DOCUMENT DE TREBALL
XREAP2016-03

WHAT IS THE ROLE OF INNOVATION
STRATEGIES? EVIDENCE FROM SPANISH
FIRMS
Elisenda Jové-Llopis (GRIT, XREAP)
Agustí Segarra-Blasco (GRIT, XREAP)

What is the role of innovation strategies? Evidence from Spanish firms
Elisenda Jové-Llopis (§), Agustí Segarra-Blasco (§)
Abstract:
This paper discusses the determinants of two alternative measures of innovative
success/output by looking at firm’s innovation strategies. These relationships are also
discussed by distinguishing between firms belonging to manufacturing and services
sectors.Our econometric analysis is based on an extensive sample of 3,919 firms taken
from the Spanish Technological Innovation Panel (PITEC) for the period 2008–2012.
Alongside the empirical analysis we applied a two-step procedure. We first identified a
diverse range of innovation strategies by applying a principal component analysis
(absence, mixed and oriented). Then, after controlling for positive skewness of the
dependent variables, we used a generalized linear model (GLM) to examine the impact
of these strategies. Our empirical results have some interesting aspects. Firstly, firms that
do not design innovation strategies have a lower probability of being a successful
innovative firm. Secondly, firms that design a strategy, but one that is not oriented on any
specific direction, are prone to achieving lower success rates than firms with an oriented
strategy. Finally, the results also show that there is a good fit between the oriented strategy
pursued by a firm and its innovation success.
Keywords: innovation strategy, innovation success, innovation objectives, Spain, PITEC
JEL Classification Numbers: D21. O31. O32
Corresponding author: elisenda.jove@urv.cat
(§) Research Group of Industry and Territory
Department of Economics – CREIP, Universitat Rovira i Virgili
Av. Universitat, 1; 43204 – Reus, Spain Tel. + 34 977 759 816 Fax + 34 977 300 661

This paper is part of research carried out with the financial support of the Consolidated Group of Research
2014-SGR-1395, the Xarxa de Referència en Economia Aplicada (XREAP) and the Secretaria
d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya. We
would like to thank Mercedes Teruel and Liliana Herrera for their valuable comments and Verònica
Gombau for her research support. The usual disclaimers apply.

1. INTRODUCTION
It is well known that innovation is a decisive tool in ensuring the competitive position of
firms in their markets. In fact, innovation is a process with high levels of uncertainty and
a right innovation strategy can help firms to guide the process in order to enjoy a durable
competitive advantage in dynamics environments (Cooper 1984b; Smith 2010). Hence,
firms may dedicate efforts and time to design, ex ante which innovation strategies they
wish to pursue to meet their objectives (Burgelman, Christensen, and Wheelwright 2004;
Cooper and Edgett 2010). In this paper, we analyse empirically the role that innovation
strategies play in achieving innovation outputs. In particular, we ask both which strategy
has the greatest odds of improving innovation success and whether there is a fit between
the innovation strategy pursued and innovation success measured in terms of product and
process innovations.
In recent decades, empirical research has attempted to identify why some firms
have been more innovative than others, and also how firms may improve their odds of
successful innovation. Today, a large body of research exists on the determinants of
innovation, as well as on the effects of innovation on firms1. An agreement factor that
emerges from these body of literature and enhances innovative success is an explicit
innovation strategy because it provides a guideline for dealing with strategic issues, such
as selecting the market to enter or developing new products (Ernst 2002; van der Panne,
van Beers, and Kleinknecht 2003; Schroeder 2013). Hence, over the last few decades, the
empirical literature has mainly discussed the role of innovation strategies using the
concept of innovation input or output representing a subset of the various innovation
strategies that firms have at their disposal. For instance, in relation to innovation output,

1

The main model explored in this literature is the CDM model based on Crepon, Duguet, and Mairesse

(1998) that links R&D expenditures, innovation output and productivity.

2

product, process, marketing and organisational strategies have been analysed (HervasOliver, Sempere-Ripoll, and Boronat-Moll 2014; Jayaram, Oke, and Prajogo 2014;
Karlsson and Tavassoli 2015; Tavassoli and Karlsson 2015), or according to R&D
sources (innovation input) empirical literature has distinguished between internal strategy
(to make), external strategy (to buy) and, more recently, cooperation strategy (Veugelers
and Cassiman 1999; Vega-Jurado, Gutiérrez-Gracia, and Fernández-de-Lucio 2009;
Goedhuys and Veugelers 2012; Mata and Woerter 2013; Love, Roper, and Vahter 2014).
However, this literature has hardly explored a broader and long term relationship
between innovation strategies and innovation success, it has mainly focus solely on
innovation inputs or output point of views. Our approach to addressing this gap is to take
a step back compared to previous studies on innovation determinants, starting with the
innovation objectives since these are found to be the starting point of the innovation
process and provides a broader and long term vision of the process (OECD -Eurostat
2005; Cooper and Edgett 2010). To do this, our data comes from the Spanish
Technological Innovation Panel (PITEC), a dataset that comprises the annual Spanish
Community Innovation Survey (CIS) questionnaire and follows the methodological
guidelines defined in the OECD’s Oslo Manual. Using a broad sample of 3,919 Spanish
manufacturing and services firms for the period 2008–2012, we carry out our empirical
analysis in two stages. Firstly, we identify the different kind of innovation strategies that
a firm can design. Applying a principal component analysis to thirteen innovation
objectives listed in the innovation survey, we define the innovation strategies (absence,
mixed and oriented towards quality, production, cost and environmental and regulatory)
that firms may pursue to improve their odds of successful innovation. Then, we examine
the impact on innovation success of these strategies and their degree of fit applying a
GLM model to check for positive skewness in the dependent variables.

3

Our contribution differs from the previous literature in a number of ways. Firstly,
we expand the scope of analysis of innovation strategies beyond only the field of input or
output innovation only point of view in order to provide a much richer understanding of
firms’ choices of innovation strategies as well as of the effects of different strategies on
innovation success. Secondly, given the current increasing importance of service firms in
most industrialized countries and the distinct nature of the innovative processes between
manufacturing and service firms (Segarra-Blasco 2010; Leiponen 2012) we consider it
appropriate and relevant to explore and study in more depth the differences between
manufacturing and service firms. This allows us to detect and quantify differences
between sectors.
The remainder of the paper is structured as follows. Section 2 consists of a
literature review. Section 3 presents the database, the variables and some descriptive
statistics. Section 4 contains the econometric methodology. Section 5 shows our main
findings. The last section presents our conclusions and the consequent policy
implications.

2. INNOVATION STRATEGIES
The empirical literature has paid attention to specific classifications of innovation
strategies. First, according to R&D sources or innovation input, three strategies have been
distinguished, internal (or make), external (or buy) and cooperation (Oerlemans, Meeus,
and Boekema 1998; Veugelers and Cassiman 1999; Goedhuys and Veugelers 2012; Love,
Roper, and Vahter 2014). This research has found that a combination of internal and
external knowledge sources is a key element of a successful innovation strategy instead
of only undertake R&D in-house. Closely related to the role of networks, partnerships
and linkages, a new growing body of literature investigate how resources allocation

4

strategies (measured as breadth of external search for new ideas) impact upon
performance (Laursen and Salter 2006; Leiponen and Helfat 2010; Leiponen 2012). Their
empirical results suggest that strategies based on allocation resources to a broader range
of information sources is likely to affect innovation outcomes.
Second, related to the four type of innovations proposed by the Oslo Manual (3rd
edition, 2005), some empirical papers have differentiated between technological
strategies -product and process innovations- and non-technological strategies -marketing
and organizational innovations- (Jayaram, Oke, and Prajogo 2014; Karlsson and
Tavassoli 2015; Oh, Cho, and Kim 2015; Tavassoli and Karlsson 2015). For instance,
Karlsson and Tavassoli (2015) for a sample of Swedish firms distinguished between
sixteen strategies, which compose four type of innovation outputs from Oslo Manuals,
i.e. product, process, marketing and organizational, called simple innovation strategies,
plus various combinations of these four types, called complex innovation strategies. They
found that complex innovation strategies are better off in terms of their future productivity
as compared to those firms that choose simple innovation strategies. Although these are
a useful classification, we think that these classifications are simplistic view of looking at
innovation strategies of firms.
It is well known that innovation is a dynamic process subject to a complex
sequence of decisions. Considering it as a process, from a temporal dimension, a firm's
first strategic decision is whether to innovate or not. That is, take on new challenges in
order to survive or grow in the markets or, on the contrary, opt for dynamic routines and
keep doing the same thing as always, not taking into account changes in the environment
and their consequences. When the decision to innovate has been taken, and innovation is
a priority in the firm, the second step consists in deciding which innovation strategy to
develop.

5

The main role of an innovation strategy is that guides the decisions on how
resources are to be used to meet a firm's objectives for innovation and, consequently
provides value and builds competitive advantage. Gilbert (1994) highlighted that
innovation strategy designates to what degree and in what way a firm uses innovation to
perform its business strategy and to enrich its performance. Hence, a planned and wellcommunicated innovation strategy is necessary in order to achieve maximal effectiveness
and efficiency (Ramanujam and Mensch 1985; Ernst 2002; van der Panne, van Beers, and
Kleinknecht 2003; Oke 2007).
Design a correct innovation strategy is an essential tool for a constant growth when
the environment is dynamic, unpredictable, competitive and specially in difficult times.
In addition, to obtain a successful innovations determination of a strategic orientation and
top management team support are needed (Cooper 1984a; Cooper and Edgett 2010; Talke,
Salomo, and Rost 2010). In addition, because in innovation activities require the
acquisition of highly specialized assets (sunk costs), the presence of highly-educated and
skilled employees (knowledge-related intangible assets), and involve a significant degree
of uncertainty (Hall 2002) following a long-term horizons when a firm allocate their
critical and scarce resources have been shown to be important for producing high-quality
innovations and avoiding quick decisions (Akman and Yillmaz 2008; Talke, Salomo, and
Rost 2010).
Although an innovation strategy provides a guideline for survival in today’s
competitive environment and is helpful to the firm’s technological capabilities, according
to Page (1993) and more recently to Dobni, Klassen and Nelson (2015), it does not seem
to be common practice among firms. Clearly, one of the most important barriers to
innovation is the absence of well-defined innovation goals and objectives that provide a

6

clear direction for the innovation process to follow (Oke 2007; Dobni, Klassen, and
Nelson 2015).
Those firms that do not design a clear innovation strategy tend to have lower
returns on R&D and innovation activities – this is because firms that wish to innovate in
all areas may end by innovating in none, may innovate in areas not essential for the firm,
may invest in innovation projects not aligned with the objectives of the firm, or their
innovation efforts may just become a matter of chance. For instance Akman and Yillmaz
(2008) highlighted that without a strategy for innovation, innovation success is harder and
frequently not possible. This lead us to formulate the following hypothesis:
H1. Firms that do not design clear innovation strategies have lower odds of being a
successful innovative firm.
When it comes to oriented strategies, the literature emphasises that an innovation
strategy focus on specific innovation fields increases a firm performance. To build an
oriented strategy implies the use of common resources between related objectives within
an innovation field and has been found to increase innovation outcomes thanks to
avoiding additional costs, coordinating resources or sharing learning processes (Salomo,
Talke, and Strecker 2008; Bowonder et al. 2010; Leiponen 2012; Aniruddha 2013).
Hence, firms with strong degree of focus may perform better than firms with an absence
of focus that are more likely to fail.
However, that not all innovative orientations are suitable for a given environment
and different innovation orientations are associated with differences innovation success
(Manu and Sriram 1996). For example, product innovation outputs will primarily relate
to innovations orientations towards competition, market and demand (e.g increasing
market share, range of products) while process or organisational innovations will tend to
relate to supply or new legislation orientations (e.g. reducing costs, improving production

7

capabilities, reducing environmental impacts) (Balachandra and Friar 1997; van der
Panne, van Beers, and Kleinknecht 2003; Tidd, Bessant, and Pavitt 2005; Paulraj 2009;
Hervas-Oliver, Sempere-Ripoll, and Boronat-Moll 2014; Jayaram, Oke, and Prajogo
2014). This lead us to test the fit between innovation strategy and formulate our second
hypothesis:
H2. Firms that design oriented innovation strategies have higher odds of being a
successful innovative firm.
Based on the “recombinant growth” expression, the recombination of different
types of knowledge or different types of innovations, it is accepted that the probability of
obtaining innovation success is higher when there is more variety to be recombined
(Weitzman 1998). Here, variety is taken as diversity in innovation orientations, which is
reflected in the breadth of fields in firm’s innovation objectives.
One of the questions that have significance relevant in the management studies is
the effects of diversity on firm performance. Some authors showed that diversified
technology base positively affects innovative potential of firm (Garcia-Vega 2006;
Quintana-García and Benavides-Velasco 2008; Lin and Chang 2014). However, other
studies find that the level of diversity matters and two much diversity cause high levels
of coordination and integration costs and may lead to reduced opportunities for innovation
(Leten, Belderbos, and Van Looy 2007). In the same line, Laursen and Salter (2006),
Leiponen and Helfat (2010) and Leiponen (2012) investigate how resources allocation
strategies (measured as breadth of external search for new ideas or pursuing multiple
parallel objectives) impact upon performance. In general, their empirical results suggest
that strategies based on allocation resources to a broader range of information sources or
objectives are associated with successful innovation. Some particularities regarding the
sector is found, for instance, (Leiponen 2012) showed that breadth in terms of pursuing

8

parallel innovation objectives appears to have a negative effect on innovation in service
industries because some services firms may do not have enough accumulated managerial
processes and capabilities to benefit from these strategies.
In summary, based on the above literature review and discussion, the last
hypothesis is proposed:
H3. Firms that design mixed innovation strategies have higher odds of being a successful
innovative firm.

