Sucess Factor for Innovatiopn Management in SMEs

7/23/2019 Sucess Factor for Innovatiopn Management in SMEs

1/18

Success factors for innovationmanagement in networks of smalland medium enterprises

Alexandra Rese and Daniel Baier

Chair of Marketing and Innovation Management, Brandenburg University of Technology Cottbus,PO Box 101344, D-03013 Cottbus, Germany. [email protected]; [email protected]

Because firms today operate in increasingly turbulent and complex environments, they need to

be more proactive and innovative. Networks are gaining in importance, especially for small

and medium enterprises with limited resources as R&D cooperations or cooperations along the

value chain seem to be the only way to succeed with technologically challenging and promising

but also expensive and risky product innovations. One of the key problems of these networks,

however, is the question of how to plan, organize and control the innovation processes that are

distributed over several partners. Theoretically derived and empirically proven success factors

could help as much here as in the traditional success/failure discussion of new product

development within firms. This paper discusses the effects of such factors, which partly derivefrom the traditional success/failure discussion within firms (e.g. market potential, product

advantage, technological synergy, proficiency of technological or marketing activities) but also

factors derived from recent network research (e.g. trust or dependence on partners). Their

effect on new product performance is discussed on the basis of a comprehensive survey with 271

participating networks. The results confirm the traditional success factors, especially the

product advantage and proficiency factors. But they also show that network-related success

factors (especially network cohesion and organization) are of similar major importance.

1. Introduction

N etworks of manufacturers, suppliers, mar-keting intermediaries, service providers andresearch institutes trying together to determine

future market needs, to accomplish complex or

interdisciplinary R&D tasks, to convert ideas and

concepts into marketable products and to launch

them, have already been the subject of research

for some time (Kowol and Krohn, 1995;

Koschatzky et al., 2001; Kueppers, 2002). The

characteristics of such networks, their stability

over time and possible control and management

mechanisms, but in particular the question ofwhich network configurations are particularly

suitable in which industries and for which enter-

prises, have been discussed. At the same time, the

opinion that R&D cooperations or cooperationsalong the value chain offer the (only) chance to

take part in technologically challenging and eco-

nomically promising, but also expensive and risky

product innovations, especially for small and

medium enterprises (SME) in emergent industries

or in structurally weak regions, has been increas-

ingly propagated (Semlinger, 1998; Harms, 2001).

Many empirical and theoretical studies on the

management of networks in general can be found

in the literature (see Sydow, 2006 for a general

overview). But there are hardly any recommenda-

tions with respect to innovation management inthese networks (Thoms, 2003). On the other hand,

research on the success/failure of new product

R&D Management 41, 2, 2011. r 2011 The Authors. R&D Management r 2011 Blackwell Publishing Ltd.,1389600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
mailto:[email protected]:[email protected]


2/18

development within one firm has uncovered, accu-

mulated and synthesized the relevant success fac-

tors (see e.g. the overviews of Montoya-Weiss and

Calantone, 1994; Henard and Szymanski, 2001;

van der Panne et al., 2003; Cooper and Kleinsch-

midt, 2007). Because meta-analyses consistently

found predictive factors for new product success,

we also expect some of the firm-level success

factors to have an influence if products are devel-

oped not within a firm but within a network.

In recent years, the concept of innovation has

changed drastically, shifting the focus of attention

to the interactive nature of the innovation process

and the role played by networks involving differ-

ent organizations (Fischer, 2006). For product

innovation, R&D cooperations or cooperationsalong the value chain do not represent a new

phenomenon, but the current scale and prolifera-

tion, especially as an organizational component in

the innovation process, is new (for documentation

of this upward trend, see e.g. Hagedoorn, 2002).

In the face of rapid and radical technological

change, shorter product life cycles and intensified

competition (Henderson and Clark, 1990), inter-

firm cooperation is related to the belief that

networks offer time advantages over internal

development in realizing innovations in a shorter

time interval (Fischer, 2006, p. 103). Other as-sumptions underlying the network mode are a

higher degree of flexibility, shared risks and costs,

increased scale and scope of activities, or im-

proved competencies and know-how. They appeal

in particular to SMEs as they reduce existing

(economic) disadvantages.

These cooperative forms were examined in the

scientific literature for different sectors of industry

(e.g. Tether and Tajar, 2008) and countries (e.g.

Tether, 2002; Miotti and Sachwald, 2003). Other

research topics include input-related motives for

cooperating in innovation (Schmidt, 2007) and

effects surrounding R&D cooperation at the firmlevel (e.g. knowledge spill-overs, access to com-

plementary knowledge, risk-sharing). The partner

selection process in the formation stages of colla-

borative new product development has been ana-

lyzed by Emden et al. (2006). The outcome of

R&D cooperations is of equal importance. For

example, Aschhoff and Schmidt (2008) have in-

vestigated the effect of past R&D cooperation on

current new product performance, but the deter-

minants of the performance of new products

developed in networks have not yet been analyzed.

While in research on innovative networksusually inter-organizational relationships between

companies were investigated on the firm level (e.g.

Hagedoorn, 1993; Shan et al., 1994; Dyer and

Singh, 1998; Madhavan et al., 1998), the focus

of interest has recently shifted to the personal

level. For example, in the still rather new debate

about innovation communities, networks of

innovators from different organizations promot-

ing a specific innovative project together are used

to define this concept (Fichter, 2009). Based on

the assumption that networks of (SME) firms

can be compared with teams of people, the

literature on team and inter-team interactions

was taken as another starting point to derive

factors that determine new product performance

in networks (e.g. Hoegl and Gemuenden, 2001;

Hoegl et al., 2004). In this context, we looked at

so-called longevity factors of cooperations (seeTeusler, 2008). The focus was on those factors

that support the interaction of the partners and

develop during the course of cooperation. In

addition, other longevity factors dealing with

the characteristics of the partners or the coordi-

nation of the network were included in the con-

siderations.

Given these findings and considerations, in this

paper, we want to explore the underlying factors

that affect the performance of new product devel-

opment by networks of firms and research insti-

tutes. One aim of the study is to investigate theclassic within-firm success factors known from

the literature with respect to their transferability

for product development activities in networks.

In addition, the study also attempts to clarify

whether additional factors have to be taken into

account for new product development carried

out in networks. Therefore, in addition to tradi-

tional success factors, network-specific factors

that describe the inter-firm cooperation were

also examined. In the following section, based

on the existing literature on within-firm new

product development and the success factors

of team performance, we derive hypotheseswith respect to driving factors in networks. While

the determinants of successful within-firm new

product development are already well described

and operationalized in the literature, this is not

the case for determinants at the network level.

With respect to network cooperation, the ques-

tion of its measurement is addressed as is the

question of its influence on performance in com-

parison with traditional success factors. We de-

rive measures and constructs and test their

influence using a sample of 271 interviewed Ger-

man networks. Overall recommendations for theoptimization of innovation management in net-

works will be given.

Success factors for innovation management in networks of SMEs

r 2011 The Authors

R&D Managementr 2011 Blackwell Publishing Ltd

R&D Management 41, 2, 2011 139


3/18

2. Theoretical background and researchhypotheses

Success factors of new product development within

firms have been examined and described for dec-

ades. Attempts have been made to answer the

following decisive questions in particular: What

makes new products successful? What must be

changed during their development to make them

more successful? The keys to success, the so-called

success factors, were the focus of interest. In his

NewProd studies (Cooper, 1979; Cooper and

Kleinschmidt, 1987b; Cooper and Kleinschmidt,

1993), Cooper examined the factors that influence

the potential success of new products (see Brown

and Eisenhardt, 1995 for an overview). He used apaired comparison approach, comparing success-

ful and unsuccessful new products, and identified

success factors by looking for correlations and

significant mean value differences. A theory or an

underlying theoretical concept in the strict sense

was not tested.

