Challenges for individual assessment in digitalised welfare administration: the case of social assistance
Amanda Tuomola & Paula Saikkonen
Abstract
Purpose This study examines how individual assessment in social assistance is shaped within screen-level bureaucracy, focussing on the role of guidelines and ICT (information and communication technology) systems in decision-making in digitalised welfare administration. Design/methodology/approach Using Finnish social assistance as a case study, we applied qualitative content analysis to government bills and guidelines for social assistance decision-making in order to explore the shaping of individual assessment within screen-level bureaucracy. Findings The rationalising logic of screen-level bureaucracy, which aims for consistency, conflicts with the flexibility required by individual cases. Efforts to preserve discretion within systems designed for mass processing may ultimately undermine both appropriate individual assessment and accountability. Research limitations/implications The findings are based on a single centralised system. Future research should examine how guidelines and ICT systems are perceived and translated into workers’ practices when conducting individual assessment within screen-level bureaucracy. Practical implications Designing a digitalised welfare administration requires a comprehensive approach that recognises both the advantages and limitations of screen-level bureaucracy, while acknowledging the requirements of individual assessment, given its essential role in social assistance. Originality/value This study shifts the focus from front-line practices to how organisational conditions shape individual assessment, highlighting the challenges inherent in individual assessment in digitalised welfare administration.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.