Abstract
Circular startups (CSUs) play a crucial role in the circular transition by developing circular business models (CBMs) that minimise resource use and narrow material and energy loops. However, empirical research on how CBMs shape growth strategies and how ecosystems enable or constrain scaling remains limited. This study aims to fill this gap by analysing the growth strategy of CSUs, addressing their circularity, business model and scalability strategies. It analysed 44 CSUs operating in packaging and plastics, textiles and food, water and nutrients value chains, using a qualitative multiple-case design. Results show that CSUs predominantly adopt Commercial and ecosystem scalability strategies, linking replication and geographical expansion with access to partners, resources and markets, and implementing platform- or waste-based CBMs. The study expands existing frameworks by conceptualising Ecosystem Strategy as a core scalability approach and clarifying its mechanisms, offering guidance for entrepreneurs and policymakers seeking to foster circular transformation.

Abstract

Abstract
Companies are increasingly adopting technologies such as Robotic Process Automation (RPA) to reduce costs and improve productivity. RPA is deployed in areas like accounting, payroll, and finance to automate business processes. While RPA does not necessarily result in unemployment, it has notable effects on employees and company governance. This study explores the impact of RPA implementation on employees and company governance, using a qualitative methodology based on thirteen semi-structured interviews with RPA experts from four multinational companies. The results indicate that the impacts of RPA vary depending on the automation strategy adopted (task-oriented or process-oriented). In task-oriented strategies, citizen developers often play a central role, contributing to rapid implementation. In contrast, process-oriented strategies tend to rely on professional developers and require more structured governance. The findings also point out that RPA influences not only task execution but also employee upskilling, job role redefinition, and the evolution of governance models. The study proposes an integrated framework linking automation strategy, governance, upskilling, and employee adaptation, offering both practical insights and theoretical contributions to digital transformation research and for managing risks and enhancing workforce capabilities. It also advances academic understanding by linking real-world RPA implementations to organisational and technological impacts.

Abstract

Abstract
The United Nations' 2030 Agenda for Sustainable Development highlights the importance of energy-efficient software to reduce the global carbon footprint. Programming languages and execution models strongly influence software energy consumption, with interpreted languages generally being less efficient than compiled ones. Lua illustrates this trade-off: despite its popularity, it is less energy-efficient than greener and faster languages such as C. This paper presents an empirical study of Lua's runtime performance and energy efficiency across 25 official interpreter versions and just-in-time (JIT) compilers. Using a comprehensive benchmark suite, we measure execution time and energy consumption to analyze Lua's evolution, the impact of JIT compilation, and comparisons with other languages. Results show that all LuaJIT compilers significantly outperform standard Lua interpreters. The most efficient LuaJIT consumes about seven times less energy and runs seven times faster than the best Lua interpreter. Moreover, LuaJIT approaches C's efficiency, using roughly six times more energy and running about eight times slower, demonstrating the substantial benefits of JIT compilation for improving both performance and energy efficiency in interpreted languages. © 2026 IEEE.

Abstract
The incorporation of large language models (LLMs) into clinical decision support systems (CDSS) offers potential for enhancing the quality of healthcare. Nevertheless, there is a dearth of comprehensive standards and understanding about the application of explainable and responsible AI practices in this context. The objective of this study is to present an overview of the literature on the utilization of LLMs in CDSSs, with an emphasis on Explainable AI (XAI) techniques and responsible AI principles. The present study was conducted through a systematic literature review analyzed of 36 studies on the application of LLMs in CDSS, with a focus on XAI and responsible AI. Human-centered AI is essential to design CDSSs that not only provide accurate recommendations but also align with clinicians' trust and workflow realities. The results suggest that while LLM-based CDSSs demonstrate potential in enhancing clinical decision-making, concerns regarding model interpretation into healthcare workflows persist as challenges.