Citation
Hervas-Oliver, J.-L. and Capone, F. (2024), "Guest editorial: Clusters and industrial districts in the twin transition", Competitiveness Review, Vol. 34 No. 5, pp. 857-863. https://doi.org/10.1108/CR-09-2024-313
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
Introduction
The concepts of clusters and industrial districts are still at the centre of an important debate in the literature, but more recently a rethinking of the concepts and their implications has been addressed (Lazzeretti et al., 2021; Hervás-Oliver et al., 2021), also to face digital and environmental transitions of localities (Lazzeretti et al., 2022; Sedita and Blasi, 2021).
The so-called “twin” digital and green transitions (Cooke, 2021) are two relevant processes that have an impact on territories, localities, regions and nations. The digital transition is related to new promising research lines in the clusters/industrial districts research, such as digitization, I4.0 and the Fourth Industrial Revolution and artificial intelligence (AI) (e.g. Hervás-Oliver, 2021; Bettiol et al., 2020; De Propris and Bailey, 2021; Capone et al., 2023). Clusters and I4.0 have been investigated for some time (Götz and Jankowska, 2017; Hervas-Oliver et al., 2019), but there are still several research gaps (Fraske, 2022).
Hence, it is crucial to highlight that a portion of the literature has examined the role of I4.0 in various specific contexts, including clusters and industrial districts (Hervas-Oliver et al., 2019; De Propris and Bailey, 2021). Some studies explored the theoretical connection between I4.0 and clusters, while others focused on case studies of particular clusters, investigating aspects, such as the adoption, implementation and effects of I4.0 in different locations [e.g. Poland/Germany (Götz and Jankowska, 2017)]. The number of works examining industrial clusters is limited, primarily concentrating on countries such as Italy and Spain (Bettiol et al., 2020; Pagano et al., 2020; Hervas-Oliver, 2022a; Burlina and Montresor, 2021). These studies underscore the potentially significant impact of I4.0, for instance, in Italy (Bellandi et al., 2020), and focus on firm level and the adoption of I4.0 technologies (Büchi et al., 2020; Cugno et al., 2021; Bettiol et al., 2023a, 2023b).
More recently, an increasing interest has been devoted to AI, also focusing on territories, regions, clusters and industrial districts (Doloreux and Turkina, 2021; Lazzeretti et al., 2023a; Xiao and Boschma, 2023). Some authors specifically focus on the relevance of AI in regional sciences (Lazzeretti et al., 2023b) or the role of cultural heritage (Lazzeretti, 2023; Lazzeretti et al., 2024). Others, nearer to the twin transition, investigate the geography of environmental technologies and whether AI supports green-technological specialization (Cicerone et al., 2023).
In the face of digital transition, the role of local forces, networks and social dynamics is endangered. In the era of AI and Industry 4.0, knowledge is becoming more and more chaotic (Nannelli et al., 2023; Lazzeretti, 2023). New challenges for territorial models are emerging (Broekel et al., 2021). Could these processes threaten social dynamics and the role of localities? This and other questions remind cluster scholars of the potential challenges that digitalization and recent developments in AI may pose to clusters and industrial districts.
On the other hand, there is increasing attention from customers, policy-makers and managers towards the green transition of territories (Hansen and Coenen, 2015; Coenen et al., 2012). Additionally, the relevance and importance of addressing climate change have been underscored by recent global initiatives, such as COP26, which has emphasized the need for urgent and coordinated action to mitigate environmental impacts and promote sustainable development.
Sustainability in cluster and industrial districts is a major research stream these days (Belso et al., 2024; Sedita and Blasi, 2021; Chaminade and Randelli, 2020). However, not much is known about the green transition of cluster and industrial districts intertwining with digital processes. How do these two processes interact at the local level?
The debate in the literature focuses on several themes, such as carbon zero emission strategies and policies (Sedita and Blasi, 2021; Bassetti et al., 2020), sustainable green innovations (Ghisetti and Pontoni, 2015; Cainelli et al., 2015), sustainability and climate change issues (Sedita and Blasi, 2021). How might all these processes affect clusters and industrial districts? Moreover, the green transition has so far been investigated mainly by adopting a mono-disciplinary approach. This opens up new interesting research avenues for the adoption of multidisciplinary approaches (Sedita and Blasi, 2021).
