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Модели Прогнозирования Спроса На Инновационную Продукцию // Models For Innovative Products Demand

Author

Listed:
  • T. Marshalkina V.

    (Financial University)

  • Т. Маршалкина В.

    (Финансовый университет)

Abstract

The importance of innovation nowadays can’t be overestimated. The introduction of innovations in usual life goes through the innovative projects, which are includes the stage of research, development, production and market realization. Like any other commercial project, the innovative project must be cost-effective in the long run to pay off and make a profit. However, the calculation of potential profits from an innovative product or service remains the most unexplored area in the innovative projects assessment. This factor makes the topic of this article particularly urgent. This article examines different approaches to assess the demand for innovation in the evaluation of innovative projects. The article provides arguments in favor of S-form of curve of innovative product adoption. Consistently discusses the stages of the model modification by adding the factors of price, advertising, market potential. Explores the advantages and disadvantages of different methods for determining Bass model parameters. It is concluded that model is practical applicable in the management of innovation projects. Значимость инноваций в современном мире трудно переоценить. Внедрение их в жизнь обычно проходит через инновационные проекты, включающие стадии исследований, разработок, производства и вывода на рынок. Как и любой другой коммерческий проект, инновационный проект должен быть экономически эффективным, в конечном счете окупиться и принести прибыль. Однако расчет потенциальной прибыли от инновационного продукта или услуги остается наиболее не изученной сферой в оценке инновационных проектов. Данный фактор делает особенно актуальной тему статьи, в которой рассматриваются различные подходы к оценке спроса на инновации в рамках оценки инновационных проектов. В статье изучается модель Басса для моделирования и прогнозирования распространения инновационных продуктов, а также процесс развития модели; приводятся аргументы в пользу S-вида кривой накопленного «принятия», т.е. покупки инновационного продукта; автором предлагаются модификации модели с добавлением в нее факторов цены, рекламы, рыночного потенциала; исследуются преимущества и недостатки различных методов определения параметров модели Басса. Делается вывод о практической применимости модели оценки спроса на инновации в оценке инновационных проектов.

Suggested Citation

  • T. Marshalkina V. & Т. Маршалкина В., 2015. "Модели Прогнозирования Спроса На Инновационную Продукцию // Models For Innovative Products Demand," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, issue 6, pages 171-178.
  • Handle: RePEc:scn:financ:y:2015:i:6:p:171-178
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    References listed on IDEAS

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    1. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. Xiaoying Liang & Lei Xie & Houmin Yan, 2015. "Self‐Restraining Bass Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(6), pages 472-477, September.
    3. Bonus, Holger, 1973. "Quasi-Engel Curves, Diffusion, and the Ownership of Major Consumer Durables," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 655-677, May-June.
    4. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    7. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    8. Fruchter, Gila E. & Van den Bulte, Christophe, 2011. "Why the Generalized Bass Model leads to odd optimal advertising policies," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 218-230.
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