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    6.4 Effects of user cost on access

    In this section, we measure the extent to which user costs to access PEAK content affected the quantity and composition of articles actually accessed. Clearly the costs and benefits of accessing the same information via other means, particularly via an institution's print journal holdings, will have an enormous impact on a user's willingness to bear costs associated with PEAK access. We do not explicitly model these costs, although we do control for them at an institutional level. Kingma (this volume) provides estimates of some costs associated with information access via several non-electronic media.

    As noted above, user costs for accessing PEAK content depended on a variety of factors. One factor is the type of content requested ("metered" versus "unmetered"). Looking only at metered content, the pecuniary and non-pecuniary costs associated with access depended in large part on the access products purchased by a user's institution. Further, the access costs faced by users within a given institution depended on the specific products selected by an institution (i.e. the specific journals to which an institution holds a traditional subscription, and the number of generalized subscription tokens purchased), individual actions (whether a password had already been obtained) and also on the actions of other users at the institution (whether a token had already been used to purchase a requested article, and how many tokens remain). In the following sections, we estimate the effects of these incremental costs on the quantity and composition of metered access.

    Non-pecuniary costs

    To gauge the impact of user cost of usage on aggregate institutional access, we compared the access patterns of institutions in the Red group with those in the Blue group. Red institutions had both generalized and traditional subscriptions available; Blue had only traditional. Users at both institutions could obtain additional articles at the per-article price. We constructed a variable we call "Normalized Paid Accesses" to measure the number of "paid" accesses to individual articles (paid by generalized tokens or by per-article fee) per 100 unmetered accesses, normalized to account for the number of traditional subscriptions. Adjusting for traditional subscriptions accounts for the amount of prepaid content provided by the user's institution; adjusting for unmetered accesses adjusts for the size of the user community and the underlying intensity of usage in that community.[11]

    Table 6.2:Normalized paid access per 100 unmetered accesses, by institution.
    Institution Access group Normalized paid accesses per 100 unmetered accesses
    3 Red 13.5
    9 Red 20.4
    10 Red 31.7
    11 Red 7.59
    12 Red 26.4
    Average Red 15.1
    13 Blue 51.0
    14 Blue 15.1
    15 Blue 4.72
    NOTE: Average not reported for Blue institutions because of variations in experimental conditions; see text for details.

    We use our statistic, Normalized Paid Accesses, as a measure of relative (cross-institution) demand for paid access. We present the statistic in Table 6.2. Even after controlling for the size of an institution's subscription base and the magnitude of demand for unmetered content, paid demand differed among institutions with the same access products. This suggests that there are institution-specific attributes affecting demand for paid access. It is also possible that we incompletely control for subscription size. One possibility is that the number of traditional subscriptions affects the cost a user expects to have to pay for an article before the actual cost is realized. Users at an institution with a large traditional subscription base, such as institution 3, would have had a lower expected marginal cost for access as a large percentage of the articles are accessible at zero cost. Some users at these institutions might attempt to access articles via PEAK, expecting them to be free, while not willing to pay the password cost when the need arises. This difference between expected and actual marginal cost may be important; we return to this point later.

    We can make some interesting comparisons between institutions in the Red group and those in the Blue group. While institution number 13, as a member of the Blue group, only had traditional subscriptions and per-article access available, users at this institution did not need to authenticate for any content, and thus faced no marginal cost in accessing any paid content. Most users at Red institutions faced the cost of authenticating to spend a token.[12] We would therefore expect a higher rate of paid access at institution 13, and this is in fact the case.

    Paid access at institution 14 was similarly subsidized by the institution. However, in contrast to institution 13, authentication was required. Thus the marginal user cost of paid access at institution 14 was exactly the same as at the Red institutions. We therefore expected that demand for paid access would be similar. This is in fact the case: Normalized Paid Access is 15.1 at both. Finally, per-article access for users at institution 15 was not automatically subsidized. Thus, users faced very high marginal costs for paid content. In addition to the need to authenticate with a password, users at this institution needed either to: a) pay the $7.00 per-article fee and enter their credit card information; or b) arrange for the request to be handled via the institution's interlibrary loan department. In either case, the user cost of access was higher than password only, and, as we expected, the rate of paid access was much lower than in the Red group.

