In the era of the new technological revolution and industrial transformation, the core of international competition has migrated toward foundational and frontier scientific domains. As China transitions from a manufacturing powerhouse to an innovation leader, the government has prioritized original innovation as a critical lever for strategic industrial upgrading. Original innovation is driven by major scientific discoveries and breakthrough technological principles, representing a profound integration of propositional knowledge and prescriptive knowledge. While theoretical frameworks suggest that the synergy between scientific research and technology R&D is vital for innovation sustainability, empirical evidence remains limited. Current studies primarily focus on one way knowledge flows from science to technology or static correlations between the two. This leaves several critical research gaps. First, there is a lack of empirical verification regarding the bidirectional and time lagged feedback loop between science and technology within a single innovation system. Second, the internal mechanisms through which specific knowledge attributes such as scientific quality and technological breadth facilitate this transformation are not well understood. Third, the boundary effects of collaboration intensity at the firm level in moderating these iterative paths remain unclear. This study addresses these gaps by exploring the positive feedback mechanisms within the original innovation system of the New Energy Vehicle industry.

To examine these dynamics, this study focuses on the New Energy Vehicle industry because it represents a sector that has achieved rapid growth through the integration of complex scientific research and technological breakthrough. The research sample includes 42 prominent enterprises, encompassing both vehicle manufacturers and suppliers, observed over a ten year period from 2015 to 2024. The dataset consists of 14,395 academic papers and 115,293 patents, which serve as proxies for scientific research and technological R&D respectively. Methodologically, the study employs dynamic structural equation modeling (DSEM) to analyze the longitudinal data. This approach allows for the decomposition of variance into between firm and within firm components, facilitating the assessment of dynamic, time lagged causalities while controlling for auto regressive effects and time trends. Variables in the model include scientific research intensity, technology R&D intensity, scientific research quality measured by high impact publications, technology R&D breadth measured by the diversity of patent classifications, and cooperation intensity reflecting joint outputs with universities and research institutes.

The empirical analysis confirms the existence of a robust positive feedback loop between scientific research and technology R&D within the original innovation system. Firstly, the results demonstrate a significant bidirectional and time lagged causal relationship at the firm level. Specifically, the scientific research intensity of an enterprise in a given year positively predicts its technology R&D intensity in the following year, indicating that scientific accumulation effectively transfers to technological applications. Conversely, technology R&D intensity also positively drives future scientific research intensity as technological development identifies new theoretical gaps that require scientific inquiry. Second, the study reveals the mediating roles of knowledge quality and breadth within this cycle. Scientific research quality, representing theoretical rigor and authority, acts as a bridge for translating discovery into application. Meanwhile, technology R&D breadth facilitates the feedback from technology to science by expanding the diversity of the knowledge base and generating complex, cross domain research questions. Thirdly, the findings highlight the moderating role of innovation collaboration. Technology cooperation intensity reinforces the path from scientific quality to technological output, whereas research cooperation intensity strengthens the transformation of technological diversity into scientific advancement. These results confirm a knowledge complementarity effect through external partnerships.

The current study offers significant contributions to innovation management. Theoretically, it provides the first systematic empirical validation of the bidirectional positive feedback mechanism in the original innovation system, shifting focus from static linkages to dynamic sustainability. By integrating the knowledge based view, it clarifies how qualitative and structural attributes of knowledge function as catalysts for co evolution. Furthermore, it clarifies the micro level enabling role of university industry collaboration in fostering internal R&D iterations. Practically, the study advises managers to prioritize the simultaneous development of basic research and applied technology. Firms should establish dual capability systems where scientific breakthroughs serve as triggers for technology and engineering bottlenecks act as incubators for scientific exploration. Policymakers are encouraged to support collaborative innovation platforms that facilitate the seamless integration of high quality science with diversified technological applications to foster new quality productive forces.