Infrastructure Sharing as a Digital Platform Model for Sustainable Manufacturing: Lessons from Two Case Studies

Abstract

Physical manufacturing and research infrastructures are essential for advanced innovation but often remain inaccessible to SMEs, start-ups, and research institutions that cannot justify ownership of capital-intensive assets. This study examines whether platform-mediated infrastructure sharing can function as a sustainable open-innovation mechanism in advanced manufacturing. Using the SCIP/SYNPRO platform developed in the SYNERGY and IDEATION projects, an exploratory case-study design combines descriptive analysis of a registry of 290 infrastructure items across 11 countries with qualitative analysis of 23 documented access requests, interaction records, and pilot reports. The results show that the Provider–Taker model facilitates observable access-enabling interactions, including infrastructure publication, request submission, provider–taker communication, negotiation, and selected documented use, although it does not measure population-wide access outcomes. Sharing potential is uneven: modular and emerging technologies, especially VR/AR infrastructures, attract higher request intensity than production-integrated assets. Users and providers favour negotiated access, flexible pricing, operator support, and contractual clarification rather than standardised rental models. Qualitative evidence shows that value is created through access to otherwise unavailable equipment, postponed investment, experimentation, technology familiarisation, student training, capability development, and new inter-organisational research links. The findings indicate that infrastructure sharing can support more resource-efficient innovation but depends on discoverability, governance, trust, and support mechanisms to scale.

1. Introduction

Sustainable and competitive manufacturing increasingly depends on firms’ ability to access and combine advanced technological resources across organisational boundaries. Open innovation has become a central paradigm in this regard, shifting attention from closed, firm-centric R&D to purposive inflows and outflows of knowledge and resources [1]. Over the last two decades, open innovation has moved from a niche idea to a mainstream management approach embedded in corporate strategies and innovation portfolios [2]. In parallel, manufacturing systems are undergoing deep digital transformation. Digital platforms, data analytics, the Internet of Things (IoT) and Artificial Intelligence (AI) provide the technical infrastructure for distributed innovation but they also require new capabilities and governance models [3]. Innovation platforms act as virtual environments coordinating knowledge and resource flows among various actors [4], and their effectiveness depends on alignment between ecosystem governance and firms’ digital capabilities [5]. In the context of sustainability-oriented business transformation, these developments raise questions about how organisations can use digital collaboration not only to improve efficiency but also to create longer-term environmental, economic, and stakeholder value.

The sharing economy adds a complementary perspective by focusing on access rather than ownership. Sharing economy research conceptualises platform-mediated, temporary access to underutilised assets as a way to increase utilisation and reduce transaction costs [6,7]. Systematic reviews highlight diverse business models including peer-to-peer, Business-to-Consumer (B2C), and Business-to-Business (B2B) models and link them to economic and environmental objectives [8,9]. Circular-economy (CE) perspectives extend this logic by aiming to narrow, slow and close resource loops. Evidence from manufacturing shows that circular-economy implementation often involves collaborative arrangements and the shared use of capital-intensive assets to avoid redundant investment and stranded infrastructure [5]. Platform-based CE models provide digital marketplaces and coordination mechanisms that improve visibility of available resources and support new circular business models [10]. At the same time, empirical research warns that sharing and circular models can generate rebound effects that partially offset expected environmental gains [11,12]. For this reason, the idea of a sustainable sharing economy requires not only access-based business models but also responsible governance, transparency, and integration of sustainability objectives into platform design and corporate decision making.

Despite rapid progress in open innovation, sharing economy and CE research, a critical gap persists: the coordinated sharing of physical research and manufacturing infrastructure. Existing open innovation platforms widely support idea contests, crowdsourcing, crowdfunding, and microworking, but systematic analyses of platform functionalities show that infrastructure sharing—making free capacity in machines, laboratories, and specialised equipment accessible to external users—is rarely implemented as a dedicated service [13]. Earlier conceptual work proposed an infrastructure sharing model as support for sustainable manufacturing and illustrated its potential to increase utilisation and reduce redundant investment [14,15], yet empirical evidence from real platform implementations remains very limited.

This gap is particularly relevant for small and medium-sized enterprises (SMEs), start-ups, and research teams in advanced manufacturing domains such as additive manufacturing, robotics, micro- and nanotechnologies, and Industry 4.0. These actors are central to innovation-led growth but often lack the capital to invest in state-of-the-art infrastructure. In such domains, sophisticated equipment, specialized testbeds, and dedicated laboratories are required to validate concepts, perform experiments, and build demonstrators. Recent reviews on knowledge management and SME digital transformation highlight the importance of innovation ecosystems and digital platforms in providing access to technologies and capabilities that SMEs cannot develop alone [16]. Studies of SME digital-platform capabilities show that well-designed platforms can enhance innovation and transformation performance when they are oriented towards value co-creation and ecosystem integration [17]. For these actors, the practical challenge is therefore not access to information alone, but access to the physical infrastructures required for experimentation and prototyping. This makes infrastructure sharing relevant not only for innovation performance but also for sustainable entrepreneurship, inclusive access to advanced technologies, and more balanced participation in regional innovation ecosystems.

The SYNERGY project (SYnergic Networking for innovativeness Enhancement of central european actoRs focused on hiGh-tech industrY)—case study 1—addresses this strategic gap by developing a multi-service open innovation environment for advanced manufacturing in Central Europe. Within the project, the Synergic Crowd Innovation Platform (SCIP) integrates several open innovation services and incorporates a dedicated infrastructure-sharing module (SYNPRO), which enables organisations to register their research and manufacturing infrastructures, specify access conditions and explore sharing arrangements with external users [18].

Also, IDEATION, an EIT Higher Education Initiative project—case study 2, strengthens universities’ innovation capacity by improving how partners share and access innovation infrastructure by implementing SCIP. It promotes joint use of labs, makerspaces, testbeds, and prototyping equipment across institutions and with regional stakeholders, supported by practical operating models (access rules, booking, safety/compliance, and cost-sharing). By sharing not only facilities but also technical expertise and support services, IDEATION reduces duplication, lowers barriers for students, researchers, and startups, and accelerates co-development and validation of new solutions within the regional innovation ecosystem [19].

In this article, SMEs, start-ups, and research institutions are not treated as mutually exclusive analytical categories, but as representative examples of resource-constrained innovation actors operating in advanced manufacturing ecosystems. Although these groups differ in formal classification—SMEs by firm size, start-ups by organizational stage, and research institutions by organizational type—they are brought together here because they share a common practical challenge: limited direct access to advanced research and manufacturing infrastructure required for experimentation, prototyping, testing, and technology validation. The infrastructure-sharing model analyzed in this study is therefore directed not at one narrowly defined organizational class, but at a broader set of actors for whom access, rather than ownership, is the critical condition of participation in innovation processes.

The analysed sharing model extends open innovation from knowledge flows to physical resource coordination, aligning with sharing-economy principles of temporary access optimisation [6] and circular-economy strategies aimed at narrowing, slowing, and closing resource loops [20,21]. At the same time, it speaks directly to the broader idea of a sustainable sharing economy, in which digital intermediation, collaborative value creation, and responsible resource use are combined to deliver environmental, economic, and social benefits for multiple stake-holder groups.

In this context, the overall objective of this article is to investigate the effectiveness, feasibility, and sustainability implications of infrastructure sharing as a complementary open innovation service for advanced manufacturing, focusing on its implementation through the SCIP. More specifically, the study examines how a digital platform can coordinate cross-organizational access to physical infrastructures, under which conditions such a model delivers utilization efficiency, economic viability, and sustainability benefits, and how infrastructure sharing can be interpreted as a sustainable sharing-economy arrangement. The analysis focuses on actors that are active in manufacturing innovation but often face financial, organizational, or institutional barriers to direct ownership of advanced infrastructure, including SMEs, start-ups, and research organizations.

