Research project S4P/25E/MAIJobCare (Research action S4P)
The MAIJobCare project (Managing Job Quality and Labour Shortages with AI/Algorithmic Management in Long-Term Care) addresses the growing sustainability challenges facing long-term care (LTC) systems across Europe. These challenges include demographic ageing, persistent labour shortages, limited productivity growth in a highly labour-intensive sector, and increasing pressure on traditional care arrangements in the context of rising female labour market participation. At the same time, LTC work is often characterised by low wages, poor working conditions, high turnover and limited worker voice, despite its central societal importance. Recent advances in digitalisation, artificial intelligence (AI) and algorithmic management (AM) are increasingly promoted as potential solutions to these challenges, yet their implications for job quality, care quality and power relations within LTC remain insufficiently understood.
The project is grounded in the observation that AM and AI are becoming more pervasive in LTC, particularly in areas such as scheduling, task allocation, performance monitoring, recruitment and care coordination. While proponents argue that these technologies can optimise work organisation, improve care delivery and support recruitment and retention, critics warn of risks including work intensification, heightened surveillance, reduced autonomy, deskilling and new forms of inequality. Existing research has largely focused on platform work and management–worker relations, leaving significant gaps in understanding how AM/AI operate in LTC settings, how they affect job quality across different welfare and industrial relations regimes, and how care recipients and families are involved in their deployment.
The overarching objective of MAIJobCare is to examine how AM and AI shape job quality, labour shortages and care quality in LTC, and under what conditions they may contribute to more sustainable and high-quality care systems. The project pursues this objective through a comparative study of five countries (Austria, Belgium, Sweden, Spain and the United Kingdom), representing distinct care, welfare and industrial relations regimes. It focuses on three LTC subsectors, with particular attention to home (domiciliary) care services, while also including more traditional and emerging platform-based care providers. Special emphasis is placed on job quality dimensions that are particularly relevant in algorithmically managed environments, including intrinsic aspects such as occupational health and safety, work intensity, surveillance, autonomy and voice, as well as extrinsic aspects such as wages, working hours, and skills development.
The project is structured around five interrelated work packages and aims to produce several key outputs while adopting a multidisciplinary and mixed-methods research design. Work Package 1 focuses on a structured literature review and the development of an analytical framework including key concepts, and indicators linking job quality and AM/AI in LTC. It combines desk research and secondary analysis of existing survey data. Work Package 2 maps national models of care, regulatory frameworks and labour market contexts through desk research and stakeholder interviews – which will result in five national stock-taking reports. Work Package 3 conducts in-depth qualitative fieldwork based on company case studies. Across the five countries, a total of fifteen company case studies will be conducted, involving interviews with management, supervisors, workers, workplace representatives, care recipients and family members. Work Package 4 undertakes comparative analysis and synthesis across the national case study reports, resulting in a cross-national comparative report. The comparative case study approach allows the project to analyse how national institutional contexts, care models and regulatory frameworks mediate the effects of AM/AI on job and care quality. Work Package 5 is dedicated to stakeholder engagement and dissemination, including a project website, national workshops, and a final international conference. Based on the work in all Work Packages, peer-reviewed academic publications and policy-relevant outputs will be created. Together, these work packages aim to generate robust, policy-relevant and socially grounded knowledge on how AI and AM can be governed to support job quality and care quality, and address labour shortages in LTC.
