Competitiveness Of Industrial Clusters Research Articles

Assessment of new energy industrial clusters: An MCDM approach using DEA and GEMS

Industrial clusters play a pivotal role in securing competitive advantages and achieving national strategic objectives within the new energy sector. The assessment of new energy industrial clusters is crucial for sustaining their swift development. However, prior research has rarely addressed this domain. In this study, a Multi-Criterion Decision-Making (MCDM) approach is introduced for the assessment of new energy industrial clusters. This approach employs the Data Envelopment Analysis (DEA) super efficiency method alongside the Grounding-Enterprise-Market-Surrounding (GEMS) model to evaluate the efficiency and competitiveness of new energy industrial clusters, respectively. Subsequently, this approach categorizes new energy industrial clusters based on their efficiency and competitiveness scores. Clusters exhibiting similar scores in these areas are grouped together for the identification of key new energy industrial clusters. Ultimately, the selection of key new energy industrial clusters is performed based on varying criteria. A case study on the selection of prominent solar energy industrial clusters is presented to demonstrate the efficacy of the proposed approach. The findings indicate that this approach facilitates an effective selection process for key new energy industrial clusters.

A study on the evaluation of competitiveness in the aviation logistics industry cluster in Zhengzhou

As the global economy continues to evolve, air transportation is increasingly seen as a crucial factor in enhancing regional competitiveness. In particular, aviation logistics industry clusters have emerged as a new driving force for regional economic development. In this context, the current study aims to evaluate the competitiveness of the aviation logistics industry cluster in Zhengzhou, China. To achieve this goal, the study employs the “GEM model” and constructs a GKA evaluation model using evaluation index data from 21 logistics node cities across China in 2021. The entropy-weighted TOPSIS method is used for empirical analysis of the data. The results of the study reveal that the competitiveness of Zhengzhou’s aviation logistics industry cluster is moderately low. This is primarily due to the weak competitiveness of its foundational and regulatory subsystems. Specifically, the study finds that Zhengzhou’s resources, facilities, markets, government, and industry aspects are all less competitive when compared to other cities in China. In order to enhance the competitiveness of Zhengzhou’s aviation logistics industry cluster, the study recommends that efforts be made to improve the competitiveness of key elements such as resources, facilities, markets, and government. In particular, the focus should be on elevating industry competitiveness, followed by the development of appropriate regulatory strategies. By doing so, the aviation logistics industry cluster in Zhengzhou would be better positioned to compete with other clusters within China and globally.

RETRACTED ARTICLE: Network Attribute Analysis and Competitiveness Evaluation of Auto Parts Industry Cluster for e-Commerce Platform

The automotive parts industry cluster is a complex operating system, and it is no longer possible to use a simple theory to make a scientific and reasonable explanation for the parts industry cluster. In view of this, the study takes the automotive parts industry cluster as the research object, innovatively invokes the theory of organizational ecology and constructs a competitiveness analysis mechanism based on population co-evolution. The study conducts performance analysis and simulation tests on the constructed model, integrating the CS–AHP evaluation method to evaluate the competitiveness of the parts industry cluster. The performance comparison results show that the research method has a minimum training error of 0.0108, when the number of iterations of the system is 69th. This method has a stable loss function value at the fifth iteration, which is a stable convergence state earlier than the GA algorithm, differential evolution and distribution estimation algorithm. The A-level index score of the off-line platform auto parts industry cluster is 0.5174, which is significantly greater than 0.5. The competitive cooperation effect, technological innovation effect and cluster culture scores of the e-commerce platform are 0.5885, 0.6899 and 0.5329, respectively, which are significantly higher than those of the off-line. The above results show that the accuracy of the research algorithm is higher than that of the off-line industry. The above results show that the accuracy of the research algorithm is better, which can make a reasonable scientific basis for the auto parts industry and carry out competitiveness evaluation. To a certain extent, it can provide technical reference for the future development of the auto field industry.

