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
- Nuryakin, M. Competitive Advantage and Product Innovation: Key Success of Batik SMEs Marketing Performance in Indonesia. Acad. Strat. Manag. J. 2018, 17, 1–17. [Google Scholar]
- Nunes, M.; Abreu, A. Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective. Sustainability 2020, 12, 3132. [Google Scholar] [CrossRef]
- Hansen, M. Collaboration: How Leaders Avoid the Traps, Create Unity, and Reap Big Results; Harvard Business School Press: Cambridge, MA, USA, 2009. [Google Scholar]
- Lutfihak, A.; Evrim, G. Disruption and ambidexterity: How innovation strategies evolve? Soc. Behav. Sci. 2016, 235, 782–787. [Google Scholar]
- Lee, K.; Woo, H.; Joshi, K. Pro-innovation culture, ambidexterity, and new product development performance: Polynomial regression and response surface analysis. Eur. Manag. J. 2017, 35, 249–260. [Google Scholar] [CrossRef]
- Workday Studios. In Good Company—Michael Arena, Chris Ernst, Greg Pryor: Organizational Networks. 2018. Available online: https://www.youtube.com/watch?v=6faV0v0yVFU (accessed on 12 January 2021).
- Arena, M. Adaptive Space: How GM and Other Companies are Positively Disrupting Themselves and Transforming into Agile Organizations; McGraw Hill Education: New York, NY, USA, 2018. [Google Scholar]
- Nunes, M.; Abreu, A. Applying Social Network Analysis to Identify Project Critical Success Factors. Sustainability 2020, 12, 1503. [Google Scholar] [CrossRef][Green Version]
- Chesbrough, H. How to Capture All the Advantages of Open Innovation at HBR. 2020. Available online: https://hbr.org/podcast/2020/01/how-to-capture-all-the-advantages-of-open-innovation (accessed on 20 February 2021).
- Deichmann, D.; Rozentale, I.; Barnhoorn, R. Open Innovation Generates Great Ideas, So Why Aren’t Companies Adopting Them? Available online: https://hbr.org/2017/12/open-innovation-generates-great-ideas-so-why-arent-companies-adopting-them (accessed on 1 January 2020).
- Camarinha-Matos, L.M.; Afsarmanesh, H. Cooperative networks. In Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management. PROLAMAT 2006. IFIP International Federation for Information Processing; Wang, K., Kovacs, G.L., Wozny, M., Fang, M., Eds.; Springer: Boston, MA, USA, 2006; Volume 207. [Google Scholar]
- Santos, R.; Abreu, A.; Anes, V. Developing a green product-based in an open innovation environment. Case study: Electrical vehicle. In Cooperative Networks and Digital Transformation. PRO-VE 2019. IFIP Advances in Information and Communication Technology; Camarinha-Matos, L., Afsarmanesh, H., Antonelli, D., Eds.; Springer: Cham, Switzerland, 2019; Volume 568. [Google Scholar]
- PMI® (Project Management Institute). Project Management Body of Knowledge (PMBOK® Guide), 6th ed.; Project Management Institute, Inc.: Newtown Square, PA, USA, 2017. [Google Scholar]
- ISO—The International Organization for Standardization. Available online: https://www.iso.org/home.html (accessed on 1 January 2021).
- Narsalay, R.; Kavathekar, J.; Light, D. A Hands-Off Approach to Open Innovation Doesn’t Work. 2016. Available online: https://hbr.org/2016/05/a-hands-off-approach-to-open-innovation-doesnt-work (accessed on 4 May 2021).
- Wigmore, I.; Rouse, M. January. 2013. Available online: http://whatis.techtarget.com/definition/data-driven-decision-management-DDDM (accessed on 4 May 2021).
- Abreu, A.; Nunes, M. Model to Estimate the Project Outcome’s Likelihood Based on Social Networks Analysis. KnE Eng. 2020, 5, 299–313. [Google Scholar] [CrossRef]
- Ladley, J.; Redman, T. Use Data to Accelerate Your Business Strategy. Harvard Business Review. 3 March 2020. Available online: https://hbr.org/2020/03/use-data-to-accelerate-your-business-strategy (accessed on 29 September 2020).
- Krackhardt, D.; Hanson, J. Informal Networks the Company behind the Charts; Harvard College Review: Cambridge, MA, USA, 1993; Available online: https://www.andrew.cmu.edu/user/krack/documents/pubs/1993/1993%20Informal%20Networks.pdf (accessed on 5 September 2020).
- Stefani, S.; Torriero, A. Formal and informal networks in organizations. In Advanced Dynamic Modeling of Economic and Social Systems. Studies in Computational Intelligence; Proto, A., Squillante, M., Kacprzyk, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; Volume 448. [Google Scholar]
- Borgatti, S.P.; Everett, M.; Johnson, J. Analyzing Social Networks, 2nd ed.; Sage Publications Ltd.: London, UK, 2017. [Google Scholar]
- Arena, M.; Cross, R.; Sims, J.; Uhl-Bien, M. How to Catalyze Innovation in Your Organization. MIT. 2017. Available online: http://mitsmr.com/2rrgvqM.summerissue or https://www.robcross.org/wp-content/uploads/2017/10/how-to-catalyze-innovation-in-your-organization-connected-commons.pdf (accessed on 1 January 2020).
