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


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

  1. 1. Research on the Knowledge Creation Process of the University-Industry Collaboration: a Case from China Wei Yao, Jin Chen, Yaqi Si,Jue HuZhejiang University, School of Public Administration, YuQuan Campus, P.O.X 1715, Hangzhou, 310027, ChinaAbstract: This paper elaborates theories of intra-organizational knowledge creation by exploring theknowledge conversion processes of University-Industry Collaboration in Chinese contexts. To describe theknowledge transform tendency, a theoretical framework is developed by reference to the Information Spacewhich of Boisot (1995). The application of the framework is described in the exploratory case study of CAESystem for Vibration Analysis. Analysis of the results suggests that seven certain stages can be especiallyindicative of cross-organizational knowledge creation, namely: Demand Codification, Knowledge Gain,Knowledge Digestion, Knowledge Sharing, Knowledge Propagation, Knowledge Spillover and KnowledgeDegeneration. And a new knowledge creation theory: GDSP knowledge creation theory which enriches andadvances the typical SECI knowledge creation theory in some aspects is proposed. Furthermore the paperconcludes 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 changerapidly, continuous innovation and the knowledge that enables such innovation have become important sourcesof sustainable competitive advantages. In the knowledge-based economy of the 21st century a key source ofsustainable competitive advantages and superior profitability within an industry is how a company creates andshares its knowledge(Boisot,1998).The importance of innovation has skyrocketed in the present times, and thesuccess of a firm largely depends on how it innovates and, by implication, creates knowledge. successfulcompanies are those that consistently create new knowledge, disseminate it widely throughout the organizationand quickly embody it in new technologies and products (Nonaka, 1991). Knowledge creation is representing aprimary 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 threemost pressing concerns for national development (Tsui, Zhao, & Abrahamson, 2007). To effectively fosterinnovation, 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-Icollaboration innovation: (i) the tendency of knowledge transform; (ii) knowledge creation mechanisms. Thispaper is structured as follows. We start by providing the review and analysis of the existing criticisms of theSECI model from cross-organizational perspective. Next, we propose a framework and apply this framework toanalyze the knowledge conversion processes in Chinese University-Industry context for knowledge creationand 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, thereis 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 andexplicit 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 explicitknowledge to tacit knowledge). It is what we called SECI knowledge creation theory shown as Figure 2 below. 1
  2. 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 andmost influential models in knowledge management literature. The SECI theory appears to have attracted littlesystematic 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 amatter 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 thepractice 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 explicitknowledge, with misreading important organizational writers, and of not using better accounts of westernphilosophy. Bereiter (2002) argued Nonaka’s model does not explain how new ideas are produced, nor howdepth of understanding develops. Another comprehensive critique by Gregorio (2008) proposed that four modesof knowledge conversion are flawed and SECI framework omits inherently tacit knowledge. They alsosuggested that different kinds of knowledge are created by different kinds of behaviors. The neglect of theexternal knowledge input. It is unrealistic to create new knowledge only through the existing knowledge withinthe organization and the generation of novel ideas and directions will be scarce if based on the existingknowledge totally(Haapasalo and Kess,2001).Both the knowledge difference between different organizationsand 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 singleenterprise, but in the context of U-I collaboration which is a kind of cross-heterogeneous organizationcooperation, it would have some limitations as below:Proposition 1The knowledge creation processes in University-Industry Collaboration begins as explicit knowledge Nonaka and Takeuchi(1991) posit four knowledge conversion processes that are essential to organisationalknowledge creations. According to their theory, most knowledge begins as tacit knowledge, which may resideonly within an individual and only at an unconscious level.And the evidence adduced in support of the startpoint of knowledge creation is inadequate. It is not clear why knowledge conversion has to begin withsocialization if tacit knowledge is the source of new knowledge. New tacit knowledge is also generated byinternalization, if reading and writing are both instrumental in tacit knowledge formation, then knowledgecreation might also begin with the creative synthesis of explicit knowledge (‘combination’) (Gourlay,2006).Proposition 2The Knowledge creation processes may evolve in different orders because of the input of the knowledge outside 2