3. DATABASE, VARIABLES AND DESCRIPTIVE STATISTICS
3.1 Database
This analysis is based on firm level data from the Technological Innovation Panel
(PITEC). PITEC is a specific statistical instrument for studying the innovation activities
of large sample of Spanish firms over time and it is jointly developed by the Spanish
National Institute of Statistics (INE), the Spanish Foundation for Science and Technology
(FECYT), and the Foundation for Technical Innovation (COTEC).
PITEC is designed as a panel survey, based on the Community Innovation Survey
(CIS), one of the most used datasets in innovation studies.2 These innovation surveys are
collected following the general guidelines of the Oslo Manual (OECD–Eurostat, 2005).
The PITEC has two main advantages for this study3. First, and most importantly,
this database has detailed information about firms' innovation objectives. Innovation

2

See Vokoun (2015), Cainelli et al. (2015), and Hashi and Stojčić (2013) for recent examples of empirical

work using the Community Innovation Survey dataset and Segarra-Blasco and Teruel (2014); Barge-Gil
and López (2014); Costa-Campi et al. (2015) for recent examples of empirical work using the PITEC
dataset.
3

However, PITEC data base is not free of limitations. One of the limitations of the innovation surveys like

PITEC is the subjective nature of many of the questions addressed to the firm’s management or those
responsible for R&D departments. However, Mairesse and Mohnen (2005) provide evidence that the

9

surveys are constantly improving their quality and relevance and, from 2003 on, the
innovation survey has been updated and new questions have been included, allowing
researchers to pursue new lines of research in depth. Specifically, in 2008, Spanish firms
were for the first time asked to indicate the importance of items in a list of innovation
objectives when carrying out innovation activities4. Such information is essential to this
study. Second, PITEC is characterized by its time dimension. It has panel data for the
period 2003–2012 which facilitates researchers in dealing more accurately with
innovative behaviour of Spanish firms longitudinally and also treat standard econometric
issues, such as unobserved heterogeneity and simultaneity problems that are hard to detect
in simple cross-sectional data or time series (Baltagi 2008). In such temporal panels,
containing data on the firms' innovation performance, it is easier to control common
endogeneity problems by introducing lagged explanatory variables in the empirical
specification or by using new methods which take into account the initial conditions of
the model's dependent variable and firms' individual-specific effects (Semykina and
Wooldridge 2010).
Our final database selection was subject to a process of filtering. The main filters
were as follows: 1) data referred the period 2008–2012, because objectives questions were
not included in the survey until 2008; 2) only innovative firms were examined, that is,

subjective measures of innovation surveys tend to be consistent with more objective measures of
innovation, such as the probability of holding a patent and the share in sales of products protected by
patents.
4

In general, empirical research on innovation at the firm level has yet to incorporate the role of the

objectives, in particular, in the studies of determinants of eco-innovation (Cainelli, De Marchi, and
Grandinetti 2015; Costa-Campi, García-Quevedo, and Segarra-Blasco 2015; Jakobsen and Clausen 2015)
and in the studies of how the breadth of innovation objectives impacts on innovation (Leiponen and Helfat
2010; Leiponen 2012).

10

firms that had introduced product or process innovations or firms with an intention of
being innovative (i.e. firms that had taken an innovative project but later abandoned or it
still remain to be completed);5 3) firms from the manufacturing and service sectors were
analysed;6 4) firms that report confidentiality issues, mergers, employment incidents and
so on were not incorporated in the sample.
After all filtering, our empirical analysis is based on a balanced panel of 19,595
observations for the period 2008–2012. At this point, the dataset included 3,919 Spanish
innovative firms of which 2,850 firms belong to the manufacturing sector and 1,069 firms
to the service sector.

3.2 Variables
We consider two types of dependent variables: product innovation (the introduction of a
good or service that is new or significantly improved during t-2 to t) and process
innovation (the implementation of a new or significantly improved production or delivery
method during t-2 to t).
The key explanatory variables in our analysis represent the different innovation
strategies that firms may design when engaging in innovation activities. In 2008, the
Spanish CIS introduced a new question7 “Innovation activities carried out in your firm
could be oriented to different objectives, how important were each of the following

5

For instance, we take into account those firms that may be pursuing a certain innovation strategy and yet

fail to attain innovation outcomes in a given period. Excluding non-innovative firms is based on the reason
that these firms are unlikely to have any aspiration to innovation, in line with other studies using innovation
dataset (D’Este et al. 2012; Jakobsen and Clausen 2015).
6

See Appendix 1 for a detailed classification.

7

The question was modified by the INE. In 2008, the question regarding the effects of innovation was

replaced by innovation objectives. While "objectives" relate to a firm's motives for innovating, "effects"
concern the actual observed outcomes of innovations (OECD - Eurostat, 2005).

11

objectives8 for your innovation activities during the three last years9?.” Firms were asked
to evaluate the importance of each innovation objective on a Likert scale of 1 to 4, where
1 represents "high importance", 2 represents "intermediate importance", 3 represents "low
importance" and 4 represents "factor not experienced". For each objective, listed in Table
1,10 we assign a binary value depending on its survey response. These dummy variables
are equal to 1 when firm considers the innovative objective to have high importance and
0 when the importance is intermediate, low or not experienced.
First, we distinguished between these firms that innovation process is guided by
an innovation strategy and those that do not design a strategy. Firms designing an
innovation strategy also are divided into two groups: mixed and oriented strategy. The
former strategy includes firms that have an innovation strategy but without any specific
orientation (firms pursue some innovation objectives but not inter-related ones). The latter
encompasses these firms with a clear innovation strategy oriented towards quality,
production, costs or environmental and regulatory dimension.11
In order to identify the oriented strategies, we group the thirteen innovation
objectives by applying a multivariate statistical method. A principal component analysis
(PCA) is undertaken on the thirteen innovation objectives reported from the innovation

8

See Table 1 for a detailed classification.

9

Some of qualitative questions in innovation surveys refer to a 3-year period, while quantitative ones refer

to the actual year of the survey. In particular, questions on innovation objectives refer to a 3-year period.
10

In 2008, the innovation survey included thirteen innovation objectives. In addition, in 2009, three new

objectives relating to employment such as the increase in total employment, the increase in skilled
employment and the maintenance of employment were appended to the thirteen objectives added the
previous year. Due to the lack of data for the full period under analysis, the latter objectives about
employment are not considered in this study.
11

The exact definition of these variables (in the way that we use them in our analysis) is presented in

Appendix 2. See Appendix 2 for a detailed definition.

12

survey.12 PCA analyses should be ideally applied to continuous variables or ordinal
measures with broad enough scales. Hence, the categorical variables with relatively
narrow scales (binary variables) are corrected for by using a tetrachoric correlation matrix
as the input correlation matrix in the standard PCA, under the assumption that observed
binary variables correspond to latent continuous variables.

Table 1
Component loadings after orthogonal rotation
Innovation objectives

Quality

Production

Cost

1. Increase range of goods or services
2. Replace products being phased out
3. Enter new markets
4. Increase market share
5. Improve product quality
6. Increase flexibility of production
7. Increase capacity of production
8. Reduce labour costs per unit output
9. Reduce material costs per unit output
10. Reduce energy costs per unit output
11. Reduce environmental impacts
12. Improve health or safety of
employees
13. Fulfil government regulation or
standards requirements

0.4982
0.3115
0.5118
0.5077
0.3662
-0.0132
-0.0166
0.0066
0.0027
-0.0182
-0.0045

-0.0393
0.0898
-0.0862
-0.0134
0.1635
0.6920
0.6509
0.2003
-0.0560
-0.0628
-0.0617

-0.0648
0.1152
0.0064
0.0312
-0.0453
-0.0536
0.0287
0.4676
0.6421
0.5781
0.0919

Environmental
and regulatory
-0.0072
-0.0665
0.0131
-0.0154
0.0732
0.0139
-0.0043
-0.0677
-0.0282
0.0846
0.5444

-0.0038

0.0467

-0.0261

0.5808

0.0093

0.0189

-0.0379

0.5859

Cronbach's alphas
0.7270
0.7195
0.7634
0.8339
Note: Seventy percent of total variance was explained by the four components; principal components
factoring with orthogonal varimax rotation. N=19,595. Larger components loadings appear in bold.

After the extraction of principal components, orthogonal rotation13 of retained
components was applied in order to enhance interpretability (Kline 1994). The number of
components to retain for rotation was subjective, based on the trade-off between

12

The main interest in this study is to use PCA to identify patterns of association across innovation

objectives.
13

Orthogonal rotation rotated components remain uncorrelated while oblique rotation allows for correlation

between the rotated components. For additional robustness in analysing the patterns identified, we used
oblique rather than orthogonal rotation, but the same patterns emerged.

13

simplicity (retaining as few as possible factors) and completeness (explaining most of the
variation in the data). There are some standard recommendations in this area. Kaiser's
rule, for example, recommends retaining only components with eigenvalues larger than
one. Another common strategy is to examine the plot of the eigenvalues and determine
whether there is a point beyond which the remaining factors explain considerably less
variation. Taking these recommendations into account, four components were retained.
Cronbach's coefficient is also used to evaluate internal consistency for each
component retained. The Cronbach alphas for the four components are greater than 0.70,
generally indicating an acceptable level of internal consistency.
Table 1 shows the component loadings that emerged after having retained four
components. According to the results, the objectives can be broadly categorized as quality
strategy (competing with better and more products), production strategy (improving the
capacity and flexibility of production), cost strategy (competing with lowering production
costs) and environmental and regulatory strategy (being environmentally friendly and
satisfying standard requirements).
In addition to our variables of interest, innovation strategies, following the
economic literature on the determinants of innovation (Souitaris 2002; Galende and de la
Fuente 2003; Becheikh, Landry, and Amara 2006; Vega-Jurado et al. 2008; Keupp,
Palmié, and Gassmann 2012) a set of variables related to the firm's assets, competences
and capabilities are also included as internal factors (size, group, export and training in
innovation activities). Then, the firm's industry (high tech manufactures and high
knowledge intensive services); technological opportunity (cooperation); appropriability
conditions (legal mechanisms of protection) and government and public policies
(subsidies) variables are included in the analyses as external factors. Appendix 2

14

summarises the list of variables and their definition, Appendix 3 descriptive statistics of
variables included in the empirical analysis and Appendix 4 shows the correlation matrix.

3.3 Descriptive statistics
Based on an extensive sample of Spanish innovative firms, this section offers an overview
of innovation strategies that can be designed. Table 2 lists the thirteen objectives that
innovative firms can pursue in the course of their innovation activities, as well as the
strategies proposed in this study. It can be seen that a large number of Spanish innovative
firms have not designed an innovation strategy (24%). Some heterogeneity exists within
the group of firms with an innovation strategy, in the sense that some firms have a mixed
strategy (27%) and some firms specialize in a specific type of strategy. A quality strategy
is the one most common across the sample. Nevertheless, if we compare strategies by
sectors, this result changes slightly. A greater percentage of manufacturing firms pursue
an environmental and regulatory strategy, while service firms are more interested in
pursuing a production strategy. We also highlight that services firms have a higher
percentage of mixed or, no strategy, than manufacturing firms.
Analysing the importance of the innovation objectives, over the 2008–2012
period, 55% of firms consider improving quality of goods or services to be their key
innovation objective. Increasing the range of goods or services is indicated as the next
most important objective (52%), and increased market share ranks third (42%); these
results are in accord with the German ones, c.f. Aschhoff et al. (2013), and suggest that
the main concern of most firms is their product and its characteristics.
Consequently, during the period analysed (2008–2012), Spanish firms try to keep
their market position and survive by creating differentiated products and services and by
distinguishing themselves from competitors. This is the opposite to Chinese firms, where

15

the main innovation objectives pursued relate to lowering production costs (Guan et al.
2009; Zheng 2014).
Table 2
Importance of different innovation objectives and strategies (mean score in the sample)
All sample
Manufactures
(% of firms)
Obs=19,595
Obs=14,250
F=3,919
F=2,850
1. Increase range of goods or services
0.5192
0.5341
(0.4996)
(0.4988)
2. Replace products being phased out
0.3399
0.3473
(0.4737)
(0.4761)
3. Enter new markets
0.4118
0.4264
(0.4921)
(0.4945)
4. Increase market share
0.4209
0.4387
(0.4937)
(0.4962)
5. Improve product quality
0.5492
0.5349
(0.4975)
(0.4987)
6. Increase flexibility of production
0.3371
0.3280
(0.4727)
(0.4695)
7. Increase capacity of production
0.3466
0.3397
(0.4759)
(0.4736)
8. Reduce labour costs per unit output
0.2715
0.3040
(0.4447)
(0.4600)
9. Reduce material costs per unit output
0.1695
0.2032
(0.3752)
(0.4024)
10. Reduce energy costs per unit output
0.1692
0.1994
(0.3750)
(0.3995)
11. Reduce environmental impacts
0.2546
0.2870
(0.4356)
(0.4523)
12. Improve health or safety of employees
0.2662
0.3018
(0.4420)
(0.4590)
13. Fulfil government regulation or standards requirements
0.3041
0.3430
(0.4600)
(0.4747)
Absence of strategy

Services
Obs=5,345
F=1,069
0.4795
(0.4996)
0.3202
(0.4666)
0.3728
(0.4836)
0.3734
(0.4837)
0.5874
(0.4923)
0.3614
(0.4804)
0.3648
(0.4814)
0.1848
(0.3882)
0.0798
(0.2711)
0.0888
(0.2845)
0.1683
(0.3742)
0.1711
(0.3767)
0.2005
(0.4004)

0.2370
(0.4252)
0.2263
(0.4185)

Oriented strategy
Quality
Production
Cost
Environmental and regulatory

0.2510
(0.4336)
0.2660
(0.4419)

0.2733
(0.4457)
0.2432
(0.4290)
0.1743
(0.3794)
0.2643
(0.4409)

Mixed strategy

0.2317
(0.4219)
0.2115
(0.4083)
0.2865
(0.4521)
0.2352
(0.4242)
0.2091
(0.4067)
0.3016
(0.4589)

0.2381
(0.4260)
0.2645
(0.4411)
0.081
(0.2734)
0.1650
(0.3712)

F: number of firms. Standard deviation in brackets.

