Assessments of tourism competitiveness at the macro level typically rely either on composite international indices, such as the World Economic Forum's Travel & Tourism Development Index (TTDI), or on author-assigned weight coefficients derived from a review of theoretical literature. Both approaches have a common limitation: the relative importance attached to individual indicators is rarely validated against the judgement of practitioners and experts who observe destination competitiveness directly. This article addresses that gap using primary survey data collected from 117 respondents, who rated (a) the relative importance of six macro-level competitiveness indicators and (b) the comparative attractiveness of thirteen destinations across four distinct dimensions of appeal.

The aim of this study is to derive expert-validated weight coefficients for macro-level tourism competitiveness indicators and to position Uzbekistan's comparative destination attractiveness against global and regional benchmarks using this same primary dataset. The objectives are: (1) to compute descriptive statistics and normalized importance weights for the six surveyed macro-level indicators; (2) to compute comparative attractiveness scores and rankings for thirteen destinations across ecological, historical-cultural, tourism-recreational, and pilgrimage tourism dimensions; (3) to examine Uzbekistan's relative position, with particular attention to any divergence between expert-perceived attractiveness and Uzbekistan's position in international composite indices such as the TTDI; and (4) to discuss implications for the weighting schemes used in macro- and meso-level competitiveness models.

Expert elicitation methods occupy a well-established place in competitiveness research. Saaty's (1980) Analytic Hierarchy Process (AHP) formalized the derivation of indicator weights from pairwise expert comparisons, while the simpler approach of converting mean Likert-scale importance ratings into normalized weights — applied in this study — offers a more easily administered alternative suited to larger, less specialized respondent pools. The theoretical destination-competitiveness models of Ritchie and Crouch (1999, 2003) and Dwyer and Kim (2003) both identify resource endowment, infrastructure, and destination management as core competitiveness determinants, while Porter's (1990) national competitiveness framework similarly emphasizes factor conditions and institutional environment — all six indicators surveyed in this study map directly onto constructs identified in this literature.

Survey-based comparative destination-attractiveness research has a parallel tradition in tourist-satisfaction and image studies, including Vavra's (1992) and Pizam and Milman's (1993) work on perceived destination quality. The four-dimensional structure used in the present survey — ecological, historical-cultural, tourism-recreational, and pilgrimage attractiveness — reflects an attempt to disaggregate destination appeal into components that are each separately relevant to different market segments, an approach consistent with destination image segmentation research but rarely applied comparatively across thirteen destinations spanning Europe, the Gulf, the South Caucasus, and Central Asia simultaneously, which is the specific contribution of this study's dataset.

The data were collected via a structured online questionnaire completed by 117 respondents. The instrument comprised two parts. Part 1 asked respondents to rate the relative significance of six indicators proposed for inclusion in an integral macro-level tourism competitiveness index — sustainability of tourist demand, development of tourism infrastructure, tourism resources potential, institutional environment, price competitiveness, and attractiveness of the region/territory — on a 5-point scale (1 = low importance, 5 = high importance). Part 2 asked respondents to rate thirteen destinations (Spain, China, Italy, the United Arab Emirates, Georgia, Turkey, Kazakhstan, Azerbaijan, Iran, Armenia, Uzbekistan, Tajikistan, and Kyrgyzstan) on the same 5-point scale across four separate dimensions: ecological attractiveness, historical-cultural appeal, tourism-recreational appeal, and pilgrimage tourism attractiveness. All 117 respondents provided complete ratings for every item analyzed in this article (n = 117 for each indicator and each destination–dimension pair).

For Part 1, the mean and standard deviation were computed for each of the six indicators, and a normalized weight coefficient was derived for each indicator as its mean score divided by the sum of all six mean scores, so that the resulting weights sum to unity — a direct, survey-grounded analogue to the AHP-derived weighting procedure used in the author's earlier meso-level (МТРИ) model. For Part 2, the mean attractiveness score was computed for each destination within each of the four dimensions; a composite score was then calculated for each destination as the unweighted mean of its four dimension-level scores, and destinations were ranked by this composite score. Limitations of the dataset include its cross-sectional, single-wave design and the absence of respondent-level demographic or professional-background variables in the available data, which precludes subgroup analysis by respondent expertise or nationality; these limitations are addressed in the concluding section.

4.1. Weighting of macro-level competitiveness indicators

Table 1 presents the descriptive statistics and derived weights for the six surveyed macro-level indicators. The overall pattern is one of relatively flat perceived importance: mean scores range narrowly from 3.38 (sustainability of tourist demand) to 3.60 (development of tourism infrastructure), yielding normalized weights that cluster between 0.162 and 0.173 rather than showing the sharp differentiation sometimes assumed in single-author weighting schemes. Development of tourism infrastructure and the general attractiveness of the region/territory were rated marginally most important, while institutional environment and demand sustainability were rated marginally least important — though all six indicators were rated, on average, above the scale midpoint, indicating that respondents regarded none of the six as dispensable.

Table 1

Importance ratings and derived weights of macro-level competitiveness indicators (N = 117)

This study derived expert-validated weight coefficients for six macro-level tourism competitiveness indicators and comparatively assessed thirteen destinations across four attractiveness dimensions using an original 117-respondent survey. Three findings stand out. First, the surveyed experts perceive the six macro-level indicators as having relatively similar importance (normalized weights ranging from 0.162 to 0.173), cautioning against overly sharp a priori weight differentiation in future macro-level index construction. Second, Uzbekistan ranks 6th of 13 destinations overall and, more strikingly, 3rd of 13 in pilgrimage tourism attractiveness — its strongest dimension and a position ahead of every other Central Asian destination surveyed. Third, this resource-based attractiveness advantage coexists with a comparatively weaker macro-level (TTDI) position, suggesting that Uzbekistan's principal competitiveness gap lies in institutional and infrastructure factors rather than in underlying destination appeal.

Based on these findings, the following recommendations are proposed: (1) macro-level competitiveness indices should consider near-equal indicator weighting as a more empirically defensible default than sharply differentiated author-assigned weights, pending further expert-survey validation; (2) Uzbekistan's tourism-marketing strategy should prioritize pilgrimage and cultural-heritage tourism segments, where its comparative position is strongest, while explicitly targeting infrastructure and institutional-environment improvements to close the gap implied by its weaker macro-index position; (3) future surveys should collect respondent-level professional and demographic data to permit subgroup analysis (e.g., by respondent expertise, nationality, or sector); and (4) the apparent divergence between expert-perceived resource attractiveness and composite macro-index rankings warrants further research using larger and stratified expert samples, ideally replicated longitudinally to track whether infrastructure and institutional reforms close this gap over time.