  3. 3. the organizationSECI 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 arguethat 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 existingknowledge within the organization and the generation of novel ideas and directions will be scarce if based onthe existing knowledge totally(Haapasalo and Kess,2001).Both the knowledge difference between differentorganizations and the cooperation mechanism of knowledge inside or outside the organizations should not beignored.Proposition 3The 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 iscomplicated 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 itsapplicability, and only a few concerns have been raised recently with respect to whether the model can besuccessfully 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-sourcephenomenon, which means that analyzing knowledge creation should be in certain industrial and geographicalcontext. Therefore the potential for its’ critical analysis is limited by the lack of empirical data that couldsupport or refute its’ideas. Ⅲ Framework and Methodology In order to observe the tendency of knowledge transforming during the U-I collaboration process, wedevelop a research framework called K-Space which is short for Knowledge Space. In fact, our analysisframework is modified from the Information Space which is proposed by Boisot (1995). The Information Spaceor I-Space is a conceptual model that relates data structuring to data sharing among a population of dataprocessors. 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 ofthe diffusion dimension in inter-organizational contexts. Then largely through the approximate location of knowledge in K-space determined by scales on the threedimensions, the characteristic of knowledge can be distinguished and identified. The tendency of knowledgetransformed in the process of knowledge creation in the U-I collaboration can be described by connecting thedots of knowledge forms of different stages in K-space. Therefore, based on judgments and consensus, a matrixof 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 dimensionsLocation Codification Abstraction Diffusionon 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? 3
  4. 4. The dominant approach does not necessarily support the utilization of creative resources hiding in theprocesses and in the structure of the organization. The process of knowledge creation in U-I collaboration isseen as an unfolding process consisting of stages in which characteristic factors not only appear in greater orlesser degree but also in a certain order of occurrence. The next section of the paper will describe the results ofa case study illustrating how the knowledge is transformed and created during the process of U-I collaborationin China. The case presents a joint-development project of CAE (Computer-aided engineering) system for thedesign of air conditioner between Hefei University of Technology and Midea Group in China, which is one ofthe 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 PipingAn overview of the partners The cooperation project was conducted between CAD/CAE Technology Platform under the TechnologyManagement Department of Midea Air-Conditioning & Refrigeration Group and School of Machinery andAutomobile Engineering of Hefei University of Technology. Midea Air-Conditioning & Refrigeration Group(MIDEA) is one of the largest, strongest and mostdiversified white household appliance makers in the world, and its sales revenue of 2008 hit a record of 52billion Yuan. MIDEA has been attaching importance to scientific research and has established TechnologyManagement Department, to conduct the technology management of the whole group. There are 30 engineersin Technology Management Department, who most of them have rich experience in R&D or production, aswell as strong practical operational capability. CAD/CAE Technology Platform is one of the four basic technology research platforms under theTechnology 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 hasbeen continually improving scientific and technological innovation capability and contributing to regionaleconomic and social development. The School of Machinery and Automobile Engineering (MAE), one of the earliest departments at HefeiUniversity 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 ofUniversity-Industry cooperation. There are about 100 research projects annually in average and more than 20million Yuan research funding in total, which 85% of them (17 million) are related to University-Industrycollaboration. From 2001 to 2007, MAE has undertaken up to 62 large-scale University-Industry cooperationprojects 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 ofthe Air-Conditioning (AC) with the piping design to be the most important, because the quality of the pipingdesign will influence the vibration and noise of the outdoor unit directly. It has been proven that the structuraldestruction caused by excessive vibration from an inappropriate design is the most substantial reason todecrease 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 thedynamic response of the product structure before the physical prototype is produced. So the defects in thedesign structure can hardly be discovered until an AC unit gets verified and tested, resulting in a longer designcycle and higher R&D cost. However, with years of design experience, MIDEA Group is now making asignificant improvement in piping design, while the very problems do exist in MIDEA and they cannot besolved by MIDEA Group alone.Stage One 4
