1

TESTING THE SCHUMPETERIAN HYPOTHESES ON AN OPEN INNOVATION

FRAMEWORK.

Abstract

The purpose of this paper is to explore the unclear relationship between industry structure and

Open Innovation. The focus of the study is on firms that received external help to develop

their products or that helped third parties in developing their products. The hypotheses were

tested on a large panel of more than 7,000 firms using Generalized Estimating Equations. The

results show that Open Innovation adoption is positively related to technology complexity and

market uncertainty while it is negatively related to market concentration. Larger firms are

more likely to adopt Open Innovation strategies. The research makes an important

contribution to the literature by examining on a large sample of firms the moderating effects

of industry concentration, industry research and development intensity and the technology life

cycle stage on the adoption of Open Innovation.

Keywords: Cooperation, Innovation, Alliances, Industry Competitiveness, Product

Innovation, Patents, Research and Development.

Introduction

In the last decade the concept of Open Innovation has rapidly gained attention from both

academics and practitioners. Despite the growing literature, theory yields ambiguous

recommendations about the most efficient strategies to adopt Open Innovation. In fact,

significant heterogeneity can be observed in the strategies and degrees of adoption of Open

Innovation among industries. While, in some industries such as food, telecommunications or

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pharmaceuticals a considerably large number of firms have adopted Open Innovation

principles, in some other industries such as banking or utilities Open Innovation adoption is

rather unusual. Industrial organization economics has been largely employed as a theoretical

framework that explains industries’ heterogeneity. Innovation strategy is not an exception and

depends on industry structure. In fact, one of the largest bodies of literature in the field of

industrial organization is devoted to the validation of the hypotheses advanced by Schumpeter

concerning innovation and industrial market structure. The first hypothesis was formulated in

1934 and suggested that innovation was promoted by small and medium firms in competitive

environments. The second and opposite hypothesis considers large firms in concentrated

markets as the main drivers of innovation (Schumpeter, 1942). This study tests these two

hypotheses from an Open Innovation perspective, examining how industry structure defines

the way companies exploit Open Innovation strategies. More specifically, this study focus on

how two characteristics of industry structure-concentration and maturity- both influence the

likelihood of adopting Open Innovation strategies. The empirical validation of hypotheses is

based on a large panel of more than 7,000 firms. This large number of firms in the sample is a

relevant contribution of this paper since it might help to reach a better understanding of the

real Open Innovation dynamics throughout an industry. However, with some exception most

of the research on Open Innovation is focused on large corporations. Some large corporations

might have enough bargaining power to define incentives and disincentives to Open

Innovation adoption or to shape coordination mechanisms or knowledge exchange best

practices. Nevertheless and since usually from the large firm’s perspective Open Innovation

implies more of a reactive than proactive approach (Christensen et al., 2004), understanding

the real dynamics of Open Innovation in an industry requires looking at the behaviour of

myriads of innovative small firms.

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Theoretical Background

Firms traditionally develop new products using internal sources of knowledge. The Open

Innovation approach considers that external knowledge is equally relevant to develop new

products. New innovation strategies that seek to combine internal and external knowledge

into the product development processes are comprised within the concept of Open Innovation.

Under this new perspective, companies would benefit from certain advantages such as

reduced time and research and development (R&D) costs as well as the emergence of

innovations that would otherwise not have been developed by the company for lack of time,

knowledge and / or technological means (Chesbrough et al., 2006). The transition to Open

Innovation models implies significant challenges in how to address the management of

external and internal knowledge and in any case it would be a process modulated by a

complex set of interrelated factors. The literature has identified some factors related to

industry market structure and to the firm’s resources and capabilities.

Among other factors related to industry market structure, the literature has studied the role

played by industry concentration, industry life cycle and industry technological intensity in

shaping firms’ innovation strategies. The lack of complementary assets such as brand or

access to distribution channels makes it difficult for small firms to exploit their inventions.

