network configuration and innovation success - an
TRANSCRIPT
Network Configuration and Innovation Success - An Empirical Analysis in German High-Tech Industries1
Hans Georg Gemunden • Thomas Ritter • Peter Heydebreck
Prof. Dr. .
Hans Georg Gemunden
Head of IBU
University ol Karlsruhe
P. O. Box 6980
76128 Karlsruhe
Tel.: ++ 49-721-608-3431
Fax: ++49-721-608-6046
e-mail: hans.gemuenden
@wiwi.uni-karlsruhe.de
Dipl. Wi. Ing.
Thomas Ritter
Lecturer at the IBU
University of Karlsruhe
P. O. Box 6980
76128 Karlsruhe
Tel.: ++49-721-608-3432
Fax: ++49-721-608-6046
e-mail: thomas.ritter
@wiwi.uni-karlsruhe.de
Dipl. Kfm.
Peter Heydebreck
Lecturer at the IBU
University of Karlsruhe
P O. Box 6980
76128 Karlsruhe
Tel.: ++ 49-721-608-3435
Fax: ++ 49-721-608-6046
e-mail: peter.heydebreck
@wiwi.uni-karlsruhe.de
The authors thank the Ministry of Research and Technology for financial support.
477
Network Configuration and Innovation Success - An Empirical Analysis in German High-Tech Industries
Abstract
Assuming that intensity and structure are the most important dimensions of a firm's
technological network the authors identify seven different real types of technological
interweavement. Drawing upon a database of 321 high-tech companies they can
show that innovation success is significantly determinated by a firm's network
position, different innovation targets demanding for different types of
interweavement.
478
Network Configuration and Innovation Success - An Empirical Analysis in German High-Tech Industries
Table of contents
1 Network configurations and innovation success
1.1 Theoretical frame of reference
1.2 Real type network configurations
1.3 Hypotheses on the impact of network configurations on innovation
success
2 Empirical findings
3 Discussion and Outlook
List of References
479
Network Configuration and Innovation Success - An Empirical Analysis in German High-Tech Industries
1 Network configuration and innovation success
1.1 Theoretical frame of reference
"No business is an island" (Hakansson and Snehota 1989). Co-operation with
external partners will provide helpful if not necessary know-how and resources into
a company's innovation processes. Figure 1 illustrates potential innovation partners
and the functions they can fulfil.
Figure 1:
Innovation partners and their resources
(Source: Gemunden, Heydebreck and Herden 1992, p. 360)
Empirical research on the impact of collaboration with external partners on a firm's
product and process innovation success has mainly been carried out in the form of
case studies. Specific relationships between specific partners and their
connectedness have been studied in depth (see particularly the IMP work, cf. e. g.
Axelsson 1986; Hakansson 1987; Hammarkvist, Hakansson and Mattsson 1982).
Whenever large scale studies have been carried out, the level of abstraction was
changed from analysing the relationships between the focal company and specific
partners (e. g. supplier E, customer B, university G) to analysing the relationships
between a focal company and a specific set of partners (e. g. customers in general
or suppliers in general) (cf. e. g. Gemunden,Heydebreck, and Herden 1992; Hahn
et al. 1995). In these studies the central idea of the a network approach - namely the
interconnectedness of the relationships - has been neglected.-Thus, there is a high
demand for large scaled empirical studies analysing the strategic importance of a
network configuration matching a companies innovation aims. This paper is '
intended to tackel the challenging task of identifying real types of network
configurations and their adaquacy for persureing different innovation aims.
480
The configuration of a network is defined by two dimensions: the intensity (degree of
interaction with external partners regardless of the type of partners) and the pattern
(relative importance of collaboration for all types of partners in relation to the
collaboration with all other partners) of technological interweavement.
The authors assume that different innovation tasks require different network
configurations. In this paper, the suitibility of different network configurations for
different innovation aims is analysed.
Figure 2 illustrates the theoretical framework of this paper.
