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Article

Tourism Business in Spanish National Parks: A Multidimensional Perspective of Sustainable Tourism

by
Esteban Pérez-Calderón
1,*,
Vanessa Miguel-Barrado
2 and
Francisco Sánchez-Cubo
3
1
Faculty of Economics and Business, University of Extremadura, 06006 Badajoz, Spain
2
Faculty of Business, Finance and Tourism, University of Extremadura, 10071 Cáceres, Spain
3
Faculty of Social Sciences, University of Castilla-La Mancha, 16002 Cuenca, Spain
*
Author to whom correspondence should be addressed.
Land 2022, 11(2), 190; https://0-doi-org.brum.beds.ac.uk/10.3390/land11020190
Submission received: 13 December 2021 / Revised: 13 January 2022 / Accepted: 21 January 2022 / Published: 25 January 2022 / Corrected: 7 September 2022
(This article belongs to the Special Issue Use of Natural and Cultural Resources through Tourism Activity for Regional Development)
Versions Notes

Abstract

:
In recent years, nature tourism has increased its prominence in the tourism market due to sociocultural change and greater concern for the environment in our society. In this sense, Spanish national parks have become important tourist destinations, increasing the number of visitors significantly in the last decade, exceeding 14.81 million in 2019. In addition to their incalculable ecological value, these protected natural spaces are a key factor in achieving the socioeconomic development of their rural area of influence. The main objective of the study is to contrast the development experienced by tourist businesses in the areas of socioeconomic influence of the Spanish national parks. This has been done from a multidimensional perspective: infrastructures, socioeconomic development, and the perception of the residents belonging to the area of influence of the parks. The indicators associated with each dimension have been compiled and a logit model was used to contrast the relationships between the different variables. The results confirmed that perceived economic development and infrastructure have a significant impact on tourism businesses. In conclusion, local socioeconomic development requires greater effective public–private partnerships to achieve business prosperity and a better quality of life as factors for the sustainability of nature tourism in national parks.

1. Introduction

The tourism industry has a great weight in the world economy. Specifically, in 2019, it accounted for 10.4% of global GDP [1]. In the case of Spain, this percentage was even higher, reaching 12.4% [2].
According to the World Tourism Organization, Spain is a world-wide benchmark in the tourism sector, positioning itself as the second tourist destination in the world, behind France. In 2019, it received approximately 84 million international travellers [3].
Among the different types of tourism, nature tourism is highly relevant, representing 15% of total tourism in 2018 [4,5,6].
In this scenario, it is necessary to value the fact that Spain is a country with a high natural and ecological interest, being one of the nationalities with the greatest biodiversity in the European Union and, in general, in the world [7]. Proof of this is that 28.12% of its land surface and 12.76% of its marine surface is covered by some form of protection, either by national, regional, or international instruments [8]. Furthermore, within the European framework, it is the country with the highest number of protected natural areas accredited with the European Charter for Sustainable Tourism [9]. Although the total number of protected areas it possesses exceeds 4000, its 16 national parks stand out especially [8].
Spain’s relationship with environmental protection dates back more than a century, being a pioneer in joining the protectionism of natural spaces, approving the first law of national parks in the world in 1916 [10,11]. Throughout history, the public use of these environments has evolved favourably. Regarding the number of visits, it has increased by 47% in the last ten years, exceeding the 14.81 million visitors in 2019 [12].
The benefits of protected areas are innumerable. Among them, the conservation of nature for future generations, the mitigation of the impacts of climate change, in addition to being an excellent source of economic and social development and poverty reduction, particularly in its closest areas of influence [13,14].
For all these reasons, the strengths that Spain has regarding its tourism leadership and the authenticity of its natural resources must be maximized, in such a way that they result in a series of advantages for the rural development of its inland areas.
It should also be mentioned that the pandemic caused by COVID-19 has seriously affected the tourism industry in general, however, it is true that its effect has been less in sectors such as rural or nature tourism [15]. This further supports, if possible, the relevance of this type of tourism today, as it can act as a driving force in the recovery of the tourism industry. Thus, one of the consequences of the pandemic has been the enhancement of protected areas in terms of human health and well-being [16].
In previous works it is common to find studies that analyse the business tourism development of different rural areas, or else focusing only on some specific protected areas. However, this analysis has not been carried out from a global perspective at the national park network level.
In this context, the objective of this study is to analyse rural development as a sustainable tourist destination experienced by the peninsular Spanish national parks through the study of the various dimensions, such as business, infrastructure, socioeconomic, and perception dimensions of the local residents. Ultimately, this work tries to answer the following question: what factors are determining factors in the tourism business development of Spanish national parks?
This work may be of great interest for achieving rural development and tourism competitiveness in rural inland destinations in a sustainable way.
Regarding the structure of the work, firstly, reference will be made to the theoretical framework on nature tourism in protected areas and its consequent effects. Secondly, it will refer to the materials and methods used, a description of the sample and the study variables. Fourth, the results obtained will be presented, with a brief explanation. Finally, the work will end with the discussion and conclusions based on the results explained above.

