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The tourism sector provides opportunities for developing countries to create productive and inclusive jobs, grow innovative firms, finance the conservation of natural and cultural assets, and increase economic empowerment, especially for women, who comprise the majority of the tourism sector’s workforce. Before the COVID-19 pandemic, tourism was the world’s largest service sector—providing one in ten jobs worldwide,  almost seven percent of all international trade and  25 percent of the world’s service exports —a critical foreign exchange generator.  In 2019 the sector was valued at more than US$9 trillion and accounted for 10.4 percent of global GDP.

Tourism offers opportunities for economic diversification and market-creation. When effectively managed, its deep local value chains can expand demand for existing and new products and services that directly and positively impact the poor and rural/isolated communities. The sector can also be a force for biodiversity conservation, heritage protection, and climate-friendly livelihoods, making up a key pillar of the blue/green economy. This potential is also associated with social and environmental risks, which need to be managed and mitigated to maximize the sector’s net-positive benefits.

The impact of the COVID-19 pandemic has been devastating for tourism service providers, with a loss of 20 percent of all tourism jobs (62 million), and US$1.3 trillion in export revenue, leading to a reduction of 50 percent of its  contribution to GDP  in 2020 alone. The collapse of demand has severely impacted the livelihoods of tourism-dependent communities, small businesses and women-run enterprises. It has also reduced government tax revenues and constrained the availability of resources for destination management and site conservation.

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• Competitive and Productive Tourism Markets. We work with government and private sector stakeholders to foster competitive tourism markets that create productive jobs, improve visitor expenditure and impact, and are supportive of high-growth, innovative firms. To do so we offer guidance on firm and destination level recovery, policy and regulatory reforms, demand diversification, investment promotion and market access. 
• Blue, Green and Resilient Tourism Economies. We support economic diversification to sustain natural capital and tourism assets, prepare for external and climate-related shocks, and be sustainably managed through strong policy, coordination, and governance improvements. To do so we offer support to align the tourism enabling and policy environment towards sustainability, while improving tourism destination and site planning, development, and management. We work with governments to enhance the sector’s resilience and to foster the development of innovative sustainable financing instruments.
• Inclusive Value Chains. We work with client governments and intermediaries to support Small and Medium sized Enterprises (SMEs), and strengthen value chains that provide equitable livelihoods for communities, women, youth, minorities, and local businesses. 

The successful design and implementation of reforms in the tourism space requires the combined effort of diverse line ministries and agencies, and an understanding of the impact of digital technologies in the industry. Accordingly, our teams support cross-cutting issues of tourism governance and coordination, digital innovation and the use and application of data throughout the three focus areas of work.

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Examples of our projects:

• In Indonesia , a US$955m loan is supporting the Government’s Integrated Infrastructure Development for National Tourism Strategic Areas Project. This project is designed to improve the quality of, and access to, tourism-relevant basic infrastructure and services, strengthen local economy linkages to tourism, and attract private investment in selected tourism destinations. In its initial phases, the project has supported detailed market and demand analyses needed to justify significant public investment, mobilized integrated tourism destination masterplans for each new destination and established essential coordination mechanisms at the national level and at all seventeen of the Project’s participating districts and cities.
• In Madagascar , a series of projects totaling US$450m in lending and IFC Technical Assistance have contributed to the sustainable growth of the tourism sector by enhancing access to enabling infrastructure and services in target regions. Activities under the project focused on providing support to SMEs, capacity building to institutions, and promoting investment and enabling environment reforms. They resulted in the creation of more than 10,000 jobs and the registration of more than 30,000 businesses. As a result of COVID-19, the project provided emergency support both to government institutions (i.e., Ministry of Tourism) and other organizations such as the National Tourism Promotion Board to plan, strategize and implement initiatives to address effects of the pandemic and support the sector’s gradual relaunch, as well as to directly support tourism companies and workers groups most affected by the crisis. 
• In Sierra Leone , an Economic Diversification Project has a strong focus on sustainable tourism development.  The project is contributing significantly to the COVID-19 recovery, with its focus on the creation of six new tourism destinations, attracting new private investment, and building the capacity of government ministries to successfully manage and market their tourism assets.  This project aims to contribute to the development of more circular economy tourism business models, and support the growth of women- run tourism businesses.  
• Through the Rebuilding Tourism Competitiveness: Tourism Response, Recovery and Resilience to the COVID-19 Crisis initiative and the Tourism for Development Learning Series , we held webinars, published insights and guidance notes as well as formed new partnerships with Organization of Eastern Caribbean States, United Nations Environment Program, United Nations World Tourism Organization, and World Travel and Tourism Council to exchange knowledge on managing tourism throughout the pandemic, planning for recovery and building back better. The initiative’s key Policy Note has been downloaded more than 20,000 times and has been used to inform recovery initiatives in over 30 countries across 6 regions.
• The Global Aviation Dashboard  is a platform that visualizes real-time changes in global flight movements, allowing users to generate 2D & 3D visualizations, charts, graphs, and tables; and ranking animations for: flight volume, seat volume, and available seat kilometers.  Data is available for domestic, intra-regional, and inter-regional routes across all regions, countries, airports, and airlines on a daily, weekly, or monthly basis from January 2020 until today. The dashboard has been used to track the status and recovery of global travel and inform policy and operational actions.

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• Global investment promotion best practices: Winning tourism investment” Investment Climate  (2013)

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• Hartwig Schafer ,  Regional collaboration can help South Asian nations rebuild and strengthen tourism industry  | July 23 rd  2020
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An analysis of the competitiveness of the tourism industry in a context of economic recovery following the COVID19 pandemic

José antonio salinas fernández.

a Dpt. of International and Spanish Economy, Universidad de Granada, c/ Paseo de Cartuja, 7, 18011 Granada Spain

José Manuel Guaita Martínez

b Department of Economics and Social Sciences, Universitat Politècnica de València, c/ Camino de Vera s/n, 46022 Valencia Spain

José María Martín Martín

Business activities within the tourism industry are especially suffering from the consequences of the COVID19 pandemic. Those countries whose economy depends largely on tourism will experience a troublesome situation for years to come. Their return to a normal situation will be conditioned by the competitiveness of their tourism sector. The study begins by pinpointing the countries that have been more hardly stricken by the pandemic and in which tourism accounts for a greater share of the GDP. A comparative analysis of the competitiveness of these countries with that of world-leading countries will be carried out so as to conclude which will face the recovery period in a more vulnerable situation. The measurement of tourism competitiveness will be supported by the creation of a synthetic indicator based on the P 2 distance method. A group of 13 countries has been identified as the most vulnerable, and it is advisable to act urgently in the following areas: the promotion of cultural elements and the historical and artistic heritage, the protection of natural areas, the availability of information and communication technologies, the international openness of the destination, and the availability of transportation infrastructures and tourist services.

1. Introduction

COVID-19 was officially declared a pandemic by the World Health Organization (WHO) on March 12, 2020. This pandemic has had significant impacts on the global economy, as a result of the containment measures adopted ( Sigala, 2020 ). One of the most affected sectors has been tourism, at the end of December 2020 it was confirmed that international tourist arrivals fell by 72% in the first ten months of 2020 ( UNWTO, 2020 ). The tourism industry has traditionally been highly sensitive to socio-economic, political and environmental risks, yet it is also a very resilient industry ( Novelli, Gussing, Jones and Ritchie, 2018 ; Jiménez, Martín and Montero, 2014 ). It is true that, in recent decades, the tourism industry has faced several crises —terrorism, earthquakes, Ebola, SARS, Zika— but it is understood to some extent that the current crisis is not comparable to those mentioned. The reason behind this is that, in previous pandemics, mass tourism was not developed in the way it is today and it was not until the 1960s that it became a global phenomenon ( Menegaki, 2020 ). Additionally, a number of health crises that have affected the tourism industry in recent years, such as SARS, did not develop into a pandemic ( Chen, Jang and Kim, 2007 ; Henderson and Ng, 2004 ). The unfolding events make us think that this crisis, besides being different from the previous ones, can bring about deep long-term changes in tourism ( Sigala, 2020 ). Some researchers have pointed out that a crisis like this may lead to the emergence of nationalist sentiments or a rejection of foreigners ( Donthu and Gustafsson, 2020 ), even fear associated with the transmission of pathogens by tourists ( Hall, 2020 ; Seong and Hong, 2021 ). In this regard, media broadcasting can influence the behavior of tourists and citizens’ attitudes during the recovery process. ( Kantar, 2020 ).

Based on the scientific production on the impact of Covid-19 on economic activities, three main lines of research can be defined: "Changes in society's consumption habits", "Impact on the public health management model" and "Economic effects of Covid-19 on business organisations" ( Carracero et al., 2021 ). The far-reaching changes that the tourism industry is undergoing and the expected long -term repercussions point towards a major economic impact. The decrease in tourism activity is expected to be the most intense in history, seven times greater than that resulting from the September 9 th terrorist attacks ( UNWTO, 2020 ). This impact, although unpredictable, derives from the great importance of tourism as an economic activity for many countries, given that it is a great source of employment and wealth: 1 out of every 10 jobs are directly or indirectly related to tourism ( UNWTO, 2020 ) and responsible for 10.3% of the world's GDP ( WTTC, 2020 ). This figure is much higher in the countries that have turned this activity into the center of their development strategy, which has resulted in a great dependence upon such an activity ( Martín, Salinas, Rodríguez and Ostos, 2020 ; Martín and Guaita, 2019 ). The strong growth of tourism at an international level ( Gómez-Vega and Picazo-Tadeo, 2019 ), has made this activity surpass economic sectors that had traditionally been the economic backbone of some countries ( Mendola and Volo, 2017 ). In fact, tourism plays a central role in the development strategies of many developing countries ( Joshi, Poudyal and Larson, 2017 ; Martín, Guaita and Burgos-Mascarrell, 2019 ). As such, the collapse of tourism as a result of the pandemic and its consequences in the medium and long term will strongly impact the economies that are highly dependent on tourism.

The competitiveness of the tourism sector in each country determines the strength of this activity, its capacity to attract flows of visitors, and, ultimately, its ability to generate wealth ( Guaita, Martín and Salinas, 2020 ). Therefore and, now more than ever, the degree of competitiveness of the different countries will be key for the recovery of the tourism industry. The pandemic has increased the gap between countries and it is expected that those with a better competitiveness will be facing the outcome of the pandemic with greater guarantees ( Sigala, 2020 ). This paper focuses on this issue, as it aims to identify which countries are the most vulnerable in view of the crisis in the tourism industry and the expected recovery. To this end, we will use three separate datasets: the weight of tourism in the country's economy, the impact of COVID19 in the country, and the degree of competitiveness of its tourism industry. This analysis will make it possible to point out the main weaknesses of the countries in terms of tourism competitiveness, but it also proposes to identify the dimensions of competitiveness on which the most vulnerable tourism destinations should focus their efforts in order to improve their position. This analysis, not carried out so far, offers a valuable contribution to the academic literature as well as contributing to the improvement of the knowledge needed for the recovery phase. In relation to previous academic literature, this analysis provides the first assessment of tourist destinations by comparing the data on competitiveness, the weight that tourism has on their GDP and the impact of the pandemic. This study identifies the specific areas that need strengthening in order to improve the situation of the most vulnerable countries. This is an entirely new contribution to the literature, as well as the way in which this analysis is carried out. In particular, it is based on a synthetic DP2 indicator designed to measure tourism competitiveness. This study provides both a framework for future analysis and an opportunity to monitor the situation. It also offers a clear contribution to the academic literature on the vulnerability of tourist destinations and their recovery after crisis situations. This can be of great use in defining public policies to strengthen the situation of the most vulnerable destinations, even ahead of crisis situations.

Measuring tourism competitiveness is a controversial and complex issue ( Abreu-Novais, Ruhanen and Arcodia, 2018 ; Salinas, Serdeira, Martín and Rodríguez, 2020 ). Several proposals have been made without a clear consensus ( Mazanec and Ring, 2011 ). In this work, we have chosen to measure tourism competitiveness based on the pillars indicated by the Travel & Tourism Competitiveness Index (TTCI) ( World Economic Forum, 2017 ). Although the final model for aggregating information is based on the P 2 Distance (DP2) method defined by Pena (1977) . This method allows for the creation of a synthetic indicator that overcomes many of the problems associated with this kind of procedure ( Rodríguez, Martín and Jiménez, 2018 ) and has been used in several studies related to the tourism industry (Rodríguez, Aguilera, Martín and Fernández, 2018 ). Based on this proposal, two research questions are posed. RQ1: Which countries are the most vulnerable in a context of crisis in the tourism industry? RQ2: In what dimensions of competitiveness should they work to improve such a situation? This will help to bridge the research gap identified in the academic literature, which advises to conduct studies that include proposals to manage this crisis ( Sigala, 2020 ). Academic research should provide useful information on the necessary transformations to be made in the tourism sector so as to address the sanitary crisis ( Lew, 2020 ).

The paper is structured as follows: first, after outlining the research gap and the research questions in the introduction, a review of the academic literature on the role of competitiveness in the tourism industry is provided. Next, we describe in detail the methodology used to create the synthetic indicator and the procedure to determine which variables offer the greatest discriminatory power. In the following section, we report on the results obtained in accordance with the initial objectives. Finally, the conclusions section presents the implications of the results of the study, its limitations, recommendations, and future lines of research.

