Shaping innovation normative pressure: how algorithmic management drives employee innovation

Purpose Despite the growing adoption of algorithmic management (AM) in organizations, scholarly discourse remains disproportionately focused on its detrimental effects, often overlooking its potential to catalyze workplace innovation. Addressing this gap, this study draws on structuration theory and social influence theory to examine how AM fosters employee innovation by institutionalizing innovation norms and exerting innovation normative pressure and how this process is moderated by the quality of leader–member exchange (LMX). Design/methodology/approach Using a three-wave survey of 445 employees in Chinese organizations that implement AM systems, we tested the proposed moderated mediation model through Mplus 8.3 software and the SPSS macro program PROCESS V4.1. Findings The results indicate that AM enhances employee innovation by amplifying innovation normative pressure through two complementary mechanisms: institutional constraints and social signaling. This mediating effect is significantly stronger under low LMX conditions, suggesting that AM can compensate for weak leadership relationships, whereas high-quality LMX mitigates the normative influence of AM. Originality/value This study offers a novel theoretical integration that reframes AM as an institutional enabler of innovation, challenging dominant control-oriented narratives. By identifying a key psychological mediator and a relational boundary condition, it provides theoretical advancement and actionable insights for designing algorithmic systems that support innovation while preserving human agency.