推荐系统对消费者网购支出的影响研究

doi:10.16381/j.cnki.issn1003-207x.2020.06.015

Abstract

Abstract: The recommendation system, with information filtering technology, is used to recommend products and services of interest to consumers in e-commerce. The relationship between consumers’ online shopping behaviors, including preferences and loyalty, and their personal attributes such as age, gender and region is analyzed by collecting a large number of questionnaires concerning online shopping with which the reliability and validity requirement for data analysis are met. Propensity Score Matching Method (PSM) is used to study the impact of using recommendation system on expenditure of online shopping. Instrumental Variables Method (IV) is conducted to test the robustness of PSM research results. The results show that the consumer online shopping expenditure using the recommendation system is 14.7% higher than that of the unused one.Online shopping spending is positively correlated with education and income, and negatively correlated with age.Urban consumers and women are more willing to use the recommendation system.Generally, recommendation effect is affected by social relations,complementary products, store reputation. The research results play an important role in assessing the economic benefits of recommendation system, promoting recommendation system to help consumers purchase satisfactory products, and enhancing consumer loyalty.

Key words: consumer online shopping expenditure, recommendation system, propensity score matching method, instrumental variables method

CLC Number:

F724.6

HU Chun-hua, ZHAO Hui, TONG Xiao-qin, REN Jian. Research on the Impact of Intelligent Recommendation System on Consumer Online Shopping[J]. Chinese Journal of Management Science, 2020, 28(6): 158-170.

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Research on the Impact of Intelligent Recommendation System on Consumer Online Shopping

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