2. Competitiveness as a vaccine for the crisis of the tourism industry

Once acknowledged the historical crisis that the tourism industry is and will continue to experience, some authors point it out as a transformative opportunity ( Mair, 2020 ). As seen in other sectors, tourism should be re-imagined and reshaped for the new normal ( McKinsey and Company, 2020 ). Crises can be a trigger for change, but no crisis has meant to date a significant transitional event for tourism ( Hall, Scott and Gössling, 2020 ). It is estimated that the tourism industry has lost 2.7 trillion USD in 2020. The most affected region is Asia-Pacific, with 63.4 million jobs lost. In Europe, job losses are estimated at 13 million ( European Data Portal, 2020 ). We can expect the pandemic to have a more lasting effect on international tourism, while other sectors will recover more quickly. Things will be especially sensitive in the countries whose economies are highly dependent on tourism, where it is crucial to monitor the situation closely and implement measures to protect this industry and mitigate the impact of the crisis ( European Data Portal, 2020 ). Therefore, it is important to generate helpful knowledge in order to promote transformations that strengthen the tourism sector and make it more competitive, otherwise it will simply be hit by successive crises ( Lew, 2020 ; Sigala, 2020 ). The crisis derived from this pandemic is highlighting weaknesses and bad practices in the tourism industry; indeed, the way in which its effects are felt could be associated with the characteristics of the growth model itself ( Ötsch, 2020 ). The chain of events that has occurred since the beginning of the crisis can be traced back to processes of large-scale urbanization, changes in the environment, and a highly interconnected world, among others (Allen, Murray, Zambrana-Torrelio, Morse, Rondinini, Di Marco, Breit, Olival, and Daszak, 2017 ). The future of the tourism industry is uncertain, given that the real impact of the pandemic in the medium and long run has yet to be determined. It is possible that a feeling of rejection towards tourism and the tourists themselves may arise from sanitary concerns. ( Donthu and Gustafsson, 2020 ; Hall, 2020 ; Seong and Hong, 2021 ). Hence the importance of planning adequate and effective recovery policies that address aspects related to the very nature of this pandemic, which is different from previous crises in the sector ( Strielkowski, 2020 ; Lew, 2020 ). In fact, one of the main lines of research that has gained momentum in the context of the pandemic focuses on the study of its economic impact ( Carracero et al., 2021 ). Therefore, at this point in time, the revival of the tourist activity is highly conditioned by the attitude of citizens and tourists ( Sigala, 2020 ; Seong and Hong, 2021 ). This differs from what has been observed in other periods of recovery, when tourist activity was linked only to economic recovery. Current forecasts point to the beginning of the recovery in the second half of 2021 ( UNWTO, 2020 ), as conditioned by the speed of vaccination and the effects of potential variants of the virus. The duration of the crisis may require profound changes in the sector, improvements in sanitation protocols and a strengthening of communication ( Chang et al., 2020 ), something for which the most competitive destinations will be better prepared. In fact, this paper's initial hypothesis assumes that: destinations that are more competitive will face the recovery in better conditions.

Bearing in mind the above, the years marked by the pandemic and the coming years after the start of mass vaccination campaigns will be extremely negative for the tourism sector. Such years will put the competitiveness of the countries to test, as it will have much to say in the race for recovery among countries. In order to progress on improving competitiveness, this concept must be correctly understood. Although it is a widely analyzed concept, there is a great deal of controversy surrounding its definition ( Mazanec, Wöber and Zins, 2007 ). The fact that there are numerous factors influencing the competitiveness of a destination makes it difficult to come up with a definition ( Gooroochurn and Sugiyarto, 2005 ; Croes and Kubickova, 2013 ). The different definitions proposed have focused on a number of aspects associated with the competitiveness of a destination. Thus, a destination will be more or less competitive depending on its ability to generate long-term benefits ( Buhalis, 2000 ), to maintain a favorable market position ( Hassan, 2000 ) and increase the economic welfare of the population ( Crouch and Ritchie, 1999 ). An updated perspective of competitiveness, which serves as a reference for this study, identifies tourism competitiveness as the optimization of the destination's resources, allowing for its development in a way that is compatible with the well-being of the locals and the preservation of resources ( Dupeyras and MacCallum, 2013 ; Martín, Guaita, Molina and Sartal, 2019 ). These same authors identify competitiveness with the optimization of the destination attractiveness, so as to gain market share. Based on this perspective, this paper analyzes the best optimization of resources for an appropriate development of tourism.

The analysis of tourism competitiveness, and therefore the assessment of the countries' situation, should consider the following dimensions: attractiveness and satisfaction with the destination, economic dimensions, dimensions associated with the well-being of the local population and sustainability ( Abreu-Novais et al ., 2018 ). In a context where it is key to reflect on the most appropriate strategies to gain in competitiveness, it is necessary to identify the factors that foster it ( De Castro, Fernández, Guaita and Martín, 2020 ; De Castro, Pérez-Rodríguez, Martín and Azevedo, 2019 ). The academic literature has described numerous factors that influence competitiveness, such as the following: basic resources and attractions, culture and the historical-artistic heritage, geography, climate or the planning of cultural or leisure events, tourism destination accessibility, transport and accommodation infrastructures, services for tourists, the willingness of the political authorities to implement a tourism-developing strategy, strategic management of the destination, human resources, service quality, marketing policies, investment-seeking, research and data treatment, international image, the level of security and safety, its location and proximity to other destinations, the cost-benefit relation, the carrying capacity, healthcare, political stability, socioeconomic relations with markets, cultural and religious matters, language, hospitality of the local residents, service excellence, quality experiences, the participation and involvement of all public and private agents in an efficient manner, the existence of continuous and transparent channels of communication, the balance between involvement and benefits for stakeholders, information management, tracking and monitoring competitiveness indexes, sustainable development policies, global strategic and marketing management, resources created by men, private competitiveness, government support, tourism demand-awareness, perception and preferences, among others (Ritchie and Crouch, 2003, 2010 ; Crouch and Ritchie, 2005 ; Heath, 2003 ; Dwyer and Kim, 2003 ). Although a long list of factors have been identified as influencing tourism competitiveness, there is no general consensus as to which are the most important ( Crouch, 2011 ).

If the definition of tourism competitiveness is not a simple task, even less so is its analysis. In the context of a crisis in the tourism industry —and subsequent recovery— it seems important to measure the level of competitiveness. At the same time, it is important to analyze which elements contribute to increasing the overall level of competitiveness, so that recovery policies take these factors into account and optimize resources ( Barbosa, Oliveira and Rezende, 2010 ). The problem of this type of analysis lies in the large number of variables that must be handled, some qualitative and others quantitative ( Kozak and Rimmington,1999 ; Guaita, de Castro, Pérez-Rodríguez and Martín, 2019 ). Usually, measuring tourism competitiveness has been based on the construction of synthetic indicators, which integrate the information of the variables with which we work ( Croes and Kubickova, 2013 ). The problems in this respect are related to the selection of the variables to be included and how they are aggregated, the availability of data, and the weighting of each variable. One of the most widespread proposals for analysis was issued by the World Economic Forum, which calculates the Travel & Tourism Competitiveness Index every year (TTCI). This synthetic indicator is made up of 90 variables organized in 14 pillars. One of the shortcomings of this methodology is that it assigns the same weight to all variables, regardless of their importance or impact. In addition, this methodology does not reveal which factors have the strongest influence on the improvement of competitiveness, something that this work aims to accomplish. In this sense, several authors have noted the importance and usefulness of highlighting the factors that drive competitiveness ( Abreu-Novais et al ., 2018 ).

3. Methodology

Three data sets were used. First, the data needed to construct the synthetic indicator of tourism competitiveness (TTCI), provided by the World Economic Forum (WEF) in the 2019 edition. It provides 90 variables in total, all of which have been used for this study. The second data set refers to the impact of COVID19 in each country. The official data from the European Centre for Disease Prevention and Control, up-to-date at the time of writing, were used for this purpose. These data reflect the cumulative incidence of the number of infected persons in relation to the country's population. The last set of data refers to the weight of the tourism industry in each country's GDP. Again, the data have been obtained from the WEF, and indicate the weight of tourism and transportation services in the total GDP of each country.

3.1. The DP 2 synthetic indicator

In this paper, Pena's P 2 distance method ( 1977 ) will be used to build a synthetic indicator of tourism competitiveness. In doing so, we will be able to classify a group of 80 countries whose tourism industry has a relevant presence in their economy. This indicator will identify the countries with the greatest vulnerability in the short and medium term, as a result of a higher number of cases of COVID-19 and for registering low levels of tourism competitiveness. The DP2 synthetic indicator —based on Ivanovic's (1974) distance— was developed by Pena (1977) by modifying the weighting of simple variables. To do so, the correlation coefficient was replaced by the determination coefficient, which operates as a corrective factor. As Somarriba and Pena (2009) point out the main advantages of the DP 2 synthetic indicator, compared to other aggregation methods such as Principal Components Analysis (PCA) or Data Envelopment Analysis (DEA), are: it eliminates the redundant information that simple variables incorporate when integrated into a synthetic indicator, it also avoids the arbitrary assignment of weights to simple variables, and solves problems related to the addition of variables expressed in different units ( Ribeiro-Navarrete, Marqués-Palacios, Martín and Guita, 2021 ). This methodology can be consulted in detail in Pena's ( 1977 ; 2009 ), Zarzosa's (1996 ; 2005 ) and Somarriba's ( 2008 ) publications and has been used by many researchers since then. Among the extensive collection of works that have used the P 2 distance method to construct synthetic indicators, those focused on welfare, quality of life, and economic and social development are the most relevant. However, in recent years, new applications have emerged in other fields or subjects, including tourism, mainly applied to the measurement of seasonality, sustainability, and competitiveness of tourist destinations. Among these works we find those of Pérez et al. (2009) , Lozano-Oyola et al. (2012) , Martín et al. (2017 , 2019 , 2020 ), Guaita et al. (2019) and Salinas et al (2020) .

Since one of the aims of our work is to measure the competitiveness of tourist destinations, the DP 2 synthetic indicator is best suited to determine the differences at a country level, since the deviation to a minimum is used as distance. This means that each country will be compared with a hypothetical baseline reference; that is, an imaginary country that shows the minimum value for all the variables —or simple indicators— thus yielding a value of zero on the DP 2 synthetic indicator. To solve the problem of variables expressed in different units of measurement, the standard deviation is used, converting them into abstract units ( Somarriba and Zarzosa, 2016 ).

According to Pena (1977) , the DP 2 indicator for a j th country is as follows:

• X ij is the value of i th variable in the j th country .
• d ij  = ࣦ x ij – x i* ࣦ is the difference between the value taken by i th variable in the j th country and the minimum of the i th variable in the whole set of countries.
• n is the number of variables.
• σ i is the standard deviation of i th variable.
• R i , i −1, i−2, ……, 1 2 , is the determination coefficient in the regression of variable x i over x i -1, x i-2 , ….., x 1 already included, where R 1 2  = 0.

By using the determination coefficient ( R i , i − 1 . i − 2 , … 1 2 ) , we are measuring the proportion of the total variance of the variable x i explained by the linear regression with respect to the variables x i-1 , x i-2 ,…., x 1 , which are previously integrated in the synthetic indicator. As a result, Pena (1977) defined the "correction factor" as ( 1 − R i , i − 1 . i − 2 , … 1 2 ) , with the purpose of eliminating the duplicated information produced by the simple variables when they enter the synthetic indicator with respect to the preceding variables, due to the existing correlation between them. As Somarriba, Zarzosa and Pena (2015) report, the DP 2 indicator only includes the new information provided by each variable or simple indicator, eliminating that which is redundant. Therefore, the correcting factors act as weights for the variables, avoiding the need to assign weights arbitrarily. If there were no correlation between the variables, the weighting of these within the synthetic indicator DP 2 would be identical. Pena's works in 1977 and 2009 show that the DP 2 synthetic indicator verifies all the mathematical properties demanded by aggregation methods. For these properties to be fulfilled, all the simple variables must progress in the same direction, so that an increase in their value always means an improvement in the objective they intend to measure, in our case, tourism competitiveness. For this purpose, the variables whose increase implies a worsening of competitiveness must be multiplied by -1 before being incorporated into the synthetic indicator. The calculation of the DP 2 indicator follows an iterative process, whereby the entry of variables or partial indicators is ordered according to the amount of information they provide with respect to the phenomenon to be measured. To do this, the absolute correlation coefficient of each variable is used in relation to the constructed synthetic indicator, ordering the variables from highest to lowest correlation, following a series of iterations until a convergence is reached in the values of the DP 2 synthetic indicator, as described by Zarzosa (1996 and 2005 ).

