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A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory Cover

A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory

Open Access
|Jun 2018

Abstract

Purpose

This study aims at identifying potential industry-university-research collaboration (IURC) partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.

Design/methodology/approach

The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.

Findings

Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.

Research limitations

In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.

Practical implications

Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.

Originality/value

Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.

DOI: https://doi.org/10.2478/jdis-2018-0008 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Language: English
Page range: 38 - 61
Submitted on: Nov 17, 2017
Accepted on: May 3, 2018
Published on: Jun 22, 2018
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Haiyun Xu, Chao Wang, Kun Dong, Rui Luo, Zenghui Yue, Hongshen Pang, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.