Management of Innovation Clusters – An Empirical Analysis

Isabel Gull

M.Sc. Economics Institute of Cooperative Research

University of Münster Germany

Telephone: (+49)251/83-22894 Fax: (+49)251/ 83-22804

Email : isabel.gull@ifg-muenster.der

Presented at the Economics and Management of Networks Conference

(EMNet 2013) (http://emnet.univie.ac.at/)

Robinson Hotel and University Ibn Zohr

Agadir, Morocco

November 21-23, 2013

1 Introduction Innovation clusters are generally regarded as an instrument for fostering innovativeness and competitiveness of a region’s economy, although criti-cism of the concept has recently been growing. That is partly because young clusters are often times not as successful as expected. Because of the substantial level of public funding, many clusters have emerged since the 1990s, especially innovation clusters in research-intensive industries. Those complex organizations might be able to foster innovation, due to the inten-sive communication and cooperation between member organizations (e.g. large firms, SMEs, research organizations, venture capital firms, and edu-cational organizations) which support the transfer, aggregation, and genera-tion of knowledge. A cluster can also present opportunities for the mem-bers to concentrate on their core competences which can provide ad-vantages of specialization.

However, clusters need effective management in order to take full ad-vantage of these opportunities. In the past, the issue of clusters has often been analyzed qualitatively from economic, geographic, or sociological perspectives. Until now, few analyses of configuration, business manage-ment, and governance of clusters have been undertaken. The specific cha-racteristics of clusters and the consequences for managing these complex and dynamic organizations have not yet been analyzed explicitly. There is also a lack of quantitative research owing to the variety of different types of clusters and their uniqueness. This poses serious problems for large-scale empirical analysis. Generally valid indicators have to be identified for ex-amining determinants of cluster success. Nevertheless, it is necessary to conduct further research about the configuration and management of inno-vation clusters in order to exploit their innovative potential.

This paper provides initial results of a large-scale empirical analysis of management-related determinants of the success of innovation clusters. Chapter two presents the underlying success definition, the theoretical background of cluster management, and deduces hypotheses concerning the success of innovation clusters. In a next step a concept for operationalizing the success of innovation clusters for a large-scale empirical analysis is de-veloped. A description of the method used to analyze empirically manage-ment-related determinants of success of innovation clusters follows. Re-

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sults are presented and in the final paragraph limitations of this paper and directions of further research are discussed.

2 Theoretical Background and Hypotheses In this paper, innovation clusters are understood as specialized companies and associate institutions that cooperate and compete in R&D in technolog-ically related areas and are located in geographic proximity (Porter 2000). Following the approach of Fasbender et al. (2010), this paper focuses on clusters with network character. This approach emphasizes the cooperative character of the cluster, the network ties between the members, and the contractual basis of the organization. The cluster management is institu-tionalized centrally by either a focal company, a public institution or a neu-tral cluster manager.

The management of clusters differs from that of companies. In clusters the complexity is increased due to the higher number of actors. This also leads to a trade-off between the members’ autonomy and the level of coordina-tion through the cluster management. Generally, cluster management activ-ities can be differentiated into three stages, the institutionalization, the op-erative cluster management and the success control (Schreyögg 1991; Syd-ow/Duschek 2011; Theurl 2005). The stages are introduced in this chapter and for each stage hypotheses concerning the empirical analysis of man-agement-related determinants of the success of innovation clusters are de-duced. First, the concept of success used in this paper is defined.

2.1 Definition of Success for a Large-Scale Empirical Analysis The complexity of clusters makes it difficult to define success for a large-scale empirical analysis because of the variety of possible objectives whose importance may be evaluated differently by the actors. Additionally, the development of causal relationships allocating activities to effects is diffi-cult due to the vast number of parallel activities (Sölvell 2009).

Because of the legal independence of the actors, using measures of corpo-rate accounting to define and assess success, like profit and loss accounts or key performance indicators, is not possible. Moreover, in the case of large-scale empirical analyses, one also has to take into account that the neces-sary data has to be available or can be collected with a reasonable effort.

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Under these conditions, a suitable definition of success is the extent of tar-get achievement (Bode et al. 2011). This approach has evolved as one of the main tendencies in organizational research. This way, success is opera-tionalized as effectiveness. One basic condition for measuring the extent of target achievement has to be fulfilled in the stage of institutionalization. The objectives must be formulated explicitly and provide the opportunity to be operationalized.

