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越览（153）——精读博士论文的5.4实证结果分析

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“越览（153）——精读博士论文

《供应链网络结构视角下的产业链韧性研究》

的5.4 实证结果分析

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Dear you, this is LearningYard Academy!

Today, the editor brings you

5.4 Analysis of empirical results

of "Yuelan (153)——Intensive reading of

the doctoral dissertation

‘Research on the resilience of

the supply chain from the perspective of

supply chain network structure’".

Welcome to visit!

一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍博士论文《供应链网络结构视角下的产业链韧性研究》的5.4 实证结果分析。

This issue of tweets will introduce 5.4 Analysis of empirical results of the doctoral thesis "Research on Industrial Chain Resilience from the Perspective of Supply Chain Network Structure" from three aspects: mind mapping, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

（一）描述性统计（Descriptive statistics）

本段内容基于彭博数据库中供应商客户数据，构建了汽车产业五级供应链网络，并对142家上市企业的样本特征进行了描述性统计分析。企业主要分布于东部地区（104家），其余为非东部地区（38家），其中国有企业占比约28%。在财务指标方面，企业股价和存货周转率的下跌幅度均值分别为43.5%和74.4%，显示出整体波动较大，差异显著。

This section is based on the supplier customer data in the Bloomberg database, constructs a five-level supply chain network for the automotive industry, and conducts a descriptive statistical analysis of the sample characteristics of 142 listed companies. The companies are mainly distributed in the eastern region (104 companies), and the rest are in non-eastern regions (38), of which state-owned enterprises account for about 28%. In terms of financial indicators, the average decline in corporate stock prices and inventory turnover rates was 43.5% and 74.4% respectively, showing a large overall fluctuation and significant differences.

结构性指标显示，样本企业在度数中心性、介数中心性、离心度、接近中心性、特征向量中心性及聚类系数等方面也存在显著差异，表明其在供应链网络中的位置和结构特征高度异质。

Structural indicators show that the sample companies also have significant differences in degree centrality, betweenness centrality, eccentricity, closeness centrality, eigenvector centrality and clustering coefficient, indicating that their positions and structural characteristics in the supply chain network are highly heterogeneous.

（二）回归结果分析（Regression results analysis）

在回归分析前，研究通过SPSS软件进行相关性检验，结果显示主要自变量间的相关系数均较低，方差膨胀因子小于3，说明不存在严重共线性问题。随后采用普通线性回归模型分析供应链网络结构对企业组织韧性的影响。

Before the regression analysis, the study conducted a correlation test using SPSS software, and the results showed that the correlation coefficients between the main independent variables were low, and the variance inflation factor was less than 3, indicating that there was no serious collinearity problem. The ordinary linear regression model was then used to analyze the impact of the supply chain network structure on the organizational resilience of enterprises.

结果表明，点度中心性、介数中心性和特征向量中心性对企业韧性均有显著正向作用，支持假设 H1、H2 和 H5，说明企业处于网络核心、资源丰富，有利于提升韧性。离心度对企业韧性虽显著，但与假设 H3 相反，呈正向影响，可能因远离中心的企业在危机中反应时间更充裕。接近中心性对绩效韧性有显著正向作用，但对运营韧性无显著影响，仅部分支持假设 H4。聚类系数对企业韧性影响不显著，假设 H6 不成立。

The results show that point degree centrality, betweenness centrality and eigenvector centrality all have significant positive effects on enterprise resilience, supporting hypotheses H1, H2 and H5, indicating that enterprises are located at the core of the network and are rich in resources, which is conducive to improving resilience. Although centrifugality has a significant effect on enterprise resilience, it is contrary to hypothesis H3 and has a positive impact, which may be because enterprises far from the center have more time to react in a crisis. Proximity centrality has a significant positive effect on performance resilience, but has no significant effect on operational resilience, only partially supporting hypothesis H4. The clustering coefficient has no significant effect on enterprise resilience, and hypothesis H6 does not hold.

（三）异质性分析（Heterogeneity analysis）

1. 区域异质性（Regional heterogeneity）

本部分通过区域异质性分析企业在供应链网络结构特征对韧性影响中的差异。研究发现，东部地区企业由于制度优势、信息获取便利和交通发达，供应链网络结构对绩效韧性的正向作用更明显，支持了原有假设。而在非东部地区，仅介数中心性和特征向量中心性与绩效韧性呈负相关，说明企业因过度依赖关键节点，反而增加风险暴露，削弱韧性。

This section analyzes the differences in the impact of supply chain network structure characteristics on resilience by regional heterogeneity. The study found that due to institutional advantages, convenient information acquisition and developed transportation, the positive effect of supply chain network structure on performance resilience is more obvious in enterprises in the eastern region, supporting the original hypothesis. In non-eastern regions, only betweenness centrality and eigenvector centrality are negatively correlated with performance resilience, indicating that enterprises increase risk exposure and weaken resilience due to over-reliance on key nodes.

