MAIJobCare – Managing job quality and labour shortages with AI/AM in longterm care
Short description
This project investigates the role of Artificial Intelligence in future-proofing long-term care (LTC). The sector faces multiple challenges: an ageing population, limited productivity gains in a labour-intensive sector, labour shortages, and the crisis of traditional modes of care provision amid rising female employment rates. This project explores AI’s potential to help the sector respond to long-standing and growing workforce challenges, improve its value proposition and care quality. We will inform industry standards, care models, and share best practices to inform viable and high-quality care solutions.
The project focus is on algorithmic management (AM) as an element of AI and its effects on job & care quality. AM may alter the dynamics of the manager-worker-care recipient. However, existing literature rarely engages systematically with the job quality-AM/AI nexus. By offering a research study that compares five countries’ (Austria, Belgium, Sweden, Spain and the UK) this project will systematically investigate efforts to address issues affecting the sustainability of LTC.
A key research focus is to explore the challenges and potential of AM/AI to (i) address recruitment & retention challenges through enhancing working conditions and (ii) improve care quality with potentially better outcomes for workers, care recipients and their families, using company case studies in three subsectors within LTC. Special attention will be paid to certain dimensions of job quality relevant to AM/AI: intrinsic elements (OH&S, work intensification, surveillance, consultative rights, and voice) and extrinsic elements (wages, working hours, benefits, employment conditions, skills). Empirically, the project will strengthen the LTC and job quality literature by exploring the less researched role of how public and private care providers across LTC regimes have embraced AM/AI. It will advance the understanding of the effects of deploying AM/AI at workplace level to shed light on the dynamics between AM and job quality within LTC. Theoretically, it will extend the job quality and LTC literature by refining and reframing present job indicators in the context of AM/AI with the aim to develop viable solutions to address labour shortage challenges. On the societal level the project contribute to national & EU policy agendas on the digital transformation of care work.
Participating researchers
The project consist of an Austrian, Belgian, Swedish, Spanish and British team. The Swedish team consist of: Ewa Wikström (University of Gothenburg), Christian Gadolin (University of Gothenburg), Monica Andersson Bäck (University of Gothenburg), Bertil Rolandsson (University of Gothenburg), Elin Siira (Halmstad University), Carin Håkansta (Karolinska Insitutet), Virgina Gunn (Karolinska Institutet), Theo Bodin (Karolinska Insitutet), Emma Brulin (Karolinska Insitutet), Pille Raat-Strauss (Karolinska Insitutet).