1 Introduction

Businesses and industries are actively reconfiguring global value chains (GVCs), as they develop new mechanisms to create and capture value on a global scale (Gereffi et al. 2005; Dachs et al. 2019; Awan et al. 2022; Bilbao-Ubillos et al. 2024; Szalavetz 2024). An ecosystem perspective on GVCs (Schmidt et al. 2021) encompasses not only the network of activities leading to the production of goods and services, focusing on the location of these activities and the interactions between participating actors (Gereffi et al. 2005), but it also includes non-traditional actors, such as research institutions and governments, thereby broadening the perspective on GVCs (Teece 2011).

Technological adaptation is bound to have very powerful territorial effects because of the territorial constraints linked to the adaptation process itself. However, economic literature has often overlooked these effects when examining the impact of changes in the GVC due to the widespread implementation of Industry 4.0 technologies (I4.0T). Orzes and Sarkis (2019) acknowledge that the link between reshoring, or the reconfiguration of global supply chains, and environmental sustainability remains largely unexplored. However, as Strange and Zucchella (2017) point out, the emerging digital technologies driven by I4.0 have significant potential to disrupt the organisation and location of activities within GVCs, as well as influence who benefits from the added value in these chains.

This paper delves into the territorial challenges that emerge when digital technologies are integrated into the knowledge bases of the manufacturing industry's product fields. It examines two fundamental dimensions: the proximity constraints that stem from the information flows between entities related to innovation goals, and how they affect decisions on business operation locations and explain the clustering of production in specific areas.

From an evolutionary perspective, technical solutions can be seen as unique cognitive combinations. Accordingly, they define distinct areas of knowledge that develop over time, aiming to improve their features cumulatively, with learning processes playing a pivotal role (Breschi and Malerba 1997; Lundvall 1992; Dosi 1982; Foray 2000; Dosi and Nelson 2011; Bilbao-Ubillos et al. 2023).

Thus, the challenge of adaptation will be unique to each firm and must address the cognitive demands that arise from its transition to Industry 4.0, leveraging the available technological resources. In order to carry out this adaptation and bridge the cognitive divide, the plant will need to draw on (a) the resources that constitute the firm's technological potential; and (b) the technological resources that are likely to be outsourced (technological context).

The role of the external context will be particularly significant, given that we are analysing the adaptation issues related to a scientific body of knowledge that stands apart from the other knowledge forming the technical solutions used by the firm. External sources are essential for addressing two technology-related needs within firms: on one hand they allow firms to acquire external information that enriches their potential, and on the other, they aid in forming collaborative links with external bodies to conduct joint research projects, leading to new developments and ultimately to new products and processes.

The technological context plays a vital role, acting both as a source of cognitive resources constrained by proximity that the firm needs to integrate, and as a local setting for providing additional technology with industrial applications. This is an important consideration, given how swiftly the digital technologies sector can generate new and increasingly sophisticated applications. These conditions favour the agglomeration of productive activities and the reversion of previous offshoring decisions, a phenomenon known as reshoring.

While digital technologies are a highly effective tool for the exchange of information among interacting actors, they do not remove the proximity constraints that affect these transfer processes. Proximity constraints are connected to the cognitive attributes of the information being exchanged and are especially influenced by the significance of the tacit knowledge content involved. A significant influence is in the knowledge of scientific basis and high degree of novelty that shape the current of new digital developments.

On the other hand, as noted by Moser (2024), the concept of sustainable manufacturing is anchored in producing manufactured goods through economically viable processes that limit waste, while taking into account the three ESG pillars: environmental, social, and governance. Moser (2024) suggests that reshoring has the potential to cut decarbonisation by 25%–50%, put an end to human rights abuses in offshore production facilities, and, by manufacturing closer to the markets they serve, produce less waste.

In this paper, we aim to link the gradual adoption of Industry 4.0 technologies with the reshoring processes, which in turn positively impact the sustainability of value chains.