It is difficult to give a complete picture of success of innovation clusters just considering universal indicators. Each cluster has individual objectives which cannot be taken into account in a large-scale analysis. When inter-preting the results, one has to be aware of the fact that there is no exact va-lue of success. Therefore, this paper does not aim to give an absolute eval-uation of success or failure but to detect general management-related de-terminants of success of innovation cluster and thus provide possible start-ing points for enhancing effectiveness.

2.2 Institutionalization of Innovation Clusters1 The management stage of institutionalization generally comprises the plan-ning, negotiations, and development of governance structures. The first steps are the formulation of objectives and regulations which provide the framework for management activities (Hoffman 2001). Afterwards, it is important to select appropriate members and allocate tasks and resources efficiently (Sydow/Duschek 2011).

Formulating Objectives

Formulating objectives is essential for the organization of a cluster. This procedure should be carried out as early as possible involving every cluster actor. In a first step, an individual environmental analysis is necessary to evaluate the strengths, weaknesses, and potentials of the cluster (Schuh et al. 2005). This is the basis for negotiations between the actors. One obsta-cle is to overcome barriers to communication between different member organizations (Lindqvist/Sölvell 2012). The final document must be formu-lated clearly but has to provide enough flexibility to ensure the capability of reacting to changing circumstances (Raschke 2009). The feasibility of objectives and possibilities to measure the level of achievement must be

1 For a detailed theoretical analysis of the institutionalization of innovation clusters, see Gull (2013a).

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H_1: The existence of clear objectives is positively related with the suc-cess of innovation clusters.

H_2: The number of members is positively related with the success of innovation clusters.

considered as well in order to raise realistic expectations and give a basis for effective success control (Scheer/Von Zallinger 2007).

A common strategy creates transparency in a complex organization and offers orientation and motivation to the actors (Beck 2005). Thus, hypothe-sis 1 proposes:

Number of Members

It is assumed that a critical mass of cluster members initiates a self-augmenting agglomeration process which attracts more companies to the cluster region. Their objective is to benefit from the agglomeration econo-mies which arise from the spatial proximity to the cluster (Brenner 2004). This gives the cluster the opportunity to select suitable members according to its selection criteria from a broader mass of candidates (Sydow/Duschek 2011). This can also affect the allocation of resources and tasks inside the cluster. An efficient division of labor between the cluster members can cre-ate economies of scale, reduce transaction costs, and achieve a flexible spe-cialization between the member organizations (Deigendesch 2004).

This influence might be recursive. In a successful cluster, more organiza-tions are likely to strive for a membership. So these two factors may be mu-tually reinforcing. This has to be taken into account when interpreting the results.

Hypothesis 2 proposes:

Percentage of Supra-regional Members

In the process of selecting suitable members, the decision about the per-centage of supra-regional members – both at national and international lev-el – is a balancing act. On the one hand, the regional roots of a cluster favor the development of informal institutions and thus can establish competitive advantages (Lorenzen/Fos 2004). On the other hand, external knowledge flows are needed in order to be able to meet the requirements of the global-

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H_3: The percentage of supra-regional members is positively related with the success of innovation clusters.

H_4: The degree of heterogeneity of the members is positively related with the success of innovation clusters.

ized markets and for preventing regional lock-in effects (Nuur/Gustavsson/Laestadius 2009). In addition, a cluster with only regional members is highly dependent on the economic situation in the particular region. Thus, the advantages gener-ated by supra-regional members predominate the disadvantages, in particu-lar when members from outside the cluster region are included into the per-sonal network and the community of values and informal institutions. The selection of cluster members should focus rather on functionality than on regionality because a regional cluster can benefit from external members (Miklis 2004).

Therefore, hypothesis 3 proposes:

Degree of Heterogeneity

The selection process also determines the variety of different cluster mem-bers concerning their organization type and their objectives. In an ideal si-tuation, all necessary competences in a cluster should be covered by its members. However, their strategic objectives and organizational cultures should fit. So in the process of the member selection, the cluster manage-ment must weigh up the degree of heterogeneity which fosters the flexible specialization and the transfer of knowledge in the systemic innovation process against the potential for conflicts between the different types of member organizations (Niu/Miles/Lee 2008). In the case of a conflict, the cluster management can take the role of an arbitrator, which can reduce the negative effects.

