67471 research outputs found
Sort by
Meeting Users’ Needs in National Health Information Infrastructures - Lessons from the Case of Germany
Get PDFNational health information infrastructures (HII) are critical for enabling interoperable and efficient healthcare delivery. However, many national initiatives struggle with misaligned stakeholder interests and unclear roles of regulators. This study investigates a national HII, which includes three important public health applications. Drawing on interviews, participatory observations, and regulatory documents, we examine how stakeholder dynamics and regulatory constraints shape the development process. While existing research has often described such cases, our study adopts a solution-oriented perspective. We derive three actionable recommendations to better align national HII with end-user needs, emphasizing balanced stakeholder involvement, user-centered design, and governance structures that promote healthcare delivery outcomes. Our findings contribute to the literature by extending empirical insights on national HII and offering guidance for governments and policymakers designing similar infrastructures in other countries
Introduction to the Minitrack on Personalized Health Assistance with Intelligent Digital Solutions
No full textIntroduction to the Minitrack on Resilient Digital Communities: Social Media, Crisis Response, and Collective Action
Get PDFA vs. I in AI: Is there a Threshold to “Engineered” Intelligence?
Get PDFDespite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within higher cardinality ontologies
Information Technologies and the Search for Top Talent in Competitive Job Markets
Get PDFThe search for highly talented professional employees has been dramatically reshaped by new information technologies. On the one hand, the rise of both dedicated and general-purpose job platforms has significantly reduced the cost and effort for applicants to apply to many more positions. In addition, the introduction of powerful LLM-based tools enables applicants to submit more polished and better-targeted materials, further lowering the signal-to-noise ratio in the application pool. On the other hand, recruiters now face an overwhelming number of applications per opening and have turned to more sophisticated selection rules and AI tools to identify candidates worth interviewing. Because application materials are not perfectly correlated with an applicant’s true inherent quality or fit, and because applicants often conceal their job preferences, recruiters continue to rely on personal interviews—both to better gauge candidate quality and to market their positions. Overall, while applicants can now submit large batches of applications easily and at low cost, recruiters, flooded with submissions, find that the search process has become increasingly expensive and time-consuming. This study situates the U.S. National Resident Matching Program (NRMP) within the broader problem of hiring in markets where specialized platforms dramatically increase application volume. As in other professional labor markets, residency programs compete for applicants in an environment where individuals can cheaply submit large numbers of applications, generating excess competition and noise. We focus on program-level strategies for managing the transition from applications to interviews—through adjustments in interview volume, screening accuracy, or interview cutoffs. Our analysis considers both symmetric settings, where programs are identical in applicant perceptions, and asymmetric settings, where one program is uniformly more desirable. A key and somewhat counterintuitive result is that additional effort by one program does not necessarily disadvantage its competitors and can, in some cases, improve overall match outcomes
Innovating in Shifting Landscapes: Digital Innovation in the Era of Generative AI
Get PDFEmerging technologies such as generative AI challenges our current understanding of digital innovation, which addresses recombination of physical and digital components into novel products. These technologies introduce dynamic and evolving capabilities that disrupt existing possibilities, what actors imagine to be feasible, valuable, or achievable. Through an 18-month longitudinal case study of an international legal firm developing an AI assistant, we observed how spaces of possibilities shift under external technological disruption, and identified core phases of innovation – surfacing new possibilities, shifting space of possibilities, and actualizing selected possibilities – through which actors envisioned and actualized emerging possibilities where distinct versions of AI evolved. Our study contributes to organizing logic of digital innovation in rapidly evolving technological landscapes, by showing how ongoing disruption during innovation shifts space of possibilities and drives iterative cycles of surfacing, shifting and actualizing new possibilities
Toward a Holistic Conceptualization of Green Coding: A Multi-Level Perspective on Sustainable Software Practices
Get PDFThe rising use of information and communication technology (ICT) is contributing to global greenhouse gas emissions (GHGEs), with projections indicating further growth. As ICT systems are software-driven, improving software energy efficiency through green coding has become more important. However, research on green coding remains disintegrated, focusing mainly on tools, green coding practices, implementation in specific phases of the software development life cycle (SDLC), and specific application domains. This paper addresses the conceptual gap by offering a multi-level conceptualization of green coding through the theoretical lens of responsible innovation, sociotechnical systems theory, organizational theory, and institutional theory. Our conceptual framework highlights how technical skills, sustainability mindsets, and ethical values influence the adoption of green coding, shaped by internal organizational dynamics and external institutional pressures. It also explores how green coding has varying impacts across application domains. We propose three propositions and offer a foundation for advancing sustainable software development
UWB-PostureGuard: A Privacy-Preserving RF Sensing System for Continuous Ergonomic Sitting Posture Monitoring
Get PDFImproper sitting posture during prolonged computer use has become a significant public health concern. Traditional posture monitoring solutions face substantial barriers, including privacy concerns with camera-based systems and user discomfort with wearable sensors. This paper presents UWB-PostureGuard, a privacy-preserving ultra-wideband (UWB) sensing system that advances mobile technologies for preventive health management through continuous, contactless monitoring of ergonomic sitting posture. Our system leverages commercial UWB devices, utilizing comprehensive feature engineering to extract multiple ergonomic sitting posture features. We develop PoseGBDT to effectively capture temporal dependencies in posture patterns, addressing limitations of traditional frame-wise classification approaches. Extensive real-world evaluation across 10 participants and 19 distinct postures demonstrates exceptional performance, achieving 99.11% accuracy while maintaining robustness against environmental variables such as clothing thickness, additional devices, and furniture configurations. Our system provides a scalable, privacy-preserving mobile health solution on existing platforms for proactive ergonomic management, improving quality of life at low costs
Unpacking Passive Innovation Resistance and Openness to Data Sharing in Pay-as-You-Drive Tariff Acceptance
Get PDFDrawing on innovation resistance theory, the present study investigates the influence of cognitive and situational passive innovation resistance on the acceptance of data-driven tariff models in the car rental sector (i.e. pay-as-you-drive). A structural equation modeling (SEM) approach was used to analyze survey data from Germany (N = 599) and the United States (N = 585). The findings indicate that passive innovation resistance has a detrimental effect on openness to data sharing and increases risk perceptions, which in turn influence attitudes towards tariffs and behavioral intention. The findings of this study offer valuable implications for practitioners seeking to increase acceptance of personalized and behavior-based tariff offerings. The implications of this study relate to the importance of tackling psychological barriers and improving data transparency in order to enhance user acceptance. The present study makes a valuable contribution to extant literature by combining innovation resistance theory with privacy and acceptance research in a novel empirical context