The study is organised around one overarching research objective and three operational objectives. The overarching objective is to explain whether platform-mediated infrastructure sharing can function as a sustainable open innovation mechanism in advanced manufacturing. The operational objectives are: (1) to verify whether the Provider–Taker model enables observable resource-sharing interactions; (2) to identify which infrastructure characteristics make sharing more or less feasible; and (3) to determine which access, pricing, and governance arrangements are preferred in practice. Governance is treated as a cross-cutting element linking the research objective, theoretical background, and empirical analysis. It is examined theoretically as the set of rules and coordination mechanisms enabling platform-mediated access, and empirically through access schedules, economic terms, user responsibilities, contractual arrangements, operator support, and risk-related conditions. In line with the available empirical evidence, the first operational objective is understood as testing whether the model supports observable access-enabling interactions, rather than measuring the full scale of access to resources across the wider population of potential users.

To operationalize this objective, the article addresses three research questions:

• RQ1: How does the Provider-Taker model support resource-sharing interactions and documented access-related outcomes?

RQ1 does not measure population-wide access to resources. Rather, it examines whether SCIP/SYNPRO enables observable access-facilitation steps, including infrastructure publication, access requests, provider–taker communication, negotiation of conditions, and documented use or pilot activity where evidence is available. In this model, the Infrastructure Provider offers access to equipment or facilities, while the Infrastructure Taker seeks to use them for research, testing, or innovation.

• RQ2: Which types of infrastructures are most suitable for sharing, and which technical and organizational characteristics enable successful sharing?

RQ2 examines the composition of the infrastructure registry and request patterns to identify infrastructure categories that attract more or fewer requests, and to infer characteristics such as modularity, safety profile, ownership type and documentation that appear to facilitate sharing.

• RQ3: What access models, pricing structures, and contractual arrangements do infrastructure providers and takers prefer in practice?

RQ3 uses registry and request data to analyze the prevalence of different access models, the dominance of negotiation-based versus standard pricing, and the types of general conditions that emerge.

This structure is used throughout the article to improve coherence. RQ1 is linked to the platform-coordination layer, RQ2 to the infrastructure-shareability layer, and RQ3 to the governance layer. The Results and Conclusions follow the same sequence, so that each empirical finding is interpreted in relation to the theoretical construct and operational objective that motivated it. The remainder of the paper is structured as follows. Section 2 reviews the relevant literature on open innovation platforms, the sharing economy, the circular economy, and SME support, and synthesises design principles for infrastructure-sharing services. Section 3 presents the conceptual framework. Section 4 describes the methodology, data sources, and analytical procedures used to examine the SYNERGY infrastructure registry and request dataset. Section 5 presents the empirical results on infrastructure characteristics, access and pricing models, utilisation patterns, and pilot outcomes. Section 6 discusses these findings in light of the theoretical framework, highlighting implications for platform design, policy, and future research. Section 7 concludes with the main contributions, limitations, and directions for further development of infrastructure-sharing models for sustainable manufacturing.

2. Literature Review and Integrative Analytical Framework

2.1. Infrastructure Sharing as a Complementary Open Innovation Service (RQ1)

Research on infrastructure sharing in manufacturing is at the intersection of four closely related streams: open innovation, the sharing economy, circular economy, and SME/platform-support research. These streams should not be treated as competing explanations, but as complementary perspectives on the same phenomenon. Open innovation explains why firms increasingly seek access to external knowledge, capabilities, and assets across organisational boundaries [1,2,22]. Sharing-economy research explains how platform-mediated, temporary access to underutilised assets can replace ownership-based models [6,7,8,9]. Circular-economy research adds a sustainability lens by asking whether such access-based arrangements improve utilisation, extend asset life, and reduce redundant investment [10,20,21,23,24]. Finally, research on SMEs, ecosystems, and platform governance explains which actors are most constrained in accessing advanced technologies and which coordination mechanisms reduce adoption barriers [16,17,25,26,27]. Taken together, these perspectives suggest that infrastructure sharing should be analysed not merely as a technical platform feature, but as a complementary open innovation service and a potentially sustainable access-based business model.

Open innovation has become a well-established paradigm in innovation management, shifting attention from closed, firm-centred R&D toward purposive collaboration across organisational boundaries [1,2,22]. Recent studies show that openness is no longer treated as occasional experimentation, but is increasingly embedded in strategy, ecosystem participation, and portfolio decisions [22]. At the same time, digital transformation has provided the infrastructures through which such openness can be operationalised. Digital platforms, data analytics, IoT, AI and related tools enable firms to identify partners, exchange knowledge, and coordinate innovation processes across time and space [1,3,4]. In this sense, open innovation platforms are not only communication tools but organisational mechanisms that structure interactions among heterogeneous actors within broader innovation ecosystems [28].

However, most open innovation research—and most open innovation platforms in practice—still focus on intangible exchanges, such as ideas, knowledge, funding, challenges, or data [13,16,17]. As a result, the open innovation perspective is especially useful here when extended from knowledge exchange to resource coordination in contexts where access to equipment, laboratories, pilot lines, and testbeds often constitutes the real bottleneck for experimentation and prototyping in advanced manufacturing. This extension is particularly important for SMEs, start-ups, and research teams that participate in innovation ecosystems but cannot justify full ownership of capital-intensive infrastructure [16,17]. In such contexts, infrastructure sharing can be interpreted as an extension of open innovation from knowledge exchange to resource coordination. The sharing-economy perspective strengthens this argument by focusing on access rather than ownership and by showing how digital platforms can coordinate temporary access to underutilized assets in B2B environments [6,8,9,29].

For the purposes of this study, infrastructure sharing is therefore conceptualised as a complementary open innovation service: complementary because it extends rather than replaces existing open innovation services, and open-innovation-oriented because it enables external actors to access capabilities that would otherwise remain unavailable or underutilised. This framing directly informs RQ1, which asks whether the Infrastructure Provider–Infrastructure Taker model facilitates resource sharing through a digital platform.

2.2. Infrastructure Suitability and Sharing Potential in Advanced Manufacturing (RQ2)

If infrastructure sharing is viewed as a complementary open innovation service, the next question is whether all infrastructures are equally suitable for sharing. Prior research suggests they are not. In shared-manufacturing environments, successful sharing depends not only on the existence of supply and demand but also on asset characteristics such as modularity, safety profile, technical complexity, dependence on operator support, and the extent to which the asset is embedded in a proprietary production process [9,29,30]. In practice, some infrastructures can be accessed relatively easily by external users, while others require complex scheduling, supervision, certification, or contractual protections.

The circular economy provides an additional explanatory perspective. Circular-economy research is concerned with narrowing, slowing, and closing resource loops [20,21]. In manufacturing, this means increasing the utilisation of existing assets, extending equipment lifetime, and reducing redundant investment. From this perspective, infrastructure sharing can be interpreted as a circular business model, because it intensifies the use of existing capital-intensive resources instead of encouraging parallel duplication across organisations [10,20,23,31]. At the same time, the literature warns that the sustainability effects of access-based and sharing-based models are not automatic. They depend on platform design, user behaviour, logistics, and governance conditions, and may be weakened by rebound effects or additional transaction burdens [11,12].

This issue is particularly relevant in advanced manufacturing, where infrastructures vary substantially in their potential for external use. Emerging and modular technologies may attract demand because they lower barriers to experimentation and skill development, whereas production-integrated assets may be more difficult to share because of calibration, downtime, safety, confidentiality, or process-integration requirements. In addition, visibility and documentation matter: infrastructures that are well-described, searchable, and easy to interpret are more likely to be matched with potential users than infrastructures that remain poorly documented or institutionally “hidden”. Accordingly, RQ2 examines which infrastructures are most suitable for sharing and which technical and organizational characteristics enable successful sharing.