On Strategic Emerging Industry Cluster Development in Anhui based on the Perspective of Global Value Chain

With the development of economic globalization, the role of strategic emerging industries embedded in global value chains (GVCs) as clusters to actively participate in the global industrial division of labor has become increasingly significant. The present situation and problems of strategic emerging industry clusters in Anhui are analyzed qualitatively from three aspects: dynamic mechanism, development mode and governance mode, and their status in GVC is inferred quantitatively by using GVC index. Secondly, Bengbu silicon-based new materials industry is used as an example for empirical research, and the GEM model and multiple linear regression model are used to build a data evaluation system and analyze the factors affecting the competitiveness of industrial clusters. Finally, we propose a scientific and reasonable upgrading path and policy suggestions based on GVC perspective, which can provide guidance and reference significance for promoting regional industrial economic development.

COMPARATIVE DESIGN STRATEGI PENINGKATAN DAYA SAING KLASTER USAHA KECIL DAN MENENGAH PENGOLAHAN KOPI

Indonesia is one of the largest coffee exporting countries in the world. Increasing the competitiveness of small and medium coffee industry clusters is a strategic issue that needs to be considered. The small and medium coffee industry in Indonesia can become a mainstay commodity for export to foreign countries. However, the small and medium coffee industry still has many obstacles, including low human resources capabilities, difficulty in penetrating access to capital, and low levels of technology adoption. This study was conducted to determine the strategy model for increasing the competitiveness of relevant small and medium industry clusters of coffee processing. One methodological approach alone is not enough to strive to increase the competitiveness of the small and medium coffee industry in Indonesia. Comparing strategic management is a choice of an alternative approach to able to increase optimal competitiveness. By comparing the structural equation model and SWOT analysis, it is hoped that the right program recommendations will be found to increase the competitiveness of the sustainable small and medium coffee industry.

Методические подходы к формированию индикаторов устойчивой конкурентоспособности инновационных промышленных кластеров

The present article introduces a new system of indicators for assessing the sustainable competitiveness of innovative industrial clusters. The authors reviewed foreign and domestic approaches to monitoring and evaluating industrial clusters and classified them according to assessment type, research level, costs, and effects. Sustainable competitiveness proved to be the most complex characteristic of any cluster. It could be divided into two groups of parameters, i.e. 1) long-term competitiveness and 2) stability as a socio-ecological and economic system. Each group was given a list of indicators for quantitative and qualitative assessment, which included indicators of potential development, organization and management, socio-ecological and economic stability, etc. The research revealed some gaps in the Russian statistics of cluster development, which hinder the information support for monitoring and evaluating the sustainable competitiveness of clusters. As a result, the current databases on the social, economic, and environmental aspects of Russian clusters are fragmented and chronologically incompatible.

Indicators of sustainable competitiveness of innovative industrial clusters

Indicators of sustainable competitiveness of innovative industrial clusters

Competitiveness of IoT industrial clusters based on G2EM-CI model

Competitiveness of IoT industrial clusters based on G2EM-CI model

Countermeasures for Tobacco Industrial Cluster Development

Nowadays, the deepening globalization of the world economy promotes the economic development to present more and more obvious regionalization characteristics, and industrial clusters are increasingly becoming an important source for a country or region to achieve competitive advantages. China is the largest tobacco country in the world, and the tobacco industry is the largest source of tax revenue in China, which has made outstanding contributions to the national financial accumulation. After joining the World Trade Organization (WTO) and signing the Framework Convention on Tobacco Con trol (FCTC) o f the World Health Organization (WHO), the competition in the domestic and foreign tobacco markets has become increasingly fierce, and more attention to health issues has put forward higher requirements for the development of China's tobacco industry. Therefore, the Chinese tobacco industry urgently needs to use industrial clusters to form economies of scale and enhance market competitiveness. Overall, the development level of China's tobacco industry is not high. Due to some existing problems that restrict the development of tobacco industry clusters, some measures must be taken to enhance the competitiveness of China's tobacco industry clusters in the world economy, such as strengthening o verall coordination and planning guidance , guiding industries from agglomeration to cluster development, actively supporting related industries, and vigorously enhancing innovation ability. Therefore, it is necessary to find out the breakthrough point of the development of tobacco industry clusters, select the appropriate industrial clusters to focus on cultivating, strengthening the leading role, supplementing the chain and clustering, and guide and promote the healthy growth of tobacco industry clusters, so as to stimulate the growth of the national economy.

