Modeling the intangible assets’ elasticity profile as a tool of financial benchmarketing

Kozenkova V. D.; Kozenkova N. P.

Article

Issue:	2021 №4 (76)
Section:	Finances
UDK:	338:005.94+658.014
DOI:	https://doi.org/10.33271/ebdut/76.114
Article language:	Ukrainian
Pages:	114-125
Title:	Modeling the intangible assets’ elasticity profile as a tool of financial benchmarketing
Authors:	Kozenkova V. D., Dnipro State Agricultural and Economic University, Kozenkova N. P., Ukrainian State University of Science and Technology
Annotation:	Methods. The results are obtained through the application of the following methods: analysis and synthesis – in highlighting the essence of benchmarking and visualization of information; comparisons – to determine the advantages and disadvantages of benchmarking and visualization of information; methods of elasticity theory – for the formation of methodological principles for determining the impact of individual packages of intangible assets (IA) on the value of enterprises, determining the complementarity and substitution of packages; methods of economic and mathematical modeling – to determine the elasticity of the impact of IA on the value of enterprises; methods of graphic analysis – for visualization of calculation material; abstract-logical analysis – for generalization, formulation of conclusions. Results. There are considered approaches to the definition of benchmarking as a tool for improving the activities of enterprises, types of benchmarking and their scope in the activities of enterprises. The need to use financial benchmarking in the system of financial management of enterprises is pointed out. The main indicators of evaluation of enterprises and their structural subdivisions are considered, their main shortcomings are identified and the necessity of using cost indicators as the basis of enterprise development in the long run is shown. The role of IA in improving the efficiency of modern enterprises and increasing their value is considered, the approach to the allocation of certain packages in the IA is presented. The possibility of modeling the elasticity of the impact of IA on their types on the value of the enterprise is shown. The necessity of using Tobin`s coefficient to determine trends in the demand function of the enterprise business, evaluation of IA packages to form a system for monitoring their vertical, horizontal and cross-elasticity of impact on the value of the enterprise. The essence of the system of monitoring the elasticity of the impact of IA on the value of enterprise is shown, and its tasks at the micro level are defined. Possibilities of using information visualization mechanisms in the financial benchmarking system are determined. Novelty. Conceptual provisions and answers of economic and mathematical tools of methods of estimation and monitoring of elasticity of influence of IA on the cost of the enterprise are developed. It is proposed to determine the profile of the enterprise on the indicators of elasticity, which allows visualizing information on the level of elasticity of IA and their packages and justifying the priorities of IA development in order to improve the efficiency of the enterprise. Practical value. The approach of construction of a profile of the enterprise on indicators of elasticity of IA which gives the chance of visualization of the information on level of elasticity and gives the chance to form directions of development of support of separate packages is offered.
Keywords:	Benchmarking, Value, Visualization, Elasticity, Modeling, Intangible assets, Elasticity profile, Financial benchmarking
File of the article:	EV20214_114-125.pdf
