Designing a dynamic model for evaluating the research and development projects cost focused on technical indicators and market share in knowledge based companies

Sina Laleh, Nosratollah Shadnoush, Abbas Toloie Ashlaghi


In this research, according to the previous studies and the extraction of factors affecting the economic valuation of research costs and the identification of cause and effect circles, a dynamic model has been developed. Subsequently, using the DEMATEL technique, the relationships between them and the effective coefficients were determined and included in the model. Hence, in order to test the accuracy of the model and determine the behavior of the state variables and the rate of information gathering from the eight knowledge based companies in the science and technology parks of Alborz, Pardis and Tehran University in the period of 24 months, by assessing the 24-month behavior of research in the framework of the model as well as sensitivity analysis, the validity of the designed dynamic model was evaluated.


Dynamic Model, Economic Valuation of Costs, Research and Development Projects, Knowledge Based Companies

Texto completo:

PDF (English)


Acemoglu, D., Akcigit, U., Alp, H., Bloom, N., & Kerr, W. (2018). Innovation, reallocation, and growth. American Economic Review, 108(11), 3450-91.

Ambe, I. M. (2010). Agile supply chain: Strategy for competitive advantage. Journal of Global Strategic Management, 7, 5-17.

Biancardi, M., & Villani, G. (2017). A fuzzy approach for R&D compound option valuation. Fuzzy Sets and Systems, 310, 108-121.

Engsner, H., Lindholm, M., & Lindskog, F. (2017). R & D expenses insurance assessment: a calculation period The cost of capital approach. Insurance: Mathematics and Economics, 72, 250-264.

Gierczak, M. (2014). The quantitative risk assessment of MINI, MIDI and MAXI Horizontal Directional Drilling Projects applying Fuzzy Fault Tree Analysis. Tunnelling and Underground Space Technology, 43, 67-77.

Jinfa, L., & Biting, L. (2017). Valuation Method of R&D Investment Value of Intelligent Manufacturing Enterprise Based on Growth Option. Procedia Engineering, 174, 301-307.

Jun, S. P., Kim, S. G., & Park, H. W. (2017). The mismatch between demand and beneficiaries of R&D support programs for SMEs: Evidence from Korean R&D planning programs. Technological Forecasting and Social Change, 116, 286-298.

Khoshnevis, P., & Teirlinck, P. (2017). Performance valuation of R&D active firms. Socio-Economic Planning Sciences.

Laurel F., Suresh R. (2009), The value relevance of R&D across profit and loss firms. Account. Public Policy 28, pp. 16–32.

Lee, C. K. & Chang T. Y. (2007). “Transnaonal corporaons' R&D localization in a developing nation - A game theory analysis”. Journal of American Academy of Business, 10(2), pp. 225-232.

Lewandowska, A., & Stopa, M. (2017). SMEs Innovativeness and Institutional Support System: The Local Experiences in Qualitative Perspective (No. 60/2017).

Livotov, P. (2018). Competitive Capability Assessment of Industrial Companies within the Framework of Advanced Innovation Design Approach. In DS92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 1903-1914).

Martín-Barrera, G., Zamora-Ramírez, C., & González-González, J. M. (2017). Impact of flexibility in public R&D funding: How real options could avoid the crowding-out effect. Renewable and Sustainable Energy Reviews, 76, 813-823.

Morgan, P., Brown, D. G., Lennard, S., Anderton, M. J., Barrett, J. C., Eriksson, U., ... & Matcham, J. (2018). Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nature Reviews Drug Discovery, 17(3), 167.

Resutek Robert J. [2015], R&D intensity, future performance, and operational distress, Electronic copy available at:

Rudnik, K., & Małgorzata, A. (2015). System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects. Expert Systems with Applications. In Press.

Shannon P. Pra, Roger J. Grabowski (2010). “Esmang the cost of equity capital and the overall cost of capital”. Business and Economics, pp. 314-315.

Sharma, P., Davcik, N. S., & Pillai, K. G. (2016). Product innovation as a mediator in the impact of R&D expenditure and brand equity on marketing performance. Journal of Business Research, 69(12), 5662-5669.

Sharma, S. K., & Chanda, U. (2017). Developing a Bayesian belief network model for prediction of R&D project success. Journal of Management Analytics, 1-24.

Suresi A, (2017). Not-invented-here syndrome and innovation performance: the confounding effect of innovation capabilities as organisational routines in service firms. International Journal of Innovation Management, 21[01], 1750036.

Suzuki, K., & Chida, R. (2017). Contribution of R&D capital to differences in Tobin's q among Japanese manufacturing firms: Evidence from an investment-based asset pricing model. Journal of the Japanese and International Economies, 43, 38-58.

Wang T, Guo S, Liu Y. (2013). "Pareto process optimization of product development project using bi-objective hybrid genetic algorithm". Advances in Engineering Software 65:12-22

Wang, J. (2017). Structuring innovation funnels for R&D projects under uncertainty. R&D Management, 47[1], 127-140.

Yu, H. (2017). Mechanism Design in Scientific Research Collaboration between Library Consortium and R&D Institutions. Procedia Engineering, 174, 756-759.


Métricas do artigo

Carregando Métricas ...

Metrics powered by PLOS ALM


  • Não há apontamentos.

Direitos autorais 2019 Revista Gestão & Tecnologia

Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição - NãoComercial 4.0 Internacional.