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DOI: 10.1177/097172180501100105 2006; 11; 109 Science Technology Society

Martin Berger and Javier Revilla Diez from Innovation Surveys in Singapore, Penang and Bangkok

Technological Capabilities and Innovation in Southeast Asia: Results

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TECHNOLOGICAL CAPABILITIES AND INNOVATION IN SOUTHEAST ASIA n 109

Science, Technology & Society 11:1 (2006)SAGE PUBLICATIONS NEW DELHI/THOUSAND OAKS/LONDONDOI: 10.1177/097172180501100105

Technological Capabilities and Innovationin Southeast Asia: Results from InnovationSurveys in Singapore, Penang and Bangkok∗

MARTIN BERGER and JAVIER REVILLA DIEZ

An essential part of the catching-up process by firms in late industrialising countries isthe development of technological capabilities. It can be assumed that these capabilitiescorrelate with firms’ innovation activities (including cooperation with external partners).At the same time, it can be assumed that the quality of the national or regional innovationsystem influences the development of firms’ technological capabilities. Consequently, thisarticle compares groups of firms with different technological capabilities in three innov-ation systems. Analysing innovation activities, cooperation behaviour and the perceptionof the business environment, conspicuous differences between the innovation systems arefound. Contrary, the comparison of the different technological capability-groups bringsabout less conclusive results, which indicate only a limited interrelation between tech-nological capabilities and innovation-related activities.

Martin Berger is at the Institute of Technology and Regional Policy, Joanneum Research,Wiedner Hauptstrasse 76, A-1040 Wien, Austria, e-mail: [email protected] Revilla Diez has a Chair of Economic Geography, Institute of Geography, Christian-Albrechts Universität Kiel, Ludewig-Meyn-Straße 14, 24098 Kiel, Germany, e-mail:[email protected].

∗∗∗∗∗The authors would like to thank their cooperation partners in the case study regions whomade this research project possible: Mrs Anna Ong, Socio-Economic and EnvironmentalResearch Institute Penang, Professor Wong Poh Kam, Centre for Management ofInnovation and Technopreneurship at the National University of Singapore, Dr ChatriSripaipan and Dr Patarapong Intarakumnerd of the National Science and TechnologyDevelopment Agency, Thailand and Dr Peter Brimble of Asia Policy Research. Helpfulcomments of two anonymous referees on an earlier version of this article are gratefullyacknowledged. The project was funded by the German Research Foundation (DFG).



110n Martin Berger and Javier Revilla Diez

Intr oduction

TODAY’S WORLD ECONOMY has been characterised as a ‘knowledge-basedeconomy’ (OECD 1996) with knowledge being the most important re-source and learning being the most important process (Lundvall 1996).According to this assumption it is essential not only for developed but alsofor developing countries to foster the innovativeness of their companies.

This article scrutinises empirical data about firms’ innovative activitiesand cooperation from Singapore, Penang (Malaysia) and the GreaterBangkok Region (Thailand) in order to establish similarities and differ-ences between these regions and between companies at different stagesin respect to their technological capabilities (TCs). Key questions are:

l Why is it important for companies in developing countries to de-velop TCs and how do TCs relate to innovation?

l Do companies with different TCs also differ in respect to their innov-ation activity, if yes, how do they vary?

l In what way does a company’s location influence its TC-level, itsinnovation and cooperation behaviour?

The article is structured in the following way: First, a brief theoreticaloverview is given, stressing the importance of knowledge, learning andinnovation for economic development, summarising the concepts of spa-tial systems of innovation and presenting the concept of technologicalcapabilities. The latter is used in our research to distinguish companiesat different stages of their catching-up process. Second, we present themethodology of the surveys conducted in Singapore, Penang and Thailandand its predecessor-surveys in Europe. Finally, the dataset and its keycharacteristics is introduced. Moreover, in this final chapter two hypoth-eses are tested: First, companies with advanced TCs are more innovative,cooperative and assess the business environment conditions in a differentway than companies with less advanced TCs. Second, distinctive differ-ences can be observed between companies’ innovation and cooperationbehaviour as well as their assessment of the business environment con-ditions in first-tier newly industrialised countries (NICs) like Singapore,fast-followers like Penang and ‘laggards’ like Thailand respectively(Intarakumnerd et al. 2002).




Theoretical Background: Knowledge, Learning,Innovation and Economic Development

Technological change used to fall like manna from heaven, at least as longas it comes to economic theory, until the early 1990s, when Paul Romer(1990) introduced technological change into the neo-classical model ofeconomic growth. In his model innovation is explained endogenouslyand is seen as the driving force of economic growth. Key determinantsof technological change are human capital and knowledge. While humancapital encompasses all the knowledge and skills that are bound to aperson, the term knowledge describes information in the form ofpublications or blueprints.

Additionally, the understanding of the innovation process has changedfundamentally. Instead of being a linear process (research and develop-ment or R&D–production–marketing) it is now seen as a chain-linkedprocess, which is achieved in an interactive way between partners internaland external to the firm (Kline and Rosenberg 1986). This change is atleast partly caused by increased uncertainty, complexity and costs of theinnovation process. Since this process is interactive and therefore basedon a division of labour, the transfer of knowledge between the partnersis all important.

In the field of innovation research it is generally accepted that knowledgecan be classified as being tacit or codified. While codified knowledge iswritten down in articles and manuals or is embedded in technology, tacitknowledge is bounded to specific persons or organisations. Tacit knowledgeis reflected by a person’s skills or a firm’s routines. Since it is difficult toarticulate tacit knowledge, its transfer is restricted to face-to-face contacts.This does not hold true for codified knowledge, which is globally avail-able by means of modern communication technologies or trade. But evencodified knowledge has to be internalised, which means that it has to beconverted into tacit knowledge, in order to use it in a different context(Nonaka and Takeuchi 1995: 65).

Learning can thus be defined as a process in which an individual ororganisation acquires tacit or codified knowledge. The ability to learndepends on the stock of previously accumulated knowledge, and it be-comes easier with an increased knowledge stock. Therefore, a company’slearning capabilities depend on its ability to assess, embrace and utilise




new knowledge, which has been termed ‘absorptive capacity’ by WesleyCohen and Daniel Levinthal (1990).

In general four means of learning can be differentiated:

1. Learning by searching: companies learn while conducting R&Dactivities in order to explore new knowledge and technology.

2. Learning by doing/using: companies learn while producing goods(learning by doing; see Arrow 1962) as well as by using products,for example, capital goods (learning by using; see Rosenberg 1976).

3. Learning by training/hiring: companies learn by acquiring humancapital, either through personnel training (learning by training) orthrough recruitment of professionals (learning by hiring).

4. Learning by interacting: companies learn while interacting withother companies, especially customers and suppliers (Lundvall1988). Due to its interactive and therefore social character, learningby interacting is strongly influenced by the institutional and organ-isational framework. While learning by interacting seems possiblebetween remote partners under the condition of stable and stand-ardised technology (ibid.: 355), it is fostered by spatial and culturalproximity in an uncertain business environment with complex tech-nologies and rapid technological change.

Since learning by interacting is supposed to be of particular im-portance for innovations (ibid.; Gertler 1995), the factors influencingthis kind of learning have received much attention by recent researchwork, which finally resulted in the elaboration of the concepts ofnational and regional systems of innovation.

