Determinants of research partnership formation by
Japanese high-tech start-ups∗
Hiroyuki Okamuro†
Yuji Honjo‡
Masatoshi Kato§

Abstract
This paper explores the determinants of R&D cooperation in Japanese high-techstart-ups. Using a sample from an original survey conducted in 2008, we examine
the effects of founder-, firm-, and industry-specific characteristics on R&D cooperation by the type of partners. Our findings indicate that founder-specific characteristics, such as educational background, academic affiliation and prior innovation
output, are fairly important in determining R&D cooperation with universities or
public research organizations. We also find that the roles of founders’ work experience and prior innovation output are significant in determining R&D cooperation
with business partners. With respect to firm-specific characteristics, it is foundthat firms investing more in R&D tend to conduct R&D cooperation, regardless ofthe type of partners. It is also found that independent firms are less likely to cooperate on R&D with universities or public research organizations, than subsidiaries
and affiliated firms.

Keywords: R&D cooperation; Start-up; University; Public research organization; Business partner; Signaling.
Paper to be presented at the Comparative Analysis of Enterprise Data
(CAED) Conference, Hitotsubashi University, Tokyo, October 2–4, 2009∗This study is supported by the Grant-in-Aid for Scientific Research (A) (No.20243018) of theJapan Society for the Promotion of Science. We thank Sadao Nagaoka and Nobuya Fukugawa for theiruseful comments. We also thank Kenta Ikeuchi for excellent research assistance. Needless to say, any
remaining errors are our own.
†
Corresponding author
Graduate School of Economics, Hitotsubashi University
Address : 2-1 Naka, Kunitachi, Tokyo 186-8601, Japan
E-mail: okamuro@econ.hit-u.ac.jp‡
Faculty of Commerce, Chuo University§
Institute of Economic Research, Hitotsubashi University

1.

Introduction

Small businesses have been regarded as important players of innovation since the seminal work by Acs and Audretsch (1990). More recently, much attention has been paid
to start-ups as the sources of regional innovation and productivity (e.g., Audretsch etal. 2006; Acs and Armington 2006; van Stel, 2006). However, it is not easy for small
start-ups to be successful in innovation because of their limited business resourcesand experience. Therefore, alliance formation, especially for research and development(R&D) cooperation with external organizations, may be an essential strategy for startups to obtain complementary assets, share costs and risks, and improve performance.
In these respects, it is important to understand the nature of R&D cooperation ofstart-ups both for academics and practitioners. The purpose of this paper is thus toprovide evidence on factors determining R&D cooperation of start-ups.
In the fields of economics and management, a number of studies have examinedthe determinants of R&D cooperation among firms, in particular, by focusing on large
established firms (e.g., Vonortas 1997; Bayona et al., 2001; Angel, 2002). Some recent
studies have examined the determinants of R&D cooperation between firms and universities (e.g., Veugelers and Cassiman, 2005). As for small- and medium-sized enterprises(SMEs), Fontana et al. (2006) investigated the determinants of R&D cooperation between firms and public research organizations, using a sample of innovative SMEs in
European countries. Muscio (2007) also examined the impact of absorptive capacity
on SMEs’ collaboration with firms, universities, and technology transfer centers. To
date, however, little is known about R&D cooperation of start-ups, except for Colombo
et al. (2006), which examined the determinants of alliance formation of Italian hightech startups. In addition, the role of founders’ human capital in R&D cooperation
tended to be ignored in the prior literature. In this paper, therefore, we highlight theeffects of founder-specific as well as firm- and industry-specific characteristics on R&D

2

cooperation of high-tech start-ups with universities, public research organizations, andbusiness partners.
Regarding R&D cooperation in Japan, for example, Branstetter and Sakakibara(1998, 2002) investigated government-sponsored research consortia among large firms.
Motohashi (2005) also examined the determinants of university-industry collaborationsin Japan.1 On the other hand, Okamuro (2007) examined the impact of R&D cooperation on performance, using data on Japanese SMEs. However, R&D cooperation ofJapanese start-ups has not been examined yet. Whereas, as is often argued, Japan has
finished the technological catch-up and strives for technological leadership, Japan ischaracterized by almost the lowest ratio of business start-ups among the OECD countries. The present study highlighting R&D cooperation of high-tech start-ups in Japan
would provide a comparative perspective on the national innovation system to othercountries.
The remainder of the paper is organized as follows. In Section 2, we explain the
background of this paper and the related literature. In Section 3, we develop our
hypotheses on the determinants of R&D cooperation. Section 4 describes the data
used in the analysis. The empirical results are presented in Section 5. The final section
includes some concluding remarks.

