Algorithmic HRM and freelancer capabilities in Sri Lanka: constraints and the role of social support

Purpose This study examines how algorithmic human resource management (HRM) constrains the capabilities of platform-based freelancers in Sri Lanka, and how informal social support helps them sustain these capabilities. Design/methodology/approach Using a qualitative design, the research draws on 41 in-depth interviews and applies reflexive thematic analysis, informed by the capability approach and social support theory. Findings Four interconnected themes were identified: algorithmic work allocation constrains income security and temporal autonomy, digital enforcement and dispute processes undermine procedural fairness and recognition, artificial intelligence (AI)-driven market changes constrain capabilities through skill devaluation, and informal social support functions as an extra-platform capability infrastructure. Practical implications The findings point to implications for platform design, regulatory policy and emerging forms of worker organising, particularly in contexts marked by weak institutional protections. Originality/value This study draws on the capability approach and social support theory to make two contributions. First, it identifies a cumulative, reputation-based pathway through which algorithmic HRM constrains capabilities in knowledge-intensive freelancing. This extends current understanding beyond the transaction-based gig work that has dominated prior research. Second, it shows that informal social support contributes directly to capability development but cannot alter the structural algorithmic mechanisms that govern visibility, income security and procedural voice.