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2018 | Buch

Strategy and Performance of Knowledge Flow

University-Industry Collaborative Innovation in China

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Faster technological development, shorter product life cycles, and more intense global competition have transformed the current competitive environment for most firms (Bettis and Hitt 1995). This new competitive landscape forces organizations to actively acquire knowledge since a firm’s competitive advantage is now more dependent on continuous knowledge development and enhancement (Santoro and Gopalakrishnan 2000). As competitive pressures increase, firms are often placed in positions where they have neither the time nor resources to internally develop the knowledge needed to achieve competitive success through product and process innovations. Thus, knowledge acquisition from external source partners has been identified as a key competency for sustained success in the competitive marketplace. A common and frequently viable option in this situation is the acquisition of technological knowledge from outside sources.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 2. Measuring the Performance of Knowledge Value-Added in University-Industry Collaborative Innovation
Abstract
Universities play an important role as a source of fundamental knowledge and, occasionally, relevant industrial technology in modern knowledge-based economies. Universities are the main knowledge dissemination and innovation places in national innovation system (NIS). Higher education in China has played an important role in the nation’s economy, science progress, and social development by bringing up a large scale of advanced talents and experts for the construction of socialist modernization. In 2015, all together there were 2560 higher education institutions (HEIs), among which 1219 were universities, 275 were independent colleges, and 1341 were higher vocational colleges. There were also 292 higher education institutions for adults. In 2015, the total number of new entrant admitted by and the total enrollment of undergraduates in regular HEIs were, respectively, 7,378,495 and 26,252,968. The total number of new entrants admitted by and the total enrollment of postgraduates in regular HEIs were, respectively, 645,055 and 1,911,406. The total number of new entrants admitted by and the total enrollment of new recruitments and the total enrollment of adult higher education institutions were 2,367,455 and 6,359,352. China spent nearly 3.9 trillion yuan ($565.6 billion) on education in 2016, an increase of 7.57% from 2015, according to preliminary statistics released by the Ministry of Education. Expenditure for higher education exceeded 1 trillion yuan, up 6.22% from 2015. Every year, universities input a great deal of knowledge and employ teachers and researchers to create new knowledge value on the original knowledge basis in order to achieve knowledge value-added through knowledge accumulation, knowledge sharing, knowledge internalization, and other knowledge-based activities. Universities also input a large number of knowledge resources to create new knowledge in order to improve the university knowledge stock. In view of this, it is important to discuss the efficiency of university knowledge value-added to find out the influence factors of this value added. It will help universities to optimize their allocation of knowledge resources so as to achieve highly effective knowledge value-added efficiency.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 3. Efficiency Evaluation of Knowledge Flow in University-Industry Collaborative Innovation in China
Abstract
Under economic globalization, innovation is increasingly more open, and the creation, innovation, and application sectors of technological knowledge need to build an open collaborative innovation. Collaborative innovation is a transdisciplinary approach for developing global synergy to improve the competitiveness of an organization through holistic, competitive, and complementary interactions between and among innovation participants in a specific environment (Swink 2006). A collaborative innovation system essentially consists of three sectors: industry, universities, and the government, with each one interacting with the other two, while at the same time playing its own role.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 4. Big Five Personality Traits and Knowledge Flow in University-Industry Collaborative Innovation
Abstract
The knowledge flow along with the whole process of the collaborative innovation of industry, academia, and research essentially defines that innovation subjects gain the advantages of knowledge in the way that they acquire, transfer, apply, and get feedbacks so as to promote the sharing, transfer, and creation of knowledge. At the same time, they exert the “externalities” and “spillover effects” of it.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 5. Personality, Team Goals, Motivation, and Tacit Knowledge Sharing Performance Within a University Research Team
Abstract
As important bases to cultivate high-level innovative talents and as also two of the main forces of original innovation in basic research and high-technology field, colleges and universities continually supply fresh troops to address the issue of the national economy and to accomplish the successful transfer of technology and achievements. The research team is a group made up of researchers having complementary skills and being responsible for each other under a common research objective, research goal, and working method. In colleges and universities, the cultivation of discipline leaders, the integration of research direction, the nurturing of characteristic discipline, the promotion of overlapping discipline, the solution to important scientific problems, the acceleration of major scientific research achievements, etc. can all be achieved by forming a research team. Research teams in universities are the main conduit of knowledge dissemination and innovation in the national innovation system, as intellectual activity runs throughout the whole process. For research teams in universities with an academic organization form having the purpose of studying, tacit knowledge sharing plays a decisive role for the completion of team tasks.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 6. Colored Petri Net Model of Knowledge Flow Based on Knowledge Life Cycle
Abstract
