与星同行：突破式创新、网络结构与技术扩散

王群勇　武文杰

（南开大学）

摘要：创新扩散是促进中国关键核心技术进步、加快实现高水平科技自立自强的重要路径。本文基于2018—2022年沪深A股上市公司间五百余万条专利引用数据构建技术扩散网络，发现企业在创新过程中呈现“与星同行”特征，即偏好引用技术前沿企业的高质量创新成果。因此，突破式创新的明星企业对技术扩散发挥了重要作用。本文基于时间指数随机图模型验证了企业突破式创新程度的提升会吸引更多外部企业引用其技术，加速技术扩散；而创新网络的互惠关系与传递结构则会强化这种促进效应。异质性分析发现，中国技术扩散呈现资源结构性分化，资源禀赋充裕、知识存量丰厚的企业更倾向于获取外部高水平的突破式创新技术，而其余企业倾向于更广泛的技术引用。本文为技术扩散相关研究提供新的视角和分析框架，并对推动创新技术的扩散与高效应用、完善科技创新体系、加快实现高水平科技自立自强提供了有益启示。

关键词：技术扩散；突破式创新；网络结构；时间指数随机图模型

作者简介：王群勇，南开大学经济学院/经济行为与政策模拟实验室教授、博士生导师，天津，300071；武文杰，南开大学经济学院博士研究生，通信作者。

基金项目：国家社会科学基金一般项目“社会网络的计量经济理论与应用研究”（22BJY160）

引用格式：王群勇,武文杰.与星同行:突破式创新、网络结构与技术扩散[J].经济与管理研究,2026,47(2):55-71.

Following the Stars: Breakthrough Innovation, Network Structure, and Technology Diffusion

WANG Qunyong, WU Wenjie

(Nankai University, Tianjin 300071)

Abstract: Innovation diffusion serves as a vital pathway for advancing China’s core technologies and accelerating the achievement of greater self-reliance and strength in science and technology. In the policy context of strengthening breakthroughs in core technologies in key fields, it remains unclear whether and how breakthrough innovation (BI) more effectively facilitates technology diffusion, and how network structures condition this process. Addressing these questions is crucial for optimizing the configuration of the national innovation system and enhancing its overall efficiency.

Therefore, this paper constructs a large-scale technology diffusion network based on more than five million inter-firm patent citation links among Chinese listed companies. Using this network, this paper systematically examines the micro-level mechanisms and structural features of technology diffusion in China. Preliminary evidence indicates that firms exhibit a pronounced tendency to cite BI achievements produced by technological frontier firms, leading to a distinct star-following phenomenon. This observation motivates the argument of the paper: Beyond their superior technological value, BI generates stronger external identification signals, while reciprocal and transitive structures within innovation networks enhance the visibility and influence of these signals. As a result, technology diffusion emerges from a composite mechanism linking BI, network structures, and diffusion dynamics.

This paper identifies both the direct effect of BI on technology diffusion and the interaction effect of network structures. Employing the temporal exponential random graph model (TERGM) alongside the linear probability model (LPM), the empirical analysis yields three main findings. First, BI increases the likelihood of being cited by external firms, providing robust evidence for the star-following phenomenon in China’s technology diffusion process. Second, reciprocal and transitive network structures strengthen the diffusion-enhancing effect of BI, indicating that diffusion is not driven solely by technological value, but rather unfolds through a network-based “signal amplification” mechanism. Third, this mechanism exhibits pronounced resource-based heterogeneity. Firms with abundant resources and strong knowledge bases tend to engage in targeted search to selectively absorb high-quality BI achievements, whereas resource-constrained firms are more inclined toward broad exploration through more dispersed citation behavior.

This paper develops an integrated analytical framework that explicitly links innovative capability with network structures, shedding light on their interactive role in shaping technology diffusion. Methodologically, by constructing a comprehensive, nationwide, and industry-wide inter-firm technology diffusion network and applying advanced social network econometric techniques, this paper addresses key limitations in prior research on sample coverage and identification, thereby enhancing the generalizability and explanatory power of the findings. Empirically, it provides the systematic evidence of resource-based structural differentiation in China’s technology diffusion process, offering a new foundation for subsequent research. Moreover, these findings carry important policy implications.

Keywords: technology diffusion; breakthrough innovation; network structure; temporal exponential random graph model

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