Competitive Benchmarking of Tourism Resources and Products in Extremadura as Factors of Competitiveness by Identifying Strengths and Convergences of Spanish Regions in the Period 2010–2018

Abstract

In a globalized and interconnected world, and with increased leisure time, competition between tourism destinations has increased considerably. For this reason, the periodic measurement of tourism competitiveness has become a basic tool for tourism management today. It is not only necessary to determine the tourism competitiveness of destinations in a given period of time (static analysis); the continuous changes which are now occurring in the tourism industry also make it necessary to measure them from time to time (dynamic analysis). The periodic measurement of the competitiveness of Spanish regions as destinations is the objective of the Monitur report. This paper aims to analyze the evolution of one of its most important mainstays, namely the diversification and structuring of regional tourism products. Based on a set of 25 indicators, the relative evolution of the 17 Spanish Autonomous Regions regarding this mainstay between 2010 and 2018 has been compared, and those aspects in which the regions have shown increased relative competitiveness (convergence or competitive reinforcement) and those in which they have shown reduced relative competitiveness (divergence or competitive weakening) in each region have been identified. Based on the results obtained, a competitive benchmarking exercise was carried out to compare the evolution of Extremadura with that of the four regions with the best overall record in terms of competitiveness between 2010 and 2018. In this way it has been possible to suggest improvements in the region’s range of products and services so as to increase its tourism competitiveness in the medium and longer terms.

1. Introduction

The current context of tourism activity is characterized by a notable increase in competition between destinations. This increase in competition has been encouraged by a relevant fact in the evolution of the sector, the diversification of tourist products, which has led to an emergence of new destinations which have become part of the traditional tourist circuit to create a differentiated tourist offer. Parallel to this growing trend in tourism, the effects of the COVID-19 health crisis on this market must be considered. The mobility restriction measures, both national and international, imposed by the various authorities to contain the spread of the disease have been a real burden, which has limited the size of the tourist market. In this context therefore, increasing competitiveness becomes particularly relevant if sustainable development of tourist activity in a destination is to be achieved.

The combination of the above factors leads us to a scenario in which the proper management of a destination’s competitiveness becomes a crucial issue owing to the increase in existing competition. Indeed, in a scenario in which competition between destinations has increased significantly [1,2], competitiveness becomes an essential factor for success. As a result it is essential that the destination is aware of what it needs to do to become more competitive than other destinations [3,4,5].

In this sense, monitoring the competitiveness of a destination becomes a key task if it is to explore its potential and minimize its limitations by increasing its attractiveness [6]. For this reason, since the study of the phenomenon of competitiveness began with the first research carried out at a company level by Porter [7], a number of scientific studies have appeared which adapt this concept to the peculiar characteristics of tourism activity, which have focused their interest on trying to delimit said concept [8,9] and pinpoint the factors affecting the competitiveness of a destination [1,10,11,12,13].

According to Abreu-Novais et al. [14], there is a growing trend towards measuring the competitiveness of a destination and identifying the factors or aspects which help to improve its competitive position. At a management level, this interest materializes in the development of different tools that measure the relative competitive position of a destination, such as the Travel & Tourism Competitiveness Index (TTCI). The latter has been developed by the World Economic Forum (WEF) and analyzes a total of 139 countries to establish a ranking based on three main dimensions: the regulatory framework; the business environment and infrastructure; and human, cultural and natural resources.

Focusing its attention on the area of interest of this research, the destination of Spain, Exceltur has drawn up the Monitur report with the initial objective of measuring the relative competitiveness of tourism in the 17 regions which make up the country. This report is a valuable tool for the management of competitiveness, since through its mainstays and indicators it focuses on which aspects must be improved in order to achieve a better competitive position for the destination, allowing an overview of the current situation of each of the 17 destinations analyzed.

However, one of its main limitations to management is its static nature, as it is restricted to portraying the situation of each of the destinations for the different stages at which the consultant has drawn up this report; it does not take into account key management issues such as the initial situation of the destination or how it has evolved since the last monitoring, which would be a fundamental tool when assessing the strategies developed by destination managers to improve their relative competitive position. For this reason, this paper goes a step further and proposes a dynamic analysis which allows us to perceive greater implications from the results of the Monitur reports. To be precise, a dynamic approach to the study of competitiveness makes it possible not only to determine the relative competitive position of a destination at a given moment but also to analyze how each region evolves over the period under study. It therefore shows which regions have developed the most effective strategies for managing their competitive position. In parallel to this analysis, therefore, and once the situation of each region has been identified, benchmarking is positioned as an appropriate technique for guiding the lines of action to be developed by a destination in order to improve its competitive position.

The results achieved will therefore make it possible to identify a series of convergences or divergences together with a set of competitive reinforcements or weakening points, making it possible to identify which regions have developed the most effective strategies and to identify the aspects on which those regions which have improved their relative competitive positions have focused their efforts.

In order to demonstrate the effectiveness of this method and the improvement in the applicability in terms of management which a dynamic analysis to monitor competitiveness represents for a specific destination, the destination of Extremadura is used as a case study. This is an inland destination which, according to the degree of development of its tourism sector, can be classified as an emerging destination. In terms of its competitive situation, it should be noted that the region has traditionally occupied one of the lowest positions in the ranking of communities in the Monitur report [15,16]. It is therefore considered that the region constitutes a suitable case study for the purposes of this research; it shows the suitability of the dynamic approach in the study of competitiveness in order to verify the effectiveness of the strategies developed by managers and destinations and to help define strategic lines of action to improve their competitive positions.

To this end, the situation of the 25 indicators making up the mainstay analyzed, the diversification and structuring of tourism products, is compared for each of the Spanish regions throughout the study period. As a result, relative changes in the indicators are identified. The comparison of these changes with regard to the position of the region for the indicator at the end of the period makes it possible to determine four possible scenarios: competitive strengthening, competitive weakening, convergence and competitive divergence. Then the standard deviations of the differences between indicators are calculated, which makes it possible to recognize with which indicators each of the regions has made a greater effort. Subsequently the calculation of the average value of these differences helps to recognize whether there has been a net gain or a loss of competitiveness for the period analyzed. The combination of this last result with the comparison of the current scenario of the region for each indicator then measures the intensity of the change which has taken place. Finally, the strategies developed by the best positioned destinations are analyzed in order to establish lines of action to improve competitiveness.

The main novelty of this research is that as far as the authors are aware no previous studies have analyzed the Monitur report results from a dynamic perspective; the results obtained therefore constitute valuable information for destination managers in Spanish regions.

In order to achieve its objectives, this research work is structured as follows: this introduction is followed by a theoretical framework which allows us to contextualize the phenomenon studied. The methodology used is then detailed followed by a list of the main results obtained. The study ends with a final section which synthesizes the conclusions reached and also their implications for destination management.