Knowledge has become the most precious property of any commercial or academic institution. Knowledge management plays the key role in upgrading the competitiveness of a team. Knowledge management concerns innovating, spreading, sharing, and using knowledge. Research on knowledge management targets the management aspects, including organizational learning, personal management, culture, etc. (Drucker 1998), and the technical aspect includes models, support tools, and environments (Zhuge 2002a, b). Knowledge is power, but knowledge is not just statically stored. It evolves through being shared and developed by roles, people, and various resources within the cyber-physical-socio environment. Knowledge flow is the passing of knowledge between people or through machinery. It has three crucial attributes: direction (sender and receiver), carrier (medium), and content (shareable). Good knowledge flow enables intelligent participants (people, roles, and devices) to cooperate effectively (Zhuge 2004). The literature has investigated multiple types of flows, e.g., material flow (Brunner and Rechberger 2004), energy flow (Odum 1968), message flow (Nierstrasz 1985), control flow (Heintze 1995), etc., and the rules they follow in respective domains.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 7. Evolutionary Game Model of Knowledge Transfer in University-Industry Collaborative Innovation
Abstract
It is frequently noted that innovation has become a strategic source for creating firms’ sustainable competitive advantages. Therefore, continuously generating new knowledge to enable such innovation has become a key agenda for policy makers as well as business organizations (Nonaka 1994; Grant 1996). Knowledge is viewed as a competitive advantage and a source of power for those who possess it at the right place and at the right time, while the process of knowledge transfer between organizations is essentially the game between two different knowledge agents. In the context of certain social environments, knowledge transfer is a process of transferring knowledge from a knowledge source to a knowledge receptor and from an organization that has high knowledge stock to an organization that has low knowledge stock. The successful transfer of knowledge is closely related to the willingness of the knowledge provider to transfer such knowledge and the willingness of the knowledge recipient to accept such knowledge.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 8. Cellular Automaton and Tacit Knowledge Sharing
Abstract
With the advent of the knowledge economy era, knowledge has become the major source for an organization to gain its core competence, and the full absorption and utilization of knowledge resources outside the organization are the key to increasing productivity and gaining a competitive advantage. An organization’s knowledge stocks determine its core competitiveness directly. Polanyi (2015) divides knowledge into two types, explicit knowledge and tacit knowledge, while Allee (1997) analogizes tacit knowledge and explicit knowledge to oceans and icebergs, respectively. In a traditional economic society, people only give credit to the role of explicit knowledge and focus on the management and utilization of themselves in their daily work. As a matter of fact, explicit knowledge is merely the “iceberg” above the water. With the advent of the knowledge economic society, people have started to draw attention to the enormous tacit knowledge under the water. Polanyi (2015) points out that in modern industries, knowledge is hard to describe as an indispensable part of technologies, thus making the sharing of tacit knowledge hard to codify as an essential component of knowledge sharing. The formation of tacit knowledge is a long-term accumulation process of personal experience, insights, and deep comprehension, which are extremely difficult to imitate and steal; therefore, tacit knowledge is the basis and source for an organization to build up its core competitiveness. Knowledge possesses abstractness and externality, which makes it possible to share, i.e., knowledge’s externalities allow it to be shared at a low cost, and the more it is shared, the more valuable it becomes; on the other hand, such qualities of knowledge serve also as the obstacles to knowledge sharing. More specifically, the high cost, high risk, and uncertainty of income distributions of the knowledge innovation processes determine knowledge owners’ monopolistic attitudes toward knowledge out of their own selfishness and needs for competition, which deter the dissemination and spreading of knowledge.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 9. Small-World Network and Knowledge Sharing
Abstract
Knowledge sharing is a two-way communication process, and the compact degree and frequency of knowledge exchange are both different due to the individual cognitive background of organization members. The mutual learning and communication among members can promote continuous sharing and enhance the organization’s overall knowledge level by sharing and expanding more benefits of the knowledge receiver. During communication and understanding, a knowledge dialogue is accrued, and there is a complex interactive relationship between individuals or populations, thus forming a relative network.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 10. Differential Game Model of Knowledge Flow in University-Industry Collaborative Innovation
Abstract
The world is currently in the era of grand developments, reforms, and adjustments. Multi-polarization and economic globalization are deepening further, and the international business structure is changing due to fierce competition among national economies. Innovation has become the main driving force of economic social development, and intellectual innovation has become the core factor of national competitiveness. Under such circumstances, each country undertakes a strategy of further exploring human resources to realize innovative development, hence grasping the initiative of global competition.
Yu Yu, Yao Chen, Qinfen Shi
Chapter 11. Conclusion and Further Research
Abstract
This book has presented systematical research for several key issues of knowledge flow in University-Industry Collaborative Innovation (UICI) in China.
Yu Yu, Yao Chen, Qinfen Shi
Backmatter
Metadaten
Titel
Strategy and Performance of Knowledge Flow
verfasst von
Yu Yu
Prof. Yao Chen
Qinfen Shi
Copyright-Jahr
2018
Electronic ISBN
978-3-319-77926-3
Print ISBN
978-3-319-77925-6
DOI
https://doi.org/10.1007/978-3-319-77926-3

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