In numerous countries, electricity Distribution System Operators (DSOs) function as local monopolies. To counter potential abuse of monopoly power, regulators, especially in Europe, often employ mechanisms like DSO-specific revenue caps to encourage cost reductions among regulated DSOs. Despite its widespread use, literature concerning ex-post evaluation of the effectiveness of revenue cap regulation, particularly divided into its individual components, is lacking. This paper offers two contributions: First, it shows the advantages of utilizing a semi-parametric panel data StoNED framework methodology as a tool for assessing the impact of revenue caps by evaluating the cost efficiency of regulated DSOs in its individual components. Second, the effectiveness of revenue cap regulation is assessed using the Danish DSOs as a case study. The empirical analysis finds evidence that part of the revenue cap incentive scheme appears to promote cost reductions among regulated Danish DSOs.JEL Classification: C14, C23, C51, L43, L51, L94, L98

Objective

This study explores children’s and adolescents’ experiences and opinions of routine inquiries about violence within specialised outpatient care. Utilising a mixed method with a convergent parallel design, the research combines quantitative data from 184 respondents aged 6–17 collected through survey data and qualitative interviews with four participants aged 7–14. The data presented is a byproduct of an ongoing research project that evaluates a questionnaire designed to ask children about violence.

Results

Findings indicate that most children and adolescents view routine questioning about violence positively or neutrally. The study highlights the importance of healthcare professionals’ responses to disclosures of violence, emphasising that supportive and empathetic reactions can impact children’s willingness to disclose such experiences in the future. The results underscore the necessity for routine inquiries about violence in healthcare settings to ensure that affected children receive appropriate support and intervention.

The metaverse has gradually come into the public eye and has become a hotspot in cyberspace, but it still faces many technical difficulties to be solved. Blockchain is a key component of the metaverse, enhancing the development of the metaverse by connecting the real and virtual worlds seamlessly and solving some of the difficulties faced by the metaverse. Our paper comprehensively studies the development and application of blockchain technology in the metaverse. First, there is an introduction to blockchain and the metaverse, followed by a discussion of why blockchain should be integrated into the metaverse. Second, an overview of the main blockchain technologies is provided to evaluate blockchain's role in the metaverse and the value is summarized. Third, the development of future integration of blockchain and metaverse is presented from the perspective of social life and technology. For social life, how to use blockchain in the metaverse to enhance and improve social life is discussed. Then, from the technical perspective, it discusses how blockchain shapes the metaverse. Finally, challenges associated with the integration of blockchain into metaverses are analyzed and some promising research directions and solutions are proposed.

Context: Organizations opt for continuous delivery of incremental updates to deal with uncertainty and minimize waste. However, applying continuous engineering (CSE) practices requires a continuous feedback loop with input from customers and end-users.

Challenges: It becomes increasingly challenging to apply traditional requirements elicitation and validation techniques with ever-shrinking software delivery cycles. At the same time, frequent deliveries generate an abundance of usage data and telemetry informing engineering teams of end-user behavior. The literature describing how practitioners work with user feedback in CSE, is limited.

Objectives: We aim to explore the state of practice related to utilization of user feedback in CSE. Specifically, what practices are used, how, and the shortcomings of these practices.

Method: We conduct a qualitative survey and report analysis from 21 interviews in 13 product development companies. We apply thematic and cross-case analysis to interpret the data. Results: Based on our earlier work we suggest a conceptual model of how user feedback is utilized in CSE. We further report the identified challenges with the continuous collection and analysis of user feedback and identify implications for practice.

Conclusions: Companies use a combination of qualitative and quantitative methods to infer end-user preferences. At the same time, continuous collection, analysis, interpretation, and use of data in decisions are problematic. The challenges pertain to selecting the right metrics and analysis techniques, resource allocation, and difficulties in accessing vaguely defined user groups. Our advice to practitioners in CSE is to ensure sufficient resources and effort for interpretation of the feedback, which can be facilitated by telemetry dashboards. 

