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This paper aims to investigate theoretically and empirically the interactions between smart economic development (SED) and competitiveness in Central and Eastern European (CEE) countries. The main argument to uphold here is that smartness approach has been traditionally more focused on smart urban planning and smart specialization.

An evaluation by index, correlation and significance analysis is used to present original empirical evidence from six CEE countries.

Smartness approach integration into economic development justifies the identification of SED determinants: basics (welfare, digitality, environmental, social responsibility) and enhancers (learning, networking, agility, innovations and knowledge-driven). The interaction between SED and countries’ competitiveness in CEE countries might be described by two approaches, namely, focus-based (several most important basics and enhancers) and balance-based (equal importance of basics and enhancers).

The limitations relate to the particular sample of CEE countries and gathering opportunities of statistical data.

The combination of SED-Index sub-indices and WEF GCI might aid a more accurate ex ante measurement. Despite common global challenges, each country should choose its own combinations for smartness determinants to achieve long-term competitiveness.

The findings are important for fostering smartness approach in economic development for long-term competitiveness.

This paper contributes to economic development literature by discovering basics and enhancers for SED. By linking well-known term of competitiveness and economic development with a concept of smartness, the new approaches, namely, focus-based and balance-based, to policy making in CEE countries emerged.

The purpose of this paper is to examine how the ranking of countries based on the World Economic Forum's (WEF') competitiveness index is changed when the underlying primitive data dimensions of this composite index are aggregated using weights that are endogenously determined for each country, instead of aggregated using the WEF's fixed set of weights applied to all countries.

The paper presents a method based on data envelopment analysis to determine weights for aggregating the underlying primitive data dimensions of any composite indicator. The approach determines endogenously the “best” weights a given observational unit (e.g. country) on the basis of its revealed performance on each primitive sub‐dimension underlying a composite index. The ranking of countries based on the values of a composite competitiveness index that uses the proposed endogenous weight method is then compared to the ranking based on the WEF's competitiveness index for the year 2006. The rankings are then compared and assessed to determine if the observed difference in the rankings are statistically significant.

A comparison of the ranking of countries on the basis of the value of each index reveals that countries do undergo a change in their competitiveness rank when endogenous weights are used. The results suggest the WEF's competitiveness index, which uses the same fixed weights applied to every country (or group of countries), creates a bias that favors countries that score high on the “technology” sub‐dimension of the index.

The study presents an alternative to the current practice of using a fixed set of weights applied uniformly to the basic unit of analysis. The method serves as a starting‐point for further research on the biases created by different weighting schemes to construct a composite indicator that aggregates primitive data, with the resulting composite index values then used to rank entities.

The method to determine endogenously the weights to be applied to each unit of analysis when constructing a composite indicator is novel and has wide applicability to the general issue of comparing performance across different units of analysis based on a composite index of performance (i.e. benchmarking).

Regional competitiveness refers to the capacity of a region to manage its resources and competencies to increase the well-being of its people. Measuring regional competitiveness is, thus, a major consideration for policymakers, businesses and the academic community in their endeavour to improve the same. This paper aims to demonstrate a novel way to calculate the regional competitiveness index under a two-stage objective general index (OGI) framework.

The authors compute the regional competitiveness index under a two-stage OGI framework. In the first stage, they aggregate the sub-factor level information into a factor level index; in the second stage, they use the factor level index to obtain a regional competitiveness index.

The authors discuss the properties of the proposed index in detail. They further analyse five periods of regional competitiveness of Peru spanning the period 2008-2015. Among others, the results reveal the existence of the resource curse of the mining regions of Peru.

The paper is a contribution to the practical measurement of competitiveness.

The calculation of a regional competitiveness index is vital for improving the competitiveness of the countries and for reducing regional inequalities.

When compared to the existent methods available in the literature, the advantage of the proposed method resides in the fact that the derived index has a positive correlation with the factor-level indices and the factor-level indices have a positive correlation with the sub-factor-level information.

This paper aims to investigate the smart economic development (SED) patterns in Europe in relation to competitiveness. Motivational focus corresponds to global events: the fourth industrial revolution, transition to a low-carbon economy, economic shocks (such as the 2008 financial crisis, Brexit or the coronavirus pandemic), which requires rethinking development policies, targeting competitiveness increase and reducing imbalances in economic development.

