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Innovation Networks and Innovation Behaviour (inib_110_Region.doc)

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The Impact of Innovation Networks on Firms’ Innovation Behaviour:

Some Empirical Evidence

Birgit Soete, Rainer Vosskamp

May 2004

DIW Berlin, German Institute for Economic Research,

Koenigin Luise-Str.5, 14195 Berlin, Germany [email protected], [email protected]

Paper presented at:

Conference “Regionalization of Innovation Policy – Options and Experiences”

DIW Berlin, June 4-5, 20054, Berlin

Abstract

In the last twenty years innovation networks, co-operation and research joint ventures have become important in order to expand firms’ resources of innovation and to strengthen innovative activities. Using survey data of the programme InnoRegio funded by the German Ministry of Education and Research we analyse several determinants of innovation behaviour. We test three hypotheses about firms’ innovation behaviour. We find empirical evidence that innovation networks reduce firms’ internal resource restrictions. Furthermore, network spill-over have an impact on innovation behaviour. However, the market structure is also a determinant for the innovation behaviour of firms.

JEL-Classifications L1, O3

Key Words regional innovation networks, innovation behaviour, resource-based theory of the firm, industrial economics, evolutionary economics


1 Introduction

For nearly twenty years innovation processes have no longer been understood as linear

processes of invention, innovation and diffusion, but as complex and evolutionary processes.

Moreover, the importance of learning and knowledge accumulation as driving forces of

innovation processes has been detected. As a consequence innovation networks, co-operation

and research joint ventures have become important in order to expand firms’ resources of

innovation and to strengthen innovative activities. There is extensive literature on this issue,

but there are only few empirical studies that analyse the impact of innovation networks on

firms’ innovation behaviour.

Kaiser (2002) concludes that joint research tends to stimulate research expenditures. Becker

and Dietz (2004) find empirical evidence that research and development (R&D) co-operation

is used to complement internal resources in the innovation process and enhance R&D

intensity. Love and Roper (2001) show that firms with strongly developed external links are

more innovative than firms without external links. However, there is still no clear cut

evidence. Theoretical papers on R&D networks derive arguments for both: increasing and

decreasing R&D investment.

In this paper, we analyse empirically the impact of spill-over effects in innovation networks

on firms’ innovation behaviour. Moreover, we study whether innovation networks are

complementary or substitutive resources for the intra-firm allocation of resources which are

relevant in the innovation process. In particular, we take into consideration the role of

qualified personnel and different experiences for the absorptive capacity of tacit knowledge

within a firm and for the innovative activities of firms. Beside, we test classical economic

determinants of innovation such as firm size and market structure.

The study is based on survey data of the innovation programme InnoRegio, which is funded

by the German Ministry of Education and Research (Bundesministerium für Bildung und

Forschung, BMBF) and evaluated by the DIW Berlin. The aim of InnoRegio is to use regional

innovation networks in the "Neue Länder" (eastern part of Germany) in combination with

funded innovation projects as a vehicle to strengthen the innovation capacity of small and

medium size enterprises as well as of regions in the "Neue Länder".

The paper is organised as follows. Section 2 briefly reviews the existing literature and derives

the relevant hypotheses. The data and variables are described in Section 3. In Section 4

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empirical findings are presented and discussed. The final section contains a conclusion and

suggestions for further research.

2 Theoretical Framework and Hypotheses

In this section we introduce four theoretical approaches, which provide the theoretical

underpinning of our empirical analysis. Moreover, the empirical results from similar studies

are surveyed with respect to our analysis.1

2.1 Industrial Organisation

J. A. Schumpeter (1934, 1942) was the first prominent economist who emphasised the

importance of innovations ("new combinations") as the central determinant of economic

growth and technological change. Following Schumpeter's "Theory of Economic

Development" (Schumpeter 1934), entrepreneurs use inventions in order to realize

innovations, which allows for the appropriation of (temporary) monopoly rents. Later

Schumpeter (1942) underlines firm size and the market concentration as determinants of

innovation and technological progress (Cohen 1995; Galende, de la Fuente 2003).

Following Schumpeter, large firms have an advantage due to internally generated funds for

risky projects, higher sales, complementarities to R&D and better appropriation possibilities.

Stimulated by Schumpeter’s writings, industrial organisation economists have conducted

theoretical and empirical studies on the determinants of innovative activity and performance

(Cohen 1995, p. 182).

A main starting point for many studies is the structure-conduct-performance-approach (Bain

1956). This approach suggests that the market structure and in particular barriers to entry

determine the incentives to invest in R&D in order to achieve strategic advantages (Tirole

1990). An innovation leads to a firm’s competitive advantage, if the firm is able to

appropriate monopoly rents due to patents or intellectual property rights. The empirical results

show that firm size and innovation (respectively R&D) are positively correlated (Cohen 1995,

p. 184ff., Bhattacharya, Bloch 2004; Rogers 2004). However, Acs and Audretsch (1990,

1991) provide evidence that smaller firms tend to account for an above average share of

innovations relative to their size. Furthermore, they state that R&D productivity tends to

1 In general, there is extensive theoretical and empirical literature on innovation and innovation behaviour (Dosi

1988, Stoneman 1995), which we cannot review in detail in this paper.

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decline with firm size. Market concentration, which is the second determinant suggested by

Schumpeter, has no clear impact on innovation (Cohen 1995, p. 196, Rogers 2004).

