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数据交易对企业数字创新的影响研究
Research on the impact of data transactions on enterprise digital innovation
数字创新对强化企业竞争力、驱动价值链升级极为关键,数据交易则在加速数据高价值转化、助力数字创新突破中发挥重要作用。本文基于2010—2022年上市公司数据,使用爬虫技术、文本分析以及人工识别等方法识别数据交易企业,并构建双重机器学习模型实证分析数据交易对企业数字创新的影响,进一步拓展了数据交易影响数字创新的微观效应和作用机制。研究结果表明:数据交易对企业数字创新具有显著正向影响,且这种正向影响在非国有企业、大规模企业、知识产权保护程度高以及数字基础设施强的企业中更强。机制分析显示数据交易通过知识溢出效应、要素配置效应和公司治理效应促进了企业数字创新。此外,供应方数据交易、数据服务交易以及直接数据交易方式促进企业数字创新更明显,且数据交易能够提高企业数字创新的质量。本研究对激励企业参与数据交易、做大做强数据要素市场、利用数据市场化推动企业数字创新具有重要启示。
Digital innovation is extremely critical to strengthening corporate competitiveness and driving value chain. Data transactions play an important role in accelerating the high value conversion of data and helping digital innovation breakthroughs. Based on the data of listed companies from 2010 to 2022, this paper used crawler technology, text analysis and manual recognition to identify corporate data transactions, and constructed a dual machine learning model to empirically analyze the impact of data transactions on corporate digital innovation, further expanding the micro-effects and mechanisms of data transactions on digital innovation. The results of the study showed that data transactions have a significant positive impact on corporate digital innovation, and this positive effect is stronger among non-state-owned corporations, large corporations, corporations with high levels of intellectual property protection, and corporations with strong digital infrastructure. The mechanism analysis showed that data transactions have promoted corporate digital innovation through knowledge spillover effect, factor allocation effect, and corporate governance effect. In addition, the effect of the supply-side data transaction, data service transactions, and direct data transactions on promoting corporate digital innovation are more obvious, and data transactions can improve the quality of corporate digital innovation. This study has important implications for encouraging corporates to participate in data transactions, it will expand and strengthen the data factor market, and promote corporate digital innovation through data marketization.
数据交易 / 企业数字创新 / 知识溢出效应 / 要素配置效应 / 公司治理效应
data transaction / corporate digital innovation / knowledge spillover effect / resource allocation effect / corporate governance effect
| [1] |
张超, 陈凯华, 穆荣平. 数字创新生态系统:理论构建与未来研究[J]. 科研管理, 2021, 42(3): 1-11.
|
| [2] |
|
| [3] |
马鸿佳, 肖彬, 王春蕾. 大数据能力影响因素及效用:基于元分析的研究[J]. 南开管理评论, 2023, 26(2):143-153+165.
|
| [4] |
张振刚, 叶宝升, 户安涛, 等. 制造企业如何整合数据资源赋能产品创新绩效?:组织间计算型与关系型信任的作用[J]. 科学学研究, 2024, 42(3):649-659.
