Paper Number
ECIS2025-1993
Paper Type
CRP
Abstract
Antimicrobial resistance (AMR), frequently termed the ‘silent pandemic,’ represents a profound global health challenge that necessitates robust public health monitoring systems. We examine the asymmetries of AMR monitoring in India, uncovering a fragmented landscape where data practices are inconsistent across national and subnational levels. Despite national policies and subnational implementation plans, governance gaps persist, reflecting systemic challenges. Building on Viljoen’s relational theory of data governance, we extend the theory by conceptualizing relational data equity. We categorize relational inequities into capacity-based, demand-based, and power-based, emphasizing the socio-political and structural dynamics that shape data practices, aligning with broader efforts to design data ecosystems that prioritize inclusion, reliability, and shared value creation. By critiquing traditional individual-centric governance models, we shift the focus toward relational interdependencies among stakeholders. Our study contributes to IS research by advancing theoretical discussions on data governance and offering pathways for designing inclusive and data-driven health management information systems (HMIS).
Recommended Citation
Thakral, Yogita and Müller, Sune Dueholm, "DATA AS SOCIAL RELATIONS: RETHINKING INFORMATION SYSTEMS FOR AMR MONITORING" (2025). ECIS 2025 Proceedings. 5.
https://aisel.aisnet.org/ecis2025/datamgmt/datamgmt/5
DATA AS SOCIAL RELATIONS: RETHINKING INFORMATION SYSTEMS FOR AMR MONITORING
Antimicrobial resistance (AMR), frequently termed the ‘silent pandemic,’ represents a profound global health challenge that necessitates robust public health monitoring systems. We examine the asymmetries of AMR monitoring in India, uncovering a fragmented landscape where data practices are inconsistent across national and subnational levels. Despite national policies and subnational implementation plans, governance gaps persist, reflecting systemic challenges. Building on Viljoen’s relational theory of data governance, we extend the theory by conceptualizing relational data equity. We categorize relational inequities into capacity-based, demand-based, and power-based, emphasizing the socio-political and structural dynamics that shape data practices, aligning with broader efforts to design data ecosystems that prioritize inclusion, reliability, and shared value creation. By critiquing traditional individual-centric governance models, we shift the focus toward relational interdependencies among stakeholders. Our study contributes to IS research by advancing theoretical discussions on data governance and offering pathways for designing inclusive and data-driven health management information systems (HMIS).
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