数字化转型对企业创新产出的非线性影响研究

罗瑾琏, 王象路, 耿新

科研管理 ›› 2023, Vol. 44 ›› Issue (8) : 1-10.

PDF(1346 KB)
PDF(1346 KB)
科研管理 ›› 2023, Vol. 44 ›› Issue (8) : 1-10.
论文

数字化转型对企业创新产出的非线性影响研究

  • 罗瑾琏1,王象路1,耿新2
作者信息 +

Research on the nonlinear impact of digital transformation on innovation output of enterprises

  • Luo Jinlian1, Wang Xianglu1, Geng Xin2
Author information +
文章历史 +

摘要

本文基于中国上市公司2007~2020年A股上市公司的数据,实证检验了数字化转型对企业创新产出的非线性影响及其作用机制。结果显示,数字化转型对企业创新产出存在倒U型影响,即随着数字化水平由低向高的转变,企业创新绩效呈现先上升后下降的非线性演化趋势,以上结论在经过一系列稳健性检验后依然成立。机制检验表明,研发授权和人力资本结构在企业数字化转型和创新产出之间发挥倒U型中介作用。本文研究结论丰富并拓展了数字化转型与企业创新效益之间的非线性逻辑探索,为企业完善数字化转型战略并提升创新效益提供了经验证据和决策支持。

Abstract

    Under the background of the accelerated development of digital economy, human economy and society has rapidly entered a "digital" era. Driven by both policy orientation and practical development, digital transformation is also deeply engraved in the evolution of Chinese enterprises. Although digital technology provides new development opportunities, the effect of digital transformation of most enterprises in practice is not ideal. It should be noted that the essence of digital transformation lies in the reconstruction of productivity and whether enterprises carry out all-round innovation with digital technology in the process of transformation is the key to realize digital drive and build core competitiveness. However, the research on enterprise digital transformation and innovation output has long been controversial. For that reason, a more systematic study on how digital transformation affects enterprise innovation output and what is the internal mechanism of the complex relationship between digital transformation and innovation output has significant theoretical value and practical significance.Based on the data of A-share listed companies in China from 2007 to 2020, this paper empirically tested the nonlinear impact of digital transformation on enterprise innovation output and its mechanism. In the research design, this paper used the natural logarithm of the number of invention patents to measure the innovation output. Meanwhile, the ratio of digital intangible assets to total intangible assets and the natural logarithm of the frequency of digital related words in the annual reports of listed companies were used to measure the digitization degree of enterprises. The empirical analysis results showed that digital transformation has a significant inverted U-shaped impact on enterprise innovation output. With the transformation of digital degree from low to high, enterprise innovation performance presents a nonlinear evolution trend of first rising and then falling. The above conclusions are still valid after the endogenous tests of enterprise level and industry level with the fixed effect model, two-stage least squares model, sys-GMM dynamic panel model and a series of robustness tests. The further mechanism tests by stepwise regression and bootstrap sampling showed that R&D authorization and human capital structure play an inverted U-shaped mediated role between digital transformation and innovation output. On the one hand, digital technology promotes the upward movement of the information cost curve and the downward movement of the agency cost curve to realize R&D authorization, but the limitations of departmental decision-making and resource constraints increase accordingly, making R&D authorization play an inverted U-shaped mediated role between digital transformation and innovation output; On the other hand, digital technology optimizes the human capital structure of enterprises through "substitution effect" and "complementary effect", but the advanced digital construction has a crowding out effect on human capital investment, making the human capital structure act as an inverted U-shaped intermediary between digital transformation and innovation output.The conclusion has enriched and expanded the nonlinear logical exploration between digital transformation and enterprise innovation benefits, and provided important enlightenment for enterprises to improve digital transformation strategy and improve innovation benefits from the following aspects: (1) enterprises should grasp the opportunity of the rapid development of digital economy, actively and steadily implement digital transformation, and continuously improve the integration of enterprises and digital technology in the process of transformation. Meanwhile, At the same time, enterprises need to combine their resource capacity endowment in digital construction to avoid the negative effect of blindly expanding digital investment on innovation output. (2) When using digital technology to reconstruct and integrate the original innovation mode, enterprises should attach great importance to the allocation of R&D power suitable for digitization, and make systematic planning according to the organizational characteristics. (3) Highly skilled talents should play a key role in the process of digital transformation. When introducing digital technology, enterprises should not only systematically consider the coordination of digital investment and human capital investment, but also strengthen the work commitment to employees and give full play to the role of human capital structure in promoting innovation output.

