Index

Abstract

This research analyzed the effect of the main touristic product components (Cultural and Natural Attractions, Tourism Services Infrastructure, Transportation Facilities, Price Accessibility, and Image) on tourists’ destination selection in the seven most-visited destinations in the world in 2020 (France, Spain, the USA, China, Italy, Turkey, and Mexico). The selected destinations were analyzed and compared with the PROMETHEE technique, using the data in the 2019 reports of the “Travel and Tourism Competitiveness Index” and the "Tourism Brand Country Ranking." The results of the analysis showed the order of superiority of the destinations as Spain, France, Italy, China, the USA, Mexico, and Turkey. The criteria and effect levels that affected this ranking are examined in detail for each destination in the findings section of this paper. It was concluded that Turkey fell behind in the ranking due, in particular, to its low performance in the Cultural and Natural Attractiveness category, despite its rich cultural and natural values. This research reveals that differences in the ranking of the 7 countries that attract the most tourists in the world depend on the characteristics of the various touristic product components, and the reasons for these differences are within the touristic product components framework.

Keywords: Destination attractiveness, Destination competitiveness, Multi-criteria decision-making methods, PROMETHEE.

Received: 8 August 2022 / Revised: 23 September 2022 / Accepted: 6 October 2022 / Published: 20 October 2022

Contribution/ Originality

In this study, quantitative data on the touristic product components were obtained from the 2019 Travel and Tourism Competitiveness Index (TTCI; renamed the Travel and Tourism Development Index (TTDI)) and the Tourism Brand Country Ranking reports. The PROMETHEE technique, a Multi-Criteria Decision Making Method, was used in the analysis. It is thought that the research will contribute to the literature on this subject.

1. INTRODUCTION

Tourism is an important driver of local and international economic growth. After six decades of steady growth, tourism has become one of the world's most important economic industries. With a growing service economy, it plays an important role in creating jobs, generating income and foreign exchange, promoting regional development, and supporting local communities. Compared to other export sectors, tourism exports make a greater contribution to a country's economy. For example, the average US dollar spent by international tourists in Organisation for Economic Co-operation and Development (OECD) countries generates approximately 89 cents of domestic added value, compared to 81 cents for overall exports. The tourism industry was one of the industries most affected by the Covid-19 pandemic in 2020, with a contraction of 74% (UNWTO, 2021a), compared to 1.5 billion international tourist arrivals in 2019 and a direct contribution of 2.9 trillion dollars to the world’s Gross Domestic Product (GDP) – statistics that outperformed long-term growth forecasts (Lock, 2021). Although the short-term outlook for tourism is mixed due to the uncertain economic outlook, health fears, and external shocks such as extreme weather events, tourism is expected to continue to grow in the long term (OECD, 2020).

According to the "Tourism Model Diagram" developed by Leiper (1990) within the framework of the physical, economic, sociocultural, political, and technological environment, the driving factors in tourist-generating regions are ticketing services, tour operators and travel agencies, and marketing and promotional activities. Similarly, the attractive factors in touristic destinations are accommodation, food and beverages, shopping and entertainment services, touristic attractions, and other touristic products and services. From this point of view, transportation facilities and communication channels between tourist-generating and tourist-attracting destinations play an important role (Leiper, 1990). Kotler, Bowen, and Makens (2003) emphasized that the factors affecting tourist behavior in tourism and accommodation marketing, in order of importance, are cultural (culture, social class, subculture), social (family, roles and status, reference groups), personal (occupation, age, lifestyle, income status), and psychological (attitudes, beliefs, learning, and motivation/perception) (Kotler et al., 2003).

To determine the quantitative importance of touristic product components in terms of destination competitiveness, the main touristic product components and their importance levels have been found to include cultural attractions (39%), natural attractions (22%), tourism services infrastructure (12%), transportation facilities (11%), prices (10%) and image (5%) (Goral & Tuna, 2018). This study aimed to compare the strengths and weaknesses of the countries that attract the most tourists in the world, using the PROMETHEE technique, a Multi-Criteria Decision Making (MCDM) method, by investigating the factors that make up the touristic product components. In this way, the effect of the importance of touristic product components on the rankings of popular destinations were attempted to reveal. To achieve this, the main touristic product components and their importance levels as identified by Goral and Tuna (2018) were employed as research criteria and weights within the scope of this research. The top seven destinations with the highest number of visitors in the world have been identified as France, Spain, the USA, China, Italy, Turkey, and Mexico. In this context, the study aimed to determine the differences that occur in the order of superiority when using the PROMETHEE method, based on the main touristic product components weighted for these top seven destinations, and to determine the reasons for these differences within the framework of touristic product components. In doing so, it aimed to determine the effect of the main touristic product components on the decisions of tourists in destination selection and to create the most appropriate destination selection order. It is thought that this research will contribute to the related literature.

