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M PRA Munich Personal RePEc Archive Knowledge hubs and knowledge clusters: Designing a knowledge architecture for development. Evers, Hans-Dieter Center for Development Research (ZEF), University of Bonn 2008 Online at http://mpra.ub.uni-muenchen.de/8778/ MPRA Paper No. 8778, posted 16. May 2008 / 17:21 ZEF Working Paper Series 1 Center for Development Research Department of Political and Cultural Change Research Group Culture, Knowledge and Development 27 Knowledge Hubs and Knowledge Clusters: Designing a Knowledge Architecture for Development. Department of Political and Cultural Change Hans-Dieter Evers ISSN 1864-6638 Bonn 2008 2 ZEF Working Paper Series, ISSN 1864-6638 Department of Political and Cultural Change Center for Development Research, University of Bonn Editors: H.-D. Evers, Solvay Gerke, Peter Mollinga, Conrad Schetter Nr. 1 Evers, Hans-Dieter and Solvay Gerke (2005). Closing the Digital Divide: Southeast Asia’s Path Towards a Knowledge Society. Nr. 2 Bhuiyan, Shajahan and Hans-Dieter Evers (2005). Social Capital and Sustainable Development: Theories and Concepts. Nr. 3 Schetter, Conrad (2005). Ethnicity and the Political Reconstruction of Afghanistan. Nr. 4 Kassahun, Samson (2005). Social Capital and Community Efficacy. In Poor Localities of Addis Ababa Ethiopia. Nr. 5 Fuest, Veronika (2005). Policies, Practices and Outcomes of Demand-oriented Community Water Supply in Ghana: The National Community Water and Sanitation Programme 1994 – 2004. Nr. 6 Menkhoff, Thomas and Hans-Dieter Evers (2005). Strategic Groups in a Knowledge Society: Knowledge Elites as Drivers of Biotechnology Development in Singapore. Nr. 7 Mollinga, Peter P. (2005). The Water Resources Policy Process in India: Centralisation, Polarisation and New Demands on Governance. Nr. 8 Evers, Hans-Dieter (2005). Wissen ist Macht: Experten als Strategische Gruppe. Nr. 8a Evers, Hans-Dieter and Solvay Gerke (2005). Knowledge is Power: Experts as Strategic Group. Nr. 9 Fuest, Veronika (2005). Partnerschaft, Patronage oder Paternalismus? Eine empirische Analyse der Praxis universitärer Forschungskooperation mit Entwicklungsländern. Nr. 10 Laube, Wolfram (2005). Promise and Perils of Water Reform: Perspectives from Northern Ghana. Nr. 11 Mollinga, Peter P. (2004). Sleeping with the Enemy: Dichotomies and Polarisation in Indian Policy Debates on the Environmental and Social Effects of Irrigation. Nr. 12 Wall, Caleb (2006). Knowledge for Development: Local and External Knowledge in Development Research. Nr. 13 Laube, Wolfram and Eva Youkhana (2006). Cultural, Socio-Economic and Political Constraints for Virtual Water Trade: Perspectives from the Volta Basin, West Africa. Nr. 14 Hornidge, Anna-Katharina (2006). Singapore: The Knowledge-Hub in the Straits of Malacca. 3 Nr. 15 Evers, Hans-Dieter and Caleb Wall (2006). Knowledge Loss: Managing Local Knowledge in Rural Uzbekistan. Nr. 16 Youkhana, Eva, Lautze, J. and B. Barry (2006). Changing Interfaces in Volta Basin Water Management: Customary, National and Transboundary. Nr. 17 Evers, Hans-Dieter and Solvay Gerke (2006). The Strategic Importance of the Straits of Malacca for World Trade and Regional Development. Nr. 18 Hornidge, Anna-Katharina (2006). Defining Knowledge in Germany and Singapore: Do the Country-Specific Definitions of Knowledge Converge? Nr. 19 Mollinga, Peter M. (2007). Water Policy – Water Politics: Social Engineering and Strategic Action in Water Sector Reform. Nr. 20 Evers, Hans-Dieter and Anna-Katharina Hornidge (2007). Knowledge Hubs Along the Straits of Malacca. Nr. 21 Sultana, Nayeem (2007). Trans-National Identities, Modes of Networking and Integration in a Multi-Cultural Society. A Study of Migrant Bangladeshis in Peninsular Malaysia. Nr. 22 Yalcin, Resul and Peter M. Mollinga (2007). Institutional Transformation in Uzbekistan’s Agricultural and Water Resources Administration: The Creation of a New Bureaucracy. Nr. 23 Menkhoff, T., Loh, P. H. M., Chua, S. B., Evers, H.-D. and Chay Yue Wah (2007). Riau Vegetables for Singapore Consumers: A Collaborative Knowledge-Transfer Project Across the Straits of Malacca. Nr. 24 Evers, Hans-Dieter and Solvay Gerke (2007). Social and Cultural Dimensions of Market Expansion. Nr. 25 Obeng, G. Y., Evers, H.