, 2020), intensifying their efforts to increase the industry’s size and the tourist flows (Thommandru et al., 2023). However, given the high level of competitiveness between destinations (González-Rodríguez et al., 2023) and the importance of the tourism sector in many economies (Antonakakis et al., 2019), it is not a simple task, and even more so if we take into account that the wealth of a country will help to configure a tourism product that incorporates the essential characteristics demanded by the tourist.

In this context, it is essential for Governments to consider which strategies are the most appropriate to be competitive in tourism markets and on which drivers should these strategies be based (Bazargani and Kiliç, 2021; De Castro et al., 2020; Michael et al., 2019). Thus, there is an extensive scientific literature that has addressed which drivers determine the competitiveness of a destination such as the price (Dwyer et al., 2000), the infrastructure (Adeola and Evans, 2020), natural and cultural resources (Salinas-Fernández et al., 2020), sustainability (Goffi et al., 2019), human resources (Nazmfar et al., 2019), safety and security (Uyar et al., 2022), or an appropriate environment (Bazargani and Kiliç, 2021), among others.

In addition, there are a number of studies that address the relationship between tourism and economic development as a function of a country’s income level (Yazdi, 2019), but without examining in depth which drivers determine this competitiveness according to income level. Moreover, the studies that try to identify the factors that lead to competitiveness in tourism taking into account the income level of the country are scarce, identifying single strategies (e.g., Kim, 2012) that are not able to capture the heterogeneity that exists between different economies. Therefore, the aim of this paper is to identify different strategies or solutions, composed of different drivers of tourism competitiveness, leading to tourism development for countries with different income levels (high, upper-middle, lower-middle and low).

The Travel & Tourism Development Index (TTDI), developed by the World Economic Forum (WEF, 2022), will be used to answer the research questions raised. This index has been selected as it is considered the most comprehensive index for measuring the competitiveness of tourist destinations (Uyar et al., 2022). In addition, this paper proposes the use of an alternative methodology such as fuzzy set Qualitative Comparative Analysis (fsQCA), which overcomes the two limitations presented above (Rihoux and Ragin, 2008). Given the heterogeneity of the different countries, these results offer a closer view of the reality by allowing different options to reach tourism development through the implementation of the different pillars identified within each sub-index, allowing each country, policy makers or DMO to choose the strategy that best suits the characteristics of their area and thus promote the correct development of tourism.

Finally, this paper is structured as follows: Section 2 addresses the relationship between tourism development and economic development, the definition of tourism competitiveness, the main factors of tourism competitiveness addressed by the literature and the tourism competitiveness index, developed by the WEF. Section 3 addresses the methodology carried out to answer the research questions posed. Finally, Section 4 presents the results of the study, Section 5 addresses the discussion of results and Section 6 collects the main conclusions reached in the work.

2. THEORETICAL FRAMEWORK

2.1 Tourism development and economic development

The tourism industry, with few exceptions, such as the recent pandemic or the 2008 financial crisis, has experienced rapid growth (Sokhanvar et al., 2018); which has generated significant benefits, both economically and socially. So, the contribution of the tourism industry at the country level is not only limited to the improvement of certain macroeconomic indicators, but also contributes to the improvement of the income level of the local population, increases employment opportunities and promotes the growth of the social economy (Adu-Ampong, 2018), promotes the development of other related industries (Wang et al…, 2022), help to reduce the poverty (Gnangnon, 2020), stimulate investment in infrastructure, human capital and technology (Shahzad et al., 2017), improve the quality of life of local people (Fu et al., 2020) and enhance human development (Zhang and Yang, 2023), among others.

Therefore, if we follow the economic development concept as the improvement in the level of well-being of the resident population (Pulido-Fernández and Sánchez-Rivero, 2010), it could be stated that tourism development, understood as the improvement of infrastructure, services, or conditions in a tourist destination that contribute to the increase of tourists (Pulido-Fernández and Cárdenas-García, 2020), leads to the economic development of the region (Cárdenas-García and Pulido-Fernández, 2019; Cárdenas-García et al., 2015; Comerio and Strozzi, 2019; Li et al., 2018; Pulido-Fernández and Cárdenas-García, 2020; Sokhanvar et al., 2018). Regardless of this, the results are not as conclusive as they may seem. Several authors claim that it is economic growth, defined as the increase in the size of a country’s or region’s economy, that leads to tourism development (Antonakakis et al., 2019; Proença and Soukiazis, 2008), with the economic growth seen as an intermediate objective that will lead to the final objective of economic development (Cárdenas-García et al., 2015). Additionally, there are other authors who claim that economic development and tourism are bidirectional (Brida et al., 2016; Fonseca and Sánchez-Rivero, 2020; Kim et al., 2006), while other authors claim that there is no causal relationship between the two (Paramati et al., 2016; Po and Huang, 2008).

In any case, it does seem clear that in order for a destination to achieve a certain economic and tourist development, or simply to grow in economic terms, it must have certain preconditions in place (Cárdenas-García, 2010; Pulido-Fernández et al., 2014). Thus, some authors emphasise the fundamental role played by infrastructures (Pulido-Fernández and Cárdenas-García, 2020; Sokhanvar et al., 2018), others highlighted the safety and security (Pulido-Fernández and Cárdenas-García, 2020; Sokhanvar et al., 2018) and the education (Sokhanvar et al., 2018), among others. Therefore, those countries with a higher level of economic development (Antonakakis et al., 2019), investment capacity (Proença and Soukiazis, 2008; Sokhanvar et al., 2018; Wu and Wu 2018) and income level (De la Mata and Llano-Verduras, 2012; Eilat and Einav, 2004) will have greater guarantees of being competitive; being able to configure a tourism product that incorporates the essential characteristics demanded by the tourist (De la Mata and Llano-Verduras, 2012; Eilat and Einav, 2004).

