Group Social Capital and Performance in MMOGs
- 1. INSNA ‘15 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 1
- 2. INSNA ‘15 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 2
- 3. INSNA ‘15 Types of MMOG Teams 3 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
- 4. INSNA ‘15 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 4
- 5. INSNA ‘15 Research directions How does social structure within and across teams affect group performance? Does the social structure differ from a corporate organization? 5
- 6. INSNA ‘15 Group Social Capital •DV: Group-level performance measure •IVs: Group-level social capital measures 6
- 7. INSNA ‘157
- 8. INSNA ‘15 Within Teams Low Closure High Closure 8
- 9. INSNA ‘15 H1 Closure and group effectiveness Inverted U relationship (Oh, Chung, Labianca, 2004) 9
- 10. INSNA ‘15 Across Teams •Diverse ties to other teams •Leader to leade ties 10
- 11. INSNA ‘15 H2 and H3 Intergroup Bridging Diversity Group Effectiveness Leadership Centrality Group Effectiveness 11
- 12. INSNA ‘15 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 12
- 13. INSNA ‘15 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) 13
- 14. INSNA ‘15 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 14
- 15. INSNA ‘15 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 15
- 16. INSNA ‘15 Three Guild-Guild Networks Social Task Exchange 16
- 17. INSNA ‘15 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 17
- 18. INSNA ‘15 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 18
- 19. INSNA ‘15 IVs – Intragroup Closure across 3 networks •Density •Density squared 19
- 20. INSNA ‘15 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 20
- 21. INSNA ‘15 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 21
- 22. INSNA ‘15 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 22
- 23. INSNA ‘15 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 23
- 24. INSNA ‘15 Discussion Successful teams – for any network – are bigger, experienced, with a curvilinear relation with closure 24
- 25. INSNA ‘15 Discussion Social network teams with less intragroup density were more successful 25
- 26. INSNA ‘15 Discussion Achievement-oriented networks teams with a moderate to high intragroup density were more successful 26
- 27. INSNA ‘15 Discussion Achievement-oriented networks high brokerage may have a greater impact on performance 27
- 28. INSNA ‘15 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? 28
- 29. INSNA ‘15 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 29
- 30. INSNA ‘15 Acknowledgements Help and comments from faculty and student participants of the Virtual World Observatory (www.vwobservatory.org) are instrumental to the work reported here. 30
- 31. INSNA ‘15 Thank you! Questions? Comments? Suggestions? Grace A. Benefield grbenefield@ucdavis.edu @grbene Cuihua Shen shencuihua@gmail.com @cuihua 31