As the data economy is growing, businesses increasingly utilize data marketplaces to share data with external parties. Due to the specialized nature of data products, data marketplaces exhibit a high degree of heterogeneity, often focusing on specific countries and industries. This specialization results in high transaction costs when sharing data in data marketplaces. Additionally, multi-homing costs are high, making it difficult for users to participate in multiple data marketplaces to expand their reach. The existing literature recognizes meta-platforms as a potential measure to reconcile highly heterogeneous digital platforms, thereby reducing transaction and multi-homing costs. Nevertheless, while sharing business data on a data marketplace is already difficult due to data sovereignty concerns, these concerns will likely intensify in a meta-platform setting because data may flow from one data marketplace to another. Control mechanisms can enhance data sovereignty; however, due to the novel and intricate nature of meta-platforms, existing knowledge on designing such mechanisms may not be directly transferable to this complex setting. Therefore, this research aims to create design knowledge for developing and evaluating control mechanisms for data sharing through meta-platforms for data marketplaces, focusing on investigating their efficacy in enhancing data sovereignty in the societal context of the data economy. To achieve the objective, this study employs the Design Science Research (DSR) approach. This study is structured based on the three DSR domains: the problem space, the solution space, and the evaluation space.

We find that data providers have different views on the efficacy of control mechanisms (i.e., smart contracts and certifications) to enhance data sovereignty facets in the context of business data sharing via data marketplace constellations federated by a meta-platform. Our research finds no significant differences in data providers’ perception of their ability to retain ownership and maintain control over shared data products in meta-platforms, regardless of the presence of smart contracts. In addition, our findings suggest that data providers using meta-platforms with certifications feel more confident in meeting data sharing compliance requirements compared to those using meta-platforms without certifications. Additionally, these data providers perceive a clearer division of responsibility between meta-platform and data marketplace operators. When combined with smart contracts, the responsibility divisions become even clearer. Contrary to our expectations, however, we find no significant difference in the perceived security of data providers when sharing data on meta-platforms with certifications compared to those without.

Considering the impacts of data sovereignty on the broader societal context of the data economy, we find that when data providers feel sovereign over their data products, they are more likely to trust both a) meta-platform operators facilitating data sharing and b) data consumers with whom they share data. Surprisingly, we do not identify a correlation between the trust and their willingness to share data. This suggests that when data providers possess data sovereignty, trust in platform operators and data consumers becomes a less important factor for data sharing. In addition, we discover that data providers, feeling sovereign over their data products, perceive lower risks in sharing their data. The reduced perceived risks subsequently increase their willingness to share data through meta-platforms. Therefore, our study emphasizes the significance of data sovereignty in the growth of the data economy by a) promoting trust toward meta-platform operators and data consumers, b) reducing perceived risks, and c) increasing the willingness to share business data through meta-platforms.

Our study contributes to the Information Systems literature, particularly in the intersection between data sharing and digital platform literature. We contribute by being among the first to create design knowledge to develop and evaluate control mechanisms for business data sharing through meta-platforms for data marketplaces, focusing on investigating their efficacy in enhancing data sovereignty in the societal context of the data economy. Specifically, our primary contributions are four-fold: 1) theorizing the potential impact of control mechanisms on data sovereignty, 2) outlining design options and principles as prescriptive knowledge, 3) defining goodness criteria to enhance data sovereignty, and 4) advancing context understanding of a meta-platform as a business data sharing setting. In addition, our secondary contributions are 1) providing evidence on the potential impact of data sovereignty on the broader data economy and 2) extending the applicability of theories employed in this research in the market-based data sharing context.

