Author
Abstract
This thesis examines how social partners utilize modern algorithmic management systems as a new policy issue to increase their participation in policymaking within different industrial relations systems. The thesis focuses on Slovakia and Italy, two EU Member States subject to the same regulatory framework through the AI Act, but belonging to different industrial relations regimes. Building on Scharpf’s actor-centered institutionalism and the conceptualization of industrial relations systems, the thesis analyzes how institutional settings, actor strategies, actor constellations, modes of interaction, and participation channels shape social partner participation. The research is based on a comparative qualitative case study using semi-structured interviews with trade union and employer organization representatives at national and sectoral levels. The findings show that the AI Act created regulatory space for social partner participation, as it sets minimum standards that can be further developed by Member States and through collective agreements. However, this space was utilized differently in the two cases. In Italy, social partners used AI/AM systems more actively through participation channels, consultations, and collective bargaining. In Slovakia, participation remained more limited, mostly consultative, and often constrained by unilateral employer action, although a new participation channel was created. The thesis argues that new policy issues do not automatically increase participation in policymaking. They can create opportunities, but participation depends on the industrial relations system, the strength of social partners, the role of the state, and the salience of the issue for social partners and their members. The thesis contributes to debates on AI governance, industrial relations, and participatory policymaking by showing how AI/AM systems can become a resource for social partners to strengthen their role in policymaking.
Suggested Citation
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cel:report:72. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Martin Kahanec (email available below). General contact details of provider: https://edirc.repec.org/data/celsisk.html .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.