Showing papers by "Yao Chen published in 2018"

Data Envelopment Analysis

Abstract: The outcomes from the previous chapters provide useful information from the literature of operations research and economics on measuring the performance of a set of homogenous firms with multiple input factors and multiple output factors as well as ranking and benchmarking firms. If firms are not homogenous, the situation is the same as when each factor has a different unit of measurement from one firm to another, and therefore, no meaningful discrimination can be expressed, unless the simple conditions of discrimination, which are represented in Sect. 2.3, are satisfied. In this chapter, the concepts introduced in the previous chapters are adapted with the literature and the philosophical background is discussed.

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.

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.

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.

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.

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.

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.

Production Planning Problem

Abstract: Production in large firms with a centralized decision-making unit usually involves the contribution of several individual units. Supermarket chains or organizations with several workshops include more than one unit in which each unit contributes to a part of the entire production. For example, a sales plan is decided by a bank’s cooperate management for its divisions regarding the number of credit cards to be issued and the number of loans to be processed. The central division defines a plan to optimize the average or overall production performance in the entire organization after planning. In this chapter, a production-planning problem which is regularly faced by the central decision-making units is illustrated. Two planning ideas, proposed by Du et al. (2010), are discussed to arrange new input and output plans for all specific units when demand deviations can be predicted.

Delta Neighborhood Extension

Abstract: In this chapter, the introduced mathematical model in Chap. 6 is improved to fairly rank firms in various conditions, and the chapter is finished with several outcomes of the model.

Context-dependent DEA

Abstract: In this chapter, the context-dependent DEA is discussed. Since a product can appear attractive in comparison with a contextual of less attractive or unattractive alternatives, the performance of firms can be influenced by the context. For an example, twenty-three Tokyo public libraries are considered and a context-dependent DEA proposed by Chen et al. (2005) is discussed. The attractiveness of each library on a particular performance level in comparison with other libraries are measured. Libraries are classified on several empirical efficient frontiers, where each frontier is used to evaluate the attractiveness. The performance of the technically efficient libraries changes as the technically inefficient libraries change their performance. The context-dependent DEA also represents another view to differentiate the performance of efficient DMUs. When DMUs in a particular level are observed as having the same performance, the attractiveness measure lets us discriminate the “equal performance” based upon the third option or the same particular evaluation context. We also develop the VBA procedure to measure the attractiveness with just one click.

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.

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.

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.

The ratio of output to input factors

Abstract: In the previous chapters, we provided transparent steps to learn the foundation of DEA to estimate the performance of a set of homogenous firms. In this chapter, we demonstrate mathematical properties to describe the natural relationships between the DEA frontier and the ratio of output to input factors.

Efficiency change over different times

Abstract: In the literature of macroeconomics and the business economic press, there is a strong interest in the variation of efficiency over different times (Ray 2004). There are two methods to address this issue, such as, the Tornqvist and the Fisher productivity indexes. These two indexes use the price information of input and output factors without requiring the production technology of firms. Caves et al. (1982) introduced an index called the Malmquist efficiency index to construct a production frontier to represent the production technology of firms. Fare et al. (1992) combined ideas on the Farrell measurement and Caves et al. (1982) efficiency measurement to construct a Malmquist efficiency index directly from the input and output factors using DEA. The Malmquist efficiency index can be decomposed into two components. One component is to measure the technical change and the other component is to measure the frontier shift. In this chapter, we first illustrate the basic Malmquist index. After that, we illustrate the works of Chen and Ali (2004) to provide an extension to the Malmquist index by analyzing the above two components with an example of computer industry. Finally, a non-linear Malmquist index, proposed by Chen (2003), is discussed.

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.