2.2 Evolutionary Economics

Traditional economic approaches explain firms’ innovation behaviour mainly by external

determinants. Contrarily, the evolutionary economics approach (Nelson, Winter 1982)

explains innovation and technological change by internal and external factors. It adopts a

dynamic view of technology and innovation processes that are no longer understood as linear

processes of invention, innovation and diffusion, but as complex and evolutionary processes.

Evolutionary economists point out that innovation processes depend on their history and their

irreversible nature with regard to the technological path (Dosi 1988). Moreover, enterprises

are the most important, but not the only actors in innovation processes. Others are

universities, the government and intermediaries. As a rule, an innovation is created within

such an innovation system, which is characterized by interaction, learning and accumulation

(Arrow 1962, Edquist 1997, Lundvall 1992, Nelson 1993). Firm specific technological

capacities and built up knowledge determine the firm’s innovation behaviour (Cohen 1995, p.

208; Galande, de la Fuente 2003). If an enterprise, which is part of an innovation system, is

depending on external information as well as on in-house innovation capacities, the enterprise

has also to invest in absorptive capacities (Cohen, Levinthal 1989, 1990; Grünfeld 2003;

Oerlemans et al. 1998). Knowledge and information, which are used and combined to

generate new knowledge and innovations, are not per se public goods. A firm has to invest to

develop the ability to identify, assimilate and exploit knowledge from the environment

(Cohen 1995).

2.3 A Resource-based View of Innovation

For a long time in the field of strategic management firms’ activities have been analysed

mainly with respect to product markets. However, for the firm, resources and products are

two sides of the same coin (Wernerfelt 1984, p. 171). Firms’ resources define the assets in a

given time for production and strategic activities. Respectively, firm's resources define the

strength or weakness of the firm. Resources are tangible (e. g. machine capacity, cash flow

and revenues) or intangible (e.g. customer loyalty, production experience or technological

lead) (Foss, Knudsen 2003; Wernerfelt 1984). Especially intangible resources can serve as an

important source of competitive advantage. The availability of resources determines how

efficiently and effectively a firm can perform its activities. Therefore, firms' available

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resources explain differences in firms' performance over time. This approach can be

combined with the presented ideas of evolutionary economics. It allows an analysis of

innovation, which gives special emphasis to the internal characteristics of firms (Galende Del

Canto, Suárez González 1999; Galende, Fuente 2003).

Several empirical studies analyse how firms’ characteristics, other than firm size, determine

innovative effort (Cohen 1995). This discussion comes up due to the observation that within

industries firms differ concerning their R&D related capabilities. In many studies cash flow is

positively associated with higher levels of R&D intensity. However, the role of cash flow is

embedded in the relationship between corporate finance and R&D investment. Therefore, it is

not clear how to interpret the empirical results (Cohen 1995). Other authors argue that

differences in innovative performance result from differences in the organisation. However,

due to the absence of data little research has been conducted on how firm characteristics

might condition the allocation of R&D resources (Cohen 1995).

Galende Del Canto and Suárez González (1999) evaluate the effect of internal or

organisational factors on R&D activities on the basis of a sample of 100 Spanish firms. They

distinguish three types of firm-specific factors: financial, physical and intangible resources.

They derive five hypotheses on how these resources have an impact on firms’ R&D activities.

The empirical results suggest that intangible factors (especially employees’ qualifications) are

the main determinants of the probability that a firm carries out internal R&D. Galende, Fuente

(2003) analyse internal factors determining firms’ innovative behaviour based on a sample of

152 Spanish innovative firms. They distinguish tangible factors (such as size and debt),

intangible factors (such as human, commercial and organisational resources) and strategies

(such as diversification or internalisation) all of which determine the firm’s innovation

activities. The paper shows evidence concerning the extent to which firms’ innovative

activities are explained by their internal resources and factors.

2.4 Innovation Networks

In the last two decades one important area of innovation research has become the analysis of

spill-over and their role for promoting innovation. Therefore, industrial cluster or innovation

networks respectively in R&D co-operations or R&D joint ventures generating spill-over are

analysed. As innovation processes have become more and more complex, risky and cost-

intensive conducting R&D in co-operations or networks becomes more and more crucial.

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Usually, innovation networks include different actors such as firms, universities, research

institutions etc. Moreover, innovation networks are more frequently long term designed than

R&D co-operations. They are often part of a regional innovation system (Cooke 1998).

Innovation networks are founded to increase the efficiency of the innovation process and to

shorten the period from the development of new products or processes to the launch to the

market. In particular, small and medium sized firms may rely more on innovation networks

than large firms. Innovation networks might expand scarce internal resources such as

knowledge or financial constraints (Rogers 2004). But it is still an open question whether

R&D co-operations are substitutive or complementary to firms’ R&D investment (Kaiser

2002). There is much theoretically and empirical literature on this issue (D’Aspremont,

Jacquemin 1988; Becker, Dietz 2004; Cohen 1995; Freeman 1991; Fritsch, Lukas 2001;

Hagedoorn 2002; Kaiser 2002; Kleinknecht, Reijnen 1992; Love, Roper 2001; Peters, Becker

1999; Rogers 2004; Tether 2002).

Important results from theoretical approaches are as follows (Kaiser 2002, p. 752):

(1) Effective R&D investment is larger under R&D joint venture than under competition

if spill-over is sufficiently large.

