Associations between micro- and macro level social network properties and individual productivity in virtual collaboration - Dongning Deng; Julia Koltai (Scientific Reports)
Abstract Although the connection between social network properties (SNPs) and team productivity has been studied extensively, there is still room to deepen our understanding, particularly regarding individual-level dynamics, the non-linear nature of these relationships, and the interactions between individual and structural factors. To do this, we analysed 58 Open Source Software Development (OSSD) projects, using a comprehensive set of SNPs and measuring individual productivity by code editing contributions. Our findings reveal that SNPs have significant and complex dynamics in their associations with individual productivity. Highly productive individuals present SNP traits with a moderate number of connections, being indirectly connected but having influential peers, and being in a decentralised yet locally cohesive environment. Centralised team structure with direct connection with central nodes or influential clusters benefits individual productivity, especially for those who are peripheral or have powerful peers. The highly productive members in the influential clusters also form and reinforce “coordination chambers”. Low individual productivity or even the free riding phenomenon may be more prevalent in a highly closed local and global environment. This is especially true when the structure is not diverse. Taking on a brokerage role with access to diverse knowledge is generally key to active participation, especially when connections are non-redundant. However, productivity may suffer when individuals become too embedded in the bridging role. To minimise the cost of such brokerage role, how and where to be a broker matters. One can become active either with unique ties in networks with centralised bridging brokerage, decentralised accessibility, or clustered structure, or bridging disconnected groups in less clustered but locally cohesive networks with evenly-distributed influence. Our analytical framework shows how non-linear and contextual interaction dynamics can be uncovered using social network and statistical methods. The findings inform not only how open-source workspaces can be better structured according to governance goals, but also potential inequalities in OSSD teams and a possible approach for more open and inclusive team structures.