Beyond Platform Work: Algorithmic Management in Platform Labor and Traditional Organizations—The Hungarian Experience

Abstract

Algorithmic management (AM) is spreading rapidly in platform work and traditional employment, yet empirical research remains platform-centric and skewed towards Western Europe, North America and China, leaving Central and Eastern Europe, especially Hungary, largely absent. This paper reports qualitative fieldwork on AM in two contrasting Hungarian firms, Data Analytics, a knowledge-intensive business services company, and ConLog, a contract logistics provider. Fieldwork in the EU-funded INCODING project (January 2022–July 2023) combined 15 semi-structured interviews, twelve internal and three external, with participant observation and documentary analysis. A sample limitation applies to one of the two firms: the Data Analytics evidence rests on only four internal interviews (two employees and two managers), so the findings for that case are indicative and exploratory rather than firm-level conclusions. A deliberately asymmetric comparison sets these findings against Hungarian platform labor, using secondary evidence on Uber, Wolt and Bolt from the CrowdWork21 project (2019–2021). Results show heterogeneity. In traditional firms, algorithmic systems sit within managerial hierarchies and produce hybrid human-algorithmic control. The older platform evidence documents real-time monitoring, opaque decision rules and market-mediated feedback that tighten control and erode autonomy. The contrast is a proposition for testing, not a measured difference. Workers in both settings adapt opportunistically, accommodating or partially resisting control. Hungary’s weak industrial relations institutions remain theoretically instructive despite limited generalizability. The contribution lies in the platform versus non-platform comparison, the institutional-bypassing argument and the design authority continuum.