(2) An increase of spill-over leads to a reduction of research efforts if goods are

complements and tends to reduce incentives to collaborate in R&D.

(3) An increase in market demand leads to an increase in research efforts and has a

positive effect on the likelihood of R&D co-operations.

(4) Increasing research productivity leads to increasing incentives to invest in R&D and

to conduct joint research.

However, there is still no clear empirical evidence with respect to the question whether R&D

co-operation and the inherent spill-over lead to increasing or decreasing firm’s R&D

investment (Kaiser 2002). Furthermore, Kaiser (2002) concludes for the German services

sector that joint research tends to stimulate research expenditures. Becker and Dietz (2004)

find empirical evidence that in the German manufacturing industry R&D co-operation is used

to complement internal resources in the innovation process and that it enhances firms’ R&D

intensity. Furthermore, the number of co-operation partners has a positive impact on the R&D

commitment. On the output side, the co-operation leads to a higher probability of developing

new products. This result for the output side was already confirmed by Tether (2002). His

analysis shows that co-operation for innovation is more common amongst firms that introduce

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innovations newly to the market. Furthermore, he shows that the intensity of R&D activities

tends to increase the likelihood to co-operate with external partners. Contrary to this, the

analysis by Love and Roper (2001) provides no support for the hypothesis that firms with

more strongly developed external links develop greater innovation intensity. Using a sample

of 597 small manufacturing firms, Freel (2003) concludes that the influence of various types

of innovation-related networking is likely to differ, depending upon the availability of internal

competencies, and the balance between product and process innovation.

Further empirical work focuses on the determinants of R&D co-operation. Kleinknecht and

Reijenen (1992) analyse R&D co-operation based on a large sample of firms from the

manufacturing and service industries in the Netherlands. They state that firm size, market

structure, R&D intensity and high shares of product related R&D have, surprisingly, only

little impact on R&D co-operation between firms. Contrary to this, Fritsch and Lukas (2001)

find empirical evidence for German manufacturing enterprises that enterprises engaged in

R&D co-operations are relatively large. Moreover, these firms show a high R&D intensity.

2.5 Hypotheses

As the previous investigation has shown, innovation behaviour is influenced by many factors.

Moreover, the relationship between innovation behaviour and these factors is very complex.

The aim of the empirical part of the paper is to shed some light on the question whether and

how innovation networks have an impact on firms’ innovation behaviour, especially on small

and medium sized enterprises. In detail, we analyse the firms’ innovation input and define

innovation behaviour by

(1) the R&D intensity and

(2) the intended level of novelty with respect to (product and process) innovation

projects.

The following hypotheses are based on the mentioned arguments.

Our starting point is the resource-based view of innovation (Galende Del Canto, Suárez

González 1999; Galende, Fuente 2003). We suppose that firms’ performance in the output

market differs due to different resources and capabilities. Consequently, the resources should

have an impact on firms’ strategies and behaviour. As resources we consider sales, human

capital, financial and personnel restrictions and experiences in R&D. Moreover, we take into

account that different resources might be relevant for different types of innovation.

Hypothesis 1: A firm’s resources determine its innovation behaviour.

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Beside the resources of a firm we look at the impact of an innovation network. In doing so,

we are able to analyse whether spill-over effects in the innovation network have an impact on

the firm’s resources and thus on the innovation behaviour. Based on theoretical arguments we

suppose that innovation networks might expand or complement the innovative resources of

enterprises. As spill-over effects we consider the flow of information support between the

network partners. These spill-over effects may be also different for firms developing a

product and firms developing a process innovation.

Hypothesis 2: Innovation networks have an impact on a firm’s innovation behaviour.

Moreover, we take into account that firms have to be endowed with a sufficient level of

absorptive capacity to be able to internalise the spill-over effects of innovation networks. We

define the absorptive capacities as the share of personnel that is employed in the firm’s R&D

project, the share of the firm’s academic personnel, the experiences in R&D and in co-

operation with the network partners. Most of these variables characterise at the same time the

firm’s resources for innovation.

Hypothesis 3: Absorptive capacities intensify the impact of spill-over effects on the

innovation behaviour of the firm.

Due to the theoretical and empirical evidence outlined in the previous subsections we also

investigate the market structure.

Summarizing, we will carry out regressions analyses for two dependent variables: the R&D

intensity and the intended level of novelty. For the latter variable we will distinguish three

cases: product or process innovation, product innovation and process innovation. As

independent variables serve resource variables (including variables which measure the

absorptive capacity), network variables and market structure variables.

3 Data

3.1 InnoRegio

Since the German reunification the economic situation in the “Neue Länder” (eastern part of

Germany) has been unsatisfactory. Many regard the lack of R&D by firms as an important

reason. Moreover, an inadequate level of co-operation between firms, research organisations

(universities, other research organisations such as the Fraunhofer institutes) and also other

facilities seems to be responsible for the low level of innovative activities.

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Therefore, the German Federal Ministry of Education and Research (Bundesministerium für

Bildung und Forschung, BMBF) has designed the policy programme InnoRegio that started in

1999 with a contest for regional innovation networks. The aim of InnoRegio is to use regional

innovation networks as a vehicle to foster innovation and the competitiveness of firms and

regions. In the 23 selected regional innovation networks different actors are organized: firms,

universities, research institutes and other organisations. The BMBF gives financial support to

organize the network and for conducting R&D projects, which have to be in line with the

aims of the corresponding regional innovation network.2

3.2 Sample

The empirical analysis is based on data which is drawn from a survey of InnoRegio. In

summer 2003 all persons involved in the 23 innovation networks were asked to complete a

questionnaire. For our analysis, we will focus on the answers of 199 producing and service

firms which got financial support for 238 R&D projects.

