Determinants of innovativeness and sourcing
strategies among European manufacturing
firms in an era of Global Economy
Aderajew Shumet Tamirat
Research Master Variant (RMV), MCB
Supervisor: Assistant Professor Stefano Pascucci
Co-supervisor: Assistant Professor Kolympiris Christos
Wageningen University and Research Center, Netherlands
September 2013
I
Abstract
Since the allocation of the limited resources for innovative activities is one of the most
difficult decisions for firms as results often are ambiguous, it is worth to study the driving
forces that affect firm’s decision to engage in innovation and the sourcing strategy selection.
The aim of this work was to investigate the drivers of firms’ decision to engage in innovation,
characterize the organisation of those innovative firms, analysis the determinants of
innovation sourcing decisions and finally to investigate whether the innovation-sourcing
options are indeed complementary. The focus was on the European manufacturing firms using
a database from EFIGE (European Firms in Global Economy) on 14, 759 manufacturing firms
operating in seven European countries.
We tackled the question of the firm’s innovative activities and sourcing decisions in two steps.
In a first step, the firms decide whether to innovate, while in the second step the innovating
firms decide on how to organize their innovation. We used the same set of company and
industry characteristics to identify the innovation decision and distinguish the choice among
the strategies. In order to have the most complete picture, we make use of three theoretical
frameworks: transaction costs economics, strategic management and resource‐based view. We
estimate a logistic regression and multinomial logit model to examine the drivers for
companies to engage in innovation activities and the combinations of sourcing strategies
respectively.
The empirical results indicate that internationalisation of firms, intensity of competition, and
human capital as major drivers of innovativeness. Whereas those firms that face competition
only in their country seem less active in engaging in innovation. Furthermore, firms’ internal
resource jointly with strategic global orientation, investment on infrastructure, intensity of
competition, belongingness, and access to information technology facilities explain the
innovation selection strategies of manufacturing firms in Europe.
1
1. Introduction
The consistent market pressure in the era of globalization to generate competitive advantage
urges firms to engage in innovative activities. Product, process, market, and organizational
innovations are the usual four innovation types found in literatures and the first two usually
considered as technological innovations.
Innovation is broadly seen as an essential component of competitiveness, embedded in the
organizational structures, processes, products, and services within a firm(Oke et al., 2007).
Study of innovation and strategies hardly needs justification, as scholars, policy makers,
business executives, and public administrators maintain that innovation is a primary source of
economic growth (Scozzi et al., 2005; Tan and Perrons, 2009), industrial change (Tan and
Perrons, 2009), and public service (Boyne et al., 2006). It is also not uncommon that studies
in economic, organizational theory, strategic management, and marketing have focused on
innovation and have claimed it to provide organizations with a means of creating a sustainable
competitive advantage that is imperative in today’s turbulent environment (Johnson et al.,
2009; Porter, 1985).
Studies that looked the role of innovation in firm’s performance present early mover
advantage and performance gap, as theoretical arguments in favour of innovation regardless
of its forms and organisations. Cohen and Levinthal (1989) and (Lieberman and Montgomery,
1988); Roberts and Amit (2003), for example, argue in their studies that engaging in
innovation activity enables first and early mover organizations to be aware of the latest
developments, absorb new and related knowledge, and increases their chances of benefiting
from innovation activities over time. Whereas, performance gap, namely the difference
between what an organization is accomplishing and what it can potentially accomplish,
creates a need for change in the organization which would in turn provide motivation to adopt
innovations in order to reduce the perceived gap (Lieberman and Montgomery, 1988; Zaltman
et al., 1973).
However, the organisation of innovation along the internal versus external sourcing
dimension remains a complex issue. The theoretical literature drawn on transaction costs
economics and property rights considers the choice between external sourcing and internal
development as substitutes: the make or buy decision (Arrow, 1962; Coase, 1937).
2
Besides, there are ample arguments to stress the complementarity between in-house R&D and
external know-how, because internal R&D capabilities allow to effectively absorb external
knowledge (Cohen and Levinthal, 1990 ). The difficulty of being a good ‘buyer’(outsource
innovation) when one is not also a ‘maker’ (Innovate in-house) has long been recognized
(Radnor, 1991 as sighted by (VEUGELERS and CASSIMAN, 1998). Most of the empirical
evidence on the complementary nature of technology sourcing strategies are circumstantial
(Veugelers and Cassiman, 1999) as it depends heavily on the internal characteristics of firms
and the external environment in which the firm operates.
Since the allocation of the limited resources for research and development is one of the most
difficult decisions due to the uncertainty and the ambiguity results of the investments, it is ,
therefore, worth to study the driving forces that affect firm decision to engage in innovation
and its sourcing strategy selection determinants in a large scale. Moreover, studies reviewed
in this paper were predominately cross-sectional and did not examine characteristics of those
firms and the cumulative consequences of the adoption of different types of innovation
strategies over time. Besides, most of the studies focused in limited industries that are
operating mainly in one country, which ultimately will call the generalizability (external
validity) of the findings in to question though it is not surprising given the difficulties of
quantifying a multi-dimensional phenomenon like innovation.
This paper aims at filling this literature gaps by investigating the drivers to innovate or not
and determinants of the make, buy or make and buy decision applied to innovation strategies.
The focus is on the European firms using a database from EFIGE (European Firms in Global
Economy) on 14, 759 firms operating in seven European countries (UK, Italy, France,
Germany, Hungry, Spain, and Austria). The fact that the data is from different economic
status of the European economies from small and weak/average to big and stronger western
European companies enhances the representativeness
of the data and their by external
validity of the findings.
In order to have the most complete picture, we make use of three theoretical frameworks to
develop hypothesis: transaction costs economics, strategic management and resource‐based
view. We tackle the question of the firm’s innovative activities and sourcing decisions in two
steps. In a first step, the firms decide whether to innovate, while in the second step the
innovating firms decide on how to organize their innovation. We use the same set of company
3
and industry characteristics to identify the innovation decision and distinguish the choice
among the strategies.
Following Cassiman and Veugelers (2006) and Beneito (2006), we estimate a logistic and
multinomial logit model, examining the drivers for
companies to engage in innovation
activities and the combinations of sourcing strategies (in this case: Make Only, Buy- Only,
and Make &Buy) respectively. To analyse the probability of engaging in innovation activities,
a binary variable is defined that takes the value of 1 if the firm has expended on any kind of
R&D and assigns one or more employees for the activity during one or more years of the
observed period, and equal to 0 if the firm does not report positive expenditures and assigns
no employees in the R&D department. For this case, we use a standard logistic regression
estimation model. In order to analyse the probability of firms to choose the innovation
sourcing strategies, we run multinomial logit model using STATA.
The empirical results indicate that internationalisation of firms, intensity of competition, and
human capital as major drivers. Whereas those firms that face competition only in their
country seem less active in engaging in innovation. Furthermore, firms’ internal resource
jointly with strategic global orientation, investment on infrastructure, intensity of competition,
belongingness, and access to information technology facilities explain the innovation
selection strategies of manufacturing firms in Europe.
This paper proceeds as follows. In section 2, the research objectives and research question are
presented under the research statement. In section 3, introduction of the theoretical
background on drivers of innovation and basic determinant of decision on innovation
strategies as used in this paper and an elaboration on the theoretical elements used to develop
our hypotheses. In section 4, the empirical analysis is presented where the data, variables and
the econometric model are discussed. Section 5 shows the results, while in section 6 the
results are discussed and conclusions are made.
