推荐系统对消费者网购支出的影响研究

推荐系统能在电子商务中利用信息过滤技术为消费者推荐感兴趣的商品和服务。本文通过收集大量消费者网购调查问卷,问卷的信度和效度均符合数据分析的要求。首先分析了消费者的产品偏好、忠诚度等网购行为与其年龄、性别、地域等个人属性的相互关系,之后运用倾向得分匹配法(Propensity Score Matching,PSM)研究推荐系统对消费者网购支出的影响,同时使用工具变量法((Instrumental Variable,IV)对PSM研究结果进行稳健性检验。结果显示使用推荐系统的消费者网购支出比未使用的消费者高出14.7%,网购支出与受教育程度和收入水平呈正相关、与年龄呈负相关,城市消费者和女性更愿意使用推荐系统;同时统计分析表明推荐效果受电子商务平台中社交关系、互补产品、店铺声誉等因素影响。研究结果对评估推荐系统的经济效益、增强消费者忠诚度和提高商家营销的精准性等方面起到了重要作用。

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

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.