现有研究强调了数据资源要素的重要作用,但缺乏探讨如何整合数据资源以赋能产品创新绩效。遵循“条件-行为-结果”的分析思路,研究基于信任视角,探究组织间计算型(理性)与关系型(感性)信任如何通过稳定调整型、开拓创造型两种数据资源整合方式赋能产品创新绩效。利用华南地区两时点调查的222家制造业企业问卷数据,研究采用PLS-SEM工具进行实证检验,得出以下结论:(1)计算型信任分别能够通过稳定调整型、开拓创造型两种数据资源整合方式赋能产品创新绩效;(2)关系型信任通过稳定调整型、开拓创造型两种数据资源整合方式与产品创新绩效存在非线性中介作用关系,即适度的关系型信任能够分别通过两种数据资源整合方式提升产品创新绩效,但是过高的关系型信任则无法通过两种数据资源整合方式提升产品创新绩效;(3)稳定调整(“拿来主义”)与开拓创造(“二次创新”)两种数据资源整合方式对于产品创新绩效的影响存在交互效应,当企业通过计算型、关系型信任同时提高两种数据资源时,能够更好地赋能产品创新绩效。研究回应了制造业企业如何进行数据资源整合发挥数据要素价值的重要问题,能够为充分发挥数据作用赋能企业高质量发展提供重要启示。
Existing research emphasizes the important role of data resources, but lacks a discussion on how to integrate data resources to enable product innovation performance. Following the analysis logic of “condition-behavior-outcome”, this paper explores how calculative and relational inter-organizational trust enable product innovation performance through the two ways of data resources integration including stabilizing and pioneering. Using the questionnaire data of 222 manufacturing enterprises in the two-time survey in South China areas, we use PLS-SEM (Partial Least Squares-Structural Equation Modeling) tool to conduct the empirical test, and draw the following conclusions: (1) Calculative inter-organizational trust can enable product innovation performance through the two ways of data resources integration including stabilizing and pioneering; (2) Relational inter-organizational trust has a nonlinear mediating relationship with product innovation performance through two data resource integration ways, stabilizing and pioneering, that is, moderate relational trust can improve product innovation performance through two data resource integration ways, but too high relational trust cannot improve product innovation performance via two data resource integration ways; (3) There is an interactive effect between the two ways of integrating data resources (stabilizing and pioneering) on product innovation performance. When enterprises improve the two ways of data resources integration simultaneously affected by calculative and relational inter-organizational trust, they can better enable product innovation performance. The study responds to the important question that how manufacturing enterprises integrate data resources and exert the value of data elements, providing important enlightenment for emphasizing data resources integration to enable the high-quality development of enterprises.
|
| [5] |
戴魁早, 王思曼, 黄姿. 数据要素市场发展与生产率提升[J]. 经济管理, 2023, 45(6):22-43.
|
| [6] |
张叶青, 陆瑶, 李乐芸. 大数据应用对中国企业市场价值的影响:来自中国上市公司年报文本分析的证据[J]. 经济研究, 2021, 56(12):42-59.
|
| [7] |
In this essay, we argue that pervasive digitization gives birth to a new type of product architecture: the layered modular architecture. The layered modular architecture extends the modular architecture of physical products by incorporating four loosely coupled layers of devices, networks, services, and contents created by digital technology. We posit that this new architecture instigates profound changes in the ways that firms organize for innovation in the future. We develop (1) a conceptual framework to describe the emerging organizing logic of digital innovation and (2) an information systems research agenda for digital strategy and the creation and management of corporate information technology infrastructures.
|
| [8] |
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.
|
| [9] |
匡慧姝, 刘政, 左勇华, 等. 信息技术背景高管能否推动企业数字创新?[J]. 科学学研究, 2023, 42 (12): 2656-2667.
|
| [10] |
胡增玺, 马述忠. 市场一体化对企业数字技术创新的影响:兼论数字技术创新衡量方法[J]. 经济研究, 2023, 58(6):155-172.
|
| [11] |
梁睿昕, 李姚矿. 政府创新政策对数字企业技术创新激励效应研究[J]. 统计研究, 2023, 40(11): 40-52.
|
| [12] |
陈国青, 任明, 卫强, 等. 数智赋能:信息系统研究的新跃迁[J]. 管理世界, 2022, 38(1):180-196.
|
| [13] |
谢康, 夏正豪, 肖静华. 大数据成为现实生产要素的企业实现机制:产品创新视角[J]. 中国工业经济, 2020(5):42-60.
|
| [14] |
焦豪, 杨季枫, 王培暖, 等. 数据驱动的企业动态能力作用机制研究:基于数据全生命周期管理的数字化转型过程分析[J]. 中国工业经济, 2021(11):174-192.
|
| [15] |
陈琳, 高悦蓬, 余林徽. 人工智能如何改变企业对劳动力的需求?:来自招聘平台大数据的分析[J]. 管理世界, 2024(6):74-91.
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
龙小宁, 刘灵子, 张靖. 企业合作研发模式对创新质量的影响:基于中国专利数据的实证研究[J]. 中国工业经济, 2023(10):174-192.
|
| [20] |
陶锋, 朱盼, 邱楚芝, 等. 数字技术创新对企业市场价值的影响研究[J]. 数量经济技术经济研究, 2023, 40(5):68-91.
|
| [21] |
吴非, 胡慧芷, 林慧妍, 等. 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+10.
|
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