Strategy and Performance of Knowledge Flow: University-Industry Collaborative Innovation in China

ISBN-13: 9783030085766 / Angielski / Miękka / 2019 / 172 str.

W. Yu; Yao Chen; Qinfen Shi

Spis treści

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.

O autorze

Yu Yu is a lecturer in the College of Auditing and Evaluation, Nanjing Audit University, China. His research interests are in the areas of knowledge management, innovation management, and performance evaluation. He received his PhD in Management Science and Engineering from Hohai University in China in 2016. He also worked as a research trainee at Desautels Faculty of Management in McGill University from Sept. 2014 to Aug. 2015. He has published over 20 articles in international and Chinese Journals.

Yao Chen is Professor of Operations Management, Manning School of Business, University of Massachusetts at Lowell. She is also a distinguished Professor in College of Auditing and Evaluation, Nanjing Audit University, China. Her current research interests include efficiency and productivity issues of information systems, information technology’s impact on operations performance, and methodology development of Data Envelopment Analysis. Her research are published in journals such as European Journal of Operational Research, OMEGA, Computers and Operations Research, Annals of Operations Research, International Journal of Production Economics, and others.

Qinfen Shi is Professor in Business School, Suzhou University of Science and Technology. Her research interests are in the areas of knowledge management and innovation management. She has published over 100 articles in Chinese Journals and International Journals and more than 1,000 citations. She received her PhD from Soochow University in 2006. She has served as Principal Investigator of five general programs of National Natural Science Foundation of China (NSFC). She is also the member of Chinese Association for Science of Science and S&T Policy.

 

 

Blurb

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.