Relational network of innovation ecosystems generated by digital innovation hubs: a conceptual framework for the interaction processes of DIHs from the perspective of collaboration within and between their relationship levels

Journal of Intelligent Manufacturing (2025) 36:1505–1545 https://doi.org/10.1007/s10845-024-02322-5 Relational network of innovation ecosystems generated by digital innovation hubs: a conceptual framework for the interaction processes of DIHs from the perspective of collaboration within and between their relationship levels Julio C. Serrano-Ruiz1·José Ferreira2·Ricardo Jardim-Goncalves2·Ángel Ortiz1 Received: 23 December 2022 / Accepted: 4 January 2024 / Published online: 2 March 2024 © The Author(s) 2024 Abstract Collaboration plays a key role in the success attained to date by networks of innovation ecosystems generated around entities knownasDigitalInnovationHubs(DIHs),recentlycreatedfollowingEuropeanCommissioninitiativestoboostthedigitisation of the European economic fabric. This article proposes a conceptual framework that brings together, defines, structures and relates the concepts involved in the collaborative interaction processes within and between these innovation ecosystems to allow comprehensive conceptualisation. The developed framework also provides an approach that helps to tangibilise collaboration as a management process. Here the goal is to ultimately move towards not only qualitative, but also quantitative modellingto bridgethe researchgapin thestate ofthe artin thisrespect. Thedata-drivenbusiness-ecosystem-skills-technology (D-BEST) model, devised to configure DIHs service portfolios in a collaborative context, provides the reference basis for the interorganisational asset transfer methodology (IOATM). This is the keystone that structures the framework and constitutes its main contribution. Through the IOATM, this conceptual framework points out collaboration quantification, and serves as a lever for its modelling to deal with collaboration accounting by: turning it into a more controllable management element; guiding practitioners’ efforts to improve collaborative processes efficiency with an approach that pursues objectivity and maximises synergies. Keywords Innovation ecosystem ·Digital innovation hubs ·Collaboration ·Interorganisational asset transfer methodology Abbreviations CIGIP Centro de Investigación en Gestión e Ingeniería de la Producción BJulio C. Serrano-Ruiz [email protected].es José Ferreira [email protected] Ricardo Jardim-Goncalves [email protected] Ángel Ortiz [email protected].es 1Research Centre on Production Management and Engineering, CIGIP, Universitat Politècnica de València (UPV), Alcoy, Spain 2Centre of Technology and Systems, CTS, UNINOVA, Lisbon, Portugal CPES Cyber–physical energy system CPS Cyber–physical system CKTI Competences, knowledge, technology and infrastructure CTTE Cross-domain technology transfer experiment D-BEST Data-driven business–ecosystem–skills–technology reference model DIHs Digital Innovation Hub DIH4CPS Digital Innovation Hubs for Cyber–Physical Systems EC European Commission EDIHs European Digital Innovation Hubs Ei2Network European Network for Interoperability and Innovation ETB Ecosystem–technology–business model ETBSD Ecosystem–technology–business-skills-data model 123 1506 Journal of Intelligent Manufacturing (2025) 36:1505–1545 EU European Union FTTE Focused technology transfer experiment GDPR General Data Protection Regulation iAE6 Application experiment number 6 of the DIHCPS project InnDIHs Digital Innovation Hubs for the economic promotion of the Valencian Community IOATM Interorganisationalassettransfermethodology ITI Instituto Tecnológico de Informática I-VLab European Virtual Laboratory for Enterprise Interoperability i4OPT Industrial Production and Logistics Optimisation in Industry 4.0 I4Q Industrial Data Services for Quality Control in Smart Manufacturing KTE Knowledge transfer experiments MaaS Marketplace-as-a-Service MES Manufacturing execution system MOM Manufacturing operations management M2M Machine-to-machine communication OPC UA Open platform communications united architecture PAE Pathfinder Application Experiment PoC Proof of concept R&D Research and Development SME Smalland medium-sized enterprises UPV Universitat Politècnica de València ZDMP Zero-Defect Manufacturing Platform Introduction European smalland medium-sized enterprises (SMEs) generallyfaceavolatile,complexandfiercelycompetitiveglobal scenario. They have a significant disadvantage in relation to large companies and global corporations, which usually better access financing, resources and skills acquisition; all this enables the latter to compete with higher performance levels and they, therefore, have more sustainability expectations (Bakhtiari et al., 2020). Indeed the obstacles that SMEs usually encounter in acquiring, implementing and exploiting digital technologies and skills respond to this pattern, which implies slowing down their journey towards digital maturity. SMEs are vital to the European Union’s (EU) economy, and account for 99% of all EU companies and two thirds of all private sector jobs. However, only 20% of European SMEs are highly digitised, unlike large corporations whose percentage reaches 50% (Gouardères, 2021). The European Commission (EC) is aware of this. This is why it has been promoting programmes and initiatives for years to foster innovation and to facilitate SMEs’ digital transformation, especiallythoseinthemostdigitallyimmaturesectors.Inline with this, a notable support line is to finance projects to create and strengthen a series of regional innovation ecosystems interconnected in a pan-European network. The main drivers of such ecosystems are the so-called Digital Innovation Hubs (DIHs), regional cooperation organisations made up of many diversepartners whose mission is twofold: regionally, to help entrepreneurs in the region to overcome innovation obstacles and digital transformation difficulties by providing them with easy access to knowledge and digital solutions (Miörner et al., 2019), essential tools for success in today’s global market; at the European level, to promote the implementation of an interdisciplinary and collaborative network of innovation providers, which will help to keep Europe in a position of technological leadership in today’s complex geopolitical scenario. DIHs were initially conceived by the EC in 2016 as part of the first industry-related initiative of the Digital Single Market package and as one of the most important pillars of the Digitise European Industry effort (Rissola & Sörvik, 2018). Since 2021, this effort is being complemented by the figure of European DIHs (EDIHs) within the framework of the Digital Europe Programme (HaDEA, 2022). This programmeinitiallyplanstoincreasethecapacitiesofthecentres selected by each member country to cover activities with a clear European added value based on the collaborative networking of hubs and promoting knowledge transfer. EDIHs will also support companies and public sector organisations in using digital technology to improve the sustainability of their processes and products, particularly regarding energy use and reducing carbon emissions (HaDEA, 2022). DIHs and EDIHs generally provide access to know-how and experimentation, and to the possibility of “try before you invest” by helping companies to improve business/production processes, products or services that employ digital technologies. They also provide innovation services, such as advice on financing, training and skills development, which are necessary for the success of digital transformation (EC, 2022). This set of innovation-driving entities is generating a relational network within the geographical framework of EU territory in which, from a hierarchical perspective, four relationship levels can be identified (Fig. 1): (i) DIH level: that existing within each DIH between the organisations making it up. A DIH is a one-stop-shop type structure that helps organisationsto be more competitiveby improvingprocesses and innovating products and services via digital technologies. It may include research centres, technology centres, universities, private technology service providers, associations, Chambers of Commerce, incubator or accelerator organisations, regional development agencies, and even governmental organisations (EC, 2022) within a regional scope. It is a space where it offers support services to organisations, usually through a partner platform. The support services 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1507 Fig. 1 Relationship levels within the pan-European space of DIHs action that a DIH can offer include awareness raising of digitisationtechnologies,innovationexploration,developingvisions and strategies for companies, training, access to funds and investments, collaborative research, advocacy and networking events, among others (Georgescu et al., 2021). DIHs have four main functions that characterise them, namely networking, skills and training, pre-investment testing and access to finance (Asplund et al., 2021); (ii) DIH ecosystem: that generated in each individual regional innovation ecosystem promoted around a specific DIH between it as an innovation, knowledge, and technology provider, plus the organisations receiving any services included in the DIH portfolio, typically SMEs that need support towards digital transformation, albeit not exclusively; (iii) EDIH level: that which integrates a set of networked DIHs into a group and turns it into a collaborative network with presence beyond a specific EU country. Such integration occurs by either belonging to a joint project funded for this purpose or the existence of some othertypeof transactionalagreement betweenDIHs; (iv)The highest level: the pan-European EDIH network: the level of potential interaction in which the different existing DIH groups in Europe, supposedly acting in competition, can create cooperation channels by generating schemes of so-called coopetition (Planko et al., 2019) or similar; e.g. in terms of association or federation to defend their common interests vis-à-vis administrations and society at large, which is still an underexplored and underexploited space. The interaction flows within this relational network depend on several factors. They are mainly determined by 123 1508 Journal of Intelligent Manufacturing (2025) 36:1505–1545 the involved actors and their typology, their interests, strategies, objectives, resources, capacities, initiatives, willingness to work in collaboration, and the transactional formal or informal agreements, that exist between actors. They also depend on factors such as: the needs, in terms of digital knowledge and skills, of the regional business fabric in which they operate; the local, regional, national and European legislation applicable in each case; or the degree and sense of governments and public administrations’ intervention in the cooperation processes at each interaction level. The literature does not contain numerous contributions on the study of DIHs. There are also some frameworks and models that address some types of collaborative interaction between elements of some of the mentioned levels of interaction, such as collaboration itself, or derivatives such as cooperation, coopetition, funding, or institutional support, among others. Yet, as far as we know, there is no general conceptual framework that addresses the whole above-described