The work undertaken followed two complementary methodological approaches: the first was theoretical, and the second focused on applied research. During the initial phase, we conducted an exhaustive review of the literature and engaged in extensive theoretical reflection on the relevant aspects of the research object and the connections between phenomena. This effort aimed to contextualise the study, identify knowledge gaps, specifically the territorial implications of using new digital technologies in terms of potential reshoring, and to establish a solid conceptual framework for analysis. In the subsequent phase, data gathered from a detailed field study conducted in the Autonomous Community of the Basque Country (ACBC) in Spain between December 2023 and February 2024 was then used to test the main theoretical assumptions established in the initial phase. This included 24 in-depth semi-structured expert interviews with senior executives and industry authorities holding extensive knowledge of the present conditions in the automotive sector. Interviews lasted 60–100 min.

We visited 19 manufacturing firms, selected according to their size, locally sourced funding—believed to afford them decision-making autonomy—their participation in international value chains, and their technological importance. The pool of interviewees was then expanded to include 5 specialists from advanced manufacturing technology centres (Teknalia) and from cluster organisations (IMH, AFM-Clúster, ACICAE, SERNAUTO), which encompass hundreds of companies within the sector (Bilbao-Ubillos et al. 2025).

The paper is structured as follows: In Section 2, we begin by outlining the theoretical arguments that provide guidance for our study. In Section 3, we analyse the proximity restrictions arising from the technological adaptation process to industry 4.0. In Section 4, we connect the proximity restrictions to reshoring processes. Section 5 outlines new sustainability drivers emerging from changes in production and regulatory dynamics. Lastly, we draw a few conclusions regarding academic contributions and managerial learnings.

2 Theoretical Framework

Our analysis of the technological adaptation processes within the framework of industry 4.0 begins with an understanding of technical development solutions as a synthesis of scientific knowledge from various fields. This technical solution will materialise a process designed to achieve a technical objective in terms of product design. Each technical solution implies a distinct cognitive combination and, in this sense, defines a unique field of knowledge that will evolve, seeking to enhance its performance according to a cumulative evolutionary dynamic in which learning processes play a fundamental role (Manoury 1968; Lundvall 1992; Edquist 1997; Dosi 1982; Dosi and Nelson 2011; Foray 2000). The nature of the knowledge base of an industry will shape the patterns of the trajectory of its technical progress (Breschi and Malerba 1997; Breschi et al. 2000; Dosi et al. 2006; Marsili and Verspagen 2002; Bilbao-Ubillos and Camino-Beldarrain 2021).

This perspective requires giving preferential attention to the analysis of the cognitive problems that arise in each product field, factoring in the essential organisational knowledge for coordinating the operations of value chains. A vision that references, on one side, the literature on Innovation Systems, underscoring the crucial role of the external context in explaining a firm's innovation dynamics, and on the other side, the Cognitive Economy literature, which examines how cognitive distance influences the exchange and assimilation of technological information. In this context, geographical distance and innovation are related (Nooteboom 1999, Nooteboom 2010; Moodysson et al. 2008) and the determining factors of proximity restrictions that affect adaptation processes are identified (Davids and Frenken 2018; Carrincazeaux 2009; Asheim et al. 2007; Mattes 2012; Torre 2008; Torre and Rallet 2005; Storper 1992, 1998). One particularly relevant aspect in the domain of digital technologies is its immense re-combinatorial potential, which greatly values proximity for generating new evolutionary possibilities (Foray et al. 2011; Foray and Goenaga 2013).

The body of literature that analyses industrial dynamics in the context of the digital economy will help us identify the specific cognitive requirements arising from the main evolutionary trends put forward by Industry 4.0, primarily servitisation (Ardolino et al. 2018 ; Vendrell-Herrero et al. 2021; Zheng et al. 2019; Niu and Qin 2021; Paiola et al. 2022). Finally, we will leverage the outcomes of our analysis to assess the potential effects of emerging production trends through the lens of the circular economy (Bressanelli et al. 2018, 2020; David et al. 2021; Kolagar et al. 2022).

