M PRA

Munich Personal RePEc Archive

Barriers to Innovation faced by

Manufacturing Firms in Portugal: How
to overcome it?
Maria Silva and Joao Leitao and Mario Raposo
University of Beira Interior

23. October 2007

Online at http://mpra.ub.uni-muenchen.de/5408/
MPRA Paper No. 5408, posted 23. October 2007

Barriers to Innovation faced by Manufacturing Firms in Portugal:

How to overcome it?
Maria Jos Silva, msilva@ubi.pt *, Joo Leito jleitao@ubi.pt * and Mrio Raposo* mraposo@ubi.pt
* Departamento de Gesto e Economia
Universidade da Beira Interior
Estrada do Sineiro Covilh
Phone: +351 275 319 625; Fax: +351 275 319 601

Abstract
This paper aims to identify the barriers to innovation that influence the innovation capability of
Portuguese industrial firms. The literature review about innovation makes use of two references
approaches: (i) the systemic; and (ii) the networks and inter-organizational relationships. The database
is obtained through the Community Innovation Survey II (CIS II) conducted by EUROSTAT.
Furthermore, from the results several public policies are proposed in order to overcome the restraining
factors of the entrepreneurial innovative capability.

1. Introdution
In the context of globalisation, innovation is a key-factor for enhancing the competitiveness of firms.
This paper aims to identify and analyse the determinant factors of innovation capability of Portuguese
industrial firms.

Thus, it is intended with this article to develop a theoretical support for the empirical method that is
going to be used, by taking into consideration two reference approaches: (i) the systemic; and (ii) the
networks and inter-organizational relationships. The selection of these approaches is due to the
adequacy they present for the study of the determinant factors of entrepreneurial innovative capability.

The database is the one that belongs to the Second Community Innovation Survey II (CIS II).
According to the data granted by the OCT Observatrio das Cincias e das Tecnologias (Sciences
and Technologies Observatory). This questionnaire was applied in distinct European countries, under
the coordination of EUROSTAT and following the guidelines presented at Oslo Manual (OCDE,
1997, 2005). From 819 firms that answered the questionnaire, 470 carried through innovations in the
product or process or these firms are involved in innovation activity, during the period of 1995-1997.
In order to identify the significant restraining factors of entrepreneurial innovative capability, a logistic
regression is preformed.

This study is structured as follows. In section two presents a literature review is made and the
hypotheses are formulated. In section three, the research methodology based on a logistic regression is
presented. In section four, the results are presented and discussed. In section five, the concluding
remarks and guidelines for futures research are presented.

2. Literature Review
The innovation is not seen as something periodical that happened by accident nor something that
results from the action of an individual agent. Innovation is seen as the result of an interactive and non
linear process between the firm and the environment. (Kline and Rosenberg, 1986; Dosi et al., 1988;
Lundvall, 1988, 1992; Nelson, 1993; Edquist, 1997; Maskell and Malmberg, 1999; Lundvall et al.,
2002; Godinho, 2002; Silva, 2003; Silva et al., 2005; Leito, 2006; Silva and Leito, 2007). The
results of this process are designated as entrepreneurial innovation capability. The term entrepreneurial
innovation capability was adopted to integrate the components that result from the innovative process
of a firm, namely: product innovation, process innovation, organisational innovation and marketing
innovation (OECD, 2005). This paper is focused on the study of entrepreneurial innovation capability
regarding the product innovation or process innovation undertaken by the firm.

This way, it is considered that the firm is innovative, when it introduces a new technological or
improved product or process during the period of 1995-1997. It is defined as new product when the
products characteristics or its use, differ significantly from those products previously produced (CIS
II, 1999:3). An improved product consists on an existing one, whose performance was significantly
widened or developed (CIS II, 1999:3). It is defined as process innovation the implementation of a
new or significantly improved production or delivery method. This includes significant changes in
techniques, equipment and/or software (OECD, 2005: 49).

In the last decades, there has been an increasing interest in studying innovation. More recently, the
systemic approach about innovation and the networks and inter-organizational approach have made
progress in the framework of innovation.

The approach of networks and inter-organizational relations, despite coming from several theoretical
approaches, has shown a considerable convergence of ideas regarding the process of innovation. The
reason why these approaches are considered is due to the fact that, overall they gather fundamental
elements to the study of the factors that stimulate and limite the innovative capacity. More than
contradictory perspectives, these approaches are seen as complementary in the study of the process of
innovation. The Industrial Cluster approach stresses the competitive pressure of the environment on
the firm (Porter, 1990, 1998; Stern et al., 2000; Porter and Stern, 2001; Furman et al., 2002), while the
2

role of cooperation amongst firms is highlighted in the Industrial Districts approach (Becattini, 1990;
Sengenberger et al., 1990; Brusco, 1992; Schmitz, 1992). The Industrial Networks approach enhances
the role of the agents, activities and resources (Hakansson 1987; Hakansson and Johanson, 1988,
1992; Johanson and Mattson, 1991); whereas the Resource-Based View points out, mainly, the
resources and the internal capacities essential to the process of innovation (Pfeffer and Salancik, 1978;
Wernerfelt, 1984, 1995; Prahalad and Hamel, 1990; Cohen and Levinthal, 1989, 1990).