  5. 5. In 2007, a Batch of Broken Piping Accident(BBPA) was discovered in of an export–oriented airconditioners produced by Residential Air-conditioning International Business Division of MIDEA Group whichis a serious quality accident for AC manufacturers. A special team was established for investigation. Afterinvestigation, it turned out that the piping of the accidental model was designed by reference to a successfulsolution of a domestic–oriented model, which had been sold well for many years. Then more hints werediscovered by comparing the two kinds of piping. Though no change was found in the overall layoutdifferences did exist in the length of each linear portion, leading to corresponding changes in the ModalFrequency and Vibration Mode. The break occurred to the piping when the cohesion of metal was graduallybroken down and finally gave away where repeated stresses was caused by the inappropriate design. So in orderto protect Air-conditionings from BBPA, it is essential to predict and measure the piping reliability accuratelywhen strong vibration is applied to the piping. However, there is no ideal approach that the MIDEA Groupcould conduct currently and the accidental broken piping will be inevitable if the reliability of trial andscientific experiments to control the adequacy cannot be guaranteed. Obviously, the traditional way of relyingon 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 cameinto 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 andhighly-efficient platform for vibration and reliability analysis, and the deficiency of scientific basis ofevaluation criteria for testing are responsible for the problems and defects in the Air Conditioners’ designingand testing.Stage Two After examining the problems above, the investigation team proposed a new solution to better the originalpiping 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 aset 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-doctoralworking colleagues, CC Platform got in contact with Professor Lu of Hefei University of Technology. As anexpert on digital design, dynamic performance simulation and mechanical vibration noise control, Professor Luis rich in practical experience of piping design and simulation, as well as has a keen and profound sense of bothtechnology 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 of2008. A prototype (DEMO) of CAE system for vibration analysis and optimization design specific to airconditioners piping was developed by Professor Lu, basing on the investigation results and Professor Lu’sleverage of piping analysis cases and data. The DEMO could conduct data extraction, modeling, analysis andoptimization automatically to a pretreated UG model of AC piping, and generate a standard format report. Thesystem not only achieved to extract model data of assemblies from CAD, but also realized to link up model datain CAD with analyzed data in CAE. Then, based on a 3D simulation model, the system could calculate thenatural frequency and vibration response of a whole set of AC outdoor unit. Finally an optimized piping designwas created by the CAE system. Equipped with this user-friendly system, the engineers were able to run thewhole 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-marketAir-conditioning models whose piping design solutions were proven to be reliable. The tests covered all fields 5
  6. 6. of performance, including the applicability of devices, the convenience to users, the accuracy of simulationresults and the logical debug. To the great surprise of Midea’s engineers, there turned out to be 32 seriousproblems in the final test report, with the poor versatility of the devices being the sharpest. Meanwhile, due tothe 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-useddesigns. After the tests, limited product lines were permitted to get through the system, and finally only onemodel, 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 thistime, engineers and researchers of the two sides communicated officially for dozens of times. Then in February2009, the system was finally applicable to the majority models of the household fixed-frequency airconditioners. 