Next to objectives related to competition, demand and market, firms also take into
account increasing the capacity and flexibility of production (34%) and fulfilment of laws
or regulations (30%) Only the increase in health security (26%), the reduction in

16

environmental impacts (25%), the reduction in labour costs (27%) and the reduction in
material and energy unit costs (16%) seem to be less strongly pursued among the highly
important objectives.
When we distinguish between manufacturing and services firms, the results show
only small changes in the innovation objectives rankings. In the manufacturing and
services sectors, the improvement of product/service quality and the increasing range of
product or services still rank as the two most frequently stated objectives. Then, if we
look at the increase in capacity and flexibility of production objectives, a greater
percentage of services firms state that they pursue these objectives than is the case for
manufacturing firms. However, the three objectives related to reducing costs are more
followed by manufacturing firms than by service ones. Finally, the percentage of firms
that state that environmental and regulatory objectives are an innovation objective of high
importance is significant. For instance, in the manufacturing sector this percentage rises
to 29 percent, however, in the services sector this percentage is much lower (17%). As
Cainelli et al. (2015) remark, manufacturing firms are increasingly challenged to include
environmental innovations in their business activities.

4. ECONOMETRIC METHODOLOGY
Generally, the economics analysis of the determinants of product or process innovation
has been carried out using standard logit or probit models or bivariate models. However,
binary logit and probit models assume that the numbers of dependent variable cases
scored as one, and scored as zero, are fairly equal. When there is a significant disparity,
as in our case (76% of firms have introduced product innovations and 73% of firms have
introduced process innovations), generalized linear models (GLMs) with a binomial
family and log-log link provide better estimations because of their asymmetric nature

17

(Hardin and Hilbe 2012). We choose to opt for GLMs models, as we prefer to prioritise
the positive skewness of the dependent variables.
The GLMs also control for over-dispersion, which can be an important problem
in models with binary responses, causing underestimation of the standard error of the
estimated coefficient vector, and consequently non-significant variables can spuriously
appear to have significant influences. In order to recognize possible over-dispersion, the
GLMs provide the value of the Pearson χ2 or the deviance divided by the degrees of
freedom. A Pearson's statistic close to 1 indicates that the models are not over dispersed
(they are well specified). The Huber-White Sandwich technique was used to correct for
possible heteroscedasticity problems.
Specifically, the following equation is estimated:
𝑦 𝑖𝑡 = 𝛽0 + 𝛽1 𝑆𝑇𝑅𝐴𝑇𝑖𝑡−1 + 𝛽2 𝑋 𝑖𝑡−1 + 𝛼 𝑡 + 𝛿 𝑖 + 𝜀 𝑖𝑡

(1)

being i = 1,…, N firms and t= 1,…, T years and where yit is the binary outcome variable
that distinguishes between product innovation and process innovation. Among the
explanatory variables in Equation (1), STRATi,t-1 is a vector of explanatory variables
containing information about innovation strategies that firms can pursue, Xi,t-1 includes a
set of firm characteristics and 𝛽0, 𝛽1 and 𝛽2 are unknown parameter vectors to be
estimated. Additionally, a set of dummy variables related the temporal αt and sector
dimension δi are included in all of the regressions to control for cyclical effects and
specific industry characteristics, respectively.
Innovation efforts need some time to impact on innovation outputs, for that reason, our
data take into account a potential time lag between innovation efforts and new product or
process innovations.14 Following Audretsch et al. (2014); Barge-Gil and López (2014)

14

Since we are working with a short panel, we decided to lag the independent variables by just one period

of time, although further lags may be needed.

18

and Santamaría et al. (2012), in the regression analyses, the dependent variables refer to
the year t while the explanatory variables refer to the year t–1. This time difference is used
in order mitigate endogeneity problems arising from reverse causality.
Even though panel data is available, a pooled estimation has been carried out for
the whole period. The period for which the dependent variable data is available is very
short and most of the independent variables like strategies and R&D are highly persistent
(Clausen et al. 2012) and there is very little variation over time.
In addition, in order to control for potential multicollinearity problems, the
variance inflation factor (VIF) was calculated. The individual VIF values were
substantially below the recommended cut-off point of 10, indicating that multicollinearity
problems do not exist in any of the models (the mean VIF was 1.54).

5. RESULTS
The main results of the empirical analyses are presented in this section. Tables 3 and 4
report the results of the generalized linear model (GLM) for the whole sample, and for
the manufacturing and services firms, respectively15. Both tables present two econometric
models, first the baseline model, which includes the most common innovation
determinants is presented, followed by the innovation-strategy model, where we analyse
the effect of different innovation strategies. Pearson's statistics with respect to all of the
models were close to one, indicating that the models were not over dispersed.
As we expected, for innovative firms, not designing an innovation strategy has a
negative and significant impact on the likelihood of achieving successful innovation
measured in terms of product or process innovations. Whereas firms that design an

15

The smaller values of Akaike's information criterion (AIC) in Models innovation strategy indicate that

these models had improved explanatory power compared to the Baseline Model.

19

innovation strategy show mixed results, depending on the innovation strategy and the
innovation success pursued (product and process innovation). Our results also indicate
that, when innovation strategies are mixed, this increases the probability of innovation in
products, while it decreases the probability of innovation in processes; however, the latter
coefficient is not significant.
Regarding oriented strategy, our results seem to confirm that there is a good fit
between the innovation strategy pursued, and the innovation output obtained. Firms that
follow a quality strategy show a positive and significant impact on product innovation
and negative but no significant impact on process innovation. In particular, those, firms
that pursue a quality strategy would increase their likelihood of being successful
innovative firms in product innovations by 11 percent. Comparing the marginal effects
between mixed strategy and focus on quality strategy, the results show that the impact of
oriented strategy on product innovation is about seven percent higher, as we expected.
The results also show that production, cost and environmental and regulatory strategy
have a positive and significant impact on process innovation and a negative impact on
product innovation.

20

Table 3
Generalized linear models (GLMs, whole sample)
Product innovation
Baseline model

Process innovation

Innovation strategies

Baseline model

Innovation strategies

Coeff.

lSize t-1

MEMs

Coeff.

MEMs

Coeff.

MEMs

Coeff.

MEMs

0.0815***

Variables

0.0159***

0.0882***

0.0168***

0.2128***

0.0457***

0.1962***

0.0405***

(0.014)

Group t-1

(0.003)

(0.014)

(0.003)

(0.015)

(0.003)

(0.015)

(0.003)

-0.1001***

-0.0195***

-0.1045***

-0.0199***

-0.0253

-0.0054

-0.0595

-0.0123

(0.038)

Export t-1

(0.007)

(0.038)

(0.007)

(0.036)

(0.008)

(0.036)

(0.007)

0.2325***

0.0453***

0.1970***

0.0375***

0.0562

0.0121

0.0473

0.0098

(0.035)

(0.007)

(0.035)

(0.007)

(0.034)

(0.007)

(0.035)

(0.007)

0.4530***

0.0883***

0.4173***

0.0794***

0.8771***

0.1883***

0.8059***

0.1664***

(0.049)

(0.009)

(0.049)

(0.009)

(0.052)

(0.011)

(0.052)

(0.010)

0.0795***

0.0155***

0.0669***

0.0127***

-0.0279***

-0.0060***

-0.0381***

-0.0079***

(0.005)

(0.001)

(0.005)

(0.001)

(0.005)

(0.001)

(0.005)

(0.001)

-0.0036

-0.0007

-0.0045

-0.0009

-0.0001

-0.0000

0.0013

0.0003

(0.005)

(0.001)

(0.005)

(0.001)

(0.005)

(0.001)

(0.005)

(0.001)

0.3848***

0.0750***

0.3529***

0.0671***

0.3267***

0.0702***

0.2769***

0.0572***

(0.039)

(0.008)

(0.040)

(0.007)

(0.036)

(0.008)

(0.036)

(0.007)

-0.0022

-0.0004

-0.0087

-0.0017

-0.1024***

-0.0220***

-0.0985***

-0.0203***

(0.038)

(0.007)

(0.039)

(0.007)

(0.035)

(0.008)

(0.036)

(0.007)

0.2826***

0.0551***

0.2697***

0.0513***

-0.3368***

-0.0723***

-0.3293***

-0.0680***

(0.034)

(0.007)

(0.034)

(0.006)

(0.032)

(0.007)

(0.032)

(0.007)

Absence strategy t-1

-0.3234***

-0.0615***

-0.2954***

-0.0610***

(0.060)

(0.011)

(0.061)

(0.013)

Mixed strategy t-1

0.2033***

0.0387***

-0.0309

-0.0064

(0.064)

(0.012)

(0.062)

(0.013)

Quality strategy t-1

0.5855***

0.1114***

-0.0019

-0.0004

(0.050)

(0.009)

(0.051)

(0.010)

Production strategy t-1

-0.1399***

-0.0266***

0.6601***

0.1363***

Human resources t-1

lInternal R&D t-1

lExternal R&D t-1

Cooperation t-1

Subsidy t-1

HT manuf. and HKIS t-1

(0.049)

(0.011)

0.2580***

0.0533***

(0.010)

(0.055)

(0.011)

-0.0418

-0.0079

0.1418***

0.0293***

(0.049)

Environ. and regulatory
strategy t-1

(0.052)

-0.0144

(0.050)

Cost strategy t-1

(0.009)

-0.0755

(0.009)

(0.050)

(0.010)

0.2410***

0.3381***

0.6401***

0.7462***

(0.073)

(0.089)

(0.076)

(0.094)

(1/df) Pearson

0.9854

0.9761

0.9997

0.9935

AIC

1.0313

1.0100

1.08688

1.0515

Constant

BIC

-135,161.9

Log pseudolikelihood

-135,448.7

-8,070.4

-7,898.0

Observations

-134,290.8

-134,799.1

-8,506.1

-8,222.8

15,676

Estimations control for time and industry dummies. Marginal effects calculated at their mean (MEMs). For dummy variables, change in probability for a
discrete change of the dummy variable from 0 to 1. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Given the different nature of manufacturing and service sectors, we also focus on
the differences that an innovation strategy may exert on the probability of innovating in

21

these two sectors (Table 4). In general, the lack of an innovation strategy to pursue has a
significant and negative influence on innovation success in manufacturing and services
firms. However, the effect of this variable is quite heterogeneous across both sectors. A
service firm not having an innovation strategy is associated with a 13 percent decrease in
the probability of being a successful innovative firm as measured in terms of process
innovation, while manufacturing firms are associated with a four percent decrease. This
reveals notable sectorial differences.
Then, regarding oriented strategies our results seem to confirm that there is also a
good fit between the innovation strategy pursued, and the innovation output obtained by
sectors. Again, firms that follow a quality strategy show a positive and significant impact
on product innovation and negative but no significant impact on process innovation.
While firms that design production, cost or environmental strategies show a positive and
significant impact on process innovation. However, in services firms these results change
slightly. The sign of the last innovation strategy (environmental and regulatory strategy)
becomes negative and significant in respect to process innovation.
In addition, services firms that design an environmental and regulatory strategy
would decrease by five percent the probability of being a successful innovative firm, as
measured by process innovation, whereas manufacturing firms would increase the
probability by three percent. The sizes of the effect of these three strategies (production,
cost and environmental and regulatory) on process innovation success are quite different.
Production strategy shows the strongest effect, followed by cost and environmental and
regulatory strategy. In addition, the results also show sectorial differences. The likelihood
of being a successful innovative firm, measured by process innovation, would increase
by 15 percent when manufacturing firms follow a production strategy or but only by 11
percent for a service firm following the same strategy.