Therefore the systemic perspective of innovation enriched its analysis, by considering organisational
and environmental factors that influence the innovative performance and the entrepreneurial
competitiveness. According to this approach, innovation is originated from a collective learning
process where institutions have a determinant role. Since the innovation capability is the result of an
interactive process, which embraces firms and environment, by enhancing the inherent synergies of
learning that belong to the economic system and by stimulating the institutions that support innovation
(Lundvall, 1985, 1988, 1992; Nelson, 1993; Cooke, Uranga and Etxebarria, 1997; and Braczyk et al.,
1998; Cooke et al., 2000; Kaufmann and Tdtling, 2001). The systematic approach enhances that these
institutions, when connecting several agents, may play a crucial role in the creation and diffusion of
innovation (Godinho, 2003). This approach provided a better understanding about the connections
established between firms and external partners, as well as it allowed the acknowledgement of several
agents that are crucial for disseminating innovation within the system.

There is an extensive literature that discusses the main determinants of entrepreneurial innovative
capability. This capability varies from firm to firm and it is determined by a vast and complex number
of aspects both stimulating and restraining factors that seem to present a significant impact on the
innovative process of firms. Through the analysis of the barriers to innovation, the restraining factors
of innovation, at the firm level, are presented in Table 1.
TABLE I
FACTORS AND BARRIERS TO INNOVATION
Barriers to innovation
The high economic risk
The high cost of innovation
The lack of financing
The organisational rigidities
The lack of skilled personnel
The lack of information about technology
The lack of information on market
The lack of customers responsiveness
The Government regulations

Factors
Economic

Internal

Other

Source: CIS II (1999:7).

The research question of the present paper is: What are the barriers to innovation faced by Portuguese
industrial firms? For addressing this research question, we formulate hypotheses to be empirically
tested through the use of a logistic regression.

The hypotheses presented below aim to identify the significant determinant factors: stimulating or
restraining; on the Portuguese firms innovative capability, regarding product innovation or process
innovation.

(H1): The high economic risk is negatively related to the firms propensity for innovating the
product or process.
(H2): The high cost of innovation is negatively related to the firms propensity for innovating the
product or process.
(H3): The lack of financing is negatively related to the firms propensity for innovating the
product or process.
(H4): The organisational rigidities are negatively related to the firms propensity for innovating
the product or process.
(H5): The lack of skilled personnel is negatively related to the firms propensity for innovating the
product or process.
(H6): The lack of information about technology is negatively related to the firms propensity for
innovating the product or process.
(H7): The lack of information on market is negatively related to the firms propensity for
innovating the product or process.
(H8): The lack of customers responsiveness is negatively related to the firms propensity for
innovating the product or process.
(H9): The Government regulations are negatively related to the firms propensity for innovating
the product or process.

In this sense the Portuguese reality is selected as an adequate laboratory for testing the hypotheses,
aiming to provide several insights and guidelines for public and private managers, in terms of the
future promotion of entrepreneurial innovative capability, at the firm level.

3. Research Methodology

After presenting the research question and proposing the hypotheses to be empirically, the next step is
to identify the data and variables. Afterwards, the hypotheses and logistic regression model are
presented.
A. Data: Presentation
The data used in this study were collected by the OCT. The data was collected during the second
semester of 1998, through a survey that consisted in a questionnaire titled as Community Innovation
Survey II. The surveyed year was 1997 and there is a great deal of indicators that concern the period:
1995 - 1997.

The population includes all the industrial firms with less than 20 employees. The economic activity
classes belonging to the population, more specifically to the industry, are the ones that follow: from 15
until 37 and from 40 until 41. The sample was built by the INE Instituto Nacional de Estatstica
(National Institute of Statistics), according to the methodological specifications of EUROSTAT. The
INE has selected an initial sample of industrial firms, selected from the 9289 firms that are registered
at the FGUE Ficheiro Geral de Unidades Estatsticas do INE (Global File of INEs Statistical
Units). Thus, an initial sample of 1556 industrial firms was extracted from the population. The firms
that answered the questionnaire in a valid way, following the guidelines defined by EUROSTAT,
came to a total of 819 firms, represented a global answer rate of 57, 3%.