인도 산업클러스터 경쟁력 분석과 시사점: 마하라슈트라 주를 중심으로 (An Analysis on the Competitiveness of Industrial Clusters in India: A case study of Maharashtra)

인도 산업클러스터 경쟁력 분석과 시사점: 마하라슈트라 주를 중심으로 (An Analysis on the Competitiveness of Industrial Clusters in India: A case study of Maharashtra)

PENGEMBANGAN KAWASAN INDUSTRI TELEMATIKA DALAM UPAYA MENDORONG DAYA SAING REGIONAL (KASUS KOTA CIMAHI)

The ICT industry is developing greatly because human dependency of information technology increases rapidly. In order to drive ICT industry competitiveness, well planned development should be carried out based on its vision. One of ICT industry cluster in Indonesia is in Cimahi City. The research objectives were to identify general characteristics of the ICT industry and its core competence, and also to acquire internal and external factors of competitiveness; and also to identify key factors that could be developed to drive regional competitiveness of ICT industry in Cimahi City. Primary data survey conducted to obtain information regarding (1) ICT industry characteristics, (2) core competence of ICT industry and (3) internal factor of competitiveness. Meanwhile, secondary data survey was conducted to obtain information regarding external factors of competitiveness. A prospective analysis was conducted to build a scenario in developing ICT industry cluster in Cimahi City. Research result showed that the core competence of ICT industry was animation, film and information system. Key factors to drive regional competitiveness of ICT industry cluster were (1) telecommunication service quality; (2) skilled labor availability; (3) responsiveness on change and innovation; (4) business ethics and (5) ICT industry regulation.

Evaluation of industry cluster competitiveness: a quantitative approach

PurposeThe purpose of this paper is to provide a quantitative approach to measure industry clusters competitiveness.Design/methodology/approachAn attempt has been made to construct a composite indicator backed up by a conceptually grounded framework, by means of Analytical Hierarchical Process technique. Four industry clusters from auto sector in India are chosen for manifestation of the methodology.FindingsThe proposed methodology sufficiently emphasises on the order of significance of the factors/indicators that make a cluster competitive. The study demonstrates the comparative competitiveness performance of four select industry clusters from India.Research limitations/implicationsThe methodology only focusses on auto clusters from India, application of the model/methodology needs to be extended to other set of industries that follows tier structure, or belong to other developing nations to corroborate the findings.Practical implicationsThe proposed approach is a useful tool to provide guidance to policy-makers and in monitoring industry clusters progress.Originality/valueThe paper offers an empirical approach for measuring competitiveness of industry clusters. So far there has been only a minuscule research on cluster competitiveness using empirical methods specifically in case of developing countries like India. Because of the heterogeneity of actors in industry clusters and absence of cluster relevant databases, its performance has been mostly captured via means of case studies. This study is one of its kind that renders comparison of competitiveness across industry clusters by combining secondary data with the perception of cluster actors.

Investigation of Competitive Advantage of the Dairy Industry Cluster: The Case of Rwanda

This paper explores the dairy industry cluster in Rwanda and its competitive advantage basing on the Porter’s diamond model. Additionally, this study identifies other factors critical to the competitiveness of the dairy industry cluster in Rwanda and includes them to the diamond model to create a multiple diamond model. This study argues that the direction taken to increase milk supply in Rwanda by increasing the number of cattle farmers through the government program known as “One cow per poor family” is not suitable in that it exerts more pressure on already scarce resources such as land. In fact, this study suggests that given the challenges facing the dairy industry, more emphasis should be put on improving efficiency and increasing productivity in the value chain. This study proposes the multiple-diamond model which extends Porter’s Diamond model to include other factors central to the competitiveness of the dairy industry cluster in Rwanda. To test the effect of the proposed multiple-diamond determinants, ARDL test was run. The findings confirmed the effect of trade openness on the dairy cluster industry competitiveness. Development assistance and milk cattle were found to have a negative and significant effect on the dairy cluster industry competitiveness while FDI inflow and market sophistication were found to have non-significant effect. The study concludes by providing recommendations for future studies in this field.

