Abstract
Various tourism destination competitiveness indicators and models have been developed and applied by different researchers and regions. Scholars emphasize that there is no single set of competitiveness indicators applicable to all destinations, and a more specific approach for each destination is required. Therefore, this paper aims to present the development of a tourism destination competitiveness model tailor-made for Serbia, adapted to the common specificities of this developing economy, with inputs from Serbian tourism experts and industry representatives. The development of the model required a multi-step process using mix-methodology. This included a literature review, a targeted workshop (applying Delphi method), pilot testing and model validation using structural equation modelling. The final model resulted in four factors: Natural and cultural heritage, Quality of tourist offer and infrastructure, Tourism policy and sustainable development of tourism, and Marketing and experience, with 48 competitiveness indicators in total. The study offers theoretical and practical contributions with generally applicable insights for Serbia and similar developing destinations.
Introduction
The evaluation of tourism destination competitiveness (TDC) is becoming a critical part of strategic positioning and marketing analyses (Moradi et al., 2022). Although several conceptual models have been developed in previous studies to address destination competitiveness in general, researchers indicate that there is no universal competitiveness model or approach that can be used for all tourism destinations and that tailor-made models for specific types of destinations can also be very beneficial (Moradi et al., 2022). Due to the rapid worldwide developments, the tourist industry's megatrends, the uniqueness of the destinations, and changes in consumer behaviour, existing models of competitiveness for destinations should be tailored to a particular destination, or new models should be developed for each destination and/or group of destinations that share a comparable set of characteristics (e.g., developing destinations). Furthermore, the tourism industry is confronted with complex challenges, including strategies for attracting visitors or re-engaging them. Crafting efficient marketing strategies that cater to various stakeholders in the upcoming period, with a growing emphasis on sustainability, necessitates a reassessment of how destinations evaluate their competitive edge.
Developing economies, such as Serbia, largely differ in the set of competitiveness indicators that are relevant and that should and can be measured (they often lack some monitoring processes, statistics, and data. For instance, aspects like high-quality drinking water, a reliable electricity supply, or adherence to high ethical standards in delivering tourist products or services are typically considered standard in developed nations. However, in developing countries, especially those still grappling with fundamental challenges to tourism development such as low living conditions, low-quality infrastructure, or adverse environmental impacts, assessing these indicators becomes essential in assessing the destination's competitiveness. Moreover, the majority of existing TDC models were tested on developed tourist destinations, and did not adequately highlight the participatory approach in model development. Such an approach is needed to generate and carefully select indicators suitable for developing countries that face challenges in establishing their position in the highly competitive tourism market. Hence, there is a need for a new model specifically designed for developing countries, one that integrates both theoretical and practical insights. Thus, this research strives to develop such model on the example of developing country and emerging tourist destination – Serbia. According to Dwyer and Kim (2003), creating a destination competitiveness model enables private and public sector players in tourism to better understand their destination's strengths and limitations and discover new possibilities for destination development. Moreover, bearing in mind that most models apply to the example of specific tourism destinations (from a theoretical point of view) and that the generally accepted practical model of World Economic Forum (WEF) Travel & Tourism Competitiveness Index has been criticized a lot, especially in the domain of methodology, general applicability, specific inputs for decision makers in countries as tourism destinations (Gómez-Vega and Picazo-Tadeo, 2019; Pulido-Fernandez and Rodriguez-Diaz, 2016; Rodríguez-Díaz and Pulido-Fernández, 2020; Wu et al., 2012), it appears that this is an additional argument for a more concrete model that synthesizes good practices and examples of the most widespread theoretical and practical implications.
Therefore, the aim of this paper is to provide a model tailor-made for measurement of the competitiveness of developing countries on the example of Serbia as a tourism destination, based on the existing body of knowledge on destination competitiveness models and inputs from Serbian tourism experts and industry representatives.
This research attempts to answer the following questions:
What are existing destination competitiveness models, as well as methodologies (approaches) used to measure TDC? What are the most relevant indicators for measuring the competitiveness of Serbia as a tourism destination from the perspective of the tourism industry? What are the main findings of the proposed competitiveness model for Serbia?
There have been previous attempts to assess TDC of Serbia, but to our knowledge, this issue has not been examined from the perspective of developing a model for the evaluation of TDC of Serbia. Creating a model for evaluation of Serbia's TDC, that can be applied to other similar developing countries, is crucial for multiple reasons, including ongoing performance monitoring of the tourism industry, pinpointing strengths and weaknesses to enhance competitiveness, making informed decisions and strategic improvements, optimizing marketing strategies, adapting to evolving circumstances, fostering innovation, and promoting accountability and transparency in destination management. Such an evaluation model serves as the foundation for the sustained enhancement of Serbia's TDC and the sustainable growth of its tourism industry.
