OPEN INNOVATION: EMPIRICAL EVIDENCE HOW INBOUND OPEN INNOVATION ACTIVITIES AFFECT THE INNOVATION BARRIERS -PERFORMANCE RELATIONSHIP.

University of Groningen Faculty of Economics and Business

Master Thesis MSc Business Administration - Strategic Innovation Management

June 19th, 2014

Word count: 9.347

Supervisor: Dr. John Qi Dong

By

Daniel Kroesen

Student number: s2248115

   

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Abstract This longitudinal study aims to empirically investigate the moderating effects of open innovation on

the innovation barrier performance relationship. The investigated barriers are cost and knowledge

constraints. The investigated open innovation activities are collaboration and sourcing. I use the data

of the fourth and fifth Community Innovation Survey, which were conducted between 2002 and 2006.

Containing 215 industry observations, the sample represents 226.290 surveyed innovative firms

among 20 European countries and 41 industry sectors. I find that knowledge barriers hampering firms

innovation performance, and that they cannot be overcome by collaborating and sourcing. The reason

might be that firms need a strong knowledge base to create the needed absorptive capacity to identify,

assimilate, and exploit external knowledge. Regarding the cost barriers I find that being aware of

financial constraints has a positive effect on innovation performance. This effect cannot be

strengthened by collaborating, which might be caused by the collaboration accompanied coordination

costs. However, sourcing has a positive effect. This might be because sourcing supports development

processes by finding solutions to occurring problems, which leads to an enhancement in efficiency.

1. Introduction “Open Innovation” became one of the most studied topics in innovation literature within the last

decade. Chesbrough (2003a) claimed the term to emphasize the phenomenon that in today’s

environment of plentiful information and knowledge, companies cannot stay competitive based solely

on their own ideas and developments. They need to leverage external sources to stay innovative.

Building on observations from companies like Xerox PARC and IBM, the open innovation concept is

defined as the use of inbound and outbound information flows to enhance internal innovation

processes and create external paths to market (Chesbrough, Vanhaverbeke & West, 2006). In contrast

to previous literature on external knowledge sources and collaboration (e.g. Cohen & Levinthal, 1989;

Cohen & Levinthal, 1990; Hagedoorn & Duyster, 2002; Van Hippel, 1986; Hagedoorn, 1993; Katz &

Allen, 1982), open innovation emphasizes the importance of simultaneously managing and balancing

internal and external innovation activities (Van de Vrande, 2010). Prior literature has extensively

elaborated on what open innovation conceptually is (e.g. Chesbrough, 2003), on how it is

differentiated from other concepts (e.g. Chesbrough et al., 2006; Van de Vrande, 2010), which

concepts it encompasses, and how it is related to innovation performance (e.g. Laursen and Salter,

2006). However, until now it has not been empirically tested how it might help organizations to tackle

innovation barriers. Also the role of innovation barriers has not been conceptually investigated

intensively so far. Knowledge barriers find fragmented attention among multiple theoretical streams,

but have not been conceptualized into a single term. Cost barriers found a little more empirical

attention. However, survey based studies didn’t produce congruous results so far. Some found

   

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negative performance relations (e.g. Savignac, 2006; Tiwari et al., 2007) other surprisingly positive

performance relations (Baldwin and Lin, 2002; Immarino et al., 2002).

The investigation of those concepts would also be highly important from a managerial perspective.

The underlying reason for this are environmental changes, which need to be anticipated: markets are

getting more dynamic due to globalization, resulting in highly competitive market places with shorter

product-life cycles and consequently shorter development cycles, while technology is getting more

complex. As consequence firms are increasingly exposed to innovation barriers due to cost and

knowledge shortages, hampering their attempts of holding pace with customer demand and needs

under competitive pressure. Therefore, the goal of this study is to discover whether means of inbound

open innovation can help firms to overcome knowledge and cost barriers towards their innovation

performance. More specifically: does inbound openness significantly moderate the innovation barrier

-performance relationship as shown in Figure 1? In order to test this moderation it has first to be

tested whether knowledge and cost barriers significantly hamper a firm’s innovation performance.

Furthermore it is interesting to see how collaboration and sourcing affect each of the barrier

performance relationships.

It is assumed that a lack of knowledge and cost hamper a firm’s innovation success. This is because

relatively weak knowledge makes it more difficult to explore new opportunities, which is the pathway

to innovation. A lack of financial means deters managers to venture large and risky investments. As a

result, firms, which are opposed to such situations, are threatened to inertia and overexploitation of

existing knowledge (Ahuja and Lampert, 2001). Overexploitation can harm a firm’s absorptive

capacity, the ability to identify, assimilate, and exploit new, diverse or distant knowledge, which is

indispensable for being innovative (e.g. Cohen and Levinthal, 1990; Vasudeva and Anand, 2011). To

tackle this problem and to overcome the overexploitation trap, inbound open innovation can help

stimulating internal R&D and a firm’s absorptive capacity by providing new knowledge and

opportunities. Collaboration, the joint development of new products or processes, helps transferring

knowledge, sharing costs and risks while sourcing offers insights into emerging technologies and

Figure 1 The Conceptual Model

   

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markets as well as new qualified employees. Therefore, this study proposes that these inbound open

innovation activities help firms to deal with innovation barriers.

