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
https://mpra.ub.uni-muenchen.de/8778/
ISSN 1864- 6638
ZEF
W orking
Paper
Series
Department of Political and Cultural Change
Research Group
Culture, Knowledge and Development
Knowledge Hubs and Knowledge
Clust ers:
Designing a Knowledge
Archit ect ure for Development .
Bonn 2008
Hans- Diet er Evers
27
ZEF W orking 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 M ollinga, Conrad Schetter
Nr. 1 Evers, Hans- Diet er and Solvay Gerke (2005). Closing t he Digit al Divide: Sout heast Asia’s Pat h Towards a Knowledge Societ y.
Nr. 2 Bhuiyan, Shajahan and Hans- Diet er Evers (2005). Social Capit al and Sust ainable Development : Theories and Concept s.
Nr. 3 Schet t er, Conrad (2005). Et hnicit y and t he Polit ical Reconst ruct ion of Afghanist an.
Nr. 4 Kassahun, Samson (2005). Social Capit al and Communit y Efficacy. In Poor Localit ies of Addis Ababa Et hiopia.
Nr. 5 Fuest , Veronika (2005). Policies, Pract ices and Out comes of Demand- orient ed Communit y Wat er Supply in Ghana: The Nat ional Communit y Wat er and Sanit at ion Programme 1994 – 2004.
Nr. 6 M enkhoff, Thomas and Hans- Diet er Evers (2005). St rat egic Groups in a Knowledge Societ y: Knowledge Elit es as Drivers of Biot echnology Development in Singapore.
Nr. 7 M ollinga, Pet er P. (2005). The Wat er Resources Policy Process in India: Cent ralisat ion, Polarisat ion and New Demands on Governance.
Nr. 8 Evers, Hans- Diet er (2005). Wissen ist M acht : Expert en als St rat egische Gruppe.
Nr. 8a Evers, Hans- Diet er and Solvay Gerke (2005). Knowledge is Power: Expert s as St rat egic Group.
Nr. 9 Fuest , Veronika (2005). Part nerschaft , Pat ronage oder Pat ernalismus? Eine empirische Analyse der Praxis universit ärer Forschungskooperat ion mit Ent wicklungsländern.
Nr. 10 Laube, Wolfram (2005). Promise and Perils of Wat er Reform: Perspect ives from Nort hern Ghana.
Nr. 11 M ollinga, Pet er P. (2004). Sleeping wit h t he Enemy: Dichot omies and Polarisat ion in Indian Policy Debat es on t he Environment al and Social Effect s of Irrigat ion.
Nr. 12 Wall, Caleb (2006). Knowledge for Development : Local and Ext ernal Knowledge in Development Research.
Nr. 13 Laube, Wolfram and Eva Youkhana (2006). Cult ural, Socio- Economic and Polit ical Con-st raint s for Virt ual Wat er Trade: Perspect ives from t he Volt a Basin, WeCon-st Africa.
Nr. 15 Evers, Hans- Diet er and Caleb Wall (2006). Knowledge Loss: M anaging Local Knowledge in Rural Uzbekist an.
Nr. 16 Youkhana, Eva, Laut ze, J. and B. Barry (2006). Changing Int erfaces in Volt a Basin Wat er M anagement : Cust omary, Nat ional and Transboundary.
Nr. 17 Evers, Hans- Diet er and Solvay Gerke (2006). The St rat egic Import ance of t he St rait s of M alacca for World Trade and Regional Development .
Nr. 18 Hornidge, Anna- Kat harina (2006). Defining Knowledge in Germany and Singapore: Do t he Count ry- Specific Definit ions of Knowledge Converge?
Nr. 19 M ollinga, Pet er M . (2007). Wat er Policy – Wat er Polit ics: Social Engineering and St rat egic Act ion in Wat er Sect or Reform.
Nr. 20 Evers, Hans- Diet er and Anna- Kat harina Hornidge (2007). Knowledge Hubs Along t he St rait s of M alacca.
Nr. 21 Sult ana, Nayeem (2007). Trans- Nat ional Ident it ies, M odes of Net working and Int egrat ion in a M ult i- Cult ural Societ y. A St udy of M igrant Bangladeshis in Peninsular M alaysia.
Nr. 22 Yalcin, Resul and Pet er M . M ollinga (2007). Inst it ut ional Transformat ion in Uzbekist an’s Agricult ural and Wat er Resources Administ rat ion: The Creat ion of a New Bureaucracy.
Nr. 23 M enkhoff, T., Loh, P. H. M ., Chua, S. B., Evers, H.- D. and Chay Yue Wah (2007). Riau Veget ables for Singapore Consumers: A Collaborat ive Knowledge- Transfer Project Across t he St rait s of M alacca.
