Regional Studies Enterprise and Entrepreneurship in Different Regional Environments

Regional Differences in Innovation Performance of New High-tech SMEs and Technology Business Incubators: a Longitudinal Study from China Li Xiao

Abstract

This paper examines the effects of both incubator and regional characteristics on the three level innovation activities of technology-based start-ups in China. Using a set of data base of all technology business incubators and incubated technology-based start-ups from three surveys conducted over consecutive years 2009, 2010, and 2011, we find that the impacts of incubator on technological innovation differ from a geographic area to another, linking to the regional business environment. The availability of scarce public resources to technologybased start-ups through a technology business incubator has improved the ability of technology-based start-ups to innovate in the both richer and peripheral regions, although an unchanged small proportion of technology-based start-ups in the peripheral Western region. In contrast, the greater availability of private sources venture capital in particular to technology-based start-ups has made the majority of technology-based start-ups to selectively establish their business in the richer Eastern region, and helped in bringing technological innovation into the economy. Introduction Technology business incubators have been considered by governments at the national and regional level as a particularly important means to foster technology-based entrepreneurial activity and keep their economies in good shape (Amin and Thrift, 1995; Storper, 1997; 1

Porter 1998; Porter and Kettels, 2003; Raco et al, 2003). Increasingly, the authorities in different regions have invested scarce public resources in technology business incubators, and are looking them to play a key role in encouraging technology-based entrepreneurship for promoting economic dynamism in the less advanced, peripheral regions. It remains unclear whether investments from public resources in new technology-based SMEs through a technology business incubator can change the nature of relationship between the innovation of technology-based firms and development level of regional economies (Keeble, 1997; Athreye and Keeble, 2004), or local resources play a different role in supporting entrepreneurial innovative activity compared to the publics. A technology lead requires a larger amount of investment and higher level of risk taking, and thus allows firms a higher return compared to a follower who re-invents the original innovation (Wernerfelt, 1984; Perez-Luno et al., 2011). However, at a time when product and business life span are shortening, both firms and investors may strategically choose between and balance how much they invest into different level of innovative activity. Moreover, investment preferences in the level of innovative activity may vary depending on the type of resources available (private versus public). Focusing on this key question, this paper looks into three levels of innovation activity including approved intellectual property, patenting, and national science and technology project, as defined by the China National Bureau, to examine the varying effects on innovative performance between incubator and regional resources. It examines China, an area comparatively understudied, but a particularly useful context in which to study the effects of incubator’s and regional characteristics on innovative activity of new technology-based SMEs given the exceptional levels of economic growth the country has experienced over the last 30 years. Furthermore, it is characterised by large regional differences in terms of economic development, differences that are becoming more exaggerated as the Chinese economy grows. It has been divided into three general geographical regions by the Chinese government since 1999, consisting of the richer Eastern coast, the relatively poor Central, and the poorest Western. This study contributes to our understanding about the effects of both regional resources and incubator resources on innovative performance of new technologybased SMEs.

The empirical results provide insight into how an incubator successfully

navigates different regional environments and contributes to the innovation of new technology-based SMEs and regional economies.

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The rest of the paper is organised as follows. It first outlines the theoretical background and empirical evidence of potential benefits of particular geographical location before proposing hypotheses. Second, the data, covering all technology business incubators and incubated new technology-based firms for a period of three years 2009 to 2011, is described. Third, the empirical results will be presented before moving to discuss the findings in terms of the combinations of incubator and region as well as offering their implications. Theoretical background and empirical evidence of benefits to geographical location Resource-based theory acknowledges the importance of geographic location to the competitive position of young technology-based firms (Ellison and Glaeser, 1994). Economic development of geographic location where new technology-based start-ups reside determines the availability of various resources to and actual amounts of investments in innovative projects of a firm. Resource-based view theorises and empirically validates that various innovation outcomes are associated with the resources inputs related to geographic location (Wernerfelt, 1984; Fritsch and Slavtchev, 2011). However, the empirical results from earlier studies have shown little consistency and no consensus has been reached in terms of the relationship between innovation and growth performance of firms (Brettel et al. 2012 and Perez-Luno et al. 2011). These inconsistent findings may indicate that previous studies failed to address the link between the scope of newness in innovation and types of resources and between the scope of newness in innovation and growth performance of firms. The location refers to in this study both incubator and region in which a technology-based firm resides, determining the accessibility of many crucial elements (e.g. research resources, skilled labour, sources of finance, market size, and policy incentives). A technology business incubator plays an important role in improving the availability of resources to technologybased start-ups that have difficulties in sourcing external finance elsewhere. The ability of an incubator to drive a firm for conducting different level of innovation may be also influenced by the availability of regional resources to technology-based firms (Acs and Storey, 2004). The effects of an incubator on entrepreneurial innovative activity and regional economic development may also vary, depending on not only the quality of service support offered by an incubator but the provision of regional resources to technology-based SMEs. In a more developed region, investments from an incubator may make a technology-based firm more attractive to suppliers of finance. In contrast to a less developed region, the provision of

