Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective
1.1. Relevance and Novelty of the Research
1.2. The Importance of Organizational Sustainability in the Global Sustainability Context
1.3. Structure of this Work
2. Literature Review
2.1. Collaborative Networks
2.1.1. Open Innovation
2.1.2. Open Innovation Model Benefits and Challenges
2.2. Risk Management and Critical Success Factors in Project Management
2.3. Social Network Analysis in Organizations
2.4. The Application of Social Network Analysis in Project Management
SNA Centrality Measures in Project Management
3. Model Development and Implementation
3.1. The Proposed Model: The OI-RM (Open Innovation Risk Management) Model
3.2. OI-RM Model Function Principle
3.2.1. Research Methodology
3.2.2. Introduction to the Functioning Principles of the Proposed Model
3.2.3. Functioning Principle of the Proposed Model—an Application Case
- ATD = average organizational network total degree
- x = number of existing links attached to one organization o
- n = total number of project organizations (o = 1, …, n)
3.2.4. Conclusions and Interpretation of Results
3.3. OI-RM Project Success Profile and Project Failure Profile
3.4. OI-RM Model Application Span
3.5. OI-RM Model Part 1 and Part 2
3.6. OI-RM Model Requirements
3.7. OI-RM Model four Interactive Collaborative Dimensions (4-ICD) and Respective Metrics
3.8. IO-RM Model Implementation Process
3.9. IO-RM Legal and Ethical Considerations
4. Conclusions, Implications, and Further Developments
4.1. Proposed Model and Researched Literature
4.2. Managerial Implications
4.3. Suggestions for Future Research
Conflicts of Interest
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|OI Project Lifecycle||Four Scientific Fields||Individual Contributions for Proposed Model|
|Project management||Provides the definitions and structure of an open innovation project where the application of the proposed model will be deployed.|
|Collaborative networks||Provides the definitions of the different dimensions of collaboration (networking, coordination, cooperation, and collaboration) |
that are used to define the four different informal collaboration dimensions (4-ICD).
|Social network analysis||Provides the tools and techniques to uncover and quantitatively measure the four informal collaborative dimensions (4-ICD), between organizations across an open innovation project lifecycle, where the 4-ICD are (1) key project organization communication and insight degree, (2) organizational control degree, (3) project information dependency degree and (4) feedback readiness degree.|
|Risk management||Provides definitions, approach, and standard risk management process, to be adopted throughout the process of identifying, analyzing, measuring, treating, monitoring, and updating (continuous improvement cycle) dynamic collaborative risks, in other words, project critical success factors.|
|Closed Innovation Model|
|Open Innovation Model|
|Risk Types||Uncertainty Types||Description||Management Approach|
|Event Risk||stochastic uncertainty||Also called event risks, are risks related to something that has not yet happened, and it may not happen at all, but if it does happen, it will impact on one or more project objectives.||There is a set of well-established techniques for identifying, assessing and managing them, based on risk management standards and best practices.|
|Variability Risk||aleatoric uncertainty||Are a set number of possible known outcomes, but one does not exactly know, which one will really occur.||Advanced analysis models such as the Monte Carlo simulation, are the actual solution to model and manage these risk types.|
|Ambiguity Risk||epistemic uncertainty||Are uncertainties, arising from lack of knowledge or understanding. Also called of know-how and know-what risks, comprise the use of new technology, market conditions, competitor capability or intentions, and so on.||Learning from experience from past, or others–lessons learned. Prototyping and simulating, before taking real action.|
|Emergent Risk||ontological uncertainty||Known as “Black Swans”, these risks are unable to be seen because they are outside a person´s experience or mindset, so one doesn’t know that he should be looking for it at all. Usually they arise from game-changers and paradigm shifters, such as the release of disruptive inventions or products.||Contingency planning, is the key to manage such risk types.|
|OI-RM Key Concept||Description|
|Open Innovation Project||For the proposed model in this work, an open innovation project is a temporary endeavor undertaken to create a unique product, service, or result.|
|Open Innovation Project Outcome||The proposed model assumes only two types of project outcomes. They are successful and unsuccessful project outcomes. The criterion that defines both types is not given by the proposed model.|
|Number of Open Innovation Projects||The model does not preview a maximum number of open innovation projects to be analyzed. However, at least two projects—one with a successful and one with an unsuccessful outcome—are required as input.|
|Open Innovation Project Organizations||Project organization is any organization that participated in a project, across its lifecycle, or/and is officially assigned to participate in an open innovation project. This means it is any organization that has participated in project meetings and email project information-related exchange.|
|Open Innovation Project Organization-Competencies||Project organization competencies are the different competencies that different organizations play as they participate in delivering open innovation projects. They can be from the most diverse fields such as engineering, marketing, sales, human resources, and so on.|
|Open Innovation Project phases and Lifecycle||Every project used as input for the model has a finite number of project-phases. Usually four generic phases can be used (but not necessarily four only), (phase 1—Starting the project, phase 2—Organizing and preparing, phase 3—Carrying out the work, phase 4—Ending the project). The sum of all project phases of a project is the so-called project lifecycle.|
|Collaborative Interaction||The dynamic collaborative interaction of project organizations, which is characterized by the four interactive collaborative dimensions (4-ICD) that usually occur as a project is being delivered, comprises the formal and informal networks of collaboration.|
|Open Innovation Projects Information|
|Project Meetings||- Total number of project meetings occurred in each open innovation project phase, across a project’s lifecycle|
- Total number of participating organizations in each open innovation project meeting, in each project phase, across a project´s lifecycle
- Organization Project Official Competency, from each of the participating organizations across an open innovation project lifecycle
|Project Mails||- Total number of emails sent/received in each phase of an open innovation project, that relate to project information data related to.|
- Organization Project Official Competency, from each participating organization that sent/received emails project related information.
