数字技术应用对企业创新的影响研究

温湖炜^1,2，王圣云^1,2

（1.南昌大学 中国中部经济社会发展研究中心，江西 南昌330031；

2.南昌大学 经济管理学院,江西 南昌330031）

摘要：新一代信息技术革命推动下，数字技术正以前所未有的广度和深度嵌入到产品与服务中。本文基于数字化、网络化、智能化等企业创新生态系统新情境，利用2007—2017年中国制造业上市公司数据系统考察数字技术应用如何影响企业创新。研究表明：智慧城市建设有助于促进数字技术在制造业领域的应用，显著地提高了企业创新；“机器联物换人”融合模式的研发投入显著增加而创新产出没有显著增加，智能制造领域的数字技术应用创新有较长的周期性；“群体信息交互”协同模式的研发投入和创新产出都显著为正，数字技术推动了产品创新与商业模式创新的繁荣。此外，国有企业与大中型企业具有数字资源的优势，数字技术应用创新的表现更为突出。

关键词：数字技术；融合创新；智慧城市；机器换人

主要研究结论：在全球制造业竞争格局加剧和国内传统要素增长红利放缓背景下，创新驱动制造业高质量发展是我国经济新常态下的经济工作重点。数字化转型被认为是推动制造业创新发展和产业竞争力提升的重要出路。本文以我国始于2012年的大规模智慧城市建设为准自然实验，将数字化、网络化、智能化等企业创新生态系统的新情境纳入分析框架，运用双重差分法考察数字技术应用的外生冲击对企业研发投入与创新产出的综合影响。实证分析表明：

（1）智慧城市建设有助于促进数字技术在制造业领域的应用，对企业的研发投入和创新产出都存在显著的正向影响。

（2）数字技术应用到不同的价值链环节会造成差异化融合模式，对企业的创新产生异质性影响。当数字技术与生产制造环节融合时，制造企业会增加机器设备投资，通过“机器联物换人”融合模式促使企业增加研发投入，但是对企业创新产出没有显著的正向影响，我国智能制造领域的数字技术应用创新具有较长的周期性。当数字技术嵌入到产品设计、市场营销等环节时，企业和用户“群体信息交互”协同模式会显著影响企业的研发投入和创新产出，数字技术应用推动了我国制造业领域产品创新与商业模式创新的繁荣。

（3）国有企业与大中型企业具有数字资源的优势，数字技术应用对国有企业和大型企业创新活动的影响更加突出，数字鸿沟可能是制约中小企业和民营企业数字技术应用创新的重要因素。

Research on the effect of digital technology application on enterprise innovation

Wen Huwei^1,2， Wang Shengyun^1,2

(1. Research Center of Central China Economic and Social Development, Nanchang University, Nanchang 330031, Jiangxi, China;

2. School of Economics & Management, Nanchang University, Nanchang 330031, Jiangxi, China)

Abstract:With the revolution of information technology, digital technologies are being embedded into the process of products and services with unprecedented breadth and depth. It not only penetrates into the value chain of product design, manufacturing and marketing, but also promotes the digitization, networking and intelligent development of manufacturing industry, and fundamentally changes the innovation ecosystem of manufacturing enterprises. The integration of digital technology and industrial development has gone beyond the traditional innovation theory. However, there are relatively few studies discussing the innovation activities of manufacturing firms in the new scenarios of innovation ecosystem. Whether Chinese manufacturing enterprises can achieve a successful transformation by the application of digital technology? How does the digital technology affect enterprise innovation? And what is the mechanism of its effect? 

With the increasing popularity of digital technology, a growing body of literature has analyzed the role of digital technology in manufacturing transformation and upgrading. Due to the limitation of data, these studies generally adopt the methods of qualitative discussion and case analysis, and there is no empirical evidence to validate the innovative effect of digital technology. Fortunately, the nationwide projects of smart city construction in China, which started in 2012, can be regarded as an exogenous event for manufacturing firms to apply digital technology. Therefore, we define a time dummy variable to indicate whether the time is before or after the intervention of smart city construction, and divide manufacturing enterprises into two groups according to the digital intensity. Then we design the quasi-natural experiment method to carry on the empirical analysis. 