National and Regional Systems of Innovation

Using the paradigm shift from the linear to the chain-linked modelof innovation as a starting point and taking into account the importanceof intra- and inter-firm cooperations for the successful development ofinnovations, a group of researchers has developed concepts of nationalsystems of innovation (NSI) since the mid 1980s (Dosi et al. 1988; Freeman1987; Lundvall 1985; Nelson 1993).

The NSI approach is at the same time a theoretical and analytical con-cept. Theoretically it is rooted in institutional and evolutionary economics.While some authors (for example, B.Å. Lundvall) support an institutional




economics approach, which examines the relevance of an economy’sinstitutional and organisational structure for the efficient allocation ofproduction factors, others (for example, C. Freeman) belong to the schoolof evolutionary economics, which contemplates the behaviour of eco-nomic agents and their path-dependent decision-making processes aswell as the grown structures of the economy and its information channels(Grupp 1997; Revilla Diez 2002a). The analytical part of the NSI ap-proach was first elaborated by Richard Nelson (1993) in order to comparethe NSI of fifteen countries.

The main components of NSI are organisations, institutions and therelations/interactions among them.

Organisations are defined as formal structures with an explicit purpose,which are consciously created (Edquist and Johnson 1997: 47). Importantorganisations are companies, knowledge-producing and knowledge-diffusing organisations like universities, political organisations such asparliaments or ministries, bridging organisations that facilitate technologytransfer between science and business and finally social organisationslike trade unions.

Institutions are ‘sets of common habits, routines, established practices,rules, or laws that regulate the relations and interactions between indi-viduals, groups and organisations’ (ibid.: 46). They can be either formal(laws) or informal (traditional way of doing business).While organisationsare regarded as players of the game, institutions are seen as the rules ofthe game.

Bengt-Åke Lundvall et al. (2002: 220) view the following three institu-tional dimensions as having a major impact on learning and innovationbehaviour: first, the time horizon of the agents (short-term in Anglo-Saxoncountries vs. long-term in Japan); second, trust between agents; and third,the pre-dominating rationality (communicative rationality rather thaninstrumental rationality seems to support innovative behaviour).

Interactions between organisations are either market or non-marketrelationships. The latter is supposed to be highly relevant for learning(Edquist 2001; Lundvall and Maskell 2000). Interaction can take theform of flows of knowledge and information as well as flows of invest-ment and funding, but also informal arrangements like networks (Cookeet al. 1997: 478).

The NSI concept is based on empirical work in developed countries.A simple transfer and implementation of the very same concept in devel-oping countries does not seem to be appropriate. Rather an analysis of




the NSI in NICs and developing countries has to take into account thefollowing four aspects:

1. In developing countries companies are rarely working at the tech-nological edge. Rather, it is crucial for these companies to acquire,utilise, adopt and improve technologies that are already establishedin advanced countries (Lall 2000; Wong 1995). This is the reasonwhy in the context of developing countries we use the broad defin-ition of the Oslo Manual (OECD 1997), which defines innovationas new products, processes and organisational arrangements thatare new to the firm rather than new to the world (see also Ernst et al.1998b: 13). Furthermore, industrial innovation in developing coun-tries is mainly conducted in-house and in an informal manner, where‘R&D activities are not clearly and formally articulated with theenterprise strategy’ (Arocena and Sutz 1999: 13).1

2. Despite being of major importance for the development of absorp-tive, learning and technological capabilities, human resource devel-opment has so far been largely neglected. Therefore, future researchhas to consider the science and education infrastructure (Lundvallet al. 2002; Wong 2001).

3. International links offer learning opportunities for developing coun-tries, but they are not well accounted for in the NSI concept. Dueto a dualistic and inhomogeneous economic structure and a weakdomestic knowledge base, interactions between national agents aresupposed to be less important in developing than in advanced coun-tries (Ernst 2002; Wong 2001). Since the NSI is hardly developed,‘international linkages need to prepare the way for the developmentof national innovation systems’ (Ernst 2002: 500).

4. For developed countries the NSI approach is an ex-post concept,which is based on empirical observations. It was utilised to describe,analyse and compare well developed NSI with a strong institutionalbase and advanced infrastructure. For developing countries on theother hand it is an ex-ante concept, with the NSI being rather anaim, that has to be built up along with economic development, thana given asset (Arocena and Sutz 1999; 2002). For this reason thefocus of analysis in developing countries should be on ‘system con-struction’ and ‘system promotion’ (Lundvall et al. 2002: 226).

Regarding these criticisms, Poh Kam Wong (2001) has elaborated a modi-fied NSI concept especially for NICs. The main organisations in his NSI




approach are (a) companies; (b) public R&D and Science and Technology(S&T) support institutions; and (c) manpower development institutions.By contemplating science and education separately, Wong shifts humanresource development into the centre of his research. The key objectivesof his NSI model are ‘to build up the stock of scientific and technologicalresources and to allocate and deploy these resources to the respectiveinnovation actors’ (Wong 2001: 544). Hereby he takes into account thediffusion of technology which is crucial for developing countries.Moreover, he includes ‘international technology linkages’ to overcomethe limited focus on domestic interactions. As a result of these changesand the widening of the concept, it is necessary to consider all directand indirect policies effecting either the agents of the system or theirinteractions.

In the course of the debate on globalisation, different scholars claimedthe end of the nation state and the rise of the regions as ‘natural economiczones’ (Ohmae 1993: 78). Even though these claims seemed exaggerated,they resulted into a stronger research interest in regions. In the field ofinnovation research this led to the formulation of the concept of regionalsystems of innovation (RSI) (Braczyk et al. 1998; Cooke 1992). Takingon the basic ideas of the NSI concept, the key notion of the RSI is thatregions offer particular environment conditions and opportunities forinteractions that can either foster or hinder the cooperation between innov-ative actors in a region. Additionally, the amount and quality of regionalinnovation actors, manufacturing companies, business service companies,research institutes and universities, influences the opportunities for learn-ing by interacting (Cooke and Morgan 1998).

In conclusion, the endowment of a region with innovative actors andenvironment conditions that favour cooperation and innovation activitiesconstitute the extent and utilisation of the regional innovation potential.Bearing in mind the positive effects of spatial proximity for innovation-related social capital building (trust) and knowledge spillover, the regionalscale provides an important research level.

A special case in point are metropolitan systems of innovation, thatare restricted to major metropolitan areas and their hinterland (Fischeret al. 2001; Revilla Diez 2002b). Since these regions often encompassthe major ‘growth engines’ of the national or global economy, their systemof innovation, their endowment with innovative actors and the environ-ment conditions they offer are of special interest.




Technological Capabilities

Like the NICs of East Asia (South Korea, Taiwan) the NICs of SoutheastAsia (Singapore, Malaysia and Thailand) have experienced a period ofremarkable growth during the final decades of the twentieth century.Being all rather late-industrialising countries they managed a pretty suc-cessful catch-up process despite at least two severe competitive disadvant-ages: first, they are far away from the lead user markets in North America,Europe and Japan and therefore disconnected from essential user-producer links; and second, they are distant from the leading sources oftechnological innovation (Hobday 2000; Wong 1999).

