Open innovation models adopted in practice: an extensive study in Italy

Open innovation models adopted inpractice: an extensive study in Italy

Valentina Lazzarotti, Raffaella Manzini and Luisa Pellegrini

Summary

Purpose – Many companies claim they are adopting an open approach to innovation, but each of them

with its own way. This paper aims to explore the different models for opening up the innovation process

adopted in practice.

Design/methodology/approach – The paper utilises an extended survey among Italian manufacturing

companies, cluster analysis and ANOVA.

Findings – The study distinguishes four different open innovation models with respect to two variables,

representing the degree of openness: the number and type of partners with whom the company

collaborates (partner variety), and the number and type of phases of the innovation process open to

external collaborations (innovation phase variety). They are: open and closed innovators, integrated and

specialized collaborators. The paper describes each cluster in terms of firm-specific variables that

characterize Open Innovation choices; at last, it tries to draw some tentative explanation of the influence

of openness on the innovative performance of companies.

Research limitations/implications – The number of respondents is still limited (about 100). Moreover,

it studied only the relationship between some firm-specific factors and the degree of openness (in terms

of partner and phase variety): a wider investigation is recommended to include more variables to define

the openness degree and more contextual factors.

Practical implications – The paper provides managerial implications because it suggests that open

innovation is not an ‘‘on/off’’ choice, but it can be interpreted and adopted with different degrees,

consistently with the company’s specific context.

Originality/value – The paper introduces a new perspective that integrates the number/typology of

both partners and phases, in order to understand if such perspective can confirm the existence of

different open innovation models.

Keywords Innovation, Manufacturing industries, Information modelling, Italy

Paper type Research paper

1. Introduction

The concept of ‘‘open innovation’’ (OI) is often studied supposing an artificial dichotomy

between closed and open approaches, whilst the idea of exploring different degrees and

types of openness in a sort of continuum seems to be promising. Prior research has

highlighted that OI may be pursued in different ways:

B organizational form of acquisition/commercialization and consequent level of integration

and time horizon (van de Vrande et al., 2006);

B number and typologies of partners (Laursen and Salter, 2004; Pisano and Verganti, 2008;

Enkel et al., 2009; von Hippel, 1988; Lundvall, 1992; Brown and Eisenhardt, 1995;

Szulanski, 1996; Laursen and Salter, 2006; Keupp and Gassmann, 2009);

B phases of the innovation process actually open (Gassmann and Enkel, 2004);

DOI 10.1108/13683041011093721 VOL. 14 NO. 4 2010, pp. 11-23, Q Emerald Group Publishing Limited, ISSN 1368-3047 j MEASURING BUSINESS EXCELLENCE j PAGE 11

Valentina Lazzarotti is

Assistant Professor and

Raffaella Manzini is

Associate Professor, based

at Universita Carlo

Cattaneo-Liuc, Castellanza,

Italy. Luisa Pellegrini is

Associate Professor at

Universita di Pisa, Pisa,

Italy.

B direction of openness: inbound and/or outbound (Lichtenthaler, 2008); and

B governance: hierarchical or flat (Pisano and Verganti, 2008).

Moreover, previous research has attempted to study also the relationships among different

OI models and several contextual factors, driven by the idea that these factors could explain

or, at least, characterize the companies’ choices in terms of degree of openness. Lastly,

different OI models, defined according to the degree of openness and models ‘‘dropped’’ in

a certain context, were analyzed in some preliminary work in terms of their performance

(Lichtenthaler, 2008, 2009).

In this paper we intend to enrich this stream, by introducing a new perspective that

considers two variables representing the openness degree, which still are not deeply

investigated: the number and type of partners (partners variety), and the number and type of

phases of the innovation process open to external contributions in and/or out (innovation

phase variety). The choices in terms of OI will be investigated in terms of contextual factors

whose role is still controversial (Lichtenthaler, 2008), or otherwise it can be better understood

in light of the concept of openness suggested here.

Our investigation will take place in Italy where, although the scarcity of empirical evidence

about OI, there is a plenty of institutional pressures towards the establishment of

collaborative models (Global Business Summit, 2010). To investigate whether, how and with

what results the companies work together becomes thus a relevant issue for both Italian

scholars and practitioners.

The objective of this paper is threefold:

1. To confirm, with an extensive study, that different models of OI can be found in practice in

terms of partner/innovation phase variety; hence, a survey on a sample of 99

manufacturing Italian companies was conducted and a cluster analysis was planned.

2. To study each different identified model, in terms of the main firm-specific variables

identified in previous works.

3. To verify whether different models imply for a different level of innovative performance

(according to definition by Calantone et al., 2002).

Next two sections describe the literature premises (2) and the empirical study methodology

(3); section 4 illustrates the main results while section 5 draws the main conclusions.

2. Theoretical background

Figure 1 depicts our theoretical framework. Below there is the analysis of the relationships

between the firm-specific factors and the OI models (paragraph 2.1). Then the analysis of

the impact of the OI models on innovative performance follows (paragraph 2.2).

