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Nowadays, the tourism industry is considered the largest and most diverse industry in the world, which can play a significant role in the economic growth and development of a country as the main source of income and employment. In this regard, the purpose of this study is to focus on evaluating the competitiveness indicators of Shiraz city tourism destination based on a combined model.

For this purpose, a questionnaire with 78 questions was used to evaluate the indicators, in which a total of 1,432 tourists participated, including 927 domestic tourists and 505 international tourists. The collected data were analyzed to determine the hierarchical relationship between the indicators using equation structural modeling.

The research findings indicate that the most effective and influential indicators in the field of tourism competitiveness are natural and handmade resources, and the most influential factors are demand conditions and cultural and natural resources. In this hierarchy, each factor affects its previous level and influences the next level.

This study offers significant potential for uncovering credible and robust approaches to further investigate the contextualization of tourism competitiveness on both national and international scales, thereby generating valuable new insights. By conceptualizing the diverse dimensions of tourism competitiveness and delving into the variations in its impacts across multiple levels, this research not only challenges existing notions but also aids destinations in maintaining and enhancing their market position and share over time. This study offers valuable insights and practical implications for both researchers and practitioners in the field of urban tourism. It enhances the understanding of destination competitiveness, informs policy decisions, facilitates benchmarking and best practices, guides strategic decision-making and promotes sustainable tourism development.

The purpose of this study involves analysing the factors of and barriers to innovation and their respective impacts (directly and indirectly) on the competitiveness of both destination and their host companies.

Based on primary data collected by questionnaire from a sample of 119 companies operating in the tourism sector in the autonomous region of Madeira (Portugal), the authors applied a quantitative methodology with recourse to econometric models and multiple linear regression.

Comprehensive results based on a conceptual model are obtained through the analysis of competitiveness tourism-based and innovation factors. The study identifies and empirically tests the existing and underlying relationships between innovation (factors and barriers) as the means of leveraging the competitiveness of destinations and their companies.

The results hold important theoretical and practical implications contributing towards innovation for competitiveness and filling a shortcoming identified in the literature.

This study aims to provide a systematic review of the literature on destination competitiveness. It explores research themes and approaches, and outlines an agenda for future research.

One hundred and eighty-three journal articles on destination competitiveness published in English since 2010 were collected. Content analysis was performed.

The authors found that sustainable development and smart tourism were popular themes related to destination competitiveness. Recent studies still relied heavily on Ritchie and Crouch’s (2000) model or Dwyer and Kim’s (2003) model. The authors also offer constructive insights for future research, proposing that research seek more input from residents, take an advanced integrated approach between supply and demand, adopt more core theories, focus on less researched types of destinations, explore the further contribution of smart tourism and adopt a negative lens to study the antecedents and consequences of destination competitiveness. The adoption of a longitudinal study approach is also suggested.

This paper contributes to the literature because of the scope of its review and perspective of discussion. Previous review papers have only focused on the factors and indicators of tourism destination competitiveness in different settings. We extend our literature review to research themes and approaches, and make suggestions for future research directions.

本研究对目的地竞争力相关的文献进行了系统性回顾, 以探讨研究主题、方法和未来议程。

本研究收集了自2010年以来以英文发表的183 篇关于目的地竞争力的期刊文章, 并进行了内容分析。

我们发现可持续发展和智慧旅游是与目的地竞争力相关的热门主题。近期的研究仍然主要依赖Ritchie 和Crouch（2000）的模型或Dwyer和Kim（2003）的模型。我们为未来的研究提供了建设性的意见, 建议研究寻求更多居民的意见, 采取进阶的供给与需求的综合方法, 采用更多的核心理论, 关注较少研究过的目的地类型, 探索智慧旅游的进一步贡献, 并采用反向的视角来研究目的地竞争力的前因和后果。我们还建议采用纵向研究的方法。

本研究在评论的范围和讨论的角度对文献作出了贡献。以往的文献综述性文章只关注不同环境下的旅游目的地竞争力的因素和指标。我们在回顾研究主题、方法和对未来研究的建议等方面进行了扩展。

Se recopilaron ciento ochenta y tres artículos de revistas sobre la competitividad de los destinos publicados en inglés desde 2010. Se realizó un análisis de contenido.

Este estudio presenta una revisión sistemática de la bibliografía sobre la competitividad de los destinos. Se analizan temas y enfoques de investigación y se presenta una agenda para futuras investigaciones.