In conclusion, this study resolves the tensions in the European policy-making agendas that promote a single market for data and interoperable data sharing (e.g., in EU Data strategy, Data Act) while, at the same time, pushing sector-specific data marketplaces to exist (e.g., the eight verticals in the Digital Europe program). Furthermore, policy agendas also emphasize adherence to data sovereignty principles. As data sovereignty is vital for data providers to share their data via meta-platforms, addressing this concern may increase meta-platform adoptions. Hence, we hope a meta-platform can realize its potential to be one distinguished instrument to fulfill what we hope (and are optimistic) for in the data economy: a single European Data Market in 2030.
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In the data economy, data sovereignty is often conceptualized as data providers’ ability to control their shared data. While control is essential, the current literature overlooks how this facet interrelates with other sovereignty facets and contextual conditions. Drawing from social contract theory and insights from 31 expert interviews, we propose a data sovereignty conceptual framework encompassing protection, participation, and provision facets. The protection facets establish data sharing foundations by emphasizing baseline rights, such as data ownership. Building on this foundation, the participation facet, through responsibility divisions, steers the provision facets. Provision comprises facets such as control, security, and compliance mechanisms, thus ensuring that foundational rights are preserved during and after data sharing. Contextual conditions (data type, organizational size, and business data sharing setting) determine the level of difficulty in realizing sovereignty facets. For instance, if personal data is shared, privacy becomes a relevant protection facet, leading to challenges of ownership between data providers and data subjects, compliance demands, and control enforcement. Our novel conceptualization paves the way for coherent and comprehensive theory development concerning data sovereignty as a complex, multi-faceted construct.

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The landscape of platform ecosystems is becoming increasingly complex, with new types of platforms emerging that glue together otherwise fragmented ecosystems. One recent case is metaplatforms that can contribute to the European Data Economy by interconnecting data marketplaces; however, meta-platforms may intensify data sovereignty concerns: the inability of data providers to own and control the exchanged data. While smart contracts and certification can generally enhance data sovereignty, it is unknown whether data providers perceive these control mechanisms as valuable in the complex meta-platform setting. This study aims to evaluate the perceived efficacy of the control mechanisms to ensure data sovereignty in meta-platforms. The findings from a survey study (n=93) indicate that respondents perceive high data sovereignty. One potential explanation is that smart contracts can potentially enable providers to maintain ownership and control over their exchanged data; meanwhile, certification may signal metaplatforms’ responsibility to deliver secure data exchange infrastructure and assist providers in adhering to relevant regulations. This study contributes to advancing design knowledge for meta-platforms, showcasing that meta-platforms can be designed in a way to resolve fragmentation without neglecting data sovereignty principles.@en

Investigating meta-platforms has been a continuing concern within information system literature due to the increasingly complex constellations of platforms in ecologies of ecosystems. A meta-platform is a platform built on top of two or more platforms, hence connecting their respective ecosystems. One promising case to benefit from meta-platforms is data marketplaces: a particular type of platform that facilitates responsible (personal and non-personal) data sharing among companies. Given that business models for meta-platforms are largely unexplored in this emerging case, how they can create value for data marketplaces remain speculative. As a starting point toward business model investigations, this paper explores value creation of a meta-platform in the case of data marketplaces. We interviewed fourteen data-sharing consultants and six meta-platform experts. We identify three potential value creation archetypes of a meta-platform. The discovery aggregator archetype emphasizes searching and dispatching value, while the brokerage one focuses on promoting and supporting value. Finally, the one-stop-shop archetype creates value by standardizing, regulating, sharing, and experimenting. This study is among the first that explore value creation archetypes for a meta-platform, thus identifying core value as a base for further business model investigations.@en

Meta-platforms have received considerable Information Systems scholarly attention in recent years. Meta-platforms enable platform-to-platform openness and are especially beneficial to amplifying network effects in highly-specialized markets. A promising emerging context for applying meta-platforms is data marketplaces—a special type of digital platform designed for business data sharing that is vastly fragmented. However, data providers have sovereignty concerns: the risk of losing control over the data that they share through meta-platforms. This research aims to explore antecedents and consequences of data sovereignty concerns in meta-platforms for data marketplaces. Based on interviews with fifteen potential data providers and five data marketplace experts, we identify data sovereignty antecedents, such as (potentially) less trustworthy data marketplace participants, unclear use cases, and data provenance difficulties. Data sovereignty concerns have many consequences, including knowledge spillovers to competitors and reputational damage. This study is among the first that empirically develops a pre-conceptualization for data sovereignty in this novel context, thus laying the groundwork for designing future data marketplace meta-platform solutions.@en

