Exploring customer satisfaction in cold chain logistics using a text mining approach

Ming Lim, Yan Li, Xinyu Song

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
316 Downloads (Pure)


With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

Research limitations/implications
The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

Original languageEnglish
Pages (from-to)2426-2449
Number of pages24
JournalIndustrial Management and Data Systems
Issue number12
Early online date20 Aug 2021
Publication statusPublished - 10 Nov 2021

Bibliographical note

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.


Chongqing Science and Technology Commission (cstc2019jscx-msxmX0189)


  • Cold chain logistics
  • Customer satisfaction
  • Customer-generated review
  • Text mining
  • Topic modelling
  • Sentiment analysis


Dive into the research topics of 'Exploring customer satisfaction in cold chain logistics using a text mining approach'. Together they form a unique fingerprint.

Cite this