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The purpose of this conceptual paper is to substantiate the idea that clusters will cope with the challenges of Industry 4.0, Industry 5.0 and remain one of the drivers for the development of the local economy.

For the preparation of this paper, the author reviewed the literature on Cluster 4.0, Industry 4.0, Industry 5.0, green and blue recovery, as well as the future of jobs and systems. Data, reports and surveys (UN, OECD, World Economic Forum, McKinsey Global Institute, D2L, etc.) were analysed in detail, in order to create a clear picture related to the research topic. The opinions expressed in the article are depicted graphically, and were developed by the author.

First, predictions on Industry 4.0 challenges are outlined, and the reasons as to why clusters are expected to succeed in the digital era are substantiated; second, the direction of development in the digital era is depicted in a two-dimensional plane; and third, the direction of development in digital era within the clusters is graphically presented as a system.

For this purpose, the author has developed a new approach to connect clusters and digitalisation with the self-designed graphical images.

This study aims to determine how much company participation in a type of cluster affects its economic performance.

This study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.

Results show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.

This research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.

From the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.

This study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.

This paper aims to focus on the perceived role of clusters in inter‐firm cooperation and social networks.

The study was carried out in a region of Latin America where limited research has been conducted in terms of inter‐firm relationships. Managers from three key natural resources‐based industries in Chile participated in the survey; one of these industries constituted a well‐defined cluster whereas the other two did not. The survey assessed managers' perceptions of the benefits and opportunities of inter‐firm cooperation in strategic marketing activities.

Results support the advantages of clusters. Managers of firms which are part of clustered industries tend to perceive more benefits and opportunities for inter‐firm co‐operation in marketing activities. Additionally, significant differences between clustered and non‐clustered industries in terms of their co‐operation behavior and objectives were found.

The findings shed light on strategies for the enhancement of inter‐firm cooperation in marketing, of particular value for marketers in small‐and‐medium sized enterprises. The paper suggests establishing new clusters and promoting more regional clusters policies since clustering seems to provide better and positive inter‐firm interaction leading to cooperation.

There are lessons to be learned at national and regional levels for Latin American and emerging economies fostering new industry cluster policies.

Clustered firms and industries may result in more innovative marketing strategies at both local and international levels than non‐clustered firms. The authors encourage regional development bodies to foster more cooperation among firms and trade associations.

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.

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.

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.

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.

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.

This study investigates the existence of Marshall, Jacob and Porter’s type of externalities in Mexico. We measure the impact of industrial specialization, competition and diversity on employment growth for the period 2004 to 2008.

The analysis is based on data from 41 highly dynamic industrial clusters originally obtained by applying Porter’s (1998) methodology. We use a cross-section specification estimated via instrumental variables and two-stage least square estimation (2SLS) to control for endogeneity.

On average, we find that industrial specialization exerts a negative impact on employment growth within states and within clusters, indicating that traded industries in Mexico carry very little innovation, operate in early stages of the life cycle, face high costs of employment reassignation or exhibit low adaptability. A negative impact of specialization on employment conforms with Jacobs (1969) type of externalities and confirms what other studies have found in France (Combes, 2000), Korea (Lee et al., 2005) and the USA (Delgado et al., 2014). The authors also find that competition generates more employment.

Industrial data at the sub-branch level were obtained from the Economic Census (EC) of the National Institute of Geography and Statistics (INEGI). The EC information for 2004 was still not fully compatible with the North America Industry Classification System (NAICS), with 262 of the 309 data at the fourth-digit level aligned to the USA. In addition, industrial information from the EC is recorded every four years, which prevents this study to use panel data techniques and it makes it impossible to use time series methods.

Policymakers can clearly identify competition forces having a significant impact on employment growth. This can orient policymakers to implement measures to encourage the development of some of these clusters, as well as to identify some of the sources that drive specialization, competition and diversity.

This paper contributes to the debate on the existence of Marshallian (MAR), Jacobian and Porter externalities. This is the first study using the definition of traded clusters in Mexico, which allows the authors to identify how specialization, competition and diversity forces drive the dynamics of regional employment growth.

The purpose of this paper is to provide a quantitative approach to measure industry clusters competitiveness.

An 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.

The 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.

The 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.

The proposed approach is a useful tool to provide guidance to policy-makers and in monitoring industry clusters progress.

The 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.

This study aims to advance the state of knowledge of the relationship between transportation and economic development by investigating how firms in competitive industry clusters use transportation networks and what role those networks play in the competitiveness of these clusters.

