Data element valorization, innovation strategies and entrepreneurial quality

The effective utilization of data element valorization to decode innovation decision-making processes, thereby achieving high-quality entrepreneurship, has emerged as a critical challenge for enterprises operating within the competitive digital economy landscape. Grounded in resource-based view theory and dynamic capability theory, this study develops a theoretical model that investigates how scenario-driven data applications mediate the impact of data valorization on entrepreneurial quality. Through empirical analysis of 355 entrepreneurial firms, the research reveals three key insights: First, data element valorization significantly enhances entrepreneurial quality by optimizing innovation pathways. Second, while data valorization exhibits a linear positive relationship with focused innovation strategies, it shows a concave nonlinear association with dispersed innovation strategies, indicating diminishing marginal returns rather than a fully realized inverted U-shaped pattern within the observed sample range. Third, focused strategies improve entrepreneurial quality through resource clustering effects, whereas decentralized approaches constrain such improvements. Additionally, scenario-driven data application orientation moderates the interplay between data valorization, innovation strategies, and entrepreneurial outcomes. This study advances innovation management theory by challenging data homogeneity assumptions and proposing a dual-balance framework for optimizing data-driven innovation efficiency alongside entrepreneurial quality. The findings further offer policymakers actionable insights for shaping institutional mechanisms that enhance data ecosystem governance.