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数据要素何以成就颠覆性创新——源于技术要素耦合协调的证据
How data elements power disruptive innovation? Evidence of the coupling coordination of technology elements
在数字经济的浪潮中,数据要素与技术要素的耦合协调已成为推动企业颠覆性创新的关键力量。从知识基础视角出发,以专利知识宽度和人才知识转移程度为中介变量、知识产权保护为调节变量,结合2012—2021年中国储能上市企业的数据,采用面板负二项模型探究区域数据要素和技术要素耦合协调与颠覆性创新之间的非线性关系。结果发现:(1)数据要素的非竞争性、规模效应递增等特性,使其与技术要素的耦合协调具有资源配置与激励约束的双重属性,对颠覆性创新的影响呈倒U型变化;(2)企业通过专利知识宽度、人员知识转移等知识管理能力,将要素协调提供的外部创新资源转化成自身资源,进而作用于颠覆性创新,这一结论验证了“要素协调-知识管理-创新颠覆”传导机制;(3)知识产权保护水平的提高将引发产权分配冲突和增强路径依赖,弱化要素协调与颠覆性创新之间的关系。研究结论为企业开展数据驱动的颠覆性创新实践提供了理论支持,对科技政策制定具有一定启示。
In the wave of digital economy, the coupling and coordination of data and technology elements have become a key driver of enterprises' disruptive innovation. From the perspective of knowledge foundations, using patent knowledge width and talent knowledge transfer degree as mediating variables, and intellectual property protection as a moderating variable, this study combined data from China's energy storage listed enterprises (2012-2021) and employed a panel negative binomial model to explore the nonlinear relationship between the coupling coordination of regional data and technology elements and disruptive innovation. The findings revealed that:Firstly, the non-rivalrous and increasing returns to scale characteristics of data elements endow their coupling coordination with technology elements with dual attributes of resource allocation and incentive constraints, leading to an inverted U-shaped impact on disruptive innovation. Secondly, enterprises transform external innovation resources derived from element coordination into internal resources through knowledge management capabilities, including patent knowledge width and talent knowledge transfer degree, which subsequently influence disruptive innovation. This finding validated the "element coordination-knowledge management-innovation disruption" transmission mechanism. Thirdly, an increased level of intellectual property protection may trigger conflicts over property rights allocation and reinforce path dependence, thereby progressively weakening the relationship between element coordination and disruptive innovation. The findings will provide theoretical support for enterprises to implement data-driven disruptive innovation practices, and have implications for science and technology policy making.
disruptive innovation / data elements / technology elements / coupling coordination model
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Based on data valuation theory, this study takes Chinese A-share listed manufacturing firms from 2011 to 2021 as samples to examine the impact of data factor on firm innovation in terms of both direct and combined effects. The results show that data factor drives firm innovation. The mechanism analysis shows that data can enter firms as an independent production factor to reduce technological uncertainty and market uncertainty and thus drive firm innovation. The combination of data factor and traditional production factors can reinforce the reduction effect of innovation uncertainty, which in turn drives firms to innovate.
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基于创新采用的分析框架，从创新的相对性出发，提出一种同时满足创新的内部特征和外部特征的颠覆性技术识别方法。首先，分别使用Bertopic主题模型、专利信息挖掘技术获取新技术功能、不连续的技术标准，识别颠覆性技术的创新内部特征；其次，使用CD指数衡量专利颠覆性潜力，识别颠覆性技术的创新外部特征；然后，识别出同时满足创新内外部特征的颠覆性技术，划分出新市场、低端和高端的颠覆性技术；最后，以储能领域专利为例，验证识别方法的可行性和有效性。该方法能将颠覆性技术的定义与识别进行匹配，体现出技术创新和市场采纳的相互作用，区分不同类型的颠覆性技术，为企业确定一项新技术是否产生颠覆性影响提供了参考。
Based on the framework of innovation adoption, this study proposes a method for identifying disruptive technology that simultaneously satisfies the internal and external characteristics of innovation to identify potential disruptive technologies from the relative nature of innovation. First, Bertopic theme model and patent text information mining techniques are used in stages to obtain new technology functions and discontinuous technology standards respectively, in order to extract the internal characteristics of innovation of disruptive technologies; Second, the CD index, a measure of disruption, is used to extract the external characteristics of innovation in disruptive technologies; Third, the disruptive technologies that simultaneously satisfy the internal and external characteristics of an innovation are identified and classified into new market, low-end and high-end disruptive technologies as well. Finally, taking patents in the field of energy storage as an example, we verify the feasibility and effectiveness of the disruptive technology identification method in this paper. The identification method in this paper is able to match the definition of disruptive technology with its identification, express the interaction between technological innovation and market adoption, and distinguish different types of disruptive technologies. This identification method could provide a reference for enterprises to determine whether a new technology has a disruptive impact.
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