Industrial data ecosystems and data spaces

Möller, Frederik et al. Article — Published Version Industrial data ecosystems and data spaces Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Möller, Frederik et al. (2024) : Industrial data ecosystems and data spaces, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 34, Iss. 1, https://doi.org/10.1007/s12525-024-00724-0 This Version is available at: https://hdl.handle.net/10419/315780 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Electronic Markets (2024) 34:41 https://doi.org/10.1007/s12525-024-00724-0 FUNDAMENTALS Industrial data ecosystems anddata spaces FrederikMöller1,3 · IlkaJussen2,3· VirginiaSpringer4· AnnaGieß1,3· JuliaChristinaSchweihoff1,2· JoshuaGelhaar2,3· TobiasGuggenberger2,3· BorisOtto2,3 Received: 24 February 2024 / Accepted: 17 July 2024 / Published online: 6 August 2024 © The Author(s) 2024 Abstract Industrial data ecosystems are inter-organizational forms of cooperation emerging around sharing data. They arise from a digital infrastructure, giving data providers and data users a platform to share and (re-)use data. Data spaces are among the digital infrastructures frequently associated with data ecosystems, as they supply a shared digital space for its participants to share data in a sovereign way. Data spaces aim to close a gap in the digital infrastructure landscape, addressing concerns of organizations when sharing data, such as data misappropriation or a lack of control of shared data. They do this by implementing data sovereignty—typically through Usage Control Policies—that give data providers the means to formalize semantically and technically how data users are allowed to use their data. In this fundamentals article, we address the following issues: (1) contextualizing and demarcating data spaces and data ecosystems, (2) systematizing data spaces in the research and policy landscape, and (3) elaborating on a research agenda for Information Systems (IS) research. Keywords Data ecosystems· Data sharing· Data spaces JEL Classification A1 Introduction The ever-increasing availability of data and diffusion of digital technologies leads to a shift in perception of data from a byproduct to a strategic resource that offers more and more opportunities for staying competitive and finding new angles for diversification (Legner etal. 2020). Leading management consultancy Gartner predicts that “organizations that promote data sharing will outperform their peers on most business value metrics” (Goasduff 2021). Additionally, new legislation such as the German Act on Corporate Due Diligence Obligations in Supply Chains (Supply Chain Act) or the Corporate Social Responsibility Directive (CSRD) requires organizations to collect data about their direct and indirect suppliers as well as to ensure that they themselves and their suppliers do not engage in activities that are harmful to the environment or promote inhumane working conditions. Realizing the positive intentions of the Supply Chain Act and navigating the additional cost of bureaucracy and inter-organizational cooperation require organizations to collect data from multiple parties, such as suppliers, government agencies, customers, non-government organizations, or whistleblowers (Federal Ministry for Economic Cooperation & Development 2023; Germany’s Federal Parliament 2021). A recent study by the German research institution IW Köln (Kolev-Schaefer & Neligan 2024) finds that most companies, even if they are not involved directly, are now tasked to supply information to their suppliers or customers who fall under the Supply Chain Act1. This can be particularly challenging. For instance, the automotive industry is characterized by extremely complex supply chains in which car manufacturers employ thousands of direct Responsible Editor: Mark de Reuver * Frederik Möller [email protected] 1 TU Braunschweig, Data-Driven Enterprise, Braunschweig, Germany 2 TU Dortmund, Industrial Information Management, Dortmund, Germany 3 Fraunhofer ISST, Dortmund, Germany 4 University ofSydney, NewSouthWales, Australia 1 Companies with at least 1000 employees fall under the Supply Chain Act. Electronic Markets (2024) 34:4141 Page 2 of 17 and even more indirect suppliers2. At the same time, car manufacturers are tasked to create transparency and continuously monitor their (in-)direct suppliers for risks regarding environmental or human rights infringement (CSR-in-Deutschland 2023). Creating transparency, continuously monitoring their (in-)direct suppliers, and fostering collaboration across the supply chains are challenging for the involved car manufacturers (Leckel & Linnartz 2023). For instance, BMW is currently under pressure since one of their suppliers in Morocco supposedly mines cobalt while releasing arsenic in nearby waters, subsequently causing environmental damage and potentially harming humans (Blum etal. 2023). Collecting the data to fulfill these requirements and generate novel business can hardly be achieved alone or be done solely in sequential supply chain relationships but requires inter-organizational industrial data ecosystems (Legenvre & Hameri 2024; Oliveira etal. 2019). However, these data ecosystems rarely emerge naturally (i.e., without legislative force) because of a range of concerns organizations have when sharing their data. Among these is the fear that their data will be used against them, negative effects in competition, data misappropriation, or a general lack of control in deciding what can be done with their data (Fassnacht etal. 2023; Jussen etal. 2024; Opriel etal. 2021). A recent study from Germany finds that issues of data sovereignty, such as access control, data protection, and unclear usage rights of data, are among the top concerns of organizations when sharing data (Röhl etal. 2021). Data spaces are inter-organizational information systems (IOISs) that explicitly address these concerns and aim to support inter-organizational data sharing by organizationally and technically implementing data sovereignty (Otto & Jarke 2019). They are one way to realize flourishing industrial data ecosystems, and in our opinion, one of the most promising, in which organizations can access multiple data sources in a distributed system while agreeing on a set of bilateral (peerto-peer) and multilateral (ecosystem) set of data usage rules (Otto 2022b). These rules can include access control and usage control policies, which formalize how data providers’ data can be accessed and used by data users through technical implementation, e.g., so-called data space connectors (Otto 2022a; Otto & Jarke 2019).3 When entering a data space, participants are required to accept its rules defined in the governance framework (Data Spaces Support Center 2023). The political and economic importance of these data spaces is reflected in a range of European initiatives enacting a significant effort to promote data spaces. For instance, GaiaX is a European initiative providing architectural guidance for creating data ecosystems in many domains, such as Agriculture, Logistics, Energy, and Health (Gaia-X 2022). In their view, data spaces are the “sum of all its participants, which may be data providers, users and intermediaries” designed to uphold data sovereignty and trust among its participants (Gaia-X 2023). The German initiative Catena-X Automotive Network aims to create an open data ecosystem based on the Catena-X data space covering the automotive supply chain tailored explicitly to its needs and including typical stakeholders taking on different roles (e.g., OEMs, Suppliers, IT Services). Members must use a standardized interface—a data space connector—to access this data ecosystem (Catena-X 2022b). In a recent article in the German news outlet Süddeutsche Zeitung, Catena-X is positioned as the data-based enabler to comply with the German Supply Chain Act (see above) (Martin-Jung 2022). Thus, one can recognize the stark effort put into implementing data spaces complemented by multiple initiatives, research projects, or large-scale community events (e.g., the Data Space Symposium with more than 1000 participants interested in data spaces). Against this background, data spaces are poised and tasked to prove their value in practice and, correspondingly, their interest to the IS research community. However, while technical standards are critical, data spaces, as of now, lack proven business models for sustainable, long-term success (Bub 2023). Additionally, legislation, such as the Data Governance Act (DGA), regulates data intermediation services by limiting data usage and setting conditions for the provision of services and infrastructures (Richter 2023), which complicates data space implementation in practice (Gemein etal. 2023). While there is a large landscape of projects working on data spaces and the facilitation of data ecosystems, these two concepts, in conjunction with each other, are a blind spot in IS research. This is problematic since they are often used interchangeably, prompting a range of adverse effects. First (1), giving the perception that data spaces are the only way to initiate and maintain data ecosystems could exclude or suppress other data infrastructures. For instance, data marketplaces, which are not data spaces (although they could play a role in them), generate data ecosystems of data providers and users using their centralized technical infrastructure to buy, sell, and generally trade data. This means that data spaces require a specific definition as one kind of data infrastructure that enables data ecosystems while fully knowing that there are other infrastructures capable of generating data ecosystems that could be more relevant to the underlying use case. Second (2), this blurry overlap prevents sharp distinctions between each concept and prevents clear scholarly discussion and alignment (Schönwerth 2022). As such, it is necessary to position data spaces as data infrastructures that can generate one or multiple data ecosystems, as well as posit that there is 2 Two examples: Mercedes-Benz Group (2024) reports around 40,000 suppliers, and BMW Group (2024) reports around 12,000 suppliers. 