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Beguiled by Bananas: A retrospective study of usage & breadth of patron vs. librarian acquired ebook collections
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Beguiled by Bananas: A retrospective study of usage & breadth of patron vs. librarian acquired ebook collections

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Presentation given at Charleston Conference, November 5, 2009

Presentation given at Charleston Conference, November 5, 2009

Published in: Education, Business

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  • Conclusions from the cautionary tale: Phrase in terms of the banana story
  • Read online - Can think of as in library useDownload – can be thought of as a checkout
  • We are interested in studying how usage varies by selection method for ebooks. Ultimately, we would like to better understand if user-selected, or patron-initiated, selection for a library collection is any better or worse than librarians doing selection, either title-by-title selection or approval plan profiling. A few obvious research questions emerge: Does usage vary by who selects a book for the collection? And if so, what are the effects? If we know those effects, can we build better acquisition models? And if not qualitatively better, at least through less effort or staff commitment.
  • To emphasize the 2nd point…
  • To emphasize the 2nd point…
  • To emphasize the 2nd point…
  • To emphasize the 2nd point…
  • To emphasize the 2nd point…
  • To emphasize the 2nd point…
  • To emphasize the 2nd point…
  • Levene's Test of Equality of Error Variances F=50.145, sig = .001
  • Levene's Test of Equality of Error Variances F=50.145, sig = .001
  • Transcript

    • 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
    • 14. Analysis Levels
    • Usage & Unique Users
    • 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
    • 23.
    • 24. Librarian Acquired
    • 25. ANOVA of uses per year
    • 26.
    • 27. Unique Users
    • 28.
    • 29.
    • 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