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Research on the Knowledge Creation Process of the University-Industry Collaboration: a Case from China

Wei Yao, Jin Chen, Yaqi Si,Jue Hu Zhejiang University, School of Public Administration, YuQuan Campus, P.O.X 1715, Hangzhou, 310027, China

Abstract: This paper elaborates theories of intra-organizational knowledge creation by exploring the knowledge conversion processes of University-Industry Collaboration in Chinese contexts. To describe the knowledge transform tendency, a theoretical framework is developed by reference to the Information Space which of Boisot (1995). The application of the framework is described in the exploratory case study of CAE System for Vibration Analysis. Analysis of the results suggests that seven certain stages can be especially indicative of cross-organizational knowledge creation, namely: Demand Codification, Knowledge Gain, Knowledge Digestion, Knowledge Sharing, Knowledge Propagation, Knowledge Spillover and Knowledge Degeneration. And a new knowledge creation theory: GDSP knowledge creation theory which enriches and advances the typical SECI knowledge creation theory in some aspects is proposed. Furthermore the paper concludes with a discussion of the theoretical implications of this model and suggestions for further research. Keywords: knowledge creation, U-I collaboration, collaboration innovation

ⅠIntroduction

In a world where markets, products, technologies, competitors, regulations and even societies change rapidly, continuous innovation and the knowledge that enables such innovation have become important sources of sustainable competitive advantages. In the knowledge-based economy of the 21st century a key source of sustainable competitive advantages and superior profitability within an industry is how a company creates and shares its knowledge(Boisot,1998).The importance of innovation has skyrocketed in the present times, and the success of a firm largely depends on how it innovates and, by implication, creates knowledge. successful companies are those that consistently create new knowledge, disseminate it widely throughout the organization and quickly embody it in new technologies and products (Nonaka, 1991). Knowledge creation is representing a primary basis for organizations’ global competitiveness (Bhagat, Kedia, Harveston & Triandis, 2002), especially for the Chinese enterprises. The Chinese government has identified innovation as one of its three most pressing concerns for national development (Tsui, Zhao, & Abrahamson, 2007). To effectively foster innovation, organizations will need to hone their capacities for knowledge creation.

To meet the goals stated above, the research questions in this study focus on the process of U-I collaboration innovation: (i) the tendency of knowledge transform; (ii) knowledge creation mechanisms. This paper is structured as follows. We start by providing the review and analysis of the existing criticisms of the SECI model from cross-organizational perspective. Next, we propose a framework and apply this framework to analyze the knowledge conversion processes in Chinese University-Industry context for knowledge creation and develop a set of propositions concerning the applicability of the SECI model in cross-organization context. Finally we conclude the paper with some implications for both knowledge management theory and practice.

Ⅱ Literature review

Introduction and the comments about SECI model

Despite the widely recognized importance of knowledge as a vital source of competitive advantages, there is little understanding of how organizations actually create and manage knowledge dynamically. Nonaka(1994) start from the view of an organization as an entity that knowledge creates through the conversion of tacit and explicit knowledge continuously. There are four modes of knowledge conversion. They are: (ⅰ) socialization (from tacit knowledge to tacit knowledge); (ⅱ) externalization (from tacit knowledge to explicit knowledge); (ⅲ) combination (from explicit knowledge to explicit knowledge); (ⅳ) internalization (from explicit knowledge to tacit knowledge). It is what we called SECI knowledge creation theory shown as Figure 2 below.

Fig. 1 The Knowledge Conversion and Sharing Process in the SECI Model (Nonaka & Takeuchi, 1995; Nonaka & Nonno, 1998;

Nonaka et al., 2000)

Nonaka’s theory has achieved paradigmatic status and has been described as one of the best known and most influential models in knowledge management literature. The SECI theory appears to have attracted little systematic criticism. Even though it has been criticized for emphasizing the need to convert tacit knowledge (Tsoukas, 2003) and assuming cultural universality (Glisby & Holden, 2003).Essers and Schreinemakers (1997) concluded that Nonaka’s subjectivism tended towards a dangerous relativism because it made justification a matter of managerial authority, and neglected to consider how scientific criteria relate to corporate knowledge. Furthermore, the theory failed to recognize that the commitment of different groups with different ideas and the practice of resolving the conflicts by managerial authority cannot bode well for creativity and innovation. Jorna(1998) charged Nonaka with overlooking learning theory, earlier discussion of tacit and explicit knowledge, with misreading important organizational writers, and of not using better accounts of western philosophy. Bereiter (2002) argued Nonaka’s model does not explain how new ideas are produced, nor how depth of understanding develops. Another comprehensive critique by Gregorio (2008) proposed that four modes of knowledge conversion are flawed and SECI framework omits inherently tacit knowledge. They also suggested that different kinds of knowledge are created by different kinds of behaviors. The neglect of the external knowledge input. It is unrealistic to create new knowledge only through the existing knowledge within the organization and the generation of novel ideas and directions will be scarce if based on the existing knowledge totally(Haapasalo and Kess,2001).Both the knowledge difference between different organizations and the cooperation mechanism of knowledge inside or outside the organizations should not be ignored.