2. Conceptual Framework

2.1. Protected Areas

Protected areas are terrestrial or marine spaces declared by means of legal instruments and specially oriented to environmental conservation as well as biological and cultural resources [17].
According to the International Union for the Conservation of Nature in the world there are a total of 252,402 terrestrial protected areas and 17,959 marine areas, which represents 15.73% and 7.92%, respectively, of the planet’s surface. In the last ten years, the growing trend in the declaration of protected areas worldwide has been very notable, especially as regards marine areas [8,18].
The origins of protectionism date back to 1872, in the United States, when Yellowstone national park was created. This same protectionist model was transferred to countries such as Canada (1888), Sweden (1909), Russia (1912), Switzerland (1914), and Spain (1918) [19,20].
These first protected areas, declared in the mid-nineteenth century, were mainly based on preserving certain spaces in a sacred way from human activity. However, this purpose evolved over time to encompass other areas and needs of society, such as recreation and sustainable development [21,22].
Therefore, the declaration of these spaces is a powerful tool at the global level to achieve goals and face such extremely important challenges today, from the point of view of sustainability, such as the Sustainable Development Goals of UNESCO and the Rio Conventions of the United Nations [23,24].

2.2. Sustainable Tourism in Natural Areas

Tourism is considered as an engine of regional and local economic development, which improves the economic situation and quality of life of the population [25,26,27,28,29]. Among the socioeconomic benefits generated by tourism, it is worth highlighting its capacity to generate employment, the distribution of income. and its consequent reduction in economic inequality or the improvement of tourist infrastructures related to leisure, accommodation, transport, etc. [30,31].
With the passage of time, the awareness and concern of humans for the conservation of the environment has increased and this is reflected in the rise of sustainable tourism, becoming a typology with great notoriety in the tourism scene [32].
Sustainable tourism can be defined as that type of tourism that respects the natural resources, customs, traditions, and culture of the local population and takes into account the interests of all stakeholders [33,34,35]. The foregoing must also be extended to the long term, so that it must provide the necessary protection to ensure a situation of use and enjoyment of the natural environment such as the current one in the future [36,37].
All the socioeconomic improvements of the tourism industry can be transferred to the modality of rural and nature tourism. It becomes a mechanism that stimulates the economy of rural areas, since it promotes socioeconomic development through their culture and traditions [38,39,40], generating employment [41], promoting their local products and services [42,43], providing new sources of income [44,45], providing services and infrastructures and, in general, improving the quality of life of the local population [46,47].
As has been said at the beginning of this work, Spain is one of the countries in Europe richest in biodiversity and whose rural environment reaches 85% of its territory. This wealth in both areas makes rural and nature tourism a great engine of economic development [48].
Previous literature determines that responsible and nature tourism in territories characterized by their rich resources is positioned as an excellent way to achieve economic development [49,50,51,52]. Along the same lines, national parks are highly attractive destinations, and this makes them ideal figures for tourism development [53,54]. This is highly beneficial for its local economy, which can recover from the economic losses posed by the restrictions inherent to its declaration, through tourist spending [55,56,57,58].
Likewise, tourism must consider numerous dimensions given the multi-dimensionality that characterizes it [59]. However, this multidimensionality is further enhanced when we refer to sustainable tourism, since there are even more areas that closely interfere (social, economic, environmental, cultural, technological, or political), in addition to being interdependent between themselves [60,61,62]. Consequently, this must be transferred to the search for indicators when conducting studies, which must cover different dimensions to obtain an integrative and holistic character [60].
