Improving team performance prediction in MMOGs with temporal communication networks

Autor: Jürgen Pfeffer, Siegfried Muller, Raji Ghawi
Rok vydání: 2021
Předmět:
Zdroj: Social Network Analysis and Mining. 11
ISSN: 1869-5469
1869-5450
DOI: 10.1007/s13278-021-00775-7
Popis: 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 Travian collected over a 12-month period that included 4753 teams with 18,056 individuals and their communication networks. For predicting team performance, we selected several social network analysis-based attributes frequently used in team and leadership research. We find that using these features, the accuracy of predicting the team performance, in terms of $$R^2$$ R 2 , is about 60%; whereas the accuracy of classifying the top-performing teams exceeds 95%. Moreover, we examine the ability to predict the team performance based on historic data of the network features, i.e., before several weeks. We find that the best accuracy can be achieved using the features in the present and the past, as well as the past performance. For a delay of one week, the accuracy of this model is about $$R^2$$ R 2 = 97%.
Databáze: OpenAIRE
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