Research Propositions

The SECI theory furnishes a satisfactory explanation for the knowledge creation process of a single enterprise, but in the context of U-I collaboration which is a kind of cross-heterogeneous organization cooperation, it would have some limitations as below: Proposition 1 The knowledge creation processes in University-Industry Collaboration begins as explicit knowledge

Nonaka and Takeuchi(1991) posit four knowledge conversion processes that are essential to organisational knowledge creations. According to their theory, most knowledge begins as tacit knowledge, which may reside only within an individual and only at an unconscious level.And the evidence adduced in support of the start point of knowledge creation is inadequate. It is not clear why knowledge conversion has to begin with socialization if tacit knowledge is the source of new knowledge. New tacit knowledge is also generated by internalization, if reading and writing are both instrumental in tacit knowledge formation, then knowledge creation might also begin with the creative synthesis of explicit knowledge (‘combination’) (Gourlay,2006). Proposition 2 The Knowledge creation processes may evolve in different orders because of the input of the knowledge outside 2

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the organization SECI model implies a certain order in which these four cognitive processes occur: socialization, externalization, combination, and internalization (captured in the model’s title). Some authors disagree with this view and argue that these cognitive processes may evolve simultaneously or in the different order (Gourlay, 2003; Zhu, 2004，Tatiana Andreeva and Irina Ikhilchik，2011).It is unrealistic to create new knowledge only through the existing knowledge within the organization and the generation of novel ideas and directions will be scarce if based on the existing knowledge totally(Haapasalo and Kess,2001).Both the knowledge difference between different organizations and the cooperation mechanism of knowledge inside or outside the organizations should not be ignored. Proposition 3 The form of knowledge with maximized value is explicit knowledge rather than tacit knowledge

The SECI model exaggerate the role of individual tacit knowledge due to neglect the differences of culture, values, strategic goals and knowledge structures between heterogeneous organizations. The situation is complicated by the fact that SECI model in its original format resists empirical verification (Gourlay, 2003). The SECI model has been internationally accepted, usually without questioning the cultural limits of its applicability, and only a few concerns have been raised recently with respect to whether the model can be successfully applied in different cultural contexts (Glisby and Holden, 2003; Weir and Hutchings, 2005).Gregorio (2008) proposed that knowledge creation is not a ‘mappable’ process but a multi-source phenomenon, which means that analyzing knowledge creation should be in certain industrial and geographical context. Therefore the potential for its’ critical analysis is limited by the lack of empirical data that could support or refute its’ideas.

Ⅲ Framework and Methodology

In order to observe the tendency of knowledge transforming during the U-I collaboration process, we develop a research framework called K-Space which is short for Knowledge Space. In fact, our analysis framework is modified from the Information Space which is proposed by Boisot (1995). The Information Space or I-Space is a conceptual model that relates data structuring to data sharing among a population of data processors. The K-Space follows the three dimensions of the I-Space: Codification, Abstraction and Diffusion (Boisot ,1995). However, compared with Boisot’s framework, we make a more precise definition of the scale of the diffusion dimension in inter-organizational contexts.

Then largely through the approximate location of knowledge in K-space determined by scales on the three dimensions， the characteristic of knowledge can be distinguished and identified. The tendency of knowledge transformed in the process of knowledge creation in the U-I collaboration can be described by connecting the dots of knowledge forms of different stages in K-space. Therefore, based on judgments and consensus, a matrix of a two-point scale (Table 1) is produced to describe the features of the three dimensions: Codifiability, Abstraction and Diffusion.

Table 1 Features of the three dimensions

Codification Abstraction Diffusion Location on Axis Is this kind of knowledge:

Easily available to other organizations in need?

High Easy to be recorded by graphics and formulas? Easy to be standardized and automatized?

Generally applicable to all situations? Primarily science-based? Only available within the

enterprise?

Low Difficult to be clearly expressed Only be expressed clearly by demonstration?

Limited to the unique context? Needing a wide range of transformation to fit its specific situation?

Only available to the unique person within the enterprises

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The dominant approach does not necessarily support the utilization of creative resources hiding in the processes and in the structure of the organization. The process of knowledge creation in U-I collaboration is seen as an unfolding process consisting of stages in which characteristic factors not only appear in greater or lesser degree but also in a certain order of occurrence. The next section of the paper will describe the results of a case study illustrating how the knowledge is transformed and created during the process of U-I collaboration in China. The case presents a joint-development project of CAE (Computer-aided engineering) system for the design of air conditioner between Hefei University of Technology and Midea Group in China, which is one of the largest white household appliance production firms and export bases in the world.

Ⅳ A Case of University-Industry Collaboration Project ：

Development of the CAE System for Vibration Analysis and Optimization of Design of AC Piping

An overview of the partners The cooperation project was conducted between CAD/CAE Technology Platform under the Technology

Management Department of Midea Air-Conditioning & Refrigeration Group and School of Machinery and Automobile Engineering of Hefei University of Technology.

