TY - GEN
T1 - Using Communication Networks to Predict Team Performance in Massively Multiplayer Online Games
AU - Muller, Siegfried
AU - Ghawi, Raji
AU - Pfeffer, Jurgen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Virtual teams are becoming increasingly important. Since they are digital in nature, their 'trace data' enable a broad set of new research opportunities. Online Games are especially useful for studying social behavior patterns of collaborative teams. In our study we used longitudinal data from the Massively Multiplayer Online Game (MMOG) Travian collected over a 12-month period that included 4,753 teams with 18,056 individuals and their communication networks. For predicting team performance, we selected 13 SNA-based attributes frequently used in team and leadership research. Using machine learning algorithms, the added explanatory power derived from the patterns of the communication networks enabled us to achieve an adjusted R2 = 0.67 in the best fitting performance prediction model and a prediction accuracy of up to 95.3% in the classification of top performing teams.
AB - Virtual teams are becoming increasingly important. Since they are digital in nature, their 'trace data' enable a broad set of new research opportunities. Online Games are especially useful for studying social behavior patterns of collaborative teams. In our study we used longitudinal data from the Massively Multiplayer Online Game (MMOG) Travian collected over a 12-month period that included 4,753 teams with 18,056 individuals and their communication networks. For predicting team performance, we selected 13 SNA-based attributes frequently used in team and leadership research. Using machine learning algorithms, the added explanatory power derived from the patterns of the communication networks enabled us to achieve an adjusted R2 = 0.67 in the best fitting performance prediction model and a prediction accuracy of up to 95.3% in the classification of top performing teams.
KW - Communication Network
KW - Machine Learning
KW - Massively Multiplayer Online Game
KW - Performance Prediction
KW - Social Network Analysis
KW - Virtual Teams
UR - http://www.scopus.com/inward/record.url?scp=85103685886&partnerID=8YFLogxK
U2 - 10.1109/ASONAM49781.2020.9381481
DO - 10.1109/ASONAM49781.2020.9381481
M3 - Conference contribution
AN - SCOPUS:85103685886
T3 - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
SP - 353
EP - 360
BT - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
A2 - Atzmuller, Martin
A2 - Coscia, Michele
A2 - Missaoui, Rokia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Y2 - 7 December 2020 through 10 December 2020
ER -