Increasing global competition and pressures to drive down costs are eroding long-term relationships in project networks. This relational instability, when coupled with task interdependence in project networks, can impede the rate at which networks can absorb innovation and organizational changes. If the pace of change exceeds the capacity of a project network to gain productive use of the innovation or organizational change, it can lead to negative productivity growth. This research initiative integrates the results of lab experiments, empirical network analyses, and in-depth fieldwork of the lab into a general purpose multi-agent simulation to develop project network dynamics theory.

According to a recent report based on data from the Bureau of Labor Statistics, the average rate of construction labor productivity has decreased over the last several decades. According to the same report, over the same time-period the labor productivity for all non-farm industries (including construction) has increased substantially. Although many attribute this change in on-site productivity to be a result of increasing use of off-site prefabrication, there has been a change in the stability of the business and operational environment. This change impacts what strategies will lead to the best productivity for organizations and project networks of organizations. The ability to adapt to this change is a determining factor in predicting which organizations will adapt and survive and which will be unable to adapt and will ultimately fail.

In this research initiative we are examining productivity through the lens of organizational and interorganizational adaptation performance in project networks. We are developing a general purpose multi-agent simulation model which enables a user to define the relationship between nodes in the network, to populate the network with nodes that differ in terms of type of specialization, to set interaction behaviors (e.g., contracting strategies) and interaction efficiency (e.g., collaboration effectiveness). The simulation then predicts network adaptation performance over time when boundary spanning innovation or organizational change occurs. We have employed the Network Dynamics Simulation to model; performance impact of learning decay, exploitation of knowledge asymmetries, the economic hold-up problem in subcontractor selection, absorptive capacity of networks, innovation diffusion curves, and simulations of energy use behavior and practice diffusion through building occupant networks.

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Jiayu Chen
Assistant Professor, City University of Hong Kong

Raymond Levitt
Professor, Stanford University

Anna Laura Pisello
Post-doctoral Researcher, University of Perugia

Chris Tucci
Professor, Swiss Federal Institute of Technology Lausanne (Switzerland)

Andrei Villarroel
Assistant Professor, Catholic University of Portugal

Xiaoqi Xu
Post-doctoral Researcher, Harvard University


Ryan Wang
PhD Student, Virginia Tech