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An empirical study on knowledge integration, technology innovation and experimental practice
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Expert Systems with Applications Expert Systems with Applications 35 (2008) 177–186 www.elsevier.com/locate/eswa
An empirical study on knowledge integration, technology innovation and experimental practice Hsu-Fang Hung b
a,*
, Hsing-Pei Kao a, Yee-Yeen Chu
b
a Institute of Industrial Management, National Central University, No. 300, Jungda Road, Jhongli City, Taoyuan 320, Taiwan, ROC Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 300, Taiwan, ROC
Abstract In the global market, inter-firm collaborative product development has become an increasingly significant business strategy for enhanced product competitiveness. Engineering knowledge is a key asset for technology-based enterprises to successfully develop new products and processes. Experimental practice is a crucial process for knowledge integration and technology innovation. This research explores this in inter-firm collaborative product development through experimental practice. We conducted a series of in-depth case studies to investigate the patterns of knowledge integration in the collaborative development of system-on-a-chip (SoC) by semiconductor firms. Our studies focused on the central interactive process for engineering applications and experimental practice to enhance knowledge integration and technology innovation for rapid development. Furthermore, we identified factors critical for experimental practice in effective engineering knowledge integration and innovation. 2007 Elsevier Ltd. All rights reserved. Keywords: Knowledge integration; Technology innovation; Experimental practice; Inter-firm collaboration; Collaborative product development
1. Introduction Developing a set of successful and profitable products and processes are key to the success of technology-based enterprises. Effective product and process development depends on the integration of a variety of specialized capabilities, strong functional groups with interdisciplinary teams and multiple progressive pressures (Nellore & Balachandra, 2001). New product development (NPD) is an important and complex business process. It involves cross-function integration, a complicated interdisciplinary activity that requires many knowledge inputs to generate a robust product solution in a time-competitive environment. Compared with other forms of cross-functional integration, research and development (R&D) integration of * Corresponding author. Present address: Macronix International Co., Ltd. (MS420), No. 16, Li-Hsin Road, Science Park, Hsinchu 300, Taiwan, ROC. Tel.: +886 3 5786688x78306; fax: +886 3 5779590. E-mail address: [email protected] (H.-F. Hung).
0957-4174/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.06.017
knowledge from past projects contributes most to variations in the length of the product development cycle (Sherman, Souder, & Jenssen, 2000). Technical knowledge is the key asset for product development by technology-based enterprises. For example, the effective utilization and application of this knowledge can help to generate feasible design alternatives and assist the decision-making process, factors crucial for successfully developing a project (Hicks, Culley, Allen, & Mullineux, 2002). Thus, successful NPD depends on knowledge integration capabilities to create appropriate technologies for the full spectrum of project management activities. Most of these capabilities result from long-term investment and knowledge accumulation within firms. Knowledge acquisition strategies can help to reduce the cost and risk of technology and market development, shorten time to market, and exploit scale economies (Tidd & Izumimoto, 2002). Collaboration is an aggressive strategy for knowledge acquisition; it is based on the use of ‘‘complementary
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assets’’ (Teece, 1986), and encompasses a wide range of activities across the product life cycle. A collaborative product development scheme could be regarded as an innovation network, the efficient function of which is closely related to the dynamic interactions within and between participating firms (Lawton Smith & Dickson, 2003). The external alliances and networks for innovation have been analyzed in terms of cost savings, control of technological uncertainties, and knowledge acquisition from external sources (Coles, Harris, & Dickson, 2003; Ernst & Kim, 2002). Thus, many NPD activities involve inter-firm collaboration to achieve knowledge synergy benefits (Parker, 2000). Therefore, enterprises should be directed to integrate this with all aspects of business activity, including customers and suppliers, covering all phases of the product lifecycle. Engineering knowledge has been considered a valuable strategic capital asset that can enhance proprietary competitive advantage. However, unlike a packaged commodity, engineering application knowledge cannot be readily acquired and used (Choi & Lee, 2002; Henriksen, 2001). Most engineers constantly confront new problems and challenges. External and internal pools of knowledge can be utilized to resolve problems only through an internalization process, turning externalized knowledge into internalized knowledge before solving problems. Problem-solving practice is related to knowing the sources of the problem and using pertinent knowledge via engineering experiments. It is a critical determinant of knowledge integration and internalization that is necessary for technology innovation (Thomke, von Hippel, & Franke, 1998). Through experimental problem-solving, knowledge integration and technology innovation are performed, allowing efficient NPD. Our research explores knowledge integration and technology innovation in inter-firm collaborative product development (ICPD) via systematic experimental practice. We address the research questions: ‘‘How can experimental practice enhance knowledge integration and technology innovation?’’ and ‘‘What are the critical factors that influence experimental practice?’’ Our research involved a series of in-depth case studies, which included five embedded ICPD projects in a Taiwanese semiconductor company. We focused on the central interactive process related to experimental practice that can enhance knowledge integration and technology innovation. We examined projects that represent highly influential experimental practices for knowledge integration and technology innovation. Furthermore, we investigated the critical factors that can influence experimental practice. Our results could improve understanding of crucial factors for influencing product and process development performance. 2. Background and literature review The concept of integration is defined as ‘‘inter-organizational and cross-functional procedures’’, which focuses on
two attributes: interaction and collaboration (Kahn, 1996). Interaction emphasizes the use and exchange of communication between functional units. Collaboration focuses on the collective work across departments. Grant (1996) developed the theory of knowledge integration to synthesize earlier knowledge management research, as he noted, ‘‘the primary role of the firm, and the essence of organizational capability, is the integration of knowledge’’. An organization’s knowledge integration capability is determined by two critical mechanisms: direction and organizational routines. Direction enables communication between specialists by codifying tacit knowledge into explicit rules. Organizational routines can reduce the need for communicating explicit knowledge (Grant, 1996). The existing internal capabilities of firms and their interaction with external knowledge sources will affect their level of innovative ability (Caloghirou, Kastelli, & Tsakanikas, 2004; Cohen & Levinthal, 1990; Lin, Tan, & Chang, 2002). When the knowledge domains within the different organizations are incongruent, the result of cluster and collaboration will increase the efficiency of knowledge utilization. Therefore, inter-firm collaboration offers riskspreading benefits when there is uncertainty over future knowledge needs (Dayasindhu, 2002; Nightingale, 2000; Parker, 2000). The efficiency of knowledge integration is crucial for successful inter-firm collaboration. Janczak (2002) analyzed the process model of knowledge integration within the organization into three stages: (1) awareness, (2) exploring versus exploiting knowledge, and (3) codifying and assessing results. Morosini (2004) argued that both the degree of knowledge integration between an industrial cluster’s agents and the scope of their economic activities, are critical dimensions behind their economic performance. Therefore, it is important to explore the processes of knowledge integration within inter-firm collaborative projects. Under the differentiation and integration business strategies, an enterprise’s competitiveness relies on the diversity and strategic value of unique knowledge, as well as on the organization’s capability to integrate that knowledge in an efficient manner (Huang & Newell, 2003). Ravasi and Verona (2001) argued that three structural properties of the new organization emerged as the cornerstones of the knowledge integration process: multi-polarity, fluidity and interconnectedness. They showed how these properties enhance the effectiveness, efficiency and flexibility of knowledge integration processes. They are in accord with Grant (1996) who argued that an organization’s competitiveness derived from knowledge integration is determined by three factors: the efficiency, scope and flexibility of integration. Although technical knowledge is an important asset for enterprises, it cannot be used for solving problems directly. It must be represented and embedded in a concrete form, as procedures, rules and recipes, and recognized as practical technologies that can be used to resolve real problems (Choi & Lee, 2002; Dayasindhu, 2002; Henriksen, 2001). Therefore, technology innovation is represented as the