2.3. Access Models, Pricing, and Governance Conditions for Infrastructure Sharing (RQ3)

The third issue concerns the conditions under which infrastructure sharing becomes practically viable. In consumer-oriented sharing platforms, access is often standardised and highly automated. In industrial and research infrastructure sharing, however, access is more likely to be negotiated, conditional, and relational. Prior research on platform ecosystems shows that participation and value creation depend not only on platform availability but also on governance arrangements that regulate access, participation rights, pricing logic, accountability, and dispute resolution [5,26,27]. This is especially relevant where infrastructures are expensive, technically complex, and closely tied to organisational routines and liabilities.

The sharing-economy literature shows that B2B sharing models differ from consumer-oriented sharing because they require stronger trust mechanisms, clearer allocation of responsibilities, and more flexible pricing and contractual structures [6,9,30]. In advanced manufacturing, these arrangements may include negotiated access terms, project-based pricing, operator support, insurance requirements, confidentiality rules, and hybrid service bundles. In many cases, the value proposition is not based on equipment access alone, but on access combined with expertise, setup, supervision, or collaborative experimentation. Infrastructure sharing should therefore not be treated as a simple rental marketplace, but as a governed collaborative arrangement mediated by a digital platform.

To situate the analyzed model within sharing-economy typologies more explicitly, this article treats infrastructure sharing as a B2B-oriented, platform-mediated access model rather than as a peer-to-peer consumer-sharing arrangement. Existing sharing models can be differentiated by market structure, such as P2P, B2C, and B2B, as well as by resource type, platform role, ownership-transfer logic, and value-capture mechanism. Within this typology, SCIP/SYNPRO corresponds to a hybrid B2B infrastructure-sharing model in which organizations temporarily access tangible production and research assets, while the digital platform supports discovery, coordination, negotiation, and trust-building rather than only processing standardized transactions [32,33,34,35,36].

This B2B positioning also explains why operational design is central to the study. Compared with P2P or B2C sharing, inter-organizational infrastructure sharing involves higher asset specificity, stronger safety and liability requirements, and greater dependence on operator expertise, calibration, confidentiality, and scheduling. The operational challenge is therefore not simply to list idle resources, but to coordinate capacity, match technical requirements with suitable providers, define responsibilities, and maintain service quality under negotiated access conditions [29,30,37,38,39].

In this article, governance is understood as the set of platform- and agreement-level mechanisms that regulate who may access an infrastructure, under what technical and economic conditions, who carries operational responsibility, and how risk, confidentiality, intellectual property, liability, and support obligations are allocated. This definition links the theoretical background directly to RQ3 and to the empirical indicators used later in Section 5: access scheduling, economic conditions, user responsibilities, contractual framing, support mechanisms, and risk-management provisions.

The SME literature adds an important user-side perspective. SMEs and start-ups often face persistent barriers to accessing advanced infrastructure because they lack capital, internal technical support, or the organisational capacity to navigate complex institutional environments [16,17]. Well-designed digital platforms can mitigate these barriers, but only if they offer more than basic catalogues. They must also support interaction, project scoping, brokerage, service bundling, and the reduction in organisational complexity [16,17,25]. For that reason, RQ3 is positioned not merely as a descriptive inventory of pricing formats, but as a governance-oriented question concerning which access models, pricing structures, and contractual arrangements are preferred in practice.

2.4. Integrative Synthesis and Analytical Framework

The four literature streams reviewed above should be synthesised into one analytical argument rather than treated as parallel background sections. Open innovation explains the strategic rationale for crossing organisational boundaries in search of external capabilities [1,2,22]. The sharing economy explains the access-based, platform-mediated mechanism through which underutilised assets can be mobilised [6,7,8,9]. Circular economy research explains why such models may matter for sustainability, resource efficiency, and the avoidance of redundant investment [10,20,21,29]. SME and governance research explains which actors are most likely to benefit and which platform features are necessary to translate potential access into actual usage [16,17,25,26,27].

In this integrated view, infrastructure sharing is conceptualised as a complementary open innovation service embedded in a sustainable sharing-economy model. It is complementary because it extends existing open innovation functions from intangible exchanges to physical resource coordination. It is sharing-economy-based because it relies on temporary, platform-mediated access instead of ownership. It is circular-economy-relevant because it can improve the utilisation of existing assets and reduce duplication of investments. And it is governance-dependent because the success of such arrangements depends on access rules, pricing mechanisms, trust, support services, and stakeholder alignment.

This synthesis provides a direct bridge to the research questions. RQ1 addresses whether a digital platform can operationalize infrastructure sharing as a complementary open innovation service. RQ2 addresses which infrastructures and conditions make such sharing technically and organizationally feasible. RQ3 addresses which access, pricing, and governance arrangements are preferred and workable in practice. In this way, the literature review becomes an explicit analytical foundation for the empirical part of the paper, rather than a set of loosely related theoretical overviews.

Accordingly, the theoretical framework is not used only as background, but as the organizing logic for the empirical interpretation: open innovation explains the platform-mediated coordination process examined in RQ1; sharing- and circular-economy perspectives explain why some infrastructures are more shareable than others in RQ2; and governance theory explains why negotiated access, responsibility allocation, pricing, support, and contractual clarification are central to RQ3.

3. Research Conceptual Framework

The research is grounded in three complementary theoretical perspectives:

• Open Innovation Platform Perspective

The research conceptualizes infrastructure sharing as an emergent service within the broader Open Innovation ecosystem. Open Innovation platforms are understood as digital intermediaries that reduce transaction costs and enable knowledge flows across organizational boundaries. Infrastructure sharing extends this paradigm from knowledge exchange to resource coordination, requiring specialized platform capabilities for asset discovery, capacity management, scheduling and contractual facilitation.

• Sharing Economy Business Model Perspective

The research applies sharing economy concepts to understand infrastructure sharing as a business model enabling temporary access to capital-intensive assets without full ownership transfer. This perspective emphasizes how digital platforms reduce transaction costs, enable efficient matching between supply and demand, and create value through asset utilization optimization.

• Circular Economy Sustainability Perspective

The research positions infrastructure sharing within circular economy strategies aimed at resource efficiency, extended product lifecycles and closed-loop value chains. This perspective emphasizes the sustainability imperative: infrastructure sharing can narrow resource loops (reducing need for new equipment), slow resource loops (extending equipment lifetime through shared use) and close loops (enabling remanufacturing and recovery services).

Together, these perspectives provide a conceptual framework that allows infrastructure sharing to be analyzed simultaneously as a coordination mechanism, a business model, and a sustainability instrument. The framework is operationalized through four linked theoretical constructs. First, value creation refers to access to otherwise unavailable capabilities, avoided or postponed investment, and capability development. Second, value delivery refers to platform-supported search, matching, scheduling, brokerage, and operator-assisted use. Third, value capture refers to negotiated pricing, voucher-supported access, project-based agreements, or other economic arrangements. Fourth, governance and operations refer to risk allocation, liability and IP safeguards, service-quality control, monitoring, and support mechanisms. These constructs translate the sharing-economy business-model lens into observable features of the registry, request records, and pilot documentation [34,35,36,38,39].

The research model follows an input–process–output logic. The input layer consists of available infrastructures, infrastructure providers, potential takers, and access conditions registered in SCIP/SYNPRO. The process layer covers platform-mediated search, matching, brokerage, negotiation, contractual clarification, and supported use. The output layer captures documented infrastructure use, improved access opportunities for resource-constrained actors, utilization efficiency, capability development, and sustainability-oriented effects. This model links the theoretical perspectives to the empirical research questions: RQ1 concerns the platform-mediated process of access facilitation, RQ2 concerns the fit between infrastructure characteristics and shareability, and RQ3 concerns the governance arrangements that make outputs achievable in practice.