Thus, hypothesis 4 proposes:

2.3 Operative Cluster Management2 The development and implementation of cluster services are important ac-tivities in operative cluster management. Those specific services for cluster

2 For a detailed theoretical analysis of the operative cluster management, see Gull (2013b).

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members can establish competitive advantages and thus contribute to in-crease the success of the cluster and its members.

Especially for innovation clusters knowledge management plays a crucial role. It includes the provision of knowledge for the systemic innovation process and the protection of the strategically important knowledge of the individual members.

Range of Cluster Services

The cluster management may create endogenous growth factors by supply-ing business services to the members. Cluster services can generally be de-fined as long-term oriented services, tailored to the members’ require-ments, integrated into the overall concept, and agreed upon with or offered by the cluster management (Zeichardt/Sydow 2009). Cluster services can support activities like internationalization, human resources management, networking, and marketing as well as promoting regional entrepreneurship as an investment in future cooperation partners and in the region as a busi-ness location (Gagné et al. 2010). The supply of cluster services depends on the cluster’s objectives and the members’ requirements. Competitive advantages for the members can be generated by a specialization of the cluster management in these services.

Therefore, hypothesis 5 proposes:

Knowledge Management

A centrally organized knowledge management can lead to closer network ties between the members and provides the opportunity to generate a broader knowledge basis through a more intense knowledge transfer bet-ween the members. It also includes regulations concerning the protection of the members’ intellectual property in order to prevent unintended know-ledge transfer and thus the loss of competitive advantages for companies in research intensive industries. This way, the innovative potential of a cluster can be increased (Bader 2008; Eckert 2005).

Hence, hypothesis 6 proposes:

H_5: The range of cluster services is positively related with the success of innovation clusters.

H_6: A centrally organized knowledge management is positively related with the success of innovation clusters.

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Joint R&D Activities

According to the principle of flexible specialization, in the course of joint R&D activities, the cluster members are able to concentrate on their core competences and thus contribute to the generation of specialized knowledge and innovation within the cluster. Implementing joint R&D ac-tivities can induce economies of scale, skills, and risk (Deigendesch 2004).

Thus, hypothesis 7 proposes:

2.4 Success Control Success control reviews the achievement of objectives set up in the stage of institutionalization. This allows the detection and removal of inefficiency. (Iristay 2007). The enhanced transparency disciplines all actors by setting incentives for behaving in a conform manner to the expectations and rules (Peitz 2002). Presenting achievements can also affect the success by foster-ing motivation and establishing reputation. However, these relations may be indirect and underlie overlapping effects which are difficult to measure empirically. This has to be considered when interpreting the results of the empirical analysis.

Hypothesis 8 proposes:

3 Method

3.1 Data In order to examine the management-related determinants of cluster man-agement, a survey approach was applied. The empirical data stems from a survey performed by VDI/VDE Innovation + Technik GmbH on behalf of

H_7: The implementation of joint R&D activities is positively related with the success of innovation clusters.

H_8: The implementation of a success control is positively related with the success of innovation clusters.

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the Danish Ministry of Economics, the Scandinavian Council of Ministers and the German Federal Ministry of Economics, Technology and Innova-tion. For the project called NGPExcellence, a database of cluster organiza-tions was built for conducting a Europe-wide benchmarking as an instru-ment of evaluation. The data comprises 270 sets with 34 categories of ques-tions and is the largest cluster benchmarking database in Europe.

The data was collected in personal interviews with cluster managers using standardized questionnaires by employees from VDI/VDE Innovation + Technik GmbH in the period from October 2010 to May 2012. It can be assumed that the cluster managers are well informed about the four main topics of the survey, structure, financing, management and governance, and achievements and thus are key informants in the sense of Mitchell (1994). The method of personal interviews reduces the risk of distortions and miss-ing values due to problems of understanding and interpretation (Berthold 2004; Kaya 2007).

The data quality can be evaluated as high. The respondents are in manage-ment positions of the cluster organizations. So it can be assumed that they are well informed and that the responses reflect the actual situation of the cluster.

