Difference Analysis and Prediction of the Helpfulness in Online Reviews of Experience-based, Search-based, and Mixed-based Goods

Online reviews (ORs) have shown evidence to help consumers to reduce hesitation in the last stage of the purchase and has been also found that ORs help online businesses increase sales. However, ORs are increasing faster, becoming every time more robust with better ways and media to express useful and helpful information. Therefore, the way ORs help online business and consumers are constantly changing. Previous studies have intended to analyze helpfulness in different ways. However, they have not totally yet identified the most appropriate influence significance of the factors to test and predict the helpfulness of ORs due to the constant change and evolution of ORs in E-commerce platforms. I based this study on the economics of information, media richness, and negativity-bias theories, proposing a model that shows the influencing factors in the helpfulness of ORs (such as length, sentimental Analysis, score rating, number of images, video and published days). To find a closer optimal helpfulness analysis and prediction, a data set of 17,119 samples of three types of online goods have been extracted from different products on Amazon.com. For the analysis, we have considered employing a regression model to analyze the significance level of the factors in ORs for every type of online goods. The findings in this research prove that in fact there is a different perception of helpfulness for every type of good.