Exploring the Tourism Competitiveness of a Destination: A Case Study of Georgia

1. Literature Review

Interest in research on tourism destination competitiveness has grown alongside the global expansion of the tourism industry. Over the past three decades, more than 1,100 articles on tourism destination competitiveness have been published in reputable journals (Haiyang et al., 2024). These studies address various facets of the subject, including theoretical foundations, competitive environment modeling, destination market segmentation, the identification of competitive variables and indicators, and the determination of competitive environments for competing destinations.

The concept of competitive strategy was introduced by Harvard Business School professor Michael Porter in 1979 through his widely recognized Porter’s 5 Forces framework. This model highlights the key competitive forces that a company must consider to strengthen its market position (Porter, 1979). Later, Porter expanded on this idea with the Diamond Model (1990), which evaluates an industry’s international competitiveness. Rugman and D’Cruz (1993) extended the Diamond Model further with the Double Diamond Model (DDM), incorporating competitive strategy on global, regional, and national levels.

Dwyer and Kim (2002) developed a Tourist Destination Competitiveness Model designed to facilitate comparisons between countries and tourism sectors. Initially tested in Australia, this model emphasized key elements of competitiveness from the broader literature while addressing the unique issues associated with destination competitiveness. Their subsequent work refined the methodology, expanding the range of competitive indicators used in the analysis (Dwyer & Kim, 2010).

Ritchie and Crouch (2003) proposed the Tourism Destination Competitiveness (TDC) and Sustainability Model, which focuses on the macro and micro levels of the competitive environment. This model identifies seven key components that influence a destination’s competitiveness and sustainability, highlighting the synergy between comparative and competitive advantages (Ritchie & Crouch, 2003; 2010). This dual perspective of competitiveness is essential for understanding a destination’s market position (Gonzalez-Rodríguez, 2023). A special study was dedicated to systematizing and explaining the definitions popularized in connection with the development of these concepts and models (Mazanec et al., 2007).

Various studies have explored specific components of the TDC model, particularly those related to the supply side of tourism destinations of different levels such as countries, regions, or specific locations. Their competitive indicators include factors like natural and cultural attractions, infrastructure, and services (Smith, 1988; Marais et al., 2017; Stalmirska, 2021). On the demand side, segmentation researches have focused on demographic, behavioral, psychographic, firmographic, and technographic factors, as well as geographical segmentations based on factors such as population density and climate (Ioannides & Debbage, 1998; Gillian Partin, 2011, Kotler & Armstrong, 2018; Cibinskiene, Snieskine, 215, Lopes, Munoz, Alarcon-Urbistondo, 2018). Many destinations use country of origin as a segmentation criterion to tailor their marketing strategies to specific tourist profiles (Dolnicer, 2008; A. Nella & E. Christou, 2021). Research on diverse aspects of tourists as buyers and territorial classification by their origin and visiting places significantly depends on the specifics of destinations and tourism sectors (A. Nella, E. Christou, 2021).

The competitive environment of a destination is multi-dimensional, encompassing core resources and attractors, supporting factors, destination policy, planning, development, and management. Ritchie and Crouch (2003) categorized these factors into the global (macro) and competitive (micro) environments. The macro-environment includes economic, technological, ecological, political, sociocultural, and demographic factors, while the micro-environment refers to the immediate forces influencing tourism activities within the destination itself (Ritchie & Crouch, 2003).

Some studies have criticized previous research for focusing too narrowly on either comparative or competitive advantages. Gonzalez-Rodríguez et al. (2024) proposed a hybrid approach that integrates both dimensions, offering a more holistic view of destination competitiveness (Gonzalez-Rodríguez et al., 2023).

In terms of research methodologies, scholars have applied classical and innovative analytical tools to assess destination competitiveness. Cracolici et al. (2006) used the Crouch-Ritchie Model to analyze destination efficiency and competitiveness, while Lusticku and Bednarov (2018) explored factors of competitiveness using the Integrated Model of Destination Competitiveness. These approaches provide valuable insights into how competitiveness influences destination performance.

Vobek K. emphasizes that “There is a vast body of literature about competition, competitive advantage, and competitive identity in tourism. Also, there is a dearth of studies linking tourism competitiveness (TC) to tourism performance” (Hossein et al., 2021). Destinations could be compared by their ability to adapt and maintain competitive positions in the tourism market, as changes in tourism affect destination performance and success” (Vodeb K., 2012, pp. 273-278).