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achievement of knowledge integration, and is often generated through problem-solving (Fong, 2003; Koskinen & Vanharanta, 2002; Sheremata, 2002). Thomke et al. (1998) developed a conceptual framework of new product development as a process of finding and solving problems. NPD project teams should both acquire and integrate this ‘‘knowledge’’ to solve the problems that keep them from their goals; how the teams find problems also matters. ICPD projects that combine practices from participating firms to implement both knowledge acquisition and integration could increase their chances to meet product goals of quality, delivery and cost. In the knowledge integration and technology innovation process, engineering experimentation plays a fundamental and significant role at the firm level. With reference to research-intensive industries, D’Adderio (2001) established a paradigm for knowledge management, under which experimentation as a form of problem-solving is fundamental to technology innovation. As described by Thomke et al. (1998), the general nature of the trial-and-error problem-solving process and strategies of experimentation are significant for new product development. The rapid progress being made in problem-solving methods and the impact on such progress could improve the competitive position of adopting firms. West and Iansiti (2003) argued that two organizational mechanisms support innovation and retention of knowledge: experience and experimentation. He also established the correlation of the use of these mechanisms with R&D performance. Practice embedded within the organization, past integration experience and social capital play a key role in shaping the level of coordination that influences the efficiency and scope of knowledge integration (Huang & Newell, 2003; Robey, Khoo, & Powers, 2000; Shin, Holden, & Schmidt, 2001). The R&D knowledge outputs are ‘‘self-created’’ achievements with the aid of specialized problem-solving processes. They involve experimental design, experimental conditions or even the nature of the desired solution (Thomke et al., 1998). Experimentation generates new kinds of organizational capabilities that help to create ‘‘requisite variety’’ in products and processes, as well as to establish a positive cycle of improvement. They also guard against core rigidities by introducing new sources of knowledge, new channels of information and new methods for solving problems (D’Adderio, 2001). Through experimental problem-solving practice, the process of knowledge integration will be enhanced, and lead to productive technology innovation. 3. Research framework and case background 3.1. Research framework From the above discussion, we can see the importance of knowledge integration, technology innovation and experimental practice. However, recent research fails to investigate neither the linkage between these three, nor
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the dimensions of and factors influential in experimental practice. Therefore, we would like to address the research questions: ‘‘How can experimental practice enhance knowledge integration and technology innovation?’’ and ‘‘What are the critical factors that can influence experimental practice?’’ An exploratory case study approach was chosen since it enables in-depth data collection and a complete understanding of contextual factors. It is best suited to empirical inquiry that investigates bounded contemporary phenomena within a real life context (Creswell, 1997; Yin, 1994). This research was based on many years of experience working in the industry, and information collected via formal and informal interviews with managers of the selected projects across the firms. A series of in-depth case interviews was conducted to explore the research issues. The study focused on five internationally collaborative projects in advanced IC (integrated circuit) product development over seven years in a Taiwanese semiconductor company. In these projects, the processes of knowledge integration and technology innovation during the inter-firm collaborative product development were investigated, across the design and manufacture phases. We then analyzed our information related to experimental practices for problem-solving and synthesized the process of knowledge integration and technology innovation. The factors crucial to experimental practice, as well as their influences, were investigated and cross-examined to ensure the validity of our findings. 3.2. Case background and data collection Over more than 50 years of development, the semiconductor industry has continued to evolve rapidly. With the accelerated development of both technology and market, even competing firms may need to collaborate on R&D in an attempt to control costs and risks. Rapid improvement of IC technology has allowed whole electronic systems to be encoded on a single silicon chip containing, e.g., application-specific logic, analog interface, memory block, processor core and more. The heterogeneous system IC is usually referred to as SoC (system-on-a-chip). With shorter time to market and higher performance characteristics in a modern IC development environment, SoC technology holds the key to numerous complex applications in electronics, consumers, communications and so on by enabling high-performance, embedded processor solutions on a low-cost single chip (Lahtinen, Kuusilinna, Kangas, & Hamalainen, 2002; Rajsuman, 2000). Individual companies usually own these specific technologies; therefore SoC product development typically involves inter-firm collaboration for technology synergy. Established in 1989, Macronix International Co., Ltd. (referred to here as MCo) is an integrated device manufacturer in the semiconductor industry, located in the Hsinchu Science Park, Taiwan. It designs and manufactures innovative IC products. It grew from 28 employees in 1989 to
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about 3200 in 2005, with revenue of US $715 million. MCo was the largest non-volatile memory company in Taiwan and ninth largest worldwide in 2005. Using their own flash technology, MCo seeks to attract global electronic system companies to develop advanced IC product collaboratively. Our research discussed representative MCo NPD projects that involved international collaboration. Five embedded projects are included in this case study. Table 1 summarizes some of the key features of the projects. MCo collaborated with several international companies, three American and two Japanese, in the cases studied. In each project, an inter-firm development team was formed, and a weekly development meeting was held via phone or video conference, while email and telephone were frequently used. For example, Fig. 1 shows the team organization of the ATDi project and the linkages between team members. A third party from Silicon Valley, NTC, with know-how in pulse width modulator technology, joined the development team to design the integrated system product. In this research, 18 interviews over five projects were carried out with project managers, R&D managers and engineering managers. The sample questions raised in investigative interviews were listed as follows, and the outcomes of cross-project analysis are presented in the next section.
ATDi Dept. of Marketing
Dept. of Product R&D
Dept. of Engineering
3rd party NTC Dept. of Product R&D
Product Development Meeting
MCo Dept. of Marketing
Dept. of Product R&D
Dept. of Product Eng.
SOC Business Unit
Dept. of Product R&D
Dept. of CAD
Dept. of Integration Eng.
Dept. of Testing Eng.
Memory Business Unit
LSI Design Center
Fabrication Manufacture Center
Backend Operation Center
: Relationship within project team : Relationship between functional units
Fig. 1. The project team organization and the linkage among ATDi project team.
4. Analysis and discussion
– Contrasting with general project, what is the key process in the ICPD project? – Based on the experience of ICPD project, how can improve the efficiency of product development? – In the ICPD project, what is the relationship between knowledge integration, technology innovation and experimental practice? – Can experimental practice enhance the knowledge integration and technology innovation? How can it do? – What are the major issues of experimental practice? – What are the critical factors that influence the experimental practice? – What is the relationship between those critical factors? – How these critical factors influence the experimental practice?
4.1. A process model of knowledge integration in the prototyping of SoC From this empirical study of intensive inter-firm collaboration projects, the process of knowledge integration and technology innovation were investigated. In general, project and product targets were accomplished through a process centered on experimental cycles, covering product design and manufacture phases. Via a series of interviews with managers of the project teams, the key knowledge elements of these projects were summarized (Table 2), including knowledge integration, experimental practice and technology innovation. Based on the analysis of process routines and rules across the projects, we derive a reference process model of knowledge integration, and identify
Table 1 Key features of the studied projects Collaborative partner
Phil-MCU
ATDi
RNS
F-Semicon
ZiT
Country Project nature
US Customer-joined collaborative product development 1997–2000 8-bit MCU 64 KB emflash ICP structure
US Customer-joined collaborative product development 1999–2002 24-bit DSP 16 KB hispeed em-flash 10-bit ADC 2-Ch PWM 0.4 lm embedded flash
Japan Technology transferbased collaborative product development 2001–2003 128 Mb hiperformance DINOR flash
Japan Customer-joined collaborative product development 2002–2004 8-bit MCU 32 KB Lopower em-flash 12bit ADC 0.35 lm embedded flash
US Supplier-joined collaborative product development 2003–2005 16-bit MCU 64 KB hiendurance em-flash hispeed ADC 0.35 lm embedded flash
Duration Product features Technology
0.5 lm embedded flash
0.15 lm DINOR flash
Table 2 Elements of knowledge integration in the studied projects Type of knowledge
Existing knowledge
Knowledge from partner
Knowledge from third party
Experiment iterations
Duration of experiment (months)
Created technology
Phil-MCU
Product design knowledge
Standard flash design
None
2
3
ICP patentb
Product design knowledge
Standard flash design
MCU system application MCU interface
None
2
2
Manufacture knowledge
Standard flash testing
MCU interface
Novel testing knowledge from vendor
3
2.5
Em-flash design method Em-flash testing method
Product design knowledge
Em-flash designa
DSP inter face
4
4
Manufacture knowledge
Digital IC testing
ADC design and testing
High-speed flash from memory BU ADC testing knowledge from vendor
5
4.5
Product design knowledge
Standard flash design
None
3
5
Manufacture knowledge
Standard flash process
DINOR flash structure DINOR flash device and technology
Process knowledge from ERSO
2
7
Product design knowledge
Em-flash designa
3
Em-flash testinga
Low-power flash from memory BU Novel testing knowledge from vendor
2
Manufacture knowledge
Low-power control logic 8-bit MCU interface
4
6
Product design knowledge
Em-flash designa
16-bit MCU interface
Hi-endurance flash from memory BU
2
2.5
ATDi
RNS
F-Semicon
ZiT a b
High-speed em-flash design Low-cost ADC testing DINOR flash design DINOR flash process Low-power em-flash design Em-flash accelerative testing Hi-endurance em-flash design
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Project
Created knowledge from previous projects. Processor with embedded in-circuit programming structure, November 21, 2000, US patent no. 6,151,657.