3.2. Discrimination power of the variables and amount of individual relative information provided to the DP2 synthetic indicator

In addition to measuring the level of competitiveness of a group of tourist destinations, another important contribution of this methodology is the possibility of identifying the variables that provide greater individual relative information to the DP2 synthetic indicator. In so doing, it is possible to identify which dimensions of competitiveness are more decisive for explaining the variability of the indicator between the countries analyzed and, consequently, implement specific policies to make the tourist destination more competitive ( Rodríguez, Martín and Salinas, 2019 ). In order to calculate the amount of individual relative information provided by the variables, it is necessary to previously determine their discrimination power. For this purpose, we will use Ivanovic's Discrimination Coefficient (1974) , which expresses the degree of inequality in the distribution of the values of each simple variable for the 80 selected countries. It is defined as follows:

m is the number of countries in the set P

x ji is the value of the variable X i in country j and x li is the minimum value taken by variable X i in country l

m ji is the number of countries where the value of X i is x ji

X ¯ i is the average of X i

k i is the number of different values that X i takes in the set P.

The "Ivanovic-Pena Global Information Coefficient" is then calculated, combining the Ivanovic Discrimination Coefficient ( 1974 ) and the Pena correction factor ( 1977 ). With this coefficient, it is possible to know the global information provided by the simple variables to the synthetic indicator DP2, defined as

where n is the total number of variables —or partial indicators— DCi is Ivanovic's discriminant coefficient and (1- R i , i − 1 , i − 2 , . . . . . . , 1 2 ) is Pena's correction factor.

Finally, in accordance with Zarzosa (1996) , we define the “individual relative information coefficient” as:

This coefficient measures the relative weight of each simple variable included in the DP 2 synthetic indicator, considering both the useful information provided by each variable and its discrimination power. The values range from 0 to 1, allowing the identification of the variables that contribute most to explaining the differences between countries in the measurement of a pre-established objective ( Rodríguez, Jiménez, Salinas and Martín, 2016 ).

3.3. The process of construction of the TTCI according to the P 2 distance method

The synthetic indicator of tourism competitiveness proposed in this study follows a two-step construction process, as described in Salinas et al . (2020) . The goal is to integrate every useful piece of information provided by the 90 variables that make up the Travel & Tourism Competitiveness Index, featured in the last report published by the World Economic Forum in 2019. The data have been downloaded from the website of this organization; whose link can be found in the bibliography ( World Economic Forum, 2019 ).

In a first stage, we have developed the partial synthetic indicators corresponding to each of the 14 pillars that make up the TTCI by taking into account all the simple variables and in accordance with the P 2 distance methodology. In a second stage, a synthetic global indicator of tourism competitiveness has been constructed, named Travel & Tourism Competitiveness Index - DP 2 (TTCI-DP2), which integrates the 14 pillars previously calculated with the same methodology. Likewise, we calculated the coefficients of individual relative information for all the variables that comprise both the partial synthetic indicators of the 14 pillars and the global synthetic index of tourism competitiveness TTCI-DP 2 . This has allowed for the identification of the key variables of competitiveness, which will have to be emphasized so as to improve the competitive situation of tourist destinations.

4. Results and discussion

Following the methodology described above, a synthetic indicator of tourism competitiveness (TTCI-DP 2 ) has been calculated for a total of 80 countries, all of which hold top positions in the international ranking. Therefore, tourism and traveling have a relevant impact on their GDP. The advantages of the indicator created in comparison with WEF's TTCI reside in the greater precision in measuring the level of competitiveness of tourism destinations, as it only takes in the non-redundant information of the simple variables and avoids the arbitrary weighting of the same. Table 1 shows the pillars or dimensions of tourism competitiveness, which represent the variables forming part of the synthetic indicator. These variables follow an entry order that is determined by the values of the absolute correlation coefficients, ordered from highest to lowest. Likewise, Table 1 also shows the corrective factors, which reveal the new, non-redundant information provided by the variables when entering the synthetic indicator with respect to previous ones. As can be seen, pillar 5 "ICT readiness" enters first into the synthetic index with the highest correlation coefficient, which means that 100% of the information provided by this variable is incorporated into the TTCI-DP2. The rest of the variables contribute less information to the synthetic indicator, although in no case is their contribution less than 30%. The pillars that contribute more new information when entering the synthetic indicator are "P7. International openness" (72.24%) and "P2. Safety and security" (63.52%), while in last place is "P1. Business environment" (30.97%).

Structure of the Travel & Tourism Competitiveness Index - DP 2

Source: own elaboration

Once the structure of the TTCI-DP 2 indicator has been examined, the following step is to determine which are the pillars or dimensions that explain, to a greater extent, the differences in tourism competitiveness of the countries. For this purpose, the Individual Relative Information Coefficient (α), defined by Zarzosa (1996) , will be calculated. This coefficient combines the useful information provided by each variable —through corrective factors— to the synthetic indicator with their discrimination power, as calculated by Ivanovic's Discrimination Coefficient. Table 2 shows the values of the Individual Relative Information Coefficient for each of the 14 pillars of competitiveness analyzed. Such a coefficient determines the importance of each pillar in the TTCI-DP2. As can be seen, the first seven pillars contribute a total of 75.6% of individual relative information to the synthetic indicator, while the remaining seven only contribute 24.4%. Therefore, the differences in competitiveness of the countries whose tourism sector accounts for the largest share of GDP are explained, to a greater extent, by the first seven dimensions. Consequently, these dimensions are key factors in the design of policies, strategies and measures to improve the competitiveness of tourism destinations.

Coefficient of individual relative information contributed by each pillar to the TTCI-DP 2 .

Source: own elaboration.

The two most relevant pillars are related to the supply of cultural (pillar 14) and natural resources (pillar 13) available at the destination. Table 3 shows in detail which variables make the greatest individual relative contribution to each pillar. Regarding Pillar 14, it is important for tourist destinations to have "Oral and intangible cultural heritage" and a high number of "World Heritage cultural sites", while in Pillar 13, the presence of "World Heritage natural sites" and protected natural areas is fundamental.

Contribution of information by variable to the key pillars of competitiveness in the TTCI-DP 2 indicator.

The next pillars that best explain the variability of the synthetic indicator TTCI-DP 2 are related to the availability of information and communication technologies (ICT), to the international openness of the destination and to the supply of transportation infrastructure and tourist services. In Pillar 5 "ICT readiness", the variables "Individuals using Internet", "Active mobile broadband Internet subscriptions" and "Fixed broadband Internet subscriptions" are decisive, which together explain more than 70% of the differences between the countries analyzed. In "P7. International openness", the "number of regional trade agreements in force" is key, as this variable contributes almost 50% of the information related to the synthetic indicator of this pillar. The territorial differences in "P12. Tourist service infrastructure" are mainly explained by the variables "Presence of major car rental companies" and "Hotel rooms", which together contribute slightly over 60% of the total information of this pillar. Then, there are two pillars related to land and port (pillar 11) and air transportation infrastructures (pillar 10). The determining variables in Pillar 11 have to do with rail network density and the efficiency of land transportation. In Pillar 10 stand out those related to the capacity of airlines to transport passengers, both domestically and internationally, and to the number of aircraft departures. The information provided in Tables 2 and ​ and3 3 allows for the identification of the pillars or dimensions that most influence the level of tourism competitiveness of destinations, as well as the particular variables to be addressed to help countries climb up the international rankings and become more competitive.

The analysis will now focus on identifying which countries are more vulnerable in the short and medium term, as they have suffered more intensely the effects of COVID-19 and have a more tourism-dependent economy. To this end, we took into account at the same time the virus incidence —in terms of cumulative number of cases per million inhabitants up to December 31 st , 2020— with the relevance of tourism in the economy of the country and with the degree of tourism competitiveness, as measured by the synthetic indicator TTCI-DP 2 . Countries whose economies are more tourism-dependent, have suffered a greater impact from COVID-19 and have a medium or low level of competitiveness will find it more difficult to return to their previous growth and employment rates in the coming years, which places them in a more vulnerable position.

The impact of the pandemic on the countries analyzed has been measured by setting a threshold of 10,000 cases per million inhabitants; above this level, the incidence is considered high. As for tourism, it is considered that its contribution to the economy is medium-high when its weight exceeds 5% of GDP. Finally, in order to classify countries according to their level of tourism competitiveness, the average of the synthetic indicator TTCI-DP 2 has been taken as a reference value, namely 21.01 points, so that those countries above that figure will be the most competitive. Based on these criteria, Table 4 has been created. It shows 8 groups of countries according to their degree of vulnerability. Similarly, Table 5 in the Annex shows the complete ranking of the 80 countries selected, according to their level of tourism competitiveness and the vulnerability group in which they fall. These 80 countries account for 95% of the industry production out of a total of 140 countries included in the latest edition of the Travel & Tourism Competitiveness Report, as well as hosting 91% of international tourist arrivals ( World Economic Forum, 2019 ). As shown in Table 4 , 13 countries with very high vulnerability and 31 countries with medium-high vulnerability have been identified. The rest of the countries are in a more favorable position with regard to the recovery of tourism activity, as their degree of vulnerability is relatively low.

Criteria for classifying countries according to their degree of vulnerability when facing the recovery of the tourism industry

Classification of countries in Travel & Tourism Competitiveness Index - DP 2 and degree of vulnerability to recover tourism activity

Source: World Economic Forum – TTCI Report 2019 (T&T share of GDP). European Centre for Disease Prevention and Control (COVID-19 cases). The authors.

Among the 13 most vulnerable countries are Mexico and Morocco, two of the tourist destinations that receive the most international travelers (around 40 and 11.5 million per year, respectively), and whose tourism sector accounts for more than 8% of their GDP. The tourism industry of three other countries has a significant presence in their economy, such as Cape Verde (18.39% of GDP) and Montenegro and Georgia, where tourism accounts for more than 10% of their GDP. Tunisia and the Dominican Republic, which receive 6-7 million international travelers every year, are also worth mentioning. The remaining six countries with the greatest vulnerability (Albania, Bahrain, Honduras, Jordan, Lebanon and Panama) receive less than 5 million international travelers per year, although the weight of tourism in their GDP ranges between 5 and 10%. In the medium-high vulnerable countries, there are some of the world's main tourist destinations in terms of the number of international arrivals and, although they occupy the top positions in the world ranking of competitiveness, their vulnerability is due to the fact that they have been strongly affected by the pandemic. Given that the tourism industry also has a significant weight in the GDP of these countries, they are expected to experience a slow recovery due to the mobility restrictions imposed to control the spread of the coronavirus. It is worth mentioning in this group the European Mediterranean countries (Spain, Italy, Greece, Portugal, Croatia and Malta), as well as Austria and the United Arab Emirates. Other relevant tourist destinations, which stand out in terms of number of international arrivals and exhibit medium-high vulnerability, are Egypt, the Russian Federation, Saudi Arabia, South Africa, Turkey, Vietnam, Seychelles, Cambodia, Philippines, and Jamaica should also be mentioned for the considerable weight of their tourism sector in GDP.

In addition to identifying the countries that show the greatest vulnerability to recover economic activity derived from tourism in the short and medium term, it is essential to examine toward which pillars or dimensions of tourism competitiveness these countries should devote the greatest efforts in order to become more competitive at the international level. Undoubtedly, only those destinations that reinforce their competitiveness will be able to face the difficult recovery of the tourism industry in the coming years. Figure 1 shows the degree of competitiveness of the most vulnerable countries for each of the 14 pillars included in the ITPGR-DP 2 .

Degree of competitiveness, by pillar, of the most vulnerable countries (Percentage reached with respect to the maximum value recorded for each pillar).

For each pillar, the average value of the most vulnerable countries has been calculated, divided by the maximum value recorded in each pillar and expressed as a percentage. As the data reveal, the most vulnerable countries perform worse in the key pillars of competitiveness, as shown in Table 2 , most of them scoring below 60%. The greatest distance from the maximum value is found in the pillars "P14. Cultural resources and business travel" (15.1%); "P10. Air transport infrastructure" (29.3%), and "P13. Natural resources" (40.6%). Therefore, these countries should focus on developing policies aimed at improving the worst aspects of the pillars that have the greatest impact on the competitiveness of tourism destinations. To do so, countries should prioritize improving the indicators shown in Table 3 , since they are the ones that explain the greatest territorial differences in each pillar.

5. Conclusions

As a consequence of the COVID-19 pandemic, the tourism industry has been significantly affected. This crisis situation is expected to continue in the medium and long term, so those countries where tourism is one of the main sources of income will take longer to recover. The impact on economies will depend partially on the competitiveness of each country's tourism sector. The most competitive destinations will be in a better position to face the recovery process and will even be more robust in withstanding the crisis. This situation can generate an opportunity, as long as tourist destinations opt for improving their competitiveness and move towards a transformation that will make them stronger. Thus, identifying the most vulnerable countries and the variables that explain their vulnerability is a very interesting contribution to support crisis response policies. This study focuses on such an objective. Basically, it seeks to identify the most vulnerable countries as regards their tourism industry in the context of a pandemic. This pioneering contribution to the academic literature will make it possible to understand the character of these countries' vulnerability and thus facilitate the development of public policies to promote tourism. Therefore, this research, in addition to being innovative, is of great social utility.

