Digital finance is expected to provide high-quality services for economic and social development. The data processing technologies of digital finance based on the advantages of underlying digital financial technologies have enabled enterprises to realize the interaction of internal cognition and the external environment, enabling enterprises to strengthen potential absorption capabilities and transform external innovation resources into internal innovation capabilities. As an important subject in modernization in China, the manufacturing industry has long been stuck with the issue that the added value of manufacturing products is low in China and the development of manufacturing industry faces bottlenecks due to core-technology dependence. It is essential for China to cultivate core technology and improve the quality of innovation to achieve high-quality development. The high-quality development of manufacturing industry requires not only a good financial environment to solve the difficulty of high innovation investment but also precise product innovation through big data analysis and predictive models to improve the quality of innovation and development. Therefore, the mechanism of analyzing the improvement of the innovation quality of digital financial-driven manufacturing enterprises is of great significance for the high-quality development of China's manufacturing industry.
This study uses data from A-share manufacturing listed companies from 2015 to 2021 as a sample to explore the impact of digital finance on the quality of corporate innovation output, as well as the intermediary role of potential absorptive capacity. By selecting the generalized least squares model and using Stata software, empirical research shows that digital finance promotes the improvement of the quality of innovation output of manufacturing enterprises, and potential absorptive capacity plays a mediating role in the relationship between digital finance and the quality of innovation output of enterprises. Further analysis shows that the effect of digital finance on the quality of corporate innovation output presents significant differences depending on the attributes of the company or the intensity of regional financial supervision. This relationship is more significant in scenarios where non-state-owned enterprises, high-tech enterprises, or financial regulatory intensity in the region where the enterprise is located is moderate. Through robustness testing using replacement variables, reduced samples, and the Heckman two-stage model, the above conclusions remain valid.
The results of this article have enriched the theoretical research on high-quality innovation in enterprises against the backdrop of digital finance and provided management inspiration for the high-quality innovation and development of enterprises, financial institutions and government supervision. It is suggested that, first of all, enterprises should actively use digital financial instruments and platforms to strengthen the construction of innovative teams, integrate resource advantages, absorb information from the market and customers, realize resource sharing and advantage complementarity, absorb and use technology and scientific and technological talents, improve the potential absorption capacity of the enterprise, enhance innovation capabilities, and produce better products and services. Second, financial institutions should use digital finance technology to obtain more data sources, use analysis tools to accurately evaluate enterprises' acceptance and application capabilities of external technology and innovation, and credit risks of the enterprise. Innovation capabilities and innovative output quality indicators could be included in the credit risk assessment system to ensure the security of capital flow and improve the value creation capabilities of financial institutions through precise services, helping enterprises realize innovation and improve the quality of innovation output. Third, government regulatory agencies should pay close attention to the development trend of digital finance, supervise the operation of the financial market in a timely manner, and actively guide and promote the in-depth integration of digital finance and manufacturing to provide new opportunities for manufacturing enterprises. There should be relevant financial regulatory policies, standards, and moderate supervision on the platform to prevent "anti-supervision science and technology" behavior, improve the regulatory mechanism, ensure that digital finance does not bring financial risks while promoting innovative output, and protect the stability and fairness of the market while promoting the stability and fairness of the market. Meanwhile , it should be stressed that open innovation will promote coordinated innovation between financial institutions and enterprises, and create a good financial ecological environment for the high-quality innovation and development of manufacturing enterprises.
[1] HANER U.Innovation quality—a conceptual framework[J].International Journal of Production Economics, 2002,80(1):31-37.
[2] 荆宁宁, 黄申奥, 李德峰. 创新文化、顾客创新、社交媒体与创新质量之间的关系——有调节的中介效应模型[J]. 宏观质量研究, 2017,5(4):117-130.
[3] 梁宗正, 张煜, 史冬梅. 校企战略科技合作﹑产业双元创新和行业高质量发展——基于我国信息技术行业的分析[J]. 河南大学学报(社会科学版), 2023,63(5):40-48.
[4] 赵景艳, 邓敏, 徐政. 异地子公司的设立能否提升母公司创新水平——来自A股上市公司异地投资的经验证据[J]. 广东财经大学学报, 2023,38(2):60-74.
[5] 张治河, 郭星, 易兰. 经济高质量发展的创新驱动机制[J]. 西安交通大学学报(社会科学版), 2019,39(6):39-46.
[6] 宋晓玲, 王婉濛, 王光远. 数字普惠金融能否为中小企业创新提供动能——来自新三板挂牌企业的经验证据[J]. 投资研究, 2023,42(1):120-137.
[7] 朱俊丰. 数字金融对企业技术创新的效应识别与机制检验[J]. 统计与决策, 2023,39(7):173-178.
[8] 康卫国, 李梓峻. 数字普惠金融与技术创新——来自企业生命周期的新视角[J]. 宏观经济研究, 2022,44(12):21-42.
[9] 熊正德, 黎秋芳. 数字金融对企业技术创新的影响——基于370家数字创意产业上市公司的证据[J]. 湖南农业大学学报(社会科学版), 2022,23(3):80-89.
[10] 赵晓鸽, 钟世虎, 郭晓欣. 数字普惠金融发展、金融错配缓解与企业创新[J]. 科研管理, 2021,42(4):158-169.
[11] 申明浩, 谭伟杰. 数字金融发展能激励企业创新吗——基于中国上市企业的实证检验[J]. 南京财经大学学报, 2022,40(3):66-77.
[12] 李朝阳, 潘孟阳, 李建标. 数字金融、信贷可得性与企业创新——基于金融资源水平的调节效应[J]. 预测, 2021,40(6):39-46.
