电子商务环境下生鲜电商冷链物流服务质量评价研究

Research on the Evaluation of Cold Chain Logistics Service Quality for Fresh E-Commerce in the E-Commerce Environment

摘要: 在电商行业蓬勃发展的当下，生鲜电商面临着物流服务质量的挑战。该研究聚焦于构建适用于电子商务环境的生鲜产品冷链物流服务质量评价体系，以PZB模型的感知–期望差距为基础，综合SERVQUAL量表及生鲜产品特点，搭建起涵盖专业性、可靠性、响应性、便利性、信息性5个一级指标和19个二级指标的评价体系。运用模糊最优最劣法确定指标权重，通过构建感知–期望差距指数(PEGI)对服务水平进行量化评估，并以京东生鲜为例展开实证分析。结果显示，不同指标的服务质量差距各异，如R11、C11、P11等指标的PEGI值较高，服务质量亟待提升；P12、P13等指标的PEGI值较低，表现相对较好。研究建议企业重点优化高PEGI值指标，并提出了相关建议，这一研究为生鲜电商冷链物流优化服务质量、合理分配资源提供了有力支撑。

Abstract: Amid the rapid development of the e-commerce industry, fresh e-commerce faces challenges in logistics service quality. This study focuses on constructing a quality evaluation system for fresh product cold chain logistics suitable for the e-commerce environment. Based on the perception-expectation gap of the PZB model, and integrating the SERVQUAL scale with the characteristics of fresh products, an evaluation system is established, covering five primary indicators—professionalism, reliability, responsiveness, convenience, and informativeness—and 19 secondary indicators. The Fuzzy Best-Worst Method (FBWM) is applied to determine indicator weights. A Perception-Expectation Gap Index (PEGI) is constructed to quantitatively assess service levels, with JD Fresh as a case study for empirical analysis. The results show varying service quality gaps across different indicators; for example, indicators such as R11, C11, and P11 have higher PEGI values, indicating an urgent need for improvement, while indicators like P12 and P13 have lower PEGI values, reflecting relatively better performance. The study suggests that companies should prioritize optimizing indicators with high PEGI values and allocate resources reasonably. This research provides strong support for fresh e-commerce to enhance service quality.