Data on innovative firms from the European Community Innovation Survey four and five has been

used to empirically test the model. The data has been collected over a period of four years (2002-

2006) and represents 226.290 firms among 41 industrial sectors and 20 countries. Therefore, the

results should be meaningful and significant from a theoretical and managerial perspective.

This report is structured in the following way: section 2 provides a review about the relevant

literature, section 3 continues with developing the theory and hypotheses, right before section 4

follows with describing the Community Innovation Survey, the extracted data, measurements and

statistical methods. The section closes with a detailed presentation of statistical quality measures.

Consecutively section 5 presents the statistical results. Finally, this report ends with section 6, which

discusses the findings and provides theoretical and managerial implications, the report’s limitations

and an outlook onto prospective studies.

2. Literature Review At the heart of the concept of openness lies the idea that enterprises cannot innovate effectively in

isolation (Dahlander & Gann, 2010). This is caused by the fact that a single company cannot employ

all smart people and have all possibly needed knowledge, which means that there are smart

individuals outside a company from whose expertise a company could benefit (Chesbrough, 2003b).

Based on these thoughts and some large enterprise observations, Chesbrough (2003a) coined the term

“Open Innovation”. His definition, which is the most commonly used one, states that open innovation

is “…the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and

expand the markets for external use of innovation, respectively” (Chesbrough et al., 2006:2). The

process of knowledge inflows is called inbound open innovation, which describes outside-in activities

like collaborating and sourcing. The process of outflows is called outbound open innovation, which

means inside-out activities like selling and revealing.

Although prior research emphasized the importance to use external knowledge sources and pathways

(Cohen & Levinthal, 1989; Cohen & Levinthal, 1990; Hagedoorn & Duyster, 2002; Van Hippel,

1986; Hagedoorn, 1993; Katz & Allen, 1982) Chesbrough (2003a) claimed a paradigm shift from a

closed to an open innovation model. And in fact, the concept “Open Innovation” made some

important contributions to the literature. First of all, Chesbrough established a single term covering

different academic developments, which enables scholars to research in this direction (Huizingh,

2011). Secondly, the open innovation literature integrates the inbound and outbound knowledge flows

(Chesbrough, 20006). Thus, it emphasizes that some innovation activities contain both processes,

   

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which additionally stresses the possibility that knowledge needs to be managed outside of

organizational boundaries (Lichtenthaler, 2008). Thirdly, open innovation literature displays the need

for simultaneously organized processes of internal and external knowledge management, to overcome

either-or decision making about whether a task should be performed internally or externally

(Chesbrough, 2003a). This firm-level view emphasizes the integrated and simultaneous exploration of

internal and external knowledge (Cohen & Levinthal, 1990). In conclusion, it can be stated that within

the closed model, external knowledge is more an addition to internal innovation, whereas the open

paradigm explicitly emphasizes a balanced role of internal and external innovation and knowledge

assimilation (Van de Vrande, 2010).

One important distinction has to be made towards open source methods for software development, as

it is often conflated with open innovation. Open source software (OSS) allows everyone to view and

modify the source code, as opposed to closed source software, which is protected by property rights

(Lessig, 1999). Therefore, the focus of OSS lies on value creation whereas the concept of open

innovation also intends value capturing (Chesbrough et al., 2006). Furthermore, it has to be stated that

the mentioned paradigm shift does not intend to create a binary classification in which it has to be

chosen between the open and closed concept, (Chesbrough, 2003a; Dahlander and Gann, 2010) but to

place these concepts on a continuum with varying degrees of openness (Chesbrough et al., 2006).

Also, the concept is not intended to be a substitution for internal R&D. This is because enterprises

need a certain level of competencies in areas related to their innovation partners, to be able to

assimilate or co-develop externally originated ideas (Brusoni, Prencipe and Pavitt, 2001; Granstrand,

Patel and Pavitt, 1997). This is also the idea behind Cohen’s and Levinthal’s (1989; 1990; 1994)

papers on absorptive capacity, what makes internal competencies and external linkages complements

instead of substitutes. Therefore, absorptive capacity is a highly important theoretical concept for this

research. It refers to the three fundamental learning dimensions: identifying, assimilating and

exploiting external knowledge (Cohen and Levinthal, 1989). It is of high importance for firms to build

and maintain their absorptive capacity, as it helps strengthening, complementing and re-orienting a

firm’s knowledge base (Lane et al., 2006). Absorptive capacity does not refer to becoming better at

what a firm already does, but learning something different (Lane et al., 2006). Therefore, two criteria

have to be met regarding the knowledge bases of knowledge transferring firms. First, the knowledge

bases should have certain similarities to enhance the mutual understanding. Second, in order to create

an advantage through the transfer, the knowledge needs to be somewhat different (Cohen and

Levinthal, 1990). This is in line with later findings of e.g. Sampson (2007), who found that a

moderate technological diversity between firms is most beneficial for their innovation performance,

and Vasudeva and Anand (2011), who found that a firm’s absorptive capacity peaks when the

transferred knowledge is either diverse (new but familiar) or distant (new and unfamiliar) but not both

simultaneously. From an environmental perspective, Cohen and Levinthal (1989; 1990) showed that

   

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factors like technological opportunities, the industry demand, and low degrees of intellectual property

rights (IPR) increase R&D spending, which in turn enhances a firm’s absorptive capacity. These

findings demonstrate that a rich environment stimulates R&D and knowledge assimilation, which

then again makes a firm better in assimilating knowledge. This positive self-enhancing cycle shows

the importance of inbound openness. On the one hand it helps firms to get stimulated to create

something innovative, and on the other hand it helps to remain the firm’s potential to get stimulated in

the future, which is their absorptive capacity.

