The determinants of R&D cooperation. A study on small and micro firms in the Netherlands.
University of Groningen Faculty of Economics and Business
Master Thesis
MSc Business Administration - Strategic Innovation Management January 18th, 2016
Word Count: 14041 First Supervisor: Dr. Pedro de Faria Second Supervisor: Dr. Killian McCarthy
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Abstract
This paper analyses the determinants of R&D cooperation and aims to add knowledge to the cooperation literature by investigating small and micro firms. R&D cooperation is defined as an innovation-based relationship of two or more independent firms on either the public or private sector. Furthermore, I divided R&D cooperation into two subgroups of market and knowledge cooperation in order to assess if the determinants of R&D cooperation are different when considering different types of partners. Knowledge cooperation includes cooperation with consultants, universities and other public research institutions whereas market cooperation includes cooperation with competitors, customers and suppliers. First I conducted literature research in order to find the gap that needs to be filled. I found that there is little research on small firms and even less on micro firms. With the help of a survey I created a final sample of 190 small and micro sized firms. Hypotheses are then tested with an empirical analysis whether the level of absorptive capacity, the breadth of appropriability, the level of cost-share or the level of risk-share are important factors when determining if a company participates in R&D cooperation in general, knowledge or market cooperation. With the help of the final sample it was found that all four independent variables are significant determinants of R&D cooperation. The results contribute to the theoretical literature of R&D cooperation by adding knowledge about small and micro sized firms. As well this study aids managers that are looking for new cooperative partners by providing them with insights on which factors determine R&D cooperation.
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1. Introduction
Widely considered as a critical source of competitive advantage, innovation gained increasing importance within companies that are faced with a continuously changing environment (Crossan & Apaydin, 2010). The way companies innovate has changed from being mostly focused on internal R&D (Nelson, 1990) towards a more external/open research and development (R&D) perspective (Chesbrough, 2003, 2006; Laursen & Salter, 2006) in which collaborating and external sourcing are the norm. Chesbrough (2003, 2006) states that in today‟s environment of abundant information and knowledge; companies cannot stay competitive based solely on their own ideas and developments. Similar Powell, Koput & Smith-Doerr (1996) argue that the source of innovation cannot be found exclusively inside a company but rather can be found in between collaborations of companies, research laboratories, universities, suppliers and customers. These researchers look at internal and external sourcing of innovation not as substitutes but as complements (De Faria, Lima & Santos, 2010). In line with the research theme of this paper and in order to have a single taxonomy of the research field, R&D cooperation is defined as an innovation-based relationship of two or more independent firms on either the public or private sector (Hagedoorn, Link & Vonortas, 2000; Hagedoorn, 2002). In general firms can collaborate in many activities; however I specifically will look at partnerships where innovation is at least part of the collaborative effort.
4 that absorptive capacity, incoming spillovers, firm size, appropriability and cost-risk sharing were important factors when analysing the willingness of a company to engage in R&D cooperation. However up to now, most studies mainly include SMEs and large firms in their sample. There is yet to be a study done on the determinants of R&D cooperation in small and micro firms.
Since the introduction of new types of communication and production technologies small and micro firms have changed the way in which they do business in the past decades (De Faria et al., 2010; Kamalian, Rashki, Hemmat & Jolfaie, 2015). Companies are considered small if they have less than 50 employees and are considered micro firms with less than 10 employees (European Commission, 2014). Small and micro firms similar to large firms need the ability to adapt and respond to the dynamic economies around them. These days it is common for small and micro firms to outsource their secondary activities in order to add more value to their core activities (Kamalian et al., 2015). Especially in the communication and technology industry small and micro firms use a “providers” network (eg.: cable providers that distribute their network among internet providers) in order to receive supportive services (Kamalian et al., 2015). Cooperating with suppliers, customers and R&D partners seems to be necessary in order to stay competitive. Kamalian et al. (2015) argues that small companies cannot operate as an island in order to succeed. They need the help of cooperating partners. However the research on small and particular on micro firms in regard to R&D cooperation is very scarce. Because of that there is a steadily growing need for more knowledge on the topic. The sample used to test the hypotheses is constituted by small and micro firms as these companies have had an increasing role of significance in industrial and developing countries and started to create more cooperative relationships in order to achieve economies of scale and exploit new opportunities (Hagedoorn et al., 2000; Kamalian et al., 2015).
5 I organized the paper as follows: first I will look at the appropriate theory of R&D cooperation. I will look at previous empirical as well as theoretical literature. Furthermore I will clarify what R&D cooperation is and why it is so important to look at small and micro firms. Secondly I will develop testable hypotheses by looking at different determinants of R&D cooperation such as absorptive capacity, appropriability, cost and risk-sharing. These hypotheses will be tested and the results will be presented and discussed.