3 The Data Act defines more roles, such as the data holder who has the right to grant access to data. In this article, we will concentrate only on the relationship between the data provider and the data user, because the legal terms do not necessarily correlate with technical roles (Data Spaces Support Center (2023). Electronic Markets (2024) 34:41 Page 3 of 17 41 not one data space but likely a whole array tailored to different industries and communities (e.g., see the overview given by the Common European Data Spaces, European Commission 2024b). This conflation could provoke unrealistic expectations by researchers and practitioners and misrepresent their intertwined roles. Third (3), data spaces generate data ecosystems, and participants—often data providers and data users—can be part of the digital infrastructure by using a dedicated data space connector or contribute or consume data or services to the larger data ecosystems outside of the digital infrastructure. The differentiation between actors engaged in the data space and the data ecosystems is necessary since one is integrated technically (data space), while the other does not necessarily have to be (data ecosystem). Disentangling this overlap is relevant as being engaged in a data space poses a different and unique set of requirements (e.g., adhering to specific rules and technical integration) than being part of a more comprehensive data ecosystem. Fourth (4), the blurriness above might prevent IS researchers from exploring data spaces and their ecosystems with maximum effectiveness and results in a lack of a distinct research stream for the IS community. To summarize, the fundamentals article addresses these issues and aims to clarify the role of data spaces in conjunction with data ecosystems (1), illustrate and systematize data spaces and their initiatives (2), and craft a research agenda for IS research (3 and 4). From data sharing todata spaces Over the years, research has started to acknowledge that data sharing is and has been a pivotal activity in many industries, such as e-commerce (e.g., Ghoshal etal. 2020) or healthcare (e.g., Li & Qin 2017). In the following, we illustrate, narratively (Schryen etal. 2020), the field of industrial data sharing between at least two organizations. We start by exploring supply chain data sharing and outline how this notion developed into what we call—today—data ecosystems. Supply chain data sharing4 Data sharing is not a novel activity, but it has been discussed for over 40 years in the literature. Early papers explored data sharing within organizations, responding to the introduction of computers and databases (e.g., Brathwaite 1983). Data is mandatory for organizations to conduct cardinal business functions, such as record-keeping or documentation of business transactions (Davenport & Prusak 1998). Early research also identified the value of information partnerships that produce shared benefits such as cost sharing and distributing excess capacity through customer data sharing (Konsynski & Mcfarlan 1990) or the benefits of the introduction of Electronic Data Interchange (EDI) for sharing electronic business documents between organizations (Hansen & Hill 1989; Mukhopadhyay etal. 1995). These ISs posed many benefits for supply chains, at least for those organizations that could afford the implementation and integration cost of such EDI systems (Stefansson 2002). Another example is information sharing in Vendor Managed Inventories (VMI), in which suppliers manage the inventory on-site and require information to handle replenishment (Lee etal. 2000). Consequently, inter-organizational data sharing is necessary to perform fundamental coordinating activities in supply chains in all their individual parts (e.g., resource collection, assembly) (Cachon & Fisher 2000; Luo etal. 2013; Stefansson 2002; Wang etal. 2021). It impacts upstream and downstream business processes of supply chain participants, such as production, and is a known strategy to mitigate the bullwhip effect, which occurs when fluctuations in production or orders outweigh inventory and variations in consumer demands impact a supplier’s production (Lee etal. 1997; Wang & Disney 2016). Coping with the bullwhip effect in supply chains without data sharing is barely possible since supplier and OEM relationships lag behind and require time to act, and sharing information can help respond to long-lead-time bullwhips (Bray & Mendelson 2012). The hypothesis is that when customers provide more complete data, the supplier can improve forecasting and accordingly have more room to address bullwhips (Moyaux etal. 2007). For instance, Aviv (2007 p. 790) found that if retailers share information upstream, “it makes sense that the manufacturer will end up with a gain due to his improved ability to anticipate demand.” However, this data and information that is shared—typically—is restricted to very specific data in bilateral relationships (Adner 2017) that are necessary to achieve a specific purpose (Legenvre & Hameri 2024; Wixom etal. 2020). Potential reasons for this narrow scope most likely originate in prevailing fears of misconduct or breaches of confidentiality by data users (Kuo etal. 2014). Cachon & Fisher (2000 p. 1033) already explored the value of shared data between “traditional information sharing” restricted to only orders and “full information sharing” with a range of benefits (e.g., improving order quantity decisions or allocating batches based on inventory positions). Refusing to share data and mitigating issues of information asymmetry can result in the loss of sales and supply chain inefficiencies (Wang etal. 2021), but using shared data requires “trust in the veracity of the reported information” (Cachon & Lariviere 2001 p. 629). One party in the supply chain could exploit another party’s lack of information and adapt buying and selling strategies according to that advantage (Cachon & Lariviere 2001; Makadok 2010). 