This monthly selection of articles is been carried out by Philippe Gugler and Basile de Raemy, from the Center for Competitiveness, University of Fribourg. The entire selection, carried out since 2013, can be consulted on the academic articles page of our web.

Collective shared value creation as emergent strategy for  cluster management organizations

By: S. F. Grimbert, J. R. Wilson, X. Amores Bravo, A. Pezzi. Competitiveness Review, DOI: 10.1108/CR-07-2022-0103, January 2024.

Abstract: “Purpose – Cluster 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/approach – Based 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. Findings – The 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/value – By 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.” [ABSTRACT FROM AUTHORS]

Environmental challenges and  innovative responses of local agri-food systems: a theoretical approach

By: L. Collado, P. Galaso, M. de las Mercedes Menédez, A. R. Miranda. Competitiveness Review, DOI: 10.1108/CR-08-2023-0210, February 2024.

Abstract: “Purpose – This paper aims to analyse how local agri-food systems (LAFS), compared to other production models, can offer innovative responses to the important environmental challenges facing food production under the twin transition. These responses are more conducive to community inclusion and local development. Design/methodology/approach – The paper combines territorial development, clusters and industrial districts literature with studies on agri-food industry environmental problems and twin transition technologies to develop an agri-food systems typology. This typology is based on a territorial approach to environmental challenges of food production and serves to illustrate the ways in which LAFS can provide innovative responses to these challenges. Findings – The study allows to visualise the differences between LAFS and other agri-food production models, showing how the operationalisation and implementation of digitisation occur at territorial level and how rural communities are involved in the process. The theoretical proposal emphasises not assuming that technology is inherently beneficial but ensuring that its implementation is inclusive and generates social value for the communities. Originality/value – The paper aims to enrich future research by adopting a territorial perspective to study the twin transition challenges associated with food production systems.” [ABSTRACT FROM AUTHORS]

How much do cluster institutions drive a firm’s green  innovation? A multi-level analysis

By: J-A. Belso, I. Díez-Vial, G. Martín-de Castro, J-L. Hervas-Oliver. Regional Studies, DOI: 10.1080/00343404.2023.2298317, January 2024.

Abstract: “This research explores the complementary role of formal institutions at the macro-level and informal ones at the cluster level on a firm’s green product innovation. Using mixed methods in a sample of 177 firms dedicated to the footwear industry, belonging to three clusters in three different countries, findings suggest that: (1) the cluster effect positively influences a firm’s green product innovation; and (2) informal cluster-level institutions’ effect on green product innovation is jointly and positively moderated by national institutions. Green innovation in clusters requires coupling different multi-scalar institutional systems effectively.” [ABSTRACT FROM AUTHORS]

The implementation of green  transformation through clusters

By: A. Maria Lis, M. Mackiewicz. Ecological Economics, DOI: 10.1016/j.ecolecon.2023.107842, January 2024.

Abstract: “The paper addresses a poorly documented issue in the literature, namely the role of clusters in green transformation, including processes related to green, low-carbon, and circular economies. The purpose was to identify and understand the practices of clusters in this area. The adopted mixed research strategy consisted of both qualitative and quantitative research. Both research phases were conducted in a group of Polish Key National Clusters. Through qualitative research, the authors abductively identified practices that are vital for green transformation and categorized them into three thematic areas: Integration, Access to resources, and Education and awareness building. On this basis, the authors developed a method to measure the clusters’ level of advancement in green practices, which was used in quantitative research. The results show that the surveyed clusters undertake a number of practices to promote green transformation. They are active in each of the distinguished areas and also engage in its most demanding forms. The study sheds new light on the concept of the clusters, showing how such organizations can be used as agents of change in favor of green transformation.” [ABSTRACT FROM AUTHORS]

Will technology innovation uncertainty affect the  distribution of benefits from low-carbon innovation activities in industrial  clusters? A study based on gray Shapley values

By: X. Tang, J. Feng, X. Mao, B. Feng, J. Wu. Managerial and decision economics, DOI: 10.1002/mde.4095, January 2024.

Abstract: “Technological innovation of industrial cluster is an important measure to realize low-carbon development. However, the uncertainty associated with new technologies can have a significant impact on the earnings of enterprises. This paper examines the benefit distribution of collaborative technological innovation in industrial clusters through the gray Shapley value, which makes up for the problem that existing research ignores the impact of the uncertainty of technological innovation on the distribution of benefits and provides a new research perspective. In addition, this paper also provides some countermeasures for the Chinese government to encourage enterprises in industrial clusters carry out low-carbon innovation activities.” [ABSTRACT FROM AUTHORS]

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

By: Z. Sun. Scientific Reports, DOI: 10.1038/s41598-024-52697-x, February 2024.

