Sustainability transition research seeks to understand the patterns and dynamics of structural societal change as well as unearth strategies for governance. However, existing frameworks emphasize innovation and build-up over exnovation and break-down. This limits their potential in making sense of the turbulent and chaotic dynamics of current transition-in-the-making. Addressing this gap, our paper elaborates on the development and use of the X-curve framework. The X-curve provides a simplified depiction of transitions that explicitly captures the patterns of build-up, breakdown, and their interactions.Using three cases, we illustrate the X-curve’s main strength as a framework that can support groups of people to develop a shared understanding of the dynamics in transitions-in-the-making. This helps them reflect upon their roles, potential influence, and the needed capacities for desired transitions. We discuss some challenges in using the X-curve framework, such as participants’ grasp of ‘chaos’, and provide suggestions on how to address these challenges and strengthen the frameworks’ ability to support understanding and navigation of transition dynamics. We conclude by summarizing its main strength and invite the reader to use it, reflect on it, build on it, and judge its value for action research on sustainability transitions themselves.
MULTIFILE
Knowledge of how professional youth work might prevent individual and social problems in socially vulnerable youngsters is poorly developed. This article presents a conceptual framework that clarifies the implicit methodical process used by professional youth workers and focuses on what stakeholders regard as the potential of professional youth work as a preventive service. A qualitative research synthesis approach was used to combine the findings of six practice-based studies conducted in six European countries. This synthesis revealed that professional youth workers employ a multi-methodic approach in their prevention efforts, strengthening the social skills and self-mastery of youngsters, reinforcing their social network, enhancing their civic participation and helping them find additional social or health services. Twelve methodic principles were identified as contributing to achieving these prevention efforts, shedding light on the process taking place between youngsters and youth workers. This conceptual framework provides essential information for future evaluation research.
This leaflet showcases a design framework for buildingcommunity resilience in urban neighbourhoods. Atits core, the framework challenges designers andother professionals to not only consider resilience inhuman communities, but also in other-than-humancommunities, including plants and animals. Theframework proposes a set of five concepts that helpbridging these two perspectives; each concept describesan important condition for community resilience toemerge for both humans and non-humans.
The hospitality industry in the Netherlands has been slow to adopt artificial intelligence (AI), despite its potential to improve service efficiency and address workforce challenges. While some industries have embraced AI agents—automated systems interacting with users—for customer service, hospitality adoption remains limited. Many hotels struggle to integrate AI in ways that enhance guest experiences while ensuring workforce sustainability, a paradox. Workforce sustainability means keeping employees skilled and adaptable. This research addresses this paradox observed in professional practice, focusing on three key gaps in AI integration: • Hotel employees lack the skills and knowledge to adapt to AI-enhanced workplaces. • Hotel managers lack clear AI strategies that maintain service quality and employee well-being, ensuring AI complements rather than replaces human service. • AI developers often lack a clear understanding of the hospitality industry’s specific needs, hindering the development of effective solutions. This leads to the central question: How can AI agents be co-developed by hotel professionals and technical experts to enhance service efficiency while supporting a sustainable hospitality workforce? A one-year KIEM project provides the ideal framework for an agile, practice-based investigation in real hospitality environments. The project will unfold in four phases: (1) co-developing conversational AI chatbots with hotel businesses and technology providers, (2) testing the chatbot integration in hotels, (3) evaluating the impact on service efficiency and workforce sustainability, and (4) initiating a community of AI agent practice in service industry. Conducted in collaboration with industry partners, the research ensures findings are directly applicable to real-world hospitality challenges. By bridging academic research and industry needs, this project will generate insights into AI-driven service innovations that benefit hotel operations, employees, and AI developers. Beyond hospitality, its findings will offer scalable strategies for responsible AI adoption in sectors like healthcare, banking, and retail, fostering a more sustainable future of work.