4
2. Statement of the problem
In order to conduct R&D activities, firms must select the most adequate innovation strategy,
which objective is to guide the firm in acquiring, developing and applying the technology in
order to generate competitive advantages (Swan and Allred, 2003). Traditionally, four
innovation strategies have been analysed in the literature: make; buy; make-buy (Scozzi et al.,
2005; Subramanian and Nilakanta, 1996; Veugelers and Cassiman, 1999) and; cooperate in
R&D activities (Colombo and Garrone, 1996). However, this last one has usually been
studied independently due to its specificity and complexity, Bayona Sáez and Huerta Arribas
(2002) for instance.
Cassiman and Veugelers (2006) underlined that most of the literature based on transaction
costs concentrates on the choice between internal and external sourcing for individual
transactions, as substitute modes for generating innovation. Although the availability of
external technology may substitute for own research investment by the receiver firms, there
are also arguments to stress the complementarity between in-house R&D and external knowhow, as the recent literature suggests (Arora and Gambardella, 1994; Cockburn and
Henderson, 1998; Freeman, 2002; Von Zedtwitz and Gassmann, 2002)
It is not uncommon that most of the existing theoretical literature concentrates on the
exclusive choice between internal sourcing and external sourcing of technology (Neely et al.,
2001). On the combination between internal and external sourcing, the theoretical literature is
scarce as it is known little in innovation process literature, while the empirical literature
provides mainly indirect evidence on the importance of the phenomenon (Arora and
Gambardella, 1994; Geroski and Machin, 1992). While the theoretical literature has only
started to unravel the complex links between internal and external sourcing, it is not surprising
that the existing empirical literature is far from being able to provide hard evidence on
complementarity in the innovation strategy.
This paper presents thus an empirical analysis of the drivers to engage in innovation, different
innovation sourcing strategies and complementarity among them where we restrict attention
to own R&D, external technology acquisition and combination of both.
5
The present work aims (a) to study the determinants of firms’ decision to engage in
innovation (usually defined in terms of R&D expenditures and number of employees assigned
formally to engage in R&D activities). (b) Characterize the innovative activities of firms, (c)
analysis the determinants/drivers of innovation sourcing decisions on the part of the firm.
First, the decision to do R&D in-house and develop their own technology, which we consider
the firm’s MAKE decision. Second, the decision to conduct R&D activities through acquiring
other companies for their technology content, or, it can hire away skilled personnel. In this
study, we call it a BUY decision. The third decision facing firms is all about the extent and
relationship (we call it here HYBRID) between in-house and contractual forms of R&D
activities which are practically less studied and few, if not none, empirical literatures exist,
and finally, (d) to investigate whether all the above three innovation sourcing options are
indeed complementary.
In line with the above objectives, the following research questions are addressed.

What are the basic drivers of firms to engage in innovation activities?

How do manufacturing firms organize their innovation?

What are the determinants of firm’s decision to choose from innovation sourcing
strategies?

Are these innovations sourcing strategies indeed complementary?
6
3. Theoretical Framework and Hypotheses
The innovation process consists of a complex sequence of decisions. For the sake of
simplifying using theoretical frameworks, we structure the decision of a firm on how to
innovate as a two-step process. First, the firm decides whether to innovate and second, the
firm decides which innovation strategy to employ and how to acquire the necessary
technology to accomplish its innovation goals.
3.1 Organizations of Innovation
According to Williamson (2000), firm can rely on a combination of different strategies to
engage in innovation. First, firms can do R&D in-house and develop their own technology,
which we see as the firm’s make decision. A second alternative strategy is to acquire
technology externally, the buy decision. A firm can acquire new technology, which is
embodied in an asset that is acquired such as new personnel or parts of other firms or
equipment. Nevertheless, new technology can also be obtained disembodied such as in blue
prints through a licensing agreement or by outsourcing the technology from an R&D
contractor or consulting agency. A third, more hybrid form of obtaining and developing new
technology is through cooperative agreements between firms or other research institutions.
Pascucci et al. (2012) looked back at the literature on the organization and management of
innovation processes and argues that an important aspect of companies’ strategies are found to
be decisions to innovate in-house, to collaborate or to outsource.
As stated by Cruz-Cázares et al. (2010), the increasing rapidness of new technologies
development made some firms prefer the externalization of the R&D activities since it is not
feasible for them to develop internally such specific technology. Besides, Barney and Arikan
(2001), revealed that firms do not need to own all the resources and capacities while they
could access them externally. Some of the advantages of developing external R&D activities
are that it is more reliable and the results are more predictable since the technology has been
already developed and tested (Kessler and Bierly III, 2002).
On the other hand, the remarkable complexity of the R&D activities suggests the creation of
internal departments for developing these activities (Cruz-Cázares et al., 2010; Dosi, 1988).
The information flow between the R&D department and those which will use the new
technology could considerable increase by integrating the R&D activities (Vega-Jurado et al.,
2009).
7
At the same time, in-house R&D constitutes a unique source of knowledge and allows an
objective valuation of the real innovation needs (West, 2002)
As reconciliation on the on-going make or buy debate on innovation strategies, the
complementarity of both buy and make has begun getting much attention in scientific research
arena in the last few decades. Needless to say that the vast technological changes, most of
the products and services offered in the market need to embody a specific set of technologies,
each of which requires high specialized knowledge and capacities to develop, so firms can no
longer hope either to do everything in-house (Cassiman and Veugelers, 2006; Iansiti and West,
1997; Oke et al., 2007; Pascucci et al., 2012; Walker, 2006) or outsource everything. Hence,
the firms need the ability to draw their strategies by combining the internal and external R&D.
Furthermore, Cruz-Cázares et al. (2010) in their paper on the innovation strategies of Belgian
manufacturing companies also stated one of the most important concept which highlights the
complementarity of the make and buy strategies; the absorption capacity as sighted in Cohen
and Levinthal (1990). This is the firm ability to recognize the value of external knowledge, to
assimilate it and to apply it to commercial ends.
Complementarity between the R&D
strategies is highlighted since firms must achieve in-house R&D in order to generate or
increment their capabilities to scan i.e. acquisition-assimilation and to integrate i.e.
transformation-exploitation the external knowledge acquired through the buy strategy (Arora
and Gambardella, 1990). In simple words, a firm will not make the most of the buy strategy
efficiently if the firm does not develop R&D activities internally. Furthermore, the more the
knowledge gained through in-house R&D may serve to modify or improve external
technological acquisitions (Veugelers and Cassiman, 1999)
3.2 Determinants of innovation strategy decisions
The theoretical literature that exists on organisation/governance of innovation stresses the
choice between external sourcing and internal development as substitutes, i.e., the classical
make or buy (Williamson, 2000). Building further on the general literature on make or buy
decisions, i.e., transaction cost economics - (Becheikh et al., 2006; Geroski and Machin, 1992)
-and property rights theory, Lieberman and Montgomery (1988); the theoretical frameworks
to explain R&D outsourcing also stresses the advantage of tapping existing often more
specialized knowledge if available.
8
Instead of discussing make, buy, or cooperate as substitutes, the potential for combining
internal and external sourcing modes, as complementary innovation strategies should not be
ignored. The Sappho (Rothwell, 1991)study identified better internal and external
communication networks, allowing a more efficient use of external know-how, as a distinct
feature of successful innovative firms. While examining the critical success factors of 40
innovations, (Hart & Moore, 1990) found external sources of technical expertise combined
with in-house basic research that facilitate these external linkages to be crucial in explaining
success of the innovation and its impact on the business.
