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Computer Games: Learning, Meaning and Method (London Knowledge Lab, January 2007)
Citation preview
Making e-friends and influencing people in
Second LifeAleks Krotoski
University of SurreySPERI
What I’ll talk about
• Interpersonal relationships in cyberspace
• How I measure relationships in Second Life
• How relationships are defined
Before I get ahead of myself
• The differences between online and offline:– Anonymity– Physical appearance– Physical proximity– Greater transience (more weak ties)– Absence of social cues
• So how can we expect community to grow?
Online community I
• In traditional definitions of “community”, there’d be no such thing in cyberspace– Tied to place – To misquote AOL ads, how can you fall for
someone you’ve never met?
• But we know that’s not true– Chatrooms, forums, MySpace, Craig’s List,
London Memorial
• These virtual worlds are the places which the online communities are tied to
Online Communities I (cont)
• Transient and formal communities– London Memorial in the virtual world Second Life– Between 12-1pm on 7 July 2005, over 150 Second Life residents
visited. It was open for 7 days and racked up thousands of visitors– Fewer than 10% claimed any British ties– Maker’s motivations were altruistic and purely community-driven
Online community II
• Form for the same reasons offline communities do:– Make friends, provide motivation, offer support, meet
like-minded others
• Whatever role trust plays in offline communities, it plays in online communities because these interactions are human-bound
• What we know about online relationships– Proximity and frequency of contact– Similarity– Self-presentation– Reciprocity & self-disclosure– Consistency
• Perpetuity: don’t mess with the orc if you’ve already PO’d the governor.
Trust in virtual communities I: we’re all in it together
• Returning to Anonymity– Perceived similarity (levelling the
playing field)– No social cues, so lots of
uncertainty– Expectations of openness and
honesty engenders a culture of mutual sharing
• Relevant Social Psychological dimension of trust– Similarity of goals and values– Expectations of future interaction
Trust in virtual worlds III: Rep (cont)
• Trust is based upon– past experience…– …which is either based upon functional goals or pre-existing
social relationships…– …or some kind of disinterested third party (e.g., Craig’s List or
MySpace)
• And speaking of social networking applications, the same principles work in-world too
• Finally, you must comply:– A non-official policing force in a space where an
official police is absent– The emphasis is on friendship and dedication to the
group– Rejection is cruel
How measure friendships? Social Network Analysis
…studies social relationships as a series of interconnected webs.…focuses on inter-relationships rather than individuals’ attributes
Asking personal questions
• Surveys– Who do you know?
• Who do you communicate with?• Who do you trust?
– Define your relationship:• Who’s trustworthy? (Poortinga & Pidgeon, 2003;
Cvetkovich (1999); Renn & Levine, 1991)• Who’s credible? (Renn & Levine, 1991)• Who do you compare yourself with? (Lennox &
Wolfe, 1984)• Who’s the most prototypical?
ResultsN (respondents)
= 33N (total
network) = 650
Picking apart communication network closeness
• But what does it mean in Second Life if someone in this community is rated “close” or “distant”?
Results: Single explanatory variable (General Communication)
y β0 (Std. Error)
β (Std. Error)
σ2e
Loglikelihood (fixed model LL)
Prototypicality 0.026 (0.101)
0.305 (0.066)
0.543 (0.035)
1292.354T (1335.299)
Credibility -0.093 (0.102)
0.519 (0.071)
0.531 (0.035)
1272.354T
(1404.954)
Social Comparison -0.098 (0.118)
0.399 (0.064)
0.408 (0.027)
987.966T
(1132.416)
General Trust -0.135 (0.098)
0.645 (0.064)
0.408 (0.027)
1114.31T
(1345.777)
*N=538; **N=539; σ2e: variance accounted for between avatars; Tp<0.000, df=2
• The greatest prediction comes from general trust followed by credibility, which is not surprising, as this is proposed in Sherif’s (1981) contact hypothesis.
Single explanatory variable: General Trust & SNC
categoriesExplanatory Variable
β0 (Std. Error)
β (Std. Error)
σ2e Loglikelihood
(fixed model LL)
Online Public Communication
0.085 (0.093)
0.370 (0.052)
0.476 (0.031)
1124.182T
(1345.777)
Online Private Communication
0.070 (0.094)
0.442 (0.062)
0.407 (0.027)
1115.396T
(1345.777)
Offline Communication
0.070 (0.090)
0.459 (0.047)
0.427 (0.028)
1159.681T
(1345.777)N=539; σ2
e: variance accounted for between avatars; Tp<0.000, df=2
• Effect of interpersonal closeness on mode of communication (e.g., Garton et al, 1997)
• Offline communication contributes the most to the estimate of General Trust. Online public communication contributes the least.
In Sum• Closeness has implications for influence and
persuasion, even in the virtual environment• Virtual communities operate in very similar ways
to other communities – both on and offline• They bring together distributed individuals based
on common experience, motivations and reputation
• This is particularly true for virtual world participants because of the explicit social design of the software
• Trust varies according to communication medium• Trust is paramount
Thank you!Aleks Krotoski (Mynci Gorky)