1. Understanding User Behavior Carol Tenopir University of Tennessee ctenopir@utk.edu

2. Disclaimers There is no one “user”, only indicative user types of groups. User behavior is like evolution. We are talking about averages of groups or subsets—typical behaviors.

3. Three Main Points Expectations grow faster than technology. It must appear simple, but be complex on the inside. Differences in information behavior are due to subject discipline, workplace, role of user, etc.

4. Expectations grow faster than technology.

6. Credit: NASA, The Hubble Heritage Team and A. Riess (STScI)NASASTScI

7. If I may be so bold as to inquire, to what degree do you wish to interact with Franz Kafka?

8. Search Experts Want: Control over the search process (want to see what is happening) Boolean logic Sorting by date or relevance ranking Field searching Drop down menus or commands

9. Subject Experts Want: More information/sources Larger backfiles Easy desk-top access, including links No barriers to use (no fees, passwords, etc.) High quality materials

10. Students Want: “If I can’t find it in 30 seconds, it’s not worth finding.” “Students want what they can print out. Immediate gratification. They need it now. Quick and easy.” “If something is from.edu it has credibility.”

11. NSF Phase 2 Tentative Conclusions: Most students use feature labeled “easy search” or “quick search” One male engineering student used browsing by subject clusters of journals (even though he didn’t recognize specific titles) No students used help files or search tips (faculty did) Few use field limitation or advanced features

12. http://web.utk.edu/~tenopir/nsf/ index.html http://www.clir.org/pubs/reports/ pub120/pub120.pdf

13. “Excuse me, I’m lost. Can you direct me to the information superhighway?”

14. The simpler it appears, the more complex it must be.

19. Understanding differences in experts’ information behavior.

20. Data From: 16,000 + scientists and social sciences 1977 to the present University and non-university settings

21. Average Time Spent and Number of Articles Read Per Year Per Scientist

22. Scholarly Article Reading Work FieldArticles Reading (Per Year) Time Spent (Hours) Time Per Article (Min) University Medical Faculty ~32211822 Chemists~27619843 Physicists~20415345 Engineers~729781

23. Sources of Readings Scientists appear to be reading from more journals—at least one article per year from approximately 23 journals, up from 13 in the late 1970s and 18 in the mid-1990s. % and amount of readings from separate copies use of personal subscriptions

24. “Patterns of Journal Use by Scientists Through Three Evolutionary Phases.” D- Lib Magazine v. 9, no. 5 (May 2003). http://www.dlib.org/dlib/may03/king/ 05king. html

25. Stages in Electronic Journal Reading Early Evolving Advanced (1990-1995) (2000 ) (2001 ) Electronic emphasis Mix of print and electronic ~35% total readings from electronic ~80% total readings from electronic Almost all print Pre-web

26. How Scientists Learned About Articles Early Evolving Advanced Browsing Online Search Citations Colleagues 58% 46% 21% 16% 22% 21% 6% 13% 16% 9% 14% 39%

27. How Scientists Learned About Articles Electronic versions provide additional functions (searching, citation linking) which replace some browsing Online Searching by Topic Browsing Complete Journals

28. Browsing Searching Core titles Current issues Background Current awareness New topics Old articles Primary research For writing

29. Sources of Reading

30. Age of Reading from Digital Media

31. Conclusions for Subject Experts More reading in all work fields in not much more time Users prefer convenience and familiarity Browsing journal titles for core sources (print or electronic) Searching separates and other means (citation links) for additional sources

32. Conclusions for Subject Experts continued Amount of reading of older materials remains steady (and older materials are reported to be more valuable) Complete journals and databases of separates will coexist. Differences in work fields and levels of users (build it right and they will come.)

33. Overall Conclusions Understanding user behavior to improve systems is never complete The simpler a system appears, the more complex it must be Subject experts, search experts, and novices all have different styles and desires (and simpler is in the eye of the beholder)