The theoretical frameworks we used to develop the hypothesis to be tested are in line with the
paper from Pascucci et al. (2012) about the determinants of the make or buy decision. As
mentioned in section 1, these theories are transaction cost, resource based view (RBV) and
strategic management.
3.2.1 Transaction Cost Economics
Transaction costs economics argues that firms that invest huge capital on infrastructure may
want to amortize these costs by innovating in-house (Gooroochurn and Hanley, 2007). On the
other hand, we can also think that for strategic reasons, firms may want to invest in that to
raise their absorptive capacity. As such, we develop the following hypothesis.
Hypothesis 1. Firms that invest heavily on infrastructure will be more inclined to innovate
in‐house.
Recent literatures revealed that belonging to a certain group is expected to determine the
innovation strategy decision of firms. Nobel and Birkinshaw (1998) argue that one advantage
for being within a group is that the strategy could be grouped in technology terms. When there
is a complementarity, in the technologies/innovation strategies between the firm and the group,
firm could access the group resources and it would diminish considerably the transaction costs
by developing the external R&D activities. In the same way, firm could experiment some
economies of scale and scope, minimizing the probability that a firm internalize the R&D
activities when it belongs to a certain group(Roberts and Amit, 2003). We develop the
following hypothesis based on the above arguments.
Hypothesis 2. When a firm belongs to a group, the buy strategy will have more probabilities
to be selected.
9
Research works of Hitt et al. (1997) have proposed a relationship between international
diversification (like export orientation and FDI) and firms’ innovation sourcing strategies as
it provides greater opportunities to achieve optimal economic scale and to amortize
investments in critical functions such as R&D and brand image over a broader base.
Additionally, internationally diversified firms can gain competitive advantages by exploiting
market imperfections (differences in national resources, for instance) and cross-border
transactions and can also gain the increased flexibility and greater bargaining power that
result from a multinational network and from larger economies of scale, scope, and learning
(Shan et al., 1994)
Hence, firms with activities abroad are expected to combine both make and buy strategies
since when a firm becomes international; it gains access to foreign information and
communication technologies, production methods, transportation, and international logistics,
which could reduce business transactions costs with potential suppliers facilitating the buy
strategy. In line with Cruz-Cázares et al. (2010), We develop the following hypothesis
Hypothesis 3. The greater the international activities a firm engaged in, the greater the
probability of selecting the make-buy strategy will be.
3.2.2 Strategic management
Pascucci et al. (2012), claimed in their paper that though a clear cut relationship doesn’t exist,
the results confirmed the hypothesis that process innovation is more likely to be outsourced
than product innovation. Their result is consistent with Veugelers and Cassiman (1999) who
have noticed
also that process and generic products innovation are more likely to be
outsourced. Since product innovation is considered as a firm‐specific input, leakage through
outsourcing has more important strategic implications than for generic process innovations.
The supplier firm cannot use leakage over generic R&D innovations opportunistically since
most firms are contracting these innovations. Therefore, we make the following hypothesis.
Hypothesis 4. Process innovation is more likely to be outsourced than product innovation.
Swan and Allred (2003) found that external acquisition technology is positively and highly
related to a high competition level because it allows cost reduction and a quickly entrance to
the market. Unlikely, Pisano (1990) argued that in sectors where the competition is very high,
the make innovation strategy is preferred by firms in order to gain the first mover advantage.
Here, we consider the two approaches very valuables. Therefore, innovative firms should not
10
look solely for the flexibility and speed needed in high competitive industries gained through
the buy strategy, but also should deem generating the barriers to imitation relying in the make
strategy.
Hypothesis 5: The make-buy strategy will be selected when the firm face a tough competition
3.3.3 Resource‐based view (RBV)
It is less arguable that highly skilled employees are very important for innovation. The
question however is how they are going to affect the decision of firms on the innovation
sourcing strategies. Arora and Gambardella (1994) have argued that internal knowledge
resources allow using foreign knowhow more effectively in the firm, which would stimulate
external innovation sourcing. Pascucci et al. (2012) also hypothesised the same in line with
the above argument and their result holds the hypothesis. However there is also a chance that
high internal employees competences may be an incentive for firms to innovate in‐house with
available resources (Veugelers and Cassiman, 1999). Thus, we develop the following
hypothesis
Hypothesis 6. Firms with skilled human resources are more likely to engage in Hybrid
(Make-Buy) innovation strategies than those with less skilled employees.
Empirical evidence less arguably point out that the organizational resources have a positive
impact in firm innovativeness (Becheikh et al., 2006; Beneito, 2006; Cruz-Cázares et al., 2010;
Egbetokun et al., 2008; Galende and de la Fuente, 2003; Kessler and Bierly III, 2002). As far
as the researcher knows, there have been very few, if not none, investigation that has been
dealing with the firm age as a determinant of the innovation sourcing strategy selection,
leading a gap in the literature that this paper aims to fill. Young firms often do not have the
high economical and human resources needed to develop the in-house R&D activities, that the
make strategy is usually more risky and expensive (Tsai, 2001; West, 2002). Hence they are
prone to select the buy strategy since young firms look for externalizing risk for overcoming
environmental uncertainties (Poon and MacPherson, 2005). In line with the above arguments,
the following hypothesis is developed.
Hypothesis 7: When firms are younger, the probability for selecting the buy strategy will be
the highest.
11
Better internal and external communication networks are important features of innovative
firms. We learn from literature that there is strong positive correlation between investment in
ICT and making innovation decisions (Cruz-Cázares et al., 2010; Egbetokun et al., 2008;
Pascucci et al., 2012). By extension, better communication network is strongly linked to a
better communication system, the presence of information and communication technology
(ICT) in the firm should influence their willingness to outsource innovation. On top of that
better internal and external information communication facilities, allowing a more efficient
use of external know-how, as a distinct feature of successful innovative firms (Rothwell,
1991). As such, we propose the following hypothesis
Hypothesis 8. Firms with access to information communication technology/system are more
likely to choose make-buy innovation strategy.
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4. Empirical Analysis
4.1 Data and Descriptive Analysis
The data for this paper is from EU-EFIGE/Bruegel-UniCredit dataset, which is a unique firmlevel database of representative samples of manufacturing firms (with a lower threshold of 10
employees) across European countries. The questionnaire covers six different broad fields:
Structure, workforce, R&D, internationalisation process, market structure and financial
structure. Almost 150 variables are organised in more than 450 different sub items for each
one of the 14,759 firms across seven European countries (443 from Austria, 2,973 from
France, 2,935 from Germany, 488 from Hungary, 3021 from Italy, 2832 from Spain and 2,067
from UK).
According to EFIGE data set road map, the data collection has been performed through a
survey carried out by a professional Contractor (GFK, the fourth largest market research
company in the world), with the aim of gathering both qualitative and quantitative
information at the firm level.
Table 1 below shows the descriptive of the innovativeness and innovation strategies by
country. First, within the whole sample, 48.8 % of the observations (companies) are not
developing R&D activities (innovating) in the years 2007-09. Almost half of the companies in
UK and France don not engage in innovation activities. As showed there, majority of
companies in Hungary and Spain do not conduct R&D activities for the last three years. Many
companies, in relative, operating in Italy and Germany do innovate more. Besides, we can
observe from the table that the Make only strategy is the most selected in all countries
followed by Buy only and both Buy and Make.
