Innovation as Clusters in Knowledge Intensive Business Services: Taking ICT services in Shanghai and Bavaria as an Example
Working Paper Version
YAN ZHAO School of Management, Shanghai University No. 99 Shangda Road, Shanghai, PR China 200444
[email protected] WEN ZHOU School of Computer Engineering and Science Shanghai University, No 149, Yancheng Road, Shanghai, PR China 200072
[email protected] STEFAN HUESIG Chair for Innovation and Technology Management Faculty of Business, University of Regensburg, Germany, D 93040
[email protected]
Final Version published in: International Journal of Innovation Management Vol. 14, No. 1 (Feb. 2010) pp. 1–18 © Imperial College Press DOI: 10.1142/S1363919610002520
Abstract Due to the quick advancement of science and technology, the services sector which has a high content of knowledge and technology has experienced globally expeditious development in the past decade. Developments in general and the growth of Knowledge Intensive Business Services (KIBS) such as Information and Communication Technology services (ICT services) in particular are at the core of the major trends that are restructuring the economic landscape of not only German but also Chinese economies. In Germany, in the new era of service economy, more emphases are put on KIBS instead of traditional famous giant clusters of steel manufacturing and auto manufacturing. Especially in Bavaria, high technology clusters are prospering. The ICT services in Bavaria accounts for 40% of all software companies in Germany. A lot of ICT services clusters can be found, including IT Speicher, FIWM, BICC-NET, etc. Similar cases can be found in Shanghai, where a number of government driving as well as market pulling ICT services cluster are also coming into being. Previous empirical evidences lead to an asymmetric bipolarity in the location behavior of KIBS: general predominance of low concentration due to equal diffusion of these services in many regions, and a high concentration in some regions located at the top of the spatial hierarchy, particularly capital cities. The current exploratory research, drawing upon cluster theory and network theory, aims at discovering the cluster features both from the perspective of the company executives in the cluster. Using the data collected through interview and questionnaire survey from company managers, incorporated with current theoretical framework and, through integration and analysis, important features of the cluster such as network mechanism are calculated both in Bavaria and Shanghai. We check the supply side as well as the demand side of the reasons why clusters are formulated in the first place, and they both have a positive effect on the network mechanism of the cluster. The network mechanism has a positive effect on innovation performance of the ICT service companies. The reasons are also discussed. Suggestions are provided for policy making about the KIBS cluster forming for both regions and the cooperation in these fields, especially in terms of service outsourcing relationship. Valuable implications for deciding the location for a KIBS company on the firm level are also provided. Key Words: Knowledge Intensive Business Communication Technology, Cluster, Innovation
Services,
Information
and
1. Introduction Innovation by the entrepreneur, as put by Schumpeter, leads to gales of “creative destruction”, as innovations cause old inventories, ideas, technologies, skills, and equipment to become obsolete. However, what Schumpeter pointed out were mainly in manufacturing industries, considering his ideas were surrounding issues of
technology and equipment. Nowadays people see much more than manufacturing in innovation, since more and more, not only in numbers, but also in quality, high value added services are integrated into the economy system. The way of doing business has been reconstructed to a large degree due to the appearance and mixing process of these services. Those knowledge intensive business services (abbreviated as KIBS), namely consulting, financial, education, health, technological agents, information and communication services, etc, are playing vital roles in economic system. KIBS emphasizes on interaction with customers, and that the high-frequency, in-depth communication and knowledge flows among the network to create value. This unique nature endows it to be highly concentrated in the global metropolis or regional central cities where there is a relatively high level of informatization and knowledgefication in the economy, such as business consulting and financial services, or, highly condensed in certain areas which are not necessarily the central part of the