One of the main premises of sustainable tourism is that it must entail sustainable economic development, properly speaking, through a uniform distribution of the benefits obtained [60].
The presence of this rural and nature tourism offers a great opportunity for local rural tourism agents [63]. To this end, these entrepreneurs must expand their tourism offer, developing their products and services to attract tourists, greater satisfaction of demand and, consequently, their loyalty [64,65].
So, from a business perspective, this type of tourism favours the economic growth of small businesses located in these rural areas, providing an impetus for their economic development through the creation and maintenance of businesses [66,67,68].
Apart from the economic factors and the natural resources that this type of destination house, infrastructures are a factor of great importance in tourist competitiveness [69,70]. In other words, having an optimal transport infrastructure can be considered the foundation of the tourist development of a destination, being necessary to attract tourists [71,72].
In this sense, there are studies that determine that environmental resources, economic factors, and infrastructures are three elements that have a positive and direct impact on sustainable business tourism development [73]. In addition, the income generated by tourism in these spaces can revert to the creation of sustainable infrastructures in the vicinity of protected areas [74].
Thus, government authorities must commit to the development of infrastructures that meet both the needs of tourists and the local population [75].
Furthermore, numerous studies have confirmed the existence of a link between the declaration of national parks and local development [76,77,78,79]. In this way, it can be confirmed that protectionism not only responds to environmental problems, but also affects the rural development of local communities [80,81,82].
Likewise, tourist activity in natural areas, understood as a clean industry, stands out for its role in sustainability, thanks to its power to generate wealth in mainly rural environments, while ensuring the preservation of natural resources. customs and culture of the place [83,84]. In other words, sustainability from the social point of view implies respecting the cultural identity of the local population and their customs [60].
Regarding the local population, there are numerous studies that analyse their perceptions of tourism. From them, it can be deduced that the perception of residents plays a key role in the process of sustainable tourism development [85].
One of the aspects most appreciated by the local population in terms of tourism is the economic benefit that it brings [86]. In this sense, as the local inhabitants recognize the economic development obtained by the protection of nature, the more they will become involved in its management [87,88]. Likewise, reducing negative impacts on the environment and improving the quality of life of residents lead to greater success in the tourism sector and its approach towards sustainability [75,85,89].
Therefore, given the great importance of the local population, their participation in decision-making in the tourist management of these spaces is essential, and their actions should not be reduced simply to a secondary role [90,91].
In this way, showing the importance that the business dimensions, infrastructure, as well as socioeconomic and local perception have on the sustainable tourist development of natural destinations, in order to demonstrate its effect in the case of national parks, the following hypotheses can be raised:
Hypothesis 1 (H1).
Infrastructure has a significant effect on business development associated with sustainable tourism in Spanish national parks.
Hypothesis 2 (H2).
Socioeconomic dimension generates a significant effect on business development associated with sustainable tourism in Spanish national parks.
Hypothesis 3 (H3).
The perception of the residents has a significant effect on business development associated with sustainable tourism in Spanish national parks.

