Editing Long tail (section)

==Academic research==

===Effects of online access===
[[Erik Brynjolfsson]], [[Yu (Jeffrey) Hu]], and [[Michael D. Smith (economist)|Michael D. Smith]] found that a large proportion of [[Amazon.com]]'s book sales come from obscure books that were not available in brick-and-mortar stores.  They then quantified the potential value of the long tail to consumers. In an article published in 2003, these authors showed that, while most of the discussion about the value of the Internet to consumers has revolved around lower prices, consumer benefit (a.k.a. [[consumer surplus]]) from access to increased product variety in online book stores is ten times larger than their benefit from access to lower prices online.<ref>Brynjolfsson, Erik; Yu (Jeffrey) Hu, and Michael D. Smith, [https://ssrn.com/abstract=400940 "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers"], ''Management Science'', 49 (11), November 2003. [https://ssrn.com/abstract=400940 working paper version, April 2003]</ref>  

===The longer tail over time===
A subsequent study by [[Erik Brynjolfsson]], [[Yu (Jeffrey) Hu]], and [[Michael D. Smith (economist)|Michael D. Smith]]<ref>Bynjolfsson, Erik; Yu (Jeffrey) Hu, and Michael D. Smith, 2010, [https://ssrn.com/abstract=1679991 "The Longer Tail: The Changing Shape of Amazon's Sales Distribution Curve"]</ref> found that the long tail has grown longer over time, with niche books accounting for a larger share of total sales. Their analyses suggested that by 2008, niche books accounted for 36.7% of Amazon's sales while the consumer surplus generated by niche books has increased at least fivefold from 2000 to 2008. In addition, their new methodology finds that, while the widely used power laws are a good first approximation for the rank-sales relationship, the slope may not be constant for all book ranks, with the slope becoming progressively steeper for more obscure books.

In support of their findings, Wenqi Zhou and Wenjing Duan not only find a longer tail but also a fatter tail by an in-depth analysis on consumer software downloading pattern in their paper "Online user reviews, product variety, and the long tail".<ref name="zd-2010">{{cite journal |url=https://ssrn.com/abstract=1742519 |last1=Zhou |first1=Wenqi |last2=Duan |first2=Wenjing |title=Online User Reviews, Product Variety, and the Long Tail: An Empirical Investigation on Online Software Downloads |date=21 November 2010 |journal=Electronic Commerce Research and Applications |volume=11 |issue=3 |pages=275–289 |doi=10.1016/j.elerap.2011.12.002 |s2cid=23070895 |url-access=subscription }}</ref> The demand for all products decreases, but the decrease for the hits is more pronounced, indicating the demand shifting from the hits to the niches over time. In addition, they also observe a superstar effect in the presence of the long tail. A small number of very popular products still dominates the demand.

==="Goodbye Pareto Principle"===
In a 2006 working paper titled "Goodbye Pareto Principle, Hello Long Tail",<ref>Brynjolfsson, Erik; Yu (Jeffrey) Hu, and Duncan Simester, 2006, [https://ssrn.com/abstract=953587 "Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales"]</ref> [[Erik Brynjolfsson]], [[Yu (Jeffrey) Hu]], and Duncan Simester found that, by greatly lowering [[search engine technology|search]] costs, information technology in general and Internet markets in particular could substantially increase the collective share of hard-to-find products, thereby creating a longer tail in the distribution of sales.

They used a theoretical model to show how a reduction in search costs will affect the concentration in product sales. By analyzing data collected from a multi-channel retailing company, they showed empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution, when compared with traditional channels. An 80/20 rule fits the distribution of product sales in the catalog channel quite well, but in the Internet channel, this rule needs to be modified to a 72/28 rule in order to fit the distribution of product sales in that channel. The difference in the sales distribution is highly significant, even after controlling for consumer differences.

===Demand-side and supply-side drivers===
The key supply-side factor that determines whether a sales distribution has a long tail is the cost of inventory storage and distribution. Where inventory storage and distribution costs are insignificant, it becomes economically viable to sell relatively unpopular products; however, when storage and distribution costs are high, only the most popular products can be sold. For example, a traditional movie rental store has limited shelf space, which it pays for in the form of building [[Business process overhead|overhead]]; to maximize its profits, it must stock only the most popular movies to ensure that no shelf space is wasted. 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"<ref>{{Cite journal |first1=Erik |last1=Brynjolfsson |first2=Yu "Jeffrey" |last2=Hu |first3=Michael D. |last3=Smith |date=Summer 2006 |title=From Niches to Riches: Anatomy of the Long Tail |url=https://sloanreview.mit.edu/article/from-niches-to-riches-anatomy-of-the-long-tail/ |journal=[[MIT Sloan Management Review]] |volume=47 |issue=4 |pages=67–71 |ssrn=918142}}</ref> 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-tailer]]s' expanded, centralized warehousing allows for more offerings, thus making it possible for them to cater to more varied tastes.<ref>{{Cite magazine |last=Anderson |first=Chris |date=1 July 2006 |title=The Rise and Fall of the Hit |url=https://www.wired.com/wired/archive/14.07/longtail_pr.html |magazine=[[Wired (magazine)|Wired]] |volume=14 |issue=7}}</ref>

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 School of the University of Pennsylvania|Wharton]] study details this phenomenon along with several ideas that may promote the long tail and greater diversity.<ref>{{cite journal| last1= Fleder | first1= Daniel | first2= Kartik |last2= Hosanagar | title=Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity|journal=Management Science |date=May 2009|ssrn=955984 | doi = 10.1287/mnsc.1080.0974 | volume=55 | issue= 5 | pages=697–712| url= http://archive.nyu.edu/handle/2451/28488 }}</ref>

A 2010 study conducted by Wenqi Zhou and Wenjing Duan<ref name="zd-2010"/> 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.