Figure 2:
Theoretical frame of reference
Innovation success in turn is an important determinant of a company's overall
economic success. The network configuration is determined by a large variety of
internal and external context factors, most of them can be grouped under the
headings of motivation and capability of networking. It is not the aim of this paper,
though, to test the possible influence of a firm's position in the value chain, its age,
size, and industry, the legal framework or the intensity of competition on the intensity
and pattern of technological interweavement. This paper focuses on efficiency
analyses, i. e.: What are the impacts of different network configurations on success
of product and process innovations? In particular, we are interested in the following
issues: Which network configurations are required for successful innovations? Do
product and process innovations require different configurations? Do minor
innovations require different configurations from major innovations?
The authors test their theoretical frame of reference drawing upon a database of
321 high-tech companies operating in the fields of microelectronics, edp, sensor
technology, biotechnology, and medical equipment. For a description of the
database see e. g. Gemunden and Heydebreck 1995; Heydebreck 1995; Ritter
1995.
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1.2 Real type network configurations
In order to analyse the intensity and pattern of a firm's technological
interweavement a vast range of indicators of network dimensions have been
collected. An exploratory factor analysis resulted in four network dimensions
explaining 66% of the variance. Each factor describes collaboration with a specific
type of external partner:
factor 1 is called supplier-interaction factor.
factor 2 is called customer-interaction factor.
factor 3 is called university-interaction factor.
factor 4 is called consultant-interaction factor.
Allthough the factor analysis provides a very clear structure the factors are impure,
meaning they load on indicators not used for their interpretation. Therefore, the
authors performed four unifactorate analyses including only indicators with factor
loading higher than 0.50. Table 1 illustrates the four dimensions of interweavement.
Table 1:
The dimensions of technological interweavement
For a description of this analysis in more detail see Gemunden and Heydebreck
1995; Heydebreck 1995; Ritter 1995.
Intensity of technological interweavement
The authors operationalise the intensity of technological interweavement as the
average factor score of the four interaction factors. This measure represents the
degree of a company's network activity independent of the type of its partners. For
further analyses the authors only differenciate between four levels of intensity: very
low, low, high, and very high intensity, each group containing 25% of the
companies.
Patterns of technological interweavement
In order to identify real type patterns, the authors clustered the cases according to
their factor scores on the four network dimensions. As the focus lies on patterns a
similarity measures was used for the cluster analysis (cosine) together with
baverage as aglomeration (using SPSS procedure). With regard to the contens the
authors have chosen to interpret the five cluster solution, which was supported by
the elbow-criteria.
Figure 3 shows the real type patterns of technological interweavement. The radar
charts illustrate differences in the relative importance of the four types of external
partner. The grey square represents equal importance of all external partners.
Figure 3:
Radar charts of network patterns
As figure 3 shows, there are substantially different patterns of interweavement.
Network configuration: Combination of intensity and patterns
The following crosstable illustrates real combinations intensity and patterns of
technological interweavement.
Table 2:
Intensity and pattern of technological interweavement
Table 2 shows that intensity and structure do not seem to be fully independent of
each other. Whereas network pattern 3 highly correlates to a low intensity of
interweavement, patterns 4 and 5 correspond to a high intensity of technological
481
interweavement. The situation is less clearcut concerning patterns 1 and 2.
Therefore, the authors have chosen to interpret two network configurations for
pattern 1 as well as for patterns 2 (one with low and one with high level of intensity).
The following seven network configurations exist (names will be used in a further
documentation of analyses):
1. The island (intensity low or very low and pattern 3): Compared with all other
groups this cluster has the lowest intensity of interweavement. The strikingly
low importance of customer interaction is due to operationalisation, because
the importance is not measured in absolute terms but in relation to the
relative importance of customer interaction of all other network configurations.
In contrast to all other groups which regard the customer as their definetely
most important external partner, the island regards its customers only as
equally important as other partners.
2. The manufacturer (intensity low or very low and pattern 2): This type of
company interacts with suppliers and customers much more intensively than
with universities and consultants. Manufacturer are heavily production
orientated. The total degree of interaction with external partners is low.