Theoretical framework
To assess the available literature dedicated to TDC, the SCOPUS database was used. This review aims to give a general overview of TDC studies, with a focus on the methodologies (approaches) and the models used to measure TDC. A total of 559 articles in the English language, mentioning the words ‘tourism destination competitiveness’, and ‘sustainable tourism indicators’ were identified in SCOPUS from 2000 to March 2022. They were included in further analysis. From the database of 559 articles, 42% of studies were identified as most relevant. Those scientific studies were the basis for preliminary indicator selection.
There are various models of TDC, and many authors have contributed to the understanding of the concept (Cracolici and Nijkamp, 2009; Dwyer and Kim, 2003; Enright and Newton 2004; Ritchie and Crouch, 1993, 2000, 2003). The most widely used models are those that take a general approach to the problem of managing a TDC (Dwyer and Kim, 2003; Heath, 2003; Hassan, 2000; Ritchie and Crouch, 2003).
In 2003, Ritchie and Crouch established TDC model, which has since become the foundation for several conceptual models (Andrades-Caldito et al., 2014). Their work highlighted the importance of a well-designed and executed destination management programme in enhancing TDC. Although the list of attributes for measuring TDC comprises five broad dimensions and 36 sub-factors, Crouch (2011) cautions that not all indicators carry equal weight, and some may be more or less relevant to certain market segments. Moreover, not all indicators may be available in every destination, limiting comparisons across destinations (Vila et al., 2015).
Dwyer and Kim (2003) developed another popular model for measuring TDC, which shares many variables with the model proposed by Ritchie and Crouch (1993, 2003), but also introduces some differences. While the Ritchie and Crouch model focuses mainly on supply-side factors, the Integrated model recognizes the role of demand-side factors in determining TDC. The authors provide an extensive list of 85 indicators for measuring TDC, grouped into six categories: inherited resources, created resources, supporting factors and resources, destination management, demand conditions, and situational conditions. One key difference between the Integrated model and the Ritchie and Crouch model is that the former includes causal links between the various elements, indicating their interdependence (Vanhove, 2010).
Hassan (2000) developed a competitiveness model that considers the relationships between all stakeholders involved in creating and integrating additional value to maintain a favourable market position relative to competitors. The model identifies four key determinants of destination competitiveness: comparative advantage, demand orientation, the structure of the tourist industry, and environmental protection. While environmental protection is important for a country's overall competitiveness, Hassan argues that it is particularly critical in the tourism industry, where the quality of the natural and cultural environment is a vital component of the tourist experience, but also of residents’ well-being (Dwyer, 2020). Heath's (2003) model builds upon the key indicators of TDC found in earlier studies such as Ritchie and Crouch (2000) and Dwyer and Kim (2003) but places a particular emphasis on the importance of the human factor in tourism development.
Most of the studies used previously created models in TDC evaluation, relying particularly on Integrated destination competitiveness model (Dwyer and Kim, 2003) or the Ritchie and Crouch (2003) model. Several studies have delved into the complex realm of country tourism competitiveness, seeking to understand the multifaceted factors that influence a nation's ability to attract and retain tourists (e.g., Andrades and Dimanche, 2017; Armenski et al., 2018; Bu et al. 2021; Dwyer et al., 2012; Goffi et al., 2019; Martínez-González et al., 2021; Medina-Muñoz et al., 2013; Vila et al., 2015). The assessment of a country's tourism competitiveness involves analysing various dimensions, including infrastructure, policy environment, natural and cultural resources, and overall attractiveness to tourists. The literature analysis showed that not many papers focus on or emphasize longitudinal studies on measuring TDC in a specific destination, or its elements in relation to TDC (Islam et al., 2021). Although a longitudinal assessment of competitiveness is not the aim or a research question of this study, the model developed in this study should be applied continuously in cooperation with national DMO, in order to monitor the progress and timely detect areas that should be improved in order to reach a better competitive position.
Longitudinal studies are a fundamental tool in the assessment of TDC, providing insights into trends, policy impacts, and causal relationships over time. They enable the observation and analysis of changes in TDC indicators, providing insights into how competitiveness evolves over time and what are trends and patterns in the performance of tourism destinations. This information is essential for informed decision-making, particularly in developing economies. Longitudinal data is valuable for building predictive models that forecast future trends in TDC, enabling proactive planning and strategy development. Longitudinal studies, when shared and analysed across different stakeholders, promote collaboration, contributing to a collective understanding of factors influencing TDC and how to address them effectively. However, conducting longitudinal studies is infrequent due to associated costs, opportunities, and time constraints (Kumar et al., 2024).
