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Technological Capabilities and Innovation in Southeast Asia: Results from Innovation Surveys in Singapore, Penang and Bangkok Martin Berger and Javier Revilla Diez Science Technology Society 2006; 11; 109 DOI: 10.1177/097172180501100105 The online version of this article can be found at: http://sts.sagepub.com/cgi/content/abstract/11/1/109

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Technological Capabilities and Innovation in Southeast Asia: Results from Innovation Surveys 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 is the development of technological capabilities. It can be assumed that these capabilities correlate 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 innovation system influences the development of firms’ technological capabilities. Consequently, this article compares groups of firms with different technological capabilities in three innovation systems. Analysing innovation activities, cooperation behaviour and the perception of the business environment, conspicuous differences between the innovation systems are found. Contrary, the comparison of the different technological capability-groups brings about less conclusive results, which indicate only a limited interrelation between technological capabilities and innovation-related activities.

∗ The authors would like to thank their cooperation partners in the case study regions who made this research project possible: Mrs Anna Ong, Socio-Economic and Environmental Research Institute Penang, Professor Wong Poh Kam, Centre for Management of Innovation and Technopreneurship at the National University of Singapore, Dr Chatri Sripaipan and Dr Patarapong Intarakumnerd of the National Science and Technology Development Agency, Thailand and Dr Peter Brimble of Asia Policy Research. Helpful comments of two anonymous referees on an earlier version of this article are gratefully acknowledged. The project was funded by the German Research Foundation (DFG). Martin Berger is at the Institute of Technology and Regional Policy, Joanneum Research, Wiedner Hauptstrasse 76, A-1040 Wien, Austria, e-mail: martin.berger@joanneum.at. Javier Revilla Diez has a Chair of Economic Geography, Institute of Geography, ChristianAlbrechts Universität Kiel, Ludewig-Meyn-Straße 14, 24098 Kiel, Germany, e-mail: diez@geographie.uni-kiel.de. Science, Technology & Society 11:1 (2006) SAGE PUBLICATIONS NEW DELHI/THOUSAND OAKS/LONDON DOI: 10.1177/097172180501100105

110 n Martin Berger and Javier Revilla Diez Introduction

TODAY’S WORLD ECONOMY has been characterised as a ‘knowledge-based economy’ (OECD 1996) with knowledge being the most important resource and learning being the most important process (Lundvall 1996). According to this assumption it is essential not only for developed but also for developing countries to foster the innovativeness of their companies. This article scrutinises empirical data about firms’ innovative activities and cooperation from Singapore, Penang (Malaysia) and the Greater Bangkok Region (Thailand) in order to establish similarities and differences between these regions and between companies at different stages in respect to their technological capabilities (TCs). Key questions are: l

Why is it important for companies in developing countries to develop TCs and how do TCs relate to innovation? Do companies with different TCs also differ in respect to their innovation activity, if yes, how do they vary? In what way does a company’s location influence its TC-level, its innovation and cooperation behaviour?

The article is structured in the following way: First, a brief theoretical overview is given, stressing the importance of knowledge, learning and innovation for economic development, summarising the concepts of spatial systems of innovation and presenting the concept of technological capabilities. The latter is used in our research to distinguish companies at different stages of their catching-up process. Second, we present the methodology of the surveys conducted in Singapore, Penang and Thailand and its predecessor-surveys in Europe. Finally, the dataset and its key characteristics is introduced. Moreover, in this final chapter two hypotheses are tested: First, companies with advanced TCs are more innovative, cooperative and assess the business environment conditions in a different way than companies with less advanced TCs. Second, distinctive differences can be observed between companies’ innovation and cooperation behaviour as well as their assessment of the business environment conditions in first-tier newly industrialised countries (NICs) like Singapore, fast-followers like Penang and ‘laggards’ like Thailand respectively (Intarakumnerd et al. 2002).

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Theoretical Background: Knowledge, Learning, Innovation and Economic Development

Technological change used to fall like manna from heaven, at least as long as it comes to economic theory, until the early 1990s, when Paul Romer (1990) introduced technological change into the neo-classical model of economic growth. In his model innovation is explained endogenously and is seen as the driving force of economic growth. Key determinants of technological change are human capital and knowledge. While human capital encompasses all the knowledge and skills that are bound to a person, the term knowledge describes information in the form of publications or blueprints. Additionally, the understanding of the innovation process has changed fundamentally. Instead of being a linear process (research and development or R&D–production–marketing) it is now seen as a chain-linked process, which is achieved in an interactive way between partners internal and external to the firm (Kline and Rosenberg 1986). This change is at least partly caused by increased uncertainty, complexity and costs of the innovation process. Since this process is interactive and therefore based on a division of labour, the transfer of knowledge between the partners is all important. In the field of innovation research it is generally accepted that knowledge can be classified as being tacit or codified. While codified knowledge is written down in articles and manuals or is embedded in technology, tacit knowledge is bounded to specific persons or organisations. Tacit knowledge is reflected by a person’s skills or a firm’s routines. Since it is difficult to articulate tacit knowledge, its transfer is restricted to face-to-face contacts. This does not hold true for codified knowledge, which is globally available by means of modern communication technologies or trade. But even codified knowledge has to be internalised, which means that it has to be converted 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 or organisation acquires tacit or codified knowledge. The ability to learn depends on the stock of previously accumulated knowledge, and it becomes easier with an increased knowledge stock. Therefore, a company’s learning capabilities depend on its ability to assess, embrace and utilise

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new knowledge, which has been termed ‘absorptive capacity’ by Wesley Cohen and Daniel Levinthal (1990). In general four means of learning can be differentiated: 1. Learning by searching: companies learn while conducting R&D activities 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 human capital, either through personnel training (learning by training) or through recruitment of professionals (learning by hiring). 4. Learning by interacting: companies learn while interacting with other companies, especially customers and suppliers (Lundvall 1988). Due to its interactive and therefore social character, learning by interacting is strongly influenced by the institutional and organisational framework. While learning by interacting seems possible between remote partners under the condition of stable and standardised technology (ibid.: 355), it is fostered by spatial and cultural proximity in an uncertain business environment with complex technologies and rapid technological change. Since learning by interacting is supposed to be of particular importance for innovations (ibid.; Gertler 1995), the factors influencing this kind of learning have received much attention by recent research work, which finally resulted in the elaboration of the concepts of national and regional systems of innovation.