3. Theory Development 3.1 Innovation Barriers – Performance Relationship.

Becoming innovative requires enterprises to overcome certain barriers, as succeeding depends on how

well a firm copes with those challenges accompanied with innovation activities. These challenges

could be accessing sufficient financial means, skilled staff, or obtaining appropriate market and

customer insights (D’Este et al., 2012). These challenges can be classified based on two fundamental

resources necessarily required for being innovative: (1) certain information corridors, meaning the

individual knowledge stock of human beings, and (2) sufficient funds to invest into highly educated

people and to exploit discovered opportunities (Shane and Venkataraman, 2000; Hall, 2002).

Therefore, it can be stated that becoming innovative requires enterprises to overcome knowledge and

cost barriers. By investigating the impact of financial barriers on a firm’s innovation performance, this

research is in line with past empirical studies (Tourigny and Le, 2004; Savignac, 2006; Tiwari et al.

2007; Mancusi and Vezulli, 2010). Additionally, the role of knowledge barriers is considered which

has been conceptually ignored so far (D’este et al., 2012).

3.1.1 The Role of Knowledge Barriers

The idea that a competitive advantage is based on the information asymmetries among different

enterprises has a long tradition. It has first been emphasized by Schumpeter (1934), used by

knowledge-based-perspective scholars (Grant, 1996), and then been addressed by the entrepreneurial

literature (Shane and Venkataraman, 2000). It explains who benefits from opportunities, which means

that those who are exposed to a lack of certain information are more likely to be outperformed, or in

other words less likely to be those who innovate. A shortage of qualified or the right employees can

be expected to cause this lack of organizational knowledge and low innovativeness, since innovation

begins as creativity of imaginative people (Amabile et al. 1996). This shortage can take multiple

expressions. A lack of technical or market knowledge (Galia and Legros, 2004), and a lack of

cognitive properties for identifying appropriate opportunities among a firm’s employees (Rosenberg,

1994; Shane and Venkataraman, 2000; Galia and Legros, 2004) can lead to a mismatch of technical

possibilities and market opportunities (Freeman and Soete, 1997). Furthermore a lack of knowledge

   

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diversity or distance can lead to a low level of absorptive capacity, overexploitation of existing

knowledge, and difficulties in finding cooperation partners (Cohen and Levinthal, 1990; Ahuja and

Lampert, 2001; Alvarez and Busenitz, 2001; Vasudeva and Anand, 2011). Therefore, the following

hypothesis has been formulated:

Hypothesis 1a: The degree of knowledge barriers, a firm is

exposed to, is negatively related to its innovation performance.

3.1.2 The Role of Cost Barriers

As most survey studies that investigate cost barriers for innovation, this study will focus on the

perceived magnitude of financial barriers. Among these survey studies the effect of financial barriers

on innovation is not as consistent as expected. Some studies report the expected deterrent effect of

financial constraints on innovation activities. For instance, Savignac (2006) found that companies

exposed to financial barriers were less likely to engage in innovation activities. Similar results, related

to R&D investments, were delivered by Tiwari et al. (2007). Other studies, for instance Baldwin and

Lin (2002), who studied a large Canadian manufacturing sample, or Immarino et al. (2009) using the

third Community Innovation Survey, found that innovation active firms more frequently report

innovation barriers. They provided the explanation that the more innovation active a company is, the

more obstacles it experiences, and the higher the perceived importance of these obstacles will be.

However, since this study exclusively investigates innovative enterprises, a negative effect is

expected.

Hypothesis 1b: The degree of cost barriers a firm is exposed to, is

negatively related to its innovation performance.

3.2 The Moderating Role of Inbound Openness.

3.2.1 The Moderating Role of Collaboration

Scholars spent relevant attention to the collaboration, R&D alliance, network and cooperation

literature for the last decades. It has been elaborated that firms benefit from collaborating by accessing

complementary capabilities, using scale effects, reducing time to market of new developments, and

lowering the costs and risks accompanied by those new developments (e.g., Powell, 1990; Mowery,

Oxley and Silverman, 1996; Sampson, 2007). All these benefits represent mutual learning

possibilities for collaborating firms. Collaborating helps enterprises to assimilate knowledge, which is

new to the firm or related to emerging technologies. Additionally, it has the potential to stimulate

internal R&D onto new pathways (Cassiman and Veugelers, 2006; Noseleit and de Faria, 2013). The

new knowledge and stimulated R&D can lead to an enhancement of absorptive capacities and

cognition capabilities. The former refers to the ability to identify, assimilate and exploit external

knowledge (Cohen and Levinthal, 1990). The latter refers to the ability of identifying valuable

   

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resources and market opportunities (Marino, 1996; Denrell et al., 2004; Danneels, 2010). Therefore,

collaborating can increase a firm’s exploration capabilities, which describes the use of new

technological trajectories. Meaning that collaboration has the potential to help firms overcoming

knowledge barriers, as these affect a firm’s innovation performance mostly by lowering its

exploration performance, leading to overexploitation of existing knowledge (Ahuja and Lampert,

