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

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.

A knowledge discovery method based on analysis of multiple co-occurrence relationships in collections of journal papers

Purpose:This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Design/methodology/approach:A variety of methods such as the model construction,system analysis and experiments are used.The author has improved Morris’crossmapping technique and developed a technique for directly describing,visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Findings:The visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers.It can reveal more and in-depth information than analyzing co-occurrence relations between two entities.Therefore,this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.Research limitations:The technique could only be used to analyze co-occurrence relations of less than three entities at present.Practical implications:This research has expanded the study scope of co-occurrence analysis.The research result has provided a theoretical support for co-occurrence analysis.Originality/value:There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles.This research defines multiple co-occurrence and the research scope,develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.