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 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 metaplatforms.

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.

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-TechnologyOrganization-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.

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