2. Literature Review and Hypotheses
2.1 What is the stand of R&D cooperation literature?
R&D collaborations have been a topic of thorough research during the past decades, resulting in an extensive body of knowledge. Listed in Table 1 there is a brief collection of the findings of major empirical studies in regard to the determinants of R&D cooperation of the last decade. There are three main approaches that can be found in the cooperation literature. First, several studies focus on the impact of R&D cooperation on the innovation performance of the firm (Belderbos et al., 2004a; Tether, 2002; Tsai, 2009). Second, several studies focus on the types of partners within R&D cooperation (Arranz & Arroyabe, 2008; Brettel, 2011; Miotti & Sachwald, 2003). Third, studies that try to find determinants that help explaining why firms choose to collaborate with each other (e.g. Belderbos et al., 2004b; Cassiman & Veugelers, 2002; Chun & Mun, 2012; Lopez, 2008; Okamuro et al., 2011). The present study will focus on the latter type of research by looking at the determinants of R&D cooperation in small and micro firms. Some of the previous studies on determinants have looked at the relationship of R&D cooperation between large companies (e.g. Albino et al. 2012; Bayona et al., 2001; Fritsch & Lukas, 2001). Other studies have researched the determinants of R&D cooperation between firms and universities (Fontana et al., 2006; Veugelers & Cassiman, 2005). One of the main motives of R&D cooperation is directly connected with the presumption of expected benefits that the involved firms are able to provide to each other, for example the access to complementary assets (Stuart, 2000; Tsang, 2000). R&D cooperation is an important topic in various scientific fields, especially in the fields of policy, management and economic studies as well as technology and innovation. Various research studies have been done on the determinants of R&D cooperation from different perspectives. Based on De Faria et al. (2010) the determinants of R&D cooperation that previous research has found can be summarized along three lines of argument: 1) in regard to the partner type; 2) in regard to the firm characteristics and 3) in regard to the different innovative activities.
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Notes: Partner types include competitor (comp), supplier (sup), university (uni), customer (cust), consultant (consul) and public research institute (ins). Firm, Region, Industry and Country state that the variables used are at the firm, region, industry and country levels, respectively. Determinants within the papers are abbreviated as follows: firm size, [size]; R&D intensity, [rd int]; incoming spillover, [spill]; patent holding or application, [patent]; appropriability [appr]; venture capital financing, [vc]; the importance of cost and risk, [risk]; employee education, [empl]; public subsidy or support, [subsidy]; group affiliation, [group].
resources and capabilities externally (Albino, Dangelico & Pontrandolfo, 2012; Hagedoorn 2002). In regard to this theory many previous researchers (Cassiman & Veugelers, 2002, 2006; De Faria et al., 2010; Hagedoorn & van Kranenburg, 2003; Hall, Link & Scott, 2003)
Paper Partner type Major significant determinants Sample
Bayona et al. (2001) All types of firms Firm (size[+], rd[+]),Industry 1652 Spanish firms (SMEs, large firms) Fritsch and Lukas
(2001)
Comp, cust, sup, ins
Firm (size[+], rdint[+]), Industry, Region
1800 German firms
Tether (2002) Comp, cust, sup, uni, consul
Firm (size[+], rd[+]), Industry 1275 UK firms
Cassiman and Veugelers (2002)
Cust/sup, uni Firm (size[+], spill[+]), Industry 411 Belgian firms
Mohnen and Hoareau (2003)
Uni/ins Firm (size[+], rd[+], patent[+], group[+], subsidy[+]), Industry, Country
9191 French, German, Irish, and Spanish firms Miotti and Sachwald
(2003)
Sup/cust, comp, uni/ins
Firm (size[+], rd[+], subsidy[+]), Industry
2378 French firms
Belderbos et al. (2004)
Comp, cust, sup, uni/ins
Firm (size[+], rdint[+], spill[+]),Industry
2194 Dutch firms
Veugelers and Cassiman (2005)
Uni Firm (size[+]), Industry 325 Belgian firms
Motohashi (2005) Uni Firm (size[+], rd[+], patent[+]), Industry
724 Japanese firms
Fontana et al. (2006)
Uni/ins Firm (size[+], rdint[+], patent[+], subsidy[+]), Industry, Country
558 EU firms (SMEs) Lòpez (2008) All types of firms Firm (size[+], rdint[+], spill[+],
risk[+]), Industry
6026 Spanish firms
De Faria et al. (2010)
Comp, cust, sup, uni, consul, govern
Firm (rdint[+], ), empl (+), appr (+)
766 Portuguese firms
Chun & Mun (2012)
Comp + sup, Uni Firm (spill[+]) risk, patent 2,190 Korean firms (SMEs)
Table 1
7 found that internal R&D is not incompatible with external research agreements, be it with other firms or with research agreements that involve universities. This strengthens the idea that R&D cooperation can give companies an edge in their market by being for example more competitive through the sharing of costs and risks, having the access to new otherwise not easily obtainable resources and the development of new capabilities with their cooperation partners. A further advantage of R&D cooperation, for small and micro firms, would be that the resources they obtain through the cooperation are not getting separated from the original firm which bypasses the issue of tradeability (Ahuja, 2000). Tether (2002), with his sample of mainly large firms, found as well that high intensity of R&D activities increases the willingness to cooperate with external partners in order to innovate. Absorptive capacity has been shown to positively enhance the performance of R&D cooperation‟s as well (Tsai, 2009). However while some firms are very successful at absorbing knowledge, others face difficulties (Cassiman & Veugelers, 2006). Therefore managing these inter-organisational knowledge streams is of great importance to organisations and can lead to a sustainable competitive advantage (Cassiman & Veugelers, 2006; Lane, Koka & Pathak, 2006; Zahra & George, 2002).