-D., Akuffo, F. O., Braimah, I. and A. Brew-Hammond (2007). Solar PV Rural Electrification and Energy-Poverty Assessment in Ghana: A Principal Component Analysis. Nr. 26 Eguavoen, Irit and Eva Youkhana (2008). Small Towns Face Big Challenge. The Management of Piped Systems after the Water Sector Reform in Ghana. Nr. 27 Evers, Hans-Dieter (2008). Knowledge Hubs and Knowledge Clusters: Designing a Knowledge Architecture for Development Authors’ address Prof. Dr. Hans-Dieter Evers Center for Development Research (ZEFa) University of Bonn Walter-Flex Str. 3 53113 Bonn hdevers@uni-bonn.de 4 Knowledge Hubs and Knowledge Clusters: Designing a Knowledge Architecture for Development 1 Hans-Dieter Evers Abstract With globalisation and knowledge-based production, firms may cooperate on a global scale, outsource parts of their administrative or productive units and negate location altogether. The extremely low transaction costs of data, information and knowledge seem to invalidate the theory of agglomeration and the spatial clustering of firms, going back to the classical work by Alfred Weber (1868-1958) and Alfred Marshall (1842-1924), who emphasized the microeconomic benefits of industrial collocation. This paper will argue against this view and show why the growth of knowledge societies will rather increase than decrease the relevance of location by creating knowledge clusters and knowledge hubs. A knowledge cluster is a local innovation system organized around universities, research institutions and firms which intend to drive innovations and create new industries. Knowledge hubs are localities with a knowledge architecture of high internal and external networking and knowledge sharing capabilities. Countries or regions form an epistemic landscape of knowledge assets, structured by knowledge hubs, knowledge gaps and areas of high or low knowledge intensity. The paper will focus on the internal dynamics of knowledge clusters and knowledge hubs and show why clustering takes place despite globalisation and the rapid growth of ICT. The basic argument that firms and their delivery chains attempt to reduce transport (transaction) costs by choosing the same location is still valid for most industrial economies, but knowledge hubs have different dynamics relating to externalities produced from knowledge sharing and research and development outputs. The paper draws on empirical data derived from past and ongoing research in the Lee Kong Chian School of Business, Singapore Management University and in the Center for Development Research (ZEF), University of Bonn. Keywords knowledge and development, knowledge governance, innovation, space, Vietnam, Straits of Malacca 1 Paper presented at a conference on “Knowledge Architecture for Development: Challenges ahead for Asian Business and Governance”, Singapore, SMU 24-25 March 2008. 5 1. Introduction: The Devaluation of Space and the End of Industrial Agglomeration? With globalisation and knowledge-based production, firms now cooperate on a global scale, outsource parts of their administrative or productive units and negate location altogether. Geographical space has been theoretically downgraded and proximity or distance devalued (Brown and Duguid 2002). In fact rapid advances in ICT have enabled the emergence of global production networks (Coe et al. 2004), outsourcing, just-in-time production, high-level manpower migration (Fallick, Fleischman and Rebitzer 2006) and global “head hunting” for managers and engineers. Globalisation theorists, like Saskia Sassen (Sassen 1991) have proclaimed the existence of a “global city”, consisting of CBDs (central business districts) in major cities worldwide, amalgamated into on huge global city welded together by intense electronic communication, sharing a common language and a common corporate culture of a capitalist world economy. The extremely low transaction costs of data, information and knowledge seem to invalidate the theory of agglomeration and the spatial clustering of firms (James 2005), going back to the classical work by Alfred Weber and Alfred Marshall, who emphasized the microeconomic benefits of industrial collocation (Weber 1909). Despite this compelling theoretical argument, empirical reality shows a different picture. Industries well versed in ICT, outsourcing and cooperation via the internet still tend to cluster and form industrial agglomerations. Proximity increases a company’s innovative capacity when firms can share ideas, products, and services. Examples are the Silicon Valley, the Hyderabad IT cluster, the Munich high-tech zone and the ABC (Aachen-Bonn-Cologne) cluster in Germany, the MSC in Malaysia, Biopolis and adjacent areas in Singapore and many others. In short, it is exactly innovative non-material production, applied research and knowledge-based manufacturing that tend to cluster in specific locations. The question then arises, why do knowledge-based industries form clusters rather than making use of ICT to connect diverse locations world- wide? Following the recent trend in recognizing knowledge as a factor of