Midea Air-Conditioning & Refrigeration Group（MIDEA） is one of the largest, strongest and most diversified white household appliance makers in the world, and its sales revenue of 2008 hit a record of 52 billion Yuan. MIDEA has been attaching importance to scientific research and has established Technology Management Department, to conduct the technology management of the whole group. There are 30 engineers in Technology Management Department, who most of them have rich experience in R&D or production, as well as strong practical operational capability.

CAD/CAE Technology Platform is one of the four basic technology research platforms under the Technology Management Department, functions of which are as follows: (i) To plan, manage and promote R&D process reengineering and to manage the technology data; (ii) To plan, manage and promote the further application of CAD technology.

Founded in 1945, Hefei University of Technology is one of the top research universities in China and has been continually improving scientific and technological innovation capability and contributing to regional economic and social development.

The School of Machinery and Automobile Engineering (MAE), one of the earliest departments at Hefei University of Technology, possesses a high reputation in the appliance industry and its technological fields.

In recent years, MAE has sustained a stable scientific research work, especially in terms of University-Industry cooperation. There are about 100 research projects annually in average and more than 20 million Yuan research funding in total, which 85% of them (17 million) are related to University-Industry collaboration. From 2001 to 2007, MAE has undertaken up to 62 large-scale University-Industry cooperation projects in total.

The process of the U-I collaboration There are three principal aspects of a successful structural design in the development of the outdoor units of

the Air-Conditioning (AC) with the piping design to be the most important, because the quality of the piping design will influence the vibration and noise of the outdoor unit directly. It has been proven that the structural destruction caused by excessive vibration from an inappropriate design is the most substantial reason to decrease the reliability of air-conditioners. In China it is typical that AC manufacturers rely on engineers’ personal experience heavily to design a new product and they always overlook a qualitative judgment on the dynamic response of the product structure before the physical prototype is produced. So the defects in the design structure can hardly be discovered until an AC unit gets verified and tested, resulting in a longer design cycle and higher R&D cost. However, with years of design experience, MIDEA Group is now making a significant improvement in piping design, while the very problems do exist in MIDEA and they cannot be solved by MIDEA Group alone.

Stage One

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In 2007, a Batch of Broken Piping Accident（BBPA） was discovered in of an export–oriented air conditioners produced by Residential Air-conditioning International Business Division of MIDEA Group which is a serious quality accident for AC manufacturers. A special team was established for investigation. After investigation, it turned out that the piping of the accidental model was designed by reference to a successful solution of a domestic–oriented model, which had been sold well for many years. Then more hints were discovered by comparing the two kinds of piping. Though no change was found in the overall layout differences did exist in the length of each linear portion, leading to corresponding changes in the Modal Frequency and Vibration Mode. The break occurred to the piping when the cohesion of metal was gradually broken down and finally gave away where repeated stresses was caused by the inappropriate design. So in order to protect Air-conditionings from BBPA, it is essential to predict and measure the piping reliability accurately when strong vibration is applied to the piping. However, there is no ideal approach that the MIDEA Group could conduct currently and the accidental broken piping will be inevitable if the reliability of trial and scientific experiments to control the adequacy cannot be guaranteed. Obviously, the traditional way of relying on the engineers design experience and the limited prototype tests isn’t the optimal solution for MIDEA.

Based on the investigation above, the special team reconsidered the AC piping design progress, and came into the following problems exposed in the design and testing sections: (i) Over-reliance on empirical design and a low success rate of primary design. (ii) Inappropriate reference to “successful” design solutions and a high risk of failure. (iii) Lack of objectivity of piping test methods & processes, as well as a low consistence of test results.

In conclusion, the lack of proper design methods and technical specifications, the short of scientific and highly-efficient platform for vibration and reliability analysis, and the deficiency of scientific basis of evaluation criteria for testing are responsible for the problems and defects in the Air Conditioners’ designing and testing.

Stage Two After examining the problems above, the investigation team proposed a new solution to better the original

piping design pattern. (i) To convert from the experience-based design and “Take-ism” pattern. (ii) To emphasize simulation analysis and improve analysis efficiency. (iii) To standardize the analysis process and reduce human intervention.

Then, a U-I collaboration project, led by CAD/CAE platform (CC Platform), was established to develop a set of CAE system to conduct Vibration Analysis and Optimization Design to AC piping (CAE Project).

With deep search into scholars in related research fields and wide recommendation from post-doctoral working colleagues, CC Platform got in contact with Professor Lu of Hefei University of Technology. As an expert on digital design, dynamic performance simulation and mechanical vibration noise control, Professor Lu is rich in practical experience of piping design and simulation, as well as has a keen and profound sense of both technology development trends and the market needs of AC industries.