Study on The Industrial Cluster of Tropical Bananas Based on Gem Model

Abstract In recent years, the development of agricultural industry clusters is rapid in China. As a main producing area of bananas, the Hainan Ledong Industrial Cluster’s competitiveness is of great significance to the development of the whole banana industry in China. This paper first analyzed the cultivation of tropical banana and the market share of bananas in each region, and then analyzed the competitiveness of Ledong banana industry cluster through the GEM (Groundings- Enterprises- Markets) model. The results showed that the GEM model score was 456 points, and the domestic cluster competitiveness exceeded the average level. The “factor pair” socre suggested that the scores of the structure, strategy and competition of the enterprise were low, which restricted the development of Ledong banana industry cluster.

Situation analysis forecasting: the case of European maritime clusters

ABSTRACTWithin the literature concerned with aspects of competitiveness, innovation and strategic management of industrial clusters, the body regarding forecasting of strategic management is still nascent. This work aspires to render a contribution within the domain of strategic management forecasting, through the indicative case of European maritime clusters. For this end, a two-tier model is formulated. A quantitative strengths, weaknesses, opportunities, and threats (SWOT) methodology that derives from the fusion of a quondam situation analysis and crosstabs’ theory is generated. This leads to the utilization of the crosstab’s conditional probabilities as transition probabilities; the latter are called to compile the transition matrix of a Markov chain. Through this methodology, and the successive Markov chain’s transition matrices, strategic forecasts are extracted for a devised European maritime cluster case. This work relinquishes a novel application with respect to strategic management forecasting, that provides a dichotomy of practical interpretations and scenarios for quantitative situation analysis. Thus, it may enable effective real-time decision-making for strategic management and/or policy drafting. The situation analysis forecasting model may find applicability in a plethora of practical and theoretical cases, wherein forecasting may be desirable. It may as well pertain to an intrinsic methodology for situation analysis forecasting of maritime clusters.

The Compared Study of Competitiveness of Electron Industry Clusters: Case of Wuhan, Shanghai and Guangzhou

With electronic information product manufacturing in the world transferring to China, the electron industrial clusters have become the main form, of which regional economy gain competitive advantages. So we selected Wuhan, Shanghai and Guangzhou as samples. Then we figured out electron industrial clusters’ competitiveness score of the three cities, using the GEM model. At the same time, we systematically analyzed electron industry clusters of Wuhan Optical Valley from the advantages and disadvantages of the six aspects.

A research on the cluster competitiveness evaluation of the Chinese automobile industry based on cuckoo-AHP

