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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.
- 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.