2. THEORETICAL FRAMEWORK

2.1. Touristic Product Mix

The types of products that a destination exhibits to its target markets constitute its "Touristic Product Mix" (İçöz, 2001). Accordingly, the usable supply potential informs the development of a destination’s touristic product mix. The touristic product is the sum of the products that the destination offers its target markets to the benefit of its target audience (Buhalis, 2000). Although destinations may have different products, each destination usually has a leading product in its touristic product mix that increases its attractiveness (Özer, 2012). The Louvre Museum in France or an all-inclusive stay in Turkey are examples of top/leading touristic products in different destinations. To determine the importance of cultural attractions in terms of tourism competitiveness, the main touristic product components are cultural attractions, natural attractions, tourism services infrastructure, transportation facilities, prices, and image, as established by Goral and Tuna (2018) and mentioned in the introduction. In addition, Kotler et al. (2003) confirmed Göral and Tuna's research by emphasizing that the most important factors affecting tourist behavior are cultural, social, personal, and psychological factors.

For this reason, the main touristic product components, as discussed by Goral and Tuna (2018), were chosen as the variables of this research to quantitatively determine the strengths and weaknesses of popular destinations relative to each other.

2.2. Destination Attraction and Competitiveness

Studies analyzing destination attractiveness and competitiveness have been increasing since the 1990s (Göral & Yurtlu, 2021). It can be observed that researchers have generally employed Porter's competitiveness analysis (Goral & Tuna, 2018). In his analyses, Porter related competitiveness to local conditions. When determining the competitiveness of destinations, factors and demand conditions such as the structure of the main and supporting sectors, firms’ strategies, and the competitive situation come to the fore (Porter, 1990). Since Porter’s publication, a number of measurement models have been developed to assess the competitiveness of destinations within the scope of tourism research:

Traditionally, the most important elements in destination attractiveness are the conditions that facilitate tourism and the value ​​that is inherent in or bestowed on the destination, such as natural resources, climate, beaches, and cultural activities (Goral & Tuna, 2018). One important study that revealed the relative competitiveness of destinations was the Travel and Tourism Competitiveness Index (TTCI) report, which was published first in 2007 by the World Economic Forum (WEF), after which new versions were released in 2008, 2009, 2010, 2011, 2013, 2015, 2017, and 2019 (WEF, 2019). The report is one of the important studies in which the factors relating to the measurement and evaluation of international tourism competitiveness are comprehensively discussed (Göral & Yurtlu, 2021). The report aims to determine the factors that support the development of the travel and tourism industry around the world at the country level. In the latest version of the TTCI, published in 2019, 14 factors and 90 indicators relating to these factors were included in four main categories (WEF, 2019). The data used to create the report were obtained from primary and secondary sources. In this context, primary data were obtained through questionnaires completed by country representatives in cooperation with the WEF. Secondary data were obtained from organizations working together with The United Nations World Tourism Organization (UNWTO), the World Travel and Tourism Council (WTTC), Bloom Consulting (BC), the International Air Transport Association (IATA), Smith Travel Research (STR) Global, UNESCO (United Nations Educational, Scientific and Cultural Organization), the United Nations Statistics Division (UNSD) and the International Union for Conservation of Nature and Naturel Resources (IUCN) (WEF, 2019). The literature shows that studies have used various approaches to examine the TTCI reports, developed and published by the WEF, and determine destinations’ competitiveness levels.

Kayar and Kozak (2010) conducted research based on the TTCI data of 2007 and found that although Turkey has a competitive advantage over its competitors in the Mediterranean, such as Italy, Spain, Greece, and Portugal, in terms of prioritizing tourism and tourism infrastructure, it is comparative to low- and middle-income Eastern European countries as an inexpensive destination with close competition features. A study by Aydemir, Saylan, and Aydoğmuş (2014) used TTCI data, mainly the TTCI data of 2013, to compare Turkey's competitiveness with European countries. The competitive factors were analyzed in three categories: Regulatory Framework, Natural-Cultural and Human Resources, and Business Environment and Infrastructure. It was determined that although Turkey is in a bad situation in terms of safety, road transportation, health-hygiene, and environmental sustainability, it is one of the top destinations in the world in terms of cultural heritage; also, investment and developments in air transportation provide benefits in terms of competitiveness. Additionally, it was determined that the countries at the top of the index are "the open countries to visitors and tourism." Göral and Yurtlu (2021) and Göral (2017) used TTCI data to comprehensively examine Turkey's performance in the travel and tourism competitiveness index in 2015 and 2019. These studies found no change in Turkey’s outlook, scoring "moderate" for Environmental Factors, "good" for the Infrastructure factor, and "very good" for the Natural and Cultural Resources factor in both 2015 and 2019. In addition, however, it was determined that Turkey had increased from "moderate" to "good" in the factor Tourism Policies and Improvement of Conditions.