关键词

数字化转型 / 创新产出 / 研发授权 / 人力资本结构

Key words

digital transformation / innovation output / R&D authorization / human capital structure

引用本文

导出引用
罗瑾琏, 王象路, 耿新. 数字化转型对企业创新产出的非线性影响研究[J]. 科研管理. 2023, 44(8): 1-10
Luo Jinlian, Wang Xianglu, Geng Xin.

Research on the nonlinear impact of digital transformation on innovation output of enterprises

[J]. Science Research Management. 2023, 44(8): 1-10

参考文献

[1] 戚聿东,肖旭.数字经济时代的企业管理变革[J]. 管理世界, 2020, 36(6): 135-153.
[2] 吴非, 胡慧芷, 林慧妍, 任晓怡. 企业数字化转型与资本市场表现——来自股票流动性的经验证据[J]. 管理世界, 2021, 37(7): 130-144.
[3] Nambisan, S., Lyytinen, K., Majchrzak, A., et al. Digital innovation management: Reinventing innovation management research in a digital world [J]. MIS Quarterly, 2017, 41(1): 223-238.
[4] Frynas, J. G., Mol, M. J., Mellahi, K. Management Innovation Made in China: Haier’s Rendanheyi [J]. California Management Review, 2018, 61(1): 71-93.
[5] 肖静华, 吴小龙, 谢康, 吴瑶. 信息技术驱动中国制造转型升级——美的智能制造跨越式战略变革纵向案例研究[J]. 管理世界, 2021, 37(3): 161-179.
[6] Agrawal, A., Gans, J. Goldfarb, A. Prediction Machines: The Simple Economics of Artificial Intelligence [M]. Brighton, MA: Harvard Business Review Press, 2018.
[7] Li L., Su, F., Zhang, W., et al. Digital transformation by SME entrepreneurs: A capability perspective [J]. Information Systems Journal, 2018, 28: 1129-1157.
[8] Nwankpa, J., K., Roumani, Y. IT Capability and Digital Transformation: A Firm Performance Perspective [J]. Thirty Seventh International Conference on Information Systems, 2016: 1-16.
[9] Rachinger, M., Rauter, R., Müller, C., Vorraber, W., Schirgi, E. Digitalization and Its Influence on Business Model Innovation [J]. Journal of Manufacturing Technology Management, 2019, 30(8): 1143-1160.
[10] Autio, E., Nambisan, S., Thomas, L., Wright, M. Digital affordances, spatial affordances, and the genesis of entrepreneurial ecosystems [J]. Strategic Entrepreneurship Journal, 2018, 12(1): 72-95.
[11] 何帆, 刘红霞. 数字经济视角下实体企业数字化变革的业绩提升效应评估[J]. 改革, 2019(4): 137-148.
[12] 赵宸宇. 数字化发展与服务化转型——来自制造业上市公司的经验证据[J]. 南开管理评论, 2021, 24(2): 149-163.
[13] Hajli, M., Sims, J. M. Ibragimov, V. Information Technology Productivity Paradox in the 21St Century [J]. International Journal of Productivity and Performance Management, 2015, 64(4): 457-478.
[14] Jacobides, M. G., Cennamo, C., Gawer, A. Towards a Theory of Ecosystmes [J]. Strategic Management Journal, 2018,39(8): 2255-2276.
[15] Ekata, G. E. The IT Productivity Paradox: Evidence from the Nigerian Banking Industry [J]. Electronic Journal of Information Systems in Developing Countries, 2012, 51: 1-25.
[16] Dong, J. Q., Netten, J. Information technology and external search in the open innovation age: New findings from Germany [J]. Technological Forecasting & Social Change, 2017(120): 223-231.
[17] 刘飞. 数字化转型如何提升制造业生产率——基于数字化转型的三重影响机制[J]. 财经科学, 2020(10): 93-107.