Governance functions as the linking mechanism between the process and output layers of the model. Without governance rules, the platform may create visibility but not necessarily usable access. With appropriate governance rules, matching can be converted into negotiated, safe, and accountable infrastructure use. Governance is therefore assessed in RQ3 as a direct object of analysis, in RQ1 as an enabling condition for access facilitation, and in RQ2 as a factor influencing infrastructure suitability.

4. Methodology

4.1. Research Design and Approach

This study adopts an exploratory case study approach, combining quantitative analysis of platform data with qualitative analysis of request descriptions, interaction records, and pilot reports (Figure 1). The overall objective, stated in the Introduction, is to assess how an infrastructure-sharing service embedded in an open innovation platform can support SMEs, start-ups, and research teams in advanced manufacturing. Accordingly, the empirical research model was applied as a guiding structure for data collection and analysis. Registry data represented the supply/input side of the model; request and interaction records represented the platform process; and documented access, negotiation outcomes, and pilot evidence represented the output side. This approach clarified how the research objective was translated into measurable and interpretable empirical observations.

The study intentionally focused on capital-intensive production and research infrastructure. These assets were selected because ownership barriers, underutilisation risks, and access restrictions are particularly acute in advanced manufacturing. Equipment such as pilot lines, laboratories, robotic cells, test facilities, additive manufacturing systems, and VR/AR environments requires high upfront capital expenditure, specialised operators, safety procedures, and coordinated access to machine time. Therefore, the decision to study these infrastructures follows directly from the theoretical assumptions of the paper: if infrastructure sharing is expected to lower capital barriers and improve asset utilisation, the most relevant empirical setting is a group of assets for which capital intensity, limited availability, and technical complexity hinder ownership-based access by SMEs, start-ups, and research teams [9,10,16,17,20,21,27,29,40].

For methodological clarity, the unit of analysis was defined as an externally accessible physical or hybrid production-research resource that can support experimentation, prototyping, testing, validation, training, or technology demonstration. The study did not consider general office space, purely administrative services, or intangible knowledge-sharing mechanisms as infrastructure unless they were directly related to production or research capabilities. This boundary condition aligned the dataset with RQ2 and RQ3, ensuring that infrastructure suitability, access models, pricing, and contractual arrangements were assessed in relation to resources whose sharing involves genuine operational, security, scheduling, and cost considerations.

The empirical work focuses on the SCIP and its infrastructure module (SYNPRO). The analysis addresses the three research questions introduced in the Introduction by examining two main sources of evidence:

• a registry of infrastructure offers published on the platform, and
• a set of documented infrastructure requests, interactions and reports submitted through the platform and associated project processes.

Quantitative analysis was used to identify descriptive and structural patterns, such as geographic distribution, access models, and request trends. Qualitative analysis was used to interpret user needs, perceived constraints, negotiation processes, and implementation conditions. Both strands were analysed in parallel and integrated in Section 5 and Section 6.

In analytical terms, the pilot actions were treated as embedded moments of the platform’s operation rather than as separate demonstrators. They made it possible to observe how supply was activated through infrastructure registration, how demand was articulated through requests, how brokerage and negotiation unfolded around concrete use cases, and how the outcomes of completed arrangements returned to the platform as documented evidence, lessons learned, and visibility signals.

4.2. Pilot Action Implementation

The registry and request data were generated within a broader pilot action that implemented infrastructure sharing as a service within the SYNERGY platform (Figure 2). The pilot was based on an Infrastructure Provider–Infrastructure Taker model, in which providers register available assets, takers search the registry or submit specific needs, and the platform and project team facilitate matching and support.

The pilot action involved four main stages. First, providers uploaded infrastructure offers using a shared template containing technical, operational, and access information. Second, takers searched the registry or submitted requests describing the required capabilities and intended use. Third, matching and negotiation were carried out on the basis of technical suitability and location, with providers and takers communicating directly and using the General Conditions Framework as a reference. Fourth, implementation was supported where relevant through vouchers or facilitation, for example to cover operator time or equipment rental. After use, participants were encouraged to document outcomes through success stories and lessons-learned templates, which formed part of the qualitative dataset.

This sequence was integrated into the platform in a cumulative way. Infrastructure offers increased the visibility of available resources within the registry; requests activated the matching process; facilitation and vouchers lowered the threshold for first transactions; and the resulting success stories and lessons-learned notes fed back into the platform environment as examples that could support later sharing arrangements. The pilot actions therefore functioned not only as tests of the model but also as mechanisms that progressively populated, activated, and refined the service.

The study covers infrastructures and organisations registered in SCIP/SYNPRO within the SYNERGY and IDEATION projects across 11 Central European countries. The empirical material includes the infrastructure registry, platform-generated and manually collected request data, and qualitative pilot documentation, including the “Rent-a-Robot” pilot and other cases involving VR/AR and testing facilities. The time frame spans from the platform launch in 2020 to mid-2024.

The primary empirical investigation was conducted through structured pilot actions designed to test infrastructure-sharing service delivery. The “Rent-a-Robot” pilot was the main implementation case, complemented by pilots involving testing laboratories, additive manufacturing facilities, and VR/AR environments. Together, these pilots provided a testbed for assessing how the provider–taker model operates across different infrastructure categories.

The pilots were also linked to different phases of the platform dynamic. Some primarily generated supply-side visibility by showing how infrastructures should be described and offered. Others activated demand by revealing the kinds of external needs users were willing to formulate. More advanced cases showed how negotiation, support, and post-use documentation contributed to trust-building and follow-on collaboration. In this sense, the pilot actions formed a bridge between platform architecture and observed user behaviour.

4.3. Data Source and Collection

4.3.1. Infrastructure Registry Analysis

The first dataset is a registry of manufacturing and research infrastructure compiled within the SYNERGY project and published on the SCIP/SYNPRO platform. It contains 290 infrastructure items offered by organisations in 11 countries. Each record describes a distinct physical asset or facility, such as a laboratory, pilot line, test bed, industrial robot, or VR/AR equipment, that can be made available to external users.

The registry was populated through case studies in which infrastructure providers registered their assets using a structured online form. The form captured information including organisation name, type, and location; infrastructure name, description, and application area; alignment with SYNERGY Key Project Areas; technical parameters and equipment categories; access modalities; indicative pricing and access conditions; external links; and acquisition year where known.

Submissions were checked by platform moderators for completeness and consistency before publication. Providers could later update their entries to reflect changes in availability or technical characteristics.

For this study, all infrastructure entries visible on the platform in mid-2024 as a result of the SYNERGY and IDEATION projects were exported to a structured spreadsheet dataset. Variable names and categories were standardised to enable systematic analysis across countries, organisation types, and technology domains.

This methodological approach also shaped the design of the registry. Entries were retained for analysis when they described tangible equipment, laboratories, pilot lines, testbeds, software-hardware environments, or specialized facilities relevant to manufacturing-oriented research and innovation. The inclusion of both manufacturing and research infrastructures reflects the empirical reality of advanced manufacturing ecosystems, where universities, research centers and companies jointly provide the assets needed for early-stage experimentation before commercial-scale production. The focus was therefore not limited to factory-floor machines, but covered the broader set of infrastructures that enable innovation in manufacturing.

4.3.2. Infrastructure Request Analysis

The second dataset consists of 23 documented infrastructure access requests submitted between February 2020 and June 2024. These requests represent concrete attempts by organisations to identify and access infrastructure through the platform and related project mechanisms.