4 In the early literature, data sharing and information sharing were often used synonymously. Electronic Markets (2024) 34:4141 Page 4 of 17 Industrial data ecosystems While the IS discipline has researched digital transformation and information management for decades, data ecosystems are a novel socio-technical manifestation of digitally transformed systems of organizations, providing a bouquet of research opportunities (e.g., Curry etal. 2022; Heinz etal. 2022; Hevner & March 2003; Legner etal. 2017; Oliveira etal. 2019). Data ecosystems advocate for an alternative view of inter-organizational data sharing, skewing away from sequential and bilateral data sharing. Instead, data ecosystems center on dynamic data sharing based on common value drivers (e.g., customer value or compliance) as opposed to merely executing fundamental business functions (Jacobides etal. 2018; Legenvre etal. 2022). In particular, the seminal article of Moore (1993) has significantly contributed to popularizing business ecosystems. It originates from the biological ecosystem, which is a systemic demarcation of a natural environment consisting of organisms and other physical and environmental factors with which they interact (Tansley 1935). Contrary to other inter-organizational forms of cooperation, ecosystems are inherently more dynamic since they revolve around a shared purpose, such as customer innovation from data, and retain the involved parties through a continuous balance of value received and effort given, enabling organizations to enter and exit freely (Otto 2022b). Nowadays, the “ecosystem” concept is mainly associated with digital platforms and reorganizes the sequential supply chain logic to a more open, dynamic, and shared understanding of inter-organizational collaboration that requires alignment of actors beyond those already implemented bilateral relationships (Adner 2017; Legenvre etal. 2022). Instead of clear boundaries confining value-creation activities, these ecosystems are populated by more-or-less autonomous multilateral actors that work as complementors and generate value and network effects (Hein etal. 2020). The synthesis of inter-organizational data sharing and (platform) ecosystems spurred the concept of data ecosystems, which are “socio-technical complex networks in which actors interact and collaborate with each other to find, archive, publish, consume, or reuse data as well as to foster innovation, create value, and support new business” (Oliveira etal. 2019 p. 589). In short, data ecosystems are “creating, managing and sustaining data sharing initiatives” (Oliveira & Lóscio 2018 p. 1). Table1 juxtaposes dominant data-sharing practices within supply chains and data ecosystems based on the literature’s narrative description and our experience. What characterizes the novelty of data ecosystems is the dedicated focus on data as a transaction object for value creation and capture and the accompanying mandatory consideration of their peculiarities (e.g., Prieëlle etal. 2020). Data (as well as products and services stemming from them) can be shared and reproduced indefinitely, contrasting it distinctively with finite resources (Shapiro etal. 1998; Veit etal. 2014). At the core of data ecosystems are the capabilities of each party to contribute to data sharing by either receiving it (data users), sending it (data provider), or facilitating the process (data intermediary) (e.g., Oliveira etal. 2019). The value resulting from these relationships is manifold. For instance, companies can use external data sources to innovate existing services and business models (e.g., Beverungen etal. 2022; Lim etal. 2018; Vesselkov etal. 2019), use others’ data for internal optimization, or use existing data as new business assets (e.g., Cappiello etal. 2020). Overall, the evolving nature of data ecosystems is shaped by the inherent dynamics of ecosystems and the intrinsic characteristics of data itself. For instance, data can be reproduced ad infinitum (e.g., Veit etal. 2014) and has inherent portability, which makes them accessible and shareable quickly and independently from geography through standardized interfaces (APIs) or open data sets (e.g., Gregory etal. 2022). Table 1 Contrasting data sharing in supply chains and industrial data ecosystems (see also Legenvre etal. (2022) andAdner (2017) for a juxtaposition of supply chains and ecosystems) Data sharing… in sequential supply chains in industrial data ecosystems Purpose Data is a tool to mitigate adversarial effects in supply chains, e.g., the bullwhip effect Data is a strategic asset used to generate new business value, optimize processes, and comply with legal requirements Transaction object Predominantly physical products and resources, material