city, but rather forming a sub-center or second CBD, such as Hollywood film and television industry cluster, Silicon Valley information service industry cluster. There have been many literatures in industrial cluster theory that clarify the characteristics of clusters in KIBS as an intersection of geographical agglomeration and sectorial network. This is a group of interlinked KIBS providers, upstream and downstream and lateral industries together with other technical and institutional supporting organizations which are concentrated in a certain geographical area. This type of cluster is essentially a locational network among those firm and institutional players, between a whole market and one integrated hierarchical firm, thus belonging to broad social network. In the industrial level, similarities and differences exist among different sectors of KIBS, which lead to different situations of innovation outputs. The uneven distribution is manifested especially in information and communication technology services sector, where substantial innovation such as patents, new software and new business models emerge rapidly. In fact, the race in this sector appears more like a race of innovation for being first in idea-generating instead of being first in market share or profit exploiting, since a cutting-edge idea or innovation will undoubtedly induce more eye-balls of both clients and investors, and is therefore much easier to generate cash, and hence share and profit, later. Competition and cooperation exist at the same time, in the same region, among same players. More often it is apparent that most of these companies, like software companies, ICT providers and IST providers, are gathering together. This more distinct trend demonstrates an underlying logic, that is, the clustering of these companies affects their economic behavior, and thus impacts their innovation
performances as well. But, to prove this logic, a suitable cluster of KIBS should be found, and appropriate measure should be taken to assess the influence of this clustering on the firms’ innovation performance. In this paper the following structure is adopted. Section 1 introduces the overall background. Section 2 provides an overview of the literature on KIBS cluster. Section 3 deals with hypotheses, considering issues including social network, economic geography, firm theory, regional innovation system, and knowledge management, to provide a basis of later research, and then bring forward methodology in this paper. The results will be shown in section 4, and further discussions of the conclusions will be presented in section 5.
2. Literature Review KIBS has been regarded as vital driving force in economic growth, especially in knowledge economy of present day. In developed as well as developing countries and areas, more KIBS are emerging. Geographically, the KIBS are occupying bigger land areas, whereas the number of sorts of the services are increasing sharply during the past decades. In ICT services, innovation in terms of new software, new websites and new service packages are being offered to enterprise clients as well as individual customers. In financial services, logistics, research and development areas, the interaction between clients and service provides facilitate the process of not only manufacturing but also knowledge flowing among the players and thus improve the business procedure. Business models have been reconfigured to a tremendous degree. In real industry, the characteristic of clustering and “getting together” of KIBS are fairly obvious already. Consulting companies and financial services companies, for instance, tend to agglomerate in urban areas especially in CBDs, or sometimes in science/industrial parks, because they can find more and keep close contact with clients such as corporate headquarters and R&D institutions in vicinity, and they have access to other resources like ICT infrastructure. Software companies, on the other hand, more often than not find themselves located in software parks or clusters, due to political preferential policies or especially made infrastructures. Game theories also reveal the underlining reasons of players in the same sector to be located as neighbors. Lots of research discusses the new features of KIBS, such as the tendency of knowledge combination, interaction between KIBS and manufacturers, unification of products and services, highly client-directed, etc (Miles et al, 1995; Sundbo and Gallouj, 1998; Hauknes, 1998; Bilderbeek et al, 1998; den Hertog and Bilderbeek, 1999). Interestingly, while there have been substantial studies about KIBS, the link between KIBS and cluster remains not well studied.
