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One way to think about the future challenges is to make benchmarking analysis, where other micro or macro level organisations are compared in relation to own activities and systems. This article includes benchmarking of all the EU‐15 countries. Authors report top 15 lists of various indicators. As a general benchmarking framework, the motivation, access and skills indicators system is used. In the study the authors perform correlation analyses of competitiveness and MAS‐indicators. A special contribution of the article is that it combines competitiveness analysis to macro level MAS framework’s social indicators analysis.

Defining and measuring competitiveness has been a major focus in the business and competition literature over the past decades. The paper aims to use data-driven principal component analysis (PCA) to measure firm competitiveness.

A “3Ps” (performance, potential, and process) firm competitiveness indicator system is structured for indicator selection. Data-driven PCA is proposed to measure competitiveness by reducing the dimensionality of indicators and assigning weights according to the endogenous structure of a dataset. To illustrate and validate the method, a case study applying to Chinese international construction companies (CICCs) was conducted.

In the case study, 4 principal components were derived from 11 indicators through PCA. The principal components were labeled as “performance” and “capability” under the two respective super-components of “profitability” and “solvency” of a company. Weights of 11 indicators were then generated and competitiveness of CICCs was finally calculated by composite indexes.

This study offers a systematic indicator framework for firm competitiveness. The study also provides an alternative approach to better solve the problem of firm competitiveness measurement that has long plagued researchers.

The data-driven PCA approach alleviates the difficulties of dimensionality and subjectivity in measuring firm competitiveness and offers an alternative choice for companies and researchers to evaluate business success in future studies.

This paper aims to investigate the competitiveness of Russian regional medium-sized oil complexes with relatively small fossil oil reserves. Taking into account the urgency for competitiveness, the authors have developed a specific assessment methodology and competitive development strategy that could be implemented within the framework of a scenario approach.

The suggested methodology for assessing the competitiveness of regional production complexes is based on the pattern method. Validation is provided for a modular structure and an approach to constructing indices of competitiveness of production complexes. A system of competitiveness indicators has been devised in relation to regional oil complexes.

The case study of the oil complex of the Republic of Udmurtia has yielded competitiveness assessment forecast for a variety of development scenarios up until 2025. A methodology of competitiveness assessment is proposed as a result of the analysis of the two cases.

This analysis is based on the oil complex in Udmurtia, Russia. The main limitation of the research scope is the extent and nature of the industrial complex. The proposed design can be used for large industrial complexes operating in the field of industrial production. It needs to be extended to more clusters, more industries and other countries’ settings for the sake of comparison and generalization.

The practical effects of the study suggest a set of strategic development tools of assessment of the dynamics of industrial complexes’ development, identification of competitive advantage and “bottlenecks” and definition of the objectives and programs of their long-term development and justification of policies and programs of perspective development.

This paper reveals specific features of oil complexes’ competitiveness, which has seldom been investigated both theoretically and empirically.

This paper analyzes tourism competitiveness in Latin America, providing a country-level ranking of tourism competitiveness. The study also identifies which areas of management to focus on in order to increase competitiveness in each case.

The study is based on the variables used by the World Economic Forum (WEF) to measure tourism competitiveness. The DP2 distance method is used to create a synthetic indicator. This method helps identify which areas best explain differences in competitiveness between countries.

In tourism, the most competitive Latin American countries are Costa Rica, Chile, Panama, Mexico and Uruguay. The areas that best explain the differences between countries relate to cultural and natural resources, the implementation of information and communication technologies (ICTs), international openness and transport infrastructure. These are therefore priority areas for tourism managers.

This paper provides detailed analysis for each country. The situation in each country is presented in terms of the key areas highlighted by the analysis. This approach can aid the individual decisions of companies and public managers, thus enhancing tourism competitiveness. This greater competitiveness can strengthen the tourism sector, which is crucial in uncertain times.

