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

Lim, M. K. , Li, Y. and Song, X. (2021) Exploring customer satisfaction in cold chain logistics using a text mining approach. Industrial Management and Data Systems, 121(12), pp. 2426-2449. (doi: 10.1108/IMDS-05-2021-0283)

Abstract

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.

• Item Type: Articles
• Additional Information: This research is funded by the Chongqing Science and Technology Commission (cstc2019jscxmsxmX0189).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lim, Professor Ming
• Authors: Lim, M. K., Li, Y., and Song, X.
• College/School: College of Social Sciences > Adam Smith Business School > Management
• Journal Name: Industrial Management and Data Systems
• Publisher: Emerald
• ISSN: 0263-5577
• ISSN (Online): 1758-5783
• Published Online: 20 August 2021