Математические модели социально-экономических процессов
М. Г. Дубинина "Исследование современных подходов к моделированию процессов распространения технологий в наукоемких отраслях"
Системы управления и моделирование
Оптимизация, идентификация, теория игр
Распознавание образов
М. Г. Дубинина "Исследование современных подходов к моделированию процессов распространения технологий в наукоемких отраслях"

Аннотация.

В данной работе проводится анализ современных подходов к описанию и моделированию процессов распространения новых продуктов и услуг
в высокотехнологичных отраслях с учетом неоднородности агентов, «двойного эффекта» в развитии рынка, наличия конкурирующих брендов и нескольких поколений высокотехнологичных продуктов. Также приводятся способы учета влияния рекламных и ценовых факторов на распространение технологий, рассматриваются методы оценки параметров и применение моделей для анализа скорости и потенциала распространения инноваций в высокотехнологичных отраслях.

Ключевые слова:

инновации, диффузия технологий, технологическое замещение, модели диффузии, неоднородность агентов, поколения высокотехнологичных продуктов, информационно-коммуникационные технологии.

Стр. 43-54.

M. G. Dubinina

"A study of current approaches to modeling the diffusion of technologies in high-tech industries"

Abstract. This paper is analyzed the modern approaches to the description and simulation the diffusion of new products and services in high-tech industries with the heterogeneity of agents, «double effect» in the development of the market, the availability of competing brands and generations of high-tech products. It also provides ways to address the impact of advertising and price factors in the spread of technology, describes how to estimate the parameters and the use of models for analyzing the speed and capacity of innovation diffusion in hightech industries.

Keywords: innovation, diffusion of technology, technological replacement, diffusion models, heterogeneity of agents, multi-generational high-technology products, information and communication technologies.

 Полная версия статьи в формате pdf.

