企业的数字化转型如何发展？——基于机器学习的证据

How Can a Company’s Digital Transformation Progress?—Evidence Based on Machine Learning

摘要: 数字化转型是推动制造业高质量发展的关键路径，对提升企业资源配置效率与核心竞争力具有深远影响。多数企业虽然认识到数字化转型的重要性，但对企业如何发展数字化，其关键影响因素是什么仍模糊不清。本文基于2015~2024年中国A股上市公司数据，采用XGBoost等机器学习方法，系统识别企业数字化转型的关键影响因素，并分析其非线性影响机制。研究发现：技术人员占比、行业竞争强度与专利数量是影响数字化转型最为重要的三大因素，且均存在显著的非线性特征。具体而言，技术人员占比在突破30%阈值后呈现边际效应递增；行业竞争强度表现为“倒U型”影响，拐点位于HHI = 0.25附近；专利数量则具有持续正向但边际递减的效应。此外，XGBoost模型在预测数字化转型方面显著优于传统回归方法。基于此，本文从强化技术人才储备、优化市场竞争环境、推动高质量专利转化等方面提出政策建议，为政府与企业推动数字化转型提供理论依据与实践参考。

Abstract: Digital transformation is a key path to promoting high-quality development of the manufacturing industry, which has a profound impact on improving the efficiency of enterprise resource allocation and core competitiveness. Although most companies recognize the importance of digital transformation, the key influencing factors on how they develop digitalization are still unclear. This article is based on data from Chinese A-share listed companies from 2015 to 2024, using machine learning methods such as XGBoost to systematically identify the key influencing factors of enterprise digital transformation and analyze their nonlinear impact mechanisms. Research has found that the proportion of technical personnel, industry competition intensity, and number of patents are the three most important factors affecting digital transformation, and all have significant nonlinear characteristics. Specifically, the proportion of technical personnel shows an increasing marginal effect after exceeding the 30% threshold; the intensity of industry competition shows an “inverted U-shaped” impact, with the inflection point located around HHI = 0.25; the number of patents has a sustained positive but marginally decreasing effect. In addition, the XGBoost model is significantly superior to traditional regression methods in predicting digital transformation. Based on this, this article proposes policy recommendations from strengthening the reserve of technical talents, optimizing the market competition environment, and promoting high-quality patent conversion, providing theoretical basis and practical reference for the government and enterprises to promote digital transformation.