The questions mainly target facts and data but some answers are subjective assessments. In these cases, there is a risk of the key informant bias, which can distort the results systematically because of different levels of infor-mation, perception and motives of the actors (Kumar et al. 1993). In partic-ular, so called effects of self-representation can occur when the respondent is responsible for the queried topic (Ernst 2003). A similar phenomenon is the social desirability bias which can occur because the respondents tend to answer the questions in a way which is regarded as socially desirable (Smith 1967). So activities considered as positive tend to be exaggerated whereas those regarded as negative are being understated. In order to re-duce the risk of these distortions, the respondents were informed that the results would be anonymized. This reduces the tendency to exaggerate one’s own performance. The sample was further controlled for common method bias using Harman’s single factor test (Podsakoff/Organ 1986). Here an un-rotated main component analysis is conducted for all relevant variables (Arino 2003). The test yielded more than one factor. No factor accounted for most of the variance. The variance explained by the largest

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eigenvalue was 38.6 %. Thus, in accordance with Podskadoff et al. (2003), common method bias was no issue either.

The sample cannot be considered representative because firstly the exact population of innovation clusters in Europe is unknown. Additionally, the clusters in the sample are probably more successful than the average cluster because they are either in a promotion program for high-performance clus-ters or participate in the benchmarking program on their own initiative which also hints at a high performance. However, for the aim to detect im-provement potential the comparison to successful organizations is useful and the high number of cases provides a good overview over the topic.

The data base was selected because of the high thematic accordance to the research objectives and the high number of cases which could not have been achieved in a primary survey. For reasons of comparability, some ca-ses were deleted from the original data base. These are one cluster from India because all the other clusters are situated in Europe and clusters from the technology fields food industry (non-biotech) and construction/building sector because these technology fields are not classified as particularly R&D intensive indus- tries. The product life cycles are not exceptionally short and they are no cut-ting-edge technology fields (Rammer 2011; Gehrke et al. 2010). Therefore the data base was reduced from 270 to 227 cases. The composition of respondent clusters per country is summarized in figure 2.

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Germany 70France 61Denmark 26Poland 17Norway 13Sweden 11Finland 8Spain 6Austria 5Iceland 3Latvia 2Estonia 1Greece 1Ireland 1Portugal 1Turkey 1

Table 1: Composition of respondent clusters per country.

Apart from this data base no additional surveys were conducted on this subject, so the content of the empirical analysis is determined by the da-ta available.

3.2 Operationalizing the Success of Innovation Clusters In a next step, measurable quantities to determine the extent of target achievement are identified. Academic literature does not yet present practi-cable procedures to measure the quantitative and qualitative elements of cluster success (Bode et al. 2011). The indicators have to be directly related to the work of cluster management. Moreover, they must not be influenced only by exogenous factors (such as the stage of a region’s business cycle) and must be appropriate for the research method of large-scale analysis (i.e. universal validity of the indicators and practicability of the data collection). Possible problems are overlapping effects of variables and indirect influ-ences which can distort the results.

Four generally valid dimensions of measuring cluster success are identi-fied, the financial, the structural, the innovation, and the subjective dimen-sion. The financial dimension reflects the willingness of involved actors – both private organizations and public institutions – to pay for competitive advantages generated by the cluster. It can be assumed that certain success requirements are also a condition for public funding. Thus, it indicates one aspect of success. The financial dimension is a basic target. It is a necessary condition for financing a functioning cluster management. However, it is not adequate to compare absolute amounts, e.g. total budgets. Different siz-es and objectives of clusters would lead to results which are not compara-ble. The time period during which the financing of the cluster is guaranteed – the sustainability of cluster financing – can indicate the financial cluster success. Thereby, differences in size and activities are balanced out. In the questionaire the respondent cluster managers were asked: How sustainable is the financing of your cluster organization? The response options are: 1 = "Secured in the long term" (for at least two to three years), 2 = "Secured in the short and middle term" (for around 12 months), 3 = "Critical" (but up to now no negative impacts on daily cluster organization’s activities), and 4 = "Very critical" (with already negative impacts on daily cluster activities.

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As a second factor, structural indicators can be used to operationalize the success of clusters. They can be divided into three elements. According to the definition, the inter-organizational relationships between the cluster members are an important element of this organizational form. So the in-tensity of cooperative relationships between the members can indicate one aspect of success (Wessels/Meier zu Köcker 2008). This can be operation-alized by the percentage of members which are in contact with each other on a regular basis. In the questionaire the respondent cluster managers were asked: How many of the committed cluster participants have contacts with other committed cluster participants on a regular basis? Please indicate for the last 12 months. Moreover, it can be assumed that a successful cluster provides a significant added value for its members and cooperation partners and thus attracts potential members and cooperation partners. From this assumption, the additional structural indicators, number of cooperation requests and change in the number of members, can be deduced. In the questionnaire, those numbers where queried for the previous 24 months before the interview. The absolute numbers were converted into percent-ages from the total number of members.