Se constató que el desarrollo sostenible y el turismo inteligente eran temas populares relacionados con la competitividad de los destinos. Los estudios recientes siguen basándose en gran medida en el modelo de Ritchie y Crouch (2000) o en el de Dwyer y Kim (2003). Se presentan ideas constructivas para futuras investigaciones, proponiendo que las investigaciones se centren en más aportaciones de los residentes, adopten un enfoque integrado avanzado entre la oferta y la demanda, se basen en mayor medida en las teorías fundamentales, se centren en tipos de destinos menos investigados, exploren la contribución adicional del turismo inteligente y adopten una óptica negativa para estudiar los antecedentes y las consecuencias de la competitividad de los destinos. También se sugiere la adopción de un enfoque de estudio longitudinal.

Este trabajo contribuye a la literatura por el alcance de su revisión y la perspectiva de la discusión. Los trabajos de revisión anteriores sólo se han centrado en los factores e indicadores de la competitividad de los destinos turísticos en diferentes entornos. Se amplía la revisión de la literatura a temas y enfoques de investigación, y se plantean sugerencias para futuras direcciones de investigación.

This research aims to model the existing system of destination competitiveness, identifies leverage points and develop revised model to achieve sustainable competitiveness.

System dynamics is used as method of modeling destination competitiveness. Structure of model utilizes 9-factor model as reference. Leverage points are identified using system archetypes. Revised model is built with resource-based view (RBV). Case study was conducted in Samosir, Toba Lake. Data used are secondary data and results of in-depth interviews.

There are 3 sub-systemic characteristics (archetypes) that hinder competitiveness. They are limit to growth quality gap, fix that fails infrastructure and promotion, tragedy of common lake pollution. Destination was unable to meet tourist expectations. Tourists spending decreased, demand size was small. Industries are unable to increase capabilities. Professionals, entrepreneurs, local workers, supporting industries are less interested in entering industry. Government policies do not match with destination's needs. Lake as main attraction is getting polluted. To achieve sustainable competitiveness, destination must utilize their valuable, rare and inimitable (VRI) resources and capabilities to design unique experiences for tourists, hence sustainable.

Government policy should be shifted to prioritizing development of valuable, rare, inimitable and well-organized resources and capabilities of destination, to produce unique tourist experience and achieve sustainable competitiveness.

Methods and findings, combining system dynamics, system archetype, 9-factor model and RBV to achieve sustainable competitiveness is novel and can enrich tourism sustainable competitiveness theory/concept.

This study aims to provide current and organised insights into past published studies on tourism destination competitiveness (TDC) in the past decade through systematic literature analysis.

A comprehensive review was performed by systematically gathering the literature published from 1983 to 2021 and coded according to categories such as author, year, article title, name of journal and TDC determinants.

The key findings of this review reveal that no universal set of items, attributes or indicators to measure the competitiveness of tourism destinations exists; the complexity and variability of many definitions and measuring elements from various perspectives portray the multi-faceted concept of competitiveness; and synergistic connection between the source of comparative and competitive advantages of TDC focusing on destination image, tourism experience and loyalty.

Research works considered in the study are only from indexed and peer-reviewed journal publications.

The study findings reveal a lack of studies that address the relationship between destination image, tourism experience and loyalty within the TDC realm. Future studies should consider complementing the tourism supply and demand side to avoid a “strategic drift” of TDC concepts, perceptions and practices.

This paper aims to identify factors that affect the sports tourism destination's competitiveness on a small island. Hence, this study looks at and evaluates these factors. The study then comes up with a model that clarifies the interrelationships between these factors.

The authors broke down the data analysis process into three steps. The first step was to conduct a literature review and use industry and academia experts' help to determine the essential aspects (fuzzy Delphi method). Then, a hierarchical model was developed, and the factors were categorised using the interpretive structural modelling (ISM) approach. Factors' driving and dependency power were also determined using MICMAC analysis.

This work has identified 13 key factors related to the sports tourism destination's competitiveness on a small island. For a small island like Kish Island, the two independent variables (government support and destination political stability) that define the institutional framework for the destination are most important. Building corresponding competitive and support strategies to address these two independent variables is thus beneficial.

The research's results provide decision-makers, practitioners, and researchers with new insights into the hierarchical model of determinants. The study will fill the existing gap between theory and practice.