2. Competitiveness: A Strategic Factor in Tourism Development in the 21st Century

The competitiveness of a tourism destination is important if it is to achieve and maintain a favorable position in the global tourism market while maintaining a competitive advantage over its main competitors [17].

Despite being a recurring theme in the scientific field, to date there is no unanimity on the concept, although the definition proposed by Ritchie and Crouch [13] is widely accepted. These authors link the concept of destination competitiveness to tourist attractiveness, resource preservation and improvements in the quality of life of the resident population, bringing competitiveness into line with sustainable productivity.

According to some authors, having a competitive advantage means treating customers—tourists in the particular case of the sector on which this research focuses—better than the competition [18]. Athiyaman and Robertson [19] point out that in order to gain a competitive advantage it is necessary to devote energy and resources to strategic planning and subsequently to measuring the results obtained from the actions taken.

In this sense it is not surprising that destinations are becoming increasingly interested in measuring their competitiveness and carrying out actions to improve their competitive positions [14]. This interest has resulted in the appearance of various studies which focus on delimiting the concept while analyzing which factors play a fundamental role in the competitiveness of a destination.

Despite being a recurring theme in the scientific literature, the different theoretical approaches which have been used to address the issue have led to a high degree of discrepancy in both the definition and measurement of the tourism competitiveness of a given destination (Cracolici et al. 2008).

In view of the existing discrepancies in the definition and delimitation of the concept, the study published by Aguiar-Barbosa et al. [20] analyzes how the concept of competitiveness has evolved since the initial study by Poon [9]. The authors carry out a content analysis of the different conceptualizations proposed in a total of 130 articles, differentiating three stages in the evolution of the concept.

In an initial stage between 1999 and 2004, the authors focus on improving the efficiency and performance of the industry in relation to certain critical success factors, which is why in their analysis they classify this first stage as economistic and Porterian [8,21,22].

The second stage they identify is defined by the work carried out between 2005 and 2010, which is characterized by addressing the phenomenon of competitiveness by including key aspects such as sustainability while at the same time increasing concern for improving the tourist experience at the destination [20,23,24].

Finally, the authors identify a third period between 2011 and 2018, which represents a return to the initial economic concept, understanding competitiveness as the capacity of the destination to promote actions, conditions, products and services to improve its productivity by creating satisfaction and innovative experiences for the visitor while preserving natural resources [25,26,27,28].

As can be seen, the evolution of the concept has led to a shift in the focus of attention when conceptualizing the competitiveness of a destination from the analysis of comparative advantages, which was associated with the destination’s resource base, towards management in order to be able to increase competitive advantages while working on the sustainability of resources and improving the quality of life of residents [25]. It is precisely in this context that monitoring becomes particularly relevant.

On the other hand, researchers have focused much of their attention on the formulation of theoretical models to measure the competitiveness of a destination. The research carried out by Crouch and Ritchie [8] constitutes the initial proposal of a conceptual model to understand which factors affect the competitiveness of a given tourist destination. According to these authors, the competitiveness of a destination depends on the comparative advantages it manages to establish, based on the endowment of resources, on the competitive advantages obtained from the management of these resources, and on a series of macro-environmental forces (the global economy, terrorism and cultural and demographic trends) and a series of micro-environmental circumstances (including the determinants of qualification and expansion in addition to supporting factors and resources).

Dwyer et al. [11] developed a model in which, in addition to the endowment of natural and heritage resources and the resources created and supported, competitiveness is explained by the following: destination management including both public and private management, the demand (awareness, perception and preferences) and a series of situational conditions (which they explain as forces related to economic, social, cultural, demographic, governmental or competitive trends, among others).

Based on the models of Crouch and Ritchie [29] and Dwyer et al. [30], Heath [12] proposed an adaptation of the main elements of these models in order to design a model which serves as a theoretical reference framework for measuring the tourism competitiveness of the destination of South Africa.

Crouch [1] subsequently developed a research project that aims to investigate which attributes among those proposed in different theoretical models have the greatest impact on the competitiveness of a tourist destination. It concludes that basic resources and attractions, together with destination management, are the two main factors which in the opinion of experts have the greatest influence on the competitiveness of a tourist destination. The authors stress that although it is undeniable that the overall attractiveness of a destination is a critical aspect in the decision-making of travelers, a destination can still obtain a good competitive position through good product design, a quality tourism superstructure and the creating or hosting of sufficiently attractive events. Destination management therefore plays an essential role in making up for deficiencies in the tourism resources of a destination.

More recent works seek to investigate the influence of new technologies on the competitiveness of destinations. Thus, the work by Iglesias-Sánchez et al. [31] confirms the importance of proper online reputation management to improve the competitiveness of a destination.

Along the same lines, the work by Custodio et al. [32], which developed a model to measure competitiveness in the development of new tourism products, can be highlighted, proposing a circular model that represents a novel approach to the design, evaluation and development of new products by including a dynamic perspective that allows the new product to be continually re-evaluated, improved or discontinued.

Apart from these theoretical models, a series of indicators have been developed with the aim of becoming a tool to assist destination management by monitoring the relative competitive position of destinations by means of the Travel & Tourism Competitiveness Index (TTCI) produced by the World Economic Forum to measure the relative position of 139 countries which stand out at an international level. For Spain, the country which is the focus of this study, the Monitur report produced by Exceltur is particularly noteworthy.

Although these indicators serve as a reference, at a global level, for the purpose of measuring destination competitiveness, in recent years different models have been developed that aim to adapt to the particular characteristics of the destination to be analyzed, as is the case of COMPETITIVTOUR [33]. This is based on the fact that, as Dywer and Kim [29] state, not all indicators are applicable to each type of destination. Thus, it proposes a set of indicators adapted to the characteristics of the territory to be analyzed, a mature sun and beach destination with similar outbound markets, such as the Portuguese Algarve and the Mediterranean coast in Spain.

Indeed, monitoring the competitiveness of a destination becomes an essential task for destination managers, which allows them to explore the potential of the destination while minimizing possible limitations and increasing its attractiveness. In short, monitoring competitiveness becomes an essential tool for improving the competitive situation of any destination. Furthermore, it must be taken into account that in addition to static analysis, studying the evolution of tourism competitiveness is vital for destinations, since as Aguiar-Barbosa et al. [6] point out, this evolution represents an indicator of change in the tourism market which also allows us to evaluate the efficiency of the strategies developed to improve certain aspects. For this reason, it is considered that the results obtained by this research represent a valuable tool for destination managers, since from the starting point of static information, a dynamic analysis is performed which gives important results for each of the 17 micro-destinations of each of the autonomous regions into which the country is divided, which can become valuable input for them to evaluate which policies are the most effective for achieving their objectives.