REFERENCES

1. Varshavskiy A. Ye. Nauchno-tekhnicheskiy progress v modelyakh ekonomicheskogo razvitiya. M.: Finansy i statistika, 1984.
2. Varshavskiy A. Ye. Naukoemkie otrasli i vysokie tekhnologii: opredelenie, pokazateli, tekhnicheskaya politika, udelnyy ves v strukture ekonomiki Rossii //Ekonomicheskaya nauka sovremennoy Rossii. 2000. № 2. S. 61–83.
3. Delitsyn L. L. Kolichestvennye modeli rasprostraneniya novovvedeniy v sfere informatsionnykh i kommunikatsionnykh tekhnologiy // Kaf. multimed. tekhnol. i inf. sis. MGUKI. M.: MGUKI. 2009. 107 s.
4. Delitsyn L. L. Razrabotka i primenenie kolichestvennykh  modeley  rasprostraneniya  novykh  informatsionnykh tekhnologiy // Nauch.-tekh. informatika. Ser. Org. i metodika inf. rab. 2014. № 5 C. 24–32.
5. Dubinina M. G. Modelirovanie dinamiki vzaimosvyazi makroekonomicheskikh pokazateley i pokazateley rasprostraneniya IT v razvitykh i razvivayushchikhsya stranakh // Trudy ISA RAN. T. 65. 1/2015. S. 24–37.
6. Kuzminov Ya. I.,  Bendukidze K. A.,  Yudkevich M. M. Kurs institutsionalnoy ekonomiki: instituty, seti, transaktsionnye  izderzhki,  kontrakty.  M.:GU  VShE, 2006, 442 s.
7. Sakhal D.  Tekhnicheskiy  progress:  kontseptsii,  modeli, otsenki. M.: Finansy i statistika, 1985.
8. Churkin V. I. Prognoz prodazh innovatsionnykh tovarov  s uchetom makroekonomicheskikh faktorov (na primere malykh vetrogeneratorov) // Nauch.-tekh. vedom. SPbGPU. Ser. Ekonom. nauki. 2013. № 163 (T. 1). C. 104–112.
9. Bass F. A. New Product Growth for Model Consumer Durables // Management Science. 1969. V. 15. (5). P. 215–227.
10. Bass F. M., Krishnan T. V., Jain D. C. Why the Bass model  fits  without decision variables //  Marketing Science. 1994. V. 13. P. 203–223
11. Bemmaor A. C., Lee J.  The impact of heterogeneity and ill-conditioning on diffusion model parameter estimates // Market. Sci. 2002. V. 21. P. 209–220.
12. Boehner R., Gold S. Modeling the Impact of Marketing Mix on the Diffusion of Innovation in the Generalized Bass Model of Firm Demand // Developments in Business Simulation and Experiential Learning. 2012. V. 39. P. 75–91.
13. Boretos G. P. IS model: A general model of forecasting and its applications in science and the economy // Technol. Forecasting & Soc. Change 2011. V. 78. P. 1016–1028
14. Chanda U., Das S. Multi-stage diffusion dynamics in multiple generation high technology products // J. of High Tech. Manag. Research. 2015. V. 26. P. 88–104.
15. Chen Y., Carrillo J. E. Single firm product diffusion model for single-function and fusion products // European Journal  of  Operational  Research. 2013.  V. 1.  № 163. P. 104–112.
16. Chiang S.-Y. An application of Lotka—Volterra model to Taiwan’s transition from 200 mm to 300 mm silicon wafers // Technol. Forecasting & Soc. Change. 2012. V. 79. P. 383–392.
17. Chiang S.-Y.,  Wong G.-G. Competitive diffusion of personal computer shipments in Taiwan // Technol. Forecasting & Soc. Change. 2011. V. 78. P. 526–535.
18. Dalla  Valle A., Furlan C. Diffusion of nuclear energy in some developing countries // Technological Forecasting & Social Change. 2014. V. 81. P. 143–153.
19. Dalla  Valle A., Furlan C. Forecasting accuracy of wind power technology diffusion models across countries // International  Journal  of  Forecasting.  2011.  V. 27(2). P. 592–601.
20. Decker R., Gnibba-Yukawa K. Sales Forecasting in High-Technology Markets: A Utility-Based Approach // J. Prod. Innov. Manag. 2010. 27. P. 115–129.
21. Feng G. C. Factors affecting Internet diffusion in China: A multivariate time series analysis // Telematics and Informatics. November 2015. V. 32(4). P. 681–693.
22. Fernandez-Duran J. J.  Modeling seasonal effects in  the Bass Forecasting Diffusion Model // Technological Forecasting & Social Change. 2014. V. 88. P. 251–264.
23. Foekens  E. W.  Leeflang P. S. H.,  Wittink D. R.  Varying parameter models to accommodate dynamic promotion effects // Journal of Econometrics. 1998. V. 89. P. 249–268.
24. Goldenberg J., Libai B., Muller E. Using complex systems analysis to advance marketing theory development: Modeling heterogeneity effects on new product growth through stochastic cellular automata // Academy of Marketing Science Review. 2001. V. 9. P. 1–18.
25. Griliches Z. Hybrid Corn: An Exploration in the Econoics of Technological Change // Econometrica. 1957. V. 25. P. 501–522.
26. Guseo R, Guidolin M. Market potential dynamics in innovation diffusion: modelling the synergy between two driving forces // Technological Forecasting and Social Change. 2011. V. 78. P. 13–24.
27. Guseo R., Dalla Valle A., Guidolin M. World oil depletion models: Price effects compared with strategic or technological interventions // Technological Forecasting and Social Change. 2007. V. 74(4). P. 452–469.
28. Guseo R., Guidolin M. Heterogeneity in diffusion of innovations modelling: A few fundamental types // Technological Forecasting & Social Change. 2015. V. 90 P. 514–524.