National and Regional Systems of Innovation

Using the paradigm shift from the linear to the chain-linked model of innovation as a starting point and taking into account the importance of intra- and inter-firm cooperations for the successful development of innovations, a group of researchers has developed concepts of national systems of innovation (NSI) since the mid 1980s (Dosi et al. 1988; Freeman 1987; Lundvall 1985; Nelson 1993). The NSI approach is at the same time a theoretical and analytical concept. Theoretically it is rooted in institutional and evolutionary economics. While some authors (for example, B.Å. Lundvall) support an institutional

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economics approach, which examines the relevance of an economy’s institutional and organisational structure for the efficient allocation of production factors, others (for example, C. Freeman) belong to the school of evolutionary economics, which contemplates the behaviour of economic agents and their path-dependent decision-making processes as well as the grown structures of the economy and its information channels (Grupp 1997; Revilla Diez 2002a). The analytical part of the NSI approach was first elaborated by Richard Nelson (1993) in order to compare the NSI of fifteen countries. The main components of NSI are organisations, institutions and the relations/interactions among them. Organisations are defined as formal structures with an explicit purpose, which are consciously created (Edquist and Johnson 1997: 47). Important organisations are companies, knowledge-producing and knowledgediffusing organisations like universities, political organisations such as parliaments or ministries, bridging organisations that facilitate technology transfer between science and business and finally social organisations like trade unions. Institutions are ‘sets of common habits, routines, established practices, rules, or laws that regulate the relations and interactions between individuals, groups and organisations’ (ibid.: 46). They can be either formal (laws) or informal (traditional way of doing business).While organisations are regarded as players of the game, institutions are seen as the rules of the game. Bengt-Åke Lundvall et al. (2002: 220) view the following three institutional dimensions as having a major impact on learning and innovation behaviour: first, the time horizon of the agents (short-term in Anglo-Saxon countries vs. long-term in Japan); second, trust between agents; and third, the pre-dominating rationality (communicative rationality rather than instrumental rationality seems to support innovative behaviour). Interactions between organisations are either market or non-market relationships. The latter is supposed to be highly relevant for learning (Edquist 2001; Lundvall and Maskell 2000). Interaction can take the form of flows of knowledge and information as well as flows of investment and funding, but also informal arrangements like networks (Cooke et 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 developing countries does not seem to be appropriate. Rather an analysis of

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the NSI in NICs and developing countries has to take into account the following four aspects: 1. In developing countries companies are rarely working at the technological edge. Rather, it is crucial for these companies to acquire, utilise, adopt and improve technologies that are already established in advanced countries (Lall 2000; Wong 1995). This is the reason why in the context of developing countries we use the broad definition of the Oslo Manual (OECD 1997), which defines innovation as new products, processes and organisational arrangements that are new to the firm rather than new to the world (see also Ernst et al. 1998b: 13). Furthermore, industrial innovation in developing countries is mainly conducted in-house and in an informal manner, where ‘R&D activities are not clearly and formally articulated with the enterprise strategy’ (Arocena and Sutz 1999: 13).1 2. Despite being of major importance for the development of absorptive, learning and technological capabilities, human resource development has so far been largely neglected. Therefore, future research has to consider the science and education infrastructure (Lundvall et al. 2002; Wong 2001). 3. International links offer learning opportunities for developing countries, but they are not well accounted for in the NSI concept. Due to a dualistic and inhomogeneous economic structure and a weak domestic knowledge base, interactions between national agents are supposed to be less important in developing than in advanced countries (Ernst 2002; Wong 2001). Since the NSI is hardly developed, ‘international linkages need to prepare the way for the development of 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 institutional base and advanced infrastructure. For developing countries on the other hand it is an ex-ante concept, with the NSI being rather an aim, that has to be built up along with economic development, than a given asset (Arocena and Sutz 1999; 2002). For this reason the focus of analysis in developing countries should be on ‘system construction’ and ‘system promotion’ (Lundvall et al. 2002: 226). Regarding these criticisms, Poh Kam Wong (2001) has elaborated a modified NSI concept especially for NICs. The main organisations in his NSI

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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 human resource development into the centre of his research. The key objectives of his NSI model are ‘to build up the stock of scientific and technological resources and to allocate and deploy these resources to the respective innovation actors’ (Wong 2001: 544). Hereby he takes into account the diffusion of technology which is crucial for developing countries. Moreover, he includes ‘international technology linkages’ to overcome the limited focus on domestic interactions. As a result of these changes and the widening of the concept, it is necessary to consider all direct and indirect policies effecting either the agents of the system or their interactions. In the course of the debate on globalisation, different scholars claimed the end of the nation state and the rise of the regions as ‘natural economic zones’ (Ohmae 1993: 78). Even though these claims seemed exaggerated, they resulted into a stronger research interest in regions. In the field of innovation research this led to the formulation of the concept of regional systems of innovation (RSI) (Braczyk et al. 1998; Cooke 1992). Taking on the basic ideas of the NSI concept, the key notion of the RSI is that regions offer particular environment conditions and opportunities for interactions that can either foster or hinder the cooperation between innovative actors in a region. Additionally, the amount and quality of regional innovation actors, manufacturing companies, business service companies, research institutes and universities, influences the opportunities for learning by interacting (Cooke and Morgan 1998). In conclusion, the endowment of a region with innovative actors and environment conditions that favour cooperation and innovation activities constitute the extent and utilisation of the regional innovation potential. Bearing in mind the positive effects of spatial proximity for innovationrelated social capital building (trust) and knowledge spillover, the regional scale provides an important research level. A special case in point are metropolitan systems of innovation, that are restricted to major metropolitan areas and their hinterland (Fischer et al. 2001; Revilla Diez 2002b). Since these regions often encompass the major ‘growth engines’ of the national or global economy, their system of innovation, their endowment with innovative actors and the environment conditions they offer are of special interest.

116 n Martin Berger and Javier Revilla Diez Technological Capabilities

Like the NICs of East Asia (South Korea, Taiwan) the NICs of Southeast Asia (Singapore, Malaysia and Thailand) have experienced a period of remarkable growth during the final decades of the twentieth century. Being all rather late-industrialising countries they managed a pretty successful catch-up process despite at least two severe competitive disadvantages: first, they are far away from the lead user markets in North America, Europe and Japan and therefore disconnected from essential userproducer links; and second, they are distant from the leading sources of technological 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 of acquiring and improving on technological capabilities rather than of innovating at the frontiers of knowledge. This process essentially consists of learning to use and improve on technologies that already exist in advanced industrial economies’ (Lall 2000: 13). Because of this, innovations in developing countries are often defined as products, processes or types of organisations new to the firm (Ernst et al. 1998b; Hobday 2000). There are various ways to categorise firm-level TCs (see Bell and Pavitt 1995; Lall 1992; Marcelle 2002; Wong 1999b). A very comprehensive framework was elaborated by Ernst et al. (1998b), who see the following classification as ‘a sequential ordering of priorities for late industrialization strategies based on imported technology’ (ibid.: 17). l

Production capabilities: which define the knowledge and skills necessary to operate a plant. Basically, these capabilities encompass production management, production engineering, repair and maintenance. Investment capabilities: are the knowledge and skills that are used to conduct a new industrial project, from pre-investment activities such as feasibility studies to project execution. Moreover, the ability for efficient external sourcing is part of investment capabilities. Minor change capabilities: refer to a company’s ability for continuous improvement, adaptation and incremental innovation of products, processes and organisational arrangements. Strategic marketing capabilities: include collecting market intelligence, the development of new markets, the establishment of distribution channels and the provision of customer services. Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

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Linkage capabilities: the competence to organise knowledge- and technology-transfer networks within the firm, with other companies and with the domestic science and technology infrastructure are summarised in the term linkage capabilities. Major change capabilities: refer to the ability to conduct R&D, develop and introduce new products, processes and organisational arrangements either in-house or in cooperation with customers, suppliers, public research institutes etc.

