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Title Page

Contents

Abstract 12

Chapter 1. Introduction 14

1.1. Research Background 14

1.2. Research Questions and Objectives 15

1.3. Research Structure 18

1.4. Contributions 19

Chapter 2. Analysis of Recent Changes in Consumer Perception of Cold Chain E-commerce Logistics Service: Focusing on Frozen Seafood Products 22

ABSTRACT 22

2.1. Introduction 22

2.2. Literature Review 24

2.3. Methodology and Data 27

2.3.1. Research Framework 27

2.3.2. Topic Analysis 28

2.3.3. Key Terms Extraction 29

2.3.4. Data Collection 30

2.4. Results and Analysis 32

2.4.1. Analysis of Changes in Review Frequency and Sentiment 32

2.4.2. Analysis of Changes in Complaint Issues 35

2.4.3. Analysis of Changes in Consumer Preferences for Cold Chain Service 41

2.4.4. Analysis of Changes in Consumer Expectations for Cold Chain Service 46

2.5. Conclusions and Implications 48

2.5.1. Conclusions 48

2.5.2. Implications 49

Chapter 3. An Evaluation Methodology of Logistics Service Quality with the Online Reviews for E-commerce Cold Chain 53

ABSTRACT 53

3.1. Introduction 54

3.2. Current Methodologies 56

3.2.1. General Methods for Dimensions Determination 56

3.2.2. General Methods for Weight Evaluation 58

3.2.3. Characteristics of this Study 59

3.3. Proposed Model 61

3.3.1. Dimensions Determination 61

3.3.2. Weight Evaluation 63

3.4. Application Example of the Model 66

3.4.1. Data Collection 66

3.4.2. Modified SERVQUAL Model 66

3.4.3. Factors Evaluation 70

3.5. Implications and Conclusions 74

3.5.1. Theoretical Implications 74

3.5.2. Managerial Implications 74

3.5.3. Conclusions 74

Chapter 4. A Dynamic Customer Satisfaction Measurement Model for Logistics Service Quality in E-commerce Cold Chain 76

ABSTRACT 76

4.1. Introduction 76

4.2. Review Methodologies for Customer Satisfaction Measurement 79

4.2.1. General Methods for Indicator Weighting 79

4.2.2. General Methods for Customer Satisfaction Score 80

4.3. Proposed Model 82

4.3.1. Structure of the Proposed Model 82

4.3.2. Evaluation Criterion Determination 82

4.3.3. Sentiment Analysis 83

4.3.4. Indicator Weight Determination 84

4.3.5. Satisfaction Score 85

4.4. Application Example of the Model 87

4.4.1. Data Collection 87

4.4.2. Sentiment Scores 87

4.4.3. Indicators Weight 89

4.4.4. Customer Satisfaction Scores 91

4.5. Conclusion and Implications 93

4.5.1. Conclusion 93

4.5.2. Theoretical Implications 93

4.5.3. Practical Implications 94

Chapter 5. Conclusions 96

5.1. Conclusions 96

5.2. Future Research Directions 97

Reference 100

Appendix 117

List of Tables

Table 2-1. Modified SERVQUAL Model 31

Table 2-2. Complaint issues frequencies 36

Table 2-3. Top five consumer expectations for cold chain service before and during the COVID-19 pandemic 47

Table 3-1. Key tags extraction 67

Table 3-2. Factors description 69

Table 3-3. Result of entropy analysis 72

Table 4-1. The modified SERVQUAL model for cold chain 88

Table 4-2. Score of the evaluation index for each judgement level 92

List of Figures

Figure 1-1. Research questions and objectives 17

Figure 1-2. Research Structure 19

Figure 2-1. Research framework 28

Figure 2-2. The relative occurrence frequency distribution of the five dimensions from 2019 to 2021 32

Figure 2-3. The proportion of negative sentiment of each dimension in the reviews from 2018 to 2021 34

Figure 2-4. The proportion of positive sentiment of each dimension in the reviews from 2018 to 2021 35

