|
|
|
| Data Element Allocation and Regional Innovation: The Enabling Effect and Action Path |
| Fan Decheng,Xiao Wenxue |
| (Schoool of Economics and Management, Harbin Engineering University,Harbin 150001, China) |
|
|
|
|
Abstract With the vigorous development of digital economy, the wide application of digital technology has a significant impact on the promotion of regional research and development activities and the enhancement of innovation ability. In particular, data elements, as the new production factors derived from the era of digital economy, are printed with the brand of innovation. As a derivative factor in the information age, data elements have become an increasingly important source of value creation that cannot be ignored. In terms of both breadth and depth, the integration process of data elements and various fields of economy and society is continuously strengthened, significantly changing the way of social and economic operation, and becoming a new engine to promote economic growth and technological innovation.#br#Therefore, this paper focuses on the perspective of data elements, selects 20 secondary indicators from four dimensions of data research and development management, data application environment, data dissemination and sharing and digital society promotion, and constructs a comprehensive evaluation index system of data element allocation level by entropy weight TOPSIS method. The inter-provincial panel data from 2012-2020 as the research interval is established and the empowering effect, difference and action path of data element allocation on regional innovation are discussed. It should be noted that the data used in this paper is all from the EPS database, China Academy of Information and Communications Technology, etc. #br#This paper first makes a preliminary analysis of the spatio-temporal evolution characteristics of the allocation level of data factors and the regional innovation capability. According to the spatio-temporal distribution of the two, the region with strong regional innovation capability tends to have a higher allocation level of data factors. From the perspective of spatial evolution, it is proven that there is a significant positive correlation between the allocation of data factors and the regional innovation capability, and the enabling effect is obvious. It corresponds to the theme of "digital +" innovation and development in the context of the Fourth Industrial Revolution. The elastic coefficient of regional innovation capability at the data element allocation level is 0.064 and passes the significance test of 1%, indicating that the data element allocation effectively enables regional innovation, and this conclusion still holds after a series of robustness tests. However, further analysis shows that the enabling effect of data factors on regional innovation has obvious heterogeneity as follows:(1)under the condition of different digital infrastructure levels, data element allocation has a promoting effect on local innovation capability and passes the significance test of 5%, but the effect is stronger in the region with higher digital infrastructure level;(2)the grouping test results of different innovation types show that the enabling effect of data element allocation level on regional innovation capability is more significant and stronger for radical innovation; (3) the test results of regional heterogeneity show that the enabling effect of data element allocation on regional innovation capability is greater in the central region than in the eastern region. In order to determine whether there are differences in the innovation-driving effect of data factor allocation in different stages of regional innovation capability, quantile regression is used to further analyze the conditional characteristics of data factor allocation on regional innovation capability. The results show that when regional innovation capability is different, data factor allocation enabling regional innovation also has significant differences. In provinces with strong regional innovation capability, the enabling effect of data element allocation is more obvious, which also proves that the development of digital economy will further widen the gap of innovation capacity between regions. According to the results of action path test, data factor allocation can also enable regional innovation through three paths, namely, improving entrepreneurial activity, accelerating R&D personnel flow and reducing industrial structure distortion, and the channel effect is obvious.#br#Finally the paper puts forward four policy suggestions: developing differentiated and dynamic digital economy development strategies, narrowing the "digital divide", improving the regulatory system of data elements, and removing the evolutionary obstacles of digital economy. Meanwhile,it is necessary to accelerate the evolution of digital platforms,and deepen the integration and application of data elements in economic and social production activities.#br#
|
|
Received: 08 October 2022
|
|
|
|
|
|
[1] 中国信息通信研究院.中国数字经济发展白皮书[R/OL].[2022-07].http://www.caict.ac.cn/kxyj/qwfb/bps/202207/P020220729609949023295.pdf.
[2] 韩若冰.数字技术推动下的文化创意产业生态化发展及其向度研究[J].山东大学学报(哲学社会科学版),2020，70(2):49-59.
[3] KALLINIKOS J,AALTONEN A V,MARTON A. A theory of digital objects[J]. First Monday, 2010, 15(6): 1-22.
