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.

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.

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 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.

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.

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