These disadvantages result into relatively little innovative activities,and little output in terms of ‘new-to-the-world’ innovations. Instead,‘the process of technological change in developing countries is one ofacquiring and improving on technological capabilities rather than ofinnovating at the frontiers of knowledge. This process essentially consistsof learning to use and improve on technologies that already exist in ad-vanced industrial economies’ (Lall 2000: 13). Because of this, innovationsin developing countries are often defined as products, processes or typesof organisations new to the firm (Ernst et al. 1998b; Hobday 2000).

There are various ways to categorise firm-level TCs (see Bell and Pavitt1995; Lall 1992; Marcelle 2002; Wong 1999b). A very comprehensiveframework was elaborated by Ernst et al. (1998b), who see the followingclassification as ‘a sequential ordering of priorities for late industrializa-tion strategies based on imported technology’ (ibid.: 17).

l Production capabilities: which define the knowledge and skillsnecessary to operate a plant. Basically, these capabilities encom-pass production management, production engineering, repair andmaintenance.

l Investment capabilities: are the knowledge and skills that are usedto conduct a new industrial project, from pre-investment activitiessuch as feasibility studies to project execution. Moreover, the abilityfor efficient external sourcing is part of investment capabilities.

l Minor change capabilities: refer to a company’s ability for con-tinuous improvement, adaptation and incremental innovation ofproducts, processes and organisational arrangements.

l Strategic marketing capabilities: include collecting market intel-ligence, the development of new markets, the establishment of dis-tribution channels and the provision of customer services.




l Linkage capabilities: the competence to organise knowledge- andtechnology-transfer networks within the firm, with other companiesand with the domestic science and technology infrastructure aresummarised in the term linkage capabilities.

l Major change capabilities: refer to the ability to conduct R&D,develop and introduce new products, processes and organisationalarrangements either in-house or in cooperation with customers, sup-pliers, public research institutes etc.

Technological capabilities are the result of technological learning. Inthis process a company acquires codified knowledge (for example, know-ledge embedded in technology or written down in manuals), combines itwith existing tacit knowledge and thereby builds up a stock of firm speci-fic, tacit knowledge. This is a conscious and purposive as well as a costlyand time-consuming process, which is non-linear but path-dependentand cumulative. Because of its interactive and technology-specific nature,there is no single trajectory but a range of possible development paths(Ernst et al. 1998a: 333; Lall 2000: 16).

Whether companies develop technological capabilities and how theydo it depends on the structure and efficiency of the RSI (Fischer et al.2001) as well as the NSI: ‘successful technological learning ... requiresan effective national innovation system’ (Kim 1997: 219; cf. Wong 1999a).

Still, the mechanisms for successful technological learning have to beenquired for. The vast literature on international technology transfer hasidentified many different transfer channels, ranging from licensing, for-eign direct investment, joint ventures and subcontracting to overseastraining and education (Hobday 2000: 133; cf. Kim 1991; Mowery andOxley 1995; Pack and Saggi 1997; Wong 1999b). Meric Gertler (2001: 9)has introduced eight channels of convergence (Table 1), which identifychannels of best practice-learning but also offer a convenient frameworkfor the mechanisms of technological learning.

A prerequisite for this kind of organisational learning is individual learn-ing of the workforce. This implies that individual learning capabilitiesare essential for the development of TCs. Therefore, formal learning (forexample, learning by training in universities), non-formal learning (forexample, training on the job) and informal training,

which is defined as a lifelong process by which persons who work inforeign affiliates or in domestic companies which closely interact with




foreign TNCs [Transnational Companies] may acquire values, atti-tudes and beliefs embedded in the organizational culture of TNCsthrough daily experience, observation and exposure to indoctrination(Ernst et al. 1998b: 16)

have to be taken into account.Based on the experience of the Asian NICs Poh Kam Wong charac-

terised five generic routes for rapid technological catch-up, which canbe seen as successful development paths for technological learning (Wong1999a: 8). One route is the ‘Reverse Value Chain’ strategy that buildsupon Michael Hobday’s work on a OEM-ODM-OBM migration strategy(Hobday 1995, 2000). The notion of the concept is, that latecomers pursuea certain strategy to develop technological capabilities: first they startdeveloping process capabilities, followed by product design capabilitiesand finally new product creation/branding capabilities. ‘This is a reversalof the normal sequence of value chain activities pursued by large estab-lished high-tech firms in advanced countries’ (Wong 1999a: 8).

According to this concept a company starts as an Original EquipmentManufacturer (OEM), performing simple component subcontracting orassembly operations for a TNC. The buyer (TNC) provides detailed prod-uct specifications and sells the product under its own brand and throughits own distribution channels (Hobday 2000; Wong 1999a). So the OEMphase marks companies that solely rely on their production capabilities.At the next stage the latecomer firm becomes an Original Design Manu-facturer (ODM). The buyer still supplies the general product requirements,but the ODM firm is responsible for the detailed design and productionprocess. Consequently, the shift from OEM to ODM is based on the

TABLE 1Channels of Convergence

Passive/shallow Media/educationTravelManagement consultantsTrade (‘simple market’)Trade (‘organised market’: that is, intense interaction between buyerand seller)Alliances (strategic alliances, joint ventures, cooperation agreements)Mergers/ acquisitions

Active/deep Foreign direct investment

Source: Adapted from Gertler 2001: 9.




gaining of product design, product-process interfacing, manufacturingand sometimes component design capabilities (ibid.). The final step isthe move towards Own Brand Manufacturing (OBM).2 The companydevelops its own products and sells them under its own brand. Often iteven develops specific distribution channels. To become an OBM firm acompany needs at least basic capabilities in the fields of marketing, prod-uct development and R&D. Table 2 summarises the transition from OEMto OBM.

As Michael Hobday (2000) points out the OEM to OBM system enablesfirms to reach into international markets, export large volumes of goods,realise economies of scale and invest in automation. Furthermore, bysupplying demanding customers in the leading markets latecomerfirms learn by doing, using and interacting (with the foreign partner),and become acquainted with product and process technology as well asend-user market requirements (Wong 1999a). Therefore, the OEM-OBMsystem can be seen ‘as a training school for technological learning’(Hobday 2000: 134).

TABLE 2Transition of Latecomer Firms: From OEM to ODM to OBM

Technological transition Market transition

OEM Learns assembly process for standard,Foreign TNC/ buyer designs, brands,simple goods and distributes

ODM Local firm designs (or contributes to TNC buys, brands, and distributesthe design, alone or in partnership TNC gains post production value-with the foreign company) and learns added (PPVA)product innovation

OBM Local firm designs and conducts R&D Local firm organizes distribution,for new products uses own brand name, and captures

PPVA

Source: Adapted from Hobday 2000: 135.

Since every OEM to OBM phase indicates a particular level of techno-logical capabilities, and respective successful technological learning, wehave grouped the companies in our dataset about Singapore, Penang(Malaysia) and the Greater Bangkok Region (Thailand) accordingly (seesection titled ‘Empirical Evidence’). The aim of the following empiricalstudy is to identify differences between these groups in respect to theirinnovation and cooperation behaviour. If we manage to establish majordifferences, this could advance our understanding of the mechanisms




and importance of knowledge transfer and learning between firms forthe development of technological capabilities and therefore of economiccompetitiveness.