在运营韧性方面，非东部地区与总体样本结果一致，点度中心性、介数中心性和特征向量中心性均有正向作用；而东部地区仅离心度显著正向影响运营韧性，说明企业因距离中心较远可延缓风险传递，从而提升应对能力。整体来看，区域位置显著影响网络结构对企业韧性的作用机制。

In terms of operational resilience, the results of non-eastern regions are consistent with those of the overall sample, with point degree centrality, betweenness centrality, and eigenvector centrality all having positive effects; while in the eastern region, only centrifugality significantly and positively affects operational resilience, indicating that enterprises can delay risk transmission due to their distance from the center, thereby improving their coping capabilities. Overall, regional location significantly affects the mechanism of network structure on enterprise resilience.

2. 股权性质异质性（Heterogeneity of equity）

本部分从股权性质出发，分析网络结构特征对企业组织韧性的影响差异。研究发现，非国有企业因缺乏政府保护，更依赖自身网络资源应对突发事件，其绩效韧性显著受点度中心性等网络结构影响；而国有企业受政府政策与扶持影响较大，网络结构对其绩效韧性作用不显著。

This section analyzes the differences in the impact of network structure characteristics on corporate organizational resilience based on the nature of equity. The study found that non-state-owned enterprises, due to the lack of government protection, rely more on their own network resources to deal with emergencies, and their performance resilience is significantly affected by network structures such as point centrality; while state-owned enterprises are greatly affected by government policies and support, and the network structure has no significant effect on their performance resilience.

在运营韧性方面，非国有企业对风险规避更为敏感，虽处于网络核心，但出于稳健经营考虑，网络结构对运营韧性影响不显著。相比之下，国有企业中除聚类系数外，其他网络特征均显著提升运营韧性，原因在于资源与关系网络可缓解其行政流程缓慢、运营效率低的问题。总体而言，网络结构对企业韧性的作用受股权性质显著影响。

In terms of operational resilience, non-state-owned enterprises are more sensitive to risk aversion. Although they are at the core of the network, the network structure has no significant impact on operational resilience due to the consideration of sound operation. In contrast, except for the clustering coefficient, other network characteristics of state-owned enterprises significantly improve operational resilience, because resource and relationship networks can alleviate the problems of slow administrative processes and low operational efficiency. In general, the effect of network structure on enterprise resilience is significantly affected by the nature of equity.

（四）稳健性分析（Robustness analysis）

为验证研究结果的稳健性，本文从三个方面进行了检验：

To verify the robustness of the research results, this paper conducted tests from three aspects:

1. 控制遗漏变量：引入客户集中度和供应商集中度作为控制变量，结果显示主要网络结构特征对绩效韧性和运营韧性依然具有显著正向影响，说明结论稳健。

1. Controlling omitted variables: Customer concentration and supplier concentration are introduced as control variables. The results show that the main network structure characteristics still have a significant positive impact on performance resilience and operational resilience, indicating that the conclusions are robust.

2. 更换变量度量方式：分别调整股价下跌幅度和存货周转率的计算方法，回归结果与原结论一致，进一步验证了网络结构特征对企业韧性的正向作用。

2. Changing the variable measurement method: The calculation methods of stock price decline and inventory turnover rate were adjusted respectively. The regression results were consistent with the original conclusions, further verifying the positive effect of network structure characteristics on corporate resilience.

3. 倾向得分匹配：基于股价恢复情况将企业分组，采用PSM方法控制潜在混杂变量。匹配后样本协变量差异不显著，回归结果与原模型一致，说明网络结构特征对企业韧性的影响具有较强的因果解释力，研究结论稳健可靠。

3. Propensity score matching: The companies were grouped based on their stock price recovery, and the PSM method was used to control potential confounding variables. After matching, the differences in sample covariates were not significant, and the regression results were consistent with the original model, indicating that the impact of network structure characteristics on corporate resilience has a strong causal explanatory power, and the research conclusions are robust and reliable.

（五）组态分析（Configuration analysis）

为进一步探究供应链网络结构特征对企业韧性的组态效应，本文在实证分析基础上引入模糊集定性比较分析（fsQCA）方法，弥补传统回归方法难以揭示变量间复杂交互关系的局限，系统识别多重因素组合对高企业韧性的影响路径。

In order to further explore the configuration effect of supply chain network structure characteristics on enterprise resilience, this paper introduces the fuzzy set qualitative comparative analysis (fsQCA) method based on empirical analysis to make up for the limitation of traditional regression method that it is difficult to reveal the complex interaction relationship between variables, and systematically identify the impact path of multiple factor combinations on high enterprise resilience.