A specific measure of success for innovation clusters is the joint generation of innovations. Jointly developed products, technologies or services as well as patents or licensing fees, generated by members through cluster activi-ties are indicators for beneficial collaboration between the members and an effective knowledge management within the cluster. Therefor, the respond-ents were asked if they have “jointly developed products, technologies or services” or “generated patents or licensing fees” in the previous 12 months before the interview.

Those indicators are little susceptible to subjective bias in the survey. But they are quite generic which reduces their explanatory power. Thus, com-plementary subjective measures of success are included for evaluating the success of clusters extensively despite its complexity. This way, individual characteristics of each cluster, like importance and achievement of certain objectives and different levels of objectives like financial and non-financial or short term and long term objectives can be assessed (Kolloge 2010). A subjective assessment of the cluster management concerning the influence on the members’ R&D activities and on their business activities can amend the generic objective indicators through more individual and comprehen-sive subjective indicators. Therefor, the respondent cluster managers were

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asked to assess the impact of their work on the R&D activities respectively the business activities of the cluster members on a Likert scale from 0 “No impact yet” to 4 “Significant and sustainable impacts for a significant num-ber of cluster members”. Individual targets of the single clusters can be taken into account in the assessment. Here, the risk of subjective bias is in-creased but this is relativized by the use of the objective indicators.

Figure 1 depicts the definition of success and the derivation of success in-dicators.

Figure 1: Definition of success and success indicators.

3.3 Forming a Success Index For identifying impact channels of the management of innovation clusters on different levels of aggregation, four sub-indices, a financial, a structural, an innovation and a subjective sub-index, are formed and aggregated into a single success index.

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The selected indicators are scaled differently in the data base. In order to be able to aggregate the indicators to indices, they must be standardized. In this procedure, the indicators are transformed onto a scale from 0 to 100. The cluster with the least value of success concerning a certain indicator is given the value 0, the one with the highest value is given the value 100. The remaining cases are positioned in relation to the maximum and the minimum of the sample. This standardization procedure is carried out using the following formula (Enste/Hardege 2006).

This way, best practice can be detected. However, because of the aggregat-ed perspective, only the effectiveness of cluster management is compara-ble, not the efficiency. For this aim, qualitative methods are more adequate because they are able to analyze the issue in more detail.

In a next step, the standardized values of the single indicators are added up to the respective sub-indices and to the overall index. The values are added and then again standardizes to a scale from 0 to 100, whereas those values are not necessarily the minimum respectively the maximum anymore. Due to the lack of theoretical knowledge about the importance of the individual indicators, an equal weighting was chosen for preventing a randomly une-qual weighting as a source of error (Enste/Hardege 2006). The result is a relative placement of each cluster within the sample. It shows no absolute measurement of success.

This form of index leads to problems of aggregation. By considering the averages of several indicators, there is a loss of information. Thus, analyses on a disaggregated level are conducted as well.

High value in original data base corresponds to a great success: 𝑋𝑋𝑖𝑐 = 𝐼𝑖𝑐−𝑚𝑖𝑛 (𝐼𝑖)

𝑚𝑎𝑥(𝐼𝑖)− 𝑚𝑖𝑛(𝐼𝑖) ∗ 100 (1)

High value in original data base corresponds to a low success: 𝑋𝑋𝑖𝑐 = 𝑚𝑎𝑥(𝐼𝑖)−𝐼𝑖𝑐

𝑚𝑎𝑥(𝐼𝑖)− 𝑚𝑖𝑛(𝐼𝑖) ∗ 100 (2)

With:

𝑋𝑋𝑖𝑐: value for cluster c concerning variable i 𝑚𝑚𝑚𝑚𝑚𝑚(𝐼𝐼𝑖): Maximum of variable i in the considered sample 𝑚𝑚𝑚𝑚𝑚𝑚(𝐼𝐼𝑖): Minimum of variable i in the considered sample