3. METHODOLOGY

3.1. The Preference Ranking Organization Method for Enrichment Evaluation - PROMETHEE

The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method, which was first introduced as a quantitative decision technique by Brans (1982), has become one of the most preferred MCDM methods today (Karahan & Peşmen, 2020). The method is based on the choice of one of six predetermined preference functions for each criterion in the analysis and a pairwise comparison of the alternatives over these functions. Thus it will be able to determine the best among the decision units and create an alternative order (Rençber, 2018). The technique, which first included PROMETHEE I (partial ranking) and PROMETHEE II (complete ranking), has been expanded to PROMETHEE III, IV, V, and VI. Today, the PROMETHEE technique is used successfully in many fields, including chemistry, healthcare, pharmaceuticals, banking, workforce planning, investment decisions, and tourism (Sezgin & Yurtlu, 2021; Uygurtürk & Korkmaz, 2015).

Mareschal and Brans (1988) introduced the GAIA graphic display technique to allow decision-makers to more easily interpret the findings obtained from the PROMETHEE method (Ömürbek & Hande, 2016). Thanks to this graphical representation, decision-makers can be presented with an understandable, fast, and simple perspective rather than a simple ranking as in other MCDM methods (Tolga, 2013). The PROMETHEE method has become a preferred method in research due to the advantages it offers compared to other MCDM techniques (Karahan & Peşmen, 2020). This study opted for the PROMETHEE method because it allows the researcher to select the preference function that can be determined for each criterion in the pairwise comparison of the alternatives. The analysis of the findings can be presented in three dimensions on the Geometrical Analysis for Interactive Aid (GAIA) geometric plane in an easier, faster, and more understandable way and can be described more richly. In addition, this method also offers the opportunity to present the level of influence the criteria have on the alternatives.

In short, the PROMETHEE I (partial ranking) and PROMETHEE II (complete ranking) methods were used to compare destinations in this research. The 7 stages and formulas of the PROMETHEE technique are presented below (Ustasüleyman & Çelik, 2015):

Stage 1: In the first stage, the data matrix of the criteria and alternatives is created. The criteria and alternatives, the values ​​of the alternatives regarding the criteria, and the criteria weights are given in Table 1.

Table 1. Data matrix.

In the data matrix, “n”: (1,2,……, n) indicates the number of criteria, “m”: (1,2,……, m) the number of alternatives, and “w” the weight value for each criterion.

Stage 2: In this stage, preference functions are selected for the evaluation of each criterion. This selection is determined according to the structure of the criteria and the characteristics of the alternatives. The PROMETHEE technique includes six types of preference functions: Usual, U-shape, V-shape, Level, Linear, and Gaussian. In cases where there is no preference function in the decision-maker’s evaluation, the First Type (Usual) preference function should be selected. When the decision-maker wants to use his/her preference in favor of a decision level above the value he/she has determined, he/she should opt for the Second Type (U-type) preference function. On the other hand, the Third Type (V-type) preference function should be used when the preference is to be used both for the levels with values ​​above the mean and when the values ​​below the said value should not be neglected. The Fourth Type (Level) preference function should be used when the decision-maker desires to keep the preference within a certain value range in terms of the evaluation factor. Next, the Fifth Type (Linear) preference function should be used when the decision-maker prefers to use a decision level that is above the average in the evaluation. Lastly, the Sixth Type (Gaussian) preference function should be used in cases where the deviation values ​​from the mean regarding the values ​​in the evaluation factor determine the decision maker's preference (Ömürbek & Hande, 2016; Sezgin & Yurtlu, 2021).

Stage 3: The common preference functions of the pairs of alternatives that are compared using the preference functions are determined. The preference function for alternatives a and b is below:

Equation 1 provides the determination of common preference functions for alternatives a and b.

Stage 4: Preference indexes related to the common preference function are determined for each alternative comparison pair. The preference index of a and b alternative pairs evaluated over k criteria with wi (i = 1, 2, ……, k) weight values ​​is determined with the below formula:

Equation 2 presents preference indexes of alternatives a and b, evaluated together with the weights of the criteria.

Equation 3 determines the positive superiority of each alternative, and Equation 4 determines the negative superiority.

Stage 6: In PROMETHEE I, partial priorities are determined and the preference status of alternatives and partial priorities is determined. Alternatives that are indifferent and incomparable with each other are determined. Below are the situations in which partial priorities can be determined for sample alternatives a and b (Uygurtürk & Korkmaz, 2015).

If any of the conditions in Equations 5, 6, or 7 are met, alternative a is preferred to alternative b. The case where alternative a is indistinguishable from alternative b is as follows:

Equation 8 presents that alternative a is indistinguishable from alternative b. Finally, if the following conditions are met, alternative a cannot be compared with alternative b.

Comparisons of a and b alternatives cannot be performed under the conditions in Equations 9 and 10.

Stage 7: In PROMETHEE II, valid full priorities for alternatives are determined by the formula below. A full ranking can be made by evaluating all the alternatives on the same plane with the calculated full priority values.

Full priorities for alternatives are calculated using Equation 11.

The following decisions are based on the exact priority values ​​set for the two alternatives a and b.