Technological capabilities are the result of technological learning. In this process a company acquires codified knowledge (for example, knowledge embedded in technology or written down in manuals), combines it with existing tacit knowledge and thereby builds up a stock of firm specific, tacit knowledge. This is a conscious and purposive as well as a costly and time-consuming process, which is non-linear but path-dependent and 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 they do it depends on the structure and efficiency of the RSI (Fischer et al. 2001) as well as the NSI: â&#x20AC;&#x2DC;successful technological learning ... requires an effective national innovation systemâ&#x20AC;&#x2122; (Kim 1997: 219; cf. Wong 1999a). Still, the mechanisms for successful technological learning have to be enquired for. The vast literature on international technology transfer has identified many different transfer channels, ranging from licensing, foreign direct investment, joint ventures and subcontracting to overseas training and education (Hobday 2000: 133; cf. Kim 1991; Mowery and Oxley 1995; Pack and Saggi 1997; Wong 1999b). Meric Gertler (2001: 9) has introduced eight channels of convergence (Table 1), which identify channels of best practice-learning but also offer a convenient framework for the mechanisms of technological learning. A prerequisite for this kind of organisational learning is individual learning of the workforce. This implies that individual learning capabilities are essential for the development of TCs. Therefore, formal learning (for example, learning by training in universities), non-formal learning (for example, training on the job) and informal training, which is defined as a lifelong process by which persons who work in foreign affiliates or in domestic companies which closely interact with

118 n Martin Berger and Javier Revilla Diez TABLE 1 Channels of Convergence Passive/shallow Media/education Travel Management consultants Trade (‘simple market’) Trade (‘organised market’: that is, intense interaction between buyer and seller) Alliances (strategic alliances, joint ventures, cooperation agreements) Mergers/ acquisitions Active/deep Foreign direct investment Source: Adapted from Gertler 2001: 9.

foreign TNCs [Transnational Companies] may acquire values, attitudes and beliefs embedded in the organizational culture of TNCs through 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 characterised five generic routes for rapid technological catch-up, which can be seen as successful development paths for technological learning (Wong 1999a: 8). One route is the ‘Reverse Value Chain’ strategy that builds upon Michael Hobday’s work on a OEM-ODM-OBM migration strategy (Hobday 1995, 2000). The notion of the concept is, that latecomers pursue a certain strategy to develop technological capabilities: first they start developing process capabilities, followed by product design capabilities and finally new product creation/branding capabilities. ‘This is a reversal of the normal sequence of value chain activities pursued by large established high-tech firms in advanced countries’ (Wong 1999a: 8). According to this concept a company starts as an Original Equipment Manufacturer (OEM), performing simple component subcontracting or assembly operations for a TNC. The buyer (TNC) provides detailed product specifications and sells the product under its own brand and through its own distribution channels (Hobday 2000; Wong 1999a). So the OEM phase marks companies that solely rely on their production capabilities. At the next stage the latecomer firm becomes an Original Design Manufacturer (ODM). The buyer still supplies the general product requirements, but the ODM firm is responsible for the detailed design and production process. Consequently, the shift from OEM to ODM is based on the

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gaining of product design, product-process interfacing, manufacturing and sometimes component design capabilities (ibid.). The final step is the move towards Own Brand Manufacturing (OBM).2 The company develops its own products and sells them under its own brand. Often it even develops specific distribution channels. To become an OBM firm a company needs at least basic capabilities in the fields of marketing, product development and R&D. Table 2 summarises the transition from OEM to OBM. As Michael Hobday (2000) points out the OEM to OBM system enables firms to reach into international markets, export large volumes of goods, realise economies of scale and invest in automation. Furthermore, by supplying demanding customers in the leading markets latecomer firms learn by doing, using and interacting (with the foreign partner), and become acquainted with product and process technology as well as end-user market requirements (Wong 1999a). Therefore, the OEM-OBM system can be seen ‘as a training school for technological learning’ (Hobday 2000: 134). TABLE 2 Transition of Latecomer Firms: From OEM to ODM to OBM Technological transition

Market transition

OEM Learns assembly process for standard, simple goods

Foreign TNC/ buyer designs, brands, and distributes

ODM Local firm designs (or contributes to the design, alone or in partnership with the foreign company) and learns product innovation

TNC buys, brands, and distributes TNC gains post production valueadded (PPVA)

OBM Local firm designs and conducts R&D for new products

Local firm organizes distribution, uses own brand name, and captures PPVA

Source: Adapted from Hobday 2000: 135.

Since every OEM to OBM phase indicates a particular level of technological capabilities, and respective successful technological learning, we have grouped the companies in our dataset about Singapore, Penang (Malaysia) and the Greater Bangkok Region (Thailand) accordingly (see section titled ‘Empirical Evidence’). The aim of the following empirical study is to identify differences between these groups in respect to their innovation and cooperation behaviour. If we manage to establish major differences, this could advance our understanding of the mechanisms

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and importance of knowledge transfer and learning between firms for the development of technological capabilities and therefore of economic competitiveness.

Methodological Approach

Carried out in two phases between 1995 and 1999, the European Regional Innovation Survey (ERIS) attempted to empirically assess regional innovation potentials as well as intra-regional and inter-regional cooperation relationships between innovation actors, and at providing a comparative evaluation. For this purpose usable data were obtained from roughly 8,600 innovation actors in eleven European regions, including 4,200 manufacturing firms, 2,500 knowledge intensive business services (KIBS) and 1,900 research institutions. An overview of the first phase of 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 comparability with the ERIS survey and other empirical studies, such as the Community Innovation Survey of the European Commission (European Commission 2001) or the Mannheim Innovation Panel of the Centre for European Economic Research (among others, see Janz and Licht 1999; Janz et al. 2001). On the other hand, certain specific features of the survey region and the interests of the cooperation partners had to be taken into account as well. The resulting questionnaires thus represent a compromise in which the core elements of the ERIS questionnaires could, however, be retained: l

General information: as an introduction, questions were asked about various firm characteristics such as age, size (in terms of turnover, capital stock and employees), branch, ownership and functional status, share of exports, educational profile of staff etc. In the analysis these variables can be called upon to explain differences in the innovation and cooperation behaviour. Innovation activities: innovating firms which have introduced a new or substantially improved product or manufacturing process in the past three years were asked about details concerning their innovation behaviour. Here, input indicators (personnel and expenditure on R&D and/or innovations) as well as throughput

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indicators (e.g., patents) and output indicators were registered. A firm is considered to be innovative when new or substantially improved products contribute to at least 25 per cent of its turnover, or when 25 per cent of its output is produced with new or improved processes. Innovation cooperation: in this central part of the survey firms were asked which external sources of technical knowledge they use for their innovation processes, with which external partners they cooperate and where those partners are located.3 Here, the most important questions concern the connection between cooperation and innovation success as well as the relevance of spatial proximity to cooperation.