A popular methodology dedicated to the assessment of tourist destination competitiveness through Crouch-Ritchie’s model and the Travel and Tourism Competitiveness Index (TTCI) implies the methods of data envelopment and truncated regression with bootstrap (Gonzalez-Rodríguez et al., 2023). Benchmarking is another popular method used in competitiveness assessments. It involves identifying and comparing the strengths and weaknesses of a destination relative to its competitors (Zlatkovic, 2016).

A wide variety of analytical methods were adjusted to the variables and indicators presented in the aforementioned models of tourism destinations’ competitiveness. A special statistical tool was developed focused on the tourism decision process, which starts from the demand schedule for holidays and ends with the choice of a specific holiday destination, and was tested in the case of Italy as a tourism destination (Gardini, 2008). Studies have also employed Multi-Criteria Decision Analysis (MCDA) methods, such as ELECTRE I, to evaluate tourism destinations (Botti & Peypoch, 2013). The Importance-Performance Analysis (IPA) model (Tontini, Silveria, 2007), as well as the Importance-Performance-Competitor Analysis (IPCA), has been used to compare destinations and highlight areas for strategic improvement (Albayarak et al., 2018).

Some scholars have further refined the categorization of competitive indicators. Ferreira and Perks (2020) identified three primary themes influencing destination competitiveness: core, facilitating, and supporting indicators. These themes provide a foundation for understanding the socioeconomic dynamics between developed and developing countries. Additionally, the 4 Cs Tourism Destination Competitiveness Matrix (Diamantino et al., 2020) identifies four key dimensions: Capacity, Competence, Communication, and Creativity. These dimensions are essential for evaluating the competitiveness of destinations, as they address aspects like infrastructure, human resources, marketing, and innovation.

The role of information technologies in tourism competitiveness has also garnered attention in recent studies. Researchers have explored how digital tools and innovations enhance competitiveness in European destinations (Petrovic et al., 2016; Irtyshcheva et al., 2022).

A growing body of literature evaluates the competitiveness of specific countries as tourism destinations. Comparative studies have been conducted on destinations such as Hong Kong (Enright & Newton, 2004), Italy (Goffi, 2013), Greece (Marios, 2015), and Portugal (Mira et al., 2016), Serbia (Milutinovic, Vasovic, 2017), India (Saqib, 2019), Japan (Takatoshi et al., 2022), China (Liu et al., 2022), Georgia (Okroshidze et al., 2024), etc.

Georgia, as a relatively emerging tourism destination, presents a special unique case.

The leading segments of Georgia represented by Armenia, Turkey, and Russia and composing more than 50% of the foreign visitors, make up less than 3% of the global travelers, while the global leading tourism generators —US, China, Germany, and the UK—making up more than 25% of the global tourism market, contribute just less than 2.8% to the Georgian market (DataBank-WB; GNTA.GE). This reality underlines the need for a tailored approach to understanding Georgia’s competitive environment, specifically through the Segment-centric Geo-competitive Environment of a Tourism Destination (SGE-TD) framework.

3. The Research Design and Applied Methodology

The research design consists of the following steps:

• Identifying competing tourist destinations for the destination under study (Georgia in this case);
• Analyzing the identified competing travel destinations composing the competitive environment for the study destination;
• Identifying the features of the studied competitive environment, focusing on the driving forces and the competitive position of the study destination

Step 1: Identifying Competing Tourist Destinations for the Destination Under Study

To determine the destinations that constitute Georgia’s competitive environment, we apply the Segment-centric Geo-competitive Environment of a Tourism Destination (SGE-TD) approach (Khelashvili, 2023). First, we identify Georgia’s leading tourism-generating countries, followed by identifying the primary travel destinations for tourists from each of these countries.

For the first task of this stage, it was assumed that tourists from these countries should collectively account for at least 80% of all foreign visitors to Georgia in order to ensure adequate representation. According to the latest statistics for 2023, the number of countries that annually send 20,000 or more tourists to Georgia is 23. The share of tourists from these countries accounts for 87.6% of all foreign tourists visiting Georgia (see Graphic 1), which justifies the use of these country-related data for further analysis.