The concept of cluster The concept of cluster is derived from the work of Porter (1990), who argued that national competitive advantage is constituted by “home base” conditions (e.g., the labor market, knowledge spillovers, and supporting supplier firms) that are embedded in localized intrafirm and interfirm linkages, interorganizational collaboration, and networks. In later work, Porter (2000) emphasized spatially bound “clusters,” defined as “geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions (e.g., universities, standards agencies, trade associations) in a particular field that compete but also cooperate.” A growing body of literature in economic geography and regional studies has been critical of this cluster concept (Asheim, Cooke and Martin, 2006). In their overview of the more general literature on clusters, Malmberg and Power (2005) claimed that there is actually little evidence that firms interact or collaborate more with other local firms and concluded that “collaborative interaction with similar and related firms in the localized cluster does not come out as a major knowledge creating mechanism.” Other scholars have stressed the multiscale dimensions of innovation, especially the role of global connections (e.g., Bunnell and Coe, 2001; see Vallance, 2007 for a review). However, this does not necessarily lead to a rejection of locally bounded theories of innovation (e.g., clusters and regional innovation systems); rather, it leads to the conclusion that the insights of both approaches need to be integrated more explicitly in future research. Although the cluster approach addresses the specificity of location issues clearly, it has a tendency toward technological determinism in that technology is presented as a given to which regions respond. The cluster approach promotes the idea of studying the interactions between firms and other organization, but it largely restricts such analyses to a particular scale or type of proximity. In contrast, it is important to explore the concentration and dispersal of innovation processes across multiple scales (Malmberg, 2003; Malmberg and Power, 2005; Malmberg and Maskell, 2006) because local external economies from concentration produce both advantages and disadvantages for firms (Parr, 2002). Characteristics and functions of KIBS cluster A lot of research is combined with producer-services or production-services. It is revealed that KIBS tends to cluster inside a nation, city or region (Wood, 2002). Then much attention is paid to the importance of KIBS cluster on new economic structure and new economic center formation (Bryson et al, 2004). The role of it in regional innovation system also emphasized (Muller and Zenker, 2001; Wood, 2002). The
clustering of KIBS in a territory is also considered vital for its image and identity (Merino and Rubalcaba, 2006). Cooke and Pandit (2004) pointed out that industrial clusters in big cities are important sources for cities’ development and innovation. This actually touches the issue of innovation as clusters in KIBS sector. There is a great deal of case studies in KIBS clusters, such as ICT services (Aslesen and Isaksen, 2004); consulting services (Keeble and Nachum, 2002); broadcast and financial services (Cook et al, 2001; Cook and Pandit, 2004); creativity industries (Yusuf and Nabeshima, 2005); music and ICT services (Power and Jansson, 2004); and media (Nachum and Keeble, 2003a, 2003b). The mechanism of formation of KIBS cluster Most studies are concentrated in two aspects. On supply side, there are good infrastructure, sufficient human resources, good information flow, knowledge (especially tacit knowledge) spillover, more innovation activities, abundant financial capital supply, and collective learning process linked with innovative environment (Keeble and Wilkinson, 2000; Daniels and Bryson, 2002; Rubalcaba and Merino, 2005). Whereas on demand side other factors are found such as frequent contact with clients, low cost of searching clients, interaction with small and medium enterprises, interplay with competitors (Isaksen, 2003, 2004; Behrens, 2005). In recent studies, industries, economic structure, MNCs are integrated (Muller and Doloreux, 2007).
3. Hypotheses and Methodology The main research question in this study is: what influences a KIBS cluster’s network mechanism? What is the influence of the KIBS cluster’s network mechanism on firms’ innovation? To measure the clustering influence and innovation of the KIBS firms, different concepts have to be categorized and specified. (1)
Environment
Environment has long been addressed as a basic requirement for cluster development. On one hand, it is important for local authorities to fulfill fundamental infrastructure for enterprises to operate, such as gas, power, water supply and disposal, communication and network access, before the idea of settling down in that region come to companies’ minds. On the other hand, management system plays a vital role in measuring the government officials’ endeavor and performance, and hence acts as an incentive for their promoting the cluster. Furthermore, a sound regulation system, including reasonable taxes and laws, as well as a smooth assessment system for company managers and entrepreneurs, is in favor of the commercial and business environment since it exerts rigid and inflexible pressure for the growth of those firms