relational network and supports all the possible basic potential interaction flows between all the elements at their relationship levels. Based on the above, the main objective of this article is to present a conceptual framework that, by building on the above overview of the relational network of European innovation ecosystems driven by EC Digitise European Industry andDigital Europe Programme initiatives, supports theexistinginteractionprocesseshorizontallyalongtheirrelationship levels, andverticallybetweenthem, fromdual qualitativeand quantitative perspectives. The research questions posed are the following: •RQ1 What characteristics define the interactions that take place between the entities making up the DIHs relational networkwhen collaborativeprocessesmaterialise between them? •RQ2 What are the key dimensions of the interaction processes between the entities that constitute the DIH relational network; what are their conceptual implications in terms of a quantitative assessment of collaboration? Therestofthearticleisorganisedasfollows.Sect."Literature review" defines the main concepts, delimits the research scope, identifies the correlations between concepts shown by the literature, explains the search method to review the literature, presents the state of the art from the previously explained methodology and selects the most relevant contributions. Sect. "Discussion" firstly presents a conceptual framework by identifying the key dimensions of the interaction processes that take place within a network from dual qualitative and quantitative perspectives. Secondly, it analyses implications in interoperability and sustainability terms. Section 4 presents a use case to provide a practical example of the application of the framework. Sect. "Conclusion"discusses the proposed conceptual framework and analyses the managerial and academic implications that derive from it. Finally, Sect. 6 offers conclusions, including its contribution to theory and practice, along with limitations and possible future research lines. Literature review Main concepts This research focuses mainly on the interrelationships between two types of concepts that pivot around the main one, the DIH concept: (i) those used to designate a type of organisation among those existing in the relational network of European innovation ecosystems; (ii) those used to designateatypeofinteractionthatispotentiallypossiblewithinthe organisations of the relational network of European innovation ecosystems. To gain a better understanding, this section more profoundly introduces the main concepts employed in both categories. Table 1introduces the different types of organisations that can be found throughout the relational network of European innovation ecosystems. All these concepts are closely interrelated and mutually supportive. Thus a DIH and its users form a specific innovation ecosystem herein called the DIH ecosystem. A group of DIHs that work together form a clear example of a collaborative network. The sum of several DIH ecosystems operating as a collaborative network forms an EDIH. The network formed by the set of EDIHs operating in EU territory forms the pan-European EDIH network, an important purpose of the Digital Europe Programme. In any case, these concepts are, on the whole, interconnected around the central “DIH” Concept. From its own definition, it is clear that both the different collaborative network or innovation ecosystem types that are generated based on DIHs, and the DIH itself, make up complex structures populated by entities of very diverse typologies. This diversity gives rise to a wide range of possibilities for mutual interaction purposes. Hubs, ecosystems and networks, public and private entities, service providers and users, regional, national or pan-European action spaces, and so on, generate a multitude of relationship scenarios with sometimes overlapping, sometimes completely disparate purposes and with a considerable gradient of possibilities between these two extremes. In short, the cosmos originating around the DIH concept is a highly heterogeneous and complex environment where interaction possibilities go beyond simple collaboration. To date, this concept has been the central axis of a large number of the studies, frameworks 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1509 Table 1 Organisation types in the relational network of European innovation ecosystems Organisation type Definition Collaborative network An organisation of a variety of entities (e.g., organisations and people) that are largely autonomous, geographically distributed and heterogeneous in terms of their operating environment, culture, social capital and goals; nevertheless, these entities collaborate to better achieve common or compatible goals (Camarinha-Matos & Afsarmanesh, 2005). Nowadays, collaborative networks are being applied to a wide variety of domains from academic research to manufacturing and other industrial applications. These implementations are supported by a variety of collaboration forms, which range from “supply chains” to emerging dynamic structures in the industry, science and services (Camarinha-Matos et al, 2019) DIH A multi-partner collaborative organisation made up of regional entities, itself part of a pan-European network of similar organisations, which, in possession of infrastructure, technology, knowledge, competences, funds, or access to them, and ready to use them to serve regional business and public sector organisations, together with them form an innovation ecosystem in which the hub, as a central role, provides services to support the itinerary of these organisations towards a full and effective digitalisation that makes them more sustainable and competitive DIH ecosystem An ecosystem of organisations that is generated from a digital innovation hub as a central actor, where it acts as a source of technologies, knowledge or skills, which it disseminates to the other members that make up the ecosystem to pilot, test and experiment with digital innovations to support their digital transformation processes DIH network A collaborative network made up of DIHs that act in coordination to contribute to the development of the regions and countries of the European space from a more sustainable position. This is supported mainly by a larger organisational dimension and by a broader portfolio of knowledge, skills, technologies and solutions than that of its individual members DIH user An organisation that, through a transactional agreement, uses the service portfolio of a DIH to receive support for its digital transformation process and thus becomes part of its ecosystem European DIH (EDIHs) A specific type of DIH network born from Digital Europe Programme calls, with both local and European functions, which has increased capacities to encompass activities with a clear European added value that centre mainly on networking hubs and promoting transfer of expertise. EDIHs also have the stated mission of supporting companies and public sector organisations in using digital technology to improve the sustainability of their processes and products, particularly regarding energy use and reducing carbon emissions. Beyond that and within EDIHs, their DIHs also act as a one-stop-shop to help companies to be more competitive as regards their business/production processes, products or services using digital technologies by providing access to technical expertise and experimentation. Thus firms can “test before invest”, and provide innovation services (i.e. financing advice, training and skills development) needed for successful digital transformation (Directorate-General for Communications Networks, Content and Technology of the EC, 2021) European DIH Network A set of EDIH networks operating in the EU territory Innovation ecosystem An evolving set of actors, activities and artifacts, and institutions and relations, including complementary and substitute relations, that are important for the innovative performance of an actor or a population of actors (Granstrand & Holgersson, 2020). An innovation ecosystem refers to a loosely interconnected network of companies and other entities that co-evolve capabilities around a shared set of technologies, knowledge or skills, and work cooperatively and competitively to develop new products and services (Nambisan & Baron, 2013) 123 1510 Journal of Intelligent Manufacturing (2025) 36:1505–1545 and models in the literature about DIHs and the arbitration of the relationships between the entities making them up. Some examples are the ecosystem–technology–business model (ETB; Butter et al, 2020) and its two main evolutions, the ecosystem–technology–business–skills–data model (ETBSD; Sassanelli et al., 2020) and the data-driven business–ecosystem–skills–technology reference model (DBEST; Sassanelli & Terzi, 2022; Sassanelli et al., 2021b). D-BEST aims to act as a reference for the DIH networks specialised in the cyber–physical energy systems (CPES) domain to configure their service portfolios both flexibly and interoperably by integrating their assets (services, competences, skills, technologies) into digital platforms to achieve flexibility and interoperability. Both are critical aspects to achieve DIH networks’ sustainability. The D-BEST model is a milestone in the conceptualisation and modelling of collaboration at DIH ecosystem and EDIH levels by triggering the identification and materialisation of service-based crosscollaborative processes between DIH networks on the one hand, and between DIHs themselves and their users on the other. However, it is necessary to further characterise the collaboration concept from a holistic perspective by: (i) firstly defining the concept itself; (ii) secondly placing it in contrast to other types of interaction whose essence, objectives, method and results significantly differ; (iii) finally, by identifying the possible interaction types of among the entities making up DIH structures, their networks and ecosystems that can, to some extent, albeit partially, represent collaboration. Only with this prior characterisation is it possible to bring together, structure and interrelate the components of a comprehensive conceptual framework that addresses the collaboration phenomenon in DIH relational networks. Regarding the definition of full collaboration used herein as a reference, that chosen is of Wankmüller and Reiner (2020): “Process of strategically working together on a specific business activity where structures are aligned, communication channels are standardised, risks are shared, and resourcesarepooledinordertomakethemavailableforevery partner”. In short, and according to the perspective provided by this definition, although partners retain their entity, their level of commitment to achieve shared goals is so important that they involve their structures, communication channels and resources, as well as taking risks in the long term. In contrast to the collaboration notion as a primarily synergistic interaction, in the different environments shaped by DIHs, there may be other formulas of interaction that are significantly removed from this synergy. They range from the simple contraposition of buyer and seller interests in a commercial relationship to the antagonism shown by two competitors.Table2sets outthe main potential typesof interaction that fit this pattern. With regard to the possibilities for the entities that make up one of the above-mentioned DIH