Nr. 24 Evers, Hans- Diet er and Solvay Gerke (2007). Social and Cult ural Dimensions of M arket Expansion.
Nr. 25 Obeng, G. Y., Evers, H.- D., Akuffo, F. O., Braimah, I. and A. Brew- Hammond (2007). Solar PV Rural Elect rificat ion and Energy- Povert y Assessment in Ghana: A Principal Component Analysis.
Nr. 26 Eguavoen, Irit and Eva Youkhana (2008). Small Towns Face Big Challenge. The M anagement of Piped Syst ems aft er t he Wat er Sect or Reform in Ghana.
Nr. 27 Evers, Hans- Diet er (2008). Knowledge Hubs and Knowledge Clust ers: Designing a Knowledge Archit ect ure for Development
Authors’ address
Prof. Dr. Hans- Dieter Evers
Center for Development Research (ZEFa) University of Bonn
W alter- Flex Str. 3 53113 Bonn
Knowledge Hubs and Knowledge Clusters:
Designing a Knowledge Architecture for Development
1Hans- Diet er Evers
Abstract
Wit h globalisat ion and knowledge- based product ion, firms may cooperat e on a global scale, out source part s of t heir administ rat ive or product ive unit s and negat e locat ion alt oget her. The ext remely low t ransact ion cost s of dat a, informat ion and knowledge seem t o invalidat e t he t heory of agglomerat ion and t he spat ial clust ering of firms, going back t o t he classical work by Alfred Weber (1868- 1958) and Alfred M arshall (1842- 1924), who emphasized t he microeconomic benefit s of indust rial collocat ion. This paper will argue against t his view and show why t he growt h of knowledge societ ies will rat her increase t han decrease t he relevance of locat ion by creat ing knowledge clust ers and knowledge hubs. A knowledge clust er is a local innovat ion syst em organized around universit ies, research inst it ut ions and firms which int end t o drive innovat ions and creat e new indust ries. Knowledge hubs are localit ies wit h a knowledge archit ect ure of high int ernal and ext ernal net working and knowledge sharing capabilit ies. Count ries or regions form an epist emic landscape of knowledge asset s, st ruct ured by knowledge hubs, knowledge gaps and areas of high or low knowledge int ensit y.
The paper will focus on t he int ernal dynamics of knowledge clust ers and knowledge hubs and show why clust ering t akes place despit e globalisat ion and t he rapid growt h of ICT. The basic argument t hat firms and t heir delivery chains at t empt t o reduce t ransport (t ransact ion) cost s by choosing t he same locat ion is st ill valid for most indust rial economies, but knowledge hubs have different dynamics relat ing t o ext ernalit ies produced from knowledge sharing and research and development out put s.
The paper draws on empirical dat a derived from past and ongoing research in t he Lee Kong Chian School of Business, Singapore M anagement Universit y and in t he Cent er for Development Research (ZEF), Universit y of Bonn.
Keywords
knowledge and development, knowledge governance, innovation, space, Vietnam, Straits of M alacca
1 Paper present ed at a conference on “Knowledge Archit ect ure for Development : Challenges ahead for Asian
1.
Introduction: The Devaluation of Space and the End of Industrial
Agglomeration?
Wit h globalisat ion and knowledge- based product ion, firms now cooperat e on a global scale, out source part s of t heir administ rat ive or product ive unit s and negat e locat ion alt oget her. Geographical space has been t heoret ically downgraded and proximit y or dist ance devalued (Brown and Duguid 2002). In fact rapid advances in ICT have enabled t he emergence of global product ion net works (Coe et al. 2004), out sourcing, just - in- t ime product ion, high- level manpower migrat ion (Fallick, Fleischman and Rebit zer 2006) and global “head hunt ing” for managers and engineers.
Globalisat ion t heorist s, like Saskia Sassen (Sassen 1991) have proclaimed t he exist ence of a “global cit y”, consist ing of CBDs (cent ral business dist rict s) in major cit ies worldwide, amalgamat ed int o on huge global cit y welded t oget her by int ense elect ronic communicat ion, sharing a common language and a common corporat e cult ure of a capit alist world economy.
The ext remely low t ransact ion cost s of dat a, informat ion and knowledge seem t o invalidat e t he t heory of agglomerat ion and t he spat ial clust ering of firms (James 2005), going back t o t he classical work by Alfred Weber and Alfred M arshall, who emphasized t he microeconomic benefit s of indust rial collocat ion (Weber 1909).
