Social protection data governance and public trust: the mediating role of targeting fairness among generation z

Abstract

Digital social protection increasingly depends on integrated administrative data, making data governance, targeting fairness, and public trust closely connected. This sequential explanatory mixed-methods study examined whether perceived targeting fairness mediates the association between social protection data governance and public trust and used Generation Z interviews to explain the quantitative pattern. The quantitative dataset comprised 180 complete responses analyzed with PLS-SEM and 5,000 bootstrap subsamples. Because the available dataset does not retain recruitment metadata, the quantitative sample is not treated as probability-representative; a sensitivity power check nevertheless shows that n = 180 provides >0.99 power to detect a medium effect (f² = 0.15) with two predictors at α = 0.05. Measurement quality was satisfactory (Cronbach's α = 0.887-0.906; rho_C = 0.917-0.927; AVE = 0.680-0.714; HTMT = 0.648-0.681). Data governance was strongly associated with targeting fairness (β = 0.603; f² = 0.572), while targeting fairness had a moderate association with public trust (β = 0.411; f² = 0.196) and the direct governance-trust association was smaller (β = 0.339; f² = 0.133). The indirect effect was significant (β = 0.248), indicating partial mediation. The qualitative corpus comprised 20 Generation Z informants aged 19-29 with personal or family experience of social protection services. Six themes data updating, explainable targeting, correction and appeals, privacy and security, hybrid assistance, and visible follow-up clarified why fairness is a key pathway between governance and trust. The findings suggest that integrated data systems are more likely to be trusted when citizens can understand, challenge, and correct data-driven decisions.