Performance Evaluation of the National Innovation Systems by Network Data Envelopment Analysis

Document Type : Research Paper


1 Assistant Prof., Faculty of Management, Kharazmei University, Tehran, Iran

2 MBA Student in Entrepreneurship Management, Faculty of Management


Today's it is essential to assess and determine the efficiency of NIS to achieve the objectives of macroeconomic policies. In this study, a network data envelopment analysis models are used to evaluate the performance of the national innovation system of Iran and 73 other countries in a two-stage process is the production and commercialization of knowledge. The results show that Iran's national innovation system is inefficient. The first and second stages and the overall efficiency in the free link are 0.59, 0.74 and 0.18, in a fixed link are 0.74, 0.87 and 0.63 and SBM is 0.88. To improve performance, reduce the amount specified inputs, increase their efficiency levels and thus the efficiency of the entire system will be up to the efficient frontier.


Cai, Y. (2011). Factors affecting the efficiency of the BRICSs' national innovation systems: A comparative study based on DEA and Panel Data Analysis.‏Economics (open eJournal), Economics Discussion Papers, No. 2011-52.
Carlsson, B., Jacobsson, S., & Rickne, A. (2002). Innovation systems: analytical and methodological issues. Research policy, 31(2), 233-245.‏
Castellacci, F., & Natera, J. M. (2013). The dynamics of NIS: a panel cointegration analysis of the coevolution between innovative capability and absorptive capacity. Research Policy, 42(3), 579-594.
Chen, C. P., Hu, J. L., & Yang, C. H. (2011). An international comparison of R&D efficiency of multiple innovative outputs: The role of the national innovation system. Innovation13(3), 341-360.‏
Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, 213(1), 147-155.‏
Costa, R. (2012). Assessing intellectual capital efficiency and productivity: An application to the Italian yacht manufacturing sector. Expert Systems with Applications, 39(8), 7255–7261.
Cullmann, A., Schmidt-Ehmcke, J., & Zloczysti, P. (2009). Innovation, R&D efficiency and the impact of the regulatory environment: A two-stage semi-parametric DEA approach.‏ German Institute for Economic Research, Discussion paper №. 883: Berlin, May 2009.
Färe, R. & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Guan, J., & Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy41(1), 102-115.‏
Ivanova, I. (2014). Quadruple helix systems and symmetry: a step towards helix innovation system classification. Journal of the Knowledge Economy, 5(2), 357-369.‏
Kao, C. (2016). Efficiency decomposition and aggregation in network DEA. European Journal of Operational Research, 255(3), 778-786.‏
Kao, C. (2009). Efficiency decomposition in network DEA: A relational model.European Journal of Operational Research, 192(3), 949–962.
Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418-429.‏
Kou, M., Chen, K., Wang, S., & Shao, Y. (2016). Measuring efficiencies of multi-period and multi-division systems associated with DEA: an application to OECD countries’ NIS. Expert Systems, 46, 494-510.‏
Kravtsova, V., & Radosevic, S. (2012). Are systems of innovation in Eastern Europe efficient? Economic Systems, 36(1), 109-126.
Lindberg, M., Lindgren, M., & Packendorff, J. (2014). Quadruple Helix as a way to bridge the gender gap in entrepreneurship: the case of an innovation system project in the Baltic Sea region. Journal of the Knowledge Economy5(1), 94-113.‏
Liu, J. S., Lu, L. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33-45.‏
Liu, J. S., Lu, W. M., & Ho, M. H. C. (2015). National characteristics: innovation systems from the process efficiency perspective. R&D Management45(4), 317-338.‏
Liu, X., & Buck, T. (2007). Innovation performance and channels for international technology spillovers: Evidence from Chinese high-tech industries. Research Policy, 36(3), 355-366.‏
Mahroum, S., AlSaleh, Y. (2013). Towards a functional framework for measuring national innovation efficacy. Technovation, 33(10), 320-332.‏
Matei, M. M., & Aldea, A. (2012). Ranking national innovation systems according to their technical efficiency. Procedia-Social and Behavioral Sciences62, 968-974.‏
Pan, W., Hung, W., & Lu, M. (2010). DEA performance measurement of the NIS in Asia and Europe. Asia-Pacific of OR, 27(03), 369-392.‏
Shahriari, S., Razavi, M. & Asgharizadeh, A. (2013). Fuzzy DEA and new approach FIEP/AHP units for the full ranking decision makers: A Case Study of Humanities Faculty of Tehran University. Industrial Management Journal, 5(1), 21-42. (in Persian)
Sharma, S., Thomas, V. (2008). Inter-country R&D efficiency analysis: An application of DEA. Scientometrics, 76(3), 483-501.‏
Tone, K. & Tsutsui, M. (2009). Network DEA: a slacks-based measure approach.European Journal of Operational Research, 197(1), 243–252.
Wang, Y., Vanhaverbeke, W., & Roijakkers, N. (2012). Exploring the impact of open innovation on NIS-A theoretical analysis. Technological Forecasting and Social Change, 79(3), 419-428.
Yun, J. (2017). Open Innovation Policy in National Innovation System. In Business Model Design Compass (pp. 49-60). Springer Singapore.