Since this study is focused on the entrepreneurial innovation capability of the firms, regarding their
product and/or process innovations, all 298 firms that undertook product innovation or process
innovation in the period 1995-1997, were considered.
B. Data: Description and Characterization
The analysis of innovation barriers, turning to the CIS data, has been carried out by several researchers
using, for this effect, data from European companies (Arundel, 1997; Silva, 2003; Glia and Legros,
2004; Tourigny and Le, 2004; Fernandez, 2005; Silva and Leito, 2007) and Canadian companies
through the adjustment of the questionnaire (Baldwin e Lin, 2002).

The firms were qualified as innovative if they introduced in the market or firm, products or processes
technologically new or improved during the period of 1995-1997. As observed in Figure 1, from the
sample of 819 firms, 298 answered they had innovated in the product or process.

In order to evaluate the importance of each restraining factor to innovation, it is attributed to each one
of them the value equal to 1, in case the firm answered the factor made it difficult to carry through the
projects, and the value equal to 0, else wise. The result of the distribution of the sample firms, along
with the difficulties in innovating, is presented in the following Figure 1.
FIG .1. BARRIERS TO INNOVATION
T he high cost of innovation
T he lack of financing
T he lack of skilled personnel
T he high economic risk
T he organisational rigidities
T he Government regulations
T he lack of customers responsiveness
T he lack of inform.technology
T he lack of information on market
0

40

80

120

In accordance with the total of the sample firms and the analysis of the Figure 1, we observe that the
main barriers to innovation are economic factors namely, high cost to innovation, lack of financing
and high economic risk. In what concerns the internal factors the lack of skilled personnel and
organizational rigidities, should be stressed. The results obtained are similar to those of other
researches carried out in Portuguese firms (CISEP, 1992). The factors associated with the lack of
information on technology and the lack of information on market are less restraining to innovation.
C. Logistic Regression Model
According to what has been previously defined, the Innovation (I) is a binary variable, which is equal
to 1, if the firm innovates; or equal to 0, if the firm does not innovate. The binary data are very
common among the several types of categorical data and their modelling is part of the general linear
regression models (McCullagh and Nelder, 1989). The logistic regression model the most common
one (Agresti, 1996, Ferro, 2003), regarding the way it facilitates the substantive interpretation of
parameters. Thus, logit regression is an approach used in studies of manufacturing firms (Kaufmann
and Tdtling, 2001; Silva, 2003, Silva et al. 2005, Silva and Leito, 2007) and services firms (Tether,
et al. 2001; Tether, 2005; and Freel, 2006).

Considering the variable answer (or dependent) I, let p (I) be the probability of the firm to innovate:
p (I)=Pr [I=1]

(1)

The extension of this model to multiple explanatory variables, represented by Cn, is processed through
their inclusion in the linear predictor. Since all the referred variables are nominal categorical and
recoded through dummy variables, the linear predictor of the model is specified according the
equation (2):
IN i 0 1C1 2 C 2 3 C 3 4 C 4 5 C 5
6 C 6 7 C 7 8 C8 9 C 9 i

(2)

In the estimation process the maximum likelihood procedure is used.

4. Results: Presentation and Discussion

The results of the estimated models are presented at the following Table II.
TABLE II
LOGIT REGRESSION MODELS RESULTS FOR BARRIERS TO INNOVATION

Barriers to innovation

The high economic

risk
The high cost of
innovation
The lack of financing
The organisational
rigidities
The lack of skilled
personnel
The lack of
information about
technology
The lack of
information on
market
The lack of
customers
responsiveness
The Government
regulations
Constant
Model summary
Correct Predict (%)
Chi-Square
Log likelihood
Number cases (n)

Model A
Parame
ter
Sig
Estimat
or

Final Model
Parameter
Estimator

Sig

EXP (B)

0,05

0,88

-1,13

0,00

-1,13

0,00

0,32

-1,34

0,00

-1,36

0,00

0,26

-0,22

0,53

-0,87

0,01

-0,93

0,00

0,40

0,29

0,49

-0,28

0,57

-1,27

0,01

-1,23

0,00

0,29

-0,09

0,82

1,54

0,16

1,43

0,00

4,62

72,3
%
123,7
2
493,6
5
470

0,00

71,9
%
122,6
6
494,7
1
470

0,00

The Model A explains the results of the systematic relations between the entrepreneurial innovative
capability at the level of product and/or process innovation, and the barriers to innovation. Since some
of the variables associated to the barriers are not statistically significant at a level of 5%, the
hypothesis, H1, H4 , H6, H7 and H9 were not empirically tested. Next, the estimation of the model was
set forth without considering those variables, from which the final model resulted.

