Constructing Algorithms for Forecasting High (Low) Project Management Performance

Olajumoke A. Awe (), Arch G. Woodside (), Sridhar Nerur () and Edmund Prater ()
Additional contact information
Olajumoke A. Awe: Coastal Carolina University, Wall College of Business of Administration
Arch G. Woodside: Yonsei University, Yonsei Frontier Lab
Sridhar Nerur: University of Texas, Department of Information Systems and Operations Management
Edmund Prater: University of Texas, Department of Information Systems and Operations Management

Chapter Chapter 2 in Accurate Case Outcome Modeling, 2019, pp 25-55 from Springer

Abstract: Abstract 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.

Keywords: Algorithms; Configurations; KME; Performance; Project management; Social capital (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-26818-3_2

Ordering information: This item can be ordered from
http://www.springer.com/9783030268183

DOI: 10.1007/978-3-030-26818-3_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().