The stark reality underscores the importance of market diversification, which is essential for strengthening Georgia’s competitive position in its foreign tourism market.

To achieve the objective of the second task of the same step, we determined the top 10 preferred tourism destinations (countries) from each of the 23 segments previously outlined. This analysis revealed 61 tourism destination countries, which collectively constitute Georgia’s geo-competitive environment, as defined by the SGE-TD concept (see Table 1).

To assess the validity of considering Georgia’s international tourism competitive environment (SGE-TD of Georgia) separately from the global competitive tourism environment, a comparison was conducted using two alternative calculations.

The first calculation shows the presence of the top 10 most popular tourist destinations globally in within the list of the top 10 preferred destinations for countries generating foreign tourism to Georgia. The second calculation is similar but includes ranking scores for destination preferences, ranging from 10 for the most popular to 1 for the least popular destination

(a) P%(ps) = (SUM DS/SUM DG) 100% = (79/230) *100% = 34.3%;

(b) P(rs)% = [SUM DS(s)/SUM DG(s)] 100% = (453/1265) *100% = 35.8%

Where,

P% (ps) - Share of top preferred global destinations among the 10 preferred

destinations of the studied segments by presence scores;

DS (ps) - Number of the top preferred global destinations present among the

top 10 preferred destinations of the studied segments;

DG - Top 10 preferred tourism destinations in the global market.

The calculation in (b), which incorporates ranking scores for globally popular destinations present in the top 10 preferred destinations of the studied segments, yielded similar results. Both calculations indicate that the preferred travel destinations among Georgia’s tourism-generating countries do not align with those in the global tourism market. Approximately 35% of the preferred destinations overlap with around 75% being different.

These calculations validate the consideration of Georgia’s SGE-TD as distinct from the global tourism market.

Regarding the selectivity of destinations within Georgia’s SGE-TD, it is important to highlight that despite the considerable differences among destinations, Turkey stands out as particularly popular. It is followed by 31 relatively popular destinations (with ranking scores of 5 and higher), including Georgia itself (see Graphic #2, Appendix A).

Table 2. Selectivity of the world’s top tourist destinations in Georgia’s tourism-generating countries.

Table 2. Selectivity of the world’s top tourist destinations in Georgia’s tourism-generating countries.

Step 2: Analyzing the identified competing travel destinations composing the competitive environment for the study destination;

Following the identification of key components in the competitive environment, such as market segments and their preferred travel destinations, the next step involves:

(A) Determining relevant competitive indicators for the study;

(B) Analyzing the competitive environment composed of the identified competing

tourism destinations.

To address Objective A, relevant competitive characteristics, indicators, pillars, and indexes are considered. Given the methodology, scope, and content of these indicators—as well as data availability for the studied countries—this study utilizes data from the World Economic Forum’s Travel & Tourism Competitiveness Index (WEF TTCI).

For Objective B, Principal Component Analysis (PCA) was applied to reduce the complexity of the dataset, which comprises five competitive indexes across 61 tourism destination countries. The PCA results revealed substantial data variation. To adequately capture this diversity, three principal components were selected for further analysis and interpretation based on their contribution to overall data variance:

• PC1 (42.5%)

• PC2 (20.4%)

• PC3 (18.9%)

Together, these components account for approximately 82% of the total variance (see Graphic 3 and Table 3).

Table 3. Eigenvalues.

ParameterParameter	PC₁	PC₂	PC₃	PC₄	PC₅
Eigenvalue	2.1269	1.0198	0.9386	0.7216	0.1932
% of Variance	42.538	20.396	18.771	14.431	3.8634
Cumulative (%)	42.538	62.934	81.705	96.137	100

Table 3. Eigenvalues.

ParameterParameter	PC₁	PC₂	PC₃	PC₄	PC₅
Eigenvalue	2.1269	1.0198	0.9386	0.7216	0.1932
% of Variance	42.538	20.396	18.771	14.431	3.8634
Cumulative (%)	42.538	62.934	81.705	96.137	100

This high variance coverage demonstrates that PCA effectively identifies the most critical underlying factors, reinforcing its robustness as an analytical tool for assessing competitive dynamics among tourism destinations.

The visualization of this diversity implies a 3D graphical expression (See graphic #4, Table 4, and Table 5)

Graphic 4. 3D PCA graphic of Georgia’s SGE-SD.

