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论文编号：	14776
作者编号：	1120191041
上传时间：	2024/6/10 0:08:27
中文题目：	数字化时代的公司创业投资：影响因素与战略效应
英文题目：	Corporate Venture Capital in the Digital Age: Influencing Factors and Strategic Effects
指导老师：	张玉利
中文关键字：	CVC投资；数字化；影响因素；战略效应
英文关键字：	CVC investments; Digitalization; Influencing factors; Strategic effects
中文摘要：	在数字经济快速发展的背景下，新兴数字技术（如人工智能、区块链、云计算和大数据等）向各行业深度渗透，技术变革的速度和复杂性给组织带来了许多不确定性，反过来又迫使组织不断以各种手段应对不确定性以增强竞争力。许多组织转向外部活动，如联盟、合资、并购和CVC投资，这些战略中任何一种战略的选择及实施都会对企业竞争优势的构建产生影响。CVC投资作为当前企业日益重视的公司创业形式显示出独特的优势，CVC投资是指大型投资企业对独立运作的创业企业进行的少数股权投资（Dushnitsky，2012）。投资企业能够在缺乏资源的情况下对创业企业低资源投入，视创业企业发展情况追加或终止投资，相比于联盟、合资和并购等具有较大的灵活性。 当前，中国已经成长为全球第二大CVC市场。作为CVC投资后起之秀的中国，随着中国股权投资市场不断发展，企业对CVC投资日益重视，旨在利用CVC投资达到战略驱动、核心业务赋能、新兴行业布局等目的。在数字化时代背景下，一方面，当前全球各国均高度重视数字技术发展，将推动企业数字化升级作为增强国家竞争力、培育新动能的重要抓手。数字化已经从部分头部企业的“可选题”，转变为更多行业企业共同的“必选题”。另一方面，数字化领域的创业企业成为投资机构重要的捕捉对象，投资企业在寻找合适的CVC投资目标时，已经将战略重点放在数字化技术上。 从已有CVC投资的文献来看，目前的研究还没有关注到数字化时代的CVC投资问题，这在当前是一个重要的研究话题，本研究致力于弥补这一空白。具体来说，围绕数字化时代CVC投资的影响因素和战略效应探讨以下三个紧密关联的具体问题：（1）采用机器学习模型能否在影响因素与数字经济企业CVC投资的关系中产生新发现？（2）创新型产业激励对投资企业CVC投资产生什么影响，数字化转型紧迫性在其中起到什么作用？（3）CVC投资对投资企业数字技术创新有什么影响，知识产权保护和投资企业投资阶段在其中起到什么作用？在以上三个研究问题中，研究问题一是以数字创新为发展驱动力的数字经济企业切入，采用机器学习模型挖掘影响因素与CVC投资关系的新解释；研究问题二是基于研究问题一的文献回顾，发现已有影响因素研究不足驱动的，认为应关注创新型产业激励的作用，并将数字化转型作为情境，提供作为数字化时代CVC投资的背景理解；研究问题三是研究问题二的研究发现驱动的，探讨CVC投资能否促进投资企业数字技术创新，制度层面知识产权保护和组织层面企业投资阶段如何影响此关系。三个研究问题之间层层递进，共同服务于数字化时代CVC投资的影响因素和战略效应研究。 基于上述研究问题，首先，系统性回顾了CVC投资国内外的研究内容和主要理论。其次，通过整理沪深A股上市企业2010-2021年的CVC投资事件，搭建起一个企业CVC投资的大样本数据库。在此基础上，围绕上述研究问题展开实证分析与检验，得出以下主要研究结论：（1）三种机器学习模型（GBDT、XGBoost和RF）在预测数字经济企业CVC投资方面均优于传统的线性逻辑回归模型。在三个机器学习模型中，XGBoost是预测效果最好的模型；行业层以及企业创新相关的影响因素是预测数字经济企业CVC投资的关键影响因素；各个影响因素与数字经济企业CVC投资之间的关系呈现非线性特点；（2）创新型产业激励对投资企业CVC投资有显著的正向影响；企业数字化转型内外部紧迫性正向调节创新型产业激励与CVC投资之间的关系；（3）CVC投资对投资企业数字技术创新有显著的正向影响；投资企业所在地的知识产权保护水平负向调节CVC投资与投资企业数字技术创新之间关系；投资企业投资阶段也负向调节CVC投资与投资企业数字技术创新之间关系。 通过对上述研究问题的解答，本研究在CVC投资研究领域的贡献包括：（1）丰富了CVC投资影响因素的研究文献。主要体现在以下两方面：一是基于机器学习模型发现影响因素与CVC投资的新关系，为已有研究影响因素与CVC投资多线性关系为主的结论提供了新解释；二是通过研究创新型产业激励与CVC投资的因果关系，并将数字化转型作为一种情境，也丰富了CVC投资影响因素的研究文献；（2）丰富了CVC投资战略效应的研究文献。通过利用可供性理论，发现CVC投资影响投资企业数字技术创新。同时，地区知识产权保护和投资企业投资阶段影响可供性作用的发挥。对于制度理论和可供性理论，本研究的贡献为：（1）将制度理论和可供性理论应用于新的研究领域，拓宽了理论的研究情境；（2）将制度理论应用于CVC投资影响因素研究，为该领域研究增加了新的因果解释机制（不同于技术创新和市场拓展机制）；（3）将可供性理论应用于CVC投资战略效应研究，为该领域研究增加了新的因果解释机制（不同于组织学习机制）。
英文摘要：	In the context of the rapid development of the digital economy, emerging digital technologies such as artificial intelligence, blockchain, cloud computing, and big data are deeply penetrating various industries. The speed and complexity of technological transformation bring a multitude of uncertainties to organizations, which in turn compels them to constantly adopt various means to cope with these uncertainties to enhance competitiveness. Many firms turn to external activities, such as alliances, joint ventures, mergers and acquisitions, and CVC investments. The choice and implementation of any of these strategies can impact the construction of a firm's competitive advantage. CVC investments, as an increasingly valued form of corporate entrepreneurship, exhibit unique advantages. CVC investment refers to the minority equity investments made by large corporations into independently operated entrepreneurial ventures (Dushnitsky, 2012). Firms making CVC investments can afford to invest minimal resources in entrepreneurial ventures when resources are scarce, with the option to increase or terminate investments based on the development status of the entrepreneurial ventures, offering greater flexibility compared to alliances, joint ventures, and mergers and acquisitions. Currently, China has grown into the world's second-largest CVC market. As a newcomer to CVC investment, China has witnessed an increasing emphasis on CVC investments by firms against the backdrop of the continuous development of China's equity investment market. These firms aim to apply CVC investments for strategic drives, empowerment of core businesses, and layout in emerging industries. In the context of the digital era, on one hand, countries globally place a high value on the development of digital technologies, considering the promotion of firm digital upgrading as a crucial lever for enhancing national competitiveness and fostering new drivers of economic growth. Digitalization has transformed from an "optional" choice for some leading firms to a "mandatory" choice for many firms. On the other hand, entrepreneurial ventures in the digital sector have become significant targets for investment institutions. When seeking appropriate CVC investment targets, investing firms have already placed a strategic focus on digital technologies. From the existing literature on CVC investments, current research has not focused on the topic of CVC investments in the digital age, which constitutes an important research topic at present. This study aims to filling this gap. Specifically, it explores the following three interrelated questions regarding the factors influencing CVC investments and their strategic effects in the digital age: (1) Can the adoption of machine learning models reveal new insights into the relationship between