H2020 RIA Proposal: ACT-FAST (eGoose) - Concept and Methodology / Concept for Review

Dear all - below a first draft of this proposal section for review.

"Describe and explain the overall concept underpinning the project. Describe the main ideas, models or assumptions involved. Identify any inter-disciplinary considerations and, where relevant, use of stakeholder knowledge. Where relevant, include measures taken for public/societal engagement on issues related to the project. Describe the positioning of the project e.g. where it is situated in the spectrum from ‘idea to application’, or from ‘lab to market’. Refer to Technology Readiness Levels where relevant. (See General Annex G of the work programme)" 

The overall concept underpinning the proposal is that the degree of digital security for extended supply chain ecosystems of SMEs and MEs[1] is based on the attention given to the issue by the individual ecosystem participants themselves. Indeed, without this attention, the importance of the issue will not be recognized in the first place and the relevant knowledge cannot flow from its point of origin to point of highest need[2],[3]. This attention is created by trusted ecosystem partners sharing knowledge[4] about being (potentially) compromised and the (potentially) compromised / informed then seeking expert guidance from trusted digital security experts. Once this attention is created, it is then of utmost importance to diffuse the needed actions rapidly and deeply throughout the ecosystem participant community. Due to the continuously growing (especially digital) integration and interdependence of supply chain participants the potential group of individuals is all participants in all organizations that are directly linked in commercial and / or technical manners. Digital security in ecosystems is thus a social phenomenon and as such is best described through diffusion of innovation principles applied to living systems. 

The specific factors enabling rapid diffusion can be broken down into those associated with the specific digital security challenge and those associated with the individual ecosystem participants aware of the challenge[5],[6]: 

· Digital Security Challenge 

o Degree of Innovativeness 

o Technical Readiness Level 

o Budget and Resources 

o Number of Competitors 

o Degree of Complexity 

o Compatibility with Existing Ways of Work 

o Ease of Understanding 

o Ease of Use 

o Ease of Adaptation 

o Ease of Trialling 

o Observability of Impact 

o Urgency of Need 

o Degree of Certification (Legal / Policy Alignment) 

· Ecosystem Participant 

o Considers the Diffusion as Urgent 

o Places Priority on the Diffusion 

o Is Motivated 

o Is Domain Competent 

o Is Collaborative 

o Engages Voluntarily 

Additionally, for each factor the subjective confidence of the participant in their assessment of these is a significant driver of adoption speed. 

The overall concept is currently implemented using a social and technology solution (currently at technical readiness level 3 – proof of concept). The proof of concept supports the proposal participants, and peers they refer, by serving as a “living laboratory” for developing social and technical design criteria for future scaling. 

The proposed outcome of a technology readiness level 7 (prototype in operational environment) will be developed by enriching the proof of concept solution by gathering (un-) successful case studies of digital security behaviour from business participants in order to develop an explicit and generic ecosystem simulation model[7] of how relevant knowledge actually flows through the ecosystem[8]. Once the proposed solution is implemented in the ecosystem of specific SMEs and / MEs, the generic model will be regularly calibrated to the specific behaviours identified in such. The dynamics of the simulation will be evaluated using value[9], social[10] and organizational[11] network analysis approaches[12][13]. The simulation model will then provide (semi-) automated controls for a mobile technology solution that enables the social behaviour needed to ensure timely notification and attention to digital security incidents, risks and treatments. 

The main assumptions (axioms) of the proposal are: 

· The extended supply chain ecosystem is a social phenomenon and can be described and orchestrated based on living systems principles. 

· A high-level system dynamics model of the ecosystem is “good enough” to describe and forecast ecosystem behaviour in a robust manner. 

· Participants participate based on personal / individual motivation rather than any motivation to support the ecosystem. 

· Information about actual and potential cyber safety compromises of an individual are considered as private as information about personal health, financial situation, sexual preferences etc. 