Methodological Approach

Carried out in two phases between 1995 and 1999, the European RegionalInnovation Survey (ERIS) attempted to empirically assess regional innov-ation potentials as well as intra-regional and inter-regional cooperationrelationships between innovation actors, and at providing a comparativeevaluation. For this purpose usable data were obtained from roughly8,600 innovation actors in eleven European regions, including 4,200manufacturing firms, 2,500 knowledge intensive business services(KIBS) and 1,900 research institutions. An overview of the first phaseof this project is provided by Fritsch et al. (1998), while Rolf Sternberg(2000) reports on the situation after completion of the second phase.

When designing the questionnaires for the postal surveys in Singapore,Penang and Thailand, it was necessary to ensure maximum comparabilitywith the ERIS survey and other empirical studies, such as the CommunityInnovation Survey of the European Commission (European Commission2001) or the Mannheim Innovation Panel of the Centre for EuropeanEconomic Research (among others, see Janz and Licht 1999; Janz et al.2001). On the other hand, certain specific features of the survey regionand the interests of the cooperation partners had to be taken into accountas well. The resulting questionnaires thus represent a compromise inwhich the core elements of the ERIS questionnaires could, however, beretained:

l General information: as an introduction, questions were askedabout various firm characteristics such as age, size (in terms ofturnover, capital stock and employees), branch, ownership andfunctional status, share of exports, educational profile of staff etc.In the analysis these variables can be called upon to explain differ-ences in the innovation and cooperation behaviour.

l Innovation activities: innovating firms which have introduced anew or substantially improved product or manufacturing processin the past three years were asked about details concerning their in-novation behaviour. Here, input indicators (personnel and ex-penditure on R&D and/or innovations) as well as throughput




indicators (e.g., patents) and output indicators were registered. Afirm is considered to be innovative when new or substantially im-proved products contribute to at least 25 per cent of its turnover, orwhen 25 per cent of its output is produced with new or improvedprocesses.

l Innovation cooperation: in this central part of the survey firmswere asked which external sources of technical knowledge they usefor their innovation processes, with which external partners theycooperate and where those partners are located.3 Here, the mostimportant questions concern the connection between cooperationand innovation success as well as the relevance of spatial proximityto cooperation.

The postal survey of manufacturing firms in Singapore was carried outbetween September 1999 and January 2000 in close cooperation withthe Centre for Management of Innovation and Technopreneurship (CMIT)at the National University of Singapore and with Singapore’s highlyinfluential economic promotion agency, the Economic DevelopmentBoard (EDB, cf. Schein 1996). Of the 1,869 questionnaires sent out itwas possible to receive 374 usable returns, resulting in a response rate of20 per cent. The initial results were presented to the EDB in a report(Wong et al. 2000), and a more detailed analysis of the data set as well asthe more extended case study material obtained in interviews is presentedby Matthias Kiese (2004).

The Penang State Innovation Survey carried out in the summer of2000 in cooperation with the Socio-Economic and EnvironmentalResearch Institute (SERI) was based on a database comprising 951 manu-facturing firms. Of the 921 questionnaires sent out, 192 were returned ina quality that was usable, which corresponds to a response rate of 20.8 percent. The initial results of this survey were presented to the governmentof the Federal State of Penang in an unpublished report as well as at aworkshop (Ong 2001; SERI and University of Hanover 2001), and amore comprehensive evaluation of the data as well as additional inter-views can be found in Simeon Stracke (2003).

On the basis of the questionnaires used in Singapore and Penang,Thailand’s National Science and Technology Development Agency(NSTDA) commissioned the Bangkok-based consulting firm TheBrooker Group Public Company Limited to carry out the first countrywideR&D and innovation survey between January and April 2001. This was




accompanied scientifically by the authors. Of the 13,415 largest Thaifirms by turnover, a sample of 2,166 companies was drawn using stratifiedrandom sampling based on firm size and industry. Of these firms 1,019returned usable replies, which corresponds to an outstanding return rateof 47 per cent (cf. Virasa and Brimble 2001).

Table 3 compares the surveys of manufacturing firms carried out inSingapore, Penang and Thailand with the results of the ERIS. It becomesclear that the samples gained in Southeast Asia are within the range ofthe ERIS project, both absolutely (sample size) and relatively (responserates).

TABLE 3Project History and Response Rates (Manufacturing Only)

Region Country Year1 Responses Response rate

Baden Germany 1995 430 15.8%Hanover-Brunswick-Göttingen Germany 1995 372 20.6%Saxony Germany 1995 1,004 16.7%Alsace France 1997 263 15.0%Barcelona Spain 1997 395 15.3%Gironde France 1997 101 12.7%Slovenia Slovenia 1997 416 31.2%South Holland Netherlands 1997 261 13.7%South Wales UK 1997 280 17.6%Stockholm Sweden 1997 451 24.0%Vienna Austria 1997 204 19.9%ERIS-11 4,177 19.7%Singapore Singapore 1999 374 20.0%Penang Malaysia 2000 192 20.8%Thailand Thailand 2000 1,019 47.0%

Sources:Data from European Regional Innovation Survey; EDB/NUS-CMIT NationalInnovation Survey Singapore; Penang State Innovation Survey; and ThailandR&D/Innovation Survey 2000.

Note: 1launch.

Empirical Evidence

Characterising the NSI of the Case Study Regions

In order to give a brief overview over the performance of the NSI in thethree countries that host the case study regions, Table 4 compares somekey secondary statistics on science and technology. It is striking that




Singapore is doing quite well when it comes to allocating resources forR&D. Gross Domestic Expenditure on R&D as a percentage of GDP(GERD) is at about the same level as in European countries such as Italy(1.12) or Austria (1.51) but still some way behind Korea (2.79) andTaiwan (1.86). Also the number of R&D personnel per capita (in 1,000)is remarkably and clearly higher than that in Italy (2.46), Korea (2.77)and Austria (3.06), but below the figures for Taiwan (4.72) (IMD 1998;1999; 2001).

The figures for Malaysia and Thailand lag behind quite significantly,with a distinctively lower GERD and R&D personnel intensity. Ad-ditionally, the number of patents in force is significantly lower than inSingapore. Despite having a similar GERD and R&D personnel inten-sity, the numbers for private sector involvement (R&D Expenditure byBusiness Enterprises in percentage of all funds (BERD) and R&D person-nel intensity in business) as well as for patents in force in Thailand lieclearly behind those of Malaysia.

TABLE 4Overview of the Key Indicators of the National Systems of Innovation in the Host

Countries of the Case Study Regions: GERD, BERD, R&D Personnelin Total and in Business, Number of Patents in Force

Total R&D R&D personnel Number ofpersonnel (FTE) business (FTE) patents in force

per capita per capita (per 100,000GERD BERD (in 1,000s) (in 1,000s) inhabitants)

Country (1996) (1996) (1997) (1997) (1996)

Singapore 1.37 63.3 3.23 2.12 368.4*Malaysia 0.20* 72.8* 0.21 0.11 28.6§

Thailand 0.13 20.2 0.22# 0.01# 4.2$

Sources:IMD 1998; 1999; 2001.Notes: FTE = full time work equivalent $1998; *1997; #1995; §1994.

This indicates that Singapore has obviously managed to catch up inallocating resources to innovation processes and has established a quitemature NSI. The national figures for Malaysia and Thailand are markedlylower, indicating a fairly weak NSI, with some higher business and patentactivity in Malaysia.