Beyond the two processes of digital and green transitions, the cluster/industrial districts literature is building up new research avenues while re-elaborating and advancing knowledge on traditional topics, such as innovation (radical vs incremental) (Hervás-Oliver et al., 2018; Hervas-Oliver et al., 2022b), networks (rethinking their role at the local level) (Belso-Martínez et al., 2020; Ruiz-Ortega et al., 2020; Capone et al., 2021; Innocenti et al., 2022, Broekel et al., 2021; Galaso and Kovářík, 2021) and the role of multinationals (in localities) (Belussi, 2018; Hervas-Oliver et al., 2022c), among others.
This special issue gathers high-quality articles that delve into how clusters and industrial districts can navigate and adapt to ongoing changes while uncovering opportunities in emerging areas. The focus is particularly, but not exclusively, on digitalization, environmental sustainability and related issues. These contributions explore innovative strategies and practices that clusters, firms and regions can employ to remain competitive and resilient in a rapidly evolving landscape, emphasizing the importance of embracing new technologies and sustainable practices.
Structure of the special issue
The study by Collini and Hausemer (2023) explores how systemic change agents influence the twin digital and green transitions. Building upon agency-based theories, the study analyses transition pathways and place-based characteristics, pointing out, through a literature review, the different steps of the twin transition. It also identifies how systemic change agents and geographic characteristics determine the direction and speed of the transition pathway. Findings suggest that each transition involves three steps: framing, piloting and scaling. Each of these steps is driven by systemic change agents who engage local actors in trust-based collaboration, pool resources, create network effects and exchange information to source solutions for industry-level challenges. Then, the combination of place-based characteristics and the actions of local systemic change agents define the path of the transition and the new (post-transition) equilibrium. Implications are important for policymakers.
In the same line of thought, Brueck (2024) examines the twin transition in China, focusing on the organization of innovation processes in AI and green technology (GT) development and the role of foreign multinationals in these systems. Using a qualitative research approach, the study involves interviews with executives from German multinationals experienced in AI and GT in China. Eleven semi-structured interviews were analysed using thematic qualitative text analysis. The findings reveal that AI applications for GT are mainly developed in cross-company projects led by local and regional authorities through industrial districts and clusters. German multinationals are either integrated, remain autonomous or are excluded from these innovation processes. This paper addresses a gap in the literature by providing a qualitative perspective on twin transition innovation processes in China and exploring the role of multinational enterprises in cluster organizations, making it one of the first studies of its kind in emerging economies.
From a theoretical perspective, and taking stock of what we know about sustainability and clusters/IDs, Hervas-Oliver et al. (2024), through a literature review, explore and conduct a critical literature review to answer a fundamental question in the industrial district literature: are clusters and industrial (clusters/IDs) really driving sustainability innovation? By intersecting different yet related strands of literature, this study takes stock of what we know about sustainability innovation in clusters/IDs. Insights point out that the sustainability innovation process (development and diffusion) in clusters/IDs and their firms couples into the mainstream cluster/IDs framework; clusters/IDs enable sustainability innovation through the usual mechanisms, fostering collective change towards sustainability innovation, vis-à-vis other settings and strengthening firm sustainability innovation and performance. Sustainability innovation in clusters/IDs requires coupling different multi-scalar institutional systems effectively, and cooperation of local organizations and policymakers for co-designing dedicated policies. Collective actions are important and firm heterogeneity needs to be considered in the clusters/IDs framework.
Applying theory to a practical case, Mackiewicz and Kuberska (2024) presented a very interesting case study which through qualitative methods, explores how cluster organizations foster green transformation in Poland. Their findings suggest that cluster organizations primarily manage and participate in actions that create favourable conditions for pursuing low-carbon and circular economy ventures. They not only assist their members in overcoming obstacles related to green transformation but also engage non-members – which can lead to spill overs reaching beyond their borders. Their engagement takes place across all phases of the green transformation process. Interestingly, the study shows how cluster organizations have emerged as drivers of circular transition by promoting sustainable practices such as material recycling, biological recovery and parts harvesting. These initiatives contribute to reducing waste, conserving resources and minimizing the environmental footprint of industries. These organizations can be active agents of transformation, orchestrating collaborative efforts that have a far-reaching impact on industries and economies.
Another interesting case is presented by Lis and Radzio (2023), a work that explores about implementing sustainability in energy transformation through industrial clusters. They focus on meta-organizations involved in decarbonization, highlighting the role of industrial clusters in promoting sustainable development and energy innovation. The study uses a case study of Mazovia Cluster ICT (MC ICT) to demonstrate the evolution of industrial clusters in supporting energy transition.
