Designing the Hybrid Cooperative: A Socio-Technical Architecture for Scalable, Global Coordination Using Blockchain

Blockchain has been promoted as a remedy for coordination in fragmented, multi-stakeholder ecosystems, yet many projects stall at pilot stage. Using a design-science approach, we develop the Hybrid Cooperative (HC), a digitally native governance architecture that combines smart-contract coordination with a minimal, code-deferent legal interface and jurisdictional modules. This selective decentralization decentralizes rules where programmability lowers agency and verification costs, and centralizes only what is needed for enforceability. A post-case evaluation against two traceability initiatives in supply chains illustrates how the HC improves distributed task management, verifiable information, incentive alignment, institutional interoperability, and scalable, contestable governance. The paper contributes to Information Systems by specifying a socio-technical model for scalable, multi-stakeholder coordination across regulatory and organizational boundaries

MoCap2Radar: A Spatiotemporal Transformer For Synthesizing Micro‑Doppler Radar Signatures from Motion Capture

We present a pure machine learning process for synthesizing radar spectrograms from Motion‑Capture (MoCap) data. We formulate MoCap‑to‑spectrogram translation as a windowed sequence‑to‑sequence task using a transformer‑based model that jointly captures spatial relations among MoCap markers and temporal dynamics across frames. Real‑world experiments show that the proposed approach produces visually and quantitatively plausible doppler radar spectrograms and achieves good generalizability. Ablation experiments show that the learned model includes both the ability to convert multi-part motion into doppler and an understanding of the spatial relation between different parts of the human body. The result is an interesting example of using transformers for time-series signal processing. It is especially applicable to edge computing and Internet of Things (IoT) radars. It also suggests the ability to augment scarce radar datasets using more abundant MoCap data for training higher-level applications. Finally, it requires far less computation than physics-based methods for generating radar data

Is AI Agreement Reassuring? It Depends on What Patients Believe About AI

People often consult online information when choosing a doctor prior to their first visit. As AI increasingly supports medical decision-making, we examined how incorporating an AI agreement cue in a doctor's online profile influences patients’ perceptions of credibility and their intention to visit. In a user study (N = 415), participants reviewed a doctor’s profile indicating 90% diagnostic agreement with either AI decision support systems or human experts. The results show that neither the AI nor the expert agreement cue significantly influences credibility perceptions or visit intention. However, among participants with low or moderate beliefs in the positive machine heuristic, or low beliefs in the negative machine heuristic, the AI agreement cue reduces perceived competence, trustworthiness, and intention to visit. These findings highlight how beliefs about AI shape user responses to the AI agreement cue and offer implications for effective self-presentation strategies for healthcare professionals collaborating with AI

Navigating the Digital Product Passport Landscape: From Implementation Challenges to Future Opportunities

Digital Product Passports are a central instrument for enabling a digitized circular economy. They provide transparency over processes and life cycles. Almost every sector must deal with them as they become mandatory in the following years. Yet, they are still in their infancy. Much theoretical-conceptual work emerges while consolidating research, but it is still missing. We tackle this gap with a review of Digital Product Passports. We could identify eleven often-used technologies for data collection, curation, and sharing with the passports. Furthermore, we present several challenges clustered into nine categories ranging from conceptual challenges via the architecture to the operation and ecosystem aspects. Moreover, we close our study with six paths for future research

Digital Coordination of Work: How Data, Intelligence, and Transparency Reshape Organizational Practice

Digital advancements have transformed how people work, interact, and experience their environments, demanding a revised understanding of work coordination. While current coordination theories remain influential, they struggle to explain emerging digital work practices. This paper critically examines the literature that applies Malone and Crowston’s coordination theory, uncovering their underlying assumptions. Building on concepts like ontological reversal and organizational digitization, we identify key needs for an updated theory. Our goal is to spark renewed interest in long-standing coordination theories and encourage scholars to explore how coordination theories can evolve to address the realities of today’s digital work environments

Suicide Prevention Among People Recently Released from Jail: Linking Data from Jail Release Reports to Electronic Health Records

Suicide risk among people recently released from jail is a critical problem of public health significance. Half (40-50%) of incarcerated individuals report lifetime suicidal ideation or behavior and 13-20% have attempted suicide. Incarcerated individuals are more likely to die by suicide than the general population. Jail release is a particularly high-risk period, as individuals often struggle with mental health concerns, including acute life stressors, and many fall through the cracks without community connection to mental health services. A new large trial is underway testing a novel informatics solution linking public jail release data with health system records via an automated computer program consisting of a set of common demographic and other data elements to stimulate immediate virtual care coordination, suicide risk screening, brief intervention, and an evidence-based telemedicine-based suicide prevention program called Coping Long Term with Active Suicide Program (CLASP) for those who screen positive. In this nested study, we provide data on the informatics matching approach to link jail records with health system records at one large health system in the US to initiate the telemedicine-based intervention. Data were examined from 1,363 individuals released from a county jail over a 2-month period. The informatics algorithm successfully matched 1,050 individuals to their health records. These data support initiation of the innovative intervention and provide promise that health systems can offer a solution to fill the gap in suicide risk after jail release

Designing Educational Games for Financial Literacy and Bias Awareness: Reflections on Challenges and Opportunities

Finance is an important yet underexplored context in the development of prescriptive knowledge for education interventions addressing cognitive biases. Previous research indicates that games are a promising tool for cognitive bias mitigation, but our understanding of how to effectively design games to enhance bias awareness remains limited, particularly in financial education. This study explores challenges and opportunities therein based on seven focus groups conducted with upper secondary school students after playing a financial education game. A thematic analysis of the data indicates that students consider finance uncertain and complex, and wish for games to match this reality through simulations that are challenging and applicable to real-life contexts. Based on these, we propose four design principles to guide the design of effective financial education information systems and games—adopting a constructivist approach to bias education; supporting agency through scaffolding; contextualizing decision-making; and paying special attention to learners' attention and motivation limitations

Quantifying Founder-Market Fit: A Machine Learning Approach to Startup Success Prediction

The high failure rate of early-stage startups poses persistent challenges for venture capitalists and innovation policymakers alike. Although Founder-Market Fit (FMF), defined as the alignment between a founder's background and the domain of their startup, has rarely been systematically quantified, it is widely acknowledged in practice as a key determinant of success. In this paper, we present a novel, data-driven framework to operationalize and predict FMF using machine learning and natural language processing. We construct high-dimensional representations of founder profiles by aggregating structured data from Crunchbase, LinkedIn, and X, and apply transformer-based embeddings to quantify semantic alignment with industry verticals. FMF scores, together with features related to prestige, experience, seniority, and inferred personality traits, are incorporated into supervised models to predict startup success. Our findings show that FMF significantly improves predictive performance over baseline models and remains robust across weighting schemes and learning algorithms. By providing a scalable, interpretable, and auditable approach to founder evaluation, this study advances algorithmic entrepreneurship and offers practical insights for investors, accelerators, and policymakers seeking to improve early-stage startup assessments