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incubator finance may compensate for a disadvantaged business/economic environment associated with a particular region. Incubator factor and new technology-based firms The literature on geographical agglomeration suggests that a common principal driver for the establishment of a technology business incubator is to encourage technological entrepreneurship, facilitate technology-based firms, and promote regional economic development (Markman et al., 2005; Link et al., 2007; Lockett and Wright, 2005; McAdam and McAdam, 2008; Lee et al, 2004; Rothaermel and Thursby, 2005; Siegel et al., 2007). An incubator is designed to attract potential technology entrepreneurs to create a technologybased business, improve regional innovation system, and promote the regional economic development by providing and improving the availability of specific resources. Studies rooted in resource-based theory found that technology-based start-ups benefit from service support from an incubator, contributing to the formation of new technology-based firms. The literature indicates associations between spatial proximity and knowledge transfer from the lab bench to industry (Markman et al., 2005). Evidence suggests technology-based firms benefit from being in location together with other firms, sharing greater access to specialised labour, and knowledge spillovers. An incubator first facilitates the knowledge transfer by offering professional services (i.e. licensing, training, etc) to incubated technology-based start-ups. An incubator also facilitates the knowledge transfer through organising social events in which a firm can develop social networks with various partnerships (i.e. strategy alliances, supply contacts, technological experts, skilled workers) and exchange key information (i.e. technology development). An incubator aiming at knowledge transfers through linking a new technology-based firm with relevant partnerships and providing support to seeking for invention protection may have positive effects on both incubator and firm-level innovation.

Studies report that firms located in a cluster of similar firms enjoyed a better performance compared to those that can be found outside the clusters since the former shared greater access to specialised labour and knowledge spillovers than the latter. Martin and Sunley (2003) stated that the empirical results on the firm performance effect of a cluster’s size have been inconsistent. A study by Folta et al. (2006), based on 806 U.S. private and public biotechnology firms founded between 1973 and 1998, found that the ability of the firms to 4

innovate increased as a cluster initially becomes larger, but then reach a point after which the ability to innovate begins to decrease with an increase in cluster size. The dividing line of clusters’ size is about 65 firms. McCann and Folta (2011) found that the effects of clusterspecific factors on the innovation performance of firms are moderated by firm-level attributes or capabilities associated with differential abilities to access and generate knowledge. These empirical findings support our argument that a key determinant to innovative types is access to both incubator and regional resources. Many studies of geographical agglomeration focus typically on a particular technology park/cluster and technology business incubator, and empirical findings from these studies reveal links between incubator-specific factors and new firm formation in the local context. However, these studies do not take a larger scale perspective, providing comparative finding on technology-based start-ups by considering region-specific factors (Cruz and Teixeira, 2010). The following section discusses the regional variations in the performance of SMEs. Region factors and start-up performance Research looking at the underlying social characteristics of a region associated with entrepreneurship or firm formation suggested that cities and regions function as an ‘incubator’ to attract talented people from various backgrounds and stimulate their entrepreneurial capacities (Lee et al., 2004; Acs and Storey, 2004; Keeble, 1997; Lucas, 1988). It has been found that region-specific factors comprising tax rates, transportation costs, industrial intensity, unemployment, population density, industrial clustering, and the availability of financing, income growth, population growth, human capital, academic research expenditure, etc. contribute to regional variations in new firm formation (Armington and Acs, 2002; Renolds et al., 1994). The size of the local market for a series of products is associated with a region’s attraction for firm establishment (Van and Storey, 2004; Keeble, 1997). A study looking at bio-tech startups in France reported that the relation between local market and creation of bio-tech startups varies depending on the development stage of the industry (Autant-Bernard et al., 2006). The firm creation effects of a research market is stronger over the emerging phase of the industry, whereas it is weaker when the industry matures and becomes more consumer and market-oriented. Following this line of the argument, one would expect that the creation of Chinese technology-based firms that where existing advanced technologies are applied to