In the meantime, further empirical research on

new product performance has provided a wide

variety of factors that can influence the outcomes

of new product development activities. In differ-

ent meta-analyses, these factors were accumu-

lated, synthesized and analyzed with respect totheir effect on new product performance. Mon-

toya-Weiss and Calantone (1994) identified a

total of eighteen factors that can be assigned to

the following four major categories: (1) strategic

factors (e.g. product advantage, technological

synergy), (2) development process factors (e.g.

proficiency of technological activities, proficiency

of marketing activities), (3) market environment

factors (e.g. market potential) and (4) organiza-

tional factors (e.g. intensity or quality of external

relations). The framework of Henard and Szy-

manski (2001) with twenty-four predictors and

the product, strategy, process and marketplacecharacteristics categories resemble Montoya-

Weiss and Calantones typology, except that

product-related factors were separated out into

the product category and organizational factors

were integrated into the process category. Pro-

duct- and market-related factors can also be

found in the meta-analysis of van der Panne et

al. (2003), but here, the firm-related factors cate-

gory comprises strategic and organizational

(structure) factors while the project-related fac-

tors category consists partly of development pro-

cess factors (see Table A1 in the Appendix A foran overview of the categories and factors sum-

marized in the meta-analyses).

In the first meta-analysis of Montoya-Weiss

and Calantone (1994) looking at 18 causal studies

(i.e. using correlational, regression, path or struc-

tural equation analyses), strategic factors and

development process factors were identified as

being most frequently included in the studies

reviewed, having the most consistently reported

supportive statistics and being significant deter-

minants of new product performance. The four

most frequently utilized factors proficiency of

technological activities, proficiency of marketing

activities, protocol (product and project defini-

tion) and product advantage were identified as

primary discriminators between success and fail-

ure in new product performance. With respect to

the two market environment factors and organi-zational factors categories, the question of insig-

nificance was raised. The meta-analysis of van der

Panne et al. (2003) confirmed that product-related

(product advantage) and firm-related (e.g. strate-

gic and also organizational) factors had a signifi-

cant influence on success. Product advantage (and

meeting customers needs) also had a strong

significant impact on market performance in the

meta-analysis of Henard and Szymanski (2001),

together with predevelopment task proficiencies

(and market potential and dedicated resources). It

is expected that well-known success factors fromnew product development within firms (e.g. pro-

duct advantage, proficiency of technological ac-

tivities, proficiency of marketing activities,

protocol) are also relevant for new product devel-

opment in inter-firm networks. In contrast to

Montoya-Weiss and Calantone (1994) and van

der Panne et al. (2003), market potential is a

success factor in the meta-analysis of Henard

and Szymanski (2001). This factor will therefore

be included in the empirical analysis. In addition,

we believe that organizational factors will play

a major role in networks due to the complex

organization.

Hypothesis 1: The new product performance in

inter-firm networks is positively related to the

traditional success factors product advantage

(in the eyes of the customer), proficiency of

technological activities, proficiency of marketing

activities, protocol (quality of product and project

definition), market potential and (quality of) pro-

ject team organization.

With respect to additional potential success

factors, we started with research dealing with

team and inter-team interaction within and acrossfirms (Hoegl and Gemuenden, 2001; Hoegl et al.,

2004). The results show that communication and


140 R&D Management 41, 2, 2011 r 2011 The AuthorsR&D Management r 2011 Blackwell Publishing Ltd


4/18

coordination (intensity and quality), balance of

member contributions, mutual support, effort

and cohesion within and between these teams

are positively related to the success of innovative

projects. Therefore, it can be assumed that these

aspects are also related to success in inter-firm

new product development.

Similar to research on team interaction, research

on inter-firm cooperation (Powell, 1998) also stems

from sociology and organizational theory (e.g.

Granovetter, 1985; Uzzi, 1996). This perspective

analyzes the relational capability of organizations

and adopts a process-based focus in which the

factors securing the continuation of a cooperation

are the focus of interest. The longevity of networks

has been used alongside classical performancemeasures, e.g. achieved milestones, budget and

intended quality, as a proxy for success. We are

concentrating here on internal longevity factors

(for a differentiation of internal and external long-

evity factors, see Schwerk, 2000). They are further

distinguished by Teusler (2008) into factors specific

to the enterprises when entering the network and

factors specific to the cooperation, e.g. supporting

the interaction of the partners or regarding the

coordination of the network. In the following, we

describe the factors that we have chosen for our

analysis.Factors supporting the interaction of the part-

ners in a network are conceptualized most fre-

quently based on the discussion in the literature of

the commitment of the partners to the network

and trust between the network partners (Teusler,

2008). The concept of commitment as defined as

the intention of exchange partners to continue a

relationship has been addressed by several theor-

ists. These authors have suggested that the invest-

ments made to establish and maintain exchange

relations lead to attachment between the partners,

because the (specialized) investments cannot be

sold outside without a loss of value (Blau, 1964;Williamson, 1975; Cook, 1977). Besides this cal-

culated commitment for inter-organizational re-

lationship, affective commitment is important.

The underlying motive to maintain a relationship

is a positive regard for and emotional attachment

to the partners (for an overview, see Seabright et

al., 1992; Geyskens et al., 1996). Another impor-

tant antecedent of cooperation is trust, which is

defined as an expectancy held by an individual or

group that the word, promise, verbal or written

statement of another individual or group can be

relied on (Rotter, 1967, p. 651). Bradach andEccles (1989) claim that trust increases the will-

ingness to share resources, because the fear of

opportunistic behavior by one of the exchange

partners is alleviated. Thus, trust is considered

essential for the emergence and repetition of

cooperative behavior (see Thoms, 2003).

If firms cooperate, they automatically give up

part of their autonomy and proceed into a rela-

tionship ofdependency, as they need to maintain a

relationship with their partner(s) to achieve their

goals. Symmetric interdependent relationships,

where the partners are equally dependent on

each other, in particular lead to higher stability

for cooperations because of less conflict, stronger

motivations to use positive and collaborative

methods to gain a partners cooperation, or

valued resources are invested in the network

(Kumar et al., 1995, 1998). Regarding antece-dents of the partners themselves to cooperate,

the compatibility of network partners is another

important factor for an efficient and smoothly

run cooperation that is frequently mentioned in

the literature. Network partners should keep in

mind the superordinate goal of the network and

align their goals to this (Schein, 1969; Deutsch,

1973). In addition, compatibility in strategic and

organizational aspects is advantageous, e.g. simi-

lar quality or other evaluation standards and

ways of proceeding (Teusler, 2008).

Altogether, we argue that more stable networksare beneficial for new product performance. Net-

work stability and the dimensions related to the

social exchange are likely to bring sustainable

advantages in terms of innovation and cost eco-

nomics (Lorenzoni and Lipparini, 1999). For

example, trust-based relationships promote infor-

mation exchange with the partners, ease of inter-

action and constructive management of conflict

(Gulati, 1998). If new technologies are to be

developed in networks, trust seems to be a basic

ingredient so that complementary knowledge can

be shared between partners. With respect to cost

economics, repeated transactions can lower thetransaction costs over time (Lorenzoni and Lip-

parini, 1999). Network-related success factors are

therefore expected to have a significant positive

influence on the new product performance in

inter-firm networks.

Hypothesis 2: The new product performance in

inter-firm networks is positively related to the net-

work-related success factors trust, commitment,

dependency and compatibility.

In addition, we assume and test exploratorily

that the influence of network-related successfactors is as strong as the influence of strategic

and process development factors.


r 2011 The Authors




5/18

Hypothesis 3: Network-related success factors are

of equal importance for new product perform-

ance in inter-firm networks as traditional success

factors.

3. Research methodology

3.1. Sample

In Germany, there are no statistical data on the

total number of networks available (see Maa

and Wallau, 2003, p. 28). However, in order to

identify relevant enterprise networks, detailedsingle-network-related information from web-

sites, industry-related workshops, promotional

competitions, the funding programs of different

German federal and federal state ministries and

network lists of (regional) chambers of Industry

and Commerce is widespread and can be used.

The focus was on networks that jointly develop

innovative products (including processes and pro-

cedures) and have a high proportion of SME, in

order to avoid too strong an impact on the net-

work by a dominant (large) enterprise. In the

questionnaire, the current definition of the Eur-opean Commission was used to explain the cate-

gory of SME. SMEs were described according to

the definition as enterprises that employ fewer

than 250 persons and that have either an annual

turnover not exceedingh40 million or an annual

balance sheet total not exceeding h43 million

(European Commission, 2006, p. 13).