environments to interact according to a formula that denotes a certain degree of collaboration, there are several alternatives. Table 3presents the most relevant possibilities. Now that the levels of the relationships in the network of European innovation ecosystems are known, the types of organisations that exist in the network are outlined, and the types of interaction that can potentially take place between the entities making up these organisations are presented, the construction of the conceptual framework herein proposed requires an additional concept, a final piece, for the complete mapping of the relational network: services resulting from collaboration. The entire DIH environment sketched so far holds a prominent circumstance: some entities, those possessing competences, knowledge, technology, infrastructure or funds, or have access to them, offer a service to others, the companies making up the regional business and public sector organisations, to achieve their full and effective digitalisation. Although collaboration is also possible for internal organisational reasons as in any other environment, it is when exercising its main function, providing support services in digital transformation processes, that a deeper understanding of collaboration frameworks and their characteristics becomes valuable for this research. In this sense, it should be stated that the classification of services in an DIH environment is a task already faced by academia, with the D-BEST reference model by Sassanelli and Terzi (2022) being the most evolved and updated exponent of the state of the art. The D-BEST reference model is structured on three levels: macroclasses, types and classes. All the five macroclasses included in the model (ecosystem, technology, business, skills and data) is divided into types of service, and these, in turn into classes of services (Sassanelli & Terzi, 2022) (Table 4). With the above classification, all the main concepts involved in this research are introduced, and the research scope is also configured (Fig. 2). Search query and selected results AsearchintheScopusdatabasewasperformedinaccordance with the defined research scope to look for the intersection of the above-indicated semantic fields. The search was done with the title, abstract or keywords of articles, reviews, conference papers and conference reviews published in English in compatible subject areas. For this purpose, we used the search chain TITLE-ABS-KEY [(“digital innovation hub”) AND(collaboration OR“commercial relationship”OR competition OR cooperation OR coopetition OR coordination OR financing OR funding OR “institutional support” OR investment OR “knowledge transfer” OR partnership OR sponsorship OR “technology transfer”)] AND (LIMIT-TO 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1511 Table 2 Types of interaction not aligned with the collaboration concept Interaction type Definition Commercial relationship Any legal relationship of a commercial nature, whether contractual or not, and includes, but is not limited to, a relationship arising from the following transactions: any trade transaction for the supply or exchange of goods or services; distribution agreement; commercial representation, or agency; factoring; leasing; construction of works; consulting; engineering; licensing; investment; financing; banking; insurance; exploitation agreement or concession; joint venture and other forms (Commercial relationship definition, 2013) Competition Rivalry between individuals, groups, organisations or nations that arises whenever two parties or more strive for something that they all cannot obtain (Stigler, 1988) Financing Money is loaned by an individual or organization for a particular purpose Investment The act of putting money, effort, time, etc. into something to make a profit or get an advantage, or the money, effort, time, etc. used to do this (Cambridge Academic Content Dictionary, 2020) Table 3 Types of interaction aligned with the collaboration concept Interaction type Definition Cooperation Process of working on independent business activities towards a common (agreed-on) goal in a long-term view, where communication is relatively informal, resources are separated and risks are shared (Wankmüller & Reiner, 2020) Coopetition Hybrid behaviour exhibited by two or more competitors that involves cooperative and competitive elements but, instead of fighting one another in fierce competition, consists of organising themselves into, for example, groups or associations, to meet common objectives that would be difficult to achieve individually vis-à-vis other organisations. Some examples are other competitors or groups of competitors, administrations, banks, consumer associations, among others. It involves gaining access to additional know-how, skills and resources. This behaviour allows risk sharing and the creation of secure contacts, while protecting one’s own assets (Bouncken & Kraus, 2013) Coordination Process of aligning, organising and managing actors’ operational business activities where private information, risks and resources are shared (Wankmüller & Reiner, 2020) Funding Money is given by an individual or organisation for a particular purpose Institutional support Support offered by government authorities and institutions, or those directly supported by a government, that comes in the form of policies, plans, laws, regulations, financial or non-financial aid to promote a particular individual or organisation’s interests (for the purposes of this research, support of a financial nature, considered separately, is excluded) Knowledge transfer The process of transferring experience, skills, and tangible and intellectual property (University of Cambridge, 2009) from an individual or an organisation to another one Networking Networking is a form of goal-directed behaviour, both inside and outside an organisation, which focuses on creating, cultivating and utilising interpersonal relationships (Gibson et al., 2014) Partnership A partnership is an arrangement in which parties, known as partners, agree to cooperate to promote their mutual interests. The members of a partnership, individuals or organisations, join together to increase the likelihood of each party achieving its mission and broadening its scope (Partnership, Wikipedia, 2022a,b) Sponsorship The position or function of a person who or group that vouches for support, advises or helps to fund another person or an organisation or project (Sponsorship, Wikipedia, 2022a,b) Technology transfer The process of transferring technology from an individual or organisation that owns or holds it to another one (Technology transfer, Wikipedia, 2022a,b) 123 1512 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Table 4 The D-BEST model service classification (Sassanelli & Terzi, 2022) Service macroclass Service type Service class 1. Ecosystem 1.1. Community building 1.1.1. SME and people engagement and brokerage 1.1.2. Innovation incitation, awards, and challenges 1.1.3. Technology scouting 1.2. DIH innovation development 1.2.1. Communication and trend watching 1.2.2. Visioning and strategy development 1.3. Ecosystem governance 1.3.1. Service impact assessment 1.3.2. Ecosystem management 2. Technology 2.1. Ideas management and materialisation Ideas generation, assessment, and feasibility study 2.2. Contract research 2.2.1. Strategic and specific research and development (R&D) 2.2.2. Technology concept development/proof of concept (PoC) 2.3. Provision of infrastructure 2.3.1. Access to infrastructure and technological platforms 2.4 Technical support on scale up 2.4.1. Concept validation 2.4.2. Prototyping 2.5. Verification and validation 2.5.1. Product qualification and certification 2.5.2. Product demonstration 3. Business Incubation acceleration support 3.1.1. Basic facilities 3.1.2. Specialised facilities 3.1.3. Business development 3.1.4. Guidance 3.2. Access to finance 3.2.1. Financial engineering 3.2.2. Connection to funding source services 3.2.3. Methods and tools 3.3. Business training and education 3.3.2. Secondment 3.4. Project development 3.4.1. Identification of opportunities 3.4.2. Creating consortia 3.4.3. Development of proposals 4. Skills 4.1. Process and organisational maturity 4.1.1. Maturity assessment 4.1.2. Maturity strategy development 4.2. Human capabilities maturity 4.2.1. Human skills maturity 4.2.2. Skill strategy development 4.3. Skills improvement 4.3.1. Human up-skilling and re-skilling training 4.3.2. Educational programmes 4.3.3. Scouting and brokerage 5. Data 5.1. Data acquisition and sensing 5.1.1. Data acquisition 5.1.2. Data protection 5.2. Data processing and analysis 5.2.1. Data storage 5.2.2. Data analytics 5.3. Decision-making 5.3.1. Cognitive big data architecture 5.3.2. Decision support and development 5.4. Physical-human action and interaction 5.4.1. Collaborative intelligence 5.4.2. User experience 5.4.4. Feedback loop 5.5. Data Sharing 5.5.1. General data protection regulation (GDPR 5.5.2. Data spaces 5.5.3. Data Platform 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1513 Fig. 2 Research scope representation from the triple perspective of DIH collaboration (SUBJAREA, “COMP”) OR LIMIT-TO (SUBJAREA, “DECI”) OR LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “BUSI”)) AND (EXCLUDE (DOCTYPE, “er”)) AND (LIMIT-TO (LANGUAGE, “English”)). It was not necessary to narrow down the searched time period because all the research publications about DIH are very recent, as is the concept itself. The search for the indicated terms finally yielded 17 results, all of which are limited to the period between 2018 and 2022. The selected results are identified in Table 5. Thematic analysis Using the content of the titles and abstracts of the selected literature as a source, a map of co-occurring expressions was drawnupusingtheVOSviewerv.1.6.16softwareapplication. This allows the concepts present in the literature review with more than three occurrences to be visualised, as well as their dimension and interrelationships (Fig. 3). Four thematic groups or clusters were automatically identified by VOSviewer: (i) that headed by expression DIHs, which is red-coloured in the figure, grouping others like innovation ecosystem, collaboration, investment, networking, service, service portfolio, training or cyber–physical systems (CPS); (ii) the heading for expression digital transformation, coloured green in the figure, which groups others like cooperation, innovation, digital technology, region, the EC or opportunity; (iii) the heading for expression knowledge, coloured yellow in the figure, which groups others like platform, knowledge transfer, knowledge management, SMEs or medium-sized enterprises; (iv) the heading for expression technology, which is blue-coloured in the figure and groups others, such as technology transfer, flexibility, product, production or robotics. As expected, as they constitute the thematic axis of the research, the biggest number of co-occurrences appears around expression DIHs, DIH, digital innovation hubs and digital innovations hubs. The highest density of cooccurrences can be seen in the clusters headed by DIHs and digital transformation (red and green); both are related to: several of the concepts under study, such as collaboration, cooperation, investment or networking; service and service portfolio, which are intrinsic concepts to a commercial relationship, and another of the researched concepts; the organisation which, depending