Table 1: Innovativeness and Innovation Strategy by Country
Country
Austria
France
Germany
Hungary
Italy
Spain
UK
TOTAL
Innovativeness
INNOVATE
Don’t INNOVATE
246
1506
1601
131
1661
1303
1100
7548 (51.2%)
197
1467
1334
357
1360
1529
967
7211(48.8%)
Innovation strategy
MAKE
228
1436
1515
108
1519
1156
1053
7015
BUY
74
266
326
37
365
276
312
1656
MAKE_BUY
24
117
54
9
60
42
74
380
13
4.2 Variables
There exists at present a huge body of both empirical and theoretical literature on the
determinants of innovative activities, which in most cases entails factors behind the yes/no
type response. Following the existing literature on this topic, the empirical estimation will
include those standard factors discussed in the previous section that are considered to
influence the decision to enroll in R&D activities and organisation/governance of innovation.
Table 2 in the following page presents the precise definitions and measures of all these
variables.
Dependent Variables: As mentioned in section 1, we have two dependent variables: the
decision to innovate and choice among innovation sourcing strategies. The first dependent
variable deals with the innovation decision of the manufacturing firms in the sample in a
multivariate analysis. Given that both innovating and non-innovating firms responded to some
parts of the questionnaire, we can attempt to discriminate between innovators and noninnovators in the sample. We use a logistic regression model where the dependent variable is
1 when the firm claims to innovate and specified a positive innovation budget, otherwise 0
when it doesn’t engage in any innovative activates or have 0 spending on research and no
employees assigned purposely for R&D activity.
The second dependent variable is all about the innovation strategy. Three levels compose the
variable: 1 = MAKE, 2 = BUY and, 3 = MAKE-BUY. This variable is categorical unordered
and is taken directly from the database corresponding to activities. The different levels are, by
definition, mutually exclusive. In order to assure that the make-buy strategy was substantially
different from make or buy isolated, it was recorded from the original data following the next
criteria:

“In-house innovation (MAKE)” refers to the presence of R&D activities carried out
within the company.

“Out-sourcing innovation” (BUY) is when the company acquired R&D activities,
patents and/or know-how from other companies with the specific purpose of
introducing new processes and/or products

“Hybrids (MAKE-BUY)” relates to R&D activities carried out together with external
entities through networks of collaboration, strategic alliances or joint ventures. i.e.,
when a firm uses a combination of both make and buy strategies.
14
Independent Variables: the following are used as explanatory variables

The total amount of investment in plants, machines, equipment and ICT in 2007‐
2009 are used as proxy for the variable total investment on firms infrastructure.

Belong to a group: the variables is operationalized as discrete that taking value of 1 if
the firm does belong to either a national or foreign group, if the firm acquired other
company or being acquired or 0 otherwise.

Internationalisation: operationalized as when the firm engages at least in one of the
following activities: exporter, importer of materials and services, outsourcer (passive
and active) and FDI.

Industry Type: following the pavitt classification, the industry’s effect on the
innovation strategy selection is operationalized by four dummies, the first one for
firms belonging to traditional sector, and the second one for High-tech, the third one
specialised and finally for economies of scale.

The variable belonging to intense of competition is operationalized as discrete, taking
value of 1 if the firm does face intense competition from the domestic and
international firms in the industry, 0 otherwise.

Type of innovation is explained in dummy taking value of 1 if a firm engage in
product, process and market innovations and 0 otherwise.

Other variables like human capital, age, and access to IT are also used as explanatory
variables. The detail of these variables is explained in Table 2 blow.
Control Variable: The debate on whether large companies have advantage to become
innovative or innovation emerge in small entrepreneurial firms always ended up in a mixed
result. On one hand, following the RBV, large firms have greater resources to innovate
internally due to the fact that they can stand more risky activities than small firms since they
used to have more financial resources and more qualified personal (Leiblein et al., 2002; Tsai,
2001). Contrary, due to the lack of resources, following less risky activities, small firms trend
to select the buy strategy (Swan and Allred, 2003). In this way, Stock et al. (2001) found that
large firms trend to do in-house R&D since they want to take advantage of the scale
economies that they generate in the in-house R&D, marketing, and production. On the other
hand, empirical studies (Johnson et al., 2009; Roper and Love, 2002; Wischnevsky and
Damanpour, 2006) point in the opposite row. Finally, Veugelers and Cassiman (1999) argue
that small firms restrict their innovation strategy to make or to buy R&D solely while large
firms usually combine both strategies at the same time. This controversial on the relationship
15
between firm size and innovation strategy selection, leaves us only the chance to control the
model through firm size.
Variations in innovation sourcing decisions may also be associated with the industrial branch
of the firms involved. Industries with a big amount of technological changes deem innovation
externalization as the better option for the reason that it is not worth to trust in internal R&D
when the market is changing in a high degree (Noori, 1990). Likewise, when there is large
technology diversity in the market, firms are influenced to externalize R&D (Cesaroni, 2004).
Cruz-Cázares et al. (2010), however, revealed that following the absorption capacity approach,
firms need to develop in-house R&D in order to integrate efficiently the acquired technology
and gain competitive advantage. The data we use for this study has no clear-cut classification
Therefore, it would be appropriate to control the impact of the industrial branch on sourcing
decision of firms.
Table 2: Description Of Variables Used In The Empirical Analysis
Dependent Variables
INNOVA
MAKE
BUY
MAKE-BUY
Theory
Transaction
Cost
Economics
Strategic
Management
Dummy for innovativeness: In the last three years (2007-2009), the firm
has undertaken R&D activities
Dummy for Make; 1 if the firm has undertaken R&D activities in-house in
the last three years (2007-09) and 0 otherwise
Dummy for Buy; 1 if the firm has undertaken R&D activities acquired
from external sources in the last three years (2007-09) and 0 otherwise
Dummy for Hybrid; 1 if the firm uses both make and buy strategies i.e has
undertaken R&D activities in collaboration with other firms in the group
in the last three years (2007-09) and 0 otherwise
Independent Variables
Hypothesis
Variables (codes)
Total investment on in plants, machines, equipment and ICT in
H1
2007‐2009 (per-Invest-plant)
Belong to either a national or foreign group (firm_belong)
H2
Acquire (totally or partially) or incorporate other firms in the last three
years 2007-2009 (firm_acquire)
Acquired (totally or partially) or incorporated by other firms in the
last three years 2007-2009 (firm_acquired)
Internationalisation: the firm engages at least in one of the following
H3
activities: exporter, importer of materials and services, outsourcer
(passive and active) and FDI (active-abroad)
H4
Firms that carried out any product innovation in years 2007‐09 (prodinnov)
Firms that carried out any process innovation in years 2007‐09
(proces-innov)
Firms that carried out new to the market innovation (mkt-innov)
The firm has competitors in home country (compet-home)
H5
The firm has competitors abroad (compet-abroad)
16
H6
Resource
Based View
H7
H8
Controls
Dummy for firm that has a higher share of graduate employees with
respect to the national average share of graduates (human_k)
Number of white collar workers (white-collar)
Number of skilled blue collar workers (skilledbl-collar)
percentage of employees participated in training programs (per-train)
Age: year of establishment (age-company)
Dummy for firms that has access to a broadband connection i.e highspeed transmission of digital content (inter-access)
Access to IT systems/solutions for internal information management
(IT-info)
Access to IT systems/solutions for E-commerce (IT-e-comrce)
Access to IT systems/solutions for management of the sales/purchase
(IT-mgmt)
Size: Total number of employees (tot_employee)
PAVITT classification: a dummy for firms industry/sector:
Traditional" - "High Tech" - "Specialized" - "Economies of Scale"
(pavitt-class)
4.3 The Empirical Model
We tackle the question of the firm’s innovative activities and sourcing decisions in two steps.
