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The effects of individual status and group performance on network ties among teammates in the National Basketball Association

MPG-Autoren
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Koster,  Jeremy
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Koster_Effects_PloSOne_2018.pdf
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Zitation

Koster, J., & Aven, B. (2018). The effects of individual status and group performance on network ties among teammates in the National Basketball Association. PLOS ONE, 13(4): e0196013. doi:10.1371/journal.pone.0196013.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-4FA3-F
Zusammenfassung
For individuals, status is derived both from their personal attributes and the groups with whom they are affiliated. Depending on the performance of their groups, the status of individuals may benefit or suffer from identifying closely with the group. When the group excels, high-status members potentially receive much of the credit and increased status. Conversely, high-status members of underperforming groups potentially suffer disproportionate declines in their status relative to the low-status group members. We therefore predict an interaction between group performance and individual status on the willingness to associate with the group and its members. We test our prediction by examining social media ties among teammates in the National Basketball Association. Specifically, we investigate the “following” ties of teammates on Twitter at the end of the 2014–2015 season. Elections to All-Star games are used to measure the status of players, and team performance is measured by recent success in the postseason playoffs. The results show that compared to high-status players on successful teams, high-status players on underperforming teams are less likely to follow their teammates. This result aligns with research on status inconsistency, suggesting that individuals deemphasize their group affiliation when it jeopardizes their individual status. An additional contribution is the advancement of the probit Social Relations Model for the analysis of binary ties in social networks.