3.2. Research Model and Sample

As stated previously, this study aimed to determine the differences between the rankings of the top seven destinations that attract the most visitors in the world and their order of superiority created using the PROMETHEE method based on weighted main touristic product components, as well as to determine the reasons for these differences. Thus, the most suitable destination selection order was made using the framework of criteria determined within the scope of the study. In this way, it was possible to examine whether or not the main touristic product components affected tourists' choice of destination. Given this context, six criteria and criteria weights that are counted as the main touristic product components, as revealed by Goral and Tuna (2018), were chosen as the criteria and weight values ​​of this research.

Concerning the research sample, the International Tourism Highlights report published by the World Tourism Organization (UNWTO) in January 2021 identified the top seven most visited destinations worldwide. These destinations are based on their number of visitors: France (89 million), Spain (84 million), the United States (USA; 79 million), China (66 million), Italy (65 million), Turkey (51 million), and Mexico (45 million) (UNWTO, 2021b).

In addition, the Visual PROMETHEE data analysis program was used to analyze the data obtained in the research.

3.3. Criteria and Weight Values

In this study, the main touristic product components put forward by Goral and Tuna (2018) to determine the importance of cultural attractions in terms of tourism competitiveness were used as the research criteria and weights.

For the main touristic product components covered in the study, the sub-components that make up the average criterion scores of the destinations in the TTCI and BC reports are:

In this context, the criteria and weight values ​​obtained in the study are presented in Table 2:

Table 2. Criteria and weight values employed, in order of effect on destination superiority.
Criteria
Weight Values
Cultural Attractions
0.394
Natural Attractions
0.220
Price Accessibility
0.101
Transportation Facilities
0.112
Tourism Services Infrastructure
0.123
Image
0.050
Source: Goral and Tuna (2018).

The criteria weights in Table 2 and the normalized scores of the criteria in the 2019 TTCI and BC reports were used to establish the ranking of destinations’ superiority using the PROMETHEE method. The criteria scores of the countries (except “image”) were obtained from the 2019 TTCI report published by the WEF. The country values ​​for the image criterion were taken from the BC report Country Brand Ranking Tourism Edition for 2019. To develop its tourism brand ranking of countries, BC uses the five tourism variables of tourism attractiveness, investment attractiveness, natural beauty, export capabilities, and the country's reputation. The report used a 10-point scale for the country rankings, ranging from “AAA=Extremely Strong” to “D=Bad” (Bloom Consulting, 2021). For this study, the alphabetic values ​​used by BC in the country rankings were digitized in accordance with the scale and ranged from 9 to 0: “AAA=9”, “AA=8”, “A=7”, “BBB=6”, “BB=5”, “B=4” ”, “CCC=3”, “CC=2”, “C=1” and “D=0.” In the TTCI report, on the other hand, since the raw data obtained from various international sources were evaluated according to separate criteria, they were not initially suitable for comparison. For this reason, WEF normalized the relevant data on a 1-7 (“1 = extremely weak, 7 = extremely strong”) scale to make them usable in calculations. The scores used for the criteria in this study thus consisted of these normalized data.

3.4. Limitations of the Study

The first limitation of the study was the fact that the TTCI and BC reports for 2021 had not yet been published as of December 2021, when this study was conducted. In addition, the data generated in the tourism dimension could not fully reflect the real potential of destinations due to the travel restrictions applied around the world in response to the Covid-19 pandemic.

This study’s analyses were based on WEF and BC data. Consequently, the validity and reliability of the research depended on the validity and reliability of the data published by WEF and BC. Another limitation of the research was that the number of destinations in the sample was limited to seven to ensure measurement consistency over 70% in the PROMETHEE analyses.

Given these limitations, the TTCI and BC data for 2019 were used in this study.

4. FINDINGS

4.1. Generating the PROMETHEE Data Matrix

Information on the alternatives, criteria, and criterion weights analyzed in the scope of this study is presented in Table 3.

Table 3. PROMETHEE data matrix.
Touristic Product Components
Cultural Attractions
Natural Attractions
Price Accessibility
Transportation Facilities
Tourism Services Infrastructure
Image
Unit
Score
Score
Score
Score
Score
Score
Cluster/Group
◆
◆
◆
◆
◆
◆
Preferences
Min/Max
max
max
max
max
max
max
Weight
0.39
0.22
0.10
0.11
0.12
0.05
Preference Fn.
Linear
Linear
Linear
Linear
Linear
Linear
Thresholds
absolute
absolute
absolute
absolute
absolute
absolute
- Q: Indifference
1.23
0.77
0.65
0.54
0.83
0.8
- P: Preference
2.90
1.64
1.56
1.32
1.88
1.87
- S: Gaussian
n/a
n/a
n/a
n/a
n/a
n/a
Statistics
Minimum
3.80
2.80
4.40
3.60
3.50
6.00
Maximum
7.00
6.00
5.70
5.40
6.60
8.00
Average
5.83
4.79
5.03
4.59
5.46
7.00
Standard Dev.
1.15
0.89
0.48
0.62
1.03
0.76
Evaluations
■ France
6.80
4.90
4.50
5.20
5.70
6.00
■ Spain
6.70
4.80
5.00
5.10
6.60
7.00
■ USA
4.70
5.00
4.70
5.40
6.60
8.00
■ China
7.00
5.10
5.70
4.10
3.50
8.00
■ Italy
6.50
4.90
4.40
4.55
6.00
7.00
■ Turkey
3.80
2.80
5.60
4.15
5.00
6.00
■ Mexico
5.30
6.00
5.30
3.60
4.80
7.00