Consequently they are often forced to cooperate with companies that have the complementary

resources to exploit this knowledge (Teece, 1986; Arora and Gambardella, 1990, Turner et al.,

2010). In concentrated markets when small firms look for potential partners with these

resources they will find often a large corporation as potential or suitable counterpart. This is

usually because in concentrated markets large corporations will more likely have the

complementary assets required to exploit innovations (see for instance Rothaermel, 2001 or

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Rothaermel and Deeds, 2004). Cooperating with large corporations often implies heavy

transaction costs for small firms. Small firms dealing with large corporations that have high

internal organizational complexity may incur in heavy negotiation costs. In this scenario,

small firms have comparatively weaker bargaining power and they can suffer from problems

of opportunistic behaviour where large corporations may end up appropriating critical parts of

their technology without a fair compensation (Christensen et al., 2004). A second important

effect of market concentration on the adoption of Open Innovation strategies is related to

market uncertainty and firm’s access to financial resources. Some of the motivations for the

adoption of Open Innovation strategies are the need to share the risks of developing new

technologies with some partners in uncertain markets and the lack of financial resources

needed to internally develop new products. Schumpeter (1942) already suggested that market

concentration reduces market uncertainty and provides the cash flow required to engage in

costly and risky innovation projects. A third factor related to market concentration and Open

Innovation adoption is given by the relationship between market concentration and

technological opportunity. Industries with high technological opportunity appear to be highly

concentrated and unfavourable to small firms (Geroski, 1990). Consequently, higher

opportunity costs of sharing the profits from innovation in these concentrated markets will

discourage the adoption of Open Innovation strategies. A fourth variable linked to Open

Innovation strategies is the link between market concentration and appropriability regimes. As

advanced by Cohen and Levinthal (1990), there is a positive relationship between market

concentration and appropriability levels. The appropriability regime influences the ability of

an innovator to capture the benefits from innovation (Teece, 1986). Weaker appropriability

regimes are a typical feature of market environments favourable to the development of Open

Innovation strategies. In fact, low levels of appropriability generate more opportunities for

cumulative advances in knowledge (Levin et al, 1987). An example of this phenomenon has

5

been the development of open source, which remains open to external collaborations and

focus less on intellectual property than on raising opportunities to involve new participants in

establishing a standard (West and Gallagher, 2006). Accordingly to the discussed above, it is

possible to make the following assumption:

H1: In concentrated markets firms’ likelihood of adopting Open Innovation will be lower.

The stage of the technology life cycle may also influence Open Innovation adoption. In

emerging sectors there may be an initial positive effect of openness to innovation and firms

may tend to use mostly external sources of innovation (Hagedoorn, 1993; Laursen and Salter,

2006). A first reason for higher likelihood of adoption of Open Innovation at the early stages

of technology is the speed of growth of the industries. In fast-growing industries it may be

difficult for companies to develop the complementary resources needed to exploit their

technological opportunities before the information reaches others in the field or before the

opportunity is replaced with (Gooroochurn and Hanley, 2007). Another reason for adopting

Open Innovation strategies in these early stages is their role as search processes. In fact, in

these early stages of industry life cycles, market and technological uncertainties are high and

to opt for a specific technology is not an easy bet for most firms. Hence, the adoption of the

Open Innovation paradigm in the industry creates an atmosphere where firms seek to mitigate

this risk through knowledge sharing. In this sense, Open Innovation strategies facilitate the

sharing of tacit knowledge between firms in these early stages of innovation when

appropriability regimes are usually weak (Dussuage et al., 2000). Accordingly, taking into

consideration the stage of technology life cycle it can be hypothesized the following:

H2: In emerging industries firms’ likelihood of adopting Open Innovation will be higher.