In terms of methodology approaches, some studies on this topic utilized primary data acquired through surveys and interviews with industry stakeholders (Drakulić Kovačević et al., 2018; Goffi et al., 2019), residents (Bu et al., 2021), tourists (Dalakis et al., 2018; Tešin et al., 2023) whereas others combined qualitative and quantitative techniques (e.g., focus groups, workshops, interviews, questionnaires) (Ribeiro et al., 2021) or secondary data (Martínez-González et al., 2021). Survey questionnaires are the most commonly used method among the sources examined (70.3%), while others choose interviews, focus groups and other methods.
Sustainability has become a much more prominent topic in recent studies, and since 2017, the number of scientific papers on this topic has increased (Rasoolimanesh et al., 2020). Even though sustainability plays a crucial role in fostering TDC nowadays, it has rarely been considered in tourism studies in developing economies, such as Serbia. Therefore, our model will implement the sustainability approach in measuring TDC of Serbia, primarily by identifying the sustainable competitiveness indicators relevant for Serbia from an industry perspective.
Although each of the comprehensive models provides exhaustive lists of determinants for identifying destination competitiveness, not all attributes are suitable for different types of destinations, life cycle phases, or market segments (Goffi, 2013; Mior Shariffuddin et al., 2022). However, any destination is competitive against relevant competitors (Goffi and Cucculelli, 2018). As a result, future model evaluations should be based on a thorough selection of indicators that can provide the market with a distinct value for effort in comparison to competitors. Furthermore, one of the significant criteria for selecting competitiveness indicators is that they must be policy-relevant (Goffi and Cucculelli, 2018). The authors of the abovementioned competitiveness models, as well as many other researchers, call for more detailed empirical studies of different TDC indicators, respecting destination uniqueness. Therefore, in this study, existing theoretical models with a comprehensive range of indicators are adjusted and tailored by tourism experts and industry representatives, aiming to establish a novel practical model for a specific destination, focusing on Serbia. The model developed in this study will be a milestone guideline for national policymakers to improve and maintain Serbia's competitive position in the international tourism market and enhance sustainable tourism development in the country.
Study background
Serbia, positioning itself as both an emerging tourism destination and a developing economy, is actively pursuing a greater market presence in the global tourism arena. Situated strategically in Southeast Europe, serving as a nexus of diverse cultures and histories, and boasting a wide array of tourism attractions, a rich cultural legacy, as well as encountering notable developmental challenges, sustainability endeavours, deliberate market positioning, and impactful policy influence, Serbia offers an intriguing and compelling case for the study of TDC. According to the WEF Travel & Tourism Competitiveness Report 2019, Serbia does not hold a significant competitive position in the global market. The country was ranked as 83rd among the 140 countries listed in the report. A TDC analysis of Europe and the Eurasia region puts Serbia in 40th place among the 46 countries included in the report (WEF, 2019). According to the World Travel & Tourism Council (WTTC) 2022, in 2021, travel and tourism's direct, indirect, and induced impact accounted for US$ 5812 billion contribution to the world's GDP, which represents 6.1% of global GDP. In 2021, the total contribution of travel and tourism to the GDP of the Republic of Serbia was 3.6% (US$ 2182.4 million) and to employment was 5.4% (WTTC, 2022).
Tourism is recognized as one of the priority areas in Serbia and can contribute to overall economic and social growth. One of the main objectives of the Tourism Development Strategy of the Republic of Serbia for the period from 2016 to 2025 is to improve the competitiveness of the tourism industry and related activities in the domestic and international markets (Government of the Republic of Serbia, Ministry of Trade, Tourism, and Telecommunications, 2016).
Academic studies on TDC of Serbia are quite scarce and mostly focused on the application of Ritchie and Crouch (2003) and Dwyer and Kim (2003) Integrated model (Armenski et al., 2012; Dragićević et al. 2012; Drakulić Kovačević et al. 2018; Gajić et al., 2018). In these studies, a similar methodology was employed (Cimbaljević et al., 2023) – questionnaires were used as a tool to conduct a survey on TDC and Serbian tourism experts and industry practitioners were survey population. All studies identified some of the advantages and disadvantages of the Serbian tourism industry. However, none of these studies proposed a unique and tailored model for measuring the competitiveness of Serbia as a tourism destination. Furthermore, there have been no longitudinal studies and no official tool for monitoring Serbian TDC over time. Moreover, WEF Travel & Tourism Competitiveness Report, which is globally accepted approach to assessing the competitiveness of tourism destinations worldwide, (Fernández et al., 2020) overlooks unique contextual factors of destinations and unrealistically assumes that the variables used in the computations hold uniform importance for all destinations worldwide (Rodríguez-Díaz and Pulido-Fernández, 2020). Hence, this study is of importance as it aims to fill these gaps by developing a novel model built on the existing TDC models, respecting destination uniqueness, and acknowledging Serbian tourism industry attitudes towards the relevance of competitiveness indicators generated from the literature.