8 De Faria et al. (2010) described cooperation as a knowledge exchange of firms. Firms generate and receive spillovers to and from their cooperation partner (De Faria et al., 2010). That becomes relevant when investigating the relationship of cooperation and appropriability, the ability to effectively absorb incoming spillovers (Cassiman & Veugelers, 2002). Firms must manage the external information flows in order to maximize the incoming spillovers from partners and partners but at the same time firms must control the spillovers to non-partners (De Faria et al., 2010). On the other hand by increasing the investment in “absorptive capacity” of a company, it can help to maximize the incoming knowledge flows which in combination with appropriability will help to reap the highest benefit from R&D cooperation. When a company is able to reap higher benefits by maximizing incoming knowledge and protect their own knowledge at the same time it will be more likely for them to engage in R&D cooperation. Cassiman & Veugelers (2002) found that incoming spillovers have a positive as well as a significant effect on the willingness to cooperate. On top of that they found that incoming spillovers have a significant positive effect on companies that want to cooperate with universities or other public research institutions while at the same time the positive effect of incoming spillovers loses significance when looking at vertical cooperation with suppliers and/or customers. Chun & Mun (2012) found that incoming spillovers play an important role for SMEs to choose to cooperate. Additionally the role of incoming spillovers seems to be even more important for small companies. Interestingly some research (De Faria et al., 2010) in regard to spillovers, found that the protection of own knowledge (appropriability) seems to be more important than the search for knowledge from public available sources (incoming spillovers) when deciding to cooperate. That finding is contradicting to the common prediction that firms with a high value to protection might be less inclined to cooperate.
While absorptive capacity and the effects of appropriability seem to play an important role when discussing determinants of R&D cooperation there are other aspects that require attention as well. One such aspect would be the importance of the barriers to innovation and how they affect the willingness to engage in R&D cooperation. In their study Bayona et al. (2001) performed empirical analyses on Spanish companies that were involved in R&D activities. Their main findings in regard to barriers of innovation included that cost barriers were lowering the willingness to cooperate while risk barriers were perceived as a motivation to cooperate. In contrast Lopez (2008) found that both risk and cost barriers were important factors when looking at the willingness to cooperate. It seems that the current knowledge is not conclusive enough and requires further research. When looking at Table 1 it can be seen that most research in regard to the determinants of R&D cooperation found that size is another important factor for R&D cooperation. Bayona et al. (2001) found that large companies are more willing to cooperate when they are looking for technological developments and if there is a high amount of uncertainty when innovating. Fritsch & Lukas (2001) as well found that large companies are more willing to enter R&D cooperation especially when they have a large share of R&D employees.
9 their behaviour in regard to R&D cooperation (Veugelers & Cassiman, 2005). One example by Veugelers & Cassiman (2005) is that large companies are more capable to cooperate because they possess the necessary internal capabilities to effectively interact with universities. However in their paper they state that small high-tech firms may be better suited to interact with universities because they have “sprung off from university research” (Veugelers & Cassiman, 2005). Literature shows that not many researchers recognize the importance of small and micro firms could possess. There are only a selected few researchers (Chun & Mun, 2012; Fontana et al., 2006; Freel, 2000; Madrid-Guijjaro et al., 2009; Okumao, 2011) that have grazed the determinants of R&D cooperation with small sized firms most of the time by including them in a larger sample that as well includes medium sized companies. There are even less researchers (Kamalian et al., 2015) that address micro firms and their determinants of R&D cooperation. In his research Okumao (2011) stresses the importance of Japanese SMEs as well as in particular small firms for a large fraction of innovations. However he as well argues that one of the problems for small firms is their limited experience and resources. Fontana et al. (2006) looked at the determinants of R&D cooperation between companies and universities and other public research institutions by making use of a sample of innovative SMEs in European countries. Their research shows that companies with a high intensity of R&D are more likely to cooperate with universities or other public research institutions. Additionally it seems a low amount of absorptive capacity will reduce the likelihood to cooperate completely (Fontana et al., 2006). Chun & Mun (2012) found that SMEs with high levels of absorptive capacity are more willing to cooperate for R&D as it allows them to absorb incoming knowledge. Given the results of the literature there is now a clear guide on which variables need to be discussed in the next section of the paper as well as a clear reason on why research in regard to small and micro firms will add considerable new knowledge to the existing literature.
2.2 Hypotheses development
2.2.1 Absorptive Capacity and R&D cooperation
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Hypothesis 1a: The level of ACAP positively influences the probability of small and micro
firms to engage in R&D cooperation activities.
Hypothesis 1b: The level of ACAP has a higher positive effect on the probability of small
and micro firms to engage in knowledge cooperation than market cooperation.
2.2.2 Appropriability and R&D cooperation
12 Regarding the research that has been done so far knowledge protection mechanisms will have a positive effect on the willingness to engage in R&D cooperation in general. In regard to the partner types I expect that companies who are looking for especially market cooperation have an incentive to be more effective at controlling the knowledge that they are willing to share with their partners because commercially sensitive information could otherwise leak out to competitors through for example common customers and suppliers. Hence, firms that can protect their knowledge sufficiently are more willing to engage in market cooperation, this problem is not present with knowledge cooperation as universities do not try to misappropriate the knowledge of their partners. Veugelers & Cassiman (2005) even propose the theory of non-exclusive exchange when cooperating for R&D with universities and other public research institutions. Thus the next two hypotheses are formulated.
