Sunbelt INSNA '15 presentation Group Closure and Brokerage: Social Capital and Group Effectiveness in MMOGs By: Grace A. Benefield and Cuihua Shen

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Group Closure and
Brokerage: Social Capital
and Group Effectiveness in
MMOGs
Grace A. Benefield
Cuihua Shen
Communication
University of California, Davis
INSNA ‘15
Brighton, UK
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Massively Multiplayer Online Role-
Playing Games (MMORPGs)
• Players develop avatar  interact with other users
in virtual world
• Earn money
• Make transactions
• Chat
• Can be similar to a second job
• Many play average 20 hours/wk (Yee, 2006)
• Requires teamwork, creativity, long hours to progress
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Types of MMOG Teams
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Pick up Groups (PUGS)
•Short-term
•Members may be
unique or repeated
•Group together to beat
a
level/dungeon/monster
then separate
Guilds
•Longer term
•More stable social
structure
•Different purposes
•Gain access to
resources

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MMOGs as a test bed for org’l
research
(Assmann et al., 2010)
•More diverse population
•Western, university-students in lab
experiments
•Individual and group-level processes
•Longitudinal studies of real teams
•Studies on leaders
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Research directions
How does social structure
within and across teams
affect group performance?
Does the social structure
differ from a corporate
organization?
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Group Social Capital
•DV: Group-level performance
measure
•IVs: Group-level social capital
measures
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Within Teams
Low Closure High Closure
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H1
Closure and group effectiveness
 Inverted U relationship (Oh, Chung,
Labianca, 2004)
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Across Teams
•Diverse ties to
other teams
•Leader to leade
ties
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H2 and H3
Intergroup Bridging Diversity
Group Effectiveness
Leadership Centrality
Group Effectiveness
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Data
• Chinese version
• DN
• Fantasy game developed by Eyedentity Games and
Nexon
• Available in Korea, China, North America, South East
Asia, Europe
• Free to play
• Purpose: awaken a poisoned goddess
• Defeat dungeons and dragons
• Discover power stones
• Players can interact with others:
• Chat
• Teams
• Guilds
• Trading currency/items
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Sampling
•Guilds
•Started during the collection period
•>3 characters in the guild on the last day of
collection
•804 total guilds (Max = 97 guild members)
•Guild members
•11,549 characters
•Level (Min = 2, Max = 40)
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Player Networks
Social
• Tie – User connects
to alter as “friends”
Task
• Tie – User and alter
play together in a
team during
collection period
• Weight – # of shared
teaming instances
Exchange
• Tie – User and alter
trade together
during collection
period
• Weight – Total # of
trade transactions
*Both user/alter must be in the sample of guild members
R-squared = .25 R-squared = .05
R-squared = .13
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Nodes
Nodes = guilds
Node size = degree
centrality
Node color = # of
guild character
members
Edges
Purple = Task ties
Blue = Exchange ties
Pink = Social ties
Inter-Group Network
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Three Guild-Guild Networks
Social Task Exchange
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DV
Guild Effectiveness
•Average of the total guild points a guild
has on the last day of the collection
period
•Guild points  complete quests
•Advance guild level  guild pts,
currency, a minimum number of
guild members
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Controls
•Character count
• Total number of guild members on the last day
of collection
•Average guild member level
• Sum of all the guild member’s max levels / # of
characters in a guild
•Total intragroup ties
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IVs – Intragroup Closure across 3
networks
•Density
•Density squared
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IVs - Intergroup Brokerage
•Bridging diversity (Blau, 1977)
• 𝐵𝑑 = 1 − 𝑝𝑖
2
*Pi
• Pi = proportional tie influence of each
group’s ties based on the total number of
groups
• Ranges from 0 to 1
•Leader degree centrality
• Sum each guild leader’s total number of ties
with other leaders
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Results Social Task Exchange
Model 4 Model 4 Model 4
7.75*** 7.11***
7.64***
Controls
Guild members 0.03*** 0.02 ***
0.02***
Experience 0.19*** 0.11***
0.13*
Total ties 0.01 0.01*** -0.01
Closure
Density 0.73 16.49*** 4.58
Density sqd -2.73** -19.29** -6.85*
Brokerage
Bridging diversity 0.20 0.99*** 0.64***
Leader centrality 0.02 0.01* -0.01
R 2
0.17 0.29 0.22
F 23.46*** 46.27*** 31.15***
Infl Pt. 0.10 0.27 0.75
*** p < .001; ** p < .01; * p < .05
•Group members
•Average experience
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Results Social Task Exchange
Model 4 Model 4 Model 4
7.75*** 7.11***
7.64***
Controls
Guild members 0.03*** 0.02 ***
0.02***
Experience 0.19*** 0.11***
0.13*
Total ties 0.01 0.01*** -0.01
Closure
Density 0.73 16.49*** 4.58
Density sqd -2.73** -19.29** -6.85*
Brokerage
Bridging diversity 0.20 0.99*** 0.64***
Leader centrality 0.02 0.01* -0.01
R 2
0.17 0.29 0.22
F 23.46*** 46.27*** 31.15***
Infl Pt. 0.10 0.27 0.75
*** p < .001; ** p < .01; * p < .05
•Density sqd
• Negatively related
• All 3 networks
•Peak
•Social .10
•Task .27
•Exchange .75
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Results Social Task Exchange
Model 4 Model 4 Model 4
7.75*** 7.11***
7.64***
Controls
Guild members 0.03*** 0.02 ***
0.02***
Experience 0.19*** 0.11***
0.13*
Total ties 0.01 0.01*** -0.01
Closure
Density 0.73 16.49*** 4.58
Density sqd -2.73** -19.29** -6.85*
Brokerage
Bridging diversity 0.20 0.99*** 0.64***
Leader centrality 0.02 0.01* -0.01
R 2
0.17 0.29 0.22
F 23.46*** 46.27*** 31.15***
Infl Pt. 0.10 0.27 0.75
*** p < .001; ** p < .01; * p < .05
•Bridging
• Task
• Exchange
•Leader Centrality
•Task
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Discussion
Successful teams – for any network –
are bigger, experienced, with a
curvilinear relation with closure
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Discussion
Social network  teams with less
intragroup density were more
successful
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Discussion
Achievement-oriented networks 
teams with a moderate to high
intragroup density were more
successful
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Discussion
Achievement-oriented networks 
high brokerage may have a greater
impact on performance
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Limitations
•Sample newly formed guilds (3 month
period)
•Do digital traces reflect actual
strength of ties?
•One case study of Chinese players
•Do the results expand across players?
MMOGs?
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Strengths
•Find similar social structures in both
organizational work groups (Oh et al.,
2004) and an MMOG
•Examine multiple types of networks
•Further research on self-organized
teams
•Use unobtrusive behavioral data
instead of surveys
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Acknowledgements
Help and comments from faculty and
student participants of the Virtual
World Observatory
(www.vwobservatory.org) are
instrumental to the work reported here.
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Thank you!
Questions? Comments?
Suggestions?
Grace A. Benefield
grbenefield@ucdavis.edu
@grbene
Cuihua Shen
shencuihua@gmail.com
@cuihua
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