Work Packages

WP1 involves a structured literature review on algorithmic management (AM), Artificial Intelligence (AI), job quality, and long-term care (LTC).
The work will identify, assess, and reframe existing job quality indicators within the context of AI-driven work environments, recognising the need for new or adapted metrics.
Using secondary data from cross-national surveys (e.g. LFS, ECS, EU-SILC), the team will map levels of digitalisation and AM/AI deployment across countries and subsectors.
The outcome will be a comparative literature review and an analytical framework for assessing the relationship between AM/AI, job quality, and institutional structures.

WP2 involves mapping national care models, labour market structures, and regulatory environments shaping the use of AM/AI in LTC.
The team will analyse policy frameworks, workforce composition, and ongoing debates about labour shortages, cost pressures, and job quality.
Desk research and interviews with key stakeholders—policymakers, providers, unions, and care advocates—will inform five national stock-taking reports.
These reports will describe the institutional settings and digital transformation trajectories within each welfare regime, establishing the empirical and comparative foundation for the subsequent fieldwork and analysis phases.

WP3 involves fieldwork through three company case studies per country (15 total).
These will examine how AM/AI affects job and care quality, including working conditions, autonomy, work intensity, and care outcomes.
Interviews will be carried out with managers, employees, workplace representatives, and care recipients or family members.
Findings will reveal how AM/AI is reshaping daily work practices, relationships, and organisational structures. Each partner will produce a national case study report, providing rich, comparative evidence on how AI-enabled management influences sustainability and workforce experiences in LTC.

WP4 involves synthesising findings from all countries and case studies. This phase will compare national approaches to implementing AM/AI, analyse context-specific outcomes, and assess the transferability and sustainability of observed practices.
The comparative analysis will identify how institutional, cultural, and organisational factors mediate AM/AI’s impact on job quality and care provision.
The work will culminate in a 25-page comparative report highlighting cross-national lessons, innovative practices, and risks, offering policy and practice recommendations for supporting fair, high-quality AI deployment in LTC systems across Europe.

WP5 will ensure effective stakeholder engagement, knowledge mobilisation, and dissemination. It includes creating a project website, social media presence, newsletters, and ongoing engagement through an advisory committee of academics, practitioners, and care providers.
Each country will host one in-person stakeholder workshop and one webinar to share results and good practice examples.
A final international conference will showcase comparative findings and facilitate dialogue among policymakers, researchers, and industry leaders.
WP5 aims to promote evidence-based guidance on responsible, inclusive, and sustainable use of AI and AM in long-term care.