Sports tourism destination managers on small islands may benefit from the proposed model since the model will enable them to organise the managers' priorities better to enhance the managers' destinations' competitiveness and provide tourists with a more accurate depiction of the destination.

According to the authors' knowledge, the research design presented in this article has provided the first attempt to hierarchical analyse these factors and develop a model for sports tourism destination competitiveness on small islands and destinations with less-developed economies. This study fills the gap in the destination competitiveness and sports tourism literature by not only identifying the key influencing factors but also examining the interactions between these factors and providing empirical evidence supporting their relationships.

Meetings, incentives, conferences and exhibitions (MICE) tourism has established as a tourism segment that is growing in popularity. It is less seasonality dependent, promotes the offer of services and contributes to the development of the sector. Therefore, this study aims to analyze the competitiveness factors for the improvement of MICE tourism in the city of Mazatlan.

It was developed with a mixed approach, using quantitative and qualitative data collection techniques, such as interviews with experts, surveys of stakeholders in the tourism sector and documentary analysis. Based on the theoretical review, the following four competitiveness factors were defined for MICE tourism: 1) resource factors, 2) destination management factors, 3) conditioning factors of the environment and 4) conditioning factors of the demand, applying and importance-performance analysis.

The results indicate that the factors of competitiveness in the case of the study that had greater importance and better performance are the conditioning factors of the demand and resource factors. However, the development and implementation of comprehensive destination management strategies are required to improve this segment, as well as giving due importance to taking into account the important conditioning factors of the environment.

This study makes a theoretical contribution to the literature on the competitiveness of tourist destinations in the MICE segment by identifying the factors for its development, as well as the practical implications for the specific case study. In addition to this, it was identified that there are few empirical studies that analyze the factors that contribute to improving the competitiveness of this segment.

The current study is designed to investigate the factors that foster the framing of destination competitiveness and establish the factors that drive the contribution of tourism innovations to economic growth in smart tourism destinations.

A four-year panel data were extracted from the World Economic Forum's travel and tourism competitiveness index and data were analysed using Poisson Pseudo Maximum Likelihood regression model.

The findings demonstrate that both the enabling environment and airport infrastructure significantly affect tourism's impact on the economy of the selected smart European tourism destinations. Conversely, human resources and general infrastructure display a negative correlation with tourism's contribution to the economy. However, no data in the sample support the idea that tourism policies, government prioritization or readiness of tourism information and communication technologies impact tourism's contribution to the economy. Additionally, the marginal effects indicate that improving the enabling environment and airport infrastructure can generate additional benefits for the economy through tourism.

The uniqueness of this study is the integration of smart tourism destinations with the measure of destination competitiveness to provide an empirical bridge that links tourism competitiveness to economic growth.

This research paper aim at providing a new approach of calculating the destinations competitiveness index. How can these variables been aggregated in other to reflect the realities of very distinct productive environments? We assume that: The weighting of variables provides a better measure of destinations competitiveness. Base on the Neo-Technological theory, after a life cycle differentiation, we used a panel data approach to calculate the weight of each variable as the spearman correlation coefficient of its contribution to tourism inflows growth. After integrating these weights, we came to the point that by applying an appropriate weight to its components, we end up having a competitiveness index that significantly improve the correlation between competitiveness and tourism inflows growth.

This study aims to determine and evaluate the artificial intelligence (AI) development and competitiveness of the top 20 countries that receive the highest number of tourists with the entropy technique for order of preference by similarity to the ideal solution (TOPSIS)-integrated method.

This study is based on Global AI Index data published by Tortoise Media. Based on this index, according to the World Tourism Organization (UNWTO) report, the top 20 destinations that will host the highest number of tourists in 2022 were evaluated in seven different subpillars, which are talent, infrastructure, operating environment, research, development, government strategy and commercial. These seven subpillars of the index were considered as criteria, and the top 20 tourist destinations were included in the research as decision alternatives.

The analysis results show that the three most important AI criteria are operating environment, infrastructure and government strategy. Furthermore, the first three countries with the best AI performance according to the weighted criteria were the USA, China and the UK, respectively.

Considering that AI technologies will direct tourist behavior in a world where technology is rapidly developing, it is recommended that the countries that receive the highest number of tourists improve their AI performance.

When the relevant literature is examined, there is a limited number of studies examining the AI development and competitiveness of the top tourist destinations and weighting the Global AI Index values. Therefore, this study contributes to the gap in the relevant literature.