Bearing this national pattern in mind, we take a closer look at theregional /metropolitan scale by investigating firm-level data in the nextsection. While Singapore obviously combines the national and regional




scale, we try to find out if the regional/metropolitan innovation systemin the national ‘powerhouses’ of Bangkok4 and Penang5 are somewhatmore developed than their national equivalent.

Key Characteristics of the Dataset

The data analysis has been restricted to the machinery and electronicsindustry which encompasses fabricated metal products, electrical machin-ery and equipment as well as electronic products.6

This sector has been chosen because it offers an appropriate basis forinter-regional comparison: it is one of the most important sectors withinmanufacturing in all three regions, it is fairly technology-intensive andinnovative. The restriction to one sector seemed necessary because firstanalyses found that the sector affiliation poses a strong influence on theinnovation behaviour.

In a next step the companies were grouped according to their techno-logical capabilities, for example, a company was labelled OEM if it mademore than 50 per cent of its turnover/sales with OEM products (see sectiontitled ‘Technological Capabilities’). Following TC groups are distinguishedin the dataset:

l Manufacturing arm of par ent company or MA: products manu-factured by the company according to design specifications providedby parent company or associate in the corporate group.

l Original equipment manufacturing or OEM : products manu-factured by the company according to design specifications providedby external buyers.

l Original design manufacturing or ODM: products developed anddesigned by the company according to performance requirementsof buyers.

l Original brand manufacturing or OBM : products developed anddesigned by the company and sold under its own brand.

To allow a cross comparison, the dataset was restricted to the followingthree metropolitan regions:

l Greater Bangkok Region (GBR): it includes the Bangkok Metro-politan Region and the Eastern Seaboard.

l Penang (PNG): the island of Penang is a high-tech enclave inMalaysia.

l Singapore (SGP).




Table 5 depicts the size of the sample in the three regions and the distri-bution over the four technological capabilities groups. It is striking thatthe percentage of OBM firms is highest in the GBR, which contradictsthe assumption that advanced countries host more companies with higherTCs. Instead of indicating a higher than expected innovativeness andcompetitiveness of the companies in Bangkok, this result instead givesevidence that the TC level in itself is not sufficient to evaluate a company’sinnovative capability.

To get the broad picture straight, it seems necessary to present figuresreflecting the differences in economic performance between the regions.

TABLE 5Frequency of Companies According to Technological Capability

GBR PNG SGP

MA 84 15 5734.6% 20.0% 29.8%

OEM 78 37 7632.1% 49.3% 39.8%

ODM 28 16 3311.5% 21.3% 17.3%

OBM 53 7 2521.8% 9.3% 13.1%

Total 243 75 191

100.0% 100.0% 100.0%

Table 6 shows the means of sales per employee for 1999 in US$.Despite the disadvantage of displaying turnover rather than value addedfigures, Singapore’s outstanding position is documented clearly. Surpris-ingly, the total figures for Penang do not confirm higher sales per em-ployee than in the GBR. Nevertheless, the MAs and OBMs in Penang dohave an obviously higher mean than those in Bangkok, which supportsthe assumption of Penang-based companies having a higher position inthe value chain.

Another way of setting the TC structure into context is to use the notionof the industry life cycle. Like products industries are supposed to experi-ence a life cycle (Klepper 1996; Revilla Diez 2002a: 76), which consistsof four phases:

1. Intr oduction: mainly small companies with a high innovation out-put dominate the industry.




2. Growth: successful small companies witness growing turnover andprofits as well as an increase in employees. They therefore becomemedium- to large-size companies, that are still characterised by anabove average innovation output.

3. Maturity : in this phase the products of the industry are standardised.The market is very competitive with many producers trying to real-ise economies of scale. The innovation activity decreases and com-panies are more likely to conduct process than product innovations.

4. Decline: companies are not competitive any more and thereforeexperience a steady decline in profits and employees. As a resultcompanies in this phase are small and do rarely innovate.

To assign a company to one of the four life-cycle stages two indicatorsare used: first, the size of the company, measured by the total number ofemployees; second, the innovation output, measured by the share of new/improved products of the total turnover.

In a first step, we calculated the industry-specific median for the shareof new/improved products of total annual sales for both, small (less than100 employees) and medium/large companies (100 employees and more).For both types of companies the median is 2 (the variable has been codedon a 5-point ordinal scale with following labels: 1: less than 10 per cent;2: 10–24 per cent; 3: 25–49 per cent; 4: 50–74 per cent; 5: 75 per centand above).

Then we arranged:

l the small companies with an average or above share of new/im-proved products at the total annual turnover in the intr oductionphase;

TABLE 6Mean of Sales per Employee 1999 in US$ (Based on Exchange Rates 31.12.1999)

GBR PNG* SGP*

MA 107,435 127,602 248,128OEM 54,600 45,098 159,161ODM 45,971 43,771 159,179OBM 40,275 79,602 207,795Total 68,951 65,183 192,080

Note: *While Penang and Singapore count employees as full-time equivalents; in Bangkokthe employees were counted as headcount.




l the medium/large companies with an average or above share ofnew/improved products at the total annual turnover in the growthphase;

l the medium/large companies with a below average share of new/improved products at the total annual turnover in the maturityphase; and,

l the small companies with a below average share of new/improvedproducts at the total annual turnover in the decline phase.

As can be seen from Table 7, Penang as well as Singapore have explicitlymore companies in the first two industry life-cycle phases than Bangkok(although this is not statistically significant). This indicates that com-panies in Singapore and Penang are more often in an early phase of theirdevelopment, offering good prospects for future growth and employment.In comparison, firms in Bangkok are more often positioned in the growthto maturity phases. This result puts into perspective the TC frequenciesin the three regions and shows that other factors also have a strong influ-ence on economic performance. At the same time it underpins the theor-etical argument of the ‘reverse value chain strategy’ (see section titled‘Technological Capabilities’), which states that companies in Asian NIEsstart in more mature, standardised productions processes and developtowards more sophisticated design, development and branding activities.

TABLE 7Number of Companies According to Industry Life Cycle

GBR PNG SGP

% cum.% % cum.% % cum.%

Introduction 21.6 21.6 16.2 16.2 27.6 27.6Growth 40.5 62.1 64.9 81.1 46.1 73.7Maturity 24.3 86.4 10.8 91.9 13.2 86.9Decline 13.5 100.0 8.1 100.0 13.2 100.0Total 100.0 100.0 100.0Chi-Sq. 0.293

Another important factor, influencing innovative behaviour is the owner-ship structure of a company, since it is relevant for the resources andopportunities available for learning. Table 8 displays the prevailingownership status of the different TC levels.

Overall, there is clear evidence that OBM companies are more likelyto be local, while MAs (especially in Penang and Singapore) have a high




probability to be part of or associate with a multinational company (inBangkok more MAs are organised as joint ventures than purely foreignowned). Especially the companies in Bangkok offer a straightforwardrelationship: the higher the local ownership, the higher the TC.

Innovation Indicators

Two hypothesis are the starting point for the examination of the datapursued here: Hypothesis 1 (H1): the level of technological capabilitiesof companies corresponds with their ability to conduct product innov-ations/process adaptations. In accordance with the theoretical literatureit is assumed that companies improve their innovation capabilities whilelearning and developing technological capabilities.