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3.4 The Model The empirical analysis is carried out in a two-stage procedure. First, for identifying impact channels of management instruments on a disaggregated level, correlation analyses are applied in order to reveal the relations be-tween the examined aspects of cluster management and the individual suc-cess indicators. In a second step, a Polytomous Logit Universal Model (PLUM; an extension of the general linear model to ordinal data) is esti-mated in order to reveal management-related determinants of the success of innovation clusters. Here, the dependent variable is the success index de-rived in the previous chapter. As the success index is a ranking of the re-spondent clusters in the sample concerning the indicator variables, it is coded ordinally. Consequently, the model is estimated by maximum likeli-hood in the following form:

𝑋𝑋𝑖 = 𝛽′ 𝑧𝑖 + 𝑢𝑖 with (i = 1,2,…,n) for cluster i (3)

The density function can be factorized as:

𝑓(𝑧1, 𝑧2, … , 𝑧𝑛;𝛽′) = ∏ 𝑓 𝑋𝑋𝑖(𝑧𝑖;𝛽′)𝑛𝑖=1 (4)

Regarding the density as a function of 𝛽′ leads to the maximum likelihood function

𝐿(𝛽′) = ∏ 𝑓 𝑋𝑋𝑖(𝑧𝑖;𝛽′)𝑛𝑖=1 (5)

The coefficient 𝛽′ has to be determined in such a way that L, the likelihood, is maximized. This yields the model’s parameters that help to determine the probability (Maddala 1983) that a cluster has a certain value in the ranking. Thus, the parameters in the regression function can be estimated and the influence of the independent variables on the cluster success can be deter-mined. The logic operation function is logit.

4 Results

4.1 Correlation Analyses For testing the hypotheses proposed in chapter 2, correlation analyses are conducted for each relation between the determinants identified in chapter 2 and the success indicators developed in chapter 3. Table 1 sums up the parameters which have been significant in the correlation analyses. Six of the eight hypotheses are substantiated. They indicate that “the existence of

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clear objectives”, “the number of members”, “the percentage of supra-regional members”, “the range of cluster services”, “a central knowledge management”, and “the implementation of joint R&D activities” are posi-tively correlated with the success index. This does not necessarily imply that “the degree of heterogeneity of the members” and “the implementation of success control”, whose correlations with the cluster success are not sta-tistically significant, are not relevant for the success of innovation clusters. Due to indirect or overlapping effects, inter-relations might not be account-ed for using statistical methods. Therefore, these determinants are also in-cluded into the ordinal regression analysis. A significant statistical correla-tion does not necessarily imply a causal relationship. But it substantiates the theoretical considerations.

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4.2 PLUM Regression

Table 2: Correlation results.

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Hypo-thesis

Determinant Positively correlated with Coefficient, Significance

Sustainability of cluster financing 0.13 *Intensity of cooperative relationships between the members 0.465 **Number of cooperation requests 0.238 ***Subjective assessment: influence on the members’ R&D activities 0.216 ***Subjective assessment: influence on the members’ business activities 0.139 **Sub-index: structure 0.205 ***Sub-index: innovation 0.198 **Sub-index: subjective assessment 0.244 ***Success index 0.223 ***Sub-index: structure 0.178 ***Sub-index: subjective assessment 0.34 ***Success index 0.287 ***Subjective assessment: influence on the members’ R&D activities 0.143 **Sub-index: structure 0.115 **Sub-index: innovation 0.239 **Sub-index: subjective assessment 0.168 **Success index 0.127 *Patents or licensing fees generated by members through cluster activities 0.45 **Subjective assessment: influence on the members’ R&D activities 0.165 **Subjective assessment: influence on the members’ business activities 0.172 **Sub-index: subjective assessment 0.21 ***Success index 0.078Number of cooperation requests 0.147 **Patents or licensing fees generated by members through cluster activities 0.21*Subjective assessment: influence on the members’ R&D activities 0.278 ***Subjective assessment: influence on the members’ business activities 0.298 ***Sub-index: structure 0.203 ***Sub-index: subjective assessment 0.335 ***Success index 0.344 ***Subjective assessment: influence on the members’ R&D activities 0.179 ***Subjective assessment: influence on the members’ business activities 0.217 ***Sub-index: structure 0.152 **Sub-index: subjective assessment 0.238 ***Success index 0.336 ***Intensity of cooperative relationships between the members 0.303 *Number of cooperation requests 0.167 **Subjective assessment: influence on the members’ R&D activities 0.197 ***Subjective assessment: influence on the members’ business activities 0.242 ***Sub-index: structure 0.224 ***Sub-index: subjective assessment 0.274 ***Success index 0.324 ***Number of cooperation requests 0.255 **Sub-index: structure 0.218 **Success index 0.138

H_8Implementation of success control

H_1Existence of clear objectives

Significance levels (two-tailed): *** p<0.01;** p<0.05; * p< 0.1.