In this study, "score" was defined as a unit because the evaluation score was used to evaluate the criteria of the alternatives. High criteria values ​​are beneficial; therefore, the maximization type was chosen. The values ​​determined within the scope of the study were defined according to the criteria weights. The fifth type of "linear" preference function was chosen because it was preferable to use all criteria values ​​with above-average values for the decision level. Since the criteria values ​​were numerical, they were defined as absolutes instead of percentages. The values ​​suggested by the program were entered as the “q” and “p” values ​​in the parameters section using the help assistant in the Visual PROMETHEE program. The Gaussian preference function was not used in the analysis; therefore, there was no “s” value in the relevant part.

4.2. Identifying Positive and Negative Advantages of Alternatives

In this step, positive and negative values ​​in the range of +1 to -1 were determined for each alternative. Here, "positive value" indicates the superiority of the analyzed alternative over other alternatives, and "negative value" indicates how weak the alternative is compared to other alternatives. Table 4 shows the positive advantages and negative advantagesfor each alternative, as calculated with the PROMETHEE method.

When the positive advantages in Table 4 are examined, the Spain (0.2372) alternative shows the highest positive advantage. This is followed by France (0.2156). The Turkey alternative, on the other hand, has the lowest performance, with a positive advantage value of 0.0363. When the negative advantages are examined, the Spain (-0.0224) alternative shows the best performance. This is followed by Italy (-0.0542). It can be seen that the Turkey alternative has the weakest performance (-0.5808) here as well.

4.3. PROMETHEE I (Creating a Partial Ranking)

The partial ranking of the alternatives, the positive and negative advantages of which were calculated with PROMETHEE I, is presented in Figure 1:

Figure 1. PROMETHEE I: the partial ranking of the alternatives.

Figure 1 shows the positive and negative Phi values ​​of the alternative destinations. The positive superiority values ​​of the destinations are in the left column (sequentially: Spain, France, China, USA, Italy, Mexico, and Turkey), and the negative superiority values ​​are in the right column (sequentially: Spain, Italy, France, USA, Mexico, China, and Turkey). Destinations at the upper levels in the two columns are more dominant than the alternative destinations. Accordingly, Turkey has no superiority over any other alternative. From this point of view, the next step was to determine the PROMETHEE II values to create a clear ranking among the destinations.

4.4. PROMETHEE II (Creating a Complete Ranking)

Net superiority values ​​can be examined with PROMETHEE II by considering the positive and negative values ​​for each destination in combination. Figure 2 presents the complete ranking results generated with PROMETHEE II.

Figure 2. PROMETHEE II complete ranking of alternatives.

According to Figure 2, Spain ranked first with the highest net Phi value among the destinations within the scope of the research. France, Italy, China, USA, Mexico, and Turkey followed Spain, respectively. The ranking of destinations obtained with the PROMETHEE II method is presented in Table 5:

Table 5. Ranking of destinations.
Number
Country
1
Spain
0.2149
2
France
0.1560
3
Italy
0.1065
4
China
0.0639
5
USA
0.0246
6
Mexico
-0.0213
7
Turkey
-0.5445

The Phi values ​​in Table 5 show the priority ranking of the destinations. Accordingly, while Mexico and Turkey have negative Phi values, the other destinations have positive Phi values. It can be seen that China, which ranked fourth among the destinations, was the only destination in the study that did not change its place in the ranking when compared with the list of the countries with the highest number of visitors in the world.

France, which was first on the list of countries with the highest number of visitors, ranked second as a result of the analysis, while Spain took first place. Meanwhile, the USA, which was third on the list, regressed to fifth place, and Italy moved to third place. Turkey, which was in sixth place according to the number of visitors, was replaced by Mexico and moved to seventh place.

4.5. Displaying Criteria and Alternatives on the GAIA Plane

A GAIA plane allows decision-makers to present PROMETHEE results in a way that is easier to understand through graphical representation. In the PROMETHEE method, alternatives and criteria for problem solutions are presented in two dimensions on the GAIA plane. Here, alternatives are shown as square dots, and criteria are shown as vectors. The GAIA plane is used to show the effect of criterion weights in the PROMETHEE technique (Kalender & Aygün, 2019). The GAIA plane is also used to determine the possible losses in the subjective decision-making capabilities of the decision-makers towards the alternatives. In this way, conflicting criteria can be easily observed. In the plane, alternatives and criterion vectors in the same direction show compatibility, while criteria vectors and alternatives in the opposite direction reveal incompatibility (Sakarya & Aytekin, 2013).