Purpose The purpose of this study is that on the basis of the competitive edge theory, source mechanism and evaluation approaches of industrial cluster competitiveness, combined with international trends in the automobile industry and the features of Chinese automobile industrial cluster development, an evaluation index system about cluster competitiveness of auto industry is built with comprehensive consideration of factors such as cluster development environment, external scale effect and internal competitiveness from the perspective of value chain of automobile industry. Design/methodology/approach An evaluation index system for automobile industrial cluster competitiveness was realized by integrating current strengths and future growth capacities with multidimensional, dynamic and comprehensive characteristics, which included 3 second-level, 10 third-level and 16 fourth-level indices. In the light of evaluation methods, a group intelligence optimization algorithm – (cuckoo search) – and traditional methods of complex decision-making system – analytic hierarchy process (AHP) – were combined to propose the cuckoo-AHP evaluation method. It was applied for the calculation and optimization of weight values in an automobile industrial cluster competitiveness evaluation index for the purpose of obtaining better scientific and more reliable results. Findings The research might further enrich the evaluation theory of automobile industrial cluster competitiveness and also can be useful for showing how traditional evaluation methods can be combined with intelligent algorithms to carry out better automobile industrial cluster competitiveness evaluations. In addition, studies of channels for kick-starting Chinese auto industrial cluster competitiveness are expected to provide references for how to enhance the cluster competitiveness of the Chinese automobile industry. Practical implications Changsha and Liuzhou, the Guangxi automobile industrial clusters as the two empirical analysis objects selected for this paper, are geographically adjacent to each other. The automobile industries of the two cities are local pillar industries with the strong support of the local government. Both clusters have their own advantages and weak points with different characteristics of cluster development, and they enjoy a representative significance amongst China’s numerous auto industrial clusters that are taking shape. Comparative analysis of both clusters serves as a good reference for the objective evaluation of the competitiveness of Chinese automobile clusters in terms of their real and practical developments and in respect of the success of reasonable scientific and industrial cluster policies. Originality/value Multidimensional, dynamic, integrated evaluation index systems are constructed around automobile industrial cluster competitiveness, which has taken into account developments in current strengths and future growth capacity. The cuckoo-AHP evaluation method has been formed by combining the traditional decision-making method known as AHP with a new meta-heuristic optimization algorithm called “cuckoo search”. Both have been used in evaluations of automobile industrial cluster competitiveness in Liuzhou and Changsha, which will be beneficial for enriching automobile industrial cluster competitiveness evaluation theory and new evaluation methods that will enable better evaluations of automobile industrial cluster competitiveness.

4th party logistics service providers and industrial cluster competitiveness

Purpose – Firms within industrial clusters are subject to challenges such as globalization, limited resources, volatility of international markets and financial instabilities. 4th party logistics (4PL) service providers are supporting individual firms to overcome such challenges by using collaborative operational capabilities from within an industrial cluster to their enhance competitiveness. The purpose of this paper is to focus on China and proposes a collaborative operational capabilities framework to illustrate the role of 4PL in industrial cluster competitiveness. Design/methodology/approach – The paper follows an extensive literature review and structured interviews in two types of clusters, drawing on resource-based view and importance-performance matrix analysis. Findings – The paper proposes six elements (that is, synergy of logistics, expansion of industrial chain, financial ability, creativity and innovation ability, cooperation of companies and flexibility of supply chain) that comprise collaborative operational capabilities, and highlights the role of “creativity and innovation ability” and “supply chain flexibility” in the use of 4PL for industrial cluster competitiveness in Chinese context. Research limitations/implications – The paper focusses on China and hence it could also be tested in the developed countries’ context with the support of large-scale empirical data to investigate further its usefulness and to identify other constraints. Originality/value – The study contributes to the 4PL literature in that it proposes a framework that extrapolates the importance of 4PL in industrial cluster competitiveness in China.

Competitiveness of Industrial Clusters: A Comparative Study of Pithampur and Indore Clusters, India

Industry clusters have proved critical for a region’s economic prosperity and a nation’s overall competitiveness. This study aims to evaluate and compare competitiveness performance of two select industry clusters from India viz. Pithampur Auto Cluster and Indore Pharma Cluster. For this purpose, the study has developed a comprehensive framework and a simplified methodology that incorporate both quantitative as well as qualitative data on various aspects of cluster competitiveness. It can be used as a foundation for future works related to assessing competitiveness of the clusters.

The Collective Learning Model and Competitiveness of Industrial Clusters

There are few enterprises in economy scale among the majority of enterprises in industrial clusters, and they cannot carry out independent research-and-development of new products. Therefore, they have to rely on innovating resources of the clusters to enhance their comprehensive strength in competition. In this study, we analyzed the driving forces for collective learning in industry clusters, and try to establish a model for collective learning and competitiveness of industry clusters, and provided a new approach for upgrading industrial clusters.