This study investigates the potential of Self-Sovereign Identity (SSI) to empower individuals within Indonesia's health data ecosystem, particularly in the context of a Low- and Middle-Income Country (LMIC). The central concern is the loss of control that individuals face when their personal data becomes part of a broader health data ecosystem, utilized by various stakeholders like the government, healthcare facilities, and insurance companies. SSI, a decentralized and blockchain-enabled system, offers a solution by giving individuals control and ownership over their data, allowing them to decide how and to what extent their data is shared with third parties.

The research question focuses on how SSI functionalities should be designed to ensure the retention of personal data sovereignty (PDS) in an LMIC context. The study employs the Design Science Research (DSR) methodology, which includes the creation and evaluation of design artifacts to advance understanding in this domain. The research process involved developing SSI interface artifacts and testing them through a qualitative study with 15 Indonesian respondents, including individuals with socially stigmatized diseases like HIV/AIDS and TB. This population was chosen to ensure the study's inclusivity, given their heightened sensitivity to data privacy issues.

The study’s findings highlight the importance of data minimization and revocation functionalities in SSI systems. Data minimization allows users to share only the necessary information, while revocation gives them the ability to withdraw their consent for data sharing. These functionalities are crucial for fostering a sense of control, which is directly linked to a feeling of ownership over one's data. The study also revealed that a user’s digital literacy and affinity with data sharing significantly affect their perception of SSI functionalities. Users with a higher understanding of data implications tend to prefer more complex interfaces that encourage careful decision-making, whereas those with limited knowledge are more comfortable with simpler interfaces.

Additionally, the study underscores the importance of trust and contractual agreements as foundational elements in a user's willingness to participate in a data ecosystem. Without these, users may be hesitant to share their data, regardless of the control mechanisms in place.

This research contributes to the growing body of knowledge on SSI, particularly in the context of an LMIC like Indonesia. By taking a user-centric approach, the study explores the link between SSI functionalities and personal data sovereignty, providing insights that are currently underrepresented in existing literature. The inclusion of vulnerable populations further enriches the study’s findings, offering a comprehensive understanding of how SSI can enhance participation in the health data ecosystem while safeguarding personal data sovereignty.

Future research should aim to broaden the respondent pool to include more diverse socio-economic backgrounds, which would improve the generalizability of the findings. Additionally, further refinement of the SSI design artifact, informed by user feedback, is recommended to enhance its usability and effectiveness. Exploring the study's hypotheses through a quantitative approach could also provide deeper insights into the relationship between digital literacy, data sharing affinity, and personal data sovereignty.

The benefit of secondary use of data leads to the belief that data can be monetized through exchanging and trading between businesses. Many new data marketplaces emerge from this, resulting in the heterogeneity of the data marketplace that leads to market fragmentation in the industry. The aggregator business model can offer various values to users in a fragmented market. Therefore, the idea of designing aggregator business models for data marketplaces could be worthwhile investigating. Design Science Research (DSR) methodology is used in this research. Business model requirements to design aggregator business models were defined in the literature review. The business models of various aggregators are analyzed with case studies. From the requirements and case studies, four new aggregator business archetypes are derived: search engine, advanced search engine, comparison sites, and one-stop shop. These four aggregator business models are distinguished by their service offerings and their degree of the network. It is also discovered from case studies that there is a connection between the degree of network and aggregators' embedded technology. The high degree of network aggregators tend to use API, while the low degree of network aggregators tend to use information crawlers. The aggregator business model archetypes are then demonstrated to the data marketplace cases by translating the business models into business activities and services that can be offered to the users. Then, we evaluate the data marketplace aggregator business model through semi-structured interviews with experts. From the evaluation, it is discovered that data marketplace aggregators could offer value to the users, e.g., navigating users to find data sales and providing a single access point to collect data. Different technology aspects that could help to realize the aggregation services have also been developed by various projects. Additionally, data providers are willing to join the aggregation services due to the economic benefit. However, although the benefit and possibilities, multiple challenges need to be addressed by the data marketplace aggregator. There are issues with technical standardizations and interoperability that could be problematic in integrating various platforms for aggregation. Maintaining collaboration is difficult due to the need to provide data assurance, which could be expensive and exhausting. Additionally, although it is beneficial to navigate users in finding sales, data marketplace aggregators might offer little value to data buyers with market knowledge because they will directly buy the data from the sellers.