Hypothesis 2 (H2): Since Singapore, Malaysia and Thailand are atdifferent stages of the catch-up (learning) trajectory and their NSI showmarked differences in respect to key science and technology statistics, itcan be expected that this is also reflected by the innovation related indi-cators of the firm-level data in the case study regions. We therefore assumestrong differences between companies in these regions, Singapore’s firmbeing the most advanced, followed by companies in Penang and—withsome distance—companies in the GBR. Furthermore, we assume thatthe regional systems of innovation in Penang and Bangkok are betterdeveloped than their national equivalent.

TABLE 8Ownership Structure

Region Owner MA OEM ODM OBM Total Chi-Sq.

local 13.4% 43.8% 60.7% 82.7% 43.8% 0.000GBR JV 47.6% 35.6% 28.6% 15.4% 34.5%

foreign 39.0% 20.5% 10.7% 1.9% 21.7%local 6.7% 51.4% 43.8% 57.1% 41.3% 0.039*

PNG JV 13.3% 16.2% 25.0% 14.3% 17.3%foreign 80.0% 32.4% 31.3% 28.6% 41.3%local 17.5% 60.5% 63.6% 56.0% 47.6% 0.000*

SGP JV 8.8% 15.8% 15.2% 8.0% 12.6%foreign 73.7% 23.7% 21.2% 36.0% 39.8%

Notes: JV = Joint Venture; *at least one cell with expected frequency <5.




Input Indicators

H1: All the available ‘hard’ input indicators (Tables 9–11) have been an-alysed without any conclusive picture. Only the data for Bangkok isshowing a straight relation between TCs and all relevant input indicators.But even here a correlation analysis of the single values and the TCgroups found just a very weak correlation between these variables. Thiscan partly be explained by the structural weakness of input indicators:high input figures do not automatically result into innovation success,for example, a high number of R&D personnel in itself does not leadnecessarily to a large number of new products. This depends, on the onehand, on other determinants like the qualification of the human capitaland on the other hand on the way the innovation process is organised.Therefore, one should not be mislead by these results.

H2: In contrast, the hypothesis that there is an apparent differencebetween the case study regions is supported by the data. Despite contra-dictory figures for some TCs, the resources committed to R&D and innov-ation in Penang and Singapore are generally explicit higher than inBangkok. The differences between Singapore and Penang are rather small.While the R&D intensity and the innovation intensity is higher in MAand OEM companies in Penang than in Singapore, ODM and OBM firmsin Singapore lead over their counterparts in Penang. Obviously ODMand OBM companies in Singapore have reached a level where they canafford more investments in the development of new products and pro-cesses. The R&D personnel intensity indicates a leading position bySingapore. Comparing these findings with the national secondary dataleads to the conclusion that the regional innovation activity in Penang isindeed more advanced than that for the nation as a whole, indicating ahigher performance of the RIS. The figures for Bangkok on the otherhand back up the impression received by the secondary data.

TABLE 9R&D Intensity 1999 (Mean of R&D Expenditure/Sales in %)

GBR PNG SGP

MA .01 8.81 4.63OEM .16 5.13 3.50ODM .44 2.00 3.28OBM 1.19 4.67 9.05Total .37 5.56 5.07

Note: PNG and SGP based on mid-point estimation.




TABLE 10R&D Personnel Intensity 1999

(Mean of Employees Involved in R&D/Total Employees)*

GBR PNG SGP

MA .15 2.93 20.42OEM .35 1.54 29.80ODM 1.26 2.17 6.56OBM 2.31 2.32 20.55Total .81 2.10 19.82

Note: *Total employees: in SGP and PNG full-time equivalent, in GBR headcount.

Additionally, companies were asked if they carry out R&D. The outcomeof this ‘soft’ input indicator is shown in Table 12.

TABLE 11Innovation Intensity 1999 (Mean of Innovation Related Expenditure/Sales in %)

GBR PNG SGP

MA .12 17.94 8.03OEM .35 12.10 9.50ODM .46 3.50 6.91OBM 1.80 3.58 11.81Total .62 11.13 8.98

Note: PNG and SGP based on mid-point estimation.

TABLE 12Share of All Companies that Carry Out R&D

MA OEM ODM OBM Total Chi-Sq.

GBR 8.3% 10.3% 14.3% 17.0% 11.5% 0.473*PNG 33.3% 27.0% 26.7% 85.7% 33.8% 0.023*SGP 35.1% 21.1% 27.3% 44.0% 29.3% 0.107

Chi-Sq. 0.000

Note: *At least one cell with expected frequency <5.

H1: The hypothesis that companies at different TC stages show signifi-cant differences in their R&D behaviour could not be backed by the data(see figures for Chi-square significance for each region). Nonetheless,there is a low positive correlation between the TC stage and the share ofcompanies that carry out R&D (r

s = 0,259). If one just analyses the correl-

ation between TCs and OEM–OBM the strength of the correlation in-creases (r

s = 0,474). This can be explained by the different behaviour of

manufacturing arm companies, of which only very few carry out R&D




in Bangkok, but many in Penang and Singapore. Since MA companiesare mainly (at least partly) owned by multinationals, the R&D strategyof MNCs seems to be fairly different between the regions.

H2: A very clear and significant (5 per cent level) distinction in thetotal share of companies that carry out R&D can be noted betweenthe three regions. The companies in Bangkok lag far behind, whileSingapore is approximately head-to-head with Penang.

Throughput Indicators

H1: It can be observed that OBM firms applied for and obtained morepatents than the other TC groups, except MA in Penang and Singapore.MA firms in Penang and Singapore do extremely well in comparison toBangkok when it comes to applying for patents (Table 13). There canbe two explanations for this pattern: first, there might be a different ap-proach towards innovation by the MA companies (which are mainlyMNCs) in these regions, i.e., MNCs see their Thailand affiliates mainlyas assembly plants with no responsibility for process or product develop-ment, while MNCs assign some of these tasks to their operations inPenang and Singapore. Second, the human capital base in Thailand couldsimply not be sufficient for the scientific work connected with acquir-ing patents, which would also explain the poor patent activity in all TCgroups. However, the relation between application for and acquisitionof patents is fairly poor in Penang and Singapore. In Singapore the meannumber of obtained patents increases with the TC level (except MA).

TABLE 13Mean of Patents (Domestic and Foreign;

Figures for the Latest Three Years Prior to Survey)

GBR PNG SGP

No. of No. of No. of No. of No. of No. ofpatents patents patents patents patents patents

applied for obtained applied for obtained applied for obtained

MA .23 .14 1.89 .22 2.30 .83OEM .01 .00 .80 .40 .46 .07ODM .00 .00 .40 .20 .33 .25OBM .79 .55 5.33 4.50 .79 .36Total .26 .17 1.80 1.03 1.16 .42

No. ofpatents (abs.) 62 41 63 36 97 35




H2: The data hints at a difference between Bangkok on the one handand Penang and Singapore on the other hand. Again, compared to thenational figures, companies in Penang are doing very well, while thosein Bangkok seem to be just in line with the national numbers. Surprisingly,companies in Singapore have got a pretty poor relationship between pa-tents applied for and patents granted. Furthermore, the OBM companiesin Singapore seek and obtain patents just as often as companies inBangkok do. The interpretation of these findings are hampered by theweakness of the indicator: the sheer number of patents does not includeany information about their quality, i.e., some of the Singaporean patentsmight be ‘path-breaking’ product patents, while some of the Thai patentsmight be more basic design patents or process-related patents. Furtherresearch in this aspect seems to be necessary.

