Cluster Competitiveness Research Articles

АУДИТ ВПРОВАДЖЕННЯ РИНКОВОЇ ІНФРАСТРУКТУРИ В УПРАВЛІННІ КЛАСТЕРАМИ

The article discusses the issue of the audit of the implementation of market infrastructure in the management of clusters, which is a key factor for increasing the efficiency and competitiveness of modern economic systems. Special attention is paid to the analysis of the role of market infrastructure in the development of cluster formations, which contributes to the synergistic effect, the unification of enterprises, scientific institutions and other business entities to achieve common economic goals. The purpose of the study is to carry out a comprehensive analysis of the implementation of market infrastructure in the management of clusters and determine its impact on the effectiveness of the functioning of cluster formations in Kyrgyzstan. The main attention is paid to the assessment of infrastructural components, such as logistics, financial services, information technology and institutional support, which ensure the competitiveness and sustainable development of clusters in the conditions of the modern economy. The object of the study is auditing methods and approaches used to assess the effectiveness and risks of innovative processes of introducing market infrastructure during cluster management. Research methods: logical and meaningful method, methods of comparison, systematization, induction and deduction, analysis and synthesis, coefficient method. The main hypothesis of the follow-up. Effective implementation of market infrastructure in the management of clusters has a positive effect on their competitiveness and ability to adapt to changes in the economic environment. The lack of proper infrastructure or its shortcomings can significantly limit the potential of clusters in achieving a synergistic effect and ensuring economic growth. Present of the main material. The authors highlight methodological approaches to the audit of market infrastructure, including the analysis of key indicators such as logistics and information networks, financial and credit institutions, as well as government support mechanisms. The importance of conducting an audit to identify problematic aspects that prevent the efficient functioning of clusters and to develop recommendations for their elimination is substantiated. Originality and practical significance. The study also presents the results of the analysis of the impact of the introduction of market infrastructure on the competitiveness of clusters and their ability to adapt to changes in the global economic environment. The issue of integration of cluster strategies with regional and national economic plans was separately considered. On the basis of empirical data, conclusions are drawn regarding the effectiveness of infrastructure changes and practical recommendations for improving cluster management are offered. Conclusions and prospects for further research. The results of the audit show that the market infrastructure is a key element that contributes to the development of clusters, ensuring effective interaction between the participants of the cluster association, improving their productivity and competitive advantages. Implementation of infrastructure components, such as modern information and communication technologies, financial institutions and logistics networks, is crucial for improving the efficiency of cluster management.

Creating shared value to enhance the competitiveness of the agricultural cluster in Qalqilya governorate

PurposeThe aim of this study is to provide an empirical investigation of the agricultural cluster’s economic, social and environmental values. By doing so, the authors aim to offer policymakers and decision-makers a strategic approach that promotes competitiveness and economic development through shared value.Design/methodology/approachThe authors used AMOS software and applied structural equation modeling to achieve the study’s objectives. The study used this approach with path analysis through the Analysis of Moment Structures software.FindingsThe empirical results indicate that creating shared value (CSV) can enhance the agricultural sector’s competitiveness through clustering. Rather than enhancing competitiveness directly, CSV plays a crucial role in improving the relationship between clustering and competitiveness. The authors also examined Porter’s diamond of competitiveness and evaluated factors for creating a shared value strategy, such as factor conditions, demand conditions, related and supporting industries, strategy, structure, rivalry and the role of government.Research limitations/implicationsThis study focuses solely on the agricultural cluster in Qalqilya governorate and cannot be applied to other regions without additional research.Practical implicationsEnsuring that stakeholders in the agricultural sector are kept informed about the activities of the cluster and the benefits of their participation is crucial. Empirical findings and conclusions have demonstrated that a shared value strategy can enhance the competitiveness of this sector. To achieve this, institutions involved in developing the agricultural cluster must increase their efficiency and capacity. Consulting experts in this field and drawing on experiences from other countries can aid in achieving this goal. Additionally, enhancing farmers’ productivity should be a priority, and the Ministry of Agriculture can provide training and workshops to improve their skills and expertise.Originality/valueThis study suggests that Palestinian policymakers should establish effective partnerships between the government and the agricultural sector’s firms in Qalqilya to reinforce the cluster’s competitiveness. This strategy can stimulate competitiveness and promote economic and social development in Palestine.