In light of the above, it is not surprising that according to the Travel & Tourism Development Index (TTDI), developed by the World Economic Forum (WEF, 2022), twenty-nine of the top thirty countries with the highest score in the ranking belong to the group of High-income economies. Only China, belonging to the Upper-middle income economies group, appears in twelfth place. However, this does not imply that countries with a lower-middle- or low-income level cannot achieve tourism Development. Within this framework, studies by Yazdi (2019) address the relationship between tourism development and economic development as a function of the country’s income level. However, these studies do not elaborate on the drivers that determine tourism competitiveness

In addition, the work of Kim (2012) does address which drivers are determinants of destination competitiveness according to income level. More specifically, and based on a Partial Least Square, the author identifies that for both groups of countries (high-income and low-income) resources/attractions, destination management and globalisation are core, although with different levels of intensity. However, this work only identifies a single solution or strategy, made up of different drivers of tourism competitiveness, for all those countries with the same level of income, and this single solution does not reflect the heterogeneity between countries with the same level of income. Therefore, the main goal of this work is to identify different strategies or solutions, composed of different drivers of tourism competitiveness, leading to tourism development for countries with different income levels (high, upper-middle, lower-middle and low), so the following research questions arise:

RQ1: Are there different strategies to achieve tourism development depending on the different income levels of a country?

RQ2: Are there different strategies that lead to tourism development for the same level of income?

2.2 Tourism competitiveness drivers and models

Although many authors have tried to conceptualise tourism destination competitiveness (hereafter, TDC), the lack of a commonly accepted definition may be due to its complex nature when it comes to definition and measurement (Cracolici and Nijkamp, 2009), the different dimensions it encompasses (De la Peña et al., 2019) and the heterogeneity of factors it includes (Enright & Newton, 2005). However, there are certain definitions that are generally accepted, such as the one provided by Dwyer and Kim (2003), who associate destination competitiveness with the ability to provide goods and services that perform better than other destinations in those aspects of the tourism experience considered important by tourists, the one provided by Croes (2011) who conceptualises TDC as the destination’s ability to create a high quality product, or that of other authors such as Ritchie and Crouch (2003:2) who define it 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”.

The various definitions provided suggest that countries compete with each other to have adequate conditions and attraction factors (Enright and Newton, 2004), which make it possible to configure an attractive product that manages to attract a considerable number of tourists (Roman et al., 2020) so, the definition of tourism competitiveness is closely related to the notion of the ability to attract tourist flows and generate welfare for the resident population. (Abreu et al., 2018). Thus, in a context of high competition between destinations, it is essential that the National Governments think about which strategies are the most appropriate to be competitive in tourism markets and on which drivers these strategies should be based (Bazargani and Kiliç, 2021; De Castro et al., 2020; Michael et al., 2019). The Table 1 shows the different drivers identified in the literature:

Table 1. Factors influencing destination competitiveness

Source: Own elaboration.

In addition, there are several models developed from Michael Porter’s Diamond Model, also known as Theory of National Competitive Advantage of Industries (Porter, 1990), which have tried to analyse the competitive advantages of destinations and their attractiveness in the market through the resources they have. However, numerous models and indices have subsequently been developed to measure the competitiveness of tourist destinations, whether qualitative (Cracolici and Nijkamp, 2009) or quantitative (Croes, 2011; Kubickova and Martin, 2020; Mazanec et al., 2007; Salinas-Fernández, et al., 2020).

In this sense, since 2007, the World Economic Forum (WEF) has been producing one of the most widely accepted indices (Uyar et al., 2022), thanks to its methodological superiority and comprehensiveness in terms of the range of issues captured and the geographical coverage (Martins et al., 2017), and being used to analyse the competitiveness of destinations (see Gónzalez-Rodríguez et al., 2023). Thus, based on the theoretical references for competitiveness models (Dwyer & Kim, 2003; Kim, 2001; Porter, 1990), the Tourism & Travel Development Index aims to measure different aspects identified as determining factors of tourism competitiveness in 117 countries around the world. This synthetic index is comprised of five subindexes, 17 pillars and 112 individual indicators, distributed among the different pillars (WEF, 2022) (Graph 1). However, according to Uyar et al. (2022:2) «tourism policymakers and investors have limited knowledge on how this index influences the tourism performance of countries«, and it is not possible to identify which components of this index are most relevant (Kubickova and Martin, 2020).

The TTDI (2022) is therefore justified as a widely accepted, comprehensible and methodologically better index than the others, which addresses what conditions are necessary in terms of resources and capacities for countries to develop tourism successfully (González-Rodríguez et al., 2023), and ranking countries on the basis of their income level. Therefore, through the methodology detailed in the following section, different strategies or solutions can be identified, composed of different drivers of tourism competitiveness, leading to the achievement of tourism development in countries with different income levels, as suggested by other researches, but at the regional level (Barreal et al., 2023).  Moreover, given the heterogeneity of the different countries, the results obtained offer a closer view of the reality by allowing different options to reach tourism development through the implementation of the different pillars identified within each sub-index, allowing each country, policy makers or DMO to choose the strategy that best suits the characteristics of their area and thus promote the correct development of tourism, the following research question is formulated:

RQ3: Are there different competitiveness factors, in each identified strategy, that have a greater influence on the tourism development of a destination?