on the context, can be 123 1520 Journal of Intelligent Manufacturing (2025) 36:1505–1545 reality urges us to advance in the understanding of collaboration towards a sort of conceptualisation that does not avoid quantitative characterisation. This section presents a conceptual framework whose objective is to provide a tool that contributes to identify, characterise, organise and quantify the elements, structure, parameters and variables that determine, as a whole, the interaction processes involved in the collaboration that takes place between the entities making up the relational network of European innovation ecosystems. This framework supports the interaction processes that exist in collaboration between entities: horizontally along their relationship levels, and vertically between them. It is important to note that this framework is developed using a bottom-up approach. This involves initially establishing, from the existing literature, what the core elements are that make up the structure of the construct that the conceptual framework represents, and allow collaboration to be understood as tangible and measurable. This conceptualisation approach is crucial for developing a solidly based conceptual framework. Without a robust approach that provides measurability, collaboration would remain close to abstract and intangible and would, therefore, difficult to model. The extant literature support this with the D-BEST reference model (Sassanelli & Terzi, 2022). The ultimate purpose of a DIH, as a provider within an innovation ecosystem, is to provide a service to end users. In this context, collaboration between entities has always directly or indirectly been the ultimate purpose of transferring or exchanging some kind of asset among organisations to improve or provide services to end users. From this angle, collaboration is closely related to service provision. According to this basis, an interaction to collaborate implies, as in the provision of services, the transfer or exchange of assets between collaborating service provider organisations, and before and during the provision of a service. This is where the D-BEST model comes into play because it not only identifies what assets are, but the assets that it identifies happen to be measurable in some way. According to the D-BEST model, the assets required for service provision purposes are classified according to their typology into competences, knowledge, technology, infrastructure and funds (Fig. 5). All these assets are susceptible to measurement in some way and, as the assets they are, can be translated somehow into monetary terms: e.g. competences and knowledge can be measured by the monetary value of the working time spent in exchange; technology and infrastructure by the monetary value of the amortisation time share of the capital invested in them, whose unit is the currency used in the valuation; funds simply for the total amount of money financed, plus its associated costs, regardless of them being interest, fees, stamp duty or guarantees. Fig. 5 Elementary asset decomposition of a D-BEST service As previously mentioned, the D-BEST model divides the possible services that an organisation offers into five macroclasses, and these, in turn, into 20 service types (Table 4), which are identified by two digits: the first one indicates the macroclass, while the second denotes the service type is in the indicated macroclass. Thus with the help of assets as an instrument, it is possible to map all the asset types that an organisation needs to have to cover the complete D-BEST model services catalogue by identifying each service type and its corresponding assets (Fig. 6). This asset map includes 100 categories. Based on the premise of this categorisation and the mapping of the services provided by the D-BEST model, moving towards the definition of a framework requires considering new elements, especially introducing the asset flow concept, which has not yet been contemplated by research studies to deal with interorganisational collaboration. Materialising collaboration in the collaborative interaction process between two DIH organisations or more occurs by creating a flow of tangible or intangible assets from ceding organisations to beneficiary organisations to alleviate any deficits in the latter and to enable them to provide some specific service types immediately or in the future. This reality is observable in practically all organisational ecosystems, and is the main rationale for a new framework for collaboration. To facilitate the understanding of the reasoning behind the assets flow concept, the following example is provided: let two organisations decide to undertake a mutual collaboration process to, on the one hand, improve the aptitude of OrganisationAfortheprovisionofbusinesstrainingandeducation services (service type 3.3) in terms of infrastructure, and skills improvement (service type 4.3) in terms of knowledge; on the other, Organisation B to acquire capabilities in terms of providing services for data acquisition and sensing (service type 5.1), data processing and analysis (service type 5.2) and data sharing (service type 5.5), and all in technology terms. Seen the other way around, to materialise this collaborative process, it is necessary for organisation A to act as 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1521 Fig. 6 Asset type map of the D-BEST services catalogue Fig. 7 Collaboration example between two organisations in a DIH ecosystem a transferor of some specific enabling assets to Organisation B to provide services types 5.1, 5.2 and 5.5. Organisation B has to transfer them to A to provide service types 3.3 and 4.3. These individual asset flows can be depicted as shown in Fig. 7. They are designated as FA−T O−O, with O being the transferor organisation designation, Othe beneficiary organisation, A the type of transferred asset and T the service type numbered from 1 to 20. This collective designation indicates that individual flow FA−T O−Oof asset type A is transferred from Organisation O to Organisation Oto enable the provision of service T. It is possible to add an additional layer of characterisation to the exchange of collaborative relationships between entities because, for each service type, the D-BEST model provides an additional level of classification called service class (Table 4). By simply counting the number of service classes involved in each individual asset flow, e.g. on a percentage basis, an additional characterisation of collaboration can be provided, which is referred to here as service depth. This aspect is addressed again later in this article. This approach to define the origin, destination, channel and asset transferred in the collaboration process is called the InterorganisationalAssetTransferMethodology(IOATM).It constitutesthecornerstoneoftheproposedconceptualframework. It is pertinent here to comment on or explain this methodology and the relationship levels between the entities making up the network of European innovation ecosystems. Whether it is the transferor or the beneficiary of a certain asset, the fact that an organisation is at a certain relationship level does not prevent it from transferring assets to entities at different levels or, on the contrary, receiving them. The vertical transfer of assets, or the transfer between different levels, is 123 1522 Journal of Intelligent Manufacturing (2025) 36:1505–1545 not restricted in actual practice, and although due to the very nature of levels the most usual are horizontal or intralevel transfer processes, collaboration between entities at different levels, especially in the first three, is also common; i.e. DIH, ecosystem and EDIH levels. Accordingly, it should be noted that IOATM, as a potential means to quantitatively assess collaboration between the entities of innovation ecosystems, does not restrict this circumstance in any way and, therefore, confers the framework flexibility in this respect. This holistic approach, based on defining the origin, destination, channel and transferred asset involved in the asset flow of the collaboration under study, answers research question RQ1, formulated about the characteristics of the interaction that occurs between the entities composing DIHs’ relational network when collaboration takes place between them. Thanks to this construct, collaborative processes between organisations can be translated in asset flow terms. This translation is particularly significant because the mapping and identification of the individual asset flows involved in collaboration enable IOATM as a tool for the quantitative assessment of collaboration processes. On the one hand, the possibility of individualising each asset transfer facilitates its characterisation and the establishment of measurable technical specifications, which are crucial for quantifying the gross magnitude Tmb acquired by such a transfer; e.g. in purely monetary terms, in terms of the time spent or use of the asset, or by other quantification means. On the other hand, theidentification of the individualchannel that conducts each individual transfer also helps to characterise it and to establish its specifications. This is fundamental to quantify the performance or efficiency eff tof the channel in the transfer process; in other words, to quantify organisations’ capacity to collaborate. This ability to collaborate, whose value would be between 0 and 1, depends not only on the parameters that define and characterise the transfer channel, especially those related to interoperability, but also on some parameters specific to the intervening organisations, all of which could be the subject of further research. This efficiency eff tmodifies downwardly the gross magnitude of transfer. Thus their joint product leads to the net magnitude of transfer Tmn,an artifice that would make it possible to accurately measure collaboration in hypothetical quantitative modelling. From this perspective, both the assets flow and transfer channels can be considered the two key dimensions in collaborative interaction processes, and the elements that most shape the quantitative assessment of collaboration, dimensions around which the other elements of the proposed framework are positioned: the service catalogue, the involved relationship levels, the collaborative interaction types, and origin and destination, all of which essentially do not shape, but condition assessments. This approach provides an answer to research question RQ2, formulated in the Introduction of this article. Regarding the interaction types aligned with collaborative processes, on the contrary it is necessary to make some distinctions because each type presents its own peculiarities in relation to the involved entities typology or the types of transferred assets: (i) cooperation involves transferring all or some of the first four asset types, i.e. competences, knowledge, technology and infrastructure (CKTI), and admits the exposed methodology without restrictions; (ii) coopetition presents two faces, cooperative and competitive, and the IOATM scope is restricted to collaboration that materialises from the cooperative perspective with the transfer of CKTI; (iii) coordination forces organisations to align and organise business activities by sharing information, risks and resources and, therefore, producing the controlled transfer of CKTI; (iv) in collaboration through funding, basically a money transfer occurs in