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experimental practice influencing the efficacy of knowledge integration and technology innovation. In contrast to previous models of the knowledge process (Janczak, 2002; West & Iansiti, 2003), this research emphasizes the interdependence and interactive nature of experimental practice in the knowledge integration process. The process of knowledge integration is triggered by new requirements, e.g., new product features or testing methods that probably cannot be resolved by existing knowledge; the situation often occurs in new product development. First, the project team needs to analyze the problem and identify new knowledge requirements to solve it. Perhaps several existing or new knowledge items are recognized as relating to the solution. As observed in the Phil-MCU project, the customer required a new product feature, in-circuit programming. Existing knowledge could not satisfy this requirement. The project team, through discussions with the customer, recognized the need for new knowledge of system applications employing existing knowledge of the flash interface relating to the technology of in-circuit programming. Second, team members need to acquire the knowledge through all possible channels. As a key observation of these projects, knowledge of analog-to-digital converter (ADC) testing was acquired from the testing equipment vendor. Then, based on existing understanding, the project team would combine several streams of those diverse knowledge sources and generate a potential solution to problems confronted. For instance, in the F-Semicon project, low-power control logic knowledge (including low-power detector and charge booster) and flash knowledge were combined in a potential solution for low-voltage embedded flash design. As a project progressed and moved to the next step, an experimental plan would be proposed for the potential solution, including experimental design and resource allocation. Through the execution of the experiment, the potential solution would be examined to see if it was validated for the problem at hand or quality requirements. If not validated, engineering effort would be directed to return to the previous step to re-identify the sources of the problem and the knowledge required. Given the acquisition of additional knowledge and the generation of new solutions, the project would proceed, followed by a new set of experiments to conduct the validation routines. After the iterations of experimental practice, a final validated solution would be confirmed with the problem solved and the knowledge integrated. As we observed in this study, the high-speed embedded flash design of ATDi project was completed in four cycles of prototype verification, which was accomplished by performing design simulation, fabrication experiments and testing analysis. The cycle lasted for four months. The embedded flash accelerative testing method was adopted through four iterations of test experimentation and analysis for six months. In the meantime, the new knowledge was internalized into team members’ tacit knowledge through intensive experimental cycles, and required technology was created
to present as explicit knowledge, in the form of patents, methods, procedures, recipes and rules. After the required knowledge was integrated, the process restarted for the next iteration until the next problem or requirement was resolved. In the product development process, the knowledge integration and technology innovation processes would repeat until project targets were achieved. Through the knowledge integration process, not only is the confronted problem solved but also organization capability is enhanced, and opportunities are also created to reach the new technology market. The identification of a process model of knowledge integration is summarized and illustrated in Fig. 2. As we observed in this study, the experimental practice would expand the cycles of the knowledge integration process, from days and weeks, to months. Improving the efficiency of experimental practice significantly enhanced knowledge integration as well as product development.
New problem or requirement raised
Identify problem and required knowledge
Internal existing knowledge
Acquiring and combining knowledge External new knowledge Design
Experimental practice Validate
Execute
Achievement of knowledge integration tacit
explicit
Knowledge internalization
Technology innovation
Enhance organization capability
Reach new technology market
Fig. 2. A basic process of knowledge integration via experimental practice.
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4.2. Factors and influences of experimental practice The other research issue relates to the critical factors in the iterative cycles of experimental practice. Based on the interviews, many contributing factors are screened and extracted from the common experimental routines, rules and procedural artifacts. In total, seven factors were identified, and they are discussed as follows: • Field expertise: Basic knowledge of the specialized field was found to be essential across projects. Field expertise built a knowledge base for problem identification as well as for experiment performance. In this case study, four out of five projects needed novel, embedded flash design technologies (high-speed, low-power and high-endurance). R&D teams utilized existing flash memory design technology to perform the experiment to achieve the desired performance level. Experience from previous projects can help solve the current problem efficiently. • Extensible capacity: Because knowledge integration involves new and diverse knowledge, the extensible capability can expand the existing knowledge base by searching, accessing and absorbing new and diverse knowledge to generate additional feasible solutions. Thus, extensible capacity is a key to consolidate knowledge in experimental practice. As we observed in the ATDi project, a new ADC testing method was needed due to cost concerns. The project team Testing engineer was not versed in ADC testing. Based on their knowledge of IC testing, new ADC design and testing knowledge were acquired from ATDi and the test equipment vendor, new knowledge was absorbed and recombined to generate feasible solutions in experimental practice. • Practical experience: Knowledge implementation also needs practical experience; it is the key factor for the implementation of experimental practice. As indicated in the series of cases studied, the practical experience of solution implementation from past projects is a significant factor, which can accumulate firms’ prior capability level to extend experiential and experimental know-how. For example, the design and verification of high-endurance flash in the ZiT project were performed more efficiently based on previous projects’ practical experience of rules and routines. • Knowledge channel: New knowledge is very important for experimental practice as well as knowledge integration. An adaptable knowledge channel can help the project team search, access and acquire new knowledge efficiently and rapidly. As we observed in the RNS project, a mix of knowledge channels and media helped acquire novel failure analysis technology from the Electronics Research and Service Organization of the Industrial Technology Research Institute, Taiwan. Based on this technology transfer process, a series of experiments were planned and performed, then the DINOR flash process technology was successfully developed.
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• Experimentation strategy: Under time and resource limitations, the experimental strategy is a key consideration for experiment planning. It is related to resource allocation, environment, procedures and methods of experimentation. In the case study, we observed the experimentation strategy providing overall planning for experimental practice that helped effectively plan, execute and validate the experiments. Replicability, imitability and appropriability are important considerations here. • Communication platform: Because interaction is a key concept for integration, an adaptable common platform (including media) can help communication and knowledge sharing, and is a key contributor to the success of experimental routines and practice. Both formal and informal routines for product review meetings can be viewed as an efficient communication platform for experimental practice as well as project performance. • Project team: Because project organization is a key factor for project management performance and individual interaction, the structure and culture of the project team play an important role in experimental practice execution. As we observed in the case study, the structure of the project team and involvement of functional departments played a key role in conducting experimental practice, which resulted in project success. Supportive organization culture can help smooth execution of the process. Linking the case findings, we summarize the critical factors and classify them under two headings: capability and mechanism. Capability relates to specialized project field techniques, which provide the technology base of experimental practice, including field expertise, extensible capacity and practical experience. Mechanism relates to managing practices, which includes knowledge channel, experimentation strategy, communication platform and project team. Following Grant’s (1996) model of knowledge integration, we analyzed the key elements and potential influences of experimental practice on knowledge integration. We inferred three major issues for experimental practice execution: flexibility, scope and appropriability. Fig. 3 provides an overall perspective of the linkages and influences of experimental practice on knowledge integration and technology innovation. We discuss this as follows: • Flexibility is concerned with the level of flexibility of planning, execution and validation. In general, the flexible nature of the process will affect the result of experimental practice positively by allowing for easy and low-cost changes in the process. Flexibility is strongly associated with the mechanisms, i.e., more efficient communication, resource allocation and teamwork can enhance the flexibility of experimental practice. In the ATDi project, the project team structure, communication platform and experimentation strategy enhanced the flexibility of experimental practice, i.e., the experi-
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Knowledge integration
Efficiency of experimental practice
Flexibility of experimental practice
Scope of experimental practice
Mechanism
Appropriability of experimental practice
Capability
Knowledge channel
Field expertise
Experimentation strategy
Extensible capacity
Communication platform
Practical experience
Project team : Strong linkage or influence : Weak linkage or influence
Fig. 3. The critical factors influencing experimental practice and knowledge integration.
