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In this paper, the authors attempt to understand how labour market attachment during the ages of 30–59 influences individuals' transition out of the labour market.

Using high-quality Swedish register data, the authors follow individuals born in 1950 and observe their labour market attachment during mid-life and their exit from the labour market.

The authors find evidence that labour market attachment in different stages of the career is differently related to exit from the labour market. At the age of 30, as well as between the ages 50–59, low attachment is related with earlier exit from the labour market. On the contrary, low labour market attachment during the ages 40–49 is related with later exit from the labour market. However, regardless of age, lower labour market attachment increases the risk of work-related benefit receipt in the exit year. The authors also find evidence that gender, migration status and childhood socioeconomic disadvantages may represent obstacles to longer working lives, while high education is a consistent factor in avoiding early exit from the labour market.

This study provides insights on the link between labour market attachment in different stages of the career and the exit from the labour market as well as work-related benefits dependency in the year of exit.

Architectural theorists have a long tradition of acknowledging the centrality of building users to architectural production. This article contributes to the discourse on architecture, actor–network theory (ANT), and users by proposing a typology of user translations ranging from supporting to tinkering to adjusting to resisting.

The research utilises an ANT-inspired ethnography of sustainable lighting scripts at the Masdar Institute of Science and Technology (MIST). It comprises semi-structured interviews with MIST designers and students, and site visits and participant observation to understand how the users interpret the scripts and how they interact and change them on a daily basis.

There is a shared understanding that users do not simply receive architectural designs but interpret and change them to suit their preferences. The findings reveal the multiple ways that users interpret and respond to the assumptions of designers and in the process, recast the relations between themselves and their material surroundings.

The research contributes to acknowledging the centrality of users to architectural design processes and the interpretation of design scripts, addressing the limitation in current literature in demonstrating the diversity of ways that users react to such scripts. The research suggests that user actions have significant implications on long-term building performance. It accordingly points to the need for devising multiple means of user involvement in the design process and allowing greater flexibility in design scripts to improve the alignment with user preferences.

The aim of administrative reform— a special public policy instrument of governments— is to improve the capacity of public service delivery systems. This study examines the reform-led service delivery situation of public institutions at the local level in South Asia. The study offers an in-depth qualitative study with gleaned data, focusing on primary education in the country in relation to MDGs basic education. Local administration in the country has been a ‘low modicum of self-governance to provide public services. Institutional capacity dealing with educational management has called into question. Primary education in the country is quantitatively discussed while qualitatively is in a state of disarray. As a thorough academic qualitative study with a country-specific case has by far not been explored yet, it fills the information gap. Little is known about the public service delivery capacity with primary education at the local level. This study implicates governments and development partners such as the UN SDGs for effective partnership in development in an era of post-CVID-19 crisis.

This study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven digital transformation (DT) processes. The study seeks to define a framework termed “responsible living lab” (RLL), emphasizing transparency, stakeholder engagement, ethics and sustainability. This emerging issue paper also proposes several directions for future researchers in the field.

The research methodology involved a literature review complemented by insights from a workshop on defining RLLs. The literature review followed a concept-centric approach, searching key journals and conferences, yielding 32 relevant articles. Backward and forward citation analysis added 19 more articles. The workshop, conducted in the context of UrbanTestbeds.JR and SynAir-G projects, used a reverse brainstorming approach to explore potential ethical and responsible issues in LL activities. In total, 13 experts engaged in collaborative discussions, highlighting insights into AI’s role in promoting RRI within LL activities. The workshop facilitated knowledge sharing and a deeper understanding of RLL, particularly in the context of DT and AI.

This emerging issue paper highlights ethical considerations in LL activities, emphasizing user voluntariness, user interests and unintended participation. AI in DT introduces challenges like bias, transparency and digital divide, necessitating responsible practices. Workshop insights underscore challenges: AI bias, data privacy and transparency; opportunities: inclusive decision-making and efficient innovation. The synthesis defines RLLs as frameworks ensuring transparency, stakeholder engagement, ethical considerations and sustainability in AI-driven DT within LLs. RLLs aim to align DT with ethical values, fostering inclusivity, responsible resource use and human rights protection.

The proposed definition of RLL introduces a framework prioritizing transparency, stakeholder engagement, ethics and sustainability in LL activities, particularly those involving AI for DT. This definition aligns LL practices with RRI, addressing ethical implications of AI. The value of RLL lies in promoting inclusive and sustainable innovation, prioritizing stakeholder needs, fostering collaboration and ensuring environmental and social responsibility throughout LL activities. This concept serves as a foundational step toward a more responsible and sustainable LL approach in the era of AI-driven technologies.