production, cluster research has increasingly turned away from an emphasis on agglomeration economics and the minimisation of transaction cost. Michael Porter in his well known study The Competitive Advantage of Nations produced a “diamond of advantage” to explain why clusters developed (Porter 1990). This diamond consisted of the following elements: • Factor conditions – a region’s endowment of factors of production, including human, physical, knowledge, capital resources, and infrastructure, which make it more conducive to success in a given industry • Demand conditions – the nature of home demand for a given product or service, which can pressure local firms to innovate faster • Related and supporting industries – networks of buyers and suppliers transacting in close proximity to foster active information exchange, collective learning, and supplychain innovation • Firm strategy, structure, and rivalry – a climate that combines both intense competition among localized producers, with cooperation and collective action on 6 shared needs, making it fertile for innovation and regional competitive advantage (Porter 2000; Porter 1990). His widely accepted view was recently challenged by Henry and Pinch. They argued that more important are “the competitive advantages secured by firms through gaining rapid access to knowledge concerning the innovations, techniques and strategies of competitor firms” (Henry and Pinch 2006:114). In view of the high ICT capabilities of high-tech firms, this argument reveals only half the truth. Why is rapid access to knowledge not gained through video conferencing, networking with other technical staff through the world-wide- web, through accessing data banks that could be located anywhere on the globe, via chat rooms on the internet or just using old-fashioned telephone connections? All these modern means of communications are used to negate geographical distance by allowing ad-hoc communication within seconds. Still, high-tech firms and knowledge-based industries show an avid tendency to cluster in geographical space. Why should this be the case? 2. Types of Knowledge: A revised Nonaka thesis To answer this question we have to go back to the basics of knowledge management. In his much cited work Nonaka and Takeuchi distinguish between tacit and explicit knowledge (Nonaka and Takeuchi 1995). Tacit knowledge is basically experience gained through action and explicit knowledge refers to knowledge stored and made available in books, databanks or other media. Maintaining competence within an organisation despite a high turnover of employees, either through retirement or retrenchment poses a major management challenge, as tacit knowledge is lost. Michel Polanyi in an earlier work emphasised that tacit knowledge is based primarily on doing rather than cognition. A person can therefore “do” more than he or she “knows” (Polanyi 1967). In fact, Botkin and Seeley estimate that eighty percent of knowledge is tacit (Botkin and Seeley 2001). One of the most difficult tasks of knowledge management is therefore to facilitate the transfer of tacit knowledge into explicit knowledge or to transfer personal into organisational knowledge, i.e. turning a firm or government agency into an intelligent learning organisation. The conversion of tacit to explicit knowledge is difficult and provides an essential challenge to the practise of knowledge management. The best way to transmit tacit knowledge or experience is still by observation, by face-to-face contacts and learning from doing. Routine work can easily be outsourced, but innovative, knowledge-based work needs team work and the existence of communities of practice, frequent social interaction and capacity building by direct face-to-face learning. This line of argument eventually leads to the hypothesis that “the transfer of tacit knowledge is a major factor in the emergence of knowledge clusters. The more important tacit knowledge is for production the more localised production is likely to be” (knowledge transfer hypothesis). There is, up to now, only some empirical evidence to support our “knowledge transfer hypothesis”, but the fact remains that clusters are still emerging and keep going by banking on their competitive advantage. We believe that our hypothesis holds both for pre-industrial handicraft manufacturing as well as for modern research and development work and knowledge based production. Pre-modern handicraft production tended to be clustered in special quarters or streets (Enright 2003:100). The craftsmen quarters in European medieval cities or the Hang (merchandise) streets in the Hoan Kiem district of Hanoi are, indeed, knowledge clusters driven