===Networks, crowds, and the long tail===
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 [[Peer-to-peer (meme)|peer-to-peer]] collaboration groups that produce open-source software or create [[wiki]]s such as [[Wikipedia]].
* The [[crowdsourcing]] model, in which a company outsources work to a large group of market players using a collaborative online platform.
* The model of [[crowdcasting]], is the process of building a network of users and then delivering challenges or tasks to be solved with the purpose of gaining insights or innovative ideas.
* Work performed by individuals in commons-like, non-market networks, described in the work of [[Yochai Benkler]].<ref>{{cite web|url=http://www.benkler.org/ |title=Benkler, The Wealth of Networks |publisher=Benkler.org |access-date=25 December 2011}}</ref>

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. Their data across 200 subject areas shows that a doubling of this influence leads to a 50% increase in revenues from the least popular one-fifth of books.<ref>{{cite journal
 | ssrn=1324064
 | title=Recommendation Networks and the Long Tail of Electronic Commerce
 | journal=MIS Quarterly
 | volume= 36
 | author=Oestreicher-Singer, Gal and Arun Sundararajan
| year=2012
 | issue=1
 | pages=65–83
 | doi=10.2307/41410406
 |jstor=41410406
 | url=http://archive.nyu.edu/handle/2451/29496
 }}</ref>

===Turnover within the long tail===
The long-tail distribution applies at a given point in time, but over time the relative popularity of the sales of the individual products will change.<ref>{{cite journal | doi = 10.1016/j.evolhumbehav.2006.10.002 | volume=28 | issue=3 | title=Regular rates of popular culture change reflect random copying | year=2007 | journal=Evolution and Human Behavior | pages=151–158 | last1 = Bentley | first1 = R. Alexander | last2 = Lipo | first2 = Carl P. | last3 = Herzog | first3 = Harold A. | last4 = Hahn | first4 = Matthew W.| citeseerx=10.1.1.411.7668 }}</ref>  Although the distribution of sales may appear to be similar over time, the positions of the individual items within it will vary. For example, new items constantly enter most fashion markets. A recent fashion-based model <ref>{{cite news|url=https://www.telegraph.co.uk/connected/main.jhtml?xml=/connected/2004/06/16/ecfpop16.xml&sSheet=/connected/2004/06/14/ixconn.html |archive-url=https://web.archive.org/web/20060223153629/http://www.telegraph.co.uk/connected/main.jhtml?xml=/connected/2004/06/16/ecfpop16.xml&sSheet=/connected/2004/06/14/ixconn.html |url-status=dead |archive-date=23 February 2006 |title=Why are they so popular? |first=Roger |last=Highfield |newspaper=The Daily Telegraph |date=16 June 2004 |access-date=25 December 2011}}</ref> of [[consumer choice]], which is capable of generating power law distributions of sales similar to those observed in practice,<ref>{{cite journal | last1 = Bentley | first1 = R.A. | last2 = Hahn | first2 = M.W. | last3 = Shennan | first3 = S.J. | year = 2007 | title = Random drift and culture change | url= | journal = Proceedings of the Royal Society | volume = 271 | issue = 1547| pages = 1443–50 | doi = 10.1098/rspb.2004.2746 | pmid = 15306315 | pmc = 1691747 }}</ref> takes into account turnover in the relative sales of a given set of items, as well as innovation, in the sense that entirely new items become offered for sale.

There may be an optimal inventory size, given the balance between sales and the cost of keeping up with the turnover.  An analysis based on this pure fashion model<ref>{{cite journal | arxiv=0808.1655 | doi=10.1016/j.physa.2008.11.009 | title=Physical space and long-tail markets | year=2009 | last1=Bentley | first1=R. Alexander | last2=Madsen | first2=Mark E. | last3=Ormerod | first3=Paul | journal=Physica A: Statistical Mechanics and Its Applications | volume=388 | issue=5 | pages=691–696 | bibcode=2009PhyA..388..691B }}</ref> indicates that, even for digital retailers, the optimal inventory may in many cases be less than the millions of items that they can potentially offer. In other words, by proceeding further and further into the long tail, sales may become so small that the marginal cost of tracking them in rank order, even at a digital scale, might be optimised well before a million titles, and certainly before infinite titles.  This model can provide further predictions into markets with long-tail distribution, such as the basis for a model for optimizing the number of each individual item ordered, given its current sales rank and the total number of different titles stocked.

===Long-tailed distributions in diplomacy===
From a given country's viewpoint, diplomatic interactions with other countries likewise exhibit a long tail.<ref>{{cite journal |last1=Nishikawa-Pacher |first1=Andreas |date=2023 |title=Diplomatic complexity and long-tailed distributions: the function of non-strategic bilateral relations |journal=International Politics |volume=60 |issue=6 |pages=1270–1293 |doi=10.1057/s41311-023-00510-3|doi-access=free }}</ref> Strategic partners receive the largest amount of diplomatic attention, while a long tail of remote states obtains just an occasional signal of peace. The fact that even allegedly "irrelevant" countries obtain at least rare amicable interactions by virtually all other states was argued to create a societal surplus of peace, a reservoir that can be mobilized in case a state needs it. The long tail thus functionally resembles "[[Interpersonal_ties#Weak_tie_hypothesis|weak ties]]" in interpersonal networks.