29. Guseo R.,  Mortarino C.  Within-brand  and  cross-brand word-of-mouth  for  sequential  multi-innovation  diffusions // IMA. Journal of Management Mathematics. 2013. P. 1–25.
30. Hung H.-C.,  Tsai Y.-S.,  Wu M.-C.  A  modified  Lotka— Volterra model for competition forecasting in Taiwan’s retail industry // Computers & Industrial Engineering. 2014. V. 77. P. 70–79.
31. Kandler A.,  Steele J.  Innovation  Diffusion  in  Time  and Space: Effects of Social Information and of Income Inequality // The Open-Access Journal for the Basic Principles  of  Diffusion  Theory, Experiment and  Application. diffusion-fundamentals.org. 11.2009. V. 3. P. 1–17.
32. Karmeshu & Goswami D. Stochastic evolution of innovation diffusion in heterogeneous groups: study of life cycle patterns // IMA J. Manag. Math. 2001. V. 12. P. 107–126.
33. Kim S. H., Srinivasan V. A Conjoint-hazard Model of the Timing of Buyers’ upgrading to Improved Versions of High-technology Products // J. Prod. Innov. Manag. 2009. V. 26. P. 278–290.
34. Kreng V. B., Wang B. J. An Innovation Diffusion of Successive Generations by Systemd Dynamics — An Empirical Study of Nike Golf Company // Technol. Forecast. Soc. Change. 2013. V. 80. P. 77–87.
35. Kreng V. B.,   Wang H. T.   A   Technology   Replacement Model with Variable market potential — An Empirical Study of CRT and LCD TV // Technol. Forecast. Soc. Change. 2009. V. 76. P. 942–951.
36. Laciana C. E.,  Gual G.,  Kalmusa D.,  Oteiza-Aguirre N., Rovere S. L.  Diffusion  of  two  brands  in  competition: Cross-brand effect // Physica A. 2014. V. 413. P. 104–115.
37. Lee D., Kim H. The effects of network neutrality on the diffusion of new Internet application services // Telematics and Informatics. August 2014. V. 31(3). P. 386–396.
38. Lee H., Kim S. G., Park H., Kang P. Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach // Technological Forecasting & Social Change. 2014. V. 86. P. 49–64.
39. Mahajan V., Muller E. Timing, Diffusion and Substitution of Successive Generations of Technological Innovations: the  IBM  Mainframe  Case  //  Technol.  Forecast.  Soc. Change. 1996. V. 51. P. 109–132.
40. Mansfield E. Technical Change and the Rate of Imitation. Econometrica. 1961. V. 29(4). P. 741–766.
41. Miranda L. C. M., Lima C. A. S. Technology substitution and innovation adoption: The cases of imaging and mobile communication markets // Technol. Forecasting & Soc. Change 2013. V. 80. P. 1179–1193.
42. Muller E., Yogev G. When does the majority become majority? Empirical analysis of the time at which main market adopters purchase the bulk of our sales // Technol. Forecasting and Soc. Change. 2006. V. 73(9). P. 1107–1120.
43. Norton J. A., Bass F. M. A  Diffusion  Theory Model  of Adoption and Substitution for Successive Generations of High-technology  Products  //  Manag.  Sci.  1987.  V. 33. P. 1069–1086.
44. Papagiannidis S.,  Gebka B.,  Gertner D.,  Stahl F.  Diffusion of web technologies and practices: A longitudinal study // Technol. Forecasting & Soc. Change. 2015. V. 96. P. 308–321.
45. Pegoretti G.,  Rentocchini F.,   Marzetti G. V.  An  agent-based  model of innovation diffusion: network structure and  coexistence  under  different  information  regimes  // J. of   Economic   Interaction   and   Coordination.   2012. V. 7(2). P. 145–165.
46. Penard T., Poussing N., Mukokoc B., Piaptie G. B. T. Internet  adoption  and  usage  patterns  in  Africa:  Evidence from  Cameroon  //  Technol.  in  Society.  August  2015. V. 42. P. 71–80.
47. Peres R., Muller E., Mahajan V. Innovation diffusion and new product growth models: A critical review and research directions // Intern. J. of Research in Marketing. 2010. V. 27 P. 91–106.
48. Phuc P. N., Yu V. F.,  Chou S.-Y.  Manufacturing production plan optimization in three-stage supply chains under Bass model market effects // Computers & Industrial Engineering. 2013. V. 65 P. 509–516.
49. Rogers E. Diffusion of Innovations (5th  ed.). New York: Free Press, 2002.
50. Shi X., Fernandes K, Chumnumpan P. Diffusion of Multi-Generational High-technology products // Technovation. 2014. V. 34. P. 162–176
51. Suarez F. Battles for technological dominance: an integrative framework // Res. Policy. 2004. V. 33 P. 271–286.
52. Tilman D. Resource Competition and Community Structure // Princeton Univ. Press. New Jersey. 1982. P. 14.
53. Tsai B. H. Predicting the Diffusion of LCDTVs by Incorporating  Price  in  the  Extended  Gompertz  Model  //Technol. Forecast. Soc. Change. 2013. V. 80. P. 106–131.
54. Van den Bulte C., Joshi Y. V. New product diffusion with influentials and imitators // Marketing Sci. 2007. V. 26(3). P. 400–421.
55. Von Arb R. Predator Prey Models in Competitive Corporations // Olivet Nazarene Univ. Honors Program Proj. Paper 45. 2013.
56. Young H. P. Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning // Am. Econ. Rev. 2009. V. 99(5). P. 1899–1924.
 

2024-74-3
2024-74-2
2024-74-1
2023-73-4

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".