Despit e t his compelling t heoret ical argument , empirical realit y shows a different pict ure. Indust ries well versed in ICT, out sourcing and cooperat ion via t he int ernet st ill t end t o clust er and form indust rial agglomerat ions. Proximit y increases a company’s innovat ive capacit y when firms can share ideas, product s, and services. Examples are t he Silicon Valley, t he Hyderabad IT clust er, t he M unich high- t ech zone and t he ABC (Aachen- Bonn- Cologne) clust er in Germany, t he M SC in M alaysia, Biopolis and adjacent areas in Singapore and many ot hers. In short , it is exact ly innovat ive non- mat erial product ion, applied research and knowledge- based manufact uring t hat t end t o clust er in specific locat ions. The quest ion t hen arises, why do knowledge- based indust ries form clust ers rat her t han making use of ICT t o connect diverse locat ions world- wide?
Following t he recent t rend in recognizing knowledge as a fact or of product ion, clust er research has increasingly t urned away from an emphasis on agglomerat ion economics and t he minimisat ion of t ransact ion cost .
M ichael Port er in his well known st udy The Compet it ive Advant age of Nat ions produced a “diamond of advant age” t o explain why clust ers developed (Port er 1990).
This diamond consist ed of t he following element s:
• Fact or condit ions – a region’s endowment of fact ors of product ion, including human, physical, knowledge, capit al resources, and infrast ruct ure, which make it more conducive t o success in a given indust ry
• Demand condit ions – t he nat ure of home demand for a given product or service, which can pressure local firms t o innovat e fast er
• Relat ed and support ing indust ries – net works of buyers and suppliers t ransact ing in close proximit y t o fost er act ive informat ion exchange, collect ive learning, and supply-chain innovat ion
shared needs, making it fert ile for innovat ion and regional compet it ive advant age (Port er 2000; Port er 1990).
His widely accept ed view was recent ly challenged by Henry and Pinch. They argued t hat more import ant are “t he compet it ive advant ages secured by firms t hrough gaining rapid access t o knowledge concerning t he innovat ions, t echniques and st rat egies of compet it or firms” (Henry and Pinch 2006:114). In view of t he high ICT capabilit ies of high- t ech firms, t his argument reveals only half t he t rut h. Why is rapid access t o knowledge not gained t hrough video conferencing, net working wit h ot her t echnical st aff t hrough t he world- wide- web, t hrough accessing dat a banks t hat could be locat ed anywhere on t he globe, via chat rooms on t he int ernet or just using old- fashioned t elephone connect ions? All t hese modern means of communicat ions are used t o negat e geographical dist ance by allowing ad- hoc communicat ion wit hin seconds. St ill, high- t ech firms and knowledge- based indust ries show an avid t endency t o clust er in geographical space. Why should t his be t he case?
2.
Types of Knowledge: A revised Nonaka thesis
To answer t his quest ion we have t o go back t o t he basics of knowledge management .
In his much cit ed work Nonaka and Takeuchi dist inguish bet ween t acit and explicit knowledge (Nonaka and Takeuchi 1995). Tacit knowledge is basically experience gained t hrough act ion and explicit knowledge refers t o knowledge st ored and made available in books, dat abanks or ot her media. M aint aining compet ence wit hin an organisat ion despit e a high t urnover of employees, eit her t hrough ret irement or ret renchment poses a major management challenge, as t acit knowledge is lost . M ichel Polanyi in an earlier work emphasised t hat t acit knowledge is based primarily on doing rat her t han cognit ion. A person can t herefore “do” more t han he or she “knows” (Polanyi 1967). In fact , Bot kin and Seeley est imat e t hat eight y percent of knowledge is t acit (Bot kin and Seeley 2001). One of t he most difficult t asks of knowledge management is t herefore t o facilit at e t he t ransfer of t acit knowledge int o explicit knowledge or t o t ransfer personal int o organisat ional knowledge, i.e. t urning a firm or government agency int o an int elligent learning organisat ion.
The conversion of t acit t o explicit knowledge is difficult and provides an essent ial challenge t o t he pract ise of knowledge management . The best way t o t ransmit t acit knowledge or experience is st ill by observat ion, by face- t o- face cont act s and learning from doing. Rout ine work can easily be out sourced, but innovat ive, knowledge- based work needs t eam work and t he exist ence of communit ies of pract ice, frequent social int eract ion and capacit y building by direct face- t o- face learning. This line of argument event ually leads t o t he hypot hesis t hat
“t he t ransf er of t acit know ledge is a major f act or in t he emergence of know ledge clust ers. The more import ant t acit know ledge is f or product ion t he more localised product ion is likely t o be” (knowledge t ransfer hypot hesis).
t hrough keen observat ion of t he pract ices in neighbouring shops. Imit at ion of successful compet it ors and early access t o crucial informat ion is conducive t o clust ering (M eusburger 2000:259). Observat ions of t he pract ices of compet it ors rat her t han blind market forces of supply and demand appear t o be t he most salient fact ors driving economic processes in t his cont ext . This insight has also been used t o argue for a sociological t heory of market s and prices (Evers and Gerke 2007; Fligst ein 2002; Whit e 1981).
