Intelligent performance evaluation of e-commerce express services using machine learning: A case study with quantitative analysis

Huwei Liu

doi:10.1016/j.eswa.2023.122511

2024-04-01

PRE

DOI：10.1016/j.eswa.2023.122511

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摘要

En 中文

Amidst the robust development of the service economy and information technology, the information age has significantly transformed consumption concepts and service demands. The requirements for logistics services from customers have become increasingly stringent. Beyond price and speed, quality has steadily emerged as the most crucial factor. When evaluating express services, experts primarily choose indicators from an enterprise perspective, mainly overlooking customer perception. The purpose of this paper is to establish a key index system for assessing the quality of express services aimed at improving the service quality of express deliveries within the development mode of intelligent logistics. In terms of methodology, we proposed a quantitative and qualitative e-commerce logistics service evaluation system. Specifically, we developed a key index system for assessing the quality of express services based on the theory of the six senses and the selection from an index library at first. And then, we ascertained the weight of each index in the evaluation by analyzing the proportion of customer comments. We employed analysis methods of machine learning, text data processing, mathematical statistics, and big data technology to determine the evaluation index and evaluation model of express service. Finally, our primary findings from the empirical verification and analysis of a real express enterprise indicate that this evaluation model can effectively assess the quality of express services. Our model can evaluate the current service quality and predict the quality of future services. We have derived some interpretations and conclusions based on these analyses and findings. Firstly, the level of customer perception is of great importance in the evaluation of express service quality. Secondly, our model can provide valuable feedback for express enterprises to improve their services. Lastly, we proposed corresponding improvement strategies to enhance the service quality of express enterprises under the intelligent logistics development mode.

Keyword:

Express enterprise

Evaluation system

Text mining

Machine learning