UNDERSTANDING THE SOCIO-TECHNICAL ASPECTS OF LOW-CODE ADOPTION FOR SOFTWARE DEVELOPMENT

The digital transformation of organizations’ leverages several approaches for creating software applications that meet the requirements of the specific context. Besides well-researched approaches like software development, outsourcing, or customizing commercial software packages, low-code platforms today offer a new approach for creating software. The low-code approach allows to develop software without or with limited actual coding, but by combining executable software components into workflows. While the low-code approach simplifies software development and offers a reduction in effort and time, we lack explanations on why organizations adopt it, and which challenges are associated with this adoption. We, therefore, investigate the adoption of the low-code approach based on the technology-organization-environment framework. We identified ten aspects supporting and six aspects hindering the adoption of the low-code approach. For practice, we propose a model that can assist organizations in determining the adequacy for adopting the low-code approach

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Dividing Apples and Pears: Towards a Taxonomy for Agile Transformation

Agile Transformation (AT), the process of adopting agile methods and practices in organizational settings, has received grappling attention in research due to its extensive emergence in practice. Although the complexity of ATs is well known and its use cases widespread, research has not yet developed a comprehensive classification of AT. This lack limits the comparability of existing studies and our possibility to draw theoretical generalizations from their results. In this paper, we fill this gap by presenting a taxonomy for AT based on a systematic literature review. We abstracted the taxonomy in an analysis of 92 articles, including empirical and theoretical papers as well as experience reports. We contribute to the existing literature by providing a taxonomy that presents an analytical theory, offering a characterization of ATs which helps researchers and practitioners analyze ATs, identify how they differ, and provide insight into combinations of agile characteristics

Evidence-based narratives in European research programming

Abstract The article introduces and exemplifies the approach of evidence-based narratives (EBN). The methodology is a product of co-design between policy-making and science, generating robust intelligence for evidence-based policy-making in the Directorate General for Research and Innovation of the European Commission (DG RTD) under the condition of high uncertainty and fragmented evidence. The EBN transdisciplinary approach tackles practical problems of future-oriented policy-making, in this case in the area of programming for research and innovation addressing the Grand Societal Challenge related to climate change and natural resources. Between 2013 and 2018, the EU-funded RECREATE project developed 20 EBNs in a co-development process between scientists and policy-makers. All EBNs are supported with evidence about the underlying innovation system applying the technological innovation systems (TIS) framework. Each TIS analysis features the innovation, its current state of market diffusion and a description of the innovation investment case. Indicators include potential future market sizes, effects on employment and environmental and social benefits. Based on the innovation and TIS function analyses, the EBNs offer policy recommendations. The article ends with a critical discussion of the EBN approach