Clustering enterprises into eco-industrial parks: Can interfirm alliances help small and medium-sized enterprises?

Chen, Luyi; Zhou, Yuan; Zhou, Dillon K.; Xue, Lan

doi:10.1016/j.jclepro.2017.09.104

Cited by 35 publications

(17 citation statements)

References 37 publications

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“…However, many of these studies have focused on the supportive role of government and public agencies on clusters performance (e.g., Kamran et al, 2017;Richardson, 2013), indicating that when the social bonding in a given cluster is weak, government interventions are of greater significance. Another stream of literature has investigated other aspects of clustering in developing countries, including trust and inter-firm relationships (e.g., Chen et al, 2017;Sarach, 2015;Vanzettine et al, 2017), value creation and competitive advantage (e.g., Lei & Huang, 2014), knowledge systems and innovation (e.g., Hashino & Otsuka, 2013;Richardson, 2013), and social capital and responsibility (e.g., Kamath, 2013;Lund-Thomsen & Pillay, 2012).…”

Section: Industrial Cluster In Developing Countriesmentioning

confidence: 99%

“…Examples of these clusters include the Silicon Valley cluster in California; the information technology cluster of Bangalore in India; and the Australian and Chilean wine clusters (Oliver, Lieo, & Cervello, 2017;Pe'er & Keil, 2013;Porter, 2000;Tan, 2006). According to the aforementioned considerable effects and achievements, clusters have risen to prominence in the academic community and policymakers (Chen, Zhou, & Xue, 2017;Morales, Martinez, Verdu, & Chafar, 2015;Tsang & Siu, 2016;Vanzettine, Corsano, & Montagna, 2017;Wasiluk, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Industrial clusters in the developing economies: Insights from the Iranian carpet industry

et al. 2020

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Industrial clusters are perceived as potential drivers of small and medium‐sized enterprises development and efficient policy instruments to lead national and regional innovation and growth. However, these clusters in developing economies are typically placed in complex environments that impose a mix of serious challenges that adversely affect their overall performance. Therefore, this study aims to analyze the nature of these challenges and understand their dynamics using a case study of a carpet industry cluster in Iran. Using multiple sources of evidence, the study reveals two distinct, yet interrelated, levels of challenges: micro and macro. Under each level, a number of key dimensions were identified and theoretically linked which helped to conceptualize the structure of these challenges and model their dynamics.

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Section: Industrial Cluster In Developing Countriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Industrial clusters in the developing economies: Insights from the Iranian carpet industry

et al. 2020

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“…Thus, according to breadthfirst search for traversing the graph with restrictions, it is advisable to combine the following 17 enterprises into a cluster: No. 5,No. 10,No.…”

Section: Enterprises That Tend To Cluster Depending On a Search Radiusmentioning

confidence: 99%

“…Lu Yi focused on the problem of 'a transfer' of small enter prises into industrial clusters. The researcher concluded that politics, market conditions, governance, finance and techno logical progress are key factors that influence making deci sions on the integration of small businesses into interfirm al liances [5].…”

mentioning

confidence: 99%

Clustering as a tool for managing industrial enterprise

Ivanova¹,

Faizova²,

Boichenko³

et al. 2020

Nauk. visn. nat. hirn. univ.

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CluSTerinG aS a Tool for ManaGinG inDuSTrial enTerPriSe Purpose. Substantiation of methodological foundations for forming a cluster of industrial enterprises and establishing a system of relationships between their cluster groups. Methodology. Specific analogue modelling techniques were used to identify the relations between manufacturing enterprises; economic and mathematical modelling was used to search for multilayered network communities. findings. A fundamentally new methodological basis has been proposed for identifying a cluster of industrial enterprises. This has been done through a comparison of the results of the three approaches to clustering. It has been revealed that hierarchical cluster analysis does not allow identifying similar groups of enterprises or relationships between them, since this approach lacks a single strict criterion for an optimal split of the dendrogram into clusters. The competitive approach of the geometric distance of neurons to objects, which is based on the technique of selflearning neural network and Kohonen's selforganizing maps, also identified a nonuniform cluster structure. The study proposes to form a cluster of industrial enterprises through the method of searching for communities in multilayered network graphs. This method was a breakthrough in building a cluster as a merger of extractive and processing industrial enterprises, together with academic and research institutions. originality. A new methodological approach to the formation of industrial enterprise cluster has been proposed, whose math ematical basis was developed by T. Kamada. This approach uses multiple object proximity matrices, which take into account sup plierconsumer relationships, geographical distances, and patterns of ownership. It has been proved that this method for clustering is more advantageous, since it allows identifying the communities of enterprises, which are network analogues to a cluster; it also takes into account the relationships of the analysed metallurgical enterprises of the mining and processing industry with educa tional and research institutions of the enterprises. The development of these relationships creates the basis for the productive de velopment, efficient operation and additional competitive advantages for industrial enterprises. Practical value. Under conditions of a crisis in the metallurgical industry, it is recommended to create a cluster, which will significantly increase the competitiveness of each enterprise included in the cluster and fully use the potential of the metallurgical complex.

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“…Recently, enterprise cluster, as an effective form of industrial space organization, has gradually become a common phenomenon in the process of modern industry and internationalization [1][2][3][4]. e competition-cooperation relationship widely exists in the real enterprise clusters and has a major impact on the evolution of enterprise clusters [5][6][7][8][9]. To study the influence of competition and cooperation on the evolution of enterprise clusters, researchers propose a competition-cooperation enterprise cluster model [10] based on the ecology model [11][12][13][14][15], which is described as follows:…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis of a Competition‐Cooperation Enterprise Cluster with Core‐Satellite Structure and Time Delay

Dong

Zhao

2021

Complexity

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Core and satellite structure is one of the common structures in enterprise clusters. In core and satellite structure, there are one core enterprise and at least two satellite enterprises. There exist a competitive relationship between satellite enterprises and a cooperative relationship between satellite enterprise and core enterprise. However, the dynamic evolution of competition-cooperation enterprise clusters with core-satellite structure is not well understood. In this paper, a novel competition-cooperation enterprise cluster model with core-satellite structure is proposed. The boundedness of the positive equilibrium is investigated. It is found that there exists upper bound of both core enterprise output and satellite enterprise output and the upper bound of core enterprise not only depends on its own production capacity but also depends on the production capacity of two satellite enterprises. Then, by selecting the production period as bifurcating parameter, the conditions of local stability and Hopf bifurcation are obtained. Once the production period passes a critical value, the output of both core enterprise and satellite enterprise loses stability and displays periodic fluctuations. This may lead to the decline of efficiency of enterprise and resource mismatch. Furthermore, the fluctuation properties are studied. Finally, a numerical example is presented to show the effectiveness of theorem.

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Clustering enterprises into eco-industrial parks: Can interfirm alliances help small and medium-sized enterprises?

Cited by 35 publications

References 37 publications

Industrial clusters in the developing economies: Insights from the Iranian carpet industry

Industrial clusters in the developing economies: Insights from the Iranian carpet industry

Clustering as a tool for managing industrial enterprise

Dynamic Analysis of a Competition‐Cooperation Enterprise Cluster with Core‐Satellite Structure and Time Delay

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