Altogether, 623 German networks consisting of

firms and research institutes were identified. They

received a questionnaire in summer and autumn

2005. The networks so-called network manager

was asked to respond. Altogether, 271 question-

naires were returned, resulting in a very highresponse rate of 43.5%. The significant interest in

the study was reflected in numerous additional

telephone inquiries and expressions of interest by

the respondents. Besides, four out of five network

managers (79.4%) believed that enterprise net-

works will significantly increase in importance

over the next 10 years. Because of the large sample

and the high response rate, it can be assumed

despite some statistical bias (e.g. by the greater

willingness to respond of networks supported by

state funding) that the sample represents German

product development SME networks (first pretestresults with a smaller sample were presented in

Baier et al., 2006).

3.2. Descriptive statistics

Networks occurred in particular in the research

and innovation intensive industries (see Table 1).

These industries are also confirmed to be coop-

eration intensive for Germany in other empirical

studies (e.g. Eggers and Kinkel, 2002; Frietsch,

2007). The networks were set up on a long-term

basis and had existed on average for 3.45 years.

Regarding the number of partners involved, net-

works were relatively small, with 60.9% of the

networks having up to 10 partners (see Table 1).

Most networks were distributed either regionally

(40.3%) or nationally (46.6%); only a few had

international partners (9.7%). Smaller networks

tended to be coordinated monocentrically by oneof the partners (e.g. 54.5% of the networks with

less than four partners), while in larger networks,

all partners were more frequently equally in-

volved in decision making (e.g. 81.3% of the

networks with over 50 partners).

With respect to network formation, above all,

the active efforts of the network management

played an important role in seeking potential

partners intensively. Previous cooperation in pro-

jects as well as meeting opportunities, e.g. confer-

ences, also helped when looking for partners.

Motives and goals for taking part in a networkincluded, in addition to the development of new

products and/or technologies, financial and eco-

nomic advantages, for example state funding, the

improvement of the participating enterprises mar-

ket position and synergy effects. Substantial ob-

stacles and difficulties for the networks concerned

in particular a lack of resources, e.g. financial

resources, R&D resources or manpower. The co-

ordination between partners was also assessed as

difficult, e.g. due to the allocation and assignment

of work packages to partners far away, or the

emergence of conflicts because of different interests

and goals, or even because of personalized dis-agreement or individual disaffection.

Regarding innovativeness, about half of the

products developed in such networks can be clas-

sified as incremental innovations (see Table A2 in

the Appendix A for more detail). The differentia-

tion between radical and incremental innovations

is based here on the newness of the knowledge

generated (for different dimensions of radical in-

novations, see e.g. Danneels and Kleinschmidt,

2001; Garcia and Calantone, 2002). Incremental

innovations were quicker to market. Products that

were already introduced on the market were pre-dominantly based on existing knowledge (81.3%).

So far, only very few networks had successfully




6/18

finished the product development process, which

took about 5.4 years. About half of the networks,

being rather young, with on average 2.4 years,

were still in the planning stage (with respect to the

stage gate approach, see Cooper, 1983, 1994;

Cooper and Kleinschmidt, 1987a).

3.3. Measures

All constructs are developed as multidimensional

concepts measured using a seven-point scale ranging

from 1 strongly disagree to 7 strongly agree.

The items were mostly drawn from existing studies

and adapted especially with respect to the network

situation (see Appendix B). Preliminary versions of

the questionnaire were pretested to reduce ambigu-

ities or difficulties in responding to the scale items

and to ensure clarity. The measures and items arediscussed below.

3.4. Dependent variable: new productperformance

When analyzing firms and their position with

respect to new product development, the success

of individual products is used as a dependent

variable. Griffin and Page (1993) identified, in

their broad approach, several categories of new

product performance measures at the firm level.

We relied here on the three categories of marketperformance, technological performance and fi-

nancial performance, which were used in the

studies included in the meta-analysis of Mon-

toya-Weiss and Calantone (1994). These three

categories should be assessed with respect to the

products developed in the networks. The items

measuring financial and market performance

were derived from Cooper et al. (1994). Financialperformance is measured using two items cover-

ing sales and the total sales (revenues) of the new

products. Market performance is captured by one

item on the degree to which the innovations

opened up new markets. Two product-related

items asked whether the innovation was techni-

cally successful (Griffin and Page, 1993) and led

to the development of further new products

(Cooper et al., 1994). In new product develop-

ment success studies, financial and market share

objectives are summarized as measures for com-

mercial performance while technical measures

were rarely considered (Montoya-Weiss and Ca-lantone, 1994). Besides new product performance

measures, product-level measures were also taken

into account, e.g. cost goals were met, the product

was launched on time or met quality guidelines

(Griffin and Page, 1993). The triple constraints of

time/achieved milestones, budget and quality

are also referred to as efficiency when measuring

team performance (Hoegl and Gemuenden, 2001;

Hoegl et al., 2004). In the following, therefore,

these items are referred to as network efficiency.

The constructs are conceptualized as stand-alone

measures to test performance effects separately,but they are also compiled into a second-order

factor for a general overview.

Table 1. Descriptive statistics of the networks

Industry sector of the network Number of partners Age of the networks

Mechanical engineering 16.6% o4 4.1% Under 1 year 8.3%Biotechnology 12.2% 410 56.8% Under 2 years 14.7%Environmental technology 8.5% 1120 15.4% Under 3 years 14.3%Micro-system technology 8.1% 2150 13.5% Under 4 years 24.1%Medicine technology 6.6% 51100 6.0% Under 5 years 10.9%Sensor technology 4.8% 4100 4.1% 5 years and more 27.8%

Finding partners Motives, goals Obstacles and difficulties

Active effort of networkmanagement

34.9% Development of new products

44.3% Lacking resources 42.4%

Personal contacts ofnetwork managers

33.6% Financial/economicadvantages

33.2% Coordination betweenpartners

34.3%

Previous cooperation inprojects

25.5% Strengthening ofmarket position

28.4% Schedule problems 17.7%

Conferences, workshops,trade shows

19.1% Acquisition of know-how

22.9% Bureaucracy 16.2%

Recommendation ofnetwork partners

4.3% Synergy effects 22.5% Finding partners/partnerfluctuation

15.1%


r 2011 The Authors




7/18

We are relying here on subjective assessments

of performance, which have been shown to be

sufficiently reliable for performance assessment

(Dawes, 1999). One reason for using a self-assess-

ment construct is that innovations are often

highly complex and uncertain (Calantone et al.,

2003). On the other hand, because the networks in

our sample were rather recently formed, we as-

sumed that clear and precise performance objec-

tives (e.g. financial data) would often not be

available (see similar Talke, 2007). The items

were formulated in the past with respect to

realized success, but it was made clear in the

questionnaire that if the products developed in

the network had not been introduced on the

market yet, the questions should be answeredwith respect to expected success.

3.5. Antecedents: Traditional successfactors

We based our research on the factors of the meta-

analysis of Montoya-Weiss and Calantone (1994)

and selected those factors that were most fre-

quently included in the studies reviewed. Of the

strategic factors product advantage, technolo-

gical synergy and marketing synergy and of thedevelopment process factors, proficiency of tech-

nological activities, proficiency of marketing ac-

tivities, protocol (product and project definition)

and the proficiency of predevelopment activities

were included. The items are based for the most

part on Cooper (1979), Cooper and Kleinschmidt

(1987b) and Cooper and Kleinschmidt (1994),

and were textually adapted, if necessary, with

respect to networks. In the case of the quality

of executing marketing activities, two items of

Bstieler (2005) dealing with the marketing concept

were added. Of the three market environment

factors, only market potential was included inthe analysis, because market competitiveness was

shown to not be a significant determinant of new

product performance (see Montoya-Weiss and

Calantone, 1994) and external environment was

also rather rarely included in empirical studies.

Finally, the (quality of the) networks project

team organization was taken into account as an

organizational factor. Here, the items of Cooper

and Kleinschmidt (1987b) were adapted to inves-

tigate whether the project team was interdisciplin-

ary (see Roure and Keeley, 1990), the team was

accountable insomuch that it undertook the pro-ject from the beginning to the end, the team was

led by a strong champion, the team was only

dedicated to this project (two items) and had the

commitment of the top management of the net-

work partners. In addition, it was asked whether

the teams were motivated, the communication

between the partners was intensive and the project

organization was supported by software.