Policymakers and analysts are heavily promoting data marketplaces to foster data trading between companies. Existing business model literature covers individually owned, multilateral data marketplaces. However, these particular types of data marketplaces hardly reach commercial exploitation. This paper develops business model archetypes for the full array of data marketplace types, ranging from private to independent ownership and from a hierarchical to a market orientation. Through exploratory interviews and case analyses, we create a business model taxonomy. Patterns in our taxonomy reveal four business model archetypes. We find that privately-owned data marketplaces with hierarchical orientation apply the aggregating data marketplace archetype. Consortium-owned data marketplaces apply the archetypes of aggregating data marketplace with additional brokering service and consulting data marketplace. Independently owned data marketplaces with market orientation apply the facilitating data marketplace archetype. Our results provide a basis for configurational theory that explains the performance of data marketplace business models. Our results also provide a basis for specifying boundary conditions for theory on data marketplace business models, as, for instance, the importance of network effects differs strongly between the archetypes.
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Data platforms are the keystone of the data economy. When opened up, data platforms allow data owners, data consumers and third parties to interact. Yet, openness may also harm business and societal interests. Literature on platform openness does not cover data platforms, and data economy scholars rarely study platform openness. Therefore, this paper develops a research agenda on the openness of data platforms. We explore how data platforms differ from conventional digital platforms (e.g., software platforms). From those differentiating characteristics, we identify areas for future work: (1) The specific characteristics of data require reconceptualizing the object of platform openness; (2) New ways in which data platforms can be opened should be conceptualized; (3) As data platforms are tailored to specific industries, platform-to-platform openness should be a novel unit of analysis; (4) Because opening up data platforms create novel risks, new reasons to (not) open up data platforms should be studied.@en

Data marketplaces are expected to play a crucial role in tomorrow’s data economy, but such marketplaces are seldom commercially viable. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially not of neglected research topics that may advance such marketplaces toward commercialization. This study provides an overview of the state-of-the-art of data marketplace research. We employ a Systematic Literature Review (SLR) approach to examine 133 academic articles and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.@en

Data marketplaces can fulfil a key role in realizing the data economy by enabling the commercial trading of data between organizations. Although data marketplace research is a quickly evolving domain, there is a lack of understanding about data marketplace business models. As data marketplaces are vastly different, a taxonomy of data marketplace business models is developed in this study. A standard taxonomy development method is followed to develop the taxonomy. The final taxonomy comprises of 4 meta-dimensions, 17 business model dimensions and 59 business model characteristics. The taxonomy can be used to classify data marketplace business models and sheds light on how data marketplaces are a unique type of digital platforms. The results of this research provide a basis for theorizing in this rapidly evolving domain that is quickly becoming important.@en

Data Marketplace Meta-platforms (DMMPs) federate the fragmented set of data marketplaces and are expected to become a pivotal instrument to realize a single European Data Market in 2030. However, one critical hindrance to foster the adoption of business data sharing via DMMPs is data providers' risk of losing control over data. Generally, the literature on interorganizational data sharing has highlighted that data governance mechanisms can help data providers to retain control over their data. Nevertheless, data governance mechanisms in the DMMP context are yet to be explored. Therefore, this research aims to design data governance mechanisms for business data sharing in DMMPs by employing the Design Science Research (DSR) approach. This study contributes to the literature by identifying root causes and consequences of losing control over data and defining prescriptive knowledge regarding design requirements, design principles, and a framework for designing data governance mechanisms within the novel setting of meta-platforms.@en

The risk of being impacted by a cyberattack is high, because of more professional attacks. Thereby, cyber criminals are bypassing technological countermeasures through tricking users. Recently collected data during the SARS-CoV-2 pandemic demonstrate, that cyberattacks including social engineering are among the main threats, especially for Small and Medium-sized Enterprises (SME). (Information) Security Education and Training Awareness (SETA) is proposed to be an effective countermeasure. However, the effects of SETA fade rapidly over time and learnings are not applied in practice sustainably. Thus, we state that a method is required to create SETA programs with sustainable learning outcomes for SME. To develop such a method, we follow the Design Science Research Methodology and share insights of our first design cycle in this article. We conducted a literature review and analyzed factors of failure and success regarding the design of sustainable SETA programs. Furthermore, we sketch our plans for design cycle 2.@en

Data marketplaces are expected to play a crucial role in tomorrow’s data economy but hardly achieve commercial exploitation. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially neglected research topics that may contribute to advancing data marketplaces towards commercialization. This study provides an overview of the state of the art of data marketplace research. We employ a Systematic Literature Review (SLR) approach and structure our analysis using the Service-Technology-Organization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.@en