mental plan and its execution could be performed with new concepts and options with dynamic alignments. Information was collected and passed within team members in real time, allowing corrective actions to be rapidly decided and executed. • Scope refers to the level of knowledge covered and organization underlying the experimental practice. Broader scope in the planning, executing and verifying process allows a more comprehensive and complete coverage of the integration and application of the knowledge. Scope can be affected by both capability and mechanism; a higher level of capability and mechanism will induce broader scope and level of experimental practice. In the RNS project, the knowledge channel helped access and acquire external knowledge, and then extensible capacity of the project team enabled knowledge internalization. A broader scope in general will help produce more robust experimental results, more depth in the new technology created, and a better capacity building effort that enhances the firm’s competitiveness. • The replicability, imitability and appropriability of experimental practice are related to capturing the opportunities and benefits of the experimental knowledge. Teece (2000a, 2000b) identified several dimensions for the appropriability of the returns to a firm’s intellectual property: the nature of technology, strength of the property rights regime, complementary assets, ease of replication and ease of imitation. Similarly, in the cases of collaborative experimental practice discussed here, the replicability and appropriability of the experimental practice can influence its efficiency due to the ease of embedding the knowledge in the product or service the next time around. It also appears that replicability and
appropriability are affected by the mechanism and capability of the experimental practice. For example, a wellrounded knowledge channel and communication platform help replication and transfer of technology, while extensive field expertise and experience help strengthen property rights. • Efficiency of experimental practice is related to the spending of time and other resources; i.e., the less time and resource spending for a given outcome signifies higher experimental practice efficiency. It will further enhance knowledge integration as well as technology innovation. In this case study, all projects showed that the efficiency of experimental practice was strongly dependent upon capability, as well as the mechanism. It was evident in our cases that the higher the capability level of a project team helped preserve the key characteristics of experimental practice: flexibility, scope and appropriability, each of which leading to better practice efficiency. That is, the experiment can be performed more flexibly, broadly and appropriately, to the benefit of improved efficiency of experimental practice. And the efficiency of experimental practice can lead to effective knowledge integration and technology innovation as discussed earlier. This case study has provided an overall depiction of the mechanisms and characteristics that lead to efficient experimental practice for knowledge integration and technology innovation, which has not been discussed extensively in current research. This result is based on the analysis of a group of SoC prototype development projects and the observation of critical factors (i.e., the various factors classified as mechanism or capability), which may vary from case to case. Nevertheless, it is our belief that the case results show that an organization can explore and understand the factors critical to their knowledge integration and technology innovation. Our study has highlighted the importance of flexibility, scope and appropriability in collaborative projects. Not only technical capability but also the managerial mechanism is an important driver of effective knowledge integration. The competence of the team engaging in knowledge integration and technology innovation does not depend only on individual ability, but also on the involvement of team linkage and mechanisms, and the institutional characteristics fostering flexibility, scope and appropriability. For example, the availability of a knowledge channel could be a key factor for experimental practice and knowledge integration. By improving the flexibility of the practice in the available channel, valuable knowledge can be acquired expeditiously, and then experimental practice can be performed efficiently, thus improving new product development. 5. Conclusions In this exploratory research, we have identified the process model of knowledge integration and experimental
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practice, as well as the factors that could help improve experimental practice and enhance knowledge integration and technology innovation. These results were derived from an in-depth case study of five inter-firm collaboration projects of a Taiwanese semiconductor company. The theoretical implications of this research are that we have identified a combination of technical and managerial dimensions influencing the efficiency of experimental practice in inter-firm collaboration projects. This is significant due to the heavy use of experimental practice for knowledge integration in NPD. This research shows that both technical capability and the managerial mechanism have significant influences on experimental practice and knowledge integration. The factors critical for capability and mechanism can be screened and extricated using detailed comparative case studies. Improving the capacity level of these factors can contribute to the flexibility, scope and appropriability of the practice and thus to the efficiency of experimental practice and knowledge integration in inter-firm collaboration projects. Our study provides a practical way to address research issues related to experimental practice and knowledge integration. Contributions made by our research are reflected not only in formulating a systematic approach to the examination of the knowledge integration process in the context of inter-firm collaboration projects, but also in synthesizing the empirical findings into conceptions of management concerns and practice. This result could help the understanding of the factors critical to improvement of the performance of product and technology development, and provide business insights for product and technology strategy from a practical point of view. The exploratory approach of the study also gave rise to new questions. Further research is needed to test the proposed process in a number of areas of knowledge integration and experimental practice. First, to generalize the findings, the model described here may require further comparative studies along with an analysis of the whole collaborative network, not just from the project or firm levels, which may lead to broader insights and implications. Second, we need to know how to effectively utilize the information and communication technology infrastructure to enhance the efficiency of experimental practice and knowledge integration. This is a challenging issue as the rules of game may change in the evolving complex knowledge network environment. Third, How to manage intellectual capital and maintain the balance of supply and demand for the efficient execution of inter-firm collaborative projects, while protecting property rights? This issue needs additional work on the strategic perspective and the institutional environment and requires synergy for interdisciplinary understanding and achievement. We believe that the results of our study can be used as a starting point for further research, which may deepen the understanding of what makes knowledge integration in collaborative product development a success or a failure.
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Acknowledgement The authors thank Macronix International Co., Ltd., Taiwan for providing practical data and support. References Caloghirou, Y., Kastelli, I., & Tsakanikas, A. (2004). Internal capabilities and external knowledge sources: Complements or substitutes for innovative performance? Technovation, 24, 29–39. Choi, B., & Lee, H. (2002). Knowledge management strategy and its link to knowledge creation process. Expert Systems with Applications, 23, 173–187. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity. A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152. Coles, A.-M., Harris, L., & Dickson, K. (2003). Testing goodwill: Conflict and cooperation in new product development networks. International Journal of Technology Management, 25, 51–64. Creswell, J. W. (1997). Qualitative inquiry and research design: Choosing among five traditions. London: Sage Publications. D’Adderio, L. (2001). Crafting the virtual prototype: How firms integrate knowledge and capabilities across organisational boundaries. Research Policy, 30, 1409–1424. Dayasindhu, N. (2002). Embeddedness, knowledge transfer, industry clusters and global competitiveness: A case study of the Indian software industry. Technovation, 22, 551–560. Ernst, D., & Kim, L. (2002). Global production networks, and knowledge diffusion, and local capability formation. Research Policy, 31, 1417–1429. Fong, P. S. W. (2003). Knowledge creation in multidisciplinary project teams: Empirical study of the processes and their dynamic interrelationships. International Journal of Project Management, 21, 479–486. Grant, R. (1996). Prospering in dynamically-competitive environment: Organizational capability as knowledge integration. Organization Science, 7, 375–387. Henriksen, L. B. (2001). Knowledge management and engineering practices: The case of knowledge management, problem solving and engineering practices. Technovation, 21, 595–603. Hicks, B. J., Culley, S. J., Allen, R. D., & Mullineux, G. (2002). A framework for the requirements of capturing, sorting and reusing information and knowledge in engineering design. International Journal of Information Management, 22, 263–280. Huang, J. C., & Newell, S. (2003). Knowledge integration processes and dynamics within the context of cross-functional projects. International Journal of Project Management, 21, 167–176. Janczak, S. (2002). How middle managers contribute to organizational knowledge integration. In Paper presented at European academy of management 2nd annual conference, Stockholm, May 9–11. Kahn, K. B. (1996). Interdepartmental integration: A definition with implications for product development performance. Journal of Product Innovation Management, 13, 137–151. Koskinen, K. U., & Vanharanta, H. (2002). The role of tacit knowledge in innovation processes of small technology companies. International Journal of Production Economics, 80, 57–64. Lahtinen, V., Kuusilinna, K., Kangas, T., & Hamalainen, T. (2002). Interconnection scheme for continuous-media systems-on-a-chip. Microprocessors and Microsystems, 26, 123–138. Lawton Smith, H., & Dickson, K. (2003). Geo-cultural influences and critical factors in inter-firm collaboration. International Journal of Technology Management, 25, 34–50. Lin, C., Tan, S., & Chang, S. (2002). The critical factors for technology absorptive capacity. Industrial Management and Data Systems, 102, 300–308. Morosini, P. (2004). Industrial clusters, knowledge integration and performance. World Development, 32, 305–326.
