Presentation given at Charleston Conference, November 5, 2009

1. Beguiled by Bananas : A retrospective study of usage & breadth of patron vs. librarian acquired ebook collections Jason Price & John McDonald Libraries, Claremont University Consortium November 5, 2009 (with data & discussion from Kari Paulson & Alison Morin of EBL)

2. Bananas tipped the boat: a cautionary tale Early patron-driven deal with a major platform Assignment on economics of banana plantations UC Boulder ‘bought every book with banana in the title’ Used by librarians & vendors(!) as evidence that user-driven selection is a bad idea

3. Patron-driven model objections:straight off the boat Books will be selected based on click-thrus that don’t indicate interest Users will select ebooks that no one (else) is interested in User selected collections will be unbalanced turkeys

4. Definitions Purchase type Patron selected = Demand Driven = User-selected Librarian selected ≈ Library selected ≈ Pre-selected Ebook usage measured conservatively Use data gathered post-purchase Did not count uses that lead to user-selection 1 use ≈ 1 ‘read online’ ≈ 1 ‘download’ read online = >10 min w/click thru OR copy OR print download = to adobe Digital Editions for multiple days Transaction level data – each use recorded separately with user anonymously identified

5. Questions we’ll address Are user-selected ebooks used less than pre-selected ebooks? Do user-selected ebooks have a narrower audience? Are user-selected collections less balanced? Do we have anything to fear in patron-initiated selection? (Can we use this to build better acquisition models?)

6. Overall Scope of the dataset 1 Ebook Vendor – EBL (Ebook Library) 11 Libraries 28,322 ebooks bought from 2006 - 2009 212,887 uses Purchase Models: User Selected, Pre-Selected, or Mixed

7. Total Books & Usage

8. Total Books & Usage

9. Total Books & Usage

10. Total Books & Usage

11. Scope of this study 5 libraries Books owned more than 6 months

12. Definitions Usage User Selected Pre-Selected: could be user request, approval profile, librarian ‘firm’ order Post acquisition usage Unique Users Read Online v. Download

13. Data 1 Ebook Vendor 11 Libraries Full purchase history Bibliographic Data Models: User Selected, Pre-Selected, or Mixed Transaction level usage data

15. By selection model

16. By library profile

17. By unique users

18. By time since owned

19. By publisher

21. Outline Does User Driving purchasing result in higher downloads? Does Librarian Driven purchasing result in usage? Do Librarians select the right books? What are the end results of having an open catalog and what are the trigger points to ensure it doesn’t eat up your whole budget? Limit to Mixed Model Libs, limit to >182 days owned.

22. Total Usage

24. Librarian Acquired

25. ANOVA of uses per year

27. Unique Users

30. Subject Area Analysis Pie charts of each discipline by model (or bar charts Another thing – is the collection too skewed towards one LC class or subject areas or do demand-driven selection result in a good collection. Do ratios of each discipline as a proportion of total books bought by model. Is publisher content skewed as well? What about price/cost?

31. User-selected collections have similar subject profiles Proportion of collection User Pre User Pre User Pre User Pre User Pre Library

32. User-selected collections have similar LC profiles

33. User-selected collections have similar LC profiles Blue = User selected Green = Pre selected

34. User-selected collections have fewer unused titles

35. ANOVA of unique users per year