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 asknowledge of software applications.Stage Four At the success of development, CC Platform carried out a training and promotion campaign for CAEsystem. Generally speaking the operation was going smoothly with some resistance encountered,and CCPlatform 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 orSunday, mainly to the promotion staffs of CC Platform, young engineers without design experience andengineers 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 engineerswith the new system. The application of the new system has achieved good results. For example, the R&D Department ofResidential Air-conditioning International Business Division, who is the first department equipped with theCAE system, started to design new product in February 2009 with the CC Platform personnel involved and asearly as in the first half of the year, the first series of products designed were launched. With the assistant ofCAE system, prototype cost reduced and design optimization process accelerated. The development speed upand 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 integrateand utilize the knowledge learning from the cooperation project in order to gain further advantages. It is the keyreason for the success of the CAE university-industry collaboration project and the great improvement of thepiping design ability of MIDEA Group. Firstly, MIDEA Group improved to the piping design criterion according to the introduction of CAEsystem. Simulation is introduced as a must in design process and the analysis results are required to be filedproperly as process documents, while the simulation analysis report is regarded as a necessary technicaldocumentation 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 commondesign 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 thestandards and commonality of products designed.Stage Six During the interview, we learned that the CAE system developed by Professor Lu was just licensed toMIDEA Refrigeration Group, rather than transferred, and Professor Lu had been conducting similar cooperationprojects with other AC manufacturing companies such as Kelon Air Conditioner Co. Ltd. and Yangzi Air 6
  7. 7. Conditioner Co. Ltd. Therefore, it can be predicted that , along with these cooperation projects, knowledgeabout CAE system for vibration analysis and optimization design of AC piping will soon spread to otherenterprises. When it came to the future development of the core technology of this project, the engineers noted that theFinite Element Method which CAE was based on is currently developing very rapidly. This could be seen fromthe increasingly wide application of the US ANSYS Finite Element Analysis software and the U.S. PTCs Pro /ENGINEER Software in the three-dimensional AC piping system modeling. With further development of theFinite Element Method and modeling technology, it is inevitable that current systems are going to be replacedby 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-frequencymachines, and failed air-conditioner of higher complexity like central air-conditioners and vertical typepackaged air conditioners it is necessary to develop CAE Systems of stronger applicability and more powerfuldesign functions to meet technology demands. In addition, with inverter air conditioners (convertible frequencyair-conditioners) being the unavoidable trend of air conditioning industry, demand forinverter-air-conditioner-piping-design-applicable CAE systems is urgent. Ⅴ Summary of the Exploratory CaseStage 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 theexploratory 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 cooperationdemands derived from the product defects, such as a Batch of Broken Piping. During this stage, the companyabstracts specific needs from the surface indications of defects, like in the CAE project the investigation teamand 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-organizationalcommunication with science research institutes. Therefore in the second stage of U-I cooperation, externalknowledge, mainly knowledge from science research institutes is introduced to the enterprise in the form ofbasic algorithms, theories, principles, etc, and specifically in the case, the Finite Element Method wasintroduced in the very stage. During this stage, primarily by means of testing and improving, the company goesthrough a process from accessing and importing knowledge from science research institutes to externalizingknowledge to form prototype or product concept according to company’s actual situation. The CAE DEMOsupplied by Hefei University of Technology is an example of prototype developed in this way. So we currently 7
  8. 8. refer to this stage as “knowledge-gain stage.” Since it is in this stage that external knowledge from scienceresearch institutes is introduced, we define this stage as the beginning of the process of knowledge creation inthe U-I collaboration. In the case, it is also in this stage that MIDEA Group acquired knowledge of testing andimproving 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-doingways during the knowledge-gain stage, engineers of Midea involved start to learn and absorb new technologyand 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 CAEsystem during this stage. However, it should be pointed out that such experiences and know-hows are picked upby engineers individually and they are too fragmented to be easily expressed. This process is somewhat similarto the internalization process proposed by Nonaka, and we refer it as “knowledge- absorption stage” for thetime being. At the fourth stage, the company experiences a process of promoting the knowledge learned at the thirdstage. In the CAE project, the CC Platform systemized the experiences and know-hows of CAE utilization tomake them more conducive to learning, and then a one to one mentoring approach of teaching-by-doing wascarried out to drive the whole group of design and R&D engineers to make use of CAE system in new productdevelopment 