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Open Innovation adoption will also depend on the R&D intensity of the industry. Companies

that opt for sharing knowledge with other partners or for adopting external innovations are

more likely to be found in R&D intensive industries ( Lichtenthaler, 2008; Santamaría et al.,

2009 among others). In technology intensive industries on average firms tend to be more

R&D intensive. In this sense, the returns to the acquisition of external know-how increases

significantly only when firms are engaged in internal R&D activities and have relevant

absorptive capacity (Cassiman and Veugelers, 2006). Moreover, from the transaction costs

literature perspective, in these industries it will be more likely that firms incur in large sunk

costs in R&D. Consequently, they may be willing to obtain a higher return to these sunk

investments through the external exploitation of these innovations (Love and Roper, 2002;

Piga and Vivarelli, 2004). On the other hand, according to the so-called "necessity effect" that

applies to small businesses with lack of internal resources for R&D, these small firms in

technology intensive industries will be more likely to have stronger need to collaborate with

others to develop innovation projects ( Bayona et al., 2001; Tether, 2002, Mazzanti and

Boboli, 2009). Finally, technology intensive markets tend to be more uncertain and therefore

firms in these markets tend to benefit more from risk and knowledge sharing associated to the

adoption of Open Innovation strategies. Firms in industries with low-complexity technologies

face relatively few technological challenges. Therefore, in these industries the gains from

collaboration do not offset the costs associated with searching, establishing and maintaining

external cooperation (Singh, 1997). For all these reasons, the following hypothesis can be

stated:

H3 In technology intensive industries firms’ likelihood of adopting Open Innovation will be

higher.

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In addition to the characteristics of the industry, it is necessary to control for some internal

characteristics of the firm that the previous literature has identified as potential drivers of

Open Innovation adoption. In first place, the size of the firm will be considered as a control

variable. Several authors suggest that larger companies with a greater stock of co-specialized

resources will be more successful in adopting Open Innovation strategies (Veugelers, 1998;

Licthentaler, 2008). Besides, small companies often lack the capacity to structure the process

of search and selection of external innovations, especially in relation to the decision to

disclose a patent or innovation (Dahlander and Gann, 2010), or to anticipate the potential

value of inbound innovation (Chesbrough and Rosenbloom, 2002). Finally, it is possible that

large incumbents prefer to maintain a stable industrial structure by controlling the industry

innovative routines, encouraging knowledge spillovers through external innovation (Turner et

al., 2010). Consequently, size seems to have a positive impact on the likelihood of adopting

Open Innovation strategies. A second internal control variable considered in this research is

the R&D intensity of the firm. This variable may have contradictory effects on the adoption of

Open Innovation. High R&D intensity is usually employed as a proxy for high absorptive

capacity. High absorptive capacity will be related to higher capacity of the firm to integrate

external knowledge into the product development process and consequently a smoother

adoption of the Open Innovation paradigm. On the other hand, firms with lower R&D

intensity would need more external knowledge. Hence, Open Innovation adoption necessity

will be stronger for firms with low R&D intensity. A third control will be whether the firm’s

R&D activities result into radical or incremental innovations. It would be expected that firms

developing more radical innovations will be encouraged to share risk and knowledge through

the adoption of Open Innovation strategies.

The data

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Based on the theoretical background discussed above, it has been defined a model of adoption

of Open Innovation based on the industry market structure. It will be expected that firms in

less concentrated markets, in emerging industries and in technology intensive industries will

be more likely to engage into Open Innovation activities. The hypotheses will be tested on a

large sample of firms. The main source of information is the PITEC (Technological

Innovation Panel) database. This database is managed by INE (Spanish National Statistics

Institute), with the support of the FECYT (Spanish Science and Technology Foundation) and

COTEC (Foundation for Technological Innovation). The PITEC database contains detailed

information on the innovation activities of Spanish firms. Data is structured as a panel with a

number of firms that ranges from 7283, for 2003, to 12813, for 2008. This panel has been

carefully designed to ensure a representative anonymized sample. Due to the anonymization

process employed by the INE, working with anonymized data could lead to some biased and

inaccurate estimations. It was asked permission to the INE to gain access to the non

anonymized data. Once this permission was granted, non anonymized data was employed to

perform the empirical analysis. Additionally, these data have been combined with some

information at the industry level from the SABI database. It contains information extracted

from the annual reports of hundred of thousands of firms operating in Spain. A short

description of the variables used to test the model, and their source are included in Table 1.