In more detail, the sample contains 168 enterprises conducting one R&D project. 24 firms are

involved in two R&D projects and six firms in three. One enterprise is conducting four funded

R&D projects. As we analyse, amongst others, the innovation process for every R&D project,

the enterprises had to fill out the questionnaire for each of their R&D projects.

We take into account 238 observations, which are assigned to the 238 projects. The R&D

intensity is a firm specific variable. As a consequence, information about up to four projects

of a firm should be used to estimate firms' R&D intensity. To tackle this problem, in the case

of the estimation of the R&D intensity the 238 observations are weighted by 1/ Ni, where Ni is

the number of firm i’s projects.

3.3 Variables and Summary Statistics

In the previous section two variables were introduced which represent firms' innovation

behaviour. First, we use a variable, which we define as "intended level of novelty". The firms

were asked whether they intend to develop (a) a basically new product respectively process,

(b) a comprehensively improved product respectively process, or (c) a partially improved

product respectively process. Based on the answers, we define three binary variables. Each

variable indicates the highest intended level of novelty. The variable “product or process”

2 For a discussion of the InnoRegio Initiative see, e. g., Eickelpasch et al. (2002) and the Initiative’s home page:

www.innoregio.de.

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equals 1, if it is intended to develop a basically new product or process. Otherwise “product or

process” equals 0. Due to the supposition that the determinants of product and process

innovation differ we define the binary variables “product” and “process” in the same way.

Table A shows that in 45.8 percent of all cases a product innovation is aspired to as a result of

the R&D project. In 38.2 percent of all cases the firms’ goal is a process innovation.

Furthermore, 9.7 percent aspire to both a product and a process innovation. Consequently,

74.4 percent are targeting a product or a process innovation.

Second, we take firms' R&D intensity into consideration. Firms were explicitly asked to

declare their intensity as R&D expenditures divided by sales. Table E shows that InnoRegio

firms show high R&D intensities on average: the firms spend 26.6 percent of sales for R&D.

The median is given by 16.5 percent. This difference is a result of the R&D intensities of

some very small firms. The 90 percent fractile of the distribution of the R&D intensities

indicates that 10 percent of the firms show R&D intensities which are higher than 70 percent.

Moreover, a few firms state that the R&D intensity is higher than 100 percent. At a first

glance, this fact seems to be impossible. Taking start-ups (in particular biotechnology start-

ups) into account this result becomes reasonable.

In Section 2 it is assumed that there are three different types of forces that have an impact on

firm’s innovation behaviour: (a) network variables, (b) resource variables, and (c) market

structure variables.

For each type of force the survey offers several variables. Table 1 presents a list of all

variables used in this analysis. Tables B, C and D present the frequencies for all categorical

variables. Mostly, a scale with five categories is chosen. In some cases two adjacent

categories are recoded into one category because the number of observations for the separate

categories is too small. However, two categories of “external support” are recoded for a

reason of symmetry, although the frequencies for the two categories are quite high. The

reason is that the three other variables, which characterize support (“internal support”,

“network support” and “supporting network”), have to be recoded.

Table E shows some summary statistics for important metrically scaled variables. In

particular, Table E shows that the sample contains only small and medium sized firms. The

median for sales is 1.275 million €. Only 10 percent of the firms sell products for more than

14.000 million €. The numbers of employees show the same picture: The median value is

given by 18 employees. 13 percent of the firms employ more than 100 employees. 10.4

percent of the firms have four or fewer than four employees.

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Although the firms of the InnoRegio programme are small, the share of employees which

hold an academic degree is quite high (median: 47.1 percent). With respect to the aims of

InnoRegio this result is not surprising. As a rule innovation projects require human resources

and therefore employees with higher and academic education.

An considerable share of the firms are involved in export activities: only 69 of 173 firms

(39.9 percent) answer that they do not export. The median export share, based on firms which

are exporting is 15 percent.

The project employee ratio, which is a project specific variable, measures the importance of

the R&D project within a firm. This ratio divides the number of employees involved in the

R&D project by the total number of the firm’s employees. Table E shows that the median for

this variable is given by 18.2 percent. For 16.0 percent of all cases more than 50 percent of all

employees are involved in the R&D project. Consequently, in many cases the R&D projects

have a substantial impact on firms.

4 Results

4.1 Intended level of novelty

In this subsection we present logit estimates for the three dummy variables we introduced in

the previous section. Firms’ innovation behaviour is analysed on the project level and defined

by the intended level of novelty of product and process innovation. The results are presented

in Table 2.

Table 2 shows that the results for the estimation of the variable “product or process”, based on

the presented set of independent variables, are unsatisfactory. The estimation is significant

only at a 31.5 percent level. At a first glance, Nagelkerkes R-square and the percentage of

correct predictions as well are quite high. However, these statistics have to be discussed with

respect to the number of observations (respectively the degree of freedom), which is not very

high.

