This paper analyzes strategies for platform owners to increase complementor participation on their platform. Specifically, it draws on open innovation (OI) to theorize the impact of three drivers of complementor participation, namely category variety offered on the platform, the extent of boundary resources provided to facilitate complementary innovation, and exclusivity of content offerings. We hypothesize that higher levels of each of these drivers increase the platform’s attractiveness to future complementors and thereby increase complementor participation. Based on negative binomial fixed-effects regressions in the context of video game consoles, we show that category variety has no effect on future complementor participation, while boundary resources and exclusive content do. The results have implications for the orchestration of platform ecosystems.

Mental health problems are increasing among young adults, and growing evidence points to social media platforms as a potential influence. Design decisions made by platform developers have the potential to fundamentally impact mental well-being. However, translating abstract values such as “well-being” or “mental health” into concrete norms and design features is challenging. We explore the potential of using a value sensitive design approach towards redesigning a social media environment that promotes mental well-being. We interviewed social media experts, held a focus group with adolescent users, and created a values hierarchy that translates abstract well-being values into norms and design requirements. Results reveal that mental well-being is negatively impacted by the inherent value tensions found in the design of social media platforms, tensions such as authenticity and connection, personalization and autonomy, and control and autonomy. We propose a value-sensitive design approach, informed by involving young users and other stakeholders, to show how to integrate key values into social media redesign.

Background: The transition from adolescence to young adulthood (age 10‐25 years) constitutes a sensitive developmental period marked by rapid biological, psychological, and social change, during which preventive health interventions can shape long-term outcomes. Mobile health tools offer accessible opportunities for tailored support for this population, but often adapt poorly to dynamic contexts, resulting in inconsistent engagement and effects. Just-in-time adaptive interventions (JITAIs), which tailor support in real time using ongoing data, are increasingly explored as precision health strategies. However, how these mechanisms are designed, implemented, and evaluated for adolescents and young adults (AYAs) has not been systematically reviewed. Objective: This review aimed to synthesize the evidence on JITAIs developed for AYAs, examine how their adaptive mechanisms have been designed to support specific health goals and changing AYA contexts, and assess methodological reporting quality to inform future precision health intervention development. Methods: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and SWiM (Synthesis Without Meta-Analysis) reporting guidelines. Twelve databases were searched for peer-reviewed studies published from 2013 to 2025. Eligible studies focused on participants aged 10 to 25 years and reported real-time adaptive mobile health interventions consistent with JITAI design principles. Two reviewers independently conducted screening, data extraction, and methodological quality appraisal using the Joanna Briggs Institute checklists. AYA coauthors contributed to all phases. Due to substantial heterogeneity in study populations, intervention content, adaptive mechanisms, comparators, and outcome measurements, findings were synthesized narratively, and no meta-analysis was conducted. Results: A total of 61 unique interventions were included. JITAIs for AYAs addressed substance use (n=24, 39.3%), mental health (n=23, 37.7%), and physical health or chronic conditions (n=14, 23%). JITAI tailoring mechanisms relied predominantly on self-reported behavioral data. Decision rules were typically symptom threshold–based, and decision points were commonly daily or event-triggered. Methodological concerns with reporting on intervention administration, participant selection, and outcome measurement reliability were pervasive across all studies, limiting the interpretability of observed effects and cross-study comparisons. Ethical considerations, including researcher positioning and reflexivity, alongside the depth of reporting around participatory AYA engagement in design and implementation, were also inconsistent. Conclusions: This review contributes a novel perspective to AYA digital health by moving beyond intervention outcomes to examine how core adaptive mechanisms are operationalized for AYAs across multiple health domains, while also integrating AYA perspectives into the interpretation of findings and recommendations. Unlike prior reviews focused primarily on adults or specific conditions, it identifies broader contextual, methodological, and ethical considerations relevant to AYA precision health. These findings highlight the need for more transparent, contextually responsive, and youth-centered adaptive interventions, alongside more rigorous designs for evaluating adaptive intervention components in daily life contexts.