3 Territorial Proximity Constraints Associated With the Technological Adaptation Process

The dynamic characteristics of technology presented by Industry 4.0 stem from the advanced technical complexity of the solutions that digital technology developments use themselves, and the pace at which the composition of these solutions changes. This increases the intensity of proximity constraints in their technological relationships with external organisations (Carrincazeaux et al. 2001).

Additionally, the prolific nature of digital technologies in producing innovative solutions increases the frequency of adaptation processes, thereby perpetuating the continual reliance on external contexts for such endeavours. Within a cognitive framework where tacit knowledge is pivotal, there will be a need to reinforce the intensity of proximity constraints. This is a change of pace that makes the cognitive and resource needs very difficult to predict along the evolutionary path of a product field.

As they look for technical contexts that can offer a vast supply of resources, we can expect the technological adaptation processes in Industry 4.0 to have very powerful territorial effects and, consequently, to reinforce the processes of production clustering.

In a descriptive analysis of the proximity restrictions that affect the different cognitive flows involved in the technological adaptation processes, we can say the following:

3.1 The Provision of Human Resources

For manufacturing firms engaged in Industry 4.0 activities, the most significant requirement is the change in the cognitive profiles of human resources. There will be a need for new qualifications and specific knowledge, along with a new typology of creative skills and entrepreneurship, greater versatility in human resources, and a more prominent emphasis on digital expertise (Smith and Anderson 2014; Brettel et al. 2014). Figure 1 shows the main skills gap identified by interviewed experts that need to be addressed in order to meet the challenges posed by Industry 4.0. During our fieldwork, participants were asked to assess several skills, using a scale from 1 to 6, with 1 being not at all sensitive and 6 being very sensitive.

As shown in Figure 1, the most valued skill shortages are technology design and programming, leadership and social influence, complex problem-solving, and analytical thinking and innovation (4 out of 6).

We need to highlight not only the changes in the qualifications required for work in Industry 4.0, but also the limitations posed by its mobility. Human resources mobility is difficult to delimit as it is also conditioned by factors that are not purely technological. First, the firm's ties with its local community will be relevant, in addition to the social, cultural, and other contexts in which it is located, which function as factors of attraction (Castells and Hall 1994). Second, the firm's appeal as a workplace destination in which to build a professional career will be important. Large manufacturing firms and those with a higher technological potential are more likely to be attractive to skilled workers than smaller firms with lower technological potential (Bilbao-Ubillos et al. 2021a). When we consider the broad mobility limitations affecting human resources and add the fact that most workers will need new qualifications, it becomes clear that the fundamental factor will be the ability of the external context, near the plant, to provide human resources with the required profiles (Strange and Zucchella 2017).

The fundamental importance of the qualification of human resources highlights the need to push forward new mechanisms aimed at facilitating the mobility of qualified personnel from context providers (universities and technological centres) to firms, through different forms of temporary stays or part-time contracts which would facilitate an efficient technological adaptation at a reasonable cost for lower potential firms. These adaptable frameworks offer a chance to leverage the available qualifications of human resources within the context, which is vital given their role as a key localisation factor (Bolter and Robey 2020; Raza et al. 2021).

The literature provides many examples of the role of human resource provision as a location factor. Freeman Schwabe Machinery cited faster market access and a greater pool of skilled workers as reasons for relocating operations from Taiwan to Cincinnati, Ohio. Similarly, according to the company, Ford's decision to relocate its hybrid transmission plants from India and its battery pack facilities from Mexico is driven by an aim to improve innovation processes and address difficulties in finding qualified personnel (Moser 2013). ‘Fine Scandinavia’, part of the Konga Mekaniska Verkstad AB group, despite possessing a strong internal technological potential that allowed for the incorporation of digital applications in the design of its components, in 2018, relocated certain product lines it was manufacturing at its Vietnam facility to its Anderstorp headquarters. This decision was based mainly on the availability of highly skilled labour in the Anderstorp region (Mellergårdh 2018). Ford Motor Company aimed to gain greater control over design and production engineering, along with access to skilled workers, by moving the assembly of its F-Series pickup trucks from Mexico to Ohio (Bals et al. 2016). Research focusing on Industry 4.0 through case studies confirms the link between digital servitisation and the availability of qualified and skilled human resources in specific locations (Lampon and Gonzalez-Benito 2020; Moradlou et al. 2022).