Methodology
The creation of the model is performed in four stages: (1) In-depth literature review, (2) workshop – Delphi method (3) Pilot testing, (4) Model set and validation.
In-depth literature review
As explained in the theoretical overview, a total of 559 papers with the main keywords ‘destination competitiveness’ and ‘sustainable tourism indicators’ were selected for the analysis. In the first round of analysis, 232 papers were selected as relevant for further analysis. In the second round of analysis, the experts reviewed the indicators and deleted repetitive items and those items that were not relevant for the analysis of TDC. After a long process of purifying the relevant indicators, a list of 165 indicators was extracted and included in the questionnaire for stakeholders.
Workshop – Delphi method
Secondly, the Delphi method was used to find consensus on which indicators are most relevant for measuring TDC of Serbia. Its main aim is to obtain a consensus among experts rather than compromised opinions. The Delphi method allows experts’ knowledge to be explicit to arrive at a set of criteria or standards as a way of acquiring knowledge, which can serve as references for decision-making in the future (Aichholzer, 2009). No strict order exists for Delphi procedures. Usually, the research scope and theme are determined first, followed by experts being selected and answering questionnaires in three to four rounds. The first round mainly revolves around the theme and knowledge provision. Opinions are exchanged, and a consensus is achieved between the experts throughout the rounds. During the final round, previous conclusions are provided to the experts to yield a conclusion (Doke and Swanson, 1995).
Delphi experts’ selection
Because the Delphi method targets experts and scholars as survey participants, the selection of these people is one of the key factors affecting the accuracy of the research results. Experts must meet the following criteria: (1) Be representative, authoritative, and widely convincing. (2) Have professional diversity and completeness (having too many experts from the same fields is avoided). Experts, scholars, government officials, and private-sector business people from tourism-related fields were invited to participate in this study to arrive at a strategic consensus. The databases of YUTA (National Association of Travel Agencies), HORES (Business Association of the Hotel and Hospitality Industry), Tourism organisation of Serbia (TOS) and personal stakeholders’ contacts were used as a basic list of experts’contacts. It is a common practice in tourism competitiveness research for the survey population to be experts and practitioners in the tourism industry, as this is the population seen to be the most knowledgeable about management and competitiveness (Crouch, 2011).
In the current study, 60 experts were invited to join the study, adhering to the suggested sample size guidelines for a uniform sample (ranging from minimum 10 to 15 individuals) (Skulmoski et al., 2007). In the first round, 42 of them responded to a questionnaire. In the next two rounds, a total of 35 respondents participated in the research. Their profile is shown in Table 1.
The profile of the experts involved in the Delphi method.
Source: Author's calculation.
Basic characteristics of the respondents involved in pilot testing.
Source: Author's calculation
The first round of the Delphi method was performed to select key tourism stakeholders in Serbia (experts in the industrial, governmental, and academic sectors) who were invited to fill in the questionnaire before the official workshop (first round). The respondents were asked to estimate how relevant each of the 165 indicators is for measuring TDC of Serbia. For these purposes, a Likert scale from 1 to 5 was used (1-not relevant at all, 5 – very relevant). In this step, indicators were divided into logical groups, in order to assist the process of elimination and evaluation. A total of 42 answers were collected. Afterwards, the answers were analysed, and indicators were ranked based on the mean values of the answers and standard deviations. All indicators with a mean value below 4 and with high standard deviations were marked to be excluded from the study. After this step, 131 indicators were extracted for the second round.
The second round of Delphi was performed during the workshop held in Belgrade in June 2022. The response rate was 83.3%, as 35 answers were collected. After the discussion of indicators, the participants were asked to fill in the questionnaire again (third round). The procedure of analysis and elimination was the same as in the first round, and for the third round, 103 indicators were left to be estimated. After repeating the procedure for the third time (35 experts), a total of 101 indicators were extracted as relevant for measuring TDCof Serbia.
It is important to note that, in addition to quantitative data analysis, the authors considered comments from experts regarding the formulation of specific indicators, the division of some indicators into several, more concrete indicators, and the elimination of some indicators that were partially repetitive. This hybrid of qualitative and quantitative approaches resulted in the final list of TDC indicators, which was then pilot-tested.
Pilot testing
Pilot testing was conducted during July and August 2022 via email to stakeholders who were randomly selected from a database of tourism-related stakeholders in Serbia. The pilot research aimed to eliminate any shortcomings and misunderstandings that may arise during the next stage – the final research when model validation will be done. The respondents were asked to access on a scale from 1-I totally disagree, to 5-I totally agree, statements regarding the current state of TDC of Serbia. They were also offered to mark 6 if the question is ‘not well formulated’.
Pilot testing for the target group of internal stakeholders has been done on a sample of 64 participants (Table 2). According to Connelly (2008), extant literature suggests that a pilot study sample should be minimum 10% of the sample projected for the larger parent study, so the sample size even exceeds the recommended threshold. It showed that internal stakeholders considered all questions as understandable and clear, which was quite expected, as they were largely involved in scale creation through three rounds of the Delphi method.
