Name
Title | ||
|---|---|---|
Using Communication Networks to Predict Team Performance in Massively Multiplayer Online Games |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ASONAM49781.2020.9381481 | 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) |
Keywords | DocType | ISSN |
Performance Prediction,Virtual Teams,Social Network Analysis,Communication Network,Machine Learning,Massively Multiplayer Online Game | Conference | 2473-9928 |
ISBN | Citations | PageRank |
978-1-7281-1057-8 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siegfried Müller | 1 | 1 | 0.35 |
| Raji Ghawi | 2 | 2 | 1.73 |
| Jürgen Pfeffer | 3 | 1 | 1.02 |