In any case, it should not be forgotten that in recent years the scientific literature has addressed the analysis of the tourism competitiveness of destinations, considering aspects that go beyond the strictly economic (such as growth, tourism expenditure or volume of tourists). Among these new aspects that are beginning to be considered to measure the tourism competitiveness of a destination are the quality of life of residents and tourists [34,35,36] or the so-called “capitals approach” [37].

3. Methodology

3.1. Data

The data used in this study have been obtained from the Monitur reports of the Alliance for Excellence in Tourism in Spain [15,16]. These reports provide an overview of the relative tourism competitiveness of the Spanish Autonomous Regions. To this end, a total of seven mainstays of tourism competitiveness are independently analyzed. The mainstay examined in this paper is Mainstay 4, “the diversification and structuring of tourism products”. The aim of this mainstay is the quantification of the competitiveness of the tourism products offered by Spanish regions. To be more precise, this mainstay addresses three essential issues of tourism competitiveness:

(a)

Comprehensive management of the offer by product clubs at destinations: in the face of increasingly demanding, better informed and multi-motivated tourists and markets, strategies for integrating the offer through public–private management models are essential. This has the aim of generating greater added value and therefore greater final expenditure at destinations and greater multiplier effects on the social and business fabric. In this sense, product clubs have an essential role to play in improving the competitiveness of tourism destinations.

(b)

Diversification of the product offer: the new types of tourism which have emerged in recent years as a result of the multi-motivation of tourists are allowing the development of certain resources which make it possible to diversify the tourism products on offer. It is clear that the destinations with the most diversified offer will also be the most competitive destinations.

(c)

Qualification of the offer of accommodation: the growing demands of the 21st century tourist make it necessary to offer quality accommodation. Four- and five-star hotels are currently the most in demand and at the same time they offer the highest tourist quality. The competitiveness of a destination is therefore not only directly related to its accommodation capacity but will also depend directly on its quality.

To measure the aforementioned issues, the Monitur report uses a total of 25 indicators which are measured in the 17 Spanish Autonomous Regions. Owing to the heterogeneous nature of these indicators and in order to obtain a single synthetic index for each mainstay analyzed, the Monitur report rescales the value of all the indicators, giving a value of 100 to the indicator at a national level and expressing the value of each indicator in each region in terms of percentage points of dispersal with regard to the national average value. A region with a value of 85 for an indicator will therefore be 15 percentage points below the national average. This rescaling of the indices makes it possible not only to know which regions are above or below the national average but also to quantify the competitive advantages and disadvantages of each region, and when the indicators are measured in two different time periods to determine whether there are convergences or divergences of each region with respect to the country as a whole. For all of the above reasons, Monitur’s measure of tourism competitiveness is relative since the improvement in absolute terms of the value of an indicator in a region will not count if the remainder of the Spanish regions has improved, also in absolute terms, more than that region.

3.2. The Identification of Relative Changes in the Tourism Competitiveness of Spanish Regions

Given that the aim of this research is to compare the diversification and structuring of the tourism products offered by Spanish regions between 2010 and 2018, the first calculation that has been made is the difference between the values of indicator i in region j in those two years, i.e.:

$$D_{ij}^{2010-2018}=I_{ij}^{2018}-I_{ij}^{2010}$$

(1)

It is clear that a positive value of $D_{ij}^{2010-2018}$ denotes a competitive improvement of the region while a negative value means competitive worsening. However, this relative variation in the tourism competitiveness of the regions will have a different reading depending on whether the value of the indicator, measured at the time closest to the present, is above or below 100. Thus, if $I_{ij}^{2018}>100$ region j is more competitive than the national average for indicator i in 2018, region j will be even more competitive than in 2010 if $D_{ij}^{2010-2018}>0$ (competitive strengthening) but less competitive than in 2010 if $D_{ij}^{2010-2018}<0$ (competitive weakening). Conversely, if $I_{ij}^{2018}<100$, region j will be less competitive than the national average for indicator i in 2018, a situation which will worsen if $D_{ij}^{2010-2018}<0$ (competitive divergence) but will improve if $D_{ij}^{2010-2018}>0$ (competitive convergence).

In this research, four competitive scenarios have therefore been considered in the dynamic analysis of the relative competitiveness of Spanish regions in the structuring and diversification of their tourism products:

Competitive strengthening: $I_{ij}^{2018}>100$ and $D_{ij}^{2010-2018}>0$.

Competitive weakening: $I_{ij}^{2018}>100$ and $D_{ij}^{2010-2018}<0$.

Competitive convergence: $I_{ij}^{2018}<100$ and $D_{ij}^{2010-2018}>0$.

Competitive divergence: $I_{ij}^{2018}<100$ and $D_{ij}^{2010-2018}<0$.

From the total number of indicator values analyzed (425 = 25 indicators × 17 regions), those which do not allow each region to be positioned in one of the four competitive scenarios defined above have been eliminated, i.e.:

Value of the indicator equal to 100 in the year: $I_{ij}^{2018}=100$ (5 cases).

Zero difference between the years 2010 and 2018: $D_{ij}^{2010-2018}=0$ (3 cases).

Unavailability of the indicator value in 2010 or 2018 (1 case).

The 416 indicator values used in this research have therefore been classified into 80 competitive strong points, 66 competitive weak points, 113 competitive convergences and 157 competitive divergences. The distribution of these competitive scenarios by Autonomous Region will be presented in the Results Section of this paper.

Finally, it is interesting not only to determine the number of competitive scenarios in each region but also to know the number of competitive changes, i.e., the value of $D_{ij}^{2010-2018}$. In fact, obtaining certain statistical estimates of this difference will allow other interesting complementary analyses to be carried out. Therefore, and given the rescaling process carried out by Monitur, it is clear that the average value of $D_{ij}^{2010-2018}$ for each indicator, calculated from the data of the 17 Spanish regions, will always be equal to 100. However, the calculation of the standard deviation of $D_{ij}^{2010-2018}$ will allow us to know in which specific indicators the Spanish regions have worked harder to improve their competitiveness between 2010 and 2018. In turn, the calculation of the average value of $D_{ij}^{2010-2018}$ for each Spanish region will show whether there has been a net gain or loss of competitiveness between the two years considered. Finally, obtaining the average value of the differences for each region in each of the four competitive scenarios will allow us to find out the intensity of the changes produced; i.e., it will allow us to know whether the strengthening, weakening, convergence and divergence have been strong or weak in each Spanish region.