Mouse neural tube organoids self-organize floorplate through BMP-mediated cluster competition

During neural tube (NT) development, the notochord induces an organizer, the floorplate, which secretes Sonic Hedgehog (SHH) to pattern neural progenitors. Conversely, NT organoids (NTOs) from embryonic stem cells (ESCs) spontaneously form floorplates without the notochord, demonstrating that stem cells can self-organize without embryonic inducers. Here, we investigated floorplate self-organization in clonal mouse NTOs. Expression of the floorplate marker FOXA2 was initially spatially scattered before resolving into multiple clusters, which underwent competition and sorting, resulting in a stable "winning" floorplate. We identified that BMP signaling governed long-range cluster competition. FOXA2+ clusters expressed BMP4, suppressing FOXA2 in receiving cells while simultaneously expressing the BMP-inhibitor NOGGIN, promoting cluster persistence. Noggin mutation perturbed floorplate formation in NTOs and in the NT invivo at mid/hindbrain regions, demonstrating how the floorplate can form autonomously without the notochord. Identifying the pathways governing organizer self-organization is critical for harnessing the developmental plasticity of stem cells in tissue engineering.

Empirical Insights - Mapping the Key Factors that Have an Influence on Clusters Competitiveness

In today's global economy, clusters have emerged as crucial drivers of regional economic development and competitiveness. This article presents a comprehensive literature review that sheds light on the factors that influence the competitiveness of clusters. Drawing upon a broad range of scholarly sources across various fields, including economics, geography, and management, we synthesize existing research to identify and analyze the key determinants that shape cluster competitiveness. The review highlights the importance of several factors such as industry specialization, knowledge spillovers, innovation networks, infrastructure, and institutional support in enhancing the competitive performance of clusters. Furthermore, the paper critically examines the methodological approaches employed in studying cluster competitiveness, providing valuable insights for policymakers, practitioners, and researchers. By analyzing and evaluating the literature, this paper provides a deeper understanding of the dynamics of cluster competitiveness. Therefore, the research contributes to the growing body of knowledge on this important subject and underscores the significance of clusters in shaping regional economic development and competitiveness.

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.

Coopetition in social commerce: What influences livestreaming knowledge sharing in agricultural clusters?

Given the increasing popularity of livestreaming and agricultural e-commerce, it is important to understand what influences knowledge sharing among competing local farmer-livestreamers. Using ordinal logistic regression and agent-based modeling, we examine several drivers of livestreaming knowledge sharing in an agricultural cluster. Our results show that peer effects and peer interaction can speed up knowledge sharing among local farmer-livestreamers. Furthermore, regional knowledge-sharing activities organized by local governments can meaningfully motivate farmer-livestreamers to share their livestreaming skills and knowledge. To promote knowledge sharing in agricultural clusters, local governments also can use communication platforms and education to change the idea of some farmer-livestreamers that knowledge sharing only benefits their local competitors. Highlighting several drivers for the evolution of livestreaming knowledge sharing, our study provides a theoretical basis for leveraging knowledge sharing to enhance the competitiveness of agricultural clusters in cyberspace.

Analysis of Competitiveness in the Domestic Semiconductor Cluster Using Entropy Technique: Based on the GEM-ESG Model

Purpose - The study aims to conduct an empirical analysis of semiconductor cluster integrated competitiveness with a focus on the GEM-ESG model. Specifically, the study seeks to compare and analyze the development factors of semiconductor clusters in different regions. The results of the empirical study will reveal a clear concentration phenomenon in the semiconductor industry clusters of some regions.
 Design/Methodology/Approach - Previous research has established the factors influencing the competitive advantage of industrial clusters. The study collected competitive indicators from major South Korean cities and regions, incorporating these into the GEM model, and exploring ESG indicators relevant to the semiconductor industry. The study utilized Entropy Technique analysis to empirically assess the competitiveness of the South Korean semiconductor cluster and conduct comparative testing of the research findings.
 Findings - The results revealed a concentration phenomenon in the semiconductor industry clusters of Yongin and Chungbuk Province. Additionally, the study highlighted the importance of factors such as “groundings (G)”, “enterprise management (E)”, “market conditions (M)”, and ESG practices in determining competitiveness.
 Research Implications - Through empirical analyses, the study emphasizes the significance of GEM and ESG factors in the analysis of industrial clusters, particularly in contributing to long-term sustainability and competitive advantage focusing on the collaboration and alignment of ESG values.

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.