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develop new/distinctive products is associated with the size of consumer markets for these products. A study focusing on SMEs in general rather than technology-based SMEs in particular, based on a sample of 698 British firms with up to 500 employees over a period of five years 1991 to 1995, reported that SMEs in the core regions performed better compared to peripheral ones in terms of employment, turnover, profitability, and internationalisation (Keeble, 1997). These empirical results are explained by resource-based and regional competitiveness theory. SMEs in core regions have greater access to resources compared to those located in peripheral regions. SME development is generally positively associated with regional environments where resources required for business (i.e. sources of finance, educated population, and supportive business climate) are more available and accessible (Hansen, 1992; Storper, 1995; Henry et al., 1996). Keeble (1997) also found that peripheral SMEs exhibit higher levels of technological intensity, as measured by R&D input indicators, compared to their counterparts in the core region. This empirical result was consistent with work done by Vaessen (1993). These findings may suggest that innovation level that a firm is prepared to pursuit may vary depending on regional environment. However, it is unclear whether this positive relationship of region-performance applies to technology-based SMEs that differ from SMEs in general, or in the Chinese context. Hypotheses We argue that both incubator and region are essential in improving business environments for technology-based start-ups pursuing innovation. There are many factors including both incubator and region which influence the types of innovative activity that technology-based start-ups within a technology business incubator conduct. The availability of resources to new technology-based firms is the key to determine their ability to selectively invest in either science-driven innovation or innovation adoption (Fritsch and Slavtchev, 2011). It has been found that innovative strategies (i.e. developing new technologies versus applying existing advanced technologies to develop distinctive products) that firms employ are associated with the availability of the resources from both a technology business incubator and region in which a firm resides (Xiao and North, 2011). Before moving to key factors determining the innovative level and performance of new technology-based firms and technology incubators, multiple measures employed in this study are discussed.

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To examine the three levels of innovation performance, making it possible to generate a more complete picture of any empirical relationships in different regions. The three levels of innovative performance allow this study to compare regional variations in the invention levels, corresponding to the availability of incubator and regional resources to new technology-based SMEs. The use of multiple measures enables this study to indicate firms’ preferences of innovative activities. Moreover, it overcomes problems associated with a single measure, for instance, a measure of innovation basing on the number of granted patents might underestimate actual innovation output (Fritsch and Slavtchev, 2011). The first measure level of innovative performance is based on the number of approved intellectual property that technology-based SMEs within an incubator as a whole are granted. This measure indicates innovations that may not be patentable, but has useful modification and practical value for an existing invention. Likely, it does not require a large amount of investment capital and take long time to bring it the market. The second measure level is based on the number of granted patents that incubated technology-based start-ups within an incubator as whole gained. The protection period for a patent is much longer than for an improved intellectual property. The third measure level is based on the number of granted national science and technology projects that represent high-level basic research and some advanced applied research on the key scientific aspects identified by the Chinese government. Technology-based start-ups that were granted a national science and technology project were given several million Chinese Yuan of national government grant, and expected to deliver highly advanced technologies that are essential to the China economic growth and development. Incubator characteristics: The total amount of investments in the public service platform of a technology incubator may be an appropriate indication of the relevance of service support that an incubator provides to incubate new technology-based SMEs. It is expected that the quality of physical service support (i.e. low cost office space, property service, telephone line, etc.) and professional service support (i.e. information, training programme, consulting, networking, etc.) offered by an incubator to technology-based start-ups is positively associated with the efforts and resources that governments at both national and regional level put into the platform of an incubator. The better infrastructure of an incubator may be beneficial for the establishment of technology-based firms and improvement of innovative activity. The literature suggests that professional services provided by an incubator including licensing, technology partnership, strategy alliances, social events, etc. help to stimulate 7