The postal survey of manufacturing firms in Singapore was carried out between September 1999 and January 2000 in close cooperation with the Centre for Management of Innovation and Technopreneurship (CMIT) at the National University of Singapore and with Singapore’s highly influential economic promotion agency, the Economic Development Board (EDB, cf. Schein 1996). Of the 1,869 questionnaires sent out it was possible to receive 374 usable returns, resulting in a response rate of 20 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 as the more extended case study material obtained in interviews is presented by Matthias Kiese (2004). The Penang State Innovation Survey carried out in the summer of 2000 in cooperation with the Socio-Economic and Environmental Research Institute (SERI) was based on a database comprising 951 manufacturing firms. Of the 921 questionnaires sent out, 192 were returned in a quality that was usable, which corresponds to a response rate of 20.8 per cent. The initial results of this survey were presented to the government of the Federal State of Penang in an unpublished report as well as at a workshop (Ong 2001; SERI and University of Hanover 2001), and a more comprehensive evaluation of the data as well as additional interviews 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 The Brooker Group Public Company Limited to carry out the first countrywide R&D and innovation survey between January and April 2001. This was Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

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accompanied scientifically by the authors. Of the 13,415 largest Thai firms by turnover, a sample of 2,166 companies was drawn using stratified random sampling based on firm size and industry. Of these firms 1,019 returned usable replies, which corresponds to an outstanding return rate of 47 per cent (cf. Virasa and Brimble 2001). Table 3 compares the surveys of manufacturing firms carried out in Singapore, Penang and Thailand with the results of the ERIS. It becomes clear that the samples gained in Southeast Asia are within the range of the ERIS project, both absolutely (sample size) and relatively (response rates). TABLE 3 Project History and Response Rates (Manufacturing Only) Region Baden Hanover-Brunswick-GĂśttingen Saxony Alsace Barcelona Gironde Slovenia South Holland South Wales Stockholm Vienna ERIS-11 Singapore Penang Thailand

Country

Year1

Responses

Response rate

Germany Germany Germany France Spain France Slovenia Netherlands UK Sweden Austria

1995 1995 1995 1997 1997 1997 1997 1997 1997 1997 1997

Singapore Malaysia Thailand

1999 2000 2000

430 372 1,004 263 395 101 416 261 280 451 204 4,177 374 192 1,019

15.8% 20.6% 16.7% 15.0% 15.3% 12.7% 31.2% 13.7% 17.6% 24.0% 19.9% 19.7% 20.0% 20.8% 47.0%

Sources: Data from European Regional Innovation Survey; EDB/NUS-CMIT National Innovation Survey Singapore; Penang State Innovation Survey; and Thailand R&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 the three countries that host the case study regions, Table 4 compares some key secondary statistics on science and technology. It is striking that

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Singapore is doing quite well when it comes to allocating resources for R&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) and Taiwan (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. Additionally, the number of patents in force is significantly lower than in Singapore. Despite having a similar GERD and R&D personnel intensity, the numbers for private sector involvement (R&D Expenditure by Business Enterprises in percentage of all funds (BERD) and R&D personnel intensity in business) as well as for patents in force in Thailand lie clearly behind those of Malaysia. TABLE 4 Overview of the Key Indicators of the National Systems of Innovation in the Host Countries of the Case Study Regions: GERD, BERD, R&D Personnel in Total and in Business, Number of Patents in Force

Country Singapore Malaysia Thailand

GERD (1996)

BERD (1996)

Total R&D personnel (FTE) per capita (in 1,000s) (1997)

1.37 0.20* 0.13

63.3 72.8* 20.2

3.23 0.21 0.22#

R&D personnel business (FTE) per capita (in 1,000s) (1997) 2.12 0.11 0.01#

Number of patents in force (per 100,000 inhabitants) (1996) 368.4* 28.6§ 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 in allocating resources to innovation processes and has established a quite mature NSI. The national figures for Malaysia and Thailand are markedly lower, indicating a fairly weak NSI, with some higher business and patent activity in Malaysia. Bearing this national pattern in mind, we take a closer look at the regional/metropolitan scale by investigating firm-level data in the next section. While Singapore obviously combines the national and regional

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scale, we try to find out if the regional/metropolitan innovation system in the national ‘powerhouses’ of Bangkok4 and Penang5 are somewhat more developed than their national equivalent. Key Characteristics of the Dataset

The data analysis has been restricted to the machinery and electronics industry which encompasses fabricated metal products, electrical machinery and equipment as well as electronic products.6 This sector has been chosen because it offers an appropriate basis for inter-regional comparison: it is one of the most important sectors within manufacturing in all three regions, it is fairly technology-intensive and innovative. The restriction to one sector seemed necessary because first analyses found that the sector affiliation poses a strong influence on the innovation behaviour. In a next step the companies were grouped according to their technological capabilities, for example, a company was labelled OEM if it made more than 50 per cent of its turnover/sales with OEM products (see section titled ‘Technological Capabilities’). Following TC groups are distinguished in the dataset: l

Manufacturing arm of parent company or MA: products manufactured by the company according to design specifications provided by parent company or associate in the corporate group. Original equipment manufacturing or OEM: products manufactured by the company according to design specifications provided by external buyers. Original design manufacturing or ODM: products developed and designed by the company according to performance requirements of buyers. Original brand manufacturing or OBM: products developed and designed by the company and sold under its own brand.

To allow a cross comparison, the dataset was restricted to the following three metropolitan regions: l

Greater Bangkok Region (GBR): it includes the Bangkok Metropolitan Region and the Eastern Seaboard. Penang (PNG): the island of Penang is a high-tech enclave in Malaysia. Singapore (SGP). Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

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Table 5 depicts the size of the sample in the three regions and the distribution over the four technological capabilities groups. It is striking that the percentage of OBM firms is highest in the GBR, which contradicts the assumption that advanced countries host more companies with higher TCs. Instead of indicating a higher than expected innovativeness and competitiveness of the companies in Bangkok, this result instead gives evidence that the TC level in itself is not sufficient to evaluate a company’s innovative capability. To get the broad picture straight, it seems necessary to present figures reflecting the differences in economic performance between the regions. TABLE 5 Frequency of Companies According to Technological Capability

MA OEM ODM OBM Total

GBR

PNG

SGP

84 34.6% 78 32.1% 28 11.5% 53 21.8% 243 100.0%

15 20.0% 37 49.3% 16 21.3% 7 9.3% 75 100.0%

57 29.8% 76 39.8% 33 17.3% 25 13.1% 191 100.0%

Table 6 shows the means of sales per employee for 1999 in US$. Despite the disadvantage of displaying turnover rather than value added figures, Singapore’s outstanding position is documented clearly. Surprisingly, the total figures for Penang do not confirm higher sales per employee than in the GBR. Nevertheless, the MAs and OBMs in Penang do have an obviously higher mean than those in Bangkok, which supports the assumption of Penang-based companies having a higher position in the value chain. Another way of setting the TC structure into context is to use the notion of the industry life cycle. Like products industries are supposed to experience a life cycle (Klepper 1996; Revilla Diez 2002a: 76), which consists of four phases: 1. Introduction: mainly small companies with a high innovation output dominate the industry.