2.1 The company’s firm-specific factors and open innovation

2.1.1 Relationship between R&D intensity and open innovation models. Many authors

investigated the role of R&D intensity. Lichtenthaler (2008) and Lichtenthater and Ernst

(2009) analyze the effect of R&D intensity and find that the greater the level of R&D intensity

the greater the technological exploration. This provides support for the assumption that firms

pursue external technology acquisition as a complement to internal R&D and not as a

substitute (Cohen and Levinthal, 1990; Zahra and George, 2002).

Calantone and Stanko (2007) underpin that firms’ exploration activities can not occur

frequently: therefore, given the high costs for developing specialized structures, firms are

more likely to require outside expertise. Moreover, they state that firms performing a great

deal of exploratory research in house are likely to be led by this exploration away from their

competencies, and will therefore be more likely to require outside expertise.

Sofka and Grimpe (2008) study the effect of internal R&D investments on breadth and

depth of research strategies. They argue that firms building absorptive capacities through

PAGE 12 jMEASURING BUSINESS EXCELLENCEj VOL. 14 NO. 4 2010

internal R&D have both broader and deeper search strategies. However, the effect on

depth is stronger than on breadth. Committing internal resources to in-house labs is

therefore the primary path for innovation managers to achieve more depth in their search

strategies.

Hence, the role played by R&D intensity is studied in relation with two of the abovementioned

perspectives through which it is possible to investigate how the innovation process can be

opened: the direction of openness and the types of partners. Thus, literature lacks the

investigation of the role that R&D intensity plays with regard to the perspective offered in this

article (partner variety and phase variety).

2.1.2 Relationship between approach to innovation and OI models. A relevant concept

investigated in the literature is that of ‘‘technology aggressiveness’’. Lichtenthaler and Ernst

(2009) find that technology aggressiveness is negatively related to the extent of external

technology acquisition and is positively related to external technology exploitation, in that

commercialization nurtures benefits in terms of setting industry standards, entering into new

markets, and realizing learning effects.

In another publication Lichtenthaler (2008) studies the implications connected with the firms’

emphasis on radical innovation and finds that the degree of openness seems to rise with the

degree of emphasis on radical innovation, especially concerning the degree of external

technology commercialization. Two are the reasons: first, the opportunity to commercialize

knowledge which, not applied in the organization, reveals to be residual; second, the

possibility to facilitate the acceptance on the market, and the creation of a standard.

Lichtenthaler (2008) also finds that firms which emphasize radical innovation are obviously

not able to develop all knowledge internally, but they have to strongly rely on complementary

external sources and thus they use technology acquisition (Perrons et al., 2005).

Hence we can draw even more restrictive considerations than those regarding R&D

intensity: technological aggressiveness is studied in the literature in relation only with the

perspective connected with the direction of openness. If we add that this factor’s role is still

controversial, it emerges that new empirical investigation is needed to analyze the impact

exerted by technological aggressiveness on OI models, analyzed according to the

number/typology of both partners and phases.

2.1.3 Company’s objectives of collaboration and OI models. The main reasons that push

firms towards OI choices are the needs of reducing costs-innovation risk and of extending

skills, competences and creativity (Huang et al., 2009).

Figure 1 Theoretical framework

Firm-specific factors

- R&D intensity- Approach to innovation- Company’s objectives - Organisational and managerial actions

Open Innovation models

Number/type of phasesof the innovation funnel

Number/type of partners

Innovative Performance

VOL. 14 NO. 4 2010 jMEASURING BUSINESS EXCELLENCEj PAGE 13

As regards the objective of diminishing costs and risks, Calantone and Stanko (2007)

analyze outsourcing as a tool for increasing staffing efficiency measured in terms of

employee sales efficiency. They infer that the decision to reduce employees’ number is

related to the outsourcing of innovation in the short run but not over the long term. Gassmann

and Enkel (2004) state that usually research-driven companies aim at reducing the R&D’s

fixed costs and sharing risk.

Chiaroni et al. (2009) state that the reason for accessing external sources is the willingness

to minimize risk by investing in technologies that are already proven in other applications.

Another main reason for firms to undertake R&D outsourcing include accessing to

specialized skill sets and creativity exposing the internal development staff to new

knowledge, technology, and organizational development processes (Calantone and Stanko,

2007; Chesbrough and Teece, 1996; Linder, 2004; Lynch, 2004), although this strategy

implies drawbacks in terms of opening the market to new entrants (Porter, 1980) and

exposing core competencies to imitation and substitution (Piachaud, 2005).

In comparison with the other firm-specific variables, the objectives of collaboration are

studied even more restrictively in that they are analyzed in relationship with only one of the

two directions (inbound process). Hence the impact exerted by the objectives of

collaboration on the OI models analyzed according to the number/typology of partners and

the number/typology of phases has to be covered.