The vector length in the GAIA plane shows how discriminating the relevant criterion is. The criteria that show similar preferences are in the same direction, and the criteria showing overlapping preferences move in different directions on the axis. Criteria that are unrelated in terms of preference are shown with vertical axes. On the other hand, points that are close to each other represent similar alternatives (Brans & Vincke, 1985).

The GAIA plane illustrating the alternative destinations within the scope of this research and the criteria used to evaluate them is presented in Figure 3.

The optimal decision bar (red bold line) in Figure 3 represents the direction in which the alternatives can be found that are most suitable to the decision-maker. Destinations close to the decision bar are the most suitable within the framework of analyzed criteria. The length of the thin vector lines of the criteria in Figure 3 shows the importance and distinguishing feature of each criterion in terms of its influence on the decision bar. The GAIA chart allows both the integrated selection and the appropriate selection for each criterion to be determined (Bağcı & Esmer, 2016). A short criterion bar indicates that the discriminating power is low. Because the relevant criterion is located in a more upright position on the GAIA plane, it appears short on the GAIA graph (Tolga, 2013).

Figure 3. GAIA plane.

Accordingly, Italy, France, Spain, and the USA are the optimal destination choices. Destinations in regions close to the criteria indicated by the thin vectors are primarily preferable for the analyzed criterion. For example, the USA is the most suitable choice in terms of Transportation Facilities and Tourism Service Infrastructure. For these criteria, the USA is followed by France and Spain. In terms of Price Accessibility, the most suitable destinations are Mexico, China, and Turkey. However, since Turkey is one of the destinations with the lowest performance in terms of Cultural Attractiveness, Natural Attractiveness, and Image among the alternatives, it is located opposite the vectors of the relevant criteria in the GAIA plane.

The measurement quality of the analysis is 80%, as seen in the lower right corner of the GAIA plane. To obtain healthy results using the PROMETHEE GAIA method, this value should be above 70% (Karahan & Peşmen, 2020; Sezgin & Yurtlu, 2021). In this sense, in the analysis conducted, the performances determined according to the order of superiority of the destinations show a good measurement quality.

4.6. Levels and Directions of Influencing Alternatives by Criteria

The effect levels and directions of the various criteria on the destination alternatives can be presented graphically in the Visual PROMETHEE program. The effect levels of the analyzed criteria on the alternatives are shown in Figure 4:

Figure 4. Levels and directions of influencing alternatives by criteria.

In Figure 4, each destination analyzed in the study is divided into slices that match the number of criteria. Here, the net criterion values ​​obtained with PROMETHEE II for the alternatives are presented in the slices. The positive contribution or negative effect of the criteria (the net Phi value) of the relevant alternatives is shown in the slices within each alternative, as well as the criterion-calculated weight values. As stated previously, the net Phi score of an alternative is the value obtained by subtracting the sum of the negative slices from the sum of the positive slices of the relevant alternative.

According to Figure 4, the Price Accessibility criterion (-0.0092) had a negative effect on the net Phi value of the Spain alternative, while the Image criterion (0.0000) had no (neutral) effect. The Tourism Service Infrastructure (0,4540) criterion made the greatest contribution to this alternative. This criterion was followed by Transportation Facilities (0.3547), Cultural Attractions (0.2625), and Natural Attractions (0.0843), respectively.

While the criteria of Image (-0,4268) and Price Accessibility (-0,2106) had a negative effect on the net Phi value of the France alternative, the other criteria, Transportation Facilities (0.4188), Cultural Attractions (0.2804), Tourism Service Infrastructure (0.1556), and Natural Attractions (0.1034), all made positive contributions.

On the other hand, Price Accessibility (-0.2656) and Transportation Facilities (-0.0043) had a negative effect on the net Phi value of the Italy alternative, while the Image criterion did not have any effect. The other criteria, Tourism Service Infrastructure (0.2524), Cultural Attractions (0.2036), and Natural Attractions (0.1034), contributed positively.

The Tourism Service Infrastructure (-0.8476) criterion had the greatest negative impact on the net Phi value of the China alternative, followed by Transportation Facilities (-0.3803). The other criteria contributed positively, as follows: Image (0.4268), Cultural Attractions (0.3204), Price Accessibility (0.2930), and Natural Attractions (0.1418).

While Cultural Attractiveness (-0.3273) and Price Accessibility (-0.1099) had a negative effect on the net Phi value of the USA alternative, the other criteria had positive effects: Transportation Facilities (0.5470), Tourism Service Infrastructure (0.4540), Image (0.4268), and Natural Attractions (0.1226).