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Nellore, R., & Balachandra, R. (2001). Factors influencing success in integrated product development (IPD) projects. IEEE Transactions on Engineering Management, 48, 164–174. Nightingale, P. (2000). The product-process-organisation relationship in complex development projects. Research Policy, 29, 913–930. Parker, H. (2000). Interfirm collaboration and the new product development process. Industrial Management and Data Systems, 100, 255–260. Rajsuman, R. (2000). System-on-a-chip: Design and test. Boston: Artech House. Ravasi, D., & Verona, G. (2001). Organising the process of knowledge integration: The benefits of structural ambiguity. Scadinavian Journal of Management, 11, 41–66. Robey, D., Khoo, H. M., & Powers, C. (2000). Situated learning in crossfunctional virtual teams. IEEE Transactions on Professional Communication, 43, 51–66. Sheremata, W. A. (2002). Finding and solving problems in software new product development. Journal of Product Innovation Management, 19, 144–158. Sherman, J. D., Souder, W. E., & Jenssen, S. A. (2000). Differential effects of the primary forms of cross functional integration on product development cycle time. Journal of Product Innovation Management, 17, 257–267.
Shin, M., Holden, T., & Schmidt, R. A. (2001). From knowledge theory to management practice: Towards an integrated approach. Information Processing and Management, 37, 335–355. Teece, D. J. (1986). Profiting from technological innovation: Implications for interaction, collaborative licensing and public policy. Research Policy, 16, 285–305. Teece, D. J. (2000a). Managing intellectual capital: Organizational, strategic, and policy dimensions. Oxford: Oxford University Press. Teece, D. J. (2000b). Strategies for managing knowledge assets: The role of firm structure and industrial context. Long Range Planning, 33, 35–54. Thomke, S., von Hippel, E., & Franke, R. (1998). Modes of experimentation: An innovation process – and competitive – variable. Research Policy, 27, 315–332. Tidd, J., & Izumimoto, Y. (2002). Knowledge exchange and learning through international joint ventures: An Anglo-Japanese experience. Technovation, 22, 137–145. West, J., & Iansiti, M. (2003). Experience, experimentation, and the accumulation of knowledge: The evolution of R&D in the semiconductor industry. Research Policy, 32, 809–825. Yin, R. K. (1994). Case study research: Design and methods. London: Sage Publications.
Expert Systems with Applications Expert Systems with Applications 35 (2008) 177–186 www.elsevier.com/locate/eswa
An empirical study on knowledge integration, technology innovation and experimental practice Hsu-Fang Hung b
a,*
, Hsing-Pei Kao a, Yee-Yeen Chu
b
a Institute of Industrial Management, National Central University, No. 300, Jungda Road, Jhongli City, Taoyuan 320, Taiwan, ROC Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 300, Taiwan, ROC
Abstract In the global market, inter-firm collaborative product development has become an increasingly significant business strategy for enhanced product competitiveness. Engineering knowledge is a key asset for technology-based enterprises to successfully develop new products and processes. Experimental practice is a crucial process for knowledge integration and technology innovation. This research explores this in inter-firm collaborative product development through experimental practice. We conducted a series of in-depth case studies to investigate the patterns of knowledge integration in the collaborative development of system-on-a-chip (SoC) by semiconductor firms. Our studies focused on the central interactive process for engineering applications and experimental practice to enhance knowledge integration and technology innovation for rapid development. Furthermore, we identified factors critical for experimental practice in effective engineering knowledge integration and innovation. 2007 Elsevier Ltd. All rights reserved. Keywords: Knowledge integration; Technology innovation; Experimental practice; Inter-firm collaboration; Collaborative product development
1. Introduction Developing a set of successful and profitable products and processes are key to the success of technology-based enterprises. Effective product and process development depends on the integration of a variety of specialized capabilities, strong functional groups with interdisciplinary teams and multiple progressive pressures (Nellore & Balachandra, 2001). New product development (NPD) is an important and complex business process. It involves cross-function integration, a complicated interdisciplinary activity that requires many knowledge inputs to generate a robust product solution in a time-competitive environment. Compared with other forms of cross-functional integration, research and development (R&D) integration of * Corresponding author. Present address: Macronix International Co., Ltd. (MS420), No. 16, Li-Hsin Road, Science Park, Hsinchu 300, Taiwan, ROC. Tel.: +886 3 5786688x78306; fax: +886 3 5779590. E-mail address: [email protected] (H.-F. Hung).
0957-4174/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.06.017
knowledge from past projects contributes most to variations in the length of the product development cycle (Sherman, Souder, & Jenssen, 2000). Technical knowledge is the key asset for product development by technology-based enterprises. For example, the effective utilization and application of this knowledge can help to generate feasible design alternatives and assist the decision-making process, factors crucial for successfully developing a project (Hicks, Culley, Allen, & Mullineux, 2002). Thus, successful NPD depends on knowledge integration capabilities to create appropriate technologies for the full spectrum of project management activities. Most of these capabilities result from long-term investment and knowledge accumulation within firms. Knowledge acquisition strategies can help to reduce the cost and risk of technology and market development, shorten time to market, and exploit scale economies (Tidd & Izumimoto, 2002). Collaboration is an aggressive strategy for knowledge acquisition; it is based on the use of ‘‘complementary
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assets’’ (Teece, 1986), and encompasses a wide range of activities across the product life cycle. A collaborative product development scheme could be regarded as an innovation network, the efficient function of which is closely related to the dynamic interactions within and between participating firms (Lawton Smith & Dickson, 2003). The external alliances and networks for innovation have been analyzed in terms of cost savings, control of technological uncertainties, and knowledge acquisition from external sources (Coles, Harris, & Dickson, 2003; Ernst & Kim, 2002). Thus, many NPD activities involve inter-firm collaboration to achieve knowledge synergy benefits (Parker, 2000). Therefore, enterprises should be directed to integrate this with all aspects of business activity, including customers and suppliers, covering all phases of the product lifecycle. Engineering knowledge has been considered a valuable strategic capital asset that can enhance proprietary competitive advantage. However, unlike a packaged commodity, engineering application knowledge cannot be readily acquired and used (Choi & Lee, 2002; Henriksen, 2001). Most engineers constantly confront new problems and challenges. External and internal pools of knowledge can be utilized to resolve problems only through an internalization process, turning externalized knowledge into internalized knowledge before solving problems. Problem-solving practice is related to knowing the sources of the problem and using pertinent knowledge via engineering experiments. It is a critical determinant of knowledge integration and internalization that is necessary for technology innovation (Thomke, von Hippel, & Franke, 1998). Through experimental problem-solving, knowledge integration and technology innovation are performed, allowing efficient NPD. Our research explores knowledge integration and technology innovation in inter-firm collaborative product development (ICPD) via systematic experimental practice. We address the research questions: ‘‘How can experimental practice enhance knowledge integration and technology innovation?’’ and ‘‘What are the critical factors that influence experimental practice?’’ Our research involved a series of in-depth case studies, which included five embedded ICPD projects in a Taiwanese semiconductor company. We focused on the central interactive process related to experimental practice that can enhance knowledge integration and technology innovation. We examined projects that represent highly influential experimental practices for knowledge integration and technology innovation. Furthermore, we investigated the critical factors that can influence experimental practice. Our results could improve understanding of crucial factors for influencing product and process development performance. 2. Background and literature review The concept of integration is defined as ‘‘inter-organizational and cross-functional procedures’’, which focuses on