As privacy concerns grow, organizations and policy makers promote the use of privacy-enhancing technologies (PETs) to improve user trust and data-sharing behaviors. However, privacy-enhancing technologies (PETs) are often technologically complex and opaque to lay users. It is challenging to understand and effectively communicate the functionality of complex PETs to the users, such as Secure Multi-Party Computation (MPC). Studies typically assess the impact of new PETs by presenting users with a high-level description of the technology before measuring how this treatment changed their attitude or behavior. These results influence business and regulatory decisions (see Gartner's Hype Cycle for Emerging Technology [123]). In the present study, we question this approach. We assess whether naming specific PETs and providing generic descriptions impact users' willingness to put trust in service providers and share their data. Our survey presented three randomized controlled trials with 1,457 participants in a data marketplace scenario. The first group was treated with a PET (MPC), the second group with a fictional PET, and the third with a non-PET, serving as a control group. Our findings reveal that user trust and data-sharing willingness increased with MPC and the fictional PET, indicating that the high-level description, rather than the technology name, shapes user perception. We conclude that claiming the use of a PET is not an effective method to measure the impact of actually using this technology. However, given their mental model, lay users cannot verify the privacy claims of such descriptions presented in studies or by service providers. This increases the risks of users being deceived into a false sense of privacy, leading them to expose more private data than they otherwise would.

During an executive meeting, the senior vice president of a large technology firm discussed recent sustainability developments with the managing director of a global implementation firm. They concluded that sustainability is gaining traction and significantly impacts data collection, analysis, and reporting. They agreed to jointly invest in developing a sustainability module to integrate into the technology firm’s digital platform. By reaching out to a client interested in becoming a “launching customer,” they established a digital platform ecosystem and created the sustainability module. This case outlines the real design challenges faced by the ecosystem partners. Seven (7) design challenges have been identified, ranging from selecting and importing data tracking metrics against goals and targets to creating a dashboard. Three environmentoriented features (e.g., decarbonization, travel emissions, energy consumption) were launched as a minimum viable product and rolled out to the client. This teaching case consists of two parts: the first part introduces the concept of sustainability, digital platform ecosystems, a case description, and the design framework, while the second part discusses the seven identified design challenges.

Digital wallets are emerging as new tools that provide citizens with control over their personal data while allowing innovation in service delivery. Wallets promise various functionalities, from authentication, authorisation, and signing, to storage and generating qualified electronic attestations. Given the potential, policymakers and service providers face significant challenges in selecting, developing, implementing, and regulating digital wallets due to the lack of clarity about their characteristics. This paper focuses on this lack of clarity. Through a socio-technical systems theory lens, we developed a comprehensive taxonomy for classifying digital wallets. Our empirical analysis reveals a taxonomy of 47 characteristics in 14 dimensions. The contribution of this taxonomy is two-fold. Firstly, it can help develop a more precise theory on specific types of wallets. Secondly, actors in the field can use the taxonomy for more granular communication on wallet development and adoption challenges, standardisation efforts, and policy development.

Data platforms enable actors to exchange personal and business data. While data is relevant for any digital platform, data platforms exclusively revolve around data artifacts. This paper argues that the specific characteristics of data artifacts challenge the authors’ understanding of platform openness. Specifically, it is argued that data artifacts are editable, interactive and distributable, which means that the consequences of opening up a data platform extend far beyond the focal platform and its context. From this, the study infers that the scope of platform openness extends beyond the data platform on which data artifacts originate. At the same time, the very nature of data artifacts afford new mechanisms to realize and reduce the risks of openness. New avenues are suggested to study platform openness in the realm of data platforms. These avenues include (1) exploring and incorporating novel consequences of platform openness in a data platform setting, (2) examining new arenas for defining openness beyond a focal platform’s confines, and (3) theorizing the implications of new mechanisms for realizing openness while maintaining apparent control over data artifacts.