3.6. Antecedents: Network-relatedsuccess factors

Altogether, we concentrate on the following four

constructs that have been shown to be important

longevity factors of cooperations: commitment of

the network partners, dependency of the networks

partners, trust between the network partners andcompatibility of the network partners.

The five-item scale assessing commitment ad-

dressed the interest of the partners in long-term

investments in the network (Anderson and Weitz,

1992), the loyalty of the partners to the coopera-

tion (Mehta et al., 2006), e.g. partners would not

leave or join another network and the relevance of

the cooperation for the partners (Hoegl and

Gemuenden, 2001). The measurement scale for

the dependency of the network partners consisted

of five items on the interdependence of the part-

ners on the network, the importance of the

partners for the cooperation (Kumar et al.,1998), e.g. covering the entire value-chain, neces-

sary strategy changes if partners exit and the high

quality of collaboration with the present partners.

If the latter were not the case, this would e.g.

result in the search for alternative partners (Kim

and Frazier, 1997). The measurement scale for

trust was based on the beliefs in the trustworthi-

ness of the partners (Jap, 1999) e.g. due to

previous cooperation, the willingness to exchange

information (Monczka et al., 1998), the openness

for change and equal rights for all partners. The

network partners compatibility is expressed ascompatibility in goals, financial affairs, quality

specifications, schedules and deadlines and per-

formance evaluation. As an additional factor, the

ability of the network partners to cooperate with

a special focus on the resource facilities of the

network partners was included, capturing the

communication behavior, bureaucratic struc-

tures, manpower resources, information technol-

ogy resources and financial resources.

3.7. Reliability of the constructs

To verify the reliability of the constructs, Cron-

bachs a was calculated. Altogether, four of the




8/18

eighty-six items were excluded, because of their

low item to total correlation. Of the factor tech-

nological synergies, the (synergy in) production

item was omitted, with respect to the project team

organization, the assignment of team members to

only one project and the organizational support

by software applications were left out and for the

dependency factor the change in strategy after

partners left was excluded. The number of (initial

and remaining) scale items, related scale reliabil-

ities and the mean and standard deviation for

each of the constructs above described are pre-

sented in Table 2. All Cronbachs a values are

40.6, which is suggested as acceptable by Mal-

hotra (1999) and Murphy and Davidshofer

(2001). Ten of the fifteen constructs even metNunnallys (1978) criteria of 0.70 for exploratory

research. All items corresponding to a construct

were totalled to yield a composite score for that

construct. The mean value of the commitment

factor is the lowest, with 4.65, while the highest

mean value was for the technological synergy

factor, with 6.50.

4. Research results

The results of hypothesis testing are presented inthis section. Hypotheses were tested using group-

level data, analysis of correlation, a t-test for

difference and regression analysis.

4.1. Hypothesis testing

A correlation analysis of the success factor scales

with the new product performance scale (see

Table 3) showed in agreement with traditional

success factor studies that product advantage is

the most important success factor and the only

success factor significantly correlated with all

detailed success measures. Similarly, high correla-

tion values could be found for the traditional

success factor project team organization, the net-

work success factors compatibility and depen-

dency and the traditional success factor

proficiency of technological activities. In addi-

tion, the networks were grouped according to

their new product performance construct by

means of a one-dimensional cluster analysis

(method: Ward) in 167 successful and 90 less

successful networks (14 networks could not be

assigned due to missing data). The four successfactors product advantage, project team orga-

nization, compatibility and dependency also

came up with the highest t-values when compar-

ing the mean values of the successful and less

successful networks (see Table 4). The data are

very robust in that only the proficiency of activ-

ities during development (proficiency of techno-

logical activities, proficiency of marketing

activities) was significantly higher in networks

being more progressed in product development.

Networks that had proceeded in product devel-

opment (e.g. being either shortly before launch orhad products on the market) were also more

optimistic about the outcome of the network

because they had received feedback from the

market.

Altogether, we found that hypothesis 1 and

hypothesis 2 are supported. The traditional and

the network-related success factors are positively

associated with the networks new product per-

formance. Network-related success factors as well

Table 2. Summary statistics, construct reliability

Number of items Mean

1

(standard deviation) Cronbachsa

New product performance 9 5.49 (0.91) 0.854Product advantage 9 5.37 (0.92) 0.766Technological synergy 2(3) 6.50 (0.71) 0.660Marketing synergy 5 4.93 (1.38) 0.891Proficiency of technological activities 4 4.77 (1.56) 0.741Proficiency of marketing activities 7 4.93 (1.38) 0.850Proficiency of predevelopment activities 6 5.51 (0.89) 0.685Protocol (product and project definition) 5 5.56 (0.95) 0.778Market potential 5 4.94 (1.10) 0.732Project team organization 6(8) 5.94 (0.75) 0.640Commitment 5 4.65 (1.07) 0.678Trust 5 5.57 (0.79) 0.650Dependency 4(5) 5.47 (1.01) 0.747

Compatibility 5 5.62 (0.96) 0.843Ability 5 5.19 (1.03) 0.795

1Mean construct values across respondents on Likert-scales ranging from 1 . . . is low to 7 . . . is high.


r 2011 The Authors




9/18

as the project team organization are especially

important for the efficiency of a project concern-

ing the achievement of constraints as time/

achieved milestones, budget or quality. Looking

at the financial performance, we found a high

correlation with marketing activities and market

potential (of the product) as well as with product

advantage and product definition (protocol),

commitment and dependency. These two net-

work-related success factors (and market poten-

tial) are also highly correlated with market

performance. Product-related success factors(product advantage, protocol) play an important

role together with technological factors (techno-

logical synergy, proficiency of technological acti-

vities) with respect to technological performance.

4.2. Importance of the success factors

Regarding the individual predictive power of the

success factors on new product performance, the

data set was first tested for multicollinearity. In a

multiple regression model with highly correlated

explanatory variables, the computational accuracy

of the individual coefficients and therefore theirinterpretation is disturbed. First, pairwise, simple

correlation coefficients were calculated for the

Table 3. Correlations between success factors and performance in general (new product performance) and differentaspects (e.g. market performance, technological performance, network efficiency)

Networksuccess Marketperformance Technologicalperformance Financialperformance Networkefficiency

Product advantage 0.477** 0.320** 0.533** 0.419** 0.367**Technological synergy 0.284** 0.163* 0.307** 0.076 0.313**Marketing synergy 0.223** 0.157* 0.162* 0.255** 0.177**Proficiency of technological activities 0.342** 0.275** 0.304** 0.322** 0.251**Proficiency of marketing activities 0.290** 0.302** 0.258** 0.498** 0.189**Proficiency of predevelopment activities 0.283** 0.300** 0.281** 0.315** 0.212**Protocol (product and project definition) 0.233** 0.264** 0.339** 0.378** 0.267**Market potential 0.285** 0.340** 0.177* 0.400** 0.189**Project team organization 0.430** 0.265** 0.270** 0.242** 0.469**Commitment 0.346** 0.320** 0.284** 0.421** 0.262**Trust 0.226** 0.104 0.199** 0.180* 0.323**Dependency 0.381** 0.314** 0.327** 0.362** 0.335**

Compatibility 0.423**

0.158*

0.258**

0.201*

0.464**

Ability 0.320** 0.105 0.197* 0.131 0.385**

**Significant withPo0.01,*Significant withPo0.05.

Table 4. Mean values of success factors in successful and less successful networks (sorted in descending orderwith respect to the size of the t-values)

Successful networks Less successful networks t-value(n167) (n90)

Product advantage 5.66 4.83 7.29**Compatibility 5.89 5.13 6.33**

Project team organization 6.17 5.55 6.28**Dependency 5.74 5.00 5.80**Ability 5.42 4.76 4.83**Proficiency of marketing activities 4.36 3.50 4.70**Commitment 4.88 4.26 4.57**Proficiency of predevelopment activities 5.69 5.18 4.52**Technological synergy 6.66 6.24 4.48**Proficiency of technological activities 5.10 4.20 4.40**Protocol (product and project definition) 5.74 5.25 4.01**Trust 5.70 5.32 3.67**Market potential 5.11 4.58 3.66**Marketing synergy 5.16 4.55 3.21**

**Significant withPo0.01.




10/18

success factor constructs (see Table A3 in the

Appendix A). The highest correlation between

the constructs was 0.58, which is in a range similar

to those found in other success factor studies (see

e.g. Hoegl and Gemuenden, 2001; Hoegl et al.,

2004). The correlations were below the suggested

multicollinearity threshold of 0.6 at which it is

considered to be a problem (Grewal et al., 2004).