flow Digital (data) products, applications, data, and services Modus Typically sequential, bilateral data sharing Simultaneous, multilateral data sharing Dominant business drivers Supply chain efficiency (Data) Network effects, innovation, value creation, and capture Data types Highly restricted, specific Versatile Goal Resolve information asymmetries and conduct essential business functions and transactions. Innovation, optimization, compliance, complementation Organization Peer-to-peer (up-/downstream) Data intermediaries (collaborative, network) Electronic Markets (2024) 34:41 Page 5 of 17 41 Different sets of algorithms and combinations of data can then produce a variety of information or applications from the same data (e.g., Yoo etal. 2010). Data sharing infrastructure Industrial data sharing requires technical infrastructure. Since there are many variants of how data ecosystems can evolve, we will only discuss some of the more prominent ones (e.g., see Ditfurth & Lienemann 2022). Broadly, we categorize data-sharing infrastructure as either based on data intermediaries (as digital platforms engaged in facilitating data sharing) or IOIS. Referring to inter-organizational data sharing in supply chains, the literature predominantly discussed IOISs, which connect individual systems of organizations (e.g., Enterprise Resource Planning (ERP) systems) for deeper supply chain integration and automated data sharing (Holland 1995; Johnston & Vitale 1988). These shared systems mitigate manual data exchange, contribute to productivity gains and flexibility (Cash & Konsynski 1985), and replace “traditional” means of inter-organizational communication such as the telephone or fax (Suomi 1992). They are typically engraved in bilateral data-sharing scenarios or multilateral networks that revolve around a focal entity (e.g., a supplier) (Kumar & van Dissel 1996). Scaling inter-organizational data sharing beyond bilateral IOIS is faced with a range of challenges. First and foremost, sharing data between organizations—as opposed to consumers—requires consideration, at scale, of the potential harms (e.g., business secrets) that could be spilled, even unintentionally (Zrenner etal. 2019). Retaining control over one’s data is commonly referred to as data sovereignty, which can be technically implemented through formalized usage control policies (Otto & Jarke 2019). Data spaces are technical data sharing infrastructures that share characteristics with IOIS and data intermediaries. On the one hand, they aim to open up a shared space for organizations to find trusted data sources and, in that, aim to be open and fertile soil for inter-organizational optimization and business innovation. This resembles their position as data intermediaries—“a mediator between those who wish to make their data available, and those who seek to leverage that data” (Janssen & Singh 2022 p. 2), i.e., two-sided markets for inter-organizational data sharing (Ditfurth and Lienemann 2022). On the other hand, a data space itself is decentralized and does not store data centrally in one platform; it uses socalled data space connectors to facilitate data sharing (as an IOIS) between two parties. In this, data spaces are IOIS as they “enable the movement of information across organizational boundaries” (Johnston & Vitale 1988 p. 153) but do so by accommodating organizational barriers by spanning boundaries with technically implemented data sovereignty. The synthesis of this results in the dual nature of data spaces, both as data intermediaries (multilateral, ecosystem view) and IOIS (bilateral, data sharing view), which use connectors to ensure technical data sovereignty and onboarding mechanisms to generate a trusted pool of data ecosystem actors (Braud etal. 2021). Figure1 illustrates our conceptual understanding of data spaces as data intermediaries (the big picture) and IOIS (a zoomed-in transaction). In the following, we summarize and discuss options to operationalize industrial data sharing in data ecosystems based on existing literature (e.g., Oliveira etal. 2019; van den Broek & van Veenstra 2015) and our observations in practice (see Table2). We will then discuss data spaces in detail and align them with data ecosystems. • First (1), data and information extracted are essential to supply chains to communicate and work (Ahmed & Omar 2019). They are the basis for generating IOISs that automatically share information between supply chain participants (Holland 1995). Typically, these datasharing relationships are sequential in that participants in the supply chain share data to ensure compliance or to improve processes (van den Broek and van Veenstra 2015). However, there is also a growing tendency for data to be shared across supply chain partners and supply chains. For example, in the area of the circular economy, data is shared between different material suppliers, battery manufacturers, and recyclers to decide how, for example, a battery should be reused or recycled. • Second (2), data ecosystems emerge around data intermediaries (Ditfurth & Lienemann 2022), in which data providers offer data that can be searched and accessed by data users based on various decision criteria (e.g., the