Request data were compiled from several internal sources: online request forms, automatically recorded metadata such as submission time and planned usage period, project documentation of follow-up interactions between providers and takers, and success stories and lessons-learned templates prepared by project partners.

Each request record includes information on the requesting organisation, desired infrastructure characteristics, intended use case and outcomes, requested duration and timing, preferred access and pricing model where indicated, and evidence of follow-up communication, negotiation, and utilisation where available.

Only requests with sufficiently complete metadata and documented follow-up to assess at least basic matching outcomes were included in the analysis.

4.4. Research Variables and Measurement Approach

The research questions (RQ1–RQ3) were operationalised through variables derived from the two datasets: the infrastructure registry and the infrastructure request records.

Registry- and platform-level variables, mainly used for RQ1 and RQ2, include location, owner organisation type, technology area, infrastructure category, access model, pricing information, online presence, and acquisition year as a proxy for technological currency.

Request-level variables, mainly used for RQ1 and RQ3, include requested infrastructure type and category, geographic relation between requester and provider, requested duration and timing, preferred access and pricing model, and matching outcome, ranging from no documented follow-up to communication established or infrastructure use.

Process and governance variables, mainly used for RQ3, include the presence and clarity of general conditions, evidence of negotiation, involvement of platform moderators or support mechanisms such as vouchers, and reported barriers and facilitators drawn from success stories and lessons-learned documents.

Categorical variables were coded using a predefined coding scheme. Where original descriptions were ambiguous, coding decisions were discussed between two researchers until agreement was reached. Missing values were recorded explicitly and considered in the analysis. The theoretical perspectives also guided the operationalization of empirical variables. The sharing-model typology informed the classification of market structure, resource type, access model, and platform role. The business-model perspective informed indicators of value creation, value delivery, and value capture. Transaction-cost and operations-management perspectives informed the coding of negotiation, matching outcome, contractual terms, operator support, scheduling, liability, and governance conditions.

In the coding procedure, infrastructures were interpreted as capital-intensive and manufacturing-relevant when their descriptions indicated at least one of the following attributes: substantial acquisition or maintenance cost, need for specialized technical operation, relevance to production, prototyping, testing or validation, limited substitutability for smaller actors, or dependence on formal access conditions such as safety rules, operator support, scheduling or liability arrangements. These criteria explain why the analysis concentrates on infrastructures for which sharing is theoretically meaningful and practically consequential, rather than on all resources that could be listed on a digital platform.

To make the mixed-methods design fully traceable, variables were operationalized separately for the quantitative and qualitative strands before the findings were integrated. A variable was treated as any registry field, request attribute, process indicator, or coded theme that could be linked directly to RQ1, RQ2, or RQ3. Raw platform fields were first cleaned and standardized, and then recoded into analytical categories. Missing information was not imputed; instead, it was retained as missing and excluded from category-specific denominators where necessary.

Two operationalization matrix for quantitative and qualitative variables are presented in Appendix A. The matrices served as the bridge between the research questions and the empirical evidence: quantitative indicators described distribution, intensity, and outcomes, while qualitative variables explained mechanisms, constraints, and governance conditions underlying the observed patterns.

4.5. Data Analysis Methods

4.5.1. Quantitative Analysis

Quantitative analysis focused on descriptive statistics and exploratory cross-tabulations. For the infrastructure registry, the analysis examined the distribution of infrastructure items by country and city, Key Project Area, technology category, access model, pricing model where specified, and acquisition year as a proxy for technological currency.

For the request dataset, the analysis examined the distribution of requests across infrastructure categories and technology areas, the share of infrastructure items receiving at least one request, the geographic relation between requesters and providers, planned access duration, submission year, and basic indicators of matchmaking effectiveness, such as whether communication, negotiation, or utilisation was documented.

Because the data are exploratory and pilot-based, inferential statistical tests were not used. Instead, quantitative results were used descriptively to characterise patterns and identify areas of relatively high or low demand and access facilitation.

For transparency, quantitative measures were calculated as simple frequencies, percentages, and cross-tabulations using the operationalized variables. Request intensity was calculated as the number of infrastructure items in a category that received at least one documented request divided by the total number of registered items in that category. Model effectiveness was assessed through an ordinal outcome variable distinguishing registration-only visibility, communication or negotiation, and documented infrastructure use. Because several registry fields were incomplete, percentages were interpreted descriptively, and denominators were kept category-specific rather than normalized across the full dataset.

For RQ1, this outcome variable should be interpreted as an access-facilitation indicator rather than as a direct measure of improved resource access. It captures the observed progression from visibility and request initiation to communication, negotiation, and selected documented use, while perceived benefits are analyzed separately in the qualitative strand.

4.5.2. Qualitative Analysis

Qualitative analysis was applied to three types of textual material: request descriptions, interaction documentation such as email summaries and meeting notes, and success stories and lessons-learned documents produced after completed sharing arrangements.

A thematic coding approach was used. Initial codes were derived deductively from the research questions and conceptual framework, including motives for sharing, perceived barriers, trust mechanisms, preferred contractual terms, and perceived benefits. Additional inductive codes were introduced when new patterns emerged, such as concerns about data security or operator availability. Coding was performed manually by two researchers. They first coded a subset of the material jointly and then divided the remaining material, discussing ambiguous cases to maintain consistency.

To enhance analytical transparency, the qualitative analysis retained traceable evidence from the collected material, including recurring phrases in request descriptions, documented implementation notes, and post-pilot success stories. These materials were used not only for illustration but also to verify whether the same mechanism appeared in more than one source of evidence.

The qualitative analysis was used to reconstruct the organisational value-creation mechanisms associated with platform participation, particularly improved resource access, deferred investment needs, capability development, and network expansion in documented pilot cases. It had three main aims: to explain quantitative patterns, identify implementation conditions and governance mechanisms in actual sharing arrangements, and illustrate sustainability and economic impacts through empirical examples.

Qualitative variables were therefore not treated as statistical variables, but as thematic constructs used to interpret the quantitative findings. Each theme was coded as present when it appeared in request wording, follow-up documentation, or pilot reports. Where possible, themes were compared across more than one evidence source to support triangulation. This procedure made it possible to connect observable platform indicators, such as requests and access models, with explanatory mechanisms such as trust, operator support, capability development, and perceived risk.

4.6. General Conditions Framework Application

The analysis of governance and contractual aspects (RQ3) is guided by the General Conditions Framework developed in the SYNERGY and IDEATION projects for infrastructure sharing. The framework identifies four key dimensions that should be defined in a sharing arrangement: access schedule requirements, economic conditions, user responsibilities, and contractual framework.

These dimensions were used as an analytical lens for reviewing infrastructure proposals, request descriptions, and documented agreements. Where sufficient information was available, each case was assessed in terms of how these dimensions were addressed and whether they met the framework requirements or required case-specific adaptation.

5. Results

This section presents the empirical findings in direct relation to RQ1–RQ3. To strengthen analytical coherence, the results are organised around three corresponding themes: the access-facilitation capacity of the infrastructure-sharing model, the differentiated suitability of infrastructure categories, and the access, pricing, and governance conditions preferred by providers and takers. The interpretation combines quantitative patterns with selected qualitative evidence to explain how value is generated for participating organisations and why some platform-based arrangements progress more effectively than others. Additional descriptive statistics, detailed category breakdowns, and supplementary evidence supporting the results are provided in Appendix A.

5.1. RQ1: How Does the Provider-Taker Model Support Resource-Sharing Interactions and Documented Access-Related Outcomes?

