Information Cascades among Product Reviews
Using apps such as yelp, we begin to construct our opinions of products or experiences before we even try them out for ourselves. After discussing information cascades in our lectures, I began to wonder how these reviews may be affected by ideas such as ‘following the herd.’ Many people use a product, and before writing a review read the analysis of other people, which is very likely to make them change their opinion before completing the review.
For example, suppose a person just watched a movie and thought it was decent based on their own opinions, although their friend gives them ‘signals’ that they loved the movie. Now the reviewer is analyzing these signals as well as depending on their own private research to come up with a final product. In this case, coming from one previous signal it would be rational for the reviewer to conform to their friend’s rating although it is likely that they will follow their own intuition based on their signal. If the first two people give a similar review for the same movie, it makes it extremely likely that the next person will view the first two, knowing that they enjoyed the movie will say that they enjoyed it as well, even if it is against their own private signal. In fact, the most rational outcome would be for everyone after this point to use the previous information given over their signals in every scenario creating a massive information cascade.
While this may seem like a major issue because of the mass of people who may be ill-informed due to the fact that these ratings are essentially biased, it is more of an issue when the ratings are solely numbers. One way many companies have solved this issue is through the implementation of an area in which you can explain your reading for the respective rating. This allows other users to make high quality, informed opinions based on their own intuition as well as the analysis of others. Essentially, the explanation for the rating is equivalent to sharing of signals from our lectures, which has the influence to break the cascade.
Source Link: https://repository.upenn.edu/cgi/viewcontent.cgi?article=1324&context=marketing_papers