Machine learning algorithms show promise in assisting clinical decision-making; however, only a few have been successfully implemented in practice. To bridge this gap, it is essential to analyse the clinicians’ perspective on the compatibility of Artificial Intelligence-based clinical decision support systems (AI-CDSSs) with their clinical tasks. We therefore conducted a literature review of 21 empirical qualitative studies that examined the interaction between health professionals and AI-CDSSs. We synthesised the research through the lens of the Task-Technology Fit (TTF) model, analysing task, technology and individual characteristics of AI-CDSS applications, to identify design elements that are (mis)aligned with clinicians’ needs. Three key findings emerged from our analysis. First, clinicians often expressed scepticism about the clinical judgements of AI-CDSSs, particularly questioning the system’s ability to compete with clinical expertise in the absence of contextual information. Users valued AI primarily for specific strengths, such as identifying trends in patient trajectories, consolidating large datasets and pattern recognition, and comparing similar patient cases, but were hesitant to rely on it for clinical decisions. Second, actionability emerged as a desired characteristic of AI-CDSSs. For instance, clinicians particularly appreciated features of AI-CDSSs that enabled them to explore how different clinical actions might influence outcomes, as well as Explainable AI for identifying modifiable variables that impacted prediction scores, allowing them to take informed action. Third, we identified various ways AI-CDSSs could be used in clinical practice, including for patient prioritisation, patient monitoring, care acceleration, risk communication and workflow efficiency. In essence, AI-CDSSs functioned either as an alert system, preventing oversights, or as a tool for more informed decision making. Our analysis challenges the assumption that AI-CDSSs add little value when clinicians disregard its predictions, as it frequently prompts them to critically reassess their judgments through additional testing, consultation with colleagues, and other actions. Overall, our findings underscore the importance of an in-depth understanding of how AI-CDSSs are used in clinical practice. To optimise for effectiveness, the design of AI-CDSSs should prioritise supporting clinicians’ cognitive processes and information needs. This approach ensures that we move beyond the hype, focusing on the responsible integration of AI-CDSSs, and ultimately enhancing patient care.

Artificial intelligence based tools are being developed for decision support in healthcare, however, they are frequently found to lack the required functionality to achieve the clinical goals for which they were built. This results in wasted time, money and resources for hospitals attempting to implement and operate such tools. To determine how functionality issues can be resolved prior to tool implementation, it is necessary to understand why such tools are being designed and then built. Our research focuses on clinical decision support tools with functionality issues arising from target variable invalidity. In this paper, we analyze published articles which present clinical decision support tool designs related to clinical goals. These tools use machine learning models trained on electronic health record data. We find that design decisions driven by data availability can introduce construct invalidity in clinical decision support tool designs, leading to an inability of the tool to address the clinical goal. We observe that alternative goals to the main clinical goal are used to justify continued development. We show that functional limitations of the tool related to the clinical goal can be obscured by imprecise terminology in the model’s stated functionality. Finally, we highlight the need for reconsidered approaches to dataset creation, defining success criteria, and the reporting and transparency of research outcomes as they relate to clinical goals.

The European Union (EU) Digital Identity Wallet (DIW) intends to give citizens control over personal data sharing. The DIW users will have full and sole control over their data. The EU intends to address the risk to citizens' privacy in cases where data from and about users is gathered and exchanged by online service providers. However, it is unclear how users of the EU DIW can decide what data to share and how to prevent sharing too much data with online service providers. In order to reduce this risk, we need to understand it first. Drawing on expert interviews, this paper presents a novel analysis of the risk of over sharing through the EU DIW. It defines the risk and what aspects influence the risk from literature, documentation and expert interviews. Over-sharing data occurs when users share more data than strictly required for the service or product acquired online and multiple aspects influence this risk, specifically the user capabilities and orientation, the loss of context awareness, the quality of the data and the ease of sharing.

This paper analyses strategies for platform owners to increase complementor participation on the platform. Specifically, it draws on open innovation (OI) and the resource-based view (RBV) to isolate three drivers of complementor participation, namely breadth of content offerings and boundary resources (related to OI), and exclusive content (associated with RBV). We hypothesize that higher levels of each of these drivers increase the platform's attractiveness to future complementors and increase complementor participation. Based on negative binomial fixed effects regressions in the context of video game consoles, we find that boundary resources and exclusive content, but not breadth of content offerings, are positively related to complementor participation. This shows that drivers from both OI and RBV relate to complementor participation. The results have implications for the orchestration of platform ecosystems.