two attributes: interaction and collaboration (Kahn, 1996). Interaction emphasizes the use and exchange of communication between functional units. Collaboration focuses on the collective work across departments. Grant (1996) developed the theory of knowledge integration to synthesize earlier knowledge management research, as he noted, ‘‘the primary role of the firm, and the essence of organizational capability, is the integration of knowledge’’. An organization’s knowledge integration capability is determined by two critical mechanisms: direction and organizational routines. Direction enables communication between specialists by codifying tacit knowledge into explicit rules. Organizational routines can reduce the need for communicating explicit knowledge (Grant, 1996). The existing internal capabilities of firms and their interaction with external knowledge sources will affect their level of innovative ability (Caloghirou, Kastelli, & Tsakanikas, 2004; Cohen & Levinthal, 1990; Lin, Tan, & Chang, 2002). When the knowledge domains within the different organizations are incongruent, the result of cluster and collaboration will increase the efficiency of knowledge utilization. Therefore, inter-firm collaboration offers riskspreading benefits when there is uncertainty over future knowledge needs (Dayasindhu, 2002; Nightingale, 2000; Parker, 2000). The efficiency of knowledge integration is crucial for successful inter-firm collaboration. Janczak (2002) analyzed the process model of knowledge integration within the organization into three stages: (1) awareness, (2) exploring versus exploiting knowledge, and (3) codifying and assessing results. Morosini (2004) argued that both the degree of knowledge integration between an industrial cluster’s agents and the scope of their economic activities, are critical dimensions behind their economic performance. Therefore, it is important to explore the processes of knowledge integration within inter-firm collaborative projects. Under the differentiation and integration business strategies, an enterprise’s competitiveness relies on the diversity and strategic value of unique knowledge, as well as on the organization’s capability to integrate that knowledge in an efficient manner (Huang & Newell, 2003). Ravasi and Verona (2001) argued that three structural properties of the new organization emerged as the cornerstones of the knowledge integration process: multi-polarity, fluidity and interconnectedness. They showed how these properties enhance the effectiveness, efficiency and flexibility of knowledge integration processes. They are in accord with Grant (1996) who argued that an organization’s competitiveness derived from knowledge integration is determined by three factors: the efficiency, scope and flexibility of integration. Although technical knowledge is an important asset for enterprises, it cannot be used for solving problems directly. It must be represented and embedded in a concrete form, as procedures, rules and recipes, and recognized as practical technologies that can be used to resolve real problems (Choi & Lee, 2002; Dayasindhu, 2002; Henriksen, 2001). Therefore, technology innovation is represented as the
H.-F. Hung et al. / Expert Systems with Applications 35 (2008) 177–186
achievement of knowledge integration, and is often generated through problem-solving (Fong, 2003; Koskinen & Vanharanta, 2002; Sheremata, 2002). Thomke et al. (1998) developed a conceptual framework of new product development as a process of finding and solving problems. NPD project teams should both acquire and integrate this ‘‘knowledge’’ to solve the problems that keep them from their goals; how the teams find problems also matters. ICPD projects that combine practices from participating firms to implement both knowledge acquisition and integration could increase their chances to meet product goals of quality, delivery and cost. In the knowledge integration and technology innovation process, engineering experimentation plays a fundamental and significant role at the firm level. With reference to research-intensive industries, D’Adderio (2001) established a paradigm for knowledge management, under which experimentation as a form of problem-solving is fundamental to technology innovation. As described by Thomke et al. (1998), the general nature of the trial-and-error problem-solving process and strategies of experimentation are significant for new product development. The rapid progress being made in problem-solving methods and the impact on such progress could improve the competitive position of adopting firms. West and Iansiti (2003) argued that two organizational mechanisms support innovation and retention of knowledge: experience and experimentation. He also established the correlation of the use of these mechanisms with R&D performance. Practice embedded within the organization, past integration experience and social capital play a key role in shaping the level of coordination that influences the efficiency and scope of knowledge integration (Huang & Newell, 2003; Robey, Khoo, & Powers, 2000; Shin, Holden, & Schmidt, 2001). The R&D knowledge outputs are ‘‘self-created’’ achievements with the aid of specialized problem-solving processes. They involve experimental design, experimental conditions or even the nature of the desired solution (Thomke et al., 1998). Experimentation generates new kinds of organizational capabilities that help to create ‘‘requisite variety’’ in products and processes, as well as to establish a positive cycle of improvement. They also guard against core rigidities by introducing new sources of knowledge, new channels of information and new methods for solving problems (D’Adderio, 2001). Through experimental problem-solving practice, the process of knowledge integration will be enhanced, and lead to productive technology innovation. 3. Research framework and case background 3.1. Research framework From the above discussion, we can see the importance of knowledge integration, technology innovation and experimental practice. However, recent research fails to investigate neither the linkage between these three, nor
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the dimensions of and factors influential in experimental practice. Therefore, we would like to address the research questions: ‘‘How can experimental practice enhance knowledge integration and technology innovation?’’ and ‘‘What are the critical factors that can influence experimental practice?’’ An exploratory case study approach was chosen since it enables in-depth data collection and a complete understanding of contextual factors. It is best suited to empirical inquiry that investigates bounded contemporary phenomena within a real life context (Creswell, 1997; Yin, 1994). This research was based on many years of experience working in the industry, and information collected via formal and informal interviews with managers of the selected projects across the firms. A series of in-depth case interviews was conducted to explore the research issues. The study focused on five internationally collaborative projects in advanced IC (integrated circuit) product development over seven years in a Taiwanese semiconductor company. In these projects, the processes of knowledge integration and technology innovation during the inter-firm collaborative product development were investigated, across the design and manufacture phases. We then analyzed our information related to experimental practices for problem-solving and synthesized the process of knowledge integration and technology innovation. The factors crucial to experimental practice, as well as their influences, were investigated and cross-examined to ensure the validity of our findings. 3.2. Case background and data collection Over more than 50 years of development, the semiconductor industry has continued to evolve rapidly. With the accelerated development of both technology and market, even competing firms may need to collaborate on R&D in an attempt to control costs and risks. Rapid improvement of IC technology has allowed whole electronic systems to be encoded on a single silicon chip containing, e.g., application-specific logic, analog interface, memory block, processor core and more. The heterogeneous system IC is usually referred to as SoC (system-on-a-chip). With shorter time to market and higher performance characteristics in a modern IC development environment, SoC technology holds the key to numerous complex applications in electronics, consumers, communications and so on by enabling high-performance, embedded processor solutions on a low-cost single chip (Lahtinen, Kuusilinna, Kangas, & Hamalainen, 2002; Rajsuman, 2000). Individual companies usually own these specific technologies; therefore SoC product development typically involves inter-firm collaboration for technology synergy. Established in 1989, Macronix International Co., Ltd. (referred to here as MCo) is an integrated device manufacturer in the semiconductor industry, located in the Hsinchu Science Park, Taiwan. It designs and manufactures innovative IC products. It grew from 28 employees in 1989 to
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about 3200 in 2005, with revenue of US $715 million. MCo was the largest non-volatile memory company in Taiwan and ninth largest worldwide in 2005. Using their own flash technology, MCo seeks to attract global electronic system companies to develop advanced IC product collaboratively. Our research discussed representative MCo NPD projects that involved international collaboration. Five embedded projects are included in this case study. Table 1 summarizes some of the key features of the projects. MCo collaborated with several international companies, three American and two Japanese, in the cases studied. In each project, an inter-firm development team was formed, and a weekly development meeting was held via phone or video conference, while email and telephone were frequently used. For example, Fig. 1 shows the team organization of the ATDi project and the linkages between team members. A third party from Silicon Valley, NTC, with know-how in pulse width modulator technology, joined the development team to design the integrated system product. In this research, 18 interviews over five projects were carried out with project managers, R&D managers and engineering managers. The sample questions raised in investigative interviews were listed as follows, and the outcomes of cross-project analysis are presented in the next section.
ATDi Dept. of Marketing
Dept. of Product R&D
Dept. of Engineering
3rd party NTC Dept. of Product R&D
Product Development Meeting
MCo Dept. of Marketing
Dept. of Product R&D
Dept. of Product Eng.
SOC Business Unit
Dept. of Product R&D
Dept. of CAD
Dept. of Integration Eng.
Dept. of Testing Eng.
Memory Business Unit
LSI Design Center
Fabrication Manufacture Center
Backend Operation Center
: Relationship within project team : Relationship between functional units
Fig. 1. The project team organization and the linkage among ATDi project team.