Variance inflation factors with the highest being

2.48 were also well below the suggested limit of

seven to ten (Belsley, 1991, p. 28). In order to

detect more complicated patterns of multicolli-

nearity, auxiliary regressions were used. Each in-

dependent variable was sequentially regressed on

all the remaining variables in the original model.

The resulting R2

ks (k indicates the regressed in-dependent variable) were then compared with the

original full model R2, but not all were below this

value. The evidence therefore suggested that multi-

collinearity was a problem in this data set.

Consequently, similar to the NewProd model, a

factor analysis of the fourteen success factor

constructs was used to find dimensions that

were independent of each other (Cooper, 1985).

We computed uncorrelated varimax-rotated prin-

cipal components and used them as predictors.

With the help of the elbow criterion, six factors

could be identified that are still quite similar tothose used previously (see Table 4). For (textual)

interpretation of the new factors, all variables

with a factor loading above 0.6 were included.

The four most relevant success factors of new

product development within firms product ad-

vantage, proficiency of technological activities,

proficiency of marketing activities and protocol

are reflected in the newly formed second-order

factors. Again, product advantage is the most

important success factor, followed by a factor

we have called network cohesion and organiza-

tion, which comprises almost all network success

factors and additionally the classic success factorproject team organization. A third relevant and

significant factor is the proficiency of activities

during development, which is followed by market

potential and activities before development. In

contrast, marketing synergy did not have an effect

on new product performance.

With respect to other variables of potential

influence on networks new product performance

or the robustness of the model, we considered

several control variables, e.g. innovativeness, ma-

turity of the networks new products or number of

partners (see Table 5). Innovativeness and thematurity of the new products had a low significant

influence on new product performance. Altogether,

the model is very stable, with product advantage,

network cohesion and organization and profi-

ciency of activities during development being the

most important success factors for networks new

product performance. Not surprisingly, the impor-

tance (significance) of market potential and profi-

ciency of activities before development decreased

as the product development process proceeded

(maturity of new products). Overall, we found

support for hypothesis 3. With network-related

success factors ranking between product advantage

and proficiency factors, they are of equal impor-

tance for new product performance in inter-firm

networks as traditional success factors.

5. Discussion

This research studied success factors for innova-

tion management in networks of SMEs. In a

broad-based approach, altogether fourteen differ-

ent success factors were included in the analysis to

explain (networks) new product performance.

Well-known traditional success factors for new

product development within firms as well as new

network-specific success factors were used for

hypotheses development. These hypothesized re-

lationships were tested on the basis of a compre-hensive sample of 271 German networks of SMEs

and research institutes.

It was shown that besides the traditional suc-

cess factors (e.g. product advantage), the new

network-specific success factors (e.g. compatibil-

ity of the network partners, dependency of the

network partners) are also of major importance.

In agreement with traditional success factor stu-

dies, product advantage was derived as the most

important success factor, followed by a construct

comprised of network-related success factors and

the organizational factor (quality of) project team

organization. These factors in particular support

the smooth and efficient running of the project

within the single firms and the network. Activities

during development, e.g. marketing and techno-

logical activities, are also considered to be im-

portant. In contrast, the proficiency of activities

before development, market potential and mar-

keting synergies is rather lower-ranked in terms of

importance.

5.1. Research limitations

From a methodological point of view, this study

is confronted with the limitations of success factor


r 2011 The Authors




11/18

analysis. There are the usual problems of survival

bias, key informant bias or retrospective bias

(Nicolai and Kieser, 2002). Information about

the dependent variable itself can have an impact

on the possible causes (March and Sutton, 1997,

p. 701). Therefore, there is a problem of self-

fulfilling prophecy, with successful networkstending to overestimate the explanatory success

factors and success factors being affected by

multicollinearity. Nevertheless, Homburg and

Krohmer (2004) defend good empirical success

factor analysis as a substantial contribution to

progress in management science. Other shortcom-

ings are that only networks of SMEs were ana-

lyzed and the focus was on a single country.

Additional work could compare these results

with those of networks including large companies,

concentrating on specific industries, or SME net-

works in other countries. Another research lim-itation is that one person was questioned for an

entire network even if the networks were rather

small, allowing one person to provide complete

insight. Although pretests of our survey had

shown that the interviewed network managers

were able to judge the specific items validly, it

would be interesting to include multiple perspec-

tives from each network in the analysis (e.g.

Hoegl and Gemuenden, 2001; Thoms, 2003).

5.2. Suggestions for further research

In our analysis, we still remained on the firm level

even if longevity factors took organizational or

sociological aspects of networks into account.

The starting point of the analysis was the assump-

tion that networks of (SME) firms can be com-

pared with teams of people. On the personal level,

the specified facets of the collaborative team

process (see Hoegl and Gemuenden, 2001) could

be investigated in more detail within networks. Inaddition, the formation, dynamics and stability of

these innovative groups could be analyzed taking

boundary and interface issues into account.

Within one company, the initiators and key

participants of the innovation process have al-

ready been empirically extensively examined re-

garding different functional roles (Gemuenden

et al., 2007; Rost et al., 2007). In this context,

the distribution of the functional roles of key

people in networks would be of interest. To gain

a complete picture, all persons in a network

should be included in the analysis.

5.3. Implications for managerial practice

The results of this study also offer some valuable

insights for managers. They indicate that network-

related success factors should not be neglected and

that measurement scales could help to conduct an

assessment of the status quo and show deficits

regarding the network features and management

but also regarding the important traditional suc-

cess factors, e.g. product advantage and the profi-

ciency of marketing and technological activities.The study also confirms well-known deficits

of SME networks. The networks in general still

Table 5. Results of regression analyses of network success measure on the success factors (Standard regressioncoefficients)

Key factor or dimension Variables loading on factor Model 1 Model 2 Model 3 Model 4

Product advantage Product advantage 0.392** 0.366** 0.392** 0.388**Network cohesion andorganization

Project team organization, dependency,compatibility, ability, trust

0.331** 0.321** 0.331** 0.336**

Proficiency of activitiesduring development

Proficiency of technological activities,proficiency of marketing activities

0.304** 0.345** 0.306** 0.261**

Proficiency of activitiesbefore development

Proficiency of predevelopment activities,protocol (product and project definition)

0.134* 0.160** 0.140* 0.099

Market potential Market potential 0.132* 0.145* 0.136* 0.099Marketing synergy Marketing synergy 0.094 0.089 0.096 0.091Innovativeness 0.104+

Number of partners 0.025Product maturity 0.118+

R2 0.400 0.431 0.401 0.397

Adjusted R

2

0.380 0.408 0.377 0.372F 20.020 19.026 17.105 16.339r 0.000 0.000 0.000 0.000

**Significant at the 0.01 level,*Significant at the 0.05 level,+Significant at the 0.10 level.




12/18

focus too much on niche markets. About half of

the networks (40.6%) placed their products on a

small market with a relatively low customer

demand (41.0%). At least the growth of the

market was predominantly expected to be positive

(70.9%). Other problems include market research

as well as resources. Not surprisingly, over half of

the networks (57.8%) did not have sufficient skills

and resources for market research. Likewise,

finances proved to be problematic. Less than

half of the partners (44.3%) had sufficient finan-

cial resources available; therefore, the willingness

to invest even more in their network was rather

low (43.4%).

Acknowledgements

The authors would like to thank two anonymous

referees for providing helpful comments to im-

prove the form and the contents of this paper.

The authors would also like to thank Dipl.-Ing.

Steffen Freund and Dr. Marko Queitsch for their

support in developing the questionnaire and

collecting the data.

References

Anderson, E. and Weitz, B.A. (1992) The use of pledges

to build and sustain commitment in distribution

channels. Journal of Marketing Research, 29, 1, 18

34.