Model validation
Participants
A total of 358 internal stakeholders from all over Serbia and from various public and private organizations, non-governmental organizations and academia participated in the final stage of the research. As mentioned before, the databases of YUTA (National Association of Travel Agencies), HORES (Business Association of the Hotel and Hospitality Industry), TOS and personal stakeholders’ contacts were used as a basic list of experts’contacts. For the purpose of the model validation, the sample was divided into two parts: Sample 1 (N = 151 respondents) was used to conduct exploratory factor nalysis (EFA), while sample 2 (N = 207 respondents) was used to conduct confirmatory factor analysis (CFA). The sample size was in line with Pallant (2020) who suggested that an acceptable sample size falls within the range of 150–300, while MacCallum et al. (2001) proposed that for conducting both descriptive and factorial analyses, a sample size of 100–200 observations would be sufficient. Regarding the years of work experience in tourism, the minimum was 1 year and the maximum was 46 years, while the average work experience of respondents in tourism is 13.12 years with Std. 10.17, and 11.64 years with Std. 9.32 (sample 2). The type of organization the respondents work for is shown in Table 3.
Type of organization the respondents work for (in %).
Source: Author's calculation.
Procedure and research instrument
The survey with internal tourism stakeholders of Serbia was carried out from October 2022 until March 2023. The survey consisted of 101 items measuring TDC of Serbia, which were extracted in the previous phases of model building. The participants were asked to evaluate their agreement with the statements referring to TDC of Serbia on a 5-point Likert scale (1 – I totally disagree, 5 – I totally agree) compared to the main competitors. In the pilot research and workshop, the respondents were asked about Serbia's main competitors. The results showed three main competitors: Croatia, Slovenia, and Hungary, thus, the evaluation has been done compared to these main competitors. The research was conducted through an electronic survey that was sent via email to a defined mailing list of stakeholders in Serbian tourism. The respondents were familiar with the goal and purpose of the research. The survey was anonymous and voluntary, but respondents were asked to list only the organization they work for.
Results
Exploratory factor analysis
In order to identify the latent dimensions of Serbia's TDC, EFA was conducted. Item analysis showed a high KMO = 0.915, and the statistically significant value of Bartlett's sphericity test was confirmed (χ2 = 16,625.83, df = 5050, p < 0.000). The analysis of item correlations revealed a significant number of correlations with values above 0.3, which led to the conclusion that the data are suitable for EFA. Items that have factor loadings lower than 0.3 were excluded from the further analysis, – a total of 30 items. The method of principal components with Promax rotation (due to established correlations between factors) and Kaiser normalization was used to extract factors. As a result, and taking into account only factors whose eigenvalues were equal to or greater than one, five factors were extracted, which explain 49.55% of the variance. The five identified factors are (F1) natural and cultural heritage, (F2) tourist offer and infrastructure, (F3) situational conditions for tourism development, (F4) marketing and experience, and (F5) pollution. The results of EFA are presented in Table 4.
The results of EFA – Factor structure and factor loadings.
Note: EFA=exploratory factor analysis; NTO= National Tourist Organization. Item numbers are original item numbers from the survey.
Source: Author's calculation.
Confirmatory factor analysis
CFA is used to validate and confirm the factor structure obtained by EFA. In order to test the hypothesized models posited in the research, structural equation modelling (SEM) was used. The AMOS programme for the Windows operating system was used for SEM and CFA. The Mardia index of multivariate kurtosis was above 3 for all tested models, indicating significant multivariate kurtosis. Therefore, it was justified to use robust methods and indices based on this method (Bentler, 2006). The fit or appropriateness of the model was assessed using the following indices: Sattora-Bentler χ² (S–B χ²) – if it is insignificant, then the model has a good fit, but since it is sensitive to the number of respondents, it is mostly significant in a greater number of cases, standardized root mean-square residual (SRMR), Root Mean-Square Error Of Approximation (RMSEA) – SRMR and RMSEA should be less than 0.08 (Browne and Cudeck, 1992), comparative fit index (CFI), normed fit index (NFI), non-normed fit index (NNFI) – if the CFI, NFI and NNFI are over 0.90, the model has a good fit (Hoyle, 1995). In order to achieve adequate fit indices, modification indices were used with which AMOS suggests and proposes changes to the model.
