Constructing Algorithms for Forecasting High (Low) Project Management Performance

Applying complexity theory tenets, the study here provides a unique asymmetric modeling perspective for examining causal conditions indicating high (low) project management performance (PMP). Complexity theory tenets include (tenet 1) recognizing that the causal conditions resulting in high PMP frequently have different components (i.e., ingredients) than the causal conditions resulting in low PMP—adopting this perspective supports the usefulness of asymmetric rather than the currently pervasive symmetric approach to theory construction and empirical modeling. A second complexity theory tenet is that the same causal condition can foster, be irrelevant, or inhibit high PMP, depending on how it is configured with other causal conditions—thus, high knowledge management effectiveness (KME) by itself is neither a sufficient nor a necessary causal condition for indicating all cases of high PMP. A third tenet is that the disparate configurations of causal conditions are equifinal in leading to adoption. The study here constructs a general model and specific configurational propositions that include social capital, project management types, processes, and complexity as causal conditions indicating case outcomes of high versus low PMP. The study includes examining the model and propositions empirically using survey data on the causal conditions for completed projects (n = 302, US sample of product and service industrial firms). The findings support the perspective that high (as well as low) PMP depends on combination effects—not the additive or net effects of causal conditions. For project managers, adopting a configurational approach to the study of project outcomes can reveal which combinations of causal conditions consistently lead to high PMP as well as which combinations indicate low PMP and the conditions when high KME associates with low PMP.