Vi undersöker i två delstudier samverkan mellan kommuner och Arbetsförmedlingen i tio kommuner. I delstudie 1 analyseras samverkan utifrån gruppintervjuer med chefer och handläggare i syfte att ringa in innehållet i samverkan. Vi genomför även en kvalitativ innehållsanalys av samverkansöverenskommelser. Delstudien fokuserar på tidsperioden strax innan Januariavtalet 2019, ett avtal som förändrade Arbetsförmedlingens roll i flera viktiga dimensioner. I delstudie 2 undersöker vi om samverkan har förändrats efter Januariavtalet genom gruppintervjuer som genomfördes 2021. Flera intressanta resultat framträder i rapporten: Vi identifierar exempelvis i delstudie 1 tre av fyra teoretiskt tänkbara samverkansarrangemang i kommunerna: kooperativ, komplementär och konfrontativ samverkan. Kooperativ samverkan återfinns bara i en av tio kommuner. I delstudie 2 finner vi att samverkan har försvårats efter Januariavtalet till följd av stängda lokalkontor, användning av privata utförare av arbetsmarknadspolitik samt ökad digitalisering av tjänster. Kommunala företrädare upplever att Arbetsförmedlingen har blivit mer svåråtkomlig. Intervjuade från Arbetsförmedlingen är mer positiva och anser att en stor del av problemen handlar om ”barnsjukdomar”. 

Entreprenörer har specifika färdigheter som skiljer sig från de som förvärvats genom utbildning eller tidigare anställning. Entreprenöriella förmågor och erfarenheter utgör ett slags humankapital och bör ses som en separat produktionsfaktor i en modern ekonomi. I en rapport från Entreprenörskapsforum, framtagen på uppdrag av Produktivitetskommissionen, argumenteras för en politik med brett och tydligt entreprenörskapsperspektiv som främjar produktivitetsvinster från det entreprenöriella humankapitalet.

Rapporten Göteborg borgar för tillväxt undersöker om Sveriges ekonomiska centrum håller på att förskjutas västerut till Göteborgsregionen.

The AI marketplace ecosystem accelerates multiple modules of the AI engineering pipeline by fostering collaboration between stakeholders. However, marketplace collaborators often face a dilemma in striking a balance between sharing artifacts and protecting intellectual property (IP) rights. Thus, there is a need for a license management system within the AI marketplace to facilitate the exchange of artifacts in a trusted and secure manner. 

This work shares experiences while building such a license management system within the Bonseyes marketplace (BMP), a functional crowdsourcing AI marketplace that specializes in deploying real-time applications on edge devices. The BMP was developed, and its applicability is proven through the European H2020 project by a series of open calls and workshops, for gathering stakeholders and orchestrating the marketplace operations. 

The main contributions of this work are (i) implementation of an end-to-end license management system that deals with selecting license templates, license agreement interaction between seller and buyer, and the generation and enforcement of human- and machine-readable license files, and (ii) introduction of "Synchronization licenses'' concept from the music industry to the AI marketplace context where consumers acquire a license to integrate the artifact into another application, and a respective BMP use-case for collaborative AI engineering. 

Considering rapid digital transformation and recent changes in the macro-level “rules of the game” in entrepreneurship, we aim to problematize and understand the progressive encounter and new relationship between opportunity-driven and necessity-driven entrepreneurs within the context of the gig economy. In such a setting, both traditional entrepreneurial roles and the very division between them are brought into question. Their encounter implies a deviation from the basic assumptions of what entrepreneurship entails: being the bearer of risk, taking on uncertainty and individual responsibility, and pursuing unlimited profit potential. This deviation results in a change in the rules of the game. Consequently, we learn more about how the context of moving from a pre-gig economy toward the gig economy has implications for our societal understanding of entrepreneurship as a phenomenon. 

Context: Generative AI (GenAI) brings new opportunities to the software industry and the digital economy in a broader context.

Objective: This study aimed to explore and capture the practitioners' perception of GenAI adoption in the fast-paced software industry in the context of developing countries. Method: We conducted online focus group discussions with 18 practitioners from various roles to collect qualitative data. The practitioners have an average of 7.8 years of working experience and have used GenAI for over a year. We employed thematic analysis and the Human-AI Collaboration and Adaptation Framework (HACAF) to identify the influencing factors of GenAI adoption, such as awareness, use cases, and challenges.

Results: The adoption of GenAI technology is evident from practitioners. We identified 22 practical use cases, three of which were novel, i.e., contextualizing solutions, assisting the internal audit process, and benchmarking the internal software development process. We also discovered seven key challenges associated with the GenAI adoption, two of which were novel, namely, no matching use cases and unforeseen benefits. These challenges slow GenAI adoption and potentially hinder developing countries from entering a high-skill industry.