ASSESSMENT OF THE IMPACT OF GLOBALIZATION ON THE REGIONAL DEVELOPMENT OF INNOVATION CLUSTER

The article delves into the complex dynamics of innovation clusters moving through the complex landscape of global trends and challenges inherent in the comprehensive process of globalization. The multifaceted influence of global factors on the evolution of innovation clusters within the framework of the modern economic situation is also meticulously studied. In the course of the analysis, a special emphasis has been placed on a detailed examination of the challenges arising as a result of the current processes of globalization and the rapid development of innovative technologies. The present research identifies the key role played by innovation clusters as integral components of regional development and essential contributors to the tangled web of global production chains. One of the most important findings is the reconciliation of local and global interests in the circumstances surrounding the establishment and operation of innovation clusters. It has also been determined that globalization is a stimulus for innovative development and the emergence of new challenges to ensure the sustainable competitiveness of regional clusters in the global economic environment has been substantiated. It is noted that countries where innovation clusters demonstrate successful integration into global production chains actively use the advantages of globalization to develop their regional innovation systems. The primary purpose of the analysis was to determine the factors contributing to the successful integration of clusters into global processes. These factors include effective cluster management, the development of a flexible innovation strategy, and the ability to adapt to changes in the global economic environment. The role of innovation management in the context of globalization is considered separately, in particular, the need for constant monitoring and analysis of global innovation trends.

Collective shared value creation as emergent strategy for cluster management organizations

PurposeCluster management organizations (CMOs) have emerged over the past few decades as intermediaries that support the competitiveness of place-based clusters of economic activity. Despite their economic origins, policymakers are now starting to experiment with a broader use for cluster policies that seeks to leverage CMOs to tackle societal challenges in approaches aligned with the concept of creating shared value (CSV). However, there remains a void in conceptual understanding around the specific roles that CMOs might play in overcoming the barriers faced by their members for CSV, which this paper aims to address. Bridging this gap presents an opportunity for cluster practitioners and policymakers in a context in which environmental and social sustainability are at the top of policy agendas.Design/methodology/approachBased on analysis of literature around collaborative approaches to CSV for mitigating transaction costs, the authors define the contours of a new conceptual framework for the roles that CMOs can play in fostering collective CSV. The authors illustrate how the different components of the framework are reflected in emerging cluster practice in the context of a new wave of European cluster-based projects tackling CSV elements.FindingsThe resulting framework reconciles the concepts of clusters and CSV by explicitly positioning CMOs as intermediaries for facilitating the CSV strategies of their members. CMOs embrace emergent strategy making that targets (tangible and intangible) collective CSV capabilities and addresses collective CSV challenges. Collective CSV can provide a theoretical anchor guiding future cluster policies to fully leverage the transformative potential of CMOs. This conceptual framework opens a promising empirical research agenda, particularly around evaluating the plurality of impacts of CMOs.Originality/valueBy stressing the social impact of CMOs alongside their well-understood economic impacts, and by enabling a categorization of functions that can support the monitoring of CMO activities toward collective CSV strategies, the framework provides a novel basis for inspiring further empirical research into the evidencing of these roles.

Foreign experience in the development of the food industry and the possibility of its use in Uzbekistan

This paper undertakes a comprehensive analysis of the food industry's development across various foreign countries, with a focus on key sectors such as meat and meat products, dairy products, sugar, cheese, and tobacco products. The examination extends to the activities of global corporations and clusters involved in food production, forming the basis for informed proposals to enhance the efficiency and functionality of food clusters. The analysis delves into the nuanced dynamics of each sector, considering factors like production methods, market trends, and global consumption patterns. By scrutinizing the practices and strategies employed by global corporations, the paper seeks to distill valuable insights applicable to the improvement of food clusters. Understanding the success stories and challenges faced by these entities becomes instrumental in formulating actionable recommendations. The proposals put forth in the paper aim to elevate the performance and competitiveness of food clusters. These recommendations likely span areas such as technological innovation, supply chain optimization, sustainability practices, and market positioning. Drawing on the strengths observed in successful global corporations, the paper strives to provide a roadmap for food clusters to adapt and thrive in a dynamic and evolving industry landscape. This research contributes to the ongoing discourse on enhancing the food industry's efficiency and sustainability, offering practical suggestions derived from a thorough analysis of both global trends and the activities of leading corporations and clusters. The insights gleaned from this paper have the potential to guide policymakers, industry stakeholders, and researchers in their efforts to foster innovation and resilience within the food sector.