Hypothesis 2a: The breadth of knowledge protection mechanisms positively influences the
probability of small and micro firms to engage in R&D cooperation activities.
Hypothesis 2b: The breadth of knowledge protection mechanisms has a higher positive effect
on the probability of small and micro firms to engage in market cooperation than knowledge cooperation.
2.2.3 Cost and Risk sharing
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Hypothesis 3a: The level of perceived risks positively influences the probability of small and
micro firms to engage in R&D cooperation activities.
Hypothesis 3b: The level of perceived risks has a higher positive effect on the probability of
small and micro firms to engage in knowledge cooperation than market cooperation.
Hypothesis 4a: The level of perceived cost positively influences the probability of small and
micro firms to engage in R&D cooperation activities.
Hypothesis 4b: The level of perceived cost has a higher positive effect on the probability of
small and micro firms to engage in market cooperation than knowledge cooperation.
3. Data and Methods
3.1 Data
15 63 firms were able to answer the questions of the survey which makes up a total of 263 firms 14.60% of the initial companies. For this research I only included small and micro firms that have developed either new or significantly improved processes, goods or services within the last three years. In other words, only innovation active firms have been considered. Small companies have less than 50 employees or a turnover of less than 10 million euro (European Commission, 2014). Micro firms consist of less than 10 employees and have a turnover of less than 2 million euro (European Commission, 2014). On top of that I did not include respondents that had too many missing values in regard to the important variables. In the end the data extraction under these criteria yielded a final sample of N=190 firms.
3.2 Dependent variables
There are three dependent variables in the study which will be analysed individually. This will lead to three models that show if the independent variables can be considered determinants of R&D cooperation in small and micro firms. The dependent variables of the study are R&D cooperation as a whole and the subcategories of market and knowledge R&D cooperation. R&D cooperation is defined as a dummy variable and is dichotomic, taking the value of one if the firm engages in R&D cooperation, otherwise it takes the value of zero. In that manner the two subcategories of knowledge cooperation (K_Coop) which includes consultants, universities or other public research institutes as well as and market cooperation (M_Coop) which includes competitors, customers and consumers are created. In both cases companies that engaged in R&D cooperation are asked which types of partners they have engaged with in cooperation. Companies that engaged in R&D cooperation with consultants, universities or other public research institutes are assigned the value of one for K_Coop, otherwise zero. Companies that engaged in R&D cooperation with competitors, suppliers and customers are assigned the value one for M_Coop, otherwise zero.
3.3 Independent variables
16 qualitative approach they will make use of single case studies or multiple cases studies (Lane et al., 2006; Todorova & Durisin, 2007). There is as well a mix method approach where both qualitative and quantitative measurement approaches are combined (Duchek, 2013; Flatten et al., 2011). This heterogeneity in concept and measurement does make it more difficult to compare results and even challenges the validity of findings in past research (Lane et al., 2006). For this mainly quantitative study I choose to use the more common proxy of total R&D expenditure in order to research if ACAP and see if it is a determinant of cooperation for small and micro firms. The aim is to find some correlation that can add knowledge to the cooperation literature. This variable is derived out of the question that asked the company how much of their total turnover is spent on internal R&D. The answer creates the independent variable ACAP.
The second independent variable of the study is appropriability (Approp). Previous studies (Cassiman & Veugelers, 2002; Chun & Mun, 2012; De Faria et al., 2010) show that the most common method to measure appropriability is to look at the sum of the scores of knowledge protection methods. In order to do so the survey included five questions regarding knowledge protection methods. The representatives of each company were asked if the company has the following protection methods for innovation: patents, registration of design patterns, trademarks, copyrights and confidentiality. Each individual question was so formulated that the result was either a 1 if they made use of the knowledge protection mechanisms or 0 if not. Depending on the amount of protection mechanisms a company has the score of the variable Approp could vary between zero (no protection mechanisms at all) to five (all five protection mechanisms). This way to measure appropriability is a very direct one and simply adds the knowledge protection mechanisms together. Other ways to measure appropriability within past literature would be for example to measure appropriability by looking at the potential economic returns of a company‟s own knowledge which it is able to appropriate (Geroski, 1995).
17 perceived cost for innovation the more likely it is for a company to engage in R&D cooperation in general and the more likely it is for a company to engage in market cooperation over knowledge cooperation.
The last independent variable of the paper is risk-share. It has been used in previous literature (Chun & Mun, 2011; Lopez, 2008; Miotti & Sachwald, 2003) usually alongside cost-share in order to find determinants of R&D cooperation. However just like cost-share both variables have not had conclusive results. Especially in the regard to micro firms the research is scarce. In this study risk-share is categorized as the sum of perceived obstacles for innovation in regards to risk. The survey asked the representatives of each company in regard to lack of market information and perceived rigid regulations. These two questions were scaled from one (large obstruction) to four (no obstruction). Exactly like the costshare variable I transformed the answers into a new scale ranging from zero (no obstruction) to three (large obstruction). Compared to cost-share there were only two obstacles to innovation that related to risk-share, hence the lowest possible score for the variable riskshare is zero and the highest possible score is six. As mentioned in section two the reason for choosing this independent variable was that previous literature showed that risk-share can influence the willingness to cooperate for R&D.