2.1.4 Managerial-organizational actions supporting open innovation and OI models.

Managerial-organizational actions allow OI to be pursued easily and more deeply: between

them the commitment of the top management in promoting the transition towards an OI

approach (Vanhaverbeke, 2006; van de Meer, 2007; Chiaroni et al., 2009; Pisano and

Verganti, 2008), the need for a champion supporting the integration of the external

technology into an existing product development phase-gate process (Chesbrough, 2006;

Chesbrough and Crowther, 2006), the exploitation of the personal relationship of the R&D

managers for starting technological collaborations, the formal evaluation of collaboration’s

objectives and risks, as well as the analysis and selection of the potential partners with a

formal and explicit process (Sakkab, 2002; Huston and Sakkab, 2006). Although these

works shed light on how organizational and managerial factors facilitate the implementation

of open models, enriching this line of inquiry with new empirical evidence is anyway

important.

2.2 The impact on innovative performance

The impact of OI models on performance has been analyzed in terms of company’s

competence base, development costs and time to market of new products/processes, the

level of innovativeness of new products/processes and sales volume/market acceptance of

new products.

Literature is unidirectional in showing the impact of the outside-in process on the access and

integration of internal company capabilities with new and complementary knowledge of

external firms (Gassmann and Enkel, 2004). Instead, literature results are not unidirectional

as far as the reduction of development time is considered: for instance, if on the one side

Gassmann and Enkel (2004) state that the benefits of co-operation are seen in an

improvement in the competitive position and in a risk minimization, but not in a reduction of

development time, on the other side, according to Kolk and Puumann (2008) firms not

concentrating on open innovation strategies fail, as rising development costs and shorter

product life cycles make it increasingly difficult to justify investments in innovation.

Many contributions in the literature support the impact of open innovation on level of

innovativeness (Lichtenthaler, 2008; Dahlaner and Gann, 2007). Lichtenthaler (2008)

underpins the effect of OI on firms’ innovativeness and hence competitive position; similarly,

Dahlaner and Gann (2007) show that interorganizational relationships help firms to increase

innovativeness.


3. Research design and methodology

3.1 The survey design

The empirical study focused on companies belonging to a Northern Italian region –

Lombardia – that in 2008 applied for funding to the ‘‘Camera di Commercio’’ (a public local

institution supporting companies belonging to the region) to conduct innovative activities

within different manufacturing sectors.

This ‘‘engagement in innovation’’, combined with the fact that Lombardia presents a

particular propensity for innovation (if measured by number of patents, Lombardia ranks first

among the Italian regions according to European Patent Office data elaborated for Italy by

Osservatorio Unioncamere Brevetti e Marchi, 2008) make them very interesting to study in

innovation topics.

The data were collected through questionnaires whose items regarded company

characteristics, innovation strategy; organization for innovation; collaborations and

innovative performance.

Before sending the questionnaire to the companies, a pilot test was conducted with a group

of senior managers and academics with working experience in innovation in order to assess

the quality of the measure items. The resulting questionnaire was sent to the company key

informant: the R&D manager (if present) or in the company’s owner, if deeply involved in

strategic firm’s orientation and thus in innovation topics (as it is very common for the Italian

companies).

3.2 The statistical analysis

Among the companies that have applied for funding (about 1,500), 500 were randomly

selected: 99 firms responded (response rate ¼ 20 percent).

For the operationalization of the partner variety and the phase variety, we used subjective

measures based on four-point Likert-type scales (1 ¼ strongly disagree; 4 ¼ strongly agree;

see the Appendix).

In order to better specify the partner variety and the phase variety we introduced the

variables: intensity of collaboration with partner and intensity of collaboration on phases in

the innovation funnel (based on the Likert-type scale). The study of correlations between

these two variables highlighted the most typical combination partner/phase.

To study OI approaches, we firstly carried out a cluster analysis (‘‘complete linkage

method’’, recommended when researcher wants to identify groups which are distinct from

each other as much as possible (Barbaranelli, 2006); and with the squared Euclidean

distance as a measure) based on partner and phase variety.

Secondly, concerning the firm-specific variables, we accomplished the following procedure.

Items of the questionnaire were defined on the basis of scales already used (sometimes

partially reworked) in previous works (still Likert-type). Anyway, we applied an exploratory

factor analysis (principal axis factoring as extraction method and promax rotation in the case

of initially unclear solution) in order to delete weakly related items, to understand the factor

structure and the measurement quality. The Eigenvalues and the Scree plot were used to

identify the number of factors to retain. In addition, all factor loadings were above the

acceptance level of 0.50 (Hair et al., 2006; Barbaranelli, 2006; Cheng and Shiu, 2008), thus

indicating the unidimensionality of the various factors. These were saved as variables and

employed in the subsequent analysis.

The factors/firm-specific variables were the following (see the Appendix):

1. Objectives of collaborations classified in:

B aims to extend skills, competences and creativity (three items, inspired by the work of

Huang et al. (2009); Cronbach’s a: 0.71); and

B aims to share risks and costs (two items, based on Calantone and Stanko (2007);

Cronbach’s a: 0.84).


