dynamics of innovation in a globalizing china: regional ... of innovation in a globalizing china:...

Dynamics of innovation in a globalizing china:regional environment, inter-firm relationsand firm attributesCassandra C. Wang� and George C. S. Lin��

�Department of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, China (310027)[email protected]��Department of Geography, Hong Kong University, Pokfulam Road, Hong Kong, [email protected]

AbstractExisting theoretical attempts to understand the dynamics of technological innovationhave focused on the influence of regional environment and inter-firm relations. Morerecently, however, a number of studies have suggested that firm-specific attributes betaken as the key to innovation. This study examines the determinants of technologicalinnovation in China’s ICT firms based on a large-scale questionnaire survey. We revealthat firm-level attributes are of great importance to innovation whereas the influencesof region/relation-specific factors are modified by the types of innovation and by firms’strategies and motivations. The role of regional and relational assets should not beover-emphasized at the expense of firm-level attributes. Research emphasis should beplaced on the process of how firm attributes interacted with regional environment andinter-firm relations to shape innovation. The article concludes with a plea to bring ‘thefirm’ back to the centre and adopt an interactionist approach to understandingtechnological innovation.

Keywords: Technological innovation, regional environment, inter-firm relations, firm attributes,China

JEL classifications: R3, O11, O31, O32Date submitted: 22 July 2011 Date accepted: 15 June 2012

1. Introduction

Over the past two decades, the dynamics of uneven regional growth and innovation inthe globalizing world have attracted much scholarly attention (Fagerberg et al., 2005).Although technological innovation has been seen as a firm-level activity by economists,geographers have paid much attention to the effects of regional and external factors onfirms’ innovation (Sternberg and Arndt, 2001). Since the 1990s, a great amount ofink has been spilled to describe, explore and explain the innovation behavior and outputof firms from the perspective of ‘territorial innovation’ (Lagendijk, 2006; Crescenziet al., 2007). Among many other things, the role of regional environment and inter-firmrelations has been the subject of the related literature on clusters, industrial districts,

Journal of Economic Geography 13 (2013) pp. 397–418 doi:10.1093/jeg/lbs019Advance Access Published on 28 July 2012

� The Author (2012). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]

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regional innovation systems, innovative milieu and learning regions (Beugelsdijk, 2007;Lin et al., 2011). While this line of theoretical inquiry has contributed to ourunderstanding of technological innovation, a number of recent studies have argued thatthe extant literature laid too much emphasis on regional and relational factors in theprocess of technological innovation but ignored the influence of intra-firm attributesthat are the key to understand innovation (Giuliani, 2007; Lin and Wang, 2009; Wanget al., 2010). In addition, the issues of what kind of regional and relational factors exertan influence on technological innovation and how have remained elusive and vague.

The phenomenal growth of China’s ICT industry in the recent decade has providedan interesting case for scrutiny to advance theoretical enquiries into the dynamism oftechnological innovation. Within a time span of510 years, China’s ICT industry hasexperienced dramatic expansion so that it has now occupied an important position inthe world economy (Wang and Lin, 2008; Ning, 2009; Zhou et al., 2011). This articleexamines the determinants of technological innovation in China’s ICT industry basedon the results of a major firm-level and cross-regional questionnaire survey conductedin 2006–07 in China’s four most important city regions—Beijing, Shanghai, Shenzhenand Suzhou. In recognition of the importance of intra-firm attributes on innovation, themain concern of this study is with the ways in which regional environment, inter-firmrelations and firm-level attributes have contributed to the practices and performance oftechnological innovation of China’s ICT firms. The remainder of this paper is organizedin four parts. It begins with a critical evaluation of the competing interpretations of thedeterminants of technological innovation. This is followed by a clarification of research,design and methodology. Attention is then turned to the growth and innovative inputsof China’s ICT manufacturing sector in recent decades. A set of logistic regressionanalyses is conducted to probe into the factors and forces that explain the innovativeperformance of individual firms in the Chinese context. Important findings of theresearch are highlighted and their theoretical implications are discussed in the end.

2. Understanding the dynamics of technological innovationin a globalizing world

Technological innovation as an important instrument to sustained economic growthhas received great attention in recent decades (Feldman, 1999; Acs and Varga, 2002;Asheim and Isaksen, 2002; Fagerberg and Verspagen, 2009). Much of the scholarlyeffort has been made to identify the factors that stimulate and facilitate the productionof technological innovation (Evangelista et al., 1997; Baptista, 1998; Lin and Wang,2009). However, the dynamics of technological innovation has remained controversial.Whereas the earlier approach focused on the role played by entrepreneurship in theprocess of technological innovation, the current intellectual trend is to emphasize theregional environment under which individual firms make innovation-related decisionsand the inter-firm relations in which firms could absorb spilled knowledge andinformation (Susskind and Zybkow, 1978; Beugelsdijk, 2007; Lin and Wang, 2009).More recently, a number of researchers have started to question the effects of regions/territory and inter-firm linkages and shifted attention toward internal firm dynamics inthe process of technological innovation (Giuliani, 2007; Lin et al., 2011). Essentially,three main issues have been addressed in the existing literature, namely the effect ofregions/territory, inter-firm linkages and internal firm dynamics.

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2.1. The effects of regions and territory

While the advanced ICT technologies allow capital, labor, information to be footloose

worldwide, there are still certain regions that could catch up and take good advantageof these production elements to become the ‘sticky places in slippery space’, whereas

others were unable to do so (Markusen, 1996). This increasing unevenness in regionalgrowth in a globalizing world has contested the notion that ‘the world is flat’ and

pointed to the fact that ‘the region matters’ (Beugelsdijk, 2007, 182). Since the 1990s,research has been focused on the conception of industrial cluster, innovative milieu,

learning region and regional innovation system to explain the innovativeness andcompetitiveness of firms and regions. It is generally believed that the regional

innovation-supportive environment and knowledge structure, such as the presence ofuniversities and research institutes, technology policy, R&D facilities, high-qualified

labor force, etc., could induce R&D input and facilitate the process of technologicalinnovation (Sternberg and Arndt, 2001; Beugelsdijk, 2007). Geography plays a key role

because it allows co-location of firms to share collective resources and grasp usefulinformation, produces agglomeration economies, facilitates inter-firm interaction andlinkages, stimulates competition and cultivates a relationship of mutual trust (Porter,

1990; Gertler, 1995; Gordon and McCann, 2000; Fan and Scott, 2003; Wolfe andGertler, 2004).

The importance of ‘region’ or ‘geography’ does not lie so much in the space per se,

but rather in the inter-firm or extra-firm relationships as well as the local innovativemilieu that are crucial to innovative performance and regional growth. Among many

other things, the mechanism of collective learning and localized knowledge spilloverstands out to be one of the most significant factors in the process of technological

innovation (Maskell, 2001). Here, ‘space is not seen merely as a ‘‘container’’ in whichattractive location factors may happen to exist or not, but rather as a milieu for

collective learning through intense interaction between a broadly composed set ofactor’ (Malmberg et al., 1996, 91). Inspired by the endogenous growth theory that

sees knowledge as a public good with highly localized characteristics, a large numberof economists and geographers have placed the emphasis of research on therelationship between industrial clustering, knowledge spillover and the geography of

innovation (Giuliani, 2007). A core argument underpinning this line of reasoning isthat some firms’ spilled knowledge increases the knowledge stock of a region and

provides a significant complementary to other firms’ technological innovation. At thesame time, knowledge spillover is considered to be some localized assets over which

firms outside the region are difficult to reach (Wang et al., 2010). As such, knowledgespillover is seen as an important factor to shape the regional conditions for

technological innovation (Fritsch and Franke, 2004). Empirical research hasidentified a robust positive relationship between localized knowledge spillover and

innovative performance (Giuliani, 2007). Baptista and Swann (1998, 538) pointed outthat ‘a firm is more likely to innovate if located in a region where the presence of

firms in its own industry is strong’ and argued that knowledge spillover makesgeographical proximity vital for innovative activities. Kesidou and Romijn (2008)

revealed that localized knowledge spillover, through labor mobility, spin-offs andinformal interactions among actors, exerts a significantly positive impact on the

innovative performance of firms in not only developed economies but also developingcountries.