In particular, the table shows that only three independent variables have significant estimated

coefficients at the 10 percent level. First, the results show that the probability of intending a

basically new product or process is increasing if cooperation experience is decreasing. At first

sight, this result is in opposition to the basic idea of innovation networks. Later on, we will

discuss once again the impact of cooperation experience on innovation behaviour. Second,

contrarily to the first result, which is surprising, the estimated impact of network support is as

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supposed. If firms cannot use network support, the probability to develop a basically new

product or process decreases. Third, the independent variable “staff restriction” shows a

significant coefficient. However, the impact of the several categories is not clear. Other

variables, for example market structure variables, are not significant at all.

Summarizing, the estimation of the intended level of novelty does not shed much light onto

the determination of innovation behaviour if there is no distinction between product and

process innovations. This is completely different if we use such a distinction.

In the case of the variable “product” network variables, resource variable and market structure

variables determine the dependent variable significantly. With respect to the significance of

the estimation, the r-square statistics and the goodness of fit, the estimation yields results

which are much more convincing. However, in the case of “process” the significance level of

the estimation is substantially lower than in the case of “product”.

The results for “product” show that some more factors have a significant impact on the firms’

innovation behaviour. We find significant coefficients for all types of factors (network,

resources, market structure). However, the results for “process” are not convincing.

The estimates for the network variables show that networks are functioning as expected. If

network support is low (category (1/2)), firms’ probability to develop a basically new product

decreases. Surprisingly, the impact is not linear. If the support from the network is high

(category (4)), then on average the intended level of novelty is increasing. External support

from partners outside the network is not significant, while the independent variable “internal

support” shows interesting results. One might expect that internal and network support will

have a quite similar impact on innovation behaviour. This hypothesis has to be rejected. Low

or medium internal support (categories (1/2) and (3)) has a positive impact on “product” while

a high level of support (category (4)), which is less than the support of the reference category

(very much, (5)), implies a negative impact.

Furthermore, some estimated coefficients for the variable “co-operation experience” are

significant at the 10 percent level. Obviously, co-operation experience with all network

partners (this is reflected by the reference category “all”, (5)), reduces the probability for the

development of a basically new product or process. At a first glance, this result is not in line

with theoretical results. One reason might be that the subgroup of firms which had already co-

operated with all firms is not typical for the sample.

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With respect to the resource variables the “share of project employees” and “R&D

experience” have a positive impact on the probability of developing a basically new product

or process. Both variables determine the resources and, moreover, the absorptive capacity of

firms. Therefore, with respect to these variables we find empirical evidence for the theoretical

results presented in the previous section. Furthermore, we observe a higher probability if the

firms can satisfy personnel requirements which result from the innovation project, due to

employed personnel. Contrarily, the variable “staff restrictions”, which indicates recruitment

difficulties during the preparation of the project, is not significant.

The variable “financial restrictions” indicates whether firms had difficulties to raise money

for that part of the project which is not financed by InnoRegio. This independent variable

shows an impact at a high level of significance. However, the estimates prove a negative

impact: if a firm had no difficulties bearing its own part, the probability of developing a

product or process innovation is small. This result gives evidence for the hypothesis that

innovation projects with a high “intended level of novelty” are characterized by financial

risks. Sales and the share of graduates have no significant impact.

Summarizing, in the case of "product" resource variables show the expected impact for most

independent variables. However, some resource variables show no significant coefficients.

The result for the estimation of "process" show that resource variables play no significant

role. Obviously, process and product innovation processes are quite different.

The estimated coefficients for the market structure variables underline this difference between

product and process innovation. While in the case of "product" the "intended level of novelty"

is affected by the variables "export share", "market demand", "customer relations" and

"approval agencies", in the case of "process" only the independent variable "customer

relations" is significant.

In more detail, Table 2 shows that firms with high export shares tend to have a higher

probability of intending to develop a basically new product. Furthermore, an increasing

market volume is also stimulating innovative activity. Contrarily, hardened customer relations

result in a positive impact on the dependent variable. This holds for both "product" and

"process". The result is in line with one standard argument of industrial economics: To drive

competitors out of fixed customer relations challengers should reinforce their innovative

activities. Moreover, the estimated coefficients for the variable "approval agencies" indicate a

negative correlation between "product" and "approval agencies". An interpretation should

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take into account that the sample contains some biotechnology firms which are confronted

with tight approval agencies.

4.2 R&D intensity

In this subsection we present estimation results for the firms’ R&D intensity (Table 3). We

apply an estimation method for generalised linear models because we include categorical

variables as well as metrically scaled variables. The firms’ R&D intensity is a firm specific

variable. As a consequence observations which are related to projects have to be weighted by

the reciprocal of the number of projects a firm is involved in.

The estimation results for the R&D intensity show some similarities to the previous estimates.

In particular, networks, resources and market structure are important.

In detail, we find a significant network effect at the 10 percent level. However, the effect is

less significant than in the previous subsection. If firms do not receive the maximum level of

support (“very much” support) from network partners, firms have to use their own resources.

Consequently, the R&D intensity is higher than for the case that a firm receives “very much”

support. This result holds for the second highest ("much" support, category (4)).

As in the previous section sales show no significant impact. With respect to extensive

literature on the Schumpeter hypotheses, which is also contradictory, this result is not

surprising. Obviously, the size of a firm is not an indicator for its (relative) R&D resources.

