数据要素流动如何影响企业创新持续性？

单浩男¹　赵凯²

（1.青岛理工大学；2.青岛大学）

摘要：数据要素的低成本流动性与可复用性为解释企业创新持续性提供了新视角。本文选取2011—2023年沪深A股上市企业与地级及以上城市数据，实证分析数据要素流动对企业创新持续性的影响。研究发现，数据要素流动促进了企业创新投入持续性和创新产出持续性。机制分析表明，数据要素流动以降低信息不对称、提升信息完备性为基础，沿着资源保障、竞争激励、信息决策和协同创新四条路径影响企业创新持续性。异质性分析结果显示，数据要素流动对企业创新持续性的促进作用在发展潜力较高的企业以及人才引进强度较高地区的企业中更强。本文的研究不仅深化了对数据要素流动影响企业创新持续性内在机制的理解，也为理解数据要素流动的微观创新效应提供了更为具体的经验证据。

关键词：数据要素流动；企业创新持续性；融资约束；市场竞争；不确定性感知；合作意识

作者简介：单浩男，青岛理工大学商学院讲师，青岛，266520；赵凯，青岛大学经济学院教授、博士生导师，通信作者，青岛，266071。

基金项目：国家社会科学基金后期资助项目“数智化赋能高质量发展：多维解构与实践路径研究”（25FJYB016）

引用格式：单浩男，赵凯. 数据要素流动如何影响企业创新持续性？［J］. 经济与管理研究，2026，47（10）：83-99.

How Does Data Factor Flow Affect Firm Innovation Persistence?

SHAN Haonan¹, ZHAO Kai²

(1. Qingdao University of Technology, Qingdao 266520;

2. Qingdao University, Qingdao 266071)

Abstract: Against the backdrop of the ongoing Fourth Industrial Revolution, characterized by digitalization, intelligentization, and networking, technological iteration is accelerating and market demand is becoming increasingly dynamic. Firms are therefore facing intensifying internal and external uncertainty, while the competitive advantage generated by a single innovation is steadily diminishing. Relying solely on isolated innovation is no longer sufficient to sustain long-term corporate development. Consequently, innovation persistence has become essential for firms to escape innovation traps and maintain long-term competitiveness. In this context, the low-cost, reusable nature of data factors provides a new perspective for addressing the challenge of sustaining corporate innovation..

Using data from A-share listed companies and prefecture-level and above cities in China from 2011 to 2023, this paper empirically examines the impact of data factor flow on firm innovation persistence. The results show that data factor flow enhances both innovation input and output persistence. By reducing information asymmetry and improving information completeness, data factor flow affects firm innovation persistence through four channels: resource support, competitive incentives, information-based decision-making, and collaborative innovation. Specifically, the resource support channel operates by alleviating financing constraints, the competitive incentive channel aligns with intensified market competition, the information-based decision-making channel presents as reduced uncertainty perception, and the collaborative innovation channel reflects enhanced cooperation awareness. Furthermore, the positive effect of data factor flow is stronger for firms with higher development potential and for firms located in cities with greater talent attraction. These findings suggest that firms’ internal growth potential and cities’ external talent support are important conditions for transforming data factor flow into sustained innovation momentum.

The marginal contributions are threefold. First, in terms of measurement, this paper combines a modified gravity model with complex network analysis to construct a multidimensional indicator of data factor flow that captures both flow intensity and network characteristics. This approach reduces reliance on a single proxy variable and offers an alternative method for quantitatively measuring cross-regional data factor flow. Second, in terms of research perspective, this paper integrates data factor flow and firm innovation persistence into a unified analytical framework. Unlike existing studies, which tend to focus on the static innovation effects of data factors, it extends the economic effects of data factors and firm innovation, paying sufficient attention to their cross-regional flow characteristics and to innovation persistence. Third, in terms of analytical dimensions, this paper classifies innovation persistence into innovation input persistence and innovation output persistence and further examines their differentiated transmission mechanisms. This distinction provides a more detailed understanding of how data factor flow supports sustained innovation and offers more specific empirical evidence on the micro-level channels through which data factors affect corporate innovation activities.

Keywords: data factor flow; firm innovation persistence; financing constraints; market competition; uncertainty perception;  cooperation awareness

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