3. Materials and Methods

3.1. Sample and Variables

The sample is made up of the municipalities located in the zones of socioeconomic influence of the peninsular Spanish national parks. For this, the zoning established in the declarative laws of each of the national parks has been considered [92]. The work sample was of convenience and a total of 103 valid cases were obtained when performing the cluster conglomerate analysis. In the selection of the sample of national parks, those that were located in archipelagos (Balearic or Canary) have not been included because it would be very difficult to dissociate sun and beach tourism from nature tourism. In the inland parks, the visits are surely more motivated by the values associated with nature tourism, which is the objective of our analysis. Table 1 shows the national parks under study, as well as their main characteristics.
The variables used correspond to different representative dimensions of the sustainability of protected natural areas, such as the business sphere, infrastructures, socioeconomic and local perception (see Table 2).
Regarding data collection, to obtain the information on the study variables, we have worked with secondary and primary data.
Regarding secondary data, the economic variables referring to operating income and economic profitability of tourist companies were obtained through the SABI (Iberian Balance Analysis System) database [93]. Data have been extracted from the annual accounts of the companies for the years 2017, 2018 and 2019. The companies were selected by filtering by the CNAE code (National Classification of Economic Activities) [94]. In all cases, the selected companies had their headquarters in the villages near the national parks analysed.
Regarding the dimension related to infrastructures, the distance to the nearest airport or train was obtained through Google Maps. The limitation of having a greater number of representative variables of the village infrastructures reduces the infrastructures to only two variables associated with the accessibility to the tourist destination, such as distances from an airport to a railway station.
Regarding the socioeconomic indicators, these were extracted from Spanish government sources, such as the National Institute of Statistics and the Ministry of Finance and Public Function [95,96]. As in the previous group of variables, only socioeconomic information associated with city councils and population budgets has been collected. In the analyses, the variations between years were used as a proxy for the improvement of the socioeconomic situation of the villages.
Regarding primary data, local perception variables were collected through a questionnaire used in the previous study by Pérez-Calderón et al. [97]. Data referring to the location and year of declaration of the national park have been compiled from the annual reports published by the government ministry [98].

3.2. Data Analysis

The methodology used was a K-means cluster analysis that classifies the data according to the observed variances, forming homogeneous clusters that are different from each other. Thus, the municipalities that reside in the zones of socioeconomic influence of the national parks have been categorized based on the variables of the tourist companies located in them and the perception of the economic development of the local managers. These business variables are, on the one hand, operating income, an indicator of the productive capacity of companies; and, on the other hand, the economic profitability, revealing of the operation of these.
First, Ward’s method and the Euclidean distance were followed to ascertain the optimal number of clusters through the dendrogram [99]. The level of significance that has been considered is greater than 99%.
Additionally, a binary logistic regression analysis was used to find out the variables that influence the tourism development of companies located in Spanish national parks.
The logistic regression model is used to know the probability of occurrence of an event, through a linear predictor based on the maximum likelihood method [100,101,102]. The probability of the event taking place or not is expressed as follows:
Prob (Y = 1) = F (x, β)
Prob (Y = 0) = 1 − F (x, β)
where x are the independent variables and β are the parameters that determine how changes in these variables modify the probability.
In this work, this model is used to predict the probability of belonging to a group of municipalities with more profitable tourism companies and with a higher volume of income.
On the one hand, the dependent variables refer to two types of municipalities classified by the cluster analysis. In other words, the dependent variable determined belonging to the group of municipalities with the most profitable companies and with the highest volume of income (G1) or to the group of municipalities with companies with profitability and low-middle income (G2-3). That is, two binary logistic regression models were performed, one for each group of municipalities, where Y acted as a dichotomous variable with value Y = 1 if it belonged to the group of municipalities in question; while Y = 0 otherwise.
On the other hand, the independent variables were socioeconomic, infrastructure, and local perception of the sustainability of national parks. The purpose of the hypothesis test is to check the effect of the independent variables on the dependent variable.
The equation that represents the proposed logit model is the following:
Logit P (Yi = 1|β, Xi) = β0 + β1Xi1 + β2Xi2 + … + βkXik, i = 1, …, n + ε
where Y is the dependent variable in the linear regression model and ε the random error.
The independent variables were introduced into the logistic regression model, and it was verified if there was a significant sign change of the β coefficient to verify the proposed model.
The Wald test was used to determine the significance of the independent variables, and its significance value must be less than 0.05 to be able to affirm that the regression coefficient is significant at a confidence level of 5% [103].

