Group Social Capital and Performance in MMOGs
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Transcript of Group Social Capital and Performance in MMOGs
INSNA ‘15
Group Closure and Brokerage: Social Capital and Group Effectiveness
in MMOGsGrace A. Benefield
Cuihua Shen
Communication
University of California, Davis
INSNA ‘15
Brighton, UK
1
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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
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 leader 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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NodesNodes = guildsNode size = degree centralityNode color = # of guild character members
EdgesPurple = Task tiesBlue = Exchange tiesPink = 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)• *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 ExchangeModel 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*** -0.01Closure
Density 0.73 16.49*** 4.58Density sqd -2.73** -19.29** -6.85*
BrokerageBridging diversity 0.20 0.99*** 0.64***Leader centrality 0.02 0.01* -0.01
F 23.46*** 46.27*** 31.15***Infl Pt. 0.10 0.27 0.75
•Group members•Average experience
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Results Social Task ExchangeModel 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*** -0.01Closure
Density 0.73 16.49*** 4.58Density sqd -2.73** -19.29** -6.85*
BrokerageBridging 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
•Density sqd•Negatively related• All 3 networks
•Peak•Social .10•Task .27•Exchange .75
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Results Social Task ExchangeModel 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*** -0.01Closure
Density 0.73 16.49*** 4.58Density sqd -2.73** -19.29** -6.85*
BrokerageBridging 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
[email protected]@grbene
Cuihua [email protected] @cuihua