The approach combines quantitative and qualitative techniques to geographically identify competitive industry clusters and to investigate the role of transportation. The US Cluster Mapping tool is used to identify competitive clusters by employment location quotients in 25 Minnesota metropolitan and micropolitan regions. A total of 12 competitive clusters were selected for further study, and in-depth interviews and site visits were conducted with businesses in each cluster to explore the competitive importance of different modes of transportation.

Minnesota’s economic competitiveness is dependent on a well-functioning transportation system in all modes – truck, air, rail, and water. Access to global markets requires rail and truck to reach coastal ports. Air transportation is critical for high-value, low-weight, time-sensitive products such as medical devices or Mayo lab testing samples. Air service is important for customers at Minneapolis – St. Paul, St. Cloud, and Rochester, Duluth, as well as other Minnesota cities. Highway access and reliability is critical for key statewide clusters such as processed food and heavy machinery.

Study limitations include the representativeness of company interviews in generalizing for a cluster and industry employment as a measure of competitiveness.

These methods can yield valuable insights into how transportation functions as an input within competitive industry clusters and how it can inform economic development strategies tailored to certain locations and industries.

This is a first-of-its kind study using industry clusters as a framework for examining the role that transportation plays in economic competitiveness.

The purpose of this paper is to identify and map trends in the Canadian apparel industry (in a global context) and, through the application of Porter's models, establish strategies that could be employed by Canadian small and medium enterprizes (SMEs) in response to the move toward trade liberalisation since the phasing out of the multi‐fibre arrangement.

The literature review established trends in the apparel industry both in Canada and globally. Qualitative research in the form of case studies highlighted apparel suppliers' perceptions of Canada's strengths and weaknesses as a business setting and provided preliminary information on possible supplier activities which provide value and competitive advantage. The analysis of the primary data also allowed the development of preliminary questions, answers to which will further enhance the understanding of clusters and their applicability to Canada's apparel SMEs.

Canada's apparel manufacturing industry is winding down while imports are continuing to grow. At the same time, the Canadian market is not large enough to sustain all the suppliers, thus forcing those who are competitive to export, primarily to the USA, which is Canada's major apparel export destination. The morphology of related and supporting industries to apparel suppliers is changing. The findings suggest that Canada's apparel supply is becoming more of a service and less of a manufacturing industry.

This paper provides an understanding of Canada's position in the global apparel map and ascertains whether competitive cluster strategies exist for the Canadian apparel industry. Furthermore, it sets the stage for further research by identifying knowledge gaps pertaining to the applicability of clusters to the apparel industry and providing data and findings to bridge these gaps.

This paper aims to examine whether in old industrial regions, the trajectory of clusters follows that of their corresponding industry or deviates from it and which are the factors that account for cluster evolution. This paper deals with the issue of how established clusters either renew or transform themselves in such regions and how they adapt to changes in their corresponding international industries.

This research paper draws from in-depth case studies on six industrial clusters, takes a longitudinal perspective and uses a multi-level and qualitative analysis. Based on existing literature, the paper suggests and exploratory analytical framework with four alternative scenarios for cluster evolution and three broad factors: cluster knowledge base, social capital at cluster and region-level and public policies.

Clusters do not always follow the life cycle of its dominant industry. The paper clearly shows a diversity of cluster evolution across clusters and even within clusters (at subcluster level). This study suggests that cluster knowledge diversity and heterogeneity allow to broaden the scope of evolutionary trajectories available; the same goes for social capital at cluster and region levels.

The main limitation of this paper lies in its qualitative approach that makes its conclusions more suggestive than conclusive. In any case, further research on other Basque clusters may corroborate or question its findings.

The paper offers an empirical and longitudinal study on cluster evolution, very much needed to the ongoing theoretical discussion on this issue. So far, there are very few empirical studies on cluster evolution with this perspective. At the same time, it presents a theoretical framework to analyse diversity of cluster evolution in old industrial regions that builds on Menzel and Fornah’s (2010) model.

The paper aims to investigate the role of specialization and agglomeration forces on industry performance in an emerging market, namely, Thailand. In particular, the impact of clusters and the influence of complexity will be tackled.

The methodology used is based on the work of Delgado et al. (2014). Industries and clusters are assigned to a certain category according to their respective level of specialization and complexity. Performance measures are then computed for each category.

It was found that the agglomeration of similar industries and co-located and related industries increase the performance of firms in terms of gross output per employee and remuneration per employee. Moreover, the increase of performance induced by the complexity level of an industry was closely related to the level of specialization.

Building on a cluster mapping, this study brings new insight on the effect of specialization and agglomeration on performance in emerging markets. In fact, the paper show how performance can be enhanced in less sophisticated and developed economies.