In a first step, the firms decide whether to innovate, while in the second step the innovating
firms decide on how to organize innovation once they decide to innovate. The same set of
company, industry and market characteristics identifying the innovation decision is used to
distinguish the choice between making, buying and/or buying-making innovation strategies.
Following Cassiman and Veugelers (2006) and Beneito (2006),
We estimate a logistic
regression and multinomial logit model, examining the drivers for companies to engage in
innovation activities and the combinations of sourcing strategies (in this case: Make Only,
Buy- Only, and Make &Buy) respectively.
To analyse the probability of engaging in innovation activities, a binary variable is defined
that takes the value of 1 if the firm has expended on any kind of R&D and assigns one or
more employees for the activity during 1 or more years of the observed period, and equal to 0
if the firm does not report positive expenditures in R&D and assigns no employees in the
R&D department.
In order to analyse the probability of firms to choose the innovation sourcing strategies, we
used multinomial logit model. We choose these models, as the number of categories is not too
large and there is sufficient variation in each category. We estimate the following model
17
Consider:
ϒ =1 If the firm decides to engage in innovation activity (INNOV))
ϒ =2 If the firm decides to innovate in-house (MAKE);
ϒ =3 If the firm decides to outsource innovation activities (BUY).
ϒ =4 If the firm decides to innovate in collaboration with other firms (MAKE-BUY)
The general model related to the firm j to choose an innovation strategy can be written as
follow:
ϒ
..................................................................................................(1)
Where,
ϒ
Where the error terms are independently distributed, the observable dependent variable ϒ i
is linked with its latent counterpart ϒ* i via:
ϒ
{
ϒ
ϒ
.........................................................................................................(2)
Then the respective probabilities can be written as
. .................................................................... (3)
{
Where;
β1, β2, β3, and β4 are vectors of parameters and
Z1, Z2, Z3, and Z4 are vectors of explanatory variables.
Under the assumption of the probability of choice at each stage being independent of the
choice at the previous stage, the vectors of parameters β1, β2, β3, and β4 can be obtained by
estimating separately the probability of occurrence of each possible realization of the variable
Ї. For this purpose, the probability of ϒ = 1 will be analyzed first, then the probability of ϒ =
2, the and the probability of ϒ = 3, finally, the probability of ϒ = 4.
18
Our aim was to model probabilities for the N different outcomes of the dependent variable ϒi
in such a way that they sum up to unity:
ϒ
ϒ
ϒ
ϒ
ϒ
.............................(4)
Implies that the probability for choosing innovation strategy ʝ is given by
(ϒ
| )
∑
...................................................................................(5)
{
}
where,
 Prob (ϒ = ) is the probability of firms choosing alternative innovative strategies j and
 Zi is a vector of characteristics of firm i.
In order to show the complementarity of all the three innovation strategies, we follow the
adoption approach as used by Cohen and Levinthal, (1990); Cruz-Cázares et al. (2010). The
approach works in a way that we examine simple correlations between the different
innovation activities. Positive correlation is a necessary condition for complementarity (Corr
(Ai, A ) > 0. Further evidence consistent with complementarity can be found in the frequency
with which combined choices are observed in the sample companies.
19
5. Results
5.1 Innovation Decision.
In this section we discuss the innovation decision of European manufacturing firms in the
sample in a multi-variety analysis. Given that both innovating and non-innovating firms
responded to some parts of the questionnaire, we use a logistic regression model where the
dependent variable is 1 when the firm claims to innovate.
Following the existing literature we include age, sector, firm belong internet access,
investment on ICT and infrastructure, internationalization, employee skill and intensity of
competition as explanatory variables. For detailed description of the variables included see
table 2.
The results of the estimation are presented in the following Table. The high Chi-squared of
the model indicates the high joint explanatory power of the independent variables.
Table 3: Innovation Decisions
20
The coefficients in Table 3 are the estimated partial derivatives of probabilities with respect to the
vector of characteristics. The coefficient tells us how much the probability that the firm innovates
increases with an increase in that independent variable, holding the other independent variables
constant. The signs of most of the coefficients are as expected.
Interesting is the highly significant coefficient of the internationalizations of the firms (active abroad)
and the tough competition abroad (compet_abraod). All else equal, a firm that engages at least in one
of the following activities: exporter, importer of materials and services, outsourcer (passive and active)
and FDI, has a 7.7% higher probability of being an innovating firm. Similarly those firms who face a
tough competition outside their country have a 4.5 % higher probability of being an innovating firm.
Competitive pressures in the international markets could account for the fact that constant innovation
is the only way to hold on to international market share. Whereas, firm acquired (those firms that are
partially or totally incorporated by other firms in the last three years) has the expected sign, but does
not show up significant in the decision whether or not to innovate. Surprisingly those forms that face
tough competition in their home country seem a little reluctant to go for innovation.
It is not surprising though; firms that have a higher share of graduate employees with respect to the
national average share of graduates have the expected positive coefficients and are highly significant.
Whereas, those firms who have many blue collar employee compared to white collars have a negative
coefficient as expected, but does not show up significant in the decision whether or not to innovate.
Larger firms are more likely to innovate as the coefficient that tells the probability of being an
innovative firm for an extra employee they have (a proxy for size) increases significantly. Against the
expectation, the result shows that for an extra year the firms stays in business, all else equal, the
probability of being innovative decreases and is statistically significant at alpha value p=0.046. Results
from table 3 further indicate that firms that belong to a group and have access to broadband internet
connection seem less active to engage in research and development.
In the next, we present the result of the analysis on how those innovative firms organise their
innovative actives along the make, buy and make _buy decisions.
5.2 Determinants of Make, Buy and Make-Buy Decisions
In table 4 we present the traditional table result where we have the no innovation as the reference
category in the model. In this table results are interpreted as the probability of selecting one of the
innovation strategies over the reference category. This table gives us the insight of which strategy will
be selected when firms decide to start achieving R&D activities. Since results in table 4 failed to
inform whether one of the strategies is significantly more probable to be selected over the others given
other variables in the model the same, it is needed to rerun the model changing the reference category
21
until crossing all possibilities. These results are presented in table 5 and table 6 for make and buy as
references categories, respectively.
Table 4: Multinomial Logit: No innovation as reference
Make
Buy
Make and Buy
Variables
Coef.
RRR
Coef.
RRR
Coef.