    Table 6.3: Estimated effects of user cost on access.
    No month dummies Month dummies
    Constant 87.535* 108.615*
    (10.394) (14.643)
    Blue: Credit Card (Inst. 15) -280.490* -270.879*
    (37.627) (35.508)
    Red + Institution 14 -58.999* -57.764*
    (7.900) (7.186)
    Out of Tokens -25.070* -25.665*
    (1.635) (2.533)
    Graduate Students/Faculty Ratio 43.821* 41.748*
    (7.301) (6.912)
    Percentage Engineering, Science and Medicine -225.913* -215.767*
    (7.535) (36.553)
    Sample Size 530 530
    R2 0.171 0.229
    NOTE: Standard errors are shown in parentheses.
    Dependent variable is weekly normalized paid access per 100 free accesses.
    * Significant at the 99% level.

    Table 6.3 summarizes the results from a multiple regression estimate of the effects of user cost on access. We controlled for differences in the graduate student / faculty ratio and the percentage of users in Engineering, Science and Medicine.[13] The dependent variable, Paid accesses per 100 unmetered accesses, controls for learning and seasonality effects. We thus see the extent to which paid access, starting from a baseline of access to paid content at zero marginal user cost, falls as we increase marginal costs. Imposition of a password requirement reduces paid accesses by almost 60 accesses per 100 unmetered accesses (Red and institution 14), while the depletion of (institution-purchased) tokens results in a further reduction of approximately 25 accesses (per 100 unmetered).

    We use the distinction between metered and unmetered access to further test the extent to which increased user costs throttle demand. As a reminder, full-length articles from the current year are metered: either the institution or the individual must pay a license fee to gain access. Other materials (notes, letters to the editor, tables of contents, and older full-length articles) are not metered: anyone with institutional access to the system can access this content after the institution pays the institutional participation license fee. Some of the unmetered content comes from journals that are covered by traditional subscriptions, some from journals not in subscriptions. We calculate the ratio of this free content accessed from the two subsets of content. If we make the reasonable assumption that, absent differential user costs, the ratio of metered content from the two subsets would be the same as the ratio of unmetered content, then we can estimate what the demand would be for metered content outside of paid subscriptions if that content were available at zero user cost (e.g., if the institution added the corresponding journals to its traditional subscription base). Our estimate is calculated as:

    Table 6.4: Paid access as percentage of average predicted for zero user cost.
    Institution Year Actual Per Predicted Percent Free Access Psswd. Authent. Credit Card Required Password Entered When Prompted
    3 1998 21.1% 11.1% 0 6.69%
    10 1998 146.2% 45.4% 0 13.5%
    11 1998 16.4% 8.81% 0 2.6%
    12 1998 83.3% 51.7% 0 7.14%
    13 1998 125.9% 98.8% 0 100.0%
    14 1998 79.3% 54.5% 0 44.4%
    15 1998 0.00% 22.2% 1 8.06%
    3 1999 31.4% 19.1% 0 10.4%
    10 1999 123.4% 43.9% 0 13.4%
    11 1999 20.8% 18.5% 0 14.1%
    13 1999 77.7% 100.0% 0 100.0%
    14 1999 56.7% 63.2% 0 17.8%
    15 1999 19.5% 12.2% 1 2.39%
    "Percent free access password authenticated" indicates the percentage of times that users accessing free material were already password authenticated (which isn't in fact necessary for free accesses).
    "Credit card required" means the user was required to pay a per-article fee.

    In Table 6.4 we present actual paid access (when customers face the actual user cost) as a percentage of predicted access (at zero user cost) for all institutions that had traditional subscriptions in a given year. All observations except three (institutions 10 and 13 in 1998, and institution 10 in 1999) show actual access substantially below predicted when users bear the actual user cost. We conjecture that the surprising result for institution 10 might be partially due to the fact that they had the fewest traditional subscriptions. Because relatively little was available at zero user cost, users at this institution might have expected to bear the user cost (password recollection and entry in this case) for every access. If this were the case, then our method of predicting access at zero user cost is biased and the results for institution 10 are not meaningful. As for institution 13, recall that its users in fact faced no incremental user cost to access paid materials. We thus expect its paid accesses to be closer to that predicted for zero user cost, and are not surprised by this result.

