پیش بینی عملکرد مدیریت پروژه
Abstract
1- Introduction
2- Literature review on project success/failure modeling
3- Complexity theory tenets
4- Methods
5- Findings
6- Discussion
7- Conclusion
References
Abstract
Applying complexity theory tenets, the study here contributes an asymmetric modeling perspective for examining resources orchestrations that indicate high (separately low) project management performance (PMP) accurately. Complexity theory tenets include recognizing that the causal conditions resulting in high PMP have different conditions (i.e., ingredients) typically than the causal conditions resulting in low PMP—adopting this perspective supports the usefulness of asymmetric point or interval estimation rather than the currently pervasive symmetric approach to theory construction and empirical modeling of variable directional relationships (VDR). This study constructs a general model and specific configurational propositions that include social capital, processes, and knowledge management effectiveness as causal conditions indicating case outcomes of high and separately low PMP. Using survey data, the study includes examining propositions and models empirically on the causal conditions for completed projects (n = 302, USA sample of executives in product and service industrial firms). The findings support the perspective that high (as well as low) PMP depends on resource orchestration (configurational) antecedent conditions. The findings serve to support the general proposition that applications of complexity theory in project management research respond effectively in building in the requisite variety for deep understanding and accurate forecasting of performance outcomes. This study includes contributions to theory and empirical research that support the perspective that separate sets of resource orchestrations of alternative complex antecedents (rather than a VDR, symmetric, net effects perspective) forecast high (low) project management performances accurately.
Introduction
Gigerenzer’s (1991) wisdom underpins the present study: what tools scientists use influences the theories that they construct and restricts/focus their data collection and interpretation of findings. He further notes, “The power of tools to shape, or even to become theoretical concepts is an issue largely ignored in both the history and philosophy of science” (Gigerenzer, 1990: 254). The pervasive adoption of correlation (r), multiple regression analysis (MRA), and structural equation modeling (SEM) in constructing and testing in research in general along with null hypothesis significance tests (NHSTs) without questioning the usefulness of these tools versus alternative tools illustrate the two quoted statements from Gigerenzer (1991). Given their pervasive use in project management, and the telling weaknesses of using r, MRA, and SEM as Armstrong (2012), Fiss (2007, 2011), Hubbard (2015), McCloskey (2002), Trafimow and Marks (2015), Wasserstein and Lazar (2016), Woodside (2019), and Ziliak and McCloskey (2008) describe, supports the call that the present study illustrates theoretically and empirically for moving away from symmetrical to asymmetrical theory construction and empirical analysis. This study proposes and tests two sets of issues in project management research.