Conclusion: While the adoption of GenAI technology is promising, industry-academia collaboration is needed to find solutions and strategies to address the challenges and maximize its potential benefits.

There are both facilitating and hindering factors when it comes to screening for intimate partner violence (IPV). While research indicates that health-care providers’ emotions regarding screening serve as an influencing factor, there is little scholarly work that has systematically considered the role of emotions in inquiring about IPV. Addressing this research gap, the article explores the affective aspects of routinely asking questions about violence in women’s health care. The findings show that emotions serve as both antecedents and consequences of routine inquiry, indicating that the role of emotions should be viewed as integral to any effort to improve screening practices for IPV.

In this study, we propose to explore multi-layered clustering to provide a context-aware data analytics tool for monitoring the network behavior of subsystems, such as a district heating (DH) network. Multi-layer clustering, in contrast to multi-view clustering, does not assume conditional independence of layers. The main idea of our approach is based on the integration of clustering models produced by considering different perspectives that capture information about the monitored subsystems' operational behavior or performance as well as their contextual environment. The initial clustering layer can reflect a static context, which is important for the subsystems' performance. It will be used as a base on which clustering models produced with respect to other analyzed operational characteristics and contexts will be layered. This will facilitate analysis and comparison of the subsystems' behavior in two comparable time periods and, eventually, identification of deviations that need attention. The proposed approach is evaluated and validated in a use case from the DH domain. The multi-layered clustering is applied and demonstrated to be robust for continuous context-aware analysis of the performance of a network of DH substations. 

Work in software development companies has become increasingly hybrid with employees altering days of working in the office with days of working remotely from home. Yet, little is know about the efficiency of such way of working because the current scale of remote working is unprecedented. In this paper, we present our findings from a company-wide survey at Storebrand - a large-scale Norwegian fintech company, focusing on perceived performance. Our analysis of 192 responses shows that most employees report being able to perform the planned tasks. Further, half of respondents perceive to have increased work hours. Through qualitative analysis of open-ended commentaries of respondents we learned that remote working has dual effects on the perceived work hours - some employees report working longer hours and others report having more work time due to efficient use of the time throughout the day. Finally, we recommend managers to discuss and address the concerning habits of employees caused by increased connectivity and inability to stop working, before these lead to burnout and disturbances in the work/life balance. 

Federated Learning (FL), a revolutionized privacy-preserving distributed Machine Learning (ML), enables models to learn from data distributed across multiple devices at the edge, empowering Edge Intelligence (EI) applications. However, a significant challenge within FL is the issue of data skew, where data distribution across devices varies significantly, potentially impairing model performance. This paper investigates this challenge by exploring the application of FL in a complex facial ethnicity classification, including blurry label boundaries and non-IID data distribution. The paper systematically examines the effects of data skews on FL aggregation algorithms over five algorithms and three different datasets using multiple scenarios. In particular, in scenarios involving sensitive non-IID data such as facial attributes. Our approach involves a novel methodology that adapts aggregation techniques to handle better the heterogeneous data distributions typical of real-world FL environments, demonstrating the potential for more robust and equitable model performance across diverse edge devices. Key findings highlight the importance of FL in preserving data privacy while facilitating model improvement, exemplifying its potential in diverse fields beyond biometrics. 

Federated Learning (FL) faces significant challenges, such as handling non-IID (Non-Independent and Identically Distributed) data and efficiently aggregating distributed models, which can lead to slower convergence and reduced model accuracy. This paper proposes a novel framework, Bayesian Federated Learning with Stochastic Variational Inference (BayFL-SVI), to address these issues. Stochastic Variational Inference (SVI) is a scalable approximation method for Bayesian inference that optimizes the Evidence Lower Bound (ELBO) using mini-batches of data through stochastic gradient descent. By computing the ELBO for each client update, our approach quantifies the significance of these updates, effectively managing the heterogeneity of non-IID data and improving the aggregation process. This approach results in a more accurate and robust integration of client contributions, enhancing convergence rates and overall model performance. We provide theoretical analysis with convergence guarantees. Our empirical results demonstrate significant improvements in convergence rates and model accuracy, establishing a solid foundation for future studies. 

