Employee surveys are governance choices: how employee-reported data inform organisational decision-making
Purpose This study aims to examine employee surveys as governance choices rather than merely as measurement tools. It argues that questionnaire content, referents, cadence and intended uses shape organisational attention and influence how issues are interpreted. Design/methodology/approach This conceptual, practitioner-oriented study brings together research on the attention-based view, institutional logics, performance measurement, evidence-based management and people analytics. It identifies four logics underpinning the use of employee surveys and derives principles for integrating employee-reported data into organisational decision-making routines. Findings This study identifies four logics: protective, instrumental, partnership-oriented and image-oriented. Each serves a legitimate purpose, but their coexistence can fragment interpretation by generating separate perspectives and decision-making pathways. This study proposes linking employee-reported data to issues already managed by the organisation, then examining perceptions alongside other indicators without conflating the two. Practical implications Human resources should ensure that the employee perspective is considered within the same decision-making processes as financial, operational or customer data when these sources shed light on the same organisational issue. This symmetry of consideration implies neither equivalence nor primacy between sources. Originality/value This study moves the question of actionability upstream to the design of the survey itself. It conceptualises the questionnaire as an architecture of attention, introduces four logics of survey use and proposes an integrated view of employee listening as input into organisational decision-making.
Authors
- Thibault Bordeaux (ORCID: https://orcid.org/0009-0007-7620-3997)
Publication Details
- Journal
- Strategic HR Review
- Published
- 2026-10-29
- DOI
- https://doi.org/10.1108/shr-08-2026-0102
- Primary Topic
- AI and HR Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00