Starting from the first half of 2008, the cooperation project reached its first milestone in the second half of 2008. A prototype (DEMO) of CAE system for vibration analysis and optimization design specific to air conditioners piping was developed by Professor Lu, basing on the investigation results and Professor Lu’s leverage of piping analysis cases and data. The DEMO could conduct data extraction, modeling, analysis and optimization automatically to a pretreated UG model of AC piping, and generate a standard format report. The system not only achieved to extract model data of assemblies from CAD, but also realized to link up model data in CAD with analyzed data in CAE. Then, based on a 3D simulation model, the system could calculate the natural frequency and vibration response of a whole set of AC outdoor unit. Finally an optimized piping design was created by the CAE system. Equipped with this user-friendly system, the engineers were able to run the whole way from data extraction, process simulation to results viewing quickly and easily.

Stage Three Treasured by MIDEA Group, the DEMO was tested and verified by over 30 different kinds of on-market

Air-conditioning models whose piping design solutions were proven to be reliable. The tests covered all fields

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of performance, including the applicability of devices, the convenience to users, the accuracy of simulation results and the logical debug. To the great surprise of Midea’s engineers, there turned out to be 32 serious problems in the final test report, with the poor versatility of the devices being the sharpest. Meanwhile, due to the limitations of Finite Element Method (FEM) of the CAE system and the detail neglect in piping design, there remained some errors between simulation results and the actual operating conditions of many well-used designs. After the tests, limited product lines were permitted to get through the system, and finally only one model, the household fixed-frequency air conditioner, was left to be approximately perfect to fit the system, though there were still some parameters of the system to adjust later.

For the next months, Professor Lu worked with engineers of CC Platform to refine the system. During this time, engineers and researchers of the two sides communicated officially for dozens of times. Then in February 2009, the system was finally applicable to the majority models of the household fixed-frequency air conditioners. As for the related personnel of CC Platform, during the process of improving and perfecting, they acquired a good command of knowledge about how to match the system with product models, as well as knowledge of software applications.

Stage Four At the success of development, CC Platform carried out a training and promotion campaign for CAE

system. Generally speaking the operation was going smoothly with some resistance encountered，and CC Platform worked on a variety of ways to advance it.

Firstly, Professor Lu and other industry experts were invited to give public lectures weekly on Saturday or Sunday, mainly to the promotion staffs of CC Platform, young engineers without design experience and engineers with no emergent assignments. Secondly, a one to one mentoring approach of teaching-by-doing was introduced. Lastly, the promotion staffs of CC Platform were involved in many development projects to acquaint engineers with the new system.

The application of the new system has achieved good results. For example, the R&D Department of Residential Air-conditioning International Business Division, who is the first department equipped with the CAE system, started to design new product in February 2009 with the CC Platform personnel involved and as early as in the first half of the year, the first series of products designed were launched. With the assistant of CAE system, prototype cost reduced and design optimization process accelerated. The development speed up and the average cost of the newly designed products decreased by 1% at least, sometimes higher to 5-8%.

Stage Five MIDEA Group has not been satisfied merely to the introduction of the CAE system, but to further integrate

and utilize the knowledge learning from the cooperation project in order to gain further advantages. It is the key reason for the success of the CAE university-industry collaboration project and the great improvement of the piping design ability of MIDEA Group.

Firstly, MIDEA Group improved to the piping design criterion according to the introduction of CAE system. Simulation is introduced as a must in design process and the analysis results are required to be filed properly as process documents, while the simulation analysis report is regarded as a necessary technical documentation archive.

Secondly, MIDEA Group established a database of optimal devices and designs based on the CAE system. Devices and designs with relatively stable performance are collected into the database and some common design patterns and common devices, such as mats, compressors and four-way valves, can be invoked flexibly. The application of the database saves time from repeated development and design, as well as enhances the standards and commonality of products designed.

Stage Six During the interview, we learned that the CAE system developed by Professor Lu was just licensed to

MIDEA Refrigeration Group, rather than transferred, and Professor Lu had been conducting similar cooperation projects with other AC manufacturing companies such as Kelon Air Conditioner Co. Ltd. and Yangzi Air

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Conditioner Co. Ltd. Therefore, it can be predicted that , along with these cooperation projects, knowledge about CAE system for vibration analysis and optimization design of AC piping will soon spread to other enterprises.

When it came to the future development of the core technology of this project, the engineers noted that the Finite Element Method which CAE was based on is currently developing very rapidly. This could be seen from the increasingly wide application of the US ANSYS Finite Element Analysis software and the U.S. PTC's Pro / ENGINEER Software in the three-dimensional AC piping system modeling. With further development of the Finite Element Method and modeling technology, it is inevitable that current systems are going to be replaced by systems of higher accuracy and better versatility.

Stage Seven (Future) Based on the status that the application of the current CAE system is limited to household fixed-frequency

machines, and failed air-conditioner of higher complexity like central air-conditioners and vertical type packaged air conditioners it is necessary to develop CAE Systems of stronger applicability and more powerful design functions to meet technology demands. In addition, with inverter air conditioners (convertible frequency air-conditioners) being the unavoidable trend of air conditioning industry, demand for inverter-air-conditioner-piping-design-applicable CAE systems is urgent.