Abstract: “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.” [ABSTRACT FROM AUTHOR]

Ecological resilience of city clusters in the middle reaches of Yangtze  river

By: C-C. Lee, J. Yan, T. Li. Journal of Cleaner Production, DOI: 10.1016/j.jclepro.2024.141082, February 2024.

Abstract: “As sustainable development has become a trending consensus, urban ecological resilience is now a significant metric for assessing the extent of urban ecological advancement. In view of the critical roles for promoting coordinated regional development, this research evaluates the ecological resilience of 28 cities in city clusters in the middle reaches of the Yangtze River from 2009 to 2020 using the entropy weight method and analyzes the spatio-temporal evolution pattern of ecological resilience. This study uses the Moran index and the spatial Durbin model to explore the spatial correlation of ecological resilience as well as the influencing factors and presents the following conclusions. First, ecological resilience appears to be on an upward trend in time, spatially characterized by a transition from higher resilience in the east region to lower resilience in the west region. Second, spatial autocorrelation in ecological resilience exhibits a positive relationship. Most cities in Jiangxi Province are high-high agglomerative cities, while most cities in Hunan and Hubei Provinces are low-low homogeneous cities and high-low polarized cities, respectively. Third, given that population density, economic development, and financial development negatively affect ecological resilience, government green investment has a positive effect and positive spatial spillovers. Based on the findings, this paper offers targeted policy recommendations on how cities can improve urban ecological resilience.” [ABSTRACT FROM AUTHORS]

Self-organizing maps: a novel approach to identify and map business  clusters

By: F. Bowen, J. Siegler. Journal of Management Analytics, DOI: 10.1080/23270012.2024.2306628, February 2024.

Abstract: “Business cluster identification is an essential topic for helping understand regional and global supply chains and establishing economic policies and logistics. This work aims to leverage the benefits of self-organizing maps (SOM), combined with traditional clustering algorithms and image processing techniques, to identify business clusters that are described by high-dimensionality feature vectors. It is advantageous over previous work because the algorithm is unsupervised and makes no assumptions about the number of clusters for a given feature set. The proposed algorithm was evaluated using recent datasets for US metropolitan cities from the Indiana Business Research Center (Innovation 2.0) and the Occupational Employment Statistics Survey. Data involving innovation metrics, education levels, economic well-being, connectivity, local GDP, and STEM are aggregated to demonstrate the effectiveness of the proposed neural network. The clustering results are compared to traditional approaches, including K-means clustering, both quantitatively and qualitatively. The unsupervised nature of the proposed SOM approach, and the acceptable computational complexity of the overall algorithm, suggests that self-organizing maps offer several advantages over traditional methods. In this work, we present a novel architecture coupling a SOM model with processing techniques for automatically identifying business clusters derived from high-dimensionality feature vectors, the first use case of SOMs in business cases affecting supply chains and other economic decisions. Preliminary results confirm the viability of architecture as an unsupervised approach for identifying business clusters.” [ABSTRACT FROM AUTHORS]

The Spatial-Temporal Evolution of China’s Automobile Manufacturing  Cluster Network and Its Influencing Factors

By: B. Lin. Journal of the Knowledge Economy, DOI: 10.1007/s13132-024-01735-0, January 2024.

Abstract: “The global cluster networks (GCN) theory has improved the theory concerning industrial clusters. This study attempts to conduct empirical research on the spatial and temporal evolution of China’s automobile industry using GCN. By using cooperative applications for patent data and buyer–supplier data between cluster firms, a 203×203 inter-cluster association network was constructed. The main findings are as follows. First, the scale and density of the networks increased from 2000 to 2012. R&D cooperation sub-networks became increasingly localized, forming communities composed of several local clusters. The number of community buyer–supplier sub-networks was relatively stable, while the linkages between communities gradually increased. Second, cluster networks existed at various spatial scales. The spatial scale of the R&D cooperation sub-networks changed from a macro to micro scale, and the buyer–supplier sub-networks expanded to a larger spatial scale. Third, the intra-industry division of labor was an important factor for cluster networks. Among them, vertical division within the industry promoted R&D cooperation sub-networks and buyer–supplier cooperation. The horizontal division only affected buyer–supplier cooperation. Buyer–supplier cooperation significantly affected R&D cooperation. The impact of local specialization on R&D cooperation was not robust.” [ABSTRACT FROM AUTHOR]