跨国电商人才来华就业意愿及其行为决策机理——基于决策树方法的分析

The Mechanism of Willingness and Behavioral Decision Making of Cross-Border E-Commerce Talents to Work in China—Analysis Based on the Decision Tree Approach

摘要: 在全球经济互联互通日益增强的背景下，跨国电商人才作为连接不同国家和地区市场的桥梁，是我国经济高质量发展的重要力量。本研究基于决策树算法，对多变量进行系统分析，构建预测模型及其预测规则。通过问卷形式收集1894份数据，使用决策树机器学习方法分析该数据。探索自变量对跨国电商人才来华就业意愿的预测作用。研究结果发现，留学经历、是否在华、工作状态这几个自变量能够预测跨国电商人才来华就业意愿，预测准确率达76.8%，效果较好。探索用决策树的方法建立预测来华就业意愿模型，对促进来华就业意愿有重要作用，以期进一步推动跨国电商人才来华就业意愿，完善我国跨国电商国际人才生态圈，推进国际数字人才枢纽建设。

Abstract: In the context of increasing global economic connectivity, transnational e-commerce talents, as a bridge connecting different national and regional markets, are an important force for high-quality development of China’s economy. This study employs decision tree algorithms to systematically analyze multiple variables and construct predictive models along with their prediction rules. Data from 1894 respondents were collected through questionnaires and analyzed using decision tree machine learning methods. The study explores the predictive power of independent variables on the willingness of cross-border e-commerce talent to work in China. The findings reveal that factors such as study abroad experience, presence in China, and employment status can predict the willingness of cross-border e-commerce talent to work in China, with an accuracy rate of 76.8%, indicating a good effect. Exploring the use of decision trees to establish a model for predicting willingness to work in China is significant for promoting such willingness, aiming to further enhance the willingness of cross-border e-commerce talent to work in China, improve the international talent ecosystem for cross-border e-commerce in China, and advance the construction of international digital talent hubs.