The analysis includes self-organising neural networks cluster analysis and correlations, comparative analysis of SED indicators structure and cumulative index estimation with World Economic Forum (WEF) global competitiveness index. The panel data set of 19 years from 2000 to 2018 for 30 European countries.

Overall, cross-country examination suggests that European countries of higher competitiveness illustrate higher estimates in SED. The key determinants are juridical fairness, social responsibility, competence building, intelligence and welfare employment to develop smart patterns for reaching higher competitiveness.

The limitations relate to the particular sample of European countries and gathering statistical data and a methodology of the SED index calculation. In addition, the paper contains a macroeconomic environment focus on competitiveness estimation. Further research may be improved with micro and mezzo environment incorporation at a cross-country analysis level.

By linking well-known terms of competitiveness and economic development with a concept of smartness, new approaches to policymaking emerged. The methodology presented in this paper has implications for territorial cohesion policies, competitiveness and branching strategies. The combination of SED sub-indexes and WEF GCI might aid a more accurate ex ante measurement.

The findings are essential for fostering a smart approach in economic development for long-term competitiveness.

This paper provides original empirical evidence about the relationship between SED and competitiveness and adds new knowledge that smartness becomes a way for building countries’ competitiveness by identified two profiles of SED patterns by development stages, namely, integrated to economic development and institutional-based which is divided to focus and balanced.

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.

The purpose of this paper is to incorporate factors that characterize the agricultural activity as productivity indices to compute the agricultural competitiveness of regions in order to rank the regions, and compare the results with those obtained by applying other commonly used social and economic indicators.

The authors identify regional factors related to the use of water, soil, production, revenues, and rural population, which conform a total of six productivity indices, that the authors then employ to calculate the regional agricultural competitiveness index.

The agricultural-related indices are informative in supporting the regional ranking related to resources and technology utilization. The results reveal that the coastal regions are the most competitive when compared to the regions located in the highlands and the jungle. Nevertheless, in contrast with other existing competitiveness rankings, the present study identifies the regions with the greatest potential for agriculture.

The authors identify the regions which have a higher potential of development considering the natural resources and agricultural production. The authors hope that this paper can assist regional and national policymakers in their endeavor to improve regional and national competitiveness.

The authors identify the regions with a higher potential of development considering natural resources and agricultural production and the possibilities to improve their competitiveness.

The study also bears social implications, given that the rural activities in Peru are carried out by approx. 7 million inhabitants, whose contribution to the gross domestic product (GDP) is as much as 7 percent, making use of about 94 percent of the available water.

The originality of the present paper resides in the attempt to compute a regional competitiveness index by taking agricultural resources as determinant factors. The authors rank the regions based on their agricultural competitiveness.

This study aims to evaluate the competitiveness of nations and seeks to answer the following research question: how does the competitiveness of nations include improvements in the quality of life, thus influencing and contributing to social progress in both social and economic indices?

This paper collected secondary data from the World Economic Forum and the socioeconomic dimensions of the Social Progress Imperative Index and considered the dimensions of these indices, which were demonstrated using Pearson’s correlation coefficient. The main focus was on the documentary analysis that was carried out to explain the realities of 121 countries from 2014 to 2017 as taken from these indices, considering the 10 countries at the top and bottom.

This study showed the use of new measures for the performance of nations that are less dependent on economics and focus more on social development, which may be a trend for the future of nations, and produce a more holistic view for the study. “Innovation” is the factor with the weakest relationship with social progress, which is justified by a weaker relationship with one of the subcategories, “basic human needs”, when analyzed in isolation. However, when the authors analyze the best and worst nations, the authors observe that economic factors are still prevalent, with the “institutions” and “infrastructure” factors being effective for improving competitiveness and the quality of life.

The findings represent a new, emerging configuration in country performance, but the study has its limitations, such as the use of only two pooled variables and the fact that it does not correlate their dimensions or variables.

This study can represents an expansion logic for measuring the performance of countries considering social factors. The main contributions of this study are its statistical evidence and documentary analysis of the relationship between economic and social variables. The main contribution of this paper is to show that over time (2014–2017) economic factors, as measured by the competitiveness index of nations, relate to aspects of social welfare, as measured by the social progress index.

This study aims to (1) identify the agricultural competitiveness of ASEAN countries in the global markets; (2) analyze the dynamics of these indicators for the period 1997–2015; and (3) test the consistency between trade indices.