2001; Vasudeva and Anand, 2011). However, collaborating only can be beneficial if the collaboration

partners have a somewhat different knowledge base. Uzzi (1997) stated that the more embedded a

firm is in its network, the more its diversity towards its partners decreases by isomorphism. This

happens when firms do not pursue the creation of new and diverse collaborative ties with firms

outside their network. Sampson (2007) supports this and found that firms, which moderately differ

from each other, gain most from their collaborative R&D activities. Consequently, ignoring the need

for new stimuli and cooperation partners can have severe consequences. Non-cooperative firms,

which hesitate to exchange intellectual property, diminish their knowledge base and lose the

capability to create exchanging relationships – their absorptive capacity (Cohen and Levintahl 1990;

Koschatzky, 2001). This may lead to a vicious circle, ending with being outperformed. Therefore, it is

crucial to cooperate externally to enhance a firm’s knowledge base, its absorptive capacity and

innovativeness, which leads to following hypothesis:

Hypothesis 2a: Collaboration activities strengthen the negative

relationship between knowledge barriers and innovation performance.

 Besides the stimulating effects collaboration activities have on the knowledge base of a firm, they

provide further potential associated with new joint developments. Even though collaborations bring

along coordination costs and the risk of misappropriation due to opportunistic behavior (Williamson,

1985; Mitchell et al., 2002), its benefits are expected to exceed its threats. This is due to appropriate

governance mechanisms. For instance, Hoetker and Mellewigt (2009) developed governance

mechanisms depending on the alliance asset type, knowledge-based vs property-based, to mitigate

potential threats of collaborating. The literature on collaborative activities is congruous regarding its

benefits and offers three substantial benefits. The first originates from the complementarity of the

firms’ assets. These complementary assets of alliance partners have the potential to create greater

rents than each of the collaborating firms would be able to obtain individually (Dyer, 1998).

Regarding innovation activities this could for instance mean finding pathways into new market

segments. Secondly economies of scale can dramatically lower developing and production costs

(Mariti and Smiley, 1983). And thirdly, risk sharing offers the potential to pursue disruptive

innovation, which may have been canceled otherwise (Mariti and Smiley, 1983). Therefore, it can be

concluded that those companies actively pursuing collaborative agreements will perform better with

respect to innovation activities.

   

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Hypothesis 2b: Collaboration activities strengthen the negative

relationship between cost barriers and innovation performance.

3.2.2 The Moderating Role of Sourcing

Organizational knowledge sourcing deals with the assimilation of knowledge and technology from

external sources. It is different from collaboration in so far that it assimilates information from

external sources without joint developments. Conceptually it belongs to the organizational knowledge

transfer research stream, which refers to the process of exchanging, receiving, and being influenced

by external experiences and knowledge (Wijk et al., 2008). This process is essential for enterprises

due its importance for the development of new applications (Henderson and Cockburn, 1994; Kogut

and Zander, 1992). Prior studies support the positive effect knowledge transfers have on enterprise

performance (e.g. Steensma and Lyles, 2000). Research showed that firms, which have the capability

to learn from their market environment, are more likely to match their products with emerging market

demand (Day, 1994). The transfer of knowledge makes new product developments more likely, due to

the stimulation new knowledge has on the existing knowledge stock (Jansen et al., 2005). Enkel et al.

(2009) further contribute that a diverse set of sources is of high importance for innovation, as most

innovation come from recombined knowledge and technology. This shows the relevance of different

sources for knowledge assimilation, which is supported by studies showing the importance of the

science-industry link on the one hand, and the benefits of strong market oriented firm culture on the

other hand. For instance, Quinn (1986) found that all innovative enterprises studied by him showed

high degrees of market orientation. Conducting a three-wave-survey study between 1997 and 2005,

Kumar et al. (2011) even found that being market oriented has become a fail preventer opposed to

formerly being seen as a success provider, by having great impact on long and short term profits.

Regarding the science-industry link, it has been found that public research does not suggest new

product ideas, which just have to be picked up, but rather provide problem-solving solutions (Cohen

et al., 2002). More precisely, these sources can offer insights on emerging technologies and provide

students as potential employees leading to enhanced knowledge bases (Caloghirou et al., 2001). This

is in line with the findings of Nelson and Rosenberg (1994), who found that scientific research has

stimulating and enhancing effects on a firm’s internal R&D. Accordingly, prior studies suggest that

the transfer of knowledge due to multiple sources enhances a firm’s innovativeness by providing

information and knowledge on technology, markets, and by providing qualified employees. As a

consequence hypothesis 3 has been formulated.

Hypothesis 3a: Sourcing activities strengthen the negative relationship

between knowledge barriers and innovation performance.

   

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Additionally scholars found that strong knowledge, which can be most effectively assimilated by

good sourcing, enhances R&D efficiency and the accuracy of reflecting market demand and needs

(Nelson, 1982). Mowery (1995) argues into the same direction stating that scientific research can

increase the development efficiency of technologies, which is supported by Cohen’s et al. (2002)

problem-solving argument mentioned earlier. Accordingly, it can be assumed that the efficiency

enhancing effect of good and diverse sourcing lowers the impact of cost-barriers towards a firm’s

innovation performance.

Hypothesis 3b: Sourcing activities strengthen the negative relationship

between cost barriers and innovation performance.

4. Methodology

4.1 Data Collection - The Community Innovation Survey

This research makes use of secondary data from The Community Innovation Survey 2004 (CIS4) and

2006 (CIS5), which was executed by Eurostat, the European statistical office, and its member states.