information exchanges about topics (i.e. technology development), and facilitate the knowledge transfer (Pouder and St. John, 1996; Almeida and Kogut, 1997). It is worth to note that there is no positive or negative sign between the total amount of investments in the public service platform of each incubator and the development level of regional economic growth. The explanation for this may be that richer regions may prefer to invest larger amounts of investments available in establishing more technology business incubators; in contrast, peripheral regions may invest smaller amounts of investments available in fewer incubators. We therefore propose: H1a the total amount of investments in the public service platform of an incubator has a positive impact on the number of approved intellectual properties that new technology-based SMEs in an incubator as a whole gained. H1b: the total amount of investments in the public service platform of an incubator has a positive impact on the number of patents that technology-based start-ups in an incubator as a whole gained. H1c: the total amount of investments in the public service platform of an incubator has a positive impact on the number of national science and technology projects that technologybased start-ups in an incubator as a whole registered. The literature suggests that a key determinant to success technology-based firms is investment in R&D leading to market offers being renewed (Acs and Audretsch, 1988, North and Smallbone, 2000, Colombo et al., 2010, Perez-luno et al., 2011). For the highest innovative activity of national science and technology projects, the length of time that is needed to fully develop, test, and commercialise product innovation often span several years, and thus access to public sources of finance through an incubator has proved particularly essential for compensation of the constrained access to other sources of finance (i.e. funding from banks and venture capitalists) (Xiao and North, 2011; Markman et al., 2005). For patent and approved intellectual property, access to incubator finance enables technology-based start-ups to invest in innovative projects necessary for sustaining the advance of products offered. The total amount of funding available from a technology incubator to incubated firms may determine start-ups’ access to the source of incubator finance aiming to support the highly innovative product development and process technologies. The larger amounts of funding that the incubator provides lead to either a higher probability or a more sufficient amount of funding an incubated firm could receive from the incubator. Incubator finance 8

could be the only external source available to some young technology-based firms particularly at the start-up stage of business development. In addition, technology-based startups that received finance from an incubator could be a positive sign to other suppliers of finance, making them in a better position in raising external funding. We therefore propose: H2a the size of incubator funding available has a positive impact on the number of approved intellectual property that technology-based start-ups in an incubator as a whole gained. H2b the size of incubator funding available has a positive impact on the number of patents that technology-based start-ups in an incubator as a whole gained. H2c the size of incubator funding available has a positive impact on the number of national science and technology projects that technology-based start-ups in an incubator as a whole registered. Regional characteristics: The literature suggests that resources required for business (i.e. sources of finance, educated population, and supportive business climate) are more available and accessible in a more developed region (Hansen, 1992; Storper, 1995; Henry et al., 1996). Regions with the higher level of economic growth and development have a larger size of funding from both business angels and venture capitalists, which can be invested in new high-tech SMEs, compared to less developed regions. Following this line of the argument, new technology-based SMEs locating in a rich region are at a competitive advantage to obtain equity finance that is considered as a more appropriate source to the innovation activity of new technology-based firms and incubators (Bertoni et al., 2010). However, it seems impossible to gather information about the amount size of equity finance available from a region to new technology-based SMEs. The total amount of equity finance received by new technology-based SMEs is therefore a measure of the regional economic development, indicating to the availability of venture capital. We assume that the ability of new technology-based SMEs in richer regions to obtain equity finance and/or venture capital is greater than that in poor regions. It has been reported that private investors are more likely to invest in technology-based start-ups that, in the Western context, are located within commuting distance of where the investors are working or living (Colombo et al., 2010). In transition economies such as China, informal investors are yet to “hands-on” and contribute to their knowledge, skills, expertise, and contracts in a variety of formal and informal roles in their investee firms (Xiao and Ritchie, 2011). However, a

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network-based approach has been commonly used by informal investors to assess an investment application for making an investment decision (Ahlstrom and Bruton, 2010). Spatial proximity to the wealth of a region therefore makes incubated technology-based startups in a better position to build networking with potential investors, thus have greater access to venture capital. We anticipate: H3a the amount of actual venture capital investment has a positive impact on the number of approved intellectual property that technology-based start-ups in an incubator as a whole gained. H3b the amount of actual venture capital investment has a positive impact on the number of patents that technology-based start-ups in an incubator as a whole gained. H3c the amount of actual venture capital investment has a positive impact on the number of national science and technology projects that technology-based start-ups in an incubator as a whole registered. A fact of a greater availability of equity finance and/or venture capital to new technologybased SMEs in a more developed region may apply to the availability of all external finance. As explained earlier in the previous section, it is difficult to collect data on the amount size of external finance available to new technology-based SMEs. A more practical measure of availability of various sources of finance from a region may be the number of new technology-based SMEs that received external funding. Spatial proximity to the wealth of a region allows firms to have more opportunities to collaborate with various suppliers of finance. A larger size of local market for a series of products enables new technology-based SMEs in a rich region more attractive to investors of finance in comparison with those in a peripheral region. We propose: H4a the number of firms received external finance has a positive impact on the number of approved intellectual properties that technology-based start-ups in an incubator as a whole gained. H4b the number of firms received external finance has a positive impact on the number of patents that technology-based start-ups in an incubator as a whole gained. H4c the number of firms received external finance has a positive impact on the number of national science and technology projects that technology-based start-ups in an incubator as a whole registered. 10

