Abstract

The purpose of the article is to analyse conceptual approaches to determining the financial feasibility of an innovative project and to develop a methodology for assessing the expected integral economic effect of an innovative project based on a set of performance indicators while ensuring the compatibility of the considered scenarios. Analysed are theoretical approaches to assessing the effectiveness of an innovative project in conditions of uncertainty. The features of evaluating the effectiveness of innovative projects are identified, and the need to assess the expected integral economic effect of socially significant innovative projects is substantiated. The indicators of efficiency and feasibility of evaluating an innovative project in the context of applicability in various economic conditions are critically studied. A set of indicators for evaluating the effectiveness of innovative projects has been determined based on the provisions of the integral approach, and these indicators have been systematised based on their relationship. The expected integral economic effect or possible damage from the implementation of an innovative project has been calculated. The expected integral effect of an innovative project with given intervals of probabilities for individual scenarios is calculated. An integrated methodology for assessing the integrated effectiveness of innovative projects is proposed, which combines quantitative and qualitative performance indicators, economic and non-economic effects. The key directions for the selection of effective innovative solutions in the presence of uncontrollable factors, taking into account a variety of indicators, have been determined. A methodology has been developed for evaluating effective innovative projects with a lack or absence of information about the conditions for their implementation and functioning. Methods have been developed for determining the best options for innovative projects based on the theory of multicriteria choice while ensuring the compatibility of the considered scenarios.

Keywords: innovative project, assessment, expected integral effect, net present value.

JEL Classification: С52, С6, H43, L21, О39.

Cite as: Andros, S., Akimov, O., Akimova, L., Chang, S., & Gupta, S. K. (2021). Scenario analysis of the expected integral economic effect from an innovative project. Marketing and Management of Innovations, 3, 237-251. https://doi.org/10.21272/mmi.2021.3-20