by the transfer of expertise and experience of master craftsmen to apprentices as well as 7 through keen observation of the practices in neighbouring shops. Imitation of successful competitors and early access to crucial information is conducive to clustering (Meusburger 2000:259). Observations of the practices of competitors rather than blind market forces of supply and demand appear to be the most salient factors driving economic processes in this context. This insight has also been used to argue for a sociological theory of markets and prices (Evers and Gerke 2007; Fligstein 2002; White 1981). By now a fair number of relevant studies provide empirical evidence that proximity and face-to-face interaction indeed facilitate the transfer of tacit knowledge and form a decisive asset in the emergence of knowledge hubs. A study in modern Italy e.g. examines the approaches used in determining communication and innovation in technological districts in Italy to identify their distinctive features and provide a framework for empirical analysis (Antonelli 2000). The study found that clusters cannot rely solely on agglomeration for their success but develop differently due to different knowledge sharing and research and development chances. This view is contested by Håkanson, who raises doubts that privileged access to "tacit knowledge" alone provides competitive advantages that cause the growth and development of both firms and regions (Håkanson 2005). His point is acceptable in so far as indeed tacit knowledge is always embedded in cultural and social contexts that need to be taken into account together with market conditions. Menkhoff et al studied knowledge in science parks and found that intense ethnic based interaction played a decisive role in the dynamics of knowledge hubs (Menkhoff et al. 2005). Similarly close interaction in socially diverse communities of practice were more productive than homogeneous knowledge hubs (Menkhoff et al. 2008). A study on rural areas in the US emphasizes the importance of local actors and argues that “rural knowledge clusters are specialized networks of innovative, interrelated firms …, deriving competitive advantages primarily through accumulated, embedded, and imported knowledge among local actors about highly specific technologies, processes, and markets” (Munnich, Schrock and Cook 2002). Another US wide study concludes that tacit knowledge is an important factor in creating innovation (Audretsch and Feldman 1996). In a different social arena in high-tech research laboratories empirical studies by Karin Knorr-Cetina have shown that face-to-face interaction between scientists inside and outside the laboratory have a decisive impact on the “manufacture” of knowledge (Knorr Cetina 1981). Knowledge production is always a social process that requires interaction. This may take place to a certain extend in cyber space, but innovation and discovery are also driven by emotions, by fun and anger, excitement and frustration which are projected at persons in direct interaction. Emotions are a less studied, but nevertheless important enabler (or hindrance) of knowledge sharing (Chay et al. 2005). From these studies we can conclude that whereas industrial clusters gained their competitive advantage primarily from a reduction of transaction costs (Iammarino and McCann 2006), knowledge clusters emerge primarily through a direct transfer of tacit knowledge. 8 3. Knowledge Architecture The marshalling of tacit knowledge and the use of proximity (Boschma 2005) for competitive gains needs a specific institutional frame, a specific “knowledge architecture” (Evers, Kaiser and Müller 2003). In a social science context Fligstein uses the term “architecture” to describe the interrelation between markets and governments (Fligstein 2002). In ICT research the term architecture “typically describes how the system or program is constructed, how it fits together, and the protocols and interfaces used for communication and cooperation among modules or components of the system” (www.courts.state.ny.us/ad4/LIB/gloss.html). “IT architecture is a design for the arrangement and interoperation of technical components that together provide an organization of its information and communication infrastructure” (http://www.ichnet.org/glossary.htm). The ICT architecture is by now the backbone of knowledge clusters in knowledge based societies, but the impact of different architectures or ICT regimes on knowledge flows is not known, except for the fact that ICT speeds up communication. The following diagram depicts a general internet architecture conceptualization (Jerez, Khoury and Abdallah 2008:3). Figure 1 Conceptualization of an