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 intooriginal 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 asdeveloped a corresponding database to achieve a better use of knowledge. For the time being we refer this stageas “knowledge-propagation stage.” At the sixth stage, knowledge gradually spills from Midea’s CAE cooperation project. Professor Lu ofHefei University of technology had published several papers based on the project and the CC Platform appliedfor several patents individually or jointly with research institutions. We refer this stage as “knowledge- spilloverstage” 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 existingtechnology. For example, in the CAE project case, the continuous development of Finite Element Method andthe constant improvement of 3D modeling technology, pose a potential threat to the CAE systems developed inthe cooperation. And the seventh stage, which MIDEA Group has not yet experienced, is supposed to be an expected stageduring which new problems and demands emerge from the potential threats of new technologies of the sixthstage and the actual needs of enterprise development. In the CAE project case, the engineers of CC Platformwere eager to increase the CAE Systems of applicability for the AC piping design industry based on the future3D modeling technology and the Finite Element Method, because the application of the current CAE system isonly limited to household fixed-frequency machines. We refer this stage as “knowledge-degeneration stage” forthe time being. We can see that knowledge begins to spill out from the enterprise in the "knowledge-spillover" stage. So inthe 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 matrixof a two-point scale is produced to describe the features of the three dimensions: Codifiability, Abstraction andDiffusion. The scales shown in Table 4, which is to measure the forms of knowledge transformed in the process ofknowledge creation in the U-I collaboration, were applied in in-depth interview for the exploratory case studyin 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 inTable 3 and Figure 2 below. 8
  9. 9. Table 3 Knowledge conversion during the stages of the U-I collaborationStages Knowledge conversion Codification Abstraction Diffusion PerformanceThe beginning of the A Batch of Broken Low Low High The Batch of Broken Piping Accident occurred to type of air-conditioningProject Piping(BBP) The reason for the BBP accident was concluded into three seriousFrom the end of Finite Element Method problems(over-reliance on empirical design, high risk from copy, lack ofDemand introduced from Hefei objectivity of piping test methods and processes) and three major solutions (toCodification 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 wasKnowledge Gain demand Summary introduced from Hefei University of Technology. DEMO of CAE system was completed. The general framework and concepts Knowledge of CAEEnd of Knowledge of CAE system were basically built up. DEMO was comprehensive tested and DEMO testing and High Low LowGain 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 betterEnd of Knowledge experience and Low Low Low understanding of the system, as well as acquired a good command ofDigestion 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. EngineersEnd 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 processEnd of Knowledge database of optimal High High Low and design criterion, as well as established a database of optimal devices andPropagation designs and stable designs for further knowledge-sharing. devices Three-dimensional modeling technology Professor Lu published several papers based on the project. With theEnd of Knowledge and More advanced constantly rapid development Related technologies, such as Finite Element High High HighSpillover 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 convertibleEnd of Knowledge demands for CAE Low Low High frequency air-conditioners, central air-conditioners and vertical type packagedDegeneration systems of better air conditioners. versatility 9
  10. 10. Knowledge creation process in U-I cooperation Knowledge Knowledge Knowledge Knowledge Gain Digestion Sharing PropagationDemandCodification 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 10
  11. 11. Ⅵ DiscussionGDSP knowledge creation mechanism in K-Space The evolution of knowledge conversion during the stages of the U-I collaboration shown above can bedescribed in the K-space. It can be seen from Figure 3 that knowledge transforms along the curve ofACDEFGCA during the process of the U-I collaboration. And based on the curve, this section will focus onanalyzing and defining the stages of knowledge conversion during U-I collaboration, which have beensummarized 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 collaborationDemand Codification Shown as AC in Figure 3, at the Demand Codification stage, phenomenological knowledge converts intoacademic knowledge after codification and abstraction. During this process, the company identifies threats andseeks opportunities from the problems and facts (This kind of problems and facts are refers as phenomenonknowledge which is concrete, uncodified and inter-organization diffused) which are encountered in practice andusually 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 (academicknowledge which is abstract, codified and inter-organization diffused) are formed. This process is an essentialstep in U-I collaboration, because academic knowledge is of the strongest abstract and codified nature which istherefore 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 supplythe 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 productsand deliver corresponding service. Correspondingly, the academic knowledge is modified and improvedaccording to the specific demands of company to produce prototypes and product concepts, which are referredas conceptual knowledge. During this process, the company firstly searches universities that meet cooperationrequirements, regarding to the “profound views” summarized at Demand Codification. Thereafter, academicknowledge spreads into the enterprise from C (inter-organization) with its diffusion going down and isembodied into company’s actual situation with the abstraction decreasing. At the same time, conceptualknowledge is gained by the company simultaneously during the testing and improving process with the science 11
  12. 12. institute. Thus, at the Knowledge Gain stage, the company learns and gets to understand the new knowledgeintroduced from the universities, which is then externalized into explicit knowledge, in the form of self-usedlanguage and concepts related to prototypes and product concepts.Knowledge Digestion Shown as DE in Figure 3, at the Knowledge Digestion stage, conceptual knowledge converts intooperational knowledge after uncodification. The company staffs, mainly the front-line engineers or R&Dpersonnel, incorporate the conceptual knowledge into their tacit knowledge bases by shared mental models orthe know-how approach, in the manner of learning by doing or learning by using. This process is somewhatsimilar to the internalization process of SECI Model.Knowledge Sharing Shown as EF in Figure 3, at the Knowledge Sharing stage, operational knowledge converts into proprietaryknowledge after abstraction. During this process, the tacit experience of the front-line engineers is structuredand simplified to the most essential characteristics by means of learning by doing. Then the well-codifiedabstract 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 ofenterprise; 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 intosystematic knowledge after codification. During this stage, the abstract knowledge is codified deeply by furtherresearch 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 companys products and services bysome innovative applications and brings material wealth for the enterprise; or the knowledge will be“solidified” in the companys philosophy, systems, process, databases, management forms and cultures ascorporate knowledge assets and achieves asset appreciation. The processes of materialization and solidificationare 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 academicknowledge at the Knowledge Spillover stage. As previously assumed, the codification and abstraction nature ofknowledge has strengthening effects on the knowledge diffusion, so the systematic knowledge of highcodification and abstraction will inevitably flow out of the enterprise, along with the development of industrytechnology 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 KnowledgeDegeneration stage. With the advance of industry and technology, new problems and demands will generate andencounter during the further enterprise practice later, which will then turn into the starting point for the nextco-operation. Meanwhile, with the continuous expansion of human cognition, the original academic knowledgecan only be used to explain concrete questions, such as Newtons law, which was regarded as a classic, wasdiscovered to be limited to the macro issues of low-speed after the theory of relativity being raised. It followsthat 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 introducedfrom Point C, and flows out of the firm because of the knowledge spillover effect from G. Thus, we define PointA as the start point of the knowledge creation in U-I collaboration in the broad sense while take Point C as thestart point and Point G as the end point in the narrow sense. Correspondingly in the later study, we name the 4 12
  13. 13. stages of knowledge creation, including Knowledge Gain, Knowledge Digestion, Knowledge Digestion andKnowledge Propagation, as GDSP knowledge creation mechanisms in the process of U-I collaboration. Whilethe seven stages, which are Demand Codification, Knowledge Gain, Knowledge Digestion, Knowledge Sharingand Knowledge Propagation, Knowledge Spillover and Knowledge Degeneration, are presented as the GDSPknowledge creation cycle in the process of U-I collaboration. Yet, it should be pointed out that what’s this studyreveals is a general tendency of knowledge creation in the U-I collaboration process, and the order of the stagesis just an specific example for many of the very stages run simultaneously and sometimes repeat in a smallscale. 