When the first model obtained by EFA was tested, satisfactory fit indices were not achieved. First of all, it was suggested that the F5 Pollution should be deleted because the factor saturations were very low. In addition to this, it was suggested that certain items should be part of another factor. It was also suggested that the following items should be excluded from the model due to very low factor saturation: Items number 11, 12, 16, 17, 18, 19, 20, 27, 28, 29, 22, 24, 52, 56, 60, 64, 65, 66, 67, 68, 72, 73. After these changes, adequate model fit indices were obtained. Also, due to the new structure, the original factor F3 Situational conditions for tourism development was renamed to F3 Legal frameworks and sustainable development of tourism. The final model thus consists of four factors: F1 Natural and cultural heritage, F2 Quality of tourist offer and infrastructure, F3 Tourism policy and sustainable development of tourism, and F4 Marketing and experience.
The final fit indices are shown in Table 5. The final model is shown in Table 6, it consists of the four mentioned factors and a total of 47 indicators.
Fit indices of the tested model.
Note: S–Bχ2=Sattora-Bentler χ²; SRMR=standardized root mean-square residual; RMSEA=root-mean-square error of approximation; CFI=comparative fit index; NFI=normed fit index.
Source: Author's calculation.
Structure of the final model of TDC (from internal stakeholders’ perspective).
Note: Item numbers are original item numbers from the survey. TDC=tourism destination competitiveness; NTO=National Tourist Organization.
Source: Author's calculation.
Factor 1: Natural and cultural resources are factors that include the assessment of Serbia's natural and cultural resources, as well as the wealth of tangible and intangible cultural heritage. Factor 2: Quality of tourist offer and infrastructure – refers to how natural and cultural resources are shaped into a tourist offer, the quality of that offer and activities, and the supporting infrastructure, signalization and accessibility of destinations and attractions. Factor 3: Tourism policy and sustainable development of tourism – refers to the extent to which the environment in Serbia is conducive to the development of tourism, what are the legal regulations, political environment, concessions, subsidies, investment opportunities, etc. This factor also contains items related to sustainability, i.e., how many companies in tourism respect the principles of sustainability, how many apply certified programmes, local community support for tourism development, etc. This factor is of particular importance for the development of a new model of competitiveness because it incorporates elements of sustainability that were not part of previous models of TDC. Factor 4: Marketing and experience - refers to the perception of the brand, the image of Serbia as a tourist destination, awareness of the brand, as well as the availability of information about Serbia both during the stay at the destination and for foreign potential visitors. This factor also includes an assessment of the quality of the experience at the destination, the possibility of booking services, and the willingness to recommend Serbia as a destination to others.
Descriptive statistics and measurement model validity
Descriptive statistics for all variables are presented in Table 7. It can be seen that Cronbach's alpha coefficient for all variables/dimensions is above 0.7. This means that the instruments used in the study are reliable and that they measure the given constructs. The results show that Natural and cultural resources are the best-rated factor in Serbia's competitiveness as a tourist destination, which means that Serbia has rich cultural and natural resources and great potential for tourism development. The lowest rated factor is the quality of the tourist offer and infrastructure, especially the arrangement of pedestrian and bicycle paths, road infrastructure, level of hygiene and cleanliness, adaptation of products and services to people with disabilities, and local needs. Only one item in this group exceeds 3.5, which indicates that the current situation in Serbia, when it comes to the quality of tourism offer and infrastructure, is not satisfactory compared to the major competitors. This is followed by factor Tourism policy and sustainable development of tourism, where the items related to the general political situation, access to funding, subsidies, public-private partnerships, involvement of stakeholders in decision-making in tourism, as well as the application of eco-certification, are particularly poorly rated. Factor Marketing and experience is also rated below 3.5 Internal stakeholders’ rate particularly low the awareness of Serbia on the international market, the positioning of Serbia as a tourist destination, and the brand of Serbia. The data obtained from the research of internal stakeholders pointed to the basic shortcomings that affect the competitiveness of Serbia as a tourist destination, and they should be the focus of further tourism strategies and initiatives.
Descriptive statistics and measurement model validity.
Source: Author's calculation. CR=composite reliability; AVE=average variance extracted.
Correlation estimates and average variances extracted.
Source: Author's calculation.
Before conducting CFA, convergent and divergent validity of the constructs was calculated to check the measurement model validity. The convergent validity of each dimension was examined by calculating the score of the average variance extracted (AVE, Fornell and Larcker (1981)). A substantial convergent validity is achieved when all item-to-factor loadings are significant and the AVE score is higher than 0.50 within each dimension, but AVE higher than 0.40 is still acceptable if composite reliability (CR) is higher than 0.60 (Huang et al., 2013). Results showed that all dimensions had AVE higher than 0.40 and CR higher than 0.60 (Table 7) which indicates good convergent validity. Discriminant validity was then checked by comparing the AVEs for each latent factor with the squared correlation estimates between latent constructs (Table 8). Fornell and Larcker (1981) noted that the discriminant validity is guaranteed when the AVEs are greater than the squared correlation estimates.