3.4 Control variables
18 Table 2
Description of variables
3.5 Regression formula
The goal of the paper is to investigate the determinants of R&D cooperation. In order to do so I make use of logistic regression since the dependent variables are dichotomous dummy variables, which are equal to 1 when a firm co-operates on R&D and otherwise 0 if they do not cooperate. The outcome is not be a forecast of a numerical value, as in linear regression, but a probability of belonging to one of two conditions, that take on values between 0 and 1. This specification of the observable dependent variables, , can be described according to the rule:
if , otherwise
The associated latent variable is
Logistic regression calculates the probability of success over the probability of failure in the form of an odds ratio. Similar to what previous research (Arranz & de Arroyabe, 2008; Belderbos et al., 2004b; Chun & Mun, 2012; Okamuro, 2011) did, I created 2 subgroups
Variables Type Definition
Dependent variables
R&D Cooperation Binary Dummy variable: 1 if the firm engages in R&D cooperation, 0 otherwise.
Knowledge Cooperation Binary Dummy variable: 1 if the firm engages in R&D cooperation with consultants, universities and/or other public research institutes, 0 otherwise.
Market Cooperation Binary Dummy variable: 1 if the firm engages in R&D cooperation with competitors, suppliers and/or consumers,. 0 otherwise
Independent variables
Absorptive Capacity (R&D expenditure) Continuous Total R&D expenditure (internal) on innovation activities as a percentage of total turnover
Appropriability Continuous Sum of the number of strategic and formal protection methods for innovations (confidentiality, patents, registered designs, copyrights, trademarks)
Cost sharing Continuous Sum of scores of importance of the following obstacles to innovation: lack of qualified personnel, lack of financial resources in enterprise, lack of proper infrastructure lack of external financial resources,lack of funding opportunities, lack of innovation networks. Scores [number between 0 (no obstruction) and 3 (large)]. Risk sharing Continuous Sum of scores of importance of the following obstacles to innovation: lack of market information, rigid regulations.
Scores [number between 0 (no obstruction) and 3 (large)]. Control variable
AGE Continuous The amount of years the company has been operated up to now (2015)
19 based on the theoretical findings that I mentioned earlier. R&D cooperation with consultants, universities and other research institutions is considered knowledge cooperation. The remaining types of cooperation partners of my sample namely competitors, suppliers and customers are considered market cooperation. The final formula of the logistic regression including all independent variables and control variables is formulated based on the logistic regression formula including multiple predictors is as follows (Field, 2009).:
is the probability of occurring, e is the base of natural logarithms. The constant is ( ), the first predictor variable ( ) and a coefficient attached to that predictor ( ), this is followed by the second predictor variable ( ) and the appropriate coefficient which is attached to that predictor ( ) (Field, 2009).
4. Results
4.1 Descriptive statistic
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Variables N Mean Std. dev Min Max VIF
Dependent variables Cooperation 190 0.605 0.490 0 1 M_Coop 190 0.542 0.500 0 1 K_Coop 190 0.526 0.501 0 1 Independent variables ACAP 190 14.928 20.820 0 100 1.15 Approp 190 1.374 1.306 0 5 1.25 Costshare 190 8.289 3.356 0 18 1.18 Riskshare 190 2.421 1.373 0 6 1.39 Control variables SIZE 190 10.184 10.754 0 45 1.39 AGE 190 18.205 21.920 0 132 1.05 IND 190 3.011 2.116 1 11 1.10 Table 3
Summary of the statistics on R&D cooperation by industry.
Notes: N indicates the number of observations. This table contains all small and micro sized firms of the final sample. Knowledge cooperation comprises consultants, universities and other public research institutes. Market cooperation comprises competitors, suppliers and consumers. “Coop” denotes the number of firms engaged in R&D cooperation in that particular category.
Appendix 3 shows the Pearson correlation matrix of the key variables in relation with each of the different dependent variables. The correlation matrix indicates that a various key variables are highly correlated (eg.: Model 1; Riskshare and Costshare r=.36 p<.01). Interesting to see is that in model 2 the dependent variable (K_Coop) is highly correlated with all independent variables whereas in model 1 and model 3 the dependent variables (Cooperation and M_Coop) are highly correlated only with ACAP, Approp and Costshare. High correlations could be an indication that there is multicollinearity within the sample. Multicollinearity indicates “high correlations among the latent exogenous constructs” (Grewal, Cote & Baumgartner, 2004). In order to check for multicollinearity the Variance Inflation Factors (VIF) of the independent variables for R&D cooperation as well as the sub-samples by type of partners are calculated in Table 4. If the VIF values would be 10 and higher it would signal that there is a problem for multicollinearity (Chun & Mun, 2012). The results of Table 4 show that there is no VIF value close or even greater than 10, which indicates robust results in regard to multicollinearity problems. Table 4 as well provides the standard deviation (std. dev.), mean and min/max values of the variables.