By now a fair number of relevant st udies provide empirical evidence t hat proximit y and face- t o- face int eract ion indeed facilit at e t he t ransfer of t acit knowledge and form a decisive asset in t he emergence of knowledge hubs. A st udy in modern It aly e.g. examines t he approaches used in det ermining communicat ion and innovat ion in t echnological dist rict s in It aly t o ident ify t heir dist inct ive feat ures and provide a framework for empirical analysis (Ant onelli 2000). The st udy found t hat clust ers cannot rely solely on agglomerat ion for t heir success but develop different ly due t o different knowledge sharing and research and development chances.
This view is cont est ed by Håkanson, who raises doubt s t hat privileged access t o "t acit knowledge" alone provides compet it ive advant ages t hat cause t he growt h and development of bot h firms and regions (Håkanson 2005). His point is accept able in so far as indeed t acit knowledge is always embedded in cult ural and social cont ext s t hat need t o be t aken int o account t oget her wit h market condit ions.
M enkhoff et al st udied knowledge in science parks and found t hat int ense et hnic based int eract ion played a decisive role in t he dynamics of knowledge hubs (M enkhoff et al. 2005). Similarly close int eract ion in socially diverse communit ies of pract ice were more product ive t han homogeneous knowledge hubs (M enkhoff et al. 2008).
A st udy on rural areas in t he US emphasizes t he import ance of local act ors and argues t hat “rural knowledge clust ers are specialized net works of innovat ive, int errelat ed firms …, deriving compet it ive advant ages primarily t hrough accumulat ed, embedded, and import ed knowledge among local act ors about highly specific t echnologies, processes, and market s” (M unnich, Schrock and Cook 2002). Anot her US wide st udy concludes t hat t acit knowledge is an import ant fact or in creat ing innovat ion (Audret sch and Feldman 1996).
In a different social arena in high- t ech research laborat ories empirical st udies by Karin Knorr- Cet ina have shown t hat face- t o- face int eract ion bet ween scient ist s inside and out side t he laborat ory have a decisive impact on t he “manufact ure” of knowledge (Knorr Cet ina 1981). Knowledge product ion is always a social process t hat requires int eract ion. This may t ake place t o a cert ain ext end in cyber space, but innovat ion and discovery are also driven by emot ions, by fun and anger, excit ement and frust rat ion which are project ed at persons in direct int eract ion. Emot ions are a less st udied, but nevert heless import ant enabler (or hindrance) of knowledge sharing (Chay et al. 2005).
3.
Knowledge Architecture
The marshalling of t acit knowledge and t he use of proximit y (Boschma 2005) for compet it ive gains needs a specific inst it ut ional frame, a specific “knowledge archit ect ure” (Evers, Kaiser and M üller 2003). In a social science cont ext Fligst ein uses t he t erm “archit ect ure” t o describe t he int errelat ion bet ween market s and government s (Fligst ein 2002). In ICT research t he t erm archit ect ure “t ypically describes how t he syst em or program is const ruct ed, how it fit s t oget her, and t he prot ocols and int erfaces used for communicat ion and cooperat ion among modules or component s of t he syst em” (www.court s.st at e.ny.us/ad4/LIB/gloss.ht ml). “IT archit ect ure is a design for t he arrangement and int eroperat ion of t echnical component s t hat t oget her provide an organizat ion of it s informat ion and communicat ion infrast ruct ure” (ht t p://www.ichnet .org/glossary.ht m). The ICT archit ect ure is by now t he backbone of knowledge clust ers in knowledge based societ ies, but t he impact of different archit ect ures or ICT regimes on knowledge flows is not known, except for t he fact t hat ICT speeds up communicat ion.
[image:9.595.74.526.389.589.2]The following diagram depict s a general int ernet archit ect ure concept ualizat ion (Jerez, Khoury and Abdallah 2008:3).
Figure 1 Concept ualizat ion of an Int ernet Archit ect ure
Pinch and ot hers have drawn at t ent ion t o t he fact t hat “agglomerat ions may develop a clust er- specific form of archit ect ural knowledge t hat facilit at es t he rapid disseminat ion of knowledge t hroughout t he clust er by increasing t he learning capacit y of proximat e firms and t hereby conferring clust er- specificcompet it ive advant ages” (Pinch et al. 2003:373). In line wit h t his argument we define t he know ledge archit ect ure of a knowledge clust er as
t he inst it ut ions of communicat ion and t he t ype and int ensit y of know ledge f low s (know ledge sharing), based on t he f ormal and inf ormal int eract ion bet w een persons and organizat ions.
in charact er…Crucially, archit ect ural knowledge is also essent ial in det ermining t he capacit y of organisat ions t o acquire, assimilat e and adopt new knowledge” (Henry and Pinch 2006). What holds t rue for individual organisat ions can also be applied t o a knowledge hub wit hin a large corporat ion or a knowledge hub, consist ing of several smaller organisat ions. In short , t he knowledge archit ect ure is a crucial det erminant for t he innovat ive capacit y of firms, knowledge hubs and, indeed, t he whole knowledge clust er.