H_7Implementation of joint R&D activities

H_2Number of members

H_5Range of cluster services

H_6Central knowledge management

H_4Degree of heterogeneity of the members

H_3Percentage of supra-regional members

To identify the directions of effects, an ordinal regression is conducted. The success index (ordinally scaled) is the dependent variable. It comprises the standardized and aggregated success indicators. The independent variables are “the existence of clear objectives” (dichotomous variable), “the number of members” (metrically scaled, grouped in steps of twenty; covariate), “the percentage of supra-regional members” (metrically scaled; covariate), “the degree of heterogeneity of the members” (metrically scaled; covari-ate), “the range of cluster services” (metrically scaled; covariate),” a central knowledge management” (dichotomous variable), “the implementation of joint R&D activities” (dichotomous variable), and “the implementation of success control” (dichotomous variable) (see table 3).

As it can be assumed that older clusters have some advantages concerning the success indicators, in particular the structural indicators, the age is one control variable. For detecting significant differences concerning sectors and countries of the clusters, they are also included as control variables.

The industry sectors are “humanities/social sciences, media, design, and service innovation”, “energy and environment”, “health and medical sci-ence”, “transportation and mobility”, “micro-, nano- and optical technolo-gies”, “biotechnology”, “information and communication”, “production and engineering”, “aviation and space”, and “new materials and chemistry” coded as dummy variables. Taking into account the number of cases, only Germany, France and the group of Scandinavian countries coded as dummy variables are included to ensure a sufficient number of cases for each coun-try (group).

Table 3: Independent variables.

Factors (dichotomous variables)Existence of clear objectives

Central knowledge managementImplementation of joint R&D activities

Implementation of success controlCovariates (metrically scaled variables)

Number of membersPercentage of supra-regional members

Degree of heterogeneity of the membersRange of cluster services

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Table 4: PLUM results.

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Coefficient Sig. Coefficient Sig.(Std. Error) (Std. Error)

1.813 ** 1.848 **(0.89) (0.927)0.147 0.368 **

(0.148) (0.178)0.014 0.018

(0.012) (0.013)-0.149 -0.396 **

(0.162) (0.192)0.963 ** 1.184 **

(0.422) (0.471)0.323 2.189

(1.472) (1.67)1.127 0.89

(1.061) (1.198)0.039 0.154

(0.550) (0.583)Control variable: age

0.052 *(0.028)

Control Variable: countries-0.07

(0.866)-0.,41

(0.657)0.318

(1.238)Control Variable: sector

-0.469(0.736)

0,469(0.736)

1.353(0.83)-0.80

(0.783)1.88 **

(0.918)2.345 ***

(0.852)1.13

(0.853)1.593

(1.216)-2.517 *

(1.446)-0.122

(1.232)Pseudo R² (Nagerkerke)Goodness of fit testTest of parallelism

Production and engineering (dummy)

New materials and chemistry (dummy)

Micro-, nano- and optical technologies (dummy)

Health and medical science (dummy)

Transportation and mobility (dummy)

Germany (dummy)

France (dummy)

Scandinavia (dummy)

Energy and environment (dummy)

Information and communication (dummy)

Percentage of supra-regional members

Range of cluster services

Implementation of joint R&D activities

Age of cluster in years

Central knowledge management

Degree of heterogeneity of the members

Implementation of success control

1.00 1.000.05 1.00

Significance levels (two-tailed): *** p<0.01;** p<0.05; * p< 0.1.

Biotechnology (dummy)

Humanities/social sciences, media, design, and service innovation (dummy)

Aviation and space (dummy)

0.198 0.355

Model 1 Model 2

Existence of clear objectives

Number of members

n=227

Model 1 shows the results of the PLUM regression without control varia-bles. However, the validity of the model is not guaranteed because the test of parallelism is significant which means that the null hypothesis testing for parallelism is violated and a basic assumption of the model is refuted. Thus, model 1 is invalid and has to be modified in order to yield significant results.