data type or the price) (Jussen etal. 2023a). Some types of data intermediaries are usually open to anyone (e.g., data marketplaces), while others are restricted only to data users that fulfill pre-defined governance policies (e.g., data trusts) (Ditfurth & Lienemann 2022). In governmental data sharing, data collaboratives revolve around one or a few private or public organizations that provide data to others to foster innovation with the distinct goal of contributing to a societal good and public governance (Klievink etal. 2018; Susha etal. 2022). • Third (3), a data ecosystem emerges around data spaces. Data spaces share some characteristics with data intermediaries. Similar to data intermediaries, data spaces need to orchestrate and bring together data providers and data users and—like digital platforms—must exploit network effects (Otto 2022b). Contrary to most data intermediaries, data spaces are only open to participants who possess technical access (i.e., a data space connector) and share data between data providers and data users. Prior to data sharing, data providers and data users must find each other’s offers and demands by providing meta-data to a data catalog (which can be operationalized through a data intermediary as a data Electronic Markets (2024) 34:4141 Page 6 of 17 space participant). The actual data is kept decentralized with the data provider and is only shared once negotiations are successful. In this regard, data spaces share characteristics with data intermediaries (as digital platforms) and as an IOIS integrating two decentralized systems through data space connectors (e.g., Zrenner etal. 2019). Data space‑enabled data ecosystems Data spaces indata ecosystems Given the explication of data spaces as motors for data ecosystems above, we will define the relevant constructs—for data ecosystems enabled by data spaces—below. Data sharing is the process of giving others access to data that they would not have access to on their own (Jussen etal. 2023a; Jussen etal. 2024). Figure2 conceptualizes three layers of data space-enabled data ecosystems, and we define data spaces as follows: Data spaces are decentralized data infrastructures designed to enable data-sharing scenarios across organizational boundaries by implementing mechanisms for secure and trustworthy data sharing—such as distributed data storage and the sharing of meta-data. They guarantee data sovereignty by ensuring that the data provider determines control over the access and use of the shared data. Data spaces allow the formation of flexible organizational forms that grant a delimited set of members access to a secure and trusted space to share data, which can be embedded in a larger data ecosystem. Data ecosystem parties can share data without explicitly using the data space technology and may contribute data but are not part of the demarcated set of members sharing data in a data space under the same set of data sovereignty mechanisms (e.g., defining data usage policies). The conceptual boundary may be formed around a technology (e.g., AI), a domain (e.g., automotive), or other factors (e.g., using a specific architecture or a shared purpose). We differentiate between organizations directly (technically) engaged in data sharing through a data space (data space members) and those that are part of the data ecosystem (data ecosystem parties) but do not directly engage in the data space themselves (see Fig.2). For example, the Catena-X Automotive Network envisions a data space integrating data from all parties alongside a supply chain (Catena-X 2024b). Members access the data space through dedicated data space connectors. This software component acts as an interface between the internal systems of the data space members and the data space itself (Pettenpohl etal. 2022). It can also be extended with additional functions, such as the International Data Spaces (IDS) Connector, which can interpret and technically enforce data usage policies (Otto Data SpacesasData Intermediaries Data Spacesas IOISs OrganizationA OrganizationB Data Data Negotiations Organizations: Data Providers, Data Consumers, Data Intermdiaries… Data Space Connectors (Potential) Data Sharing Connections Fig. 1 The dual nature of data spaces as data intermediaries and IOISs Electronic Markets (2024) 34:41 Page 7 of 17 41 etal. 2022b; Zrenner etal. 2019). The Eclipse Dataspace Connector Component (Spiekermann 2022) is used in the Catena-X Automotive Network5. The corresponding data ecosystem to Catena-X’s data space could be the Automotive data ecosystem, potentially having more than one data space. Transferred to the Catena-X case, we find many use cases, such as Manufacturing-as-a-Service (Catena-X 2023b) or Circular Economy (Catena-X 2023a), from a sub-group of the data ecosystem on the data spaces. Each use case requires different data and parties that act in the data space and are part of the data ecosystem. From a technical point of view, the term data space describes a specific data infrastructure concept, which can be characterized by four properties (Franklin etal. 2005; Halevy etal. 2006; Otto 2022a, 2022b; Otto & Burmann 2021): 1. Distributed: Data spaces are distributed by design, which means