As t he knowledge archit ect ure is basically “t acit ” in charact er, t acit knowledge t ransfer is an essent ial fact or in t he emergence of knowledge hubs, as we have argued in t he “knowledge t ransfer hypot hesis” above. A knowledge archit ect ure emerges on t he basis of knowledge (Chay et al. 2005; Chay et al. 2007). Knowledge about t he knowledge archit ect ure wit hin a clust er or wit hin a firm provides a compet it ive advant age for persons in t he know as well as for int elligent firms in comparison t o organizat ions out side a clust er. Archit ect ural knowledge must be dist inguished from “component knowledge”, which is “normally t ied t o t he t echnology of t he indust ry, is relat ively coherent and definable, and is usually acont ext ual” (Tallman et al. 2004:264). Component knowledge can easily be shared wit h expert s in t he same field or t ransmit t ed t o organizat ions. Archit ect ural knowledge, like organizat ional or managerial processes is, however, more difficult t o pass on, as it evolves as an inseparable part of a firm and is t herefore cont ext ualized (Tallman et al. 2004:265).
Knowledge flows and knowledge deposit ories const it ut e t he knowledge archit ect ure of an organisat ion or a clust er of organisat ions. A “knowledge archit ect ure” is t herefore a propert y of an organisat ion or clust er. This argument may be support ed from t he vant age point of sociological syst ems t heory (Luhmann 1984). As Helmut Willke has argued, t he int elligence of an organisat ion is more t han t he sum of knowledge of it s members. The knowledge of organisat ions is, indeed, different from personal knowledge, because “organisat ional or inst it ut ional knowledge resides in de- personalised, anonymous rule syst ems” (Willke 2007:113) and, we would argue, it s knowledge archit ect ure. In a modern knowledge societ y, Willke argues, large organisat ions t end t o be more knowledgeable, more int elligent t han individuals. No single individual is capable of building a modern airplane (Willke 2007:114). It needs organisat ional int elligence t o accomplish t his t ask and, we would add, indust rial clust ers and knowledge hubs as well.
4.
K- Clusters and K- hubs
M ost of t he current lit erat ure does not draw a dist inct ion bet ween knowledge clust ers and knowledge hubs. Policy st at ement s in part icular use bot h t erm arbit rarily. We feel t hat t urning t hese t erms int o different analyt ical concept s would enhance our underst anding of spat ial processes. The most general concept would be “agglomerat ion”, i.e. clust ers are agglomerat ions wit h ”proximit y” as a crucial variable. Henry and Pinch use t he t erm agglomerat ion and clust er synonymously “t o refer t o geographical groupings of firms (bot h large and small but oft en SM Es), broadly in t he same sect or, but ext ending beyond t o incorporat e great er part s of t he value chain” (Henry and Pinch 2006:117).The clust er concept emphasises t he organizat ional aspect of agglomerat ions, while t he t erm hub refers t o t he knowledge sharing and disseminat ion aspect . A more precise definit ion reads as follows.
are cent ral places w it hin an epist emic landscape, i.e. in a w ider st ruct ure of know ledge product ion and disseminat ion. Examples f or organisat ions in know ledge clust ers are universit ies and colleges, research inst it ut ions, t hink t anks, government research agencies and know ledge- int ensive f irms.
Knowledge hubs may exist in t he same locat ions as knowledge clust ers and may be nest ed wit hin t hem.
Know ledge hubs are local innovat ion syst ems t hat are nodes in net w orks of know ledge product ion and know ledge sharing. They are charact erised by high connect edness and high int ernal and ext ernal net w orking and know ledge sharing capabilit ies. As meet ing point s of communit ies of know ledge and int erest , know ledge hubs f ulf il t hree major f unct ions: t o generat e know ledge, t o t ransf er know ledge t o sit es of applicat ion; and t o t ransmit know ledge t o ot her people t hrough educat ion and t raining.
Knowledge hubs are always nodes in net works of knowledge disseminat ion and knowledge sharing wit hin and beyond clust ers. Their knowledge archit ect ure shows specific charact erist ics t hat can be made apparent in empirical st udies. As a st udy of t he wine indust ry in It aly and Chile has shown, firms wit h a st rong knowledge base are more likely t o exchange innovat ion- relat ed knowledge wit h ot her firms. However, t his is considered t o occur only among firms whose cognit ive dist ance is not t oo high. “This may explain t he format ion of densely connect ed cohesive subgroups and t he emergence of local knowledge communit ies” (Giuliani 2007:163), in our t erminology t o t he format ion of knowledge hubs.