The criteria that negatively affected the net Phi value of the Mexico alternative were Transportation Facilities (-0.5897), Tourism Service Infrastructure (-0.3032), and Cultural Attractions (-0.0639). Meanwhile, Natural Attractiveness (0.4444) and Price Accessibility (0.0733) contributed positively, while the Image criterion had a neutral effect on this alternative.

Price Accessibility (0.2289) was the only criterion that contributed positively to the net Phi value of the Turkey alternative, which took last place in the ranking. The other criteria had negative effects: Natural Attractions (-1.0000), Cultural Attractions (-0.6737), Image (-0.4268), Transportation Facilities (-0.3462), and Tourism Service Infrastructure (-0.1651).

Considering the effect levels of the criteria on the alternatives in light of the findings obtained in this study, Cultural Attractiveness made the greatest positive contribution to China (0.3204), while it had the greatest negative effect on Turkey (-0.6737). The greatest positive contribution of the Natural Attractiveness criterion was in Mexico (0.4444), and its greatest negative effect was again in Turkey (-1.0000). The greatest positive contribution of Price Accessibility was in China (0.2930), while the greatest negative effect was observed in Italy (-0.2656). The Transportation Facilities criterion had the highest positive effect on the USA (0.5470), while its greatest negative effect was on Mexico (-0.5897). Meanwhile, the Tourism Service Infrastructure criterion made the greatest positive contribution in Spain (0.4540) and the USA (0.4540) but had the highest negative effect on China (-0.8476). Finally, the Image criterion made the most positive contribution in China (0.4268) and the USA (0.4268) but had the highest negative effect on France (-0.4268) and Turkey (-0.4268).

5. CONCLUSION, DISCUSSION, AND RECOMMENDATIONS

This study aimed to determine the role played by the main touristic product components in tourists’ destination selection. To achieve the research purpose, the data were analyzed using the PROMETHEE technique, one of the selected MCDM methods. Aside from determining the most suitable destination among the alternatives, the competitiveness of the destinations according to the touristic product components was also assessed according to the criteria examined in this research. Thus, the study attempted to reveal the comparative strengths and weaknesses of the destinations in the research sample. In this way, it became possible to measure more clearly which touristic product components contributed to the studied countries’ destination competitiveness and in which areas they exhibited weak performance. This aspect of the research is thought to contribute to the literature.

The analyses carried out in the study revealed a different order of preference than ranking the destinations according to the highest number of visitors. The seven destinations attracting the most visitors in the world in 2020 were France, Spain, the USA, China, Italy, Turkey, and Mexico. On the other hand, the most suitable destination preference order among the alternatives according to the PROMETHEE analysis based on the criteria and the criterion weights selected in this research was Spain, France, Italy, China, the USA, Mexico, and Turkey. This result is consistent with that of Güllü and Yılmaz (2020) , who determined the performance of ten Mediterranean countries using the ENTROPI (Emerging Network To Reduce Orwellian Potency Yield)-based EDAS (Evaluation Based on Distance from Average Solution) technique and TTCI data, and found that the top three destinations were Spain, France, and Italy, respectively.

To uncover the reasons for the differences in the ranking that emerged in the scope of the study, the effect levels of the criteria on the alternatives were examined. Accordingly, it was concluded that the total score of positive and negative Phi values ​​obtained by Spain over the selected criteria was higher than those of all other alternative destinations. It was determined that the criteria that enabled Spain to get ahead of its closest rival, France, were Tourism Service Infrastructure, Price Accessibility, and Image. Although France performed better than Spain in other criteria, France nevertheless lagged behind Spain due to the low difference in their values.

The destination that had the most significantly different ranking result of the studied destinations and rose two steps in the results of this research was Italy. Here, the effect of the different criteria in which Italy was superior to both the USA and China could be seen. It could also be observed that the USA regressed from third to fifth place due to its poor performance in the Cultural Attractiveness criterion, which had the highest weight value. In addition, the USA lagged behind China in the Price Accessibility criterion. The low performance of the USA in these criteria, especially given the large effect of the Cultural Attractiveness criterion, caused the USA to regress two places in the PROMETHEE ranking when the net Phi scores were taken into account, although its performance in the Transportation Facilities and Tourism Service Infrastructure criteria was high.

Again, it can be noted that China's positive performance in the Cultural and Natural Attractiveness and Image criteria made a significant contribution to preserving its place. However, it can also be seen that Italy's performance in the Transportation Facilities and Tourism Service Infrastructure criteria was higher than China's, allowing Italy to get ahead of China. It can also be observed that Turkey, which was seventh in the PROMETHEE ranking, was overtaken by Mexico. The reason for this was that Turkey performed more poorly than Mexico on the criteria of Cultural Attractiveness, Natural Attractiveness, and Image.