3. METHODOLOGY

3.1 Data collection

In order to answer the research questions raised in the introduction of this paper, a sample of 117 countries, their income level (high, upper-middle, lower-middle and low). This division has been carried out following two criteria: the first is to try to maintain a balanced number of countries within each group, trying to respect the second criterion, that these countries have similar socio-economic characteristics between each one (in terms of the division made by the WEF), so that they are heterogeneous groups (in order to make it reasonable to differentiate between them) and at the same time are as homogeneous as possible within these groups, trying to balance the number of cases per group. The groups comprised 38%, 29% and 33% of the total 117 countries respectively (high, upper-middle, lower-middle and low), by merging the lower-middle and low groups, we avoid having too few cases and making any analysis for them impossible. The countries included in each group can be seen in the Appendix 1. The score obtained in the TTDI 2021 (WEF, 2022) for each of the five sub-indices are used as a starting point (Enabling Environment, Travel and Tourism Policy and Enabling Conditions, Infrastructure, Travel and Tourism Demand Drivers and Travel and Tourism Sustainability) and seventeen pillars that form the TTDI (Table 2). Although the original classification divides the countries into 4 groups, given that we only found 5 cases of low incomes, we have chosen to create a joint category with the lower-middle ones.

Table 2. Composition of the Travel & Tourism Development Index

Source: Own elaboration

The score obtained by each country in each of the pillars is measured through a set of 112 indicators that are measured based on the WEF Executive Opinion Survey. The responses on each indicator are rated on values ranging from 1 (worst) to 7 (best) (WEF, 2022). It should be noted that the WEF Tourism Competitiveness Index has been evolving since its first version in 2007. Thus, one of the latest deficiencies corrected in the recent version of the index is that each of the seventeen pillars has the same weight in the final score obtained by each country.

3.2 Method

In order to identify possible strategies or solutions that lead to tourism development according to the level of income for the sample of selected countries, a semi-qualitative methodology such as QCAs will be followed. This methodology has some advantages over traditional models such as SEM or PLS. The main advantage is that, while traditional variance-based methodologies examine different variables in competitive environments (trying to arrive at the best possible solution), asymmetric models such as QCA (Qualitative Comparative Analysis) examine these relationships based on combinations of variables that may result in different paths to a solution, this means that certain variables, despite having a low correlation with respect to output (dependent variable in traditional models), may be explaining part of this solution, or at least helping other conditions (independent variable in such traditional models) to better explain this result (Ragin et al., 2008), which is an advance with respect to other variance-based methodologies, which tend to simplify an often complex reality. This is known as equifinality, and is much more appropriate when working in the social sciences, where reality can be explained by more than one solution (Pappas and Woodside, 2021), since a result can be explained through different ways or solutions, and not through a single result (Rihoux and Ragin, 2008) as it seems logical when, in our case, it is a question of understanding the development of a country, which can be achieved by promoting different combinations of drivers according to the specific casuistry of each case. Moreover, this type of methodology allows working with small samples (even below 50 individuals) and is optimal when considering populations (as in our case) (Greckhamer et al., 2013). Finally, the fsQCA methodology also allows for the classification of each of the cases in the different solutions, also serving as a segmentation tool that is very useful in this type of work (Pappas and Woodside, 2021).

Among these QCA models, the most widespread in tourism studies are undoubtedly the fuzzy set QCA models (e.g. Castañeda-García et al., 2023; Corne and Peypoch, 2020; Zhang et al., 2021), as they provide greater flexibility to these models thanks to the fact that the different conditions (variables) can be recalibrated on a continuous scale between 0 and 1.

Following Martínez-Cháfer et al. (2021) or Pappas and Woodside (2021), the use of this methodology requires, like any other, the development of a series of steps and checks prior to the elaboration of the results. The first of these is the calibration of the data, or its transformation from a continuous scale to a scale between 0 and 1, necessary step to carry out a fsQCA analysis. To carry out this recalibration (a necessary prior step to carry out the analysis), the most commonly used strategy is to do so based on the 95th, 55th and 5th percentiles, with 95 being the presence of a condition in a case (country), 55th percentile being neutral and 5th percentile being the absence of such condition (DiStefano et al., 2009). Other authors such as Sukhov et al. (2023) suggest a direct calibration, however, when dealing with continuous variables the literature suggests an indirect calibration of the data (Castañeda-García et al., 2023). The following table (Table 3) shows the mean, standard deviation and the 3 cut-off points selected for each of the conditions (the 5 sub-indexes) and the outcome (Tourism Development).

Table 3: Descriptive analysis of the variables

TD: Tourism development; EE: Enabling environment subindex; TTP: Travel and tourism policy enabling; INF: Infrastructure subindex; TTD: Travel and tourism demand drivers; TTS: Travel and tourism Sustainability subindex

Following this approach, we will then perform a necessity analysis which looks at whether the relationship of a condition to the outcome is in terms of necessity (when consistency is greater than 0.9) or simply in terms of sufficiency. This consistency statistic is crucial in the use of QCAs and is defined as «the explicit connection between a combination of causal conditions and an outcome» (Rihoux & Ragin, 2008). This allows us to know the degree of the relationship between a condition and the outcome. Along with this sufficiency analysis the next step would be to check the Relevance of Necessity (RoN), or whether a necessary condition is in terms of triviality (it will appear independently of the output result) or not (Schneider & Wagemann, 2012). Finally, the last step is the calculation of the different solutions through the «truth table» (Ragin et al., 2008), which examines all possible combinations that give rise to the outcome and retains those that reach a consistency level higher than 0.8 (Fiss, 2021) and in which we can see the achieved results.

The explanation and development of the proposed methodology will be continued in the results section, as suggested by Pappas and Woodside (2021).

4. RESULTS

For the analysis of the results and following the fsQCA methodology, the scheme proposed by authors such as Martínez-Cháfer et al. (2021) or Pappas and Woodside (2021) will be followed as mentioned earlier, who suggest starting with the analysis of necessity, in order to detect those conditions that appear in terms of necessity in the different combinations that lead to the outcome (Tourism Development – TD) (Ragin et al., 2008). This analysis will show us what conditions must be present (are necessary) for the outcome (Development) to occur. Thus, Table 4 shows this analysis of necessity for the set of conditions of the two groups analysed, finding up to 3 necessary conditions (consistency > 0.9) (Ragin et al., 2008) to achieve this TD in countries with high incomes (EE, INF and TTS) and one for the set of countries with lower-middle incomes (TTP); no necessary conditions are observed for high-middle income countries.

