To access business data for compliance monitoring of the circular economy (CE), governments would need to deal with issues of both legislative complexities arising from many new regulations in the area of CE and sustainability, as well as the digital complexity for accessing business data that resides in different business systems and data spaces. While earlier research has touched upon (1) the legal complexity through the identification of common high-level concepts of what to monitor, and (2) the digital complexities through the use of upper ontologies, so far these aspects have been treated to a large extent in isolation and not been linked systematically. In this research in progress paper, we propose an approach on how to link the two, discuss advances in the area and limitations, and identify areas that need to be addressed to allow governments to tap into the rich business data sources for compliance monitoring in the future.

Monitoring the circular economy (CE) transition requires data sharing and collaboration between public and private actors. However, businesses are reluctant to share data with authorities for monitoring purposes due to fear of losing control over sensitive data. The emerging technology Multi-Party Computation (MPC), which enables collaborative data analysis while maintaining data control, could address barriers in business-to-government (B2G) data sharing and collaboration. This ongoing research aims to explore the potential of MPC in facilitating B2G data sharing and collaboration for CE monitoring under the conditions of inter-organizational trust and data control. Drawing on a B2G data sharing framework, our initial findings suggest that MPC can benefit authorities in accessing sensitive business data, while businesses can benefit from controlling shared data for compliance reporting. As MPC can be deployed in various architectures, the next research steps are to examine links between variants of MPC architectures and different data-sharing solutions.

To facilitate the transition toward a circular economy (CE), EU policymakers are drafting new policies and legislations at a high speed. This affects a wide set of sectors and leads to legislative complexity. At the same time, the legislative developments requiring Digital Product Passports (DPPs) offer opportunities for governments to tap into a rich set of business supply chain data for CE and sustainability monitoring. Nevertheless, the diversity of these legislative initiatives leads to complexity for governments on what needs to be monitored. There is a need to reduce legislative complexity, to have a more clear view on what governments need to monitor, which in turn would provide more clarity on the types of business data from the Digital Product Passports and digital infrastructures governments may need to access for CE and sustainability monitoring purposes. One approach to reduce the legislative complexity is to have a framework of high-level concepts for CE and sustainability monitoring. The question, however, is how to arrive at such a framework of high-level concepts. In this paper, we explore the potential of the concepts found in the UN Recommendation 46 (initially developed for the traceability of textiles), to serve as a basis for a generic framework of high-level concepts for CE and sustainability monitoring. We examine the suitability by applying the concepts from UN Recommendation 46 to a variety of legislations beyond textiles. Our analysis suggests that the framework has the potential to serve as a high-level framework of CE and sustainability monitoring concepts across sectors, and we identify several areas for further research.

Data sharing through data marketplaces, which rely on a Trusted Third Party (TTP), can benefit businesses and society. However, many companies and consumers are increasingly reluctant to share data due to mounting concerns over data control and privacy. Emerging privacy-enhancing technologies (PETs) like Multi-Party Computation (MPC), which enables joint computation to generate insights while keeping the input data private, could address data sharing barriers due to its differences with the traditional data sharing approach relying on a TTP. Thus, MPC could challenge the current understanding of why and how businesses and consumers share data. Nevertheless, whether businesses and consumers would be more willing to share data with MPC in place is unclear, as less attention is given to the socio-technical implications of MPC on data sharing decisions in data marketplaces and its antecedents. This research aimed to theorize the socio-technical implications of MPC on sharing through data marketplaces, by investigating how MPC potentially impacts data sharing antecedents by businesses and individuals. We do so through a mixed-method research design focusing on the automotive industry. Based on interviews with 15 MPC experts, which were structured using a Unified Business Model framework, we explored value propositions enabled by MPC use in data marketplaces. These value propositions allow MPC to potentially impact control, privacy, trust, and risks as antecedents of data sharing decisions in data marketplaces. Subsequently, we interviewed 23 automotive industry experts to investigate the potential impact of MPC use in data marketplaces on control, trust, and risks as antecedents of business data sharing. We then conducted an experiment via an online crowdsourcing platform with 1457 participants to investigate the potential impact of MPC use in data marketplaces on control, privacy, trust, and risks as antecedents of consumer data sharing. In this way, we contribute to the socio-technical understanding of MPC beyond technical perspectives. At the same time, we also demonstrate the relevance of MPC to practitioners by pointing out key aspects that should be considered while exploring the possibility of implementing MPC. Furthermore, this research provides a foundation for future studies on understanding the socio-technical implications of MPC on data sharing decisions.

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.

In this paper, we propose a framework for circular economy monitoring by looking at the issue of border crossing and levels of control.

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.

