Measuring Innovation Efficiency in the firms of Four High-tech Industries in Iran, Using Slack-Based DEA Model

Document Type : Original Article

Authors

1 Researcher, Research Institute for Science, Technology and Industry Policy at Sharif University of Technology, Tehran, Iran.

2 Assistant Professor, Research Institute for Science, Technology, and Industry Policy (RISTIP) at Sharif University of Technology, Tehran, Iran.

3 Research Institute for Science, Technology, and Industry Policy (RISTIP) at Sharif University of Technology, Tehran, Iran.

Abstract

Nowadays, evidence-based and data-based policymaking is taken into consideration in policymaking, so policymakers understand that until they don’t analyze micro data from firms, they could not form efficient and effective policies. One of the most important ways to measuring innovation is measuring its efficency. Measuring innovation efficeincy in macro level is more usual than micro level. In this paper, first, on the basis of previous reseaches, input and output indicators were identified. Then based on first Irarnian innovation survey data, innovation efficiency of four sectors (Nanotechnology. Biotechnology, Electronic, micro electronic and Telecomunication and Aerospace) firms were calculated and analysed, using Slack based DEA model. Finally, results show that 23.27% of nanotechnology firms, 20% of Electronic, micro electronic and Telecomunication, 18.33% of Biotechnology and 18.18% of Aerospace firms were efficent. In addition, the size of the firms in each sector were effective in firms efficiency.

Keywords

Main Subjects


Abbasi, F., Hajihoseini, H. & Haukka, S. 2011. Use of virtual index for measuring efficiency of innovation systems: a cross-country study. International Journal of Technology Management & Sustainable Development, 9(3), pp. 195-212.
Adam, F. 2014. Measuring national innovation performance: the Innovation Union Scoreboard revisited, Springer.
Afzal, M. N. I. 2014. An empirical investigation of the National Innovation System (NIS) using Data Envelopment Analysis (DEA) and the TOBIT model. International Review of Applied Economics, 28(4), pp. 507-523.
Arundel, A. Innovation scoreboards: Promises, pitfalls and policy applications. Conference of Innovation and Enterprise Creation: Statistics and Indicators, 2001.
Cai, Y. 2011. Factors affecting the efficiency of the BRICSs' national innovation systems: A comparative study based on DEA and Panel Data Analysis. Economics Discussion paper.
Cook, W. D. & Seiford, L. M. 2009. Data envelopment analysis (DEA)–Thirty years on. European journal of operational research, 192(1), pp. 1-17.
Cruz-cazares, C., Bayona-Saez, C. & Garcia-Marco, T. 2013. You can’t manage right what you can’t measure well: Technological innovation efficiency. Research Policy, 42(6-7), pp. 1239-1250.
Danesh kohan, Elyasi, Pilevari, Tabatabaei, Bafghi, 2015. Review and prioritize the key factors of innovation success in the UAV industry. Innovation Management, 14(4), pp. 107-130.
Diaz-Balteiro, L., Herruzo, A. C., Martinez, M. & Gonzalez-Pachon, J. 2006. An analysis of productive efficiency and innovation activity using DEA: An application to Spain's wood-based industry. Forest Policy and Economics, 8(7), pp. 762-773.
Dzemydaite, G., Dzemyda, I. & Galiniene, B. 2016. The efficiency of regional innovation systems in new member states of the European Union: a nonparametric DEA approach. Economics and business, 28(1), pp. 83-89.
EARL, L. 2006. National innovation, indicators and policy, Edward Elgar Publishing.
Feng, F., Wang, B., Zou, Y. & Du, Y. 2013. A New Internet DEA Structure: Measurementof Chinese R&D Innovation Efficiency in High Technology Industry. International Journal of Business and Management, 8(21), p. 32.
Guan, J. & Chen, K. 2012. Modeling the relative efficiency of national innovation systems. Research policy, 41(1),pp. 102-115.
Hsu, Y. 2011. Cross national comparison of innovation efficiency and policy application. African Journal of Business Management, 5(4), pp. 1378-1387.
Junwen 2016. A Research on the Evaluation of the Operating Efficiency and Innovation Efficiency of China’s Development Zones Based on Panel Data. Canadian Social Science, 12,pp. 13-19.
Mehregan, 2012. Data Envelopment Analysis Quantitative Models in Organizational Performance Evaluation. Tehran: university book publication.
Mirghafoori, Shafeiroodposhty, Nadafi, 2011. Management process and development. Comparison and ranking of financial performance of Provincial Telecommunication companies with the collective model approach of data envelopment analysis and cross-efficiency method.. management and development process, Issue pp. 103-128.
Mogha, S. K., Yadav, S. P. & Singh, S. 2015. Slack based measure of efficiencies of public sector hospitals in Uttarakhand (India). Benchmarking: An International Journal, 22(7), pp. 1229-1246.
Rahimirad, Yahyazadehfar, Miremadi, 2017. Analysis of the technological innovation system of photovoltaic solar systems in Iran. Innovation Management,Volume (6), pp. 1-28.
Revilla, E., Sarkis, J. & Modrego, A. 2003. Evaluating performance of public–private research collaborations: A DEA analysis. Journal of the Operational Research Society, 54(2), pp. 165-174.
tabatabaian, Pakzadbinab, 2006. Investigating innovation evaluation systems and providing a framework for assessing innovation in Iran. Lecturer in Human Sciences, Volume (1), pp. 161-190.), pp. 161-190.
Xu, X. & QI, L. 2015. Evaluation Research of Innovation Efficiency of the Equipment Manufacturing Industry Based On Super Efficiency DEA and Malmquist Index. International Journal of Hybrid Information Technology, 8(4), pp. 27-34.