Mapping Poverty Alleviation in Garut Regency: An Actor-Network Theory Perspective on Collaboration and Actor Interactions

This study investigates the essential yet frequently ignored function of trust in data governance networks for poverty alleviation programs in Garut Regency, Indonesia, based on Actor-Network Theory (ANT). The research employed a qualitative method which included 35 in-depth interviews with government officials from Social Service, Regional Planning Agency, and Baznas together with beneficiary community members and Focus Group Discussions with Regional Government Organizations and Village Heads. The research demonstrates that poor trust between human actors (government-community) and non-human actors (Poverty Data) creates major obstacles for essential poverty alleviation programs including Non-Cash Food Assistance and the Family Hope Program. The main obstacles that emerged during the study included strong sectoral egos, uncoordinated and non-unified data systems and unclear data management transparency practices. According to ANT "translation" analysis the network faces important challenges at interest and enrollment stages which prevent successful mobilization. The research reveals two potential trust-building approaches through the productive training provided by Baznas and the local Lapad Ruhama initiative, but success depends on better coordination and community participation. The research contributes new understanding through its analysis of poverty alleviation networks using ANT while addressing a major theoretical and empirical gap about data governance in Indonesia. This study proposes three practical recommendations to boost poverty alleviation program effectiveness in Garut Regency: data integration improvement, inter-organizational synergy promotion, continuous community empowerment training and enhanced data verification transparency through community involvement.