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.

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.

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.

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.

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.

Managers increasingly seek ways to explore insights from data for business improvements and innovation. Data Analytics (DA) platforms hold promise for businesses, especially small businesses that cannot afford tailor-made proprietary analytics services. DA platforms offer generic analytics features to a pool of businesses, saving costs and enabling benchmarking. This paper explores value propositions and adoption challenges for small businesses regarding DA. The paper offers practical insights from designing and launching a DA platform targeting small businesses in the hospitality sector. The findings of our paper show that data analytics is potentially valuable for small businesses through insights into market and customer trends. Small businesses can leverage such insights to refine their offerings. Trust and privacy concerns in sharing data are key challenges holding back adoption. We proposed measures, especially privacy-preserving technologies, to mitigate the risk of tracing a specific enterprise's data shared on the DA platform. These measures assure businesses that data shared or analysed through the DA platform is not used to harm their competitive advantage.

When allowing third parties to join their platforms, platform owners run the risk of attracting harmful third-party complements. Existing literature considers that low-quality offerings negatively affect cross-side user satisfaction and attractiveness, ultimately harming the platform's reputation and stability. However, recent events show that negative externalities from third-party offerings can also motivate platform sponsors to adapt their membership conditions. Existing platform literature does not explain the underlying theoretical mechanisms. In this paper, we examine why platform sponsors adjust the conditions that govern third parties joining their platforms in response to negative externalities. We apply legitimacy theory to a critical case on a payment transaction platform. We find that negative externalities affect both moral and regulatory legitimacy, which in turn motivate the platform sponsor to adjust the conditions under which third parties may join the platform.

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.

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.

Meta-platforms have received considerable Information Systems scholarly attention in recent years. Meta-platforms enable platform-to-platform openness and are especially beneficial to amplifying network effects in highly-specialized markets. A promising emerging context for applying meta-platforms is data marketplaces—a special type of digital platform designed for business data sharing that is vastly fragmented. However, data providers have sovereignty concerns: the risk of losing control over the data that they share through meta-platforms. This research aims to explore antecedents and consequences of data sovereignty concerns in meta-platforms for data marketplaces. Based on interviews with fifteen potential data providers and five data marketplace experts, we identify data sovereignty antecedents, such as (potentially) less trustworthy data marketplace participants, unclear use cases, and data provenance difficulties. Data sovereignty concerns have many consequences, including knowledge spillovers to competitors and reputational damage. This study is among the first that empirically develops a pre-conceptualization for data sovereignty in this novel context, thus laying the groundwork for designing future data marketplace meta-platform solutions.

In this study, we investigate how small and medium-sized enterprises (SMEs) engage in business model innovation in response to technology shifts, and the ensuing impact of this innovation on performance. Using structural equation modeling, we analyze data from a survey of 1328 European SMEs and find that technological turbulence affects the scope and novelty of business model innovation, and that these dimensions of innovation in turn affect firm performance. We show that these relationships are doubly contingent: both SME size (micro, small, or medium) and time since market entry are relevant. Early entrant firms are more responsive to technological turbulence through both dimensions of business model innovation, as the learning accruing in these SMEs since market entry motivates and informs business model innovation. There are meaningful differences in these relationships for micro, small, and medium-sized enterprises. Our findings advance the theoretical understanding of the relationships between technology, entry order, business model innovation, and performance and also serve to inform managers’ estimations of the implications of technology turbulence and business model innovation for performance.

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.

Purpose: Information and digital literacy have recently received much interest, and they are being viewed as critical strategic organisational resources and skills that employees need to obtain in order to function at their workplaces. Yet, the role of employees' literacy seems to be neglected in current literature. This paper aims to explore the roles that information and digital literacy play on the employees' perception in relation to usefulness and ease of use of digital technologies and consequently their intention to use technology in the practices they perform at the workplace. Design/methodology/approach: This paper builds a conceptual model with key constructs (information literacy and digital literacy) as new antecedents to the technology acceptance model and aims to establish that information literacy and digital literacy are indirect determinants of employees' intention to use digital technologies at the workplace. The data set used in this paper comprises of 121 respondents and structural equation modelling was used. Findings: The findings reveal that both information literacy and digital literacy have a direct impact on perceived ease of use of technology but not on the perceive usefulness. The findings also show that both literacies have an indirect impact on the intention to use digital technology at work via attitude towards use. Practical implications: Managers and decision-makers should pay close attention to the literacy levels of their staff. Because literacies are such an important skillset in the digital age, managers and chief information officers may want to start by identifying which work groups or individuals require literacy training and instruction, and then provide specific and relevant training or literacy interventions to help those who lack sufficient literacy. Originality/value: This is one of the first studies to consider information literacy and digital literacy as new antecedents of the technology acceptance model at the workplace environment.

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.

Policymakers and analysts are heavily promoting data marketplaces to foster data trading between companies. Existing business model literature covers individually owned, multilateral data marketplaces. However, these particular types of data marketplaces hardly reach commercial exploitation. This paper develops business model archetypes for the full array of data marketplace types, ranging from private to independent ownership and from a hierarchical to a market orientation. Through exploratory interviews and case analyses, we create a business model taxonomy. Patterns in our taxonomy reveal four business model archetypes. We find that privately-owned data marketplaces with hierarchical orientation apply the aggregating data marketplace archetype. Consortium-owned data marketplaces apply the archetypes of aggregating data marketplace with additional brokering service and consulting data marketplace. Independently owned data marketplaces with market orientation apply the facilitating data marketplace archetype. Our results provide a basis for configurational theory that explains the performance of data marketplace business models. Our results also provide a basis for specifying boundary conditions for theory on data marketplace business models, as, for instance, the importance of network effects differs strongly between the archetypes.

As digital platforms are dominating the digital economy, complex ecologies of platforms are emerging. While the openness of digital platforms is an important theme in platform studies, the openness between platforms has hardly been studied. This paper explores factors that affect decisions by platform owners to open their platforms to other platforms. The focus is on Internet-of-Things platforms for automotive and healthcare applications. According to the findings, platform owners make trade-offs on whether to open up on a case-by-case basis. We identify a complex array of factors relating to direct benefits and costs (e.g., revenues from selling platform data), indirect benefits (e.g., attractiveness of the focal platform to users) as well as strategic consideration (e.g., improving bargaining power towards other actors). How businesses make trade-offs on these factors depends on market-level context (e.g., maturity of the market and standards) and organizational context (e.g., strategic focus and business objectives). Our findings provide a basis for future studies on the openness between platforms, which will become increasingly important as platforms proliferate in every layer of the digital industry.

Whereas open digital platforms drive innovation in industries, platforms in primary healthcare are mostly closed. Policy-makers have been looking for ways to open up primary healthcare platforms to stimulate collaboration and innovation and need to do so even more due to the ongoing COVID-19 crisis. Yet, there is not one way of opening up platforms in primary healthcare, just as it is unclear how different ways of openness can lead to more innovation. This paper analyzes the opportunities and challenges in realizing platform openness while examining alternative forms of openness. To answer this, we (1) conceptualize different forms of platform openness (sponsor-provider-platform-user openness), (2) examine how these forms of openness can resolve barriers to innovation, and (3) examine what challenges need to be overcome to realize that form of openness in practice, such as complexity in roles, regulations, and ICT infrastructure. The findings are relevant to structure further research on how platform openness leads to more innovations in healthcare.

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.