in the cluster. These variables are categorized into Hard Environment in this study. There are more elements in environment which are vital for the cluster’s development. Local human resource is the key for any regional economy’s growth, for instance. Another issue is types of networks (e.g. friendship and communication) and their connections (e.g. strong vs. weak, negative and positive) which influence power distribution. Networks can control the distribution and utilization of information within its sphere of influence (Rocha and Sternberg, 2005; Rauch and Casella, 2001). If the local social and culture environment is conducive to network building and company’s development, which is flexible, attractive, and looks to individual accomplishment and business achievement, the firms and therefore the cluster, will obtain more resources for development. At the same time, it’s unimaginable for a good cluster to grow up, in terms of whether economic performance or technological progress, if it’s located in an undeveloped, technology lagging area. Most of the science or technology related clusters of great eminence, e.g. Silicon Valley, Toulouse aerospace cluster, Zhongguancun Park, are found located in developed countries or comparatively advanced area. Last but not least, fund is always emphasized as an ultimate factor for business development, whether for one single company, or for a whole cluster. These variables are regarded as Soft Environment in terms of their relatively unconsolidated constraint effect on the cluster’s dynamics compared with the items in Hard Environment. (2)
Supply
The cluster provides advantages to the companies, not only in terms of the relationship of deal and alliance, but also of geographical location. Cooke (2005) highlighted the importance of “regional knowledge capabilities” and the shift to diversification as a cluster develops. In other work, there has been a greater emphasis on the issue of proximity and exactly what types of proximity prove central in clusters (Coenen, Moodysson, and Asheim, 2004; Zeller, 2004; see Boschma, 2005 for more general discussion), particularly the importance of “functional proximity” (accessibility) (Coenen, Moodysson, and Asheim, 2004). Companies are cautious in making their determinations to enter a cluster. The judgment comes as a result of balancing benefits and costs. One element is a good image of the cluster and hence of the region. Another issue is whether enough quantity as well as quality of suppliers can be found in local area. This is rather an interactive process in which service providing companies prosperous and therefore provokes the booming of suppliers nearby, which in turn form a better and smoother business environment that attracts more service companies to come. Beyond this, networks of
actors also benefit from enhanced information diffusion and their relatively loose structure, and facilitate the cross-fertilization of ideas and collaboration, relative to non-networked actors. This process aids in enhancing the innovative performance of firms, and provides an alternative to formal collaborative and control structures. (e.g. research consortia or equity joint ventures) (Feldman, 2003; Desrochers, 2001; Breschi and Lissoni, 2001; Martin and Sunley, 2003; Stuart and Sorenson, 2003). Some research pointed out information redundancy as a negative factor for innovation performance (Zaheer and George, 2004; Casper and Murray, 2005). Nevertheless, information, as a primary and previous form of knowledge, still improves the mutual understanding of players, and its flow among the players helps the whole network to be better integrated into a system. It still essentially enriches the network mechanism. Altogether the above variables are classified as Supply. (3)
Demand
One of the biggest advantages for a KIBS company to locate in a cluster is that it is convenient to contact existing clients. Most of the KIBS clusters are thronged in central business districts (CBDs) of big cities, which come as the result of a natural, self-organizing process, or enter an artificially well designed territory such as a software park. Whatever the case, the environment of CBDs or parks provides the KIBS companies with excellent accesses to the clients, like big MNCs, SOEs, private firms, and governmental offices or institutes because of the geographical proximity. Furthermore, it’s also convenient for them to look up and hunt for potential clients. In the dynamics of KIBS sector, the active interactions and encouraging potential clients to adopt a service package or implement a solution scheme are everything. Therefore a good location offers unprecedented advantages. The variables before are named Demand. (4)
Network mechanism
This mechanism in cluster has been manifested in lots of aspects. In economic geography theories, rich inter-firm relationships, primarily driven by geographic proximity to competitors, supply chain members and firms in related industries, are addressed as a key factor of firms’ performance. According to transaction cost theory, motives for partnership are minimizing the sum of internal and external transaction costs, rising efficiency. Main factors during a relationship are: frequency, asset specificity, uncertainty, complexity (Williamson, 1979). Objective of partnership is economizing on transaction costs through choosing an appropriate governance structure. Also, knowledge management theory points out the importance of knowledge spillovers and creativity within the innovation process which primarily
