基于主成分分析与系统聚类分析的数字化企业经济绩效研究

A Study on Digital Enterprise Economic Performance Based on PCA and Hierarchical Clustering Analysis

随着数字化转型的深入,企业经济绩效的评估与提升成为重要议题。本研究旨在探讨数字化企业经济绩效的评估方法,通过主成分分析(PCA)和系统聚类分析,构建了一个综合性的研究框架。选取50家中国上市数字化企业为样本,基于其2022年的财务数据,运用PCA降维提取关键绩效指标,并通过系统聚类分析对企业进行分类和比较。研究发现,四个主成分累积解释了83.664%的方差,有效地代表了原始数据集中的主要信息。系统聚类分析揭示了企业间经济绩效的显著差异,其中上海钢联电子商务股份有限公司表现最为突出,而中信尼雅葡萄酒股份有限公司则需要进一步分析其绩效较低的原因。本研究为数字化企业经济绩效的评估和提升提供了新的视角和方法,同时指出未来研究可以扩展到非财务指标和全球范围的企业,以增强研究的普遍性和适用性。With the deepening of digital transformation, the evaluation and improvement of enterprise economic performance have become important issues. This study aims to explore the evaluation method of digital enterprise economic performance and constructs a comprehensive research framework through principal component analysis (PCA) and hierarchical clustering analysis. Fifty Chinese listed digital enterprises were selected as samples, and key performance indicators were extracted by PCA dimensionality reduction based on their financial data in 2022. The enterprises were classified and compared by hierarchical clustering analysis. The study found that four principal components explained 83.664% of the variance, effectively representing the main information in the original dataset. Hierarchical clustering analysis revealed significant differences in economic performance among enterprises, with Shanghai Ganglian E-commerce Co., Ltd. performing most prominently, while CITIC Ninya Wine Co., Ltd. needs further analysis of the reasons for its low performance. This study provides new perspectives and methods for the evaluation and improvement of digital enterprise economic performance, and points out that future research can be expanded to non-financial indicators and global enterprises to enhance the universality and applicability of the research.