4. Analysis and discussion
– Contrasting with general project, what is the key process in the ICPD project? – Based on the experience of ICPD project, how can improve the efficiency of product development? – In the ICPD project, what is the relationship between knowledge integration, technology innovation and experimental practice? – Can experimental practice enhance the knowledge integration and technology innovation? How can it do? – What are the major issues of experimental practice? – What are the critical factors that influence the experimental practice? – What is the relationship between those critical factors? – How these critical factors influence the experimental practice?
4.1. A process model of knowledge integration in the prototyping of SoC From this empirical study of intensive inter-firm collaboration projects, the process of knowledge integration and technology innovation were investigated. In general, project and product targets were accomplished through a process centered on experimental cycles, covering product design and manufacture phases. Via a series of interviews with managers of the project teams, the key knowledge elements of these projects were summarized (Table 2), including knowledge integration, experimental practice and technology innovation. Based on the analysis of process routines and rules across the projects, we derive a reference process model of knowledge integration, and identify
Table 1 Key features of the studied projects Collaborative partner
Phil-MCU
ATDi
RNS
F-Semicon
ZiT
Country Project nature
US Customer-joined collaborative product development 1997–2000 8-bit MCU 64 KB emflash ICP structure
US Customer-joined collaborative product development 1999–2002 24-bit DSP 16 KB hispeed em-flash 10-bit ADC 2-Ch PWM 0.4 lm embedded flash
Japan Technology transferbased collaborative product development 2001–2003 128 Mb hiperformance DINOR flash
Japan Customer-joined collaborative product development 2002–2004 8-bit MCU 32 KB Lopower em-flash 12bit ADC 0.35 lm embedded flash
US Supplier-joined collaborative product development 2003–2005 16-bit MCU 64 KB hiendurance em-flash hispeed ADC 0.35 lm embedded flash
Duration Product features Technology
0.5 lm embedded flash
0.15 lm DINOR flash
Table 2 Elements of knowledge integration in the studied projects Type of knowledge
Existing knowledge
Knowledge from partner
Knowledge from third party
Experiment iterations
Duration of experiment (months)
Created technology
Phil-MCU
Product design knowledge
Standard flash design
None
2
3
ICP patentb
Product design knowledge
Standard flash design
MCU system application MCU interface
None
2
2
Manufacture knowledge
Standard flash testing
MCU interface
Novel testing knowledge from vendor
3
2.5
Em-flash design method Em-flash testing method
Product design knowledge
Em-flash designa
DSP inter face
4
4
Manufacture knowledge
Digital IC testing
ADC design and testing
High-speed flash from memory BU ADC testing knowledge from vendor
5
4.5
Product design knowledge
Standard flash design
None
3
5
Manufacture knowledge
Standard flash process
DINOR flash structure DINOR flash device and technology
Process knowledge from ERSO
2
7
Product design knowledge
Em-flash designa
3
Em-flash testinga
Low-power flash from memory BU Novel testing knowledge from vendor
2
Manufacture knowledge
Low-power control logic 8-bit MCU interface
4
6
Product design knowledge
Em-flash designa
16-bit MCU interface
Hi-endurance flash from memory BU
2
2.5
ATDi
RNS
F-Semicon
ZiT a b
High-speed em-flash design Low-cost ADC testing DINOR flash design DINOR flash process Low-power em-flash design Em-flash accelerative testing Hi-endurance em-flash design
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Project
Created knowledge from previous projects. Processor with embedded in-circuit programming structure, November 21, 2000, US patent no. 6,151,657.
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experimental practice influencing the efficacy of knowledge integration and technology innovation. In contrast to previous models of the knowledge process (Janczak, 2002; West & Iansiti, 2003), this research emphasizes the interdependence and interactive nature of experimental practice in the knowledge integration process. The process of knowledge integration is triggered by new requirements, e.g., new product features or testing methods that probably cannot be resolved by existing knowledge; the situation often occurs in new product development. First, the project team needs to analyze the problem and identify new knowledge requirements to solve it. Perhaps several existing or new knowledge items are recognized as relating to the solution. As observed in the Phil-MCU project, the customer required a new product feature, in-circuit programming. Existing knowledge could not satisfy this requirement. The project team, through discussions with the customer, recognized the need for new knowledge of system applications employing existing knowledge of the flash interface relating to the technology of in-circuit programming. Second, team members need to acquire the knowledge through all possible channels. As a key observation of these projects, knowledge of analog-to-digital converter (ADC) testing was acquired from the testing equipment vendor. Then, based on existing understanding, the project team would combine several streams of those diverse knowledge sources and generate a potential solution to problems confronted. For instance, in the F-Semicon project, low-power control logic knowledge (including low-power detector and charge booster) and flash knowledge were combined in a potential solution for low-voltage embedded flash design. As a project progressed and moved to the next step, an experimental plan would be proposed for the potential solution, including experimental design and resource allocation. Through the execution of the experiment, the potential solution would be examined to see if it was validated for the problem at hand or quality requirements. If not validated, engineering effort would be directed to return to the previous step to re-identify the sources of the problem and the knowledge required. Given the acquisition of additional knowledge and the generation of new solutions, the project would proceed, followed by a new set of experiments to conduct the validation routines. After the iterations of experimental practice, a final validated solution would be confirmed with the problem solved and the knowledge integrated. As we observed in this study, the high-speed embedded flash design of ATDi project was completed in four cycles of prototype verification, which was accomplished by performing design simulation, fabrication experiments and testing analysis. The cycle lasted for four months. The embedded flash accelerative testing method was adopted through four iterations of test experimentation and analysis for six months. In the meantime, the new knowledge was internalized into team members’ tacit knowledge through intensive experimental cycles, and required technology was created
to present as explicit knowledge, in the form of patents, methods, procedures, recipes and rules. After the required knowledge was integrated, the process restarted for the next iteration until the next problem or requirement was resolved. In the product development process, the knowledge integration and technology innovation processes would repeat until project targets were achieved. Through the knowledge integration process, not only is the confronted problem solved but also organization capability is enhanced, and opportunities are also created to reach the new technology market. The identification of a process model of knowledge integration is summarized and illustrated in Fig. 2. As we observed in this study, the experimental practice would expand the cycles of the knowledge integration process, from days and weeks, to months. Improving the efficiency of experimental practice significantly enhanced knowledge integration as well as product development.
New problem or requirement raised
Identify problem and required knowledge
Internal existing knowledge
Acquiring and combining knowledge External new knowledge Design
Experimental practice Validate
Execute
Achievement of knowledge integration tacit
explicit
Knowledge internalization
Technology innovation
Enhance organization capability
Reach new technology market
Fig. 2. A basic process of knowledge integration via experimental practice.
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4.2. Factors and influences of experimental practice The other research issue relates to the critical factors in the iterative cycles of experimental practice. Based on the interviews, many contributing factors are screened and extracted from the common experimental routines, rules and procedural artifacts. In total, seven factors were identified, and they are discussed as follows: • Field expertise: Basic knowledge of the specialized field was found to be essential across projects. Field expertise built a knowledge base for problem identification as well as for experiment performance. In this case study, four out of five projects needed novel, embedded flash design technologies (high-speed, low-power and high-endurance). R&D teams utilized existing flash memory design technology to perform the experiment to achieve the desired performance level. Experience from previous projects can help solve the current problem efficiently. • Extensible capacity: Because knowledge integration involves new and diverse knowledge, the extensible capability can expand the existing knowledge base by searching, accessing and absorbing new and diverse knowledge to generate additional feasible solutions. Thus, extensible capacity is a key to consolidate knowledge in experimental practice. As we observed in the ATDi project, a new ADC testing method was needed due to cost concerns. The project team Testing engineer was not versed in ADC testing. Based on their knowledge of IC testing, new ADC design and testing knowledge were acquired from ATDi and the test equipment vendor, new knowledge was absorbed and recombined to generate feasible solutions in experimental practice. • Practical experience: Knowledge implementation also needs practical experience; it is the key factor for the implementation of experimental practice. As indicated in the series of cases studied, the practical experience of solution implementation from past projects is a significant factor, which can accumulate firms’ prior capability level to extend experiential and experimental know-how. For example, the design and verification of high-endurance flash in the ZiT project were performed more efficiently based on previous projects’ practical experience of rules and routines. • Knowledge channel: New knowledge is very important for experimental practice as well as knowledge integration. An adaptable knowledge channel can help the project team search, access and acquire new knowledge efficiently and rapidly. As we observed in the RNS project, a mix of knowledge channels and media helped acquire novel failure analysis technology from the Electronics Research and Service Organization of the Industrial Technology Research Institute, Taiwan. Based on this technology transfer process, a series of experiments were planned and performed, then the DINOR flash process technology was successfully developed.