3.3. Benchmarking

Once the four dynamic competitive scenarios defined above were identified and quantified for all the Spanish regions and for all the indicators of the mainstay in question, the regions which registered the highest number of competitive strengthening points and convergences were selected in order to compare them with Extremadura and to propose, on the basis of a competitive benchmarking exercise, the changes that the region should make in the coming years to improve the competitiveness of its tourism resources and products.

4. Results

As indicated above in the Methodological Section, the first calculation carried out in this paper was to obtain the $D_{ij}^{2010-2018}$ values and to classify them into strengthening points, weakening points, convergences or competitive divergences for each of the 25 indicators and in each of the 17 Spanish Autonomous Regions. The result of this classification is shown in

Table A1. Competitive scenarios for spanish regions between 2010 and 2018.

${\mathit{D}}_{\mathit{i}\mathit{j}}^{2010-2018}$		AND	ARA	AST	BAL	CAN	CANT	CLM	CyL	CAT
1. Integral management of supply by product clubs		−39.1	15.9	−1.5	−4.7	−35.1	17.9	−18.1	−7.9	−12.1
2. Diversification of product offering		2.3	2	−2.5	5.5	0.6	0.3	0.1	3.9	−9.8
3. Cultural tourism		6.6	−7.2	2.8		0.7	3.1		−5.6	−7.9
3.1. World Heritage Sites		24	−19.6	4.8	4.8	4.8	4.8	3.1	−12.5	−10.8
3.2. Assets of Cultural Interest		11	−2.5	1.4	−14.8	0.7	2.4	−0.5	−4.5	−11.8
3.3. Number of visitors to museums		−15.2	0.3	2	0.2	−3.4	2.2	3.6	0.3	−1.1
4. M.I.C.E. tourism		−9.9	2.6	−11	3.8	1.8	0.3	19.8	−7	−23
5. Nature tourism		15.2	−6.8	−20.3	4.4	−0.8	2.6	−11.1	18	−1.4
5.1. Enhancement of nature parks		26.5	5.3	−47.5	2.5	−0.8	2.5	0.5	3.9	15.5
5.2. Development of greenways		9.2	−14.3	−10.8	10.1	6.5	1.2	−14	24.8	−22.2
5.3. Provision of approved trials		10	−11.4	−2.8	0.6	−8.1	4.3	−19.9	25.5	2.6
6. Golf tourism		−1.7	−0.9	−1.4	−0.6	2.7	−0.9	−1.9	1.1	−0.8
7. Thermal tourism		21.6	38.3	−5	6.7	−1.3	−3.7			−55.1
8. Language tourism		3.9	−1.8	−1.5	6.3	−3	2.2	2.7	−3.4	−3.8
9.Gastronomic tourism		5.7	0.5	−2.6	1.2	4.2	−1.8	−0.3	1.9	−5.1
10. Rural tourism		21.9	−3	−0.7	15	−3.9	−5.1	3.1	−12.6	2.6
10.1. Number of bed places in rural tourism accommodation		14.3	−1.5	−1.3	10.4	−6.7	−5.9	8.6	−10.1	7.5
10.2 Effective demand in rural tourism accommodation		29.6	−4.5	0	19.6	−0.9	−4.2	−2.3	−15.2	−2.3
11. Wine tourism		−18.7	2.5	−0.3	−0.3	−4	−0.3	−6.3	53.3	−4.3
11.1. Number of wineries on Wine Routes		−10.9	3.1	3.4	3.4	−2.4	3.4	−8	49.5	8.9
12.Shopping tourism		1.4	−3.5	−3	13.4	−7.8	−0.1	−0.4	2	−0.1
13. Qualification of the accommodation and catering offer		2.5	0.3	−1.1	8.1	−1.1	−4.2	−3.7	−3.1	2.6
13.1. Commitment to regulated supply		−0.8	−3.9	−5.1	0.5	−3.4	−8	−0.4	−3.9	1.7
13.2. Presence of high quaility hotels		4.1	1.8	2.9	18.1	−3.2	−3.5	−2.9	1.6	7.9
13.3. Quality of catering		4	2.7	−1	5.5	3.4	−1	−7.6	−6.9	−2
$D_{ij}^{2010-2018}$		VAL	EXT	GAL	MAD	MUR	NAV	EUS	RIO
1. Integral management of supply by product clubs		50.1	6.5	−13.9	17.7	−3.5	16.8	12.5	−1.5
2. Diversification of product offering		−2.5	0	−2.4	9.8	0.5	−3.4	0.1	−2.4
3. Cultural tourism		−5.2	6.9	4.1	3.7	0.5	−3.5	3.3	−1.1
3.1. World Heritage Sites		−8.3	15.3	2.3	3.1	−6.5	−6.5	4.8	−7.4
3.2. Assets of Cultural Interest		1.8	4	11.5	−5.2	6.1	−3.6	1.5	2.5
3.3. Number of visitors to museums		−9.2	1.5	−1.3	13.4	1.9	−0.4	3.6	1.7
4. M.I.C.E. tourism		−10.6	1.4	−2.9	46.8	−2.4	2.9	0	−5.4
5. Nature tourism		−4.4	1.9	2.3	0.7	4.6	5.8	−10.2	−0.7
5.1. Enhancement of nature parks		5	2.5	−0.5	2.2	2.5	2.5	−25.7	2.5
5.2. Development of greenways		−12.7	7.1	6	−6.1	9.2	9.8	3.5	−7.3
5.3. Provision of approved trials		−5.5	−3.9	1.1	6.1	2	4.8	−8.1	2.8
6. Golf tourism		0.7	−0.9	0.8	6	1.1	−0.9	−1.4	−0.9
7. Thermal tourism		21		−17.4	−1.3	−0.7	11	−4.3	3
8. Language tourism		−2.3	−0.2	−2.3	3.8	−0.5	0.7	−0.9	−0.2
9.Gastronomic tourism		−1.8	−1.8	−12.4	14	−1.8	−4.8	5.7	−1
10. Rural tourism		−3.3	2.3	−6.6	−2.3	−2.8	−2.8	−0.8	−1
10.1. Number of bed places in rural tourism accommodation		−2.6	0.8	−6.6	−2.6	−3.9	−0.3	−0.3	0.3
10.2 Effective demand in rural tourism accommodation		−4	3.8	−6.6	−2.1	−1.7	−5.3	−1.5	−2.3
11. Wine tourism		−0.1	−4.8	−5.9	−0.3	2.1	−3.1	4.6	−14.4
11.1. Number of wineries on Wine Routes		−0.2	−3.7	−11.5	3.4	4.6	−2.6	2.9	−43.3
12.Shopping tourism		0.3	−2.3	−2.3	38.2	2	−48.3	13.6	−3.6
13. Qualification of the accommodation and catering offer		0.2	−2.9	−4.1	8.4	−2.8	5.9	−0.4	−4.5
13.1. Commitment to regulated supply		3.3	−2	−5.9	15.4	−0.8	8		0.8
13.2. Presence of high quaility hotels		−0.1	−6.3	−4.8	−0.2	−7.8	10.3	−3.1	−14.8
13.3. Quality of catering		−2.6	−0.4	−1.5	10.1	0.3	−0.6	−2.5	0.3
	COMPETITIVE STRENGTHENING
	COMPETITIVE WEAKENING
	COMPETITIVE CONVERGENCE
	COMPETITIVE DIVERGENCE

Source: own elaboration based on data from Monitur 2010 and 2018.

in Appendix A.

