Abstract
PurposeThis paper is aimed primarily at academic library managers and acquisition librarians. By analogy to Pareto studying the relationship between clients and turnover, the paper will study subscriptions to e‐journals and usage statistics. The aim is to evaluate the long tail of usage statistics and to compare it with subscription lists of individually selected titles and packages (big deals).Design/methodology/approachThe paper exploits usage statistics and subscription data from a national usage study of an academic publisher. Data are from 2010.FindingsUsage statistics are partly shaped by the long tail effect. Individual subscriptions of journals are more selective than big deals, and trend towards a traditional retail curve. Unlike subscriptions through packages, usage and individual subscriptions can be related by a similar inclination. But both types of subscriptions fail to predict the popularity of a title in its usage.Research limitations/implicationsThe paper uses data from a national usage study and tries to identify global trends. Thus, it does not distinguish between customer categories, disciplines or activity domains.Practical implicationsThe paper considers the opportunity provided by big deal for acquisition policy. Ready‐made big deals sometimes appear as an unbounded and excessive supply, not suited to true and sufficient users' needs, but on the other hand, selective acquisition policy cannot completely anticipate online usage behaviour.Originality/valueOnly a few studies distinguish Pareto from long tail distributions in usage statistics, and there is little empirical evidence on the impact of selected subscriptions versus big deals on these statistics.
Highlights
Since Chris Anderson popularized the long tail in 2004, the concept has challenged the academic library
Our question is this: Do big deal and long tail provide a new opportunity for selective acquisition policy? In the following article, we report on global statistics on journal usage and subscriptions in order to assess the reality of the long tail effect and its relationship with acquisition policy
The distribution of usage statistics for all journals (H1) Online requests show the characteristics of a long tail
Summary
Since Chris Anderson popularized the long tail in 2004, the concept has challenged the academic library. The Anderson long tail distribution is a special variant of the so-called Pareto principle, known as the 80-20 rule. This statistical principle applies in many disciplines and in library and information sciences. It states that often 80% of the effects come from 20% of the causes. 80% of the revenues are due to a larger number of products. This means that the “top of the charts” (bestsellers, most-read journals or books, highly requested and downloaded e-journals) become less important. The remaining less selling 80% disks are called a “fat” or “heavy tail”
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