Table 4. Analysis of necessity (“development” = outcome variable).

EE: Enabling environment subindex; TTP: Travel and tourism policy enabling; INF: Infrastructure subindex; TTD: Travel and tourism demand drivers; TTS: Travel and tourism Sustainability subindex

Along with this analysis of necessity, some authors suggest carrying out another complementary analysis to see if these conditions are in terms of triviality or not (Mártinez-Cháfer et al., 2021). If they are expressed in terms of triviality, it is understood that this necessary condition will appear regardless of the value taken by the outcome, being always present and not being as important a variable for the analysis as another non-trivial necessary condition might be. Therefore, the next step is to look at the Relevance of Necessity (RoN), which is shown in Table 5. As can be seen in this table, none of the conditions is presented in terms of triviality, as all of them exceed the 0.5 limit established in the literature and it has been calculated as proposed by Schneider and Wagemann (2012): (EQ1) RoN=

Table 5. Trivialness analysis (“development” = outcome variable).

EE: Enabling environment subindex; TTP: Travel and tourism policy enabling; INF: Infrastructure subindex; TTD: Travel and tourism demand drivers; TTS: Travel and tourism Sustainability subindex

Once the necessity and triviality analyses have been carried out, the truth table is calculated. In this analysis, an analysis is made of all possible combinations of conditions that can lead to tourism development and, based on their consistency (the percentage of causal configurations of similar composition which result in the same outcome value) and coverage (to the number of cases for which a configuration is valid) (Roig-Tierno et al., 2017), to retain those combinations that are most present in the different countries analysed. Before proceeding to the analysis of this Truth Table, we must remember that the maximum number of possible logical combinations is established by the 2^k rule, where k is the number of conditions (Ragin et al., 2008). In this study there are 5 conditions, so the maximum number of combinations is 32, which means that the model will be saturated in all groups as there are more cases than possible solutions. With this number of possible logical combinations, the literature suggests retaining those with a consistency above the threshold of 0.8 (Fiss, 2011). This will be the cut-off point for whether we should retain a solution for analysis. In addition to this analysis, we check whether the conditions are core or peripheral conditions. That is, if in the intermediate solution (which is the one shown in Table 6), they also appear in the most parsimonious solution, giving rise to core conditions that form the axis of the solution (Fiss, 2011).

Table 6. Sufficiency analysis: High, High-middle and Lower-middle incomes countries

A closer analysis of these results shows that there are different solutions or strategies to achieve tourism development, both in countries with high per capita income, high-middle income and low-middle+low income. More specifically, three solutions are obtained for countries with high incomes, two solutions for countries with high-middle incomes and three solutions for countries with lower-middle incomes.

Looking first at the set of high-income countries, solution 2 has the highest coverage of all, and is therefore the most repeated solution in this set of countries, i.e. most of the countries in this group reach development through this way. These destinations achieve tourism development through improvements in Enabling Environment (EE), Infrastructure (INF) and Travel and Tourism Sustainability (TTS); conditions that also coincide with those that appeared as necessary in the analysis, corresponding mainly to central and northern European countries (Germany, Denmark or Norway), according to the degree of membership (>0.5) of each of these cases to this solution. Therefore, the countries in this area tend to achieve this development by following a strategy based on the promotion of these three conditions, fostering quality and sustainable tourism. On the other hand, the first of the solutions proposes to reach this tourism development by following a policy focused on Enabling Environment (EE), Travel and Tourism Policy and Enabling Conditions (TTP) and Infrastructure (INF), which seems to be a strategy more focused on the development of tourism as a central axis of the economy. This path is mainly followed by countries in the Mediterranean basin (Spain, Italy, Malta or Greece) and others such as the United Arab Emirates, countries that, in fact, focus many of their efforts (or are doing so) on the promotion of tourism as a fundamental pillar of their economy. Finally, we come to the last of the solutions, where mainly the Baltic countries (Latvia, Estonia and Lithuania) focus their efforts on achieving this tourism development through Enabling Environment (EE), Travel and Tourism Policy and Enabling Conditions (TTP) and Travel and Tourism Sustainability (TTS), in the absence of Travel and Tourism Demand Drivers (TTD), focusing not so much on mass tourism (they do not stimulate the TTD) but on quality tourism, given the conditions under which they arrive at this development.

Turning to upper-middle income countries, we find two solutions or strategies to achieve the development of tourism. Thus, in the first of these, we find the cases mainly of Asian countries (China, Malaysia and Thailand) and Central American countries (Mexico and Panama). These countries achieve this tourism development through the enhancement of their natural and cultural resources as Drivers of Tourism Demand (MDT) and their infrastructures (INF). While they show no interest in the development of sustainable tourism in their destinations. On the other hand, we have the case of Eastern European countries (Albania, Montenegro or Romania) which reach this tourism development through a more complete path, where all the pillars influence except the Travel and Tourism Demand Drivers (TTD). In other words, on the one hand, the first of the solutions opts for a less sustainable tourism, while the second seeks to copy its European neighbours, trying to achieve development in a similar way.