Data and summary statistics The data comprises of the whole population of all the national technology business incubators and all incubated new technology-based SMEs in China, which are provided by the Ministry of Science and Technology in China. It includes 228 incubators and 27,980 incubated technology-based start-ups across the country in 2009. About 224 of the 2009 population were still operating and incubating 28,049 start-ups and that the remaining four incubators had closed down in 2010. Of the 216 survivors from the 2009 population incubated 27,298 start-ups and the remaining eight incubators had closed down in 2011. Analysis is restricted to all 216 technology business incubators which survive over the studied period of three years from 2009 to 2011. It allows our study to conduct a longitudinal investigation of all incubated new technology-based SMEs and technology business incubators that reside in different regional business environments. It spans a period of three years 2009 to 2011. Additional information about regional characteristics (i.e. average wage of employees in urban private units, average wage of employees in high-tech industrial sector, gross regional production, gross regional production per capita, and urban population) that are provided by the China Statistics Yearbooks 2009 to 2011 are also collected. Table 1 is about here Table 1 provides a summary of the definitions of and descriptive statistics for the variables. It groups the variables into three areas: 1) dependent variables - the innovative performance, 2) incubator’s characteristics, and 3) regional characteristics. The descriptive statistics for each variable shown in Table 1 is based on the average of three year value calculated. Table 2 is about here Table 2 sets up the correlations between variables. It shows positive correlations between the three independent variables. It is worthy to note that positive correlations between the amount size of venture capital, employees’ wage in urban private units, employees’ wage in high-tech industrial sector, and gross regional product per capita have been found. It is not surprising that the actual amount of venture capital invested in each incubator’ technology-based startups was larger in more developed regions than in less developed regions. However, the data do not provide support for positive correlations between the number of technology-based firms received external finance in each incubator, employees’ wage in urban private units, employees’ wage in the high-tech industrial sector, and gross regional product per capita. 11

Technology-based firms located in a rich region are not at a competitive advantage to access to external finance, measured by the number of firms obtained funding from finance supplier. A possible explanation is that both incubator finance and venture capital are the most available sources for technology-based start-ups. The difference is that a larger amount of venture capital available from more developed regions was allocated to a small number of rich firms, in contrast incubator finance may be distributed to a large proportion of firms in less developed regions. Empirical results

Hierarchical multiple regression models are employed to test the hypotheses proposed earlier in the previous section. Table 3 presents regression analyses of the impact of an incubator and region on the innovative performance of new technology-based SMEs within an incubator as a whole. Incubator’s effects on innovation Table 3 Models 1A, 2A, and 3A test the impacts of services and support from an incubator on the innovation of technology-based firms in relation to the three levels controlling for the two variables of number of incubated firms (NF) and graduated firms (NGF). The empirical results indicate clear evidence for the relevance that the amounts of investments in the public service platform of an incubator are positively associated with technology-based start-ups of innovation performance indicated by the number of approved intellectual properties, patents and national science and technology projects granted, providing support for H1a, H1b, and H1c. It shows that a 1% increase in the amount of investment capital in the public service platform of an incubator is associated with an increase of 0.28 the probability of approved intellectual property (p<0.01 at the statistically significant level), an increase of 0.24 the probability of patent (p<0.01 at the statistically significant level), and an increase of 0.24 the probability of national science and technology project (p<0.01 at the statistically significant level). The allocation of public resources to increase the quality of service and support that an incubator provide enables technology-based start-ups to more innovate and engage in all three levels of innovation. Table 3 Models 1A, 2A, and 3A show that the amount of funding available from an incubator to technology-based start-ups has positive effects on the innovative performance indicated by the number of approved intellectual property, patents, and national science and technology 12

