Analysis of Data Envelopment Analysis (DEA) Application in E-Commerce Performance Evaluation

数据包络分析(DEA)作为一种非参数效率评估方法，在电子商务企业绩效评估中展现出独特的优势。本文系统梳理了DEA模型的基本原理及其在电子商务领域的应用，包括CCR、BCC、SBM、三阶段DEA和超效率DEA等模型的特点及适用场景。研究表明，DEA能够有效处理多投入多产出的复杂系统，为电商企业提供相对效率评估，并识别资源配置的优化方向。然而，其在数据质量、模型选择及结果解释性方面仍面临挑战。未来，结合大数据技术、多方法融合以及强化理论与实践互动，有望进一步提升DEA在电商绩效评估中的精确性和实用性，为行业高质量发展提供支持。Data Envelopment Analysis (DEA), as a non-parametric efficiency evaluation method, demonstrates unique advantages in assessing the performance of e-commerce enterprises. This paper systematically reviews the fundamental principles of DEA models and their applications in the e-commerce sector, including the characteristics and applicable scenarios of models such as CCR, BCC, SBM, three-stage DEA, and super-efficiency DEA. Research indicates that DEA can effectively handle complex systems with multiple inputs and outputs, providing relative efficiency evaluations for e-commerce enterprises and identifying optimization directions for resource allocation. However, challenges remain in terms of data quality, model selection, and interpretability of results. Moving forward, integrating big data technologies, combining multiple methodologies, and strengthening the interaction between theory and practice are expected to further enhance the accuracy and applicability of DEA in e-commerce performance evaluation, thereby supporting the high-quality development of the industry.