Aschhoff, B. and Schmidt, T. (2008) Empirical evidence

on the success of R&D cooperation happy

together? Review of Industrial Organization, 33, 1,

4162.

Baier, D., Queitsch, M. and Freund, S. (2006) Erfolgs-

faktoren fuer das Innovationsmanagement in Netz-

werken aus KMU und Forschungseinrichtungen:

Eine empirische Untersuchung. In: Meyer, J.-A.

(ed.), Kleine und mittlere Unternehmen in neuen

Maerkten Aufbruch und Wachstum Jahrbuch der

KMU-Forschung und -Praxis 2006. Lohmar: Josef

Eul, pp. 197214.

Belsley, D.A. (1991) Conditioning Diagnostics: Colli-

nearity and Weak Data in Regression. New York,

NY: John Wiley & Sons.

Blau, P. (1964)Exchange and Power in Social Life . New

York, NY: John Wiley & Sons.

Bradach, J.L. and Eccles, R.G. (1989) Price, authority,

and trust: from ideal types to plural forms. Annual

Review of Sociology, 15, 97118.

Brown, S.L. and Eisenhardt, K.M. (1995) Product

development: past research, present findings, andfuture directions. Academy of Management Review,

20, 2, 343378.

Bstieler, L. (2005) The moderating effect of environ-

mental uncertainty on new product development and

time efficiency. Journal of Product Innovation Man-

agement,22, 3, 267284.

Calantone, R., Garcia, R. and Droege, C. (2003) The

effects of environmental turbulence on new product

development strategy planning. Journal of Product

Innovation Management, 20, 2, 90103.

Cook, K.S. (1977) Exchange and power in networks in

interorganizational relations. Sociological Quarterly,

18, 1, 6282.

Cooper, R.G. (1979) The dimensions of industrial new

product success and failure. Journal of Marketing,

43, 3, 93103.

Cooper, R.G. (1983) A process model for industrial

new product development. IEE Transactions on En-

gineering Management,30, 1, 211.Cooper, R.G. (1985) Selecting winning new product

projects: using the NewProd system. Journal of

Product Innovation Management, 2, 1, 3444.

Cooper, R.G. (1994) Third generation new product

processes. Journal of Product Innovation Manage-

ment, 11, 1, 314.

Cooper, R.G., Easingwood, C.J., Edgett, S., Klein-

schmidt, E.J. and Storey, C. (1994) What distin-

guishes the top performing new products in financial

services. Journal of Product Innovation Management,

11, 4, 281299.

Cooper, R.G. and Kleinschmidt, E.J. (1987a) Success

factors in product innovation. Industrial Marketing

Management, 16, 3, 215223.Cooper, R.G. and Kleinschmidt, E.J. (1987b) New

products: what separates winner from losers? Journal

of Product Innovation Management, 4, 3, 169184.

Cooper, R.G. and Kleinschmidt, E.J. (1993) Major new

products: what distinguishes the winners in the

chemical industry? Journal of Product Innovation

Management, 10, 2, 90111.

Cooper, R.G. and Kleinschmidt, E.J. (1994) De-

terminants of timeliness in product development.

Journal of Product Innovation Management, 11, 5,

381396.

Cooper, R.G. and Kleinschmidt, E.J. (2007) Winning

businesses in product development: the critical suc-

cess factors. Research Technology Management, 50,

3, 5266.

Danneels, E. and Kleinschmidt, E.J. (2001) Product

innovativeness from the firms perspective its di-

mension and relation with product selection and

performance.Journal of Product Innovation Manage-

ment, 18, 6, 357373.

Dawes, J. (1999) The relationship between subjective

and objective company performance measures in

market orientation research: further empirical evi-

dence. Marketing Bulletin, 10, 6575.

Deutsch, M. (1973) The Resolution of Conflict: Con-

structive and Destructive Processes. New Haven, CT:

Yale University Press.Dyer, J.H. and Singh, H. (1998) The relational view:

cooperative strategy and sources of interorganiza-


r 2011 The Authors




13/18

tional competitive advantage. Academy of Manage-

ment Review, 23, 4, 660679.

Eggers, Th. and Kinkel, S. (2002) Die virtuelle Fab-

rik in weiter Ferne: Verbreitung und Nutzen von

Produktionskooperationen und Produktionsnetz-

werken im Verarbeitenden Gewerbe, Mitteilungen

aus der Produktionswirtschaft, Nummer 25, Fraun-

hofer Institut fuer System- und Innovations-

forschung, Karlsruhe.

Emden, Z., Calantone, R.J. and Droege, C. (2006)

Collaborating for new product development: select-

ing the partner with maximum potential to create

value. Journal of Product Innovation Management,

23, 4, 330341.

European Commission. (2006) The New SME Defini-

tion. User Guide and Model Declaration. Luxem-

bourg: Enterprise and Industry Publications.Fichter, K. (2009) Innovation communities: the role of

networks of promotors in Open Innovation. R&D

Management, 34, 4, 357371.

Fischer, M.M. (2006)Innovations, Networks and Knowl-

edge Spill-Overs. Berlin: Springer.

Frietsch, R. (2007) Patente in Europa und der Triade:

Strukturen und deren Veraenderung. Studien zum

deutschen Innovationssystem Nr. 9-2007. Maerz,

Fraunhofer Institut fuer System- und Innovations-

forschung, Karlsruhe.

Garcia, R. and Calantone, R.J. (2002) A critical look

at technological innovation typology and innova-

tiveness terminology: a literature review. Journal of

Product Innovation Management, 19, 2, 110132.Gemuenden, H.-G., Salomo, S. and Hoelzle, K. (2007)

Role models for radical innovations in times of open

innovation. Creativity and Innovation Management,

16, 4, 408421.

Geyskens, I., Steenkamp, J.-B.E.M., Scheer, L.K. and

Kumar, N. (1996) The effects of trust and interde-

pendence on relationship commitment: a trans-

Atlantic study. International Journal of Research in

Marketing, 13, 4, 303317.

Granovetter, M. (1985) Economic action and social

structure: the problem of embeddedness. American

Journal of Sociology,91, 3, 481510.

Griffin, A. and Page, A.L. (1993) An interim report on

measuring product development success and failure.

Journal of Product Innovation Management, 10, 4,

291308.

Gulati, R. (1998) Alliances and networks. Strategic

Management Journal, 19, Special issue, 4, 293317.

Hagedoorn, J. (1993) Understanding the rationale of

strategic technology partnering: interorganizational

modes of cooperation and sectoral differences. Stra-

tegic Management Journal, 14, 5, 371385.

Hagedoorn, J. (2002) Inter-firm R&D partnerships: an

overview of major trends and patterns since 1960.

Research Policy, 31, 4, 477492.

Grewal, R., Cote, J.A. and Baumgartner, H. (2004)

Multicollinearity and measurement error in struc-tural equation models: implications for theory test-

ing. Marketing Science, 23, 4, 519529.

Harms, R. (2001) Interorganisationales Innovations-

management von KMU: Innovationsnetzwerke als

Kooperationsform. In: Meyer, J.-A. (ed.), Innova-

tionsmanagement in kleinen und mittleren Unterneh-

men. Muenchen: Vahlen, pp. 135148.

Henard, D.H. and Szymanski, D.M. (2001) Why some

new products are more successful than others. Jour-

nal of Marketing Research, 38, 3, 362375.

Henderson, R.M. and Clark, K.B. (1990) Architectural

innovation: the reconfiguration of existing product

technologies and the failure of established firms.

Administrative Science Quarterly, 35, 1, 930.

Hoegl, M. and Gemuenden, H.-G. (2001) Teamwork

quality and the success of innovative projects: a

theoretical concept and empirical evidence. Organi-

zation Science, 12, 4, 435449.

Hoegl, M., Weinkauf, K. and Gemuenden, H.-G.(2004) Interteam coordination, project commit-

ment, and teamwork in multiteam R&D projects: a

longitudinal study. Organization Science, 15, 1,

3855.

Homburg, Ch. and Krohmer, H. (2004) Die Fliegen-

patsche als Instrument des wissenschaftlichen Dia-

logs. Die Betriebswirtschaft (DBW), 64, 4, 626631.

Jap, S.D. (1999) Pie-expansion efforts: collaboration

processes in buyer-supplier relationships. Journal of

Marketing Research, 36, 4, 461475.