Industry N (A) R&D Cooperation Knowledge Cooperation Market Cooperation
Coop (B) B/A (%) Coop (C) C/A (%) Coop (D) D/A (%)
Professional and technical activities 51 33 64.71 28 54.90 31 60.78
Manufacturing 47 28 59.57 22 46.81 26 55.32
Information and communication 35 19 54.29 17 48.57 18 51.43
Wholesale and retail trade 25 15 60.00 14 56.00 11 44.00
Other Services 10 6 60.00 6 60.00 6 60.00
Construction 9 7 77.78 6 66.67 6 66.67
Administrative and support service 4 2 50.00 2 50.00 1 25.00
Healthcare services 3 1 33.33 1 33.33 1 33.33
Education 2 1 50.00 1 50.00 1 50.00
Mining and quarrying 2 1 50.00 1 50.00 1 50.00
Transport and storage 2 2 100.00 1 50.00 1 50.00
All Industries 190 115 60.53 99 52.11 103 54.21
Notes: N indicates the number of observations. VIF indicates the Variance Inflation Factor testing for multicollinearity
Table 4
21 4.2 Regression
Table 5 presents the results of the binary linear regressions. Each of the three models includes all independent and control variables. In support of H1a, ACAP is the only variable that is highly significant (p < .01) for all three models. This is in line with prior research on R&D cooperation (Bayona et al., 2001; Chun & Mun, 2012). Additionally to existing research the second and third model show that ACAP has a highly significant effect on the willingness to engage in knowledge and market cooperation. The regression indicates that with an increase of one unit in ACAP the willingness for a firm to engage in knowledge cooperation is increased by 2.6% (1.026*100-100) and the willingness to engage in market cooperation is increased by 2.4% (1.024*100-100). Even though the odds ratio for knowledge cooperation (Exp (B) = 1.026) is slightly higher than that for market cooperation (Exp(B) = 1.024). It is not possible to instantly draw a conclusion in regard to H1b. In order to truly estimate if the coefficients of ACAP can be compared like that, it is necessary to do a z-test (Paternoster, Brame, Mazerolle & Piquero, 1998). Appendix 2 holds the calculation for the z-test (Paternoster et al., 1998) in order to find out if the beta coefficient of ACAP in model 2 is significantly different from the beta coefficient of ACAP in model 3. The calculation results in a z-score of 0.14896 with the corresponding p-value of 0.44081 which can be taken from a z-score chart. The result indicates that the beta coefficients are not significantly different from each other and it is not possible to say that one is having a bigger influence than the other. Because of that H1b is rejected. The empirical findings in general in regard to ACAP
Variables ACAP 1.034*** [0.011] 1.026*** [0.009] 1.024** [0.010] Approp 1.331** [0.141] 1.411** [0.137] 1.237 [0.132] Riskshare 1.208 [0.132] 1.422** [0.136] 1.032 [0.127] Costshare 1.059 [0.055] 1.024 [0.055] 1.119** [0.055] SIZE 1.011 [0.018] 1.012 [0.019] 1.020 [0.018] AGE 1.000 [0.009] 1.005 [0.009] .998 [0.008] Constant 0.306** [0.580] 0.147*** [0.603] 0.250** [0.584] IND N (observations) Log likelihood Nagelkerke R Square 190 190 190 -446.44 -458.424 -474.692 .208 .217 .163
Exp(B)[S.E.] Exp(B)[S.E.] Exp(B)[S.E.]
Yes Yes Yes
Model 1 Model 2 Model 3
(R&D Cooperation) (Knowledge Cooperation) (Market Cooperation)
Table 5
Binary-Logistic regression of all three models
22 are contrary to research done by Lopez (2008) who found that there is only a (weak) significant effect with suppliers and customers but that this effect loses significance with competitors, universities and other public research institutions. The next variable of the regression analysis, Approp, has a significant effect (p < .05) for R&D cooperation in general which is in support of H2a. In fact the odds ratio of model 1 (Exp(B)=1.331) indicates that with an increase of one unit in Approp the willingness to engage in cooperation is increased by a factor of 1.331 (or 33.10%). Furthermore Approp has a significant effect (p < .05) for knowledge cooperation while losing significance for market cooperation (p > .1). Model 2 shows that with a unit increase in Approp the willingness to engage in knowledge cooperation is increased by a factor of 1.411 (or 41.10%). Since the effect of knowledge cooperation is significant and the effect for market cooperation is not the result is the reversed to the hypothesis hence I reject H2b. These results as well are contradicting with past research. Cassiman & Veugelers (2002) found the reverse effect of this regression in regard to appropriability, they found that it is more important for companies that are looking for cooperation with suppliers and customers (market cooperation) and that it has no effect on knowledge cooperation. Similar Lopez (2008) found that appropriability is a general determinant for all types of partner cooperation, not just for knowledge cooperation. The next variable to look at is Riskshare which seems to be only significant (p < .05) in model 2, knowledge cooperation, and has no impact on the R&D cooperation in general or market cooperation. These findings are in contrast with the research of Veugelers & Cassiman (2005) who found risk to be a barrier to innovation that discourages knowledge cooperation. As well it contradicts the findings of Lopez (2008) who found that “Cost-risk sharing” was the most important determinant for R&D cooperation in general. Thus I can reject H3a. Since
Riskshare is significant for knowledge cooperation but not for market cooperation it indicates
that indeed the level of perceived risks has a higher positive effect on the probability to engage in knowledge cooperation than market cooperation. This result leads to the support of H3b. The empirical result indicates that with an increase in one unit of Riskshare the willingness to engage in knowledge cooperation is increased by a factor of 1.422 (or 42.20%). Further contradicting to past research Costshare has no significance in neither of models 1 and 2. Because of that I can reject H4a. Costshare has however a significant effect in model 3 (p < .05) which indicates that the level of perceived cost has a higher positive effect on the probability engage in market cooperation than knowledge cooperation. The empirical results show that with each increase in one unit of Costshare the willingness to engage in market cooperation would increase by a factor of 1.119 (or 11.90%). This result is in support of H4b and verifies my prediction. The findings for this variable are in line with the research of Miotti & Sachwald (2003) who as well didn‟t find cost-share to have an impact on the willingness to cooperate for R&D in general. Looking at the different types of partners these findings seem to be different to what researchers have found in regard to SMEs and large firms. Contrary to the findings of this paper Belderbos et al. (2004b) found that cost-sharing seems to be only important for knowledge cooperation, while even having a negative impact on the cooperation with suppliers.
