EVALUATION OF RISK IMPACT ON IMPLEMENTATION OF INNOVATION PROJECTS WITHIN THE FRAMEWORK OF MACHINE-BUILDING QUASI-INTEGRATION STRUCTURES
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Abstract
The risk management of innovation project can improve the level of risk control of quasi-integration structures (QIS), so as to make more effective decisions, reduce investment losses and achieve mutual benefit as well. Therefore, an assessment of the risk impact on implementation of innovation project makes this study relevance. The purpose of this paper is to analyze the impact of market risks on the success of innovative QIS engineering projects with the involvement of an economic-probabilistic model based on a conjoint analysis. Research methodology. The article substances the feasibility and results of the application of the method of conjoint analysis – one of the methods of mathematical psychology – to assess the impact of risks on the effectiveness of innovative projects implemented within quasi-integration structures in the engineering complex. Findings. The most likely scenarios for implementing an innovative project in terms of the impact of risk events on the financial result had been found with a help of a conjoint procedure with a fractional factorial design. The relative impact of each risk on the success of the innovation project was evaluated. The rule of deciding on the eligibility of an innovative project by the participants of machine-building QIS was formulated on the basis of the technique of the internal rate of investment return (IRR). Research limitations. The developed methodology is proposed to be used in assessing the impact of risks on innovative projects within machine-building quasi-integration structures. The proposed method of assessing the impact of risks on the financial results of innovative projects within the machine-building QIS can be used in more general situations. Practical implications. The methodology was tested on the example of an innovation project within an innovation and technology cluster, which included six participants: mechanical engineering companies, a service company, a scientific institution and an educational institution. The market and specific inter-corporate risks that influence the results of innovative projects within machine-building QISs were identified and evaluated for operationalization. According to the results of implementation of the methodology, the feasibility of implementing an innovative project within the innovation-technological cluster was substantiated. Originality/value. The scientific novelty of this study is the use of a conjoint analysis methodology to assess the impact of market risks.
How to Cite
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machine-building, Ukraine, quasi-integration structures, innovative project, risks, conjoint analysis
Behrens, W., & Hawranek, P. (1991). Manual for the Preparation of Industrial Feasibility Studies. Vienna: United Nations Industrial Development Organization.
Bragina, T. E., & Tabunshik, G. V. (2011). Fuzzy analysis of project risk. Information processing systems, 3(93), 15–20.
Chen, J. M. (2014) Measuring Market Risk Under the Basel Accords: VaR, Stressed VaR, and Expected Shortfall. International Journal of Finance, 8, 184–201. doi: https://doi.org/10.2139/ssrn.2252463
Cherenkov, A. (1999). Application of the method of conjoint analysis marketing study. Marketing in Russia and abroad, 4, 24–28.
Committee of Sponsoring Organizations of the Treadway Commission (2007). Gerenciamento de riscos corporativos: estrutura integrada. 2nd ed. United States: COSO.
Dreshchinsky, V.A., & Markov, M.A. (2010). Risk assessment methodology in promotion of innovations projects. Innovations, 2(136), 100–104.
Etges, A., Souza, J., & Neto, F. (2017). Risk management for companies focused on innovation processes. Production, 27, e20162209, doi: https://doi.org/10.1590/0103-6513.220916
Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., & Veitch, B. Fault and Event Tree Analyses for Process Systems Risk Analysis: Uncertainty Handling Formulations. Risk Analysis, 31(1), 86–107. doi: https://doi.org/10.1111/j.1539-6924.2010.01475.x
Green, P., Krieger, A., & Wind, Y. (2001). Thirty Years of Conjoint Analysis: Reflections and Prospects. Marketing Engineering, 31(3), 56–73. doi: 10.1287/inte.31.3s.56.9676
Green, P., & Rao, V. (1971). Conjoint Measurement for Quantifying Judgmentеal Data. Journal of Marketing Research, 8, 355–363. doi: https://doi.org/10.2307/3149575
Green, P., & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. Journal of marketing, 54(4), 3–19. doi: https://doi.org/10.2307/1251756
Hanke, J., & Wichern, D. (2009). Business forecasting. 9th ed. Pearson Education, Prentice-Hall.
Hauser, J., & Rao V. (2004). Conjoint analysis, related modeling, and applications. Marketing Research and Modeling: Progress and Prospects, 14, 141–168. doi: https://doi.org/10.1007/978-0-387-28692-1
Havenaar, M., & Hiscocks, P. (2012) Strategic alliances and market risk. Drug Discovery Today, 17, pp. 824–827. doi: https://doi.org/10.1016/j.drudis.2012.03.008
Hsu, C.-C., & Sandford, B. A. (2007). The Delphi Technique: making sense of consensus. Practical Assesment, Research & Evaluation, 12(10), 1–7. doi: https://doi.org/10.7275/pdz9-th90
Illiashenko, S. (2010). Strategic management of enterprise innovative activity on basis of marketing of innovations. Actual problems of economics, 12, 111–119.
