Выпуск 1, 2021 год

Kazemi S. Leader identification in a research collaborative network.

There is considerable empirical evidence on the advantages of interorganizational research collaborative networks across societies and research institutes such as research and development (R&D) centers and universities. Identifying a leader in this contexts is important both theoretically for doing leadership studies, and practically for effective governmental funding allocation and private investments. Inconsistent definitions and non-homogeneous attributes with unidimensional measurement approaches such as subjective measuring of power or considering a central company as the leader made the previous efforts inefficient for identifying leaders in an interorganizational setting. This research aims to identify a leading organization among a set of homogenous R&D centers in a research collaborative network context through implementing the main leader’s attributes in different dimensions. The article presents a multidimensional common weight model based on the data envelopment analysis (DEA) approach in a parallel system with several operational dimensions each of which consumes a set of inputs (budget, lecturers, and students) to produce a set of outputs (scientific meetings and conferences, national and international papers). Centrality and visibility are two main leaders’ attributes combined with efficiency influence the contributions and outcomes of each collaborative network partner. It is demonstrated how the proposed model performs its high-efficiency score in the most influential R&D center named the “leader” among 47 R&D centers in medical universities in Iran. The comparative analysis of managerial results showed that reputation has a greater impact on leader identification than centrality. The results based on mathematical calculations showed a robust discriminating power for efficiency measurement of the proposed model. Keywords: research collaborative network, leader, data envelopment analysis, common weights, non-discretionary variables, efficiency, network centrality, reputation.

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