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2.2. The effects of inter-firm linkages

In the process of knowledge spillover, some relational assets such as the stable and

intensified production linkages among the related firms, the mobility of skilled

workers, and spin-off initiatives are believed to be among the important vehicles forknowledge transfer and absorption (Giuliani, 2007; Bathelt and Zeng, 2012).

Bathelt and Gluckler (2003) suggested that the interactions and relations betweenthe people and firms involved, such as regular personal communication as well as

joint problem-solving and adjustments, stimulate information spillovers and theprocess of knowledge creation. As well, ‘traded and untraded interdependencies’ in

the region are believed to have exerted significant influences on firm’s innovativeperformance.

Conceptualization of the effects of regional and relational assets on technological

innovation has continued to occupy and divide researchers holding different views(Martin, 1999; Boggs and Rantisi, 2003). For instance, the geographical scale involved

has remained so vague and elusive that further clarifications are required (Martin andSunley, 2003). Phelps (1992) and Phelps and Ozawa (2003) examined the circularity

involved in the concept of clustering/agglomeration and pointed out the lack of

definition of what geographic scale is actually concerned. There are also the questionsconcerning how ‘geographically proximate’ a group should require in order to produce

a self-reinforcing dynamics that stimulate and facilitate innovation (Martin and Sunley,2003). Regional environment and milieu are another concept that need better

measurement. Furthermore, the logic between milieu and innovation remains poorlydeveloped as it fails to ‘identify the economic logic by which milieu fosters innovation.

There is a circularity: innovation occurs because of a milieu, and a milieu is what existsin regions where there is innovation’ (Storper, 1999, 211). Finally, the existing literature

does not offer sufficient explanation for what and how exactly region-level factors affectthe production of innovation. Empirical studies have shown that firms as a whole in

certain location turned out to have better innovative capability than the firms elsewhere

and hence suggested that region/milieu matters, but it remains unclear as towhat kind of region-level factors and to what extent have affected firms’ innovation

(Lin et al., 2011).Region-level influential factors on innovation can be categorized into different types.

Some of them may not always exert an influence on firms’ innovative performance as

their role may change under different conditions and contexts. Sternberg and Arndt(2001) examined the influence of five region-level factors, namely population with

university degree, manufacturing employment, patent applications, R&D intensity andregional accessibility on both product innovation and process innovation of

manufacturing firms in Europe. They insisted that, among these five variables, thevariable ‘manufacturing employment’ appeared to be the only one exerting a significant

influence on process innovation. Yet production innovation was strongly affected by

not only manufacturing employment but also R&D intensity and patent applications.Beugelsdijk’s research (2007) on Dutch firms revealed that region-level variables failed

to affect both incremental innovation and radical innovation of the firms. In a similarvein, the issues about what kind of relations (formal or informal relations; vertical or

horizontal relations; local or global relations, etc.) are able to boost innovation and howstrong these linkages have to be to effectively drive innovation still need to be

addressed.

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2.3. The effects of internal firm dynamics

More recently, a number of economic geographers have called for bringing ‘the firms’

back to the center in the analysis of innovation since firms’ individual strategies

and behavior influence and shape the meso-level conditions (Giuliani, 2007;

Wang et al., 2010; Lin et al., 2011). Giuliani (2007, 143) contended that ‘throughout

most of the literature there is a tacit assumption that all district firms are relatively

homogeneous and that they do not merit attention in their own right. While local

institutions and broader social–structural features undoubtedly shape and constrain

economic behaviour within industrial districts, we want to emphasise that industrial

districts continue to be very much shaped by individual agency’ (quoted in Lazerson

and Lorenzoni, 1999, 237–238). Taylor and Thrift (1982) drew our attention to the

segmented economy in which different types of companies can co-exist in agglomer-

ations of industry. With diverse strategies, motivations, cultural background and

business models, the co-existence of different kinds of companies could play a very

different role in the innovative performance of the region and therefore cannot be taken

as a homogeneous entity.1

Recent research has also shown that to understand the relationship between

knowledge spillover and innovation, more attention should be paid to individual firms’

learning and absorptive capability and how this capability interacted with regional

environment and spilled knowledge to produce a positive influence on innovation. The

earlier cluster literature ‘emphasizes the importance of factors external to firms and

somehow residing in the local environment. In too many accounts local ‘‘territorial

learning’’ is privileged, yet what this process actually is remains ambiguous and its

interactions with firm-based learning are left completely unexamined’ (Martin and

Sunley, 2003, 17). As Feldman has pointed out, ‘the degree to which location matters to

innovation depends upon the type of activity, the stage of the industry life cycle and the

composition of activity within a location’ (Feldman 1999, 21).Given the fact that innovation is the result of individual firms’ input and output, it

would be illogical to understand innovation only through meso-scale examination

without giving any attention to firm-level attributes. A recent study conducted by Wang

et al. (2010, 1990) has suggested that ‘to understand the dynamics of technological

innovation, the importance of traded linkages and untraded interdependencies cannot

be overemphasized at the expense of the nature and attributes of the firms themselves as

active agents and actors that function in a specific global and regional context’. In this

vein, a growing number of empirical studies have shown that the firm-level attributes

make a contribution no less significant if not greater than the regional environment and

inter-firm relations to innovation (Sternberg and Arndt, 2001; Beugelsdijk, 2007). With

the data from the design consultancy sector in the UK, Sunley et al. (2008) revealed that

relations with clients, and firm routines and competences, are much more important to

innovation than inter-firm co-operation or local cultural environment. They further

stressed that exiting studies have paid too much attention to place-based creative

inspiration and downplayed the business ecosystems and firm architectures that

1 This type of analysis is particularly relevant to China where state-owned enterprises co-existed withprivate-owned firms as well as foreign-invested enterprises. This difference among different kinds of firmsin the Chinese context is primarily reflected in ownership structure, which we have taken intoconsideration in our research.


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drive innovation. The empirical analysis on the firms in the USA conducted byBalasubramanian and Lee (2008) highlighted the importance of firm attributes andpointed out that firm age is negatively related to technical quality. This effect appearseven greater in technologically active areas.

Obviously, the competing interpretations about the dynamics of technologicalinnovation identified above have been the results of different perspectives. However,they have brought up a number of intriguing and controversial issues that requireserious verifications and investigations. To what extent do intra-firm attributes exerttheir influences upon the innovative performance of firms? How does the effect ofintra-firm attributes on technological innovation change from region to region? Morebroadly, how are regional environment, inter-firm linkages, and intra-firm attributesinter-related to shape the dynamics of innovation? What kinds of regional factors andinter-firms linkages are instrumental to innovation and how are their effects modified orconditioned by different attributes of the firm such as age, size, and type of production?Recent phenomenal growth of the ICT industry in China with a large and diversifiedproduction space has offered a valuable opportunity for empirical investigations andhopefully theoretical advancement.