22

Table 4 GLMS by sector
Manufactures
Product innovation
Baseline model
Innovation strategies
Variables
lSize t-1

Coeff.
0.0939***

MEMs
0.0171***

Coeff.
0.1065***

Process innovation
Baseline model
Innovation strategies

MEMs
0.0189***

Coeff.
0.2054***

MEMs
0.0420***

Coeff.
0.1974***

MEMs
0.0386***

(0.020)
Group t-1

(0.004)

(0.020)

(0.004)

(0.020)

(0.004)

(0.020)

(0.004)

-0.0670

-0.0122

-0.0741

-0.0132

-0.0640

-0.0131

-0.1072**

-0.0210**

(0.048)

(0.045)

(0.009)

(0.045)

(0.009)

0.0144

0.0507

0.0104

0.0604

0.0118

(0.009)

(0.050)

(0.009)

(0.049)

(0.010)

(0.050)

(0.010)

0.4853***

0.0885***

0.4761***

0.0847***

0.8141***

0.1664***

0.7396***

0.1447***

(0.012)

(0.066)

(0.012)

(0.066)

(0.013)

(0.066)

(0.013)

0.0982***

0.0179***

0.0849***

0.0151***

(0.006)

lInternal R&D t-1

(0.009)

0.0811

(0.066)

Human resources t-1

(0.048)

0.0213**

(0.050)

Export t-1

(0.009)

0.1170**

(0.001)

(0.006)

(0.001)

(0.006)

(0.001)

(0.006)

(0.001)

-0.0207***

-0.0042*** -0.0299***

-0.0059***

-0.0000

-0.0109*

-0.0022*

-0.0080

-0.0016

(0.007)

(0.001)

(0.006)

(0.001)

(0.006)

(0.001)

0.3255***

0.0594***

0.2990***

0.0532***

0.3987***

0.0815***

0.3371***

0.0660***

(0.009)

(0.049)

(0.009)

(0.045)

(0.009)

(0.045)

(0.009)

0.0541

0.0099

0.0350

0.0062

0.0075

0.0015

-0.0004

-0.0001

(0.008)

(0.047)

(0.008)

(0.043)

(0.009)

(0.043)

(0.008)

0.3837

0.0700

0.4545

0.0809

0.2694

0.0551

0.1877

0.0367

(0.346)

(0.063)

(0.339)

(0.060)

(0.301)

(0.062)

(0.310)

(0.061)

-0.3119***

-0.0555***

-0.2085***

-0.0408***

(0.071)

(0.013)

(0.072)

(0.014)

0.1877**

0.0334**

-0.0436

-0.0085

(0.077)

(0.014)

(0.073)

(0.014)

0.5704***

0.1015***

0.0147

0.0029

(0.058)

(0.010)

(0.059)

(0.012)

-0.1726***

-0.0307***

0.7595***

0.1486***

(0.057)

(0.010)

(0.064)

(0.012)

-0.1257**

-0.0224**

0.2177***

0.0426***

(0.058)

(0.010)

(0.062)

(0.012)

-0.2810

-0.0050

0.1464**

0.2865**

(0.0575)

HT manufacture. t-1

-0.0003

(0.046)

Subsidy t-1

0.0001
(0.001)

(0.048)

Cooperation t-1

0.0004
(0.007)

lExternal R&D t-1

(0.010)

(0.0585)

(0.0114)

Absence strategy t-1

Mixed strategy t-1

Quality strategy t-1

Production strategy t-1

Cost strategy t-1

Environ. Regul. Strategy t-1
0.0232

0.0388

0.0806

0.1133

(0.299)

(0.300)

(0.272)

(0.290)

(1/df) Pearson

0.9798

0.9737

0.9915

0.9867

AIC

0.9934

0.9745

1.0599

1.0251

BIC

-94,9230.5

-95,090.8

Log pseudolikelihood
Observations

-5,629.8

-5,518.2

Constant

-94,165.68
-6,008.8
11,400

23

-94,518.0
-5,804.63

Table 4
GLMS by sector (continued)
Services
Product innovation
Baseline model
Innovation strategies
Variables
lSize t-1

Coeff.

MEMs

Coeff.

MEMs

0.0720*** 0.0152*** 0.0707*** 0.0147***

Process innovation
Baseline model
Innovation strategies
Coeff.

MEMs

Coeff.

MEMs

0.1872***

0.0434***

0.1742***

0.0393***

(0.023)

Human resources t-1

(0.005)

(0.024)

(0.006)

(0.025)

(0.005)

-0.1194*

-0.0249*

-0.0062

-0.0014

-0.0093

-0.0021

(0.014)

(0.068)

(0.014)

(0.065)

(0.015)

(0.066)

(0.015)

0.0996

0.0211

0.0747

0.0156

-0.1153**

-0.0267**

-0.1200**

-0.0271**

(0.065)

Export t-1

(0.024)

-0.0217

(0.067)

Group t-1

(0.005)

-0.1023

(0.014)

(0.066)

(0.014)

(0.059)

(0.014)

(0.060)

(0.013)

0.9943***

0.2306***

0.9257***

0.2088***

(0.084)

(0.018)

(0.084)

(0.018)

0.4717*** 0.0998*** 0.4005*** 0.0834***
(0.076)

lInternal R&D t-1

(0.016)

(0.076)

(0.016)

0.0516*** 0.0109*** 0.0394*** 0.0082*** -0.0293*** -0.0068*** -0.0442*** -0.0100***
(0.009)

(0.002)

(0.009)

(0.002)

(0.010)

(0.002)

(0.010)

(0.002)

Cooperation t-1

0.0127

0.0027

0.0090

0.0019

0.0079

0.0018

0.0057

0.0013

(0.009)

lExternal R&D t-1

(0.002)

(0.009)

(0.002)

(0.008)

(0.002)

(0.008)

(0.002)

0.2495***

0.0579***

0.2106***

0.0475***

(0.065)

(0.015)

(0.066)

(0.015)

0.6277*** 0.1328*** 0.5804*** 0.1209***
(0.071)

(0.015)

(0.071)

(0.015)

0.0043

0.0380

0.0079

(0.016)

(0.075)

(0.016)

(0.071)

(0.016)

0.2109
(0.288)

0.0446
(0.061)

0.3808
(0.286)
-0.2408**

0.0793
(0.060)
-0.0502**

-0.5371
(0.528)

-0.1246
(0.122)

(0.120)

(0.025)

(0.122)

(0.027)

0.2868**

0.0597**

-0.1352

-0.0305

(0.125)

HKIS t-1

0.0203
(0.075)

Subsidy t-1

(0.026)

(0.124)

(0.028)

-0.0991

-0.0223

Absence strategy t-1

Mixed strategy t-1

Quality strategy t-1

-0.2494*** -0.0578*** -0.2106*** -0.0475***

0.4801*** 0.1000***

(0.072)

(0.016)

-0.4339
-0.0979
(0.531)
(0.120)
-0.5903*** -0.1331***

(0.105)

0.1062***

(0.021)

(0.098)

(0.022)

0.0104

0.2638**

0.0595**

(0.121)

(0.025)

(0.132)

(0.030)

0.0597

0.0124

-0.2000*

-0.0451*

(0.106)
Constant

(0.023)

0.4707***

0.0502

Environ. Regul. Strategy t-1

(0.104)

0.0018

(0.101)
Cost strategy t-1

(0.022)

0.0085

Production strategy t-1

(0.022)

(0.109)

(0.025)

-0.2844

-0.3373

1.0617**

1.3614**

(0.295)

(0.308)

(0.531)

(0.544)

(1/df) Pearson

0.9810

0.9776

1.0183

1.0143

AIC

1.0675

1.0516

1.1258

1.0930

BIC

-31,065.1

-31,094.9

-30,815.78

-30,917.87

Log pseudolikelihood

-2,263.3

-2,223.6

-2,388.0

-2,311.8

Observations

4,276

Estimations control for time and industry dummies. Marginal effects calculated at their mean (MEMs). For dummy variables, change in
probability for a discrete change of the dummy variable from 0 to 1. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, *
p<0.1.

24

Finally, with respect to the other variables extensively analysed, our results for the
whole sample are in accordance with the literature (Becheikh, Landry, and Amara 2006;
Mohnen, Mairesse, and Dagenais 2006; Ahuja, Lampert, and Tandon 2008; Hashi and
Stojčić 2013). Regarding firm characteristics, size has positive and significant impact on
both product and process innovation success. A wide range of empirical studies showed
that larger firms have more capacity to generate innovations (Bhattacharya and Bloch
2004; Becheikh, Landry, and Amara 2006). In general, other characteristics of the firm
such as belonging to a group or export activity are not significant in explaining the
introduction of product or process innovation. For innovation success, firm competences
are important. They show a positive and significant impact, regardless of the type of
innovation, except for investment in internal R&D which shows a negative and significant
impact on process innovation. For instance, if firms invest in training expenditure for
innovation activities, this is associated with a 16 percent increase in the probability of
being a successful innovative firm in process innovations; if firms invest in supporting
the introduction of innovations into the market activities this is associated with a 30
percent increase in the probability of being a successful innovative firm in product
innovations.
As regards external factors, we observe that, for the whole sample, firms that have
cooperation agreements and firms that have mechanisms to protection their innovative
activities have an increased probability of being a successful innovation firm. With
respect to public subsidies, we observe that having access to public R&D subsidies has a
negative and significant impact on process innovation, however, we find no relation with
product innovation. It is also observed that high tech manufactures and highly knowledgeintensive services (KIS) have positive and significant impact on product innovation, but
a negative impact on process innovation.

25

To confirm the robustness of our results, we conducted additional analyses. First,
we tested our model introducing the thirteen innovation objectives (dummy variables
identifying firms pursue each of the objectives with high importance). When considering
the analysis of each innovation objective (see Table A.5 in Appendix 5), we find that the
results are very similar to those presented before. Quality objectives are positively related
to product innovation; in particular, we find that four of five objectives are positive and
significant, so a strong positive relationship is found. Firms that pursue Objective 1,
increase range of goods or services, show the highest likelihood of being a successful
innovative firm in product innovation. Objectives related to efficiency, such as increase
in flexibility and capacity of production and reduction in labour costs per unit output have
a positive relationship to process innovation. Firms that pursue the Objective 7 (increase
the capacity of production) would increase the probability of having process innovations
success by 10 percent. However, we do not find any positive and significant relationship
between reduction in material and energy costs objectives and process innovation. The
objectives related to reduction in environmental impacts and improvement in health or
safety of employees have a negative and significant impact on product innovation. While
the objective related to fulfil governmental regulation or standard recruitments, shows a
positive and significant impact on product innovation, i.e. in firms pursuing this latter
objective increase the probability of product innovation success by four percent.

6. CONCLUDING REMARKS
This study examines the role played by innovation strategies, which refers to strategic
decisions at firm level, on innovation success measured in terms of product and process
innovation. The analysis was performed with data from the Technological Innovation
Panel (PITEC) between 2008–2012 for a sample of 3,919 Spanish innovative firms in the

26

manufacturing and services sectors. Firstly, we identified the innovation strategies that
innovative firms can design (absence, mixed, or oriented strategy towards quality,
production, cost and environmental and regulatory) by applying a principal component
analysis. Secondly, after controlling for positive skewness of the dependent variables, a
generalized linear model was used to examine the impact of these innovation strategies.
Our econometrics results show that those firms that are able to design their
innovation strategies tend to have a greater probability of being a successful innovative
firm. Our results also show that there is a good fit between the strategies pursued by each
firm and the innovation output obtained. Quality strategy orientation is positively related
to product innovation success, whereas product, cost and environmental and regulatory
strategy are positively related to process innovation success. Product innovation requires
understanding both customers and technologies, and firms that carry out process
innovation are enhancing the efficiency, effectiveness and flexibility of the firm.
To sum up, our results highlight that there are three classes of Spanish innovative
firms: 1) a group of firms that do not have an explicit strategy and consequently perform
worst; 2) a group of firms that pursue some objectives in the innovative field and want to
innovate, but do not have enough capacity to focus their innovation and, finally, 3) a
group of firms that have a capacity to design one or more oriented innovation strategy
and experience greater innovation success.
These results are of great interest from the perspective of policy-makers and
managers. They need to take into account a broader range of characteristics that may
influence innovation success such as innovation strategy. It is crucial for management to
realize the importance of innovation strategy as a fundamental key to the success of
innovation in a highly dynamic environment. In terms of managerial implications, these
results suggest that encouraging innovation beginning with a clear and precise innovation

27

strategy is likely to enhance innovative outcomes. For policy-makers, this study reveals
a diverse range of strategic profiles in relation to innovation and emphasizes the
importance and effects of innovation strategies in the manufacturing and services firms.
From a public policy point of view, in order to develop appropriate innovation policies,
it is currently very important for governments to understand how innovative firms define
their innovation strategies. Many policies for supporting innovation would benefit from
the identification of the main forces that drive firms' innovation activities. Thus,
innovation policies should provide a series of tools to firms wishing to initiate internal
reflection on their ability to innovate. In addition, evaluating and understanding the
strategic orientation of innovative firms allows governments to develop appropriate
innovation policies.

REFERENCES
Ahuja, G., C. M. Lampert, and V. Tandon. 2008. Moving Beyond Schumpeter:
Management Research on the Determinants of Technological Innovation. The
Academy of Management Annals 2 (1): 1–98.
Akman, G., and C. Yillmaz. 2008. Innovative capability, innovation strategy and market
orientation: an empirical analysis in Turkish software industry. International
Journal of Innovation Management 12 (1). Imperial College Press: 69–111.
Aniruddha, K. P. 2013. Innovation Objectives, Strategies and Firm Performance: A Study
of Emerging Market Firms. In Driving the Economy through Innovation and
Entrepreneurship, ed. C. Mukhopadhyay, K. B. Akhilesh, R. Srinivasan, A. Gurtoo,
P. Ramachandran, P. P. Iyer, M. Mathirajan, and M. H. B. Subrahmanya, Springer
I, 3–12. Springer.
Aschhoff, B., E. Baier, D. Crass, M. Hud, P. Hünermund, C. Köhler, B. Peters, C.
Rammer, et al. 2013. Innovation in Germany - Results of the German CIS 2006 to
2010. ZEW Dokumentationen (13-01).
Audretsch, D. B., A. Segarra-Blasco, and M. Teruel. 2014. Why don’t all young firms
invest in R&D? Small Business Economics 43 (4): 751–66.
Balachandra, R., and J. H. Friar. 1997. Factors for success in R&D projects and new
product innovation: a contextual framework. IEEE Transactions on Engineering
Management 44 (3): 276–87.
Baltagi, B. 2008. Econometric Analysis of Panel Data. Chichester: John Wiley and Sons.
Barge-Gil, A., and A. López. 2014. R&D determinants: Accounting for the differences
between research and development. Research Policy 43 (9): 1634–48.