a unidirectional way; that is, when the ceding organisation transfers the asset, the receiver can, inturn,transfer otherassets to thetransferor inresponse,such as knowledge or technology, but not money; (v) Institutional supportinteractionsarecharacterisedbythetransferororganisation being a government authority or a public institution, and flow is unidirectional. The assets transferred in this collaboration type are normally skills (indirectly acquired with the support of policies, plans, laws or regulations) or financing; (vi) knowledge transfer is defined by its own name; (vii) networking basically implies transferring knowledge about who to collaborate with and in what subjects; (viii) partnership; it essentially represents the same as cooperation or coopetition, but on a larger scale and in terms of the number of involved organisations, to produce the same type of assets transfer as in these; (ix) sponsorship; a potential formula in which the transferor organisation is a person or group fromtheprivatesphere;it isunusualintheinnovationecosystems context; here the assets flow is unidirectional and it may involveknowledgetransferintheformofadviceorsometype of funding; (x) Technology transfer; its very name characterises it. In any case, albeit with their particular nuances, all collaborative interaction types occupy a place in the methodology advocated by this framework. Having clarified all this, the conceptual framework can be represented, from a general perspective, by bringing together withina singleframeall theaforementioned elements.On the one hand, those elements are based on the D-BEST model, which serves as a platform to develop the conceptual framework: asset types involved in services, macroclasses and service types, and the asset type map of the service catalogue. On the other hand, those new elements that allow the collaboration process between organisations to be shaped: transfer channels or asset flows, the four relationship levels in the network of European innovation ecosystems, collaborative interaction types and, obviously, the ceding and the recipient organisations (Fig. 8). The conjunction of all these and their interrelationships, with a special emphasis on the 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1523 Fig. 8 Collaboration Framework in the Network of European innovation ecosystems core concept of asset flow as the main rationale, make up the proposed conceptual framework. Case study: a food processing application experiment for production management and predictive maintenance within the DIH4CPS framework European project “Fostering DIHs for Embedding Interoperability in the CPS of European SMEs” (DIH4CPS) was an innovation action that received funding from the European Union’s Horizon 2020 programme. This created an interdisciplinary network of DIHs) and solution providers specialised in the Industry 4.0 technologies application in SMEs, especially on cyber–physical and embedded systems, interweaving knowledge and technologies from different domains, as well as connecting regional clusters with this pan-European expert pool of DIHs. When the project finished in December 2022, DIH4CPS’s ambition to become a sustainable network materialised early in 2023, when it was instantiated in the European Virtual LaboratoryforEnterpriseInteroperability(I-VLab)underthe name of Ei2Network, which is currently operational. DIH4CPS integrated its ecosystem with 11 initial DIHs from nine countries from all regions of Europe, and 20 additional DIHs following the first and second open calls, to provide European industry with unprecedented ease of access to world-class domain expertise in developing CPS and embedded systems. The development of this expertise revolvedarounda core experimentationclusterthat consisted of 23 application experiments covering many key industrial sectors and activities. This use case approaches the collaborative processes in Application Experiment number 6 (iAE6) carried out in the project, which aimed to address the difficulties of those companies that, despite having large and valuable production data generated by powerful automation systems, do not integrate them into the value chain and end up often representing data silos that are barely or no exploited at all. The planned experiment, implemented in practice into an industrial pilot of the food processing sector, supports the development of data-driven value-added services, both related to the 123 1524 Journal of Intelligent Manufacturing (2025) 36:1505–1545 manufacturing execution system/manufacturing operations management (MES/MOM) functional areas (e.g. production order control or performance analysis) and predictive maintenance. The experiment facilitated the development of a vertical solution that leverages the production data generated by quality inspection machines for the agri-food sector, especially in the production of olives, cherry tomatoes and other fruit, which was made possible by the integration of value-added services that collect and maximise the process data generated by state-of-the-art machine vision sorting and grading machines to optimise production and maintenance management. Food processing application experiment iAE6 The application experiment was validated with a pilot at a food processing company specialised in cherry tomatoes (Níjar, Almería, in Spain), on a cherry tomato grading and sorting line (Figs. 9,10,11), which was subject to improvement (Fig. 9). The process of sorting and grading cherry tomatoes involvesseveralsteps. Firstly,operators transportpalletscontaining cherry tomatoes and feed them into the sorting line’s roller conveyor. From there, tomatoes move along different feeding belts and enter the Multiscan MGS sorting and grading roller machine. This machine uses computer vision to detect the different features of each cherry tomato, such as shape, colour and size, as it rolls through the machine. In this way it inspects the entire surface of each fruit. The machine classifies them into different quality categories defined by the user through an intuitive user interface, which allows the thresholds for each property and category to be set up. The machine then tracks and guides all the tomatoes to strategically placed slots to place them into separate exits. The objective of the application experiment is to develop data-drivenadded-valueMES/MOMapplications toimprove manufacturing operations (Figs. 10,11). The main components or building blocks making up the system’s architecture has allowed the experiment to be developed, which is organised into clusters or tiers according to the different levels of a secure industrial network defined by the IEC/ISA 62443 series of standards as detailed below (Fig. 12). An embedded server based on the open platform communications united architecture (OPC UA) facilitates the integration of the production data generated and managed by the line inspection machine into external applications. The OPC UA is becoming a standard factor for machineto-machine (M2M) communications at different industrial network communication levels. By means of OPC UA ServiceDiscoverytechnologyand an ad hoc data model for OPC UADataAccessservices,theapplicationexperimentdelivers a turn-key solution to enable “Plug-and-Play” connectivity. This OPC UA Server allows information from not only the quality inspection machine, but also from other connected manufacturing equipment, to be exchanged. In this way, the embedded OPC UA server allows other services to exchange operational and maintenance data with the quality inspection machine so that it is no longer a data silo, which improves the performance of supply chain processes. A hybrid edge/cloud service platform provides a runtime platform and a core service to facilitate access to the data generated by the OPC UA to connected applications so that they can provide data-driven added value services. The edge/cloud service platform provides asynchronous data services to access real-time production data and synchronous data services to access historical data. This basis enables the secure access and exchange of the operational and maintenance data between the stakeholders involved through a set of data services designed specifically to support this collaboration. The edge/cloud services also allow datasets to be created for analysis and model training purposes. This edge/cloud platform manages the data storage of industrial data time series at two different levels: on-premises (systems installed within the pilot company’s boundaries); in-cloud (systems installed in a private cloud). The platform keeps the on-premises hot data generated in the near past by applying retention policies that have been specifically defined to meet the requirements of the added value services that consume these data. The collected and stored data include the industrial variables describing the process and quality of products. On the one hand, as mentioned data services store above all information, even the information collected from line controllers through the embedded OPC UA servers. This includesall the process information aboutthe real-time status of production equipment and all the product quality-related information generated by the compute-vision grading system. On the other hand, data services allow applications to enrich this information with additional context information, like the information provided by operators in natural language to better describe incidences and machine failures. The implemented MES/MOM applications are basically web applications that provide the manufacturing execution system and the manufacturing operation management functionalities, backed by the edge/cloud platform services. These MES/MOM applications focus on some demanded key functionalities, which mainly revolve around four milestones: production key performance indicators (KPIs) monitoring, production order control, production batch traceability and production process management. Finally, the objective of the anomaly detection and predictive maintenance module is to put the maintenance data to good use to provide value data-driven services in this area. Anomaly detection and predictive maintenance allow unexpected events in machine performance to be reported by 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1525 Fig. 9 Scheme of the Multiscan MGS cherry tomato grading and sorting line (source DIH4CPS project dissemination archives) Fig. 10 Multiscan MGS cherry tomato grading and sorting line (source DIH4CPS project dissemination archives) Fig. 11 Detail of Multiscan MGS cherry tomato grading and sorting line (source DIH4CPS project dissemination archives) studying baseline normality trends with contextual information, reporting anomaly detection and the estimated time to failure of machines and predicting any likely failures. In this way, the described module provides a solution to several key issues: (i) detecting and warning in real time when operating parameters deviate from expected machine performance; (ii) studying and adjusting for drift and seasonal variations in performance estimators; (iii) minimising loss of availability due to unplanned repair and adjustment downtime; (iv) reducing operating costs by planning maintenance and stocking appropriate spare parts on site in advance; (v) optimally integrating the planned downtime into the operating schedule. Of the different possible models towards this endeavour, the use of survival models and classifiers was chosen for this project. Survival models are appropriate for obtaining several probability estimations for failure in different future times by allowing maintenance to be adapted according to the taken risk. Besides, classifier models provide different probabilities for each failure type during a given time period. 