International Organization for Standardization (2009). ISO 31000:2009: risk management: principles and guidelines 2009. Geneva: ISO.
Kaplan, R., & Mikes, A. (2016). Risk Management ‒ The Revealing Hand. Applied Corporate Finance, 28(1), 8–18. doi: https://doi.org/10.1111/jacf.12155
Kovalev, P.P. (2017). Features of risk assessment of investment projects. Economics: yesterday, today and tomorrow, 7 (5A), 251–260.
Krantz, H., Luce, R., Suppes, P., & Tversky, A. (1971). Foundations of Measurement. New York, NY: Academic Press.
Laburtseva, O. (2012). Management of innovations marketing risks. Marketing and Management of Innovations, 4, 15–22.
Luce, R.D., & Tukey, J.W. (1964). Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement. Journal of Mathematical Psychology, 1, 1–27. doi: https://doi.org/10.1016/0022-2496(64)90015-X
Malhotra N., & Birks D. (2007). Marketing Research: an applied approach: 3rd European Edition. Harlow, UK: Pearson Education.
McConnell, P. (2016). Strategic Risk Management. London: Risk Books.
Merton, R., & Kendall, P. (1946). The focused interview. American Journal of Sociology, 51, 541–557. doi: https://doi.org/10.1086/219886
Miorando, R., Ribeiro, J., & Cortimiglia, M. (2014). An economic-probabilistic model for risk analysis in technological innovation projects. Technovation, 34, 485–498. doi: https://doi.org/10.1016/j.technovation.2014.01.002
Miura, R., & Shingo, O. (2000). Statistical Methodologies for the Market Risk Measurement. Asia-Pacific Financial Markets, 7, pp. 305–319. doi: https://doi.org/10.1023/a:1010077117199
Morgan, J. P. (1996). RiskMetrics: Technical Document. Retrieved January 12, 2020 from: http://www.jpmorgan.com/RiskManagement/RiskMetrics/RiskMetrics.html
Rutkauskas, A., & GinevičiusI, A. (2011). Integrated management of marketing risk and efficiency. Journal of Business Economics and Management, 12(1), 5–23. doi: https://doi.org/10.3846/16111699.2011.555357
Slepukhina, J., & Kharchenko, G. (2007). Features of modern risk assessment methods of investment projects. Journal of new economy, 1(18), 104–116.
Solntsev, S., & Ovchynnikova, A. (2011). Assessment of the marketing risks of bringing a new product to the market. Marketing education in Ukraine. 356–365.
Solntsev, S., & Ovchynnikova, A. (2013). Model of Assessment of Marketing Risks in Investment Projects. Business Inform, 12, 105–110.
Solntsev, S., & Zhygalkevych, Zh. (2019). Creation and development of quasi-integration structures on the basis of machinebuilding enterprises. Business navigator, 3(52), 128–132.
Solntsev, S., & Zhygalkevych, Zh. (2020). Determining the attractiveness of innovative projects within quasi-structures based on a conjoint approach. Marketing and Digital Technologies, 4(1), 15–28.
Steiner, M., & Meißner, M. (2018). A User’s Guide to the Galaxy of Conjoint Analysis and Compositional Preference Measurement. Marketing ZFP – Journal of Research and Management, 40(2), 3–25. doi: https://doi.org/10.15358/0344-1369-2018-2-3
The CBC System for Choice-Based Conjoint Analysis Copyright Sawtooth Software, Inc. Orem, Utah USA. Retrieved February 24, 2020 from: https://www.sawtoothsoftware.com/download/techpap/cbctech.pdf
Thuesen, K. (2007). Analysis of Ranked Preference Data. Retrieved February 5, 2020 from: http://citeseerx.ist.psu.edu/viewdoc/download?doi: https://doi.org/10.1.1.89.152&rep=rep1&type=pdf
Van Horne, J., & Wachowicz, J. (2008). Fundamentals of financial management. 13th ed. Pearson Education, Prentice-Hall.
Vargas-Hernández, J. G., Noruzi, M. R., & Sariolghalam, N. (2010). Risk or innovation, which one is far more preferable in innovation projects? International Journal of Marketing Studies, 2(1), 233–244. doi: https://doi.org/10.5539/ijms.v2n1p233
Wang, Q. (2009). Multi-agent Assessment on Marketing Risk Based on Evidence Theory. International Conference on Electronic Commerce and Business Intelligence. doi: https://doi.org/10.1109/ECBI.2009.33
Wittink, D., & Krishnalnurthi, L. (1981). Aggregation issues in conjoint analysis. Working Paper, No. 580. Retrieved February 5, 2020 from: https://www.gsb.stanford.edu/faculty-research/working-papers/aggregation-issues-conjoint-analysis
Xing Y., & Guan Q. (2017). Risk management of PPP project in the preparation stage based on Fault Tree Analysis. IOP Conference Series Earth and Environmental Science, 59. 1–9. doi: https://doi.org/10.1088/1755-1315/59/1/012050