4. Results

4.1. Descriptive Statistics

As a preliminary step, Table 3 shows the descriptive statistics for each of the variables under study in this work at the level of the peninsular Spanish national parks network.
Taking into account the business perspective, tourism companies located in the zones of socioeconomic influence of the inland national parks have an average operating income of € 315,237.30. It can also be seen that they have a slightly negative economic profitability (−0.39%).
On the other hand, considering the infrastructure dimension, the average distance from the set of national parks to the nearest airport is 96.75 km and to the train station is 37.69 km.
Regarding the sociodemographic sphere, the municipalities have a population that ranges from a minimum of 61 inhabitants to a maximum of 18,995. This great variation between minimum and maximum is also notable in public budgets.
Regarding the perception of the sustainability of national parks by the local agents of the municipalities located in the zones of socio-economic influence, we can highlight the highest score in the sphere referring to social development (3.84), followed by economic development (3.71), with the dimension that reports the lowest score being global satisfaction (2.87).

4.2. Cluster Analysis k-Means

In the first place, referring to the cluster analysis, three different groups of municipalities have been obtained. ANOVA was used to determine if the clusters were classified correctly, showing a significance level of 0.0%.
The groups have been categorized based on the differences between the average values of operating income and the economic profitability of the tourist companies located in the municipalities under study and the economic development perceived by local managers (see Table 4). Thus, cluster 1 corresponds to those municipalities whose tourism business fabric is characterized by high operating income, high profitability, and high perception of economic development. Conglomerate 2 brings together the municipalities in which the companies with average operating income and profitability and a low valuation of the perceived economic development are located. Cluster 3 groups the localities with the poorest tourist companies, with the lowest operating income, minimal profitability, and medium perception of economic development.

4.3. Binary Logistic Regression Analysis

Second, the results of the binary logistic regression analysis are presented.
First, the global fit of the model was analysed using the omnibus test. With this test, it is possible to check whether the study model represents an improvement in the reference model. For this, the Chi-square test is taken into consideration, and it is observed if there is a significant difference between the -2LL of the reference model and the proposed model [104]. The results can be seen in Table 5.
Therefore, since the Chi-square value is significant, the new model is significantly better than the reference model. According to this test, the level of significance (p = 0) implies that the model is statistically significant and can be used to make predictions.
Likewise, the R2 of the model was analysed (see Table 6), which determines the proportion of the variation that can be explained. Thus, the value zero means that the model has no predictive value, while the value one indicates a perfect fit [105]. Specifically, the Cox–Snell and Nagelkerke indicators [106,107] have been used in this study.
Thus, the coefficient of R2 of Cox–Snell (0.497) and the coefficient of R2 of Nagelkerke (0.712) indicate that the variation of the dependent variables explained by the model is very high. Using other terminology, this model explains 49.7% of the variation in the result or 71.2%, depending on the indicator that we take as a reference.
On the other hand, the goodness of fit of the logistic regression model has been calculated using the Hosmer–Lemeshow test, as can be seen in Table 7.
The results of the chi-square coefficients of the Hosmer–Lemeshow test show that there are no significant differences between the observed values and the values predicted by the model.
Finally, Table 8 presents the results of the logit model. The independent variables were introduced into the model, and it was verified if there was a significant change in the sign of the coefficient B to verify the proposed model.
To determine the significance of the variables entered in the model, the value Sig. must be fixed, and must be less than 0.05. Therefore, the variables perception of economic development (PDE) and distance to the train (IDT) are significant, while the rest of the variables introduced in the model do not have a significant impact.
Hence, the summary of the proposed logit model determines that with a percentage of cases of 89.8% correctly classified, there is a 95% probability that a greater local perception of the economic development of the municipalities located in the areas of socioeconomic influence of the national parks and a greater proximity in terms of transport infrastructures (distance to the train) determine the belonging to a group of municipalities with the tourist companies with the highest economic profitability and operating income.
Furthermore, the fact that these same variables are significant in both groups and with great differentiating power in the opposite direction support the results of the cluster.