The estimators of the final model are presented in Table 2. According to the Wald statistics, we detect
that all the estimators of the regression parameters are statistically significant up to 5%, except for the
relationships established with competitors.

The predictive capacity of the model is 71,9%, which results from the comparison between the
predicted and the observed values of the answer variable. The chi-square test statistics comprises
122,66 with a proof value inferior to the significance level of 0,05. The log-likelihood statistics,
comprising 491,71, also corroborates the global significance of the model, when compared with the
null model.

The obtained results show that most of the variables associated with barriers to innovation present a
negative signal, reason for which they are considered as stimulating and restraining factors that may
influence entrepreneurial innovative activities and consequently, to a decrease in the firms propensity
for innovating.

In what regards the statistical significance of each barrier to innovation, it is known that there are four
statistically significant variables whose identification and analysis will take place at once.

The results of the model suggest that high costs of innovation have a significant effect in the firms
propensity for innovating. Aware of this data, the null hypothesis of inexistent relation between
variables can be rejected, which sustains the H2 hypothesis. Firms that consider as excessive the
innovation costs present a smaller propensity for innovating. These results sustain the analysis of the
barriers to innovation (Figure 1) where high innovation costs are presented as the main barrier to
innovation. The obtained results are similar to other empirical studies (CISEP/GEPE, 1992; Martins,
1999; Tourigny and Le, 2004). The results show that firms which consider innovation costs as
excessive tend not to innovate, turning this factor into a barrier to innovation.

Concerning the hypothesis that intend to test if lack of financing sources is associated with the
propensity to innovate, the results show that this barrier is presented with a negative and significant
effect, for which it can be said that firms facing scarcity of financing sources have less firms
propensity for innovating. Thus, hypothesis H3 is confirmed. The obtained results are similar to those
of other researches, where the lack of adequate financing is an important barrier to innovation
(Hadjimanolis, 1999; Fernandez, 2005).

The lack of skilled personnel is presented as a statistically significant variable, for which the null
hypothesis of inexistent relation can be rejected, therefore there is a relation and a negative signal is
presented. Hence, it can be said that firms which face situations such as lack of skilled personnel, have
less propensity to innovate. Therefore, the hypothesis H5 is confirmed. The study of Hoffman et al.
(1998) supports these results, when defending the thesis that lack of qualified staff can be a serious
constraint to the development of the innovation process.

The results of the model show that lack of customers responsiveness to new products have a
significant effect in the propensity to innovate. The rejection of the null hypothesis of inexistent
relation amongst variables, allows the confirmation of H8 hypothesis. Thus, firms that perceive lack
of customers responsiveness to new products show fewer propensities to innovate. This result is in
accordance with the interactive model of innovation, with the market-pull approach and the Porter
model. These approaches demonstrate that the satisfaction of the market requires the incorporation of
innovations. Therefore, if the firm believes the market is not accepting the new products, it has no
incentive to innovate, and then this consciousness ends up creating a barrier to innovation.

5. Conclusions

The results show that firms which innovate are those that have more perception of the barriers to
innovation. However it is observed through the logistic regression model that some of the relations
established between the barriers to innovation and the entrepreneurial innovative capacity are not
statistically significant.

The results reveal that the majority of the variables associated with the barriers to innovation present a
negative signal. In this sense these variables are considered as factors that difficult or limit the
development of innovation activities and thus make firms less prone to innovate.

In what concerns the significance of each restraining factor of innovation, four significant variables
are detected. The results provide insights that high innovation costs have a negative and significant
effect on the innovation propensity. The same is detected for the barrier associated with the lack of
financing sources. For its turn, the lack of qualified personnel restrains the propensity of the firm for
innovating and also for developing the innovation process. The lack of customers responsiveness to
new products has also a negative and significant impact on the propensity for innovating.

In this sense, several public policies oriented for promoting innovation and overcoming innovation
restrains should be designed and implemented. This kind of policies is particularly important since the
majority of the Portuguese firms have a micro, small or medium dimension, which face scarce
resources and knowledge that restrain the entrepreneurial innovative capability. Thus, the conception
and the adoption of public policies for fostering innovation and overcoming barriers to innovation
should be promoted by national entities and governments.

In operational terms, the public measures should embrace financing schemes and incentives for
innovation activities, in order to promote the acquisition of new entrepreneurial and innovation
competences, and also the diffusion of innovation. The promotion of open innovation networks is also
critical. On the one hand, they promote access to information, knowledge and supportive mechanisms
for the firms. On the other hand, they promote cooperation between firms and other partners for
innovation (namely, universities, research units and other kind of public or private entities).

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