Research on the Evaluation of Fresh Product E-commerce Logistics Service Quality from the Perspective of Big Data

In the context of the big data era, e-commerce enterprises must delve into consumer demands. Evaluation results of logistics service quality derived from online comments can enhance the logistics service quality in a targeted manner, which is crucial for improving the competitiveness of fresh product e-commerce. In this research, an evaluative framework for fresh e-commerce logistics services has been developed by synthesizing insights from the SERVQUAL and LSQ models, taking into account the unique aspects of the fresh product e-commerce sector. Utilizing a Python-based crawler, this research gathers consumer online comments from the JD Fresh platform, aligns identified logistics-related keywords to specific evaluation metrics, and performs sentiment analysis on these targeted comments with the SnowNLP toolkit. The TF-IDF method is utilized to determine the weight of indicators, and the quality of JD's self-operated logistics services is assessed based on reliability, professionalism, timeliness, responsiveness, and empathy. The evaluation results reveal that JD Fresh maintains good performance in terms of reliability, professionalism, timeliness, and responsiveness, and suggests improvements in empathy, such as establishing real-time communication channels, developing online self-service systems, and providing personalized services at pick-up points. The research conclusions can provide references for fresh product e-commerce enterprises to optimize product logistics services and enhance competitiveness.