The study uses RCA, RTA, and NRCA for the first objective; OLS method and Markov matrix for the second objective; and statistic tool for the final purpose.

The results show that: (1) ASEAN countries achieve the strongest competitiveness in rice, rubber, spices, vegetable fat and oils, wood, fuel wood, fish, and crustacean. Vietnam, Thailand, and Indonesia are the strongest competitive whilst Brunei, Singapore, and Cambodia are the weakest competitive; (2) They have convergent patterns in agricultural competitiveness; (3) They successfully maintain rankings of the strong competitive sectors; and (4) ASEAN countries obtain benefits from the regional integration and the specialization in competitive products.

ASEAN countries with strong competitiveness should specialize in and maintain their rankings to enhance competitiveness and maximize social welfare while the countries with weak agricultural competitiveness should specialize in the processed products and services based on their advantages of economic resources.

Comprehensive results of the static and dynamic agricultural competitiveness of ASEAN countries as a whole are provided. The findings and policy recommendations can be used by policymakers and enterprises to improve competitiveness and benefit. The discussions and findings should be a significant reference for economists.

The objective of this study is threefold–first, to develop a Regional Competitiveness Index (RCI) for measuring competitiveness of sub-national regions for India; second, to test this index for its ability to explain regional growth, which validates usage and applicability of this index; and third, to further investigate if the competitiveness of states is in turn caused by economic growth, i.e. it is tested if there is a bidirectional causality between competitiveness and regional growth.

The data of indicators used in the index are from sources available freely in public domain. The competitiveness index is constructed using equal weightage supported by principal component analysis (PCA) technique. The causal relationship analysis is done using panel data of 10 years from 2008 to 2017 for 32 Indian states/union territories. The generalized method of moments (GMMs) is used for this dynamic regression estimation.

Based on RCI score, states have been ranked and through rank analysis, the authors observe the performance status of these sub-national regions and are able to categorize them as improving, no change or deteriorating in regional competitiveness. Using the GMM estimation, the association between RCI and economic growth is found to be significant at 10% level. This shows that regional competitiveness as captured through the RCI score is able to explain regional economic growth and economic disparity among the sub-national units. Further, that RCI score is found to Granger-cause growth, while growth does not lead to better RCI scores. This establishes the usefulness of RCI as an important policy variable to compare states and provide direction for sectoral reforms.

The limitations of the study include (1) broad assumption that these sub-national regions belong to a uniform macro-economic and technology environment, and (2) data constraints as it is a longitudinal study. The study implies that the composite index could capture differences in regional competitiveness explaining regional economic disparity and that competitiveness causes higher economic growth and not vice versa.

The RCI score can prove to be a useful indicator of economic performance of different states and can be used by national and state policymakers to compare and assess regional disparity among different states. The pillar-wise scores will be useful for in-depth study of weakness and strength of the sub-national territories.

Construction of an RCI for sub-national territories and analysis of panel data for longitudinal study of ten years is unique in the regional competitiveness literature.

The analysis of the interconnectedness between resources and capabilities, and the way businesses use them as competitive weapons is a central element of the strategic management literature. Finding the appropriate configuration of competitive pillars is particularly relevant for resource-constrained small businesses. Drawing on the resource-based view and the configuration theory, this study evaluates the effect of both competitiveness and the configuration of the competitiveness system on performance.

An index methodology based on the configuration theory was used to compute the competitiveness index on a unique sample of 625 Hungarian small- and medium-sized firms. The study hypotheses were tested via regression analysis.

Results show that the impact of competitiveness-enhancing strategies is conditional on the configuration of the system of competencies. Low-competitive businesses benefit more from investments in the weakest competitive pillar, while strategies oriented to improve more than one competitive pillar yield higher competitiveness improvements among high-competitive businesses. Our findings also indicate that competitiveness positively impacts performance, and that the exploitation of competitive strengths leads to superior results among high-competitive businesses.

By employing an index methodology, our analysis contributes to unveil how competitiveness impact business performance. The proposed analysis has value for scholars and strategy makers by showing how the configuration of the business' competitive system—in terms of competitive strengths and weaknesses—conditions the generally positive impact of competitiveness enhancing actions linked to the acquisition or development of resources and capabilities.