23 be determinants of R&D cooperation. With respect to the variables SIZE some previous studies have found a positive and significant effect in regard to R&D cooperation (e.g. Cassiman & Veugelers, 2002, Lopez, 2008; Motohashi, 2005). However, most of these studies made use of data on relatively large firms. This paper focuses on small and micro firms hence it could be argued that due to the missing medium sized and larger firms the variance of the analysed firms is too small and it seems that because of that these control variables have no role in determining the willingness to engage in R&D cooperation.
5. Discussion
This study was set out to find the determinants of R&D cooperation for small and micro sized firms. Albeit previous studies have analysed the determinants of R&D cooperation the waste amount of knowledge comes from research through samples of mainly SMEs and large companies. Especially the latter has been the sample for the most cited papers on R&D cooperation. As there is not much research in the field of small companies and next to no research in regard to micro sized companies I have attempted to fill this research gap by introducing a more focused analysis especially on those types of companies. On top of that I try to expand the research field by not simply analysing a sample consisting out of small and micro firms but as well I already go one step further and differentiate the types of partners in two major subgroups namely, knowledge and market cooperation. The empirical findings of this paper indeed show that some determinants have a very similar effect on the willingness to engage in R&D cooperation as previously research with SMEs and large firms has shown. However the empirical findings as well show that there are differences with previous research on the determinants of R&D cooperation which can most likely be related to the fact that small and micro sized firms have to look at different factors and motivations before engaging into R&D cooperation. Because of these differences with previous research only partial hypotheses have been supported as can be seen in the overview of appendix 4. Overall the findings of this study show multiple novel and interesting insights.
24 would put the company at a great advantage. The other way around, having a low ability to absorb knowledge, it would be unlikely for a company to engage in R&D cooperation because in that case they would not gain enough to make up for the risk of possible risks such as misappropriation.
25 Third, as hypothesized, the empirical findings show that risk-share had a significant effect on the willingness to engage in knowledge cooperation. This is contrary to the notion of previous research that risk-sharing has a significant effect on the cooperation with competitors, customers and suppliers (Lopez, 2008) while being seen as a barrier to cooperation with universities and other public research institutions (e.g. Veugelers & Cassiman, 2005). The difference in results could be related to at least two different factors. One, the sample is consisting out of small and micro sized firms. These types of companies, contrary to their larger counterparts, perceive risk as motivator to engage in explorative research with universities and other public research institutions and contrary to market cooperation they do not have to be afraid of misappropriation. Second, most previous research did not factor in consultants who are a big part of the knowledge cooperation variable. Consultants could be a very effective tool for small and micro sized firms to innovate in a market with high uncertainty without having to fear misappropriation.
Fourth, it was hypothesized that cost-share had an effect of the willingness to engage in market cooperation. The empirical findings show that the hypothesis was supported, showing a significant relationship with market cooperation. This finding is contrary to previous research which found cost-share to be of either no importance in regard to the willingness to engage in market cooperation (Miotti & Sachwald, 2003), to have a significant effect on the willingness to engage in knowledge cooperation (Belderbos et al., 2004b) or a significant effect on both market and knowledge cooperation (Lopez, 2008). Similar to the findings of risk-share the difference in findings to previous research could be linked with the sample itself. This paper focuses solely on small and micro sized firms while the previous research papers include medium and large firms while not including micro firms at all. Especially due to the differences in resources of small and micro firms, compared to their larger counterparts, it can be argued that cost-sharing is an important factor to engage in market cooperation. As previously stated in section 2 it is best to look at the resource-based perspective. Small and micro sized firms could choose to engage in market cooperation in order to alleviate their relative disadvantages in order to access complementary resources and innovation networks. In that case both parties stand the risk of losing their assets should the innovation process fail which could reduce the opportunism.
26 willingness to engage in R&D cooperation. Larger firms have more resources in order to engage in cooperation and as well follow different determinants as this study has shown.