[18] 刘淑春, 闫津臣, 张思雪, 林汉川. 企业管理数字化变革能提升投入产出效率吗[J]. 管理世界, 2021, 37(5): 170-190.
[19] Forman, C., McElheran, K. Firm Organization in the Digital Age: IT Use and Vertical Transactions in U. S. Manufacturing [J]. SSRN Working Paper Series, 2019.
[20] Brynjolfsson, E., McElheran, K. Digitization and Innovation: The Rapid Adoption of Data -Driven Decision-Making [J]. American Economic Review: Papers & Proceedings, 2016, 106(5): 133-139.
[21] 刘政, 姚雨秀, 张国胜, 匡慧姝. 企业数字化、专用知识与组织授权[J]. 中国工业经济, 2020(9): 156-174.
[22] 张栋, 胡文龙, 毛新述. 研发背景高管权力与公司创新[J]. 中国工业经济, 2021(4): 156-174.
[23] 孙早, 侯玉琳. 工业智能化如何重塑劳动力就业结构[J]. 中国工业经济, 2019(5): 61-79.
[24] 陈冬梅,王俐珍,陈安霓.数字化与战略管理理论——回顾、挑战与展望[J].管理世界, 2020, 36(5): 220-236.
[25] 陈国青, 曾大军, 卫强, 张明月, 郭迅华. 大数据环境下的决策范式转变与使能创新[J]. 管理世界, 2020, 36(2): 95-105.
[26] Briel, F. V., Recker, J., Davidsson, P. Not all digital venture ideas are created equal: implications for venture creation processes [J]. Journal of Strategic Information Systems, 2018, 27(4): 278-295.
[27] Clemons, E. K., Row, M. C. Information Technology and Industrial Cooperation: The Changing Nature of Coordination and Ownership [J]. Journal of Management and Information System, 1992, 9(2): 9-28.
[28] Lyytinen, K., Yoo, Y., Boland, Jr R J. Digital product innovation within four classes of innovation networks [J]. Information Systems Journal, 2016, 26(1): 47–75.
[29] 陈庆江, 王月苗, 王彦萌. 高管团队社会资本在数字技术赋能企业创新中的作用——助推器还是绊脚石?[J]. 上海财经大学学报, 2021, 23(4): 3-17.
[30] Johnson, J. S., Friend, S. B., Lee, H. S. Big data facilitation, utilization, and monetization: exploring the 3Vs in a new product development process [J]. Journal of Product Innovation Management, 2017, 34(5): 640-658.
[31] Huang J., Henfridsson O., Liu M. J., et al. Growing on steroids: Rapidly scaling the user base of digital ventures through digital innovation [J]. MIS Quarterly, 2017, 41(1): 301-314.
[32] Yoo, Y., Boland, Jr R. J., Lyytinen, K., et al. Organizing for innovation in the digitized world [J]. Organization Science, 2012, 23(5): 1398-1408.
[33] Brynjolfsson, E., D. Rock, C. Syverson. Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics [J]. National Bureau of Economic Research, 2017, Working Paper No. 24001.
[34] Wamba, S. F., Gunasekaran, A., Akter S. Big data analytics and firm performance: effects of dynamic capabilities [J]. Journal of Business Research, 2017, 70(1): 356-365.
[35] Bresnahan, T. F., Brynjolfsson, E., Hitt L. M. Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm-level Evidence [J]. The Quarterly Journal of Economics, 2002, 117(1): 339-376.
[36] Schrey?gg, G., Sydow, J. Organizational Path Dependence: A Process View [J]. Organization Studies, 2011, 32(3): 321-335.
[37] 罗仲伟, 李先军, 宋翔, 李亚光. 从赋权到赋能的企业组织结构演进——基于韩都衣舍案例的研究[J]. 中国工业经济, 2017(9): 174-192.