The general aim of the regularly conducted CIS is to obtain data regarding the innovativeness of

participating countries, enterprises and so on. Gathered information is used for academic research and

to compare the performance of the member countries by the European innovation index. The survey is

motivated by the European Union’s (EU) goal to raise growth rates and increase research investments

by making innovation topics core elements of EU policies. EU-wide methodological definitions of the

Oslo Manual were used to carry out the survey (OECD, 2005).

The process of sample selection started with a comparison of all Community Innovation Surveys

conducted so far. Until now seven Community Innovation Surveys have been conducted within the

period of 2000 and 2010. The CIS4 and CIS5 have been chosen for this analysis since all constructs to

be observed are covered by their questionnaires. In addition these two surveys show high similarity

regarding their methodology. The CIS3 covers the used constructs as well, but does not include all

measurement items, which led to its exclusion for this research. All remaining surveys do not

encompass the needed measurements items, leading to their exclusion. Launched in 2005, CIS4 was

conducted between 2002 and 2004 and includes data from all 25 EU members (as for 2004), Iceland,

Norway, Bulgaria and Romania. Launched in 2007, CIS5 was conducted within the period of 2004

and 2006 and includes data from all 25 EU members (as for 2006), Norway, Bulgaria, Rumania,

Croatia and Turkey. The surveyed industries were defined by NACE Rev 1.1, which is the statistical

classification of economic activities in the European Community (Eurostat, 2014). The target

population of each survey represents the total population of enterprises related to NACE market

activities “C-K” (European Commission, 2006a; European Commission, 2006b). Therefore, the

industry coverage includes (C) mining and quarrying, (D) manufacturing, (E) electricity, gas and

water supply, (F) construction, (G) wholesale and retail trade, (H) hotel and restaurants, (I) transport,

   

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storage and communication, (J) financial intermediation, (K) real estate, renting and business

activities. These industry categories are sub-divided into sectors, which are used in this research to

increase the sample size. All observations made by the Eurostat innovation surveys represent

enterprises with more than ten employees (Eurostat, 2014). For this research only product and/or

process innovative enterprises, regardless of organizational or marketing innovations encompassing

abandoned and/or suspended innovations, are included in the sample. In other words, only innovation

active enterprises have been considered. Data extraction regarding the related constructs yielded data

on 48 different industry sectors across all nine NACE-industries and 29 countries for 2004 and 30

countries for 2006, with N = 2.832. Since the Eurostat database only provides aggregated data on

industry level, the extracted sample represents 352.425 enterprises for 2004 and 306.965 enterprises

for 2006. A careful data examination led to the exclusion of observations showing missing values on

some measurement items. This exclusion reduced the number of observed industry-sectors to 35

across now 18 countries for 2004. For 2006 the number of observed industry-sectors has been reduced

to 39 across 4 countries. However, all nine core industries defined by NACE are still covered by the

sample and by each year’s observations, with N = 215 in total. As shown in Table 1, these data

represent 168.303 enterprises for 2004 and 57.987 enterprises for 2006. Furthermore it can be seen

that the majority of the surveyed innovative enterprises, which suit the requirements of this research,

are located in Spain and that almost the half (49,6 %) of the enterprises belong to the manufacturing

industry as shown in Table 2. In summary, the sample can be described as covering most of the

Source: Eurostat, Community Innovation Survey 4 and 5

Table 1 Enterprise Distribution regarding Country of Origin

   

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NACE industry sectors and a large proportion of the European economy by including 20 countries

and 226.290 observations among four years collected by two surveys.

4.2 Measures

4.2.1 Dependent Variable: Performance

The dependent variable in this research is innovation performance. It is measured as a second-order

hierarchical construct, consisting out of the two first order constructs “Product and Service Innovation

Effects” and “Process Innovation Effects”. The first is measured by three items and the second by

two, as can be seen in Figure 2. The respondents reported whether each effect was experienced as

high, medium or low. However, the available data only provides data on high innovation effects as the

percentage of all respondents for each observed country-sector combination as described in Table 3.

The mentioned measures have been chosen over other common measures like patenting and financial

Table 2 Enterprise Distribution regarding NACE Rev 1.1 Sector Classification

Source: Eurostat, Community Innovation Survey 4 and 5

   

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measures because of several reasons. First, financial measures do not necessarily capture all

successful innovation efforts. Often innovations, especially disruptive innovations, show their

beneficial impact first on the long run, which might be not captured by a survey study. Patenting,

which is commonly used, is also often criticized, as the commercial importance of patents vary across

industries and technologies (Levin et al., 1987). It also only represents codified knowledge, which

means that it does not effectively measures intangible, uncodified knowledge. Whereas the use of

product/service and process related measures, like increased production flexibility or increased

product ranges, directly captures the benefits of innovation efforts.

4.2.2 Independent Variable: Barriers to Innovate

Cost and knowledge barriers are the two independent variables in this research. The first construct is

measured by three items, and the latter by four, as shown in Figure 2. Respondents had to indicate the

degree of importance of several hampering factors on a scale from high, over medium, and low, to

not-experienced. As with the dependent variable, the available data only provides high degrees,

measured as the percentage of all respondents for each country-sector combination as described in

Table 3.

4.2.3 Moderating Variable: Inbound Openness

The focus of this research is the concept of inbound openness. This concept refers to inbound oriented

open innovation activities. To measure this concept two themes are used: collaboration and sourcing.