Firstly, the standard deviation of the $D_{ij}^{2010-2018}$ values for each of the indicators has been obtained and is shown in

Table 1. Standard deviation of differences in indicator values between 2010 and 2018.

Indicator	Standard Deviation
1. Integral management of supply by product clubs	20.87
2. Diversification of product offering	4.08
3. Cultural tourism	4.74
3.1. World Heritage Sites	10.33
3.2. Assets of cultural interest	6.65
3.3. Number of visitors to museums	5.71
4. M.I.C.E. tourism	14.48
5. Nature tourism	8.92
5.1. Enhancing the value of natural parks	15.28
5.2. Development of greenways	11.85
5.3. Presence of approved trails	9.58
6. Golf tourism	1.91
7. Thermal tourism	20.59
8. Language tourism	2.81
9. Gastronomic tourism	5.46
10. Rural tourism	7.72
10.1. Number of beds in rural tourism accommodation	6.42
10.2 Actual demand in rural tourism accommodation	9.87
11. Wine tourism	14.49
11.1. Number of wineries on wine routes	16.86
12. Shopping tourism	15.85
13. Qualification of the accommodation and catering offer	4.07
13.1. Commitment to regulated supply	5.52
13.2. Presence of high-quality hotels	7.32
13.3. Quality of catering	4.16

Source: own work.

. This statistical measure indicates the average variation in the indicator value between the years 2010 and 2018. Consequently, the indicators with a higher standard deviation will be those on which the Spanish regions have focused their efforts to be more competitive, since those which have made more progress in diversification and in the structuring of certain tourism products will be those which have obtained the greatest positive difference between the two years considered and at the same time have forced greater negative differences in other regions given the relative nature of the indicators (it should be remembered that the value of the index for Spain as a whole is always 100).

The creation of product clubs to achieve the comprehensive management of the offer and thermal tourism have therefore been the issues in which the greatest competitive movement between regions has taken place between 2010 and 2018, i.e., they are the two most dynamic aspects. Alongside these two aspects, the Spanish regions have also worked to diversify their offer by offering new tourism products which were less consolidated on the market in 2010 and which seem to be beginning to improve in 2018. These include, for example, wine tourism and the incorporation of wineries as a tourist attraction, MICE(Meetings, Incentives, Conventions and Exhibitions/Events) tourism and shopping tourism, as well as enhancing the value of natural parks. In contrast, the “traditional” tourism offer, which focuses mainly on cultural tourism and nature tourism and also on the quality of the offer (especially accommodation and catering), has been much less dynamic in competitive aspects; the Spanish regions have therefore made less of an effort regarding their management, structuring and/or diversification.

We then proceeded to quantify the number of indicators shown in Table 1 in which each Spanish region has sought a relative gain in competitiveness (i.e., those aspects in which the region has strengthened itself or converged towards the national average) and in those other indicators in which there has been a relative loss of competitiveness (i.e., those aspects in which the region has weakened or diverged from the national average). The results of this analysis are shown in

Table 2. Number of strengthening points, convergences, weakening points and divergences detected in the Autonomous Regions between 2010 and 2018.

Region	Strengthening Points	Convergence	Weakening Points	Divergence
Andalusia	17	1	5	2
Aragon	4	8	3	10
Asturias	0	6	3	15
Balearic Islands	5	15	2	2
Canary Islands	2	7	3	13
Cantabria	0	13	1	11
Castilla La Mancha	4	4	2	13
Castilla y León	8	4	7	5
Cataluña	8	0	14	3
Region of Valencia	5	3	11	6
Extremadura	2	10	0	11
Galicia	2	5	4	14
Region of Madrid	12	5	1	7
Murcia	2	11	2	10
Navarre	3	8	1	13
Basque Country	6	5	5	7
La Rioja	0	8	2	15

Source: own work.

.

Andalusia is undoubtedly the Spanish region which has most improved between 2010 and 2018, as in 17 of the 25 indicators analyzed it was above the national average in 2010 and has improved even more in 2018. Together with Andalusia, the region of Madrid has also achieved competitive strengthening in 12 of the 25 indicators. In contrast, the two regions with the highest number of competitive weakening points were Cataluña (14) and the region of Valencia (11), where many of the indicators in which these regions were above the national average in 2010 have registered lower values in 2018.

On the other hand, in now analyzing those indicators in which the Spanish regions were below the national average, and considering that over the course of these 8 years they may have moved closer to (convergence) or further away from (divergence) the national average, it should be noted that the Balearic Islands (15 indicators) and Cantabria (13 indicators) have been the two Spanish regions which have converged most towards national averages, a circumstance which may have led to the disappearance of some of the competitive disadvantages of these regions. Below them in the ranking, the convergence experienced by the regions of Murcia (in 11 indicators) and Extremadura (in 10 indicators) also stands out. Finally, the greatest divergences with regard to the national value are found in six regions: Asturias and La Rioja (in 15 indicators), Galicia (14 indicators), and the Canary Islands, Castilla La Mancha and Navarre (13 indicators).

However, the analysis should not be limited to the number of indicators in which each region experiences changes for the better or for the worse; it is also necessary to quantify the value of these differences. One region may have shown many competitive strengthening points or convergences, but may have moved away (in the case of strengthening points) or moved closer (in the case of convergences) only slightly from the national average, while another region may have shown a very small number of strengthening points or convergences, but these may have moved away from or moved closer to the national average to a significant extent. A similar situation may occur with competitive weakening points and divergences. It is consequently necessary to calculate the $D_{ij}^{2010-2018}$ differences and to analyze the sign of their average value. Bearing in mind that competitive strengthening points and convergences always generate positive differences, and that weakening points and divergences always produce negative differences, the fact that the average value of these differences is positive means that the competitive balance of the region between 2010 and 2018 is positive, since regardless of the number of indicators of the region in each competitive scenario (Table 2), the advances in relative terms with respect to other regions have been greater than the setbacks in relative terms. The opposite interpretation would be obtained if the average value of the differences were negative. In this sense, it should be borne in mind that it is practically impossible for a region to register only positive values of $D_{ij}^{2010-2018}$, since the budgetary constraints on tourism in the Spanish Autonomous Regions prevent competitive improvement in all indicators, which forces them to prioritize certain aspects in their competitive strategy and neglect other aspects, so that the coexistence of positive and negative differences is entirely logical. However, whether the average value of these differences is positive or negative does have a clear interpretation.