Kim, K. and Frazier, G.L. (1997) On distributor

commitment in industrial channels of distribution:

a multicomponent approach.Psychology & Market-

ing, 14, 8, 847877.Koschatzky, K., Kulicke, M. and Zenker, A. (eds),

(2001)Innovation Networks. Concepts and Challenges

in the European Perspective. Heidelberg: Physica.

Kowol, U. and Krohn, W. (1995) Innovation und

Vernetzung: Die Konzeption der Innovationsnetz-

werke. In: Weyer, J. (ed.), Soziale Netzwerke.

Muenchen and Wien: Oldenbourg, pp. 135160.

Kueppers, G. (2002) Complexity, self-organization,

and innovation networks: a new theoretical ap-

proach. In: Pyka, A. and Kueppers, G. (eds),Innova-

tion Networks: Theory and Practice. Cheltenham:

Edward Elgar, pp. 2252.

Kumar, N., Scheer, L.K. and Steenkamp, J.-B.E.M.

(1995) The effects of perceived interdependence on

dealer attitudes. Journal of Marketing Research, 32,

3, 348356.

Kumar, N., Scheer, L.K. and Steenkamp, J.-B.E.M.

(1998) Interdependence, punitive capability, and the

reciprocation of punitive actions in channel relation-

ships.Journal of Marketing Research,35, 2, 225235.

Lorenzoni, G. and Lipparini, A. (1999) The leveraging

of interfirm relationships as a distinctive organiza-

tional capability: a longitudinal study. Strategic

Management Journal, 20, 4, 317338.

Maa, F. and Wallau, F. (2003) Internationale Koo-

perationen kleiner und mittlerer Unternehmen:

Unter besonderer Beruecksichtigung der neuen Bun-deslaender. IfM-Materialien Nr. 158, Institut fuer

Mittelstandsforschung, Bonn.




14/18

Madhavan, R., Koka, B.R. and Prescott, J.E. (1998)

How industry events (re)shape interfirm relation-

ships.Strategic Management Journal,19, 5, 439450.

Malhotra, N.K. (1999) Marketing Research: An Ap-

plied Orientation. New Jersey: Prentice Hall.

March, J.G. and Sutton, R.I. (1997) Organizational

performance as a dependent variable. Organization

Science, 8, 6, 698706.

Mehta, R., Larsen, T., Rosenbloom, B. and Ganitsky, J.

(2006) The impact of cultural differences in U.S. busi-

ness-to-business export marketing channel strategic alli-

ances. Industrial Marketing Management, 35, 2, 156165.

Miotti, L. and Sachwald, F. (2003) Co-operative R&D:

why and with whom?: an integrated framework of

analysis. Research Policy, 32, 8, 14811499.

Monczka, R.M., Petersen, K.J., Handfield, R.B. and

Ragatz, G.L. (1998) Success factors in strategicsupplier alliances: the buying company perspective.

Decision Sciences,29, 3, 553557.

Montoya-Weiss, M.M. and Calantone, R.J. (1994)

Determinants of new product performance: a review

and meta-analysis. Journal of Product Innovation

Management, 11, 5, 397417.

Murphy, K.R. and Davidshofer, Ch.O. (2001) Psycho-

logical Testing: Principles and Application. Upper

Saddle River, NJ: Prentice Hall.

Nicolai, A. and Kieser, A. (2002) Trotz eklatanter

Erfolglosigkeit: Die Erfolgsfaktorenforschung weiter

auf Erfolgskurs.Die Betriebswirtschaft (DBW), 62,

6, 579596.

Nunnally, J.C. (1978) Psychometric Theory, 2nd edn.New York, NY: McGraw-Hill Publishing Company.

Powell, W.W. (1998) Learning from collaboration:

knowledge and networks in the biotechnology and

pharmaceutical industries. California Management

Review, 40 (Special issue on Knowledge and the

Firm), 3, 228240.

Rost, K., Hoelzle, K. and Gemuenden, H.-G. (2007) Pros

and cons of role specialisation for economic process.

Schmalenbach Business Review,59, 4, 340363.

Rotter, J.B. (1967) A new scale for the measurement of

interpersonal trust.Journal of Personality,35, 4, 651

665.

Roure, J.B. and Keeley, R.H. (1990) Predictors of

success in new technology based firms. Journal of

Business Venturing, 5, 4, 221239.

Schein, E.H. (1969) Process Consultation: Its Role in

Organization Development. Reading MA: Addison-

Wesley.

Schmidt, T. (2007) Motives for innovation co-opera-

tion evidence from the Canadian survey of

innovation. ZEW Discussion Paper No. 07-018.

Mannheim.

Schwerk, A. (2000) Dynamik von Unternehmenskooper-

ationen. Berlin: Duncker & Humblot.

Seabright, M.A., Levinthal, D.A. and Fichman, M.

(1992) Role of individual attachments in the dissolu-

tion of interorganizational relationships. Academy of

Management Journal, 35, 1, 122160.

Semlinger, K. (1998) Innovationsnetzwerke. Koopera-

tion von Kleinbetrieben, Jungunternehmen und kollek-

tiven Akteuren. Eschborn: RKW-Verlag.

Shan, W., Walker, G. and Kogut, B. (1994) Interfirm

cooperation and start up innovation in the biotech-

nology industry. Strategic Management Journal, 15,

5, 387394.Sydow, J. (2006) Management von Netzwerkorganisa-

tionen, 4th edn. Auflage, Wiesbaden: Gabler.

Talke, K. (2007) Corporate mindset of innovating

firms: influences on new product performance. Jour-

nal of Engineering and Technology Management, 24,

12, 7691.

Tether, B.S. (2002) Who co-operates for innovation,

and why: an empirical analysis. Research Policy, 31,

6, 947967.

Tether, B.S. and Tajar, A. (2008) The organisational-

cooperation mode of innovation and its prominence

amongst European service firms.Research Policy,37,

4, 720739.

Teusler, N. (2008) Strategische Stabilitaetsfaktoren inUnternehmenskooperationen Eine kausalanalytische

Betrachtung. Wiesbaden: Gabler.

Thoms, U. (2003) Langfristige Beziehungen zwischen

Unternehmen. Zum Wert und zur Stabilitaet inter-

organisationaler Partnerschaften. Wiesbaden: Deut-

scher Universitaets-Verlag.

Uzzi, B. (1996) The sources and consequences of

embeddedness for the economic performance of

organizations: the network effect. American Socio-

logical Review, 61, 4, 674698.

van der Panne, G., van der Beers, C. and Kleinknecht,

A. (2003) Success and failure of innovation: a litera-

ture review.International Journal of Innovation Man-agement,7, 3, 309337.

Williamson, O.E. (1975) Markets and Hierarchies:

Analysis and Antitrust Implications. New York, NY:

Free Press.


r 2011 The Authors




15/18

Appendix A

Table A1. Success categories and factors on the firm level

Montoya-Weiss andCalantone (1994)

Henard and Szymanski (2001) van der Panne et al. (2003)

Strategic factors:Product advantageTechnological synergyMarketing synergyStrategyCompany resources

Product characteristics:Product advantageProduct meets customer needsProduct priceProduct technological sophisticationProduct innovativeness

Firm strategy characteristics:Marketing synergyTechnological synergyOrder of entryDedicated human resourcesDedicated R&D resources

Product-related factors:Relative priceRelative qualityInnovativenessTechnologically advanced

Firm related factors:Firm cultureExperienceR&D teamStrategy towards innovationOrganization structureR&D intensity

Development process factors:ProtocolProficiency of technologicalactivitiesProficiency of marketing activitiesProficiency of predevelopmentactivitiesTop management support/skillSpeed to marketFinancial/business analysis

Organizational factors:Internal/external relationsOrganizational factors

Firm process characteristics:Structural approachPredevelopment task proficiencyTechnological proficiencyLaunch proficiencyReduced cycle timeMarket orientationCustomer inputCross-functional integrationCross-functional communicationSenior management support

Project-related factors:ComplementarityManagement styleTop management support

Market environment factors:Market competitivenessMarket potentialEnvironment

Market place characteristics:Likelihood of competitive responseCompetitive response intensityMarket potential

Market-related factors:Concentration of target marketTiming market introductionCompetitive pressureMarketing

Table A2. Network innovativeness, age and stage of product development

Innovation basingon . . .