The third of the combinations, which achieves the greatest unique coverage, should be highlighted when focusing our attention on the Lower-middle+low income countries. This solution corresponds to African countries, such as Mauritius, where Travel and Tourism Policy and Enabling Conditions (TTP) and Travel and Tourism Sustainability (TTS) as core conditions, together with Enabling Environment (EE), in the absence of Infrastructure (INF) and Travel and Tourism Demand Drivers (TTD), combine to reach tourism development. The profile of these countries show how they try to develop tourism activity without damaging the environment (the main attraction of these countries). They do not try to promote the construction of infrastructure for this activity, nor do they try to attract a large number of tourists (absence of Travel and Tourism Demand Drivers), with policies related to tourism and sustainability being the central axes of their strategy. Finally, the second solution is analysed, which corresponds to the one with the next largest coverage. This solution corresponds to Southeast Asian countries such as Indonesia and India. These countries, in the absence of Travel and Tourism Sustainability (TTS) and Enabling Environment (EE), achieve tourism development mainly through infrastructure development (INF) and also through Travel and Tourism Policy and Enabling Conditions (TTP) and Travel and Tourism Demand Drivers (TTD), resulting in tourism that is more focused on attraction than on sustainability. Similarly, for this group, there are two solutions that lead to tourism development in the same way: while the first is based on sustainability and the promotion of natural and cultural resources, the second relies on the development of infrastructures and the stimulation of demand to achieve this.

5. DISCUSSION

The important benefits derived from tourism activity in terms of development (both economic and tourism itself) justify the allocation of resources, by national governments, with the aim of expanding the activity. There is no doubt that the comparative advantages that a country has (natural and cultural resources, among others) act as attraction factors that condition the position of a destination in international tourism markets, but the importance that a country’s income level plays when undertaking the necessary investments and developing the appropriate strategies and policies to configure a competitive and attractive destination must be taken into account too (Sokhanvar et al., 2018; Wu and Wu, 2018). However, this does not imply that countries with a level of medium or low income cannot reach optimal levels of competitiveness that lead to tourism development, as long as they make appropriate and efficient use of the resources that the territory has (Pulido- Fernández and Rodríguez-Díaz, 2016).

In the case of high-income countries, Enabling Environment, which has drivers as diverse as business environment, safety and security, human resources and labour market and ICT readiness, is present in the three identified solutions. The importance of the drivers that make up this subindex in their contribution to tourism development partially coincides with the conclusions of Kim (2012). More specifically in destination management and its relationship with the human resources contemplated by this author. On the other hand, the competitiveness drivers included in the Enabling Environment have been identified as determining factors of tourism competitiveness in the work of Adeola and Evans (2020), Bazargani, and Kiliç (2021), Craigwell and Worrell (2008), Enright and Newton (2004), Kumar and Kumar (2020), Michael et al.  (2019), Nazmfar et al. (2019), Salinas-Fernández et al. (2020) or Uyar et al. (2022). Furthermore, the infrastructures are present in the first two solutions; contradicting the work of Kim (2012), who does not identify them as a key driver in high-income countries, but in line with the findings of Craigwell and Worrell (2008), Nazmfar et al. (2019) or Uyar et al. (2022), which do highlight the key role of infrastructure in the configuration of the destination as a tourism product.  Likewise, the Travel and Tourism Policy and Enabling Conditions, which houses factors such as Prioritization of Travel & Tourism, International Openness and Price competitiveness, also contradicts the findings of Kim (2012) and confirms the results of Bazargani, and Kiliç (2021) regarding the price variable and the International Openness. Finally, sustainability is only identified as a key factor in solution 2; in line with the work of Goffi et al. (2019) and Kim (2012).

Regarding countries with upper middle incomes, two solutions or strategies have been identified that lead to tourism development. The first of them is made up of a strong presence of the natural and cultural resources of the destination, in line with the works of Kim (2012), Nazmfar et al. (2019), and Bazargani, and Kiliç (2021), and is partially confirmed, regarding cultural resources in the works of Enright and Newton (2004), Salinas-Fernández et al. (2020) and Uyar et al. (2022). Also in this strategy, infrastructures also play a key role in line with what is proposed by Craigwell and Worrell (2008), Nazmfar et al. (2019) and Uyar et al. (2022). Regarding the second strategy, where all the pillars contemplated by the TTDI influence with the exception of natural resources, cultural resources and non-leisure resources, the results confirm the findings of Kim (2012), with the exception of attraction resources. Additionally, practically all the works included in the literature review in Table 1 identify the pillars of this tourism development strategy as key determinants of tourism competitiveness.

Focusing on the countries with a lower-middle+low-income level, three solutions have been identified that lead to tourism development. However, the discussion of solution number 3 and 2 will be carried out, as they are the ones that show the greatest coverage. In both solutions or strategies, the Travel and Tourism Policy and Enabling Conditions subindex plays a key role. These results follow the line of Bazargani, and Kiliç (2021) regarding the importance of the price variable and International Openness in the competitiveness of the destination. Focusing on solution or strategy 3, the pillars related to sustainability appear, confirming the findings of Kim (2012), Bazargani and Kiliç (2021), Goffi et al. (2019) and Nazmfar et al. (2019). The third sub-index that proves to be a determining factor in achieving tourism development is the Enabling Environment. The conclusions reached by Kim (2012) also show that for low-income countries, the role played by human resources is a determining factor in the competitiveness of the destination. In addition, and as indicated above, the competitiveness drivers encompassed by the Enabling Environment have been identified as determining factors of tourism competitiveness in the studies of Adeola and Evans (2020); Salinas-Fernández et al. (2020), Bazargani, and Kiliç (2021) and Uyar et al. (2022). In the second solution, apart from the already mentioned significant role played by Travel and Tourism Policy and Enabling Conditions, Infrastructures and Travel and Tourism Demand Drivers have been identified, as suggested by most authors regarding the importance of this driver in the tourism competitiveness of the destination. (Nazmfar et al., 2019; Uyar et al., 2022). Finally, natural, cultural and non-leisure resources play a fundamental role in the present strategy; confirming the findings of Kim (2012) and other authors such as Nazmfar et al. (2019) or Bazargani, and Kiliç (2021) who highlight the role of these drivers as attraction factors.

