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- Applying social network analysis to identify project critical success factorsPublication . Nunes, Marco; Abreu, AntónioA 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. Inthiswork,aheuristictwo-partmodel,supportedwiththreescientificfields—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.
- Identifying project corporate behavioral risks to support long-term sustainable cooperative partnershipsPublication . Nunes, Marco; Abreu, António; Saraiva, CéliaProjects are considered crucial building blocks whereby organizations execute and implement their short-, mid-, and long-term strategic visions. Projects are thought, developed, and implemented to solve problems, drive change, satisfy unique needs, add value, and exploit opportunities, just to name a few objectives. Although existing project management tools and techniques aim to deliver projects with success, according to the latest reviewed literature, projects still keep failing at an impressive pace. Among the extensive list of factors that may threaten project success, several articles from the research literature place particular importance on a still underexplored factor that may strongly lead to unsuccessful project delivery. This factor—usually known as corporate behavioral risks—usually emerges and evolves as organizations work together to deliver projects across a bounded period of time, and is characterized by the mix of formal and informal dynamic interactions between the different stakeholders that constitute the different organizations. Furthermore, several articles from the research literature also point out the lack of proper models to efficiently manage corporate behavioral risks as one of the major factors that may lead to projects failing. To efficiently identify and measure how such corporate behaviors may contribute to a project’s outcomes (success or failure), a heuristic model is proposed in this work, developed based on four fundamental fields ((1) project management, (2) risk management, (3) corporate behavior, and (4) social network analysis), to quantitatively analyze four critical project social networks ((1) communication, (2) problem-solving, (3) advice, and (4) trust), by applying the theory of social network analysis (SNA). The proposed model in this work is supported with a case study to illustrate its implementation and application across a project lifecycle, and how organizations can benefit from its application.
- A model to manage cooperative project risks to create knowledge and drive sustainable businessPublication . Nunes, Marco; Abreu, António; Saraiva, CéliaEfficient cooperation between organizations across all the phases of a project lifecycle is a critical factor to increase the chances of project success and drive sustainable business. However, and according to research, despite the large benefits that efficient organizational cooperation provides to organizations, they are still often reluctant to engage in cooperative partnerships. The reviewed literature argues that the major reason for such a trend is due to the lack of efficient and actionable supportive models to manage organizational cooperative risks. In this work we propose a model to efficiently support the management of organizational cooperative risks in project environments. The model, MCPx (management of cooperative projects), was developed based on four critical scientific pillars, (1) project risk management, (2) cooperative networks, (3) social network analysis, and (4) business intelligence architecture, and will analyze in a quantitative way how project cooperative behaviors evolve across a bounded time period, and to which extent they can turn into a cooperative project risk (essentially potential threats). For this matter, the MCPx model will quantitatively analyze five key project cooperative behavioral dimensions, (1) communication, (2) information sharing, (3) trust, (4) problem solving and (5) decision making, which show how dynamic interactions between project stakeholders evolve across time. The implementation and functioning principles of the MCPx model are illustrated with a case study.
- Applying social network analysis to identify project critical success factorsPublication . Nunes, Marco; Abreu, AntónioA 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.
- Managing open innovation project risks based on a social network analysis perspectivePublication . Nunes, Marco; Abreu, AntónioIn today’s business environment, it is often argued, that if organizations want to achieve a sustainable competitive advantage, they must be able to innovate, so that they can meet complex market demands as they deliver products, solutions, or services. However, organizations alone do not always have the necessary resources (brilliant minds, technologies, know-how, and so on) to match those market demands. To overcome this constraint, organizations usually engage in collaborative network models—such as the open innovation model—with other business partners, public institutions, universities, and development centers. Nonetheless, it is frequently argued that the lack of models that support such collaborative models is still perceived as a major constraint for organizations to more frequently engage in it. In this work, a heuristic model is proposed, to provide support in managing open innovation projects, by, first, identifying project collaborative critical success factors (CSFs) analyzing four interactive collaborative dimensions (4-ICD) that usually occur in such projects—(1) key project organization communication and insight degree, (2) organizational control degree, (3) project information dependency degree, (3) and (4) feedback readiness degree— and, second, using those identified CSFs to estimate the outcome likelihood (success, or failure) of ongoing open innovation projects.
- Measuring project performance by applying social network analysesPublication . Nunes, Marco; Abreu, António; Bagnjuk, Jelena; Tiedtke, JörnIt is often argued that the core of organizational success is efficient collaboration. Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise. However, the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives. Currently, much of the collaboration in organizations occurs through virtual network channels, such as e-mail, Yammer, Jabber, Microsoft Teams, Skype, and Zoom. These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions. This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment. A case study illustrates the proposed model's implementation and application in a real virtual project organizational context. The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative, organizational, and dynamic behavioral patterns, which can later correlate with organizational outcomes. The model analyzes three collaborative project dimensions: network collaboration cohesion evolution, network collaboration degree evolution, and network team set variability evolution. This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered. Organizations can use the proposed model to identify projects' critical success factors by comparing successful and unsuccessful delivered projects' dynamic behaviors if a substantial number of both project types are analyzed. The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general, and linear regressions in particular. Further, the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime. Finally, applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics, leading to the optimized use of resources; this will transform organizations to become more lean-oriented and significantly contribute to economic, social, and environmental global sustainability.