  This paper is about the possible negative impact of excessive collaboration
on the performance of top employees. With the rise of participatory culture and
developments in communications technology, management practices require greater
conceptual awareness about possible outcomes of increased organizational
interconnectivity. While there exists a sound theoretical basis for possible
burdens brought by collaborative overload, the literature never really manage
to measure and empirically test this phenomenon. We address this gap by
developing a methodological framework for the identification of organizational
actors at risk of operational capacity overload. Drawing on social network
analysis as the widely applied approach for the estimation of employees'
involvement in the information exchange networks, this paper describes
potential personal and organizational causes leading to the emergence of
collaborative overload. Relying on primary data gathered through a survey
conducted among employees in a large insurance company, we present a testable
model for overload detection. A second merit of the paper consists in finding a
novel identification strategy for empirical works on cross-sectional network
data, which often face the issue of endogeneity. This research suggests that
active collaborative activity does not cause a decrease throughout every aspect
of performance. We found that expertise sharing depends on a few key players
who take core knowledge assets upon themselves and thus run higher risks of
exposure to overload.