influencing factors and CVC investments by digital economy firms? (2) What impact do incentives in innovative industries have on the CVC investments of investing firms, and what role does the urgency of digital transformation play in this relationship? (3) What impact do CVC investments have on the digital technology innovation of the investing firms, and how do intellectual property protection and the investment stage of the investing firms influence this relationship? The first research question approaches from the perspective of digital economy firms driven by digital innovation, employing machine learning models to unearth new explanations for the relationship between influencing factors and CVC investments. The second research question builds on the literature review of the first question, identifying a gap in the existing research on influencing factors, suggesting a focus on the role of incentives in innovative industries, and considering digital transformation as a contextual factor, thereby providing a background understanding for CVC investments in the digital age. The third research question, driven by the findings of the second, investigates whether CVC investments can foster digital technology innovation in investing firms and how institutional-level intellectual property protection and organizational-level investment stages affect this relationship. These three questions progressively build upon each other, collectively contributing to the research on the influencing factors and strategic effects of CVC investments in the digital age. Based on the aforementioned research questions, this study first systematically reviews the domestic and international research content and main theories related to CVC investments. Second, by compiling CVC investment events of Shanghai and Shenzhen A-share listed firms from 2010 to 2021, a large-sample database of CVC investments was constructed. On this basis, empirical analyses and tests were conducted around the above research questions, deriving the following main conclusions: (1) Three machine learning models (GBDT, XGBoost, and RF) all outperform the traditional linear logistic regression model in predicting CVC investments by digital economy firms. Among the three machine learning models, XGBoost demonstrates the best predictive performance. Industry-level and firm innovation-related influencing factors are key in predicting CVC investments by digital economy firms. The relationships between various influencing factors and CVC investments by digital economy firms exhibit non-linear characteristics. (2) Incentives in innovative industries have a significant positive impact on CVC investments by investing firms. The internal and external urgency of firms' digital transformation positively moderates the relationship between incentives in innovative industries and CVC investments. (3) CVC investments have a significant positive impact on the digital technology innovation of investing firms. The level of intellectual property protection in the investing firms' locations negatively moderates the relationship between CVC investments and digital technology innovation of investing firms. The investment stage of the investing firms also negatively moderates the relationship between CVC investments and digital technology innovation of the investing firms. Through addressing the aforementioned research questions, this study contributes to the field of CVC include: (1) Enriching the research literature on the influencing factors of CVC investments. This is mainly reflected in two aspects: first, by discovering new relationships between influencing factors and CVC investments based on machine learning models, providing new interpretations for the predominantly multilinear relationships between existing research factors and CVC investments; second, by examining the causal relationship between incentives in innovative industries and CVC investments, and considering digital transformation as a context, it further enriches the literature on the influencing factors of CVC investments. (2) Enhancing the research literature on the strategic effects of CVC investments. Applying affordance theory, it was found that CVC investments influence the digital technology innovation of investing firms. At the same time, regional intellectual property protection and the investment stage of investing firms affect the manifestation of affordances functions. The contributions of this study to institutional theory and affordance theory are as follows: (1) Expanding the theoretical research context by applying institutional theory and affordance theory to a novel research domain. (2) By introducing institutional theory to the study of influencing factors of CVC investments, it adds a new causal explanation mechanism to the field (different from technology innovation and market expansion mechanisms). (3) By introducing affordance theory to the study of the strategic effects of CVC investments, it adds a new causal explanation mechanism to the field (different from organizational learning mechanisms).
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