Based on a synthetic indicator, this research offers the first country-level analysis of tourism competitiveness in Latin America. The study is also novel in its ability to detect the areas where action should be taken to improve tourism competitiveness. This analysis offers an alternative to the WEF Travel and Tourism Competitiveness Index (TTCI), which has certain weaknesses. The results can help enhance tourism competitiveness in Latin American countries through the specific recommendations presented in this paper.

Este documento analiza la competitividad del turismo en América Latina, proporcionando un ranking a nivel país. El estudio también identifica en qué áreas de gestión deben enfocarse los esfuerzos para aumentar la competitividad en cada caso.

La investigación se basa en las variables propuestas por el Foro Económico Mundial (WEF) para medir la competitividad del turismo. El método de distancia DP₂ se utiliza para crear un indicador sintético. Este método ayuda a identificar qué factores explican en mayor medida las diferencias de competitividad entre países.

Los países latinoamericanos más competitivos en cuanto a la actividad turística son Costa Rica, Chile, Panamá, México y Uruguay. Los factores que mejor explican las diferencias entre países se relacionan con los recursos culturales y naturales, la implementación de tecnologías de la información y la comunicación (TIC), la apertura internacional y las infraestructuras de transporte. Por tanto, estas serían las áreas prioritarias de trabajo para los gestores turísticos.

Este documento proporciona un análisis detallado de cada país. La situación individual se presenta mediante un análisis de las áreas clave destacadas como responsables en mayor medida de la competitividad. Este enfoque puede ayudar a las decisiones tomadas por los gestores de las empresas y los administradores públicos, mejorando así la competitividad del turismo. Esta mayor competitividad puede fortalecer el sector turístico, que es crucial en tiempos de incertidumbre.

Basado en un indicador sintético, esta investigación ofrece el primer análisis a nivel país de la competitividad turística en América Latina. El estudio también es novedoso por su capacidad para detectar los ámbitos en los que se debe actuar para mejorar la competitividad turística. Este análisis ofrece una alternativa al Índice de Competitividad de Viajes y Turismo (TTCI) del Foro Económico Mundial (WEF), que tiene ciertas debilidades. Los resultados pueden ayudar a mejorar la competitividad del turismo en los países de América Latina a través de las recomendaciones específicas presentadas en este documento.

This study aims to construct models to classify independent smallholder farmers’ (ISFs) plantation suitability and its competitiveness index. It proposes the models with the objective to accommodate ISFs as the main source of raw material for the palm oil industry. It was predicted that the supply of oil palm fresh fruit bunch would depend on ISFs’ plantations due to the government policy that restricts the expansion of the plantations.

The study was designed using a geographic information system approach and analytical hierarchy process for mapping the suitability of smallholder farmers’ oil palm plantation in the Kampar Regency. The competitiveness index was measured using a modified Diamond Porter framework and competitiveness index quantification. The model was conducted on 177 respondents from five districts in Kampar Regency.

The results indicated that it mapped 128,936.759 hectares area of ISFs’ oil palm plantation in the Kampar Regency. The results of plantation suitability showed that only 13.88% of plantations owned by ISFs were potential, 71.21% of them were in the developing category and 14.91% of plantations were non-potential. The competitiveness index showed there were only 7.91% of them at the developed competitive position, 73.45% at developing a competitive position and 18.64% at the least-developed position.

The paper includes implications for developing ISFs’ capacity building and best practice management for oil palm optimization, resulting in bargaining positions and social well-being.

The study had succeeded to visualize ISFs’ plantation area suitability and competitiveness at Kampar Regency, Riau Province. The model provides a brisk understanding and valuable information about ISFs’ conditions spatially. It offers specific outcomes and becomes important in optimize and develop the existing plantations at the right time and exact location.

Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities.

After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the first-level principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles.

The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supply-side perspective include the profit factor, cost factor, tight support factor and facilitating factor.