RRR
Pavitt_class
Per_invest_Plant
Age_comp
firm_belong
firm_acquire
firm_acquired
Total_empl
Inter_access
prod_innov
procs_innov
mkt_innov
active_abrd
human_k
white_collar
skilledbl_colla
train_emplo
compt_abrd
compt_home
_cons
-0,0210
(.0275154)
0.00001
(0.0016)
-0,1554***
(.0432383)
-0,035
(.0586327)
0,0936
(.0751959)
0,1546
(.113354)
0,0027***
(.0003081)
-0,0603
(.0780018)
1,1064***
(.0552396)
0,7218***
(.0433302)
0,7106***
(.0616102)
0,7906***
(.0562435)
0,5701***
(.0478593)
0,0003
(.0005029)
-0,0024
(.0003389)
0,0034***
(.000752)
0,4434***
(.0465353)
-0,1618**
(.0636707)
-2,0549***
(.1547532)
0,97927
1,00081
0,85605
0,9656
1,09812
1,16718
1,00273
0,94151
3, 02332
2,05814
2, 03527
2,20463
0,77838
1,00032
0,99976
1,00342
1,55804
0,85061
0,12811
0,00712
1,00714 0,05053
(.0598009)
.0407157
0.00367
1,00367
0.003
(0.0033)
(0.0025)
0,00384
1,00385 -0,16384**
(.094757)
.070149
0,66600***
1,94644 0,45379***
(.114429)
.0830698
0,01523
1,01535 0,43996***
(.1620835)
(.0996379)
-0,14362
0,86622 0,00933
(.2397279)
(.1598763)
0,00168**
1,00168 0,00342***
(.0007949)
(.0003881)
-0,23642
0,78945 -0,29145*
(.1901672)
(.1528359)
0,51514***
1,67388 1,53389***
(.1279888)
(.1022126)
0,82206***
2,27517 1,02505***
(.0959081)
(.0710571)
0,50687***
1,66008 1,02772***
(.1386036)
(.089407)
0,45923***
1,58286 1,14197***
(.1232342)
(.1248035)
0,65854***
1,93196 0,63994***
(.1002233)
(.0738024)
-0,00402*
0,99599 0,00067
(.0022487)
(.0005892)
0,00037
1,00037 -0,00065
(.0008122)
(.0004034)
0,00223
1,00223 0,00714***
(.0016216)
(.0011265)
0,08955
1,09369 0,60245
(.1034465)
(.0776504)
-0,06444
0,93759 -0,22801**
(.1423793)
(.0909104)
-3,76821***
0,0231 -4,80487***
(.3513522)
(.2773515)
14058 Log likelihood = -12307.832
1,05182
1,00295
0,84888
1,57426
1,55265
1,00937
1,00343
0,74718
4,63618
2,78724
2,7947
3,13294
1,89636
1,00067
0,99935
1,00717
1,82659
0,79612
0,00819
Number of obs =
LR Chi2(54)=5156.19
Prob > chi2 = 0.0000
Pseudo R2
= 0.1732
*P<1, **P<0.05, and*** P<0.01; Standard errors in brackets
Observe that the log-likelihood (-12307.832) and the pseudo R2 (0.1732) are the same for all models
and the only thing which varies is the significance and the sign of the coefficient when the strategies
are crossed.
22
The multinomial Logit model estimates k-1 models, where k is the number of levels of the outcome
variable, in this case 3 (4-1). For the first model, see table 4 above, we set No-innovation as the
referent group and therefore estimated a model for Make only, Buy only and Make-buy in relative to
No innovation. The Parameter estimates in table 4,5 and 6, show the logistic coefficient (β) for each
predictor variable for each alternative category of the outcome variable. Since these parameter
estimates are relative to the referent group, the standard interpretation of the multinomial Logit is that
for a unit change in the predictor variable, the Logit of outcome m relative to the referent group is
expected to change by its respective parameter estimate (which is in log-odds units) given the
variables in the model are held constant. Thus βi can be viewed as parameters of a binary logit model
between alternative J and alternative 1. So a positive coefficient from mlogi t means that as the
repressor increases, we are more likely to choose alternative j than alternative 1.
Further in multinomial logit models, interpreting relative risk ratio (RRR) gives a concrete and more
sensible argument than the β coefficients (Cameron and Trivedi, 2009). RRR can be obtained by
exponentiating the multinomial logit coefficients or by specifying the rrr option in the STATA
command. Standard interpretation of the relative risk ratios is for a unit change in the predictor
variable, the relative risk ratio of outcome m relative to the referent group is expected to change by a
factor of the respective parameter estimate given the variables in the model are held constant (Bruin,
2006). As such, the RRR of a coefficient indicates how the risk of the outcome falling in the
comparison group compared to the risk of the outcome falling in the referent group changes with the
variable in question.
Thus, an RRR > 1 indicates that the risk of the outcome falling in the
comparison group is more likely and when the RRR < 1, the outcome is more likely to be in the
referent group.
From table 4 we can see that variables like competitions abroad, size, innovation types and, human
capital are positive and significant in at least one strategy indicating that all of them influence the
decision to achieve R&D activity. However negative sings indicate for example that those firms who
face competition in their country are less active to engage in innovation. It seems form the descriptive
analysis that the make strategy is the most preferred one to start innovation than buy and make - buy.
In order to see if the make strategy is really preferred over buy and make-buy, for all variables, we
start the analysis following hypothesis statement by comparing results of table 4, 5 and 6.
We stated in hypothesis 1 that Firms that invest heavily on infrastructure will be more inclined to
innovate in‐house. Results indicate a positive effect of investment on infrastructure costs on both
buying decisions while negatively affect the likelihood of the firm to make innovation. Due to the
positive though not significant sings of buy and make-buy in table 5 results allow us to see that make
strategy is the less prone to be selected. As such, our hypothesis is not supported according to our
23
empirical results while indicating a negative correlation between the investment on infrastructures and
in-house innovation decisions.
The fact that firms that choose buy have the highest percentage of investment on infrastructure is
comprehensible since firms incur both costs of allocating plant and equipment and the transaction cost
of finding, selecting and negotiating when buying innovation.
Table:5 Multinomial Logit: Make as reference
N0_Innovation
Buy
Variables
Pavitt_class
Coef.
RRR
Coef.
0,0210
1,0212
0,0281
(0,0275)
(0,0592)
Per_invest_Plant
-.0000892
0,9991
.0035763
(0016051)
(.0032977)
Age_comp
0,1554***
1,1682
0,1593**
(0,0432)
(0,0951)
firm_belong
0,0350
1,0356
0,7010***
(0,0586)
(0,1139)
firm_acquire
-0,0936
0,9106
-0,0784
(0,0752)
(0,1593)
firm_acquired
-0,1546
0,8568
-0,2982
(0,1134)
(0,2361)
Total_empl
-0,0027***
0,9973
-0,0010
(0,0003)
(0,0008)
Inter_access
0,0603
1,0621
-0,1762
(0,0780)
(0,1927)
prod_innov
-1,1064***
0,3308
-0,5912***
(0,0552)
(0,1281)
procs_innov
-0,7218***
0,4859
0,1003
(0,0433)
(0,0961)
mkt_innov
-0,7106***
0,4913
-0,2038
(0,0616)
(0,1344)
active_abroad
-0,7906***
0,4536
-0,3313**
(0,0562)
(0,1276)
human_k
-0,5757***
0,5623
0,0828
(0,0479)
(0,0994)
white_collar
-0,0003
0,9997
-0,0043
(0,0005)
(0,0022)
skilledbl_colla
0,0002
1,0002
0,0006
(0,0003)
(0,0008)
per_train_emplo
-0,0034***
0,9966
-0,0012
(0,0008)
(0,0016)
compet_abroad
-0,4434***
0,6418
-0,3539**
(0,0465)
(0,1036)
compet_home
0,1618**
1,1756
0,0974
(0,0637)
(0,1398)
_cons
2,0549***
7,8059
-1,7131***
(0,1548)
(0,3535)
Number of obs = 14058 Log likelihood = -12307.832
LR Chi2(54)=5156.19
Prob > chi2 = 0.0000
Pseudo R2
= 0.1732
*P<1, **P<0.05, and*** P<0.01; Standard errors in brackets
RRR
Make and Buy
Coef.