5. Discussion & Conclusions

This study analyses the relationships that exist between the different dimensions present in the management of Spanish national parks, from the point of view of the tourist business offer. Specifically, variables related to business, infrastructure, as well as socioeconomic and local perception dimensions have been analysed.
According to the results of the analysis, it appears that the tourist companies located in the municipalities of the zones of socioeconomic influence of the Spanish national parks in the interior have a productive capacity and performance that are very different from each other.
The results of this study denote the importance of the perception of the local population and the infrastructures in the tourist development and management of protected natural spaces. In particular, the results show that a favourable perception of economic development by residents and the proximity to railway infrastructures have a significant relationship in the business development of tourism companies.
It is essential that residents perceive the economic development that nature tourism can provide in these types of spaces. This tourist activity can contribute to the rural development so badly needed by these rural areas, marked by depopulation and by dependence on the activities of the primary sector, currently in decline. In addition, these are areas that, due to the category of environmental protection they enjoy, have numerous limitations in terms of the exploitation of their public use, considering this sustainable tourism as a good development option for their regional, or local economy, and even more so considering the boom that this type of tourism has been experiencing for a few years due to the change in tourist demands.
Given the different dimensions that the management of protected natural spaces encompasses, it is necessary to implement integrative measures and territorial management instruments aimed at tourism sustainability, improvement of the quality of life of the local population, and preservation of natural resources.
For this, it is inescapable to bet on an improvement of tourist infrastructures, always considering the particularities of this type of pristine destination, in such a way that ecological and natural values are not affected. This infrastructure development must be focused not only on tourist needs, but also on the needs of the local population. It is important that local communities perceive this development and enjoy its benefits. Therefore, it is necessary to invest in a good transport network, in telecommunications, in medical and commercial services, among others.
At the same time, it is necessary to support those tourism companies that provide their services in these spaces, while promoting the rural development of local populations. In this sense, entrepreneurs must adapt to the environment in which they are located and offer services based on interaction with nature, in accordance with environmental requirements and tourist demands. In addition, the role played by the national, regional, and municipal governments in regulating the limitations implicit in the declaration of these spaces is essential.
Moreover, it is essential to convey to the local community, businessmen, and tourists the importance of the conservation of natural and cultural resources and tourism in these spaces. For example, this awareness can be raised through programs and workshops promoted by the public sector that extol the advantages and opportunities of living in the zones of socio-economic influence of this type of protected area.
Regarding the limitations of this work, it could be improved by debugging and expanding the search for the data. Specifically, we would be referring to an update of the socioeconomic and infrastructure variables. Another limitation of this study is the low representation of some national parks considering certain variables, such as those referring to the business dimension and local perception. Additionally, it would be very interesting to include in the study a dimension referring to ecological resources, given the uniqueness of this type of destination and the number of visitors.
Finally, one of the main problems of this type of nature tourism, especially in protected areas, is the uncontrolled growth in the number of tourists. In this sense, it must be considered that the use and exploitation of these geographical areas become highly regulated, not only for tourism, but also agricultural, livestock, and industrial purposes, which presents a traditional conflict of interest for residents in harming their economic prosperity. Despite the above, the problem of over-tourism threatens this segment of the tourism business, and it is a promising future line of research with previous reference papers [108,109].