Figure 2-5. Ratios of issues frequencies in 2019 37

Figure 2-6. Ratios of issues frequencies in 2020 39

Figure 2-7. Ratios of issues frequencies in 2021 40

Figure 2-8. Three attitudes changes of consumers towards cold chain packaging 43

Figure 2-9. Consumers' opinions changes towards online and offline shopping 44

Figure 2-10. Objections to online purchases (in percentage) 45

Figure 2-11. Reasons supporting online purchases (in percentage) 46

Figure 3-1. Model Structure 61

Figure 3-2. Average Sentiment Score 70

Figure 3-3. Frequency percentage of sentiment scores for each factor 71

Figure 4-1. Structure of the Proposed Model 82

Figure 4-2. Sentiment scores frequency ratio of each indicator 89

Figure 4-3. The sum absolute SHAP values for global measure of feature importance 90

Figure 4-4. Customer satisfaction scores of JD.com seafood cold chain service quality from 2018 to 2021 92

초록보기

The expanding global cold chain market presents an opportunity for the development of cold chain logistics. However, the sudden change and imbalance in online demand due to the COVID-19 pandemic may imply changes in consumer behavior and challenges to the quality of cold chain logistics service. To investigate this, an analysis of recent cold chain e-commerce logistics services was conducted in the Chapter 2, focusing on consumers who purchased frozen seafood products based on online reviews. Text mining and sentiment analysis were employed to compare Chinese consumers' evaluations, preferences, and expectations of cold chain e-commerce logistics services before and during the pandemic. The study identified changes in consumer sentiment and highlighted factors contributing to these changes. The findings indicate a decline in consumers' dissatisfaction with various dimensions of logistics services, improvements in complaint issues such as staleness and poor packaging, adoption of sustainable packaging with some concerns, increased acceptance of online cold chain shopping, and a shift in expectations towards speed and sanitized delivery during the pandemic. Practical implications for cold chain logistics service providers, e-commerce platforms, and retailers are discussed.

To better understand consumer needs in the rapidly developing cold chain e-commerce market, this study then proposes an evaluation model for logistics service quality from consumers' perspective using online reviews as data in the Chapter 3. The model incorporates TF-IDF, Word2Vec, and the SERVQUAL model to identify evaluation dimensions and factors. A multi-keyword query is developed to refine sentiment scores at the sentence level. The entropy weight method is then used to generate factor weights based on sentiment scores. The model is applied to online reviews of seafood products using cold chain logistics service on the JD.com platform in China, resulting in the identification of five dimensions and fourteen weighted factors. The implications for consumer behavior research and service improvement are discussed. This study contributes to a comprehensive and refined text-mining approach and provides significant weights of evaluation factors for reference.

To facilitate real-time monitoring and continuous improvement of service quality in e-commerce cold chain logistics, a dynamic evaluation model for customer satisfaction is proposed in the Chapter 4. The model integrates sentiment analysis of logistics service attributes within online reviews and overall ratings. The SHAP value methodology is used to derive weights for indicators, and a fuzzy comprehensive evaluation method is employed to establish an objective and comprehensive satisfaction score for customers. This score enables logistics service providers to conduct self-assessment and competitive benchmarking. The study presents several theoretical and practical implications in the field of customer satisfaction evaluation in e-commerce cold chain logistics.

This study has four contributions at the methodological level. Firstly, the application of machine learning, manual verification, and tailored queries has resulted in a more refined text-mining approach for online review analysis. Secondly, employing continuous sentiment scores and the entropy method to generate evaluation factor weights that enable a detailed investigation of sentiment fluctuations. Thirdly, incorporating a non-linear interpretable approach to allocate indicators' weights based on overall ratings and customer sentiment, providing a comprehensive and interpretable satisfaction evaluation. Fourthly, the dynamic model for assessing customer satisfaction based on online textual reviews. This study has two contributions at the practical level. Firstly, Augmenting the reference value of evaluation factors and weights in assessing Chinese e-commerce cold chain service quality by using a mature Chinese language sentiment analysis model and abundant online review data. Secondly, It examines Chinese customers' perceptions of e-commerce cold chain logistics service quality before and during the Covid-19.

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