The empirical material does not allow a comprehensive assessment of resource access across the full population of potential users. It does, however, allow an assessment of whether the Infrastructure Provider–Infrastructure Taker model supports access-enabling interactions through the SCIP/SYNPRO platform. The platform aggregated a registry of 290 infrastructure items across 11 countries, while the request dataset comprises 23 documented access requests (Figure 3). The available records show that most requests progressed to direct provider–taker communication and that a subset resulted in documented use or implementation-oriented pilot activity. Therefore, the RQ1 findings should be interpreted as evidence of access facilitation and coordination, rather than as proof of a general increase in resource access.

The key observation is not the absolute number of requests, but the progression of requests once they were initiated. In most documented cases, the platform-supported process enabled partner contact, negotiation of conditions, and, in several cases, actual use. In this sense, the model functions less as a high-volume transactional marketplace and more as a structured coordination mechanism for advanced manufacturing infrastructure.

The qualitative material clarifies what this operational feasibility means at the organizational level. In the documented pilot cases, the platform created value by enabling access to equipment that users did not possess internally, allowing experimentation without immediate capital expenditure, and supporting coordination around the practical conditions of use. The recorded cases also indicate benefits beyond a single transaction, including technology familiarization, student training, and the emergence of new research and collaboration links. These observations suggest that platform effectiveness should be interpreted not only as successful matching but also as the creation of organizational options for testing, learning, and collaboration.

A more detailed inspection of the cases reveals a recurring process. An initial need for external infrastructure access was typically followed by clarification of technical scope, timing, and user responsibilities. Only then did the arrangement move toward short-term use for evaluation or training, or toward a broader collaborative activity. In the documented VR/AR and other pilot cases, access was therefore not limited to one-off use; follow-up records indicate technology familiarization, student training, and the creation of new collaboration links between institutions and users.

A second recurring pattern concerns the economic logic of participation. The collected evidence indicates that actors used the platform to test ideas and capabilities before committing to capital expenditure. In practical terms, access to external infrastructure functioned as a way to postpone or avoid immediate investment in expensive equipment during the experimentation stage, thereby lowering the threshold for early prototyping and validation.

From a platform-dynamics perspective, these pilot cases show that implemented sharing did not emerge solely from a static directory. Each pilot combined several stages of the platform process: registry visibility, request formulation, provider–taker interaction, negotiated terms, and post-use documentation. The completed cases therefore served a dual purpose: they addressed a specific user access problem while also generating trust, operational knowledge, and examples that could support future platform interactions.

5.2. RQ2: Which Infrastructures Are Most Suitable for Sharing, and What Characteristics Explain Their Sharing Potential?

The results show substantial heterogeneity in the sharing potential of registered infrastructures. Demand is concentrated in selected categories rather than distributed evenly across the registry. In particular, VR/AR-related infrastructures attract the highest request intensity, while many traditional, production-integrated assets receive little or no external demand despite their numerical presence in the inventory (Figure 4). This indicates that early-stage infrastructure sharing is driven less by generic capacity optimization and more by a combination of novelty, accessibility, and suitability for experimentation.

The observed pattern suggests that infrastructures are particularly suitable for sharing when they are modular, self-contained, relatively safe to use, clearly documented, and supported by operator assistance or training where needed. By contrast, assets embedded in proprietary production processes, or those requiring extensive calibration, high safety assurance, or continuous expert supervision, appear less likely to be shared successfully (

Table 1. Infrastructure categories and observed sharing intensity (RQ2).

Infrastructure Category	Indicative Request Intensity	Interpretation
VR/AR-related infrastructures	Very high	Strong demand linked to experimentation, training, and novelty
Robotics and automation	Moderate	Sharing potential exists, but safety and application specificity matter
3D technologies	Low to moderate	Useful for prototyping, but logistics and local substitutes may reduce demand
Measurement and analysis equipment	Low	Expertise, calibration, and procedural requirements constrain access
Production-integrated/general manufacturing assets	Low	High downtime costs, liability, and process embeddedness reduce shareability

).

Qualitative evidence helps explain this asymmetry. VR/AR and other modular infrastructures appear attractive because they are visible, relatively secure, and well suited to short-term exploratory projects, demonstrations, training, and capacity building. Production-integrated resources, on the other hand, typically involve downtime costs, calibration requirements, confidentiality concerns, and dependence on limited operator time. The potential for infrastructure sharing is therefore shaped not only by the existence of unused resources but also by the practical ease with which external users can access and meaningfully use them (

Table 2. Characteristics associated with higher and lower sharing potential (RQ2).

Higher Sharing Potential	Lower Sharing Potential
Technologically novel and attractive for experimentation	Mature assets with weak external learning incentives
Modular and relatively self-contained	Strongly embedded in proprietary production lines
Relatively safe to access or demonstrate	High safety, liability, or compliance requirements
Clearly documented and visible online	Poorly described or difficult to interpret externally
Flexible scheduling and available support	Dependent on scarce operator time or complex setup

).

This interpretation is supported by the collected evidence in three ways. First, the most frequently requested VR/AR cases were related to exploratory applications, such as demonstrations, short-term evaluations, training, and research preparation, rather than routine production. Second, documented secondary effects include architecture-oriented research networking, student training, and increased familiarity with new VR technologies, suggesting that demand was linked to learning and capacity building as much as to hardware access itself. Third, low-demand categories tended to be those involving stronger dependence on calibration, operator time, process integration, or confidentiality.

These qualitative findings support the conclusion that practical usability, interpretability, and support requirements shape sharing potential more strongly than the mere existence of unused resources. The difference between high- and low-demand categories is therefore not simply a matter of technological type but also reflects the organizational conditions under which external users can safely and meaningfully use the infrastructure.

5.3. RQ3: What Access Models, Pricing Arrangements, and Contractual Conditions Are Preferred in Practice?

The results indicate a clear preference for flexible, negotiated access arrangements rather than fully standardized rental models. On the provider side, “usage according to agreement”, mixed access options, and “price for negotiation” are common. On the taker side, requests also concentrate on negotiated access rather than predefined booking terms (Figure 5). This suggests that infrastructure sharing in advanced manufacturing should not be interpreted as a simple rental service. Instead, it operates as a collaborative arrangement in which equipment access is often combined with technical support, scheduling flexibility, and case-specific agreement on responsibilities and economic terms.

Successful cases also show that governance conditions are central enablers of sharing rather than peripheral details. Access schedules, user responsibilities, and at least a basic contractual framework are usually made explicit, even when final economic terms remain flexible. In this sense, the platform supports not only search and matching but also the structured negotiation of conditions under which sharing becomes organisationally and technically acceptable (

Table 3. Preferred access, pricing, and governance conditions in practice (RQ3).

Dimension	Dominant Pattern	Interpretation
Access model	Usage according to agreement dominates on both supply and demand sides	Flexible, negotiated access is preferred to rigid booking models
Pricing logic	Price for negotiation and price on request are common	Standard tariffs are secondary to case-specific arrangements
Usage duration	Most requests concern short- to medium-term use, especially 1–3 months	The model is used primarily for pilots, testing, and R&D-related access
Governance conditions	Access schedule and user responsibilities are usually specified; contractual framing is present in many successful cases	Sharing depends on clear operational and organizational rules, not on price alone
Service logic	Access is often bundled with support, coordination, or supervision	The platform mediates collaboration rather than simple equipment rental

).

The qualitative material suggests that negotiated arrangements are preferred because users typically seek a broader service package rather than isolated machine time. In the documented cases, access to equipment was combined with scheduling flexibility, technical guidance, clarification of user responsibilities, and, where necessary, operator support. Negotiated access therefore appears to reduce organizational uncertainty for both providers and takers, making the transition from platform interest to realized collaboration more feasible.

The collected evidence also reveals a characteristic negotiation pattern. Requests that progressed further usually specified the intended period of use and project purpose at an early stage, while provider-side follow-up focused on cost conditions, operator availability, and clarification of user responsibilities. In several documented cases, access to infrastructure was effectively bundled with supervision, training, or problem-specific technical assistance. This helps explain why fully standardised rental logic was less attractive than negotiated arrangements.