In this study, we engage with current theoretical debates through the introductionand interrogation of a proposed conceptual framework centered around the nature andattributes of the firm in the understanding of the dynamics of technological innovation.Firm attributes, such as age, size, and nature of production, are believed to have directinfluences upon not only the innovation-related strategies adopted but also the scope,significance and nature of innovation activities. Smaller and newly established firms inthe labor-intensive industry usually face resource constraints in the process ofinnovation. By contrast, larger and matured firms in the capital-intensive industrytend to have a comprehensive resource base to bring about innovation. The effects offirm attributes are inter-related with those of the regional environment and inter-firmlinkages, however. More often than not, firm level attributes and regional environmentas well as inter-firm linkages are mutually conditioned in shaping the dynamics ofinnovation. On one hand, favorable regional-level factors can enlarge the resource baseof the firms and stimulate their strategic decision-making to involve innovation. Thepresence of universities, high-qualified talents and industry mix in a region may enlargethe knowledge base of firms and help them exploit their innovation potentials. As well,broader global linkages can function as important supplement to local knowledge baseand avoid regional ‘lock-in’ or ‘path dependence’. On the other hand, firms of differentage, size, and production type can have different sensitivity and reaction to regionalenvironment and inter-firm linkages. Other things being equal, younger and small firmswould be more sensitive to the functioning of regional level factors and inter-firmrelationship as well as foreign competitions. Subject to empirical verifications, aconceptual framework that takes firm attributes seriously and rests upon the interactionof regional environment, inter-firm linkages, and internal firm factors may generate abetter understanding of the actual dynamics of technological innovation.

3. Data and methodology

The purpose of this study is essentially to investigate the dynamics of technologicalinnovation of China’s ICT industry, including both hardware and software sectors.

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To accomplish this objective and to address the issues raised in the foregoing section,

three working hypotheses are made.

� H1: The innovative performance of a firm is affected by not only its regional

environment and inter-firm relations but also its nature and attributes.� H2: In the process of innovation, small-sized firms (employment smaller than 100)

are inclined to seek for inter-firm relations and are more easily affected by the

regional environment than larger firms.� H3: Compared with the firms in the labor-intensive industry such as the hardware

sector, firms in the technology-intensive sector (software sector) are more likely to

be affected by regional environment and inter-firms relations in their process of

innovation.

The empirical analyses in this study are mainly based on the data obtained from a

large-scale questionnaire survey of China’s ICT firms conducted during 2006–07 in

Beijing, Shanghai, Shenzhen and Suzhou where the lion’s share of China’s ICT industry

is found (Figure 1).2 According to Chinese official statistics, Beijing, Shanghai,

Guangdong province and Jiangsu province contributed 75% output value and 82%

export value to China’s ICT manufacturing sector in 2008 (CSSB, 2010). These four

regions also generated 62% of sales revenue and 79% of export value of the whole

software sector (MIIT, 2010). Our sample was drawn from the database developed by

China’s State Statistical Bureau (CSSB) on the basis of the 2004 first national economic

census. Four sectors were chosen to represent the ICT industry, including manufactur-

ing of computer/communication equipment (SIC 401 and 404), semiconductor

(SIC 4052 and 4053), manufacturing of electronics parts excluding semiconductors

(SIC 4051 and 406), and software designs (SIC 62). The administration of the

questionnaires was commissioned to a professional survey company out-grown from

and still closely affiliated with CSSB. A pilot survey was first conducted in Shenzhen

during October–November 2006. Subsequent surveys were then carried out in Beijing,

Shanghai and Suzhou in the spring of 2007. The sample size was predetermined with a

target of a 5% sampling rate. A total of 908 valid responses including responses from

518 hardware manufacturers and 390 software designers were received. The question-

naires were in the structural form and consisted of firm profile and attributes, location

and regional environment, market and inter-firm relations, innovation and technology

development. The questionnaires were administered by experienced researchers who

were on site to offer necessary explanations to the respondents for any questions

unclear. They were double-checked upon completion for validity and accuracy to

ensure reliability. Information from the questionnaire survey allows us to probe into the

influence of firm attributes and inter-firm relations on the innovative performance.

Data of the region-level factors are derived from the statistical yearbook of each city

region. While these city regions only occupy a tiny territory of the whole country, they

have become the most agglomerated and innovative places for China’s ICT firms. The

variety of the degree of innovation within each city region provides a good example to

explore the affect of region-level factors on technological innovation. Logistic

2 Suzhou has been under the strong effect of the industrial spills from Shanghai, but it also contains its owncharacteristics distinct from Shanghai (e.g. manufacturing out-sourcing from Taiwan, etc.) (see Wei et al.2012).


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regression analysis is used to estimate the influence of regional environment, inter-firm

relations and firm attributes on technological innovation of the ICT firms.Before we move towards the empirical analysis, several indicators need to be clarified

and quantified. In this study, technological innovation is used as a dummy-dependent

variable and measured by patent, invention patent and new products developed by

individual firms.3 We distinguish invention patent from general patent to judge whether

or not the difference in the degree of complexity of technology will lead to the difference

in determinants of innovation. Firm-specific variables include age, ownership, size,

percentage of R&D personnel, percentage of marketing personnel, export intensity,

affiliate R&D facility and intensity of R&D expenditure. Age of the firm is measured by

the years since the firm was founded. We control ownership of the firms by a dummy

Figure 1. Location of Beijing, Shanghai, Suzhou and Shenzhen.

3 According to the Chinese Patent Law, patents include invention patents, utility model patents and designpatents. Compared to the other two types of patents, invention patents are generally believed to involvemore complex technology and knowledge. Although many researchers adopted patent to measureinnovation (Feldman, 1994, 1999; Audretsch, 1998; Sun, 2002), it remains controversial how effectivepatents and new products can truly capture the nature of technological innovation. We admit that thereare limitations involved when using patents as a measure of innovation. However, our patent dataset iscurrently the most reliable and comparable measure that is available.

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variable reflecting whether the firm is invested by domestic capital or foreign/HongKong, Taiwan and Macao capital. Size of a firm is measured by the number of itsemployees. Export intensity is measured by the share of export value of the total salesrevenue. Affiliate R&D facility is measured by a dummy variable showing whether ornot the firm has an independent R&D facility. Having a separate R&D facility mayindicate a firm’s enduring commitment to R&D and its capability to mobilize resourcesto R&D activities (Sun, 2002). Intensity of R&D expenditure is measured by thepercentage of R&D expenditure of the total expenditure.

In recognition of the fact that regional environment consists of a complexconstellation of many factors, we have tried to incorporate as many factors as possibleinto our regression analyses. However, because of the limitation of data collection andthe problem of multicollinearity, region-specific factors in this study include threevariables, namely, regional R&D intensity, patent certification, and the presence ofsupportive research institutes. We use the share of R&D expenditure in the GDP of aregion to measure the regional R&D intensity. Patent certification refers to the numberof certificated patents in the year of 2006 of a region. We control for the presence ofsupportive universities by including a dummy variable that indicates whether or notuniversities existed within the region.