Table 3. Average value of the differences $D_{ij}^{2010-2018}$ among Autonomous Regions.

Region	Average Value
(a) Regions with a positive average value:
Region of Madrid	7.308
Balearic Islands	4.988
Andalusia	4.700
Castilla y León	3.879
Extremadura	1.033
Cantabria	0.340
Region of Valencia	0.280
Murcia	0.088
(b) Regions with a negative average value:
Basque Country	−0.129
Aragon	−0.224
Navarre	−0.304
Canary Islands	−2.420
Castilla La Mancha	−2.430
Galicia	−3.232
La Rioja	−3.916
Asturias	−4.084
Cataluña	−4.972

Source: own work.

shows the average value of the $D_{ij}^{2010-2018}$ differences for the 17 Autonomous Regions. As can be seen, it is possible to establish two groups of regions: those which obtained a positive competitive balance between 2010 and 2018, and those with a negative balance over this period of time. Among the first group of regions (made up of eight territories), the region of Madrid stands out, followed by the Balearic Islands, Andalusia and Castilla y León. Although the balance for Extremadura, Cantabria, the region of Valencia and Murcia is also positive, the average value is close to zero, which determines that the competitive improvement of these last four regions has been insignificant.

In the second group of regions (made up of nine territories), the most affected in terms of competitiveness was Cataluña followed by Asturias, La Rioja and Galicia. The Canary Islands and Castilla La Mancha are in an intermediate situation, while the three regions in which the competitive deterioration has been the least are the Basque Country, Aragon and Navarre.

It is even more interesting to make the average calculation of the $D_{ij}^{2010-2018}$ differences not in aggregate but for each of the four competitive scenarios we are working with (

Table 4. The average intensity of strengthening, convergence, weakening and divergence detected by Autonomous Region between 2010 and 2018.

Region	Strengthening Points	Convergence	Weakening Points	Divergence
Andalusia	12.494	1.400	−11.280	−19,950
Aragon	15.375	1.725	−10.833	−4840
Asturias	0.000	2.883	−1.600	−7640
Balearic Islands	12.260	5.253	−7.700	−2500
Canary Islands	1.700	3.143	−2.567	−6015
Cantabria	0.000	3.631	−8.000	−2791
Castilla La Mancha	8.650	1.725	−10.200	−5923
Castilla y León	22.050	2.350	−9.471	−5280
Cataluña	6.163	0.000	−6.871	−25,800
Region of Valencia	14.760	2.867	−4.445	−4417
Extremadura	10.900	3.220	0.000	−2655
Galicia	3.200	4.340	−9.075	−5186
Region of Madrid	14.392	6.020	−0.200	−2843
Murcia	4.050	2.664	−5.650	−2390
Navarra	11.533	5.488	−3.600	−6346
Basque Country	7.133	2.660	−8.380	−2471
La Rioja	0.000	1.738	−28.850	−3607

Source: own work.

). In this case, it can be seen that the most intense competitive strengthening (although it has affected a smaller number of indicators in some cases) has occurred in Castilla y León followed by Aragon, the region of Valencia and the region of Madrid. The most pronounced competitive weakening has undoubtedly taken place in La Rioja (although this has only been localized in two indicators: wine tourism and the number of wineries on wine routes. The relative nature of tourism competitiveness in the terms in which it is measured by Monitur should always be borne in mind. In 2010, La Rioja was the second Spanish region in terms of wine tourism and the third in terms of the number of wineries on wine routes. However, the incorporation of other Spanish regions into this tourism segment and the creation of new products related to it have meant that in 2018 this region fell to fifth position in the wine tourism ranking (from 123.6 to 109.2 points) and to sixth position in the number of wineries (from 149.5 to 106.2 points) followed a long way behind by Andalusia, Aragon and Castilla La Mancha.

On the other hand, the most intense convergence with the national average occurred in the region of Madrid and also in the Balearic Islands and Navarre, whereas the highest average divergences from the national average were mainly recorded in two regions: Cataluña and Andalusia.

In any case, and apart from the fact that the value of an indicator may be lower or higher than 100 in 2018, the fact is that both strengthening and competitive convergence are favorable scenarios for the Spanish regions, since in both cases there is a relative gain in competitiveness. It should be borne in mind that, depending on their relative position in the national ranking for each indicator, the average value of the strengthening points will be high in some regions and low in others, while the average of the convergences will be higher in the latter than in the former. Consequently, combining graphically the strengthening points and convergences gives a visual idea of which regions have experienced a greater relative gain in competitiveness. The same situation occurs with the competitive weakening points and divergences if the average value of these is used to determine the relative loss of competitiveness of the Spanish regions. In this way, by combining both figures (Figure 1) it is possible to determine in accordance with the position of each region in the figures the balance which the Spanish regions obtained between 2010 and 2018.

As can be seen, three regions have experienced a significant relative gain in competitiveness in certain indicators and have registered a very small relative loss of competitiveness in others. These three regions are the region of Madrid, the Balearic Islands and Navarre. The clearest case of all is that of the region of Madrid, which recorded an average competitive strengthening of 14 points and an average convergence of 6 points, while its competitive weakening in other aspects of the mainstay has been practically non-existent (−0.2 points) and its competitive divergence with respect to the national total has also been very low (just under 3 points). Important also, although to a lesser extent, has been the progress in competitiveness of the Balearic Islands, with more than 12 points of competitive strengthening and some 5 points of competitive convergence, although the relative loss of competitiveness of the Balearic Islands has been greater than that of Madrid since it has recorded almost 8 points of competitive weakening. The third of these regions is Navarre, which has worked on the diversification and structuring of its tourism products, resulting in more than 11 points of competitive strengthening and more than 5 points of competitive convergence. However, its competitive divergence with regard to the national average values has been significant in some indicators (the fourth highest in the country after Cataluña, Andalusia and Asturias).

On the other hand, other regions stand out for their behavior as it is the opposite of that described for the three previous regions. These are regions which in general show very little strengthening or competitive convergence but on the other hand very strong competitive weakening and divergence. These are La Rioja, Andalusia and Cataluña. Of these regions, La Rioja is the one with the strongest competitive weakening (almost 29 points), a circumstance which is accompanied by zero competitive strengthening and practically marginal convergence. In contrast, Andalusia and Cataluña are the two Spanish regions with the highest degree of competitive divergence (almost 26 points in the case of Cataluña and 20 points in the case of Andalusia). Andalusia, moreover, also registers the second strongest weakening in the country (11 points) which is surpassed only by the aforementioned La Rioja.