The first, collaboration, is measured as a second order hierarchical construct, consisting out of the two

first order constructs cooperation and mainly-developed-by-others as shown in Figure 2. Surveyed

enterprises reported whether their product- or process-developments were created by themselves, in

cooperation, or mainly by others. The first-order construct cooperation refers to products and

processes, which were created together with other enterprises or institutions. “Mainly-developed-by-

others” only refers to products mainly developed by other enterprises or institutions, since the item

loading for “processes-mainly-developed-by-others” was statistically too low (below 0,40), which led

to the exclusion of this item (Hair, Ringle and Sarstedt, 2011).

The second moderator, Sourcing, is measured by the use of six items as shown in Figure 2. The

surveys collected data on nine items. However, three items exhibit loadings, which were statistically

too low, below 0.40, leading to their exclusion (Hair et al., 2011). The excluded sourcing items are

suppliers, clients, and conferences. The remaining items are listed in Table 3. The items are divided

into the two categories market and institutional sources, which were initially used as two first order

constructs in a hierarchical second order construct. However, the items of these two constructs

showed high cross-loadings with each other’s construct, which led to its merger to one construct.

Each of the items has been surveyed regarding their degree of importance, which was distinguished

   

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into high, medium, low, and not-experienced. As with the independent and dependent variable, the

available data only provides high degree of importance, measured as the percentage of all respondents

for each country-sector combination.

All constructs are reflective, meaning that the causality is from construct to measure and that all

measurements are interchangeable and highly correlated. Therefore, the same results would be

expected by using different measures for the same constructs (Fornell and Bookstein, 1982).

Table 3 Measurement Overview

   

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4.3 Statistical Method and Quality Criteria

4.3.1 Statistical Method

PLS (Partial Least Squares) path modeling in SmartPLS has been used for testing. PLS is a

component based SEM (Structural Equation Modeling) technique, which has been used since it is

more suitable for predictive studies and studies, which investigates relatively new theoretical

frameworks (Chin and Newsted, 1999; Hair et al., 2011). Further benefits are the less restrictions

regarding constructs with fewer items (one or two), which is the case in this study, and its higher

robustness compared to covariance-based SEM (Hair et al., 2011). Moreover, PLS has been used, as

the investigated model is a hierarchical one. The dependent variable, innovation performance, as well

as one of the moderating variables, collaborating, are hierarchical constructs, as shown in figure 2.

Therefore, the measurement model has to be estimated besides the structural model. The structural

model shows potential cause dependencies between certain variables, while the measurement model

shows the relation between latent variables and their indicators (Fornell and Bookstein, 1982). The

typical PLS-SEM two-step procedure, first examining the measures’ reliability and validity, secondly

assessing the structural model estimates via non-parametric boostrapping, has been followed (Chin

1998; Hair et al., 2011).

Figure 2 PLS SEM Path Model

   

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4.3.2 Quality Criteria

Since a reflective measurement model has been used, as shown in Figure 2, reliability and validity

measures have to be examined in a confirmatory factor analysis (Hair et al., 2011). Regarding the

reliability, Chronbach’s alpha and composite reliability measures were used as illustrated in Table 4.

All Chronbach’s alpha measures except of “collaboration” show values higher than the .70 threshold.

However, a value of .67 is not below the cut-off of .60, which in turn would infer a lack of reliability

(Hair et al., 2011). Furthermore, all composite reliability values, including the collaboration value, are

above the .70 threshold, indicating a high reliability of each construct (Nunnally and Bernstein, 1994).

Moreover, Composite reliability is seen to be more suitable for PLS-SEM, indicating that no

reliability issues exist (Hair et al., 2011).

Next the model’s convergent and discriminant validity needs to be measured. Therefore each

construct and related measurement items were examined regarding its loadings and potential cross-

loadings. The construct and indicator correlation is also a measure for the indicator’s reliability

(Henseler, Ringle and Sinkovics, 2009). Most variables exceed the .70 threshold regarding its

construct correlation, meaning that more than half of each items variance is explained by the construct

shown in Table 5 and 6 (Chin, 1998; Henseler et al., 2009; Hair et al., 2011). However, the first order

model shows one loading below the .70 threshold and the second order model shows two further ones,

as can be seen in Table 5 and 6. Nonetheless, rejecting those, is not recommended, except they would

show loadings below the .40 threshold or the rejection would lead to a substantial increase of

composite reliability (Churchill, 1997; Henseler et al., 2009; Hair et al., 2011). As both is not the case,

these indicators were not rejected. In addition, Chin (1998) stated that loadings between .50 and .70

are still acceptable if there are additional items for that construct, which is true for this model. These

results indicate sufficient indicator’s reliability and construct’s convergent validity. Moreover, no

discriminant validity concerns seem to exist since no cross-loadings, an indicator loads higher on

another construct than the construct’s actual indicators, can be noticed (Chin 1998; Hulland, 1999;

Henseler et al., 2009; Hair et al. 2011).

