Designing Organizational Innovation Measuring Model with Dynamic Network DEA (Case Study: Iranian First Level Universities)

Document Type : Original Article


1 PhD. Student, Tarbiat Modares University.

2 Professor, Tarbiat Modares University.

3 Associate Professor, Tarbiat Modares University.

4 Assistant Professor, Tarbiat Modares University.


Nowadays the efficiency-o7666riented performance evaluation of complex systems be-comes increasingly important for investment and management decisions. This paper proposes a new formulation approach based on dynamic network DEA (DN–DEA) models for multi-period and multi-division (MPMD) systems to measure and decompose the overall efficiency. Although conventional DEA models provide magnificant modeling idea of efficiency measures in the multi-input and -output contexts, they do not account for the multi-division transformation process of decision-making units (DMUs) and present “black-box” measures of their efficiency scores wich causes incorrect estimates of the efficiency points of the units by ignoring the information about the internal operations of the system. As an illustrative case study, the present research applies the proposed approach to evaluate the efficiencies of 13 universities of Iran over the three-year period and the rate of innovation has been measured according to each division, every period, as well as total innovation.


1. Chen, K. & Guan, J. (2011). Mapping the functionality of China's regional innovation systems: A structural approach. China Economic Review, 22, 11-27.
2. Chen, K. H. & Guan, J. C. (2012). Measuring China’s regional innovation systems: an application of a relational network DEA. Regional Studies, 46(3), 355-370.
3. Cook, W. D.; Zhu, J.; Bi, G. B. & Yang, F. (2010). Network DEA: additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122-1129.
4. Faghih, N., & Askarifar, K. (2014). Ranking of Selected Countries According to National Innovation Capacity Improvement Using Data Envelopment Analysis. Journal of Business Development. 1(7), 1-16 (In Persian).
5. Färe, R. & Grosskopf, S. (1996a). Intertemporal production Frontiers: with dynamic DEA. Norwell: Kluwer.
6. Färe, R, & Grosskopf, S. (1996b). Productivity and intermediate products: A frontier approach. Economics Letters. 50(1), 65-70.
7. Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35-49.
8. Färe, R. & Whittaker, G. (1995). An intermediate input model of dairy production using complex survey data. Journal of Agricultural Economics, 46(2), 201-213.
9. Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31, 899-933.
10. Galanakis, K. (2006). Innovation process, make sense using systems thinking. Technovation, 26, 1222-1232.
11. Ghlichlee, B. & Rajabi Shahrabadi, E. (2015). Study of Relationship between Knowledge Creation, Technological Innovation and Organizational Agility (A Case Study: Iran Alloy Steel Company). Journal of Industrial Management Perspective, 16(4), 95-116 (In Persian).
12. Ghlichlee, B., Mirzaei, F., & Rahmati, H. (2017). Effect of Intellectual Capital on Innovation Capacity and Competitive Advantage in SME’s. Journal of Industrial Management Perspective, 27(7), 105-126 (In Persian).
13. Guan, J. C., & Chen, K. H. (2010). Measuring the innovation production process: a cross-region empirical study of China’s high-tech innovations. Technovation, 30(5), 348-358.
14. Guan, J. C., & Chen, K. H. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1), 102-115.
15. Hollanders, H. & Celikel-Esser, F. (2007). Measuring innovation efficiency. INNO Metrics 2007 report. European Commission. Brussels: DG Enterprise INNO Metrics 2007 report.
16. Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: a relational model. European Journal of Operational Research, 192(3), 949-962.
17. Kao, C. (2014). Network data envelopment analysis: a review. European Journal of Operational Research, 239(1), 1-16.
18. 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.
19. Kou, M., Chen, K., Wang, Sh. & Shao, Y. (2016). Measuring efficiencies of multi-period and multi-division system associated with DEA: An application to OECD countries’ national innovation systems. Expert systems whit applications, 46, 494-510.
20. Liu, J. S., & Lu, W. M. (2009). DEA and ranking with the network-based
approach: a case of R&D performance. Omega, 38(6), 453-464.
21. Lu, W. M. & Hung, S. W. (2010). Exploring the operating efficiency of
Technology Development Programs by an intellectual capital perspective- A case study of Taiwan. Technovation, 31(8), 374-383.
22. Nemoto, J. & Goto, M. (1999). Dynamic data envelopment analysis modeling intertemporal behavior of a firm in the presence of productive inefficiencies. Economic Letters, 64(1), 51-56.
23. Park, K. S. & Park, K. (2009). Measurement of multiperiod aggregative efficiency. European Journal of Operational Research, 193(2), 567-580.
24. Salehzadeh, S. J.; Hejazi, R.; Arkan, A. & Hosseini, S. M. (2011). Proposing an Integrative Approach for Efficiency Evaluation of Network Structures Including Tour and Allocation Link. Production and Operations Management, 2 (1), 47-60 (In Persian).
25. Soleimani Damaneh, R.; Momeni, M.; Mostafaei, A. & Rostami Malkhalife, M. (2017). Developing of a Dynamic Network Data Envelopment Analysis Model for Performance Evaluating Banking Sector. Journal of Industrial Management Perspective, 25(7), 68-89 (In Persian).
26. Sueyoshi, T. & Sekitani, K. (2005). Returns to scale in dynamic DEA. European Journal of Operational Research, 161(2), 536-544.
27. Tone, K. & Tsutsui, M. (2010). Dynamic DEA: a slacks-basedmeasure approach. Omega. The International Journal of Management Science, 38(3), 145-156.
28. Wang, E. C. & Huang, W. C. (2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260-273.