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Voting with Their Fingers: What Research Libraries Can Learn from User Behavior

Voting with Their Fingers: What Research Libraries Can Learn from User Behavior. Anne R. Kenney Columbia Reference Symposium March 2004. Recent Trends in User Behavior. Self service Satisfaction Seamlessness “Google is disintermediating the library.” The 2003 OCLC Environmental Scan:

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Voting with Their Fingers: What Research Libraries Can Learn from User Behavior

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  1. Voting with Their Fingers: What Research Libraries Can Learn from User Behavior Anne R. Kenney Columbia Reference Symposium March 2004

  2. Recent Trends in User Behavior • Self service • Satisfaction • Seamlessness “Google is disintermediating the library.” The 2003 OCLC Environmental Scan: Pattern Recognition

  3. How Do Libraries Stack Up? • There are 139,800 libraries in the US • They circulate about the same number of items as FedEx ships per day • Amazon ships over one fourth as many books per day as circulate in all US libraries combined

  4. Top Sites on the Web in the English Language

  5. Top Web Sites for Reference

  6. Most Popular Research Library Web Sites

  7. What do popular sites have in common? • Easy to use/low barriers to use • Reuse/recombine information • Personalization or anonymity • Communication • Community and participation • Pan and zoom from macro to micro

  8. What do popular sites have in common? • Increase personal productivity • Enhance decision making, laying out options • Relevancy and vetting • Contextualization • Prompts and suggestions • Integration with other services, databases

  9. What do popular sites have in common? • Value adds • Supporting tools (translation) • Ability to manipulate, save, and use information • What’s new/relevant; current awareness

  10. Redefining the Search Experience

  11. Auxiliary Services

  12. Limiting the Search to the University Domain

  13. Relevancy/Assessment Information

  14. Search Inside Feature Offers Granularity

  15. Personalization, Community, and Participation

  16. 1-Way & 2-Way Communication

  17. Refining Search/Offering Choices

  18. Impartial Information and Candidate Input

  19. Analogy to a Library Service

  20. Currently Relevant Information

  21. Link to respected review sources

  22. One Search. Three Responses.

  23. Concept Mapping

  24. The 88th Most Popular Site on the Web

  25. Costs to Support refdesk.com

  26. What Research Libraries Have Going For Them • Content (depth, volume, range) • Trust • Authoritative information • Content management (retrieval, classification, metadata) • Common vocabulary and processes

  27. What Research Libraries Have Going For Them • Formalized sharing mechanisms • Free and equitable access to clientele • Common technology services (authentication and authorization) • Good communication networks • Vetting information (indexes, abstracts, reviews)

  28. What Research Libraries Have Going For Them • Auxiliary information (use data) • Searching/sorting mechanisms • Some capability to link databases • Some relevance ranking capabilities • People!

  29. What Research Libraries Don’t Do Well • Complicated interface • Lingo intensive • Prerequisite user knowledge • Non intuitive distinctions • Little sense of community • Lack of collaboration tools

  30. What Research Libraries Don’t Do Well • Nascent communication tools • Lack of federated searching • Rigid categorization • Limited granular access • Little recombinant use of information, especially in public services to provide context or improve searching, vetting, relevance, and decision making

  31. What Research Libraries Don’t Do Well • Human intensive processes • Limited external linking to relevant sites • Poor marketing of services and products • Limited embedding in other domains to advertise services and provide direct access to resources

  32. Number 1 Library in College Category

  33. Most Popular Library Site on the Web

  34. Relevance Ranking at NLM

  35. RLG’s RedLightGreen Prototype

  36. Possible Next Steps? • Increase sense of community, document sharing, message sharing • Repurpose data we already collect and use it in serving our public • Abstracts, indexes, reviews • Subject headings/classification schemes • Circulation data • Recent acquisitions

  37. Possible Next Steps? • From CUL to Borrow Direct--make links to external sources overt • Provide tracking information on delivery options (including purchase) • Take the library into places where users go • Track use and prepare to adjust

  38. Conclusion: Two Quotes • “I don’t think people want a search engine, I think they want a find engine.” Seth Godin, Washington Post • “Netflix… seems to use an honest collaborative recommendation engine…it stocks almost everything and has done much to increase the visibility of foreign and independent films, and we’ve had excellent service” Walt Crawford, Cites & Insights

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