Although the extant literature highlights the influence of both traded and untradedinterdependency on innovation, we only include traded linkages between firms in ourmodels since it is almost impossible to trace untraded interdependency. In recognition ofthe importance of both local and global linkages as well as government support in thedeveloping countries (Segal, 2003; Bathelt et al., 2004; Simmie, 2004; Lin and Wang,2009), we include five relation-related variables to analyze their influence on firms’innovation, namely, vertical linkages with local firms (measured as the percentage oflocal customers to all customers), vertical linkages with foreign-invested enterprises(FIEs) (measured as the percentage of FIEs as customers to all customers), horizontallinkages with local firms (a dummy variable showing whether the firm has a sub-contracting or contracting relationship with local firms), horizontal linkages with FIEs (adummy variable reflecting whether the firm working as a subcontractor of FIEs) andlinkages with government (the share of government purchase of total sales revenue).

4. Growth and Innovative Input of China’s ICT Industry

The growth of China’s ICT industry has been a recent phenomenon. As shown in Figure2, China’s ICT industry barely existed before 1978. At the end of 1991, there were only1131 ICT manufacturing start-ups and 111 software start-ups in China. Both the ICTmanufacturing and software sectors grew during the 1990s and experienced a dramaticincrease in the period 2001–2008. The expansion of the software sector was even morestriking. There were 34,568 software start-ups during the stage of 2001–2008, nearlyeight times as many start-ups as there had been in the 1990s (Figure 2).

A close examination of the ICT manufacturing sector during the period of 1990–2006reveals a continuing growth in terms of not only the industrial scale but also S&Tactivities and technological innovation.4 The employment of the ICT large and

4 We mainly focus on the ICT manufacturing sector here because data on the innovative activities of theservice sector are unavailable in Chinese statistics.


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medium-sized enterprises in 1990 was less than 0.9 million persons, but it ascended

up to more than 4 million persons in 2006.5 Correspondingly, the output value of these

ICT enterprises had enlarged 64 times during the period of 1990–2006 (Figure 3).It is interesting to note that the ratio of the ICT manufacturing firms with

tech-activities gradually descended from 1990 to 2006 despite a substantial increase in

firm number during this period. Likewise, although the absolute amount spent in

developing new products had been expanded, the expenditure on developing new

products as a percentage of total sales revenue showed a significant decrease from 1996

to 2006. Sales revenue of new products as a percentage of total sales revenue in 2006

was only 24%, much lower than that of 1990, in spite of a sharp increase in both sales

revenues of new products and total sales revenue during the period of 1990–2006

(Table 1). This pattern suggests that more and more ICT manufacturing firms are

reluctant to devote themselves into innovative activities. The declining interest in

tech-activities and innovation may be the results of the increasing pressure on firms that

they had to raise most of their S&T funds by themselves as well as the deepening of

economic globalization that allows more foreign capital to China with their interests in

preferential policies and low production costs rather than innovative inputs. The ratio

of S&T funds from government to total S&T funds was more than 14% in 1990, but

this ratio dropped to 2.8% in 2000 and further decreased to 2.4 percent in 2006. In

contrast, the ratio of self-raised funds by ICT firms to total S&T funds had been

increasing from 61% in 1990 to 88% in 2006 (Table 1). Apparently, the state is no

longer interested in providing direct financial supports to the ICT firms for

technological innovation.Although China’s ICT manufacturing sector has functioned primarily as a latecomer

of technological advancement, China’s indigenous firms started to focus on in-house

innovative activities rather than rely on external technology import. As shown in

Table 2, domestic firms arranged a much higher ratio of S&T expenditure (96%) to

330 1131

8962

34490

6 111

4478

34568

0

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25000

30000

35000

40000

Before 1978 1978-1991 1992-2000 2001-2008

Year of establishment

Num

ber o

f sta

rt-up

s

ICT manufacturing sector

Software sector

Figure 2. ICT manufacturing and software start-ups at different stages. Source: (CSSB, 2010).

5 Large- and medium-sized enterprises refer to the enterprises with 4300 persons of employment, 430million yuan of sales revenue and440 million yuan of total assets. Retrieved from: http://www.stats.gov.cn/tjbz/t20061018_402369829.htm [Assessed on 26 June 2008].

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in-house research and development. Only 4% of S&T funds was allocated to externalcooperation by domestic firms, in sharp contrast to 14% that of FIEs.

The interests of FIEs are found to be not so much in localized R&D activities andinnovation. They preferred to import core technology and knowledge rather thanconduct localized S&T activities. Generally, Chinese indigenous firms should havespent more money on technology import in view of their low innovative capability.However, it is surprising to note that FIEs allocated nearly 64% of its intramuralexpenditure to technology import, in sharp contrast to only 7% that of domestic firms

0

500

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1990 1995 2000 2006

Year

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pers

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100

150

200

250

300

350

Out

put

Val

ue (

billi

on Y

uan)

Employment

Output Value

Figure 3. Employment and output value of the ICT large and medium-sized enterprises inselected years. Source: (CSSB and MOST, 1991, 1996, 2001, 2007).

Table 1. Economic indicators of the ICT large- and medium-sized enterprises at selected years

Year 1990 1995 2000 2006

Number of firms (unit) 611 855 887 2511

Number of firms with tech-activities (unit) 404a — 567 1076

Ratio of firms with tech-activities to total (%) 66.12 — 63.92 42.85

Sales revenue (billion yuan) 4.02 14.17 47.11 293.19

Expenditure on developing new products (million yuan) 558 1362 8660 7799

Expenditure on developing new products as a percentage of

total sales revenue (%)

13.88 9.61 18.38 2.66

Sales revenue of new products (billion yuan) 1.38 2.56 21.59 70.58

Ratio of the sales revenue of new products to all products (%) 34.38 18.08 45.83 24.07

Total funds for S&T activities (million yuan) 1119 2996 16,916 54,538

S&T funds raised from government (million yuan) 160 270 474 1330

Ratio of S&T funds from government to total S&T funds (%) 14.30 9.00 2.80 2.44

S&T funds self-raised by enterprises (million yuan) 677 2383 14,094 47,728

Ratio of enterprise-raised S&T funds to total S&T funds (%) 60.51 79.52 83.31 87.51

aThe number of firms with tech-development institutes, which should be larger than that of firms with

tech-activities.

Source: (CSSB and MOST, 1991, 1996, 2001, 2007).


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(Table 2). In addition, FIEs only arranged 27% of total intramural expenditure totechnological renovations much lower than 88% that of domestic firms.

The universities and research institutes that are normally regarded as playing asignificant role in creating cutting edge knowledge and technology turn out to beinvaluable for FIEs. Around 93% of the extramural expenditure on S&T activities ofFIEs mainly went to other enterprises, whereas the expenditure on the cooperation withR&D institutes or universities only accounted for 1% (Table 2). This is not surprising inconsideration of the fact that FIEs seldom conducted localized innovative activities inChina and they mainly obtained their technology through import. Yet domestic firmsallocated 43% of their extramural S&T expenditure to the cooperation with universitiesand research institutes. This suggests that firm attributes such as ownership have madea significant difference in firm innovation, and it further confirms that the segmentedeconomy in China cannot be examined by a meso-level analysis.

5. Determinants of China’s ICT firms’ innovation

The rapid expansion and increasing capability of indigenous innovation of China’s ICTindustry make it a rare laboratory to explore the dynamics of technological innovationin the developing countries. In this section, we report the main results of the logisticregression models. Before we start to run our logit regression models, special effortneeds to be made to ensure that there are no multicollinearity problems in our dataset.This is done in Table 3 where correlations of the independent variables are shown to below, suggesting no serious problem of multicollinearity.

