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Strategy and performance of knowledg...

Chen, Yao.

Strategy and performance of knowledge flowuniversity-industry collaborative innovation in China /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Strategy and performance of knowledge flowby Yu Yu, Yao Chen, Qinfen Shi.
Reminder of title:	university-industry collaborative innovation in China /
Author:	Yu, Yu.
other author:	Chen, Yao.
Published:	Cham :Springer International Publishing :2018.
Description:	xviii, 172 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Academic-industrial collaborationChina.
Online resource:	http://dx.doi.org/10.1007/978-3-319-77926-3
ISBN:	9783319779263$q(electronic bk.)

Strategy and performance of knowledge flowuniversity-industry collaborative innovation in China /
Yu, Yu.

Strategy and performance of knowledge flowuniversity-industry collaborative innovation in China /[electronic resource] :by Yu Yu, Yao Chen, Qinfen Shi. - Cham :Springer International Publishing :2018. - xviii, 172 p. :ill., digital ;24 cm. - International series in operations research & management science,v.2710884-8289 ;. - International series in operations research & management science ;v. 140..

Chapter 1: Introduction -- Chapter 2: Measuring the Performance of Knowledge Value-Added in University-Industry Collaborative Innovation -- Chapter 3: Efficiency Evaluation of Knowledge Flow in University-Industry Collaborative Innovation in China -- Chapter 4: Big Five Personality Traits and Knowledge Flow in University-Industry Collaborative Innovation -- Chapter 5: Personality, Team Goals, Motivation, and Tacit Knowledge Sharing Performance within a University Research Team -- Chapter 6: Colored Petri Net Model of Knowledge Flow Based on Knowledge Life Cycle -- Chapter 7: Evolutionary Game Model of Knowledge Transfer in University-Industry Collaborative Innovation -- Chapter 8: Cellular Automaton and Tacit Knowledge Sharing -- Chapter 9: Small World Network and Knowledge Sharing -- Chapter 10: Differential Game Model of Knowledge Flow in University-Industry Collaborative Innovation -- Chapter 11: Conclusion and Further Research.

This book constructs a model of the knowledge value chain in the university and analyzes the university knowledge value-added mechanism in the process of Industry-University Collaborative Innovation. The efficiency of university knowledge value-added of Provinces in China is measured. The book illustrates the operating mechanism between enterprise subsystems and college subsystems in the collaborative innovation system, and establishes a Data Envelopment Analysis (DEA) model with parallel decision making units to assess the performance of Industry-University Collaboration Innovation in China by considering the complex internal structure of the collaborative innovation system. The book also addresses various behaviors of knowledge agents in the knowledge sharing process. The research findings of this book will provide some policy implications to help policy makers to establish a more effective collaborative and interactive innovation system. The focus on China offers a unique contribution, because the form that university-industry collaborations take differs widely from country to country. The United States, the United Kingdom, Japan, and China differ vastly in the way that they implement their respective R&D policies. Some of these differences stem from national culture, others from the historical evolution of the institutions that support innovation efforts, and some from the extent of available resources.

ISBN: 9783319779263$q(electronic bk.)

Standard No.: 10.1007/978-3-319-77926-3doiSubjects--Topical Terms:

814668
Academic-industrial collaboration
--China.

LC Class. No.: LC1085.4.C6

Dewey Class. No.: 378.1035

Strategy and performance of knowledge flowuniversity-industry collaborative innovation in China /
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505 0 $a Chapter 1: Introduction -- Chapter 2: Measuring the Performance of Knowledge Value-Added in University-Industry Collaborative Innovation -- Chapter 3: Efficiency Evaluation of Knowledge Flow in University-Industry Collaborative Innovation in China -- Chapter 4: Big Five Personality Traits and Knowledge Flow in University-Industry Collaborative Innovation -- Chapter 5: Personality, Team Goals, Motivation, and Tacit Knowledge Sharing Performance within a University Research Team -- Chapter 6: Colored Petri Net Model of Knowledge Flow Based on Knowledge Life Cycle -- Chapter 7: Evolutionary Game Model of Knowledge Transfer in University-Industry Collaborative Innovation -- Chapter 8: Cellular Automaton and Tacit Knowledge Sharing -- Chapter 9: Small World Network and Knowledge Sharing -- Chapter 10: Differential Game Model of Knowledge Flow in University-Industry Collaborative Innovation -- Chapter 11: Conclusion and Further Research.
520 $a This book constructs a model of the knowledge value chain in the university and analyzes the university knowledge value-added mechanism in the process of Industry-University Collaborative Innovation. The efficiency of university knowledge value-added of Provinces in China is measured. The book illustrates the operating mechanism between enterprise subsystems and college subsystems in the collaborative innovation system, and establishes a Data Envelopment Analysis (DEA) model with parallel decision making units to assess the performance of Industry-University Collaboration Innovation in China by considering the complex internal structure of the collaborative innovation system. The book also addresses various behaviors of knowledge agents in the knowledge sharing process. The research findings of this book will provide some policy implications to help policy makers to establish a more effective collaborative and interactive innovation system. The focus on China offers a unique contribution, because the form that university-industry collaborations take differs widely from country to country. The United States, the United Kingdom, Japan, and China differ vastly in the way that they implement their respective R&D policies. Some of these differences stem from national culture, others from the historical evolution of the institutions that support innovation efforts, and some from the extent of available resources.
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