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

1. 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.
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2. 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
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3. 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
Location Codification Abstraction Diffusion
on Axis Is this kind of knowledge:
High Easy to be recorded by Generally applicable to all Easily available to other
graphics and formulas? situations? organizations in need?
Easy to be standardized and Primarily science-based? Only available within the
automatized? enterprise?
Low Difficult to be clearly Limited to the unique context? Only available to the unique
expressed Needing a wide range of person within the enterprises
Only be expressed clearly by transformation to fit its specific
demonstration? situation?
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4. 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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5. 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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6. 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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7. 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
Finite Element Method introduced from Hefei University of Technology
the beginning of Stage Two
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
Optimized process and design criterion; database of optimal designs and
End of Stage Five
stable devices
Three-dimensional modeling technology and More advanced Finite
End of Stage Six
Element Method (emerging from external sources)
New problems and demands, such as demands for CAE system applicable
End of Stage Seven
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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8. 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.
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9. Table 3 Knowledge conversion during the stages of the U-I collaboration
Stages Knowledge conversion Codification Abstraction Diffusion Performance
The beginning of the A Batch of Broken
Low Low High The Batch of Broken Piping Accident occurred to type of air-conditioning
Project Piping(BBP)
The reason for the BBP accident was concluded into three serious
From the end of Finite Element Method
problems(over-reliance on empirical design, high risk from copy, lack of
Demand introduced from Hefei
objectivity of piping test methods and processes) and three major solutions (to
Codification to the University of High High High
convert from the experience-based design, to emphasize simulation analysis,
beginning of Technology and
to standardize the analysis process). Besides, Finite Element Method was
Knowledge Gain demand Summary
introduced from Hefei University of Technology.
DEMO of CAE system was completed. The general framework and concepts
Knowledge of CAE
End of Knowledge of CAE system were basically built up. DEMO was comprehensive tested and
DEMO testing and High Low Low
Gain there turned out to be 32 serious bugs which were improved later by the
improving
cooperation of two sides.
Knowledge of CAE During the process of improving CAE system, the company had a better
End of Knowledge
experience and Low Low Low understanding of the system, as well as acquired a good command of
Digestion
application knowledge about the operation and application of the system.
CC Platform assigned specialists to carry out promotion campaign for CAE
General tips and
system by a one to one mentoring approach of teaching-by-doing. Engineers
End of Knowledge know-hows of skilled
Low High High who mastered the operational knowledge improved development efficiency.
Sharing application of CAE
Operational knowledge was solidified in new products to achieve higher
System
value, as well as to enhance the core competitiveness of MIDEA Group.
Optimized process and
design criterion; Based on the CAE system, MIDEA Group optimized the development process
End of Knowledge
database of optimal High High Low and design criterion, as well as established a database of optimal devices and
Propagation
designs and stable designs for further knowledge-sharing.
devices
Three-dimensional
modeling technology Professor Lu published several papers based on the project. With the
End of Knowledge and More advanced constantly rapid development Related technologies, such as Finite Element
High High High
Spillover Finite Element Method Method and 3D modeling technology, current CAE systems are going to be
(emerging from replaced in the future.
external sources)
New problems and
demands, such as Potential demands are urgent for CAE systems applicable to convertible
End of Knowledge
demands for CAE Low Low High frequency air-conditioners, central air-conditioners and vertical type packaged
Degeneration
systems of better air conditioners.
versatility
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10. Knowledge creation
process in U-I cooperation
Knowledge Knowledge Knowledge Knowledge
Gain Digestion Sharing Propagation
Demand
Codification Knowledge
Degeneration
Finite Element Knowledge
Method external Spillover
introduced
Knowledge of Knowledge of CAE Tips and know-hows Optimized process;
CAE system testing and of skilled application A database of
Three serious
DEMO improving of CAE System optimal designs
problems and
three major
solutions
3D modeling New problems
technology and accidents
and new
A Batch of algorithm
Broken Piping
Academic Conceptual Operational Proprietary Systematic Academic
Knowledge Knowledge Knowledge Knowledge Knowledge Phenomena
Knowledge
Phenomena
Fig. 2 The tendency of knowledge conversion
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11. 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.
C
H
Knowledge Gain
Knowledge Spillover
D
G
Demand Codified
Codification
Knowledge
Knowledge Inter-organization
Propagation B
Digestion Diffused A
Knowledge
Sharing Uncodified Inter-organization
Abstract
F Concrete E Undiffused
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
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12. 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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13. 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 Abstract explicit knowledge of Combination Explicit knowledge (of individual
Gain inter-organizational level converts into level) converts into explicit
concrete explicit knowledge of knowledge (of organization level).
organization level.
Knowledge Concrete explicit knowledge of Internalization Explicit knowledge (of
Digestion organization level concrete tacit organization level) converts into
knowledge of organization level. tacit knowledge (of individual
level).
Knowledge Tacit knowledge of organization level Socialization Tacit knowledge (of individual
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14. Sharing converts into abstract tacit knowledge of level) converts into tacit
organization level. knowledge (of organization level).
Knowledge Abstract tacit knowledge of organization Externalization Tacit knowledge (of organization
Propagation level turn into abstract explicit level) converts into explicit
knowledge of organization level. 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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