Table 4 Reliability Measures†

   

    16  

To further assess the model’s convergent reliability, the composite reliability, and average variance

extracted (AVE) has been compared. As Table 7 shows, the composite reliability of each construct

exceeds the AVE of all measures indicating sufficient convergent reliability (Fornell and Larker,

1981). Furthermore, all AVE values exceed the cut-off of .50, proposed by Fornell and Larcker

(1981), meaning that each construct explains more than the half of its indicator’s variance, which

Table 5 First Order Construct Loadings†

Table 6 Second Order Construct Loadings†

   

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additionally indicates sufficient convergent validity (Chin, 1998; Henseler et al., 2009; Hair et al.,

2011). Moreover, as shown in Table 7, the square-root of the AVE exceeds any construct correlation,

which is an additional support for discriminant validity, besides the cross-loading analysis (Chin,

1998; Fornell and Larcker, 1981; Hulland, 1999). All measures indicate sufficient construct and

indicator reliability as well as sufficient convergent and discriminant validity.

Finally, the model has to be tested for common method bias (CMB), which is the variance attributed

to the measurement method rather than to the model’s constructs represented by the measures

(Podsakoff, MacKenzie and Lee, 2003). Therefore, one of the mostly used tests has been applied,

Harman’s single factor test. The result shows that less than the majority (36.42 %) is explained by a

single factor, which means that CMB is unlikely an issue (Podsakoff et al., 2003). However, this

technique only assesses the extent to which CMB variance may be an issue, but does not statistically

control it (Podsakoff et al., 2003). To control for CMB the marker variable approach was applied

Table 7 Reliability and Validity Measures†

Table 8 Common Method Bias: Results Marker Variable Approach†

   

    18  

(Podsakoff et al., 2003). Therefore partial correlations are calculated and compared to the original

correlations to see whether original significant correlations remain significant, which was the case for

this model as shown in Table 8.

5. Results Table 9 shows the results of the bootstrapping with 1.000 replications, which was used to test the

hypotheses. Hypothesis 1a assumed a negative relationship between the level of knowledge barriers

enterprises experience and their innovation performance. The findings support the expectations and

show that there is overwhelming evidence (p = .001) to infer that a lack of knowledge has a strong

negative impact (β = -.419) on a company’s innovation performance. Cost barriers have been

predicted to be harmful for an enterprise’s innovation performance (H1b), as well. However, a strong

(β = .366) and highly significant (p = .001) positive relationship has been found, to infer that cost

barriers are beneficial. Hypothesis 2a predicted that collaboration intensity strengthens the negative

relationship between knowledge barriers and innovation performance, which is not supported due to

nonsignificant findings. The same has been found for hypothesis 2b, which assumed that

collaboration positively moderates the relationship between cost-barriers and innovation performance

and hypothesis 3a, predicting that sourcing intensity strengthens the knowledge barrier innovation

performance relationship. While a company’s sourcing intensity does not significantly affect the

influence of knowledge barriers, strong evidence (p = .05) has been found to infer that it strengthens

the relationship between cost barriers and innovation performance as shown in Figure 3. The results

are summarized in Table 10.

Table 9 Path Coefficients of the Structural Model†

   

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Tenenhaus, Vinzi, Chatelin and Lauro (2005) suggested a global fit measure (GoF), which is defined

as the geometric mean of the average communality and average R2. Since communality equals AVE,

it is calculated as follows: GoF = √(averageAVE*averageR2) (Wetzels, Odekerken-Schröder and van

Oppen, 2009). The GoF value obtained for this study, as shown in Table 9, is .699, which exceeds the

threshold of GoFlarge = .36 proposed by Wetzels et al. (2009). Hence in can be concluded that the

model has substantial explaining power. The explained variance of the endogenous variable was

assessed by calculating its R2. According to Cohen (1988) is the R2 assessed as substantial when it

exceeds the cut-off of .26. Table 9 shows that the endogenous variable has a R2 of .45, indicating a

high level of explained variance.

Table 10 Summary of the Main Results†

Figure 3 Moderation Effect of Sourcing on the Cost Barrier – Innovation Performance Relationship

   

    20  

6. Discussion & Conclusion This research was undertaken to empirically investigate the impact of innovation barriers on a firm’s

innovation performance, and more importantly whether this proposed hampering effects could be

positively affected by open innovation activities. It was hypothesized that knowledge and cost barriers

will have harmful effects. For both, significant results have been found. However, of these two

hypotheses only H1a – hampering effects of knowledge barriers – holds true. This means that

knowledge barriers, which are conceptually under-investigated, have a negative, highly significant

effect on firms’ innovation performance. This result is in line with the vast amount of theories related

to the knowledge-based perspective. This result shows how important knowledge is nowadays, and

can best be summarized as follows: “An entrepreneur’s expanding knowledge base and absorptive

capacity becomes an entrepreneurial firm’s competitive advantage” (Alvarez and Busenitz, 2001, p.

766).

Surprisingly, cost barriers have been found to have highly significant positive effects on firm

innovation performance. This result is in line with the results of Baldwin and Lin (2002) and

Immarino et al. (2009), who found that innovative firms more frequently report innovation barriers

than non-innovative firms. This assumption has not been used for the related hypothesis, as only

innovative firms are included in the sample. However there are basically two possible explanations:

First, the one offered by Baldwin and Lin (2002) as well as Immariono et al. (2009), who claimed that

the fact that a survey with subjective measures was used to observe the presence of barriers biased the

results. Being aware of barriers could be a characteristic of successful innovation managers, whereas

being not aware of those could hamper the innovation process. The second explanation could be that

the cost barrier-performance relationship is mediated by efficiency. Meaning that the higher the cost

barrier, the more efficient a firm needs to be, the better the available resources are used, which is

finally leading to better innovation performance. Furthermore, it can be assumed that financial

constraints enhance managers’ market sensitivity, which has been found to have a positive

relationship with firm performance (e.g. Marino, 1996; Denrell et al., 2004; Danneels, 2011).