123 1526 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Fig. 12 The iAE6 high-level technical architecture The pilot was prepared by provisioning plant facilities with laboratory equipment, which consisted of a food processing line simulator, whose main function is to generate values for the different industrial variables from the available historical data. With this simulator, the installation and integration of the different components were validated under laboratory conditions by evaluating the installation procedures and user interfaces to ensure that user requirements and acceptancecriteriawere met.Oncethesolution wasvalidated under laboratory conditions, the tested unit was installed on the user’s premises and finally commissioned. Managerial implications of iAE6 From the knowledge acquired during the experiment, several implications for user management processes are worth highlighting: (i) better monitoring of production KPIs; a benefit that comes from the MES/MOM applications. This is because they calculate KPIs from the collected data and display them on comprehensive dashboards designed specificallyfordifferentuserprofiles(operator,productionmanager or maintenance manager) so that everyone involved in the process can assimilate information; (ii) improved order control due to MES/MOM applications, which provide functions to dispatch production orders to the shop floor (operators and manufacturing equipment), and show the production plan current status to relevant users; (iii) enhanced process management thanks again to the MES/MOM applications by monitoring and controlling the manufacturing process status, and by showing operators the current status of the line and allowing them to specify the cause of stoppage when it is not detected by a machine; (iv) improved production performance, derived from the benefits of anomaly detection and the predictive maintenance system. This vertical solution is expected to be well accepted by the SMEs involved in food grading and sorting, which currently have lower Industry 4.0 maturity levels than larger companies, which usually access technological resources more easily. Organisational aspects of the iAE6 experiment Three organisations participated in the design and development of the iAE6 experiment: (i) The Universitat Politècnica de València (UPV), a member of the DIH for the economic promotion of the Valencian Community (InnDIH). It plays the role of team leader, system architect and DIH member that specialises in production management technologies (here mainly MES/MOM technologies) through the Centro de Investigación en Gestión e Ingeniería de la Producción (CIGIP), which belongs to this university. (ii) The Instituto Tecnológico de Informática (ITI), another InnDIH 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1527 Fig. 13 iAE6 overall organisation member.Itactsas aspecialistinmachine learningandpredictive maintenance technologies. (iii) The company Multiscan Technologies SL. It is the designer, manufacturer, installer andmaintainerofthe product(MultiscanMGScherrytomato gradingandsortinglineof referencePL18007),andis theend user of the solution. It also acts as a specialist in machine vision technology. These three organisations are the intervening actors that play an active role in developing the target solution of this experiment. SAT Costa de Níjar, a company that produces, processes and sells agricultural products, has offered its facilities to run the pilot. However, this company does not play an active role in experiment development, which is why it is not considered in the use case (Fig. 13). Implementing the collaboration framework The iAE6 experiment provides a real and sufficiently complex case to constitute a representative example of the collaborative processes that exist in the innovation ecosystems generated around DIHs. The application of IOTAM to this case initially requires defining the source, destination, channels and assets involved in the collaboration process. The example provided by the iAE6 experiment involves, as shown, three collaborating organisations, any of which can act as both the source and destination of assets during the collaboration process. This circumstance provides six potential collaboration channels (Table 6;Fig.14): The next step in this bottom-up process is to carry out an analysis to identify the different service types exchanged during the collaboration process, regardless of their origin or destination, into the most elementary services of among the 20 possible types of organisations’ services portfolio (Table 7). Table 7shows the service macroclasses and service types named and numbered according to the D-BEST portfolio, set out in Table 4, as well as their translation into the IOTAM code, which numbers them with a single digit from 1 to 20 (Fig. 6). Hereafter in this article, the code used will be IOTAM. To follow this process, collaboration activities have Table 6 Collaboration channels in the use case Channel Collaboration origin or asset transferor Collaboration destination or asset receiver Channel 1 UPV ITI Channel 2 ITI UPV Channel 3 UPV MULTISCAN Channel 4 MULTISCAN UPV Channel 5 ITI MULTISCAN Channel 6 MULTISCAN UPV Fig. 14 iAE6 detailed organisation with all the collaboration channels opened among the UPV, ITI and Multiscan Table 7 List of services identified during the collaborative exchange process Service Macroclass Service type Service type (IOTAM code) Service type name Ecosystem 1.3 3 Ecosystem governance Technology 2.1 4 Ideas management and materialisation Technology 2.2 5 Contract research Technology 2.3 6 Provision of infrastructure Technology 2.4 7 Technical support on scale up Skills 4.3 15 Skill improvement Data 5.1 16 Data acquisition and sensing Data 5.3 18 Decision-making Data 5.5 20 Data Sharing 123 1528 Journal of Intelligent Manufacturing (2025) 36:1505–1545 to be decomposed channel by channel. In the iAE6 experiment, this decomposition would take the following form (Table 8): As previously indicated in the conceptual framework presentation, there is the possibility of adding an additional characterisation layer to the exchange of the collaborative relationships among the three entities because, as for each service type, the D-BEST model provides an additional classification level called service class (Table 4). So it is feasible to measure the service depth of collaborative interrelationships by simply counting the number of service classes involved in each individual asset flow, e.g. on a percentage basis. By this approach, let us take a closer look at each elementary service to better understand their rationale from a general perspective. Tables 9,10,11,12,13 and 14 show the services provided by each collaboration, described per channel as displayed in Table 8(the truly involved service classes are marked in bold): Afteridentifying the channelswith their respectiveorigins and destinations, decomposing the collaboration process into elementary services, classifying these elementary services into types and calculating the service depth, the next stage in the process is to determine both the assets involved in each elementary service and their typology among the five possible ones: competences, knowledge, technology, infrastructure and funds (Table 15). This provides a first detailed overview of the flow of the assets involved in the collaboration process. The codes used in columns C, K, T, I and F of Table 15 result from combining the initial letter of the asset name and the IOTAM code of the involved service. For example, code K3 implies knowledge transfer in the Ecosystem Governance Table 8 Service decomposition of the collaboration process Channel Origin–destination Service Macroclass Service type Service type name Channel 1 UPV →ITI Ecosystem 3 Ecosystem governance Technology 4 Ideas management and materialisation Data 16 Data acquisition and sensing (Training dataset acquisition, preparation and sharing) Data 17 Data processing and analysis Channel 2 ITI →UPV Data 17 Data processing and analysis Data 20 Data Sharing (Predictive maintenance model training, building, and sharing) Channel 3 UPV →MULTISCAN Ecosystem 3 Ecosystem governance Technology 4 Ideas management and materialisation Technology 5 Contract research Technology 7 Technical support on scale up Skills 15 Skill improvement Data 16 Data acquisition and sensing Data 18 Decision-making Data 20 Data Sharing Channel 4 MULTISCAN →UPV Technology 4 Ideas management and materialisation Technology 6 Provision of infrastructure Technology 7 Technical support on scale up Skills 15 Skill improvement Data 16 Data acquisition and sensing Channel 5 ITI →MULTISCAN Technology 5 Contract research Skills 15 Skill improvement Data 16 Data acquisition and sensing Data 18 Decision-making Data 20 Data Sharing Channel 6 MULTISCAN →UPV Technology 6 Provision of infrastructure Skills 15 Skill improvement Data 16 Data acquisition and sensing 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1529 Table 9 Rationale of the elementary services of Channel 1 UPV →ITI Service Macroclass Service type Service type name Service class Service depth Ecosystem 3 Ecosystem governance: in the role of the iAE6 experiment leader, the UPV provides ITI with both the service impact assessment through the corresponding key performance indicators and ecosystem management, which includes engagement rules and governance structure to ease relationships among organisations 1.3.1. Service impact assessment 2 Out of 2 100% 1.3.2. Ecosystem management Technology 4 Ideas management and materialisation: the UPV, based on the pilot knowledge, generates the idea for the application experiment, evaluates it and analyses its feasibility by subsequently involving ITI in the generated idea 2.1.1. Ideas generation, assessment, and feasibility study 1 Out of 2 50% 2.1.2. Technology readiness assessment Data 16 Data acquisition and sensing (Training dataset acquisition, preparation and sharing): training data acquisition for preventive maintenance comes mainly from the MES/MOM management system, which acts as a central hub by collecting and contextualising sensor data 5.1.1. Data acquisition 1 Out of 2 50% 5.1.2. Data protection Data 17 Data processing and analysis: Data are prepared in a first stage from the side of UPV according to the requirements of the analytic models for predictive maintenance 5.2.1. Data storage 1 Out of 2 50% 5.2.2. Data analytics Collaboration average depth 5 Out of 8 63% Table 10 Rationale of the elementary services of Channel 2 ITI →UPV Service Macroclass Service type Service type name Service class Service depth Data 17 Data processing and analysis: Data are prepared from the side of ITI according to the requirements of the front-end systems 5.2.1. Data storage 1 Out of 2 50% 5.2.2. Data analytics Data 20 Data sharing: ITI, for being responsible for designing the predictive maintenance models, defines both the data space on which data models and data formats are to be used. The security standards adopted in the system architecture enable secure and reliable data exchange. ITI also provides the data and computing