28

Becheikh, N., R. Landry, and N. Amara. 2006. Lessons from innovation empirical studies
in the manufacturing sector: A systematic review of the literature from 1993–2003.
Technovation 26 (5-6): 644–64.
Bhattacharya, M., and H. Bloch. 2004. Determinants of Innovation. Small Business
Economics 22 (2): 155–62.
Bowonder, B., A. Dambal, S. Kumar, and A. Shirodkar. 2010. Innovation Strategies For
Creating Competitive Advantage. Research Technology Management 53 (3): 19–32.
Burgelman, R. A., C. M. Christensen, and S. C. Wheelwright. 2004. Strategic
management of technology and innovation. Boston: McGraw-Hill.
Cainelli, G., V. De Marchi, and R. Grandinetti. 2015. Does the development of
environmental innovation require different resources? Evidence from Spanish
manufacturing firms. Journal of Cleaner Production 94: 211–20.
Clausen, T., M. Pohjola, K. Sapprasert, and B. Verspagen. 2012. Innovation strategies as
a source of persistent innovation. Industrial and Corporate Change 21 (3): 553–85.
Cooper, R. G. 1984a. New product strategies: What distinguishes the top performers?
Journal of Product Innovation Management 1 (3). No longer published by Elsevier:
151–64.
Cooper, R. G. 1984b. The Strategy-Performance Link in Product Innovation. R&D
Management 14 (4): 247–59.
Cooper, R. G., and S. J. Edgett. 2010. Developing a product innovation and technology
for your business. Research Technology Management 53: 33–40.
Costa-Campi, M. T., J. García-Quevedo, and A. Segarra-Blasco. 2015. Energy efficiency
determinants: An empirical analysis of Spanish innovative firms. Energy Policy 83:
229–39.
Crepon, B., E. Duguet, and J. Mairesse. 1998. Research, Innovation And Productivity:
An Econometric Analysis At The Firm Level. Economics of Innovation and New
Technology 7 (2). OPA (Overseas Publishers Association): 115–58.
D’Este, P., S. Iammarino, M. Savona, and N. von Tunzelmann. 2012. What hampers
innovation? Revealed barriers versus deterring barriers. Research Policy 41 (2):
482–8.
Dobni, C. B., M. Klassen, and W. T. Nelson. 2015. Innovation strategy in the US: top
executives offer their views. Journal of Business Strategy 36 (1). Emerald Group
Publishing Limited: 3–13.
Ernst, H. 2002. Success Factors of New Product Development: A Review of the Empirical
Literature. International Journal of Management Reviews 4 (1): 1–40.
Galende, J., and J. M. de la Fuente. 2003. Internal factors determining a firm’s innovative
behaviour. Research Policy 32 (5): 715–36.
Garcia-Vega, M. 2006. Does technological diversification promote innovation? Research
Policy 35 (2): 230–46.
Gilbert, J. T. 1994. Choosing an Innovation Strategy: Theory and Practice. Business
Horizons 37 (6): 16–22.
Goedhuys, M., and R. Veugelers. 2012. Innovation strategies, process and product
29

innovations and growth: Firm-level evidence from Brazil. Structural Change and
Economic Dynamics 23 (4): 516–29.
Greene, W. H. 2002. Econometric Analysis. New Jersey: Prentice Hall.
Guan, J. C., R. C. M. Yam, E. P. Y. Tang, and A. K. W. Lau. 2009. Innovation strategy
and performance during economic transition: Evidences in Beijing, China. Research
Policy 38 (5): 802–12.
Hall, B. H. 2002. The Financing of Research and Development. Oxford Review of
Economic Policy 18 (1): 35–51.
Hardin, J. ., and J. . Hilbe. 2012. Generalized Linear Models and Extensions. Third Edit.
Texas, USA: Stata Press.
Hashi, I., and N. Stojčić. 2013. The impact of innovation activities on firm performance
using a multi-stage model: Evidence from the Community Innovation Survey 4.
Research Policy 42 (2): 353–66.
Hervas-Oliver, J.-L., F. Sempere-Ripoll, and C. Boronat-Moll. 2014. Process innovation
strategy in SMEs, organizational innovation and performance: a misleading debate?
Small Business Economics 43 (4): 873–86.
Jakobsen, S., and T. H. Clausen. 2015. Innovating for a greener future: the direct and
indirect effects of firms’ environmental objectives on the innovation process.
Journal of Cleaner Production.
Jayaram, J., A. Oke, and D. I. Prajogo. 2014. The antecedents and consequences of
product and process innovation strategy implementation in Australian
manufacturing firms. International Journal of Production Research 52 (15): 4424–
39.
Karlsson, C., and S. Tavassoli. 2015. Innovation strategies of firms: What strategies and
why? The Journal of Technology Transfer.
Keupp, M. M., M. Palmié, and O. Gassmann. 2012. The Strategic Management of
Innovation: A Systematic Review and Paths for Future Research. International
Journal of Management Reviews 14 (4): 367–90.
Kline, P. 1994. An Easy guide to factor analysis. London: Routledge.
Laursen, K., and A. Salter. 2006. Open for innovation: the role of openness in explaining
innovation performance among U.K. manufacturing firms. Strategic Management
Journal 27 (2): 131–50.
Leiponen, A. 2012. The benefits of R&D and breadth in innovation strategies: a
comparison of Finnish service and manufacturing firms. Industrial and Corporate
Change 21 (5): 1255–81.
Leiponen, A., and C. E. Helfat. 2010. Innovation objectives, knowledge sources, and the
benefits of breadth. Strategic Management Journal 31 (2): 224–36.
Leten, B., R. Belderbos, and B. Van Looy. 2007. Technological Diversification,
Coherence, and Performance of Firms. Journal of Product Innovation Management
24 (6). Blackwell Publishing Inc: 567–79.
Lin, C., and C.-C. Chang. 2014. The effect of technological diversification on
organizational performance: An empirical study of S&P 500 manufacturing firms.

30

Technological Forecasting and Social Change. Elsevier Inc.
Love, J. H., S. Roper, and P. Vahter. 2014. Dynamic complementarities in innovation
strategies. Research Policy 43 (10): 1774–84.
Mairesse, J., and P. Mohnen. 2005. The importance of R&D for innovation: a
reassessment using French survey data. Journal of Technology Transfer 30: 183–97.
Manu, F. A., and V. Sriram. 1996. Innovation, marketing strategy, environment, and
performance. Journal of Business Research 35 (1): 79–91.
Mata, J., and M. Woerter. 2013. Risky innovation : The impact of internal and external
R&D strategies upon the distribution of returns. Research Policy 42 (2). Elsevier
B.V.: 495–501.
Mohnen, P., J. Mairesse, and M. Dagenais. 2006. Innovativity: A comparison across
seven European countries. Economics of Innovation and New Technology 15 (4-5).
Routledge: 391–413.
OECD -Eurostat. 2005. Oslo Manual. Guidelines for collecting and interpreting
innovation data. Third edit. Paris: OECD.
Oerlemans, L. A. G., M. T. H. Meeus, and F. W. M. Boekema. 1998. Do Networks Matter
for Innovation? The usefulness of the economic network approach in analysing
innovation. Tijdschrift voor Economische en Sociale Geografie 89 (3): 298–309.
Oh, C., Y. Cho, and W. Kim. 2015. The effect of a firm’s strategic innovation decisions
on its market performance. Technology Analysis & Strategic Management 27 (1).
Routledge: 39–53.
Oke, A. 2007. Innovation types and innovation management practices in service
companies. International Journal of Operations & Production Management 27 (6).
Emerald Group Publishing Limited: 564–87.
Page, A. L. 1993. Assessing new product development practices and performance:
Establishing crucial norms. Journal of Product Innovation Management 10 (4): 273–
90.
van der Panne, G., C. van Beers, and A. Kleinknecht. 2003. Success and Failure of
Innovation: A Literature Review. International Journal of Innovation Management
7 (3): 309–38.
Paulraj, A. 2009. Environmental motivations: a classification scheme and its impact on
environmental strategies and practices. Business Strategy and the Environment 18
(7): 453–68.
Quintana-García, C., and C. a. Benavides-Velasco. 2008. Innovative competence,
exploration and exploitation: The influence of technological diversification.
Research Policy 37 (3): 492–507.
Ramanujam, V., and G. O. Mensch. 1985. Improving the strategy-innovation link.
Journal of Product Innovation Management 2 (4): 213–23.
Salomo, S., K. Talke, and N. Strecker. 2008. Innovation Field Orientation and Its Effect
on Innovativeness and Firm Performance. Journal of Product Innovation
Management 25 (6). Blackwell Publishing Inc: 560–76.
Santamaría, L., M. J. Nieto, and I. Miles. 2012. Service innovation in manufacturing

31

firms: Evidence from Spain. Technovation 32 (2): 144–55.
Schroeder, H. 2013. Strategic Innovation for Business Performance: The Art and Science
of Transformation. Technology Innovation Management Review 3 (9): 6–12.
Segarra-Blasco, A. 2010. Innovation and productivity in manufacturing and service firms
in Catalonia: a regional approach. Economics of Innovation and New Technology 19
(3). Routledge: 233–58.
Segarra-Blasco, A., and M. Teruel. 2014. High-growth firms and innovation: an empirical
analysis for Spanish firms. Small Business Economics 43 (4): 805–21.
Semykina, A., and J. M. Wooldridge. 2010. Estimating panel data models in the presence
of endogeneity and selection. Journal of Econometrics 157 (2): 375–80.
Smith, D. 2010. Exploring innovation. London: McGraw-Hill Higher Education.
Souitaris, V. 2002. Technological trajectories as moderators of firm-level determinants
of innovation. Research Policy 31 (6): 877–98.
Talke, K., S. Salomo, and K. Rost. 2010. How top management team diversity affects
innovativeness and performance via the strategic choice to focus on innovation
fields. Research Policy 39 (7): 907–18.
Tavassoli, S., and C. Karlsson. 2015. Innovation strategies and firm performance: Simple
or complex strategies? Economics of Innovation and New Technology. Routledge:
1–20.
Tidd, J., J. Bessant, and K. Pavitt. 2005. Managing innovation : integrating technological,
market and organizational change. Chichester: John Wiley and Sons.
Vega-Jurado, J., A. Gutiérrez-Gracia, I. Fernández-de-Lucio, and L. ManjarrésHenríquez. 2008. The effect of external and internal factors on firms’ product
innovation. Research Policy 37 (4): 616–32.
Vega-Jurado, J., A. Gutiérrez-Gracia, and I. Fernández-de-Lucio. 2009. Does external
knowledge sourcing matter for innovation? Evidence from the Spanish
manufacturing industry. Industrial and Corporate Change 18 (4): 637–70.
Veugelers, R., and B. Cassiman. 1999. Make and buy in innovation strategies: evidence
from Belgian manufacturing firms. Research Policy 28 (1): 63–80.
Vokoun, M. 2015. Innovation behaviour of firms in a small open economy: the case of
the Czech manufacturing industry. Empirica 43 (1): 111–39.
Weitzman, M. L. 1998. Recombinant Growth. The Quarterly Journal of Economics 113
(2). Oxford University Press: 331–60.
Zheng, C. 2014. The inner circle of technology innovation: A case study of two Chinese
firms. Technological Forecasting and Social Change 82: 140–8.