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 GDSPknowledge creation theory featuring the four key stages: Knowledge Gain, Knowledge Digestion, KnowledgeSharing and Knowledge Propagation. This section will mainly make a comparison of the GDSP knowledgecreation theory to the typical SECI knowledge creation theory which is similarly based on knowledgeconversion Firstly, in term of the knowledge creation dimension, the GDSP theory follows the SECI theory on theexistence dimension. As to the cognitive dimension, the GDSP theory makes a reference to the abstractiondimension proposed by Boist (1998) , as well as the codification dimension relied by the SECI model. It shouldbe mentioned that the introduction of the abstraction dimension of knowledge in the context of U-Icollaboration is quite essential, because enterprises and universities are two entirely different types oforganizations with significant differences in the knowledge background. Secondly, the comparisons between similar knowledge creation processes of the two theories are shown inTable 5. It can be seen that the GDSP theory with the abstraction dimension introduced is more convincing thanthe SECI model on the knowledge creation processes. For example, since there is no change to the knowledgeforms during the two processes of Combination (from explicit knowledge to explicit knowledge) andSocialization (from tacit knowledge to tacit knowledge), it is difficult to use the SECI theory to differ theformer type of knowledge from the later and to explain what has happens to the value of knowledge after such aprocess, while in the GDSP theory these issues can be easily explained on the analogy of Knowledge Gain andKnowledge Sharing. Actually, during the combination process, by systemizing concepts into knowledgesystems, both the abstraction and value of the explicit knowledge are increased. Likewise, the same conversionhappens 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 thisvery 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 theGDSP 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 theoryGDSP Stage Description SECI Stage DescriptionKnowledge Abstract explicit knowledge of Combination Explicit knowledge (of individualGain 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 (ofDigestion 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 13
  14. 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 organizationPropagation 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 ofknowledge conversion is summarized based on the exploratory case study, and different knowledge forms areidentified according to their locations in the K-space. Then based on the tendency of knowledge conversion, theGDSP knowledge creation theory is developed,featuring four key stages of Knowledge Gain, KnowledgeDigestion, Knowledge Sharing and Knowledge Propagation. Taking Demand Codification, the four stages ofGDSP, Knowledge Spillover and Knowledge Degeneration being into consideration, the GDSP knowledgecreation cycle with seven stages is developed. Finally, a comparison between the GDSP theory and the SECIknowledge creation theory is given, which proves that in the contexts of U-I collaboration in China, the processof 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 andfour knowledge conversion processes which are quite different from SECI theory are identified which makes upfor the neglect of the external knowledge input. Second, this research proves that it is the abstract explicit knowledge rather than personal tacit knowledgethat is the form of knowledge with maximized value, which is different from the Nonaka’s exaggeration of therole of individual tacit knowledge mystification of the collaborative work. Third, in the context of inter-organization, the knowledge creation process begins with the conversion ofexplicit knowledge rather than tacit knowledge. This result is consistent with research results presented byGourlay (2006) . The present research effort has several possible limitations. First, the generalizability of the results may belimited because the GASP theory is proposed based on a single case though it is a theoretical sample. It is notenough to prove that all the other U-I collaboration will follow this 7 stages identically, let alone to takeaccount of the additional variables of industries, firm scales. Second, the case chosen is a project-basedcollaboration 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 todifferent characteristics of knowledge transformation. In this present study, the a priori framework has been discussed on the basis of one case study only. Sinceat this stage the results are exploratory, there is clearly a strong need to test the framework further with othercase studies, such as sampling cases from different industries and of different firm scales. However, there aredoubts about the relevance of quantitative measures concerning research in cross-organizational knowledgecreation, because of the decisive role of the tacit knowledge component. In term with the different cooperationapproaches, some potential moderating factors may be explored to expend the GASP theory. In addition, toidentify the influence factors in the various stages of Knowledge creation process, further empirical researchesare needed.Reference1. Argyris, C. and D.A.Schön; “Organizational learning,” Reading, MA, Addison –Wesley, 1978.2. Bereiter, C. “Education and mind; in the knowledge age,” in Mahwah, NJ and London: Lawrence Erlbaum Associates, 2002. 14
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