The proposed study has grouped countries according to their vulnerability. Said vulnerability is determined by combining several characteristics: low competitiveness, a high incidence of COVID19 and a high weight of tourism in its economy. As a result, we have identified the 13 most vulnerable countries, namely: Panama, Georgia, Bahrain, Morocco, Montenegro, Albania, Mexico, Dominican Republic, Jordan, Tunisia, Cape Verde, Honduras, and Lebanon. This answers RQ1: Which countries are the most vulnerable in the context of the crisis in the tourism sector? It should be borne in mind that maximum vulnerability is reached when the country is highly dependent on tourism activity, has poor levels of competitiveness and a high incidence of the pandemic. The countries mentioned above comply with these criteria, so that the most effective action in the short term would be to control the incidence of the pandemic and improve tourism competitiveness, since diversification policies would take longer to be effective.

These countries show a very negative situation in the pillars or dimensions that have been identified as key to tourism competitiveness, most of them being below 60% with respect to the value achieved by the best positioned country. The pillars with the greatest distance in relation to the maximum value are "P14. Cultural resources and business travel" (15.1%); "P10. Air transport infrastructure" (29.3%), and "P13. Natural resources" (40.6%). Thus, the most vulnerable countries should define policies to improve their situation in these competitive factors, since, in addition to having been identified as key elements, they are the weakest in these areas. Specifically, the determining elements of competitiveness on which it is possible to work more effectively in the short/medium term would be those related to the enhancement of cultural elements and historical-artistic heritage; the protection of natural areas; the availability and improvement of information and communication technologies; the international opening of the destination, which, in turn, would promote regional trade agreements; and the increase in the supply of transport infrastructure, especially rail and air transport, as well as tourist services. This would answer RQ2: In which dimensions of competitiveness should they work to improve this situation? The above outlines three strategic elements for improving competitiveness. The first focuses on the management and protection of tourism resources, both cultural and natural. The second involves improving transportation and telecommunications infrastructures. And third, improving the country's external openness. The most vulnerable countries should design strategies focused on these lines, or at least on those on which they can work more effectively in the short term.

This research contributes, in the first place, to identifying the countries with the worst departing point in the process of recovery after the peak of the pandemic. Secondly, it sets out a roadmap of factors on which the countries should focus in order to improve the competitiveness of tourist destinations. It would be interesting to continue this research by carrying out a follow-up study during the recovery period, the recovery period, related to the evolution of arrivals to each of the destinations defined as vulnerable. It would also be very interesting and useful to compare the nature of the policies adopted by the countries to support their tourism sector with the factors on which intervention has been recommended.

Declaration of Competing Interest

Biographies.

Jose Antonio Salinas Fernández is a senior lecturer at the University of Granada, Spain, Department of Spanish and International Economics. The interests of his research focus on tourism management, economic impact, social and economic indicators. He holds a Ph.D. in Economics and Business and a MA in Economy of the European Union. He has worked as an economic analyst and consulting projects director for various companies and financial institutions.

José Manuel Guaita Martinez has been the Director of the master's degree in Business Administration at the Valencian International University since 2014 until 2020. He received a PhD in Economics after completing a degree in Business Administration and Contemporary History. He has also been the head of the international financial markets department at several banking institutions. He conducts basic research into financial markets, sport, entrepreneurship, innovation, sustainability, and tourism economics. He has guest edited special issues in Journal of Business Research and Technological Forecasting and Social Change. He belongs to the editorial review board of top journals such as Journal of Business Research, Journal of Innovation & Knowledge, and International Journal of Entrepreneurial Behavior & Research. He is currently a senior lecturer at Universitat Politècnica de València

Jose María Martín Martín is a senior lecturer at University of Granada, Spain, Department of Spanish and International Economics. The interests of his research focus on tourism economics, economic and social sustainability, seasonality and sustainable development. He holds a Ph.D. in Economics and Business and a MA in International Business. He has worked as an economic analyst and consulting projects director for various companies and financial institutions. He has collaborated with the Spanish government in the development of public policies in the tourism sector. He has also led the Business Area for the International University of La Rioja.

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Competitive Advantage in Tourism

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Competitive advantage has a long history of application in industrial studies relating to competition and competitiveness at the company or firm level. Its introduction to tourism and destination management started after the publication of The Competitive Advantage of Nations (Porter 1990 ). At the industry level, competitive advantage is used to describe a firm’s ability to create more economic value (the difference between the perceived benefits by a customer who purchases a firm’s products or services and their full economic cost) than its rival firms (Barney 2007 :17).

At the global level, competitive advantage depends on the country’s ability to innovatively achieve, or maintain, an advantageous position in its key industries over others. In relation to tourism management, competitive advantage deals with the ability to use a destination’s resources efficiently and effectively over the long term (Crouch and Ritchie 1999 ). A number of researchers have provided inputs into the...

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Barney, J. 2007. Gaining and Sustaining Competitive Advantage . Upper Saddle River: Prentice Hall.

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Cronjé, D., and E. du Plessis. 2020. A Review on Tourism Destination Competitiveness. Journal of Hospitality and Tourism Management 45: 256–265.

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Crouch, G., and J. Ritchie. 1999. Tourism, Competitiveness, and Societal Prosperity. Journal of Business Research 44: 137–152.

Porter, M. 1990. The Competitive Advantage of Nations . New York: Macmillan.

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Tsai, H., H. Song, and K. Wong. 2009. Tourism and Hotel Competitiveness Research. Journal of Travel and Tourism Marketing 26: 522–546.

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Guillet, B.D. (2023). Competitive Advantage in Tourism. In: Jafari, J., Xiao, H. (eds) Encyclopedia of Tourism. Springer, Cham. https://doi.org/10.1007/978-3-319-01669-6_33-2

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Reimagining the $9 trillion tourism economy—what will it take?

Tourism made up 10 percent of global GDP in 2019 and was worth almost $9 trillion, 1 See “Economic impact reports,” World Travel & Tourism Council (WTTC), wttc.org. making the sector nearly three times larger than agriculture. However, the tourism value chain of suppliers and intermediaries has always been fragmented, with limited coordination among the small and medium-size enterprises (SMEs) that make up a large portion of the sector. Governments have generally played a limited role in the industry, with partial oversight and light-touch management.

COVID-19 has caused an unprecedented crisis for the tourism industry. International tourist arrivals are projected to plunge by 60 to 80 percent in 2020, and tourism spending is not likely to return to precrisis levels until 2024. This puts as many as 120 million jobs at risk. 2 “International tourist numbers could fall 60-80% in 2020, UNWTO reports,” World Tourism Organization, May 7, 2020, unwto.org.

Reopening tourism-related businesses and managing their recovery in a way that is safe, attractive for tourists, and economically viable will require coordination at a level not seen before. The public sector may be best placed to oversee this process in the context of the fragmented SME ecosystem, large state-owned enterprises controlling entry points, and the increasing impact of health-related agencies. As borders start reopening and interest in leisure rebounds in some regions , governments could take the opportunity to rethink their role within tourism, thereby potentially both assisting in the sector’s recovery and strengthening it in the long term.

In this article, we suggest four ways in which governments can reimagine their role in the tourism sector in the context of COVID-19.

1. Streamlining public–private interfaces through a tourism nerve center

Before COVID-19, most tourism ministries and authorities focused on destination marketing, industry promotions, and research. Many are now dealing with a raft of new regulations, stimulus programs, and protocols. They are also dealing with uncertainty around demand forecasting, and the decisions they make around which assets—such as airports—to reopen will have a major impact on the safety of tourists and sector employees.

Coordination between the public and private sectors in tourism was already complex prior to COVID-19. In the United Kingdom, for example, tourism falls within the remit of two departments—the Department for Business, Energy, and Industrial Strategy (BEIS) and the Department for Digital, Culture, Media & Sport (DCMS)—which interact with other government agencies and the private sector at several points. Complex coordination structures often make clarity and consistency difficult. These issues are exacerbated by the degree of coordination that will be required by the tourism sector in the aftermath of the crisis, both across government agencies (for example, between the ministries responsible for transport, tourism, and health), and between the government and private-sector players (such as for implementing protocols, syncing financial aid, and reopening assets).

Concentrating crucial leadership into a central nerve center  is a crisis management response many organizations have deployed in similar situations. Tourism nerve centers, which bring together public, private, and semi-private players into project teams to address five themes, could provide an active collaboration framework that is particularly suited to the diverse stakeholders within the tourism sector (Exhibit 1).

We analyzed stimulus packages across 24 economies, 3 Australia, Bahrain, Belgium, Canada, Egypt, Finland, France, Germany, Hong Kong, Indonesia, Israel, Italy, Kenya, Malaysia, New Zealand, Peru, Philippines, Singapore, South Africa, South Korea, Spain, Switzerland, Thailand, and the United Kingdom. which totaled nearly $100 billion in funds dedicated directly to the tourism sector, and close to $300 billion including cross-sector packages with a heavy tourism footprint. This stimulus was generally provided by multiple entities and government departments, and few countries had a single integrated view on beneficiaries and losers. We conducted surveys on how effective the public-sector response has been and found that two-thirds of tourism players were either unaware of the measures taken by government or felt they did not have sufficient impact. Given uncertainty about the timing and speed of the tourism recovery, obtaining quick feedback and redeploying funds will be critical to ensuring that stimulus packages have maximum impact.

2. Experimenting with new financing mechanisms

Most of the $100 billion stimulus that we analyzed was structured as grants, debt relief, and aid to SMEs and airlines. New Zealand has offered an NZ $15,000 (US $10,000) grant per SME to cover wages, for example, while Singapore has instituted an 8 percent cash grant on the gross monthly wages of local employees. Japan has waived the debt of small companies where income dropped more than 20 percent. In Germany, companies can use state-sponsored work-sharing schemes for up to six months, and the government provides an income replacement rate of 60 percent.

Our forecasts indicate that it will take four to seven years for tourism demand to return to 2019 levels, which means that overcapacity will be the new normal in the medium term. This prolonged period of low demand means that the way tourism is financed needs to change. The aforementioned types of policies are expensive and will be difficult for governments to sustain over multiple years. They also might not go far enough. A recent Organisation for Economic Co-operation and Development (OECD) survey of SMEs in the tourism sector suggested more than half would not survive the next few months, and the failure of businesses on anything like this scale would put the recovery far behind even the most conservative forecasts. 4 See Tourism policy responses to the coronavirus (COVID-19), OECD, June 2020, oecd.org. Governments and the private sector should be investigating new, innovative financing measures.

Revenue-pooling structures for hotels

One option would be the creation of revenue-pooling structures, which could help asset owners and operators, especially SMEs, to manage variable costs and losses moving forward. Hotels competing for the same segment in the same district, such as a beach strip, could have an incentive to pool revenues and losses while operating at reduced capacity. Instead of having all hotels operating at 20 to 40 percent occupancy, a subset of hotels could operate at a higher occupancy rate and share the revenue with the remainder. This would allow hotels to optimize variable costs and reduce the need for government stimulus. Non-operating hotels could channel stimulus funds into refurbishments or other investment, which would boost the destination’s attractiveness. Governments will need to be the intermediary between businesses through auditing or escrow accounts in this model.

Joint equity funds for small and medium-size enterprises

Government-backed equity funds could also be used to deploy private capital to help ensure that tourism-related SMEs survive the crisis (Exhibit 2). This principle underpins the European Commission’s temporary framework for recapitalization of state-aided enterprises, which provided an estimated €1.9 trillion in aid to the EU economy between March and May 2020. 5 See “State aid: Commission expands temporary framework to recapitalisation and subordinated debt measures to further support the economy in the context of the coronavirus outbreak,” European Commission, May 8, 2020, ec.europa.eu. Applying such a mechanism to SMEs would require creating an appropriate equity-holding structure, or securitizing equity stakes in multiple SMEs at once, reducing the overall risk profile for the investor. In addition, developing a standardized valuation methodology would avoid lengthy due diligence processes on each asset. Governments that do not have the resources to co-invest could limit their role to setting up those structures and opening them to potential private investors.

3. Ensuring transparent, consistent communication on protocols

The return of tourism demand requires that travelers and tourism-sector employees feel—and are—safe. Although international organizations such as the International Air Transport Association (IATA), and the World Travel & Tourism Council (WTTC) have developed a set of guidelines to serve as a baseline, local regulators are layering additional measures on top. This leads to low levels of harmonization regarding regulations imposed by local governments.

Our surveys of traveler confidence in the United States  suggests anxiety remains high, and authorities and destination managers must work to ensure travelers know about, and feel reassured by, protocols put in place for their protection. Our latest survey of traveler sentiment in China  suggests a significant gap between how confident travelers would like to feel and how confident they actually feel; actual confidence in safety is much lower than the expected level asked a month before.

One reason for this low level of confidence is confusion over the safety measures that are currently in place. Communication is therefore key to bolstering demand. Experience in Europe indicates that prompt, transparent, consistent communications from public agencies have had a similar impact on traveler demand as CEO announcements have on stock prices. Clear, credible announcements regarding the removal of travel restrictions have already led to increased air-travel searches and bookings. In the week that governments announced the removal of travel bans to a number of European summer destinations, for example, outbound air travel web search volumes recently exceeded precrisis levels by more than 20 percent in some countries.