2. Approach to innovation: technological aggressiveness with emphasis on radical

innovation (five items, inspired by Lichtenthaler and Ernst (2009), that use suggestion

by Brockoff and Pearson (1992). We re-adapted the scale also on SURVEY TOOL 2.1

basis, a questionnaire sponsored by Industrial Research Institute, Cronbach’s a: 0.71).

3. Organizational and managerial actions for OI (five items, scale based on Mine Survey

Tools 2.1 (n.d.), Cronbach’s a: 0.85).

Other variables, not presented in Appendix, were measured directly (and eventually

transformed in logarithmic scale to improve normality):

B R&D intensity (percentage of R&D expenses/sales); and

B indicators of company’s performance (return on assets (ROA); return on sales (ROS)).

As concerns company’s results, also a factor representing innovative performance was

defined (five items reported in the Appendix, our scale based on Calantone et al. (2002);

Cronbach’s a: 0.82).

Dummy variables were included for the existence of organizational units to support

collaboration and for the type of organizational structure for innovation activities

(input-oriented, output-oriented, matrix; Chiesa, 2001).

4. Results

Figure 2 illustrates the results of cluster analysis based on partner and phase variety.

A solution with four groups of firms emerged. The decision on the number of clusters was

determined by the criterion that suggests to stop the aggregation process at the stage that

precedes the one with the highest increase in the coefficient of agglomeration (Barbaranelli,

2006). In the four-cluster solution, the variance inside clusters is about 21 percent whereas

the variance among clusters is about 72 percent (F-tests sig. , 0.001). Despite the quite

high correlation between partner variety and phase variety variables, which suggests that

most firms adopt open or closed approach on both dimensions, small intermediate clusters

– 2 and 3 – exist (i.e. they open their innovation process more strongly in one direction rather

than in the other). Clusters are:

B open innovators, who collaborate with a wide set of partners in many phases of the

innovation process;

B specialized collaborators, who open only a small part of the innovation process to a wide

variety of partners;

Figure 2 Results of cluster analysis

2

2

43

Open innovators (1)

36

9

11

Partner variety

Phas

e va

riety

Closed innovators (4)

Integrated collaborators (2)

Specialized collaborators (3)


B integrated collaborators, who collaborate only with a limited set of partners along thewhole innovation funnel; and

B closed innovators, who open a very small part of the innovation funnel to a very limited setof partners.

To analyze differences across clusters, in particular the open and closed ones, one-wayANOVA test was used for comparing means of scale variables (i.e. R&D intensity, approachto innovation, types of objectives, organizational and managerial actions for OI; innovativeperformance) and Chi-square test was employed to compare the frequency on nominalvariables (i.e. existence of organizational units supporting collaborations). The scarcity ofobservations on integrated and specialized clusters makes not applicable Chi-square tests;as well as the results regarding scale variables are often not significant. Anyway, althoughthe following evidence is useful above all to compare open and closed companies, someclues on the other two clusters also emerge and thus they will be presented in order todeepen them with next research.

Open and closed innovators emerge as two significantly different OI models, in terms of(Table I):

B Approach to innovation: open innovators choose an aggressive technology andinnovation strategy, in which they work to be technological leader, to come first to themarket with new products, to lead the technology evolution with superior know-how, topursue even radical innovations. In other words, it can be argued that opening theinnovation process to a wide variety of partners and all along the innovation funnel isconceived as part of an aggressive strategy.

B R&D intensity: open innovators invest more than closed, and this confirms the differencein the strategy for the two clusters; aggressive innovators spend significantly more in R&Dand, as part of their effort, spend for opening their innovation process. Anotherexplanation of this result refers to the need to invest in internal competences in order to beopen, as the absorptive capacity of the company is critical to identify and exploit potentialcollaborations with external partners. In coherence with this result, there is evidence thatan input-oriented organizational structure for the R&D activities, which maximizes theabsorptive capacity, is typical for open innovators rather than for closed.

B Type of objectives: coherently with the results above, open innovators, with respect toclosed ones, mainly open their innovation process to achieve benefits in terms ofintegration of the knowledge base, increase of creativity and flexibility, achievement ofexcellence in knowledge production. On the other side, closed innovators aim at reducingcosts and risks of innovation, by sharing them with others.

B Organizational-managerial actions implemented to support openness: open innovatorshave modified their organizational structure and management techniques, by introducingroles, routines and tools dedicated to the design, development and implementation ofcollaborations with partners. Also a significant Chi-square test on the existence of anorganizational unit supporting collaboration gives evidence of the organizational andmanagerial differences between open and closed approaches.