Figure 2 Internal versus external communication:
E- mail communication of junior staff in a research institute
country-wide
internal
external
To visualize a complex mat t er in simple t erms we may say t hat clust ers are most visible as an agglomerat ion of organisat ions and buildings and hubs as a communit y of knowledge sharing and knowledge producing people.
The concept s discussed above are summarized in t he following t able.
Table 1 Concepts
Concept
Short Definition
Measurement (examples)
k-cluster
agglomerations of organisations
emphasizing knowledge as output
or input
number of organisations
per location
K-hub
local innovation systems that are
nodes in networks of knowledge
production and knowledge sharing
number of knowledge
workers and their products
(patents, papers, software)
k-architecture the
structures and institutions of
communication and the related
type and intensity of knowledge
flows
ICT governance regimes,
regular meetings,
k-sharing incentives
Epistemic landscape
areas of high or low knowledge
intensity
Regional R&D
expenditure,
location of k-clusters and
k-hubs
[image:12.595.66.506.447.676.2]5.
Epistemic Landscapes
Epist emic landscapes develop over long periods of t ime. They are seldom shaped by individual act ors, but more oft en by t he collect ive act ion of st rat egic groups. Firms connect ed by a common int erest t o capit alize on t he compet it ive advant age of clust ering have an impact on epist emic landscapes t hrough t heir locat ion decisions. M ore over government st rat egies t o develop knowledge- based societ ies and economies have oft en been decisive in shaping epist emic landscapes. Relevant development policies have been assessed in det ail elsewhere for M alaysia and Indonesia (Evers 2003), Singapore and Germany (Hornidge 2007a). Developing indust rial regions, clust ers or knowledge hubs are, indeed, st andard pract ice in many regional planning depart ment s around t he world.
In t his cont ext we define epist emic landscapes in a geographical sense, i.e. we refer t o t he spat ial dist ribut ion of knowledge asset s wit hin a predefined region. The t erm is not yet st andard scient ific t erminology. It has been used in different cont ext s. One line of argument refers back t o Bacon and 18t h- cent ury 'encyclopaedism' and defines an epist emic landscape as depict ing a synt hesis of knowledge (Wernick 2006). In Weisberg and M uldoon’s st udy a single epist emic landscape corresponds t o t he research t opic t hat engages a group of scient ist s. This may be t he t opic of a specialized research conference or advanced level monograph. Agent based modelling wit h Net Logo soft ware is used t o model t he changing epist emic landscape according t o research st rat egies of part icipat ing scient ist s (Weisberg and M uldoon 2007). In our st udy we int end t o follow a slight ly different pat h and focus on t he development st rat egies of government s, st rat egic groups, firms, research inst it ut es and t heir success in shaping t he epist emic landscape of a region2. The allocat ion of human and financial resources creat es knowledge asset s which can be measured, mapped and made t o depict t he cont ours of an epist emic landscape.
6.
Case Studies of K- Hubs and Epistemic Landscapes in ASEAN.
(1) Centres of Trade as Hubs of Learning in the Straits of M alacca.
Knowledge hubs t ake t ime t o develop. They oft en emerge on t he basis of earlier social and economic condit ions; in ot her words t hey are st rongly pat h- dependent . The inst it ut ions t hat were creat ed in earlier t imes show t heir own dynamics and st rongly influence out comes at a lat er dat e. This st at ement goes beyond t he simple assert ion t hat hist ory mat t ers and argues t hat t he knowledge archit ect ure, as defined above, has it s root s in local condit ions and local knowledge. as well as local concept s of knowledge, i.e. t he creat ion of what t ypes and forms of knowledge are especially fost ered (Hornidge 2007b). Development st rat egies aiming at t he creat ion of knowledge hubs and ult imat ely knowledge societ ies will produce different out comes dependent on which locat ion is chosen. We shall subst ant iat e t his argument on t he basis of our case st udy of knowledge hubs in t he St rait s of M alacca region (Evers and Hornidge 2007).
The hist ory of t he St rait s of M alacca is unt il t oday st rongly det ermined by int ernat ional t rade (Evers, Gerke and Hornidge 2008). At different point s in t ime different port s in t he St rait s
2 This refers t o ongoing research on knowledge management and knowledge governance in t he wat er sect or of t he
formed t he main cent res of commercial act ivit ies and as such arose as crucial cont act zones for t he exchange of not only product s but also commercial and naut ical knowledge as well as religious beliefs including st at e- craft . Reason for visit ing t hese knowledge hubs was t rade and for some t he spread of a cert ain fait h. But once t he t ravellers arrived in t hese port s, access t o knowledge became of ult imat e import ance, as it became t he precondit ion for reaching t he long-t erm goal, namely success in long-t rade or conversions.