Discussion and conclusions
The findings of this study carry significant implications for both the industry and academia. The development of a comprehensive model and indicators for the evaluation of TDC is crucial for the sustainable growth and success of the tourism industry. As the industry continues to grow rapidly, it becomes increasingly important for destinations to understand and measure their competitiveness accurately to remain competitive and attract visitors. This study provides a significant contribution to the growing body of knowledge concerning TDC, specifically by developing a model tailored to the unique characteristics of developing economies. The new model, with its detailed process of creation, starting from an extensive literature review, Delphi method in indicator selection, pilot testing and complex statistical methods of validation, contributes to theoretical bases for a broader consideration of concrete and usable indicators of competitiveness of developing tourism destinations that are clear, precise and accurate for decision-makers to take concrete measures to improve the competitiveness of a tourism destination (Figure 1). The theoretical contribution creates the basis for the practical application of the proposed model not only in Serbia, but also similar developing countries.
The study's findings provide valuable insights for policymakers, researchers, and other stakeholders involved in the development and management of tourism destinations. The model and indicators developed in this study can be used to evaluate TDC, identify areas for improvement, and implement effective strategies to enhance their competitiveness in the global tourism market. Moreover, we found no academic studies of continuous TDC evaluation, while the model presented in this paper is aimed at national destination management organizations to be used as a tool for longitudinal TDC measurement. Also, this study contributes to the popularization and utilization of a mixed methodology approach (both quantitative and qualitative) in the development of TDC models for a specific destination.
Based on the literature review and discussion with the experts and representatives of the public and private tourism sectors in Serbia, we found that a more practical tool for measuring TDC is needed in order to provide tourism policymakers with concrete recommendations on how to improve the competitive position of a destination (in this case, Serbia) on the market. This idea of developing a TDC model that respects the specificity of Serbia as a tourism destination has been supported by national DMO and several national tourism associations. The combination of qualitative and quantitative methods allowed us to develop the competitiveness indicators relevant to Serbia as a developing tourism destination. Conducting a continuous (regular) evaluation of TDC should provide better information useful for decision-makers on how to improve the destination's competitive position. To successfully implement the model in practice, it is necessary to create a more sustained and coordinated programme of regular activities (surveys among target groups), which should be the task of the national DMO. The development and later practical application of this model will have implications for improving the competitiveness of Serbia as a tourism destination and the overall tourism contribution to the Serbian economy. For countries such as Serbia, the process of measuring competitiveness is important for further development, attracting investments, differentiation in branding, and responsiveness of business, which is why it is important to have a reliable methodology that includes all aspects and stakeholders, unlike developed destinations that are self-sustainable.
It is also important to mention that the Delphi method implemented before, during and after the workshop was crucial for reaching stakeholders’ consensus regarding relevant indicators to be included in the model. Besides quantitative grades, qualitative data in the form of stakeholders’ discussions resulted in better clarity and formulation of certain indicators. Furthermore, pilot testing eliminated all shortcomings and misunderstandings, making this model well-balanced and approved by Serbian tourism experts and the industry. The model was then tested and shown to be a valid and reliable tool for measuring TDC of Serbia. The final model includes 50 indicators in four extracted factors: Natural and cultural heritage, Quality of tourist offer and infrastructure, Tourism policy and sustainable development of tourism, and Marketing and experience. These factors with the variables that constitute them align with the existing models of TDC but are labelled differently. Main elements of the existing TDC models (e.g., Dwyer and Kim, 2003; Ritchie and Crouch, 2003) are focused on resource base (e.g., natural and cultural heritage, events, etc.), supporting factors (e.g., infrastructure, accessibility, service quality etc.) and destination management and policy. Our model puts some emphasis on sustainability indicators, whose implementation is tightly connected with tourism policy and regulations. These indicators are of particular importance as they are somehow underestimated in developing countries while having a strong effect on all other destination competitiveness indicators. Developing economies like Serbia predominantly emphasize the economic impacts of tourism, potentially overlooking other crucial dimensions of sustainability. Consequently, we advocate for additional research efforts within these economies to comprehensively explore and enhance their tourism competitiveness through a more balanced consideration of sustainability. Understanding how they integrate sustainability into their tourism policies and practices while aiming for competitiveness is an area worth studying. As mentioned before, two most frequently applied TDC models in the previous studies were Ritchie and Crouch (2003) and Dwyer and Kim (2003) Integrated model. While Ritchie and Crouch (2003) model is more focused on supply-side factors, the Integrated model includes some indicators related to the demand side as well. Our model includes both types of indicators, while demand-side indicators being included in Marketing and experience. Both of these studies propose evaluation of TDC by tourism experts. In future studies, TDC should be evaluated by employing a participatory approach that engages diverse target groups at the destination, including both internal and external stakeholders. Sustainable tourism development should address the needs of all stakeholders involved in this process: industry, host community and visitors. Thus, the study proposes testing the newly created model with all mentioned target groups in order to validate separate tailor-made and specific models for each target group. Only with such a holistic approach to TDC, the realistic picture of destination competitiveness can be obtained.