    Though not related to our focus on user cost, there are two other statistical results reported in Table 6.4 that bear mention. First, usage is substantially, and statistically significantly higher when the graduate student / faculty ratio is higher. It is not implausible that graduate students make more frequent use of the research literature, reading more articles while taking classes and working on their dissertations, than more established scholars. This may also reflect life cycle differences in effort and productivity. However, it is also possible that a higher graduate student ratio is proxying for the intensity of research (by both graduate students andfaculty) at the institution, which would be correlated with higher access.

    The other, and more surprising result is that the higher is the percentage of engineering, science and medicine (STM) users, the lower is usage, by a large and statistically significant amount. We cannot be sure about the interpretation of this result, either. We were surprised because the Elsevier catalogue is especially strong in STM, reflected in breadth, depth and quality of content. Perhaps the nature of study and research in STM calls for less reading of journal articles, but this conjecture cannot be tested without further data.

    For all other institutions we generally see that the user costs associated with paid access caused an appreciable reduction in the number of paid articles demanded. We also present in Table 6.4 factors which we believe help explain this shortfall, namely the percentage of free access that is password authenticated, whether or not a credit card is required for all paid access, and the rate at which passwords were entered for paid access when prompted.

    Table 6.5: Estimation results of effects of user cost on actual paid accesses as percent of predicted accesses
    Independent variable Coefficient (standard error)
    Percent Free Psswd. Auth. 2.12*
    (.45)
    Prompted Login Percent -1.05**
    (.54)
    Credit Card Required -.213
    (.25)
    Sample Size 13
    R2 0.85
    NOTE: Standard errors shown in parentheses. Dependent variable is actual paid access as a percentage of predicted.
    *Significant at the 99% level; **Significant at the 95% level.

    In Table 6.5 we summarize the results from the estimation of the effects of user cost on actual paid access as a percentage of predicted accesses. Despite the small sample size, the results clearly demonstrate that, as we increase the number of individuals who can access paid content without additional marginal costs (proxied by the percent of free access that is password authenticated, which indicates that the password user cost has already been incurred), more paid access is demanded. The dummy variable for credit card required (for per-article payment) is not significant, but there was almost no variation in the sample from which to measure this effect.[14] The coefficient for the percent of prompted users who log in is of the wrong sign to support our hypothesis: we expected that the higher the number of users who are willing to bear the non-pecuniary costs of login, the higher would be the access to paid material.

    Pecuniary costs

    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.[15] 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.[16]

    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.

    Table 6.6: Credit card payments as a percent of requests, estimated from transaction log evidence
    Institution Estimated Credit Card Requests Credit Card Payments Percent
    3 53 13 25.5%
    6 260 194 74.6%
    9 190 1 0.5%
    11 562 61 10.9%
    15 137 73 53.3%

    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.[17] 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.

    Table 6.7: Credit card payments as a percent of requests, estimated from token expenditure rate
    Institution Credit Card Requests Credit Card Payments Percent
    3 128 13 10.2%
    9 366 1 0.3%
    11 1128 61 5.4%

    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.

    Table 6.8: Effect of token depletion on demand for paid content
    Institution 3 Institution 3 Institution 9 Institution 11
    1998 1999 1999 1999
    30 days prior 13.6 18.4 20.2 16.0
    30 days after 0.25 0.29 0.00 0.35
    Percentage Decrease -98.2% -98.4% -100.0% -97.8%
    NOTE: Units: Normalized paid access per 100 unmetered accesses.

    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.

    Summary: Effects of user costs

    The results we presented in this section demonstrate that increases in user costs substantially diminish demand for paid content. In particular, the decisions made by thousands of users demonstrate that non-pecuniary costs, such as password use, have an impact on demand that is of the same order of magnitude as direct monetary costs.