Consequent ly, knowledge flowed or was t ransferred from t he foreigners t o t he local communit ies, from one group of foreign t raders t o anot her (i.e. from Indians t o Chinese, Arabs t o Indians, Europeans t o Arabs, et c.) as well as from local communit ies t o foreign t raders. Up t o now Singapore’s cult ural diversit y provides access to a wide range of cult urally specific knowledge pools as well as of course t o mult iple et hnically defined and hist orically grown t rans- boundary business net works (Evers and Hornidge 2007:432). The t ransfer of knowledge t ook place in inst it ut ionalised modes of knowledge t ransfer (i.e. schools of religious learning, t raders associat ions, t he feudal court s) as well as in informal ways (i.e. spont aneous exchange of most ly t acit knowledge t hrough int eract ion wit h t raders from a different et hnic group). Basic fact s are known but research on t he modes and ext end of knowledge t ransfer t hrough t rade and on t he knowledge archit ect ure of t he t rading cent res st ill await s furt her analysis.
Turning t o our st udy of current knowledge hubs and clust ers in t he St rait s of M alacca region (Evers, Gerke and Hornidge 2008) it could be shown t hat modern knowledge clust ers emerged most ly at localit ies t hat had a long t radit ion of t rade and learning in t he past . The growt h and t he knowledge archit ect ure of knowledge clust ers and hubs appear t o be highly pat h dependent . This fact is oft en neglect ed in development programmes advocat ing t he est ablishment of knowledge hubs “out of t he blue” wit hout regards for t he exist ing knowledge archit ect ure and landscape.
To delineat e knowledge clust ers in t he St rait s of M alacca region we compiled a direct ory of research cent res and inst it ut ions of higher learning. Combining t hese dat a wit h geospat ial coordinat es we were able t o ident ify areas of agglomerat ion of knowledge t ransferring and producing organisat ions. These were defined as knowledge clust ers3. Combining t hese dat a wit h out put variables, i.e. numbers of int ernat ionally recognised academic publicat ions, pat ent s, number of persons graduat ed and similar dat a we could ident ify knowledge hubs. The following map shows t he knowledge clust ers, using t he number of knowledge- producing organisat ions as an indicat or. Four major clust ers emerge: a Nort hwest M alaysian clust er (around Georget own and Alor St ar), a West M alaysian clust er (Kuala Lumpur wit h t he Klang Valley, t he M SC and M alacca), t he Nort h Sumat ra clust er (cent red on M edan) and t he Singapore- Johore clust er as t he major knowledge clust er of Sout heast Asia.
3 We are now using a more refined definit ion of clust ers and hubs and t herefore deviat e somewhat from t he
Figure 2 Knowledge Clusters along the Straits of M alacca
Source: (Evers, Gerke and Hornidge 2008; Evers and Hornidge 2007:426)
Figure 2: Knowledge Output, M alaysia and Singapore. 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 Nat ional Uni ve rs ity of Singapore (NUS ) Nany ang T ec hnologic al U ni ve rs ity ( N TU ) Ins tit ut e of Mic roelec tr onic s (IME ) In st itut e of M olec ular and Cell B iology (I M C B) Ins tit ut e of Sout heas t As ia n St ud ie s ( IS EAS) Univ er si ti S ains Ma la ysi a ( U S M ) U ni ve rs ity P utr a M ala ys ia (U PM ) Int ernat ional Me di ca l Univ ers ity Uni ver si ty Ma la ya (U M) Univ er si ty Kebangs aan Ma la ysi a In te rn at ional Is la m ic Un ive rsi ty M ala ysia (II U M) Univ er si ti T ek nologi Ma la ysi a (U T M )
Singapore Penang Selangor Kuala Lumpur Johor
Tota
l N
o
. of R
e fe re nc e s qtd. in 'We b of Sc ie nc e ' Malaysia
The dat a were collect ed from t he dat abase ‘Web of Science’ on all universit ies and research inst it ut es in M alaysia and Singapore on 24t h of January 2007. Only t hose universit ies or research inst it ut es referenced in t he dat a base are included in t his diagram (Evers and Hornidge 2007:424).
(2) The Epistemic Landscape of the M ekong Delta in Vietnam
Wit h t hese maps and t ables we have st ill a long way t o go unt il we can const ruct an “epist emic landscape” showing t he cont ours and t he dist ribut ion of knowledge asset s and t he archit ect ure of knowledge product ion and disseminat ion. A first at t empt t owards t his goal is made in our current st udy of knowledge governance in t he M ekong Delt a of Viet nam4.
The following figures show t he mapping of an epist emic landscape in Sout hern Viet nam.
