Introduces the use of signaling theory as a decision aid in the people sensemaking process in deciding whom to contact for expertise. This work was presented at CHI 2009 in Boston, MA, USA.

1. Making sense of strangers’
expertise from signals in
digital artifacts
N. Sadat Shami, Kate Ehrlich, Geri Gay, Jeff Hancock

3. Proliferation of online information
“An abundance of information leads to a poverty of
attention”
- Herbert Simon

4. Outline of talk
 Research question
 Prior research
 expertise search, self presentation
 The use of signaling theory as a decision aid
 Study design
 Findings

5. General Research Question
 ‘People sensemaking’
 When looking for specific expertise using a tool, how
do individuals make sense of different information
about a stranger conveyed through digital artifacts?

6. Context of study
 Context
 Finding an expert to contact
 Evaluating them by by viewing
online profile
 Usually only after personal
networks are exhausted (Borgatti
& Cross, 2003; Cross & Sproull,
2004)

7. Prior research
 Expertise search
 Many tools built to find experts (Terveen &
McDonald, 2005)
 Focus on finding ‘best expert’
 Less attention on finding people likely to respond

8. Prior research
 Self presentation
 Selective self presentation (Goffman, 1959)
 Identity claims and behavioral residue (Vizier &
Gosling, 2004)
 Profiles on social networking sites (Donath, 2007;
Lampe et al. 2007)
 Deception can occur (Hancock et al. 2007)

9. Signaling theory
 Interpretive framework
 Theory of communication
 Process of discerning and interpreting
conveyed information
 Useful for decision making under
uncertainty where deception can occur

10. Signaling theory
Reliable signals are pieces of information that are hard to
fake (Spence, 1973; Zahavi, 1975; Zahavi & Zahavi, 1997)

11. Signals in digital artifacts
 Assessment signals
 Quality correlated with trait
 Quality is ‘wasted’ in production
 Conventional signals
 Need not possess the trait
 Social norms and mores maintain quality
Based on Donath, in press

12. Signals of expertise in digital artifacts
Conventional signal Assessment signal

13. Study: Making sense of the different pieces of
Information on a profile page

14. Enterprise expertise locator system
 SmallBlue, renamed to Atlas™ (Ehrlich et al. 2007;
Lin et al., 2008)
 Convenient platform for research
 Description
 Mines outgoing email and
instant messaging transcripts
 Data aggregator
 Opt in system

15. Participants
 Email invitation
 Performed at least 20 searches using SmallBlue
 131 employees, 67 responded (51.15%)
 Demographics
 21 countries (majority US - 43.75%)
 48 males, 19 females
 Average tenure 10.5 years
 Majority from consulting or sales (37.5%)

16. User Task
 Find an expert in ‘AJAX’
 On a committee evaluating a new project
proposal. Need a second opinion on whether
AJAX is appropriate for the project.
 Why ‘AJAX’?
 Among top searches in SmallBlue

17. “AJAX” scenario: User Task
Shami, Ehrlich, Millen, CHI 2007, Pick me! Link selection in expertise search

18. Corporate Recommended Mailing list
directory and alternate connection paths membership
information
Corporate
directory self Social
reported bookmarking
expertise tags
Blog posts
Forum posts
Social bookmarks

19. Self reported rating data
 Outcome variable (1-9 scale)
 Likelihood of contacting a person
 Predictor variables (1-9 scale)
 Social software (tags + blogs + forums)
 Social connection information
 Mailing list membership
 Corporate directory info
 Self-described expertise
 Control variables
 Familiarity with AJAX

20. Profile data
 Outcome variable
 Likelihood of contacting a person (1-9 scale)
 Predictor variables
 Participation in social software i.e. count of: tags +
blog posts + forum posts (0-1100)
 Social closeness (0-6)
 Mailing list membership (0-13)
 Control variables
 Familiarity with AJAX

21. Results from rating data: Social software
 For each point increase in perceived helpfulness of social
software, likelihood of contact increased by 0.33 points (p < 0.01).
“People who use dogear or IBM Forums are more likely
to reach out to the community with their questions and
their expertise and therefore I would think they would
be more likely to assist in sharing their own expertise.”

22. Results from rating data: social closeness
 For each point increase in perceived helpfulness of social
connection information, likelihood of contact increased by 0.37
points (p < 0.01).
“I know the people that the system recommended to go
through. If I contact them, I'll be able to get straight to him.”
“...it wouldn't be too much of a cold call to say ‘hi, I understand
you know my colleague so and so, I'm calling you about this
other topic.’ I guess it would make me feel more comfortable
knowing that I could sort of name drop.”

23. Results from profile data (counts)
 Posting one more tag, blog, or forum post increased
likelihood of contact by 0.01 points (p < 0.001).
 Each degree increase in social closeness corresponds to a
0.29 point increase in likelihood of contact (p < 0.01)

24. Signaling theory as a decision aid
 Signaling theory in ‘people sensemaking’
 Focus on information that is hard to fake
 More credible, reliable, less open to deception
 Social software related to approachability
 Social connection information related to accessibility and
verifying expertise.

25. Implications for design
 ‘Page rank’ for experts
 Analyze structural patterns to find ‘answer people’ (Wesler
et al., 2007)
 No systematic analysis of social software
 Find others likely to respond

26. Thank you!
sadat@us.ibm.com
http://www.research.ibm.com/social