Model 2 which includes the control variables shows valid results. Both the goodness of fit test and the test of parallelism are not significant. Nagelkerke’s Pseudo R² is 0.36 which reveals a good fit of the model (Tabachnik/Fidell 2007). The results document that those clusters are more successful in the sense of the modeled index that have clear objectives which are documented and can be operationalized and measured. A posi-tive relationship also holds for the covariates, “number of members” and “range of cluster services”, and the success index. “The degree of hetero-geneity of the members” has a negative influence on the cluster success.

The control variable age has a positive influence on the cluster success. This supports the assumption that older clusters tend to be more successful because they have had more time to evolve a structure of cooperative ties between the members, to acquire a critical mass of members, to generate innovations etc. The country control dummies are not significant. This shows that in this sample there are no statistically significant differences between clusters in the countries Germany, France, and the group of Scan-dinavian countries concerning the modeled success index. The sector con-trol dummies reveal a positive relationship between the affiliations to the sectors “micro-, nano- and optical technologies” as well as “health and medical science” and the success index. This shows that the clusters be-longing to these sectors are more successful than the average of the sample. A negative relationship holds for the affiliation to the sector “humani-ties/social sciences, media, design, and service innovation” and the success index, i.e. clusters belonging to this sector are less successful than the aver-age of the sample.

“The percentage of supra-regional members”, “the central knowledge ma-nagement”, “the implementation of joint R&D activities”, and “the imple-mentation of success control” have no significant influence on the success index in this model. Testing this model for overlapping effects, a PLUM regression with only these determinants as independent variables was con-ducted. Here, the test of parallelism is significant and thus, the model is

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invalid. A valid model results from the PLUM regression with the inde-pendent variables “central knowledge management”, “implementation of joint R&D activities”, and “the implementation of success control”. How-ever, the independent variables are not significant in this configuration of the model either (see results in table 4). This hints at overlapping effects with the other independent variables or the indirectness of the relationship with the cluster success not distorting the results of this model.

Table 5: PLUM results (2).

5 Discussion Innovation clusters have been in the focus of studies in geographic and economic research fields for years. Yet, little is known about their ma-nagement. This study takes a step towards developing a more detailed pic-ture of this issue. Relevant aspects have been systematized for each of the three management stages, institutionalization, operative cluster manage-ment, and success control. Following this, hypotheses have been deduced and tested. Therefor a general success index has been developed in order to offer a possibility to operationalize success in the context of a large-scale empirical analysis. It comprises four dimensions for measuring the success of innovation clusters on a general basis, the financial, the structural, the innovation, and the subjective dimension.

The results document that management aspects influence the success of innovation clusters. In particular, “the existence of clear objectives” and “the number of cluster members” which can be actively influenced by the cluster management in points of marketing and member selection have a

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Coefficient Sig.(Std. Error)

0.529(1.455)

0.696(1.026)

0.692(0.502)

Pseudo R² (Nagerkerke)Goodness of fit testTest of parallelism

0.0581.000.103

Significance levels (two-tailed): *** p<0.01;** p<0.05; * p< 0.1.

Implementation of joint R&D activities

Central knowledge management

Implementation of success control

n=227

positive effect on the cluster success in the stage of institutionalization. In the course of the operative cluster management, a positive relationship holds for “the range of cluster services” as well as for “the implementation of a central knowledge management” and “joint R&D activities” with the success index. The results show that the success of innovation clusters can be influenced by effective management and reveal possibilities to improve effectiveness. That is, especially the aspects having a significant influence on the success need to be inspected closely on an individual level for each cluster and proposals for improvements should be analyzed. For research-ers the results indicate the necessity to examine cluster management more closely and conduct additional empirical analyses to verify and comple-ment the results of this pioneering work. The collection and analysis of panel data would further a more detailed knowledge about impact channels of cluster management and changes made over time.

However, there are limitations to this study. Foremost, the complexity of the issue of innovation cluster management makes it difficult to give a comprehensive picture of modes of action in clusters and their success in a large-scale study which requires general validity of the variables. The high level of complexity and the uniqueness of cluster structures restrict the sig-nificance and the validity of the results. Additionally, the availability of data determines the empirical method to a certain extent. Thus, this analysis does not claim comprehensiveness.

Further research should conduct a benchmarking of the respondent clusters in order to verify the results of the regression analysis. Moreover, expert interviews can contribute in validating the practical significance of the re-sults.

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