Applying social network analysis to identify project critical success factors

A key challenge in project management is to understand to which extent the dynamic interactions between the different project people—through formal and informal networks of collaboration that temporarily emerge across a project´s lifecycle—throughout all the phases of a project lifecycle, influence a project’s outcome. This challenge has been a growing concern to organizations that deliver projects, due their huge impact in economic, environmental, and social sustainability. In this work, a heuristic two-part model, supported with three scientific fields—project management, risk management, and social network analysis—is proposed, to uncover and measure the extent to which the dynamic interactions of project people—as they work through networks of collaboration—across all the phases of a project lifecycle, influence a project‘s outcome, by first identifying critical success factos regarding five general project collaboration types((1) communication and insight, (2) internal and cross collaboration, (3) know-how and power sharing, (4) clustering, and (5) team work efficiency) by analyzing delivered projects, and second, using those identified critical success factos to provide guidance in upcoming projects regarding the five project collaboration types.info:eu-repo/semantics/publishedVersio

The Roles of IS Project Critical Success Factors: A Relevatory Case

Research in Critical Success Factors (CSFs) of Enterprise Systems (ES) projects has identified numerous practitioner governance mechanisms for ensuring project success. However, such research has not developed a theory of why certain critical success factors encourage project success. Our research develops such theory on a case study where even though the levels of several critical success factors were weak, the project nevertheless succeeded. Specifically, the logistics ES project succeeded even though there was (1) only marginal top management support, (2) low key user commitment, and (3) change management, training and other critical aspects of user management and communication were not well done. Using a modified dialectical lens, we highlight that project team legitimacy appears to be the underlying CSF, and many heretofore identified CSFs are really manifestations of project team legitimacy

Understanding the Meaning of “Project Success”

Fortune 500 organizations are executing their tasks using projects. Project management is the area of concentration across the world. Different stakeholders have a different perspective about project success. The meaning of project success had been explained in this article. In addition, the Project Critical Success Factors (CSFs) were mentioned. The research of Standish Group on project success and project success metrics was presented. Earlier research on the meaning of project success and project critical success factors was highlighted. The works of Jeffery K. Pinto and Dennis P. Slevin, David and Adam, DeLone and McLean, and The Standish Group Research were discussed in this article. The methodology included secondary research based on literature view of prominent empirical studies and the literature reviews by making note of findings and observations from those studies. The initial literature collected led to further search of articles based on their references. The research findings indicate that the top of the most success factors for many projects include project objective, top management commitment, competent project team, and user involvement

A Foundational Perspective on Core Competency Requirements for Project Management Initiatives

Many research studies have discussed critical factors which can affect project success or failure. In this paper, an examination of a limited number of these studies is conducted to emphasize the need to understand a foundational perspective of the human element influencing these factors. The result is the classification of three core competency areas which can lead toward more effective project management initiatives. An outline of these core competencies is provided based on the identification of key themes focusing on project critical success factors. Initial findings of the study are discussed and a foundation for future research agendas is provided

Project selection in project portfolio management: an artificial neural network model based on critical success factors

While a growing body of literature focuses in detecting and analyzing the main reasons affecting project success, the use of these results in project portfolio management is still under investigation. Project critical success factors (CSFs) can serve as the fundamental criteria to prevent possible causes of failures with an effective project selection process, taking into account company strategic objectives, project manager’s experience and the competitive environment. This research proposes an innovative methodology to help managers in assessing projects during the selection phase. The paper describes the design, development and testing stages of a decision support system to predict project performances. An artificial neural network (ANN), scalable to any set of CSFs, classifies the level of project’s riskiness by extracting the experience of project managers from a set of past successful and unsuccessful projects

Best Practice Guide : Consultants

The Guide contains the distilled findings from a major, two-year research project to explore those factors considered by industry practitioners to be critical to the successful adoption of ICT, both within their firms and between their firms and their trading partners. In the context of this project Critical Success Factors (CSFs) have been defined as, “Those things that absolutely, positively must be attended to in order to maximise the likelihood of a successful outcome for the stakeholder, defined in the stakeholder’s terms.” The guide includes: o Perceived benefits of ICT use across the consultants’ sector o Types and levels of ICT used across the sector o Self-assessment tool o CSFs for medium- and high-level ICT users, including o Best Practice Profiles o Action Statements o Barriers to ICT use for low-level users o Action Statements The material contained in this Guide has been generated following a number of principles: o For a given situation there is not a single ‘right answer’, but a number of solutions that have to be evaluated using a range of relevant factors. o As there are as many solutions as there are ‘solvers’, factors for evaluation will ‘emerge’ from collective wisdom

Project managers’ and change managers’ contribution to success

© 2016, © Emerald Group Publishing Limited. Purpose – Many projects involve an organisational change component. Project management (PM) and change management (CM) have the potential to jointly contribute to the delivery of organisational changes. However, there is a lack of clarity in the literature about the boundary and relationship between these disciplines. The purpose of this paper is to explore the contribution these disciplines make to a set of project critical success factors, to understand the ways that these disciplines can most effectively work together. Design/methodology/approach – This paper analyses data collected through an online survey, examining project managers’ and change managers’ perception of each disciplines’ contribution to critical success factors. The survey received 455 responses. Findings – This paper identifies the success factors that are most clearly influenced by PM and CM, and areas where practitioners of these disciplines hold significantly different perceptions of their contributions. The results have been used to rank and categorise success factors based on the influence of each discipline. This has been used to develop a risk-based questionnaire to guide the contribution of PM and CM to the mitigation of specific project risks. Originality/value – These findings will be of use to practitioners managing organisational change projects, or projects with a significant change component. The findings will be of assistance in determining the ways in which these disciplines should work together to mitigate risks associated with specific critical success factors

Construction procurement systems : a linkage with project organisational models

This paper constitutes a literature review undertaken at the start of a two and a half year EPSRC funded research project. As such, its purpose is to present the details of the ‘re-search’concerning construction procurement and project organizational design. The paper shows that the ‘post -Latham’construction industry provides several new developments (client power, partnering, concurrent engineering etc) which are altering the construction project process, and therefore prove worthy vehicles for investigation into project organizational structures

A Proposal Model for Estimation of Project Success in Terms of Radial Based Neural Networks: A Case Study in Iran

For improving the conditions of project intended purpose and reaching high score in the project success, project Stakeholders (including employer, contractor, consultant and its users) try to comply with the implementation of project Critical Success Factors(CSFs) at the beginning of each project. This implementation is in two terms: economic and executive. Artificial neural networks are one of the new methods which have been developed to estimate and predict parameters by using inherent relationship among data. In this research, it tried to propose a model to determine the project success score by using radial basis neural networks. For reaching this purpose, firstly, the key indicators of project success (employer, contractor and consultant) among the main elements involved in the industry of macro-civil construction projects in Iran reviewed. Secondly, ten CSFs key project success indicators were recognized in five categories: (i) financial, (ii) interaction processes, (iii) manpower, (iv) contract settings and (v) characteristic nature of the project (based on conditions of the present research in Iran). Then, some projects were selected by random sampling of projects operated during the last 5 years in the country's Ministry of Energy. Among those projects, project information was collected by managers of large projects. After training the designed neural network, the project success model was provided based on an assessment of project objectives including factors of Scope, Time, Cost, and Quality of the projects. For facilitating other researches’ use, the applied equation of the model was presented as well. Outputs, calculated by the proposed model, were in good agreement with the actual number of projects assessed in Iran. The results of this study may be used as a tool in implementing projects for the rapid assessment of achieving project goals’ facilities