6. Conclusion
This study managed to increase the field of knowledge in the R&D cooperation literature by analysing small and micro sized firms. Especially in regard to micro sized firms there has been near to no research. In doing so the study looked at the determinants of cooperation with the help of 190 small and micro sized firms which form the sample. Similar to previous research absorptive capacity had a significant effect on the willingness to engage in R&D cooperation. This means firms who increase their investments in R&D are more likely to engage in R&D cooperation on all levels (market and knowledge cooperation) because they are more able to absorb complementary resources and capabilities. Furthermore knowledge protection turned out to be a significant determinant of R&D cooperation in general and knowledge cooperation. These results are contrary to existing research which leads to the rejection of H2b and H2c. Two completely new findings were made, first, between the relationship of costsharing and market cooperation which could be a specific characteristic of small and micro sized firms. Second, the research found a new significant relationship between risksharing and knowledge cooperation which as well could be specifically in regard to small and micro sized firms. Small and micro sized firms have realized that R&D cooperation is an efficient and effective way for a company to do complex R&D and innovation processes. Knowing which determinants are related to different types of partners enables these firms to share the skills and resources with a partner in order to reduce their own asset commitment and enable them to be more flexible while innovating.
6.1 Managerial Implications
27 protection mechanisms might be not as important as previous studies have shown and needs to be revaluated, especially when a small or micro firm decides to engage in cooperation with competitors, customers or suppliers. Previous studies showed that knowledge protections can determine if a company engages in R&D cooperation however these studies did not look at small and micro firms. For small and micro firms it could be of a big disadvantage if there are too many protection mechanisms in place these small and micro firms might be not attractive enough in order to engage in R&D cooperation.
6.2 Limitations and future research
28 micro firms should look into the effects of knowledge spillovers. Fifth, future research should be to analysis not only the determinants of R&D cooperation but as well the efficiency of such cooperation and possible impact on innovative performance. Lastly, although it was controlled for in this study, there was no differentiation made between high tech and low tech industries, whereas other authors have argued that especially in high tech industry sectors R&D cooperation is more likely to occur (Miotti & Sachwald, 2003). Future research should look into the different effects within low and high tech industries.
Acknowledgements
29
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34
Appendix 1: Frequencies of both cooperation with both knowledge and
market
Appendix 2: Z test for testing the difference between coefficients across
models
Hypotheses
The digits stand for the model from which the coefficient comes from.
Coefficient values
ln(1.026) = 0.02567
ln(1.024) = 0.02371
Formula to calculate Z (Paternoster et al., 1998)
√
√
0.14896
p 0.440816 One-tailed No significant difference, support of Frequencies Percentage
No cooperation at all 75 39.47
Cooperation in either knowledge or market 27 14.21 Cooperation in both knowledge and market 88 46.32
35
Appendix 3: Correlation matrix of key variables
N=190 Model 1
Coop ACAP Approp Costshare Riskshare SIZE AGE IND
Cooperation 1 ACAP .307** 1 Approp .207** .208** 1 Costshare .179* 0.118 .150* 1 Riskshare 0.13 -0.02 -0.035 .364** 1 SIZE -0.046 -.277** -0.052 -.178* -0.022 1 AGE -0.027 -.255** -.163* 0.024 0.109 .450** 1 IND -0.016 -0.017 -0.049 0.058 0.06 0.067 -0.109 1 N=190 Model 2
K_Coop ACAP Approp Costshare Riskshare SIZE AGE IND
K_Coop 1 ACAP .265** 1 Approp .207** .208** 1 Costshare .173* 0.1177 .150* 1 Riskshare .207** -0.0204 -0.0351 .364** 1 SIZE -0.0142 -.277** -0.0516 -.178* -0.0225 1 AGE 0.01276 -.255** -.163* 0.02364 0.10857 .450** 1 IND 0.04969 -0.0166 -0.0493 0.05843 0.06039 0.06689 -0.1088 1 N=190 Model 3
M_Coop ACAP Approp Costshare Riskshare SIZE AGE IND
M_Coop 1 ACAP .219** 1 Approp .191** .208** 1 Costshare .187** 0.1177 .150* 1 Riskshare 0.06658 -0.0204 -0.0351 .364** 1 SIZE -0.0069 -.277** -0.0516 -.178* -0.0225 1 AGE -0.0117 -.255** -.163* 0.02364 0.10857 .450** 1 IND -0.0755 -0.0166 -0.0493 0.05843 0.06039 0.06689 -0.1088 1 **. Correlation is significant at the 0.01 level (2-tailed).
36
Appendix 4: Summary of hypothesis and empirical results for the
Netherlands
Variable Hypotheses Finding
Absorptive Capacity Hypothesis 1a : The level of ACAP positively influences the
probability of small and micro firms to engage in R&D cooperation activities.
Supported
Hypothesis 1b : The level of ACAP has a higher positive effect on
the probability of small and micro firms to engage in knowledge cooperation than market cooperation.
Rejected
Appropriability Hypothesis 2a: The breadth of knowledge protection mechanisms
positively influences the probability of small and micro firms to engage in R&D cooperation activities.
Supported
Hypothesis 2b: The breadth of knowledge protection mechanisms
has a higher positive effect on the probability of small and micro firms to engage in market cooperation than knowledge cooperation.
Rejected
Risksharing Hypothesis 3a: The level of perceived risks positively influences the
probability of small and micro firms to engage in R&D cooperation activities.
Rejected
Hypothesis 3b: The level of perceived risks has a higher positive
effect on the probability of small and micro firms to engage in knowledge cooperation than market cooperation.
Supported
Costsharing Hypothesis 4a: The level of perceived cost positively influences the
probability of small and micro firms to engage in R&D cooperation activities.
Rejected
Hypothesis 4b: The level of perceived cost has a higher positive
effect on the probability of small and micro firms to engage in market cooperation than knowledge cooperation.