Literature:	1. Deming, W.E. (2000). The New Economics: For Industry, Government, Education. Boston: MIT Press. 2. Crosby, Ph. (1979). Quality is Free. New York: McGraw-Hill. 3. Ishikawa, K., & Lu, D.J. (1985). What is total quality control? The Japanese way. New Jersey: Prentice- Hall, Englewood Cliffs. 4. Shewhart, W.A. (1931). Economic control of quality of manufactured product. New York: D. Van Nostrand Company. 5. Andersen B., & Pettersen P. -G. (1996). The Benchmarking Handbook: Step-by-Step Instructions. London, England: Chapman & Hall. 6. Watson, G.H. (1993). Strategic Benchmarking: How to Rate your Company’s Performance against the World’s Best. New Jersey: John Wiley. 7. Camp, R.C. (1989). Benchmarking: The Search for Industry Best Practices that Lead to Superior Performance. Milwaukee, WI: ASQ Quality Press. 8. Coer News (2002). The free Newsletter, Issue No.2. Massey University, New Zealand. 9. Harrington, H.J., & Harrington, J.S. (1996). High Performance Benchmarking: 20 Steps to Success. New York: McGraw Hill. 10. APQC's Benchmarking Methodology. (2020). Retrieved from https://www.apqc.org/resource- library/resourcelisting/apqcs-benchmarking-methodology 11. Anand, G., & Rambabu, K. (2008). Benchmarking the benchmarking models, Benchmarking: An International Journal, 15, 3, 257-291. Retrieved from http://dx.doi.org/10.1108/14635770810876593. 12. Išoraite, M. (2004). Benchmarking methodology in a transport sector. Transport 19(6), 269-275 https://doi.org/10.1080/16484142.2004.963798625. 13. Luu, V.T., Soo, Y.K., & Tuan, A.H. (2008). Improving Project Management Performance of Large Contractors Using Benchmarking Approach. International Journal of Project Management, 26, 758-769. Retrieved from https://www.academia.edu/8072211/Improving_project_ management_performance_of_large_contractors_using_ benchmarking_approach doi:10.1016/j.ijproman.2007.10.002 14. Kozak, M., & Rimmington, М. (1998). Benchmarking: Destination Attractiveness and Small Hospitality Business Performance. International Journal of Contemporary Hospitality Management 10/5 [1998] 184-188. Retrieved from http://195.130.87.21:8080/dspace/bitstream/123456789/316/1/Benchmarking%20destination%20attractiveness%20and%20small%20hospitality%20business%20performance.pdf 15. Van Horne, J.C., & Wachowicz, J.M. (2008).. Fundamentals of financial management. Harlow: Prentice-Hall, Inc. 16. Brophy, D.J., & Shulman, J.M. (1992). A Finance Perspective on Entrepreneurship Research. Entrepreneurship Theory and Practice, 16(3), 61-72. Retrieved from https://journals.sagepub.com/doi/10.1177/104225879201600304 17. Tobin, J. (1969). A General Equilibrium Approach To Monetary Theory. Journal of Money, Credit and Banking.. 1, 15-29. Retrieved from https://www.jstor.org/stable/1991374, https://doi.org/10.2307/1991374 18. Zadeh, L.A., Fu, K.S., Tanaka, K., & Shimura, M. eds. (1975). Fuzzy Sets and Their Applications to Cognhive and Decision Processes. New York: Academic Press. 19. Oxford Dictionaries, n.d. visualization - definition of visualization in English Oxford Dictionaries. Oxf. Dictionaries Engl. Retrieved from https://en.oxforddictionaries.com/definition/visualization. 20, Tory, M., & Moller, T. (2004). Human factors in visualization research. IEEE Trans. Vis. Comput. Graph.,10. 72-84. Retrieved from https://eprints.cs.univie.ac.at/5024/1/2004_-_human_ factors_in_vis_research.pdf 21. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations eedings of the IEEE Symposium on Visual Languages, Boulder, 3-6 September 1996, 336-343. Retrieved from doi:10.1109/VL.1996.545307 22. Myatt, G.J., & Johnson, W.P. (2009). Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications, 1 edition. New Jersey: Wiley, Hoboken,.. 23. Yi, J.S., Kang, Y., Stasko, J.T., & Jacko, J.A. Understanding and Characterizing Insights: How Do People Gain Insights Using Information Visualization? // in: Proceedings of the 2008 Workshop on BEyond Time and Errors: Novel evaLuation Methods for Information Visualization, BELIV ’08. ACM, New York, NY, USA, 4:1-4:6. Retrieved from https://dl.acm.org/doi/abs/10.1145/1377966.1377971 24. Lohse, G.L. (1978). The role of working memory on graphical information processing. Behav. Inf. Technol., 16, 297-308. Retrieved from https://doi.org/10.1080/014492997119707