5.1. H1: Innovation, regional environment, inter-firm relation, and firm attributes

Table 4 summarizes the results of the logistic regression analyses. Overall, all of thesethree models correctly assess 68.1%, 76.1% and 65.3% of the cases, respectively. Inother words, the independent variables chosen explained well the probability oftechnological innovation of China’s ICT firms. Firm-specific characteristics exerted a

Table 2. Comparison of expenditure on S&T activities of above-scale enterprises by sources of investment,

2008

Sources of investment Domestic

capital

Foreign

capital

Total expenditure to S&T activities (million yuan) 5.27 8.08

Ratio of intramural expenditure to total (%) 96.33 86.09

Ratio of extramural expenditure to total (%) 3.67 13.91

Ratio of expenditure on cooperating with R&D institutes/universities

to total extramural expenditure (%)

43.25 1.38

Ratio of expenditure on cooperating with other

enterprises to total extramural expenditure (%)

50.11 92.56

Ratio of expenditure on technological renovations (%) 87.89 27.46

Ratio of expenditure on technology import (%) 7.24 63.61

Source: (CSSB, 2010).

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Table

3.

Means,

standard

deviationsandcorrelations

Variables

Mean

SD

12

34

56

78

910

11

12

13

14

15

1.A

geofthefirm

6.64

5.36

2.O

wnership

0.65

0.48

0.02

3.Size

392

2883

0.03�0.12

4.%

ofR&D

personnel

29.44

29.37�0.15

0.19�0.11

5.%

ofmarketingpersonnel

2.20

332.58�0.08�0.02

0.00

0.03

6.Export

intensity

21.69

33.93

0.05�0.47

0.11�0.21

0.01

7.A

ffiliate

R&D

facility

0.84

0.41

0.07

0.15�0.01

0.20

0.00�0.07

8.Intensity

ofR&D

expenditure

25.62

22.34�0.04

0.21�0.07

0.54

0.11�0.15

0.28

9.R

egionalR&D

intensity

3.57

1.66

0.11

0.34�0.09

0.32

0.01�0.27

0.30

0.44

10.Patentcertification

9140

3155

0.09

0.23�0.14

0.29

0.01�0.12

0.19

0.40

0.33

11.Presence

ofsupportiveuniversities

0.56

0.50

0.10

0.20�0.10

0.40

0.04�0.18

0.18

0.43

0.49

0.75

12.Verticallinkages

withFIE

s18.06

27.72

0.02�0.32

0.06�0.27

0.00

0.18�0.21�0.26�0.35�0.31�0.31

13.Verticallinkages

withlocalfirm

s3.07

1.38�0.10

0.15

0.01�0.08�0.05�0.48�0.10�0.20�0.21�0.20�0.21

0.11

14.H

orizontallinkages

withFIE

s0.23

0.42

0.02�0.19

0.10�0.06

0.02

0.19�0.09�0.11�0.20�0.11�0.15

0.24�0.02

15.H

orizontallinkages

withlocalfirm

s0.42

0.49

0.01�0.08

0.06�0.11

0.03

0.06�0.04�0.11�0.14�0.12�0.18

0.15

0.08

0.55

16.G

overnmentsupport

13.41

26.59�0.02

0.19�0.03

0.16

0.02�0.23

0.14

0.19

0.27

0.16

0.24�0.23

0.08�0.12�0.02

Note:N¼908


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significant influence on all types of technological innovation. Among the eight

firm-specific independent variables, the factor of whether or not having an independentR&D facility (variable 7) stands out to be the most significant and strongest one for

innovation. The probabilities of patent certification, invention patent certification andthe production of new product for firms with an independent R&D facility was 2.8, 2.2,

2.7 times higher than those without a R&D facility. The size of the firm (variable 3),

percentage of the marketing personnel (variable 5) and the intensity of R&Dexpenditure (variable 8) also had a significant effect on innovation, although the

influence is not very strong. It is a little surprising that the age and ownership of thefirms as well as the percentage of R&D personnel did not make any difference in the

innovative performance of firms.The results for the region-specific variables are of particular interest. The presence of

supportive universities (variable 11) appears insignificant for model 1 and 3, but it is

extremely significant for the firms to achieve invention patents (Table 4). The

probability for firms located in the region with supportive universities was nearly threetimes higher than that for firms located in the region where no such institutes are

around. This suggests that firms may not need an innovation-supportive environment ifthey did not involve innovative activities or merely conducted simple R&D activities,

such as the application of utility model patent and design patent—the other two types

Table 4. Logit estimates for the probability of innovation

Model 1:

Total patent

Model 2:

Invention patent

Model 3:

New product

Firm-specific variables

1. Age of the firm 1.03 1.02 1.03

2. Ownership 0.87 0.76 1.16

3. Size 1.00* 1.00* 1.00

4. % of R&D personnel 1.00 1.00 1.00

5. % of marketing personnel 1.01* 1.02** 1.01

6. Export intensity 1.00 1.00 1.00

7. Affiliate R&D facility 2.76*** 2.15** 2.66***

8. Intensity of R&D expenditure 1.00 1.00 1.02***

Region-specific variables

9. Regional R&D intensity 1.04 1.10 0.96

10. Patent certification 1.00 1.00* 1.00

11. Presence of supportive universities 1.5 2.96*** 1.36

Relation-related variables

12. Vertical linkages with FIEs 1.00 1.00 1.00

13. Vertical localized linkages 0.92 0.95 0.81**

14. Horizontal linkages with FIEs 1.45 1.70* 0.76

15. Horizontal localized linkages 1.45* 1.39 1.61*

16. Government support 1.01** 1.01* 1.01***

Chi-square and probability 91.67*** 97.29*** 139.33***

Probability of correct prediction 68.1% 76.1% 65.3%

Note: Values in this table refer to the exponential values of beta coefficients that describe the factor

indicating the change of the estimated probability of innovation. * Statistically significant at the 0.05 level.

** Statistically significant at the 0.01 level. *** Statistically significant at the 0.001 level.

Source: Authors’ survey.

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of patent in which simpler technology and knowledge is involved. However, theregional environment, such as the presence of supportive universities and the number ofpatent certification, appears to be very important for the firms that involvedcomplicated knowledge and technology, for example, the application of inventionpatent. This result is consistent with the findings of a recent study that firms mayoperate well in a region without the presence of supportive universities and researchinstitutes at their first stages, but the desire for the development of long-termcompetitiveness will force them to go somewhere else with a better S&T environment(Wang et al. 2010).

The values of the five relation-related variables show considerably greater divergencein their influence on firms’ propensity for three types of innovation. Whereas horizontallinkages with FIEs (variable 14) had a positive and significant influence on theachievement of invention patent, the variable of horizontal localized linkages (variable15) exerted a similar influence on the production of general patent and new product.This suggests that horizontal linkages with FIEs may sometimes be more importantthan localized linkages in the production of complicated knowledge and technology.This finding echoes the recent theoretical arguments that localized interactions andknowledge base are no longer enough for innovation of individual firms, and globalknowledge/linkages are significant and necessary supplements to the production ofadvanced technological innovation (Bathelt et al., 2004; Simmie, 2004). Relations withthe government appear to be significant for all types of innovation, although the degreeof influence is low. Given the special nature of the Chinese political economy, thesupport of the government for technological innovation has remained important,although it is foreseeable that the non-state influence will play a growing part in theinnovative activities in the future (Lin et al., 2011).