The case of Greece helps illustrate the importance of clear and consistent communication. Greece was one of the first EU countries to announce the date of, and conditions and protocols for, border reopening. Since that announcement, Greece’s disease incidence has remained steady and there have been no changes to the announced protocols. The result: our joint research with trivago shows that Greece is now among the top five summer destinations for German travelers for the first time. In July and August, Greece will reach inbound airline ticketing levels that are approximately 50 percent of that achieved in the same period last year. This exceeds the rate in most other European summer destinations, including Croatia (35 percent), Portugal (around 30 percent), and Spain (around 40 percent). 6 Based on IATA Air Travel Pulse by McKinsey. In contrast, some destinations that have had inconsistent communications around the time frame of reopening have shown net cancellations of flights for June and July. Even for the high seasons toward the end of the year, inbound air travel ticketing barely reaches 30 percent of 2019 volumes.

Digital solutions can be an effective tool to bridge communication and to create consistency on protocols between governments and the private sector. In China, the health QR code system, which reflects past travel history and contact with infected people, is being widely used during the reopening stage. Travelers have to show their green, government-issued QR code before entering airports, hotels, and attractions. The code is also required for preflight check-in and, at certain destination airports, after landing.

4. Enabling a digital and analytics transformation within the tourism sector

Data sources and forecasts have shifted, and proliferated, in the crisis. Last year’s demand prediction models are no longer relevant, leaving many destinations struggling to understand how demand will evolve, and therefore how to manage supply. Uncertainty over the speed and shape of the recovery means that segmentation and marketing budgets, historically reassessed every few years, now need to be updated every few months. The tourism sector needs to undergo an analytics transformation to enable the coordination of marketing budgets, sector promotions, and calendars of events, and to ensure that products are marketed to the right population segment at the right time.

Governments have an opportunity to reimagine their roles in providing data infrastructure and capabilities to the tourism sector, and to investigate new and innovative operating models. This was already underway in some destinations before COVID-19. Singapore, for example, made heavy investments in its data and analytics stack over the past decade through the Singapore Tourism Analytics Network (STAN), which provided tourism players with visitor arrival statistics, passenger profiling, spending data, revenue data, and extensive customer-experience surveys. During the COVID-19 pandemic, real-time data on leading travel indicators and “nowcasts” (forecasts for the coming weeks and months) could be invaluable to inform the decisions of both public-sector and private-sector entities.

This analytics transformation will also help to address the digital gap that was evident in tourism even before the crisis. Digital services are vital for travelers: in 2019, more than 40 percent of US travelers used mobile devices to book their trips. 7 Global Digital Traveler Research 2019, Travelport, marketing.cloud.travelport.com; “Mobile travel trends 2019 in the words of industry experts,” blog entry by David MacHale, December 11, 2018, blog.digital.travelport.com. In Europe and the United States, as many as 60 percent of travel bookings are digital, and online travel agents can have a market share as high as 50 percent, particularly for smaller independent hotels. 8 Sean O’Neill, “Coronavirus upheaval prompts independent hotels to look at management company startups,” Skift, May 11, 2020, skift.com. COVID-19 is likely to accelerate the shift to digital as travelers look for flexibility and booking lead times shorten: more than 90 percent of recent trips in China  were booked within seven days of the trip itself. Many tourism businesses have struggled to keep pace with changing consumer preferences around digital. In particular, many tourism SMEs have not been fully able to integrate new digital capabilities in the way that larger businesses have, with barriers including language issues, and low levels of digital fluency. The commission rates on existing platforms, which range from 10 percent for larger hotel brands to 25 percent for independent hotels, also make it difficult for SMEs to compete in the digital space.

Governments are well-positioned to overcome the digital gap within the sector and to level the playing field for SMEs. The Tourism Exchange Australia (TXA) platform, which was created by the Australian government, is an example of enabling at scale. It acts as a matchmaker, connecting suppliers with distributors and intermediaries to create packages attractive to a specific segment of tourists, then uses tourist engagement to provide further analytical insights to travel intermediaries (Exhibit 3). This mechanism allows online travel agents to diversify their offerings by providing more experiences away from the beaten track, which both adds to Australia’s destination attractiveness, and gives small suppliers better access to customers.

Governments that seize the opportunity to reimagine tourism operations and oversight will be well positioned to steer their national tourism industries safely into—and set them up to thrive within—the next normal.

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Margaux Constantin is an associate partner in McKinsey’s Dubai office, Steve Saxon is a partner in the Shanghai office, and Jackey Yu  is an associate partner in the Hong Kong office.

The authors wish to thank Hugo Espirito Santo, Urs Binggeli, Jonathan Steinbach, Yassir Zouaoui, Rebecca Stone, and Ninan Chacko for their contributions to this article.

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Handbook of Business Strategy

ISSN : 1077-5730

Article publication date: 1 January 2006

This paper demonstrates how the tour operating industry must take responsibility of the sustainability of its suppliers as part of the quality expected by tourists, in order to remain competitive.

Design/methodology/approach

Case studies resulting from telephone surveys, interviews and document searches. The theoretical approach is that of using sustainable supply chain management both as a method of corporate social responsibility and a strategy for industry survival.

Price wars have forced mass tourism operators to small margins, while ignoring the growing special interest market. Sustainability is now part of quality expectations and the industry as a whole has to reinvent itself to meet changing demands, while also embedding corporate social responsibility in a way that makes business sense.

Research limitations/implications

The challenge is transferring experience to less sophisticated and mature markets, where at present there is little evidence of demand for sustainable products.

Practical implications

Industry wide standards are necessary as the lever for change in those industries where short return on investment eco‐savings will not be possible, and where the future of a whole industry relies on joint action.

Originality/value

The paper makes a contribution to the limited knowledge of sustainable supply chain management in the service sector. Most research emphasizes environmental issues in manufacturing.

• Supply chain management
• Responsibilities
• Service industries

Font, X. , Tapper, R. and Cochrane, J. (2006), "Competitive strategy in a global industry: tourism", Handbook of Business Strategy , Vol. 7 No. 1, pp. 51-55. https://doi.org/10.1108/10775730610618611

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ORIGINAL RESEARCH article

Tourism competitiveness evaluation: evidence from mountain tourism in china.

• 1 Research Center of Eco-cultural Tourism in Western Hubei, Hubei Minzu University, Enshi, China
• 2 School of Political Science and Public Administration, Neijiang Normal University, Neijiang, China
• 3 Hubei Enshi College, Enshi, China
• 4 Penshui County Development and Reform Commission, Chongqing, China
• 5 Institute of Ethnology and Sociology, Southwest Minzu University, Chengdu, China

The evaluation of tourism competitiveness is an important tool for analyzing the potential of tourism in a specific context. Enshi Autonomous Prefecture (EAP) in China is selected as a case through which to explore the potential of mountain tourism and its competitiveness in the tourism industry. This study develops EAP’s mountain tourism competitiveness model focusing on three criteria: core competitiveness of mountain tourism, the economic environment’s competitiveness, and infrastructure competitiveness. Context-specific customized evaluation index has been applied to data collected from EAP Statistical Yearbook for 2005–2014. The study reveals that the value of EAP’s mountain tourism core competitiveness, economic and environmental competitiveness, and infrastructure competitiveness are 84.292, 13.4, and 2.308%, respectively. When tourism core competitiveness is increased by one unit, EAP’s mountain tourism competitiveness will increase by 0.84292 units. Similarly, when economic environment competitiveness is increased by one unit, EAP’s mountain tourism competitiveness will increase by 0.134 units. EAP’s mountain tourism competitiveness increases by 0.02308 units when infrastructure competitiveness increases by one unit. The major reasons for low levels of competitiveness were lack of awareness of the county authority, a low level of cooperation, and weak infrastructure. The recommendations from the study’s findings are as follows. Firstly, the county authority should appropriately improve the relationship between competition and cooperation, maintaining cooperation in competition, and competition in cooperation. Secondly, the county authority should strengthen communication by establishing an effective coordinated mechanism. Thirdly, the county authority should improve the sense of cooperation and jointly develop the mountain tourism market. Fourthly, the county authority should improve the construction of tourism infrastructure and break down the barriers to tourism cooperation. The study’s findings help develop a “win-win” cooperation mechanism within the competition and support the sustainable development of the mountain tourism industry while reducing poverty and promoting the revitalization of the mountains of China.

Introduction

Mountain tourism is considered to be a key tool for poverty reduction, economic development, and environmental management. Rapidly increasing the level of the mountain tourism industry can enhance the progress of society, national income, the demand for tourism, and overall economic development ( Wang et al., 2013 ). The tourism industry also plays an important role in promoting cultural exchange, employment, and regional economic development ( Liu et al., 2022 ). At present, the tourism industry has become the fastest growing industry and is one of the important pillars of world economic activities, with this highly recognized in all countries worldwide ( Shen et al., 2019 ). China also places great importance on developing the mountain tourism industry, strengthening various measures to promote the development of this industry and encouraging mountain tourism competition between regions ( Zeng et al., 2022 ). Tourist destination competition usually affects the redistribution of market opportunities ( Patton, 1985 ; Shi et al., 2016 ). The associated challenges include scientifically evaluating the competitiveness of mountain tourism destinations and the formation of measures to enhance tourism competitiveness ( Nazmfar et al., 2019 ). In contrast to other tourism sectors (such as spa, heritage, and sightseeing tourism), Mountain tourism allows visitors to pursue wellness and associated passions ( Zeng et al., 2022 ). Because of its recreational, detaching, healing, and sporting characteristics, the mountain attracts the largest flows of tourists, and it is becoming a key social and educational component for the community ( Bacoş and Gabor, 2021 ). Considering the importance, this study focuses on the potential of mountain tourism and its competitiveness in the tourism industry.

Tourism climate focuses on the physical, thermal, and aesthetic variables. Most physical and aesthetic qualities are subjective, which plays a significant role in tourism. China’s tourist business has grown into an all-encompassing sector encompassing a wide range of activities. Tourism has emerged as a new engine for developing the national economy in China. In contrast to the falling trend in the number of incoming tourists to China, the quality of Chinese tourism services has remained “generally constant with steady growth” in recent years ( Peng and Yuan, 2019 ). As a result, the slow growth of inbound tourism in recent years has had only a negligible impact on the quality of tourism services. Intuitively, the confluence of increased haze and decreased incoming visitor numbers may explain the slow growth of inbound tourism, and the link between the two has piqued the interest of academics. Haze has a detrimental effect on inbound tourism traffic in China. Furthermore, haze has impacted inbound tourism and domestic tourism, which has seen a drop in demand and market growth, exacerbating the tourist sector’s cyclical oscillations. The Chinese government recently suggested the development strategy of constructing an ecological civilization and the grand objective of building a “beautiful China” to alleviate the threat of significant environmental degradation to the Chinese economy’s long-term viability ( Zeng et al., 2022 ). Therefore, an investigation on tourism competitiveness is urgent to develop the industry.

Several studies have been conducted on the evaluation index system of tourism competitiveness. For example, Li and Du (2021) investigated the coupling coordination relationship between culture and tourist flow in China and used the competitiveness evaluation index for measuring tourism competitiveness. Gao et al. (2021) conducted a study on tourism competitiveness and sustainability in China by using an evaluation index approach. Haahti (1986) formulated this evaluation index system, evaluated Finland’s tourism competitiveness, and proposed a series of measures to consolidate Finland’s tourism competitiveness. Kozak and Rimmington (1999) divided the indicators of tourism competitiveness into hard indicators and soft indicators, thus being able to measure the tourism competitiveness of tourism destinations by combining hard indicators with soft indicators. Sánchez et al. (2006) used social information technology (IT), national policy, and tourism talent in their evaluation index system of tourism competitiveness. The brand image of tourism is an extremely important factor in evaluating tourism competition ( Law, 2001 ; Law, 2007 ; Greene et al., 2007 ; Chon et al., 2010 ; Zhang et al., 2011 ; Zhang and Lu, 2012 ). Zhang and Lu (2012) selected evaluation indexes from the following four aspects: factor competitiveness, market competitiveness, management competitiveness, and development competitiveness. Su et al. (2003) established an index system from the perspectives of urban tourism competitive performance, competitive potential of urban tourism, and urban tourism competitive environment support. Zhang and Zhou (2005) selected evaluation indexes using four aspects: tourism development scale, outbound tourism ability, tourism organization ability, and tourism reception ability, using a province as their study area.

Tourism competitiveness is a key issue for governments and destinations seeking a competitive edge in the ever-changing global tourist industry. The relative competitiveness of tourism sites influences their performance in global marketplaces ( Sedlacek et al., 2022 ). Attracting tourists to locations has gotten more difficult as global tourism market growth has slowed and market shares have shifted. As a result, the tourist competitiveness of locations has received more attention. The strength or capacity of a place to give a great experience to tourists is at the heart of tourism competition ( Zeng et al., 2022 ). The issue of tourism competitiveness is critical for countries that want to monitor and perform effectively in the global tourism sector. Understanding a country’s tourism competitiveness is critical for policymakers and a significant task for professionals in producing evidence to support decision-making.