projects, controlling for the number of firms (NF) and graduated firms (NGF). The data provides support for H2a, H2b, and H2c. We assume that a larger size of funding available from an incubator improved incubated start-ups’ access to finance at the start-up stage. Moreover, incubator finance may be particularly important to technology-based start-ups since access to tradition finance seems weaker in comparison with established technologybased firms. Technology-based start-ups that sourced incubator’ finance are able to invest in innovation and seeking protection for the inventions, increasing the probability of being granted approved intellectual property, patent, and national science and technology project. At a detailed level, a 1% increase in the amount size of funding available is associated with an increase of 0.22 the probability of approved intellectual properties (p<0.05 at the statistically significant level), an increase of 0.21 the probability of patents (p<0.01 at the statistically significant level), and an increase of 0.40 the probability of national science and technology projects (p<0.01 at the statistically significant level). This suggests that the availability of funding from public resources to technology-based start-ups has been essential to conduct R&D and innovation for developing new products to the market. Region’s effects on innovation Table 3 Models 1B, 2B, and 3B show, controlling for average wage of employees (AWE), average wage of employees in high-tech industrial sector, gross regional production (GRP), gross regional production per capita (GRPpercapita), urban population (UP), and do not offer strong support for H3b and H3c in particular. The estimated co-efficient for the availability of venture capital to technology-based start-ups, as measured by the actual amounts of investment received, indicates a weak positive impact on innovation in terms of patenting and basic research and highly advanced applied research, but not at a significant level. It suggests that venture capital may be yet to become the most appropriate source for technology-based start-ups to conduct patenting and highly advanced inventions. The first possible explanation is that, in a more developed region, a larger size of venture capital was allocated to a smaller number of firms with the most profitable opportunities, and may be invested in commercialising innovation and expanding business rapidly. The investments and efforts that business angels and venture capitalists put into technology-based start-ups were on projects that likely generate a good return on the investments. The second possible explanation is that the majority of business angels and venture capitalists, which are more active in a more developed region, are not ready to invest in highly innovative projects requiring a long leadtime and sufficient amounts of investment capital for bringing it to market. A greater 13

availability of venture capital to technology-based start-ups in a more developed region has not improved the ability of technology-based start-ups to innovate greatly. These results clearly suggest that the current role of business angels and venture capitalists on the innovative performance of technology-based start-ups in China differs from that in advanced economies (i.e. US and European countries). The availability of venture capital from a rich region may contribute to exaggerated regional differences in the number of technology-based start-ups and entrepreneurial activity. Table 3 presents analyse of the effect of number of external finance obtained on approved intellectual property, patenting, and national science and technology project. In Models 1B, 2B, and 3B, we control for five regional variables including average wage of employees (AWE), average wage of employees in high-tech industrial sector (AWEHT), gross regional production (GRP), gross regional production per capita (GRPpercapita), and urban population (UP). The empirical results show that the number of technology-based firms was positively associated with the three level innovations, which provides support for H4a, H4b, and H4c. At a more detailed level, we find a strong support for a positive association between the number of firms that received external sources of finance and number of national science and technology projects based on Model 3B, and support for positive associations of number of firms received external finance and number of approved intellectual properties and patents based on Models 1B and 2B. It suggests that the availability of external finance is the key for technology-based start-ups to innovate. The higher level of innovations, the more external finance required. Investments from external sources of finance enable technology-based startups to invest more in R&D and innovation. Perhaps, recognition by investors makes new technology-based firms more confident with the new product development that they have been conducting. Implication and conclusions This paper first contributes to the regional literature by taking a more macro approach to join things up and take a larger-scale view in comparison with previous focused and in-depth studies (Cruz and Teixeira, 2010). The empirical results based on a population of technology business incubator and incubated technology-based start-ups in China therefore provide a comprehensive picture and better understanding of relationships between location and the innovative performance of firms. The nature of relationship between incubator and innovation of technology-based start-ups depends entirely on the availability of public 14

resources and the quality of support and service offered. One of the clear finding results is that the allocation of public resources plays an important and consistent role in facilitating innovative activity of an incubator and technology-based start-ups. Our study demonstrates that investments from public resources in technology-based start-ups through a technology business incubator cannot change the nature that the distribution of technology-based firms was spatially uneven (Keeble, 1997; Acs and Storey, 2004). It also reveals that investment from public resources cannot improve the ability of peripheral regions to catch up on the more developed regions in terms of regional innovation capacity. Peripheral technology-based start-ups relied more on public resources, directing towards investments in higher level innovation (i.e. patent, basic research, and advanced applied research), while prosperous technology-based start-ups benefited from not only public resources through an incubator but also private sources mainly from business angles and venture capitalists. In other words, prosperous technology-based start-ups are at a competitive advantage to access to venture capital for, believably, commercialising the inventions and enjoying economies of scale, whereas peripheral technology-based start-ups being in a weaker position to access to non-incubator resources. A lack of resources required making the inventions worthwhile economically may be a consequence that the majority of technology-based start-ups have selectively established a technology-based business in an incubator that resides in a more developed region. Economic development impact of technology-based start-ups varies from one geographic area to another, linking to the availability of venture capital and investment preferences of business angels and venture capitalists (Bertoni et al., 2010). . The limited provision of venture capital finance to technology-based start-ups proved to be a significant constraint on the ability of the peripheral regions to catch up on the more developed regions in terms of engagement with regional inputs. The greater availability of regional finance has made some prosperous technology-based firms to play a leading role in the market nationally and/or internationally. This paper also provides support for resource-based perspective that the availability of various resources has improved the ability and willingness of technology-based start-ups to innovate at a regional level. In comparison with prior studies that have focused primarily on innovation and technology-based start-ups, ours distinguishes that incubators function differently from regions in terms of its impact on the innovative performance of technologybased start-ups. It reinforces resource-based theory by demonstrating the availability of resources from an incubator has improved the ability of technology-based start-ups to 15