Shared Resources and Cluster Life Cycle: A study in the footwear sector in Brazil and Italy

Clusters are considered sources of competitive advantage. Cluster is understood as a set of resources; however, it is noted that these resources vary according to their trajectory. Thus, this paper seeks to analyze the relationship between shared resources in the life cycle of the cluster. We conducted a cross-country study with two clusters in footwear industry: Brazil and Italy. We present two main contributions. First, the competitiveness of clusters is sustained by their ability to renew and change their dependent trajectory. Second, we propose five elements that positively influence cluster competitiveness. These elements also help us understand the trajectory of clusters.

Nigerian Maritime

Abstract Maritime industry clustering has in recent years gained attention as a tool for strategic global and regional economic competitiveness policies, owing to their co-location synergies and agglomeration economies. The Nigerian maritime sector is known to have a large industry, which comprises shipping and logistics firms, shipyards, ship consultants and a suite of other firms, institutions and associations with maritime functions. However, nothing is known about the structural characteristics and environmental dynamics of this significant cluster. This article, capitalising on a critical and meta-analytic review of studies and conceptual modelling, explores for the first time the Nigerian maritime industry from a cluster perspective, underpinned by the foundational competitiveness of Nigerian microeconomic performance. It further evaluates broadly the quality of the Nigerian business environment, highlighting the challenges and opportunities for strategic foresight in decision-making. Targeted recommendations predicated on cluster-specific characteristics are provided to improve cluster competitiveness.

Industrial agglomeration, spatial structure and economic growth: Evidence from urban cluster in China

The industrial agglomeration and spatial structure characteristics play important roles in the economic development and improvement of competitiveness for urban cluster. The article constructs a C-D model using panel data of 130 cities in 13 urban clusters in China from 2006 to 2015, then constructs regression equations of the relationship between industrial agglomeration, spatial structure and economic growth through fixed-effects regression models, and finally analyzes the influence of industrial agglomeration and spatial structure on economic growth of urban clusters. The research results indicate that the economic structure of urban clusters is affected by both the level of industrial agglomeration and spatial structure of urban clusters. The higher the level of industrial agglomeration, the more obvious the economic effect of urban clusters. From the perspective of the spatial structure of urban clusters, the impact of monocentric spatial structure of urban clusters on economic growth shows a positive correlation. Human capital, physical capital, economic openness and resident population have significant positive effects on the economic growth of urban clusters, while transportation infrastructure is not significant. At this moment, we should further promote the industrial agglomeration, and continuously optimize the internal spatial structure of urban clusters with the linkage mechanism of land and household registration to improve the comprehensive competitiveness of urban clusters.

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.

A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this paper, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Grow-Shrink</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</i> ), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Incremental Association MB</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IAMB</i> ), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interleaved IAMB</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inter-IAMB</i> ). Our implementations are available as part of an open-source software called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ramBLe</i> , and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC'21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.

Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From ShanghaiTech University

In SC20, Srivastava <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> [6] proposed a Parallel F <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ram</b> ework for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ayesian <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Le</b> arning, or ramBLe, for short, which is a highly parallel and efficient framework for learning the structure of Bayesian Networks (BNs) from samples, particularly large genome-scale networks. As part of our participation in the SC21 Student Cluster Competition, our task was to verify conclusions from the original work [6]. Here we present the outcome of our experiments, which were performed on a four-node cluster from the Oracle Cloud HPC platform. We reproduce the numerical results from [6], namely the algorithm's performance and scaling behavior using MPI and different Python and Boost libraries on the Oracle cloud.

Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Peking University

Ankit Srivastava et al. [1] proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with Centos-compatible Oracle Linux. Our experiments, covering the same three algorithms of ramBLe, evaluate its strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions in the paper and propose our explanation of the differences.

PECULIARITIES OF THE DEVELOPMENT OF THE TEXTILE INDUSTRY AND CLUSTERING OF ENTERPRISES IN UZBEKISTAN

Functional efficiency of management processes of textile enterprises in the world is based on a sequence of production processes, the organization and management of integrated textile industry clusters, increasing management efficiency based on the use of internal capacity of enterprises, management mechanisms of enterprises included in the cluster, ready-to-eat textile products A number of scientific studies are being conducted to improve the value chain in production, increase the competitiveness of clusters through competitive advantages.