Evaluation of Logistics Service Quality Based on Text Mining
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Logistics Service Quality (LSQ) evaluation is a key issue in operations management, especially in the post-pandemic era with greater flexibility and diverse consumer demands. Companies now require real-time LSQ assessment tools. While recent studies use text mining to identify LSQ factors, they often lack analysis of factor importance and relationships, limiting practical insights. This study collected customer reviews on 11 major Chinese logistics firms from Koubei.com and applied LDA topic modeling to extract LSQ factors. A negative binomial regression linked these factors to customer ratings, revealing that delivery speed and service attitude positively affect LSQ, while logistics processes, last-mile delivery, economic efficiency, responsiveness, professionalism, and reliability have negative impacts. Co-occurrence network analysis further provided practical strategies to improve LSQ.
Abstract
Logistics Service Quality (LSQ) evaluation is a key issue in operations management, especially in the post-pandemic era with greater flexibility and diverse consumer demands. Companies now require real-time LSQ assessment tools. While recent studies use text mining to identify LSQ factors, they often lack analysis of factor importance and relationships, limiting practical insights. This study collected customer reviews on 11 major Chinese logistics firms from Koubei.com and applied LDA topic modeling to extract LSQ factors. A negative binomial regression linked these factors to customer ratings, revealing that delivery speed and service attitude positively affect LSQ, while logistics processes, last-mile delivery, economic efficiency, responsiveness, professionalism, and reliability have negative impacts. Co-occurrence network analysis further provided practical strategies to improve LSQ.