The world of scholarly publishing is rapidly changing in response to pressure from research funders and policymakers to accelerate the move to open access (OA), such as the 2018 Plan S mandates and the recent memorandum from the White House Office of Science and Technology Policy. As a result, publishers are accelerating their efforts to transform their business models in the near term to remain viable over the long term.
In part 1 of this blog series, I explored the challenges that publishers face in pulling together clean, actionable data to model out new agreement offers to propose to their institutional partners. However, data preparation is only one – albeit large – challenge that publishers face when moving new agreement proposals forward. Once the data is prepared, publisher teams have to model out new agreements in a way that is beneficial to all parties.
With the technologies that are currently available, OA pricing calculations require a significant amount of manual work. With no tools available that focus strictly on OA business models, agreement modeling is either done manually or developed in tools not designed for this specific task. This is time-consuming, error prone, and difficult to scale – making the process of building innovative new agreements and renewals challenging.
The subscription model is an easier (and time-tested) formula to manage with year-over-year increases or when contracts were renewed, leading to more clear-cut negotiations. But with OA, publishers are still navigating the best way to approach building new agreements and renewals at scale. With subscriptions, prices might rise 3% per year or when new deals are formed. With OA, publishers need a new perspective that allows for pricing to adjust over the life of the contract, depending on how many articles each author produces. All the extra information on articles (who is sponsoring, who is writing) used to be nice-to-have data sets, but with the funding organizations,consortia, and universities paying the fee, these are now need-to-have data sets.
An initial calculation to convert subscription pricing to OA may not be too difficult. Under the subscription model, a publisher might have 100 journals that a university pays $2 million per year to subscribe to. To earn the same amount under OA, the publisher determines how many authors published how many articles in those 100 journals. For example, if there were 1,000 articles, the OA price would be $2,000 per article to match the revenue of the subscription model. The problem arises, however, when publishers don’t know how many articles will be published in those journals the following year. If it’s only 800 articles, they will lose revenue under the per-article fee model. If it’s 1,200 articles, they will generate more revenue—but the funding institutions may not be happy that they have to pay more. Neither side knows for sure how many articles will be published each year.
This means that when negotiating, publishers have to come to the table with contracts that include fair pricing based on historical usage, an idea of predicted output, and clauses in case either side wants to make an adjustment. Publishers need all the data in one place so they can make quick updates for new negotiations and don’t have to repeat the process every single time a contract changes.
Using data to model agreements and show how OA programs can scale year over year is crucial in an ever-changing landscape. Automating the process in one tool, like CCC’s OA Intelligence, that digests data, enriches the dataset, and then uses that data to help model agreements increases transparency and trust throughout the agreement process, saving time and allowing for innovation in deals, helping to push the scholarly publishing ecosystem forward.
Learn more about CCC’s OA Intelligence here.