Insert Table 1 about here

For industry level or industry related variables (LIFE_CYCLE, CONCENTRATION and

INTENSITY) sectors are considered in accordance with the two-digits level classification of

economic activities CNAE, Spanish equivalent for NACE codes. Table 2 contains some

descriptive information about the variables employed in the analysis. The sample includes

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both small and large corporations, ranging from 0 to 66.500 employees, being the mean 246

employees and the median 47. Open Innovation is still an emerging phenomenon in Spain

since only one-sixth of the firms in the sample has adopted Open Innovation strategies.

Insert Table 2 About Here

Insert Table 3 About Here

The model

The econometric model that will be used to test the hypotheses is based on a binary dependent

variable (OPEN), which takes value equal to one if the firm has adopted Open Innovation,

and a set of independent variables that explain industry structure and control variables:

ε++++

++++=

YEAR7βINTENSITY6βLIFE_CYCLE5β

IONCONCENTRAT4βLEADERSHIP3βTECHNOLOGY2βSIZE1β0βOPEN

The generalized estimating equations (GEE) method, introduced by Liang and Zeger (1986),

can be used to estimate the above econometric model. In general terms, GEE can account for

temporal correlations and is valid for longitudinal studies. Like generalized linear models

(GLMs), GEE allows for non-linear relationships between independent variables and the

dependent variable. Moreover, the GEE approach relaxes some assumptions of GLM and

allows for correlated structures of grouped data. The key elements in the model are the linear

predictor, the link function and the variance function. The linear predictor, i iXη = β∑ ,

includes the explanatory variables (Xi), and the parameters to be estimated (βi ). The link

function “l”, provides the relationship between the mean of the dependent variable “y” and the

linear predictor: ( )1iE(y | X ) l−µ = = η . The variance function, “V” defines the variance of the

dependent variable as a function of its mean: ( )2 Vσ = ϕ⋅ µ . Then, it is possible to define a

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log-likelihood function, “L”, and to estimate the unknown parameters by maximum likelihood

as the roots of the score equations:

( )i i i

ij i j

yL0

V

− µ ∂µ∂ = =∂β µ ∂β∑

If only the link and variance functions are known, it is still possible to write a pseudo-

likelihood function “K” which shares the most important properties of the log-likelihood. In

order to reduce the number of parameters to be estimated, it is a common practice to impose

some parametric structure to the ( )var y matrix (Dobson, 2008). The econometric estimation

in this research considers an autoregressive (AR1) structure.

Results

The results of the GEE-GLM estimation are reported in Table 4.

Insert Table 4 About Here

Hypothesis 1 suggests that firms in concentrated markets are less likely to adopt Open

Innovation. The negative and significant coefficient for CONCENTRATION gives support to

this hypothesis. The results indicate that the specificities of concentrated markets such as

higher risk of opportunistic behaviour by large corporations, lower market uncertainty, higher

technology opportunity or stronger appropriability regimes discourage the adoption of Open

Innovation.

Hypothesis 2 advanced the notion that technology maturity is inversely related to the

likelihood of adoption of Open Innovation. The negative and significant coefficients of the

categories MATURE and DECLINING for the variable representing industry maturity

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provide support to Hypothesis 2. This implies that as the technology matures firms limit their

search for new external technologies or their dependence on external knowledge. This finding

is in line with some previous evidence suggesting that in emerging industries firms cooperate

to facilitate technological innovation, while in mature industries firms cooperate to create

diversified product offerings (Hagedoorn, 1993). According to the estimation, firms in

declining markets are less likely to adopt Open Innovation than firms in mature markets.