3. The toddler (intensity low or very low and pattern 1): The toddler shows a low
intensity of technological interaction with its environment as well. But in
contrast to the manufacturer the toddler lays relatively more emphasis on
collaboration with universities than on supplier interaction.
4. The highway (intensity high or very high and pattern 2): The highway shows a
very similar pattern of interweavement to the manufacturer. The degree of
interaction is much higher, though. The shape of the network configuration
indicates a rapid flow of information and know-how from supplier to focal
company to customer and vice versa.
5. The visionary (intensity high or very high and pattern 1): The visionary
interacts with universities on a high level and regards his customers as even
relatively more important than its suppliers and consultants.
484
6. The apart the market (intensity low, high, or very high and pattern 5): This
type of company is the only group regarding utilisation of innovation
orientated consultancy services as important for their innovation success.
Apart from the island the apart the market shows the lowest intensity of
collaboration with suppliers out of all types. The interaction with customers
and universities is at an average level.
7. The spider (intensity high or very high and pattern 4): The spider interacts
with all network partners at a very high level. (The two cases with intensity 2
and pattern 4 were rejected because low intensity seems not to be typically
for this group.)
In the following, the authors discuss the impact of these network configurations on
innovation success.
1.3 Hypotheses on the impact of network configurations on success
There is no overall successful type of interweavement. That means that network
intensity and network patterns have to suit specific innovative targets of a company.
Customer interaction is necessary but not sufficient to achieve product innovation
success (cf. e. g. Biemans 1992; Gemunden, Heydebreck, and Herden 1992;
Herstatt 1991; Shaw 1985). Additional partners are needed to bring forward new
ideas (in high-tech industries especially universities and research institutes) or to
offer new production facilities or technically improved or new product components to
realize new ideas (for example suppliers). Different combinations support different
innovations steps, for example the highway is more likely to support improvements
of existing products whereas the visionary promotes greater product innovation
steps. In addition, the authors assume that the highly interwoven spider is
particularly capable of achieving all kinds of product innovation success.
485
Hypothesis 1:
For technological product innovation success a high overall intensity of a network
and a prominent position of the customer is necessary.
In order to rapidly and efficiently improve existing products collaboration with the
suppliers is of critical importance. Therefore, the authors believe that the highway
and the spider have higher innovation s.upcess in regard of product improvements
compared with all other types of companies, (hypothesis 1 a)
In order to establish new technology platforms outstanding technological resources
and know-how from different disciplines are needed. Universities and research
institutes are particularly apt as external partners in bregk-throuqh product
innovation processes. Both suppliers apd consultants are of secondary importance
only. Therefore, visionaries and spiders are believed to realise a product innovation
success fin regard to radically new products) much higher than all other cornpany
types, (hypothesis j I?)
A lot of partners can provide valuable know how in order to stimulate process
innovation. Consultants have theoretical knowledge of best practice and are able to
assist during the implementation phase of new processes. In addition, customers
can show a company's weaknesses in processes too. Therefore, consultants and
customers can near to force companies to realise process innovations.
As suppliers promote new product ideas they also could have a positive effect in
providing new equipment to reduce production costs or decrease processing time.
Empirical studies have also shown the positive impact of universitiy interaction on
process innovations. The authors assume that suppliers and universities help a
company to overcome technical barriers to process innovations.
For these reasons, all network configurations interacting at a high or very high
degree are appropriate to support process innovations. But as developed above,
the highway and the visionary are strongly focused on product innovations and
therefore the authors expect the apart the market and the spider as best network
configurations for process innovations.
486
Hypothesis 2:
For technical process innovation success high intensity and a network patterns with
stress on customer or consultant and in addition on supplier or university is needed
(the apart the market and the soiderl
The authors assume that companies striving for process innovations will use
consultancy agencies for analysing processes and recommending partners for
problem solution. On the other hand, if process innovation is a main part of a
firm'scorporate strategy a company does not need consultants, because these
companies are very conscious about process innovations. They interact with
partners providing direct help for realising process innovations.
Hypothesis 3:
Consultant orientated networks or generally highly interwoven companies (the apart
the market and the spider} are particularly successful in realising economic profit
from rationalising their processes.
2 Empirical Findings
Table 3 provides an overview of the indicators used for measuring innovation
success.
Table 3:
Operationalisation of innovation success indicators
In the following, the authors test the hypotheses on the impact of a firm's network
configuration on innovation success. Regression analyses with configuration coded
as dummy-variables were carried out in order to test the influence of network
configuration on success. In all regression analyses the island was taken as the
reference group. In these analyses the variable 'industry 1 was included in order to
simultaniously control external effects. The results of these analyses are reported in
table A-1.
Figure 4 shows the average score for each network configuration regarding
technological product innovation success.
Figure 4:
Network configurations and product innovation
Network configuration influences technological product innovation success (p =
0.01 and p = 0.02). Furthermore, the island has significantly lower percentages of
economic successful developments. In harmony with hypothesis 1, the highway has
the highest percentage of successful improvements of products. The results of the
toddler are better than assumed. University interaction - even at a low degree of
total interweavement - is helpful to achieve product innovation success. For larger
innovation steps the visionary has high percentages of successful developments.
This result supports the assumption that university interaction leads to high
innovative products. As documented in figure 4 the spider realises high
percentages of economically successful product innovations regardless the degree
of step of innovation. This result confirms that the spider is a 'real 1 networker.
The authors analysed process innovation as well. The results of these analyses are
documented in figure 5.
Figure 5:
Network configuration and process innovation
The construct 'network configuration' is significantly influencing process innovation
success. Regarding technical process innovation success and the economic
relevance of process innovations hypothesis 2 and 3 are completely supported by
the empirical findings.
All in all, the results of our analyses support our hypotheses: different network
configurations exist and these configurations are strongly influencing innovation
488
10
success in different ways. Depending on the number of cases, not all of our results
are sufficient in a way that we can call them statistically confirmed. But "it may be of
interest to recall that Freud created psychoanalysis out of five cases and that
Hippocrates laid the foundation for medicine from seven cases" (Gummesson
1995).
3 Discussion and Outlook
Our findings are promising. They show that for a description of innovation networks
intensity and patterns are decisive. Like 'industry' there are a lot of additional
variables influencing network activities or the success of network activities. In further
research, the authors will, therefore, analyse these influences. Particularly, a firm's
corporate strategy should be included (cf. Gemunden and Heydebreck 1994).
Networks are dynamic. The authors assume that a company can develop the
network in a way that the network is changing from one configuration to another.
This process will be influenced by a lot of external variables as well. Thus, it will be
interesting to have a look on the developing process of a company's network
configuration.
489
11
Appendix
Table A-1:
Network configurations and innovation success
(results of the regression analyses)
490
12
Factor
Indicator
importance of suppliers
importance of suppliers by developing
new product ideas
importance of suppliers by product
conception
importance of suppliers developing new
products
importance of suppliers by testing new
products
importance of customers
importance of customers by developing
new product ideas
importance of customers by product
conception
importance of customers developing new
products
importance of customers by testing new
products
importance ol universities
importance of (Fach-) Hochschulen and
research institutes
importance of consultants
importance of engineering offices
explained variance [%]
Cronbachs alpha
F1
(n=294)
0.68
0.79
0.86
0.87
0.82
-
-
-
-
-
-
-
-
-
65.1
0.86
F2
(n-297)
-
-
-
_
_
0.71
0.76
0.77
0.68
0.71
_
-
-
-
52.7
0.77
F3
(n=307)
-
-
-
_
-
-
-
-
-
-
0.93
0.93
-
-
85.7
0.83
F4
(n=305)
-
-
-
-
-
-
-
-
-
-
-
-
0.88
0.88
' 77.7
0.71
Table 1:
The dimensions of technological interweavement
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13
pattern
intensity
very low
low
high
very high
1
13
40
15
2
29
35
11
8
3
24
2
4
2
26
52
5
6
12
7
Table 2:
Intensity and pattern
492
14
indicator
percentage of economic successful
developments
• improvement ot products
• new product development
technical process innovation success
economic relevance of process
innovations
operationalisation
tour categories to choose:
0 = no product innovations
1 = 'less than 25%'
2 a 'between 25 and 50%'
3 » 'between 50 and 75%'
4 =. 'more than 75%'
(actor built up with the following
indicators:
• savings in working time
• increase of maschine productivity
• reduction of process time
• savings in material and energy
factor built up with the following
indicators regarding the relevance of
process innovations for:
• survival of the company
• profit or productivity
• growth of the company
mean in sample
44%
35%
0.00
(factor score)
0.00
(factor score)
Table 3: Operationalisation of innovation success indicators
493
15
network
configuration
The manufacturer
The toddler
The highway
The visionary
The apart the market
The spider
improvement of
products
impact of industry: n. s.
impact of network configuration:
p - 0.02
beta
0.11
0.26
0.22
0.13
0.03
0.24
level of sign.
0.34
0.01
0.01
0.09
0.72
0.03
new product
development
impact of industry: n. s.
impact of network configuration:
p - 0.01
beta
0.20
0.36
0.17
0.22
0.18
0.38
level of sign.
0.07
0.00
0.05
0.01
0.04
0.00
technical process innovation success
impact of industry: p - 0.00
impact of network configuration:
p - 0.06
beta
0.32
0.21
0.19
0.04
0.13
0.34
level of sign.
0.01
0.04
0.03
0.58
0.13
0.00
economic
relevance of
process innovations
impact of industry: p - 0.08
impact of network configuration:
p - 0.04
beta
0.11
0.11
0.06
0.08
0.23
0.30
level of sign.
0.32
0.26
0.45
0.28
0.01
0.01
Table A-1 :Network configurations and innovation success
(results of the regression analyses)
494
16
Administration> Subsidy• Political support• Mediations, transfer > Laws, (de-) regulations
Co-supptiers• Complementary know-how
• Solving interlace problems
Consultants> Innovative Concepts• Structuring of processes• Financial, legal and insurance services
Suppliers,producers of means
of productionNew technologies ol components and systems
BuyersDefining new requirements] Solving problems of im plementation and market acceptance
^Reference function
Focal CompanyOwn authority
Research and training Institutes
• Research• Training
Qualified personnel
CompetitorsJoint basic research Establishing standards Getting subsidies
J1
Distributors> Changing and weightingof demands
• Gathering informationabout developments ofcompetitors
Figure 1:Innovation partners and their resources
(Source: Gemunden, Heydebreck and Herden 1992, p. 360)
495
17
overall success
growth productivity
il
innovation success
product innovation success
process innovation success
j , 1 1
llflllp:^^
: ill.i.;:'intenshyy.;11lll;-xlt - ;: -^--- -C :;¥.-• :.^-: ::f;|i|;#:
Figure 2: Theoretical frame of reference
496
18
pattern 1(68 cases)
pattern 3(26 cases)
suppliers
universities
pattern 2(83 cases)
suppliers
pattern 4(80 cases)
suppliers
suppliers
Figure 3:
Radar charts of network patterns
497
50%
25%
•oJCO CO
TD
E0)
19
.c o>
<D
improvements of products (percentage of economic successful innovations)
'5. CO0)
50%
25%
2 ns3
QJ£-c? h-E
0>-0 TJ
<p -c H-
« • S —« ®
O> T3 'CL
CO O
new product development (percentage of economic successful innovations)
Figure 4: Network configuration and product innovation
498
20
0.20
0.00-
-0.20
TJ
CO5
Qj =0
raJZ O)IcCD
The visionary apart et
technical process innovation success (average factor sores)
0.20
o.ocr
-0.20
Q)x:
§1S
economic relevance of process innovations _____(average factor scores)_____
Figure 5:
Network configurations and process innovation
4-99
21
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