· Any individual can maintain up to five close friendships (which are suited for sharing of sensitive knowledge) and can maintain up to 150 friendships (which are suited for gaining their attention)[14]. 

The proposed solution thus leverages existing schools of research and practices in the social sciences in order to activate wide scale peer-protection behaviour in a rapid manner. Knowledge from the natural sciences is drawn upon to describe the relevant social behaviour and technology knowledge is drawn upon to create the digital platform for coordinating this behaviour among widely diverse individual participants of the ecosystem that are distributed geographically and temporally. 

Since the proposal solution is designed as a voluntary and free peer-protection network (supported by open source technology) and is based on the social ties of participants within the ecosystems, it is by default suited to engage with the wider public and society through story-telling as the most powerful knowledge distribution behaviour available. 

[1] See Maynooth University Cyber Security Effectiveness Model available at https://ivi.ie/cybersecurity-effectiveness-assessment/. 

[2] Amidon D, Formica P, Mercier-Laurent E (eds) (2005) Knowledge economics: emerging principles, practices and policies. Tartu University Press, Estonia. 

[3] Amidon, D., Davis, B. (2004) Get in the zone. Knowledge Management. Vol. 8 (2) pp. 26-28. Available at http://www.entovation.com/whatsnew/kmmag-kiz.pdf. 

[4] Sveiby, K. (2001) A knowledge-based theory of the firm to guide in strategy formulation. Journal of Intellectual Capital. Vol. 2, pp. 344-358. Available at https://www.researchgate.net/profile/Karl_Erik_Sveiby/publication/230557808_A_Knowledge-based_Theory_of_the_Firm_to_guide_Strategy_Formulation/links/540d42810cf2f2b29a3836c3.pdf. 

[5] See Innovation Diffusion Litmus Test Version 3 available at https://sourceforge.net/projects/entov-hvm/files/ENTOV-HVM/. 

[6] See Schwabe, O., Bilge, P., Hoessler, A., Tunc, T., Gaspar, D., Price, N., Sharir, L., Pasher, P., Erkoyuncu, J.A., Almeida, N. Formica, P., Schneider, S., Dietrich, F., Shehab, E. (2020) A Maturity Model for Rapid Diffusion of Innovation in High Value Manufacturing. CIRPe 2020 – 8th CIRP Global Web Conference – Flexible Mass Customization 

[7] Sterman, J.D. (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill Education Ltd. 

[8] Allee, V., Schwabe, O. (2015) Value Networks and the True Nature of Collaboration. Meghan-Kiffer Press. 

[9] See in particular Verna Allee, (2008) "Value network analysis and value conversion of tangible and intangible assets", Journal of Intellectual Capital, Vol. 9 Issue: 1,pp. 5-24, doi: 10.1108/14691930810845777. Available at https://ocw.tudelft.nl/wp-content/uploads/Value_network_analysis_and_value.pdf. 

[10] Wasserman, S., Faust, K. (1995) Social Network Analysis: Methods and Applications. Cambridge University Press. 

[11] Ujwary-Gil, A. (2019) Organizational Network Analysis: Auditing Intangible Resources. Taylor & Francis Ltd. 

[12] Schwabe, O., Almeida, N., Schneider, L., Salvado, A. (2020) A Framework for Accelerating Innovation through Innovation Webs (in the Construction Industry). Sustainability and Automation in Smart Constructions. Proceedings of the International Conference on Automation Innovation in Construction (CIAC-2019), Leiria, Portugal. Springer ISBN 978-3-030-35533-3. 

[13] Schwabe, O. (2018) A Geometrical Framework for Forecasting Cost Uncertainty in Innovative High Value Manufacturing. PhD Thesis. Cranfield University. 

[14] Dávid-Barrett, T., Dunbar, R.I.M. (2013) Processing power limits social group size: computational evidence for the cognitive costs of sociality. Proc Biol Sci; 280(1765): 20131151. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3712454/ (Last accessed: 28 July 2020)