Previous literature (see for instance Rothaermel and Deeds, 2004) indicates that as industries

enter the declining stage, firms may expect to leverage their existing knowledge base and

complementary assets in new emerging industries. Accordingly, firms operating in declining

industries should have similar arguments for openness than firms in emerging markets. The

results of the estimation of this research do not support this point of view, since firms in

declining markets are the least likely to adopt Open Innovation.

Hypothesis 3 indicates that firms in R&D intensive industries are more likely to adopt Open

Innovation. The positive and statistically significant coefficient for the variable INTENSITY

provides support to this hypothesis. This result implies that higher technology uncertainty and

complexity, higher R&D sunk costs or higher absorptive capacity are related to higher

likelihood of Open Innovation adoption.

The coefficients of the control variables show that large corporations, firms with lower R&D

intensity and firms developing more radical innovations are more likely to adopt Open

Innovation.

Discussion

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Research on Open Innovation has grown in the last years. However, few works have studied

the relationship between industry and Open Innovation adoption on a large sample of firms.

This research has studied this relationship by testing the validity of the Schumpeterian

hypotheses on an Open Innovation framework.

In first place, the results of the empirical estimation have found a higher likelihood of

adoption of Open Innovation in emerging industries. This is probably explained firstly by the

fact that some firms are using Open Innovation to share or mitigate the high risk of

developing new products in emerging industries. The result may also be explained by the fact

that some other firms in emerging firms are promoting Open Innovation with the aim of

blocking Schumpeter’s creative destruction. Merck’s decision to share their DNA database or

Glaxo´s decision to share their information related to vaccines are both examples of firms

trying to control innovation in their industries. The results of this research also suggest that

firms’ actual behavior contradicts the theoretical models that consider that Open Innovation is

less efficient in emerging industries (Laursen and Salter, 2006; Almirall and Casadesus,

2010). Future research should clarify this apparently theoretically inconsistent behavior.

In second place, this research has shown that firms in concentrated markets are less likely to

adopt Open Innovation. Previous evidence suggests that in concentrated markets Open

Innovation strategies are tightly linked to modular business architectures based on platforms

or standards. For instance, in the elevator industry dominant firms such as Otis or Schindler

gained advantage by adopting Open Innovation (Gassman and Enkel, 2006). However, not all

the concentrated industries are based on platforms or standards. In this sense, the results of

this research imply that in concentrated markets large firms may not need Open Innovation to

protect their competitive position and small firms may not find enough incentives to adopt it.

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In third place, the paper demonstrated that Open Innovation adoption is more likely in

industries that are more R&D intensive. Open Innovation adoption seems to be driven more

by the lack of resources than by technological opportunity. In this sense, Open Innovation is

used by firms in these complex industries to obtain external resources to keep up with the

pace of technology change.

Finally, this paper has been a first attempt to explain the relationship between Open

Innovation and industry structure. Therefore, it has some limitations and one should be

cautious when interpreting the results. In first place, market structure variables are rude.

However, more specific data were not available for the large sample employed in this

research. Therefore, future research should explain in more detail the role of approapriability

regimes, market uncertainty or the direction of knowledge flows on Open Innovation

adoption. In second place, the research has not made any differentiation between inbound and

outbound Open Innovation strategies. In third place, it has been used a broad definition of

Open Innovation adoption. Future research should investigate the adoption of specific Open

Innovation strategies. Finally, the results arise from data defined by an expansionist economic

cycle (period 2003-2008) and a country such as Spain with an average low R&D intensity.

Further research is needed with data from contraction stages of the business cycle and

countries with higher R&D intensities.