Table 4. Eigenvectors.

		Vector2	Vector3	Vector4	Vector5
Enabling Environment	0.5217	-0.3757	-0.4386	0.1972	0.5962
T&T Policies and Enabling Conditions	-0.1618	0.7367	-0.6203	0.198	0.084
Infrastructure	0.5984	-0.002	-0.3309	-0.2493	-0.6858
T&T Resources	0.4255	0.5	0.3649	-0.5341	0.3879
T&T Sustainability	0.4032	0.257	0.4245	0.7579	-0.1293

Table 4. Eigenvectors.

		Vector2	Vector3	Vector4	Vector5
Enabling Environment	0.5217	-0.3757	-0.4386	0.1972	0.5962
T&T Policies and Enabling Conditions	-0.1618	0.7367	-0.6203	0.198	0.084
Infrastructure	0.5984	-0.002	-0.3309	-0.2493	-0.6858
T&T Resources	0.4255	0.5	0.3649	-0.5341	0.3879
T&T Sustainability	0.4032	0.257	0.4245	0.7579	-0.1293

Table 5. The covariance matrix.

Group\Group	Enabling Environment	T&T Policies and Enabling Conditions	Infra- structure	T&T Resources	T&T Sustainability
Enabling Environment	1	-0.1686	0.6865	0.099	0.2671
T&T Policies and Enabling Conditions	-0.1686	1	-0.06151	-0.0532	-0.0866
Infrastructure	0.6865	-0.06151	1	0.4719	0.2616
T&T Resources	0.099	-0.0532	0.4719	1	0.3395
T&T Sustainability	0.2671	-0.0866	0.2616	0.3395	1

Table 5. The covariance matrix.

Group\Group	Enabling Environment	T&T Policies and Enabling Conditions	Infra- structure	T&T Resources	T&T Sustainability
Enabling Environment	1	-0.1686	0.6865	0.099	0.2671
T&T Policies and Enabling Conditions	-0.1686	1	-0.06151	-0.0532	-0.0866
Infrastructure	0.6865	-0.06151	1	0.4719	0.2616
T&T Resources	0.099	-0.0532	0.4719	1	0.3395
T&T Sustainability	0.2671	-0.0866	0.2616	0.3395	1

Step 3. Identifying the features of the studied competitive environment

The principal components identified, along with the corresponding competitive variables and scores of tourist destinations, demonstrate both the general and specific features of the competitive environment studied, as well as Georgia’s competitive position within it.

PC1 (42.54% variance) emphasizes the significance of “Infrastructure” (0.60) and “Enabling Environment” (0.52), followed by “T&T Resources” (0.43) and “T&T Sustainability” (0.40). Leading destinations in these categories, with their different contributions in different variables, include the US, the UK, Spain, France, Japan, Canada, and Denmark (Score range between 2.0-3.5; refer to see Table in Appendix B).

Georgia holds a moderate position in this principal component, with a slightly negative score of -0.47, ranking 35th among all destinations. This is due to a negative score in “Infrastructure” (-0.38), partly balanced by a positive score in the “Enabling Environment” (0.37). A Similar balance is seen in relation to the other variables in this vector; Georgia has a negative score in “T&T Resources” (-0.87) and a positive score in “Sustainability” (0.23) (refer to see Appendix B, Score Tables).

Georgia’s primary competitors in PC1 are Slovenia, the Czech Republic, and Hungary, while Kazakhstan and Mexico closely follow. However, when considering this relationship across the most influential variables in PC1 individually, more competing destinations emerge. For instance, in terms of the “Infrastructure” variable, Georgia competes with Slovenia, China, Estonia, Hungary, Romania, Egypt, Malaysia, the Dominican Republic, Lithuania, and Thailand (with scores ranging from between 0.00 and -0.37) (refer to Appendix B, Score Table). (Note: The two variables mentioned in PC1—T&T Resources (0.43) and T&T Sustainability (0.40)—will be further analyzed in the following PC3, where they play a leading role.)

PC2 (20.40% variance) increases the combined coverage of all variations up to 62.95% (see Table 2 above). This component focuses on “T&T Policies and Enabling Conditions,” which shows the highest score in the list of all other variables (score 0.74). The top countries in this vector are Indonesia, India, Türkiye, and Mexico (scores between 1.54 and 2.44). Additionally, the variable driving this principal component - ‘T&T Policies and Enabling Conditions’ - is significantly influenced by Türkiye, Malaysia and the UAE (scores above 1.5) (see Appendix B).