Tourism competitiveness has become a hotspot of theoretical and practical research. Scholars have conducted a few studies on the basic connotation ( Zhang et al., 2011 ; Deng et al., 2019 ), management ( Maso et al., 2016 ), influencing factors ( Wang et al., 2013 ), rurality ( Cvelbar et al., 2016 ; Shen et al., 2019 ), and evaluation methods ( Mendola and Volo, 2017 ; Jiao et al., 2018 ). These scholars have achieved rich results, but some issues still need to be solved urgently. Firstly, evaluations have been done regarding the competitiveness of different regional tourism destinations ( Lopes et al., 2018 ; Sedlacek et al., 2022 ). Existing studies mostly focus on national, provincial, and developed urban tourism destinations ( Lee et al., 2010 ; Chen et al., 2011 ), thus belonging to macro-evaluation research. However, few studies have been conducted on the micro-evaluation of tourism destinations for underdeveloped regions, especially for regions in which ethnic minorities live, as in the Western mountains of China. Secondly, the existing evaluation index system is relatively macroscopic, incomplete, and not specific; thus, it is not suitable for the systematic evaluation of the competitiveness of local or regional tourism destinations. Thirdly, the existing research is mainly from the perspective of competitiveness but lacks the perspective of cooperation in exploring the competitiveness of tourism destinations. This study intends to fill the research gap by addressing a couple of questions, like what is the potential of mountain tourism? Is mountain tourism being competitive in the tourism industry in China? And what will be the context-specific measurement of mountain tourism competitiveness? These issues are becoming increasingly important to the government, academics, and the general public. It is critical to construct a technology-based system for evaluating mountain tourism competitiveness, to statistically evaluate and scientifically identify obstacles impeding actual demands, as well as the simultaneous pressures of the authority and competition ( Gao et al., 2021 ). Tourist destinations need both competition and cooperation ( Zhang et al., 2022 ). To fundamentally enhance the competitiveness of tourist destinations, it is necessary to strengthen cooperation between these destinations, such as in the mountain tourism industry in China, and achieve “win-win” cooperation.

Enshi Autonomous Prefecture (EAP), an important tourist destination in Western China, has eight counties and is located in China’s Western mountains. As tourism resources are mainly concentrated in mountain areas, mountain tourism is the main form of tourism. The EAP government has given higher priority to developing the mountain tourism industry to make it the prefecture’s leading industry. An analysis and evaluation of EAP’s mountain tourism competitiveness can help clarify the attractiveness of each region’s mountain tourism industry and identify the direction for its development. Therefore, the development of evaluation indicators and models, the analysis of key competitiveness factors, and the development of future directions are conducive to maintaining long-term competitive advantage. These drivers can promote the sustainable and healthy development of EAP’s mountain tourism industry. Therefore, the aim of this study is to construct an evaluation model of tourism competitiveness for the mountain tourism industry. The rest of the manuscript is arranged as follows. Section “Literature Review” presents the literature review, sections “Materials and Methods” and “Results” respectively describe the methodology, results, and discussion, while section “Discussions” concludes the manuscript with recommendations.

Literature Review

Rapid urbanization in China is stimulating its citizens to develop feelings for nature and to spend the maximum amount of their leisure time in tourist destinations. Mountain tourism provides not only a place of entertainment but also acts as a key tool for developing the mainstream economy. Tourism competitiveness is defined as the ability of a tourist destination to attract and satisfy potential tourists ( Enright and Newton, 2004 ; Zhang et al., 2011 ). Ritchie and Crouch (2000) explained the concept of tourism competitiveness in terms of the value addition from tourism destination development in a country. Pearce (1997) believed that the competitiveness of tourist destinations depends on the method of evaluation of their development. Tourism competitiveness is also the ability of tourism competitors to obtain comprehensive benefits from international and domestic perspectives. Some scholars have conducted research on tourism competitiveness in a provincial region, revealing that the essence of tourism destination competitiveness is the comprehensive quality of tourism development in different regions ( Zhang and Zhou, 2005 ; Wen and Liang, 2007 ; Zhang et al., 2011 ).

The formation and development of tourism competitiveness are affected by many factors. For example, Eraqi (2011) assessed the influencing factors of tourism competitiveness from different aspects, and discussed in depth the importance of Egyptian enterprises’ marketing strategies and their tourism management attitudes toward tourism competitiveness. Finally, he analyzed ways to better implement these aspects in the Egyptian tourism industry to make full use of the potential of tourism competitiveness, finding the suitability of a marketing strategy for this purpose ( Law and Ting, 2011 ). Mayaka and Akama (2007) , in their study in Kenya, reported that the key factor for tourism competitiveness is the quality of human resources in Kenya’s tourist destinations. Mihalič (2000) reported that tourist destinations, tourist routes, and the tourism industry are the main factors to use when measuring tourism competitiveness. Scholars have found that the main influencing factors of tourism competitiveness are human resources, talent competition, image marketing, and knowledge-based management ( Wei, 2000 ; Nie, 2006 ; Zhang et al., 2011 ; Cvelbar et al., 2016 ; Yang et al., 2020 ; Lan et al., 2021 ). In addition, tourists’ perceptions and the level of scientific and technological development of tourist destinations have a significant impact on the competitiveness of urban tourism destinations.

Mountain tourism is considered to be a vital factor for economic development and forms the soul of the tourism industry ( Chin et al., 2014 ). This adds to the competition within the mountain tourism industry. Assessment of the competitiveness of mountain tourism has a pivotal role in the sustainable development of this industry ( Lo et al., 2019 ). Appropriate assessment can help to develop this industry into becoming a competitive environment. Although mountain tourism may not be a top contributor in the mainstream economy, it makes a valuable contribution to the mountain economy in a large country like China. Previous studies have focused on mountain tourism as a way to generate income, support local communities, and create local employment ( Wang et al., 2013 ; Shi et al., 2016 ; Mendola and Volo, 2017 ; Shen et al., 2019 ). However, research is lacking on the assessment of mountain tourism competitiveness. Therefore, this study intends to fill the research gap by developing a model for assessment of tourism competitiveness and testing the model in a case in the Western mountains of China. This study adopts all available indicators from prior studies and customizes them in the EAP context.

Materials and Methods

The Enshi Autonomous Prefecture (EAP) of China is full of natural resources; its climate, topography, vegetation, and other features are distinctive, as are its tourism opportunities. The expansion of the tourism sector is a critical component of the southwestern region’s economic prosperity. As a result, the current study’s research focus is southwestern China (EAP).

Enshi Autonomous Prefecture is located in the southwest of Hubei Province. It is a typical mountainous area; thus, it is mainly mountain tourism. The prefecture’s territory covers an area of 24,000 km 2 . It comprises eight counties: Enshi County, Lichuan County, Jianshi County, Badong County, Xuanen County, Laifeng County, Xianfeng County, and Hefeng County, as shown in Figure 1 . The registered population is 4,020,000, among whom the population of minorities accounts for 54%. In the 1990s, EAP’s mountain tourism industry began to develop gradually. By the end of 2016, 31 A-level scenic spots were designated: mentionable among them were two scenic spots at 5A level and 16 scenic spots above 4A level. A-level scenic spots rank in the forefront of Hubei Province, with EAP also having 46 hotels at higher than three-star level. Among travel agencies, 13 are above 3A level, of which two are at 4A level. Accommodation is provided by, among others, 75 star-rated hotels. Tour guides holding tour guide qualification certificates number 1,558. Mountain tourism leads, directly, to the employment of more than 100,000 people and, indirectly, leads to the employment of 400,000 people ( Cao et al., 2019 ).

Figure 1. Regional distribution map of eight counties in Enshi Autonomous Prefecture (EAP), China.

Factor analysis was used in this study; specifically, the study used a multivariate statistical method that originated from Karl Pearson and Charles Spearman’s statistical analysis of psychological tests in the early 20th century. This method’s core is to use the least independent factors to reflect the vast majority of the information of the original variables. Through the analysis of the causal relationships between indicators, researchers are able to find out the main contradictions and key indicators.

Factor Analysis Model

Let, P observable indicators be X 1 , X 2 , X 3 , …, X p . The unobservable factors are F 1 , F 2 , F 3 , …, F m . The factor analysis model is described as follows:

The common factor of X is called F . Its mean vector E ( F ) = 0 and for the covariance matrix Cov ( F ) = 1. Therefore, each component of the vector ε( ε 1 ,ε 2 ,…, ε p ) is independent of each other, which is a special factor. It is independent of F , and E ( e ) = 0.

A = ( a ij ), a ij is the factor load, with it possible to be proved mathematically that the factor load a ij is the correlation coefficient between index I and factor J . If the load is larger, this influences the closeness of the relationship between the j index and the I factor; conversely, the smaller the load, the more distant the relationship.

Factor Analysis Steps

Standardization of raw data.

The non-dimensionalization of indicators is used to transform different indicators into uniform relative values through mathematical transformation, eliminating the influence of the different dimensions of each indicator.

Computation of Eigenvalues

In accordance with the eigenvalue equation | R − E | = 0, the eigenvalues λ and corresponding eigenvectors A, λ of the correlation matrix are calculated. The sizes of the eigenvalues describe the role of each factor in interpreting the object.

Factor Contribution Rate

The factor contribution rate represents the ratio of the degree of variation of each factor to the degree of variation of all factors. The formula is as follows:

C i denotes the contribution rate of variance. When the cumulative contribution rate is over 85% or the characteristic root λ is not less than 1, the number of common factors is determined.

Factor Load Matrix

With X = AF, the factor load matrix A is not unique, but this study uses different parameter estimation methods to obtain the corresponding estimation matrix. The parameter estimation methods mainly include the following: least squares, maximum likelihood, principal component, principal factor, and multiple regression.

If the factor load is relatively average, the initial factor load matrix meaning will not be properly shown. It is difficult to judge the relationship between factors if rotation in the factors is required. The contribution of common factors after rotation is more dispersed by factor rotation, which mainly includes the two methods of orthogonal rotation and oblique rotation.

The factor score coefficient matrix B of the factors is obtained through the factor load matrix. The score F = BZ of each factor is then calculated. Finally, the weight of the variance contribution rate of each factor to the total variance of the factor is taken as the weighted sum, and the comprehensive score is obtained.

By using the factor module calculation function of the analysis software, IBM SPSS Statistics (v. 20.0), the entire process of factor analysis can be quickly completed. Therefore, this study evaluates EAP’s tourism competitiveness through the use of SPSS software.

Data Variables

The success of an objective and scientific evaluation of regional tourism competitiveness depends on the rationality and integrity of the index system design. In this study, the selection of evaluation indicators for EAP’s mountain tourism competitiveness mainly referred to the existing literature, both from China and internationally, on the evaluation of tourism competitiveness ( Leong and Tan, 1992 ; Kozak and Rimmington, 1999 ; Kim et al., 2000 ; Wei, 2000 ; Zhou et al., 2002 ; Zhang and Zhou, 2005 ; Nie, 2006 ; Sánchez et al., 2006 ; Zhang and Lu, 2012 ), and was carried out using systematic analysis and screening.

In strict accordance with the basic principles of rationality and effectiveness, this study combines the characteristics of EAP’s mountain tourism industry development. A multi-dimensional index system is established, which consists of three first-level indicators and 18 second-level indicators (presented in Table 1 ). The structure of the evaluation index system of EAP’s mountain tourism competitiveness is as follows:

Table 1. Evaluation index system of EAP mountain tourism competitiveness.

Core competitiveness of tourism:

Economic environment0s competitiveness:

Infrastructure competitiveness: A 3 = (B 15 , B 16 , B 17 , B 18 )

Data Sources

By consulting the EAP Statistical Yearbook , EAP’s mountain tourism data from 2005 to 2014 were collected. From 2005 to 2014, the mountain tourist reception numbers, comprehensive income from tourism, foreign exchange income, domestic and foreign tourist reception numbers, travel agencies, star-rated hotels, A-level scenic spots, star-rated hotel rooms, etc. showed a growing trend. Among these data, the mountain tourist reception numbers increased by 2,163,300–31,004,100, indicating an increase of 17.52%; the comprehensive income from mountain tourism increased from 159 to 201 million yuan, indicating an increase of 10.26%; the number of travel agencies increased by 42; while the number of star-rated scenic spots increased by 27. Following the structural requirements of the evaluation index system for EAP’s mountain tourism competitiveness, EAP’s tourism data from 2005 to 2014 were systematically combed and analyzed, as shown in Table 2 .

Table 2. Indicators of EAP mountain tourism competitiveness.

Standardization of Data Processing

To make the data comparable and reflect the relative position of the indicators, standardization was used to eliminate the influence of the original indicators from the different kinds of raw data. Finally, the analysis was carried out as shown in Table 3 .

Table 3. Standardized results of EAP mountain tourism competitiveness indicators.

Analysis of the Core Competitiveness of Tourism

Seven of the indicators that affect the core competitiveness of the EAP tourism industry underwent the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity. Judging of the correlation coefficients and partial correlation coefficients was used to determine if these indicators were suitable for factor analysis. If the KMO statistic was greater than 0.5 and less than 1, these seven indicators could be used for factor analysis. The closer the value of the KMO statistic was to 1, the more suitable it would be as a factor.

With the KMO value of 0.547 at 5% level of significance, this indicates that the core competitiveness index data are suitable for factor analysis ( Table 4 ).

Table 4. Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity.

