Designing an Organizational Innovation Measurement Model with Dynamic Network Data Analysis and Applying Fuzzy constraint for Weight Control and Finding a common set of weights (Case Study: Iranian Universities)

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.

2 Prof., Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.

3 Assistant Prof., Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.


Objective: Measuring the efficiency of innovation to manage innovation investment in
the era of "knowledge economy" is being considered by more researchers every day. The
evaluation of innovation efficiency helps identify the best innovators for benchmarking
and identifies ways to improve efficiency by identifying the weaknesses. In this paper, a
new formulation approach for dynamic network data envelopment analysis is presented to
evaluate the efficiency of multi-period and multi-division systems (MPMDS) while
controlling the weights.
Methods: To prevent facing the black-box of innovation, at the first, a conceptual
dynamic network structure of the universities’ innovation was developed, and then, the
proposed dynamic network DEA approach is used to overcome the fundamental
shortcomings to control the weights of factors in line with enabling the desired
management weights.
Results: The findings depicted that, among 13 universities surveyed, one university
(about 7%), was recognized as efficient in the total process of innovation, and the average
efficiency was equal to 0.79 for universities. In both sub-processes of R&D and
application, one university (7%) was considered efficient and their average efficiency was
0.82 and 0.47, respectively, which indicates the poor performance of universities in
implementation and ideas commercialization. Also, the changes in the average efficiency
of the sub-process of applying the results are quite the opposite of the research and
development sub-process.
Conclusion: The results reflect that the model presented in this study, by solving the
conventional DNDEA model problems in weight control, improves the discriminating
power of efficient and inefficient units.


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