1,0285
1,0035
1,1727
2,0158
0,9246
0,7421
0,9990
0,8385
0,5537
1,1055
0,8157
0,7180
1,0864
0,9957
1,0006
0,9988
0,7020
1,1023
0,1803
0,0715
(0,0367)
.0028419
(.0023626)
-0,0084
(0,0658)
0,4888***
(0,0749)
0,3464***
(0,0860)
-0,1453
(0,1403)
0,0007**
(0,0003)
-0,2312
(0,1487)
0,4275***
(0,1005)
0,3033***
(0,0671)
0,3171***
(0,0803)
0,3514**
(0,1258)
0,0642
(0,0672)
0,0004
(0,0004)
-0,0004
(0,0003)
0,0037***
(0,0010)
0,1590**
(0,0737)
-0,0662
(0,0800)
-2,7500***
(0,2652)
RRR
1,0741
1,0028
0,9916
1,6303
1,4139
0,8648
1,0007
0,7936
1,5335
1,3543
1,3731
1,4211
1,0663
1,0004
0,9996
1,0037
1,1724
0,9359
0,0639
24
In hypothesis 2 we stated that When a firm belongs to a group, the buy strategy will have more
probabilities to be selected. When seeing results in table 4 for belong to a group, (firm_belong)
variable there is evidence that firms select the buy strategy as a first step to innovate.
Furthermore, all the negative and significant values in table 6 for this variable give total
support for H2, where we argued that firms belonging to a group would select the buy strategy
instead of developing in-house R&D or combining both strategies. Note as well that firms
belonging to a group do have a preference for the make-buy to make strategies (see table 5).
In this paper it is confirmed that internationalization activities favor innovation development.
Since we observe in table 4 that firms with activities abroad achieve one or other innovation
strategy. Active-abroad variable i.e the firm engages at least in one of the following activities:
exporter, importer of materials and services, outsourcer (passive and active) and FDI
measured as firm internationalization, are determinants for the innovation strategy selection.
Furthermore, in hypothesis 3 we have stated being international would increase the
probability of selecting the make-buy strategy is supported. In table 5 we observed that the
make-buy strategy is preferred over the other ones since the sign is positive and significant.
On the other hand, we see in table 6 that make is preferred over externalizing R&D (buy).
This indicates that in one way or other firms need to attain in-house R&D when they want to
become international firms in order to achieve optimal economic scale and to amortize
investments. Besides, deciding to make the base innovation and then combine the other
strategies bring flexibility to firms so that they can gain competitive advantages by exploiting
market imperfections.
In hypothesis 4 we indicate that process innovation is more likely to be outsourced than
product innovation. Results from the three tables partially confirm the hypothesis because
while we can highlight that more process related innovations are more likely to be make-buy
it also applies to product innovations.
Moreover it seems that product innovations, against our expectation, are more innovated inhouse and overall decisions makings seems lack an existence of a clear -cut relationship. The
result is similar with the findings of Stefano et, al. 2012.
25
Table 6: Multinomial Logit: Buy as reference
No_Innovation
Make
Variables
Pavitt_class
Coef.
RRR
Coef.
-0,0071
.9929093
-0,0281
(0,0598)
(0,0592)
Per_invest_Plant
-.0036655
0,996341
-.0035763
(.0032736)
(.0032977)
Age_comp
-0,0038
.9961629
-0,1593*
(0,0948)
(0,0951)
firm_belong
-0,6660***
.5137599 -0,7010***
(0,1144)
(0,1139)
firm_acquire
-0,0152
.9848846
0,0784
(0,1621)
(0,1593)
firm_acquired
0,1436
1,15444
0,2982
(0,2397)
(0,2361)
Total_empl
-0,0017**
.9983233
0,0010
(0,0008)
(0,0008)
Inter_access
0,2364
1,26671
0,1762
(0,1902)
(0,1927)
prod_innov
-0,5151***
.5974144
0,5912***
(0,1280)
(0,1281)
procs_innov
-0,8221***
.439527
-0,1003
(0,0959)
(0,0961)
mkt_innov
-0,5069***
.6023807
0,2038
(0,1386)
(0,1344)
active_abroad
-0,4592***
.6317683
0,3313**
(0,1232)
(0,1276)
human_k
-0,6585***
.5176081
-0,0828
(0,1002)
(0,0994)
white_collar
0,0040*
1,00403
0,0043
(0,0022)
(0,0022)
skilledbl_colla
-0,0004
.9996281
-0,0006
(0,0008)
(0,0008)
per_train_emplo
-0,0022
.9977753
0,0012
(0,0016)
(0,0016)
compet_abroad
-0,0896
.9143386
0,3539**
0,1034
(0,1036)
compet_home
0,0644
1,06657
-0,0974
(0,1424)
(0,1398)
_cons
3,7680***
4,32943
1,7131***
(0,3514)
(0,3535)
Number of obs = 14058 Log likelihood = -12307.832
LR Chi2(54)=5156.19
Prob > chi2 = 0.0000
Pseudo R2
= 0.1732
*P<1, **P<0.05, and*** P<0.01; Standard errors in brackets
RRR
.9723236
.9964301
.8527661
.4960883
1,081519
1,347443
1,001049
1,192620
1,806173
.9046057
1,226008
1,392817
.9205018
1,004351
.9993847
1,001187
1,424571
.9072346
5,546355
Make and Buy
Coef.
-0,0071
(0,0598)
-.0007344
.0037913
-0,0038
(0,0948)
-0,6660***
(0,1144)
-0,0152
(0,1621)
0,1436
(0,2397)
-0,0017**
(0,0008)
0,2364
(0,1902)
-0,5151***
(0,1280)
-0,8221***
(0,0959)
-0,5069***
(0,1386)
0,4592***
(0,1232)
-0,6585***
(0,1002)
0,0040*
(0,0022)
-0,0004
(0,0008)
-0,0022
(0,0016)
-0,0896
(0,1034)
0,0644
(0,1424)
3,7680***
(0,3514)
RRR
1,044366
.9992659
.8456226
.808793
1,529178
1,165256
1,001747
.9464574
2,7697190
1,2250690
1,6834730
1,9792900
.9815731
1,0047050
.9989828
1,0049270
1,6701200
.8491091
.3545709
Hypothesis 5, where we stated that the make-buy strategy would be selected when the firm
face a tough competition from abroad and in their home country can be supported partially
since this is valid only for firms that face competition in abroad (compet-abroad) and not for
26
the not for firms that face competition in home country (compet-home).
When the
competition is tough in other countries, firms are willing to achieve the make-buy over solely
make (table 5).
Nevertheless, when firms have to decide in achieving one of the strategies, the make-buy and
make one will be chosen (see negatives coefficients of Buy in table 5 and the positive
coefficient of make in table 6). This firm behavior should obeys to that mentioned by Swan
and Allred (2003) who found that external acquisition will be preferred in high levels of
competition because it allows cost reduction and a quickly entrance to the market. the results
are consistent with Pisano (1990) that argue firms when they face high competition, the make
innovation strategy is preferred by firms in order to gain the first mover advantage.
