Crowdfunding is a process of raising money (funding) for a project through a venture of large number of people (crowd). The popular online crowdfunding platforms Kickstarter and Indiegogo provide a stage for innovators worldwide to bring ideas to reality. Despite the popularity and success of many projects on the platforms, it is yet to be determined whether successful projects always produce high quality products. Previously, the quality of crowdfunded products (successfully funded projects from crowdfunding website that are available on Amazon) in the market (e.g., Amazon) has not been statistically and scientifically evaluated. There has been no previous study to understand whether a successful project will receive high/low ratings from customers in e-commerce sites like Amazon. To address this problem, we (i) compare crowdfunded products with traditional products in terms of their ratings on Amazon; (ii) analyze negative reviews of crowdfunded products; (iii) analyze characteristics of the successful projects (received \(\ge \) 4 Amazon rating) and unsuccessful projects (received < 4 Amazon rating); and (iv) build machine learning models at three different stages, to predict high or low star ratings for a crowdfunded product. Our experimental results show that, on average, crowdfunded products received lower ratings than traditional products. Our ensemble model effectively identifies which product will receive high star-ratings from customers on Amazon. The dataset and code used in this manuscript are available at https://github.com/vishalshar/popularity_vs_quality_data-code.

众筹是通过大量人（众）的风险为一个项目筹集资金（资金）的过程。流行的在线众筹平台 Kickstarter 和 Indiegogo 为全球的创新者提供了一个将想法变为现实的舞台。尽管平台上的许多项目都很受欢迎和成功，但成功的项目是否总是能产出高质量的产品还有待确定。此前，市场（如亚马逊）上的众筹产品（亚马逊上众筹网站成功资助的项目）的质量尚未经过统计和科学评估。之前没有研究了解一个成功的项目是否会在亚马逊等电子商务网站上获得客户的高/低评级。为了解决这个问题，我们 (i) 比较众筹产品和传统产品在亚马逊上的评分；(ii) 分析众筹产品的负面评论；(iii) 分析成功项目的特征（收到\(\ge \)4 亚马逊评级）和不成功的项目（收到 < 4 亚马逊评级）；（iv）在三个不同阶段建立机器学习模型，以预测众筹产品的高星级或低星级。我们的实验结果表明，众筹产品的平均评分低于传统产品。我们的集成模型有效地确定了哪些产品将获得亚马逊客户的高星级评价。本手稿中使用的数据集和代码可从 https://github.com/vishalshar/popularity_vs_quality_data-code 获得。