4 This st udy is carried out wit hin t he WISDOM Project by t he Cent er for Development Research (ZEF), Universit y of
Figure 3 Epistemic map of t he M ekong Delta, Vietnam
Figure 4 Epistemic landscape of the M ekong Delta, Vietnam
The ridge of high knowledge asset s and t he knowledge peak of t he provincial capit al of Can Tho are clearly visible. Using t he met aphor of height s, valleys, peaks and ridges may help us t o visualize t he uneven dist ribut ion of knowledge in t he M ekong Delt a.
7.
Towards a New Architecture of Knowledge for Development
Asian government s as well as int ernat ional development agencies are increasingly banking on knowledge as a fact or of product ion (ADB 2005; Gerke and Evers 2006:2- 3; Gerke, Evers and Schweisshelm 2005; Hornidge 2007a: 4- 10, 62- 65). In 2003 t he Asian Development Bank ident ified knowledge as t he most import ant resource in maint aining t he region's compet it iveness, given t he rapid rat e of change creat ed by globalizat ion and t echnological innovat ion. Besides banking on increased t ransfer of knowledge t hrough FDI, as well as increased invest ment in educat ion and R& D, expert s are advocat ing t he creat ion of knowledge hubs as incubat ors of fut ure economic development . The M inist ry of Educat ion, Cult ure, Sport s, Science and Technology of Japan (M EXT) launched a programme in 2003 t o set up knowledge clust ers t hroughout Japan. Knowledge clust ers are described as follows: “A “Knowledge Clust er” is a local innovat ion syst em organized around universit ies, research inst it ut ions and firms which have unique R& D t hemes and pot ent ialit ies”5.
In 2006 t he Asian Development Bank announced a programme t o develop knowledge hubs in select ed developing count ries t hroughout t he Asia and Pacific region t o support and st rengt hen research and disseminat e new development concept s and t echnologies (ADB 2005). Since 2006 ADB is support ing Tsinghua Universit y in Beijing in est ablishing a regional knowledge hub on climat e change. The knowledge hub is t o be est ablished under an ADB grant and expert ise t hat is set t ing up cent res of excellence in t he region t o support and st rengt hen
5
research and disseminat e new and emerging concept s and t echnologies. Ot her cent res are planned in Thailand and India, st rengt hening and supplement ing t he already exist ing knowledge hubs.
“These knowledge hubs should aim t o mainst ream new concept s in innovat ion, science, t echnology, management development , and relat ed fields for t he region. They should also promot e improved exchange of dat a, informat ion, and knowledge; and increase t he capabilit ies of inst it ut ions and organizat ions in t he region. Init iat ives have creat ed a wealt h of knowledge base and expert ise t hroughout t he region. However, t he capabilit ies of regional organizat ions and inst it ut es in disseminat ing and sharing t heir findings are limit ed. Informat ion is not enriched t hrough regional cooperat ion, and informat ion and expert ise bases largely remain scat t ered around t he region and fail t o provide t he mult iplier effect t hat could be achieved if it were nurt ured wit h more support for regional knowledge exchange. As t he knowledge hub will focus on new development t opics, experience and lessons learned from ADB knowledge sharing init iat ives such as t he Consult at ive Group on Int ernat ional Agricult ural Research (CGIAR) cent re of excellence will be applied in t he est ablishment of t he knowledge hubs” (ADB 2005:2).
Singapore and M alaysia have followed a similar policy of designat ing specific areas t o house knowledge clust ers and ident ifying special areas of research and development t o set up knowledge hubs. We have analysed elsewhere t he st rat egies t o develop knowledge clust ers in t he St rait s of M alacca region in great er det ail (Evers, Gerke and Hornidge 2008), in Indonesia (Evers 2003), M alaysia (Evers 2003; Evers 2004a; Evers 2004b; M enkhoff et al. 2008) and Singapore (Evers 2003; Hornidge 2007a; M enkhoff et al. 2008). So far t hese development policies have been fairly successful. It should be not ed, however, t hat t he emergence of knowledge clust ers and knowledge hubs have been embedded in a wider epist emic landscape. Knowledge capit al was creat ed by support ing colleges, universit ies, research inst it ut es and cent res of applied research and development and t acit knowledge was import ed t hrough immigrat ion of foreign t alent s and overseas t raining schemes. By t his an import ant principle of knowledge management was leveraged, namely t hat knowledge is needed t o use and creat e more knowledge. This also ent ails delet ing barriers t o knowledge flows, building an ICT backbone, increasing knowledge asset s and closing knowledge gaps and developing a legal infrast ruct ure t hat allows and encourages creat ive and diverse knowledge product ion. Wit hout t he t horough implement at ion of a knowledge archit ect ure as well as an epist emic landscape, a successful development of a knowledge- based economy and societ y will hardly be possible.
8.
Conclusions
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