Algorithmic management (AM) is transforming workplaces by automating decision-making, yet concerns about dehumanization, trust, and ethical issues persist, often leading to worker resistance. While research has focused on comparing human management and AM, hybrid systems that integrate algorithms with human oversight have received insufficient attention. This is notable, as hybrid systems are increasingly adopted by conventional organizations beyond platform-based AM models, yet we lack a clear understanding of how workers perceive decisions made under these systems. This study addresses this gap by examining workers’ perceptions of trust, competence, and dehumanization across hybrid systems, human managers, and AM. Using an online vignette-based experiment with a sample size of 97 participants, we find that hybrid systems are perceived as more competent and trustworthy than both human managers and AM individually, while also reducing feelings of dehumanization. The scenarios addressed key tasks requiring both mechanical and human skills: work assignments, scheduling, evaluation, and hiring. Additionally, participants’ algorithmic literacy, which encompasses the ability to understand, apply and evaluate algorithms, moderates trust towards decisions made by AM, with higher literacy fostering greater acceptance of AM systems. This study highlights how hybrid systems address key AM challenges, improving worker perceptions, and fostering acceptance in technology-driven workplaces.