stems from the traditional economic idea of knowledge externalities (Marshall, 1920; Jaffe, 1986) and the more modern concept of localized learning systems (see Breschi and Lissoni, 2001 for a survey). These fields emphasize the benefits accrued by firms within close spatial proximity to each other. As this process lowers the transaction and communication costs associated with knowledge transfer it is seen as lowering the costs of innovation and enhancing the innovative opportunities open to firms within the appropriate spatial proximity (Krugman, 1991; Audretsch and Feldman, 1996; McEvily and Zaheer, 1999). The regional innovation system approach does not really address the specificity of place and the consequences that it has for different actors. However, it is useful to consider how regions are constituted by multiscalar innovation processes and whether the interaction across these scales enables regions and nations to adjust and adapt to global economic change. Indeed, proximity facilitates an increased number of interactions between related firms, largely as a function of high spatial concentration, which in turn enhances the coordination and control of firm activities within the supply chain, facilitates frequent and repeated inter-firm information sharing and collaboration, lowering the costs of innovation, lowers the information search costs associated with environmental scanning as information about competitors and potential partners “spill over” from inter-firm interactions (Audretsch, 2001; McEvily and Zaheer, 1999; Bresnahan et al., 2001). Although networks can control the distribution and utilization of information within its sphere of influence (Rocha and Sternberg, 2005; Rauch and Casella, 2001), knowledge flow within this sphere still reaches as many as possible players along all available paths and links. In some studies, isomorphism is concluded as a constraining process that forces one unit in an environment to resemble other units that face similar environmental conditions (Hawley, 1968). As a result of path dependency firms begin to more closely represent each other (e.g. strategies). This hampers competition, obscures attempts for differentiation and reduces the perceived desirability of novel strategies (Desrochers, 2001). Therefore, this is also taken into account. The research summarized above has provided a valuable basis from which to understand the dynamics of network mechanism in the cluster, and demonstrates the implicit tradeoffs facing firms deciding to locate within clusters. Therefore they are brought into the construct Network Mechanism. (5)
Innovation performance
Innovation performance for a single company has been studied by many researchers. The most used variables are intellectual patent applications or
procurements, Tobin Q, new products or services, revenues or profits in total, IPO value (Gary and Michael, 2005; Buraj and David, 2007; Stephen and Kathleen, 2007; Negassi, 2004; Xiaohui and Huan, 2008; Julian and Sussex, 2003; Vladimir, 2003). These traditional scales indeed provide useful angles of measuring firms’ innovation performance. There is still another variable especially for ICT services companies. That is the time length of marketing the products or services. It is actually one of the vital elements for surviving in this industry. A software company might be unable to pull out difficult times if it doesn’t manage to market its product or service faster than its competitors. For information and communication industry, where “eyeball economy” is preached to be significant in terms of grasping the attentions of potential investors, companies strive to be the first vie with each other in announcing and marketing their new services. Thus the construct of Innovation Performance is got. What is a network for the purpose of the analysis of innovation and industry evolution? How might we define it in such a way that is understandable and useful for research on innovation and industry evolution? (Freeman, 1991) A related issue is how and why the specific features and characteristics of networks affect innovation, profitability and growth in an industry. A final, broader question regards the role of networks in different stages of industry evolution, and the related coevolutionary processes. Therefore the following hypotheses are drawn. Hypothesis 1: Supply condition is positively related to network mechanism. Hypothesis 2: Demand condition is positively related to network mechanism. Hypothesis 3: Hard environment is positively related to innovation performance of the ICT service cluster. Hypothesis 4: Soft environment is positively related to innovation performance of the ICT service cluster. Hypothesis 5: Network mechanism is positively related to innovation performance of the ICT service cluster. This study aims to discover features of innovation as clusters of Knowledge Intensive Business Services (KIBS). In this paper cluster refers to those official, formal ones, including technological or science parks, incubators, and innovation centers. The methodology used in this study is questionnaire survey. The companies are from Bavaria, Germany, where high technology clusters are prospering. The ICT services in Bavaria accounts for 40% of all software companies in Germany. A lot of ICT services clusters can be found, including IT Speicher, FIWM, BICC-NET, etc. Similar cases can be found in Shanghai, China, where a number of government driving as well as market pulling ICT services cluster are also coming into being.