The study uses factor analysis to analyze the correlation coefficient matrix of EAP’s core competitiveness and the eigenvalues, variance contribution rate, and cumulative contribution rate are calculated. From the seven indicators, this study chooses an eigenvalue greater than 1 with a common factor to ensure the method’s validity and reliability. This reveals that the eigenvalue is 6.030, and the cumulative variance contribution rate is 86.139%. Shi et al. (2016) obtained almost similar results (79.7%) for tourism competitiveness in the border counties of Liaoning and Guangxi Province of China. This common factor properly reflects the level of EAP’s tourism core competitiveness ( Table 5 ).

Table 5. Total variance of interpretation.

As shown in Table 5 , each index value’s load for the common factor is obtained, and the size of the common factor is then calculated according to the load of each index. The public factors are mainly determined by the following seven indicators: comprehensive tourism income; number of mountain tourist receptions; number of domestic tourist receptions; number of travel agencies; number of star-rated hotels; number of star-rated rooms; and A-score of tourist attractions. The revenue can be raised for specific tourism if more tourists visit there ( Liu et al., 2021 ). When tourism revenue grows faster, local tourism income rises, residents’ and staff’s income rises, local economic development improves, and the competitiveness of mountain-based other factors also rises ( Zeng et al., 2021 ). The load of these seven indicators on the public factors is 0.958, 0.984, 0.983, 0.739, 0.984, 0.948, and 0.875, respectively, as expressed in F 1 .

The formula for calculating the common factor F 1 is obtained from the component coefficient matrix, as shown in Table 6 .

Table 6. Component score coefficient matrix.

Among them, B 1 , B 2 , B 3 , B 4 , B 5 , B 6 , and B 7 are the variables of the original data after standardization. The mountain tourism core competitiveness of EAP is expressed by the public factor F 1 , from which the score of A 1 can be obtained for EAP’s mountain tourism core competitiveness and the ranking of its core competitiveness, as shown in Table 7 .

Table 7. Scores and rankings of EAP’s tourism core competitiveness (A 1 ).

The score reflects the size of EAP’s mountain tourism core competitiveness. The values in Table 7 show that EAP ranks the core competitiveness of tourism in its counties from strong to weak, in descending order, from Enshi County, Lichuan County, Badong County, Xianfeng County, Jianshi County, Laifeng County, Xuanen County, and Hefeng County. Enshi County has the highest score among the eight counties, with its core tourism competitiveness value being 1.61.

Analysis of Economic Environment’s Competitiveness

Using the factor analysis module of SPSS (v. 20.0), the standardized EAP economic environment’s competitiveness index is analyzed, with the factor analysis model’s validity judged using the KMO test and Bartlett’s test of sphericity. The KMO value is 0.562 which is higher than 0.5 at the 10% significance level, but less at the 5% significance level. These indicators, shown in Table 8 below, meet the requirements for factor analysis ( Table 8 ).

Table 8. Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity.

The factor analysis of EAP’s economic environment’s competitiveness is carried out, and the eigenvalues, variance contribution rate, and cumulative contribution rate are calculated as shown in Table 9 . According to the principle that the eigenvalue is greater than 1, two common factors, namely, F 2 and F 3 , are selected for the seven indicators of EAP’s economic environment’s competitiveness. The variance contribution rates of the two common factors are 68.921 and 21.722%, respectively. The cumulative variance contribution rate is 90.642%, and the eigenvalues are 4.887 and 1.458, respectively. These two public factors reflect the competitiveness of EAP’s economic environment ( Table 9 ).

Table 9. Total variance of interpretation.

The competitiveness of EAP’s economic environment has two public factors, namely F 2 and F 3 . On the one hand, F 2 is mainly determined by gross domestic product (GDP). The tertiary industry’s output value, investment in fixed assets, fiscal revenue, urban residents’ per capita disposable income, and mountain residents’ per capita disposable income. Their principal component loads are 0.983, 0.967, 0.958, 0.952, 0.953, and 0.498, respectively. On the other hand, F 3 is mainly focused on fiscal expenditure, with the load on the principal component being 0.918 ( Table 10 ).

Table 10. Component score coefficient matrix.

As shown in Table 10 , the scoring matrix of the two common factor components is calculated as follows:

The scores and rankings of the two public factors of EAP’s economic environment’s competitiveness, namely, F 2 and F 3 , are then used to determine A 2 , using the formula below, as listed in Table 11 .

Table 11. Scores and rankings of EAP’s economic environment’s competitiveness (A 2 ).

According to the comprehensive score, namely, A 2 , of the two public factors, this study reveals that EAP’s economic and environmental competitiveness rankings are as follows, in descending order: Enshi County > Lichuan County > Badong County > Jianshi County > Hefeng County > Laifeng County > Xianfeng County > Xuanen County. Of these, Enshi County has the strongest economic and environmental competitiveness, scoring 1.34, while Xuanen County has the weakest economic and environmental competitiveness, scoring −0.66. Zeng et al. (2022) argue that the economic environment is a key dimension of mountain tourism development. The competitive market fails to distribute resources efficiently to overcome the unfavorable consequences. As a result, worldwide cooperation is required to keep mountain tourism alive as a tourist savior ( Nguyen et al., 2022 ).

Analysis of Infrastructure Competitiveness

Through the KMO test and Bartlett’s test of sphericity, this study determines that the value of the KMO statistic is 0.524 which is greater than 0.5 at the 10% significance level, but less at the 5% significance level. Therefore, the EAP’s infrastructure competitiveness index can be analyzed by factor analysis ( Table 12 ).

Table 12. KMO test and Bartlett’s test of sphericity.

The study uses SPSS to analyze the factor values in the correlation coefficient matrix of EAP’s infrastructure competitiveness. The eigenvalues, variance contribution rates, and cumulative contribution rates are also calculated ( Table 13 ). To ensure the validity and reliability of the method, this study chooses an eigenvalue greater than 1, with two common factors, namely, F 4 and F 5 , focusing on four indicators of EAP’s infrastructure competitiveness. The variance contribution rates of these two common factors are 53.523 and 42.365%, respectively. The cumulative variance contribution rate of 95.887% is greater than an eigenvalue of 1. Therefore, these two factors can be used as public factors to reflect the competitiveness of EAP’s infrastructure.

Table 13. Total variance of interpretation.

As shown in Table 14 , the scoring matrix of the two common factor components is calculated as follows:

Table 14. Component score coefficient matrix.

This study obtains the scores and rankings of the public factors of EAP’s infrastructure competitiveness, in particular, the size of EAP’s infrastructure competitiveness A 3 ( Table 15 ).

Table 15. Scores and rankings of EAP’s infrastructure competitiveness (A 3 ).

As shown in Table 15 , the descending order of EAP’s infrastructure competitiveness is Lichuan County > Badong County > Enshi County > Laifeng County > Hefeng County > Jianshi County > Xianfeng County > Xuanen County. The counties with strong infrastructure competitiveness are Lichuan, Badong, and Enshi, measured as 1.25, 0.54, and 0.15, respectively. From a competition point of view, the competitiveness of Lichuan County, Badong County, and Enshi County is higher due to the long mileage and high road density of these three counties. Lo et al. (2019) measured the destination competitiveness of rural tourism in Malaysia and reported that the infrastructure of the tourist place has a vital effect on attracting tourist and competitiveness of the industry. Yang et al. (2020) argue that to prolong tourist travel, enhance tourism consumption, and raise tourism revenue, it is required to strengthen tourism infrastructure construction. Similarly, Reisinger et al. (2019) argue that infrastructure is important but not sufficient for tourism, and its success is dependent on several other factors.

Analysis of Enshi Autonomous Prefecture’s Mountain Tourism Competitiveness

The proportion of variances to total variances is determined, using the weighted method, from the results of EAP’s mountain tourism core competitiveness, economic and environmental competitiveness, and infrastructure competitiveness. These weighted values are 84.292, 13.4, and 2.308%, respectively ( Table 16 ).

Table 16. Total variance of interpretation.

This study also obtained EAP’s mountain tourism competitiveness A by weighting the scores of EAP’s mountain tourism core competitiveness, economic and environmental competitiveness, and infrastructure competitiveness ( Table 17 ).

Table 17. Scores and rankings of EAP’s mountain tourism competitiveness.

The values in Table 17 show that the mountain tourism competitiveness of each county in EAP is quite different. Enshi County, Lichuan County, and Badong County have positive scores in tourism competitiveness, indicating that their tourism competitiveness is relatively strong and has competitive advantages. However, five counties, namely, Xianfeng County, Jianshi County, Laifeng County, Hefeng County, and Xuanen County, had negative scores in tourism competitiveness, indicating that these five counties had weak tourism competitiveness. Enshi County’s score of 1.54 indicates that it has the strongest tourism competitiveness while Xuanen County’s score of −1.02 indicates that it has the weakest competitiveness. Although each county is in the same region, the development of the tourism industry across the counties is not balanced. The descending order of tourism competitiveness is Enshi County > Lichuan County > Badong County > Xianfeng County > Jianshi County > Laifeng County > Hefeng County > Xuanen County. Enshi County, Lichuan County, and Badong County are strong in tourism core competitiveness, economic and environmental competitiveness, and infrastructure competitiveness, while Jianshi County, Xianfeng County, Xuanen County, Laifeng County, and Hefeng County have weak competitiveness.

Therefore, EAP’s mountain tourism competitiveness is quite different in terms of the respective contributions of tourism core competitiveness, economic and environmental competitiveness, and infrastructure competitiveness. The findings are almost similar to other studies that assessed mountain tourism ( Bacoş and Gabor, 2021 ; Gao et al., 2021 ; Zeng et al., 2021 ; Zeng et al., 2022 ). The main contribution to EAP’s mountain tourism competitiveness is tourism core competitiveness, followed by economic and environmental competitiveness, and, finally, infrastructure competitiveness. When tourism core competitiveness is increased by one unit, EAP’s mountain tourism competitiveness will increase by 0.84292 units. Similarly, when economic environment competitiveness is increased by one unit, EAP’s mountain tourism competitiveness will increase by 0.134 units. When infrastructure competitiveness increases by one unit, EAP’s mountain tourism competitiveness increases by 0.02308 units. Michael et al. (2019) claim that tourism competitiveness is influenced by destination resources, destination infrastructure and support services, and the general business environment in United Arab Emirates. Similarly, Cvelbar et al. (2016) argue that tourism infrastructure and destination management are the primary competitiveness drivers in developing countries, whereas destination competitiveness in developed countries is influenced by tourism-specific factors like infrastructure, economic, and business environment.

A Large Gap Exists in Tourism Competitiveness Between Enshi Autonomous Prefecture’s Counties

Enshi County has the highest score for mountain tourism competitiveness (1.54), while Xuanen County has the lowest (−1.02); thus, the competitiveness gap between Enshi County and Xuanen County is 2.56. Analysis of these model values shows that the level of Enshi County’s tourism competitiveness gap is large, with obvious polarization and excessively strong cooperation. Therefore, this weakens the basis for cooperation between counties, leading to ineffective overall cooperation in EAP’s mountain tourism industry. There are numerous benefits of mountain tourism, including the development of suburban rural tourism, many issues eventually arise that limit its growth. Mountain tourism may be an essential driver for rural development in remote mountain villages that confront major economic, social, and environmental issues, according to case studies from Germany, Italy, Romania, Ukraine, and Poland ( Lun et al., 2016 ). As a result, retail trade, transportation, and communication strategies should focus on maintaining natural and cultural resources more successfully. A sufficient local, employable population is critical in establishing mountain tourism locations with greater employment prospects ( Zeng et al., 2022 ).

The Degree of Tourism Cooperation Between Enshi Autonomous Prefecture’s Counties Is Low

The degree of tourism cooperation focuses on the extent of relationships between two or more actors who agree to share information, technical assistance, management training, capital, and/or market intelligence, either formally or informally ( Reisinger et al., 2019 ). Interorganizational connections impact the entire local system in tourism ( Li and Du, 2021 ). An autonomous organization interacts to generate joint actions, utilizing common rules, conventions, and structures to deliberate and act on issues linked to the region’s tourism growth ( Dias et al., 2021 ). Public and private organizations are active in interorganizational collaboration to promote the tourism destination’s long-term viability and competitiveness. The inter-organizational linkages extend to tourist management agencies, which help managers, company owners, and government officials develop communication channels and narrow interpersonal interactions ( Gao et al., 2021 ). Publicity for Enshi County’s Mountain tourism product is self-centered. Even though it formulates mountain tourism routes as a unit, the mountain tourism products of various regions have not been effectively combined. Most existing high-quality tourist routes are designed around scenic spots in Enshi County, Lichuan County, Badong County, and Jianshi County, with the tourist attractions of Xianfeng County, Xuanen County, Hefeng County, and Laifeng County not listed. Even though some scenic spots in Jianshi County and Badong County are listed in the high-quality tourist routes, insufficient cooperation is evident. Therefore, tourism competition between EAP’s counties is greater than tourism cooperation, with the degree of cooperation between counties rated as low. Wilke et al. (2019) argue that organizational learning is facilitated by accessing new sources of information, which aids in the development of dynamic capacities in the tourism industry.