Overall, users were often seeking a temporary capability package rather than machine time in isolation. The organizational value of the platform therefore lies not only in exposing a catalogue of available assets but also in creating a framework within which technical, economic, and responsibility-related conditions can be configured around a concrete use case.

5.4. Cross-Cutting Observations and Implications for Platform Design

Across RQ1–RQ3, three recurring patterns can be observed. First, the platform-based sharing model is feasible, but it remains at an early stage of adoption and depends on active brokerage and network effects. Second, sharing potential is not evenly distributed across infrastructure categories; it is strongest where assets are technologically novel, modular, relatively safe, clearly documented, and supported by operator assistance where needed. Third, infrastructure sharing in advanced manufacturing functions less as a standard rental market and more as a governed collaborative arrangement in which access, scheduling, pricing, responsibilities, and support are negotiated around specific use cases. These observations suggest that future platform development should prioritize the discoverability of suitable assets, interaction support for negotiation, and clear governance rules for access, responsibilities, and contractual framing.

This cross-cutting interpretation links the empirical results back to the research model. RQ1 is supported by evidence on registry visibility, access requests, provider–taker communication, negotiation, and selected documented use; it is therefore interpreted as platform-enabled access facilitation rather than as a population-wide measure of improved resource access. RQ2 is supported by differences in request intensity and shareability characteristics across infrastructure categories, showing how theoretical arguments on asset utilization and circularity appear in practice. RQ3 is supported by the observed preference for negotiated access, pricing flexibility, scheduling arrangements, user responsibilities, contractual framing, and operator support, showing how governance converts potential access into usable sharing arrangements.

6. Discussion

The empirical analysis of 290 registered infrastructures and 23 documented requests on the SCIP/SYNPRO platform shows that infrastructure sharing is a technically and organisationally feasible open innovation service, although it remains at an early stage of market adoption. This section interprets the main findings in relation to the research questions, prior literature, and the specific context of advanced manufacturing in Central Europe, highlighting implications for platform design, policy, and future research.

6.1. Interpretation of the Main Findings

The first key finding concerns the Infrastructure Provider–Infrastructure Taker model (RQ1). The evidence shows that the model can support access-enabling interactions under real-world conditions. Although the absolute number of requests was relatively low, 70–80% of documented requests progressed to concrete communication and, in several cases, to actual access arrangements. This suggests that, once a request is initiated, the platform and the general conditions framework can support matchmaking, negotiation, and implementation. From a functional perspective, SCIP/SYNPRO therefore addresses a gap identified in other open innovation platforms, which have generally lacked dedicated and operationalised support for physical infrastructure sharing. These results support the theoretical proposition that open innovation platforms can extend from knowledge exchange to resource coordination, provided that robust governance mechanisms are in place [2,28]. They also align with findings that digital capabilities and governance structures jointly influence platform innovation performance [5].

At the same time, the answer to RQ1 should be framed cautiously. The evidence supports the model’s role in enabling and coordinating access attempts, but it does not establish a statistically measured increase in resource access beyond the documented cases. Read through the research model, governance functions as the mechanism connecting the platform process with realised use: the registry creates visibility, but access becomes feasible only after scheduling, cost, responsibility, support, and contractual conditions are clarified. This interpretation strengthens the alignment between the research objectives, the theoretical framework, and the empirical findings.

The second major finding concerns the heterogeneity of sharing potential across equipment categories (RQ2). Emerging technologies (particularly VR/AR) showed a request rate of roughly 75%, while traditional manufacturing equipment attracted only 1–3%. This indicates that demand is driven by exploration and learning motives rather than by pure capacity optimization. Such behaviour corresponds to the open innovation literature, which characterizes shared assets as “boundary objects” enabling experimentation across organizational boundaries [1]. It also resonates with shared manufacturing research, which emphasizes modularity, safety, and knowledge transfer as essential prerequisites for successful sharing arrangements [8,9].

The third finding concerns access and pricing preferences (RQ3). The study identifies a clear preference for flexible and negotiated arrangements. On the supply side, many providers rely on “for negotiation” or “price on request” terms; on the demand side, 78% of requests specify “usage according to agreement.” This pattern supports the view that collaboration and co-creation, rather than purely transactional exchange, drive value in digital open innovation ecosystems [3]. The emphasis on relationship-based interaction also supports the broader theoretical view that successful platform-mediated innovation depends on social trust, adaptive governance and shared learning among heterogeneous actors [4,41].

Qualitative evidence further clarifies what value creation means in organizational terms. Across the documented cases, the platform appears to generate value through four recurring mechanisms: access to otherwise unavailable infrastructure, avoidance or postponement of investment, capability development through guided experimentation and operator support, and network expansion through new provider–taker relationships. This mechanism-based interpretation is consistent with the broader literature on SME platform participation and ecosystem-based value co-creation [16,17,25]. The qualitative material also clarifies how pilot actions were integrated into the platform’s dynamics. Rather than functioning as external showcases appended to a digital tool, the pilots acted as activation episodes through which the service accumulated content, interaction experience, and legitimacy. Their role was therefore dynamic and recursive: each pilot not only validated a specific arrangement but also improved the conditions under which later users could discover infrastructures, formulate requests, and enter negotiations with greater confidence.

The empirical traces reported in success stories and lessons-learned notes point to a layered sequence of outcomes: first access, then experimentation, then capability development, and finally network extension. In practical terms, a single access event could therefore serve simultaneously as a testing opportunity, a learning episode, and a starting point for broader cooperation. This pattern is important because it shows that the benefits of platform participation cannot be reduced to utilization statistics alone.

Finally, the results reveal structural barriers to scale, including limited discoverability, geographic concentration, liability concerns, dependence on operator expertise, and low awareness beyond the core network. These findings resonate with broader ecosystem studies showing that digital innovation performance depends on network maturity, trust-building, and institutional support [22,25]. Overcoming these barriers will require a combination of platform design improvements and policy-level interventions that support SME participation, regional brokerage, and cross-regional infrastructure sharing.

6.2. Implications for Theory and Open Innovation Platforms

From a theoretical standpoint, the study extends the literature on open innovation by empirically validating infrastructure sharing as a distinct service within platform-mediated ecosystems. Previous studies have emphasised digital collaboration, but have paid less attention to the sharing of physical infrastructures [13,14]. This study demonstrates that integrating physical infrastructure sharing into an open innovation platform is both feasible and valuable, providing a concrete mechanism for resource-based collaboration.

The findings also clarify the article’s contribution to sharing-economy theory. The SCIP/SYNPRO case shows that B2B infrastructure sharing cannot be reduced to consumer-style platform intermediation. Although the platform lowers search and negotiation costs, realized value still depends on relational governance, technical support, risk allocation, and case-specific contracting. The dominance of “usage according to agreement” is therefore consistent with a B2B sharing model in which value capture is negotiated and service delivery combines asset access with expertise, supervision, and scheduling [34,36,37,38,39].

The evidence further refines the understanding of which infrastructures are “shareable”. Rather than treating all research equipment as equally suitable for sharing, the results support a more nuanced view: sharing potential depends on a combination of technical, organisational, and risk-related characteristics. Infrastructures show higher sharing potential when they are modular and not tightly embedded in production lines, relatively self-contained and safe to operate, accompanied by training and support, and associated with emerging technologies for which external users have strong learning incentives. The observed heterogeneity in shareability therefore supports the view that platform effectiveness depends on matching technical modularity with governance flexibility [5].