From April to June, 2008, second hand information collecting and questionnaire survey were conducted with ICT cluster managers in Bavaria, Germany. During these interviews some closed and open questions were asked and answered. The following issues were discussed: environment, including the hard and soft environment; Influencing factors for companies to locate in the cluster, including demand and supply factors; sub-cluster network mechanism; innovation performance of the companies in the cluster; and activities the cluster offers to its member companies. These questions are related with cluster and network, especially from the geographical and cooperative perspective. The same process was adopted in Shanghai, China during July and August, 2008.
4. Results The current study examined the fit of the hypothesized model in Germany and China. As shown in Figure 1, on the side of Bavaria, Germany, the model fits the data well (χ2 = 179.16, df = 225, GFI = 0.952, AGFI = 0.941, RMR = 0.079, CFI = 0.88, RMSEA = 0.003). The results showed that the hypothesized model could be moderately accepted. Supply
.14*
Network Mechanism Demand
.28** .44**
Hard Environment
1.89* *
Soft Environment
510.01*
Innovation Performance
Figure 1: Unstandardized estimates of the model in Bavaria, Germany
On the side of Shanghai, China, the model also fits the data to a moderate degree (χ2 = 195.15, df = 225, GFI = 0.948, AGFI = 0.937, RMR = 0.082, CFI = 0.81, RMSEA = 0.007). The results showed that the hypothesized model could also be moderately accepted (see Figure 2).
Supply
.08*
Network Mechanism Demand
.18* .23**
Hard Environment
Soft Environment
13.22** Innovation Performance 2.39*
Figure 2: Unstandardized estimates of the model in Shanghai, China
As shown in both figures, supply and demand are both positively related to network mechanism. Hypotheses 1 and 2 are partly accepted. The case is the same to innovation performance, since both hard environment and soft environment are positively related to it. Hence hypotheses 3 and 4 are partly accepted. The last one, hypothesis 5 is partly accepted since the regression weight is also positive.
5. Conclusions and Policy Implications The contribution of current research aims at exploring the relationships among hard environment, soft environment, supply, demand, and network mechanisms together with innovation performances of KIBS companies in the cluster, and trying to preliminarily compare the situations in those of Bavaria, Germany and Shanghai, China. The findings are fairly interesting. The discoveries show different dependence of network mechanism on supply and demand in two countries, and similar situation for innovation performance on hard environment and soft environment. This sheds light on the innovation of these clusters, and paves way to further study of how to improve the situation for Chinese clusters. In the KIBS cluster of Bavaria, Germany, the situations of supply and demand have positive link with the network mechanism. The same rule applies to those firms in Shanghai, China. However, the obvious difference appears in the sense that the regression weights on Germany side are both bigger than on China side. This might
imply that the KIBS companies are heavily relying on the supply chain, both from upstream and downstream. A good image of the cluster is critical as it provides a “halo” for its member firms under shelter. Enough good suppliers, knowledge input and access to the research institutes are all indispensable. Training system provides abilities and approaches to adapt to the environment and obtain necessary skills. In terms of the influences on innovation performance, the same thing happens to soft environment. The Germany KIBS companies depend extraordinarily heavily on the soft environment, including local human resources, social and cultural environment, technological potential and funding, while their Chinese counterparts are far less concerned about it. When it comes to the hard environment, the case goes completely opposite. Infrastructure, local management system, regulation and assessment system mean so much to Chinese firms that they address them with the highest value. This may reveal the fact that in China, the economic development or, rather, economic growth, still remains investment driven, even in the relatively high value adding sector like KIBS. And at the same time, executives and managers in KIBS firms still put their emphasis on the rigid, inflexible items, rather than such soft issues as human resources, social and cultural environment, etc. While soft environment is the most crucial factor for German KIBS companies’ innovation performance, hard environment remains the most momentous driving force for their Chinese colleagues. The regression weight between network mechanism and innovation performance offers another topic, in the sense that it’s bigger in Germany