Firms are often reluctant to share data because of mistrust, concerns over control, and other risks. Multi-party computation (MPC) is a new technique to compute meaningful insights without having to transfer data. This paper investigates if MPC affects known antecedents for data sharing decisions: control, trust, and risks. Through 23 qualitative interviews in the automotive industry, we find that MPC (1) enables new ways of technology-based control, (2) reduces the need for inter-organizational trust, and (3) prevents losing competitive advantage due to data leakage. However, MPC also creates the need to trust technology and introduces new risks of data misuse. These impacts arise if firms perceive benefits from sharing data, have high organizational readiness, and perceive data as non-sensitive. Our findings show that known antecedents of data sharing should be specified differently with MPC in place. Furthermore, we suggest reframing MPC as a data collaboration technology beyond enhancing privacy.

Consumers are increasingly reluctant to share their personal data with businesses due to mounting concerns over privacy and control. Emerging privacy-enhancing technologies like multi-party computation (MPC), which allows generating insights while consumers retain data control, are challenging the current understanding of why consumers share their data. In this research-in-progress paper, we develop and evaluate an instrument and experimental design to investigate the impact of MPC on consumers’ willingness to share data and its antecedents. Preliminary analysis from a pre-study (N=300) indicates a good fit for our model. Also, MPC enhances consumers’ control and trust while reducing privacy concerns and risk, ultimately increasing data sharing willingness. The findings suggest that privacy-enhancing technologies significantly affect both the willingness to share data itself and its typical antecedents. The next step will conduct a large-scale online experiment using the developed instruments to evaluate further the impact of MPC on consumers’ willingness to share data.

Privacy-preserving technologies could allow data marketplaces to deliver technical assurances to companies on data privacy and control. However, how such technologies change the business model of data marketplaces is not fully understood. This paper aims to bridge this gap by focusing on multi-party computation (MPC) as a cryptographic technology that is currently being hyped. Based on interviews with privacy and security experts, we find that MPC enables data marketplaces to employ a “privacy-as-a-service” business model, which goes beyond privacy-preserving data exchange. Depending on the architecture, MPC could transform data marketplaces into data brokers or data aggregators. More complex architectures might lead to more robust security guarantees and lower trust requirements towards data marketplace operators. Furthermore, MPC enables new offerings of privacy-preserving analytics and services as new revenue sources. Our findings contribute to developing business models of privacy-preserving data marketplaces to unlock the potential of data sharing in a digitized economy.

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.

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.

Data sharing facilitated by data marketplaces enable companies to generate meaningful insights and discover new opportunities. However, enterprises are reluctant to share data over platforms due to lack of trust, fear of losing control over data and concerns regarding privacy violations. Multi-party computation (MPC) is a cryptographic technique that enables joint data analyses by multiple parties while retaining data secrecy. Despite the potential of MPC, its meaning in data marketplaces setting and how MPC change firms' behavior towards data sharing is not yet researched. This research aims to explain why and how MPC could enable platform control and affect firms' participation in data sharing via data marketplaces. To do so, we will employ a mixed-method research design by combining semi-structured interviews with actors in the mobility domain and quantitative experiments using a mockup of MPC-enabled data marketplaces. Our initial findings revealed various barriers and incentives for firms in sharing their data. We expect our research to become a foundation for future research in the emerging phenomenon of platformization of data sharing via data marketplaces and the key role of MPC in enabling the data economy.

Data has become a core asset, as well as a “management fashion”, of our time. It brings about unprecedented opportunities for data-driven decision making and innovation in various spheres of public life. This concerns data held by governments, as well as companies, academic institutions, non-profits, and citizens. In our study we investigate a novel form of cross-sector partnership called Data Collaborative, and namely the business models employed by intermediaries in data collaboratives. Based on an analysis of six cases, we derived four generic business models based on the level of openness and added value of the data: Data Gatekeeper model, One-stop-shop model, Information-as-a-service model, and Data Controls model. Our study contributes to the literature on data partnerships and on intermediation and information sharing more broadly.

Digital technologies, such as online healthcare portals, enable elderly people to live independently at home for a longer period of time. Independent living, in this context, refers to the freedom elderly people have to live their lives in ways that they find important. Borrowing from the capability approach (CA) framework from Nobel Prize winner Amartya Sen, the core argument of this paper is that elderly people make decisions on whether to use digital healthcare technologies by considering how these technologies enhance their capabilities to live their lives in ways that are valuable to them. This paper develops a theoretical model of adoption of digital healthcare technologies that support independent living applying the CA framework. We follow a mixed-methods approach with a sequence of qualitative, quantitative, and qualitative methods. We find support for our theoretical model, specifically that the intention to use online healthcare portals depends on whether elderly people expect to enhance their capabilities for living independently by using them. Our study contributes to the information systems literature on adoption of digital healthcare technologies as it is the first that applies the capability approach. For adoption studies on digital technologies in healthcare and beyond, our study poses two major theoretical implications: (1) when considering how outcome expectations affect adoption, scholars should consider how digital technologies allow people to live their lives in ways that are valuable to them, rather than considering how technologies help to execute predefined tasks, jobs, or activities; (2) the availability of digital technologies should be considered as a mediator between outcome expectations and intention to use technologies.