Conclusion

This paper extends the understanding of the relationship between Open Innovation adoption

and industry structure. The predictions of the research suggest that market concentration and

technology maturity will discourage the adoption of Open Innovation, while technology

uncertainty and complexity will favour its adoption. Overall, this work appears to provide

14

incomplete support to the Schumpeterian hypotheses. Regarding the second Schumpeterian

hypothesis, large corporations are more likely to promote the Open Innovation. However, in

contrast to Schumpeter’s second hypothesis large corporations supporting Open Innovation

are more likely to operate in competitive markets than in concentrated markets. An interesting

theory arising from this research would suggest that Open Innovation is being used by large

corporations in emerging and complex markets as a defensive strategy either to block or to

keep control of the development of new technology. This would raise some questions on the

potentially positive social impact of Open Innovation, if there is some risk that it might

hamper radical innovation. On the other hand, large corporations in concentrated markets

usually do not need Open Innovation to keep this control. Further research will be needed to

confirm this emerging theory and to solve the doubts on the impact of Open Innovation on

Schumpeter’s creative destruction theory.

15

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ANNEX

Table 1. Variable description.

VARIABLE DESCRIPTION SOURCE OPEN Does the firm collaborate in product

innovation with other firms or organizations? Binary variable (YES/NO).

PITEC

SIZE Logarithm of the number of workers. PITEC TECHNOLOGY Logarithm of the number of full-time

equivalent R&D workers. PITEC

LEADERSHIP Percentage of gross revenue obtained from products that are new to the market products.

PITEC

LIFE_CYCLE Categorical variable with three levels: EMERGING, MATURE and DECLINING, which represents the current stage of the life-cycle in the industries. This variable is measured as in Algorithm 1 of McGahan and Silverman (2000).

SABI

CONCENTRATION

Industry concentration CR4 index defined as the market share of the four largest firms in the industry at two digits CNAE (Spanish Equivalent for NACE).

SABI

INTENSITY Logarithm of the ratio between R&D expenditures over the total gross revenue for the industry.

PITEC

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Table 2. Descriptive statistics.

Variable min. max. mean median std. dev.

SIZE 0.0 66,500.0 246.0 47.0 1,12.0

TECHNOLOGY 0.0 1620.0 9.4 2.5 43.6

CONCENTRATION

0.0284 1.0 0.3170 0.1880 0.3438

LEADERSHIP 0.0 100.0 16.6 2.0 27.4

OPEN NO: 24.894 YES: 5.860

LIFE_CYCLE Emerging: 12,403 Mature: 10,843 Declining: 7,508

YEAR 2003: 3382 2004: 4620 2005: 6069 2006: 5879 2007: 5445 2008: 5359

Table 3. Correlations.

SIZE TECHNOLOGY

CONCENTRATION

INTENSITY LEADERSHIP

SIZE 1.0000

TECHNOLOGY

0.1930 1.0000

CONCENTRATION

-0.0021 0.0689 1.0000

INTENSITY -0.0014 -0.0009 -0.0048 1.0000

LEADERSHIP

-0.0139 0.0602 -0.0092 0.0048 1.0000

Table 4. GEE-GLMa estimationb (N = 30754). Dependent variable: OPEN Variable Coeff. Std. Error Wald stat. P(>|W|) Intercept -1.2715448 *** 0.0799854 252.721 < 2e-16 MATURE -0.2362504 *** 0.0446843 27.953 1.24e-07 DECLINING -0.2918732 *** 0.0509749 32.785 1.03e-08 CONCENTRATION -0.1942387 ** 0.0666320 8.498 0.003556 INTENSITY 0.0238784 *** 0.0054356 19.298 1.12e-05 LEADERSHIP 0.0022470 *** 0.0006222 13.042 0.000305 SIZE 0.0467103 ** 0.0152999 9.321 0.002246 TECHNOLOGY -0.0452995 *** 0.0059490 57.983 2.64e-14 a Correlation structure = ar1 (Estimate = 0.6642, Std. error = 0.01348); Link = Identity b Year fixed effects have been included in the model, but not shown *** p<0.001; ** p<0.01; * p<0.05

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