Economics and Usage of Digital Libraries: Byting the BulletSkip other details (including permanent urls, DOI, citation information)
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If an institution did not purchase any, or depleted all of its tokens, a user wanting to view a paid article not previously accessed had three choices. She could pay $7.00 to view the article, and also incur the non-pecuniary cost of entering credit card information and waiting for verification. If the institution subscribed to the print journal, she could use the print journal article rather than the electronic product. She could also request the article through a traditional interlibrary loan, which also involves higher non-price costs (effort to fill out the request form, and waiting time for the article to be delivered) than spending a token.
Due to details of the system design, we are unable to determine the exact number of times that users were faced with the decision of whether or not to enter credit card information in order to access a requested article. We were able to identify in the transaction logs events consistent with the credit card decision (hereafter we call these "consistent events"). These consistent events are, however, a noisy signal for the actual number of times users faced this decision.
We used evidence from the experimental variation to estimate the actual rate of requests for credit card payment. In some months some institutions had unused tokens and thus there were nocredit card (per-article) purchases, since unused tokens are always employed first. For these months we divided the number of consistent events by the number of access requests handled by the system for that institution, to obtain a measure of the baseline rate of consistent events that are not actual credit card requests. For each institution that did deplete its supply of tokens, we then subtracted this estimated baseline rate from the total number of consistent events to measure requests for credit card payment. For institutions that never had tokens, we use the weighted average of the estimated baseline rates for institutions with tokens.
|Institution||Estimated Credit Card Requests||Credit Card Payments||Percent|
In Table 6.6 we present the number of actual payments as a percent of estimated requests for credit card payments. The relative percentages are consistent with our intuition. Institutions 6 and 15 never had any tokens. We thus expect that users at these institutions expected a relatively high cost of article access, and would not bother accessing the system or searching for articles if they were not prepared to pay fairly often. Among the institutions at which tokens were depleted, the payment rate is appreciably higher at institutions 3 and 11, which is consistent with the fact that at these institutions the user could make an interlibrary loan request for articles through PEAK, and the institution would pay the per article charge on behalf of the user.
We gain further understanding of the degree to which differences in user cost affects the demand for paid article access by looking at only those institutions that depleted their supply of tokens at various points throughout the project. There were three institutions in this category: institution 3 ran out of tokens in November 1998 and again in July 1999; institution 11 in May 1999; and institution 9 in June 1999.
For institutions that had tokens available at certain times, we can estimate the number of credit card requests (by PEAK, to the user) based on the number of tokens spent per free access. If we make the assumption that this rate of token expenditure would have remained constant had tokens still been available, we can estimate the number of credit card requests to be equal to the estimated number of tokens that would have been spent had tokens been available.
|Institution||Credit Card Requests||Credit Card Payments||Percent|
In Table 6.7 we present the rate of credit card payments as estimated from the rate of token expenditure. The relative percentages are consistent with our previous estimates for these institutions. The estimated number of requests for credit card payment are about twice as high as the estimates in Table 6.6. One possible explanation is that when users know they are going to face a credit card payment request (tokens have run out, which they learn on their first request for an article that is not prepaid) they may make fewer attempts to access material, which would be another measure of the effect of transaction payments on service usage.
|Institution 3||Institution 3||Institution 9||Institution 11|
|30 days prior||13.6||18.4||20.2||16.0|
|30 days after||0.25||0.29||0.00||0.35|
To further quantify the decrease in demand for paid access resulting from a depletion of tokens, in Table 6.8 we present the normalized accesses of metered content per hundred accesses of free content at these institutions for the 30 days prior and subsequent to running out of tokens. Usage plummeted after tokens ran out and users were required to pay per article for access to metered content.