B Regarding performance we obtained some preliminary indications on innovativeperformance. Indeed, we believe that the analysis of overall company’s performance iscomplex and can be ‘‘explained’’ only by considering a variety of factors that can haveopposite impacts. With this premise, we studied the differences between clusters only withan explorative purpose to define next steps of research and in terms of innovativeperformance (i.e. factor that is a combination of five items). We found that open clusterseems more performing than closed (and better than the sample average). Moreover, bystudying correlations between innovative performance and partner variety[1], we found ahigh and significant relation. Particularly strong were the relations between the single item‘‘The company’s competence base was enlarged’’ and partner variety and ‘‘the level ofinnovativeness of new products/processes was improved’’ and the partner variety,suggesting that the open is more innovative and that the innovativeness seems to be linkedto the partner variety. Another clue for this type of interpretation is given by the specialistinnovative performance: higher than integrated just in these two items. Anyway, these are


Table

IInform

ationontheclusters

andmain

differences(scale

variables)

Variab

les

Sam

ple

(n¼

99)

Clu

ster

1(n

¼43)

Op

en

Clu

ster

2(n

¼9)

Inte

gra

ted

colla

bora

tor

Clu

ster

3(n

¼11)

Sp

ecia

lized

colla

bora

tor

Clu

ster

4(n

¼36)

Clo

sed

Sig

.(A

NO

VA

test

)S

ig.

(Chi-sq

uare

test

)

Part

ner

and

phase

variab

les

Part

ner

variety

2.6

33.4

41.8

93.1

81.6

70.0

00

Phase

variety

2.6

13.4

93.1

11.8

21.6

70.0

00

Inte

nsi

tyof

colla

bora

tion

with

:U

niv

ers

ityand

rese

arc

hcente

rs1.3

81.5

91.1

61.3

61.1

80.0

05

Technic

aland

scie

ntifi

cse

rvic

ecom

panie

s1.3

71.5

61.2

91.0

51.2

50.0

2G

ove

rnm

enta

lin

stitu

tions

1.1

11.2

01.0

01.1

81.0

10.0

3C

ust

om

ers

1.7

01.7

71.5

61.8

01.6

10.6

3S

up

plie

rs1.9

32.1

21.8

01.8

71.7

40.1

7C

om

petit

ors

1.0

91.0

81.1

81.2

91.0

30.0

1Firm

sop

era

ting

ind

iffere

nt

secto

rsof

activ

ity1.4

01.6

11.2

91.3

31.1

90.0

4In

tensi

tyof

colla

bora

tion

on:

Idea

genera

tion

1.5

11.6

21.3

81.5

21.3

90.1

1E

xperim

enta

tion

1.5

61.7

21.3

51.6

91.3

60.0

00

Eng

ineering

1.4

41.6

11.4

0.1

.32

1.2

90.0

02

Manufa

ctu

ring

set

up

1.3

41.4

91.1

31.2

61.2

20.0

02

Com

merc

ializ

.1.2

81.3

51.3

51.2

51.1

80.2

3

Firm

-sp

ecifi

cconte

xtualva

riab

les/

facto

rsR

&D

inte

nsi

ty(L

og

)0.5

90.8

60.2

00.5

90.3

50.0

1In

nova

tion

ap

pro

ach

20.0

40.3

32

0.0

70.3

02

0.4

70.0

00

Ob

jectiv

eof

ext

end

ing

skill

and

com

pete

nce

20.2

00.4

52

0.1

32

0.1

12

0.4

70.0

00

Ob

jectiv

eof

sharing

risk

sand

cost

s0.0

20.2

82

0.2

92

0.1

32

0.2

20.0

5O

rganiz

atio

naland

manag

erialactio

ns

for

OI

0.0

10.3

70.2

60.3

42

0.6

10.0

00

Perf

orm

ance

Innova

tive

perf

orm

ance

20.0

40.3

00.0

80.2

92

0.4

80.0

01

RO

S(L

og

)0.7

30.7

60.8

00.6

50.7

00.8

9R

OA

(Log

)0.7

80.7

80.6

90.7

50.8

10.9

4

Org

aniz

atio

nalconte

xtO

rganiz

atio

nin

put-

oriente

d44%

53%

Not

exe

cute

dN

ot

exe

cute

d33%

0.0

5E

xist

ence

of

org

aniz

atio

nalunit

sup

port

ing

colla

bora

tion

35%

47%

Not

exe

cute

dN

ot

exe

cute

d22%

0.0

2


onlyclues,notconfirmedbytheanalysisofcompany’soverallperformance(ROSandROA),

that is even greater in the closed than in the open cluster. Thus, further investigation is

required.

Finally, to further analyze the degree of openness in terms of partner variety and phase variety

and understand which factors can have an influence on it, we focalized on two factors (i.e. the

type of objectives ‘‘aims to extend skills, competences, creativity’’ and the ‘‘approach to

innovation’’) that seem to be able to play this role. Instead, we excluded by the further analysis

thesecondtypeofobjective ‘‘share risksandcosts’’because in thepreviousANOVAanalysis it

emergedas lesssignificantand,anyway, it ishighlycorrelatedtofirst typeofgoal (theexclusion

allows toavoid thecollinearity problem). Moreover,weexcluded the factor ‘‘organizationaland

managerial actions’’, despite its significance in the ANOVA because it does not seem a factor

that conceptually can explain the degree of openness. Rather, it is a factor that can support

collaboration and it certainly cannot be missed for a successful collaboration. Thus, it is

probably more appropriate to consider it to ‘‘explain’’ performance in a next research.