infrastructure to enable the training of the model, and provides connection services to ingest the train the datasets delivered by the UPV. The trained models are then deployed using secure interfaces 5.2.2 Data analytics 2 Out of 3 67% 5.5.1. General Data Protection Regulation (GDPR) 5.5.2. Data spaces 5.5.3. Data Platform Collaboration average depth 3 Out of 5 60% 123 1536 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Fig. 15 Asset flow map of the collaboration interrelationships between the UPV, ITI and MULTISCAN 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1537 Table 16 Professional categories involved in collaboration UPV and ITI Multiscan Categories MR: Main researcher M: Department Manager ST: Senior technician PoD: PostDoc researcher JT: Junior technician PrD: PreDoc researcher SW: Skilled worker Table 17 Quantification of asset flows of the collaborative processes in iAE6 for Channel 1 UPV →ITI (person ×months) Channel Origin–destination Service type Service type name K T Type MR PoD PrD Type MR PoD PrD Channel 1 UPV →ITI 3 Ecosystem governance K3 0.05 0.25 0.90 4 Ideas management and mat K4 0.05 0.15 16 Data acquisition and sensing T16 0.25 17 Data processing and analysis T17 0.20 SUBTOTALS 0.10 0.40 0.90 0.45 Table 18 Quantification of asset flows of the collaborative processes in iAE6 for Channel 2 ITI →UPV (person ×months) Channel Origin–destination Service type Service type name T Type MR PoD PrD Channel 2 ITI →UPV 17 Data processing and analysis T17 0.05 0.20 20 Data Sharing T20 0.05 0.45 SUBTOTALS 0.10 0.65 constitute a sufficient resource to create a robust model by itself because, although it allows the definition of both the origin and destination of the assets flow and the transferred asset itself, as stated above, it is essential to pay attention to the environment through which the transfer occurs because this medium must be enable an efficient flow for collaboration to optimally materialise. Efficiency here means that the asset flow occurs between both organisations under interoperabilityconditions andthese conditionsare sustainable from a comprehensive perspective, i.e. organisations are interoperable at (i) the data level, (ii) the service level, (iii) the process level and (iv) the business level (Fig. 17), and all from the triple dimension of conceptual, technological and organisational barriers (Ducq et al., 2012). Indeed the value of the transferred assets may be altered by the greater or lesser ability of the organisations involved to access and process data from their multiple sources without losing meaning and subsequently integrating them so that any of them can locate, explore and grasp the structures and contents of datasets. Thus an efficient collaborative process requires the prior assessment of interoperability at the data level. The same applies when the asset transfer is performed through distributed systems, and the ability to cooperate in servicesthatimplydataexchanges,despitedifferencesinlanguage, interface and execution platforms (Fang et al., 2004), is also crucial. Hence troubles with the interoperability at the service level can prove to be a disturbing factor that needs evaluation and control, a task that must be done from a comprehensive perspective and involves interoperability sublevels, such as the signature, protocol, semantic, quality and context sublevels (Strang and Linhoff-Popien, 2003). In networked environments, such as the entities making up innovation ecosystems, it is no less important to ensure that the processes of those organisations that interact to collaborate are designed to work together (federated relationship approach) or are even conceived from the outset as a single common process (integrated relationship approach) (Ducq 123 1538 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Table 19 Quantification of asset flows of the collaborative processes in iAE6 for Channel 3 UPV →Multiscan (person ×months) Channel Origin–destination Service type Service type name C K T Type MR PoD PrD Type MR PoD PrD Type MR PoD PrD Channel 3 UPV →Multiscan 3 Ecosystem governance K3 0.15 0.40 4 Ideas management and mat K4 0.20 0.50 1.05 5 Contract research T5 0.40 1.00 2.05 7 Technical support on scale up K7 0.35 0.55 15 Skill improvement C15 0.40 16 Data acquisition and sensing T16 1.25 18 Decision-making T18 0.05 0.70 1.80 20 Data Sharing T20 0.55 1.45 SUBTOTALS 0.40 0.35 1.25 1.60 0.45 3.80 5.30 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1539 Table 20 Quantification of asset flows of the collaborative processes in iAE6 for Channel 4 Multiscan →UPV (person ×months) Channel Origin–destination Service type Service type name CK Type M ST JT SW Type M ST JT SW Channel 4 Multiscan → UPV 4 Ideas management and mat K4 0.10 0.20 7 Technical support on scale up K7 15 Skill improvement C15 0.50 1.20 4.00 0.05 SUBTOTALS 0.50 1.20 4.00 0.10 0.20 0.05 Channel Origin–destination Service type Service type name TI Type M ST JT SW Type M ST JT SW Channel 4 Multiscan →UPV 6 Provision of infrastructure I6 0.05 0.10 16 Data acquisition and sensing T16 0.10 SUBTOTALS 0.10 0.05 0.10 Table 21 Quantification of asset flows of the collaborative processes in iAE6 for Channel 5 ITI →Multiscan (person ×months) Channel Origin–destination Service type Service type name K T Type MR PoD PrD Type MR PoD PrD Channel 5 ITI →Multiscan 5 Contract research T5 0.30 1.00 1.75 15 Skill improvement C15 0.40 16 Data acquisition and sensing T16 1.00 18 Decision-making T18 0.05 0.65 2.05 20 Data Sharing T20 0.90 2.20 SUBTOTALS 0.40 0.35 3.55 6.00 et al., 2012). Taking this step to address the necessary interoperability at the process level is crucial to safeguard the efficiency of the transfer of such transfer-sensitive assets as knowledge. To succeed in this, a significant part of the effort should focus on modelling network processes and defining system objectives (Ducq et al., 2012) from a collaborative workingperspective.Regarding thelast interoperability level (that of business), it must be seen as the last link in the chain that joins collaboration to efficiency through interoperability. Collaborative entities must be able to collaborate both organisationally and operationally with their network partners, and regardless of them being at the same or a different relationship level, or if they are to effectively establish, conductanddeveloptheirrelationshipssupportedbyinformation and communication technologies to create value in order to prevent that different legislation, corporate cultures, general, specificworkingprocedures,ordecision-makingmethodologies to undermine the effectiveness of the transfer of assets in collaborative processes. At this level and to that end, practicality must prevail. So the task of being interoperable will require addressing four challenges: (i) the interoperability of integrated value networks; (ii) the economic evaluation of business interoperability; (iii) the determination of optimal interoperability levels; (iv) the design of internal and interorganisational systems and process architectures for interoperability (Legner & Lebreton, 2007). All this needs to be taken into account from a managerial viewpoint. Nowadays, sustainability is, and rightly so, a cross-cutting concern in practically any sector and activity. Collaboration between organisations does not escape this growing 123 1540 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Table 22 Quantification of asset flows of the collaborative processes in iAE6 for Channel 6 Multiscan →ITI (person ×months) Channel Origin–destination Service type Service type name IC Type M ST JT SW Type M ST JT SW Channel 6 Multiscan →ITI 6 Provision of infrastructure I6 0.05 0.10 15 Skill improvement C15 0.45 1.20 3.80 SUBTOTALS 0.05 0.10 0.45 1.20 3.80 Channel Origin–destination Service type Service type name T Type M ST JT SW Channel 6 Multiscan →ITI 16 Data acquisition and sensing T16 0.10 SUBTOTALS 0.10 trend,certainlynotbetweenorganisationswillingtonetwork, which is the case of the entities belonging to the innovation ecosystems created in Europe around DIHs and/or EDIHs. It is common for the results of collaboration to fall short of expectations because the ability to obtain satisfactory results depends on many factors that are not often taken into account by organisations. Collaboration can confer organisations mutual benefits by helping to bridge gaps through shared effort. However, these potential advantages should not divert our attention from the fact that collaboration does not always work well, and certainly not in all contexts. Collaborative interaction processes can be misused. It is worth remembering that the complexities involved in collaboration may be used to promote certain vested interests. Actors with resources and skills can use the legitimising power of collaborative initiatives to promote their own agendas. Therefore, the first questions to ask at the beginning of every collaborative practice are: What is the purpose of this collaboration? Whose interests does it potentially serve? To a great extent, the sustainability of a collaborative relationship depends on the answer to these questions. Conduct in collaboration can be determined by monitoring techniques, such as audits or verifications of best practices, at a more qualitative level and, crucially, by quantitative evaluation methodologies based on accounting and statistics (Fadeeva, 2005). This is where the herein proposed framework takes centre stage because it enables these evaluation avenues by taking a quantitative approach. Conclusion This article presents a conceptual framework that firstly defines and structures the relational network of European innovation ecosystems driven by the EC’s Digitise European Industry and Digital Europe Programme initiatives as a whole. Secondly, it provides theoretical and conceptual support to the existing interaction processes horizontally along their relationship levels and vertically between them from a dual qualitative and quantitative perspective. The purpose of this framework is twofold. On the one hand, the initial aim is to bridge the gap in research into collaboration between DIHs by providing a broader conceptualisation of the elements, structure, interrelationships and content making up the materialised interactions between organisations in the collaborative environment that exist in a network generated from DIHs and/or EDIHs. On the other hand, the framework must represent an effective lever for moving towards a model for collaboration in this context. This research can contribute significantly to develop advanced analysis and evaluation tools in collaborative processes, which can provide a robust response to interorganisational interaction problems. From a previous and global definition of the pan-European space of action of DIHs, on which its four levels of relationship are delineated, the article begins by defining the main concepts involved within the scope of this research work: (i) the organisation types existing in the relational network of European innovation ecosystems; (ii) the types of interaction not aligned with the collaboration concept; (iii) the types of interaction aligned with the collaboration concept. Likewise, the article indicates which definition of collaboration is adopted as the first reference for research, whose authors are Wankmüller and Reiner (2020): “Process of strategically working together on a specific business activity where structures are