32

APPENDIX
Appendix 1. Aggregations of manufacturing and services based on NACE Rev. 2
Firms are grouped depending on their technological intensity according to Eurostat,
NACE Classification.
Table A.1
Aggregations of manufacturing and services based on NACE Rev. 2
Manufacturing industries
1. Industry: High Technology
Manufacture of basic pharmaceutical products and pharmaceutical preparations

21

Manufacture of computer, electronic and optical products

26

Manufacture of air and spacecraft and related machinery

30.3

2. Industry: Medium High Technology
Manufacture of chemicals and chemical products
20
Manufacture of electrical equipment, Manufacture of machinery and equipment n.e.c., Manufacture of
27-29
motor vehicles, trailers and semi-trailers
Manufacture of other transport equipment (excluding 30.1 Building of ships and boats, and 30.3
30 –(30.1+30.3)
Manufacture of air and spacecraft and related machinery)
3. Industry: Medium Low Technology
Manufacture of coke and refined petroleum products
19
Manufacture of rubber and plastic products, Manufacture of other non-metallic mineral products,
Manufacture of basic metals, Manufacture of fabricated metal products, except machinery and
equipment
Building of ships and boats
Repair and installation of machinery and equipment

22-25
30.1
33

4. Industry: Low Technology
Manufacture of food products, beverages, tobacco products, textiles, wearing apparel, leather and
related products, wood and of products of wood, paper and paper products, Printing and reproductions
of recorded media
Manufacture of furniture, Other manufacturing

10-18
31-32

Services industries
5. High-Tech Knowledge Intensive Services
Motion picture, video and television programme production, sound recording and music publishing
activities, Programming and broadcasting activities, Telecommunications, Computer programming,
consultancy and related activities, Information service activities
Scientific research and development

59-63
72

6. Other Knowledge Intensive Services
Financial and insurance activities
Legal and accounting activities, Activities of head offices; management consultancy activities,
Architectural and engineering activities; technical testing and analysis
Advertising and market research, Other professional, scientific and technical activities
Veterinary activities

64-66
69-71
73-74
75

Human health and social work activities

86-88

Arts, entertainment and recreation

90-93

33

Appendix 2. Variable definitions
Table A.2
Variable definitions
Dependent variables
Product innovation
Process innovation

Dummy variable which takes the value 1 if the firm has introduced new or significantly
improved products during t–2 to t; 0 if not
Dummy variable which takes the value 1 if the firm has introduced new or significantly
improved production processes during t–2 to t; 0 if not

Independent variables
Firms' resources and capabilities
Absence of innovation strategy

Dummy variable which takes the value 1 if the firm pursues fewer than two objectives with
high importance during t–2 to t; 0 if not

Mixed strategy

Dummy variable which takes the value 1 if the firm purses two or more objectives with high
importance during t–2 to t without an orientation; 0 if not

Quality strategy

Dummy variable which takes the value 1 if the firm has a strategy oriented towards the quality.
That means that firm considers at least four of the following objectives with high importance
during t–2 to t: (1) increase range of goods or services, (2) replace products being phased out,
(3) enter new markets, (4) increase market share and (5) improve product quality; 0 if not

Production strategy

Dummy variable which takes the value 1 if the firm has a strategy oriented towards the
production. That means that firm considers two of the following objectives with high
importance during t–2 to t: (1) increase flexibility of production, (2) increase capacity of
production; 0 if not

Cost strategy

Dummy variable which takes the value 1 if the firm has a strategy oriented towards cost
reduction. That means that firm considers at least two of the following objectives with high
importance during t–2 to t: (1) reduce labour costs per unit output, (2) reduce material costs
per unit output and (3) reduce energy costs per unit output objectives; 0 if not

Environment and regulatory
strategy
Size
Group
Export

Dummy variable which takes the value 1 if the firm has a strategy oriented towards
environment and regulatory norms. That means that firm considers at least two of the following
objectives with high importance during t–2 to t: (1) reduce environmental impacts, (2) improve
health or safety of employees and (3) fulfil government regulation or standards requirements;
0 if not
Log of the total number of firm's employees (in logs)
Dummy variable that takes a value equal to 1 if the firm belongs to a group; 0 if not
Dummy variable that takes a value equal to 1 if the firm exports; 0 if not

Internal R&D

Investment in internal R&D per worker (in logs)

External R&D

Investment in external R&D per worker (in logs)

Human resources
High Tech manufacture and
High KIS
Cooperation
Public subsidies

Dummy variable that takes a value equal to 1 if firm invests in training expenditure for
innovation activities; 0 if not
Dummy variables which take the value equal 1 if the firm belongs to a high tech manufacturing
sector or to a high knowledge intensive service; 0 if not
Dummy variable that takes a value equal to 1 if the firm cooperates with other agents during
t–2 to t; 0 if not
Dummy variable that takes a value equal to 1 if the firm received any public financial support
for innovation activities during t–2 to t; 0 if not

34

Appendix 3. Descriptive statistics

Table A.3
Summary statistics of sample 2008-2012 (mean score in the sample)
Absence
Innovation
strategy
strategy

Variable
Size (workers)
Group1
Export by sales1
R&D training1
Internal R&D per worker (€)
External R&D per worker (€)
Cooperation1
Subsidy 1
HT manuf. and HKIS1
Product innovation1
Process innovation 1

Obs=4,645
F=929
231.32
(891.64)
0.4357
(0.4959)
0.6822
(0.4656)
0.1138
(0.3177)
6570.30
(28610.60)
1443.42
(13977.46)
0.3001
(0.4583)
0.3608
(0.4802)
0.4607
(0.4985)
0.6206
(0.4852)
0.6066
(0.4885)

Obs=14,950
F=2,990
290.93
(1156.10)
0.4936
(0.4999)
0.7305
(0.4437)
0.2134
(0.4097)
8539.51
(32156.19)
1706.74
(11187.17)
0.4723
(0.4992)
0.4922
(0.4999)
0.5117
(0.4998)
0.7983
(0.4012)
0.7662
(0.4232)

Mixed
strategy
Obs=4,436
F=887
211.88
(898.97)
0.4675
(0.4990)
0.7044
(0.4563)
0.1832
(0.3869)
9046.01
(48690.5)
1445.14
(7024.45)
0.4537
(0.4979)
0.4862
(0.4998)
0.5076
(0.4999)
0.8016
(0.3988)
0.6837
(0.4650)

Quality strategy

Obs=5,357
F=1,072
353.18
(1568.31)
0.4784
(0.4995)
0.7731
(0.4188)
0.2251
(0.4177)
8489.58
(17231.34)
1655.79
(7358.225)
0.4804
(0.4996)
0.5070
(0.4999)
0.5417
(0.4983)
0.8579
(0.3491)
0.7720
(0.4195)

Production
strategy
Obs=4,767
F=953
387.46
(1388.32)
0.5235
(0.4994)
0.7082
(0.4546)
0.2494
(0.4327)
7919.39
(25734.91)
1627.46
(13468.46)
0.4856
(0.4998)
0.4740
(0.4993)
0.4786
(0.4995)
0.7734
(0.4186)
0.8770
(0.3283)

Cost
strategy
Obs=3,416
F=683
374.42
(1344.25)
0.5901
(0.4918)
0.7854
(0.4105)
0.2330
(0.4228)
7004.77
(16712.46)
1491.96
(10136.31)
0.4947
(0.5000)
0.5014
(0.5000)
0.4812
(0.4997)
0.7827
(0.4124)
0.85681
(0.3502)

Environ. and
regulatory strategy
Obs=5,180
F=1.036
363.7089
(1281.58)
0.5376
(0.4986)
0.7698
(0.4209)
0.2438
(0.4294)
8506.80
(18949.93)
2034.50
(14958.37)
0.4872
(0.4998)
0.4996
(0.5000)
0.5075
(0.4999)
0.7949
(0.4037)
0.8148
(0.3884)

Note: All monetary variables were deflated using the Price Index of the National Statistics Institute (INE, Spain). The Industrial Price Index was used for manufacturing firms and the
Services Sector Price Index for services firms.
1
Percentage of firms.
F: number of firms.

35

Appendix 4. Correlation matrix

Table A.4
Correlation matrix
1
1.Size

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

1

2.Group

0.181*

1

3.Export

-0.027*

0.077*

1

4.Human resources

0.072*

0.031*

-0.005

1

5.Internal R&D

-0.029*

-0.002

-0.025*

0.040*

6.External R&D

-0.002

0.034*

-0.016*

0.032*

1
0.189*

1

7.Cooperation

0.095*

0.132*

0.020*

0.149*

0.115*

0.086*

1

8.Subsidy

0.044*
-0.009

0.050*
0.062*

0.123*
0.056*

0.151*

0.092*

0.367*

1

9. HT manuf. HKIS

0.029*
-0.040*

0.124*

0.052*

0.044*

0.086*

1

10. No strategy

-0.023*

-0.049*

-0.045*

-0.108*

-0.026*

-0.009

-0.147*

-0.112*

-0.045*

1

11 Quality strategy

0.042*

0.052*

0.008

0.001

0.060*

0.056*

0.054*

-0.341*

1

12. Production strategy

0.057*

0.049*

-0.013

0.086*

-0.002

-0.001

0.061*

0.014*

-0.036*

-0.316*

0.223*

1

13. Cost strategy

0.040*

0.101*

0.067*

0.050*

-0.015*

-0.005

0.058*

0.037*

0.059*

-0.256*

0.199*

0.313*

1

14. Environ. strategy

0.047*

0.069*

0.068*

0.082*

0.008

0.019*

0.067*

0.046*

0.095*

-0.334*

0.220*

0.239*

0.330*

1

15.Unoriented strategy

-0.031*

-0.012

-0.017*

-0.009

0.016*

-0.009

0.024*

0.027*

-0.027*

-0.301*

-0.331*

-0.307*

-0.248*

-0.324*

1

16.Product innovation

0.043*

0.014*

0.073*

0.105*

0.036*

0.002

0.153*

0.107*

0.090*

-0.176*

0.145*

0.022*

0.028*

0.054*

0.057*

1

0.163*

-0.021*

-0.021*

0.118*

0.023*

-0.057*

-0.152*

0.060*

0.189*

0.132*

0.116*

-0.054*

0.087*

17.Process innovation

0.067*

-0.001

0.074*

0.073*

0.018*

* Significance at 5%

36

1

Appendix 5. Innovation objectives
Table A.5
GLMs. Innovation objectives (whole sample)
Variables
lSize t-1
Group t-1
Export t-1
Human resources t-1
lInternal R&D t-1
lExternal R&D t-1
Cooperation t-1
Subsidy t-1
HT manuf. and HKIS t-1
Objective 1 t-1
Objective 2 t-1
Objective 3 t-1
Objective 4 t-1
Objective 5 t-1
Objective 6 t-1
Objective 7 t-1
Objective 8 t-1
Objective 9 t-1
Objective 10 t-1
Objective 11 t-1
Objective 12 t-1
Objective 13 t-1
Constant

Product innvoation
Coeff.
MEMs
0.0909***
0.0168***
(0.015)
(0.003)
-0.1098***
-0.0203***
(0.038)
(0.007)
0.1704***
0.0315***
(0.036)
(0.007)
0.4185***
0.0774***
(0.049)
(0.009)
0.0594***
0.0110***
(0.005)
(0.001)
-0.0034
-0.0006
(0.005)
(0.001)
0.3229***
0.0597***
(0.040)
(0.007)
-0.0185
-0.0034
(0.039)
(0.007)
0.2405***
0.0445***
(0.034)
(0.006)
0.6440***
0.1191***
(0.039)
(0.007)
0.0518
0.0096
(0.040)
(0.007)
0.1829***
0.0338***
(0.043)
(0.008)
0.2449***
0.0453***
(0.044)
(0.008)
0.1246***
0.0230***
(0.038)
(0.007)
0.0096
0.0018
(0.043)
(0.008)
-0.1266***
-0.0234***
(0.044)
(0.008)
-0.0511
-0.0095
(0.048)
(0.009)
0.0804
0.0149
(0.062)
(0.012)
-0.1427**
-0.0264**
(0.061)
(0.011)
-0.1657***
-0.0306***
(0.054)
(0.010)
-0.1468**
-0.0271**
(0.058)
(0.011)
0.2303***
0.0426***
(0.054)
(0.010)
0.0259
(0.075)
0.9718
0.9875
-135.748.3
-7,714.4

Process innovation
Coeff.
MEMs
0.1843***
0.0374***
(0.015)
(0.003)
-0.0604*
-0.0123*
(0.036)
(0.007)
0.0577*
0.0117*
(0.035)
(0.007)
0.7835***
0.1591***
(0.052)
(0.010)
-0.0353***
-0.0072***
(0.005)
(0.001)
0.0020
0.0004
(0.005)
(0.001)
0.2694***
0.0547***
(0.036)
(0.007)
-0.0959***
-0.0195***
(0.036)
(0.007)
-0.3129***
-0.0635***
(0.032)
(0.007)
0.0154
0.0031
(0.036)
(0.007)
0.0319
0.0065
(0.037)
(0.008)
0.0115
0.0023
(0.041)
(0.008)
-0.0245
-0.0050
(0.041)
(0.008)
0.0092
0.0019
(0.036)
(0.007)
0.4400***
0.0894***
(0.044)
(0.009)
0.4947***
0.1005***
(0.045)
(0.009)
0.2327***
0.0473***
(0.049)
(0.010)
0.0287
0.0058
(0.063)
(0.013)
0.0853
0.0173
(0.064)
(0.013)
0.0974*
0.0198*
(0.053)
(0.011)
0.0228
0.0046
(0.055)
(0.011)
0.0777
0.0158
(0.048)
(0.010)
0.4807***
(0.078)
0.9914
1.0373
-134,967.3
-8,104.9

(1/df) Pearson
AIC
BIC
Log pseudolikelihood
Observations
15,676
Estimations control for time and industry dummies. Marginal effects calculated at their mean (MEMs). For dummy variables,
change in probability for a discrete change of the dummy variable from 0 to 1. Robust standard errors in parentheses. ***
p<0.01, ** p<0.05, * p<0.1.