The hypothesis we made on human skills (H6); firms with skilled human resources are more likely
to engage in Hybrid (Make-Buy) innovation strategies, is supported in the analysis. since our
empirical results show that when firms have very competent employees, approximated as those firm
that has a higher share of graduate employees with respect to the national average share of
graduates (human_k) and percentage of employees participated in training programs (pertrain), the make-buy strategy will be preferred. The result is in line with the explanations
given by Arora and Gambardella (1994) and Veugelers and Cassiman, (1999) that internal
knowledge resources allow using foreign knowhow more effectively in the firm, which would
stimulate external innovation sourcing; whereas these competence may become also an
incentive for firms to innovate in‐house with available resources.
Support to hypothesis 7 that states when firms are younger, the probability for selecting the buy
strategy will be the highest, was found since results show that when firms have less experience in
the business as organizational resources, approximated as firm age (AGE), the buy strategy will be
preferred. Fist, in table 4 we observe that the make and make-buy are negative and significant,
meaning that the younger the firms will not make or make-buy innovation. Further in table 5, we
observed that buy is preferred over make and make- buy. The result is consistent with the fact that
young firms often do not have the high economical and human resources needed to develop
the in-house R&D activities and they might prefer to externalize the risk for environmental
uncertainties
The last one of the hypothesis is H8 which states that firms with access to information
communication technology/system are more likely to choose make-buy since they will have
more absorbing capacity is not supported in results while indicating a strong positive
correlation between access to information technology facilities and engaging in innovation.
27
Regarding the control variables analysis, we found support for our argument that large firms
approximated as total employees would prefer the make-buy strategy due to the physical
resources they have.
The positive and significant coefficients for make-buy in tables 4, 5, and 6 indicate that large firms in
our sample trend to select this strategy over make. Taking Leiblein et al., 2002; notes, we observe that
large firms do not prefer to externalize R&D activities since they want to take advantages of the
potential scale economies they have in the form of financial resources and more qualified personal.
Finally, the last control variable is the industry type/branches as determinants of the innovation
strategy selection. All positive but not significant coefficients values in table 4, 5 and 6 show that the
type of industries firms engage determine firms engagement in innovation since all strategies are
preferred over no_innovatiion when compare the size of the relative risk ratios.
However, no
significant differences between make and buy are showed.
To see the complementarity of the sourcing strategies, we run a simple correlation. Results from table
7 indicate that here is a statistically significant correlation between make and buy and make and makebuy strategies, r (14758) = .192, p < .001 and r (14758) = .34, p < .001 respectively.
Table 7: Complementarities of sourcing strategies
Correlations
MAKE
Pearson Correlation
MAKE
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
MAKE_BUY
1
Sig. (2-tailed)
N
BUY
BUY
Sig. (2-tailed)
N
MAKE_BUY
,192
**
,338
**
,000
,000
14758
14758
14758
**
1
,192
,000
,833
**
,000
14758
14759
14759
**
**
1
,338
,833
,000
,000
14758
14759
14759
**. Correlation is significant at the 0.01 level (2-tailed).
Most of the innovating firms in our sample have internal R&D activities (make) as we can see from
table 1 and almost three-quarters of them acquire innovation on the external market and a combination
of both. As expected, make and buy are positively correlated (0.192) see table 7. These results are
consistent with complementarity between innovation activities. Further evidence consistent with
complementarity can be found in the frequency with which firms combine these innovation activities—
firms’ innovation strategy. As we can see from table, high number of firms prefer make (78%). Only 18.5 %
choose Buy Only as a strategy, while only 4.2 % choose a Make-buy strategy. We also find that 48.8% of
the firms yet are not engaged in any innovation activity (NoMake&Buy).
28
6 Conclusions and discussion
The consistent market pressure in the era of globalization to generate competitive advantage
urges firms to engage in innovative activities. Study on innovation and sourcing strategies
hardly needs justification. The internal versus external sourcing of new technology remains however
a complex, relatively unexplored issue in innovation management. Drawing on transaction costs
economics and property rights; the literature stresses the choice between external sourcing and internal
development as substitutes, i.e. the classical make or buy decision. But, although the availability of
external technology may discourage and hence substitute for own research investment by the receiver
firms, there are also arguments from a resource-based view of the firm, to stress the complementarity
between in-house R&D and external know-how, i.e. the make and buy decision, certainly at the firm
level.
This study aimed to investigate the determinants of firms’ decision to engage in innovation,
characterize the organisation of those innovative firms, analysis the determinants/drivers of innovation
sourcing decisions and finally to investigate whether the innovation-sourcing options are indeed
complementary. We pose a certain number of research hypotheses and use the empirical data to test
them. In order to have the most complete picture, we also make use of three theoretical frameworks:
transaction costs economics, strategic management and resource‐based view. We estimated a logistic
and multinomial logit model to examine the drivers for companies to engage in innovation activities
and the determinants of sourcing strategies respectively
Using a database from EFIGE (European Firms in Global Economy) on 14, 759 manufacturing firms
operating in seven European countries, this paper tackles the question of the firm’s innovative
activities and sourcing decisions in two steps. In a first part, we analyze the determinants that
distinguish innovating firms from non-innovating firms. In addition to the standard explanatory
variables like size, investment on ICT, and international orientation, the empirical model includes
variables measuring intensity of competition and human resource competence The empirical results
indicate that internationalisation of firms, intensity of competition, and human capital as major drivers.
Whereas those firms that face competition only in their country seem less active in engaging in
innovation
The focus of the second step in the analysis is on the sourcing decision for innovative firms. In this
section we single out the determinants of the decision of the innovation-active firm, to develop
innovation by itself (Make decision) and/or to source externally (Buy decision). Most firms use the
make strategy, although there are firms that are using an exclusive buy strategy and combine both by
and make sourcing strategies. As such firms’ internal resource jointly with strategic global orientation,
29
investment on infrastructure, intensity of competition, belongingness, and access to information
technology facilities explain the innovation selection strategies of manufacturing firms in Europe.
A key conclusion of this study is that industry structure, firm characteristics affect innovativeness and
those firms that belong to a group, focus on process innovation, and young to the business they are in,
found to prefer to outsource (buy) innovation. Whereas there is a tendency to prefer to combine both
the buy and make innovation strategies when firms go international (active abroad), face tough
competition abroad, and have a very competent human capital that is usually justified for the
flexibility the strategies will bring and reducing the imitability to stay in the business.
Theoretical and managerial implications are obtained from this study. This investigation will
contributed to the literature from two streams. First, we propose some determinants of the innovation
strategy selection that barely used in the literature like the kind of innovation developed, firm age, and
industry competitiveness. Second; given the scarcity of previous empirical work on this topic, results
from this study provide some interesting hints for further theoretical work on the complementarity of
innovation activities as absorbing capacity is not getting much attention which however is one of the
highlighted argument for complementarity. Managers could be aware of the main characteristics
under which each innovation strategy is used, as well as for those non-innovative firms, whenever they
plan to start one; it will give a clue which one to choose from strategic management point of view.
This study has limitation in several ways; first panel data set would allow us to eliminate any
unobserved firm specific fixed effects, which might be driving some of these results. Second, the fact
that we only have information on the firm’s innovation strategy and only run a correlation analysis
limits the conclusions we can draw about the complementarities of the make and buy innovation
strategies. Further if innovation activities are truly complementary, the effect of their complementarity
should also show up in measures of innovation performance. As such, a detailed econometric approach
is the ideal analysis for complementarities. Finally, the analysis would have been complete if we
extend the discussion to the performance impact of different innovation sourcing strategies along
different sectors and compare the results among different branches in the manufacturing sector. This
would allow us to formulate some policy recommendations to stimulate innovation within lagging
sectors and countries and improve existing innovation policies.
30
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