Ⅴ Summary of the Exploratory Case

Stage characteristics of the process of knowledge creation in the U-I collaboration Stage characteristics of the process of knowledge creation in the U-I collaboration summarized from the

exploratory case are showed in the Table 2. Table 2 the U-I collaboration process

Stage Description

Project Origin A Batch of Broken Piping

From the end of Stage One to the beginning of Stage Two

Finite Element Method introduced from Hefei University of Technology

End of Stage Two Knowledge of CAE DEMO testing and improving

End of Stage Three Knowledge of CAE experience and application

End of Stage Four General tips and know-hows of skilled application of CAE System

End of Stage Five Optimized process and design criterion; database of optimal designs and stable devices

End of Stage Six Three-dimensional modeling technology and More advanced Finite Element Method (emerging from external sources)

End of Stage Seven New problems and demands, such as demands for CAE system applicable to inverter air conditioners and vertical type packaged air conditioners

First off, on the first stage of U-I cooperation, the origins of the project generally refer to the cooperation demands derived from the product defects, such as a Batch of Broken Piping. During this stage, the company abstracts specific needs from the surface indications of defects, like in the CAE project the investigation team and CC Platform summarized three major issues and three improvement methods. We refer to it as “demand-codification stage.”

Useful abstractions from complex phenomena of the first stage facilitate the cross-organizational communication with science research institutes. Therefore in the second stage of U-I cooperation, external knowledge, mainly knowledge from science research institutes is introduced to the enterprise in the form of basic algorithms, theories, principles, etc, and specifically in the case, the Finite Element Method was introduced in the very stage. During this stage, primarily by means of testing and improving, the company goes through a process from accessing and importing knowledge from science research institutes to externalizing knowledge to form prototype or product concept according to company’s actual situation. The CAE DEMO supplied by Hefei University of Technology is an example of prototype developed in this way. So we currently

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refer to this stage as “knowledge-gain stage.” Since it is in this stage that external knowledge from science research institutes is introduced, we define this stage as the beginning of the process of knowledge creation in the U-I collaboration. In the case, it is also in this stage that MIDEA Group acquired knowledge of testing and improving based on the theoretical knowledge from the external science research partner.

Then at the third stage of the cooperation, after the process of testing and improving in learning-by-doing ways during the knowledge-gain stage, engineers of Midea involved start to learn and absorb new technology and knowledge to enrich their tacit knowledge base by shared mental models or the know-how approach. Taking the CAE case for example, the engineers involved acquired experiences and know-hows to use the CAE system during this stage. However, it should be pointed out that such experiences and know-hows are picked up by engineers individually and they are too fragmented to be easily expressed. This process is somewhat similar to the internalization process proposed by Nonaka, and we refer it as “knowledge- absorption stage” for the time being.

At the fourth stage, the company experiences a process of promoting the knowledge learned at the third stage. In the CAE project, the CC Platform systemized the experiences and know-hows of CAE utilization to make them more conducive to learning, and then a one to one mentoring approach of teaching-by-doing was carried out to drive the whole group of design and R&D engineers to make use of CAE system in new product development and design process. So we refer stage as “knowledge-sharing stage” for the time being. At the fifth stage, the Midea, by all kinds of means, embeds the knowledge learned in the cooperation into original enterprise knowledge system to obtain further benefits. For example, due to the knowledge introduction, the CC Platform improved and optimized the original design process and design specifications, as well as developed a corresponding database to achieve a better use of knowledge. For the time being we refer this stage as “knowledge-propagation stage.”

At the sixth stage, knowledge gradually spills from Midea’s CAE cooperation project. Professor Lu of Hefei University of technology had published several papers based on the project and the CC Platform applied for several patents individually or jointly with research institutions. We refer this stage as “knowledge- spillover stage” for the present. At the same time, we can see from the case that during the "knowledge-spillover" process, there is another notable feature that new theories and methodology have the potential to replace the existing technology. For example, in the CAE project case, the continuous development of Finite Element Method and the constant improvement of 3D modeling technology, pose a potential threat to the CAE systems developed in the cooperation.

And the seventh stage, which MIDEA Group has not yet experienced, is supposed to be an expected stage during which new problems and demands emerge from the potential threats of new technologies of the sixth stage and the actual needs of enterprise development. In the CAE project case, the engineers of CC Platform were eager to increase the CAE Systems of applicability for the AC piping design industry based on the future 3D modeling technology and the Finite Element Method, because the application of the current CAE system is only limited to household fixed-frequency machines. We refer this stage as “knowledge-degeneration stage” for the time being.

We can see that knowledge begins to spill out from the enterprise in the "knowledge-spillover" stage. So in the strict sense, the process of knowledge creation in the U-I collaboration should only include the four stages: that is Knowledge Gain, Knowledge Absorption, Knowledge Sharing, and Knowledge Propagation.

The tendency of knowledge conversion in K-Space for the case The framework of Knowledge Space has been discussed in the Framework and Methodology and a matrix

of a two-point scale is produced to describe the features of the three dimensions: Codifiability, Abstraction and Diffusion.