Regarding the degree of openness, at first we carried out a standard regression with

dependent variables respectively the partner variety, the phase variety, the sum of partner

variety-phase variety and finally their product to provide an integrated view of the concept of

openness innovation (in similar vein of Lichtenthaler, 2008; see Table II for descriptive

statistics and correlations of the considered variables).

As can be seen (Table III – standard section) the ‘‘aim to extend skills, competences and

creativity’’ is significant to explain both the partner variety and phase variety whereas the

approach to innovation is significant to explain only the partner variety and with a lesser

extent than the first.

Table III Results of regression

VariablesModel 1 Model 2 Model 3 Model 4

Dependent variable Partner variety Phase variety OI product Open innovation sum

StandardInnovation approach 0.23* Non sig. 0.20* 0.18*Objective of extending skill and competence 0.37*** 0.46*** 0.45*** 0.44***R 2 0.29 0.29 0.34 0.32F 19*** 20.31*** 24.41*** 22.12***

Hierarchical (after controlling for the possible effect of R&D intensity)Innovation approach Non sig. Non sig. Non sig. Non sig.Objective of extending skill and competence 0.46*** 0.52*** 0.53*** 0.50***R&D intensity 0.25* Non sig. Non sig. Non sig.R 2 change 0.22 0.25 0.285 0.26F change 12.96*** 12.65*** 16.65*** 14.52***F 14.63*** 10.20*** 15.65*** 13.72***

Notes: *p,0.05; **p , 0.01; ***p , 0.001; number of observations: 99

Table II Descriptive statistics and correlations

Variables Mean SD 1 2 3 4 5 6 7

Partner variety (1) 2.63 0.96 1Phase variety (2) 2.61 0.98 0.71*** 1OI product (3) 7.51 4.76 0.89*** 0.91*** 1OI sum (4) 5.23 1.80 0.92*** 0.92*** 0.97*** 1R&D intensity (5) 0.59 0.70 0.40*** 0.22* 0.34** 0.33** 1Innovation approach (6) 20.04 0.87 0.42*** 0.36*** 0.42*** 0.40*** 0.30** 1Obj. of extending skill and competence (7) 20.20 0.89 0.50*** 0.53*** 0.55*** 0.53*** 0.27* 0.50*** 1

Notes: *p , 0.05; **p , 0.01; ***p , 0.001; number of observations: 99


Next, we carried out a hierarchical regression in order to control the influence of R&D

intensity on the same dependent variables. Also after controlling this type of influence

(Table III – hierarchical section), only ‘‘the aim to extend skills, competences and creativity

remains significant’’, revealing itself as factor able to explain the degree of openness. This

finding brings further support to the absorptive capacity interpretation: even if companies

invest internally, the pressure to openness remains. Indeed, perhaps they invest to open

easier due to the fact that internal R&D give them the necessary competences to

successfully absorb from outside.

5. Conclusions

In this paper, different OI models are studied, by means of a survey conducted among 99

Italian manufacturing companies, with respect to two variables: the partner and the

innovation phase variety. Four different models for OI were found: open and closed

innovators, specialized and integrated collaborators. In particular, the results concerning the

two ‘‘intermediate’’ models – specialized and integrated collaborators – are less robust,

because of the limited number of companies in these two clusters. As a consequence, only

some tentative interpretation can be put forward. However, it is relevant to reflect on such

results since they can represent the starting point for a future research aimed at verifying

whether these two OI models can actually represent a valid alternative to open and closed

models and, if so, in which contexts. By making a synthesis of all results achieved for

specialized and integrated collaborators, it seems that there is a sort of ‘‘continuum’’ in the

openness of companies, in terms of the most relevant context conditions emerged in this

study (Figure 3).

The figure shows that specialized and integrated collaborators can be really considered as

‘‘intermediate’’ models: the most significant variables that characterize the open and closed

models, in fact, have values that are between the two extremes[2]. Integrated and

specialized collaborators are thus viable options for companies that do not have a highly

aggressive approach to innovation and that do not want to invest too much for opening up

the innovation process. As a consequence, these companies have limited expectations in

terms of benefits form OI, but do not want to completely abandon the opportunity to access

to external sources of knowledge.

A widening investigation is certainly necessary to include more contextual factors, i.e.

external/environmental ones, or more variables that can help to define the openness degree.