In the following figure (Figure 1) the reader will be able to see a summary of the different combinations that lead to this development for each of the economies:

EE: Enabling environment subindex; TTP: Travel and tourism policy enabling; INF: Infrastructure subindex; TTD: Travel and tourism demand drivers; TTS: Travel and tourism Sustainability subindex; H- (high incomes – solution); HM- (high-middle incomes – solution); LML (lower-middle+low incones – solution)

6. CONCLUSIONS

The expansion of tourism activity translates into an improvement in economic development. But this connection does not occur automatically, since it is necessary to allocate public revenues to develop policies that contribute to achieving economic development. In this context, a destination will be competitive if it can attract and satisfy potential tourists. And this competitiveness is determined by the presence of both comparative advantages and competitive advantages.

In this context, this paper identifies different strategies or solutions, composed of different drivers of tourism competitiveness, that lead to achieving tourism development for countries with different income levels (high, upper-middle, lower-middle and low) and clarifies to policy makers and investors the tourism competitiveness index that enjoys greater acceptance worldwide.

Thus, through the results obtained, the three research questions posed have been answered. More specifically, with respect to the first question, the existence of different paths or strategies that make it possible to achieve tourism development is confirmed, and that these paths depend both on the characteristics of a country, its income level and the strategy chosen to achieve this development.

In addition, three different pathways or strategies have been identified for high-income countries, two pathways for upper-middle-income countries and three for lower-middle-income and low-income countries. Thus, research question 2, which asked whether there were different pathways or strategies for the same income level, has also been answered. In fact, this is one of the main novelties of the present work with respect to the existing literature, which overcomes the limitations of other contributions to respect such as those of Kim (2012).

The third and last research question asked whether there were different tourism competitiveness factors, in each of the strategies, that have a greater influence on the tourism development of a destination. Indeed, the results show that the sub-index called Enabling Environment, which encompasses the following drivers or pillars: business environment, safety and security, health and hygiene, human resources and labor market, ICT readiness is present in the three pathways or strategies identified for high income and in one of the strategies for lower-middle and low income. In addition, infrastructure also plays a key role as it is present in different strategies in the three income categories considered in this paper. For their part, the pillars or drivers related to Sustainability and those related to Travel and Tourism Policy and Enabling Conditions demonstrate their importance by appearing as determining factors in different strategies of the three income categories. On the other hand, the Travel and Tourism Demand Drivers, which include pillars related to natural and cultural factors, are key in upper-middle, lower-middle and low-income countries.

Regarding the management implications derived from this work, the results obtained clarify the interpretation of the most widely accepted tourism competitiveness index. More specifically, the results obtained will allow policy-makers and tourism investors to identify which of the identified strategies, according to their level of income, is best associated with the resources and capacities of each destination, making it possible to identify which pillars of the TTDI are most relevant in each of the strategies. Additionally, in the process of tourism development, more and more problems related to the inefficient use of resources and low-quality tourism development are occurring. Knowing which tourism competitiveness drivers are most significant in each strategy will allow policymakers to allocate resources more efficiently and rationally, develop more effective policies, and undertake more appropriate investments to create a higher quality tourism product that incorporates the essential characteristics demanded by tourists.

Additionally, management implications are also derived from the results obtained for another fundamental actor in tourism activity such as companies. Thus, both companies, business associations and chambers of commerce will be able to make significant efforts to improve those factors associated with different pillars of the TTDI, that turn out to be key in each strategy and that are within the scope of their competencies. Likewise, and through the governance process, they can also participate in dialogue spaces with policy-makers and destination managers about the diagnosis of the situation and about possible plans, policies and investment proposals that result in the development of the national tourism industry.

Finally, as in any other research, this document suffers from some limitations. The first is that the data have been obtained from secondary databases, but this is the only way to homogenise the data provided by the different countries. Another limitation could be the analysis carried out, which is mainly semi-qualitative. However, given that it is a population, there are some limitations in the number of the sample and, due to this issue, traditional analysis is not recommended. In terms of future research, the authors encourage further analysis of the key factors leading to tourism development, seeking to shed light on this research of particular interest.

7. REFERENCES

Abreu, N.M., Ruhanen, L., y Arcodia, C. (2018): “Destination competitiveness: A phenomenographic study”. Tourism Management, 64, 324–334.

Adeola, O., & Evans, O, (2020): “ICT, infrastructure, and tourism development in Africa”. Tourism Economics, 26(1), 97–114.

Adu-Ampong, E.A. (2018): “Tourism and national economic development planning in Ghana, 1964–2014”. International Development Planning Review 40 (1), 75–95.

Antonakakis, N., Dragouni, M., Eeckels, B., & Filis, G. (2019): “The tourism and economic growth enigma: Examining an ambiguous relationship through multiple prisms”. Journal of Travel Research, 58(1), 3–24.

Barreal, J., Vena-Oya, J., & Mercadé-Melé, P. (2023). Addressing regional tourism policy: Tools for sustainable destination management. Journal of Vacation Marketing, 13567667231179756.

Bazargani, R., & Kiliç, H. (2021): “Tourism competitiveness and tourism sector performance: Empirical insights from new data”. Journal of Hospitality and Tourism Management, 46, 73–82.

Brida, J. G., Cortes-Jimenez, I., & Pulinac, M. (2016): “Has the Tourism-Led Growth Hypothesis Been Validated? A Literature Review”. Current Issues in Tourism, 19 (5), 394–430.

Brida, J.G., Gómez, D.M., & Segarra, V. (2020): “On the empirical relationship between tourism and economic growth”. Tourism Management, 81, 104131.

Cárdenas-García, P.J. (2012). Tourism as an Economic Development Tool. An Analysis of Key Factors (Doctoral Thesis, University of Jaén).

