We all know that the easiest way to find a book is to search by title, author, or perhaps some well-chosen keywords. And that may be the way that most people find books most of the time.
But when we study our faculty members’ actual behavior in their offices and labs, we sometimes find out that they search for books and articles in surprising ways. Size, for example.
The title and author may slip a researcher’s mind, but she remembers that the book she wants is about three inches thick. Now, all she has to do is hunt around for a big, thick book.
This can be a great strategy for finding a book in a messy office. But what if you could search the OPAC this way? Or by color? Or binding – cloth, paper, calfskin?
It’s a whimsical example, but the point is serious. When we study the practices of the people who use the library, we may find ourselves thinking in unexpected ways about our library systems.
More seriously, we want to study how our users browse the collections, confer with others, follow leads in the literature, and so on. We believe that this will help us break through to new ways of finding unknown resources.
However, taking this path has implications not just for functionality and coding but for metadata, as well. Take the size example. If we really wanted to help our patrons find items in our OPAC by size, we would need to include the thickness of each item in our metadata records. Since we don’t currently do this, we would have to go out and start measure things like crazy and enter the information into hundreds of thousands of records. We’re not going to do that, but we could if we were convinced that it would be worth it.
We may face a similar challenge, depending on what our XC user research discovers, and what features we think we might want to add to the system. In making our decision, we will have to consider the costs not just of programming but of adding or massaging metadata when we decide which of several possible features to build.