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• Experimentation strategy: Under time and resource limitations, the experimental strategy is a key consideration for experiment planning. It is related to resource allocation, environment, procedures and methods of experimentation. In the case study, we observed the experimentation strategy providing overall planning for experimental practice that helped effectively plan, execute and validate the experiments. Replicability, imitability and appropriability are important considerations here. • Communication platform: Because interaction is a key concept for integration, an adaptable common platform (including media) can help communication and knowledge sharing, and is a key contributor to the success of experimental routines and practice. Both formal and informal routines for product review meetings can be viewed as an efficient communication platform for experimental practice as well as project performance. • Project team: Because project organization is a key factor for project management performance and individual interaction, the structure and culture of the project team play an important role in experimental practice execution. As we observed in the case study, the structure of the project team and involvement of functional departments played a key role in conducting experimental practice, which resulted in project success. Supportive organization culture can help smooth execution of the process. Linking the case findings, we summarize the critical factors and classify them under two headings: capability and mechanism. Capability relates to specialized project field techniques, which provide the technology base of experimental practice, including field expertise, extensible capacity and practical experience. Mechanism relates to managing practices, which includes knowledge channel, experimentation strategy, communication platform and project team. Following Grant’s (1996) model of knowledge integration, we analyzed the key elements and potential influences of experimental practice on knowledge integration. We inferred three major issues for experimental practice execution: flexibility, scope and appropriability. Fig. 3 provides an overall perspective of the linkages and influences of experimental practice on knowledge integration and technology innovation. We discuss this as follows: • Flexibility is concerned with the level of flexibility of planning, execution and validation. In general, the flexible nature of the process will affect the result of experimental practice positively by allowing for easy and low-cost changes in the process. Flexibility is strongly associated with the mechanisms, i.e., more efficient communication, resource allocation and teamwork can enhance the flexibility of experimental practice. In the ATDi project, the project team structure, communication platform and experimentation strategy enhanced the flexibility of experimental practice, i.e., the experi-
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Knowledge integration
Efficiency of experimental practice
Flexibility of experimental practice
Scope of experimental practice
Mechanism
Appropriability of experimental practice
Capability
Knowledge channel
Field expertise
Experimentation strategy
Extensible capacity
Communication platform
Practical experience
Project team : Strong linkage or influence : Weak linkage or influence
Fig. 3. The critical factors influencing experimental practice and knowledge integration.
mental plan and its execution could be performed with new concepts and options with dynamic alignments. Information was collected and passed within team members in real time, allowing corrective actions to be rapidly decided and executed. • Scope refers to the level of knowledge covered and organization underlying the experimental practice. Broader scope in the planning, executing and verifying process allows a more comprehensive and complete coverage of the integration and application of the knowledge. Scope can be affected by both capability and mechanism; a higher level of capability and mechanism will induce broader scope and level of experimental practice. In the RNS project, the knowledge channel helped access and acquire external knowledge, and then extensible capacity of the project team enabled knowledge internalization. A broader scope in general will help produce more robust experimental results, more depth in the new technology created, and a better capacity building effort that enhances the firm’s competitiveness. • The replicability, imitability and appropriability of experimental practice are related to capturing the opportunities and benefits of the experimental knowledge. Teece (2000a, 2000b) identified several dimensions for the appropriability of the returns to a firm’s intellectual property: the nature of technology, strength of the property rights regime, complementary assets, ease of replication and ease of imitation. Similarly, in the cases of collaborative experimental practice discussed here, the replicability and appropriability of the experimental practice can influence its efficiency due to the ease of embedding the knowledge in the product or service the next time around. It also appears that replicability and
appropriability are affected by the mechanism and capability of the experimental practice. For example, a wellrounded knowledge channel and communication platform help replication and transfer of technology, while extensive field expertise and experience help strengthen property rights. • Efficiency of experimental practice is related to the spending of time and other resources; i.e., the less time and resource spending for a given outcome signifies higher experimental practice efficiency. It will further enhance knowledge integration as well as technology innovation. In this case study, all projects showed that the efficiency of experimental practice was strongly dependent upon capability, as well as the mechanism. It was evident in our cases that the higher the capability level of a project team helped preserve the key characteristics of experimental practice: flexibility, scope and appropriability, each of which leading to better practice efficiency. That is, the experiment can be performed more flexibly, broadly and appropriately, to the benefit of improved efficiency of experimental practice. And the efficiency of experimental practice can lead to effective knowledge integration and technology innovation as discussed earlier. This case study has provided an overall depiction of the mechanisms and characteristics that lead to efficient experimental practice for knowledge integration and technology innovation, which has not been discussed extensively in current research. This result is based on the analysis of a group of SoC prototype development projects and the observation of critical factors (i.e., the various factors classified as mechanism or capability), which may vary from case to case. Nevertheless, it is our belief that the case results show that an organization can explore and understand the factors critical to their knowledge integration and technology innovation. Our study has highlighted the importance of flexibility, scope and appropriability in collaborative projects. Not only technical capability but also the managerial mechanism is an important driver of effective knowledge integration. The competence of the team engaging in knowledge integration and technology innovation does not depend only on individual ability, but also on the involvement of team linkage and mechanisms, and the institutional characteristics fostering flexibility, scope and appropriability. For example, the availability of a knowledge channel could be a key factor for experimental practice and knowledge integration. By improving the flexibility of the practice in the available channel, valuable knowledge can be acquired expeditiously, and then experimental practice can be performed efficiently, thus improving new product development. 5. Conclusions In this exploratory research, we have identified the process model of knowledge integration and experimental
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practice, as well as the factors that could help improve experimental practice and enhance knowledge integration and technology innovation. These results were derived from an in-depth case study of five inter-firm collaboration projects of a Taiwanese semiconductor company. The theoretical implications of this research are that we have identified a combination of technical and managerial dimensions influencing the efficiency of experimental practice in inter-firm collaboration projects. This is significant due to the heavy use of experimental practice for knowledge integration in NPD. This research shows that both technical capability and the managerial mechanism have significant influences on experimental practice and knowledge integration. The factors critical for capability and mechanism can be screened and extricated using detailed comparative case studies. Improving the capacity level of these factors can contribute to the flexibility, scope and appropriability of the practice and thus to the efficiency of experimental practice and knowledge integration in inter-firm collaboration projects. Our study provides a practical way to address research issues related to experimental practice and knowledge integration. Contributions made by our research are reflected not only in formulating a systematic approach to the examination of the knowledge integration process in the context of inter-firm collaboration projects, but also in synthesizing the empirical findings into conceptions of management concerns and practice. This result could help the understanding of the factors critical to improvement of the performance of product and technology development, and provide business insights for product and technology strategy from a practical point of view. The exploratory approach of the study also gave rise to new questions. Further research is needed to test the proposed process in a number of areas of knowledge integration and experimental practice. First, to generalize the findings, the model described here may require further comparative studies along with an analysis of the whole collaborative network, not just from the project or firm levels, which may lead to broader insights and implications. Second, we need to know how to effectively utilize the information and communication technology infrastructure to enhance the efficiency of experimental practice and knowledge integration. This is a challenging issue as the rules of game may change in the evolving complex knowledge network environment. Third, How to manage intellectual capital and maintain the balance of supply and demand for the efficient execution of inter-firm collaborative projects, while protecting property rights? This issue needs additional work on the strategic perspective and the institutional environment and requires synergy for interdisciplinary understanding and achievement. We believe that the results of our study can be used as a starting point for further research, which may deepen the understanding of what makes knowledge integration in collaborative product development a success or a failure.
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