3.2 The Supply of Technological Services and Consultancy in the Field of Digital Technologies

We refer to a highly diverse fabric composed of various firms and institutions offering a wide range of technological advisory and consultancy services, which have a beneficial impact on their customers' technical and organisational transformations (Wood 2002). We gathered all organisations that would have an explicit role in coordinating or connecting agents, or independently acting as knowledge disseminators, including technology centres, engineering companies, technical assistance companies, and digital services companies. Figure 2 illustrates the extensive use of external digital resources in the context of the interviewed company's location.

In recent years, 92% of the companies participating in the study had drawn on technology centres, HR trained in ICT, and engineering firms in their vicinity as resources. For universities, the figure stood at 83%.

Therefore, the activities undertaken by these entities, equipped with technological and targeted capabilities to fulfil customer needs, are essential, particularly for companies with lower potential and a limited ability to connect with leading technological institutions.

Konttinen et al. (2010) insist on the need for policy interventions in order to encourage the establishment of such institutions when the relationships between firms and universities or technological centres are ineffective.

There is a need for organisations capable of performing these knowledge dissemination tasks, which can come in many different institutional forms, from private advisory firms to formal entities such as technological centres that can achieve these goals.

The presence of clusters, which unite firms engaged in a specific product field, serves as a tool with enormous facilitating potential for technological adaptation processes. It defines a consortium of firms that create an optimal setting for implementing programmes related to technology transfer, business collaboration, internationalisation, and similar endeavours aimed at enhancing competitiveness. The cluster organisation creates a forum particularly well-suited for facilitating the supply of technological services and fostering inter-firm relationships of interest to the partners, which is especially relevant for firms with a lower technological potential (SMEs) (Hernández et al. 2013). It also constitutes a uniquely qualified platform capable of building relationships with institutions and individuals beyond the cluster (Carrincazeaux et al. 2008).

The literature examining reshoring processes is very useful in justifying the need for technological infrastructures that provide technological services in understanding decisions about localisation (relocation).

Pearce II (2014) analyses backshoring among firms in the USA. NCR Corporation is to move production from China to Georgia (USA) to seize the advantages offered by the latter location in terms of technology infrastructures. Dachs et al. (2019) study a sample of 1705 firms from Austria, Germany, and Switzerland. They confirm that the rise of Industry 4.0 has accelerated the trend of backshoring. An important reason for this shift is the infrastructure issues that arise when companies prioritise increases in productivity and quality.

4 Proximity Restrictions and Reshoring

Integrating digital knowledge involves the dual challenge of engaging with new technologies and fields of knowledge unfamiliar to the firm, thus highlighting the significance of its tacit content and, ultimately, the intensity of the proximity constraints that affect its transfer.

The role of external entities will be especially relevant in facilitating the company's adaptation processes. The constant flow of new digital developments determines the fundamental role of the external context as a source of technology. This reliance imposes strong proximity constraints arising from both the prominence of the tacit content of the knowledge being shared and the enormous difficulty of predicting in advance the ‘technological needs’ that companies may have. These conditions explain why companies choose to locate their activities in contexts of high technological potential (Bilbao-Ubillos et al. 2024).

The need to incorporate new, specific resources required by new products and processes, achievable via different mechanisms such as technology services, next-generation materials, cooperation with customers and suppliers, and workers with specific skill sets, all subjected to strong proximity constraints, strengthens the rationale for locating in contexts of excellence that cater to the demands of new product fields. This explains why those production activities are clustered.

The new context of Industry 4.0 intensifies customer-supplier relationships, increasing proximity restrictions between linked companies and reinforcing the processes of geographic agglomeration of productive activities in the most technologically developed contexts, with the densest productive fabric and the best technology centres and universities.

Functioning within the Industry 4.0 framework establishes a production scenario that favours concentration processes in areas with high technological potential, thereby boosting the process of reshoring plants that were initially set up in areas with less technological potential, in pursuit of reduced operational costs, mainly wages.