In % Stage In % Age of networks

Newknowledge(%)

Existingknowledge (%)

New knowledge 2.6 Planning 47.3 2.4 35.2 41.8Mainly new knowledge 24.8 Development 17.7 3.8 9.1 56.8New and existing knowledge 22.9 Testing and validation 16.9 4.3 23.3 53.5Mainly existingknowledge

41.7 Shortly before launch 11.9 4.5 29.0 51.6

Existing knowledge 7.9 Product on market 6.2 5.4 18.8 81.3




16/18

Appendix BNew product response scale and item list(translated from German)

Response scale: 1 strongly disagree,. . . ,

7 strongly agree

New product performance:

1. Because of the innovations new markets could

be opened (market performance).

2. Because of the innovations other new products

became possible (technological performance).

3. The innovations were technically successful

(technological performance).

4. Sales objectives could be met (financial perfor-mance).

5. Sales figure objectives could be met (financial

performance).

6. The schedule was met (network efficiency).

7. The budget was met (network efficiency).

8. The time was used efficiently (network efficiency).

9. Quality specifications could be met (network

efficiency).

Product advantage:

1. Products offered unique benefits to the customer.

2. Products were superior to competitive products

in the eyes of the customer.

3. Products were innovative (the first of its kind in

the market).

4. Product benefits are easy to deliver to the

customer.

5. The products were better value for money

compared with the competitors.

6. Our products were higher quality than compet-

ing products.

7. Products reduced costumers costs.

8. Products solved a problem the customers had

with competing products.

9. The products were based on a technologywhich is new for the network.

Technological synergy:

There is a good fit between the skills of the

network and the project with respect to:

1. Research and development (product development).

2. Engineering.

3. Production.

Marketing synergy:

There is a good fit between the skills of the

network and the project with respect to:

1. Market research.TableA

3.Intercorrelationsoftheexplanator

yconstructs

1

2

3

4

5

6

7

8

9

10

11

12

13

VIF1

(1)P

roductadvantage

1.56

(2)T

echnologicalsynergy

0.35**

1.58

(3)M

arketingsynergy

0.21**

0.21**

1.49

(4)P

roficiencyoftechnologicalactivities

0.39**

0.15*

0.27**

2.09

(5)P

roficiencyofmarketingactivities

0.29**

0.13

0.40**

0.52**

2.46

(6)P

roficiencyofpredevelopmentactivities

0.26**

0.22**

0.31**

0.38**

0.44**

1.78

(7)P

rotocol(productandprojectdefinition

)0.37**

0.30**

0.30**

0.37**

0.44**

0.48**

1.83

(8)M

arketpotential

0.31**

0.23**

0.43**

0.23**

0.40**

0.27**

0.30**

1.64

(9)P

rojectteamorganization

0.28**

0.35**

0.29**

0.32**

0.34**

0.38**

0.36**

0.23**

2.03

(10)C

ommitment

0.24**

0.17**

0.32**

0.48**

0.45**

0.37**

0.37**

0.34**

0.4

6**

2.03

(11)T

rust

0.18**

0.29**

0.18**

0.09

0.16*

0.18**

0.23**

0.12

0.4

6**

0.35**

1.75

(12)D

ependency

0.35**

0.39**

0.37**

0.38**

0.32**

0.40**

0.38**

0.40**

0.5

4**

0.58**

0.51**

2.48

(13)C

ompatibility

0.30**

0.36**

0.27**

0.30**

0.09

0.21**

0.28**

0.24**

0.5

0**

0.41**

0.46**

0.52**

2.15

(14)A

bility

0.24**

0.36**

0.23**

0.16*

0.11

0.16**

0.17**

0.18**

0.4

2**

0.28**

0.42**

0.50**

0.53*

*

1.84

**Significantatthe0.01level,

*Significantatthe0.05level,

1Varianceinflationfactor:1/(1Rk2

),Rk2

isthecoeffi

cientofdeterminationforregressionoftheithindependentvariableonalltheotherindependentvariables:Xk

Xothers.


r 2011 The Authors




17/18

2. Sales force/distribution system.

3. Advertising and promotion.

4. Customer services.

5. Management.

Proficiency of technological activities:

Within the projects the following activities were

carried out well:

1. Product development.

2. Testing the product in the network.

3. Trial/pilot production.

4. Production start-up.

Proficiency of marketing activities:

Within the projects the following activities werecarried out well:

1. Detailed market study/marketing research.

2. A marketing concept developed in advance.

3. Sufficient tests of the marketing concept.

4. General customer tests.

5. Customer tests of prototype.

6. Test on a test market.

7. Comprehensive market launch.

Proficiency of predevelopment activities:

Prior to the (product development) projects the

following tasks were carried out well:

1. Idea screen.

2. Preliminary market assessment.

3. Preliminary technical assessment.

4. Financial analysis.

5. Product concept development.

6. Product concept testing.

Protocol (product and project definition):

Prior to product development the following

items were well defined:

1. The target market.2. A positioning strategy (product definition).

3. The customers wants and preferences.

4. The product concept.

5. The product specifications and requirements.

Market potential:

1. Products are placed on a large market.

2. The market growth is large.

3. The customers requirements change rapidly on

the market.

4. Customer demand for the product is high.

5. The product is of great importance for thecostumer.

Project team organization in the network:

1. The person leading the network had the neces-

sary qualities and skills.2. The project teams in your network were inter-

disciplinary.

3. There was intensive communication between

the network partners.

4. The teams were assigned to only one project

during the life span of the project.

5. Team members did not change during the project.

6. The teams were motivated.

7. The top management of the partners was

committed to the projects.

8. The project team organization was supported

by different software applications (for exampleMS Project).

Commitment

1. Their network is important to the partners.

2. The partners would not join another network.

3. The partners would abandon the network only

as a result of serious changes.

4. The partners are willing to invest even more in

their network.

5. The partners are willing to assign people/

resources permanently to their network.

Trust1. The partners knew each other already before

starting the cooperation.

2. The partners mutually trust each other.

3. The partners are equal in their network.

4. The partners are willing to share knowledge.

5. The partners are open to the necessary changes/

adjustments.

Dependency

1. The partners cover the entire value chain.

2. The partners depend on the network.

3. The partners work well with one another.4. The partners need their network in order to

reach full potential.

5. The strategy of the network would have to be

changed if partners leave.

Compatibility of the network partners

The opinions/attitudes of the network partners

go very well together with respect to

1. Goals.

2. Financial affairs.

3. Quality specifications.

4. Schedules and deadlines.5. Performance evaluation.




18/18

Ability

The network partners are equipped with

1. Good communication behavior.

2. Adequate bureaucratic structures.

3. Sufficient man-power resources.

4. Sufficient information technology resources.

5. Sufficient financial resources.

Alexandra Reseis Assistant Professor at the Chair

of Marketing and Innovation Management, Bran-

denburg University of Technology Cottbus, Ger-

many. She received her PhD in sociology and

entrepreneurship from the University of Karlsruhewhile working at Fraunhofer Institute for Systems

and Innovation Research ISI in Karlsruhe. Her

current research focuses on innovative and entre-

preneurial teams, competencies in entrepreneur-

ship, conflicts, information need and computer-

based methods in new product development. She

teaches business planning to students.

Daniel Baier is Full Professor of Marketing and

Innovation Management at Brandenburg Univer-

sity of Technology Cottbus, Germany. He

received his PhD and his venia legendi in market-

ing-oriented product development from the Uni-

versity of Karlsruhe. His works have appeared in

Journal of Econometrics, Annals of Operations

Research, Zeitschrift fur betriebswirtschaftliche

Forschung (zfbf), Zeitschrift fur Betriebs-

wirtschaftslehre, Marketing ZFP. His current re-

search focuses on market-oriented development

of innovative products and services, innovation

management in networks as well as data analysis,statistics and operations research. He teaches

marketing and innovation management to bache-

lor and master students in economics and indus-

trial engineering, as well as PhD students and

executives.


r 2011 The Authors R&D Management 41, 2, 2011 155

Documents

Sucess Factor for Innovatiopn Management in SMEs