On the contrary, the share of graduates is significant at the 1 percent level. Two aspects drive

this result. On the one hand, the absorptive capacity of a firm is determined by the capabilities

of the employees. Therefore, a firm with a high share of graduates has quite good

prerequisites to carry out R&D projects. On the other hand, employing graduates leads to

higher staff costs and to a higher R&D intensity, because graduates are employed above

average as researchers.

Research experience increases also the R&D intensity. The result is plausible: On the one

hand, a firm which has spent R&D expenditures in the past, will usually spend R&D

expenditures also in the future. On the other hand, R&D experience can be interpreted as

absorptive capacity.

In contrast to the logit estimates financial restrictions are not significant. However, staff

restrictions seem to be crucial for the R&D intensity. The estimated coefficients show that

firms’ R&D intensity increases if the recruitment of staff is difficult.

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In the previous subsection we used several market structure variables, which are project

specific. For that reason, now we include, beside the export share, two firm specific market

structure variables. Table 3 shows that the export share and the firm specific market demand

variable are not significant at all. The estimated coefficients for the variable “market power”

indicates that firms which believe that they are not much stronger than their competitors, have

a lower R&D intensity than firms which believe they are much stronger.

5 Conclusion

Based on four important approaches to economics (industrial economics, evolutionary

economics, resource-based view of innovation and innovation networks) we derived

hypotheses on the determinants of innovation behaviour. The estimation result for the R&D

intensity and the intended level of novelty show that all three types of variables are important

predictors of the innovation behaviour of firms.

However, it seems to be important to distinguish between product and process innovations.

The results suggest that product innovations are stimulated by networks much more than

process innovation. Furthermore, firms' resources play an important role in product

innovation projects, but not in process innovation projects. And, market structure has a more

evident impact on product innovations than on process innovation.

The paper presents initial results regarding the innovation behaviour of the InnoRegio firms.

Although the paper presents answers to some interesting questions, many questions remain

open. Some of them are raised in the previous sections. In particular, it seems to be an

interesting question whether the results will hold if we use support variables which are based

on professional support and not on informational support. Moreover, an investigation for

different industries would be interesting. As mentioned above, some results have to be

interpreted with respect to specific characteristics of important industries (e. g.

biotechnology). Third, with respect to the estimation of the R&D intensities a distinction

between firms which are focusing on product innovation, and firms which are focusing on

process innovation might be a further step to find explanations for firms’ innovation

behaviour.

Summarizing, the paper presents initial results on the question how networks, resources and

market structure determine the innovation behaviour of the firms which are supported by the

InnoRegio programme. However, the paper is a first step to understand the complex

mechanism of how networks determine innovation behaviour.

15


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18


Appendix: Summary Statistics

Table A: Intended level of novelty f / p yes no all obs. product innovation p 45.8 54.2 100.0 238 process innovation p 38.2 61.8 100.0 238 product and process innovation p 9.7 90.3 100.0 238 product or process innovation p 74.4 25.6 100.0 238 f / p: firm specific / project specific variable

Table B: Binary variables f / p yes no all obs. project staff requirements p 83.2 16.8 100.0 226 R&D experience f 84.5 15.5 100.0 226 f / p: firm specific / project specific variable

Table C: Categorical variables f / p scale 1 2 3 all obs. market demand (p) p A 92.2 7.6 * 100.0 224 competition p B 18.2 56.4 25.3 100.0 225 * Categories are recoded f / p: firm specific / project specific variable A: 1 = increasing / 2 = stagnating / 3 = decreasing B: 1= not intensive / 2 = intensive / 3 = very intensive

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Table D: Categorical variables f / p scale 1 2 3 4 5 all obs. financial restrictions p A 41.8 27.1 22.7 8.4* 100.0 225 partnership p A 50.0 31.3 14.7 4.0* 100.0 224 staff restrictions p A 54.0 22.6 17.7 5.8* 100.0 226 internal support p B 9.2* 18.4 32.9 39.5 100.0 228 network support p B 13.4* 28.4 37.5 20.7 100.0 232 external support p B 55.4* 27.0 11.7 5.9 100.0 222 supporting projects p C 14.6* 36.1 38.2 11.2 100.0 233 customer relations p C 32.7 18.4 30.9 11.7 6.3 100.0 223 approval agencies p C 45.0 25.2 15.6 8.7 5.5 100.0 218 co-operation experience p D 19.3 53.9 11.4 9.2 6.1 100.0 228 market demand (f) f E 10.0* 24.4 35.3 30.3 100.0 221 market power f F 10.2* 44.2 33.0 12.6 100.0 206 * Categories are recoded f / p: firm specific / project specific variable A: 1 = not at all / ... / ... / ... / 5 = absolutely B: 1 = not much / ... / ... / ... / 5 = very much C: 1 = not at all / ... / ... / ... / 5 = very much D: 1 = none / ... / ... /... / 5 = all E: 1 = strongly decreasing / ... / ... / ... / 5 = strongly increasing F: 1 = much weaker / ... / ... / ... / 5 = much stronger

Table E: Summary statistics

R&D intensity

sales employees export share share of graduates

share of project

employees f /p f f f f f p obs. 168 178 193 173 177 219mean 26.6 4891.71 46.3 13.4 49.6 31.8

median 16.5 1275.00 18.0 5.0 47.1 18.2standard dev. 27.9 10063.82 71.0 20.2 45.5 47.3minimum 0.0 0.00 0.0 0.0 0.0 0.7maximum 200.0 73000.00 494.0 90.0 500.0 500.0quartiles

