大数据应用与中国企业创新资源错配

摘要/Abstract

摘要：

在数字化变革的浪潮中,企业创新过程不仅涉及新技术新产品的研发和应用,还涉及与新技术新产品相适应的研发资源配置方式。本文建立理论模型分析大数据应用如何影响企业创新资源错配,并利用2011-2020年中国沪深A股制造业上市公司数据对理论模型的推论进行验证。研究发现:中国52.83%的行业创新资本配置不足,62.26%的行业创新人力配置不足。大数据应用显著地改善了企业研发资本错配和研发人力错配,且在创新资源不足时这一改善效应更为明显。机制分析表明,大数据应用主要通过知识流动效应和技术壁垒效应影响企业创新资源错配。资源互补效应表明,数据要素效力的发挥还需要技术条件的支撑。本文结论为新一轮科技革命背景下以数据为关键生产要素促进我国企业创新资源合理配置、加快建设创新型国家提供了有益启示。

关键词: 大数据应用, 创新资源错配, 知识流动, 技术壁垒, 资源互补

Abstract:

In the era of digital economy, the innovation process of firms not only involves the Research and Development of new technologies and products but also involves the allocation of R&D resources by the new technologies and products. The Chinese government attaches great importance to the development of big data. Under the dual impetus of technological revolution and policy support, can Chinese enterprises apply big data to decrease misallocation in firm innovation? Big data refers to the integrated information assets derived from the circulation data containing massive information after being analyzed and processed by digital technology. Less literature directly studies how big data affects misallocation in firm innovation. Drawing on the ideas of Hsieh and Klenow (2009)’s productivity misalignment model, this paper constructs a model of enterprise innovation resources misallocation, expounded the impact of big data application on innovation resources misallocation from the theoretical level, and verified the inferences of the theoretical model by using the data of China listed firms from 2011 to 2020. It is found that 52.83% of industries in China have an insufficient allocation of R&D capital and 62.26% of industries have an insufficient allocation of R&D staff. The results show that big data significantly restrains the misallocation of R&D capital and R&D staff, and this inhibition effect is more obvious when R&D resources are insufficient. The mechanism test shows that big data application affects the misallocation of firm R&D resources mainly through the knowledge flow effect and the technology barrier effect. Furthermore, the resource complementary effect indicates that the effectiveness of data elements also needs the support of technical conditions. When enterprises effectively promote the construction of digital infrastructure, the operational efficiency of enterprises can be improved, and the information analysis and decision-making optimization of the innovation process can be realized at a smaller cost, resulting in the efficient allocation of innovation resources. The conclusion of this paper provides useful enlightenment to promote the rational allocation of R&D resources and speed up the construction of an innovative country with data resources as the key element under the background of new scientific and technological revolution.

Key words: big data application, misallocation in firm innovation, knowledge flow, technology barrier, resource complementary

中图分类号:

F270

任曙明, 马橙. 大数据应用与中国企业创新资源错配[J]. 科学学研究, 2024, 42(1): 194-204.

REN Shu-ming, MA Cheng. Effects of big data on misallocation in firm innovation[J]. Studies in Science of Science, 2024, 42(1): 194-204.

图/表 7

参考文献 28

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变量	变量说明	均值	标准差
研发资本错配τ_{RK_i}	根据公式(13)计算所得	0.3239	2.7644
研发人力错配τ_{RL_i}	根据公式(14)计算所得	0.3651	4.2216
大数据应用bigdata	大数据相关关键词出现次数加1后取对数	0.2956	0.5872
企业员工规模lnSize	企业员工数量,取对数	7.6818	1.1541
企业资本密度lnFixed	企业人均固定资产净值,取对数	14.2922	0.6868
城市人均GDPlnpgdp	城市人均GDP,取对数	11.4023	0.5049
外商直接投资lnfdi	外商实际投资额,取对数	12.3527	1.5289
城市人口规模lnpop	城市年末总人口,取对数	6.1695	0.5496
政府干预Gov	地方政府财政支出/GDP	0.4304	0.6628

变量	变量说明	均值	标准差
研发资本错配τ_{RK_i}	根据公式(13)计算所得	0.3239	2.7644
研发人力错配τ_{RL_i}	根据公式(14)计算所得	0.3651	4.2216
大数据应用bigdata	大数据相关关键词出现次数加1后取对数	0.2956	0.5872
企业员工规模lnSize	企业员工数量,取对数	7.6818	1.1541
企业资本密度lnFixed	企业人均固定资产净值,取对数	14.2922	0.6868
城市人均GDPlnpgdp	城市人均GDP,取对数	11.4023	0.5049
外商直接投资lnfdi	外商实际投资额,取对数	12.3527	1.5289
城市人口规模lnpop	城市年末总人口,取对数	6.1695	0.5496
政府干预Gov	地方政府财政支出/GDP	0.4304	0.6628

变量	研发资本错配				研发人力错配
变量	(1) τ_{RK_i}	(2) τ_{RK_i}0	(3) τ_{RK_i}0	(4) \|τ_{RK_i}\|	(5) τ_{RL_i}	(6) τ_{RL_i}0	(7) τ_{RL_i}0	(8) \|τ_{RL_i}\|
bigdata	-0.0062^*	-0.0010	-0.0078^*	-0.0056^*	-0.0614^**	0.0813^***	-0.0470^*	-0.0624^***
	(0.0037)	(0.0033)	(0.0042)	(0.0032)	(0.0259)	(0.0294)	(0.0272)	(0.0222)
控制变量	YES	YES	YES	YES	YES	YES	YES	YES
固定效应	YES	YES	YES	YES	YES	YES	YES	YES
N	9272	4209	4632	9276	6733	3289	3444	6731
Adj_R²	0.8047	0.5597	0.7743	0.7422	0.1009	0.0632	0.0594	0.0878

变量	研发资本错配				研发人力错配
变量	(1) τ_{RK_i}	(2) τ_{RK_i}0	(3) τ_{RK_i}0	(4) \|τ_{RK_i}\|	(5) τ_{RL_i}	(6) τ_{RL_i}0	(7) τ_{RL_i}0	(8) \|τ_{RL_i}\|
bigdata	-0.0062^*	-0.0010	-0.0078^*	-0.0056^*	-0.0614^**	0.0813^***	-0.0470^*	-0.0624^***
	(0.0037)	(0.0033)	(0.0042)	(0.0032)	(0.0259)	(0.0294)	(0.0272)	(0.0222)
控制变量	YES	YES	YES	YES	YES	YES	YES	YES
固定效应	YES	YES	YES	YES	YES	YES	YES	YES
N	9272	4209	4632	9276	6733	3289	3444	6731
Adj_R²	0.8047	0.5597	0.7743	0.7422	0.1009	0.0632	0.0594	0.0878

变量	替换被解释变量衡量指标		替换解释变量衡量指标
变量	smisallo	pmisallo	\|τ_{RK_i}\|	\|τ_{RL_i}\|
bigdata	-0.0020^***	-0.0143^***
	(0.0005)	(0.0021)
bigdata_d			-0.0056^**	-0.0624^***
			(0.0025)	(0.0222)
控制变量	YES	YES	YES	YES
固定效应	YES	YES	YES	YES
N	9423	9891	9276	6731
Adj_R²	0.8468	0.3222	0.7423	0.0878