Internet Architecture Pinch and others have drawn attention to the fact that “agglomerations may develop a cluster-specific form of architectural knowledge that facilitates the rapid dissemination of knowledge throughout the cluster by increasing the learning capacity of proximate firms and thereby conferring cluster-specific competitive advantages” (Pinch et al. 2003:373). In line with this argument we define the knowledge architecture of a knowledge cluster as the institutions of communication and the type and intensity of knowledge flows (knowledge sharing), based on the formal and informal interaction between persons and organizations. Steven Pinch has described the characteristics of architectural knowledge, which “tends to be specific to, or embedded in, particular organisations within which it evolves endogenously over time in a complex trajectory…architectural knowledge is highly path dependent…and tacit 9 in character…Crucially, architectural knowledge is also essential in determining the capacity of organisations to acquire, assimilate and adopt new knowledge” (Henry and Pinch 2006). What holds true for individual organisations can also be applied to a knowledge hub within a large corporation or a knowledge hub, consisting of several smaller organisations. In short, the knowledge architecture is a crucial determinant for the innovative capacity of firms, knowledge hubs and, indeed, the whole knowledge cluster. As the knowledge architecture is basically “tacit” in character, tacit knowledge transfer is an essential factor in the emergence of knowledge hubs, as we have argued in the “knowledge transfer hypothesis” above. A knowledge architecture emerges on the basis of knowledge (Chay et al. 2005; Chay et al. 2007). Knowledge about the knowledge architecture within a cluster or within a firm provides a competitive advantage for persons in the know as well as for intelligent firms in comparison to organizations outside a cluster. Architectural knowledge must be distinguished from “component knowledge”, which is “normally tied to the technology of the industry, is relatively coherent and definable, and is usually acontextual” (Tallman et al. 2004:264). Component knowledge can easily be shared with experts in the same field or transmitted to organizations. Architectural knowledge, like organizational or managerial processes is, however, more difficult to pass on, as it evolves as an inseparable part of a firm and is therefore contextualized (Tallman et al. 2004:265). Knowledge flows and knowledge depositories constitute the knowledge architecture of an organisation or a cluster of organisations. A “knowledge architecture” is therefore a property of an organisation or cluster. This argument may be supported from the vantage point of sociological systems theory (Luhmann 1984). As Helmut Willke has argued, the intelligence of an organisation is more than the sum of knowledge of its members. The knowledge of organisations is, indeed, different from personal knowledge, because “organisational or institutional knowledge resides in de-personalised, anonymous rule systems” (Willke 2007:113) and, we would argue, its knowledge architecture. In a modern knowledge society, Willke argues, large organisations tend to be more knowledgeable, more intelligent than individuals. No single individual is capable of building a modern airplane (Willke 2007:114). It needs organisational intelligence to accomplish this task and, we would add, industrial clusters and knowledge hubs as well. 4. K-Clusters and K-hubs Most of the current literature does not draw a distinction between knowledge clusters and knowledge hubs. Policy statements in particular use both term arbitrarily. We feel that turning these terms into different analytical concepts would enhance our understanding of spatial processes. The most general concept would be “agglomeration”, i.e. clusters are agglomerations with ”proximity” as a crucial variable. Henry and Pinch use the term agglomeration and cluster synonymously “to refer to geographical groupings of firms (both large and small but often SMEs), broadly in the same sector, but extending beyond to incorporate greater parts of the value chain” (Henry and Pinch 2006:117).The cluster concept emphasises the organizational aspect of agglomerations, while the term hub refers to the knowledge sharing and dissemination aspect. A more precise definition reads as follows. Knowledge clusters are agglomerations of organisations that are production-oriented. Their production is primarily directed to knowledge as output or input. Knowledge clusters have the organisational capability to drive innovations and create new industries. They