Background and Related Work:

Software startups face unique challenges in product development, including limited resources, the need for rapid innovation, and the constant pressure to adapt to market changes. Generative Artificial Intelligence (GenAI) has recently gained significant attention, offering capabilities to assist creative processes, generate content, and enhance decision-making through data analysis. However, how GenAI can be integrated into agile product development processes in software startups remains an open question.

Objective:

This study aims to identify potential use cases for GenAI in software startups and explore how GenAI can support innovation, overcome development challenges, and integrate with agile practices to improve product quality and development speed.

Method:

We identified a list of GenAI use cases from existing systematic literature reviews and mapped them to engineering process areas in software startups. Following that, we conducted workshops with experts to validate our results. Results: The results provide a descriptive overview of GenAI’s potential applications in software startup environments. Given the current state of the art, we identified areas that could benefit faster from integrating GenAI.

Conclusions:

The study delineates the prospective impact of GenAI on agile product development in software startups, showcasing areas of immediate applicability. 

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training while preserving data privacy across multiple clients. FL addresses critical privacy concerns but introduces challenges related to model drift. Model drift is a phenomenon where the model degrades over time due to changes in the underlying data distribution or the relationships between input features and target variables. This paper proposes a novel drift detection and management methodology within federated environments. Our experimental analysis demonstrates the effectiveness of the proposed drift detection framework. The study systematically evaluates the impact of drift on model performance metrics, including accuracy, F1 score, Cohen’s kappa, and ROC. The findings indicate that even minimal drift in a subset of clients can significantly degrade the global model’s performance, underscoring the importance of robust drift detection. The proposed solution enhances the reliability and accuracy of federated models and addresses the scalability and privacy-preserving requirements inherent in FL environments. 

In this study, we propose a data-driven survival risk analysis approach in support of predictive maintenance management of a large portfolio of industrial assets. The concrete use case considered is a large portfolio of industrial vehicles (trucks). However, the approach is generic (i.e., asset-type agnostic) in nature and can be applied in different industrial contexts. It is able to employ different data sources in the risk analysis workflow, e.g., time series operation data collected via a multitude of sensor measurements combined with tabular data recording the technical specifications of the assets (vehicles). Subsequently, several different risk assessment strategies can be considered: 1) operation-related risk at each time step for any asset computed on the operation data across the whole portfolio; 2) the failure predisposition of each asset determined by its technical specification; 3) hybrid risk analysis, which innovatively combines the different data types to estimate overall risk at any time in the future for any asset. Our validation, conducted on real-world data, demonstrates that the hybrid approach provides a realistic temporal risk assessment during vehicle operation that also reflects adequately the inherent (contextual) risk predisposition of the vehicle due its technical specification. The proposed approach derives diverse survival risk estimations, which are interpretable by design and in this way facilitate both prognostic health monitoring and root cause analysis of the factors impacting vehicles' risk of failure.

Do firms’ investments in digital technologies leave a footprint in their growth aspirations? We employ a unique survey of more than 4,000 firms in Sweden to investigate whether firms that invest in software development are more likely to aspire to grow. There is a positive relationship between software development and growth aspirations, and firms that develop software are particularly more likely to aspire to grow through internationalization, i.e. expanding on new markets within as well as outside the European Union. Even after controlling for innovation, the result shows that for a wide array of firms, software development has become an essential input in the pursuit of growth and internationalization.

This article attempts to bridge the gap between the concepts of Industrial Symbioses (IS) and Industrial Convergence (IC) by arguing that the two concepts can jointly help to understand the role of industrial structures and value chains that embody transformation processes through which technologies evolve in response to transformation pressure. On one hand, IS with a focus on inter-firm collaborations and resource exchange has become a useful framework to understand and capture the mechanisms that foster sustainable industrial and technological development, while on the other hand IC has been used to analyze technological development that blurs traditional borders between firms in terms of innovations and business development. However, although interrelated the two concepts have been discussed separately. This paper is using the HYBRIT initiative as an illustrative case of a climate change mitigation and as such a “flagship” project in Sweden in an effort to replace the traditional blast furnace technology as the core unit processing technology in steelmaking. It is advocated that whilst many aspects of the conceptual models of IS and IC appear to be congruent with the on-going HYBRIT eco-industrial transformation process, the overall impression is that in future eco-industrial transformations, it could be of interest to develop and deploy a more specific transformation model adapted and capturing unique process-industrial conditions for product and process innovation.