首先，使用熵权法构建企业韧性指标，并参考既有研究设定变量的校准阈值，完成模糊集隶属度转换。随后，构建真值表并进行必要性分析，发现无单一变量构成企业韧性的必要条件，说明企业韧性由多种因素共同作用形成。

First, the entropy weight method is used to construct the enterprise resilience index, and the calibration threshold of the variables is set with reference to existing research to complete the fuzzy set membership conversion. Subsequently, a truth table is constructed and a necessity analysis is performed, and it is found that no single variable constitutes the necessary condition for enterprise resilience, indicating that enterprise resilience is formed by the combined action of multiple factors.

接着，进行充分性分析，识别出三种高企业韧性的典型组态路径，具体如下：

Next, we conducted a sufficiency analysis and identified three typical configuration paths for high enterprise resilience, as follows:

1. 资源控制型（M1）：高点度中心性、高介数中心性和高特征向量中心性的组合，强调企业处于网络核心位置、掌握资源和信息，从而提升韧性。

1. Resource control type (M1): A combination of high vertex centrality, high betweenness centrality, and high eigenvector centrality, which emphasizes that the enterprise is at the core of the network and controls resources and information, thereby improving resilience.

2. 风险规避型（M2）：高介数中心性、高离心度和高特征向量中心性，说明企业虽位于关键路径但离中心较远，可有效延迟风险传导，增强应对能力。

2. Risk-averse type (M2): High betweenness centrality, high eccentricity, and high eigenvector centrality, indicating that although the enterprise is located on the critical path, it is far away from the center, which can effectively delay risk transmission and enhance response capabilities.

3. 资源交换型（M3）：高介数中心性、高接近中心性和高特征向量中心性，突出企业与重要节点紧密连接，便于资源快速流通与合作，提高组织韧性。

3. Resource exchange type (M3): high betweenness centrality, high closeness centrality and high eigenvector centrality, highlighting the close connection between enterprises and important nodes, facilitating rapid circulation and cooperation of resources, and improving organizational resilience.

综上，企业组织韧性的形成具有多重路径与因果复杂性，不同网络结构特征在不同情境下通过不同机制共同作用于韧性提升。fsQCA分析从组态视角揭示了网络结构对企业韧性影响的深层逻辑。

In summary, the formation of corporate organizational resilience has multiple paths and causal complexity, and different network structure characteristics work together to enhance resilience through different mechanisms in different situations. fsQCA analysis reveals the deep logic of the impact of network structure on corporate resilience from a configuration perspective.

四、知识补充（Knowledge supplement）

内生性检验是计量经济学中用来检验模型中解释变量（自变量）是否与误差项存在相关关系的方法。若这种相关性存在，则说明模型存在内生性问题，导致估计结果偏误且不一致，影响研究结论的有效性。

Endogeneity test is a method used in econometrics to test whether the explanatory variables (independent variables) in the model are correlated with the error term. If such a correlation exists, it means that the model has an endogeneity problem, which leads to biased and inconsistent estimation results, affecting the validity of the research conclusions.

（一）内生性通常源于以下三种原因（Endogeneity usually arises from the following three reasons:）

1. 遗漏变量（Omitted Variable）

有影响因变量的关键变量未被纳入模型，而这些变量又与自变量相关。

Key variables that affect the dependent variable were not included in the model, and these variables were correlated with the independent variables.

2. 同时性（Simultaneity）

自变量和因变量之间存在相互因果关系，如供需模型中价格既影响又受供需影响。

There is a mutual causal relationship between the independent and dependent variables, such as in the supply and demand model, price both affects and is affected by supply and demand.

3. 测量误差（Measurement Error）

自变量存在测量误差，导致其与误差项相关。

The independent variable has measurement errors, which causes it to be correlated with the error term.

（二）常见的内生性检验方法（Common endogeneity test methods）

1. 工具变量法（Instrumental Variable, IV）

通过寻找一个与内生变量高度相关但与误差项无关的变量作为工具变量，进行两阶段最小二乘法（2SLS）回归。

A two-stage least squares (2SLS) regression is performed by finding a variable that is highly correlated with the endogenous variable but unrelated to the error term as an instrumental variable.

检验方法（Inspection method）

Hausman 检验：比较OLS与2SLS估计值，显著差异则说明存在内生性。

Hausman test: compare OLS and 2SLS estimates, significant differences indicate endogeneity.

Durbin-Wu-Hausman检验：常用于内生性整体检验。

Durbin-Wu-Hausman test: often used for overall endogeneity test.

2. 控制函数法（Control Function Approach）

在第一阶段拟合内生变量的残差，并在第二阶段回归中将其引入，以控制内生性。

The residuals of the endogenous variables are fitted in the first stage and introduced in the second stage regression to control for endogeneity.

3. 倾向得分匹配法（Propensity Score Matching, PSM）

虽不直接解决内生性，但可减轻选择偏差对因果推断的干扰。

Although it does not directly solve endogeneity, it can reduce the interference of selection bias on causal inference.

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