The concept of Environmental, Social, and Governance (ESG), has captivated the attention of firms and academics, driven by an EU directive for firms to report ESG. Digital platforms fulfill an essential role to support organizations to manage their ESG initiatives. Since ESG disclosure standards are ambiguous, designing a digital platform for ESG is challenging. The design of ESG platform functionality is hindered as essential knowledge is fragmented across various stakeholders. In this paper, we show how a platform owner, complementor, and platform user co-design ESG platform module features by applying an ecosystem approach. Situated in a socio-technical context, we apply Action Based Research (ADR) that includes a cyclic process of design iterations in an actual real-life setting. We identified ten challenges that may occur in the design process, and we explain which strategies have been applied to overcome the challenges.

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.

Introduction Health behaviours such as exercise and diet strongly influence well-being and disease risk, providing the opportunity for interventions tailored to diverse individual contexts. Precise behaviour interventions are critical during adolescence and young adulthood (ages 10-25), a formative period shaping lifelong well-being. We will conduct a systematic review of just-in-time adaptive interventions (JITAIs) for health behaviour and well-being in adolescents and young adults (AYAs). A JITAI is an emerging digital health design that provides precise health support by monitoring and adjusting to individual, specific and evolving contexts in real time. Despite demonstrated potential, no published reviews have explored how JITAIs can dynamically adapt to intersectional health factors of diverse AYAs. We will identify the JITAIs' distal and proximal outcomes and their tailoring mechanisms, and report their effectiveness. We will also explore studies' considerations of health equity. This will form a comprehensive assessment of JITAIs and their role in promoting health behaviours of AYAs. We will integrate evidence to guide the development and implementation of precise, effective and equitable digital health interventions for AYAs. Methods and analysis In adherence to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines, we will conduct a systematic search across multiple databases, including CENTRAL, MEDLINE and WHO Global Index Medicus. We will include peer-reviewed studies on JITAIs targeting health of AYAs in multiple languages. Two independent reviewers will conduct screening and data extraction of study and participant characteristics, JITAI designs, health outcome measures and equity considerations. We will provide a narrative synthesis of findings and, if data allows, conduct a meta-analysis. Ethics and dissemination As we will not collect primary data, we do not require ethical approval. We will disseminate the review findings through peer-reviewed journal publication, conferences and stakeholder meetings to inform participatory research. PROSPERO registration number CRD42023473117.

Purpose: Digital platforms are essential for fostering innovation in first-line healthcare. These platforms require openness, allowing external parties to utilize, enhance, or profit from them. Yet, knowledge about barriers to realizing platform openness is lacking. This research investigates the barriers to realizing platform openness in first-line healthcare. Method: This research employed a qualitative exploratory approach. We collected data through thirteen semi-structured interviews with platform experts, application developers, and healthcare practitioners. As a study setting, we focused on Dutch first-line healthcare. We then analyzed the data using thematic analysis. Result: We identify barriers in three main categories that hinder platform openness: technology-related (e.g., redundancy in development work), business-related (e.g., profit-maximizing strategy), and healthcare-related (e.g., reluctance to change). Scientific contribution: We contribute to the platform literature in medical informatics by being among the first to examine openness barriers that hinder platform-based innovation. We thus explain why platform implementations often do not result in substantial improvements in healthcare delivery despite their transformative impact in other industries.

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.

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.

Across the European continent, governments and GovTech companies are rushing to launch digital identity wallets for citizens. These wallets should allow citizens to obtain a higher level of control over their personal data. While there are some regulations and policy directions, actors are struggling with the design, launch, and governance of these digital wallets. Those looking for help will find little guidance in academic literature. The objective of this paper is to provide insights in barriers for launching digital identity wallets by a public-private ecosystem. Drawing on the case study approach, we study the available regulations and policy directions, and collect insights from workshops with policy makers and aspiring wallet providers. The main findings indicate that barriers such as the lack of boundary resources (e.g. shared data specifications and exchange standards) and the absence of a collaborative, public-private governance impede the launch of digital identity wallets. Policy makers looking to speed up the launch of digital wallets must focus on removing these barriers, starting with the development and governance of boundary resources by the public-private ecosystem.