Since Peter Drucker’s seminal work (1993) addressed the emerging impor-tance of knowledge workers, knowledge, beyond the physical capital and land, has been viewed as the most critical resource of a firm. OECD 1996 also reports that economies are increasingly based on knowledge and information. Knowledge is now recognized as the driver of productivity and economic growth, leading to a new fo-cus on the role of information, technology and learning in economic performance.
The term “knowledge-based economy” stems from this fuller recognition of the place of knowledge and technology in modern OECD economies. The term “knowl-edge-based economy” results from a fuller recognition of the role of knowledge and technology in economic growth. Knowledge, as embodied in human beings (as “hu-man capital”) and in technology, has always been central to economic development.
In addition, tacit knowledge has been singled out as vital in gaining an emergent competitive advantage, mainly due to its difficulty in being expressed verbally or in being codified. Such difficulties also mean competitors cannot imitate, let alone du-plicate, the tacit knowledge of a competitor (Winter, 1987).
Previous studies of learning organizations are mostly based on Peter M.
Senge’s “The Fifth Discipline: The Art and Practice of the Learning Organization”, but there are more than five disciplines for developing learning organizations. Actu-ally, knowledge management will be the sixth discipline to improve the formation of learning organizations and for advancing organizational changes. This study focuses on two areas: organizational learning and knowledge management.
PART I
With the historic handover of Hong Kong by Britain to China in 1997, eco-nomic development may be leading China towards democracy (Lewis, 1997). A large virgin market, dense population, cheaper labor, and its land and energy sources make China’s presence felt in the global market, and a new economic re-source flow is being formed. China is an attractive force for Taiwan, too. Because of historical events, Taiwan’s economy has developed separately from that of Mainland China’s for almost five decades. However, due to the advantages of geographical closeness, a similar culture, and shared language, Taiwan, this small island that lacks natural resources, acts as an entrance to the Chinese market. This economic activity is what keeps Taiwan running.
The management paradigm today is experiencing a shift. While cutting costs used to be a good strategy in stable times, it is no longer suitable in today’s dynamic competition. Kim and Mauborgne (2005), the authors of Blue Ocean Strategy, con-tend that while most companies compete in (hostile) Red Oceans, strategies focusing on cost cutting to improve competitiveness are increasingly unlikely to create prof-itable growth in the future. Kanter (1983) argues that organizations cannot survive without innovating (cited by Mezias and Glynn, 1993). No industry and no firm can always be at the top without innovation; it is a key factor of survival and competi-tiveness. Knowledge is power and is the main driving force behind innovation (Swan, et. al., 1999). Organizational learning is the process by which new knowledge or insights are developed by a firm (Slater & Narver, 1995).
In today’s competitive climate, where the only certainty is uncertainty, organ-izational learning is considered a key factor of business success, and is seen as the foundation of competitive advantage. In knowledge-based societies, knowledge has
become the most important strategic asset. Organizations need to use knowledge to realize competitive advantages in the changing business environment (Sohal, Chung
& Morrison, 2004). Many fields within academe (e.g., cognitive psychology, infor-mation sciences, educational psychology, etc.) have attempted to better understand the concepts of knowledge creation, storage and retrieval, knowledge sharing, and knowledge application. Senge also describes learning organizations as organizations in which people continually expand their capacity to create desired results, where new patterns of thinking are nurtured, and where people are continually learning how to learn together (Senge, et al., 1994). How firms acquire, store and share valuable knowledge among individuals or units in the highly competitive marketplace has re-cently become a hot topic.
The Chinese market, like a black hole, sucks in global investment directly.
According to Charlene Barshefsky, a former US Trade Representative, "Over the next decade, China will become a hub of economic integration in Asia” (Business News, 2005). Facing such a challenge, a shift within management and economic paradigms is needed so that Taiwanese industries may stay competitive. A number of studies have pointed out that learning can make one more competitive (e.g. Grant, 1996; Lei, Hitt & Bettis, 1996; Simonin, 1997; Tippins & sohi, 2003); in order to keep up with the onslaught of challenges, organizations must continuously learn.