Figure 3 illustrates the impact of Industry 4.0 technologies on company location, according to the expert assessments gathered during the fieldwork. Again, participants were asked to assess three possible decisions, using a scale from 1 to 6, with 1 being not at all sensitive and 6 being very sensitive.

The options of repatriation (reshoring) and relocating a foreign-based plant to another country were the most highly valued, both scoring 5 out of 6, while offshoring received a score of 4.

Moreover, the literature provides conclusive evidence of the rising importance of reshoring: in 2020, the US saw an increase of 1484 companies relocating, setting a record with 109 000 jobs created and raising the total number of jobs announced since 2010 to over 1 million. Europe recorded 253 reshoring cases during the period 2015–2018, with an estimated total employment impact of 12 840 jobs.

Empirical evidence from the last decade highlights an upward trend in these processes (Raza et al. 2021; Dikler 2021; Ma 2020), with larger firms and medium- and high-tech industries exhibiting the highest propensity for reshoring (Raza et al. 2021). As a result, manufacturing that requires adequate infrastructure, skilled workers, intellectual property protection, and integrated supply chains finds domestic production to be the least costly and most efficient option (Bolter and Robey 2020).

5 Overall Sustainability Implications of Changing GVCs

Digital technologies (DTs) are viewed as drivers that can promote a more sustainable supply chain management (Birkel and Müller 2021; Strange and Zucchella 2017; Sanguineti et al. 2023). Stoddart (2011) defined sustainability as the efficient and equitable distribution of resources both within and across generations, while ensuring socio-economic activities are conducted within the confines of a finite ecosystem. This concept includes economic, social, and environmental aspects.

Over several decades, the reduction of trade barriers has enabled firms to offshore to predominantly developing countries offering substantial savings on labour and raw material costs, as well as access to valuable resources, technology, skills, and knowledge (Ashby 2016). To gain strategic advantages and optimise processes, the various stages of value creation were located in different countries. The GVC framework provides a thorough understanding of how value was captured, sustained, and leveraged (Gereffi et al. 2005). However, the new constraints for GVCs stemming from I4.0T and the increased environmental awareness among the population and policymakers are driving sustainability performance.

One aspect is the mentioned tendency to repatriation or regionalisation of productive activities brings production nearer the markets of developed countries. This significantly reduces the ecological footprint and promotes greater sustainability within value chains.

There is further evidence indicating that the introduction and implementation of I4.0 technologies are important in supporting the adoption of circular economy (CE) practices (Bilbao-Ubillos et al. 2025). Numerous studies highlight the significance of the Internet of Things (IoT), Big Data and cyber-physical systems (CPS) as essential facilitators for implementing circular strategies, such as recycling and reuse in smart supply chains (Ćwiklicki and Wojnarowska 2020; Montemayor and Chanda 2023). Likewise, Big Data can be used to forecast product returns, aiding the planning of remanufacturing operations and the effective scheduling of logistics (Bag and Pretorius 2022). Alongside IoT and Big Data, other studies include additive manufacturing and artificial intelligence as the primary technologies promoting the implementation of the CE (Lei et al. 2023; Rosa et al. 2020).

In their work, Pathak and Verma (2025) identify the adoption of government regulations as the sixth primary driver facilitating the successful integration of sustainability into manufacturing. Coercive pressures stemming from stricter environmental policy standards play a critical role in the CE (Mont et al. 2017). Political elements include guidelines and regulations, such as taxes, support funds and subsidy policies (Hina et al. 2022). The availability of recycled materials is also a driver for reshoring (Ashby 2016).

Finally, as highlighted by Samuel (2025), carbon pricing mechanisms, including carbon taxes and emissions trading systems, have emerged as critical tools for mitigating environmental impact while encouraging industries to adopt cleaner technologies.

All these dimensions are instrumental in shaping the techno-geographic decisions of firms, generating significant impacts on sustainability.

6 Conclusions, Academic Contributions, Managerial Learnings, Limitations and Future Research

Conflicts of Interest

The authors declare no conflicts of interest.