Development of the scientific and technological forecasting methodology based on using TIPS instruments

Economic Annals-ХХI: Volume 187, Issue 1-2, Pages: 223-231

Citation information:
Golubev, S., Efremov, A., Gorokhova, A., Gayduk, V., & Kravets, E. (2021). Development of the scientific and technological forecasting methodology based on using TIPS instruments. Economic Annals-XXI, 187(1-2), 223-231. doi: https://doi.org/10.21003/ea.V187-22


Sergei Golubev
D.Sc. (Economics),
Department of Economics and Organization,
Faculty of Economics and Management,
Moscow Polytechnic University
38 Bolshaya Semenovskaya Str., Moscow, 107023, Russian Federation
sergei.golubev56@mail.ru
il.abrorvv23@yahoo.com
ORCID ID: https://orcid.org/0000-0001-8745-6235

Andrey Efremov
D.Sc. (Economics),
Department of Economics and Organization,
Faculty of Economics and Management,
Moscow Polytechnic University
38 Bolshaya Semenovskaya Str., Moscow, 107023, Russian Federation
a.a.efremov@mospolytech.ru
ORCID ID: https://orcid.org/0000-0002-1006-1427

Anna Gorokhova
D.Sc. (Economics),
Department of Economics and Organization,
Faculty of Economics and Management,
Moscow Polytechnic University
38 Bolshaya Semenovskaya Str., Moscow, 107023, Russian Federation
agor_80@mail.ru
ORCID ID: https://orcid.org/0000-0002-5820-1687

Vladimir Gayduk
D.Sc. (Economics),
Department of Institutional Economics and Investment Management,
Faculty of Economics,
Kuban State Agrarian University named after I. T. Trubilin
13 Kalinin Str., Krasnodar, 350044, Russian Federation
vi_gayduk@mail.ru
ORCID ID: https://orcid.org/0000-0001-9992-7647

Ekaterina Kravets
PhD (Economics),
Department of Management,
Institute of Project Management and Investment Business,
Leonov Moscow Region University of Technology
42 Gagarin Str., Korolev, Moscow Region, 141070, Russian Federation
kravec_e_v@mail.ru
ORCID ID: https://orcid.org/0000-0002-1178-239X

Development of the scientific and technological forecasting methodology based on using TIPS instruments

Abstract. Scientific and technological forecasting for a long run is essential when determining priorities in science, engineering, and technology development. The forecasting methods used in its development and based on the Delphi approach cannot provide a complete analysis of development trends, depending on the experts’ professionalism, and allow facts of lobbying interests of various groups.

In this paper, the mechanisms for improving the quality of scientific and technological forecasts by using the theory of inventive problem solving (TIPS, or TRIZ) when forming and verifying forecasts have been offered. In this case, the forecast results are based not only on the expert’s subjective opinion but also on the objective laws of the development of the technical systems. The material has come with detailed practical examples.

The integration of the TRIZ instruments in the state system for determining top priority technological areas will reduce errors in choosing the number of promising technological and technical areas that will make up the basis for the formation of new models and technologies.

Keywords: Innovation; Scientific Forecasting; Technological Forecasting; Forecast Quality; Problems Solving; TRIZ; TIPS

JEL Classifications: O31; O39; O32; C87

Acknowledgements and Funding: The authors received no direct funding for this research.

Contribution: The authors contributed equally to this work.

DOI: https://doi.org/10.21003/ea.V187-22

References

  1. Borgianni, Y., Cascini, G., & Rotini, F. (2018). Investigating the future of the fuzzy front end: towards a change of paradigm in the very early design phases? Journal of Engineering Design, 29(11), 644-664.
    https://doi.org/10.1080/09544828.2018.1520971
  2. Coccia, M. (2019). The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting. Technological Forecasting and Social Change, 141, 289-304.
    https://doi.org/10.1016/j.techfore.2018.12.012
  3. Da Silva, R. H., Kaminski, P. C., & Armellini, F. (2020). Improving new product development innovation effectiveness by using problem solving tools during the conceptual development phase: Integrating Design Thinking and TRIZ. Creativity and Innovation Management, 29(4), 685-700.
    https://doi.org/10.1111/caim.12399
  4. Feng, L., Niu, Yu., & Wang, Ji. (2020). Development of Morphology Analysis-Based Technology Roadmap Considering Layer Expansion Paths: Application of TRIZ and Text Mining. Applied Sciences, 23(10), 8498.
    https://doi.org/10.3390/app10238498
  5. Golubev, S. S., Chebotarev, S. S., Sekerin, V. D., & Gorokhova, A. E. (2017). Development of Employee Incentive Programmes with Regard to Risks Taken and Individual performance. International Journal of Economic Research, 14(7), 37-46.
    https://www.elibrary.ru/item.asp?id=31034164
  6. Jia, W., Xie, Y., Zhao, Y., Yao, K., Shi, H., & Chong, D. (2021). Research on Disruptive Technology Recognition of China’s Electronic Information and Communication Industry Based on Patent Influence. Journal of Global Information Management (JGIM), 29(2), 148-165.
    https://doi.org/10.4018/JGIM.2021030108
  7. Nagimov, A. R., Akhmetshin, E. M., Slanov, V. P., Shpakova, R. N., Solomonov, M. P., & Ilyaschenko D. P. (2018). Foresight technologies in the formation of a sustainable regional development strategy. European Research Studies Journal, XXI(2), 741-752.
    https://doi.org/10.35808/ersj/1037
  8. Sengupta, S., Kim. J., & Kim, S. D. (2018). Forecasting New Features and Market Adoption of Wearable Devices Using TRIZ and Growth Curves: Case of Fitness Tracking Products. International Journal of Innovation and Technology Management, 15(1), 6-22.
    https://doi.org/10.1142/S0219877018500098
  9. Sheu, D. D., Chiu, M.-Ch., & Cayard, D. (2020). The 7 pillars of TRIZ philosophies. Computers & Industrial Engineering, 146, 106572.
    https://doi.org/10.1016/j.cie.2020.106572
  10. Tsygankova V. N. (2019). Application of the theory of inventive problem solving to amplifying creativity of employees. IOP Conference Series: Materials Science and Engineering, 483(1), 5-23.
    https://iopscience.iop.org/article/10.1088/1757-899X/483/1/012096/meta
  11. Wang, L.-Y., & Dong, Z. H. A. O. (2021). Cross-domain function analysis and trend study in Chinese construction industry based on patent semantic analysis. Technological Forecasting and Social Change, 162, 120331.
    https://doi.org/10.1016/j.techfore.2020.120331

Received 8.12.2020
Received in revised form 29.12.2020
Accepted 12.01.2021
Available online 28.02.2021