The study identifies three main areas of cluster activity: strategic (developing strategies and lobbying), operational/project (executing national and transnational energy projects) and institutional (supporting start-ups and developing distributed energy). These activities share common goals and interests, particularly at the institutional level. The qualitative research offers valuable insights for public authorities and other clusters aiming to foster pro-environmental initiatives. The paper contributes to understanding the role of industrial clusters in sustainable energy transformation and highlights their potential for broader social welfare.
Depicting a turnaround case towards sustainability, the study by Brou and Nadou addresses how the Caux Seine agglo territory in France transits from an industrial district to a “greener” industrial district. Using a case study, this study shows how to facilitate decarbonization and how to contribute to the emergence of a new, more sustainable sector (diversification of the energy mix, multiplication of symbioses to decarbonize industrial processes). This process of transition is driven by major companies and supported by local development agencies. Collaboration in the territory and with external firms is paramount for this transition.
Addressing the agri-food industry, Collado et al. (2024), targeting local agri-food systems, analyse how these systems couple and respond to environmental challenges of food production under the twin transition. The study allows the differences between LAFS and other agri-food production models to be visualized, showing how the operationalization and implementation of digitization occur at the territorial level and how rural communities are involved in the process. The theoretical proposal emphasizes that technology should ensure an inclusive implementation that generates social value for communities.
The study of a classic industrial district, Prato, is shown in Ferlito’s study, analysing the twin transition of textile firms in an industrial district, focusing on the interconnected processes of sustainable and digital transitions. Specifically, Ferlito examines sustainability goals through the triple bottom line and digitalization via I4.0 technologies, aiming to understand how Italian district characteristics influence these transitions in textile firms. The study uses a multiple-case approach, involving five firms from Prato’s industrial textile district. Findings reveal that district characteristics, such as supply chain fragmentation, lean manufacturing, territorial proximity and attachment to traditions, significantly impact sustainable goals and I4.0 technology adoption. The study proposes a framework where market and technology drive the twin transition, sustainability is the aim, I4.0 serves as an enablers and business model innovation is the outcome. These insights are valuable for textile firms, policymakers and stakeholders navigating the complexities of twin transition, contributing to the broader discourse on the subject.
Focusing on Industry 4.0, Capone et al. (2023) focus mostly on digital transition and investigate how firms’ features influence innovation performance in Industry 4.0. In particular, the authors develop a focus on the role of openness in the firms’ innovation processes. Using data from 96 Italian firms active in Industry 4.0, they examine the relationship between the openness of firms’ innovation processes and their innovation performance. The results indicate that the breadth of openness in the innovation processes is curvilinearly related to innovation performance in I4.0, forming an inverted U-shape. This suggests that while a certain level of openness can enhance innovation, too much openness may hinder it.
The results of this article offer crucial managerial insights for companies in Industry 4.0, emphasizing the importance of open innovation (OI) and external collaborations for driving innovation.
Last but not least, Cooke (2023) examines the growth of the shadow economy, which has historical roots dating back to the Middle Ages, driven by globalization, neoliberal trading regulations, and the rise of tax havens. In this study, he focuses on some key factors such as multinationals evading national regulations, profit shifting via tax havens and the expansion of international trade leading to increased money laundering. Using an abductive “pattern recognition” approach, the study interprets indirect observations. Reviewing the EU’s list of non-cooperating jurisdictions, it was found that the impact was modest due to reluctance in sanctioning major economies. Data indicated no significant harm to listed jurisdictions. Evidence of widespread fraudulent and corrupt activities was found. The study demonstrates the relevance of assemblage-third space theory and uncovers the deceptive practices of tax haven clusters.
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Further reading
Boix-Domenech, R., Capone, F. and Galletto, V. (2022), “Searching for ‘rare diamonds’? Industrial districts and innovation in Spain and Italy”, Competitiveness Review: An International Business Journal, Vol. 32 No. 5, pp. 728-742.
De Propris, L. and Bailey, D. (2020), Industry 4.0 and Regional Transformations, Taylor and Francis, Oxfordshire, p. 276.
Acknowledgements
Funding: Dr Hervas-Oliver acknowledges funding from PID2021-128878NB-100 MCIN/AEI/10.13039/501100011033.