Reversing the Accountability Chain: How Relational Power Can Shape Accountability in Public Encounters

Sara C Closs-Davies et al.

Journal of Public Administration Research and Theory2026https://doi.org/10.1093/jopart/muag009article
AJG 4ABDC A
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0.50

Abstract

Public administration plays a fundamental role in accountability relationships between citizens and the State, but how these take shape in public encounters is remarkably understudied. Analysing relational dynamics within and around public encounters expands the relational perspective on accountability in public administration–challenging core assumptions of the principal-agent model underpinning studies of citizen-State accountability relationships. We conducted a critical-interpretivist ethnography of public encounters in the UK Tax Credits (TC) system and share findings from our Constructivist Grounded Theory Analysis of multiple data sources, including 28 open interviews. We discuss four relational dynamics of account-giving–emerging from the interplay of neoliberal discourse, digital technologies, and communicative practices–that ‘reversed the accountability chain’. We demonstrate how claimants experiencing significant financial and emotional hardship, in their encounters with an unaccountable State, became accountable for their TC obligations and welfare. We explain these findings by mobilising interdisciplinary theory from critical accounting research on relational power to offer original conceptual and empirical insight into the interactive, dynamic, and emergent accountability relationships between citizens and agents of the State.

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https://doi.org/https://doi.org/10.1093/jopart/muag009

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@article{sara2026,
  title        = {{Reversing the Accountability Chain: How Relational Power Can Shape Accountability in Public Encounters}},
  author       = {Sara C Closs-Davies et al.},
  journal      = {Journal of Public Administration Research and Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/jopart/muag009},
}

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F · citation impact0.50 × 0.4 = 0.20
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R · text relevance †0.50 × 0.4 = 0.20

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