[13] LI Y, LIU X, ZHAO Q. Digital finance, absorptive capacity and enterprise dual innovation: an empirical analysis on mediation and threshold effects[J]. Asian Journal of Technology Innovation, 2022,31(2):1-32.
[14] COHEN W M, LEVINTHAL D A. Absorptive capacity: a new perspective on learning and innovation[J]. Administrative Science Quarterly, 1990,35(1):128-152.
[15] ZAHRA S A, GEORGE G. Absorptive capacity: a review, re-conceptualization, and extension[J]. The Academy of Management Review, 2002,27(2):185-203.
[16] 张晓燕, 姬家豪. 金融科技与金融监管的动态匹配对金融效率的影响[J]. 南开管理评论, 2023,26(1):43-56.
[17] 程雪军. 我国监管科技的风险衍生与路径转换:从金融科技“三元悖论”切入[J]. 上海大学学报(社会科学版), 2022,39(1):74-90.
[18] 宋宝琳, 张航, 宋凤轩. 数字金融对城市创新水平与质量的影响效应研究——基于我国284个地级市面板数据的实证分析[J]. 系统科学与数学, 2023,43(1):199-214.
[19] 刘军航, 刘书畅. 数字金融对企业价值的影响——基于影子银行的调节效应[J]. 工业技术经济, 2023,42(3):92-98.
[20] 雷晓丽. 数字金融、技术创新与流通产业绿色全要素生产率[J]. 商业经济研究, 2022,41(22):15-18.
[21] 余官胜, 田菊芳. 数字金融发展与企业对外直接投资规模增长——基于上市公司样本的实证研究[J]. 国际商务(对外经济贸易大学学报), 2023,37(1):88-104.
[22] 罗岭, 曹青青. 数字金融、企业风险承担与审计费用[J]. 审计与经济研究, 2023,38(1):40-50.
[23] 马芬芬, 付泽宇, 王满仓. 数字金融、融资约束与企业全要素生产率——理论模型与工业企业经验证据[J].人文杂志, 2021,65(7):69-79.
[24] 康鑫, 郭双叶. 知识协同视角下企业突破性创新形成机理研究[J]. 技术经济, 2022,41(12):12-24.
[25] 王雷, 王圣君. 外部社会资本、吸收能力与新产品绩效的关系——基于中国长三角地区企业样本的实证分析[J]. 技术经济, 2015,34(12):15-23.
[26] LANE P J, KOKA B R, PATHAK S. The reification of absorptive capacity: a critical review and rejuvenation of the construct[J]. The Academy of Management Review, 2006,31(4):833-863.
[27] 许军. 知识共享与互动的企业间电子商务模式研究[J]. 统计与决策, 2010,26(1):182-185.
[28] 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.
[29] LIAN G, XU A, ZHU Y. Substantive green innovation or symbolic green innovation? the impact of ER on enterprise green innovation based on the dual moderating effects[J]. Journal of innovation & knowledge, 2022,7(3):100203.
[30] 郭峰, 王靖一, 王芳, 等. 测度中国数字普惠金融发展:指数编制与空间特征[J]. 经济学(季刊), 2020,19(4):1401-1418.
[31] WANG Y,NING L, CHEN J.Product diversification through licensing:empirical evidence from Chinese firms[J].European Management Journal, 2014,32(4):577-586.
[32] 吴非, 申么, 祝佳, 等. 利率市场化对企业技术创新的驱动效应研究——异质性特征、微观机理与政府激励解构[J]. 中国软科学, 2021,36(8):120-129.
[33] 唐松, 伍旭川, 祝佳. 数字金融与企业技术创新——结构特征、机制识别与金融监管下的效应差异[J]. 管理世界, 2020,36(5):52-66.
[34] 温忠麟,侯杰泰,张雷. 调节效应与中介效应的比较和应用[J]. 心理学报, 2005,50(2):268-274.
[35] 孙莹, 王甜甜. 营商环境改善是否可以提高企业绩效——基于2008—2020年中国A股上市公司的经验证据[J]. 河海大学学报(哲学社会科学版), 2022,24(6):121-128.
[36] 王超, 余典范, 龙睿. 经济政策不确定性与企业数字化——垫脚石还是绊脚石[J]. 经济管理, 2023,45(6):79-100.
[37] 祝佳, 孙念念, 吴非. 金融市场化改革与企业“短贷长投”——基于利率市场化视角下的中国经验[J]. 商业研究, 2022,65(5):103-113.
[38] 钟凤英, 冷冰洁. 员工持股计划、内部控制与创新绩效[J]. 经济问题, 2022,44(8):120-128.
[39] 李靖远, 于文成. 数字金融素养能否助推家庭共同富裕——基于中国家庭金融调查的研究[J]. 金融发展研究, 2023,42(6):25-35.
[40] 王韧, 张奇佳, 何强. 金融监管会损害金融效率吗[J]. 金融经济学研究, 2019,34(6):93-104.
[41] 常曦, 吴非, 任晓怡. 金融监管能否治理企业房地产投资:效用识别与机制检验[J]. 河北经贸大学学报, 2023,44(1):98-109.
[42] CLEARY S. The relationship between firm investment and financial status[J]. The Journal of Finance, 1999,54(2):673-692.
[43] 肖红军, 阳镇, 凌鸿程. “鞭长莫及”还是“遥相呼应”:监管距离与企业社会责任[J]. 财贸经济, 2021,42(10):116-131.
[44] 李光武, 谭曼, 朱仁泽. 网络基础设施建设、企业投资决策和金融资产配置——来自“宽带中国”战略的准自然实验[J]. 云南财经大学学报, 2023,39(7):42-69.