126 n Martin Berger and Javier Revilla Diez TABLE 6 Mean of Sales per Employee 1999 in US$ (Based on Exchange Rates 31.12.1999)

MA OEM ODM OBM Total

GBR

PNG*

SGP*

107,435 54,600 45,971 40,275 68,951

127,602 45,098 43,771 79,602 65,183

248,128 159,161 159,179 207,795 192,080

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

2. Growth: successful small companies witness growing turnover and profits as well as an increase in employees. They therefore become medium- to large-size companies, that are still characterised by an above 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 realise economies of scale. The innovation activity decreases and companies are more likely to conduct process than product innovations. 4. Decline: companies are not competitive any more and therefore experience a steady decline in profits and employees. As a result companies in this phase are small and do rarely innovate. To assign a company to one of the four life-cycle stages two indicators are used: first, the size of the company, measured by the total number of employees; 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 share of new/improved products of total annual sales for both, small (less than 100 employees) and medium/large companies (100 employees and more). For both types of companies the median is 2 (the variable has been coded on 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 cent and above). Then we arranged: l

the small companies with an average or above share of new/improved products at the total annual turnover in the introduction phase; Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

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the medium/large companies with an average or above share of new/improved products at the total annual turnover in the growth phase; the medium/large companies with a below average share of new/ improved products at the total annual turnover in the maturity phase; and, the small companies with a below average share of new/improved products at the total annual turnover in the decline phase.

As can be seen from Table 7, Penang as well as Singapore have explicitly more companies in the first two industry life-cycle phases than Bangkok (although this is not statistically significant). This indicates that companies in Singapore and Penang are more often in an early phase of their development, offering good prospects for future growth and employment. In comparison, firms in Bangkok are more often positioned in the growth to maturity phases. This result puts into perspective the TC frequencies in the three regions and shows that other factors also have a strong influence on economic performance. At the same time it underpins the theoretical argument of the ‘reverse value chain strategy’ (see section titled ‘Technological Capabilities’), which states that companies in Asian NIEs start in more mature, standardised productions processes and develop towards more sophisticated design, development and branding activities. TABLE 7 Number of Companies According to Industry Life Cycle GBR Introduction Growth Maturity Decline Total Chi-Sq.

PNG

cum.%

21.6 40.5 24.3 13.5 100.0

21.6 62.1 86.4 100.0

% 16.2 64.9 10.8 8.1 100.0 0.293

SGP cum.%

cum.%

16.2 81.1 91.9 100.0

27.6 46.1 13.2 13.2 100.0

27.6 73.7 86.9 100.0

Another important factor, influencing innovative behaviour is the ownership structure of a company, since it is relevant for the resources and opportunities available for learning. Table 8 displays the prevailing ownership status of the different TC levels. Overall, there is clear evidence that OBM companies are more likely to be local, while MAs (especially in Penang and Singapore) have a high Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

128 n Martin Berger and Javier Revilla Diez TABLE 8 Ownership Structure Region GBR

PNG

SGP

Owner

OEM

ODM

OBM

Total

Chi-Sq.

local JV foreign local JV foreign local JV foreign

13.4% 47.6% 39.0% 6.7% 13.3% 80.0% 17.5% 8.8% 73.7%

43.8% 35.6% 20.5% 51.4% 16.2% 32.4% 60.5% 15.8% 23.7%

60.7% 28.6% 10.7% 43.8% 25.0% 31.3% 63.6% 15.2% 21.2%

82.7% 15.4% 1.9% 57.1% 14.3% 28.6% 56.0% 8.0% 36.0%

43.8% 34.5% 21.7% 41.3% 17.3% 41.3% 47.6% 12.6% 39.8%

0.000

0.039*

0.000*

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

probability to be part of or associate with a multinational company (in Bangkok more MAs are organised as joint ventures than purely foreign owned). Especially the companies in Bangkok offer a straightforward relationship: the higher the local ownership, the higher the TC. Innovation Indicators

Two hypothesis are the starting point for the examination of the data pursued here: Hypothesis 1 (H1): the level of technological capabilities of companies corresponds with their ability to conduct product innovations/process adaptations. In accordance with the theoretical literature it is assumed that companies improve their innovation capabilities while learning and developing technological capabilities. Hypothesis 2 (H2): Since Singapore, Malaysia and Thailand are at different stages of the catch-up (learning) trajectory and their NSI show marked differences in respect to key science and technology statistics, it can be expected that this is also reflected by the innovation related indicators of the firm-level data in the case study regions. We therefore assume strong differences between companies in these regions, Singapore’s firm being the most advanced, followed by companies in Penang and—with some distance—companies in the GBR. Furthermore, we assume that the regional systems of innovation in Penang and Bangkok are better developed than their national equivalent.

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Input Indicators

H1: All the available ‘hard’ input indicators (Tables 9–11) have been analysed without any conclusive picture. Only the data for Bangkok is showing a straight relation between TCs and all relevant input indicators. But even here a correlation analysis of the single values and the TC groups found just a very weak correlation between these variables. This can 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 lead necessarily to a large number of new products. This depends, on the one hand, on other determinants like the qualification of the human capital and 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 difference between the case study regions is supported by the data. Despite contradictory figures for some TCs, the resources committed to R&D and innovation in Penang and Singapore are generally explicit higher than in Bangkok. The differences between Singapore and Penang are rather small. While the R&D intensity and the innovation intensity is higher in MA and OEM companies in Penang than in Singapore, ODM and OBM firms in Singapore lead over their counterparts in Penang. Obviously ODM and OBM companies in Singapore have reached a level where they can afford more investments in the development of new products and processes. The R&D personnel intensity indicates a leading position by Singapore. Comparing these findings with the national secondary data leads to the conclusion that the regional innovation activity in Penang is indeed more advanced than that for the nation as a whole, indicating a higher performance of the RIS. The figures for Bangkok on the other hand back up the impression received by the secondary data. TABLE 9 R&D Intensity 1999 (Mean of R&D Expenditure/Sales in %)

MA OEM ODM OBM Total

GBR

PNG

SGP

.01 .16 .44 1.19 .37

8.81 5.13 2.00 4.67 5.56

4.63 3.50 3.28 9.05 5.07

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

130 n Martin Berger and Javier Revilla Diez TABLE 10 R&D Personnel Intensity 1999 (Mean of Employees Involved in R&D/Total Employees)* GBR

PNG

SGP

.15 .35 1.26 2.31 .81

2.93 1.54 2.17 2.32 2.10

20.42 29.80 6.56 20.55 19.82

MA OEM ODM OBM Total

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

Additionally, companies were asked if they carry out R&D. The outcome of this ‘soft’ input indicator is shown in Table 12. TABLE 11 Innovation Intensity 1999 (Mean of Innovation Related Expenditure/Sales in %)

MA OEM ODM OBM Total

GBR

PNG

SGP

.12 .35 .46 1.80 .62

17.94 12.10 3.50 3.58 11.13

8.03 9.50 6.91 11.81 8.98

Note: PNG and SGP based on mid-point estimation. TABLE 12 Share of All Companies that Carry Out R&D

GBR PNG SGP

OEM

ODM

OBM

Total

Chi-Sq.

