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Although organizations regularly execute projects to improve their performance, there is still no agreement in the literature on how to evaluate their eventual success. As a result, scholars end up using different scales to measure the same outcome variable of project success, thereby causing inconsistency in research results. Existing project success evaluation models suffer from their inability to apply to all project types and lack of separation of project success measurement from that of project individuals’ performance (for e.g., of the project manager). Through two longitudinal studies based on the satisficing theory, this paper develops, validates, and illustrates generic scales to measure the success of any project, as well as the performance of its two key leaders. This results in three distinct project success dimensions: 1) Project management success—evaluates the performance of the project manager in achieving the project plan; 2) Project ownership success—evaluates the performance of the project owner in realizing the business case; and 3) Project investment success—evaluates the investment performance of the project for its funder. This paper contributes to the literature by offering a theoretically robust, multidimensional evaluation model that will enhance performance evaluation of both projects and their leaders. [ABSTRACT FROM AUTHOR]

With the growing economic pressure, one lever for companies to be competitive is to efficiently monitor projects. To this goal, the different processes involved in the project must be carefully supervised and the project manager needs to be well-informed on their state and progress to take the best decisions thanks to accurate performances of indicators, process, and project. Another key factor of successful decision-making in engineering projects beyond taking into account information from the project manager on the project management (PM) is to consider information from the systems engineer on the product development. This paper proposes a process to monitor engineering projects relying on a set of project performance indicators that integrate the views of both project manager and systems engineer. This process includes three main activities: 1) defining project performance indicators related to the processes described by international guides and standards; 2) valuating and weighting these indicators by consulting project managers and systems engineers; and 3) constructing a hierarchical framework of indexes to support project monitoring. This proposal thus refers to the practices described in PM and systems engineering norms and improves them by involving project managers and systems engineers into decision-making, with the goal to take more coherent decisions. [ABSTRACT FROM AUTHOR]

Large dam projects (i.e., those exceeding 15 m) often make headlines for their poor performance and their negative social and environmental impacts. The world register characterizes dams by height, purpose (e.g., irrigation), and type (e.g., rock-fill). Thus, large dams differ in many technical ways, but because practitioners still lack a framework to sort them into different types for management purposes, they tend to manage them in a one-size-fits-all manner. Shenhar and Dvir's NTCP (novelty, technology, complexity, and pace) model (2007) may be a good fit as large dams experience high unforeseen technological uncertainty. In this paper, through observations, a case study, a qualitative analysis of 42 interviews with project managers, and a quantitative analysis, we examined 30 large dam projects in Africa and sorted them into different categories according to the NTCP model. Going beyond the rather static NTCP, we identified their underlying NTCP characteristics and the variety of roles that their project managers played throughout the lifecycle, and highlighted the dynamic fit between the roles and NTCP characteristics. Since different characteristics and project manager's roles are prominent at different phases, project managers should sort dams into different types based on the NTCP model at different phases, and tailor their roles accordingly for more success. [ABSTRACT FROM AUTHOR]

Because of resource limitations, project managers in many organizations are tasked to lead more than one project at a time. In this research, these project managers are referred to as multiple-project managers and such a management condition is referred to as management of a group of multiple projects (MGMP). This study is among the first to investigate effectiveness in MGMP, a multiple project management form that has been utilized widely in industry but not broadly researched. The objective is to empirically identify the significant predictors of MGMP effectiveness in terms of two criteria: project performance and the learning of project managers. This study is a continuation of previous research, which was conducted using a case study methodology to explore the definition of MGMP effectiveness, factors affecting MGMP effectiveness, and criteria for measuring such effectiveness. Based on a survey of 169 multiple-project managers, the findings from the research reported in this paper suggest multiple factors as significant predictors of MGMP effectiveness. First, the competencies of multiple-project managers in terms of multitasking and multiteam management were found to contribute to both dimensions of effectiveness. Second, project manager assignment and sufficient resource allocation contribute to improved project performance. Finally, management of the interdependence between projects contributes to project manager learning. [ABSTRACT FROM PUBLISHER]

This paper addresses the question of how projects are organized and how these management structures impact project success. Despite its widely accepted managerial importance, empirical studies could not provide significant evidence of a relationship between implemented management structures and project success. A major problem in finding meaningful empirical evidence is the conceptualization of the structure measure, which is derived from a typologist's perspective. In this study, we follow the taxonomists' perspective and empirically develop an alternative taxonomy of project management structures. We empirically compare both approaches, by using two different samples, collected in the United States and Germany, including together over 600 projects. Our empirical findings show that the validity of the widely accepted project organization typology is in question. The use of cluster analyses reveals an alternative taxonomy that encompasses five structural types, differentiated by the entities managing them: project coordinator, supervised project coordinator, autonomous project manager, supervised project manager, and autonomous functional project manager. The results strongly support the widely accepted proposition of a relationship between project organization and project success. The emerging taxonomy of project organization configurations enriches the theoretical and conceptual discussions of organizing projects and unravels the multiple aspects involved in organizing the execution of projects. [ABSTRACT FROM AUTHOR]

Project risks are not always independent, yet current risk management practices do not clearly manage dependencies between risks. If dependencies can be explicitly identified and analyzed, project managers will be able to develop better risk management strategies and make more effective risk planning decisions. This paper proposes a management methodology to address risk dependency issues. Through the study of three IT projects, we confirm that risk dependencies do exist in projects and can be identified and systematically managed. We also observed that, as project teams needed to deal with risk dependency issues, communications between projects were improved, and there were synergetic effects in managing risks and risk dependencies among projects. [ABSTRACT FROM AUTHOR]