When the obtained research findings on Turkey were examined, it became clear that the criterion on which Turkey showed the highest performance was Price Accessibility, and this was the only criterion for which it had a positive Phi value. This result shows that among the top seven tourism destinations in the world with the highest number of visitors, Turkey was the cheapest destination after China. This confirms that Turkey's "cheap destination" image persists after many years, as was the case in Kayar and Kozak's (2010) analysis of TTCI data. In addition, Turkey did not display the highest performance in any criterion among the alternatives. Destinations with the highest scores for the various criteria were China for the Cultural Attractions and Price Accessibility criteria, Mexico for the Natural Attractions criterion, the USA for the Transportation Facilities criterion, Spain and the USA for Tourism Service Infrastructure, and the USA and China for the Image criterion. As discussed in the findings section of this paper, according to the WEF TTCI report for 2019, Turkey scored very low ​​in the Cultural and Natural Attractiveness criteria. Also, since the mentioned criteria were those with the highest weight values ​​within the scope of this research, Turkey performed worst among the alternative destinations in the study.

It is extremely surprising that Turkey, with its thousands of years of ancient cultural history and unique natural beauties, had such low scores for the Cultural and Natural Attractions criteria in the WEF TTCI report. When the factors constituting the criteria in the 2019 report, in which 140 countries are listed, are examined, it can be observed that Turkey placed 5th and well above average in terms of intangible cultural heritage activities and oral heritage in the Cultural Attractions category, and 11th and again above average in terms of the number of world heritage cultural sites. It also placed 18th, above the European average, in terms of the number of sports stadiums; in addition, it was close to average in 34th place concerning the number of international meetings, and 27th in terms of the digital demand factor of culture and entertainment tourism (WEF, 2019).

On the other hand, however, it can be seen that for the Natural Attractions criterion, Turkey ranks below average at 50th in terms of the number of world heritage natural areas, above the European average but below the general average at 68th in terms of the number of known species, and well below average at 137th in the total number of protected areas. Besides that, it ranked 30th and above the average in terms of the natural tourism digital demand and 87th in terms of the attractiveness of natural assets, which were close to the European but below the general average (WEF, 2019). To address these issues, it is recommended that sustainable strategic development plans can be effectively created and implemented to improve all factors, especially the factors for which Turkey scores below the above-mentioned averages in terms of Cultural and Natural Attractions.

In related literature, “Long Queue Theory” is the situation in which the total sales of a large number of different niche products exceed the total sales of the best-selling popular product or products (Göral, 2018). With this in mind, it is necessary to develop alternative tourism products and services to leverage the high potential of Turkey's cultural, natural, and historical riches, as well as continue the mass tourist attraction of the "all-inclusive" system, which is one Turkey’s most popular touristic products. In this way, Turkey will be able to strengthen its destination competitiveness by achieving an efficient and effective tourism operation; by diversifying its niche markets depending on the development of alternative tourism products, the total income obtained from various niche tourism products will exceed the income generated from popular tourism products.

Another important point is the validity and reliability of the data that make up the WEF TTCI report. Sakal (2021), in his research on the source of the Turkish data that informed the WEF TTCI 2019 report, emphasized that the data obtained through the survey consists of the average of the data obtained from certain authorities directly or indirectly related to tourism; therefore, it mostly measures the perceptions of company managers. It was also underlined that these data could be improved over time with various policies. On the other hand, concerning the other groups of data that contributed to the report, it was concluded that some of the data collected by international organizations were not current, and, based on this, Turkey's ranking in the TTCI report for 2019 was negatively affected.

One example of non-current data is that Turkey’s developments in information technology in recent years are not reflected in the ranking of indicators in this category. Turkey's digital demand indicators for natural, cultural, and entertainment tourism are ranked much lower than they should be. Furthermore, the indicator regarding total protected areas is based on extremely incomplete or inaccurate data, meaning that the calculations result in a rather low value. From this point of view, special units should be urgently established under the leadership of Turkey’s Ministry of Culture and Tourism and within the body of the Tourism Development Agency to provide data to reports such as the WEF TTCI and to transmit current and consistent data to the relevant international organizations. In this way, the errors identified above will be minimized and Turkey will soon be able to rank much higher in international competitiveness reports such as the TTCI.

Focusing on the final result of this research, conclusions can be drawn concerning the absolute effects of touristic product components on tourists’ destination selection. However, in light of the analyses conducted in this study, when the ranking of the number of visitors to the destinations around the world is compared to the strengths and weaknesses of the said destinations, it is noteworthy that the only consistent destination is China, which does not change its placement in the two rankings. This situation can be explained in two ways:

First, as has been determined in previous studies conducted specifically for Turkey, similar mistakes may have been made in the evaluation scores of other countries in the said international reports. Second, tourists represent a mass of individuals with different priorities in the decision-making process regarding destination selection.

Researchers are advised to conduct further research related to each of these points. It is also recommended that future, similar studies use current reports and MCDM methods, as these will be beneficial in revealing the competitive power of destinations.

Funding: This study received no specific financial support.  
Competing Interests: The authors declare that they have no competing interests.
Authors’ Contributions: All authors contributed equally to the conception and design of the study.

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