Risk,Process Maturity,and Project Performance: An Empirical Analysis of US Federal Government Technology Projects

In recent years, there has been increasing pressure on the US federal government to reduce spending and improve the management of its technology projects. Mitigating the adverse impact of risks on the performance of these projects presents a significant challenge for its stakeholders. Our research examines this challenge in two steps. First, we identify and define a set of salient risks in federal technology projects—specifically, complexity risk and contracting risk in the planning process, and execution risk in the execution process. Next, we investigate whether higher levels of process maturity, assessed by the Capability Maturity Model Integration (CMMI) framework, mitigate the negative effect of project risks on project performance. The analysis of time‐series data collected from 82 federal technology projects across 519 quarterly time periods indicates that each of the three types of risks has a significant negative effect on project performance. This finding highlights the practical significance of managing these risks in the federal technology project context. Further, we find that increasing levels of process maturity attenuate the negative effect of project risks on the performance of federal technology projects. However, the attenuation effects are consequential only at high levels of project risks; at low levels of project risk, increasing levels of process maturity can adversely affect project performance. To demonstrate the financial implications of increasing process maturity levels in federal technology projects, we examine the magnitude of project cost savings (and overruns) across different levels of CMMI and project risks. In summary, our study contributes to the sparse literature on public sector operations by addressing the understudied context of federal technology projects, and provides a nuanced examination of the implications of process maturity in managing the risk to performance relationship in such projects.     

Consistency of Bayesian linear model selection with a growing number of parameters

Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which provide a stochastic search of informative variables, have gained popularity. In this paper, we will study the asymptotic properties related to Bayesian model selection when the model dimension p is growing with the sample size n. We consider p≤n and provide sufficient conditions under which: (1) with large probability, the posterior probability of the true model (from which samples are drawn) uniformly dominates the posterior probability of any incorrect models; and (2) the posterior probability of the true model converges to one in probability. Both (1) and (2) guarantee that the true model will be selected under a Bayesian framework. We also demonstrate several situations when (1) holds but (2) fails, which illustrates the difference between these two properties. Finally, we generalize our results to include g-priors, and provide simulation examples to illustrate the main results.     

A Multiple Objective Embedded Network Model for Human Resource Planning and an Implementation of the Tchebycheff Method

A multiple objective embedded network model is proposed to model a variety of human resource planning problems including executive succession planning, compensation planning, training program design, diversity management and human systems design. The Tchebycheff Method, an interactive multiple objective programming solution procedure developed by Steuer and Choo [32], is implemented using NETSIDE, a computer routine for solving network problems with side constraints developed by Kennington and Whisman [17]. This paper demonstrates how the network structure common to many types of human resource planning problems can be exploited to improve solution efficiency, and how our approach extends the use of network models in human resource planning by including multiple objectives and extranetwork constraints. An illustrative example demonstrating the modeling and solution approach is presented, and the potential applications of these approaches in two specific areas of human resource planning are discussed.