| 基于STERGM的风险投资网络演化动力研究 |
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| 引用本文： | 党兴华,张晨.基于STERGM的风险投资网络演化动力研究[J].科研管理,2022,43(5):182-190. |
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| 作者姓名： | 党兴华 张晨 |
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| 作者单位： | 1.西安理工大学经济与管理学院，陕西 西安710054；
2.西安邮电大学经济与管理学院，陕西 西安710061 |
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| 基金项目： | 国家自然科学基金：“风险投资网络社群形成机理及对投资绩效的影响研究”(71572146,2016.01—2019.12);“技术创新网络惯例复制机理及对创新催化的影响：悖论整合视角”(71902157,2020.01—2022.12);“面向‘技术-组织’颠覆的集群企业跨界能力递阶作用机理及对创新的影响”(71972154,2020.01—2023.12)； |
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| 摘 要： | 风险投资网络演化与网络形成和网络解散密切相关。本研究利用CVSource数据库构建风险投资网络，从机构属性、二元关系、网络结构等多维度提出影响风险投资网络形成和网络解散的假设变量，采用分离时间指数随机图模型（STERGM），探索我国风险投资网络的演化动力。研究发现，网络形成和网络解散的基础和演化驱动因子存在差异，网络形成是基于发现潜在伙伴的价值和匹配属性，而网络解散是基于对关系质量和未来价值的预期。具体而言，声誉、地理邻近性、结构嵌入和位置嵌入有利于网络关系的形成，且声誉和结构嵌入有利于网络关系持续；认知临近性和位置嵌入对网络关系解散有显著影响。研究结论有助于揭示风险投资网络演化的动力学机制。
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| 关 键 词： | 风险投资 网络演化 声誉 邻近性 网络嵌入性 STERGM |
| 收稿时间： | 2019-05-30 |
| 修稿时间： | 2020-01-06 |
Research on the evolution dynamics of venture capital network based on STERGM |
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| Dang Xinghua,Zhang Chen.Research on the evolution dynamics of venture capital network based on STERGM[J].Science Research Management,2022,43(5):182-190. |
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| Authors: | Dang Xinghua Zhang Chen |
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| Institution: | 1. School of Economics and Management, Xi′an University of Technology, Xi′an 710054, Shaanxi, China;
2. College of Economics and Management, Xi′an University of Posts & Telecommunications, Xi′an 710061, Shaanxi, China; |
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| Abstract: | The venture capital network is a complex and dynamic inter-organizational network that is gradually formed based on the accumulation of syndication. Venture capital firms use network relationships to search for investment opportunities, realize resource sharing, diversify investment risks, and improve investment performance. With the increasing competition in the venture capital industry, the role of venture capital networks has become increasingly significant. A high-quality venture capital network is conducive to the efficient allocation of regional innovation resources and the improvement of regional innovation economy development. Therefore, optimizing the structure of the venture capital network and grasping the evolution forces and trends of the venture capital network are effective driving forces for improving the efficient allocation of innovation resources and achieving the innovation-driven development strategy. However, the existing research on venture capital networks mostly focuses on the properties and effects of venture capital networks in static scenarios, and the black box on the evolution of venture capital networks has not yet been cracked. In addition, there are many theoretical and empirical studies on venture capital network formation, but there have been few rigorous studies that explore how venture capital networks dissolve. Therefore, optimizing the structure of the venture capital network and grasping the evolution forces and trends of the venture capital network are of great practical significance to China in improving the efficient allocation of innovation resources and realizing the innovation-driven development strategy.
In this research, we first select five main driving factors for the evolution of venture capital network according to previous researches. Second, we explore the impact of five driving factors on the formation and dissolution of venture capital network relationships and propose hypotheses. Reputation can increase the attractiveness of venture capital firms and facilitate the formation of syndication relationships; the network relationship formed by high-reputation VC firms tends to remain stable in view of maintaining a good reputation. Geographical proximity is beneficial to reduce the cost of information search and communication between venture capital firms, and improve the efficiency of information sharing, which affects the formation of relationships between venture capital firms; the reduction of the value of geographic proximity and the increase of alternative options easily lead to venture capital firms dissolve the network relationships. Cognitive proximity helps to generate trust between venture capital firms, improve the efficiency of information transfer and knowledge sharing, and reduce transaction costs, which are conducive to network relationships formation; however, cognitive proximity will increase the competition intensity between venture capital firms, thus increasing the chances of link dissolution. Venture capital firms with common partners are more likely to form a syndication relationship, causing a triad closure structure, and triad closure structures are more stable in the network. Venture capital firms occupying a good network position have more opportunities and better capabilities to establish network relationships, but when the network ties are formed, they lack the motivation to maintain them.
To understand how VC network evolves, we use the CVSource database to collect 3993 rounds of syndication investment made by Chinese venture capital firms during 2001–2015, and empirically investigate the dynamics of Chinese venture capital network. As venture capital network formation and network dissolution are simultaneously determined, we apply the separable temporal exponential-family random graph model (STERGM) method into our empirical study. The STERGM separates the network formation and dissolution processes, allows for the accurate designation of multiple processes that affect the dynamics of the network structure, and does not need the independent assumption that the traditional regression models need. Findings suggest that network relationships form when unfamiliar venture capital firms identify desirable and matching traits in potential partners. By contrast, network relationships dissolve when familiar venture capital firms reflect on the quality of their relationship and the expectation of future value of their relationship. Specifically, reputation, geographic proximity, structural embeddedness and positional embeddedness facilitate relationships formation, reputation and structural embeddedness suppress relationships dissolution, cognitive proximity and positional embeddedness promote relationships dissolution.
Our research has important theoretical implications in several folds. Firstly, this study expands and deepens the research on network evolution, revealing that the evolution of venture capital networks is driven by network relationships formation and network relationships dissolution. Secondly, our research breaks the original research paradigm of inferring the power of network relationships dissolution through the process of network relationships formation and finds that the dissolution of network relationships is a more complicated process than previously thought. Thirdly, it demonstrates the use of STERGM as a method for investigating formation and dissolution processes in venture capital network relationships.
This study provides the following guidance and reference for the networked development of VC industry. Venture capital firms should be aware of the need to balance short-term and long-term network relationships when configuring network. Short-term network relationships can bring immediate high returns to venture capital firms, while long-term network relationships can bring them potential long-term value. If venture capital firms want to obtain high short-term returns, they only need to adopt short-term strategies to judge the value of potential partners, and choose partners that can bring them short-term benefits. On the contrary, if long-term benefits are to be obtained from network relationships, rather than "a flash in the pan", venture capital firms need to start from the long-term strategic perspective at the beginning of the relationship and choose stable and valuable relationships that can promote the realization of long-term benefits, although they may sacrifice short-term benefits.
This study still has certain limitations, which provides a realistic basis and theoretical clues for future research. Firstly, although the direct effects of reputation, proximity, and network embeddedness on network evolution have been explored, due to the limitations of the method, this study lacks research on the interaction between factors and their impact on the evolution of venture capital networks. Secondly, the advantage of using syndication investment data in this study is that the data is highly available, but lacks an in-depth explanation of the behavioral logic of venture capital firms. Future research can increase interviews and questionnaire surveys of venture capital firms, and provide in-depth discussion on the evolution mechanism of venture capital networks. |
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| Keywords: | venture capital network evolution reputation proximity network embeddedness STREGM |
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