Cárdenas-García, P.J., & Pulido-Fernández, J.I. (2019): “Tourism as an economic development tool. Key factors. Current Issues in Tourism, 22, 2082–2108.

Cárdenas-García, P.J., Sánchez-Rivero, M., & Pulido-Fernández, J.I. (2015): “Does tourism growth influence economic development? Journal of Travel Research, 54(2), 206–221.

Castañeda-García, J. A., Sabiote-Ortiz, C. M., Vena-Oya, J., & Epstein, D. M. (2023): “Meeting public health objectives and supporting the resumption of tourist activity through COVID-19: a triangular perspective”. Current Issues in Tourism, 26(10), 1617–1634.

Comerio, N., & Strozzi, F. (2019): “Tourism and its economic impact: A literature review using bibliometric tools”. Tourism Economics, 25(1), 109–131.

Corne, A., & Peypoch, N. (2020): “On the determinants of tourism performance”. Annals of Tourism Research, 85, 103057.

Cracolici, M.F., & Nijkamp, P. (2009): “The attractiveness and competitiveness of tourist destinations: a study of Southern Italian regions”. Tourism Management, 30 (3), 336–344.

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.

De Castro, M., Fernández, P., Guaita, J.M., & Martín, J.M. (2020): “Modelling Natural Capital: A Proposal for a Mixed Multi-criteria Approach to Assign Management Priorities to Ecosystem Services”. Contemporary Economics 14 (1), 22–37.

De la Mata, T., & Llano-Verduras, C. (2012): “Spatial pattern and domestic tourism: an econometric analysis using inter-regional monetary flows by type of journey”. Papers in Regional Science, 91, 437–470.

De la Pena, M.R., Núnez-Serrano, J. A., Turrion, J., & Velazquez, F. J. (2019): “A New Tool for the Analysis of the International Competitiveness of Tourist Destinations Based on Performance”. Journal of Travel Research, 58 (2), 207–223.

DiStefano, C., Zhu, M., & Mindrila, D. (2009): “Understanding and using factor scores: Considerations for the applied researcher”. Practical Assessment, Research, and Evaluation, 14(1), 20.

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. y Kim, CH. (2003): «Destination competitiveness: determinants and indicators». Current Issues in Tourism, 6(5), 369–414.

Eilat, Y., & Einav, L. (2004): “Determinants of international tourism: a three dimensional panel data analysis”. Applied Economics, 36, 1315–1327.

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(4), 339–350.

Fiss, P. C. (2011): “Building better causal theories: A fuzzy set approach to typologies in organization research”. Academy of Management Journal, 54(2), 393–420.

Fonseca, N., & Sánchez-Rivero, M. (2020): “Significance bias in the tourism-led growth literature”. Tourism Economics, 26(1), 137–154.

Fu, X., Ridderstaat, J., & Jia, H. C. (2020): “Are all tourism markets equal? Linkages between market-based tourism demand, quality of life, and economic development in Hong Kong”. Tourism Management, 77, 104015.

Gnangnon, S. K. (2020): “Impact of international tourism receipts on public revenue in developed and developing countries”. Tourism Review, 75(5), 809–826.

Goffi, G., Cucculelli, M., & Masiero, L. (2019): “Fostering tourism destination competitiveness in developing countries: The role of sustainability”. Journal of Cleaner Production, 209, 101–115.

González-Rodríguez, M.R., Díaz-Fernández, M.C., & Pulido-Pavón, N. (2023): “Tourist destination competitiveness: An international approach through the travel and tourism competitiveness index”. Tourism Management Perspectives, 47, 101127.

Greckhamer, T., Misangyi, V. F., & Fiss, P. C. (2013). The two QCAs: From a small-N to a large-N set theoretic approach. In Configurational theory and methods in organizational research (pp. 49-75). Emerald Group Publishing Limited.

Kim, C.H. (2001): “Destination Competitiveness: Development of a Model with Aplication to Australia and the Republic of Korea, Seúl”. Tourism Research Institute.

Kim, N. (2012). Tourism Destination Competitiveness, Globalization, and Strategic Development from a Development Economic Perspective. (Doctoral Thesis, University of Illinois).

Kim, H. J., Chen, M., & Jan, S. (2006): “Tourism Expansion and Economic Development: The Case of Taiwan”. Tourism Management, 27 (5), 925–933.

Kubickova, M., & Martin, D. (2020): “Exploring the relationship between government and destination competitiveness: the TALC model perspective”. Tourism Management, 78: 104040.

Kumar, N., & Kumar, R.R. (2020): “Relationship between ICT and international tourism demand: a study of major tourist destinations”. Tourism Economics 26(6), 908–925.

Li, K.X., Jin, M., & Shi, W. (2018): “Tourism as an Important Impetus to Promoting Economic Growth: A Critical Review”. Tourism Management Perspectives, 26, 135–42.

Lin, V.S., Yang, Y., & Li, G. (2019): “Where can tourism-led growth and economy-driven tourism growth occur?”. Journal of Travel Research 58, 760–773.

Martínez-Cháfer, L., Molina-Morales, F. X., & Roig-Tierno, N. (2021): “Explaining technological innovation of the clustered firms: internal and relational factors”. Journal of Small Business Management, 1–32.

Martinez, J.M.G., Martín, J.M.M., & Fernández, J.A.S. (2020). Innovation in the measurement of tourism competitiveness. In M.A., Mendez-Picazo, M.T., Castaño-Martínez, (Eds). Analyzing the relationship between innovation, value creation, and entrepreneurship (pp. 268-288). IGI Global.

Martins, L. F., Gan, Y., & Ferreira-Lopes, A. (2017): “An empirical analysis of the influence of macroeconomic determinants on World tourism demand”. Tourism Management, 61, 248–260.