37

38

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
2006
CREAP2006-01
Matas, A. (GEAP); Raymond, J.Ll. (GEAP)
"Economic development and changes in car ownership patterns"
(Juny 2006)
CREAP2006-02
Trillas, F. (IEB); Montolio, D. (IEB); Duch, N. (IEB)
"Productive efficiency and regulatory reform: The case of Vehicle Inspection Services"
(Setembre 2006)
CREAP2006-03
Bel, G. (PPRE-IREA); Fageda, X. (PPRE-IREA)
"Factors explaining local privatization: A meta-regression analysis"
(Octubre 2006)
CREAP2006-04
Fernàndez-Villadangos, L. (PPRE-IREA)
"Are two-part tariffs efficient when consumers plan ahead?: An empirical study"
(Octubre 2006)
CREAP2006-05
Artís, M. (AQR-IREA); Ramos, R. (AQR-IREA); Suriñach, J. (AQR-IREA)
"Job losses, outsourcing and relocation: Empirical evidence using microdata"
(Octubre 2006)
CREAP2006-06
Alcañiz, M. (RISC-IREA); Costa, A.; Guillén, M. (RISC-IREA); Luna, C.; Rovira, C.
"Calculation of the variance in surveys of the economic climate”
(Novembre 2006)
CREAP2006-07
Albalate, D. (PPRE-IREA)
"Lowering blood alcohol content levels to save lives: The European Experience”
(Desembre 2006)
CREAP2006-08
Garrido, A. (IEB); Arqué, P. (IEB)
“The choice of banking firm: Are the interest rate a significant criteria?”
(Desembre 2006)
CREAP2006-09
Segarra, A. (GRIT); Teruel-Carrizosa, M. (GRIT)
"Productivity growth and competition in spanish manufacturing firms:
What has happened in recent years?”
(Desembre 2006)
CREAP2006-10
Andonova, V.; Díaz-Serrano, Luis. (CREB)
"Political institutions and the development of telecommunications”
(Desembre 2006)
CREAP2006-11
Raymond, J.L.(GEAP); Roig, J.L.. (GEAP)
"Capital humano: un análisis comparativo Catalunya-España”
(Desembre 2006)
CREAP2006-12
Rodríguez, M.(CREB); Stoyanova, A. (CREB)
"Changes in the demand for private medical insurance following a shift in tax incentives”
(Desembre 2006)
CREAP2006-13
Royuela, V. (AQR-IREA); Lambiri, D.; Biagi, B.
"Economía urbana y calidad de vida. Una revisión del estado del conocimiento en España”
(Desembre 2006)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
CREAP2006-14
Camarero, M.; Carrion-i-Silvestre, J.LL. (AQR-IREA).;Tamarit, C.
"New evidence of the real interest rate parity for OECD countries using panel unit root tests with breaks”
(Desembre 2006)
CREAP2006-15
Karanassou, M.; Sala, H. (GEAP).;Snower , D. J.
"The macroeconomics of the labor market: Three fundamental views”
(Desembre 2006)
2007
XREAP2007-01
Castany, L (AQR-IREA); López-Bazo, E. (AQR-IREA).;Moreno , R. (AQR-IREA)
"Decomposing differences in total factor productivity across firm size”
(Març 2007)
XREAP2007-02
Raymond, J. Ll. (GEAP); Roig, J. Ll. (GEAP)
“Una propuesta de evaluación de las externalidades de capital humano en la empresa"
(Abril 2007)
XREAP2007-03
Durán, J. M. (IEB); Esteller, A. (IEB)
“An empirical analysis of wealth taxation: Equity vs. Tax compliance”
(Juny 2007)
XREAP2007-04
Matas, A. (GEAP); Raymond, J.Ll. (GEAP)
“Cross-section data, disequilibrium situations and estimated coefficients: evidence from car ownership demand”
(Juny 2007)
XREAP2007-05
Jofre-Montseny, J. (IEB); Solé-Ollé, A. (IEB)
“Tax differentials and agglomeration economies in intraregional firm location”
(Juny 2007)
XREAP2007-06
Álvarez-Albelo, C. (CREB); Hernández-Martín, R.
“Explaining high economic growth in small tourism countries with a dynamic general equilibrium model”
(Juliol 2007)
XREAP2007-07
Duch, N. (IEB); Montolio, D. (IEB); Mediavilla, M.
“Evaluating the impact of public subsidies on a firm’s performance: a quasi-experimental approach”
(Juliol 2007)
XREAP2007-08
Segarra-Blasco, A. (GRIT)
“Innovation sources and productivity: a quantile regression analysis”
(Octubre 2007)
XREAP2007-09
Albalate, D. (PPRE-IREA)
“Shifting death to their Alternatives: The case of Toll Motorways”
(Octubre 2007)
XREAP2007-10
Segarra-Blasco, A. (GRIT); Garcia-Quevedo, J. (IEB); Teruel-Carrizosa, M. (GRIT)
“Barriers to innovation and public policy in catalonia”
(Novembre 2007)
XREAP2007-11
Bel, G. (PPRE-IREA); Foote, J.
“Comparison of recent toll road concession transactions in the United States and France”
(Novembre 2007)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2007-12
Segarra-Blasco, A. (GRIT);
“Innovation, R&D spillovers and productivity: the role of knowledge-intensive services”
(Novembre 2007)
XREAP2007-13
Bermúdez Morata, Ll. (RFA-IREA); Guillén Estany, M. (RFA-IREA), Solé Auró, A. (RFA-IREA)
“Impacto de la inmigración sobre la esperanza de vida en salud y en discapacidad de la población española”
(Novembre 2007)
XREAP2007-14
Calaeys, P. (AQR-IREA); Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA)
“Fiscal sustainability across government tiers”
(Desembre 2007)
XREAP2007-15
Sánchez Hugalbe, A. (IEB)
“Influencia de la inmigración en la elección escolar”
(Desembre 2007)
2008
XREAP2008-01
Durán Weitkamp, C. (GRIT); Martín Bofarull, M. (GRIT) ; Pablo Martí, F.
“Economic effects of road accessibility in the Pyrenees: User perspective”
(Gener 2008)
XREAP2008-02
Díaz-Serrano, L.; Stoyanova, A. P. (CREB)
“The Causal Relationship between Individual’s Choice Behavior and Self-Reported Satisfaction: the Case of Residential Mobility in the EU”
(Març 2008)
XREAP2008-03
Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP)
“Car ownership and access to jobs in Spain”
(Abril 2008)
XREAP2008-04
Bel, G. (PPRE-IREA) ; Fageda, X. (PPRE-IREA)
“Privatization and competition in the delivery of local services: An empirical examination of the dual market hypothesis”
(Abril 2008)
XREAP2008-05
Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP)
“Job accessibility and employment probability”
(Maig 2008)
XREAP2008-06
Basher, S. A.; Carrión, J. Ll. (AQR-IREA)
Deconstructing Shocks and Persistence in OECD Real Exchange Rates
(Juny 2008)
XREAP2008-07
Sanromá, E. (IEB); Ramos, R. (AQR-IREA); Simón, H.
Portabilidad del capital humano y asimilación de los inmigrantes. Evidencia para España
(Juliol 2008)
XREAP2008-08
Basher, S. A.; Carrión, J. Ll. (AQR-IREA)
Price level convergence, purchasing power parity and multiple structural breaks: An application to US cities
(Juliol 2008)
XREAP2008-09
Bermúdez, Ll. (RFA-IREA)
A priori ratemaking using bivariate poisson regression models
(Juliol 2008)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2008-10
Solé-Ollé, A. (IEB), Hortas Rico, M. (IEB)
Does urban sprawl increase the costs of providing local public services? Evidence from Spanish municipalities
(Novembre 2008)
XREAP2008-11
Teruel-Carrizosa, M. (GRIT), Segarra-Blasco, A. (GRIT)
Immigration and Firm Growth: Evidence from Spanish cities
(Novembre 2008)
XREAP2008-12
Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB)
Assessing the assignation of public subsidies: Do the experts choose the most efficient R&D projects?
(Novembre 2008)
XREAP2008-13
Bilotkach, V., Fageda, X. (PPRE-IREA), Flores-Fillol, R.
Scheduled service versus personal transportation: the role of distance
(Desembre 2008)
XREAP2008-14
Albalate, D. (PPRE-IREA), Gel, G. (PPRE-IREA)
Tourism and urban transport: Holding demand pressure under supply constraints
(Desembre 2008)
2009
XREAP2009-01
Calonge, S. (CREB); Tejada, O.
“A theoretical and practical study on linear reforms of dual taxes”
(Febrer 2009)
XREAP2009-02
Albalate, D. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA)
“Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona”
(Març 2009)
XREAP2009-03
Borrell, J. R. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA)
“Assessing excess profits from different entry regulations”
(Abril 2009)
XREAP2009-04
Sanromá, E. (IEB); Ramos, R. (AQR-IREA), Simon, H.
“Los salarios de los inmigrantes en el mercado de trabajo español. ¿Importa el origen del capital humano?”
(Abril 2009)
XREAP2009-05
Jiménez, J. L.; Perdiguero, J. (PPRE-IREA)
“(No)competition in the Spanish retailing gasoline market: a variance filter approach”
(Maig 2009)
XREAP2009-06
Álvarez-Albelo,C. D. (CREB), Manresa, A. (CREB), Pigem-Vigo, M. (CREB)
“International trade as the sole engine of growth for an economy”
(Juny 2009)
XREAP2009-07
Callejón, M. (PPRE-IREA), Ortún V, M.
“The Black Box of Business Dynamics”
(Setembre 2009)
XREAP2009-08
Lucena, A. (CREB)
“The antecedents and innovation consequences of organizational search: empirical evidence for Spain”
(Octubre 2009)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2009-09
Domènech Campmajó, L. (PPRE-IREA)
“Competition between TV Platforms”
(Octubre 2009)
XREAP2009-10
Solé-Auró, A. (RFA-IREA),Guillén, M. (RFA-IREA), Crimmins, E. M.
“Health care utilization among immigrants and native-born populations in 11 European countries. Results from the Survey of Health, Ageing and
Retirement in Europe”
(Octubre 2009)
XREAP2009-11
Segarra, A. (GRIT), Teruel, M. (GRIT)
“Small firms, growth and financial constraints”
(Octubre 2009)
XREAP2009-12
Matas, A. (GEAP), Raymond, J.Ll. (GEAP), Ruiz, A. (GEAP)
“Traffic forecasts under uncertainty and capacity constraints”
(Novembre 2009)
XREAP2009-13
Sole-Ollé, A. (IEB)
“Inter-regional redistribution through infrastructure investment: tactical or programmatic?”
(Novembre 2009)
XREAP2009-14
Del Barrio-Castro, T., García-Quevedo, J. (IEB)
“The determinants of university patenting: Do incentives matter?”
(Novembre 2009)
XREAP2009-15
Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA), Artís, M. (AQR-IREA)
“Human capital spillovers, productivity and regional convergence in Spain”
(Novembre 2009)
XREAP2009-16
Álvarez-Albelo, C. D. (CREB), Hernández-Martín, R.
“The commons and anti-commons problems in the tourism economy”
(Desembre 2009)
2010
XREAP2010-01
García-López, M. A. (GEAP)
“The Accessibility City. When Transport Infrastructure Matters in Urban Spatial Structure”
(Febrer 2010)
XREAP2010-02
García-Quevedo, J. (IEB), Mas-Verdú, F. (IEB), Polo-Otero, J. (IEB)
“Which firms want PhDs? The effect of the university-industry relationship on the PhD labour market”
(Març 2010)
XREAP2010-03
Pitt, D., Guillén, M. (RFA-IREA)
“An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions”
(Març 2010)
XREAP2010-04
Bermúdez, Ll. (RFA-IREA), Karlis, D.
“Modelling dependence in a ratemaking procedure with multivariate Poisson regression models”
(Abril 2010)
XREAP2010-05
Di Paolo, A. (IEB)
“Parental education and family characteristics: educational opportunities across cohorts in Italy and Spain”
(Maig 2010)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2010-06
Simón, H. (IEB), Ramos, R. (AQR-IREA), Sanromá, E. (IEB)
“Movilidad ocupacional de los inmigrantes en una economía de bajas cualificaciones. El caso de España”
(Juny 2010)
XREAP2010-07
Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB)
“Language knowledge and earnings in Catalonia”
(Juliol 2010)
XREAP2010-08
Bolancé, C. (RFA-IREA), Alemany, R. (RFA-IREA), Guillén, M. (RFA-IREA)
“Prediction of the economic cost of individual long-term care in the Spanish population”
(Setembre 2010)
XREAP2010-09
Di Paolo, A. (GEAP & IEB)
“Knowledge of catalan, public/private sector choice and earnings: Evidence from a double sample selection model”
(Setembre 2010)
XREAP2010-10
Coad, A., Segarra, A. (GRIT), Teruel, M. (GRIT)
“Like milk or wine: Does firm performance improve with age?”
(Setembre 2010)
XREAP2010-11
Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Calero, J. (IEB)
“Exploring educational mobility in Europe”
(Octubre 2010)
XREAP2010-12
Borrell, A. (GiM-IREA), Fernández-Villadangos, L. (GiM-IREA)
“Clustering or scattering: the underlying reason for regulating distance among retail outlets”
(Desembre 2010)
XREAP2010-13
Di Paolo, A. (GEAP & IEB)
“School composition effects in Spain”
(Desembre 2010)
XREAP2010-14
Fageda, X. (GiM-IREA), Flores-Fillol, R.
“Technology, Business Models and Network Structure in the Airline Industry”
(Desembre 2010)
XREAP2010-15
Albalate, D. (GiM-IREA), Bel, G. (GiM-IREA), Fageda, X. (GiM-IREA)
“Is it Redistribution or Centralization? On the Determinants of Government Investment in Infrastructure”
(Desembre 2010)
XREAP2010-16
Oppedisano, V., Turati, G.
“What are the causes of educational inequalities and of their evolution over time in Europe? Evidence from PISA”
(Desembre 2010)
XREAP2010-17
Canova, L., Vaglio, A.
“Why do educated mothers matter? A model of parental help”
(Desembre 2010)
2011
XREAP2011-01
Fageda, X. (GiM-IREA), Perdiguero, J. (GiM-IREA)
“An empirical analysis of a merger between a network and low-cost airlines”
(Maig 2011)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2011-02
Moreno-Torres, I. (ACCO, CRES & GiM-IREA)
“What if there was a stronger pharmaceutical price competition in Spain? When regulation has a similar effect to collusion”
(Maig 2011)
XREAP2011-03
Miguélez, E. (AQR-IREA); Gómez-Miguélez, I.
“Singling out individual inventors from patent data”
(Maig 2011)
XREAP2011-04
Moreno-Torres, I. (ACCO, CRES & GiM-IREA)
“Generic drugs in Spain: price competition vs. moral hazard”
(Maig 2011)
XREAP2011-05
Nieto, S. (AQR-IREA), Ramos, R. (AQR-IREA)
“¿Afecta la sobreeducación de los padres al rendimiento académico de sus hijos?”
(Maig 2011)
XREAP2011-06
Pitt, D., Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA)
“Estimation of Parametric and Nonparametric Models for Univariate Claim Severity Distributions - an approach using R”
(Juny 2011)
XREAP2011-07
Guillén, M. (RFA-IREA), Comas-Herrera, A.
“How much risk is mitigated by LTC Insurance? A case study of the public system in Spain”
(Juny 2011)
XREAP2011-08
Ayuso, M. (RFA-IREA), Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA)
“Loss risk through fraud in car insurance”
(Juny 2011)
XREAP2011-09
Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB)
“The link between public support and private R&D effort: What is the optimal subsidy?”
(Juny 2011)
XREAP2011-10
Bermúdez, Ll. (RFA-IREA), Karlis, D.
“Mixture of bivariate Poisson regression models with an application to insurance”
(Juliol 2011)
XREAP2011-11
Varela-Irimia, X-L. (GRIT)
“Age effects, unobserved characteristics and hedonic price indexes: The Spanish car market in the 1990s”
(Agost 2011)
XREAP2011-12
Bermúdez, Ll. (RFA-IREA), Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA)
“A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation”
(Setembre 2011)
XREAP2011-13
Guillén, M. (RFA-IREA), Pérez-Marín, A. (RFA-IREA), Alcañiz, M. (RFA-IREA)
“A logistic regression approach to estimating customer profit loss due to lapses in insurance”
(Octubre 2011)
XREAP2011-14
Jiménez, J. L., Perdiguero, J. (GiM-IREA), García, C.
“Evaluation of subsidies programs to sell green cars: Impact on prices, quantities and efficiency”
(Octubre 2011)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2011-15
Arespa, M. (CREB)
“A New Open Economy Macroeconomic Model with Endogenous Portfolio Diversification and Firms Entry”
(Octubre 2011)
XREAP2011-16
Matas, A. (GEAP), Raymond, J. L. (GEAP), Roig, J.L. (GEAP)
“The impact of agglomeration effects and accessibility on wages”
(Novembre 2011)
XREAP2011-17
Segarra, A. (GRIT)
“R&D cooperation between Spanish firms and scientific partners: what is the role of tertiary education?”
(Novembre 2011)
XREAP2011-18
García-Pérez, J. I.; Hidalgo-Hidalgo, M.; Robles-Zurita, J. A.
“Does grade retention affect achievement? Some evidence from PISA”
(Novembre 2011)
XREAP2011-19
Arespa, M. (CREB)
“Macroeconomics of extensive margins: a simple model”
(Novembre 2011)
XREAP2011-20
García-Quevedo, J. (IEB), Pellegrino, G. (IEB), Vivarelli, M.
“The determinants of YICs’ R&D activity”
(Desembre 2011)
XREAP2011-21
González-Val, R. (IEB), Olmo, J.
“Growth in a Cross-Section of Cities: Location, Increasing Returns or Random Growth?”
(Desembre 2011)
XREAP2011-22
Gombau, V. (GRIT), Segarra, A. (GRIT)
“The Innovation and Imitation Dichotomy in Spanish firms: do absorptive capacity and the technological frontier matter?”
(Desembre 2011)
2012
XREAP2012-01
Borrell, J. R. (GiM-IREA), Jiménez, J. L., García, C.
“Evaluating Antitrust Leniency Programs”
(Gener 2012)
XREAP2012-02
Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA), Bermúdez, Ll. (RFA-IREA)
“Solvency capital estimation and risk measures”
(Gener 2012)
XREAP2012-03
Ferri, A. (RFA-IREA), Bermúdez, Ll. (RFA-IREA), Guillén, M. (RFA-IREA)
“How to use the standard model with own data”
(Febrer 2012)
XREAP2012-04
Perdiguero, J. (GiM-IREA), Borrell, J.R. (GiM-IREA)
“Driving competition in local gasoline markets”
(Març 2012)
XREAP2012-05
D’Amico, G., Guillen, M. (RFA-IREA), Manca, R.
“Discrete time Non-homogeneous Semi-Markov Processes applied to Models for Disability Insurance”
(Març 2012)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2012-06
Bové-Sans, M. A. (GRIT), Laguado-Ramírez, R.
“Quantitative analysis of image factors in a cultural heritage tourist destination”
(Abril 2012)
XREAP2012-07
Tello, C. (AQR-IREA), Ramos, R. (AQR-IREA), Artís, M. (AQR-IREA)
“Changes in wage structure in Mexico going beyond the mean: An analysis of differences in distribution, 1987-2008”
(Maig 2012)
XREAP2012-08
Jofre-Monseny, J. (IEB), Marín-López, R. (IEB), Viladecans-Marsal, E. (IEB)
“What underlies localization and urbanization economies? Evidence from the location of new firms”
(Maig 2012)
XREAP2012-09
Muñiz, I. (GEAP), Calatayud, D., Dobaño, R.
“Los límites de la compacidad urbana como instrumento a favor de la sostenibilidad. La hipótesis de la compensación en Barcelona medida a través
de la huella ecológica de la movilidad y la vivienda”
(Maig 2012)
XREAP2012-10
Arqué-Castells, P. (GEAP), Mohnen, P.
“Sunk costs, extensive R&D subsidies and permanent inducement effects”
(Maig 2012)
XREAP2012-11
Boj, E. (CREB), Delicado, P., Fortiana, J., Esteve, A., Caballé, A.
“Local Distance-Based Generalized Linear Models using the dbstats package for R”
(Maig 2012)
XREAP2012-12
Royuela, V. (AQR-IREA)
“What about people in European Regional Science?”
(Maig 2012)
XREAP2012-13
Osorio A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N.
“Intermediary and structural determinants of early childhood health in Colombia: exploring the role of communities”
(Juny 2012)
XREAP2012-14
Miguelez. E. (AQR-IREA), Moreno, R. (AQR-IREA)
“Do labour mobility and networks foster geographical knowledge diffusion? The case of European regions”
(Juliol 2012)
XREAP2012-15
Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT)
“Ecological Footprint Inequality: A methodological review and some results”
(Setembre 2012)
XREAP2012-16
Varela-Irimia, X-L. (GRIT)
“Profitability, uncertainty and multi-product firm product proliferation: The Spanish car industry”
(Setembre 2012)
XREAP2012-17
Duró, J. A. (GRIT), Teixidó-Figueras, J. (GRIT)
“Ecological Footprint Inequality across countries: the role of environment intensity, income and interaction effects”
(Octubre 2012)
XREAP2012-18
Manresa, A. (CREB), Sancho, F.
“Leontief versus Ghosh: two faces of the same coin”
(Octubre 2012)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2012-19
Alemany, R. (RFA-IREA), Bolancé, C. (RFA-IREA), Guillén, M. (RFA-IREA)
“Nonparametric estimation of Value-at-Risk”
(Octubre 2012)
XREAP2012-20
Herrera-Idárraga, P. (AQR-IREA), López-Bazo, E. (AQR-IREA), Motellón, E. (AQR-IREA)
“Informality and overeducation in the labor market of a developing country”
(Novembre 2012)
XREAP2012-21
Di Paolo, A. (AQR-IREA)
“(Endogenous) occupational choices and job satisfaction among recent PhD recipients: evidence from Catalonia”
(Desembre 2012)
2013
XREAP2013-01
Segarra, A. (GRIT), García-Quevedo, J. (IEB), Teruel, M. (GRIT)
“Financial constraints and the failure of innovation projects”
(Març 2013)
XREAP2013-02
Osorio, A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N., Rathmann, K.
“Social Determinants of Child Health in Colombia: Can Community Education Moderate the Effect of Family Characteristics?”
(Març 2013)
XREAP2013-03
Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT)
“The building blocks of international ecological footprint inequality: a regression-based decomposition”
(Abril 2013)
XREAP2013-04
Salcedo-Sanz, S., Carro-Calvo, L., Claramunt, M. (CREB), Castañer, A. (CREB), Marmol, M. (CREB)
“An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches”
(Maig 2013)
XREAP2013-05
Alcañiz, M. (RFA), Guillén, M. (RFA), Sánchez-Moscona, D. (RFA), Santolino, M. (RFA), Llatje, O., Ramon, Ll.
“Prevalence of alcohol-impaired drivers based on random breath tests in a roadside survey”
(Juliol 2013)
XREAP2013-06
Matas, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Roig, J. L. (GEAP)
“How market access shapes human capital investment in a peripheral country”
(Octubre 2013)
XREAP2013-07
Di Paolo, A. (AQR-IREA), Tansel, A.
“Returns to Foreign Language Skills in a Developing Country: The Case of Turkey”
(Novembre 2013)
XREAP2013-08
Fernández Gual, V. (GRIT), Segarra, A. (GRIT)
“The Impact of Cooperation on R&D, Innovation andProductivity: an Analysis of Spanish Manufacturing and Services Firms”
(Novembre 2013)
XREAP2013-09
Bahraoui, Z. (RFA); Bolancé, C. (RFA); Pérez-Marín. A. M. (RFA)
“Testing extreme value copulas to estimate the quantile”
(Novembre 2013)
2014
XREAP2014-01
Solé-Auró, A. (RFA), Alcañiz, M. (RFA)
“Are we living longer but less healthy? Trends in mortality and morbidity in Catalonia (Spain), 1994-2011”
(Gener 2014)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2014-02
Teixidó-Figueres, J. (GRIT), Duro, J. A. (GRIT)
“Spatial Polarization of the Ecological Footprint distribution”
(Febrer 2014)
XREAP2014-03
Cristobal-Cebolla, A.; Gil Lafuente, A. M. (RFA), Merigó Lindhal, J. M. (RFA)
“La importancia del control de los costes de la no-calidad en la empresa”
(Febrer 2014)
XREAP2014-04
Castañer, A. (CREB); Claramunt, M.M. (CREB)
“Optimal stop-loss reinsurance: a dependence analysis”
(Abril 2014)
XREAP2014-05
Di Paolo, A. (AQR-IREA); Matas, A. (GEAP); Raymond, J. Ll. (GEAP)
“Job accessibility, employment and job-education mismatch in the metropolitan area of Barcelona”
(Maig 2014)
XREAP2014-06
Di Paolo, A. (AQR-IREA); Mañé, F.
“Are we wasting our talent? Overqualification and overskilling among PhD graduates”
(Juny 2014)
XREAP2014-07
Segarra, A. (GRIT); Teruel, M. (GRIT); Bové, M. A. (GRIT)
“A territorial approach to R&D subsidies: Empirical evidence for Catalonian firms”
(Setembre 2014)
XREAP2014-08
Ramos, R. (AQR-IREA); Sanromá, E. (IEB); Simón, H.
“Public-private sector wage differentials by type of contract: evidence from Spain”
(Octubre 2014)
XREAP2014-09
Bel, G. (GiM-IREA); Bolancé, C. (Riskcenter-IREA); Guillén, M. (Riskcenter-IREA); Rosell, J. (GiM-IREA)
“The environmental effects of changing speed limits: a quantile regression approach”
(Desembre 2014)

