When human support fails: algorithmic management as a catalyst for innovation via digital sensemaking

Purpose Although algorithmic management (AM) has become a common form of workplace governance, its relationship with employee innovation remains underexplored. Prior research has emphasized AM’s control-oriented features and possible adverse consequences, with less attention to employees’ agentic responses and the cognitive processes through which algorithmic systems may be associated with constructive employee outcomes. Drawing on sensemaking theory, this study examines whether perceived AM intensity is indirectly associated with employee innovation via digital sensemaking and whether perceived supervisor support (PSS) conditions this indirect association in human–algorithm hybrid governance. Design/methodology/approach We conducted a 3-wave field survey of 418 full-time employees in China whose work involved AM. The data were analyzed using structural equation modeling (SEM) and latent moderated structural equations (LMS) to examine mediation and moderated mediation. Findings Perceived AM intensity was positively and indirectly associated with employee innovation via digital sensemaking. This indirect association was stronger when PSS was low, suggesting that algorithmic cues may become more salient when supervisory support is less available. Originality/value By theorizing and testing digital sensemaking as a cognitive pathway, this study offers one explanation for how AM may be associated with positive employee outcomes. It also specifies the boundary role of PSS as an interpersonal sensegiving resource and points to the practical value of aligning supervisory support with algorithmic governance to facilitate employees’ sensemaking and innovation.