중국 전자상거래 추천 시스템특성과 소비자의 지각된 가치 속성이 구매의도에 미치는 영향 = The Impact of the Characteristics of China's E-commerce Recommendation System and Consumers' Perceived Value Attributes on Purchase Intention|RISS 상세보기

다국어 초록 (Multilingual Abstract)

With the rapid growth of E-commerce market, commodity information has grown exponentially, giving rise to the “information overload” and “selection overload” that has caused serious troubles to customers. Therefore, E-commerce recommendation system (ECRS) came into being. E-commerce recommendation system was an E-commerce platform that inferred the preferences and needed of potential customers according to their search history, purchase or ratings and other behavioral data, as well as the historical records of customers with similar characteristics, and then recommended products they may be interested in. It could help consumers improved decision-making efficiency, optimized shopping experience, provided precision marketing function for E-commerce platform to increase their sales revenue. Thanks to its efficiency in solving information overload and selection overload, the system has been widely used in recent years. In the mean time, relevant researches on the system by scholars had gone viral and borne fruitful results.
The existing research results in China focused more on the technical improvement and algorithm optimization of the recommendation system, although the accuracy of recommendation has been greatly improved. However, the consequence of one-sided pursuit of recommendation accuracy was that the recommended goods were lacked of variety, resulting in the system’s inability to meet the personalized interests and preferences of customers and low recommendation quality.
Therefore, it was urgent to explore an effective way to improve the recommended quality of the system from the perspective of customer perception.
At the same time, as perceived value was of dynamic, hierarchical and subjective nature, and lack of universally applied structure and model, therefore, so far, there were few studies that combine the dimension of perceived value with the characteristics of E-commerce recommendation system to explore the factors affecting purchase intention. The author took "E-commerce recommendation system", "perceived value" and "purchase intention" as the keywords to search on Chinese academic websites, and the number of documents obtained was still quite limited, which provided much room for academic research and innovation for this study.
Thus, this paper carried out an empirical research with the purpose of finding out the key factors of E-commerce recommendation system and perceived value that had impact on customers’ purchase intention.
Firstly, through careful literature review and initial analysis, the four first-order dimensions of "consistency", "diversity", "credibility" and "interactivity" were incorporated into the E-commerce recommendation system as independent variables, whereas the three first-order dimensions of "economic value", "emotional value" and "functional value" were incorporated into perceived value as intermediary variables. Customer purchase intention was the dependent variable. This paper constructed the research model built upon E-commerce recommendation system - perceived value - purchase intention, put forward four research hypotheses, and designed 8 scales with 35 items.
Secondly, the study used Spss 26.0, Amos 24.0 and other tools to analyzed 1068 effective questionnaires collected through the online platform in the following steps: step one, the descriptive statistical analysis of samples was carried out, and the difference was analyzed by t-test and analysis of variance; step two, common method biases, normality test, reliability and validity test for sample data was carried out; step three, correlation analysis, regression analysis and intermediary effect analysis were carried out for the correlation degree and influence degree between variables; step four, a test on the assumptions of the model was carried out.
The empirical analysis results showed that E-commerce recommendation system had a significant positive effect on customers' purchase intention, customers' perceived value, whereas perceived value had a significant positive effect on customers' purchase intention, and played a significant intermediary role between E-commerce recommendation system and customers' purchase intention. All research hypotheses were valid.
Based on this, the paper put forward some suggestions that E-commerce recommendation system can focused more on diversity and consistency to improved customer economic value and enhanced customer functional value from credibility and interactivity, which was conducted to upgrading the recommendation quality of the system and promoting the wider application of it to a certain extent. In the academic aspect, the research creatively put forward the four characteristics of E-commerce recommendation system, namely, consistency, credibility, diversity, interactivity, it also put forward factors that can affected customers' purchase intention the most, namely perceived value, emotional value and functional value. With some degree of innovative and practical significance, this research constructed the research model centered on E-commerce recommendation system - perceived value - purchase intention, and to a certain extent, it enriched the weak gaps of the existing research, provided a certain theoretical support for the follow-up research.

번역하기

목차 (Table of Contents)

Chapter 1 Introduction 1
1.1 Research Background and Objective 1
1.2 Research Method and Contents 5
1.3 Research Significance 7

Chapter 1 Introduction 1
1.1 Research Background and Objective 1
1.2 Research Method and Contents 5
1.3 Research Significance 7
Chapter 2 Literature Review 9
2.1 E-commerce Recommendation System 9
2.1.1 The Definition of E-commerce Recommendation System 9
2.1.2 Types of E-commerce Recommendation Systems 10
2.1.3 Characteristics of E-commerce Recommendation System 14
2.2 Perceived value 18
2.2.1 The Definition of Perceived Value 18
2.2.2 The Model of Perceived Value 19
2.2.3 The Dimensions of Perceived Value 24
2.3 Purchase Intention 27
2.3.1 Definition of Purchase Intention 27
2.3.2 Theoretical Basis of Purchase Intention 28
Chapter 3 Research Design　and Model 34
3.1 Operational Definition and Measurements of Variables 34
3.1.1 Independent Variables 34
3.1.2 Mediating Variables 39
3.1.3 Dependent Variable 43
3.2 Research Model and Research Hypothesis 44
Chapter 4 Analysis and Results 57
4.1 Data Collection and Analysis 57
4.1.1 Respondents and Data Collection 57
4.1.2 Ethical Considerations 59
4.1.3 Data Inspection and Data Analysis 60
4.2 Demographic Description 65
4.2.1 Basic Characteristics of Respondents 65
4.2.2 Variable Description 67
4.3 Common Method Bias Test 69
4.4 Correlation Analysis and Discriminant Validity 70
4.5 Reliability and Validity 71
4.5.1 Reliability Analysis 71
4.5.2 Exploratory Factor Analysis 74
4.5.3 Confirmatory Factor Analysis 76
4.6 Hypothesis Test 79
Chapter 5 Conclusion 84
5.1 Main Findings 84
5.2 Implication 86
5.3 Limitation 94
5.4 Further Studies 96
Reference 97
국문초록 115
Appendix 1 118

참고문헌 (Reference)

1. Recommender Systems, Resnick , P. and Varian , H. R., 40 ( 3 ) , 56 ? 58, , 1997

2. Value-in-use Pricing, Christopher , M., 16 ( 5 ) , 35-46 ., , 1993

3. Recommender Systems Survey, Bobadilla , J. , Ortega , F. , Hernando , A. and Guti ? rrez , A, 46 , 109-132 ., , 2013

4. Internet Recommendation Systems, Ansari , A. , Essegaier , S. and Kohli , R., 37 ( 3 ) , 363-375 ., , 2000

5. Licensing Draws Mixed Reactions, Schlossberg , H., 25 ( 1 ) , 12-13 ., , 1991

6. Overview of Customer Value Theory, Xing , S. F. , Lu , F. and Chang , Y. S., 6 ) , 40-42, , 2007

7. Broadening the Concept of Marketing, Kotler , P. and Levy , S., 33 ( 1 ) , 10-15 ., , 1969

8. E-commerce Recommendation Applications, Schafer , J . B. , Konstan , J . A. and Riedl , J ., 5 ( 2 ) , 115-153 ., , 2001

9. Research on online retail customer value, Lei , B, 3 ( 2 ) , 58-60, , 2018

10. Review of Recommendation System Research, Li , B, 03 ( 3 ) , 7-10, , 2014