Collaborating has not been found to significantly moderate the relationships between cost/knowledge

barriers and innovation performance. Even though collaboration decreased the harmful effects of

knowledge barriers, the result was not significant. However, since a positive effect was observed, it

can be assumed that collaboration activities contribute to knowledge assimilation. One reason for the

nonsignificant results could be that firms, experiencing knowledge barriers, lack an appropriate

degree of absorptive capacity, leading to the inability of collaborating. This is consistent with the

findings of Danneels’ (2010) Smith Corona study, which shows that only firms possessing valuable

resources attract other firms to collaborate. Another reason for the nonsignificant effect on the

relationship between knowledge barriers and innovation performance could be the high coordination

costs accompanied with collaboration activities. This is because firms need to accomplish two

   

    21  

substantial goals for successfully collaborating: first they need to optimize the combination of their

resource, and secondly they need to find ways to lower the risk associated with opportunistic

behavior, which is common for knowledge based collaborations (Arrow, 1962; Mitchell et al., 2002;

Nickerson and Zenger, 2004; Hoetker and Mellewigt, 2009). Moreover, knowledge based

collaborations are difficult to pre-specify regarding their coordination, which leads to higher

coordination costs (Hoetker and Mellewigt, 2009). As for knowledge barriers, collaboration has

nonsignificant effects on the cost barriers innovation performance relationship. However, this is

interesting in so far that collaboration lowers the positive effect cost barriers have on innovation

performance. An explanation could be the coordination cost reasoning above, since an activity, which

increases costs, can unlikely help overcoming cost barriers.

Hypothesis 3a predicted that a firms sourcing intensity mitigates the hampering effects knowledge

barriers have. A positive effect has been observed, which is nonsignificant. This result can be

explained by the findings of Cohen et al. (2002), who stated that public research does not provide

ideas for innovative products or processes, but rather contributes problem-solving solutions. Hence,

sourcing can be seen as a knowledge-provider and inspiring mean towards internal R&D, which is the

origin of innovative ideas. However, sourcing does have a positive and highly significant moderating

effect on the positive relationship between cost barriers and innovation performance. Hence, sourcing

strengthens the positive effect cost barriers have. Also this can be explained by the findings of Cohen

et al. (2002), arguing that public research provides solutions rather than ideas, which can be related to

the efficiency of developments. Engineers and other employees face multiple problems during the

process of innovation development, which potentially lead to the project termination. The problem

solving function of sourcing lowers this negative effect. This result supports other previous findings

related to the enhancing effect of sourcing on internal R&D (Nelson, 1983; Mowery, 1995).

The results provide substantial managerial implications. The contrary effects of cost barriers on

innovation performance show that managers need to be aware of what deter them of being innovative.

Being aware of hampering factors and being sensitive could potentially be one key characteristic of

successful innovation managers. Furthermore, this study joins the vast amount of literature advocating

the importance of knowledge for firm performance. Even though collaboration shows nonsignificant

effects, managers should not neglect it. However, the discussion indicates that a proper internal R&D

department and an adequate knowledge stock is the basic condition for being innovative. If this is the

case, collaboration partners can be attracted and mutual benefits been captured. Finally, the results

show that firms should source intensively, as this has the potential to enhance the efficiency of

internal R&D and serve as a source of problem-solving solutions.

This research and its results are limited by several data related constraints. Overall, the

generalizability can be classified as high, as the data was collected among 20 European countries and

   

    22  

41 industrial sectors. However, a closer look shows that 50% of the sectors belong to the

manufacturing industry, whereas e.g. only 0.70% belong to the financial industry. Which means that

the results can only hardly be used to explain occurrences from the financial industry. Furthermore, it

needs to be stated that almost 60% of the collected data originated from the Spanish (41,70%) and

Italian (16,40%) economies, whereas the United Kingdom was not even included. Meaning that the

results cannot directly be implied on British enterprises. Another limitation is that for all measurement

items, except of those related to the construct “Sourcing”, only the results on high degrees were

available, since a use of micro data was not possible. Each of these items was originally measured by

four different degrees: high, medium, low, not experienced. Therefore, the use of micro data

potentially would have resulted in more specific results. Moreover it is important to mention that the

observations regarding experienced barriers is probably biased in so far that managers were able to

report that those barriers existed. The ability to mention innovation barriers shows that these

managers were already sensitized. Is this sensitivity a characteristic of innovative managers? This

would mean that those being not as innovative as the high performer were just not able to report that

they were actually experiencing innovation barriers.

Potential future studies could first of all contribute new insights by investigating the mentioned

limitations. For instance, as the majority of sectors belong to the manufacturing industry, future

studies could replicate this study by using only certain industries, for instance the financial industry.

Due to that, it could be investigated whether the results still hold true. In order to do so, those studies

should apply for the use of micro data in order to increase the sample size. The use of micro data

could also help future studies to investigate the effects by each degree (high, medium, low, not

experienced). Additionally, the model could be tested for each country or different clusters, like

Benelux or Eastern Europe vs Western Europe, to see whether differences exist. Regarding the

contrary cost barrier effect, scholars could investigate the effect of managerial sensitivity towards

potential barriers on innovation performance. Additionally, it could be tested whether efficiency

mediates the relationship between cost barriers and innovation performance.

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