From the regional analysis previously carried out, the Spanish regions with the greatest relative gains in tourism competitiveness have been selected, which as indicated above are Madrid, Navarre and the Balearic Islands, to which Castilla y León must also be added as it is the region with the strongest competitive strengthening. With these four regions a competitive benchmarking exercise has been carried out regarding Extremadura, with the aim of determining in which specific aspects it is possible to improve the tourism competitiveness of the region in the mainstay analyzed.

Figure 2 shows the values of the competitive strengthening points and convergences of Extremadura and the four previously selected regions. This graph shows that the competitive improvement of Extremadura is essentially due to the management of the region’s World Heritage Sites (Cáceres, Mérida and Guadalupe) and the development of greenways. However, for other tourism products or services the region has registered almost negligible competitive improvements or no such improvements at all. Comparing the performance of Extremadura with that of the regions with the greatest net gains in competitiveness in the period analyzed, various improvements can be proposed which will be reflected in the future in an improvement in the overall competitiveness of the region.

As noted above, Madrid is the Spanish region with the greatest net gain in this mainstay of competitiveness. Although there are variations in actions which justify this situation, the fact is that the most significant progress has been made in shopping tourism and MICE tourism. Shopping tourism is one of the tourism segments that the Extremadura region should develop in the coming years. In this sense it is not reasonable to think of the development of this segment in the region as a whole, given the great territorial dispersion and the absence of large population centers. However, one city in the region should be committed to this tourism segment and that is the city of Badajoz. With a population of more than 160,000 inhabitants, Badajoz is the commercial capital of the region with shopping centers in which the main clothing shop franchises have a presence and where other important commercial projects (Ikea) will be developed in the future. These centers hold an undoubted attraction for the Portuguese population (especially for the inhabitants of the Alentejo and Centro regions), who routinely travel to Badajoz to shop. Consequently, the promotion of shopping tourism (with an aggressive promotional campaign in Portugal) in Badajoz could be a factor in improving the tourism competitiveness of the region in the future. On the other hand, the region currently has five conference centers located in the main cities of the region (Badajoz, Cáceres, Mérida, Plasencia and Villanueva de la Serena) although these are clearly disconnected from the major institutions organizing scientific or technical conferences (such as the University of Extremadura, the Center for Minimally Invasive Surgery, etc.), which tend to hold their conferences in their own facilities rather than in the existing conference centers. It is therefore necessary to establish collaboration agreements between institutions requesting the organization of congresses and the different congress centers, enabling the latter to extend their current agendas (often limited to holding concerts or graduation ceremonies) with the holding of meetings, incentives, conferences and exhibitions so as to attract tourism from other parts of Spain and elsewhere. It is hoped that this objective will be achieved in the future thanks to the creation of the ‘Extremadura Convention Bureau’ Product Club.

Another of the Spanish regions which has made the most overall progress in the competitiveness of its tourism resources and products is Navarre. The commitment of this region in recent years to improving the competitiveness of its tourism products has focused on the creation of a range of high quality hotels and the comprehensive management of supply by product clubs in addition to thermal tourism. Although Extremadura already has seven product clubs which are mainly based on the gastronomy of the region (Cheese Route, Olive Oil Route, Iberian ‘Dehesa de Extremadura’ Route, Tajo International Gastronomic Destination, Ribera del Guadiana Wine and Cava Route, Birding in Extremadura and ‘Extremadura Convention Bureau’), it still remains for the region to include its tourism resources and products in national product clubs such as the Castles and Palaces Tourism Product Club and the Ecotourism in Spain Product Club and also to increase its participation in other national product clubs of which the region is a member, such as the Biosphere Reserves Product Club or the Wine Routes Product Club. On the other hand, Extremadura should also work to improve the supply of quality hotels given that the number of 5-star hotels in the region is very low (only four) and their number of beds represents only 1.9% of the regional hotel total while their number of rooms represents 1.8% of the said total. In this sense, it would be convenient to generate the interest of the large national hotel chains (Meliá, Riu, NH, Barceló, etc.) in investing in the opening of new 5-star hotels in the region. To this end, the future Elysium City, which will be located in the west of the province of Badajoz, appears to be an excellent business opportunity for these large hotel chains and consequently for the region to improve the competitiveness of its tourism products and resources.

The third region under comparative analysis with Extremadura in this research is the Balearic Islands, the strategy of which in recent years in terms of the competitive improvement of its tourism resources and products has focused on rural tourism and the creation of a genuine demand for it. In this respect, although Extremadura has developed an ambitious strategy to create a supply of rural accommodation supported by several calls for aid and subsidies for the purpose, the fact is both the supply and national and international promotion in this segment is very fragmented, with the owners of these accommodation establishments promoting their businesses on an individual basis. However, a common and integrated strategy for the whole region is needed for the overall promotion of rural tourism in Extremadura and for the creation of a genuine demand in this segment, which should be sought not so much in Madrid (where demand has been guaranteed for many years) but rather in other large cities near the region such as Seville (to the south) or Salamanca and Valladolid (to the north) and also the two main population centers of Portugal (Lisbon and Oporto).

Finally, the fourth Spanish region with which a comparison with Extremadura is necessary is Castilla y León. This region, perhaps following in the footsteps of the pioneering Spanish region in this segment (La Rioja), has made a clear commitment to wine tourism in general and to the inclusion of wineries on the wine routes in particular in order to improve the competitiveness of its tourism resources and products. In this sense Extremadura also has the Ribera del Guadiana Designation of Origin, in relation to which there is an important production of wine and cava concentrated mainly although not exclusively in the municipality of Almendralejo. However, despite the large number of wineries in the area (more than 25), very few offer guided tours, tasting sessions, etc. In short, there is considerable room for improvement in wine tourism in the region, but in order to achieve this, and given that the offer of wineries already exists, a major promotional campaign for this tourist segment is needed to create a demand that does not yet exist.

5. Discussion and Conclusions

The competitiveness of tourist destinations has become one of the most recurring themes in scientific research, with a proliferation of studies aimed at defining the concept and establishing models to suggest measures of competitiveness to fit the characteristics of any given destination. Despite its recurrence, there is no clear consensus on the delimitation of the phenomenon; however, the different models proposed agree in pointing out the importance of adequate destination management to achieve a good competitive position (Crouch and Ritchie 1999; Ritchie and Crouch 2000; Dwyer and Kim 2003; Dwyer et al. 2004; Heath 2002). In other words, they indicate the importance of including in the study of competitiveness a measure of the level of performance of the strategies developed by the destination managers.