Further on, the model validation was based on measuring TDC of Serbia by internal stakeholders. The study results revealed that natural and cultural resources are the best-rated factor in Serbia's competitiveness as a tourist destination, while the lowest rated factors refer to the quality of the tourist offer and infrastructure and marketing and tourists experience. When we compare these results with Armenski et al. (2012)study, we can see some similarities, as they revealed that competitive advantage of Serbia is in its natural, cultural, and created resources while it lacks competitiveness in destination management and demand conditions (especially image and awareness of the destination). Moreover, the study by Dwyer et al. (2014) emphasized that improving TDC of Serbia would require a bigger focus on sustainability-related activities, while the current study confirmed that the current state in Serbia did not improve much in the last 10 years. Thus, the newly created model should be implemented longitudinally. This is very important as after each measurement some necessary actions should be defined with the aim to improve TDC of Serbia. A better understanding of the components that make up the model would be beneficial to tourism planners and managers since this will enable them to improve the competitiveness of their destinations. The results may also help policymakers to adopt strategies encouraging competitiveness and sustainable tourism development in Serbia as well as in other similar developing destinations. Thus, besides theoretical contributions, the model has high practical applicability.
Based on the research findings and conclusions drawn during the workshop held with Serbian tourism experts and industry representatives in Belgrade, Serbia, in June 2022, we propose several recommendations that can help enhance Serbia's TDC:
Infrastructure development – upgrade and expand transportation networks, to improve accessibility and connectivity to major tourism destinations within and outside Serbia. Promote sustainable tourism – emphasize sustainable practices to preserve natural and cultural heritage, that appeals to eco-conscious travellers. Diversify tourism offerings – to cater to different preferences and attract a broader audience. Enhance marketing and promotion – invest in targeted marketing campaigns both domestically and internationally to raise awareness of Serbia as a tourism destination and highlight its unique attractions and experiences. Collaborate with regional partners to create regional tourism packages and promote joint attractions, allowing travellers to explore multiple destinations in one trip. Digital presence, innovation and technology – enhance the online presence of Serbian tourism through user-friendly websites, mobile applications, and social media platforms to provide easy access to information and bookings for tourists. Provide training programmes and workshops to enhance the skills and knowledge of those working in the tourism industry, ensuring a high standard of service. Public-private partnerships – encourage collaboration between the government, private sector, and local communities to jointly invest in tourism infrastructure and initiatives, leveraging each entity's strengths. Tourist safety and security – create a welcoming and secure environment for tourists, instilling confidence in potential visitors. Tourism research and data analysis – conduct thorough research and data analysis to understand market trends, consumer preferences, and emerging tourism opportunities, enabling targeted strategies and decision-making.
The study also had certain limitations. First, some limitations can be driven by the common limitations of the Delphi method which might be partly based on the subjectivity of the participants in the research. However, it is the most used method for reaching a consensus among experts. Additionally, a lack of financial resources leads to a lack of large-scale data collection and surveys. Regarding future research, the first step will certainly include evaluation of competitiveness indicators identified in our model by the other important target groups: Visitors, residents and foreign tour operators that sell package tours for Serbia. Dwyer (2020) advocates for a more comprehensive approach to measuring the impact of tourism development on destination residents’ well-being, both presently and in the future. This involves taking into account not just economic benefits, but also social and environmental factors that affect residents’ quality of life. By doing so, tourism planners and policymakers can make informed decisions that promote sustainable tourism development, which benefits both tourists and residents. Such an approach would be beneficial not only in the short term but also in the long run, as it would help to mitigate negative impacts on residents’ well-being that might arise from tourism development. The innovation and technology indicator should be in focus in future research toward TDC as well (Cimbaljević et al., 2023). Moreover, future research will also include the application of the validated model in other countries in the region (Serbian competitors) with similar tourism industry characteristics to test the model's applicability to similar tourist destinations.
The focus on GDP alone can be a narrow approach when assessing the sustainable development of tourism destinations. To achieve sustainable tourism development, it is essential to consider both objective and subjective measures when evaluating TDC. This requires a comprehensive analysis of the links between the objective and subjective dimensions of competitiveness. Therefore, tourism researchers and policymakers should broaden their perspectives and incorporate both objective and subjective measures in their assessments to ensure a more holistic approach to sustainable tourism development.
Tourism destination competitiveness (TDC) model for Serbia.
Footnotes
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors received the following financial support for the research, authorship, and/or publication of this article: This research was supported by The Science Fund of the Republic of Serbia (Grant No. 7739076) and Tourism Destination Competitiveness – Evaluation Model for Serbia – TOURCOMSERBIA.
