Which Organizational and Individual Factors Predict Success vs. Failure in Procurement Projects

Abstract

Project manager (PM) certification and other commonly-tested independent factors were deductively examined using logistic regression to develop an explanatory model of high-priced public procurement project success versus failure. Overall 59% were successful, and 41% failed. The model correctly classified 67.3% of the 2,692 projects, yielding a significant 12% effect size. Individual factors, PM experience, certification, and contractor quality, impacted performance, but contractor size, revenue, industry type, PM age, gender, and education had negligible effect on performance. Quality was in the opposite hypothesized direction. Certified PMs were more likely to achieve successful projects rather than encounter failures. Future research advice was offered.

Impact of Individual Decision-Making Factors on Project Failure

“…Other 'a priori' factors such as PM gender, age, experience, education, and certification cannot be controlled, but they may be measured to ensure they do not confound other variables. Prior literature has found that experience and certification are sometimes related to project performance (Barrows, Clevenger, Abdallah, & Wu, 2020;Laurie et al, 2017;Pace, 2019;Strang, 2021). So, at this point, we can propose a set of control hypotheses to determine if 'a priori' factors are related to an project outcome condition, as listed below.…”

“…For several decades, project failure rates have been significantly high (Borbath, Blessner, and Olson, 2019, Eckerd and Snider, 2017, Strang, 2021. While several researchers have studied the causes of failures on an individual project basis, only a few empirical studies found individual project manager (PM) factors such as experience and certification were related to project success/failure (Anthopoulos et al, 2016, Laurie, Rana and Simintiras, 2017, Strang, 2021. Consequently, more research is needed to determine why so many PMs have failed to meet their scope, time, quality, and or budget mandates.…”

How effective is business education in the workplace: structural equation model of soft and hard skill competencies

“…In the current study, the author adopted a post-positivistic research design ideology to answer the RQ. A post-positivistic ideology refers to the researcher’s intention to focus on factual evidence to prove deductive theories through testing hypotheses: quantitative data types are preferred to facilitate analysis (Strang 2021 ).…”

“…Also having 12 items in a factor overweights it as compared to others, namely, communications with only 4 items. Additionally, having more than six items can inflate Cronbach reliability coefficients (Strang 2021 ). Furthermore, problem identification in management skills overlapped with solving technical problems in the technical factor.…”

Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods

“…One Standish Group Chaos report stated that only 29% of the IT software projects in the U.S. were considered successful by 2004 (Masticola, 2007), and a more recent analysis found the rate rose slightly to 32% by 2009 (Standish-Group, 2009). This results in an estimated IT project failure rate of 68-71% in the U.S. Other empirical studies place the IT project failure rate between 41-50% (Anthopoulos et al, 2016;Eckerd & Snider, 2017;Ghossein et al, 2018;Strang, 2021). More specifically, the IT project success/failure factors in relevant empirical studies usually included individual and or organizational attributes.…”