In this sense, the monitoring of tourism activities becomes an essential tool for establishing and measuring strategies aimed at improving the competitive position of a destination. In the case study analyzed in this paper, the work carried out by Exceltur reflected in the Monitur report stands out. The data produced by this report provide information on the competitive position of each of the 17 regions into which Spain is divided, but their static nature limits some of the possible interpretations which could be developed. For this reason, this paper proposes a dynamic analysis which in addition to focusing attention on the relative competitive position of each of the regions also focuses on the evolution of the different indicators, making it possible to measure how effective the strategies developed in each of them have been and serving as a basis for establishing on which other aspects they should focus their efforts in order to achieve the goal of improving their relative competitive position.

This dynamic approach to the study of tourism competitiveness is justified by the profound transformations that have been taking place in recent years as a result of the global COVID-19 pandemic [38,39,40,41,42]. These transformations can make tourist destinations that were competitive a few years ago uncompetitive if they do not know how to adapt to these changes. Similarly, destinations that were uncompetitive a few years ago could achieve high levels of competitiveness if they assimilate these changes and introduce them into their integrated management.

The results obtained in this paper essentially agree with those obtained by Exceltur in the national rankings it presents for Mainstay 4 in its Monitur reports [15,16]. However, the Monitur reports go no further than obtaining a synthetic index for each mainstay based on the values of the indicators used to measure them, without taking into account whether the value of the indicator at the most recent point in time is above or below 100 and therefore ignoring the various competitive scenarios which may prevail in each for each indicator. Furthermore, the disaggregated analysis by indicator has made it possible to identify the specific aspects on which the competitive strategy of many of the Spanish regions has focused, this being an improvement with regard to Monitur’s analysis of these indicators (essentially ranking each region for each indicator and identification of this as a competitive advantage or disadvantage, if applicable), while at the same time giving a dynamic and comparative character to the analysis carried out from a temporal perspective.

By comparing the changes in the relative position of each region in Mainstay 4 of the diversification and structuring of tourism products reflected in the Monitur reports, this research therefore obtains similar results. The three regions which have recorded a high gain and a low competitive loss are Madrid (which moves from 10th to 4th position in the ranking, improving the value of the mainstay from 96.2 to 108.2), the Balearic Islands (up two positions in the ranking from 8th to 6th and increasing the value of the mainstay from 98.2 to 101.1) and Navarre (up from 14th to 10th position and from 89.0 to 95.4). In contrast, the two regions with the lowest gain and the greatest competitive loss were Cataluña (which despite this continues to be the leading Spanish region in the Mainstay 4 ranking, reducing the value of the mainstay from 146.8 to 140.4) and Galicia (which fell 6 positions from 7th to 13th in the national ranking with a reduction in the value of the mainstay from 99.3 to 92.5).

In any case, it should be noted that the relative nature of the measure of competitiveness reflected both in the Monitur reports and in this paper can clearly be observed in the situation in Cataluña. Despite the results obtained in this study for this Spanish region, Cataluña’s leading position in this mainstay in 2010 (146.8 points, i.e., more than 25 points behind the second region, Andalusia with 121.1 points) means that although the remainder of the regions have moved at a faster pace than Cataluña between 2010 and 2018 to diversify and improve the structure of their tourism products, the Catalan region continued to be the leader in this mainstay in 2010 and remains the leader in this mainstay in 2018 (although its 140.3 points represent a reduction of 6.5 points compared to 2010 and the difference with regard to the second region in the 2018 ranking, the region of Valencia, is now less than 20 points).

Once a dynamic analysis makes it possible to identify the regions in which there has been a greater relative gain in the period considered, lines of action can be established. To this end, the use of benchmarking techniques takes its place as a valuable tool for this purpose, allowing the guiding of the strategies to be developed by those destinations wishing to improve their relative competitive positions.

In this case study, we therefore put forward a series of proposals for a destination which needs to improve its competitive situation, the region of Extremadura. Once the evolution of this situation in the study period was analyzed, indicators were detected for which the destination had shown a worse performance with regard to the policies developed by those responsible for the destination, and therefore the need to reorient this action to improve the relative competitive position of the region is pointed out.

To be precise, and concerning the mainstay in this study, it is recommended that improvements should be made in plans of action for the development of certain tourism products: shopping tourism, MICE tourism, thermal tourism and rural tourism. At the same time, the leading regions in these indicators are highlighted as case studies by means of which to reorient action strategies.

Within the different regions analyzed, the benchmarking results for the region of Castilla y León are particularly important, as both this region and Extremadura are similar in terms of their tourism offer and intrinsic characteristics, being inland regions particularly specializing in nature and cultural tourism. The results obtained as a result of the dynamic analysis carried out, together with the benchmarking technique, therefore focus attention on those specific indicators in which the destination must improve, allowing the reorientation of its action policies in relation to one of its main competing destinations.

After all of the above, it can be concluded that the results obtained in this paper represent an important resource for destination managers, as it allows them to monitor the extent to which they have met their objectives with the strategies developed, focusing attention on those aspects in which there has been a strengthening or competitive convergence and those others in which the lines of action developed should be questioned when some weakening or competitive divergence is identified.

Therefore, in view of the richness of the results obtained, the main contribution of this work can be summarized in its ability to demonstrate the importance of including a dynamic perspective in the analysis of destination competitiveness, since this analysis will make it possible to identify the evolution of the indicators and, at the same time, to evaluate the performance of the strategies developed, allowing them to be reoriented when necessary. Although to the authors’ knowledge there are no precedents in the literature which allow a comparison of the results obtained, it should be noted that the dynamic approach used is in line with that postulated by certain studies. The research carried out by Abreu et al. [43] therefore concludes that the phenomenographic analysis of destination competitiveness establishes three perspectives from which to approach the concept: competitiveness as perception, as performance and as a long-term process. Therefore, and in view of the results obtained, the dynamic analysis of competitiveness appears to respond to these three conceptions of the competitiveness of tourism destinations.

One of the main limitations of this research is a consequence of the very definition of competitiveness used, since as has been pointed out there is no clear consensus on how to measure competitiveness. The results obtained are therefore limited by those biases which can be identified in the source used to measure competitiveness, i.e., the methodology proposed by the Monitur report.

Another significant limitation of the study is the great heterogeneity of the Monitur report in the analysis of the diversification of the supply of tourism products, where tangible variables (such as number of beds, number of wineries) are mixed with clearly more intangible variables (such as shopping tourism, language tourism). In this sense, it would be desirable for future Monitur reports to carry out a homogenization exercise in the definition of the supply of tourism products by regions.