Lack of a Sense of Cooperation Between Enshi Autonomous Prefecture’s Counties

Cooperation between organizations is critical for enterprises to gain valuable resources like information and knowledge, commodities and services, finance, markets, and technology ( Luštický and Štumpf, 2021 ). Cooperation has been emphasized in particular in the context of tourist destinations, where ties and integration between companies involved in tourism on a direct or indirect basis contribute to providing consumers with a comprehensive tourist experience ( Nguyen et al., 2022 ). In essence, research indicates that the tourism business would not reach its full potential without cooperation amongst many stakeholders ( Wu et al., 2022 ). Currently, no sense of cooperation is found between EAP’s counties. Cooperation in EAP’s mountain tourism industry is still at a low level, with the importance of cooperation not really understood in terms of ideology and action. Each county in EAP is fighting for its own independent development and its own competitiveness, so it is difficult for them to reach consensus and form a joint force. Therefore, it is necessary to elevate the cooperative consciousness of EAP’s mountain tourism, form an industrial cluster of mountain tourism, and bring into play the cooperative effect of “1 + 1 > 2.” According to Wilke et al. (2019) , organizations create a variety of talents through collaboration, which results in sustained competitive advantage and higher performance.

Imperfect Infrastructure in Enshi Autonomous Prefecture’s Counties

The analysis of EAP’s infrastructure competitiveness shows a large gap. The tourism competitiveness of each county is closely related to its infrastructure construction. Due to the mountainous area, road construction is difficult and costly, with EAP having few tourist highways. Although the main highway has been built between counties, the quality of the highway is not at the same standard and the road is narrow, contributing to the failure of effective cooperation between counties. At present, only Enshi County, Lichuan County, Jianshi County, and Badong County have railways. Existing transportation facilities cannot meet the needs of tourists, which greatly hinders EAP’s ability to attract foreign tourists. Bazargani and Kiliç (2021) argue that the infrastructure is a fundamental driver of tourism success, but policy conditions, the institutional framework, and socio-cultural resources are all important drivers. Similarly, Lim et al. (2019) report that infrastructures alone are insufficient yet crucial aspects that attract visitors to tourism destinations and meet their needs once they arrive at a competitive destination.

Benign interaction between competition and cooperation is the driving force for the healthy development of EAP’s mountain tourism industry. To effectively overcome the problems from tourism competition between EAP’s counties, solutions need to achieve “win-win” cooperation within the competition. It is found that Enshi county is more competitive in terms of tourism than other seven counties under EAP. This study explores that core competitiveness, economic and environmental competitiveness, and infrastructural competitiveness are worth 84.29, 13.4, and 2.31%, respectively. EAP’s mountain tourism competitiveness rises by 0.84 units when tourism core competitiveness rises by one unit. Similarly, when the competitiveness of the economic environment improves by one unit, the competitiveness of EAP’s mountain tourism improves by 0.134 units. EAP’s mountain tourist competitiveness grows by 0.023 units when infrastructural competitiveness increases by one unit. Lack of awareness of the county administration, a low level of cooperation, and poor infrastructure were the main causes of low competitiveness.

The recommendations from this study’s findings are as follows. Firstly, it is necessary to properly handle the relationship between competition and cooperation, maintaining cooperation in competition and competition in cooperation, and promoting the healthy development of EAP’s regional mountain tourism industry. Secondly, county authorities should strengthen communication, establish an effective coordination mechanism, achieve effective regional coordination and benefit distribution, and jointly promote the development of EAP’s mountain tourism industry. Thirdly, the sense of cooperation must be enhanced and jointly developed within the mountain tourism market. For example, EAP could formulate joint mountain tourism image promotional plans, with the slogan “EAP’s Wonderland” to develop the overall mountain tourist market. Fourthly, the construction of tourism infrastructure must be strengthened to break down the barriers to tourism cooperation. Strengthening the construction of transportation infrastructure is the first priority for EAP’s mountain tourism competition and cooperation. Traffic links between county towns and scenic spots are also necessary to strengthen, rebuild, and optimize existing roads, and ensure the smooth flow of traffic on mountain tourist roads in each county’s area. Road network links between different counties and scenic spots should be strengthened to promote the coordinated development of the mountain tourism industry involving all counties. This study’s findings will be helpful for developing a cooperation mechanism and sustainable development, thus reducing poverty and promoting the mountain revitalization of China. The results of this study are applicable for mountain regions worldwide in general and China in particular.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

QC designed the research plan, collected and analyzed the data, and wrote the manuscript. MNIS, DZ, JS, and JD analyzed the data and revised the manuscript. TX collected and analyzed the data and revised the manuscript. All authors have checked the final version of the manuscript and approved it for publication.

The study was supported by the Key Project of Philosophy and Social Sciences, Hubei Province, China (Grant No. 18ZY003), the youth project of Philosophy Social Sciences, Hubei Province, China (Grant No. 19Q119), the youth project of Hubei Minzu University, Hubei Province, China (Grant No. MY2017Q010), and National Social Science Fund Project of China (Grant No. 18BMZ071).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : tourism psychology, Enshi Autonomous Prefecture (EAP), tourism industry, tourism competitiveness, competitiveness evaluation

Citation: Cao Q, Sarker MNI, Zhang D, Sun J, Xiong T and Ding J (2022) Tourism Competitiveness Evaluation: Evidence From Mountain Tourism in China. Front. Psychol. 13:809314. doi: 10.3389/fpsyg.2022.809314

Received: 05 November 2021; Accepted: 21 February 2022; Published: 31 March 2022.

Reviewed by:

Copyright © 2022 Cao, Sarker, Zhang, Sun, Xiong and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Md Nazirul Islam Sarker, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Competitor Analysis in the Tourism Marketing Industry

For all you curtain twitchers and wannabe detectives, spying on your closest neighbours can help you gain some invaluable insights that can help you stay one step ahead.

Taking a closer look at your competitors allows you to assess the strengths and weaknesses of both your immediate and potential competition across the digital landscape. By doing so, you will gain better insights and knowledge, helping you (and us) to create more effective marketing strategies.

Dig up the dirt on your competition

We look at your competitors’ digital presence, highlighting strengths and weaknesses, arming you with the tools to exploit any shortcomings as well as providing guidance on things you need to improve upon. Put succinctly: we help you learn from your competitors’ successes as well as their mistakes.

We will look at how competitors stack up against your business in terms of Google rankings, how they perform on social media compared with your brand, and how effective their website is compared to yours. These and many other aspects will give you invaluable insights while making you feel like a super spy.

Thinking outside the box

When conducting a competitor analysis, it is not always companies who provide similar services that you need to worry about. Instead, it sometimes makes more sense to monitor the many other activities that your potential customer may consider instead of booking one of your products. In this sense, a bike tour company could be in competition with a museum tour company or a theme park may be in competition with a rock-climbing centre.

One of the main challenges for your business is convincing your potential customer to spend time with your products rather than on some other activity that may grab their attention instead.

In its most abstract sense, you could say one of your competitors is people’s time.

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UN Tourism | Bringing the world closer

Innovation programme, share this content.

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UN Tourism Tourism Startup Competitions

The Startup Competitions are our flagship innovation programme.

They seek for the most disruptive projects that are changing the way people travel and how the Sustainable Development Goals (SDGs) are reached all over the world. They provide an exceptional platform for great ideas to continue scaling up through gathering the entire global tourism ecosystem: Member States, startups, institutions, education centers, corporations and investors. This support is translated into mentoring, exposure to the sector and investments form our network.

There are two types of competitions: the generic ones, which solutions are widely spread through the tourism value chain, and the thematic ones, focused on a specific tourism product.

4th Global Gastronomy Tourism Startup Competition (Open until June)

More information here!

UN Tourism Startup Competition for Tourism Technologies and Solutions in Hotels and New Business Models (CLOSED)

Mega Events and MICE Tourism (CLOSED)

UN Tourism Women in Tech Startup Competition: Middle East (CLOSED)

More information in English! - More information in Arabic!

UN Tourism TOURISM TECH ADVENTURES: CANARIAS 2023: GLOBAL STARTUP COMPETITION: ISLAND DESITNATION SUPPORTED BY INFECAR (closed)

The World Tourism Organization (UN Tourism), and the Institución Ferial de Canarias (INFECAR), have joined forces in this global startup competition to source the most innovative and disruptive business models and smart solutions adapted to answer the complex challenges and specific needs of the tourism sector of island destination are facing with in the aftermath of the pandemic.

This competition will source entrepreneurs and stakeholders that can offer innovative and smart solutions for island destinations in a more sustainable way. The call will identify and support those that contribute to the post-pandemic recovery and resilience of the tourism ecosystem of island destinations through the development of innovative and smart tourism solutions and products.

The winning startups will have the opportunity to pitch at the UN Tourism Tourism Tech Adventures: Canarias 2023 (15-16 March 2023), receive mentorship and training by UN Tourism and Partners, become a member of the UN Tourism Innovation Network, and more.

UN Tourism Awake Tourism Challenge (closed)

Seeking to find the most innovative startups that are working to advance the United Nations Sustainable Development Goals.

Startups from all walks of life, from all over the world and all economic sectors are invited to participate.

3rd Global Gastronomy Tourism Startup Competition (closed)

The UN Tourism in collaboration with the Basque Culinary Center is launching the 3rd Global Competition for Gastronomy Tourism Startups.  We are looking for startups which utilize new technologies, promote sustainability, and positively impact local communities.

In addition to mentorship, investment, and educational opportunities provided to the winners, this competition provides an international platform to showcase your innovative gastronomy tourism startup. The top entrepreneur will have the chance present at the 7th UN Tourism Gastronomy Tourism Forum, utilize the LABe-Digital Gastronomy Lab facilities, and more!

Apply by February 28, 2022 and propel your startup to the forefront of the international gastronomy tourism sector.

UN Tourism Global Rural Tourism Startup Competition (closed)

The COVID-19 pandemic altered the way in which travelers want to experience the world. Tourists are looking for sustainable, safe, convenient, and flexible travel options. Domestic, rural and eco tourism have emerged as the most popular travel choices. As rural tourism expands, it is crucial that tourism benefits both tourists and the local communities. The rising demand for rural tourism experiences coupled with an increased importance placed on local community engagement has provided an opportunity for the economic, social and environmental revitalization of rural areas.

The UN Tourism Global Rural Tourism Startup Competition challenges entrepreneurs to contribute both to the post-pandemic recovery and rural development. Winners will receive mentoring and support from the UN Tourism and our partners as well as investment, scholarship, and networking opportunities. Finally, the winning startups will have the chance to present at the 24th UN Tourism General Assembly in Madrid Spain.

Help us build more resilient rural communities through the UN Tourism Global Rural Tourism Startup Competition! The final pitches will be presented in October, and winners will be announced in November of 2021.

UN Tourism SDGs Global Startup Competition (closed)

The largest innovation competition to accelerate the achievement of the United Nations Sustainable Development Goals (SDGs). 

2nd UN Tourism Gastronomy Tourism Startup Competition (powered by Basque Culinary Center) (closed)

We encourage you to apply and help us accelerate tourism worldwide:

More information

1st UN Tourism Rural Tourism Startup Competition (closed)

In the wake of the year of Tourism and Rural Development, the UN Tourism encourage Spanish entrepreneurs to come up with solutions for rural development challenges through tourism. The most impactful and innovative solutions will be awarded, as it is a top priority to make our world more sustainable.

2019 competitions

2nd un tourism tourism startup competition (powered by wakalua) (closed).

The 2nd UN Tourism Tourism Startup Competition gathered over 1700 applications from 150 countries, reaching 12 more nationalities than the previous edition. The format was based on 6 categories, such as: smart mobility, rural development, disruptive hospitality, smart destinations, deep tech applied to geolocalization, and innovative tourism solutions. Each of them was supported by a corporate partner who was actively involved in the selection process. Thus far, entrepreneurs have evolved towards more scalable business models, greater focus on the Sustainable Development Goals and disruptive solutions based on state-of-the-art technologies, highlighting the use of Cloud Front Backend Systems, Artificial Intelligence, Big Data and Internet-of-Things. The Final was held in Madrid on 20 January 2020 and presented the winners of each vertical and the most exciting tourism insights from international experts.

1st UN Tourism Gastronomy Tourism Startup Competition (powered by Basque Culinary Center) (closed)

This competition was designed to track the most innovative startups in gastronomy tourism, which merges the best of travelling with the intangible heritage of the destinations. It gathered over 300 submissions from 80 countries with finalists representing Italy, Spain, Israel, the Czech Republic and Japan. These finalists presented their proposals in San Sebastián, Spain on 3 May 2019 within the framework of the 5th UN Tourism World Forum on Gastronomy Tourism.

1st UN Tourism Sports Tourism Startup Competition (powered by Barca Innovation Hub) (closed)

This competition received over 100 initiatives from over 50 countries. Participants came up with solutions ranging from new technologies, community-based tourism development platforms, software initiatives and innovation-driven sports tech projects. Finalists made their pitches in the Tourism Tech Adventures: SportsTech Macau on 13 October, 2019.

2018 competitions

1st un tourism tourism startup competition (powered by wakalua) (closed).

This first edition was launched in the framework of World Tourism Day 2018 and gathered over 3,000 applications representing 133 countries. Its Final was held on the International Tourism Fair 2019 (Fitur) and its winning start-up was the Israeli-based Refundit, which got a funding of 25 € millions from Amadeus Ventures.