The scales shown in Table 4, which is to measure the forms of knowledge transformed in the process of knowledge creation in the U-I collaboration, were applied in in-depth interview for the exploratory case study in order to determine the location of different-stage knowledge in K-space. The transforming process of the knowledge is described forms during the stages of the U-I collaboration in Table 3 and Figure 2 below.

Table 3 Knowledge conversion during the stages of the U-I collaboration

Stages Knowledge conversion Codification Abstraction Diffusion Performance

The beginning of the Project

A Batch of Broken Piping(BBP)

Low Low High The Batch of Broken Piping Accident occurred to type of air-conditioning

From the end of Demand Codification to the beginning of Knowledge Gain

Finite Element Method introduced from Hefei University of Technology and demand Summary

High High High

The reason for the BBP accident was concluded into three serious problems(over-reliance on empirical design, high risk from copy, lack of objectivity of piping test methods and processes) and three major solutions (to convert from the experience-based design, to emphasize simulation analysis, to standardize the analysis process). Besides, Finite Element Method was introduced from Hefei University of Technology.

End of Knowledge Gain

Knowledge of CAE DEMO testing and improving

High Low Low

DEMO of CAE system was completed. The general framework and concepts of CAE system were basically built up. DEMO was comprehensive tested and there turned out to be 32 serious bugs which were improved later by the cooperation of two sides.

End of Knowledge Digestion

Knowledge of CAE experience and application

Low Low Low During the process of improving CAE system, the company had a better understanding of the system, as well as acquired a good command of knowledge about the operation and application of the system.

End of Knowledge Sharing

General tips and know-hows of skilled application of CAE System

Low High High

CC Platform assigned specialists to carry out promotion campaign for CAE system by a one to one mentoring approach of teaching-by-doing. Engineers who mastered the operational knowledge improved development efficiency. Operational knowledge was solidified in new products to achieve higher value, as well as to enhance the core competitiveness of MIDEA Group.

End of Knowledge Propagation

Optimized process and design criterion; database of optimal designs and stable devices

High High Low Based on the CAE system, MIDEA Group optimized the development process and design criterion, as well as established a database of optimal devices and designs for further knowledge-sharing.

End of Knowledge Spillover

Three-dimensional modeling technology and More advanced Finite Element Method (emerging from external sources)

High High High

Professor Lu published several papers based on the project. With the constantly rapid development Related technologies, such as Finite Element Method and 3D modeling technology, current CAE systems are going to be replaced in the future.

End of Knowledge Degeneration

New problems and demands, such as demands for CAE systems of better versatility

Low Low High Potential demands are urgent for CAE systems applicable to convertible frequency air-conditioners, central air-conditioners and vertical type packaged air conditioners.

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Knowledge creation process in U-I cooperation

Finite Element Method external introduced

Three serious problems and three major solutions

3D modeling technology and new algorithm

Knowledge of CAE testing and improving

Tips and know-howsof skilled applicationof CAE System

Optimized process; A database of optimal designs

Knowledge of CAE systemDEMO

Knowledge Digestion

Knowledge Sharing

Knowledge Propagation

Knowledge Spillover

Knowledge Gain

Conceptual Knowledge

Operational Knowledge

Proprietary Knowledge

Systematic Knowledge

Academic Knowledge

Academic Knowledge

Demand Codification Knowledge

Degeneration

Phenomena

A Batch of Broken Piping

Phenomena

New problems and accidents

Fig. 2 The tendency of knowledge conversion

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Ⅵ Discussion

GDSP knowledge creation mechanism in K-Space The evolution of knowledge conversion during the stages of the U-I collaboration shown above can be

described in the K-space. It can be seen from Figure 3 that knowledge transforms along the curve of ACDEFGCA during the process of the U-I collaboration. And based on the curve, this section will focus on analyzing and defining the stages of knowledge conversion during U-I collaboration, which have been summarized in the exploratory case.

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Fig. 3 Knowledge creation mechanisms during the U-I collaboration

Demand Codification Shown as AC in Figure 3, at the Demand Codification stage, phenomenological knowledge converts into

academic knowledge after codification and abstraction. During this process, the company identifies threats and seeks opportunities from the problems and facts (This kind of problems and facts are refers as phenomenon knowledge which is concrete, uncodified and inter-organization diffused) which are encountered in practice and usually can be easily accessed to but hardly clarified. Then the phenomenological knowledge is structured, uniformized and formalized to eliminate the initially related uncertainties and finally profound views (academic knowledge which is abstract, codified and inter-organization diffused) are formed. This process is an essential step in U-I collaboration, because academic knowledge is of the strongest abstract and codified nature which is therefore most conducive to the inter-organizational diffusion and exchange.