5.2. H2: Innovation, inter-firm relation, and firm size

To test our hypothesis 2, we conduct a logistic regression analysis to investigate thedeterminants of small-sized firms’ innovation (Table 5). Compared with model 2, threeinteresting points can be found. First of all, ownership had a significant influence oninnovation. Small-sized domestic firms tend to be less innovative than non-domesticfirms. Most of the small-sized non-domestic firms in China are either the branches ofMNCs or the independent firms founded by the returnees with years-experiencesoverseas and VC investment. Their relatively ample resources in technology and capitalallow them to succeed more easily in innovative activities than their domesticcounterparts. We also found that a few of foreign-invested branches claimed a largenumber of certificated invention patents. Nevertheless, it is possible that the claimedachievement was completed by their parent company rather than by themselves.

Second, the presence of supportive universities remains important, and its effect iseven stronger for small-sized firms’ innovation. In contrast, localized inter-firm linkageswere not significant for small-sized firms’ innovation. The probability of innovation forthe firms located in a region with supportive universities was almost six times higherthan that of the firms located elsewhere. Compared with large firms, it is hard forsmall-sized firm to attract high-qualified talents, especially those from other places,which to some level disabled the innovative capability of small-sized firms. Thisproblem can be solved by locating in a region where local universities/research institutescan cultivate and agglomerate talents for local firms.


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Finally, the linkages with FIEs appear unimportant for small-sized firms. As shownin model 2, the value of horizontal linkages with FIEs was significant for innovation ofall-sized firms. However, both horizontal and vertical linkages with FIEs appearedinsignificant for small-sized firms. This can be explained by the fact that the possibilityfor small-sized firms to cooperate with MNCs is rare since the latter are inclined tochoose larger firms to cooperate. A recent research also found that FIEs in smaller citiessuch as Suzhou and Dongguan seldom accept business services provided by local firmssince the latter is usually small (Wei et al., 2012). They prefer to seek business servicesprovided by the adjacent primary cities, such as Shanghai and Shenzhen. Governmentsupport was insignificant for small-sized firms mostly because only a few of them,comparative to larger ones, are able to gain the order from governments. In the processof innovation, small-sized firms are more easily affected by regional environment thanlarger ones, but inter-firm linkages did not contribute to their innovative performance.

5.3. H3: Innovation, inter-firm relation, and technological intensity

We further examine the determinants of software firms’ innovation (Table 5). The ageof firms becomes significant for innovation although the value is low. We find that

Table 5. Logit estimates for the probability of complicated innovation (measured by invention patent

certification)

Model 4:

Firms with employment

small than 100

Model 5:

Software firms

Firm-specific variables

1. Age of the firm 1.03 1.09*

2. Ownership 0.51* 0.62

3. Size 1.01** 1.00

4. % of R&D personnel 1.00 1.00

5. % of marketing personnel 1.02*** 1.02*

6. Export intensity 0.99* 0.99

7. Affiliate R&D facility 1.79 3.14

8. Intensity of R&D expenditure 1.00 1.01

Region-specific variables

9. Regional R&D intensity 1.05 1.00

10. Patent certification 1.00 0.00

11. Presence of supportive universities 5.93** 7.59

Relation-related variables

12. Vertical linkages with FIEs 0.99 1.00

13. Vertical linkages with local firms 0.95 0.83

14. Horizontal linkages with FIEs 1.38 1.67

15. Horizontal linkages with local firms 1.58 1.94*

16. Government support 1.01 1.00

Chi-square and probability 76.799*** 71.364***

Probability of correct prediction 78.8% 75.4%

Note: Values in this table refer to the exponential values of beta coefficients that describe the factor

indicating the change of the estimated probability of innovation. * Statistically significant at the 0.05 level.

** Statistically significant at the 0.01 level. *** Statistically significant at the 0.001 level.

Source: Authors’ survey.

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the older the software firm, the higher the probability toward innovation. Given the factthat younger Chinese software firms are relatively less experienced and that resourcesare the key to innovation, this finding is understandable (Balasubramanian and Lee,2008). Nevertheless, one cannot extend the positive relationship between age andinnovation to other cases since this relationship may vary across industries and contexts(Balasubramanian and Lee, 2008). To our surprise, region-level variables did not exert asignificant influence on innovation of software firms. It suggests that firms withfavorable internal feature can be very innovative even when they are operating under anunfavorable regional environment—a point already made by Sternberg and Arndt(2001). The factor of localized horizontal linkages appears to be very important,however. The probability of firms with frequent localized relations was almost twicehigher than those without such relations. It is interesting to see that linkages with FIEsdid not improve the innovative performance of software firms. This shows that softwarefirms may be able to benefit from learning and interaction with other local businesspartner rather than FIEs. Therefore, we have to reject our third hypothesis andconclude that firms in the technology-intensive sector (software sector) are notnecessarily affected by regional environment in their production of innovation.

6. Conclusion and discussion

The dynamics of technological innovation has been one of the most controversial issuesthat never ceased to intrigue scholars in recent decades. While the existing literature hasplaced emphasis on regional environment and inter-firm relations, factors outside theboundary of a firm, recent studies have started to adopt a firm-centered approach tounderstand the technological innovation of firms (Giuliani, 2007; Wang et al., 2010). Itis argued that firms are not passive slaves to geography but active actors that directly orindirectly shape their regional environment and purposively involve all types ofinter-firm relationships. Intra-firm attributes such as its strategies, motivation, businessmodel, capability and resources are believed to have exerted influences no lessimportant if not greater than regional environment and inter-firm relations oninnovation (Beugelsdijk, 2007). It remains unclear, however, whether or not and howregion-level factors, inter-firm relations and firm-level attributes interacted themselvesto shape the innovative performance of firms.

This study investigates the determinants of technological innovation of China’s ICTindustry. A systematic examination has identified a declining ratio of innovative inputsdespite a rapid growth in employment and output of China’s ICT manufacturing sector.A further analysis has revealed that the declining interests in innovative activities are tosome extent related to the increasing foreign capital investment whose interests are notso much in localized R&D activities. The strategy of ‘market for technology’ putforward by the Chinese government in the 1980s with the hope to learn advancedtechnologies through attracting foreign capital to China is demonstrated to be not assuccessful as expected. The improvement of innovative capability of China has to relyupon in-house R&D activities of indigenous firms. Our logit regression analyses basedon a large-scale questionnaire survey have shown that not only regional environmentand inter-firm relations but also firm attributes have exerted significant influences onthe technological innovation of China’s ICT firms. Nevertheless, when we separate oursample by firm size and by sub-sector, we find that inter-firm relationships do not


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contribute to a better innovative performance of small-sized firms and regional

environment does not appear significant for software firms as we expected.In our study, firm-level attributes have played a significant role in the process of

innovation in all circumstances, no matter what types of innovation we refer to and

whether we treat the firms as a whole or separate them by firm size and by sectors. This

is not surprising given the fact that technological innovation has been seen as an

intra-firm effort by economists ever since Schumpeter brought up the concept of

innovation. Although there exists an extensive but inconclusive body of empirical workon the relation between firm size and innovativeness, our study confirm that the

innovative capability of small indigenous ICT firms is relatively low. This finding is

partly contradictory with some studies based on the Western experiences that small

firms are more flexible and specific as well as less bureaucratic and inefficient, and

therefore more easily to be innovative than large firms (Meeus and Oerlemans, 2000;

Beugelsdijk, 2007). Chinese small indigenous firms may be confronted with moreobstacles in their process of technological innovation than their counterparts (Lin and

Hu, 2011). They not only suffer from the dual difficulties in both financial shortage and

the lack of high-qualified human resources but also operate under an immature

institutional and market environment that depressed the motivation of innovation.