8.3% 33.3% 35.1%

10.3% 27.0% 21.1%

14.3% 26.7% 27.3%

17.0% 85.7% 44.0% Chi-Sq.

11.5% 33.8% 29.3% 0.000

0.473* 0.023* 0.107

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

H1: The hypothesis that companies at different TC stages show significant 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 of companies that carry out R&D (rs = 0,259). If one just analyses the correlation between TCs and OEM–OBM the strength of the correlation increases (rs = 0,474). This can be explained by the different behaviour of manufacturing arm companies, of which only very few carry out R&D Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

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in Bangkok, but many in Penang and Singapore. Since MA companies are mainly (at least partly) owned by multinationals, the R&D strategy of MNCs seems to be fairly different between the regions. H2: A very clear and significant (5 per cent level) distinction in the total share of companies that carry out R&D can be noted between the three regions. The companies in Bangkok lag far behind, while Singapore is approximately head-to-head with Penang. Throughput Indicators

H1: It can be observed that OBM firms applied for and obtained more patents than the other TC groups, except MA in Penang and Singapore. MA firms in Penang and Singapore do extremely well in comparison to Bangkok when it comes to applying for patents (Table 13). There can be two explanations for this pattern: first, there might be a different approach towards innovation by the MA companies (which are mainly MNCs) in these regions, i.e., MNCs see their Thailand affiliates mainly as assembly plants with no responsibility for process or product development, while MNCs assign some of these tasks to their operations in Penang and Singapore. Second, the human capital base in Thailand could simply not be sufficient for the scientific work connected with acquiring patents, which would also explain the poor patent activity in all TC groups. However, the relation between application for and acquisition of patents is fairly poor in Penang and Singapore. In Singapore the mean number of obtained patents increases with the TC level (except MA). TABLE 13 Mean 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 patents patents patents patents applied for obtained applied for obtained MA OEM ODM OBM Total

.23 .01 .00 .79 .26

No. of patents (abs.)

.14 .00 .00 .55 .17 41

1.89 .80 .40 5.33 1.80 63

.22 .40 .20 4.50 1.03 36

No. of No. of patents patents applied for obtained 2.30 .46 .33 .79 1.16 97

.83 .07 .25 .36 .42 35

132 n Martin Berger and Javier Revilla Diez

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

H1: First, considering product innovators (Table 14), it can be seen that the distributions are significantly different at the 5 per cent level. This suggests that the TC level has influence on a firm’s innovation behaviour. Second, one can observe a low but positive correlation between TC level and share of companies that introduced new or improved products to the market (rs = 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 behaviours of MA companies. While an above average share of MA firms are innovative in Penang and Singapore (only exceeded by OBM companies), MA firms in Bangkok are just as often innovative as the average. Since MAs are mainly owned by MNCs, this result leads once again to the conclusion that the innovation behaviour of MNCs in Bangkok is very different from that of MNCs in Penang and Singapore. Additionally, MA firms in Bangkok are less often purely foreign owned than in Singapore and Penang, which could be an alternative explanation for the dissimilar innovation behaviour of MAs. Moreover, the comparison between product innovation and process adaptation (Table 15) displays an increase in the share of innovating OEMs, reflecting the importance of competitive production technology for the economic success of OEMs. These results are also in line with the channels of convergence presented in Table 1. MAs, which are at least partly owned by MNCs, present the deepest/most active channel of technological learning since they are the result of FDI. In Penang and Singapore MAs obviously receive a lot Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

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TABLE 14 Share of All Companies that Introduced Product Innovations into the Market (in the Last Three Years Prior to the Survey)

GBR PNG SGP

OEM

ODM

OBM

Total

Chi-Sq.

9.5% 53.3% 42.1%

5.1% 32.4% 19.7%

17.9% 33.3% 24.2%

20.8% 85.7% 56.0% Chi-Sq.

11.5% 41.9% 31.9% 0.000

0.029* 0.043* 0.001*

Note: * At least one cell with expected frequency < 5. TABLE 15 Share 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 PNG SGP

OEM

ODM

OBM

Total

Chi-Sq.

9.5% 53.3% 43.9%

10.3% 40.5% 32.9%

14.3% 26.7% 24.2%

20.8% 85.7% 32.0% Chi-Sq.

12.8% 44.6% 34.6% 0.000

0.228* 0.059* 0.273*

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

of know-how from their parent/associate companies, which enables them to become successful product and process innovators. Again, the strategy of MNCs for their Bangkok-based subsidiaries seems to be very different. Moreover, the increasing innovation performance from OEM to OBM is in accordance with the depth of the channel of convergence: OEMs, whose operations can be seen as ‘simple market-based trade’, are less likely to innovate than ODMs or OBMs, whose more sophisticated design and/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 obviously lag behind companies in the other two regions in respect to their innovation output. The chi-square test reveals that the differences between the regions are significant at the 5 per cent level for product innovations as well as process adaptations. Figure 1 displays the regional differences in R&D, product innovation and process adaptation. As already stated for the input and throughput indicators, the innovation performance of companies in Penang is exceptional for Malaysia. This can be seen simultaneously 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 weakness of the NSI. Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 © 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

134 n Martin Berger and Javier Revilla Diez FIGURE 1 Share of Companies in Each of the Three Case Study Region that Carry Out R&D, Product and Process Innovation that Carry Out

The same conclusion holds true if one considers the share of companies that are regarded as innovative (see Table 16). A company is classified as innovative if either its share of new or improved products of the total turnover exceeds 25 per cent or the share of new processes of the production volume exceeds 25 per cent. TABLE 16 Share of Innovative Companies MA

OEM

ODM

OBM

Total

Chi-Sq.

Product Innovation GBR PNG SGP

2.4% 13.3% 21.1%

2.6% 21.6% 10.5%

3.6% 6.3% 3.0%

11.3% 42.9% 20.0% Chi-Sq.

4.5% 18.7% 13.6% 0.000

0.063* 0.186* 0.063*

Process Innovation GBR PNG SGP

3.6% 26.7% 31.6%

7.7% 27.0% 15.8%

3.6% 6.3% 15.2%

11.3% 14.3% 20.0% Chi-Sq.