25 6.3 500.00 8.0 0.0 19.1 8.350 16.5 1275.00 18.0 5.0 47.1 18.275 40.0 3925.00 51.0 17.5 71.4 40.0

fractiles 10 2.0 150.00 4.0 0.0 8.8 3.120 5.0 379.00 7.0 0.0 14.9 7.430 10.0 594.00 9.2 0.0 22.0 10.040 15.0 880.00 14.0 0.6 33.3 13.350 16.5 1275.00 18.0 5.0 47.1 18.260 25.0 1778.40 25.0 10.0 55.4 25.070 30.6 3030.00 36.6 15.0 66.7 33.380 40.0 5728.00 75.0 20.0 76.3 44.490 70.5 14000.00 121.2 40.0 90.0 71.4

f / p: firm specific / project specific variable

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Table 1: Dependent and independent variables variable f / p scale description

dependent variable product or process p b dummy variable

(see Section 3) product p b dummy variable

(see Section 3) process p b dummy variable

(see Section 3) R&D intensity f m R&D expenditures divided by sales

(see Section 3) network variables partnership p c 5 statement: “Difficulties in finding an appropriate partner

for cooperation occurred.” (not at all / … / … / … / absolutely)

cooperation experience p c 5 statement: “Of the InnoRegio partners …… partners were known before project star. (none / some / many / most / all)

internal support p c 5 statement: “How much informational support did you / do you receive from your own firm?” (not much / … / … / … / very much)

network support p c 5 statement: “How much informational support did you / do you receive from network partners?” (not much / … / … / … / very much)

external support p c 5 statement: “How much informational support did you / do you receive from external partners (not network partners)?” (not much / … / … / … / very much)

supporting projects p c 5 statement: “I support other sub-projects by informational support.” (not at all / … / … / … / very much)

resource variables share of project employees p m number of project employees divided by total number of

employees sales f m sales (in 1,000 €) share of graduates f m number of graduates divided by total number of

employees project staff requirements p b statement: “Are you able to meet the staff requirements?”

(yes / no) research experience f b statement: “Did you carry out research and development

during the last two years outside of InnoRegio?” (yes /no )

financial restrictions f c 5 statement: “Difficulties in financing equity ration occurred.” (not at all / … / … / … / absolutely

staff restrictions f c 5 statement: “Difficulties in recruiting appropriate personnel occurred.” (not at all / … / … / … / absolutely)

market structure variables export share f m sales to foreign countries divided by total sales market demand (p) p c 3 statement: “Total market demand is

decreasing / … / strongly increasing.”. market demand (f) f c 5 statement: “Total market demand is

decreasing / … / … / … / strongly increasing.”. market power f c 5 statement: “In comparison to our competitors our

competitiveness is much weaker / … / … / … / much stronger.”

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Table 1 (cont.): Dependent and independent variables variable f / p scale description

competition p c 3 statement: “Competition is not intensive / intensive / very intensive.”

customer relations p c 5 statement: “After completion of the project we expect that hardened customer relations will hamper the utilization of the results.” (not at all / … / … / … / very much)

approval agency p c 5 statement: “After completion of the sub-project we expect that authorizing agencies will hamper the utilization of the results.” (not at all / … / … / … / very much)

f / p: firm specific / project specific variable b: binary variable (dummy variable) c n: categorical variable with n categories m: metrically scaled variable