6.3. Practical Implications for Platform Design and Management

For practitioners, the results emphasize that demand generation, not supply, is the current bottleneck. Expanding platform awareness through SME outreach and integration with innovation support schemes will be crucial, consistent with evidence that digital platforms enhance SME innovation performance when combined with value co-creation mechanisms [16,17]. The preference for negotiation-based access suggests platforms should facilitate semi-structured interactions, combining automation with flexibility, an approach endorsed in open innovation governance research [41]. Moreover, the geographic concentration of requests reinforces the need for regional hubs and brokerage mechanisms, resounding findings that ecosystem coordination and local proximity drive networked innovation performance [1]. These implications are closely connected to responsible innovation and stakeholder engagement, because successful infrastructure-sharing schemes depend on credible intermediaries, transparent rules, and practical support that enables smaller organisations to participate on reasonable terms.

6.4. Policy and Ecosystem Implications

At policy level, the findings support the argument that infrastructure sharing can be an effective lever for regional innovation and circular economy strategies, but only if embedded in a broader ecosystem approach. The results indicate that sustainable sharing models in manufacturing require integration between digital transformation, corporate responsibility, and regional innovation policy rather than isolated platform deployment. Public support instruments should therefore incentivize not only asset utilisation but also transparent governance, stakeholder collaboration, and broader access for SMEs, start-ups, and research organisations.

The strong urban concentration of infrastructures in university and research hubs and the dominance of a single pilot institution in request activity indicate that existing institutional networks and local proximity are critical for early adoption. Policy measures that incentivize cross-regional collaboration, co-funded access schemes and joint calls involving multiple regions could help move from local to transnational sharing networks.

Circular platforms, when coupled with open innovation policies, can significantly increase resource utilization and economic efficiency [10,20]. Expanding voucher schemes, standardizing contractual frameworks and linking platforms to EU digital and green transition programmes could amplify the observed benefits, consistent with CE and sharing-economy literature highlighting the importance of systemic enablers [6]. In addition, future policy design could benefit from stronger ESG-oriented indicators that capture not only utilisation and cost effects but also inclusion of smaller actors, quality of stakeholder engagement, and the responsible governance of shared assets.

6.5. Overall Contribution

Overall, this study provides one of the first empirically grounded demonstrations that physical research infrastructure can be successfully integrated into open innovation platforms. It shows that:

• the provider–taker model is operationally viable;
• sharing potential is category-dependent and driven by modularity and novelty;
• flexible, negotiated access dominates transactional models; and
• infrastructure sharing can enhance sustainability and innovation simultaneously.

These findings prove theoretical claims about the convergence of open innovation, sharing economy and circular economy paradigms [2,6,20], and provide a solid empirical foundation for scaling such models across Europe’s manufacturing innovation ecosystems. More broadly, they position infrastructure sharing as a relevant case of the sustainable sharing economy, in which digitally enabled access models can align business sustainability, responsible innovation, and stakeholder-oriented resource governance.

7. Conclusions

This study assessed whether and how infrastructure sharing can function as a complementary open innovation service in advanced manufacturing, using empirical data from the SCIP. Based on a registry of almost 300 infrastructures across 11 countries and 23 documented access requests, the research shows that platform-mediated infrastructure sharing is feasible in practice and can generate innovation and sustainability benefits. However, its adoption remains at an early and still fragile stage. The findings should therefore be understood as evidence of access facilitation within the observed platform records, rather than as a comprehensive assessment of resource-access outcomes for all potential users.

This conclusion mirrors the logic of the research model. First, the study establishes the role of the platform in enabling access-related interactions. Second, it identifies the technical and organisational conditions that make infrastructures more or less shareable. Third, it shows that governance arrangements are required to translate platform visibility into responsible and usable sharing. This alignment strengthens the coherence between the objectives, literature review, results, and final contribution of the article.

First, the results confirm that the Infrastructure Provider–Infrastructure Taker model is operationally feasible. Once a request is made, 70–80% progress to concrete arrangements, with near-universal implementation of access schedules and user-responsibility definitions and high implementation rates for economic terms and contractual elements. This shows that the combination of a dedicated platform and a structured general conditions framework can support matchmaking, negotiation, and execution of infrastructure-sharing agreements. In this way, the study addresses a gap in existing open innovation platforms, which rarely handle physical infrastructures in a systematic way.

Second, the study shows that sharing potential is strongly category-dependent. Emerging, modular, and relatively safe technologies, especially VR/AR, reach request rates around 75%, whereas traditional production-integrated equipment categories attract only 1–3% of requests despite much larger installed bases. This indicates that infrastructure sharing is currently driven more by exploration, learning, and access to novel capabilities than by pure capacity utilisation. Infrastructures that are easy to explain, decoupled from critical production lines, and accompanied by support services are much more likely to be shared successfully.

Third, the evidence highlights a clear preference for flexible access arrangements. Both providers and users tend to favour negotiation-based pricing and “usage according to agreement” over standardised rental schemes. In research and innovation contexts, infrastructure sharing therefore functions less as a conventional rental market and more as a relationship-based collaboration mechanism in which access, knowledge exchange, and future partnership potential are bundled. Platforms and policies that assume purely transactional behaviour are therefore likely to underperform.

Fourth, even with a relatively small number of cases, the analysis points to meaningful economic and sustainability benefits. Shared use can help avoid redundant investment, particularly in high-cost emerging technologies, improve the utilisation of existing assets, extend equipment lifetimes, and support circular-economy objectives by intensifying and prolonging use. Secondary effects, such as new research collaborations, technology familiarisation, and capacity building, create additional value that is not easily captured by traditional cost–benefit analyses but is highly relevant for regional innovation ecosystems.

The qualitative evidence makes these benefits more tangible. In the documented cases, shared access was accompanied by examples of technology familiarization, student training, architecture-oriented research networking, and new collaborative pathways between institutions and firms. These materials indicate that the observed patterns were not only inferred from distributions but were also visible in request content, follow-up interactions, and post-pilot documentation.

At the same time, the study reveals significant barriers that currently constrain scale-up: limited discoverability of suitable infrastructures, strong geographic concentration of requests, operator-expertise requirements, data and IP sensitivities, low awareness beyond the core network, and the early developmental stage of cross-regional sharing networks. Addressing these barriers will require coordinated efforts in platform design, such as improved search and recommendation functions and richer interaction support, as well as public-policy instruments, including incentives, vouchers, and integration with regional innovation and sustainability programmes.

The study has several limitations that should frame interpretation of the findings. The request dataset includes only 23 documented requests, which restricts generalizability, and the registry reflects infrastructures of organizations participating in the SYNERGY and IDEATION projects rather than the full population of infrastructures available in the covered countries. The analysis also captures a specific implementation window, from February 2020 to June 2024, and is strongly concentrated in selected partner regions, while data completeness varies across registry fields and interaction records. In addition, self-selection and reporting bias may be present, as participating providers and takers may be more innovation-oriented and collaborative than the wider population. For these reasons, the findings are best interpreted as evidence from an early-stage implementation of infrastructure sharing within an open innovation platform rather than as a definitive statistical assessment. Future research should therefore combine larger datasets, broader cross-regional samples, and longitudinal pre–post designs that track organizational outcomes such as equipment access, avoided investment, capability development, and collaboration intensity over time.

Taken together, the findings suggest that infrastructure sharing should be understood and supported as a strategic complement to other open innovation instruments rather than as a universal solution to underutilised assets. Its greatest current impact lies in enabling experimentation with emerging technologies, fostering collaboration between research institutions and companies, and contributing to more resource-efficient use of specialised equipment. With targeted improvements to discoverability and ecosystem support, the model documented here has the potential to scale beyond pilot settings, deepen its role in advanced manufacturing innovation systems, and contribute not only to competitiveness and the circular economy but also to the broader development of a sustainable sharing economy based on responsible governance, stakeholder integration, and innovation.