Some of the empirical research has found that organizational learning posi-tively relates to organizational performance (Hrebiniak & Snow, 1982; Dess, 1987;
Chen & Kuo, 2004; Wang & Hsiao, 2004). Lien (2002) also adopted Marsick and Watkins’ ‘Dimensions of the Learning Organization Questionnaire’ (DLOQ) as an instrument for investigating high-tech firms in Taiwan, and found that the relation-ships between the learning organization and organizational performance were
posi-tive. Most research in this area, however, focuses on large businesses (Matlay, 2000) or specific industries (Lien, 2002; Wang & Hsiao, 2004). This literature suggests that organizational learning is one process that plays an important role in enhancing a firm’s capabilities and competitive advantage. In this stage of our research, we aim to identify the organizational learning status of different Taiwanese industries.
Through a survey of Taiwan’s industries we wish to prove that performance, a criti-cal element of competitiveness, is higher in industries where organizational learning is actively practiced. Proof of such a linkage would suggest that competitiveness could be enhanced not only by seeking opportunities for cost-cutting, but by actively promoting organizational learning.
PART II
The definition of Research and Development (R&D) is to discover new knowledge regarding products, processes or services etc., and then apply that knowledge to create (or improve) new (or existing) products, processes and services that fill market needs. It is difficult to evaluate R&D performance, as it is a complex construct (Lin & Chen, 2005). Many studies on R&D project success factors (Bala-chandra & Brockhoff, 1995; Holtzmann 1972) have reported a set of factors leading to the success of R&D projects based on personal experiences. Therefore, one of the principal determinants of R&D project success is the mode of knowledge involved (tacit/explicit) (Gassmann & Zedtwitz, 2003).
The I.C. chip industry plays an important role in the national economy of Tai-wan, and the IC manufacturing process involves complex systems and complex sci-ence. It takes one or two months and involves hundreds of processes, including the processes of diffusion, lithography, thin film and etching which are performed on
hundreds of machines such as implanters, CVDs, PVDs, furnaces, steppers, wet benches etc., and is measured by related sensors or metrologies. Within each process, the parameters of control are the key factors leading to the yield rate. The duty of R&D here is to tune up optimal process parameters; in other words, it is to come up with optimal process recipes.
Deposition of coatings by plasma enhanced chemical vapor deposition is the most complex of all plasma surface treatment techniques (Dhar, 2003, p.7). The module development of the plasma enhanced chemical vapor deposition (PECVD) process includes several kinds of process parameters, such as R.F. power, total pres-sure inside the reactor, flow rates of gases involved, substrate temperatures, type of electrodes used, and reactor type or geometry (gases, flow rate, vacuum percentage, electric and magnetic field intensity). Most of these process parameters have corre-sponding physical (direct or indirect) sensors which monitor their real-time value.
However, after the reaction of all the parameters (molecular formula) in the PECVD chamber, it forms plasma and decomposes into a state of high density ion and mole-cules. During this complex interaction of physics and chemistry in the chamber, which can be treated as a black box, direct physical sensors can only detect the spe-cific states inside the chamber. To acquire more detailed information, indirect physi-cal sensors such as RGAs (Residual Gas Analyzers), OESs (Optiphysi-cal Emitter Sensors) or VIProbers (Voltage & Ampere Probers) are employed. Thousands of individual pieces of information related to optical spectrum, voltage and ampere distribution, and the density of the magnetic field, etc. are acquired. This mass of information exceeds the ability of an engineer to handle, and s/he is therefore compelled to abandon all of it.
Applying multivariate statistical analysis to monitor the process can generate
specific results corresponding to the core of the equipment or process, and gets rid of non-accurate information using experience rating. This could therefore be an effi-cient method to lead R&D projects in the right direction.
The second part of our research therefore explores the practices of PECVD processes, focusing on the requirements, formation, applications and extensions of the model. This model can effectively manage trait knowledge externalization for guiding R&D direction, and find a way to enhance the capability of the R&D process in the semiconductor industry.