innovate in both more and less developed regions, enabling peripheral technology-based start-ups within an incubator as a whole to enjoy innovation as much as those in rich regions. Investment from an incubator is more directed towards innovation process, while it seems regional resources are more driven by commercialised process. The State has had the capacity to direct public resources to particular purposes through a technology business incubator, but has demonstrated a limited capacity to influence the investment decision of business angels and venture capitalists in particular in the pursuit of development of technology-based firms and regional economies. Public resources have been oriented towards a higher level of innovation of both prosperous and peripheral technologybased start-ups, in contrast, private resources have been directed towards prosperous firms leading to the inventions worthwhile economically. The empirical results also support a view that venture capital has not yet contributed significantly to the improved ability of technology-based start-ups to develop highly innovative technologies and products (Xiao and North, 2011). Impressive investment in establishing technology business incubators has helped in closing a gap in the innovation performance of technology-based start-ups between incubators rather than regions. Investments in technology business incubators, although helping facilitate innovation performance of an incubator, do not necessarily narrow regional differences in the development of new technology-based SMEs and economic growth. The implication for policy makers is that governments at all levels should be aware of the importance of both incubator and regional resources to the development of technology-based start-ups. A key determinant to China’ transition from the reliance in technological import to generation of radical innovation is to improve the efficiency of both public and private sources of finance available (Lee et al., 2004). Regional input of venture capital is yet to be sufficiently invested in highly innovative projects that require a long-lead time to fully develop, test, and commercialise product innovations. Peripheral technology-based start-ups that conduct high-level of basic or applied research, supported by the Chinese central government (Science and Technology resources), may not be able to commercialise the inventions without diversification of regional inputs (i.e. venture capital and entrepreneurial expertise). Peripheral highly innovative start-ups may face a challenge in terms of sustaining its technology advancement as well as its growth and development without sufficient regional inputs. Scarce public resources that are invested in highly innovative projects may therefore not help in developing regional technology-based entrepreneurial activity, creating new jobs, and narrowing gaps in the development of regional economies. 16

Our study raises further research opportunities associated with the effects of both incubator and region on the performance of technology-based start-ups and the development of regional economies in China. The research findings indicate the amount of venture capital investing in technology-based start-ups is not significantly positively associated with highly innovative activity (i.e. patent innovation, based researches, and advanced applied researches) in particular. There is therefore a need for more research on regional characteristics and technology-based start-ups to look into investment preferences of business angels and venture capitalists as well as the impact of venture capital on technology-based firm growth and performance in response to different regional environments. One of the limitations of the present study is that because of the data excluding those graduated technology-based firms it is impossible to examine identified considerable time lags in terms of the effects of innovation types on firm growth performance (Fritsch and Mueller, 2004). To advance our understanding of relationships between innovation types and long-term economic growth (Schumpeter, 1934; Therrien at el., forthcoming), other studies are needed to track those graduated technology-based firms and improve our understanding of the impact of incubator and region characteristics on the long-term development of technology-based firms and regional economies.

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Table 1 Variable definition and summary statistics (average value of three years 2009-2011) Variable Dependent variables AIP Patents NSTP Incubator characteristics IPSP (1,000 Yuan) FSI (1,000 Yuan) NF NGF Regional characteristics VC (1,000 Yuan) NFREF AWE (Yuan) AWEHT (Yuan) GRP (100 million Yuan) GRP per capita (Yuan) UP (10,000 persons)

Definition

Mean

Median

Minimum

Maximum

Standard deviation

No. of Observations

No. of approved intellectual properties No. of patents granted Total number of national science and technology projects within the Technology Incubator

58.47 19.21 7.33

46.33 13.5 8.61

3.00 2.00 1.33

711.67 210.33 53.00

63.11 23.15 8.61

210 184 97

Accumulated Total amount investments in public service platform Total amount of funding available Total number of firms within an technology incubator number of graduated firms within the technology incubator over 2009

