Strengths-based performance conversations: a randomized trial in the UK civil service

Purpose – This study evaluates impacts of training managers in a strengths-based approach to performance conversations on the perceived utility of one-to-one meetings. It also investigates changes in meeting content and subjects’ experiences to shed light on causal mechanisms of the intervention. Design/methodology/approach – A cluster randomized trial was conducted in two UK civil service organizations and a non-controlled before-after-study in a third civil service organization. The trial involved 8,843 managerial and non-managerial subjects; the impact evaluation uses data from 2,903 responses across two waves. Sixty-three intervention group subjects provided additional qualitative data. Findings – The intervention positively affected employees’ perceptions of the usefulness of performance conversations for learning and development. Evidence of a similar impact on job performance is weak. The content of conversations changed partly as hypothesized, increasing the focus on employee development and managerial support, but did not reduce emphasis on performance objectives. Improvements appear to be due both to mechanisms proposed by strengths-based theories and generic improvements in line-management relationships, supporting leader-member exchange (LMX) theory. Practical implications – Strengths-based conversations can add value in public sector contexts by focusing managers and their reports on how the latter optimally perform and foster supportive and developmental relationships. The strengths-based approach did not prompt a less target-driven approach, which may allay managerial concerns about adopting it. The study suggests a discrete training intervention is viable, but an integrated change program is likely to be more effective. Originality/value – Prior evidence on the impact of strengths-based performance management practices is limited. This study presents a large-scale field trial on this within a real-world public sector context and sheds light on causal mechanisms.