Because online video rental provider (such as Amazon.com or Netflix) stocks movies in centralized warehouses, its storage costs are far lower and its distribution costs are the same for a popular or unpopular movie. It is therefore able to build a viable business stocking a far wider range of movies than a traditional movie rental store. Those economics of storage and distribution then enable the advantageous use of the long tail: for example, Netflix finds that in aggregate, "unpopular" movies are rented more than popular movies. An "MIT Sloan Management Review" article titled "From Niches to Riches: Anatomy of the Long Tail" examined the long tail from both the supply side and the demand side and identifies several key drivers. On the supply side, the authors point out how e-tailers' expanded, centralized warehousing allows for more offerings, thus making it possible for them to cater to more varied tastes. On the demand side, tools such as search engines, recommendation software, and sampling tools are allowing customers to find products outside their geographic area. The authors also look toward the future to discuss second-order, amplified effects of Long Tail, including the growth of markets serving smaller niches. Not all recommender systems are equal, however, when it comes to expanding the long tail. Some recommenders (i.e. certain collaborative filters) can exhibit a bias toward popular products, creating positive feedback, and actually reduce the long tail. A Wharton study details this phenomenon along with several ideas that may promote the long tail and greater diversity. A recent study conducted by Wenqi Zhou and Wenjing Duan further points out that the demand side factor (online user reviews) and the supply side factor (product variety) interplay to influence the long tail formation of user choices. Consumers' reliance on online user reviews to choose products is significantly influenced by the quantity of products available. Specifically, they find that the impacts of both positive and negative user reviews are weakened as product variety goes up. In addition, the increase in product variety reduces the impact of user reviews on popular products more than it does on niche products. The "crowds" of customers, users and small companies that inhabit the long-tail distribution can perform collaborative and assignment work. Some relevant forms of these new production models are:  The demand-side factors that lead to the long tail can be amplified by the "networks of products" which are created by hyperlinked recommendations across products. An MIS Quarterly article by Gal Oestreicher-Singer and Arun Sundararajan shows that categories of books on Amazon.com which are more central and thus influenced more by their recommendation network have significantly more pronounced long-tail distributions.