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The Balanced Development of the Spatial Innovation and Entrepreneurial Ecosystem Based on Principles of the Systems Compromise: A Conceptual Framework

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Abstract

The central research question of this paper is how a regional or national (spatial) innovation and entrepreneurial ecosystem (SIEES) can function in a sustainable mode under conditions of uncertainty of an external environment. As an attempt to answer this question, the authors consider to approach the idea of sustainable development from the standpoint of a nonlinear dynamic stability of open systems through information exchange. Addressing this issue as a multi-criteria decision problem, the authors integrate the concept of the “Innovative Helix” and its modifications, which are describing the interaction of science, government and business, as well as formal methods of game theory and business simulation games as a basis for modeling the process of sustainable development in a spatial innovation and entrepreneurial ecosystem. The purpose and interest of the article is to provide input for further discussion on these and other issues related to organizing and governing the interaction of key stakeholders in such arrangements. In practical terms, also a first case study for Russia will be designed and set up for further discussion.

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Authors and Affiliations

  1. Department of International Economics, Mathematical Methods and Business-Informatics, Altai State University, 61 Lenina, Barnaul, 656031, Russia

    Igor N. Dubina

  2. Department of Mathematical Methods in Economics, Novosibirsk National Research State University, 2 Pirogova, Novosibirsk, 630090, Russia

    Igor N. Dubina

  3. Alpen-Adria-University Klagenfurt, Faculty for Interdisciplinary Studies (iff), Institute of Science Communication and Higher Education Research (WIHO), Schottenfeldgasse 29, A-1070, Vienna, Austria

    David F. J. Campbell

  4. Department of Information Systems & Technology Management, School of Business, The George Washington University, Duquès Hall, Funger Hall, Suite 515C, 2201 G Street, NW, Washington, D.C., 20052, USA

    Elias G. Carayannis

  5. Department of Anti-Crisis and Strategic Management of the Financial, University under the Government of the Russian Federation (the Financial University), 49 Leningradsky prospekt, Moscow, 125993, Russia

    Anna A. Chub

  6. School of Production Engineering and Management, Technical University of Crete, University Campus, 73100, Chania, Greece

    Evangelos Grigoroudis

  7. Center for the Analysis of the Efficiency and Effectiveness Governance of the Financial University under the Government of the Russian Federation, The Financial University, 49 Leningradsky prospekt, Moscow, 125993, Russia

    Olga V. Kozhevina

Authors
  1. Igor N. Dubina
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  2. David F. J. Campbell
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  3. Elias G. Carayannis
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  4. Anna A. Chub
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  5. Evangelos Grigoroudis
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  6. Olga V. Kozhevina
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Corresponding author

Correspondence to David F. J. Campbell.

Appendix

Appendix

Technically, the game “Lab to Industry” is defined by the following input parameters:

  • n is the number of players (groups);

  • Ri is the amount of resources available to player i, i = 1, …, n;

  • m is the number of the innovative projects in the game;

  • CDminj, CDmaxj are the minimum and the maximum development costs, respectively, of innovative project j, j = 1, …, m;

  • CIminj, CImaxj are the minimum and the maximum production and sales costs, respectively, of innovative project j;

  • ERj is the expected revenue from project j;

  • α is the investment return from a risk-free project.

In real world situations, the innovations are accompanied by risk and uncertainty. This game simulates the risks and uncertainties considered at all phases by using a set of random variables (μ, φ, ξ).

The decisions of the players are the following:

  • Xij are the resources allocated by player i to develop project j, i = 1, …, n, j = 1, …, m;

  • Yij are the resources allocated by player i to implement project j, i = 1, …, n, j = 1, …, m + 1, where Yim + 1 are the resources allocated by player i to the riskless project.

The output parameters of the game are as follows:

  • FDj = ΣiXij are the resources to develop project j;

  • pj is the probability of the successful development of project j, 0 ≤ pj ≤ 1, pj = (FDj-CDminj) / (CDmaxj-CDminj);

  • μ is a random value obtained by using the uniform distribution in [0, 1], 0 ≤ μ ≤1; when μ ≤ p the project can be successfully developed and has the potential to produce a result for the investor; if μ > p, the project cannot be developed and the investor will not receive profits from the project;

  • Fj = ΣiYij are the resources to implement project j j;

  • qj is the probability of the successful implementation of project j, 0 ≤ qj ≤ 1, qj = (Fij-CIminj) / (CImaxj-CIminj);

  • φ is a random value obtained by using the uniform distribution in [0, 1], 0 ≤ φ ≤1; if φ ≤ q, the project is successfully implemented and brings income to the investor; if φ > q, the project will not be implemented;

  • ξ is a random variable, which characterizes a commercial success of the implemented project; it may be obtained by using the uniform distribution in [0, 1], the standard normal distribution, etc.;

  • RRj is the real income received from project j, and may differ from the expected income (ERj); for example, if ξ is generated by the uniform distribution in [0, 1], 0 ≤ ξ ≤1, the result can be calculated as RRj = ERj x (1,5 ξ), and, in this case, the real income may differ from the expected return by the value of 50 % upward or downward;

  • NRi = Yim + 1 (1 + α) is the income of player i from investing in the risk-free project;

  • TRI = ΣjRRj is the total income from venture capital (innovation) projects;

  • TR = TRI + ΣiNRi is the total revenue in the game (GDP).

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Dubina, I.N., Campbell, D.F.J., Carayannis, E.G. et al. The Balanced Development of the Spatial Innovation and Entrepreneurial Ecosystem Based on Principles of the Systems Compromise: A Conceptual Framework. J Knowl Econ 8, 438–455 (2017). https://doi.org/10.1007/s13132-016-0426-0

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