that they do not require physical data integration but leave the data at the data source and make it accessible only when it is needed. Table 2 Views on data sharing in IOISs, data intermediaries, and data spaces Organizational Form Inter-Organizational Information System Data Intermediaries Data Spaces Illustration Key Function Bilateral data sharing, 1:1 and 1:n Multilateral data sharing n:m Access to multilateral data space, bilateral data sharing 1:1 and n:m DescriptionData is bilaterally shared between supply chain participants (e.g., suppliers and OEM). Data is shared through data intermediaries as digital platforms. Typically,in multilateral data ecosystems (e.g., data marketplaces) Data is shared bilaterally in a data space (infrastructure). The data space enables all its participants to search for data multilaterally. Scope Formalized relationship between supplier(s) and OEM Open and dynamic data ecosystem for ‘all’ or specific group Open and dynamic ecosystem for data space participants ExampleOEM and Supplier relationship Advaneoa, SkywisebMobility Data Spacec, SCSNd, Catena-Xe Example Literature Opriel et al. (2021), van den Broek and van Veenstra (2015),Johnston and Vitale (1988), Kumar and van Dissel (1996) Bergman et al. (2022), Driessen et al. (2022), Geisler et al. (2021), , Ditfurth and Lienemann (2022), Janssen and Singh (2022), Schweihoff et al. (2023) DSSC (2023), Otto and Jarke (2019), Zrenner et al. (2019) Legend Data Prosumer Data UsersData Provider a https:// www. advan eo. de/ last accessed: 22.02.2024 b https:// aircr aft. airbus. com/ en/ servi ces/ enhan ce/ skywi se last accessed: 22.02.2024 c https:// mobil itydatas pace. eu/ de last accessed: 22.02.2024 d https:// smartconne cted. nl/ de last accessed: 22.02.2024 e https:// catenax. net/ de/ last accessed: 22.02.2024 5 For an overview of data space connectors, see Giussani and Steinbuß (2023). Electronic Markets (2024) 34:4141 Page 8 of 17 2. No common schema: Data spaces do not require a common database schema to which data from different sources must adhere. Rather, data integration occurs at the semantic level, e.g., through common vocabularies. 3. Data redundancy: The distributed architecture of data spaces allows for redundancy of data, i.e., multiple data objects can coexist in a data space describing the same real-world object. 4. Nested and overlapping: Data spaces can be overlapping and nested so that data providers and data users can be members in multiple data spaces, and data can be shared between data spaces. Based on this understanding, we define data ecosystems based on data spaces as follows (see also Fig.2): Data ecosystems are socio-technical systems that emerge around one or multiple (federated) data spaces. They represent the sum of collaborative data-sharing activities built on the secure and trustworthy data-sharing paradigm of data spaces to realize shared goals (e.g., innovation, compliance, optimization) for their members. To summarize this understanding of data spaces and their data ecosystems, we establish four principles from practice and research to guide our understanding of how they work in data ecosystems: 1. There is more than one data ecosystem. They may be differentiated by referencing a technology (e.g., AI), domain (e.g., automotive), or other conceptual boundaries delineating one data ecosystem from another. Different data ecosystems can intersect and generate an overlapping data ecosystem through data spaces being part of more than one data ecosystem. Each data ecosystem is operationalized through at least one data space (see (2) in Fig.3). 2. A data ecosystem can span more than one data space. For example, the Gaia-X data ecosystem consists of various data spaces, such as Agri-Gaia or the Mobility Data Space (e.g., Otto 2022a). The Gaia-X-based health data ecosystem conceptualizes all relevant health stakeholders acting in multiple data spaces that should be connected (Gaia-X 2021). Data spaces can connect through technical integration (e.g., APIs) and be part of more than one data ecosystem (e.g., Otto & Burmann 2021). However, suppose data spaces exist for the same domain in different countries. Arguably, these data spaces would initially not be connected but operate in parallel (see (1) in Fig.3). The four options indicate development steps (see trajectories in Fig.3). It appears that the 4th option, i.e., overlapping data ecosystems with connected data Fig. 2 The Catena-X and Mobility Data Space data spaces as illustrative case scenarios contextualized within data ecosystems (for a detailed description of the illustrated use cases used for the scenarios, see https:// catenax. net/ en/ benefi tspros/ susta inabi lity and https:// mobil itydatas pace. eu/ usecases last accessed: 14.07.2024) Electronic Markets (2024) 34:41 Page 15 of 17 41 Fassnacht, M., Benz, C., Heinz, D., Leimstoll, J., & Satzger, G. (2023). Analyzing barriers to data sharing among private sector organizations: Combined insights from research and practice. Proceedings of the 56th Hawaii International Conference on System Sciences, Hawaii: USA. Federal Ministry for Economic Cooperation and Development. 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