2015
XREAP2015-01
Bolance, C. (Riskcenter-IREA); Bahraoui, Z. (Riskcenter-IREA), Alemany, R. (Risckcenter-IREA)
“Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches”
(Gener 2015)
XREAP2015-02
Ramos, R. (AQR-IREA); Sanromá, E. (IEB), Simón, H.
“An analysis of wage differentials between full- and part-time workers in Spain”
(Agost 2015)
XREAP2015-03
Cappellari, L.; Di Paolo, A. (AQR-IREA)
“Bilingual Schooling and Earnings: Evidence from a Language-in-Education Reform”
(Setembre 2015)
XREAP2015-04
Álvarez-Albelo, C. D., Manresa, A. (CREB), Pigem-Vigo, M. (CREB)
“Growing through trade: The role of foreign growth and domestic tariffs”
(Novembre 2015)
XREAP2015-05
Caminal, R., Di Paolo, A. (AQR-IREA)
Your language or mine?
(Novembre 2015)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP
XREAP2015-06
Choi, H. (AQR-IREA), Choi, A. (IEB)
When one door closes: the impact of the hagwon curfew on the consumption of private tutoring in the Republic of Korea
(Novembre 2015)

2016
XREAP2016-01
Castañer, A. (CREB, XREAP); Claramunt, M M. (CREB, XREAP), Tadeo, A., Varea, J. (CREB, XREAP)
Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II
(Setembre 2016)
XREAP2016-02
García-Quevedo, J. (IEB, XREAP); Segarra-Blasco, A. (GRIT, XREAP), Teruel, M. (GRIT, XREAP)
Financial constraints and the failure of innovation projects
(Setembre 2016)
XREAP2016-03
Jové-Llopis, E. (GRIT, XREAP); Segarra-Blasco, A. (GRIT, XREAP)
What is the role of innovation strategies? Evidence from Spanish firms
(Setembre 2016)

xarxa.xreap@gmail.com