- DTM-EC—Digital Transformation Scoreboard 2017, Internal Market, Industry, Entrepreneurship and SMEs, European Commission. Available online: https://ec.europa.eu/growth/content/digital-transformation-scoreboard-2017_en (accessed on 10 January 2021).
- Digital Transformation Monitor of the European Commission. Germany: Industrie 4.0. 2017. Available online: https://ec.europa.eu/growth/tools-databases/dem/monitor/sites/default/files/DTM_Industrie%204.0.pdf (accessed on 3 April 2021).
- Putnik, G.D.; Varela, L.; Modrák, V. Intelligent Collaborative Decision-Making Models, Methods, and Tools. Math. Probl. Eng. 2018, 2018, 9627917. [Google Scholar] [CrossRef][Green Version]
- Mohammed, H.; Knapkova, A. The Impact of Total Risk Management on Company’s Performance. Procedia Soc. Behav. Sci. 2016, 220, 271–277. [Google Scholar] [CrossRef][Green Version]
- Davies, D. Risk management: Holistic risk management. Comput. Law Secur. Rev. 1997, 13, 336–339. [Google Scholar] [CrossRef]
- Hillson, D. How to Manage the Risks You Didn’t Know You Were Taking; Phoenix, A.Z., Ed.; Paper Presented at PMI® Global Congress 2014—North America; Project Management Institute: Newtown Square, PA, USA, 2014. [Google Scholar]
- Abreu, A.; Martins, J.D.M.; Calado, J.M.F. Fuzzy logic model to support risk assessment in innovation ecosystems. In Proceedings of the 2018 13th APCA International Conference on Automatic Control and Soft Computing (CONTROLO), Ponta Delgada, Portugal, 4–6 June 2018; pp. 104–109. [Google Scholar] [CrossRef][Green Version]
- Karantininis, K.; Nilsson, J. The network form of the cooperative organization. In Vertical Markets and Cooperative Hierarchies; Springer: Dordrecht, The Netherlands, 2007; pp. 19–34. ISBN 978-1-4020-4072-6. [Google Scholar]
- Schalk, R.; Curşeu, P. Cooperation in organizations. J. Manag. Psychol. 2010, 25, 453–459. [Google Scholar] [CrossRef]
- Rindfleisch, A. Organizational Trust and Interfirm Cooperation: An Examination of Horizontal versus Vertical Alliances. Mark. Lett. 2000, 11, 81–95. [Google Scholar] [CrossRef]
- Durland, M.; Fredericks, K. An Introduction to Social Network Analysis. New Dir. Eval. 2006, 2005, 5–13. [Google Scholar] [CrossRef]
- Krivkovich, A.; Levy, C. Managing the People Side of Risk. McKinsey Global Institute. 2015. Available online: https://www.mckinsey.com/business-functions/risk/our-insights/managing-the-people-side-of-risk (accessed on 15 September 2020).
- Blacker, K.; McConnell, P. People Risk Management: A Practical Approach to Managing the Human Factors That Could Harm Your Business; Kogan Page Publishers: London, UK; CPI Group (UK), Ltd.: Croydon, UK, 2015. [Google Scholar]
- Cross, R.; Parker, A. The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations; Harvard Business School Press: Boston, MA, USA, 2004. [Google Scholar]
- Wasserman, S.; Faust, K. Social network analysis in the social and behavioral sciences. In Social Network Analysis: Methods and Applications; Cambridge University Press: Cambridge, MA, USA, 1994; pp. 1–27. ISBN 9780521387071. [Google Scholar]
- Liaquat, H.; Wu, A.; Choi, B. Measuring Coordination through Social Networks Quantitative Research Methods. 2013. Available online: https://www.semanticscholar.org/paper/MEASURING-COORDINATION-THROUGH-SOCIAL-NETWORKS-Liaquat-Wu/32db1daa63ae50a0eecc85d71e90d8dd5dfc404d (accessed on 4 May 2021).
- Nunes, M.; Abreu, A. A model to support OI collaborative risks applying social network analysis. In Boosting Collaborative Networks 4.0. PRO-VE 2020. IFIP Advances in Information and Communication Technology; Camarinha-Matos, L.M., Afsarmanesh, H., Ortiz, A., Eds.; Springer: Cham, Switzerland, 2020; Volume 598. [Google Scholar]
- Villegas-Ch, W.; Palacios-Pacheco, X.; Luján-Mora, S. A Business Intelligence Framework for Analyzing Educational Data. Sustainability 2020, 12, 5745. [Google Scholar] [CrossRef]
- Dedić, N.; Stanier, C. Measuring the success of changes to existing business intelligence solutions to improve business intelligence reporting (PDF). In Lecture Notes in Business Information Processing 268; Springer International Publishing: Cham, Switzerland, 2016; pp. 225–236. [Google Scholar]
- Olszak, C.M. Business Intelligence and Big Data: Drivers of Organizational Success; Auerbach Publications: Boca Raton, FL, USA, 2020; ISBN 9780367373948. [Google Scholar]
- Ponnambalam, K. Business Analytics Foundations: Descriptive, Exploratory, and Explanatory Analytics. Available online: https://www.linkedin.com/learning/business-analytics-foundations-descriptive-exploratory-and-explanatory-analytics/stages-of-business-analytics?u=77012418 (accessed on 10 May 2021).
|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
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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