Author Contributions

Conceptualization, V.M.-B. and F.S.-C.; methodology, E.P.-C. and V.M.-B.; formal analysis, E.P.-C.; investigation, E.P.-C.; resources, V.M.-B.; writing—original draft preparation, V.M.-B. and F.S.-C.; writing—review and editing, E.P.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Sánchez-Cubo, F. benefits from a predoctoral contract for training researcher staff within the frame of the Formación de Profesorado Universitario (FPU), Fellowship Programme of the Ministry of Universities of Spain. Miguel-Barrado, V. benefits from a research contract to carry out his doctoral thesis. The foregoing is thanks to a financing program from the Fundación Valhondo, Spain.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Characteristics of the national parks under study.
Table 1. Characteristics of the national parks under study.
National ParkHectaresNo. of VillagesInhabitants (2020)Total Visits (2019)LocationDeclaration Year
Aigüestortes145,057.751013,801560,723Lleida1955
Cabañeros182,292.5264.781100,493Ciudad Real, Toledo1995
Doñana200,601.86444,976388,325Huelva, Sevilla1969
Monfragüe195,500.731412,267457,555Cáceres2007
Ordesa89,290.4461822915,144Huesca,1918
Picos de Europa133,683.561114,1641,791,410Asturias, León, Cantabria1918
Sierra de Guadarrama175,593.4034150,3691,519,039Madrid, Segovia2013
Sierra Nevada266,690.914469,841789,756Granada, Almería1999
Tablas de Daimiel82,113.86330,644157,424Ciudad Real1973
Table
Table 2. Study variables.
Table 2. Study variables.
DimensionVariablesDescription
BusinessEIEOperating Income (€). Average 2017–19
EREEconomic Profitability (%). Average 2017–19
InfrastructuresIDADistance to the nearest airport (km). Average
IDTDistance to nearest train stop (km). Average
SocioeconomicSPPopulation. 2017
SPABudgets Village Halls (€). 2017
Local perceptionPDEEconomic development
PDSSocial development
PSGGlobal Satisfaction
Table
Table 3. Descriptive statistics of the variables under study.
Table 3. Descriptive statistics of the variables under study.
VariablesMinMaxAverageStd. Desv.
EIE15,565.331,134,482.73315,237.30251,549.17
ERE−104.2435.40−0.3916.76
IDA26.70223.0096.7543.61
IDT0.0097.3037.6925.66
SP61.0018,995.002037.073411.34
SPA13,742.1812,173,408.921,203,865.151,904,177.78
PDE1.336.333.711.23
PDS1.006.503.841.38
PSG1.005.602.871.33
Table
Table 4. Final cluster.
Table 4. Final cluster.
VariablesClusterANOVA
G1G2G3Sig.
EIE885,601.16380,101.01100,310.160.000
ERE10.474.49−8.680.000
PDE4.523.073.990.000
N. cases 2148340.000
Table
Table 5. Omnibus test.
Table 5. Omnibus test.
Chi-SquaredfSig.
Model33.63170.000
Table
Table 6. Model Summary.
Table 6. Model Summary.
−2 Log LikelihoodCox & Snell R2Nagelkerke R2
24.9990.4970.712
Table
Table 7. Model goodness-fit test. Hosmer and Lemeshow test.
Table 7. Model goodness-fit test. Hosmer and Lemeshow test.
Villages with the Most Profitable Companies (G1)Villages with the Least Profitable Businesses (G2-3)
Chi-squaredfSig.Chi-squaredfSig.
0.61381.0005.24280.731
Table
Table 8. Summary of binary logistic regression analysis.
Table 8. Summary of binary logistic regression analysis.
Independent VariablesDependent Variables
Villages with the Most Profitable Companies (G1)Villages with the Least Profitable Businesses (G2-3)
BSig.Exp(B)BSig.Exp(B)
PDE3.7700.01843.393−3.7700.0180.023
PDS0.9560.1972.600−0.9560.1970.385
PSG−1.8660.0600.1551.8660.0606.462
IDA−0.0050.7710.9950.0050.7711.005
IDT−0.0670.0290.9350.0670.0291.069
SP0.0010.6581.001−0.0010.6580.999
SPA0.0000.1121.0000.0000.1121.000
% Classification 89.8% 89.8%
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Pérez-Calderón, E.; Miguel-Barrado, V.; Sánchez-Cubo, F. Tourism Business in Spanish National Parks: A Multidimensional Perspective of Sustainable Tourism. Land 2022, 11, 190. https://0-doi-org.brum.beds.ac.uk/10.3390/land11020190

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Pérez-Calderón E, Miguel-Barrado V, Sánchez-Cubo F. Tourism Business in Spanish National Parks: A Multidimensional Perspective of Sustainable Tourism. Land. 2022; 11(2):190. https://0-doi-org.brum.beds.ac.uk/10.3390/land11020190

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Pérez-Calderón, Esteban, Vanessa Miguel-Barrado, and Francisco Sánchez-Cubo. 2022. "Tourism Business in Spanish National Parks: A Multidimensional Perspective of Sustainable Tourism" Land 11, no. 2: 190. https://0-doi-org.brum.beds.ac.uk/10.3390/land11020190

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