[38] Jensen, M. C., Meckling, W. H. Specific and General Knowledge and Organizational Structure [J]. Journal of Applied Corporate Finance, 1995, 8(2): 4-18.
[39] Lateef, A., F. O. Omotayo. Information Audit as an Important Tool in Organizational Management: A Review of Literature [J]. Business Information Review, 2019, 36(1): 15-22.
[40] Fort, T. C. Technology and Production Fragmentation: Domestic Versus Foreign Sourcing [J]. The Review of Economic Studies, 2017, 84(2): 650-687.
[41] Adner, R., P. Puranam, F. Zhu. What Is Different about Digital Strategy? From Quantitative to Qualitative Change [J]. Strategy Science, 2019(4): 253-261.
[42] 张永珅, 李小波, 邢铭强. 企业数字化转型与审计定价[J]. 审计研究, 2021(3): 62-71.
[43] Goldfarb, A., Tucker, C. Digital Economics [J]. Journal of Economic Literature, 2019, 57(1): 3-43.
[44] 谭洪涛, 陈瑶. 集团内部权力配置与企业创新——基于权力细分的对比研究[J]. 中国工业经济, 2019(12): 136-153.
[45] Schuster, C. P., Dufek, D. F. The Consumer or Else! Consumer-Centric Business Paradigms [M]. New York: Routledge, 2004.
[46] Milgrom, P., Roberts, J. The Economics of Modern Manufacturing: Technology, Strategy and Organization [J]. The American Economic Review, 1990, 80(3): 511-528.
[47] Alesina, A., Battisti, M., Zeira, J. Technology and Labor Regulations: Theory and Evidence [J]. Journal of Economic Growth, 2018, 23(1): 41-78.
[48] 何小钢, 梁权熙, 王善骝. 信息技术,劳动力结构与企业生产率——破解“信息技术生产率悖论”之谜[J]. 管理世界, 2019(9):65-80.
[49] Autor, D H., David, D. The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market [J]. American Economic Review, 2013, 103(5): 1553-97.
[50] 黎文靖, 郑曼妮. 实质性创新还是策略性创新?——宏观产业政策对微观企业创新的影响[J]. 经济研究, 2016, 51(4): 60-73.
[51] 赵宸宇, 王文春, 李雪松. 数字化转型如何影响企业全要素生产率[J]. 财贸经济, 2021, 42(7): 114-129.
[52] 祁怀锦, 曹修琴, 刘艳霞. 数字经济对公司治理的影响——基于信息不对称和管理者非理性行为视角[J]. 改革, 2020(4): 50-64.
[53] 袁淳,肖土盛,耿春晓,盛誉.数字化转型与企业分工:专业化还是纵向一体化[J].中国工业经济,2021(9):137-155.
[54] Chang, X., Fu, K., Low, A., Zhang, W. Non-Executive Employee Stock Options and Corporate Innovation [J]. Journal of Financial Economics, 2015,115(1): 168-188.
[55] Haans, R. F. J., Pieters, C., He, Z. L. Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research [J]. Strategic Management Journal, 2016, 37(7):1177-1195.
[56] Adhikari, B. K., Agrawal, A. Peer influence on payout policies [J]. Journal of Corporate Finance, 2018, 48: 615-637.
[57] 温忠麟,叶宝娟.中介效应分析:方法和模型发展[J].心理科学进展,2014,22(05):731-745.
[58] 罗胜强,姜嬿.管理学问卷调查研究方法[M].重庆大学出版社,2014.
[59] Arntz, M., Gregory, T., Zierahn, U. The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis [J]. OECD Social, Employment and Migration Working Papers,2016, No. 189.
[60] 丁一兵,刘紫薇.中国人力资本的全球流动与企业“走出去”微观绩效[J].中国工业经济,2020(3):119-136.

基金

国家自然科学基金面上项目:“学习-认知视角下双元领导行为的多层效应与转换过程研究”(71772138,2018.01—2021.12);国家自然科学基金面上项目:“内化于心何以外显于行:创新使命的多层次意义建构及对企业突破性创新影响效应研究” (72072128,2021.01—2024.12);山东省社会科学规划研究项目:“山东省区域人才活力评价与提升对策研究”(21CJJJ12,2021.12—2023.12)。

PDF(1346 KB)

Accesses

Citation

Detail

段落导航
相关文章

/