Maria Rucsandra Stan

Artificial intelligence (AI) development in healthcare involves multiple forms of training work, yet data annotation labor faces systematic devaluation despite its structural necessity. Drawing on institutional logics theory as our primary framework, we examine how professional logic, as the dominant logic in AI development, produces gendered hierarchies of expertise through which epistemic positioning determines whose contributions gain recognition. We identify compartmentalization—the allocation of competing logics to distinct organizational domains—as the mechanism producing gendered hierarchies of expertise. Professional logic structures formal evaluation, privileging abstract conception over execution, whereas care logic—whose patient care dimension is invoked to justify AI projects while its data care dimension is systematically excluded from labor valuation—operates asymmetrically within professional logic's evaluative framework. This asymmetric allocation devalues annotation work performed predominantly by junior women as “operational” despite its technical complexity. Through the asymmetric application of authorship criteria and internalization of professional logic's hierarchy of conception over execution, essential data work becomes invisible within formal recognition systems. Our analysis demonstrates how institutional mechanisms reproduce gendered knowledge hierarchies in AI development, echoing historical patterns where data processing was relegated to computing's feminized “machine underclass”—determining which forms of technically complex, structurally necessary labor become invisible.

Purpose – This study aims to extend knowledge management and innovation scholarship by examining the direct and indirect effects of knowledge sharing on innovation performance through purposeful knowledge sharing. It further explores the contingent roles of absorptive capacity in shaping the link between knowledge sharing and purposeful knowledge sharing, and how perceived knowledge hoarding moderates the link between purposeful knowledge sharing and innovation performance. Design/methodology/approach – Data were collected in three waves from 1,076 managerial-level employees working in Pakistan's information and communications technology industry. Structural equation modeling was used to test the hypothesized relationships and assess the proposed mediation and moderation effects. Findings – Grounded in the knowledge-based view and dynamic capabilities perspective, the findings suggest that both tacit and explicit knowledge sharing exert a positive influence on innovation performance, with purposeful knowledge-sharing practices acting as a crucial mediating mechanism. Moreover, absorptive capacity improves the positive influence of explicit knowledge sharing on innovation performance, but not that of tacit knowledge sharing. Notably, the study provides new empirical evidence that perceived knowledge hoarding attenuates the positive effect of purposeful knowledge sharing on innovation performance, highlighting a previously underexplored barrier to effective knowledge utilization. Originality/value – This research contributes to the knowledge management literature by introducing a novel integrative framework that connects knowledge sharing, purposeful knowledge sharing, absorptive capacity and perceived knowledge hoarding within a single explanatory model of innovation performance. By simultaneously examining value-creating and value-destroying knowledge dynamics, the study not only advances knowledge-process theory but also provides actionable insights for organizations seeking to foster innovation through effective knowledge management practices.