Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources.