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Network configuration and innovation success: An empirical analysis in German high-tech industries
h t t e r r ~ doun~~
ELSEVIER
Intern.I. of Research in Marketing 13(1996)'449-462
Network configuration and innovation success: An empirical analysis in German high-tech industries Hans Georg Gemiinden a. *, Thomas Ritter a, Peter Heydebreck
b
a htstitutefor Applied Management Science and Corporate Strategy, Unirersity of Karlsruhe. Box 6980. 76128 Karlsruhe. Germany b Inno GesellschaJ~fiir innocatice Unternehmensentwickhmg mbH, P. 0. Box 3366. 76019 Karlsruhe, Germany
Accepted 30 August 1996
Abstract Based on the assumption that intensity and structure are the most important dimensions of a firm's technological aetwo~'k, the authors identify seven different types of technology-oriented network configurations. Drawing upon a database of 32E high-tech companies, they show that innovation success is significantly correlated with a firm's technological network. Product and process innovations are shown to demand different types of network configurations. Keywords: Networks; Innovationstrategies;Technology
1. Network configuration and innovation success: Theoretical frame of reference There is a rapidly expanding body of theoretical and empirical literature showing that technology-oriented relationships constitute a valuable means for integrating complementary resources into a firm's innovation processes, thereby significantly increasing a firm's product and process innovation success. In particular, two different types of empirical literature deal with analysing the impact of technology-oriented relationships on innovation success and overall success. Firstly, there are the analyses of focal actors, their specific relationships and the way these relatto~ships influence each other. These studies conco,Orate on a few selected cases, data collection for
which has been executed by means of face-t~face interviews. This type of work is typical particularly of the IMP group (see e.g. Axelsson. 1986: H~kansson, 1987) but also other researchers have chosen this approach (see e.g. Biemans. 1992; Shaw. 1985). Secondly, there are a number of large-scale qtmatitative studies which analyse the impact of collaboration with a specific type of partner (customers. suppliers etc.) on innovation success. Typically, data collection is done via standardised mailed questionnaires. However, these studies do not take into account the central element of the network approach. namely the inter-connected nature of these relationships (e.g. Gemiinden, 1996; Gemiinden et al.. 1992; Hagedoorn and Schakenraad. 1994: H~kansson. 1989; Herden, 1992; Heydebreck, 1996a). t
"Correspondingauthor. Tel.: (+49)721-608.3431; fax: (+49) 721-608.6046.
I Following Cook and Emerson (1978), many network researchers define networksas sets of inter-connectedrelationship.
0167-8116/96/$15.00 Copyright © 1996ElsevierScienceB.V. All rights reserved. PIi SO 167-8116(96)00026-2
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H. (;ct~rt,,Gemihtdc,'t er aL / httern. J. O[ Research iJe Ahtrketing 13 (IO~,'~) 449-462
Ltmking at d~e existing literature, the authors detect a lack o f studie,~ which address the need to match a company's network of different types of techrmlogy-orented relationships with its innovation ~ e d s ~,ld which ~,t the same time provide the large database which is necessary lor testing statistically hypt~he~s on the impact of matching types of relationships for innovation success. Depending on a firm's innovation targets, internal resources and its external context, different external resources are needed. We assume that different types o f actors are particularly appropriate tbr contributing specific kinds of resources and know-how. Fig. I illustrates the most important types of innovation partners an,.: their contributions. Fig. 1 illustrates that many different types of partners can support a firm in i.~s innovation activities. although each type of partner offers a different ~ t of resources. Despite the high potential of technological relationsh~.ps, one has to remember that external resources are not free of costs. On the contrary. the establishment of access to external partners" re~ u r c e s is a lengthy and costly investment process (cf. e.g. Mattsson. 1988: Plinke. 1989: Valla. 1986: Williamson. 1979).
Thus. companies b'~,fld up and maintain only those relationships which are particularly valuable for them. As companies differ in respect of their needs for n~:tworking, it is p',ausible to assume that companies als,~ differ i;+ respect of ~he types of ~:xtemal partners with whom they collab(,:ratc. For some companies. universities might constitute the most important source of external know-how and technological resources, whilst other companies might not interact with universities at all but collaborate in R & D with customers and suppliers instead. The authors believe that the relative importance of technology-oriented relationships with one type of partner in relation to the ovet ail importance of networking differs significantly from company to company and that it should differ because different network patterns are suited tor pursui|~g different innovation aims. Another aspect is that some companies interact intensively with many external partner,~ whilst others hardly maintain close relationships with any external partners at all. This is due to significant differences in the companies" experience and capability of networking. Also. some companies are more willing to invest in an advantageous network position than others. This means that companies not only differ in
Administration : Subsidy
Suppliers, producers of means
Political support L : Mediations, transfer Laws, (de-) regulations
of production New technologies of L components and systems
I "C°mplementary ]know-how ~o Solving interface problems
( Researchand training~ i institutes /
/
L ~ 1
==..._/' ~
:
J . Research "[raining Qualified personnel
II~_nmncanln v v . . ,1"14, "3 Own authority
I • Innovative Concepts I • Structuring of processes ~• Financial. ~egaland ~ nsurance services Fig. I. lnnovati(~r=partnel'sand their contributions,
• Joint basic research • Establishing standards • Getting subsidies
| 1 j
H. Ge,,,~ Gemiinden et aL /httern. J. ~+]'Re.s'earchin Marketing 13 (1906) 449-462
network configuration
In"
innovatio;~ success
i
intensity of technological interweavement
451
product innovation SUCCESS • improvementof products • new product development
- - 7 process innovation | success l ° technical . economic relevance
pattern of technological interweavement
Fig. 2. Theoreticalframe of referettce.
respect of their-,,,:tw.grt" pat:ern but also in the intensity of technological interweavement. ? To conclude, we assume that p a t t e n z (importance of collaboration with each specific type of partner in relation to the overall importance of networking) and h~tensita' are the two most important dimensions for characterizing a n e t w o r k cot!~gztra~ion. In this paper, the suitability of different network configurations for different innovation aims. i.e. product and process innovation, is analysed. Specific hypotheses are developed in Section 3. Fig. 2 gives our frame of reference. The network configuration is determined by a large variety of internal and external context factors. most of which can be grouped under the headings of motivation and capability of networking. However. it is not the aim of this paper to test the possible influence of internal and external context variables (e.g. 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 a company's technological interweavement. This paper
2 The term tecl',,nologicalinterweavementis meant to describe the totality of a firm's technology-orientedrelationshipsaimed at acquiring, jointly developingor diffusing of technologicalknowhow and resources.
: : ~ e r focuses on analysing the impact of different network configurations on product and process innovation success which in turn are amongst the most important determinants of a company's overall commercial success. In particular, we are interested in the following issues: • Do product and process innovations require different network configurations? • Do minor innovations require different network configurations than major innovations, i.e. is there an overall best network configuration for product innovation, or are some network configurations part!cularly suited for product improvements and others for new product development?
2. An empirical typology- o f network configm'a-
tions 2. !. T h e d a t a b a s e
The authors test their theoretical frame of referonce drawing upon a database obtained from five studies of 321 high-tech companies operating in the fields of biotechnology. EDP. medical equipment. microelectronics and sensor technology. All the studies used mailed questionnaires, usually with a prior phone contact.
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H. Geor¢ Gemiinden et al. / Intern. J. ~ Research in Marketin. o 13 ( 19961449-462
Response rates varied from a low of 14~- to a high o f 66%. Apart from the biotcchnology study, the response rates are not overwhelmingly high. This can be explained by the nature of our t a ~ e t population which mainly consists of r.ma!l comran;.e.':. H o w e v e r low response rates increase the risks of systematic bias. Therefore, the authors performed representativeness tests in order to check for a potentim systematic bias. In respect o f context variables this was done by comparing re:;ponding companies with the total sample. In respect oi indicators o f technological interweavement and success, no data was available on the total sample. Therefore, we compared the answers to our first questionnaire with tbe responses to our reminder. It turned out that size was the only variable to show a systematic bias: large companies gave a significantly higher response rate than small companies. In addition telephone interviews were performed with non-respondents in c ' d e r to check whether there was a systematic bias with regard to technological interweavement and innovation success. Although we did not carG' out enough telephone interviews to a | l o w the use statistical inference tests, our judgemerit was that there was no systematic bias with regard to interweavement and success. Furthermore. we are not interested in frequencies but in analysing the impact one variable has upon the ~ther, which
considerably reducex the consequences of the bias of one variat,',c. For a thorough discussion ot the quality of the database see Heydebreck (1996a). 2.2. O p e r a ' . i o n a l i s a t i o n o f net~,.,;rk c o n f i g u r a t i o n
In order to analyse the intensity and pattem of a firm's technological interweavement, a vast range of indicators o f network dimensi.ans were collected. Table I lists the interweavement indicators and provides an overview o f their distributions. If not stated otherwise, all variables are measured on five-point rating scales (! = of no importance, 5 = very high importance). A factor analysis o f these indicators replicated our findings from the Lake-Constance Study (cf. Gemiinden et al., 1992), i.e. each interweavement factor describes collaboration with a specific type of external partner: the factors were labelled: supplierinteraction factor, customer-interaction factor, university-interaction factor, and the consultant-interaction factor. The exploratory solution explains 66% of the total variance of the indicators. Although this factor analysis provides a clear pattern, the factors are impure, meaning that they load on indicators not used for their interpretation. Therefore, the authors performed four uni-factorate factor analyses including only those
Table 1 [nterwea~;ement of high-tech companies with external actors Indicator
Mean value
Sta~Mard deviation
n
Importance of suppliers a.s discussion partners Importance of suppliers lbr generating new product ideas lmrxn'tance of suppliers for conceptionalising new products |mpor~,rce of suppliers lbr developing new products Importance of suppliers for testing new products Importance of custome~ as discussion partners Importance of customers for generating new product ideas |mpertance of customers for conceptionalising new products Importance of customc~ for developing new prtr;lu~_'ts Importance of customers for testing new products Importance of universities as di.~ussion partners Relationships with universities {0 = no contact. 2 = stable relationships) Importance of consultants a_sdi~ussion partners importance of engineering offices as discussion partners
2.9 1.8 2.0 2.3 2.4 4. I 3.4 3.0 2.5 3.7 2.8
1.4 1.2 1.2 I.-~1.3 I.I 1.3 1.3 1.3 1.4 1.5
312 303 299 297 298 313 306 307 3(15 309 308
1.2 1.7 2.0
0.8 1.0 1.2
320 305 306
indicators Table
with
factor
2 document<
loadings
higher
the four resultin?
than
on the four interaction
050.
dimensions
of
In order to identify
empirical
interweavement,
cases according network cosine
dimensions.
patterns
authors
factor
:s interwoven
of techno-
clustered
the
scores on the four
Since the focus is on patterns, a
measure
was used for
using a standard
at the resulting interpret
the
to their
similarity
analysis
clusters,
the five-cluster
by the elbow-criteria
SPSS
the cluster
procedure.
the authors solution.
further
sure indicates
technological illustrate
interweavemcnt.
differences
sents the average specific
firm’s specific
partners
partner.
of
differences
than other types of cornpa&., that the hgure
of a firm’s
as empirical
of substantially
different
The dimemions
Fig. in
with
3 illustrates
i.e. more
a
with or less
do. 7he authors stress
network
as indtcatactivities
distin~~~s~
of the companies. of a con
from the ty the frequency
and intensity
of tee
3 shows that intensity
but
interweavement. high intensity situation
network
for pattern
shows
patterns 4 and int
concerning
the authors
The
patterns
2 a
hav
configurations
result (the
discussion
of a ve
of technological
3 (one with
of intensity). rations
I
is less clear-cut
3. Therefore, two
independent
pattern
for pattern
low
following
2 as w
and one with
high lever
seven network
labels will
config~-
be used in the furt
of analyses):
for the existence
patterns of interweavement.
interweavement
25%
seem to be fully network
of the
collaborating
terms,
evidence
The authors operationalise technological
of
diagrams
importance
must nat be misinterpreted
ing the intensity exclusively
spider
of collaboration
partner.
in relative
of patterns
The grey square repre-
importance
type
structural
The
in the relative
four types of external
each
types
that the c
average
weavement:
and the shape of the dendro-
the different
the authors
3 illustrates
Table 3 shows
than
the degree
tions of pattern
is supported
less
independently
Table
have chosen to
which
a
very low. low. high. a
group containing activities
Looking
to
analyses.
ot iritcnsity:
gram. Fig.
factors.
an intensive
ative average factor scores indicate
interweavement. logical
for scores indicate
the intensity
of a firm’s
as the average
of technological interweavement
score
I.
The i.dmd (intensity I): Compared company
with
shows
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and pattern
all other groups,
below
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the
fowest
intensity
ef
fnter-
1 suppliers
(26 cases)
I unlversitles
I Y
suppliers
I
pattern 3 (68 casas)
I
suppliers
*
4.55
H. Georg Gemibalen et al. / htrern. .I t~['Research in Marketing 13 (1996) 449-462
Table 3 Intensityand patternof techr,ological interweavement Intensity Pattern I 2 3 4
5
Very low Low High Very high
2 26 52
2.
3.
4.
5.
6.
24 2
2t~ 35 !I 8
13 41) 15
6 12 7
weavement, in contrast to all other groups, the island only regards it.q customers to be equally as important as other partners. The ma_m.,.~wt,lrer (intensi~y below average and pattern 2): This type of company interacts with suppliers and customers much mote intensively than with universities and consultants. Manufacturers are heavily oriented towards their upstream and downstream production partners. The overall degree of interaction with external partners is low. The toddler (intensity below average and pattern 3): The toddler shows a low intensity of technological interaction with its environment too. However in contrast to the manufacturer, the toddler lays relatively more emphasis on collaboration with universities than on supplier interaction. The highway (intensity above average and pattern 2): The highway shows the same pattern of interweavement as the manufacturer but with a higher degree of interaction. We assume a rapid flow of information and know-how from suppliers to the focal company to its customer and vice versa. The ~'isionao" (intensity above average and pattern 3): The ;isionary interacts with universities at a high level and regards its customers as more important than its suppliers and consultants relative to companies with other network configurations. The competence acquirer (intensity low to very high and pattern 4): This type of company is the
only one regarding utilisation of innovation oriented consultancy services to be import,~nt for their innovation success. Apart from fhe isla;KI. the apart the market shows the lowest intensity of collaboration with st, ppliers of all types. 7. The spider (intensity above average and pattern 5): q'he spider interacts with all network partners at a very high level. (The two cases with intensity 2 and pattern 5 were excluded becau~ low intensity seems to be untypical of this group.) 2.3. Operationalisation ~ f imtoration success
In the following section, the authors present their measurement of innovation success. All indicators are company specific in the sense that activities should be innovative for the company and not nece~ sarily tbr the whole market. We distinguish between product innovations (technically improved or tota|ty new production outputs) and process innovations (modified or new ways of producing outputs). Aft concepts of operationalising innovation success applied in this paper are discussed in more detai| by Heydebreck (1996a). 2.3. I. Product inm,wation success Product innovation rates are a common indicator for innovation success (cf. e.g. Brockhofl: l q S | , i985; Cooper. 1984a,b). Nevertheless. this indicator has some weakness in the sense that not all firms strive for the highest product innovation rates (ef. the body of literature discussing new product strategy, e.g, Cooper (1984a,b). Therefore, companies were asked what percentage of their product innovation processes were commercially successful, e.g. matched their ccor:omic expectations (cf. for a similar appro;~.ch see Cooper (1984a.b)). In answeripg this question, companies were asked to take all innovation activities into ,recount which had been
Table 4 Indicatorsof product innovationsucces,~ Percentageof economically n less than 25~/~ between25 and 509;- between50 and 75c/ morethan 75c'~ no produc~:inuo. successful developments Product improvement 277 24 New prtKluctdevet:~pment 261 8-1-
38 51)
52 42
85 23
78 62
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H. Gcor¢ Gemiinden t7 t~l./ Intern..L o]'Research in Marketing 13 (1996) 449-462
carried out within the last five years. We pre-defined four categories to choose from to avoid too man), missing values. As we consider the extent of innovation to have an influence on innovation management and the external partners which are involved, the au~ors collected data on both product improvement (rnin~,r in,-..~vative changes) and new product development (new products lor known or even unknown customer groups) (cf. for a similar point of view see e.g. Biemans and de Vries (1988); Hauschildt (I993)). Table 4 documents the answers given by the companies.
Table 6 Factor Ioadingslor economicrelevanceof process innovations On profit 0.92 On corporate growth 0.90 On survivalof the company 0.85 Cronbach'salpha is (I.88
In the following section, the authors discuss the impact of the network configurations developed above on inno.'ation success.
2.3.2. P r o c e s s inlllot'aliolI s t t t t ' e s s
Process innovation success can be operationalised as the degree to which a new or improved process is superior to existent processes. Out criteria for efficiency judgements include labour costs, lead time. productivity of the equipment and co~:sumption of materials and energy. These four indicators have been int:lnded in a factor analysis resulting in one construct: technical process innovation success (see Table 5). ~'~is construct is focused on internal improvements from a technical point of view. In order to Paeasure economic success as well. we asked the companies about the economic relevance of their process innovations. The following three indicators - impact of process innovations on profit, impact of process innovations on corporate growth and impact of process innovations on survival of the company were reduced to one factor. The factor Ioadings are shown in Table 6. Technical and economic process innovation success significantly correlate with each other (coefficient of t.c,.'-relation r = 0.34). The authors interpret technical process innovation success as a pre-condition but not as a guarantee for economic process innovat;on success.
Table 5 Factor Ioadingsfor technical process innovationsuccess Reductionof labourcosts 0.73 |nc~eascof productivity 0.78 Reductionof lead time 0.8 I Decrease of consumptionof materialand energy 0.74 Cronbach'salpha is 0.72
3. H y p o t h e s e s o n the i m p a c t o f n e t w o r k c o n f i g u ration o n s u c c e s s
There is no overall successful type of interweavement: there is not even a network configuration which is superior to all other configurations as regards innovation success. From our point of view, network intensity and network pattern must suit an individual firm's specific strategic innovation aims. In what follows, we argue the suitability of different network configurations tbr process and for product innovation processes. Many theoretical and empirical studies have shown that customer interaction is necessary for achieving product innovation success (cf. e.g. Biemans. 1992: Gemtinden et al.. 1992; Herstatt, 1991; Shaw. 1985). Customers offer important contributions to a company's product innovations. Customers define targets for their suppliers" product imiovation success which are commercially viable. They contribute user know-how and perform prototype tests. In addk]on, clc,:;c col!aboratioa with customers during the innovation process creates customer commitment which results in direct sales and increases the customer's motivation to recommend the product to third parties (for a discussion of customer's contribution in more detail see, for example, Gemiinden (1996). Heydebreck (1996a,b), von Hippel (1988), Kirchmann (1995)). Nevertheless. collaboration with customers alone is no guarantee i'gr product innovation success. Some other crucial types of contributions are more likely ~o he provided by other external partners, e.g. new production facilities or technically improved or new product components to realise cus-
H. Georg Gemiimlen et al. / httern. J. qf Research in Marketing 13 (1996) 449-462
temer's requirements (for example by suppliers), in addition, universities and research institutes in particular provide new ideas which can be transformed into marketable products by a company by means of co-operation with customers. Therefore, isolated customer interaction is not sufficient for achieving product innovation success efficiently. Depending on the innovation step, different externa! partners are of different importance (cf. e.g. Biemans and de Vries, 1988). In order to improve existing products rapidly and efficiently, collaboration with suppliers is of critical importance. Therefore, the authors believe that the highway and the spider are likely to have higher innovation success in respect of product improvements in comparison to all other types of companies. Outstanding technological resources and know-how from different disciplines are needed in order to establish new technology platforms. Universities and research institutes are particularly appropriate as external partners in break-through product innovation processes. Both suppliers and consultants are likely to be of secondary importance. Therefore, cisionaries and spiders are believed to realise product innovation success (in respect to radically new products) much more often than all other company types. We summarise our arguments in hypothesis I. !. For product innovation success a high overall intensity of the technological network and a prominent customer position is necessary. In addition to customer-orientation, supplier interaction is needed for minor innovation steps (hypothesis l a) whilst university relationships primarily support major innovation step~ (hypothesis lb). Hypothesis
Regarding process innovations, a n u m ~ r of different types of partners can provide valuable knowhow in order to stimulate this type of innovation. For example, consultants sensitise companies for possible improvements in existing processes and assist them in identifying weaknesses, e.g. by bench marking core processes against world-class standards set b), other organisations. In addition, consultants are able to assist during the implementation phase of new processes. Thus, co-operation with consultants is one way of identifying and realising process innovations successfully. Customers can also point out a company's weaknesses, e.g. they continuously de-
457
mand lower prices or a higher degree of flexibility. Therefore, c u s t o m e r s can almost three companie.~. to realise process innovations. To summarise, both customers and consultants are external partners who guide a company's process innovation targets towards commercial success (cf. e.g. Gemiinden and Heydebreck. 1995a; Heydebreck 1996a.b). Given a company's need for realising process innovations, suppliers could have a positive effect on process innovation by providing new equipment to reduce production costs or to decrease processing time. This set of external partners ~upports the ideas provided by consultants a n d / o r customers. In addL tion, suppliers actively bring new ideas into the company by developing better production facilities and marketing them. Also, empirical studies have shown the positive impact of university interaction on process innovations (cf. e.g. Heydebreck. 1996a,b). The authors suggest that suppliers and universities help a company to overcome technical barriers to process innovations and thus fulfil process innovation targets. Summarising our arguments regarding process innovations, a combination of both groups of external partners is assumed to be successful, e.g. the competence acquirer has a network pattern in which consultants and universities have a prominent position and the overall degree of networking is very high. Especially within high-tech industries, a universityorientation supports innovativeness. The h(ghway and the cisionarv have a strong locus on product innovations. Therefore, the authors do not expect them to be very successthl in realising process innovation despite their seeming to have quite a suitable pattern. For process innovation success, high intensity and a network pattern with stress on customer or consultant and, in eddition, on supplier or university is needed. Hypothesis 2.
4. Empirical findings In the following section, 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
458
H Geor'., Grmihuh'n el ol. ,/Intcr~t J. ~!/Re~ear('h itt Marketing 13 ¢1996) 449-462
configuration on success. In all regression analyses. the :sland was taken as the reference group. In these analyses, the v~fiable "industry" was included in order to s~multaneously control external effects. The results of these analyses are reported in Table A.I. In order ~o illustrate our findings, we compared group means for each innovation success measure. Fig. 4 shows the average score for each network configuration regarding product innovation success.;. Fig. 4 provides evidence to support the idea that a suitable network configuration is a critical pre-co.qdifion for realising product innovation success. Both
analyses together show the differences between product improvements and new product development. In more detail, the island has significantly lower percentages of economically successful developments. This result anderlines the important role of technological interweavement as a means lor increasing product innovation success. In harmony with hypothesis I. the highwto' has the highest percentage of successful improvements of products within the database as well as within each indiviJual industry. The results of the toddler and the cish:~tarv are very similar and better than asst.,m,'d. University interac-
A A 50%
I1 ~
"5
o
~-
C--E
~-
._
o~
=
~
improvements of product3 (percentage of economic successful innovations)
50oJ
25%
V7 "5
~-
~
o
-~
.~
t'-
t'--
I---
=~ ==
I.-
I---
new product development (percentage of economic successful innovations) Fig. 4. Network configure.ions and product innovation success.
I-
H. Georg Gemiinden et al. / Intern. J. t~J'Res'earch in Marketi~,g 13 ( ;996~ 449-462
tion - even at a low degree of total interweavement - is helpful in achieving success by realising minor innovation steps. The spider as a very active netwerker (very high intensity) also has scores which are above average within each industry. For greater innovation steps, the toddler and the risionar3' have high percentages of economically successful developments. This result supports the notion that customer-orientation and university interaction lead te highly innovative products, especially in high-tech industries. In addition, the spider re-
alises high percentages of economically successfu| ne~*~ product developments too. This result confirm~s that as a "real" networker~ the spider is abte to realise more tl~au one innovation aim with its network. These three network configurations are rrmre successful within all the industries analysed. The results of the process innovation success analyses are shown in Fig. 5. The construct "network configuration" correlates significantly with process innovation success supporting the hypotheses that they are causally re!a~ed.
A 0.20
0.00
L_J [__J L_I
-0.20
>, -~
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I"
technical process innovation success
(averagefactor scores)
B 020
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F-
economic relevance of process inno~,ations (average factor scores) Fig. 5. Network configuration and process innovation success.
.16n
H. Geor'¢ 'Gen,i~mlen ct al. / httern. J. r~l'Research ht M, 'rkethtg 13 ( "995! ,149- 453
Regarding technical process innovation success and the economic relevance of process innovations, hypothesis 2 is completely supported by the empirical findings. The c o m p e t e n ~ ' e a c q u i r e r at~d the s p i d e r are the only network configurations which realise scores which are above average in both the technical process innovation success and the economic relevance of process innovations. The isltold is the worst performer, since it is the only configuration which does not support any of the innovation targets analysed. No industry specific differences could be found. All in all. the results of our analyses support our hypotheses: different network configurations exist and these configurations correlate with innovation success in different ways. Because of the relatively low number of cases for each network configuration not all of our results were statistically significant but the correlations were in the right direction. Thus we are able to claim that the interconnected nature of relationships should be taken into account when discussing the patterns of firms" innovations. Therefore. it becomes a strategic task to develop a network suitable for t~e individual firm's innovation aims.
5. Discussion and outlook Our research has shown that the network as ;r whole mugt be taken into account when analysing technological networks. This res,!t ~s important for managerial day-to-day dc~.ision making too. The portfolio of all relationships should not be divided into. for example, supplier, customer and university portfolios and managed separately from each other. It is a strategic task to develop, manage, plan and exploit a company's network as a whole. Referring to our empirical findings, it is the synergy between supplier and customer interaction which makes product improvements more successful. In addition, the university plays an important role in developing new products, ai least within high-tech industries. To summarise, customer-orientation is critical for product innovation success, but it is not the i~Aated co-operation with customers which ensures product innovation success. Our findings show that process innovation success needs multi-dimensional co-operation with multiple actors as well: be it through an intensive interaction wRh uaiversi,ti,es a n d consultants or through a bal-
anced network pattern. It is of particular interest to notice that only a high intensity of interweavement secures process innovation success. The advantage of special network configurations may be dependent on context variables, In our analyses. we differentiated the database by industry. However. no significant systematic differences between the industries could be identilied. It is possible that the high-tech industries ar:alysed here are too similar to each other to show such industry effects but that they would occur if comparisons with other, different industries e.g. consumer goods, industrial goods, primary goods, were made. Other context variables may also h::ve a moderating influence on the advantages or disadvamage of different network configurations, e.g. smaller companies may re,alise innovation success through different network configurations than larger ones. In addition, Gemiinden and Heydebreck (1994. 1995b,c) have demonstrated the relationship between business strategy and technological network activities. Thus. the impact of business; strategy on a company's network configuration and its suitability for serving strategic aims should be addressed in future research. The impact of the whole focal network on innovation success has managerial implications too. Efficient networking no longer implies optimisation of single relationships independently of each other, but instead network management requires the management of synergies and co-ordination of all relationships in an efficient way. A company has to promote organisational structures which are able to deal with suppliers and customers simultaneously (for example because special suppliers are necessary fol special customers). Cross-functional teams can be seen as a tool to realise 'network as a whole' management. On a personal level, relationship promoters are a possibility for centralising network management towards one focal point (cf. for a discussion of relationship promoters Gemiir!den and Walter (1994. 1995)).
Acknowledgements The authors thauk Geoff Easton and three anonymous reviewers lk;r their constructive comm.'nts on earlier versions of this manuscript. The authors are grateful for financial support of the German Ministry of Education, Science, Research and Technology.
H. Georg Gentiinden et al / httern. J. of Research ht Marketing 13 (1996) 449-462
461
Appendix A Table A.I. Network configuration and innovation success (results of the regression analyses) Network configuration
The manufacturer The toddler The highway The visionary The competence acquirer The spider
lmpro~,ement of products
New product development
Technical process innovation success
Economic relevance of process innovations
Impact of industry: n.s.
Impact of industry: n.s.
Impact of network configuration: p = 0.02
Impact of network configuration: p = 0 . 0 1
Impact of industry: p = 0.00 Impact of network configuration: p = I).(16
Impact of industry: p = 0.08 Impact of network configuration: p = 0.04.
Beta
Level of sign.
Beta
Level of sign.
Beta
Level of sign
Beta
Level of sign.
0.1 I 0.26 0.22 0.13 0.03
0.34 0.01 0.01 0.09 0.72
l).20 0.36 0.17 0.22 0.18
0.07 0.00 0.0-~ 0.01 0.04
0.32 0.21 O. 19 0.04 0.13
0.01 0.04 0.03 0.58 0.13
O.I I O.11 0.(16 0.08 0.23
0.32 0.26 0.45 0.28 0.01
0.24
0.03
0.00
0.38
0.34
0.00
0.31)
0.01
References
Axelsson. B.. !986. Fr'~n fluga till spindcl. Unpublished working paper. Biemans. W.G., 1992 Managing innovations within networks, Routledge: London. New Yolk. Biemans. W.G. and R. de Vries, 1988, Supplier-customer telationships in developing a tether;logical process innovation. In: P.W. Turnbull and S,J. Paliwoda. eds.. Researcil developments in industria! marketing. Proceedings of the 4th annual IMP conference, Manchester, 33-48. Brockhoff. K.. 1981. Die Produktinnovationsrate im Lageberich;.. Dcr Betrieb 34(9), 433-437. Brockhoff. K.. 1985. Die Produktinnovationsrate als Instrument der strategischen Unternehmensplanung. Zeitschrift fi]r Betriebswirtschaft 55(5). 451-476. Cook, K.S. and R.M. Emerson, 1978. Power. equity and commitmeat ia exchange networks. American Sociological Review 43 (Oct.). 721-739. Cooper, R.G.. 1984a. How new product strategies impact on perforraance. Journal of Product Innovation Management 1984(1), 5-18. Cooper, R.G.. 1984b. New product strategies: What distinguishes the top performers? Journal of Product Innovation Management 1984(2). 151-164. Gemiinden, H.G., 1996. Technological interweavement: A summary of past research and new findings concerning the moderating influence of context variables. In: P. Ghauri. ed., Advances in iuternational marketing: From mass marketing to relationships and networks. Greenwich. CT: JAI Press (~orthcoming). Gemiinden. H.G, and P. Heydebreck. 1994. Matching business strategy and technological network activities: The impac' on
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ELSEVIER
Intern.I. of Research in Marketing 13(1996)'449-462
Network configuration and innovation success: An empirical analysis in German high-tech industries Hans Georg Gemiinden a. *, Thomas Ritter a, Peter Heydebreck
b
a htstitutefor Applied Management Science and Corporate Strategy, Unirersity of Karlsruhe. Box 6980. 76128 Karlsruhe. Germany b Inno GesellschaJ~fiir innocatice Unternehmensentwickhmg mbH, P. 0. Box 3366. 76019 Karlsruhe, Germany
Accepted 30 August 1996
Abstract Based on the assumption that intensity and structure are the most important dimensions of a firm's technological aetwo~'k, the authors identify seven different types of technology-oriented network configurations. Drawing upon a database of 32E high-tech companies, they show that innovation success is significantly correlated with a firm's technological network. Product and process innovations are shown to demand different types of network configurations. Keywords: Networks; Innovationstrategies;Technology
1. Network configuration and innovation success: Theoretical frame of reference There is a rapidly expanding body of theoretical and empirical literature showing that technology-oriented relationships constitute a valuable means for integrating complementary resources into a firm's innovation processes, thereby significantly increasing a firm's product and process innovation success. In particular, two different types of empirical literature deal with analysing the impact of technology-oriented relationships on innovation success and overall success. Firstly, there are the analyses of focal actors, their specific relationships and the way these relatto~ships influence each other. These studies conco,Orate on a few selected cases, data collection for
which has been executed by means of face-t~face interviews. This type of work is typical particularly of the IMP group (see e.g. Axelsson. 1986: H~kansson, 1987) but also other researchers have chosen this approach (see e.g. Biemans. 1992; Shaw. 1985). Secondly, there are a number of large-scale qtmatitative studies which analyse the impact of collaboration with a specific type of partner (customers. suppliers etc.) on innovation success. Typically, data collection is done via standardised mailed questionnaires. However, these studies do not take into account the central element of the network approach. namely the inter-connected nature of these relationships (e.g. Gemiinden, 1996; Gemiinden et al.. 1992; Hagedoorn and Schakenraad. 1994: H~kansson. 1989; Herden, 1992; Heydebreck, 1996a). t
"Correspondingauthor. Tel.: (+49)721-608.3431; fax: (+49) 721-608.6046.
I Following Cook and Emerson (1978), many network researchers define networksas sets of inter-connectedrelationship.
0167-8116/96/$15.00 Copyright © 1996ElsevierScienceB.V. All rights reserved. PIi SO 167-8116(96)00026-2
-t-50
H. (;ct~rt,,Gemihtdc,'t er aL / httern. J. O[ Research iJe Ahtrketing 13 (IO~,'~) 449-462
Ltmking at d~e existing literature, the authors detect a lack o f studie,~ which address the need to match a company's network of different types of techrmlogy-orented relationships with its innovation ~ e d s ~,ld which ~,t the same time provide the large database which is necessary lor testing statistically hypt~he~s on the impact of matching types of relationships for innovation success. Depending on a firm's innovation targets, internal resources and its external context, different external resources are needed. We assume that different types o f actors are particularly appropriate tbr contributing specific kinds of resources and know-how. Fig. I illustrates the most important types of innovation partners an,.: their contributions. Fig. 1 illustrates that many different types of partners can support a firm in i.~s innovation activities. although each type of partner offers a different ~ t of resources. Despite the high potential of technological relationsh~.ps, one has to remember that external resources are not free of costs. On the contrary. the establishment of access to external partners" re~ u r c e s is a lengthy and costly investment process (cf. e.g. Mattsson. 1988: Plinke. 1989: Valla. 1986: Williamson. 1979).
Thus. companies b'~,fld up and maintain only those relationships which are particularly valuable for them. As companies differ in respect of their needs for n~:tworking, it is p',ausible to assume that companies als,~ differ i;+ respect of ~he types of ~:xtemal partners with whom they collab(,:ratc. For some companies. universities might constitute the most important source of external know-how and technological resources, whilst other companies might not interact with universities at all but collaborate in R & D with customers and suppliers instead. The authors believe that the relative importance of technology-oriented relationships with one type of partner in relation to the ovet ail importance of networking differs significantly from company to company and that it should differ because different network patterns are suited tor pursui|~g different innovation aims. Another aspect is that some companies interact intensively with many external partner,~ whilst others hardly maintain close relationships with any external partners at all. This is due to significant differences in the companies" experience and capability of networking. Also. some companies are more willing to invest in an advantageous network position than others. This means that companies not only differ in
Administration : Subsidy
Suppliers, producers of means
Political support L : Mediations, transfer Laws, (de-) regulations
of production New technologies of L components and systems
I "C°mplementary ]know-how ~o Solving interface problems
( Researchand training~ i institutes /
/
L ~ 1
==..._/' ~
:
J . Research "[raining Qualified personnel
II~_nmncanln v v . . ,1"14, "3 Own authority
I • Innovative Concepts I • Structuring of processes ~• Financial. ~egaland ~ nsurance services Fig. I. lnnovati(~r=partnel'sand their contributions,
• Joint basic research • Establishing standards • Getting subsidies
| 1 j
H. Ge,,,~ Gemiinden et aL /httern. J. ~+]'Re.s'earchin Marketing 13 (1906) 449-462
network configuration
In"
innovatio;~ success
i
intensity of technological interweavement
451
product innovation SUCCESS • improvementof products • new product development
- - 7 process innovation | success l ° technical . economic relevance
pattern of technological interweavement
Fig. 2. Theoreticalframe of referettce.
respect of their-,,,:tw.grt" pat:ern but also in the intensity of technological interweavement. ? To conclude, we assume that p a t t e n z (importance of collaboration with each specific type of partner in relation to the overall importance of networking) and h~tensita' are the two most important dimensions for characterizing a n e t w o r k cot!~gztra~ion. In this paper, the suitability of different network configurations for different innovation aims. i.e. product and process innovation, is analysed. Specific hypotheses are developed in Section 3. Fig. 2 gives our frame of reference. The network configuration is determined by a large variety of internal and external context factors. most of which can be grouped under the headings of motivation and capability of networking. However. it is not the aim of this paper to test the possible influence of internal and external context variables (e.g. 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 a company's technological interweavement. This paper
2 The term tecl',,nologicalinterweavementis meant to describe the totality of a firm's technology-orientedrelationshipsaimed at acquiring, jointly developingor diffusing of technologicalknowhow and resources.
: : ~ e r focuses on analysing the impact of different network configurations on product and process innovation success which in turn are amongst the most important determinants of a company's overall commercial success. In particular, we are interested in the following issues: • Do product and process innovations require different network configurations? • Do minor innovations require different network configurations than major innovations, i.e. is there an overall best network configuration for product innovation, or are some network configurations part!cularly suited for product improvements and others for new product development?
2. An empirical typology- o f network configm'a-
tions 2. !. T h e d a t a b a s e
The authors test their theoretical frame of referonce drawing upon a database obtained from five studies of 321 high-tech companies operating in the fields of biotechnology. EDP. medical equipment. microelectronics and sensor technology. All the studies used mailed questionnaires, usually with a prior phone contact.
4.52
H. Geor¢ Gemiinden et al. / Intern. J. ~ Research in Marketin. o 13 ( 19961449-462
Response rates varied from a low of 14~- to a high o f 66%. Apart from the biotcchnology study, the response rates are not overwhelmingly high. This can be explained by the nature of our t a ~ e t population which mainly consists of r.ma!l comran;.e.':. H o w e v e r low response rates increase the risks of systematic bias. Therefore, the authors performed representativeness tests in order to check for a potentim systematic bias. In respect o f context variables this was done by comparing re:;ponding companies with the total sample. In respect oi indicators o f technological interweavement and success, no data was available on the total sample. Therefore, we compared the answers to our first questionnaire with tbe responses to our reminder. It turned out that size was the only variable to show a systematic bias: large companies gave a significantly higher response rate than small companies. In addition telephone interviews were performed with non-respondents in c ' d e r to check whether there was a systematic bias with regard to technological interweavement and innovation success. Although we did not carG' out enough telephone interviews to a | l o w the use statistical inference tests, our judgemerit was that there was no systematic bias with regard to interweavement and success. Furthermore. we are not interested in frequencies but in analysing the impact one variable has upon the ~ther, which
considerably reducex the consequences of the bias of one variat,',c. For a thorough discussion ot the quality of the database see Heydebreck (1996a). 2.2. O p e r a ' . i o n a l i s a t i o n o f net~,.,;rk c o n f i g u r a t i o n
In order to analyse the intensity and pattem of a firm's technological interweavement, a vast range of indicators o f network dimensi.ans were collected. Table I lists the interweavement indicators and provides an overview o f their distributions. If not stated otherwise, all variables are measured on five-point rating scales (! = of no importance, 5 = very high importance). A factor analysis o f these indicators replicated our findings from the Lake-Constance Study (cf. Gemiinden et al., 1992), i.e. each interweavement factor describes collaboration with a specific type of external partner: the factors were labelled: supplierinteraction factor, customer-interaction factor, university-interaction factor, and the consultant-interaction factor. The exploratory solution explains 66% of the total variance of the indicators. Although this factor analysis provides a clear pattern, the factors are impure, meaning that they load on indicators not used for their interpretation. Therefore, the authors performed four uni-factorate factor analyses including only those
Table 1 [nterwea~;ement of high-tech companies with external actors Indicator
Mean value
Sta~Mard deviation
n
Importance of suppliers a.s discussion partners Importance of suppliers lbr generating new product ideas lmrxn'tance of suppliers for conceptionalising new products |mpor~,rce of suppliers lbr developing new products Importance of suppliers for testing new products Importance of custome~ as discussion partners Importance of customers for generating new product ideas |mpertance of customers for conceptionalising new products Importance of customc~ for developing new prtr;lu~_'ts Importance of customers for testing new products Importance of universities as di.~ussion partners Relationships with universities {0 = no contact. 2 = stable relationships) Importance of consultants a_sdi~ussion partners importance of engineering offices as discussion partners
2.9 1.8 2.0 2.3 2.4 4. I 3.4 3.0 2.5 3.7 2.8
1.4 1.2 1.2 I.-~1.3 I.I 1.3 1.3 1.3 1.4 1.5
312 303 299 297 298 313 306 307 3(15 309 308
1.2 1.7 2.0
0.8 1.0 1.2
320 305 306
indicators Table
with
factor
2 document<
loadings
higher
the four resultin?
than
on the four interaction
050.
dimensions
of
In order to identify
empirical
interweavement,
cases according network cosine
dimensions.
patterns
authors
factor
:s interwoven
of techno-
clustered
the
scores on the four
Since the focus is on patterns, a
measure
was used for
using a standard
at the resulting interpret
the
to their
similarity
analysis
clusters,
the five-cluster
by the elbow-criteria
SPSS
the cluster
procedure.
the authors solution.
further
sure indicates
technological illustrate
interweavemcnt.
differences
sents the average specific
firm’s specific
partners
partner.
of
differences
than other types of cornpa&., that the hgure
of a firm’s
as empirical
of substantially
different
The dimemions
Fig. in
with
3 illustrates
i.e. more
a
with or less
do. 7he authors stress
network
as indtcatactivities
distin~~~s~
of the companies. of a con
from the ty the frequency
and intensity
of tee
3 shows that intensity
but
interweavement. high intensity situation
network
for pattern
shows
patterns 4 and int
concerning
the authors
The
patterns
2 a
hav
configurations
result (the
discussion
of a ve
of technological
3 (one with
of intensity). rations
I
is less clear-cut
3. Therefore, two
independent
pattern
for pattern
low
following
2 as w
and one with
high lever
seven network
labels will
config~-
be used in the furt
of analyses):
for the existence
patterns of interweavement.
interweavement
25%
seem to be fully network
of the
collaborating
terms,
evidence
The authors operationalise technological
of
diagrams
importance
must nat be misinterpreted
ing the intensity exclusively
spider
of collaboration
partner.
in relative
of patterns
The grey square repre-
importance
type
structural
The
in the relative
four types of external
each
types
that the c
average
weavement:
and the shape of the dendro-
the different
the authors
3 illustrates
Table 3 shows
than
the degree
tions of pattern
is supported
less
independently
Table
have chosen to
which
a
very low. low. high. a
group containing activities
Looking
to
analyses.
ot iritcnsity:
gram. Fig.
factors.
an intensive
ative average factor scores indicate
interweavement. logical
for scores indicate
the intensity
of a firm’s
as the average
of technological interweavement
score
I.
The i.dmd (intensity I): Compared company
with
shows
average
and pattern
all other groups,
below
this aype of
the
fowest
intensity
ef
fnter-
1 suppliers
(26 cases)
I unlversitles
I Y
suppliers
I
pattern 3 (68 casas)
I
suppliers
*
4.55
H. Georg Gemibalen et al. / htrern. .I t~['Research in Marketing 13 (1996) 449-462
Table 3 Intensityand patternof techr,ological interweavement Intensity Pattern I 2 3 4
5
Very low Low High Very high
2 26 52
2.
3.
4.
5.
6.
24 2
2t~ 35 !I 8
13 41) 15
6 12 7
weavement, in contrast to all other groups, the island only regards it.q customers to be equally as important as other partners. The ma_m.,.~wt,lrer (intensi~y below average and pattern 2): This type of company interacts with suppliers and customers much mote intensively than with universities and consultants. Manufacturers are heavily oriented towards their upstream and downstream production partners. The overall degree of interaction with external partners is low. The toddler (intensity below average and pattern 3): The toddler shows a low intensity of technological interaction with its environment too. However in contrast to the manufacturer, the toddler lays relatively more emphasis on collaboration with universities than on supplier interaction. The highway (intensity above average and pattern 2): The highway shows the same pattern of interweavement as the manufacturer but with a higher degree of interaction. We assume a rapid flow of information and know-how from suppliers to the focal company to its customer and vice versa. The ~'isionao" (intensity above average and pattern 3): The ;isionary interacts with universities at a high level and regards its customers as more important than its suppliers and consultants relative to companies with other network configurations. The competence acquirer (intensity low to very high and pattern 4): This type of company is the
only one regarding utilisation of innovation oriented consultancy services to be import,~nt for their innovation success. Apart from fhe isla;KI. the apart the market shows the lowest intensity of collaboration with st, ppliers of all types. 7. The spider (intensity above average and pattern 5): q'he spider interacts with all network partners at a very high level. (The two cases with intensity 2 and pattern 5 were excluded becau~ low intensity seems to be untypical of this group.) 2.3. Operationalisation ~ f imtoration success
In the following section, the authors present their measurement of innovation success. All indicators are company specific in the sense that activities should be innovative for the company and not nece~ sarily tbr the whole market. We distinguish between product innovations (technically improved or tota|ty new production outputs) and process innovations (modified or new ways of producing outputs). Aft concepts of operationalising innovation success applied in this paper are discussed in more detai| by Heydebreck (1996a). 2.3. I. Product inm,wation success Product innovation rates are a common indicator for innovation success (cf. e.g. Brockhofl: l q S | , i985; Cooper. 1984a,b). Nevertheless. this indicator has some weakness in the sense that not all firms strive for the highest product innovation rates (ef. the body of literature discussing new product strategy, e.g, Cooper (1984a,b). Therefore, companies were asked what percentage of their product innovation processes were commercially successful, e.g. matched their ccor:omic expectations (cf. for a similar appro;~.ch see Cooper (1984a.b)). In answeripg this question, companies were asked to take all innovation activities into ,recount which had been
Table 4 Indicatorsof product innovationsucces,~ Percentageof economically n less than 25~/~ between25 and 509;- between50 and 75c/ morethan 75c'~ no produc~:inuo. successful developments Product improvement 277 24 New prtKluctdevet:~pment 261 8-1-
38 51)
52 42
85 23
78 62
456
H. Gcor¢ Gemiinden t7 t~l./ Intern..L o]'Research in Marketing 13 (1996) 449-462
carried out within the last five years. We pre-defined four categories to choose from to avoid too man), missing values. As we consider the extent of innovation to have an influence on innovation management and the external partners which are involved, the au~ors collected data on both product improvement (rnin~,r in,-..~vative changes) and new product development (new products lor known or even unknown customer groups) (cf. for a similar point of view see e.g. Biemans and de Vries (1988); Hauschildt (I993)). Table 4 documents the answers given by the companies.
Table 6 Factor Ioadingslor economicrelevanceof process innovations On profit 0.92 On corporate growth 0.90 On survivalof the company 0.85 Cronbach'salpha is (I.88
In the following section, the authors discuss the impact of the network configurations developed above on inno.'ation success.
2.3.2. P r o c e s s inlllot'aliolI s t t t t ' e s s
Process innovation success can be operationalised as the degree to which a new or improved process is superior to existent processes. Out criteria for efficiency judgements include labour costs, lead time. productivity of the equipment and co~:sumption of materials and energy. These four indicators have been int:lnded in a factor analysis resulting in one construct: technical process innovation success (see Table 5). ~'~is construct is focused on internal improvements from a technical point of view. In order to Paeasure economic success as well. we asked the companies about the economic relevance of their process innovations. The following three indicators - impact of process innovations on profit, impact of process innovations on corporate growth and impact of process innovations on survival of the company were reduced to one factor. The factor Ioadings are shown in Table 6. Technical and economic process innovation success significantly correlate with each other (coefficient of t.c,.'-relation r = 0.34). The authors interpret technical process innovation success as a pre-condition but not as a guarantee for economic process innovat;on success.
Table 5 Factor Ioadingsfor technical process innovationsuccess Reductionof labourcosts 0.73 |nc~eascof productivity 0.78 Reductionof lead time 0.8 I Decrease of consumptionof materialand energy 0.74 Cronbach'salpha is 0.72
3. H y p o t h e s e s o n the i m p a c t o f n e t w o r k c o n f i g u ration o n s u c c e s s
There is no overall successful type of interweavement: there is not even a network configuration which is superior to all other configurations as regards innovation success. From our point of view, network intensity and network pattern must suit an individual firm's specific strategic innovation aims. In what follows, we argue the suitability of different network configurations tbr process and for product innovation processes. Many theoretical and empirical studies have shown that customer interaction is necessary for achieving product innovation success (cf. e.g. Biemans. 1992: Gemtinden et al.. 1992; Herstatt, 1991; Shaw. 1985). Customers offer important contributions to a company's product innovations. Customers define targets for their suppliers" product imiovation success which are commercially viable. They contribute user know-how and perform prototype tests. In addk]on, clc,:;c col!aboratioa with customers during the innovation process creates customer commitment which results in direct sales and increases the customer's motivation to recommend the product to third parties (for a discussion of customer's contribution in more detail see, for example, Gemiinden (1996). Heydebreck (1996a,b), von Hippel (1988), Kirchmann (1995)). Nevertheless. collaboration with customers alone is no guarantee i'gr product innovation success. Some other crucial types of contributions are more likely ~o he provided by other external partners, e.g. new production facilities or technically improved or new product components to realise cus-
H. Georg Gemiimlen et al. / httern. J. qf Research in Marketing 13 (1996) 449-462
temer's requirements (for example by suppliers), in addition, universities and research institutes in particular provide new ideas which can be transformed into marketable products by a company by means of co-operation with customers. Therefore, isolated customer interaction is not sufficient for achieving product innovation success efficiently. Depending on the innovation step, different externa! partners are of different importance (cf. e.g. Biemans and de Vries, 1988). In order to improve existing products rapidly and efficiently, collaboration with suppliers is of critical importance. Therefore, the authors believe that the highway and the spider are likely to have higher innovation success in respect of product improvements in comparison to all other types of companies. Outstanding technological resources and know-how from different disciplines are needed in order to establish new technology platforms. Universities and research institutes are particularly appropriate as external partners in break-through product innovation processes. Both suppliers and consultants are likely to be of secondary importance. Therefore, cisionaries and spiders are believed to realise product innovation success (in respect to radically new products) much more often than all other company types. We summarise our arguments in hypothesis I. !. For product innovation success a high overall intensity of the technological network and a prominent customer position is necessary. In addition to customer-orientation, supplier interaction is needed for minor innovation steps (hypothesis l a) whilst university relationships primarily support major innovation step~ (hypothesis lb). Hypothesis
Regarding process innovations, a n u m ~ r of different types of partners can provide valuable knowhow in order to stimulate this type of innovation. For example, consultants sensitise companies for possible improvements in existing processes and assist them in identifying weaknesses, e.g. by bench marking core processes against world-class standards set b), other organisations. In addition, consultants are able to assist during the implementation phase of new processes. Thus, co-operation with consultants is one way of identifying and realising process innovations successfully. Customers can also point out a company's weaknesses, e.g. they continuously de-
457
mand lower prices or a higher degree of flexibility. Therefore, c u s t o m e r s can almost three companie.~. to realise process innovations. To summarise, both customers and consultants are external partners who guide a company's process innovation targets towards commercial success (cf. e.g. Gemiinden and Heydebreck. 1995a; Heydebreck 1996a.b). Given a company's need for realising process innovations, suppliers could have a positive effect on process innovation by providing new equipment to reduce production costs or to decrease processing time. This set of external partners ~upports the ideas provided by consultants a n d / o r customers. In addL tion, suppliers actively bring new ideas into the company by developing better production facilities and marketing them. Also, empirical studies have shown the positive impact of university interaction on process innovations (cf. e.g. Heydebreck. 1996a,b). The authors suggest that suppliers and universities help a company to overcome technical barriers to process innovations and thus fulfil process innovation targets. Summarising our arguments regarding process innovations, a combination of both groups of external partners is assumed to be successful, e.g. the competence acquirer has a network pattern in which consultants and universities have a prominent position and the overall degree of networking is very high. Especially within high-tech industries, a universityorientation supports innovativeness. The h(ghway and the cisionarv have a strong locus on product innovations. Therefore, the authors do not expect them to be very successthl in realising process innovation despite their seeming to have quite a suitable pattern. For process innovation success, high intensity and a network pattern with stress on customer or consultant and, in eddition, on supplier or university is needed. Hypothesis 2.
4. Empirical findings In the following section, 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
458
H Geor'., Grmihuh'n el ol. ,/Intcr~t J. ~!/Re~ear('h itt Marketing 13 ¢1996) 449-462
configuration on success. In all regression analyses. the :sland was taken as the reference group. In these analyses, the v~fiable "industry" was included in order to s~multaneously control external effects. The results of these analyses are reported in Table A.I. In order ~o illustrate our findings, we compared group means for each innovation success measure. Fig. 4 shows the average score for each network configuration regarding product innovation success.;. Fig. 4 provides evidence to support the idea that a suitable network configuration is a critical pre-co.qdifion for realising product innovation success. Both
analyses together show the differences between product improvements and new product development. In more detail, the island has significantly lower percentages of economically successful developments. This result anderlines the important role of technological interweavement as a means lor increasing product innovation success. In harmony with hypothesis I. the highwto' has the highest percentage of successful improvements of products within the database as well as within each indiviJual industry. The results of the toddler and the cish:~tarv are very similar and better than asst.,m,'d. University interac-
A A 50%
I1 ~
"5
o
~-
C--E
~-
._
o~
=
~
improvements of product3 (percentage of economic successful innovations)
50oJ
25%
V7 "5
~-
~
o
-~
.~
t'-
t'--
I---
=~ ==
I.-
I---
new product development (percentage of economic successful innovations) Fig. 4. Network configure.ions and product innovation success.
I-
H. Georg Gemiinden et al. / Intern. J. t~J'Res'earch in Marketi~,g 13 ( ;996~ 449-462
tion - even at a low degree of total interweavement - is helpful in achieving success by realising minor innovation steps. The spider as a very active netwerker (very high intensity) also has scores which are above average within each industry. For greater innovation steps, the toddler and the risionar3' have high percentages of economically successful developments. This result supports the notion that customer-orientation and university interaction lead te highly innovative products, especially in high-tech industries. In addition, the spider re-
alises high percentages of economically successfu| ne~*~ product developments too. This result confirm~s that as a "real" networker~ the spider is abte to realise more tl~au one innovation aim with its network. These three network configurations are rrmre successful within all the industries analysed. The results of the process innovation success analyses are shown in Fig. 5. The construct "network configuration" correlates significantly with process innovation success supporting the hypotheses that they are causally re!a~ed.
A 0.20
0.00
L_J [__J L_I
-0.20
>, -~
I--
I"- E
I--
.,.-
I"
technical process innovation success
(averagefactor scores)
B 020
O.OG
t_._.J
U
'459
I
I
J
-0.20
F-
economic relevance of process inno~,ations (average factor scores) Fig. 5. Network configuration and process innovation success.
.16n
H. Geor'¢ 'Gen,i~mlen ct al. / httern. J. r~l'Research ht M, 'rkethtg 13 ( "995! ,149- 453
Regarding technical process innovation success and the economic relevance of process innovations, hypothesis 2 is completely supported by the empirical findings. The c o m p e t e n ~ ' e a c q u i r e r at~d the s p i d e r are the only network configurations which realise scores which are above average in both the technical process innovation success and the economic relevance of process innovations. The isltold is the worst performer, since it is the only configuration which does not support any of the innovation targets analysed. No industry specific differences could be found. All in all. the results of our analyses support our hypotheses: different network configurations exist and these configurations correlate with innovation success in different ways. Because of the relatively low number of cases for each network configuration not all of our results were statistically significant but the correlations were in the right direction. Thus we are able to claim that the interconnected nature of relationships should be taken into account when discussing the patterns of firms" innovations. Therefore. it becomes a strategic task to develop a network suitable for t~e individual firm's innovation aims.
5. Discussion and outlook Our research has shown that the network as ;r whole mugt be taken into account when analysing technological networks. This res,!t ~s important for managerial day-to-day dc~.ision making too. The portfolio of all relationships should not be divided into. for example, supplier, customer and university portfolios and managed separately from each other. It is a strategic task to develop, manage, plan and exploit a company's network as a whole. Referring to our empirical findings, it is the synergy between supplier and customer interaction which makes product improvements more successful. In addition, the university plays an important role in developing new products, ai least within high-tech industries. To summarise, customer-orientation is critical for product innovation success, but it is not the i~Aated co-operation with customers which ensures product innovation success. Our findings show that process innovation success needs multi-dimensional co-operation with multiple actors as well: be it through an intensive interaction wRh uaiversi,ti,es a n d consultants or through a bal-
anced network pattern. It is of particular interest to notice that only a high intensity of interweavement secures process innovation success. The advantage of special network configurations may be dependent on context variables, In our analyses. we differentiated the database by industry. However. no significant systematic differences between the industries could be identilied. It is possible that the high-tech industries ar:alysed here are too similar to each other to show such industry effects but that they would occur if comparisons with other, different industries e.g. consumer goods, industrial goods, primary goods, were made. Other context variables may also h::ve a moderating influence on the advantages or disadvamage of different network configurations, e.g. smaller companies may re,alise innovation success through different network configurations than larger ones. In addition, Gemiinden and Heydebreck (1994. 1995b,c) have demonstrated the relationship between business strategy and technological network activities. Thus. the impact of business; strategy on a company's network configuration and its suitability for serving strategic aims should be addressed in future research. The impact of the whole focal network on innovation success has managerial implications too. Efficient networking no longer implies optimisation of single relationships independently of each other, but instead network management requires the management of synergies and co-ordination of all relationships in an efficient way. A company has to promote organisational structures which are able to deal with suppliers and customers simultaneously (for example because special suppliers are necessary fol special customers). Cross-functional teams can be seen as a tool to realise 'network as a whole' management. On a personal level, relationship promoters are a possibility for centralising network management towards one focal point (cf. for a discussion of relationship promoters Gemiir!den and Walter (1994. 1995)).
Acknowledgements The authors thauk Geoff Easton and three anonymous reviewers lk;r their constructive comm.'nts on earlier versions of this manuscript. The authors are grateful for financial support of the German Ministry of Education, Science, Research and Technology.
H. Georg Gentiinden et al / httern. J. of Research ht Marketing 13 (1996) 449-462
461
Appendix A Table A.I. Network configuration and innovation success (results of the regression analyses) Network configuration
The manufacturer The toddler The highway The visionary The competence acquirer The spider
lmpro~,ement of products
New product development
Technical process innovation success
Economic relevance of process innovations
Impact of industry: n.s.
Impact of industry: n.s.
Impact of network configuration: p = 0.02
Impact of network configuration: p = 0 . 0 1
Impact of industry: p = 0.00 Impact of network configuration: p = I).(16
Impact of industry: p = 0.08 Impact of network configuration: p = 0.04.
Beta
Level of sign.
Beta
Level of sign.
Beta
Level of sign
Beta
Level of sign.
0.1 I 0.26 0.22 0.13 0.03
0.34 0.01 0.01 0.09 0.72
l).20 0.36 0.17 0.22 0.18
0.07 0.00 0.0-~ 0.01 0.04
0.32 0.21 O. 19 0.04 0.13
0.01 0.04 0.03 0.58 0.13
O.I I O.11 0.(16 0.08 0.23
0.32 0.26 0.45 0.28 0.01
0.24
0.03
0.00
0.38
0.34
0.00
0.31)
0.01
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