- Chronological Mail Exchange Time (send/received/answered)
- Categorize emails according *:
|Description and Objective: How does important project organizations (function of their competency across the accomplishment of an open innovation project) present at the in-project meetings and emails networks, and to which extent their presence influences a certain project outcome.|
Regarding Meetings: How the presence of those important project organizations in project meetings, triggers communication and insight on what is ongoing throughout the different phases of a project lifecycle, namely at the transitional phase of the different project phases.
SNA Metric: For this case, the total-degree (CDT)  SNA metric will be applied, to first measure the project meetings participation degree of each participating organization in each open innovation project phase.
= total degree of an entity within a graph
n = total number of entities within a network (graph) for i = 1…,n
xji = number of links from entity j to entity i, where i ≠ j, and vice-versa.
After having all the total degrees for each participating organization, a linear regression will be applied to characterize the evolution within a given project phase. There are three possible outcomes. They are
SNA Metric: For this case, the density (Ds)  will be used, to characterize the amount of existing email communication channels that exist between the different organizations that participate in open innovation projects.
Nr of Maximum Possible ties = NLMAX =
n = number of entities within a graph
The outcome for this metric is:
|Description and Objective: To which extent does a given organization controls and holds “power” over the email communication network, in terms of send/receive project information related.|
Regarding Mails: How is the volume of mail communication between the different participating organizations in open innovation projects? Who holds the most volume of email communication related to project information data?
SNA Metric: For this case, the In-degree, and Out-degree (COT)  will be applied in order to identify which organization holds control over the email communication network.
= total out-degree of an entity within a graph
n = total number of entities within a network (graph) for i = 1…,n
xji = number of links from, only entity j to entity i, where i ≠ j.
The output for this metric, is:
|Description and Objective: To which extent, does the project-related information, provided by one organization to other organization is recognized as important and decisive to enable evolution in project activities? What is the degree of dependency of a given organization regarding to another organization in order to accomplish project activities or tasks?|
Regarding Mails: How is the volume of emails sent seeking vs providing vital information to project activities?
SNA Metric: For this case, will be used the Out-degree (COT) (4) and the Average degree (AD) , will be applied, which will characterize how much a given organization is dependent on other organization to accomplish project activities.
= Average degree
n = total number of entities within a network (graph)
The output for this metric is:
|Description and Objective: To which extent, does the speed of answering emails by providing or seeking project information related, influences project outcome?|
Regarding Mails: How fast or how slow is the speed of answering a request from an organization to other organization? Analyze the volume of emails sent/received crossing them with the chronological time.
SNA Metric: For this case, first the reciprocity (R)  will be used to analyze which emails were answered providing project information related, and second, the chronologic time associated to each pair sent/received.
= Number of links pointing in both directions
The output for this metric is:
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Nunes, M.; Abreu, A. Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective. Sustainability 2020, 12, 3132. https://doi.org/10.3390/su12083132
Nunes M, Abreu A. Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective. Sustainability. 2020; 12(8):3132. https://doi.org/10.3390/su12083132Chicago/Turabian Style
Nunes, Marco, and António Abreu. 2020. "Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective" Sustainability 12, no. 8: 3132. https://doi.org/10.3390/su12083132