aligned, communication channels are standardised, risks are shared, and resources are pooled in order to make them available for every partner”. Subsequently, the classification of the D-BEST model services by Sassanelli and 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1541 Table 23 Summary table on the quantifications of asset flows (person ×months) Channel Origin–destination Staff categories Asset Subtotal Total CKTI F Channel 1 UPV →ITI MR 0.10 0.45 0.55 1.85 PoD 0.40 0.40 PrD 0.90 0.90 Channel 2 ITI →UPV MR 0.75 PoD 0.10 0.10 PrD 0.65 0.65 Channel 3 UPV →MULTISCAN MR 0.40 0.35 0.45 0.80 13.15 PoD 1.25 3.80 5.45 PrD 1.60 5.30 6.90 Channel 4 MULTISCAN →UPV M 0.10 0.10 0.05 0.15 6.30 ST 0.50 0.20 0.10 0.80 JT 1.20 0.05 1.35 SW 4.00 4.00 Channel 5 ITI →MULTISCAN MR 0.40 0.35 0.35 10.30 PoD 3.55 3.95 PrD 6.00 6.00 Channel 6 MULTISCAN →ITI M 0.10 0.05 0.15 5.70 ST 0.45 0.10 0.55 JT 1.20 1.20 SW 3.80 3.80 Fig. 16 Factors altering the efficiency of collaboration 123 1542 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Fig. 17 Components that make up transfer channel interoperability Terzi (2022) is introduced, which in later sections is crucially importanttoconstructthis framework,and theresearchscope is delimited from the triple perspective of the environment, D-BEST service, and interaction type. On this basis, according to the review of the scientific literature that relates DIH concepts and all the interaction types related to the collaboration process, this research identifies, collects and performs a thematic and content analysis of the 17 selected contributions of the state of the art that is most aligned, partially or totally, and directly or indirectly, with the purpose of the framework. The literature review reaches three main conclusions: (i) most of the selected articles address collaboration between DIHs in a tangential manner; (ii) the research contributions made to date consider the interaction processes for collaboration to be an intangible element of management that is difficult to perceive and, a priori, is not measurable; and (iii) as far as we know from this review, there is still no descriptive or conceptual framework, or a qualitative or quantitative model, that deals with the conceptualisation of the interaction processes of DIHs from the collaboration perspective within and between its four relationship levels. The framework built on a bottom-up approach is presented below. The conceptualisation path guided by this approach starts by explaining the decomposition of the D-BEST services into assets and types. From this decomposition, collaboration can be conceptualised as an assets flow from ceding organisations to beneficiary organisations to alleviate any deficits in the latter and to, thus, enable them to provide some specific service types immediately or in the future. This abstraction, which is called the IOATM, lies at the heart of the conceptual framework and the article for not only establishing the precise origin, destination, channel and transferred asset, but for also representing a theoretical means to quantitatively assess the magnitude of collaboration, which is considered themain contribution ofthis research. Subsequently, toeffectively show how this framework can be implemented in a real situation, a use case that addresses collaborative processes in the iAE6 application experiment of the DIH4CPS project is presented as an example. To do so, all the steps to the full characterisation and quantification of collaboration are detailed.Finally,the contributionofthe presented framework is discussed on several fronts: (i) its suitability as a lever for moving towards a strong collaborative model; (ii) its interoperability implications; (iii) its sustainability repercussions. This approach provides answers to the formulated research questions. The main characteristic to define the interactions that take place between the entities making up the relational DIHs network when collaboration processes materialise between them is the possibility of decomposition into the origin, destination, channel and transferred asset involved in the assets flow of the analysed collaboration, which provides an answer to research question RQ1. From thisperspective,both theassetsflowandthe transferchannels canbeconsideredthetwokeydimensionsinthe collaborative interaction processes and the elements that most shape the quantitative assessment of collaboration, dimensions around which the other elements of the proposed framework are positioned: the services catalogue, the levels of relationship 123 Journal of Intelligent Manufacturing (2025) 36:1505–1545 1543 involved and the collaborative interaction types, which do not essentially shape, but condition, the assessment. This approach provides an answer to research question RQ2 formulated in the introduction of this article. In the end, the presented framework is discussed on three cardinal fronts: (i) its aptitude as a lever to move towards a collaboration model;(ii)itsconnotationsininteroperabilityterms;(iii)how it relates to sustainability. The implications of this framework are substantial. In the academic sphere, it bridges the research gap detected on the analysed topic, and not only contributes a new perspective on collaborative processes in the innovation ecosystems generated by DIHs and/or EDIHs, but also establishes the meaning andinterrelationships ofthe concepts makingup itsontology. It provides a nuance that allows quantification, and serves as a lever to model a research object in clear progression, such as collaboration. As for managerial implications, the main one to highlight is that the approach offered by IOATM constitutes the first piece of a future model that will enable dealings with the accounting of collaboration in its multiple facets and, therefore, will turn it into a more tangible and controllable management element. However, it is also worth noting that, in addition, this framework can already constitute a roadmap that helps practitioners to guide their efforts to improve the efficiency of collaborative processes with an approach that pursues objectivity, plus the maximisation of synergies between collaborative entities. Despite its strengths, the framework presents some limitations that should be outlined here: (i) the D-BEST reference model is specifically oriented to DIHs and, by extrapolation, can work in EDIHs or in organisations that integrate innovation ecosystems in general, such as service providers or end users. Beyond this collaboration scope, the service catalogues and the asset typology may vary that, in turn, implies that the presented framework lacks validity; (ii) it does not offer a plane frame of uniform application for all types of collaboration, but shows variations in interpretation depending on whether interactions are cooperation, coopetition, coordination, etc.; (iii) in multiple collaboration schemes involving more than two organisations, when the transferor organisation simultaneously transfers assets to several receiver organisations, it is not always easy to precisely delimit the assets transferred to each one, especially when intangible assets like competences or knowledge are involved, which implies further effort to specify flows and their direction from their origin through the corresponding mapping. Something similar happens in collaborative processes with several transferor organisations that simultaneously interact with a receiver organisation, which requires additional efforts to delimit asset transfers, this time in the destination. This research leaves several open doors that can guide future research on the topic under study: (i) the most obvious one is to advance towards modelling the collaborative interaction processes of DIHs and its validation through empirical methods,suchascompletecasestudiesorsurveys;(ii)insuch modelling,it wouldbe veryuseful tofacethe challengeposed by the evaluation of the factors that may alter the efficiency of collaboration, such as harnessing degree of collaboration in the receiving entity, asset transfer efficiency depending on the degree of interoperability that exists through the transfer channel, or efficiency of the transfer drive exercised by the transferor entity; (iii) with a view to collaboration sustainability, it also seems appropriate to study in more detail how to monitor and evaluate the behaviours exercised during collaboration processes to avoid misuse, but to mitigate undesired effects on its efficiency. Acknowledgements This research was carried out at the Universidade NovadeLisboa(UNINOVA)andtheUniversitatPolitècnicadeValència (UPV).The authorsare gratefulfor the support received from the Centre of Technology and Systems (CTS) of UNINOVA and the Research Centre on Production Management and Engineering (CIGIP) of the UPV to successfully carry out this research. Likewise, the authors are very grateful to all participating entities in the pilot used as a case study. Author contributions Julio C. Serrano conceptualised the method described in this framework, implemented it, applied it to the case study, and wrote the main text of the manuscript, tables and figures. José Ferreira provided overall supervision of the text, contributed to the conceptualisation and provided the case study. Ricardo JardimGoncalves and Angel Ortiz reviewed the article, contributed to the discussion section and validated the conclusions. Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The research leading to these results received funding from the European Union H2020 Research and Innovation Programme with Grant Agreements No. 872548 “Fostering DIHs for Embedding Interoperability in Cyber–Physical Systems of European SMEs” (DIH4CPS), No. 825631 “Zero-Defect Manufacturing Platform” (ZDMP), and No. 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)”, from the European Union Horizon Europe Programme with Grant Agreement No. 101057294 “AI Driven Industrial Equipment Product Life Cycle Boosting Agility, Sustainability and Resilience” (AIDEAS), and from the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana entitled “Industrial Production and Logistics Optimisation in Industry 4.0” (i4OPT, Ref. PROMETEO/2021/065). Data availability The data on which this article is based, including the developed method and case study data, are available upon request. Nevertheless, due to confidentiality considerations, detailed information regarding the development of the pilot used in the case study cannot be publicly disclosed. Declarations Conflict of interest No potential conflict of interest is reported by the authors. Ethical approval Not applicable. Informed consent Not applicable. 123 1544 Journal of Intelligent Manufacturing (2025) 36:1505–1545 Consent for publication The authors consent that the work entitled “Relational Network of innovation ecosystems generated by digital innovation hubs: a conceptual framework for the interaction processes of DIHs from the perspective of collaboration within and between their relationship levels” for possible publication in the Journal of Intelligent Manufacturing. The authors certify that this manuscript is original and has not been published in whole or in part, nor is it being considered for publication elsewhere. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitteduse,youwillneedtoobtainpermissiondirectlyfromthecopyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/. 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