Figure 3 Specialized and integrated collaborators between the two extreme models

R&D intensity

I = 0.20 C = 0.35 S = 0.59 O = 0.86

C = -0.47 I = -0.07 S = 0.30 O = 0.33

C = -0.47 I = -0.13 S = -0.11 O = 0.45

Organisational and managerial actions for OI

C = -0.61 I = 0.26 S = 0.34 O = 0.37

Min. value mean Max. value mean

Note: C = closed innovators; I = integrated collaborators; S = specialised collaborators;O = open innovators

Approach to innovation

Objective of extending skills and competences


Notes

1. Data not reported in this paper but available upon request.

2. Only R&D intensity is lowest in the case of integrated collaborators, but this is coherent with what is

already discussed in the previous section, since closed innovators need to invest a lot in R&D in

order to develop internally all tangible and intangible resources needed for innovation.

References

Barbaranelli, C. (2006), Analisi dei Dati: Tecniche Multivariate per la Ricerca Sociale, LED Edizioni,

Milan.

Brockoff, K. and Pearson, A. (1992), ‘‘Technical and marketing aggressiveness and the effectiveness of

research and development’’, IEEE Transactions on Engineering Management, Vol. 39, pp. 318-24.

Brown, S.L. and Eisenhardt, K.M. (1995), ‘‘Product development: past research, present findings and

future directions’’, Academy of Management Review, Vol. 20 No. 2, pp. 343-78.

Calantone, R.J. and Stanko, M.A. (2007), ‘‘Drivers of outsourced innovation: an exploratory study’’,

The Journal of Product Innovation Management, Vol. 24, pp. 230-41.

Cheng, C.J. and Shiu, E.C.C. (2008), ‘‘Re-innovation: the construct, measurement and validation’’,

Technovation, Vol. 28, pp. 658-66.

Chesbrough, H. (2006), Open Business Models: How to Thrive in the New Innovation Landscape,

Harvard Business School Press, Boston, MA.

Chesbrough, H. and Crowther, A.K. (2006), ‘‘Beyond high tech: early adopters of open innovation in

other industries’’, R&D Management, Vol. 36, pp. 229-36.

Chesbrough, H.W. and Teece, D.J. (1996), ‘‘When is virtual virtuous? Organizing for innovation’’,

Harvard Business Review, Vol. 74 No. 1, pp. 65-71.

Chiaroni, D., Chiesa, V. and Frattini, F. (2009), ‘‘The open innovation journey: how firms dynamically

implement the emerging innovation management paradigm’’, Technovation, DOI

10.1016/j.technovation.2009.08.007

Chiesa, V. (2001), R&D Strategy and Organization, Managing Technical Change in Dynamic Contexts,

Imperial College Press, London.

Cohen, W.M. and Levinthal, D.A. (1990), ‘‘Absorptive capacity: a new perspective on learning and

innovation’’, Administrative Science Quarterly, Vol. 35, pp. 128-52.

Enkel, E., Gassmann, O. and Chesbrough, H. (2009), ‘‘Open R&D and open innovation: exploring the

phenomenon’’, R&D Management, Vol. 39 No. 4, pp. 311-6.

Gassmann, O. and Enkel, E. (2004), ‘‘Towards a theory of open innovation: three core process

archetypes’’, paper presented at R&D Management Conference.

Global Business Summit (2010), ‘‘Crescita e Sfide Globali, Il Sole 24 ore’’, Harvard Business Review,

May, pp. 17-18.

Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (2006), Multivariate Data Analysis, Prentice-Hall,

Upper Saddle River, NJ.

Huang, Y., Chung, H. and Lin, C. (2009), ‘‘R&D sourcing strategies: determinants and consequences’’,

Technovation, Vol. 29, pp. 155-69.

Huston, L. and Sakkab, N. (2006), ‘‘Connect and develop, 2006, inside Procter and Gamble’s new model

for innovation’’, Harvard Business Review, Vol. 85, pp. 58-66.

Keupp, M.M. and Gassmann, O. (2009), ‘‘Determinants and archetype users of open innovation’’,

R&D Management, Vol. 39 No. 4, pp. 331-41.

Kolk, A. and Puumann, K. (2008), ‘‘Co-development of open innovation strategy and dynamic

capabilities as a source of corporate growth’’, Journal of Economic Literature, pp. 73-83.

Laursen, K. and Salter, A. (2004), ‘‘Searching high and low: what type of firms use universities as a

source of innovation?’’, Research Policy, Vol. 33 No. 8, pp. 1201-15.

Laursen, K. and Salter, A. (2006), ‘‘Open for innovation: the role of openness in explaining innovation

performance among UK manufacturing firms’’, Strategic Management Journal, Vol. 27 No. 2, pp. 131-50.


Lichtenthaler, U. (2008), ‘‘Open innovation in practice: an analysis of strategic approaches to technology

transactions’’, IEEE Transactions on Engineering Management, Vol. 55 No. 1, pp. 148-57.

Lichtenthaler, U. (2009), ‘‘Outbound open innovation and its effect on firm performance: examining

environmental influences’’, R&D Management, Vol. 39 No. 4, pp. 317-30.

Lichtenthaler, U. and Ernst, H. (2009), ‘‘Opening up the innovation process: the role of technology

aggressiveness’’, R&D Management, Vol. 39 No. 1, pp. 317-29.