Output Indicators

H1: First, considering product innovators (Table 14), it can be seen thatthe distributions are significantly different at the 5 per cent level. Thissuggests that the TC level has influence on a firm’s innovation behaviour.Second, one can observe a low but positive correlation between TC leveland share of companies that introduced new or improved products to themarket (r

s = 0,281). If we now limit the analysis to OEM–OBM firms,

the correlation increases to a fairly higher correlation level (rs = 0,580).

This outcome seems to be the result of the different innovation behavioursof MA companies. While an above average share of MA firms are innov-ative in Penang and Singapore (only exceeded by OBM companies),MA firms in Bangkok are just as often innovative as the average. SinceMAs are mainly owned by MNCs, this result leads once again to theconclusion that the innovation behaviour of MNCs in Bangkok is verydifferent from that of MNCs in Penang and Singapore. Additionally,MA firms in Bangkok are less often purely foreign owned than inSingapore and Penang, which could be an alternative explanation for thedissimilar innovation behaviour of MAs.

Moreover, the comparison between product innovation and processadaptation (Table 15) displays an increase in the share of innovatingOEMs, reflecting the importance of competitive production technologyfor the economic success of OEMs.

These results are also in line with the channels of convergence pre-sented in Table 1. MAs, which are at least partly owned by MNCs, presentthe deepest/most active channel of technological learning since they arethe result of FDI. In Penang and Singapore MAs obviously receive a lot




of know-how from their parent/associate companies, which enables themto become successful product and process innovators. Again, the strategyof MNCs for their Bangkok-based subsidiaries seems to be very different.

Moreover, the increasing innovation performance from OEM to OBMis in accordance with the depth of the channel of convergence: OEMs,whose operations can be seen as ‘simple market-based trade’, are lesslikely to innovate than ODMs or OBMs, whose more sophisticated designand/or development activities require an ‘organised market’ approach,i.e., close interactions between buyers and sellers.

H2: The data suggests that companies based in Bangkok obviouslylag behind companies in the other two regions in respect to their innov-ation output. The chi-square test reveals that the differences between theregions are significant at the 5 per cent level for product innovations aswell as process adaptations. Figure 1 displays the regional differences inR&D, product innovation and process adaptation. As already stated forthe input and throughput indicators, the innovation performance of com-panies in Penang is exceptional for Malaysia. This can be seen simultan-eously as an indicator and as a prerequisite for a well-performing RSI.Contrary, the RSI in Bangkok does not seem to make up for the weaknessof the NSI.

TABLE 14Share of All Companies that Introduced Product Innovations into the Market

(in the Last Three Years Prior to the Survey)


GBR 9.5% 5.1% 17.9% 20.8% 11.5% 0.029*PNG 53.3% 32.4% 33.3% 85.7% 41.9% 0.043*SGP 42.1% 19.7% 24.2% 56.0% 31.9% 0.001*

Chi-Sq. 0.000

Note: * At least one cell with expected frequency < 5.

TABLE 15Share of all Companies that Adopted Process Innovation(in the Last Three Years Prior to the Survey; N.B. GBR:

Only Process Innovations Developed in Thailand)


GBR 9.5% 10.3% 14.3% 20.8% 12.8% 0.228*PNG 53.3% 40.5% 26.7% 85.7% 44.6% 0.059*SGP 43.9% 32.9% 24.2% 32.0% 34.6% 0.273*

Chi-Sq. 0.000

Note: *At least one cell with expected frequency < 5.




The same conclusion holds true if one considers the share of companiesthat are regarded as innovative (see Table 16). A company is classifiedas innovative if either its share of new or improved products of the totalturnover exceeds 25 per cent or the share of new processes of the pro-duction volume exceeds 25 per cent.

TABLE 16Share of Innovative Companies


Product InnovationGBR 2.4% 2.6% 3.6% 11.3% 4.5% 0.063*PNG 13.3% 21.6% 6.3% 42.9% 18.7% 0.186*SGP 21.1% 10.5% 3.0% 20.0% 13.6% 0.063*

Chi-Sq. 0.000

Process InnovationGBR 3.6% 7.7% 3.6% 11.3% 6.6% 0.291*PNG 26.7% 27.0% 6.3% 14.3% 21.3% 0.341*SGP 31.6% 15.8% 15.2% 20.0% 20.9% 0.122*

Chi-Sq. 0.000

Note: *At least one cell with expected frequency < 5.

FIGURE 1Share of Companies in Each of the Three Case Study Region

that Carr y Out R&D, Pr oduct and Process Innovation that Carry Out




Cooperation and Perception of the Regional Business Environment

Since innovation is seen as a chain-linked process, which requires veryspecialised, fragmented knowledge and whose outcome is highly uncer-tain and therefore connected with high risks and costs, companies tendto cooperate with other actors either within their RSI/NSI or at a globalscale. Consequently, the cooperation behaviour of firms is an essentialpart of their innovation activity. Besides cooperation patterns, the per-ception of the business environment by the firms offers some insightinto the (perceived) quality of the national or regional system of innov-ation. This section tries to shed some light on the cooperation behaviourof firms and their perception of the regional business environment.

Tables 17 and 18 depict the share of companies that cooperate withparticular actors of the innovation system in an intense or very intensemanner. Following conclusions can be drawn from the figures presented:

H1: The data is inconclusive concerning the behaviour of the TC groups.However, there is a significant distinction in the cooperation with theparent associate company overseas in Singapore. But this result ratherowes to the ownership structure than the influence of TCs itself.H2: In contrast, characteristic features can be identified for the cooper-ation behaviour in the three regions.

Product Innovation

First of all, all companies focus basically on the same cooperation part-ners: customers are the most important partners, followed by the parentor associate company overseas, suppliers (either domestic or foreign),technical service providers and R&D institutes and universities. But, farless companies in Bangkok tend to cooperate on an intense or very intensebasis as compared to Penang and Singapore. Since intensity is, to a certainextent, related to spatial proximity, this might reflect a lack of suitablecooperation partners in Bangkok, missing absorptive capacity by the com-panies due to a lack of skilled human capital so that they cannot utilisepossible knowledge flows in cooperations, a mismatch between the tech-nological focus of the companies and possible cooperation partners inpublic research institutes, and/or a lack of awareness about the importanceof cooperation for innovation. The figures therefore hint towards a certainstructural weakness of the RSI in Bangkok.




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Process Innovation

Again, the companies in all three regions share the same cooperationpattern: most likely they cooperate with customers and parent or associatecompanies overseas. Suppliers are also important partners, followed bytechnical services. As with product innovation cooperations, the shareof companies that pursue an intense cooperation in Bangkok is by faroutnumbered in Penang and Singapore. The difference is especially strik-ing in the numbers for the most important cooperation partners.

