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

1,696 views

Published on

Presentation given at Charleston Conference, November 5, 2009

Published in: Education, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,696
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • 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
  • Beguiled by Bananas: A retrospective study of usage & breadth of patron vs. librarian acquired ebook collections

    1. 1. Beguiled by Bananas : <br />A retrospective study of usage & breadth of patron vs. librarian acquired ebook collections<br />Jason Price & John McDonald<br />Libraries, Claremont University Consortium<br />November 5, 2009<br />(with data & discussion from Kari Paulson & Alison Morin of EBL)<br />
    2. 2. Bananas tipped the boat: a cautionary tale<br />Early patron-driven deal with a major platform<br />Assignment on economics of banana plantations<br />UC Boulder ‘bought every book with banana in the title’ <br />Used by librarians & vendors(!) as evidence that user-driven selection is a bad idea<br />
    3. 3. Patron-driven model objections:straight off the boat<br />Books will be selected based on click-thrus that don’t indicate interest<br />Users will select ebooks that no one (else) is interested in<br />User selected collections will be unbalanced turkeys<br />
    4. 4. Definitions<br />Purchase type<br />Patron selected = Demand Driven = User-selected<br />Librarian selected ≈ Library selected ≈ Pre-selected<br />Ebook usage measured conservatively<br />Use data gathered post-purchase<br />Did not count uses that lead to user-selection<br />1 use ≈ 1 ‘read online’ ≈ 1 ‘download’ <br />read online = >10 min w/click thru OR copy OR print<br />download = to adobe Digital Editions for multiple days<br />Transaction level data – each use recorded separately with user anonymously identified<br />
    5. 5. Questions we’ll address<br />Are user-selected ebooks used less than pre-selected ebooks? <br />Do user-selected ebooks have a narrower audience?<br />Are user-selected collections less balanced?<br />Do we have anything to fear in patron-initiated selection?<br />(Can we use this to build better acquisition models?)<br />
    6. 6. Overall Scope of the dataset<br />1 Ebook Vendor – EBL (Ebook Library)<br />11 Libraries<br />28,322 ebooks bought from 2006 - 2009<br />212,887 uses<br />Purchase Models: User Selected, Pre-Selected, or Mixed<br />
    7. 7. Total Books & Usage<br />
    8. 8. Total Books & Usage<br />
    9. 9. Total Books & Usage<br />
    10. 10. Total Books & Usage<br />
    11. 11. Scope of this study<br />5 libraries<br />Books owned more than 6 months<br />
    12. 12. Definitions<br />Usage<br />User Selected<br />Pre-Selected: could be user request, approval profile, librarian ‘firm’ order<br />Post acquisition usage<br />Unique Users<br />Read Online v. Download<br />
    13. 13. Data<br />1 Ebook Vendor<br />11 Libraries<br />Full purchase history<br />Bibliographic Data<br />Models: User Selected, Pre-Selected, or Mixed<br />Transaction level usage data<br />
    14. 14. Analysis Levels<br /><ul><li>Usage
    15. 15. By selection model
    16. 16. By library profile
    17. 17. By unique users
    18. 18. By time since owned
    19. 19. By publisher
    20. 20. By LC class</li></li></ul><li>Usage & Unique Users<br />
    21. 21. Outline<br />Does User Driving purchasing result in higher downloads?<br />Does Librarian Driven purchasing result in usage?<br />Do Librarians select the right books?<br />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?<br />Limit to Mixed Model Libs, limit to >182 days owned.<br />
    22. 22. Total Usage<br />
    23. 23.
    24. 24. Librarian Acquired<br />
    25. 25. ANOVA of uses per year<br />
    26. 26.
    27. 27. Unique Users<br />
    28. 28.
    29. 29.
    30. 30. Subject Area Analysis<br />Pie charts of each discipline by model (or bar charts<br />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.<br />Is publisher content skewed as well?<br />What about price/cost?<br />
    31. 31. User-selected collections have similar subject profiles <br />Proportion of collection<br />User Pre User Pre User Pre User Pre User Pre<br />Library<br />
    32. 32. User-selected collections have similar LC profiles<br />
    33. 33. User-selected collections have similar LC profiles<br />Blue = User selected<br />Green = Pre selected<br />
    34. 34. User-selected collections have fewer unused titles<br />
    35. 35. ANOVA of unique users per year<br />

    ×