6.6% 21.3% 20.9% 0.000

0.291* 0.341* 0.122*

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

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135

Cooperation and Perception of the Regional Business Environment

Since innovation is seen as a chain-linked process, which requires very specialised, fragmented knowledge and whose outcome is highly uncertain and therefore connected with high risks and costs, companies tend to cooperate with other actors either within their RSI/NSI or at a global scale. Consequently, the cooperation behaviour of firms is an essential part of their innovation activity. Besides cooperation patterns, the perception of the business environment by the firms offers some insight into the (perceived) quality of the national or regional system of innovation. This section tries to shed some light on the cooperation behaviour of firms and their perception of the regional business environment. Tables 17 and 18 depict the share of companies that cooperate with particular actors of the innovation system in an intense or very intense manner. 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 the parent associate company overseas in Singapore. But this result rather owes to the ownership structure than the influence of TCs itself. H2: In contrast, characteristic features can be identified for the cooperation behaviour in the three regions. Product Innovation

First of all, all companies focus basically on the same cooperation partners: customers are the most important partners, followed by the parent or associate company overseas, suppliers (either domestic or foreign), technical service providers and R&D institutes and universities. But, far less companies in Bangkok tend to cooperate on an intense or very intense basis as compared to Penang and Singapore. Since intensity is, to a certain extent, related to spatial proximity, this might reflect a lack of suitable cooperation partners in Bangkok, missing absorptive capacity by the companies due to a lack of skilled human capital so that they cannot utilise possible knowledge flows in cooperations, a mismatch between the technological focus of the companies and possible cooperation partners in public research institutes, and/or a lack of awareness about the importance of cooperation for innovation. The figures therefore hint towards a certain structural weakness of the RSI in Bangkok. Downloaded from http://sts.sagepub.com by Juan Pardo on November 14, 2007 ÂŠ 2006 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.

R&D institutes/universities

Parent/associate company

Foreign-owned suppliers

Locally-owned suppliers

Customers buyers

GBR PNG SGP GBR PNG SGP§ GBR PNG SGP GBR PNG SGP GBR PNG SGP#

50.0% 75.0% 58.3% 25.0% 50.0% 41.7% 18.8% 50.0% NA 50.0% 75.0% 100.0% 12.5% 25.0% 25.0%

MA 33.3% 75.0% 71.4% – 25.0% 42.9% 8.3% 33.3% NA 16.7% 66.7% 42.9% – 16.7% 21.4%

OEM 50.0% 50.0% 57.1% 33.3% – 28.6% – 25.0% NA 33.3% 50.0% 14.3% – – 14.3%

ODM 50.0% 83.3% 71.4% 28.6% 33.3% 35.7% 7.1% 16.7% NA – 33.3% 14.3% 28.6% – 21.4%

OBM

45.8% 73.3% 64.4% 20.8% 30.0% 39.0% 10.4% 33.3% NA 25.0% 60.0% 55.9% 12.5% 13.3% 22.0%

Total

0.800* 0.694* 0.765* 0.221* 0.332* 0.909* 0.551 0.599* NA 0.014* 0.405* 0.000* 0.118* 0.458* 0.946*

Chi-Sq.

TABLE 17 Share of Companies that Cooperate Intensely or Very Intensely in their Product Innovation Activities with the Following Partners (Reference: All Companies that Conduct Either Product or Process Innovation Activities)

136 n Martin Berger and Javier Revilla Diez

GBR PNG SGP GBR PNG SGP GBR PNG SGP GBR PNG SGP

18.8% 37.5% 4.2% 31.3% 50.0% 8.3% 25.0% 37.5% 4.2% 6.3% 37.5% 4.2%

– 25.0% 7.1% 16.7% 33.3% 21.4% – – 7.1% – – 14.3%

– – – – – – 16.7% 25.0% – – – –

– 50.0% 7.1% 7.1% 50.0% 14.3% 14.3% 16.7% 7.1% 7.1% – 14.3%

6.3% 30.0% 5.1% 16.7% 36.7% 11.9% 14.6% 16.7% 5.1% 4.2% 10.0% 8.5%

0.094* 0.360* 0.882* 0.207* 0.328* 0.470* 0.325* 0.165* 0.882* 0.737* 0.027* 0.486*

Notes: § suppliers in general (domestic and foreign); # R&D institutes/universities in Singapore; * at least one cell with expected frequency <5.

Other firm

Competitors

Technical service providers

Business service providers

TECHNOLOGICAL CAPABILITIES AND INNOVATION IN SOUTHEAST ASIA n

137

R&D institutes/universities

Parent/associate company

Foreign-owned suppliers

Locally-owned suppliers

Customers/buyers

GBR PNG SGP GBR PNG SGP§ GBR PNG SGP GBR PNG SGP GBR PNG SGP#

12.5% 37.5% 28.0% – 37.5% 48.0% 12.5% 25.0% NA 37.5% 62.5% 72.0% 6.3% 12.5% 32.0%

MA 50.0% 73.3% 58.3% 25.0% 53.3% 29.2% 8.3% 53.3% NA 33.3% 60.0% 33.3% 8.3% 6.7% 20.8%

OEM 33.3% 33.3% 42.9% 33.3% – 28.6% 16.7% 33.3% NA 33.3% 66.7% 28.6% – – –

ODM

16.7% 25.0% 21.4% – 25.0%

14.3% 50.0% 25.0% 14.3% 50.0% 50.0% – 50.0% NA

OBM

25.0% 56.3% 40.6% 14.6% 43.8% 39.1% 8.3% 43.8% NA 25.0% 53.1% 46.9% 10.4% 6.3% 23.4%

Total

0.093* 0.301* 0.133* 0.140* 0.374* 0.466* 0.536* 0.587* NA 0.083* 0.262* 0.014* 0.412* 0.768* 0.352*

Chi-Sq.

TABLE 18 Share of Companies that Cooperate Intensely or Very Intensely in their Process Innovation Activities with the Following Partners (Reference: Companies that Conduct Either Product or Process Innovation Activities)

138 n Martin Berger and Javier Revilla Diez

GBR PNG SGP GBR PNG SGP GBR PNG SGP GBR PNG SGP

18.8% 25.0% 8.0% 18.8% 50.0% 16.0% 6.3% 25.0% 8.0% – 25.0% 12.0%

– 40.0% 12.5% – 40.0% 25.0% 8.3% 13.3% 4.2% – 6.7% 16.7%

– – – – – – 16.7% 33.3% 14.3% – – 14.3%

7.1% 50.0% 25.0% 14.3% 50.0% 25.0% – 16.7% 12.5% – – 12.5%

8.3% 34.4% 10.9% 10.4% 40.6% 18.8% 6.3% 18.8% 7.8% – 9.4% 14.1%

0.269* 0.433* 0.429* 0.320* 0.464* 0.465* 0.545* 0.818* 0.778* – 0.339* 0.971*

Notes: § suppliers in general (domestic and foreign); # R&D institutes/universities in Singapore; * at least one cell with expected frequency <5.