Knowledge Gain Shown as CD in Figure 3, at the Knowledge Gain stage, universities and science research institutes supply

the enterprise with codified academic knowledge, including principles, formulas, rules, systems, methodology, est., and then help it absorb this kind of new knowledge to cultivate capacity to produce designated products and deliver corresponding service. Correspondingly, the academic knowledge is modified and improved according to the specific demands of company to produce prototypes and product concepts, which are referred as conceptual knowledge. During this process, the company firstly searches universities that meet cooperation requirements, regarding to the “profound views” summarized at Demand Codification. Thereafter, academic knowledge spreads into the enterprise from C (inter-organization) with its diffusion going down and is embodied into company’s actual situation with the abstraction decreasing. At the same time, conceptual knowledge is gained by the company simultaneously during the testing and improving process with the science

C H

A

B

D

E F

G

Concrete

Codified

Uncodified

Knowledge Gain Knowledge Spillover

Demand

Codification

Knowledge

Propagation Knowledge

Digestion

Inter-organization

Diffused

Knowledge

Sharing Inter-organization

Undiffused Abstract

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institute. Thus, at the Knowledge Gain stage, the company learns and gets to understand the new knowledge introduced from the universities, which is then externalized into explicit knowledge, in the form of self-used language and concepts related to prototypes and product concepts.

Knowledge Digestion Shown as DE in Figure 3, at the Knowledge Digestion stage, conceptual knowledge converts into

operational knowledge after uncodification. The company staffs, mainly the front-line engineers or R&D personnel, incorporate the conceptual knowledge into their tacit knowledge bases by shared mental models or the know-how approach, in the manner of learning by doing or learning by using. This process is somewhat similar to the internalization process of SECI Model.

Knowledge Sharing Shown as EF in Figure 3, at the Knowledge Sharing stage, operational knowledge converts into proprietary

knowledge after abstraction. During this process, the tacit experience of the front-line engineers is structured and simplified to the most essential characteristics by means of learning by doing. Then the well-codified abstract knowledge is propagated and applied to other wide range of intra-organizational situations. Nonaka (2000) believed that the internalized tacit knowledge was the most critical source of the competitiveness of enterprise; likewise, proprietary knowledge in this study is an important component of firm competitiveness.

Knowledge Propagation Shown as FG in Figure 3, at the Knowledge Propagation stage, proprietary knowledge converts into

systematic knowledge after codification. During this stage, the abstract knowledge is codified deeply by further research and development and is embodied into firm’s specific practices to make greater contributions. However, throughout all these processes, the knowledge remains in the company. Through the process of gain, digestion and sharing, the knowledge will finally be "materialized" in the company's products and services by some innovative applications and brings material wealth for the enterprise; or the knowledge will be “solidified” in the company's philosophy, systems, process, databases, management forms and cultures as corporate knowledge assets and achieves asset appreciation. The processes of materialization and solidification are thereby regarded as the propagation progress of organization knowledge.

The subsequent stages of U-I collaboration: Knowledge Spillover Shown as GC in Figure 3, systematic knowledge spills from the firm and re-converts into academic

knowledge at the Knowledge Spillover stage. As previously assumed, the codification and abstraction nature of knowledge has strengthening effects on the knowledge diffusion, so the systematic knowledge of high codification and abstraction will inevitably flow out of the enterprise, along with the development of industry technology and the movement of personnel.

The subsequent stages of U-I collaboration: Knowledge Degeneration Shown as AC in Figure 3, academic knowledge re-converts into phenomenon knowledge at the Knowledge

Degeneration stage. With the advance of industry and technology, new problems and demands will generate and encounter during the further enterprise practice later, which will then turn into the starting point for the next co-operation. Meanwhile, with the continuous expansion of human cognition, the original academic knowledge can only be used to explain concrete questions, such as Newton's law, which was regarded as a classic, was discovered to be limited to the macro issues of low-speed after the theory of relativity being raised. It follows that both the abstraction and codification of the original academic knowledge decrease

It can be seen from the processes of U-I collaboration above, knowledge from universities are introduced from Point C, and flows out of the firm because of the knowledge spillover effect from G. Thus, we define Point A as the start point of the knowledge creation in U-I collaboration in the broad sense while take Point C as the start point and Point G as the end point in the narrow sense. Correspondingly in the later study, we name the 4

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stages of knowledge creation, including Knowledge Gain, Knowledge Digestion, Knowledge Digestion and Knowledge Propagation, as GDSP knowledge creation mechanisms in the process of U-I collaboration. While the seven stages, which are Demand Codification, Knowledge Gain, Knowledge Digestion, Knowledge Sharing and Knowledge Propagation, Knowledge Spillover and Knowledge Degeneration, are presented as the GDSP knowledge creation cycle in the process of U-I collaboration. Yet, it should be pointed out that what’s this study reveals is a general tendency of knowledge creation in the U-I collaboration process, and the order of the stages is just an specific example for many of the very stages run simultaneously and sometimes repeat in a small scale. So it is actually a generalization of the knowledge conversion results that this study has finally proposed.