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Table 2: Intended level of novelty product or process product process B Wald sig. B Wald sig. B Wald sig. constant 3.954 .670 .413 -9.421 3.263 .071 * 5.898 2.227 .136 network variables partnership 1.445 .695 7.163 .067 * 6.562 .087 * (1) -.222 .019 .891 1.028 .511 .475 -1.897 2.747 .097 * (2) -1.113 .471 .492 -.266 .033 .857 -.529 .210 .647 (3) -.379 .046 .830 -2.346 1.914 .167 -.006 .000 .996 cooperation experience 6.789 .147 7.332 .119 1.502 .826 (1) 2.421 2.785 .095 * 2.611 2.911 .088 * -.076 .004 .948 (2) 3.322 6.425 .011 ** 3.176 6.297 .012 ** -.861 .830 .362 (3) 1.874 1.887 .170 1.769 .960 .327 -.523 .166 .684 (4) 2.315 1.877 .171 3.123 3.487 .062 * -.693 .189 .664 internal support 2.323 .508 8.632 .035 ** 2.210 .530 (1/2) 1.907 2.148 .143 1.514 1.424 .233 -.716 .506 .477 (3) .753 .470 .493 2.692 4.196 .041 ** .744 .677 .411 (4) .008 .000 .992 -1.209 2.168 .141 .310 .211 .646 network support 7.128 .068 * 6.227 .101 2.629 .452 (1/2) -1.766 1.970 .160 -2.985 2.992 .084 * 1.277 1.207 .272 (3) -2.105 4.823 .028 * -.199 .052 .820 .320 .158 .691 (4) .364 .137 .712 1.722 2.972 .085 * 1.110 1.841 .175 external support 2.539 .468 .523 .914 5.725 .126 (1/2) -.337 .028 .867 -1.013 .300 .584 1.086 .338 .561 (3) -1.496 .468 .494 -1.346 .478 .489 2.503 1.651 .199 (4) -1.841 .694 .405 -1.057 .302 .582 .675 .108 .742 supporting projects 3.731 .292 3.383 .336 4.296 .231 (1/2) 1.048 .338 .561 2.165 1.695 .193 -1.518 1.217 .270 (3) -.406 .067 .796 -.198 .021 .885 -2.241 3.580 .058 * (4) -1.250 .672 .412 -.158 .012 .913 -1.255 1.228 .268 resource variables share of project employees .009 .243 .622 .032 3.752 .053 * -.005 .261 .610 sales .000 .636 .425 .000 .619 .431 .000 1.995 .158 share of graduates .021 1.531 .216 .027 2.469 .116 -.014 1.172 .279 project staff requirements -1.699 1.872 .171 2.512 4.963 .026 ** -1.990 5.952 .015 **R&D experience -.934 .530 .467 6.101 8.724 .003 *** -1.042 1.289 .256 financial restrictions 4.314 .229 7.447 .059 * .450 .930 (1) -2.683 1.911 .167 -4.705 5.819 .016 ** -.647 .251 .616 (2) -1.284 .440 .507 -3.890 4.362 .037 ** -.792 .341 .559 (3) -2.794 1.994 .158 -5.311 7.190 .007 *** -.416 .098 .754 staff restrictions 6.505 .089 * 3.512 .319 4.041 .257 (1) 2.565 2.632 .105 1.480 .584 .445 1.061 .485 .486 (2) 2.081 1.705 .192 .392 .044 .833 -.402 .073 .786 (3) .082 .002 .965 -.416 .037 .847 -.033 .000 .985 market structure variables export share .041 2.148 .143 .091 4.485 .034 ** .023 1.299 .254 market demand (p) (1) 1.416 1.707 .191 3.260 2.941 .086 * .348 .102 .749 competition 2.968 .227 .034 .983 1.997 .368 (1) .301 .054 .817 .115 .009 .925 1.401 1.905 .168 (2) -1.353 1.828 .176 .179 .033 .856 .850 1.268 .260 customer relations 3.150 .533 7.858 .097 * 5.993 .200 (1) -.466 .082 .775 -.291 .040 .841 -2.983 4.282 .039 **(2) .699 .181 .671 -.910 .347 .556 -2.817 3.764 .052 * (3) -1.109 .552 .457 -2.570 2.739 .098 ** -2.406 3.269 .071 * (4) -.549 .103 .748 -4.260 4.173 .041 ** -1.136 .636 .425 approval agencies 7.466 .113 12.109 .017 ** 2.707 .608 (1) -2.364 1.976 .160 -3.928 6.330 .012 ** -1.314 1.347 .246 (2) -.730 .161 .688 -1.589 .764 .382 -.631 .244 .621 (3) -1.885 1.034 .309 -2.512 2.082 .149 -1.567 1.414 .234 (4) .911 .160 .689 -.689 .129 .719 -.669 .222 .637

23


Table 2 (cont.): Intended level of novelty product or process product process number of observations 130 130 130 chi-square 45.88 85.45 51.09 significance .315 .000 .159 Cox & Snell R-square .297 .482 .325 Nagelkerkes R-square .446 .646 .439 log-likelihood 96.94 92.79 123.90 correctly predicted "yes" (in percent) 51.6 87.7 79.5 correctly predicted "no" (in percent) 92.9 80.7 63.5 correctly predicted "yes" and "no" (in percent) 83.1 84.6 73.1 B: coefficient Wald: Wald statistic sig.: level of significance ***: significant at the 1 percent level **: significant at the 5 percent level *: significant at the 10 percent level

24


25

Table 3: R&D intensity B T sig. constant 39.001 1.839 0.069 * network variables partnership (1) 7,489 ,845 ,400 (2) ,560 ,063 ,950 (3) 5,213 ,509 ,612 internal support (1/2) 2,609 ,375 ,709 (3) -7,216 -1,171 ,245 (4) -6,773 -1,406 ,163 network support (1/2) -,266 -,037 ,971 (3) 9,062 1,623 ,108 (4) 9,773 1,803 ,075 * external support (1/2) 1,727 ,155 ,878 (3) 3,898 ,346 ,730 (4) 1,545 ,135 ,893 supporting projects (1/2) -2,184 -,245 ,807 (3) 3,718 ,501 ,618 (4) 1,791 ,242 ,810 Resource variables sales ,000 -1,350 ,180 share of graduates ,352 4,917 ,000 *** R&D experience 11,563 1,820 ,072 * financial restrictions (1) -9,042 -,996 ,322 (2) -5,407 -,587 ,559 (3) -8,316 -,903 ,369 staff restrictions (1) -30,138 -3,043 ,003 *** (2) -29,311 -2,913 ,005 *** (3) -24,036 -2,315 ,023 ** market structure variables export share -,112 -1,022 ,310 market demand (f) (1/2) -7,115 -,998 ,321 (3) 3,169 ,544 ,588 (4) ,459 ,089 ,930 market power (1/2) -20,853 -2,090 ,040 ** (3) -17,489 -2,538 ,013 ** (4) -15,789 -2,194 ,031 **

weight variable: reciprocal number of firms’ projects R-square = .537, adjusted R-square: = .374 B: coefficient T: t-statistic sig,: level of significance ***: significant at the 1 percent level **: significant at the 5 percent level *: significant at the 10 percent level

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