Inter-firm relations are revealed partly important for innovation. Their influences oninnovation varied by the types of inter-firm relations, the nature of innovation and firm

attributes (such as firm size and sector involved by firms). Whether or not inter-firm

linkages play a positive role in firm innovation depends on firm-level characteristics and

the nature of linkages per se.Our research has demonstrated a picture more complicated than what is described in

the existing literature concerning the importance of regiona-level factors to techno-

logical innovation. Whether or not the region matters is essentially determined by a

firm’s attributes, its strategy, motivation and capability for innovation. If the strategy

of a firm is to occupy the high-end product market, the firm will be motivated to

conduct complicated R&D activities and touch high-end knowledge and technology.

A favorable environment may help them facilitate the process of technological

innovation. In contrast, a firm without motivation to innovation will not be interested

to pursue an innovation-supportive environment, and it will not be innovative even it

operates under such condition. This result is consistent with the research conducted by

Sternberg and Arndt (2001, 379): ‘the regional environment is not an independent

determinant of firm innovation activity but is influenced by the characteristics of local

firms’. Many other empirical studies also found that being located in a cluster does not

have a noticeable positive effect on firm innovation after controlling for firm-specific

factors (Lee, 2009, 1160).In a broader theoretical perspective, the findings of our research have raised serious

questions concerning the importance of clustering, collective learning, innovative milieu

and localized knowledge spillover in technological innovation. First, it is questionable

whether or not collective learning is going to happen after taking into account the firm

attributes. The extreme asymmetry in knowledge stock and technological capabilitybetween FIEs and indigenous firms may decrease the possibility of mutual learning.

Our research found that FIEs usually paid very little attention to the cooperation with

small domestic firms. They are also reluctant to share their knowledge unless the host

country forces them to do so (Liu, 2000). The relationship based on unwillingness and

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conflict of interests runs against the motto of collective learning that is supposed to beproduced on the basis of spontaneity, equality and mutual benefits.

Second, since the formation of inter-firm linkages is determined by firm attributes,the hybridity of firms with different sources of investment in China’s industrial clustershas complicated the social relationships and weakened the territorial embeddedness andtherefore challenged the concept of innovative milieu. Firms invested by foreign capitalhave more close social connections to their headquarters while firms invested by Taiwancapital are more likely to cooperate with Taiwanese firms who have the tendencytowards geographic co-location (Walcott, 2002, 362; Wang and Lee, 2007). It is hard todetermine whether these firms really have a sense of belonging, and it is debatable ifsynergic interactions or coherent efforts do exist between them. The concept ofinnovative milieu exaggerates the ‘collaborative and cooperative nature’ but ignores‘questions of social equity and clashes of social interests’ among firms (Sunley, 2008, 5).

Finally, because of the variation of firms’ motivation, capability and strategies, notall firms can benefit from the spilled knowledge. Some firms may be able to takeadvantages of knowledge spillover because of their abundant knowledge base butothers may fail to do so because of their poor absorptive capability or disregard for thiskind of knowledge. As Giuliani (2007, 163) puts it, since firms shaped the meso-levelconditions, ‘knowledge diffused in clusters on the basis of a purposeful and highlyselective search process, rather than pervasively or randomly’. To avoid ‘spatialfetishism’, it is time for us to bring ‘the firm’ back to the center of analyses and adopt amultilevel interactionist approach to understand technological innovation of individualfirms.

Acknowledgments

The authors wish to thank Wang Jici, Yu Zhou, Yifei Sun, Yehua Dennis Wei, Tong Xin, GangZeng, and Debin Du for their supports and assistance. They are also grateful for the comments bytwo anonymous reviewers on the earlier versions of this article.

Funding

National Natural Science Foundation of China (No. 41101112); Research GrantsCouncil of the Hong Kong Special Administrative Region, China (GRF No. 7666/05Hand GRF No. 747509H); Basic Research Fund of the Central Universities (ProgramNo. 2011QNA3042); National Science Foundation (NSF BCS 0552237; 0552265;0757615).

References

Acs, Z. J., Varga, A. (2002) Geography, endogenous growth, and innovation. InternationalRegional Science Review, 25: 132–148.

Asheim, B. T., Isaksen, A. (2002) Regional innovation systems: the integration of local ‘sticky’and global ‘ubiquitous’ knowledge. The Journal of Technology Transfer, 27(1): 77–86.

Audretsch, D. B. (1998) Agglomeration and the location of innovative activity. Oxford Review ofEconomic Policy, 14: 18–29.

Balasubramanian, N., Lee, J. (2008) Firm age and innovation. Industrial and Corporate Change,17: 1019–1047.


at Rollins C



ownloaded from


Baptista, R. (1998) Clusters, innovation, and growth: a survey of the literature. In P. Swann,M. Prevezer, D. Stout (eds) The dynamics of industrial clustering. Oxford: Oxford UniversityPress.

Baptista, R., Swann, P. (1998) Do firms in clusters innovate more? Research Policy, 27: 525–540.Bathelt, H., Gluckler, J. (2003) Toward a relational economic geography. Journal of EconomicGeography, 3: 117–144.

Bathelt, H., Malmberg, A., Maskell, P. (2004) Clusters and knowledge: local buzz,global pipelines and the process of knowledge creation. Progress in Human Geography, 28:31–56.

Bathelt, H., Zeng, G. (2012) Strong growth in weakly-developed networks: producer–userinteraction and knowledge brokers in the Greater Shanghai chemical industry. AppliedGeography, 32: 158–170.

Beugelsdijk, S. (2007) The regional environment and a firm’s innovative performance: a plea for amultilevel interactionist approach. Economic Geography, 83: 181–199.

Boggs, J. S., Rantisi, N. M. (2003) The ‘relational turn’ in economic geography. Journal ofEconomic Geography, 3: 109–116.

China State Statistic Bureau (CSSB), (2010) China Economic Census 2008. Beijing: ChinaStatistics Press. Digital Version.

China State Statistic Bureau (CSSB), (1991) Ministray of Science and Technology (MOST),(1991) China Statistical Yearbook on Science and Technology 1991. Beijing: China StatisticsPress.

China State Statistic Bureau (CSSB), (1996) Ministray of Science and Technology (MOST),(1996) China Statistical Yearbook on Science and Technology 1996. Beijing: ChinaStatistics Press.



Crescenzi, R., Rodriguez-Pose, A., Storper, M. (2007) The territorial dynamics ofinnovation: a Europe United States comparative analysis. Journal of Economic Geography,7: 673–709.

Evangelista, R., Perani, G., Rapiti, F., Archibugi, D. (1997) Nature and impact of innovation inmanufacturing industry: some evidence from the Italian innovation survey. Research Policy,26: 521–536.

Fagerberg, J., Mowery, C. D., Nelson, R. R. (eds) (2005) The Oxford Handbook of Innovation.Oxford: Oxford University Press.