On the basis of research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and conducted principal component analysis. However, because of the inaccessibility of panel data, the current data were only sufficient to conduct the research. If panel data could be acquired, further research could be conducted to perfect the current indicator system for urban MICE competitiveness.

The findings suggest that tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.

The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supply-side perspective, while the cost factor exerts a significant negative influence. The findings suggest that the tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.

The research bridge the empirical statistics and the questionnaire-based perception study on urban MICE tourism image, and advance to construct an empirical statistics based indicator system for urban MICE tourism image.

This paper aims to show how the term competitiveness has been applied and adapted since Michael Porter made it respectable in economics, strategic management and consulting. This paper connects the concept with new developments in the theory of the firm, theories of growth and, finally, with Beyond GDP literature.

This paper distinguishes between input and output competitiveness, developing a set of indictors for both. Countries are ranked according to costs, structure and capabilities (drivers of competitiveness) as well as according to economic, social and ecological performance (performance pillars). Finally, outcome competitiveness is explained by the individual drivers, using econometrics and principal component analyses.

Defining competitiveness as the ability of a country or nation to deliver Beyond GDP goals changes the policy conclusions drawn from the quest for competitiveness. Policies to reduce costs prove inferior relative to “high-road strategies” built on skills, innovation and supporting institutions. Ecological ambition and social investment are not costs, but enablers of competitiveness for high-income countries.

Connecting the well-known term competitiveness with Beyond GDP goals is a new approach. It is very different from the old concept of cost competitiveness criticized heavily by Paul Krugman. Supplying a set of indicators to measure “low-road” and “high-road” competitiveness leads to important new policy conclusions.

The purpose of this paper is to assess the strategies for improving the competitiveness of Ecuador (ECU), Colombia (COL) and Peru (PER). It is one of the first studies using 36 indicators within the context of three South-American countries. It fulfills the lack of knowledge at the scientific work and its practical objective by identifying the factors that public policy may take into consideration urgently to improve the global competitiveness level of the countries for a sustainable development and considerations for a long-term integration.

It analyzes data of four-year average of international sources such as: The International Monetary Fund, The World Bank, The United Nations, among others. The factors were selected after a careful literature review, the final selection and the weight of each determinant was calculated using partial least squares-structural equation modeling. The calculation of the national and international competitiveness indexes used the double diamond for competitiveness theoretical frame.

From a national perspective, it was found that COL is the most competitive country followed by PER in the second place and ECU is in third. Internationally, the authors have found also that COL is the most competitive economy, PER in second place and ECU is in third.

This study has found that in order to improve the level of national competitiveness, ECU has to concentrate on improving and strengthening formal and non-formal institutions, which are reflected in: four factors, PER four, and COL three. To reach international competitiveness, ECU should improve four factors, PER five, and COL five formal and non-formal institutions.

This research is the first one on its field, it uses 36 competitiveness indexes and the PLS-SEM statistic methodology to assign the weight of competitive indicators and the DD theoretical frame to determine the relevance of its factors and it is oriented to advise decision-makers and provides the appropriate police guidelines for the national competitiveness strategy and improve their quality of life of its residents from: ECU-COL-PER. On the other hand, the academic implications of these results appear when calculating the weight-load competitiveness indexes using inferential multivariate analysis; it provides researchers and practitioners an analysis tool for comparing competitive factors of emerging countries from the DD approach without any weaknesses, as a framework to assist in formulating economic policy at the national and regional level.

Due to the difficulty of competitiveness operationalization, this research uses PLS-SEM to correlate its factors as the statistical methodology and the DD as the tool for the identification of theoretical indicators. This work may be taken into consideration for an immediate and sustainable improvement in order to win competitiveness than its neighbors.

This study is unique because the factors were selected after a careful literature review; the final selection and the weight of each determinant was calculated using PLS-SEM. The calculation of the national and international competitiveness indexes used the DD for competitiveness theoretical framework applied for the first time in a research for South America with 36 determinants. The result of this analysis compares the weak and strong determinants of these three member countries of UNASUR for the development of their complementarities and therefore the recommendations of public policy.