Specifically, this paper investigates the effects of digital technology application on enterprise′ R&D investment and innovation output by using the difference-in-difference (DID) and quasi difference-in-difference（QDID）method. The method can effectively identify causal relationships without suffering from selection bias. We employ an unbalance panel data of Chinese A-share listed manufacturing enterprises of the Shanghai and Shenzhen Stock Exchanges from 2007 to 2017. We use the variables R&D intensity and intangible assets ratio, which define as the ratio of R&D expenditures to the annual income of a firm and the ratio of intangible assets to total assets, represent the explained variables of R&D investment and innovation output. According to previous studies in the field of innovation, the control variables include enterprise size, enterprise age, return on assets, Tobin′s Q, the ratio of liability to total asset, the ratio of fixed assets to total assets, share of major shareholders and ownership. We also control the industry fixed effect and time fixed effect, which capture the time invariant characteristics of manufacturing industry and trend characteristics, respectively. Our financial data are collected from the China Stock Market and Accounting Research (CSMAR) database.

Firstly, this paper displays descriptive statistics for two group of manufacturing enterprises. According to descriptive statistical, there are grouping differences of the two variables —— R&D intensity and intangible assets ratio —— between treatment enterprises and control enterprises. The grouping differences may be due to the exogenous impact of digital technology, or the differences of enterprise characteristics. We need to further investigate whether the exogenous event incurs the increase of R&D investment and intangible assets ratio of enterprises or not.

Secondly, we use the univariate DID estimation to test for the grouping differences of enterprise innovation. Specifically, the mean values of enterprise innovation are calculated across samples belonging to the treatment group versus the control group over the sample periods before versus after the intervention of smart city construction. Judging by the testing results for the DID estimation, the application of digital technology has a statistically significant effect on R&D intensity at the 5% significance level, while the effect on intangible assets ratio is insignificant at 1% level. In addition, the null hypothesis of no difference between the two groups is rejected, and thus may distort the evaluation of causality.  

Thirdly, in order to identify the causality of digital technology and enterprise innovation, this paper introduces regression-based DID method to control enterprise characteristics. In terms of enterprises′ R&D intensity, the regression coefficients are significantly positive at the 1% level, indicating that the application of digital technology has a significant effect on enterprises′ R&D investment. In terms of enterprises′ intangible assets ratio, the coefficient of DID model is not significant and the T value is 1.38, while the coefficient of QDID model is significantly positive at the level of 10%. It indicates that the digital technology has significantly positive effect on the innovation output of treated enterprises, and this conclusion is not the same as univariate DID estimation. The robustness test shows that the above conclusion is still true. There is strong evidence that the digital technology application has significant effect on enterprise innovation.

Fourthly, this paper divides the combination of digital technology and manufacturing industry into two types: the integration mode of machine coupling and substitution and the collaborative mode of group information interaction. We investigate the influence of the integration mode and the collaborative mode on enterprise innovation, respectively. For the integration mode, the effect of digital technology on R&D investment is significant, while the effect on innovation output is not significant. More time is needed to exert an effect on innovation output for intelligent manufacturing. For the collaborative mode, both the effects on R&D investment and innovation output are significant, the digital technology has promoted the prosperity of product innovation and business innovation. 

Finally, this paper divides our sample firms into two groups of state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) and examine the treatment effect separately for each of the two subsamples based on DID regression. We also divide our sample firms into three groups according to firm size and investigate the heterogeneity effects. It can be concluded that there are heterogeneity effects of digital technology on enterprise innovation. Because of the advantages of digital resources, the treatment effects are significantly larger for SOEs and for large-sized enterprises.

Our findings are enlightening for enacting better policies involving digital technology. For example, supporting policies for intelligent manufacturing, encouraging technological innovation of manufacturing enterprises, and reducing the digital divide of non-SOEs and private enterprises.

Key words:digital technology; integrated innovation; smart city; machine replacement

引用本文：温湖炜,王圣云.数字技术应用对企业创新的影响研究[J].科研管理,2022,43(4):66-74.