Moreover, for both kinds of innovation cooperations companies inSingapore seek more often intense cooperation with R&D institutes anduniversities. This is presumably a reflection of the quality of the localscience infrastructure and moreover qualitative and quantitative endow-ment of the RSI/NSI with this key actor. On the other hand, companiesin Penang strongly rely on cooperations with service companies (eitherbusiness or technical).

Additionally, the perception of current business environment conditionsby companies evidences the (perceived) quantity and quality of national/regional actors (i.e., possible cooperation partners), government policiesand regulations as well as cultural norms. Put shortly, the perception re-flects the quality of the national regional system of innovation.

H1: In general, the TC groups do not show major differences in theassessment of the business environment conditions. In some aspects aslightly higher share of OBM companies seems to evaluate conditionsas rather good or good. For the availability of suitable manpower in thebusiness sector in Bangkok and Singapore this is significant at the 5 percent level. Obviously, companies with lower TCs experience more diffi-culties in finding employees for business positions. This indicates a bottle-neck in the supply of qualified personnel, which could be an explanationfor the low innovation performance of Bangkok-based companies.

H2: As can be seen in Figure 2 there is an articulate discrepancy be-tween the quality of the business environment condition in Singaporeand the other two regions. All conditions were more often positively ratedin Singapore. In particular, Singapore’s conditions relating to governmentattitudes, policies and legal regulations (availability of government incen-tives for innovation, openness of government departments and regulatoryauthorities to innovation, intellectual property protection) and to infra-structure (quality of telecommunications and IT services) are superior.Other striking differences can be detected in the endowment of localuniversities and R&D institutions as possible advisors and/or collabor-ation partners. Little variations display the cultural variables (tolerance




FIGURE 2Share of Companies (in %) that Assess the Following Business

Envir onment Conditions as Rather Good or Good




of failure, attitude of people, customers and suppliers to innovation).Surprisingly, only the infrastructure and financial aspects are rated higherin Penang than in Bangkok, while most of the actor-related conditions(local universities and R&D institutes for technical support and R&Dcollaboration; technical services for support) are more often seen as beingpositive in Bangkok than in Penang. This could be the result of a lack ofS&T infrastructure in Penang (see Revilla Diez and Kiese forthcoming)as well as higher expectations/requirements by firms in Penang concern-ing the quality of these partners.

Conclusion

At the beginning of the article we emphasised the importance of innov-ations for economic development, described briefly how regional andnational characteristics can either foster or hinder innovation-relatedcooperations and depicted the aspects of the innovation process uniqueto late industrialising countries.

The key argument is that companies in late industrialising countriesneed to adopt and diffuse technologies already existing in advanced coun-tries to be competitive. Therefore, innovation in this context is understoodas products/processes new to the firm rather than new to the world. Tobe able to adopt and diffuse new technologies, companies need to developtechnological capabilities through technological learning.

Since TCs are the result of successful learning, we have grouped thesurveyed companies accordingly. By analysing their innovation and co-operation behaviour as well as their perception of the business environ-ment our intention has been to learn more about these groups positionedat different stages of the ‘learning curve’.

In a second step we have displayed geographical differences in theinnovation behaviour in three case study regions, GBR, Penang andSingapore, which also reflect the quality of the respective RSI/NSI. Basedon these considerations we have formulated two hypothesises:

H1: The higher a company’s level of technological capabilities thehigher its innovation and cooperation activity.H2: A company’s innovation activity is influenced by the developmentlevel of its host country and its host country NSI/ RSI. An ‘innovationhierarchy’ is therefore supposed, with Singapore on top and Bangkokat the bottom.




The subsequent data analysis led to the following conclusion:H1: While the analysis of the input indicators only partly supported

our assumption, the analysis of the output indicators showed a moreconspicuous connection between the TC level and innovation activity.There is a tendency of increasing innovation activity from OEM to OBMcompanies. However, the MAs obviously ‘behave’ differently: most ofthe MAs are owned by MNCs, which seem to follow different innovationstrategies in the three case study regions. Some appear to act as innovatorswhile others operate as extended workbenches—further research in thisrespect seems worthwhile. The examination of the cooperation behaviourand business environment condition did not produce TC-specific results.In further, more detailed research more variables (for example, innovativevs. non-innovative) will be taken into account.

H2: On the other hand the enquiry into the spatial hypothesis displaysa more conclusive pattern. Companies in Bangkok lag behind in all avail-able input and output indicators. Nevertheless, innovating companies inBangkok are mostly as active as their counterparts in Penang andSingapore with regard to innovation activities. Further research has toask whether or not there is a fundamental distinction in the innovationbehaviour of innovating/innovative companies in the three regions.Moreover, Thai companies also cooperate less often than companies inthe other two regions and fewer companies evaluate the business envir-onment conditions positively. This supports our assessment—basedon secondary statistics—and the perception in the literature (cf.Intarakumnerd et al. 2002) of the Thai-NSI as being poorly developed.Furthermore and contrary to the case of Penang, the RSI in Bangkokdoes not seem to be able to (at least partly) make up for the structuralweaknesses of the NSI, for example, by providing qualified personnel.In contrast, Singapore’s companies rate their environment conditions ashighly positive. Firms in Singapore carry out R&D more often and assignmore personnel to R&D than companies in the other two regions. How-ever, they neither apply for nor obtain more patents than Penang’s com-panies, nor do they generally show higher output figures. Even the shareof innovative companies does not exceed those of Penang. The same canbe observed in the share of intense or very intense cooperations (Singaporehas plainly a higher cooperation rate with R&D institutes and universitiesthough). Finally, Penang’s companies do very well with regard to innov-ation activities and output as well as cooperation, despite less favourableenvironment conditions than in Singapore and a fairly weak NSI.




Since the presented figures are the starting point for our future research,a number of research questions remain unanswered. For example, whatare the mechanisms by which companies in developing countries buildtechnological capabilities and how do international linkages fosterthis process? How do the generic routes for rapid technological catch up(see section titled ‘Technological Capabilities’) work in detail? Firm-specific, in-depth case studies might lead to a better understanding ofthis question and can therefore provide information about the needs oflatecomer firms for future policies. Finally, how can the lack of qualifiedpersonnel (especially in Bangkok) be met by a better interaction betweenresearch and training institutes (for example, universities) and industry?

NOTES

1. These conclusions are the result of empirical work in Latin America but are supposedto hold true for other developing countries, too.

2. Furthermore, Wong (1999) distinguishes between OBM and own idea manufacturing(OIM). The latter company is developing own product ideas, but does not market itsproducts under its own brand.

3. The surveyed firms in Thailand were not asked about the location of their cooperationpartners.

4. When the gross regional product at current market prices per capita for 1997 is set as100 for Thailand, the figures for Bangkok (324) and Vicinity of Bangkok (206) are aclear indication of the outstanding performance of the Bangkok metropolitan regionwhen compared to the rest of the country (Alpha Research 2003; own calculations).

5. When the gross domestic product (at purchasers’ value) at constant 1987 prices percapita for 1997 is set as 100 for Malaysia, the figure for Penang (142) also pointstowards an above average economic performance (Asian Development Bank 1999;SERI 1999; also the Economic Planning Unit—Prime Minister’s Office Malaysia2003).

6. Unfortunately the Thailand Statistical Classification (TSIC) two-digit code combinesthe important machinery and electronic sector, which therefore cannot be analysedseparately.

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