Fagerberg, J., Verspagen, B. (2009) Innovation studies—the emerging structure of a new scientificfield. Research Policy, 38: 218–233.

Fan, C. C., Scott, A. J. (2003) Industrial agglomeration and development: a survey of spatialeconomic issues in East Asia and a statistical analysis of Chinese regions. Economic Geography,79: 295–319.

Feldman, M. P. (1994) The Geography of Innovation. Dordrecht: Kluwer Academic Publishers.Feldman, M. P. (1999) The new economics of innovation, spillovers and agglomeration: a reviewof empirical studies. Economic of Innovation and New Technology, 8: 5–25.

Fritsch, M., Franke, G. (2004) Innovation, regional knowledge spillovers and R&D cooperation.Research Policy, 33: 245–255.

Gertler, M. S. (1995) ‘‘Being there’’: proximity, organization, and culture in the devel-opment and adoption of advanced manufacturing technologies. Economic Geography, 71:1–26.

Giuliani, E. (2007) The selective nature of knowledge networks in clusters: evidence from the wineindustry. Journal of Economic Geography, 7: 139–168.

Gordon, I. R., McCann, P. (2000) Industrial clusters: complexes, agglomeration and/or socialnetworks? Urban Studies, 37: 513–532.

Kesidou, E., Romijn, H. (2008) Do local knowledge spillovers matter for development? Anempirical study of Uruguay’s software cluster. World Development, 36: 2004–2028.

416 . Wang and Lin

at Rollins C



ownloaded from


Lagendijk, A. (2006) Learning from conceptual flow in regional studies: framing present debates,unbracketing past debates. Regional Studies, 40: 385–399.

Lazerson, M., Lorenzoni, G. (1999) The firms that feed industrial districts: a return to the Italiansource. Industrial and Corporate Change, 8: 235–266.

Lee, C. (2009) Do firms in clusters invest in R&D more intensively? Theory and evidence frommulti-country data. Research Policy, 38: 1159–1171.

Lin, G. C. S., Hu, Z. Y. (2011) Getting the China story right: Insights from national economiccensuses. Eurasian Geography and Economics, 52: 712–746.

Lin, G. C. S., Wang, C. (2009) Technological innovation in China’s high-tech sector: insightsfrom a 2008 survey of the integrated circuit design industry in Shanghai. Eurasian Geographyand Economics, 50: 402–424.

Lin, G. C. S., Wang, C. C., Zhou, Y., Sun, Y. F., Wei, Y. D. (2011) Placing technologicalinnovation in globalizing China: production linkage, knowledge exchange, and innovativeperformance of the ICT industry in a developing economy. Urban Studies, 48: 2999–3018.

Liu, W. D. (2000) Geography of China’s Auto Industry: Globalization and Embeddedness. PhDthesis, The University of Hong Kong, Hong Kong.

Malmberg, A., Solvell, O., Zander, I. (1996) Spatial clustering, local accumulation of knowledgeand firm competitiveness. Geografiska Annaler. Series B, Human Geography, 78: 85–97.

Markusen, A. (1996) Sticky places in slippery space: a typology of industrial districts. EconomicGeography, 72: 293–313.

Martin, R. (1999) The new ‘geographical turn’ in economics: some critical reflections. CambridgeJournal of Economics, 23: 65–91.

Martin, R., Sunley, P. (2003) Deconstructing clusters: chaotic concept or policy panacea? Journalof Economic Geography, 3: 5–35.

Maskell, P. (2001) Towards a knowledge-based theory of the geographical cluster. Industrial andCorporate Change, 10: 921–943.

Meeus, M., Oerlemans, L. (2000) Firm behaviour and innovative performance: an empiricalexploration of the selection-adaptation debate. Research Policy, 29: 41–58.

Ministry of Industry and Information Technology (MIIT), (2010) Statistical Yearbook of ChineseElectronic Information Industry 2009. Beijing: Publishing House of Electronics Industry. DigitalVersion.

Ning, L. (ed.) (2009) China’s Rise in the World ICT Industry: Industrial Strategies and theCatch-Up Development Model. New York: Routledge.

Phelps, N. A. (1992) External economies, agglomeration and flexible accumulation. Transactionsof the Institute of British Geographers, 17: 35–46.

Phelps, N. A., Ozawa, T. (2003) Contrasts in agglomeration: proto-industrial, industrial andpost-industrial forms compared. Progress in Human Geography, 27: 583–604.

Porter, M. E. (1990) The competitive advantage of nations. New York: Free Press.Segal, A. (2003) Digital Dragon: High-Technology Enterprises in China. Ithaca and London:Cornell University Press.

Simmie, J. (2004) Innovation and clustering in the globalised international economy. UrbanStudies, 41: 1095–1112.

Sternberg, R., Arndt, O. (2001) The firm or the region: What determines the innovation behaviorof European firms? Economic Geography, 77: 364–382.

Storper, M. (1999) The resurgence of regional economies, ten years later: the region as a nexus ofuntraded interdependencies. In J. Bryson, N. Henry, D. Keeble, R. Martin (eds) The EconomicGeography Reader: Producing and Consuming Global Capitalism. Chichester: John Wiley &Sons, Ltd.

Sun, Y. (2002) Sources of innovation in China’s manufacturing sector: imported or developedin-house? Environment and Planning A, 34: 1059–1072.

Sunley, P. (2008) Relational economic geography: a partial understanding or a new paradigm?Economic Geography, 84: 1–26.

Sunley, P., Pinch, S., Reimer, S., Macmillen, J. (2008) Innovation in a creative production system:the case of design. Journal of Economic Geography, 8: 675–698.

Susskind, C., Zybkow, M. (1978) The ecology of innovation. In P. Kelly, M. Kranzberg (eds)Technological Innovation: A Critical Review of Current Knowledge. California: San FranciscoPress, Inc.


at Rollins C



ownloaded from


Taylor, M. J., Thrift, N. J. (1982) Industrial linkage and the segmented economy: 1. Sometheoretical proposals. Environment and Planning A, 14: 1601–1613.

Walcott, S. M. (2002) Chinese industrial and science parks: bridging the gap. ProfessionalGeographer, 54: 349–364.

Wang, J. H., Lee, C. K. (2007) Global production networks and local institution building: thedevelopment of the information-technology industry in Suzhou, China. Environment andPlanning A, 39: 1873–1888.

Wang, C. C., Lin, G. C. S. (2008) The growth and spatial distribution of China’s ICT industry:new geography of clustering and innovation. Issues & Studies, 44: 145–192.

Wang, C. C., Lin, G. C. S., Li, G. C. (2010) Industrial clustering and technological innovation inChina: new evidence from the ICT industry in Shenzhen. Environment and Planning A, 42:1987–2010.

Wei, Y. H. D., Zhou, Y., Sun, Y., Lin, G. C. S. (2012) Production and R&D networks of foreignventures in China: implications for technological dynamism and regional development. AppliedGeography, 32: 106–118.

Wolfe, D. A., Gertler, M. S. (2004) Clusters from the inside and out: local dynamics and globallinkages. Urban Studies, 41: 1071–1093.

Zhou, Y., Sun, Y., Wei, Y. H. D., Lin, G. C. S. (2011) Decentering ‘‘spatial fix’’—patterns ofterritorialization and regional technological dynamism of ICT hubs in China. Journal ofEconomic Geography, 11: 119–150.

418 . Wang and Lin

at Rollins C



ownloaded from


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