11707.37

5252.83

161.67

191966.67

20979.40

168

14140.59 124.68 107.90

5762.5 92.00 66.00

630.00 33.67 13.00

206666.67 572.00 2517.33

27422.99 86.40 186.39

172 216 213

Total amount of funds received from venture private capital No. of firms received external finance Average wage of employees in urban private units Average wage of employees in high-tech industrial sector Gross regional production

77574.93 36.08 33634.86 47781.73 18051.07

23050.00 11.33 28710.25 40359.5 14337.03

518.67 1.33 22381.00 27014.5 861.76

964462.67 1010.00 56811.50 75875.00 37589.51

152433.46 113.36 10734.08 15247.37 10484.00

116 117 216 216 216

Gross regional product per capita

36592.26

34488.50

9090.00

76056.50

18507.74

216

Urban population

2871.70

2606.00

283.00

6079.00

1419.62

216

Notes: average statistics of each variable exclude observation where its value were 0 over the period 2009- 2010

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Table 2 Correlation matrix and descriptive statistics (average value of three years 2009-2011) 1

2

3

4

1. AIP 2. Patent 3.NSTP 4. IPSP (1,000Yuan)

1.000 .810** .565** .360**

5

6

1.000 .422* .291**

1.000 .345**

1.000

5. FSI (1,000 Yuan ) 6. NF

.380** .487**

.271** .304**

.460** .585**

7. NGF

.191**

.092

8. VC (1,000Yuan) 9.NFREF

.225* .340**

10. GRP (100 million Yuan) 11. GRP per capita (Yuan) 12. AWE (Yuan) 13. AWEHT (Yuan) 14.UP (10,000 persons)

7

8

9

.259** .225**

1.000 .331**

1.000

.381**

.205**

.330**

.458**

1.000

.182 .395**

.136 .681**

.055 .062

.097 .285**

.110 .266**

-.006

-.066

-.227*

.003

.140

-.022 .006 .053 .032

.023 .063 .076 -.044

-.269** -.236* -.263** -.154

-.097 -.083 -.056 .051

-.033 -.031 .036 .174*

.309** .223*

1.000 .098

1.000

-.079

-.036

-.100

-.017

1.000

-.223** -.231** -.230** -.012

-.093 -.094 -.074 .006

.262** .328** .298** -.136

-.051 -.054 -.027 .020

.161* -.030 .171** .940**

Notes: correlation matrix relates to 216 technology business incubators. *significant at the 5% level **significant at the 1% level

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10

11

12

13

14

1.000 .940** .938** -.103

1.000 .943* -.254**

1.000 -.033

1.000

Table 3 Regression analyses of effects of regional and incubators’ characteristics on innovative performance (average value of three years 2009-2011)

Constant IPSP (1,000Yuan) FSI (1,000Yuan) NF NGF VC (1,000Yuan) NFRET AWE (Yuan) AWEHT (Yuan) GRP (100 million Yuan) GRP per capita (Yuan) UP (10,000 persons) No. of observations

AIP Model 1A Coefficient 31.264 .278 .223

t-stats 4.261** 2.979** 2.298*

Model 1B Coefficient

.194 .321

210

t-stats

2.114* 3.505**

Model1 Coefficient

t-stats

.236 .142 .428 -.194

2.658** 1.449 4.199** -1.899

.163 .192 -.179 .627 .186 -.430 -.223

1.685 2.128* -.434 1.673 .406 -1.056 -.485

Patent Model 2A Coefficient

t-stats

.236 .210

2.846** 2.524**

Model 2B Coefficient

.145 .371

184

t-stats

NSTP Model 3A Coefficient

t-stats

.243 .397

2.430** 3.979**

1.563 3.987**

Model 3B Coefficient

t-stats

Model3

.070 .674

.750 7.241**

97

Notes: This table presents regression analyses of the impact of incubators and regional characteristics on innovative performance of incubators measured by number of patents granted, number of approved intellectual properties, and number of national science and technology projects over a period of three years 2009 to 2011. Models 1A, 2A, and 3A test the impacts of an incubator on the innovation controlling for the number of incubated firms (NF) and graduated firms (NGF). Models 1B, 2B, and 3Ba test the regional effects on innovation controlling for average wage of employees (AWE), average wage of employees in high-tech industrial sector, gross regional production (GRP), gross regional production per capita (GRPpercapita), and urban population (UP). *significant at the 5% level **significant at the 1% level

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