A comparison of the GDSP knowledge creation theory and the SECI knowledge creation theory After a deep analysis into the tendency of knowledge transform in the three dimensions of codification,

abstraction and diffusion in the exploratory case of U-I collaboration, this study has advanced a GDSP knowledge creation theory featuring the four key stages: Knowledge Gain, Knowledge Digestion, Knowledge Sharing and Knowledge Propagation. This section will mainly make a comparison of the GDSP knowledge creation theory to the typical SECI knowledge creation theory which is similarly based on knowledge conversion

Firstly, in term of the knowledge creation dimension, the GDSP theory follows the SECI theory on the existence dimension. As to the cognitive dimension, the GDSP theory makes a reference to the abstraction dimension proposed by Boist (1998) , as well as the codification dimension relied by the SECI model. It should be mentioned that the introduction of the abstraction dimension of knowledge in the context of U-I collaboration is quite essential, because enterprises and universities are two entirely different types of organizations with significant differences in the knowledge background.

Secondly, the comparisons between similar knowledge creation processes of the two theories are shown in Table 5. It can be seen that the GDSP theory with the abstraction dimension introduced is more convincing than the SECI model on the knowledge creation processes. For example, since there is no change to the knowledge forms during the two processes of Combination (from explicit knowledge to explicit knowledge) and Socialization (from tacit knowledge to tacit knowledge), it is difficult to use the SECI theory to differ the former type of knowledge from the later and to explain what has happens to the value of knowledge after such a process, while in the GDSP theory these issues can be easily explained on the analogy of Knowledge Gain and Knowledge Sharing. Actually, during the combination process, by systemizing concepts into knowledge systems, both the abstraction and value of the explicit knowledge are increased. Likewise, the same conversion happens to the tacit knowledge during the socialization process through processes of sharing and experiencing. In addition, the starting and ending points of the GDSP theory and the SECI theory are different, and it is this very diversity that distinguishes the two theories.

In the SECI theory, the conversion of tacit knowledge is both the start point and the end point while in the GDSP theory it is the conversion of explicit knowledge that marks the origin and destination.

Table 4 Comparisons of the processes between the GDSP knowledge creation theory and the SECI knowledge creation theory

GDSP Stage Description SECI Stage Description Knowledge Gain

Abstract explicit knowledge of inter-organizational level converts into concrete explicit knowledge of organization level.

Combination Explicit knowledge (of individual level) converts into explicit knowledge (of organization level).

Knowledge Digestion

Concrete explicit knowledge of organization level concrete tacit knowledge of organization level.

Internalization Explicit knowledge (of organization level) converts into tacit knowledge (of individual level).

Knowledge Tacit knowledge of organization level Socialization Tacit knowledge (of individual

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Sharing converts into abstract tacit knowledge of organization level.

level) converts into tacit knowledge (of organization level).

Knowledge Propagation

Abstract tacit knowledge of organization level turn into abstract explicit knowledge of organization level.

Externalization Tacit knowledge (of organization level) converts into explicit knowledge (of individual level).

Ⅶ Conclusion and Discussion

In this study, using the framework of the K-space (by reference to the I-space of Boisot) a new tendency of knowledge conversion is summarized based on the exploratory case study, and different knowledge forms are identified according to their locations in the K-space. Then based on the tendency of knowledge conversion, the GDSP knowledge creation theory is developed，featuring four key stages of Knowledge Gain, Knowledge Digestion, Knowledge Sharing and Knowledge Propagation. Taking Demand Codification, the four stages of GDSP, Knowledge Spillover and Knowledge Degeneration being into consideration, the GDSP knowledge creation cycle with seven stages is developed. Finally, a comparison between the GDSP theory and the SECI knowledge creation theory is given, which proves that in the contexts of U-I collaboration in China, the process of knowledge creation follows the GDSP theory rather than the typical SECI theory.

The research enriches and advances the typical SECI knowledge creation theory in three aspects: First, the new knowledge creation theory is proposed in the context of inter- heterogeneous organization and

four knowledge conversion processes which are quite different from SECI theory are identified which makes up for the neglect of the external knowledge input.

Second, this research proves that it is the abstract explicit knowledge rather than personal tacit knowledge that is the form of knowledge with maximized value, which is different from the Nonaka’s exaggeration of the role of individual tacit knowledge mystification of the collaborative work.

Third, in the context of inter-organization, the knowledge creation process begins with the conversion of explicit knowledge rather than tacit knowledge. This result is consistent with research results presented by Gourlay (2006) .

The present research effort has several possible limitations. First, the generalizability of the results may be limited because the GASP theory is proposed based on a single case though it is a theoretical sample. It is not enough to prove that all the other U-I collaboration will follow this 7 stages identically, let alone to take account of the additional variables of industries, firm scales. Second, the case chosen is a project-based collaboration rather than the currently prevailing strategic alliance collaboration which is longer extended, deeper interactive, and closer collaboration. The heterogeneity of these two kinds of cooperation may lead to different characteristics of knowledge transformation.

In this present study, the a priori framework has been discussed on the basis of one case study only. Since at this stage the results are exploratory, there is clearly a strong need to test the framework further with other case studies, such as sampling cases from different industries and of different firm scales. However, there are doubts about the relevance of quantitative measures concerning research in cross-organizational knowledge creation, because of the decisive role of the tacit knowledge component. In term with the different cooperation approaches, some potential moderating factors may be explored to expend the GASP theory. In addition, to identify the influence factors in the various stages of Knowledge creation process, further empirical researches are needed.

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