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    1. See MacKie-Mason and Riveros (2000) for a discussion of the economics of electronic publishing.

    2. See Bonn et al. (this volume) and Hunter (this volume) for accounts of the genesis of this project.

    3. Kingma (this volume) provides a good discussion of the role of library as intermediary.

    4. As we further discuss below, user cost may include several components only one of which is a standard price. The other components may include, for example, time and inconvenience. We expect these user costs, taken together, and not price alone, to determine usage.

    5. 120 is the approximate average number of articles in a traditional printed journal for a given year. We refer to this bundle of options to access articles as a set of tokens, with one token used for each article added to the generalized subscription during the year.

    6. For example, a Green institution first decides how many generalized subcriptions to purchse (if any). Users then access articles using generalized subscription "tokens" at zero pecuniary cost until the tokens run out, and thereafter pay a fee per article for additional articles. The library determines how many articles (not which articles) are available at the two different prices.

    7. To access PEAK from other IP addresses, users entered a password. Once access was granted, all content in these categories was available without further user cost.

    8. In the first eight months of the experiment, users paid with a First Virtual VPIN account, rather than with a credit card. Because a VPIN was an unfamiliar product, the non-pecuniary costs were probably higher than for credit card usage, although formally the user needed to undertake the same steps.

    9. When the user accessed an article for which per-article payment was required, the institution was automatically billed by the PEAK service.

    10. Paid content is metered content not including article in journals to which an institution purchased a traditional subscription.

    11. Formally, Normalized Paid Access is equal to , where Apaid is the total number of paid accesses, Aunmetered the total number of unmetered accesses, and Scale is equal total number of free accesses divided by the total number of accesses of free content in journals to which the institution does not have a traditional subscription. We multiply by Scale because the more that accesses are covered by traditional subscriptions, the less likely a user is to require paid access. Scaling by access to unmetered content also controls for different overall usage intensity (due to different numbers of active users, differences in the composition of users, differences in research orientation, differences in user education about PEAK, etc.). Unmetered accesses proxies for the number of user sessions, and therefore our statistic is an estimate of paid accesses per session.

    12. Only 28% of unmetered accesses from Red group users were password authenticated. This suggests that a large majority of users attempting to access paid content would not already be password authenticated. For these users, the need to password authenticate would truly be a marginal cost.

    13. The Elsevier journal catalogue is especially strong in these subject areas, so we expect differences in usage when the subject area concentration of the user community differs.

    14. In only two cases were credit cards are required, and both were at the same institution.

    15. Recall that all users at an institution could access, without password authentication, any article previously purchased by that institution with a generalized token. For articles purchased on a per-article basis, only the individual who purchased the article could view it without further monetary cost.

    16. The libraries at institutions 3 and 11 processed these requests electronically, through PEAK, while the library at institution 9 did not and thus incurred greater processing delays.

    17. In addition, institution 6 is a corporate institution. It is possible that its users' budgetary constraints were not as binding as those associated with academic institutions.

    18. This phenomenon was widely discussed—though not, to our knowledge, sufficiently demonstrated—during the early years of widespread public access on the Internet. Many businesses and commentators asked whether users would pay for any content after being accustomed to getting most Internet-delivered information for free.

    19. For an excellent discussion of the collection development officer's problem, see Haar (1999)

    20. The percentage of articles read through June 1999 for academic institutions participating in PEAK ranged from .12% to 6.40%. An empirical study by King and Griffiths (1995) found that about 43.6% of users who read a journal read five or fewer articles from the journal and 78% of the readers read 10 or fewer articles.

    21. Project implementation delays exacerbated the demand forecasting problem. For example, none of the institutions in the Blue Group started the project until the third quarter of the year.

    22. With print publications and some electronic products libraries may be willing to spend more on full journal subscriptions to create complete archival collections. All access to PEAK materials ended in August 1999, however, so archival value should not have played a role in decision making.

    23. As 1999 PEAK access is for 8 months, the number of 1999 generalized subscriptions was multiplied by 1.5 for comparison with 1998.

    24. One of the institutions that increased token purchases despite over purchasing in 1998 was more foresightful than our simple learning model: its usage increased so much that it ran out of tokens less than six months into the final eight-month period of the experiment.

    25. E.g., the Green group had average overspending of about 55% so a 36-point change represents a shift from about 73% in 1998 to about 37% in 1999.

    26. The calculations in the two columns are independent and should not generally sum to one. The first column indicates the percent of titles that were subscribed that should have been subscribed (given perfect foresight). A high percent means there were not many specific titles subscribed that should not have been. However, this does not indicate that a library subscribed to most of the titles that it should have. A library that subscribes to zero journals will get 100% on this measure: no journals were subscribed that should not have been. The second column addresses this question: what percent of those titles that should have been subscribed were missed? The two columns correspond to Type I and Type II error in classical statistical theory. The first should be high, and the second low if the institution is forecasting well (and following our simple model of "optimal" practice).

    27. We performed the calculation for those institutions for which we have a good estimate of the user cost effect (see Table 6.4), and for which there were enough article accesses for meaningful estimation.