Other firm

Competitors

Technical service providers

Business service providers

TECHNOLOGICAL CAPABILITIES AND INNOVATION IN SOUTHEAST ASIA n

139

140 n Martin Berger and Javier Revilla Diez Process Innovation

Again, the companies in all three regions share the same cooperation pattern: most likely they cooperate with customers and parent or associate companies overseas. Suppliers are also important partners, followed by technical services. As with product innovation cooperations, the share of companies that pursue an intense cooperation in Bangkok is by far outnumbered in Penang and Singapore. The difference is especially striking in the numbers for the most important cooperation partners. Moreover, for both kinds of innovation cooperations companies in Singapore seek more often intense cooperation with R&D institutes and universities. This is presumably a reflection of the quality of the local science infrastructure and moreover qualitative and quantitative endowment of the RSI/NSI with this key actor. On the other hand, companies in Penang strongly rely on cooperations with service companies (either business or technical). Additionally, the perception of current business environment conditions by companies evidences the (perceived) quantity and quality of national/ regional actors (i.e., possible cooperation partners), government policies and regulations as well as cultural norms. Put shortly, the perception reflects the quality of the national regional system of innovation. H1: In general, the TC groups do not show major differences in the assessment of the business environment conditions. In some aspects a slightly higher share of OBM companies seems to evaluate conditions as rather good or good. For the availability of suitable manpower in the business sector in Bangkok and Singapore this is significant at the 5 per cent level. Obviously, companies with lower TCs experience more difficulties in finding employees for business positions. This indicates a bottleneck in the supply of qualified personnel, which could be an explanation for the low innovation performance of Bangkok-based companies. H2: As can be seen in Figure 2 there is an articulate discrepancy between the quality of the business environment condition in Singapore and the other two regions. All conditions were more often positively rated in Singapore. In particular, Singaporeâ&#x20AC;&#x2122;s conditions relating to government attitudes, policies and legal regulations (availability of government incentives for innovation, openness of government departments and regulatory authorities to innovation, intellectual property protection) and to infrastructure (quality of telecommunications and IT services) are superior. Other striking differences can be detected in the endowment of local universities and R&D institutions as possible advisors and/or collaboration partners. Little variations display the cultural variables (tolerance

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141

FIGURE 2 Share of Companies (in %) that Assess the Following Business Environment Conditions as Rather Good or Good

142 n Martin Berger and Javier Revilla Diez

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

Conclusion

At the beginning of the article we emphasised the importance of innovations for economic development, described briefly how regional and national characteristics can either foster or hinder innovation-related cooperations and depicted the aspects of the innovation process unique to late industrialising countries. The key argument is that companies in late industrialising countries need to adopt and diffuse technologies already existing in advanced countries to be competitive. Therefore, innovation in this context is understood as products/processes new to the firm rather than new to the world. To be able to adopt and diffuse new technologies, companies need to develop technological capabilities through technological learning. Since TCs are the result of successful learning, we have grouped the surveyed companies accordingly. By analysing their innovation and cooperation behaviour as well as their perception of the business environment our intention has been to learn more about these groups positioned at different stages of the ‘learning curve’. In a second step we have displayed geographical differences in the innovation behaviour in three case study regions, GBR, Penang and Singapore, which also reflect the quality of the respective RSI/NSI. Based on these considerations we have formulated two hypothesises: H1: The higher a company’s level of technological capabilities the higher its innovation and cooperation activity. H2: A company’s innovation activity is influenced by the development level of its host country and its host country NSI/ RSI. An ‘innovation hierarchy’ is therefore supposed, with Singapore on top and Bangkok at the bottom.

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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 more conspicuous connection between the TC level and innovation activity. There is a tendency of increasing innovation activity from OEM to OBM companies. However, the MAs obviously ‘behave’ differently: most of the MAs are owned by MNCs, which seem to follow different innovation strategies in the three case study regions. Some appear to act as innovators while others operate as extended workbenches—further research in this respect seems worthwhile. The examination of the cooperation behaviour and business environment condition did not produce TC-specific results. In further, more detailed research more variables (for example, innovative vs. non-innovative) will be taken into account. H2: On the other hand the enquiry into the spatial hypothesis displays a more conclusive pattern. Companies in Bangkok lag behind in all available input and output indicators. Nevertheless, innovating companies in Bangkok are mostly as active as their counterparts in Penang and Singapore with regard to innovation activities. Further research has to ask whether or not there is a fundamental distinction in the innovation behaviour of innovating/innovative companies in the three regions. Moreover, Thai companies also cooperate less often than companies in the other two regions and fewer companies evaluate the business environment conditions positively. This supports our assessment—based on 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 Bangkok does not seem to be able to (at least partly) make up for the structural weaknesses of the NSI, for example, by providing qualified personnel. In contrast, Singapore’s companies rate their environment conditions as highly positive. Firms in Singapore carry out R&D more often and assign more personnel to R&D than companies in the other two regions. However, they neither apply for nor obtain more patents than Penang’s companies, nor do they generally show higher output figures. Even the share of innovative companies does not exceed those of Penang. The same can be observed in the share of intense or very intense cooperations (Singapore has plainly a higher cooperation rate with R&D institutes and universities though). Finally, Penang’s companies do very well with regard to innovation activities and output as well as cooperation, despite less favourable environment conditions than in Singapore and a fairly weak NSI.

144 n Martin Berger and Javier Revilla Diez

Since the presented figures are the starting point for our future research, a number of research questions remain unanswered. For example, what are the mechanisms by which companies in developing countries build technological capabilities and how do international linkages foster this process? How do the generic routes for rapid technological catch up (see section titled ‘Technological Capabilities’) work in detail? Firmspecific, in-depth case studies might lead to a better understanding of this question and can therefore provide information about the needs of latecomer firms for future policies. Finally, how can the lack of qualified personnel (especially in Bangkok) be met by a better interaction between research and training institutes (for example, universities) and industry? NOTES 1. 2.

3. 4.

These conclusions are the result of empirical work in Latin America but are supposed to hold true for other developing countries, too. Furthermore, Wong (1999) distinguishes between OBM and own idea manufacturing (OIM). The latter company is developing own product ideas, but does not market its products under its own brand. The surveyed firms in Thailand were not asked about the location of their cooperation partners. When the gross regional product at current market prices per capita for 1997 is set as 100 for Thailand, the figures for Bangkok (324) and Vicinity of Bangkok (206) are a clear indication of the outstanding performance of the Bangkok metropolitan region when compared to the rest of the country (Alpha Research 2003; own calculations). When the gross domestic product (at purchasers’ value) at constant 1987 prices per capita for 1997 is set as 100 for Malaysia, the figure for Penang (142) also points towards an above average economic performance (Asian Development Bank 1999; SERI 1999; also the Economic Planning Unit—Prime Minister’s Office Malaysia 2003). Unfortunately the Thailand Statistical Classification (TSIC) two-digit code combines the important machinery and electronic sector, which therefore cannot be analysed separately.

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