Linder, J.C. (2004), ‘‘Transformational outsourcing’’, MIT Sloan Management Review, Vol. 45 No. 2,

pp. 52-8.

Lundvall, B.A. (1992), National Systems of Innovation: Towards a Theory of Innovation and Interactive

Learning, Pinter, London.

Lynch, C.F. (2004), ‘‘Why outsource?’’, Supply Chain Management Review, Vol. 8 No. 7, pp. 44-51.

Mine Survey Tools 2.1 (n.d.), available at: www.minesurvey.polymtl.ca (Survey), Industrial Research

Institute, Ecole Politechnique Montreal, Social Sciences and Humanities Research Council of Canada,

Monteal.

Osservatorio Unioncamere Brevetti e Marchi (2008), Report, ISBN: 886077067X; ISBN-13:

9788860770677.

Perrons, R.K., Richards, M.G. and Platts, K. (2005), ‘‘What the hare can teach the tortoise about

make-buy strategies for radical innovations’’, Management Decisions, Vol. 43, pp. 670-90.

Piachaud, B. (2005), ‘‘Outsourcing technology’’, Research Technology Management, Vol. 48 No. 3,

pp. 40-6.

Pisano, G.P. and Verganti, R. (2008), ‘‘Which kind of collaboration is right for you?’’, Harvard Business

Review, December, pp. 1-9.

Porter, M.E. (1980), Competitive Strategy: Techniques for Analyzing Industries and Competitors, Free

Press, New York, NY.

Sakkab, N. (2002), ‘‘Connect and develop complements research and develop at P&G’’,

Research-Technology Management, Vol. 45 No. 2, pp. 38-45.

Sofka, W. and Grimpe, C. (2008), ‘‘Managing search strategies for open innovation – The role of

environmental munificence as well as internal and external R&D’’, Discussion Paper No. 08-075, Centre

for European Economic Research.

Szulanski, G. (1996), ‘‘Exploiting internal stickiness: impediments to the transfer of best practice’’,

Strategic Management Journal, Vol. 17, pp. 27-43.

van de Meer, H. (2007), ‘‘Open innovation – the Dutch treat: challenges in thinking in business models’’,

Creativity and Innovation Management, Vol. 6 No. 2, pp. 192-202.

van de Vrande, V., Lemmens, C. and Vanhaverbeke, W. (2006), ‘‘Choosing governance modes for

external technology sourcing’’, R&D Management, Vol. 36 No. 3, pp. 347-63.

Vanhaverbeke, W. (2006), ‘‘The interorganisational context of open innovation’’, in Chesbrough, H.,

Vanhaverbeke, W. and West, J. (Eds), Open Innovation: Researching a New Paradigm, Oxford

University Press, Oxford, pp. 205-19.

von Hippel, E. (1988), The Sources of Innovation, Oxford University Press, New York, NY.

Zahra, S.A. and George, G. (2002), ‘‘Absorptive capacity: a review, re-conceptualization and

extension’’, Academic Management Review, Vol. 27, pp. 185-203.

Appendix

1. Partner variety: in the last five years you have collaborated with a wide variety of externalactors.

2. Phase variety: in the last five years you have collaborated on a wide variety of phases.

3. Intensity of collaboration with (each) partner: in the last five years you have collaboratedvery strongly with the following partner (university and research centers, technical andscientific service companies, governmental institutions, customers, suppliers,competitors, firms operating in different sectors of activity).


4. Intensity of collaboration on phase: in the last five years you have collaborated verystrongly on the following phases (idea generation, experimentation, engineering,manufacturing set up, commercialization).

5. Objectives of collaboration:

B aims to extend skills, competences and creativity:

– enlarge the company’s competence base;

– increase the flexibility of the internal organization; and

– stimulate creativity and idea generation capability.

B aims to share risks and costs:

– reduce or share the risks of innovation; and

– reduce or share the costs of innovation.

6. Approach to innovation (technology aggressiveness):

B investing for technological leadership;

B aggressive acquiring new business areas by means of innovation;

B influencing the industry structure and rules by means of products characteristics;

B trying to recruit the best researchers and experts available on the market; and

B giving emphasis on radical rather than incremental innovation.

7. Organizational and managerial actions for open innovation:

B top management is committed towards the maximization of the collaborationsresults;

B personal relationship of the R&D manager are exploited to start technologicalcollaborations;

B for each collaboration, there is a ‘‘champion’’ acting as a facilitator for thecollaboration success;

B the company formally evaluates the objectives and risks of the collaboration; and

B the company analyses and selects the potential partners with a formal and explicitprocess.

8. Innovative performance:

B the company’s competence base was enlarged;

B the average development costs of new products/processes was reduced;

B the time to market of new products/processes was reduced;

B the level of innovativeness of new products/processes was improved; and

B sales volume and market acceptance of new products was improved.

Corresponding author

Valentina Lazzarotti can be contacted at: [email protected]


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Open innovation models adopted in practice: an extensive study in Italy