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Individual and team learning & performance in a virtual context: a social network & system dynamics approach


Traditionally, most social network studies have been associated with individual or organizational outcomes such as learning, coordination and performance. While such formal social network analysis techniques capture the world-view of relations and structures of human and organisational systems using a "moment in time" or a "snapshot of the current fabric of relations" analogy, as in most traditional correlational research, it is inevitable that structures, ties and positions of such systems vary over time. Human and organisational systems are therefore comprised of dynamic networks with self-organising, self-adapting and an evolving web of relations. Therefore, in dynamic networks, this translates to variation in uncertainty in networks as its properties change (e.g. multi-modes and multi-links) longitudinally. The relations are therefore probabilistic which requires non-traditional methods and special treatment of network-based statistical analyses. Questions that motivate this research are: (i) are complex and dynamic networks able to account for variation in learning and performance of individuals and team in a virtual collaboration network? (ii) if so, what are the important predictors of performance and learning that characterize complex and dynamic networks such as structure, position and ties?


Dr Kenneth Chung.

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This project will involve analysing dynamic and complex social networks by using probabilistic network statistical modelling techniques based on longitudinal data. It aims to examine how collaboration networks dynamically evolve over time by keeping the actors constant (i.e. the network is a sociocentric network), and by studying the variations of network ties periodically and its impact on individual and team learning, measured by a performance metric, accompanied by visual sociograms. The theoretical and methodological motivations from this research can be applied for living, human, organisational, computational and biological systems in order to further the understanding of how complex systems dynamically self-organise, vary, coordinate, learn, perform, absorb feed-back and evolve over time.

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The opportunity ID for this research opportunity is 1379

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