AI-Mediated Employee Voice and Performance: Disentangling Informational and Affective Pathways
Abstract: Employee voice -- the discretionary expression of ideas, concerns, and suggestions intended to improve organizational functioning -- creates value inside firms, yet organizations increasingly replace or augment human-led listening with AI-enabled systems that collect, analyze, and route employee input at scale. Whether algorithmic listening replicates the value-creation mechanisms of human voice, and whether it alters the pathways through which voice translates into performance, remains an open question. This research proposal theorizes that traditional voice bundles two distinct functions, knowledge extraction and relational signaling, and that AI mediation may decouple them: strengthening the informational pathway (implemented improvement measures) while weakening the affective pathway (the experience of being heard). We propose a randomized field intervention at a global strategy consulting firm, covering approximately 2,000 client-facing consultants, that varies the form of listening across three conditions -- no structured voice, human-led survey-based voice, and AI-enabled conversational voice -- and links weekly team survey data to longitudinal performance ratings. A dual-mediation model is tested in which voice affects performance through (1) implemented process improvements and (2) affective well-being. The study extends voice theory to knowledge-intensive professional services, disentangles the micro-mechanisms of voice, and provides causal evidence on whether AI can serve as an effective listener.