@article{1783,
  author = {Hsuanwei Michelle Chen},
  title = {Do Online Recommendations Matter?  A Multimodal Investigation of Amazons Co-Purchase Network},
  journal = {Journal of Digital Information Management},
  year = {2015},
  volume = {13},
  number = {3},
  doi = {},
  url = {},
  abstract = {Online recommendations are considered as
a useful approach to encourage consumer purchases and drive product sales in e-commerce. This paper uses a multimodal approach to examine the effectiveness of online
recommendations using the lenses of econometric
modeling and text mining. Applying data on Amazon's 'customers who bought this item also bought' recommendation, commonly referred to as the co-purchase network, this study presents several findings. First, the
effects of direct co-purchase recommendations on product sales are essentially strong, regardless of other potential
factors that can also affect those sales. Second, the sales of one product can have positive effect on the sales of another, when the latter is suggested in co-purchase
recommendations. Moreover, the similarity of the customer base between the two co-purchased products, which can be extracted using text mining from online reviews, will affect that product's sales. This finding leads to the
conclusion that online recommendations achieve by
recommending related products to like-minded
consumers, and that recommendation effect is significant. These results contribute to academics and practitioners
by providing clear economic evidence of the value of using text mining approaches for online recommendations.},
}