than that in China. The likely underlying logic is that the specialization degree in KIBS clusters in Bavaria, Germany is bigger than that in Shanghai, China, and companies there have better complements for each others’ abilities, find it easier to collaborate with one another, and become more similar in strategy and R&D policies as well. And their innovation performance does benefit from this network mechanism. The more involved they are in the clusters’ activities and interactions about business running or innovation, the more gain they procure on the return. On the other hand, the degree of concern as well as participation in clusters’ affairs of Chinese companies in Shanghai is not apparent due to their daily operation. Indeed, KIBS companies in Shanghai are sort of overwhelmed by their ordinary business activities, although it doesn’t necessarily result in high output or return in terms of whether innovation or economic revenue. The findings have significant policy implications. First, the major difference between the clusters in Bavaria and Shanghai is the difference of the innovation performance on soft environment. This is a reflection of the different operating mechanism of these clusters in two countries. The Chinese KIBS companies still rely
more on infrastructure and tax preferential policies, which are easily altered through one night by the changes of natural condition or government policy. The German ones are however more enjoying the abundant human resources and culture advantages and technology potential, which are far more stable and less affected by sudden short-term shocks. Second, the value chain in Chinese clusters is still at its primitive stage, reflected by the regression weights of supply and demand to network mechanism. Indeed, higher value added services are key to KIBS sectors. To integrate and add more value in this system require the cluster companies to collaborate more on such business activities as cooperative R&D, joint venture, strategic alliances, network association. Furthermore, it requires the cluster coordinator or manager to set out more energy to enhance and improve this situation as well. Last but not least, the network mechanism must be improved in the sense that it should be pinpointed toward the innovation of the firms. Everyday business network activities ought to be separated from real key innovation events like joint research, new product or service cooperation, and new ideas co-generating. To relate this with innovation demands more research.
Appendix Questionnaire For the questions below please give a mark from 1 to 7. (1: strongly disagree; 2: moderately disagree; 3: slightly disagree; 4: neutral; 5: slightly agree; 6: moderately agree; 7: strongly agree). For open ones please give your brief remarks and ideas about them. 1. Hard environment • •
Do you think the local infrastructure is good for the company’s development? Do you think the local management system (from the government) is good for the company’s development?
• •
Do you find a loose regulation (tax and law) in this area? Do you think the local assessment system (for the company managers and entrepreneurs) is good for the company’s development? 2. Soft environment •
Do you think there is abundant availability of local qualified human resources for the company’s staff? If so, where are they mainly from? (open)
•
Do you think the local social and culture environment is good for the company’s development? Describe them briefly. (open)
•
Do you think the local technological potential and R&D level is good for the company’s development?
• Do you find it easy to get state or local research funding? 3. Sub-cluster network mechanism • Do you think there is a clear specialization in local KIBS companies? • Is it possible for the companies to complement each other’s ability and have a good cooperation? • •
Is it easy for KIBS companies to collaborate with each other for a project? Are the companies more and more similar to each other in the cluster? (Products or services, strategy, R&D, etc) If so, what do you think the reasons are? (open) 4. Influencing factors for companies to locate in KIBS cluster Organizing and managing the local cluster here, to what degree will you agree that the following factors are good in the cluster? （1） a) b) c) d) e)
Supply side: A good image Enough good suppliers Get more knowledge from other companies such as fellow traders, suppliers and clients Can easily approach the research institutes such as universities, colleges, and research institutes Good training system
（2） Demand side: • Low cost of searching clients • Convenient contacts to clients 5. Is innovation performance of the local KIBS cluster very good? • Patents application • New products or services • Revenues and profits • It’s short to market the products or service.
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