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Group Closure and Brokerage: Social Capital and Group Effectiveness

in MMOGsGrace A. Benefield

Cuihua Shen

Communication

University of California, Davis

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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

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

grbenefield@ucdavis.edu@grbene

Cuihua Shenshencuihua@gmail.com @cuihua