A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business
1.1. Relevance and Novelty of the Conducted Research in This Work
1.2. Structure of the Present Work
2. Literature Review
2.1. Project Risk Management
2.2. Cooperative Networks
2.3. Social Network Analysis in Organizations
2.4. Business INTELLIGENCE in Organizations
3. Model Development and Implementation
3.1. Model Development
3.2. Model Implementation
4. Application of the MCPx Model—A Case Study
4.1. Introduction to the Application Case
4.2. Application of the MCPx Model
6. Academic and Managerial Implications
6.1. Proposed Model and Academic Implications
6.2. Proposed Model and Managerial Implications
7. Further Developments
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Scientific Pillars||Brief Description Regarding Individual Contributions|
|Project Risk Management||Contributes with the definitions and structure of a typical project (lifecycle, phases, and so on) according to the Project Management Institute , and with the definitions and approach process of the risk management standard process according to the International Organization for Standardization .|
|Cooperative Networks||Contributes with the definitions, importance, and key factors regarding cooperation principles between organizations. This work assumes the cooperative principle of performing joint work according to .|
|Social Network Analysis||Provides the tools and techniques (essentially centrality metrics such as in-degree, out-degree, density, average degree, closeness and so on, based on the graph theory) which will quantitatively measure the five key project cooperative behavioral dimensions that emerge and evolve as organizations cooperate to deliver projects.|
|Business Intelligence||Contributes with the typical organizational business intelligence architecture (collecting, transforming, analysing data and reporting) that enables organizations to perform business data analysis in a timely and accurate manner so that they can take more data-informed decisions.|
|Risk Types||Brief Description||Recommended Management Approach|
|Event Risk||Also known as “stochastic uncertainty”, these are risks that relate to something that has not yet occurred, but if it comes to occur, will impact on one or more project objectives.||Risk Management Standards tools and techniques.|
|Variability risk||Also known as “aleatoric uncertainty “, comprising different possible known outcomes, but no one knows which one will really occur.||Advanced tools and techniques such as the Monte Carlo simulation.|
|Ambiguity risk||Also known as “epistemic uncertainty “, emerging from lack of knowledge or understanding (also called of know-how and know-what risks). These risks comprise the use of new technology, market conditions, and competitor capability, just to name a few.||Lessons learned (learning from experience). Simulations and prototyping.|
|Emergent risk||Also known as “ontological uncertainty “or “Black Swans”, these are risks unable to be identified because they are just outside one’s experience or mindset. Usually these types of risks arise from game-changers or disruptive innovations.||Contingency planning.|
|Networks or Dimensions (D)||Data Sources||Objectives and Applied SNA Centrality Metrics|
|D1: Communication||Emails: All exchanged email data between all organizations that participated in the different phases of a cooperative project lifecycle. This project-related information is to be collected at the end of each project timing t.||SNA Metric 1: Weighted Total-Degree|
SNA Metric 2: Average weighted total-degree
|D2: Information sharing||Survey: Addressed to all organizations that participated in the different phases of a cooperative project lifecycle. This project-related information, is to be collected in each project timing t.||SNA Metric 3: In-degree|
SNA Metric 2: Average In-degree
|D3: Trust||Survey: Addressed to all organizations that participated in the different phases of a cooperative project lifecycle. This project-related information is to be collected in each project timing t.||SNA Metric 1: In-degree (see (3)).|
Objective 1: Identify who is more or less central within the project trust network. It maps the trust network and identifies who discusses in confidence sensitive information and ideas, and to whom.
SNA Metric 2: In-degree (see (4)).
Objective 2: Map the evolution across the different project phases of the trust network.
|D4: Problem solving||Survey: Addressed to all organizations that participated in the different phases of a cooperative project lifecycle. This project-related information is to be collected in each project timing t.||SNA Metric 1: In-degree (see (3)).|
Objective 1: Identify who are the organizations that belong to a given project problem solving network. It maps the problem-solving network and identifies who knows what and how.
SNA Metric 2: In-degree (see (4)).
Objective 2: Map the evolution across the different project phases of the problem-solving network.
|D5: Decision making||Survey: Addressed to all organizations that participated in the different phases of a cooperative project lifecycle. This project-related information is to be collected in each project timing t.||SNA Metric 1: In-degree (see (3)).|
Objective 1: Identifies who are the decision-making organizations with the cooperative project network.
SNA Metric 2: In-degree (see (4)).
Objective 2: Map the evolution across the different project phases of the decision-making network
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Nunes, M.; Abreu, A.; Saraiva, C. A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business. Sustainability 2021, 13, 5798. https://doi.org/10.3390/su13115798
Nunes M, Abreu A, Saraiva C. A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business. Sustainability. 2021; 13(11):5798. https://doi.org/10.3390/su13115798Chicago/Turabian Style
Nunes, Marco, António Abreu, and Célia Saraiva. 2021. "A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business" Sustainability 13, no. 11: 5798. https://doi.org/10.3390/su13115798