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Research on express service defect evaluation based on semantic network diagram and SERVQUAL model
Suishan Gu1, Kangyu Wang1, Lianyue Gao1
1School of Business and Management, Jilin University, Changchun, Jilin, China.
Frontiers in Public Health
|December 19, 2022
Summary
This study introduces a novel express service defect evaluation model using text mining and machine learning. It identifies "responsiveness" as the key area for improvement in express service quality.
Area of Science:
- Service Science
- Data Mining
- Computational Linguistics
Background:
- Online customer feedback is crucial for service quality assessment.
- Existing methods may not fully capture the nuances of express service defects.
- A systematic approach is needed to analyze and prioritize service improvements.
Purpose of the Study:
- To construct a defect evaluation model for express services.
- To identify and prioritize key areas for service quality improvement.
- To provide actionable insights for enhancing customer satisfaction.
Main Methods:
- Web crawling to collect online negative comments.
- Support Vector Machine (SVM) for emotion analysis.
- Linear Discriminant Analysis (LDA) for topic modeling and characteristic word extraction.
- SERVQUAL and Importance-Performance Analysis (IPA) models for defect classification and mapping.
Main Results:
- Extracted characteristic words of express service defects using LDA.
- Classified defects based on SERVQUAL dimensions (tangibility, reliability, responsiveness, assurance, empathy, economy).
- Identified "responsiveness" as the primary improvement direction, followed by reliability, assurance, and economy.
Conclusions:
- The developed model offers valuable insights for evaluating service industry defects.
- Prioritizing "responsiveness" defects can significantly improve overall service quality.
- The model provides a framework for data-driven service enhancement.
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