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Birger, R. (1980). The Internal Rate of Return Method – A Critical Study. Engineering Costs and Production Economics, Elsevier, 5(1): 43-52. [CrossRef]
2. Borgonovo, E. & Gatti, S. & Peccati, L. (2010). What Drives Value Creation in Investment Projects? An Application of Sensitivity Analysis to Project Finance Transactions. European Journal of Operational Research, Elsevier, 205(1): 227-236. 
3. Brem, A., Radziwon, A. (2017). Efficient Triple Helix Collaboration Fostering Local Niche Innovation Projects–A Case from Denmark. Technological Forecasting and Social Change, 123, 130–141. 
4. Cappa, F., Del Sette, F., Hayes D., Rosso F. (2016). How to Deliver Open Sustainable Innovation: An Integrated Approach for a Sustainable Marketable Product. Sustainability, 8(12): 1341. [CrossRef]
5. Cigola, M., & Peccati, L. (2005). On the Comparison Between the APV and the NPV Computed Via the WACC. European Journal of Operational Research, Elsevier, 161(2): 377-385. [CrossRef]
6. Foster, J. E., Mitra, T. (2003). Ranking Investment Projects. Econ Theory, 22(3): 469–494. 
7. Gordon, B Hazen (2009). An Extension of the Internal Rate of Return to Stochastic Cash Flows. Management Science, 55(6): 1030-1034. 
8. Guerra, M. L., Magni, C. A., Stefanini, L. (2014). Interval and Fuzzy Average Internal Rate of Return for Investment Appraisal. Fuzzy Sets & Systems, 257, 217-241. [CrossRef]
9. Helfat, E., Quinn, J. B. (2006). Open Innovation: The New Imperative for Creating and Profiting from Technology. Academy of Management Perspectives, 20(2): 86–88. [CrossRef]
10. Leyman, P. & Vanhoucke, M. (2017). Capital- and Resource-Constrained Project Scheduling with net Present Value Optimization. European Journal of Operational Research, Elsevier, 256(3): 757-776.
11. Lööf, H., Heshmati, A. (2003). On the Relationship between Innovation and Performance: A Sensitivity Analysis. Economics of Innovation and New Technology, 15(4-5): 317-344. [CrossRef]
12. Magni, C. A. (2010). Average Internal Rate of Return and Investment Decisions: A New Perspective. The Engineering Economist, 55(2): 150-180. Available at SSRN: [Link]
13. Magni, C. A. (2015). Investment, Financing and the Role of ROA and WACC in Value Creation. European Journal of Operational Research, Elsevier, 244(3): 855-866. 
14. Magni, C. A. (2013). The Internal Rate of Return Approach and the AIRR Paradigm: A Refutation and a Corroboration. The Engineering Economist, 58(2): 73–111. [CrossRef]
15. Maravas, A., Pantouvakis, J.-P. (2018). A New Approach to Studying Net Present Value and the Internal Rate of Return of Engineering Projects under Uncertainty with Three-Dimensional Graphs. Advances in Civil Engineering, Article ID 6108680, 1-9. [CrossRef]
16. Marchioni, A., Magni, C. A. (2018). Investment Decisions and Sensitivity Analysis: NPV-Consistency of Rates of Return. European Journal of Operational Research, 268, 361-372, Available at SSRN: [Link]
17. Mohamed, S., McCowan, A. K. (2001). Modelling Project Investment Decisions Under Uncertainty Using Possibility Theory. International Journal of Project Management, 19(4): 231–241. 
18. Moshe, B.-H., & Kroll, Y. (2017). A Simple Intuitive NPV-IRR Consistent Ranking. The Quarterly Review of Economics and Finance, Elsevier, 66(C): 108-114.
19. Nwogugu, Michael C. I. (2016). On Algebraic Anomalies in Polynomials and Net Present Value Decisions. Anomalies in Net Present Value, Returns and Polynomials, and Regret Theory in Decision-Making, 263-295. 
20. Nwogugu, Michael C. I. (2016). The Historical and Current Concepts of “Plain” Interest Rates, Forward Rates and Discount Rates аre or Can Be Misleading. Anomalies in Net Present Value, Returns and Polynomials, and Regret Theory in Decision-Making, 207-262.
21. Pasqual, J. & Padilla, E. & Jadotte, E. (2013). Technical Note: Equivalence of Different Profitability Criteria with the Net Present Value. International Journal of Production Economics, Elsevier, 142(1): 205-210. 
22. Robison L. J., Barry, P. J., Myers, R. J. (2015). Consistent IRR and NPV Rankings. Agricultural Finance Review, Emerald Group Publishing, 75(4): 499-513. 
23. Prokop V., Stejskal, J., Kuvíková, H. (2017). The Different Drivers of Innovation Activities in European Countries: A Comparative Study of Czech, Slovak, and Hungarian Manufacturing Firms. Ekonomicky Casopis, 65(1): 31-45.
24. Sewastjanow, P., Dymowa, L. (2008) On the Fuzzy Internal Rate of Return. In: Kahraman C. (eds) Fuzzy Engineering Economics with Applications. Studies in Fuzziness and Soft Computing, 233. Springer, Berlin, Heidelberg. [CrossRef]
25. Silva, J. L. e, Sobreiro, V. А., Kimura, H. (2018) Prepurchase Financing Pool: Revealing the IRR Problem. The Engineering Economist, 63(2): 158-170. 
26. Sorenson, G. E., Lavelle, J. P. (2008). A Comparison of Fuzzy Set and Probabilistic Paradigms for Ranking Vague Economic Investment Information Using a Present Worth Criterion. The Engineering Economist, 53(1): 42–67. [CrossRef]  
27. Wiesemann, W. & Kuhn, D. & Rustem, B. (2010). Maximizing the Net Present Value of a Project Under Uncertainty. European Journal of Operational Research, Elsevier, 202(2): 356-367. [CrossRef]
28. Yang, Kum Khiong & Talbot, F. Brian & Patterson, James H. (1993). Scheduling a Project to Maximize its Net Present Value: An Integer Programming Approach. European Journal of Operational Research, Elsevier, 64(2): 188-198. [CrossRef]