Georgia possesses a similarly moderate position in the PC2 component, although it holds a relatively high rank (29th) among all destinations. The gained score (0.22) is its only positive one across all three principal components (see Appendix B). This position reflects its relative strength in the tourism policy indicator while leaving room for further improvement.

According to the applied calculation, Georgia’s closest foremost competitors in PC2 are Lebanon, Azerbaijan, and Kazakhstan (scores between 0.21 and 0.24). In the position along the PC2 driving variable, that is “T&T Policies and Enabling Conditions”, Georgia holds a more advanced position, ranked 12th in the whole considered market. In this dimension, the closest competitors are Hungary, Albania, and Poland (scores between 0.98 and 1.07).

PC3 (variance 18.77%) accrues the combined coverage of all variance up to 81.70%, emphasizing the role of “T&T Sustainability” (0.42) and T&T Resources” (0.37). The leading destinations in this line are the US and Indonesia (scores vary between 2.54 and 2.33, respectively) (see Score Table, Appendix B).

In this component, Georgia holds a relatively low position (score -0.86) and is ranked 50th among all destinations. Similar to the PC1 case, controversial scores appear in the driving variables of PC3. In part, Georgia shows a negative score in “T&T Resources” (-0.87) and a positive one in “Sustainability” (0.23). (see page Countries vs PC - Score table, Georgia). Georgia’s lower rank and negative positioning in this component underline areas where the country could enhance its resource base and sustainability efforts. The interpretation of this position is relevant as it identifies areas for strategic improvement.

The closest competitors of Georgia in this principal component are Lebanon, Azerbaijan, and Russia (score 0.31 per the latest 2019 data), as well as Greece with an equal score and Mexico (scores between 0.20 and 0.47). (see Appendix B).

4. Generalized Outcomes

The obtained results and the compiled interpretations are summarized in graph #.5 (see Graphic 5, Table 6). It displays the close competitors of the study tourist destination—in this case, Georgia—across each analyzed competitive variable, which in turn are allocated per identified main principal components of the studied SGE-TD of Georgia.

Graphic 5.

Table 6. Competitive position of Georgia within its SGE-TD.

Table 6. Competitive position of Georgia within its SGE-TD.

According to the studied tourism destination, Georgia possesses a moderate position in its SGC-TD per PC1 and PC2 and a relatively low follower position per PC3 (SEE Graphic 5, Table 6). The positive score in PC2 near zero (0.22) suggests that Georgia’s “Tourism policies” and “Enabling Conditions” are comparable to the average of other tourism destinations; however, it leaves room for improvement. The negative scores in PC1 and especially in PC3 emphasize the importance of advancing the position, especially in raising the acknowledgment of the country’s rich and diversified T&T Resources, as well as in strengthening infrastructure to improve competitiveness. These recommendations align with the PCA results.

Georgia, within its tourism competitive environment, appears to be in tight competition with different destinations, depending on the competitive indicators. In relation to infrastructure and enabling environment, such competing destinations are Kazakhstan and Mexico, while with regard to T&T policies and enabling conditions—Türkiye, Malaysia, and the United Arab Emirates. The largest number of the closest competing destinations appears in terms of T&T Sustainability as well as T&T Resources.

In this study, we selected the ranking positions of destinations that are located in close proximity to Georgia. In particular, three positions above and two positions below Georgia, giving priority to the number of advanced destinations and to the country’s promotion in its competitive environment.

The revealed outcomes suggest options for strategies to advance the competitive position in the county’s tourism competitive environment, which can be prioritized based on the country’s general strategy for economic development and its priorities. The suggestion that these insights can contribute to Georgia’s broader economic development strategy also adds value, making the analysis more impactful.

The generalized methodological outcomes of the provided research imply the logic of sequential research stages, a set of conceptual approaches, and optional tools corresponding to each stage (see Table 7).

The suggested methodology and the developed research design can be applied to other tourism destinations and their geo-competitive environments. In addition, the same research design can include more detailed variables of a tourism competitive environment depending on the research objectives and data availability.