Research On The Impact Factors Of The Helpfulness Of Online Reviews

Connected and upgraded mobile data has become the driving force behind the continuous growth of online shopping consumption.Online shopping is booming,and massive online review data is exploding.In the online shopping environment of the e-commerce platform with overloaded information,helpful review information,as the most accessible source of product information,potentially affects brand reputation,user stickiness and product marketing.Online reviews often reveal consumer buying motivations and are a common concern of online shoppers,online retailers and consumer experts.Therefore,how to measure the helpfulness of online review information has become an urgent subject for further study.Firstly,this thesis sorts out the classic literature on the helpfulness of online reviews,and combines the Elaboration Likelihood Model and the Information Adoption Model,and uses the two paths of review content and reviewer information characteristics as the benchmark to construct a conceptual model of the impact factors of the helpfulness of online reviews.Secondly,taking 5 products reviews of the Amazon(China)platform as the data source,this research uses "octopus" big data crawler software to capture 17,149 valid review data.After data cleaning and preprocessing,it left 2213 reviews with a helpful vote of at least 3.Using Python's snow-NLP toolkit,it performs text mining on the collected data to calculate the emotional intensity in the semantics of reviews.And it analyzes the semantic features of reviews with the help of Goo-Seeker word segmentation tools.From 10 independent variable indicators of the two dimensions of the semantic and formal characteristics of review content and the characteristics of reviewer information,this thesis uses SPSS23 and STATA15 for regression modeling,which is to analyze the impact factors of the helpfulness of online reviews and explore the moderating effects of product types.The following conclusions are obtained:1.In terms of the characteristics of the review content,the headline sentiment and the review sentiment significantly have a negative impact on the helpfulness of online reviews.But the richness of reviews,the timeliness of reviews,the pictures of reviews and the responses of reviews significantly have a positive impact on the helpfulness of online reviews.2.In terms of the characteristics of reviewer information,the reviewer's rank and the reviewer's engagement significantly have a negative impact on the helpfulness of online reviews.But the reviewer's authenticity and the reviewer's authority significantly have a positive impact on the helpfulness of online reviews.3.As far as product types are concerned,compared to experiential products,the richness of reviews,the timeliness of reviews and the pictures of reviews have a higher impact on the helpfulness of online reviews in search products.Compared to search products,the responses of reviews,the rank of reviewer and the engagement of reviewer have a more prominent impact on the helpfulness of online reviews in experiential products.However,the influences of the headline sentiment,the review sentiment,the reviewer's authenticity and the reviewer's authority on the helpfulness of online reviews are not significantly different between the two types of products.Finally,from the management practice of e-commerce companies and the optimization of the review information recommendation system,a series of suggestions are proposed to promote the scientific management of the e-commerce platform,and the main research contents and conclusions are summarized to look into the future research direction.