A comprehensive review of previous literature on digital technology innovation predominantly posits that expediting the agglomeration of capital and talent yields positive externalities. However, shortages in these resources often precipitate systemic disruptions, such as talent poaching competitions and detrimental tax rate races among regions. These phenomena impede the efficient allocation of resources and obstruct the advancement of regional digital collaborative innovation. In contrast, data, as a novel production factor, can manifest agglomeration effects within virtualized environments, thereby propelling urban digital innovation. Lacking a tangible physical form, data factors necessitate integration with traditional elements, such as labor, to generate amplified, synergistic, and multiplicative impacts in production processes. This unique characteristic of data implies that digital labor, by inheriting data-factor attributes, may reconfigure the dynamics of traditional factor mobility, thereby influencing regional digital collaborative innovation.

To empirically validate this theoretical conjecture, this study operationalizes digital collaborative innovation along two dimensions (intra-regional and inter-regional) cooperationwithin the theoretical framework of innovation systems. Utilizing a panel dataset encompassing 283 prefecture - level cities from 2011 to 2023, the research undertakes a rigorous econometric analysis to examine the effects and underlying mechanisms of digital labor factors on digital collaborative innovation. Additionally, it explores the optimal trajectories of digital collaborative innovation across cities, contingent upon the cumulative development of digital labor factors.

The study's principal findings are as follows:(1) Digital labor factors demonstrably accelerate holistic regional digital collaborative innovation.;(2)Knowledge spillovers and the circulation of innovation factors remain pivotal mechanisms through which digital labor drives regional digital collaborative innovation;(3)Under the constraints of knowledge diffusion and factor mobility, the modus operandi of digital labor-driven digital collaborative innovation exhibits distinct spatial heterogeneity. Regions characterized by abundant knowledge reserves (e.g., Beijing and Wuhan), robust intellectual property protection (e.g., Shanghai and Tianjin), or limited innovation-factor competition (e.g., Wuxi and Foshan) are better suited to pursue inter-regional collaborative innovation models. Conversely, cities with proactive policy interventions in innovation-factor circulation (e.g., Guangzhou, Ningbo, and Shenzhen) tend to foster more effective intra-regional digital collaboration;(4)The accumulation of digital labor factors enhances the knowledge absorption capacity of regions with limited knowledge endowments, enabling them to build innovation capabilities through imitative innovation. In this context, intellectual property protection emerges as a critical determinant for transitioning from collaborative to independent innovation. Furthermore, excessive regulatory interventions in talent and capital flows are found to undermine the path of endogenous innovation, thereby impeding the full realization of digital collaborative innovation potential. These findings not only enrich the theoretical understanding of digital labor factor cultivation but also offer a robust foundation for harnessing digital dividends, invigorating digital economic innovation, and formulating evidence - based strategies for regional coordinated development.

This research contributes to the extant literature in three significant ways. First, by integrating the unique characteristics of digital labor, it elucidates the micro-foundations of regional digital collaborative innovation, thereby advancing the theoretical discourse on digital labor valuation. Second, it adopts a dual-dimensional analytical approach, examining both intra and inter-regional collaborative dynamics, which mitigates the potential exacerbation of regional disparities inherent in single-entity or single-city innovation studies. Third, by analyzing the evolving patterns of knowledge spillovers and factor mobility in the context of digital labor accumulation, it identifies region-specific optimal pathways for digital collaborative innovation, providing policymakers with tailored recommendations for formulating effective digital technology cooperation strategies.