Mazanec, J.A., Wober, K., & Zins, A.H. (2007): “Tourism Destination Competitiveness: From Definition to Explanation”. Tourism Economics, 14(4), 725–751.

Michael, N., Reisinger, Y., & Hayes, J. P. (2019): “The UAE’s Tourism Competitiveness: A Business Perspective”. Tourism Management Perspectives, 30, 53–64.

Nazmfar, H., Eshghei, A., Alavi, S., & Pourmoradian, S. (2019): “Analysis of Travel and Tourism Competitiveness Index in Middle-East Countries.” Asia Pacific Journal of Tourism Research 24 (6), 501–13.

Nunkoo, R., Seetanah, B., Vinesh-Sannassee, R., Khan-Jaffur, Z.R., Warren-Moraghen, P. G. (2020): “Tourism and Economic Growth: A Meta-regression Analysis”. Journal of Travel Research, 59 (3), 404–23.

Pappas, I. O., & Woodside, A. G. (2021): “Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing”. International Journal of Information Management, 58, 102310.

Paramati, S. R., Alam, M. S., & Chen, C. F. (2016): “The effects of tourism on economic growth and CO2 emissions a comparison between developed and developing economies”. Journal of Travel Research, 56(6), 712–724.

Po, W.C., & Huang, B.N. (2008): “Tourism Development and Economic Growth—A Nonlinear Approach”. Physica A: Statistical Mechanics and Its Applications , 387 (22), 5535–42.

Porter, M. (1990). The competitive advantage of nations. The Free Press: New York.

Proença, S., & Soukiazis, E. (2008): “Tourism as an economic growth factor: A case study for southern European countries”. Tourism Economics, 14(4), 791–806.

Pulido-Fernández, J.I., & Cárdenas-García, P.J. (2020): “Analyzing the Bidirectional Relationship between Tourism Growth and Economic Development”. Journal of Travel Research, 1–20.

Pulido-Fernández, J.I., Cárdenas-García, P.J., & Sánchez-Rivero, M. (2014): “Tourism as a tool for economic development in poor countries”. Tourism, 62, 309–322.

Pulido-Fernández, J.I., & Rodríguez-Diaz, B. (2016): “Reinterpreting the World Economic Forum’s global tourism competitiveness index”. Tourism Management Perspectives, 20, 131–140.

Pulido-Fernández, J.I., & Sánchez-Rivero, M. (2010): “Competitiveness versus Growth in Tourist Destinations. An Analysis Using Multivariate Techniques”. Cuadernos de Economía 33 (91): 159–182.

Ragin, C. C., Strand, S. I., & Rubinson, C. (2008). User’s guide to fuzzy-set/qualitative comparative analysis. University of Arizona, 87, 1-87.

Rihoux, B., & Ragin, C.C. (2008). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Sage Publications.

Ritchie, J.R. y Crouch, G.I. (2003). The Competitive Destination: A Sustainable Tourism Perspective, Wallingford, Oxon, UK: CABI Publishers.

Roig-Tierno, N., González-Cruz, T.F., & Llopis-Martinez, J. (2017): “An overview of qualitative comparative analysis: A bibliometric analysis”. Journal of Innovation & Knowledge, 2(1), 15–23.

Roman, M., Roman, M., Prus, P., & Szczepanek, M. (2020): “Tourism competitiveness of rural areas: Evidence from a region in Poland”. Agriculture, 10(11), 569.

Salinas-Fernández, J. A., Serdeira-Azevedo, P., Martín-Martín, J. M., & Rodríguez Martín, J. A. (2020): “Determinants of tourism destination competitiveness in the countries most visited by international tourists: proposal of a synthetic index”. Tourism Management Perspectives, 33, 100582.

Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press.

Shahzad, S.J.H., Shahbaz, M., Ferrer, R., & Kumar, R.R. (2017): “Tourism-led growth hypothesis in the top ten tourist destinations: New evidence using the quantile-on-quantile approach”. Tourism Management, 60, 223–232.

Sokhanvar, A., Çiftçioğlu, S., & Javid, E. (2018): “Another look at tourism-economic development nexus”. Tourism Management Perspectives, 26, 97–106.

Sukhov, A., Friman, M., & Olsson, L. E. (2023). Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making. Journal of Retailing and Consumer Services, 74, 103424.

Suryandaru, R. (2020): “Measuring tourism-led growth hypothesis in Indonesia”. International Journal of Culture, Tourism and Hospitality Research. 14, 295–300.

Thommandru, A., Espinoza-Maguiña, M., Ramirez-Asis, E., Ray, S., Naved, M., & Guzman-Avalos, M. (2023): “Role of tourism and hospitality business in economic development”. Materials Today: Proceedings, 80, 2901–2904.

Uyar, A., Kuzey, C., Koseoglu, M.A., & Karaman, A. (2022): “Travel and tourism competitiveness index and the tourism sector development”. Tourism Economics, 1–27.

Wang, Y., Han, L., & Ma, X. (2022): “International tourism and economic vulnerability”. Annals of Tourism Research, 94, 103388.

World Economic Forum (2022). The Travel & Tourism Development Index 2021. WEF: Geneva.

World Travel & Tourism Councial – WTTC- (2023). Economic Impact Research. Retrieved from https://wttc.org/research/economic-impact

Wu, T. P., and Wu, H.C. (2018): “The Influence of International Tourism Receipts on Economic Development: Evidence from China’s 31 Major Regions”. Journal of Travel Research, 57 (7), 871–882.

Yazdi, S. K. (2019): “Structural breaks, international tourism development and economic growth”. Economic Research-Ekonomska Istraživanja, 32(1), 1765–1776.

Zhang, M., Zhao, L., Zhang, Y., Liu, Y., & Luo, N. (2021): “Effects of destination resource combination on tourist perceived value: In the context of Chinese ancient towns”. Tourism Management Perspectives, 40, 100899.