[4] FICHMAN R G,SANTOS B L D,ZHENG Z. Digital innovation as a fundamental and powerful concept in the information systems curriculum[J]. MIS Quarterly, 2014,38(2):329-353.
[5] STIRON K J. Computers, productivity, and input substitution[J]. Economic Inquiry, 1998,36 (2):175-191.
[6] JORGENSON D W，STIRON K J.Information technology and growth[J].American Economic Review,1999, 89 (2):109- 115.
[7] 曹玉娟.数字化驱动下区域科技创新的框架变化与范式重构[J].学术论坛,2019,42(1):110-116.
[8] 王超贤,张伟东,颜蒙.数据越多越好吗——对数据要素报酬性质的跨学科分析[J].中国工业经济,2022，39(7):44-64.
[9] YOO Y，BOLAND Ｒ J，LYYTINEN K，et al. Organizing for innovation in the digitized world[J].Organization Science，2012，23( 5):1398-1408.
[10] 余菲菲,王丽婷.数字技术赋能我国制造企业技术创新路径研究[J].科研管理,2022,43(4):11-19.
[11] 李雪,吴福象,竺李乐.互联网发展水平、知识溢出与区域创新能力[J].经济经纬,2022,39(3):15-25.
[12] 韩先锋,宋文飞,李勃昕.互联网能成为中国区域创新效率提升的新动能吗[J].中国工业经济,2019，36(7):119-136.
[13] 刘洋,董久钰,魏江.数字创新管理：理论框架与未来研究[J].管理世界,2020,36(7):198-217,219.
[14] GREGORY R W,HENFRIDSSON O,KAGANER E, et al. The role of artificial intelligence and data network effect for creating user value[J]. The Academy of Management Review, 2021,46(3):534-551.
[15] GREGORY R W,HENFRIDSSON O,KAGANER E, et al. Data network effects: key conditions, shared data, and the data value duality (forthcoming in Academy of Management Review)[J]. The Academy of Management Review,2022,47(1):189-192.
[16] 荆文君,孙宝文.数字经济促进经济高质量发展:一个理论分析框架[J].经济学家,2019，31(2):66-73.
[17] 戴魁早,刘友金.要素市场扭曲与创新效率——对中国高技术产业发展的经验分析[J].经济研究,2016,51(7):72-86.
[18] KOHLI R,MELVILLE N P.Digital innovation: a review and synthesis[J].Information Systems Journal,2019,29(1):200-223.
[19] 郭美晨,杜传忠.ICT提升中国经济增长质量的机理与效应分析[J].统计研究,2019,36(3):3-16.
[20] 李治国,王杰.数字经济发展、数据要素配置与制造业生产率提升[J].经济学家,2021，33(10):41-50.
[21] 潘为华,贺正楚,潘红玉.中国数字经济发展的时空演化和分布动态[J].中国软科学,2021，36(10):137-147.
[22] 蔡跃洲,马文君.数据要素对高质量发展影响与数据流动制约[J].数量经济技术经济研究,2021,38(3):64-83.
[23] 白俊红,蒋伏心.协同创新、空间关联与区域创新绩效[J].经济研究,2015,50(7):174-187.
[24] 沈小波,陈语,林伯强.技术进步和产业结构扭曲对中国能源强度的影响[J].经济研究,2021,56(2):157-173.
[25] KOENKER R,BASSETT G W. Regression quantiles[J]. Econometrica, 1978, 46(1):211-244.
[26] 谢伏瞻.论新工业革命加速拓展与全球治理变革方向[J].经济研究,2019,54(7):4-13.
[27] 涂心语,严晓玲.数字化转型、知识溢出与企业全要素生产率——来自制造业上市公司的经验证据[J].产业经济研究,2022，21(2):43-56.
[28] JONES C I,TONETTI C. Nonrivalry and the economics of data[J]. American Economic Review, 2020, 110(9):2819-2858.
[29] 郭凯明,潘珊,颜色.新型基础设施投资与产业结构转型升级[J].中国工业经济,2020，37(3):63-80.
|
|
|
|