The purpose of this study is to develop a model for the selection of optimal development projects for an industrial complex (IC), aimed at improving its competitiveness.

Selection of optimal projects for IC development is carried out within the framework of a methodical approach to assessing and forecasting the competitiveness of IC based on the phased implementation of the following steps: assessment of the current level of competitiveness of an IC; optimization of the project portfolio aimed at improving the IC competitiveness; forecasting the IC competitiveness. The methodology for assessing the IC competitiveness is based on a comparative analysis of its activities against those of its leading competitors and evaluates two integral characteristics, namely, the level of current competitiveness and the indicator of the competitive potential. The methodical approach to the selection of the optimal portfolio of projects for the development of competitiveness is based on a step-by-step optimization of the portfolio using a dynamic programming procedure, which takes into account the implementation conditions of the projects, resource constraints and the prioritization of these projects. The targeted benchmark for the optimization is an integral indicator of IC competitiveness, comprising various aspects of its activities.

Method for multi-factor evaluation of IC competitiveness and the impact of a set of factors driving competitive advantage; set of benchmarks of IC competitiveness; the model for the selection and step-by-step optimization of the portfolio of IC development projects, aimed at increasing competitiveness in the context of resource constraints.

Methodology described in this paper was used to assess the competitiveness and optimize the project portfolio of Uralelektrotyazhmash Group of Enterprises (UETM), one of the largest power engineering complexes in Russia. The assessment was carried out in comparison with the leading domestic and global manufacturers of electrical equipment, which form UETM’s primary competition in the marketplace.

The study establishes a method for the comprehensive assessment of IC competitiveness based on the comparison of IC activity indicators with those of leading competitors. The proposed method defines an integral competitiveness index to allow for the quantitative assessment of IC competitiveness, development of measures to improve IC competitiveness and producing a methodology to forecast the impact of such measures; the study proposes a methodical approach to selecting the optimal portfolio of projects for the development of IC based on the stepwise optimization of such a portfolio while taking into account the conditions of their implementation, resource constraints and the impact on the indicator of competitiveness. This approach allows the IC to prioritize the implementation of the development projects and maximize its competitiveness in the context of available resources.

This paper aims to examine the competitiveness trends and rankings of the Irish dairy sector at the farm and trade levels, relative to selected European Union (EU) Member States, in the context of the removal of the EU milk quota in 2015.

Competitiveness indicators including partial productivity measures and accountancy-based indicators were used for farm competitiveness, and net export market share and normalised revealed comparative advantage (NRCA) were used for export competitiveness.

Amongst the countries examined, Ireland had the highest growth in partial productivity indicators and was ranked first with the lowest total costs and cash costs per kg of milk solids post-quota. However, the total economic cost sub-components showed that Irish dairy farmers had high opportunity costs for owned land and labour. While Irish dairy products such as butter and powders have demonstrated growth potential in competitiveness post-quota with Irish butter and whey ranked in top three relative to other countries, other products, i.e. cheese and liquid milk have declined in competitiveness according to key export competitiveness indicators used.

The challenge for Irish dairy farmers is how to mitigate relatively high land and labour costs, which can limit farm competitiveness in the long run. The key players in the Irish dairy industry can now better position themselves in the global dairy market, recognizing the competitiveness dynamics of the different dairy products and their competitors. Policy implications and further areas of research have been identified to help improve the overall competitiveness position. It is surprising that Irish butter is a leader in the EU, yet not much research has been done to understand the market dynamics of this sector.

To the best of the authors’ knowledge, this study is the first of its kind to use both farm and export competitiveness measures to analyse the Irish dairy industry relative to other countries in the context of quota abolition. Unlike previous studies on dairy export competitiveness, this study has disaggregated the processed dairy products, which allowed for the ranking of countries and comparability across countries using NRCA.