2.

Literature review

To date, R&D cooperation has been examined in a rich stream of literature. As isoften argued, R&D cooperation is a useful way to exploit external technologies andknowledge. Also, as mentioned earlier, R&D cooperation can allow firms to obtaincomplementary assets, share costs and risks, and improve performance.2 Recently, a1
In addition, Miyata (1995) examined the determinants of R&D cooperation in Japanese firms,
focusing on industry effects.
2
For theoretical discussions on R&D cooperation, see, for example, Katz (1986), d’Asremont andJacquemin (1988), and Suzumura (1992).

3

number of studies have addressed the determinants of R&D cooperation from various
perspectives. In this section, we review the previous studies on the determinants of
R&D cooperation in order to clarify the stylized facts and missing factors in the literature. Then, we explain some differences of our paper from the previous studies andour contribution to the literature.
As summarized in Table 1, a number of studies have empirically examined the determinants of R&D cooperation. Among them, some studies focus on R&D cooperation
between firms. For example, Bayona et al. (2001) investigate the motives of industrial
firms to cooperate in R&D, using a sample of Spanish firms. They found that firms
with large and sufficient capacity to carry out R&D tend to cooperate on R&D, andthe reasons for cooperative R&D are overall different between large firms and SMEs.
Colombo et al. (2006) examine the determinants of commercial and technological alliances by Italian high-tech startups. They provide evidence that not only firm sizeand but also prior innovation output are significant determinants of these alliances.
Most recently, Lopez (2008) emphasize the roles of incoming spillovers and the costs
and risks of innovative activities in determining R&D cooperation among firms, using
data on Spanish manufacturing firms.
The other studies address the determinants of R&D cooperation between firms
and universities or public research organizations. For example, Mohnen and Hoareau(2003) investigate the determinants of R&D cooperation between firms and universities
or government labs, using a sample of French, German, Irish, and Spanish firms.3 They
provide evidence that firm-specific characteristics, such as patent holding, group affiliation, and subsidies, affect R&D cooperation. Fontana et al. (2006) also examine thedeterminants of R&D cooperation between firms and universities or public research or3

Mohnen and Hoareau (2003) used data from the Community Innovation Surveys (CIS) in theEuropean countries. As shown in Table 1, other studies also used data from each country’s versionof the CIS to examine the determinants of R&D cooperation; Tether (2002) for the UK, Miotti and
Sachwald (2003) for France, Belderbos et al. (2004) for the Netherlands, Veugelers and Cassiman (2005)
for Belgium, and Lopez (2008) for Spain.

4

ganizations with a sample of European Innovative SMEs, and found that the opennessof firms to the external environments significantly affects the probability of R&D cooperation with universities or public research organizations. Furthermore, some studies,including Fritsch and Lukas (2001) and Miotti and Sachwald (2003), explore how thedeterminants of R&D cooperation vary according to the types of partners, and provide
evidence on some differences in the determinants of R&D cooperation between different
types of partners.
As already mentioned, these previous studies tend to focus on R&D cooperation
involving large firms, although several studies, including Bayona et al. (2001) andFontana et al. (2006), use data on SMEs. On the other hand, few studies, except for
Colombo et al. (2006), examine R&D cooperation by start-ups. For start-ups that haveneither experiences nor track records and hence no reputation, founders may play more
important roles in the management of their firms than old and large established firms.
In this respect, founder-specific characteristics may be fairly important rather thanfirm-specific characteristics in determining R&D cooperation in start-ups. Colombo etal. (2006) took into account the characteristics of founders in high-tech start-ups as
the determinants of alliances, but they generally failed to find significant results on
the effects of founder-specific characteristics. Also, as pointed out by Fontana et al.
(2006), the ability of firms to signal their research capabilities to potential partners isa key factor to determine R&D cooperation with universities. In this sense, signaling
founders’ capabilities may also be important for start-ups in the decisions of R&D
cooperation by potential partners. In this paper, we explore the effects of founderspecific characteristics on R&D cooperation, controlling for firm- and industry-specificcharacteristics.

5

3.

Hypotheses

The purpose of this paper is to examine the determinants of R&D cooperation in
Japanese high-tech start-ups by the type of partners; universities, public research organizations, and business partners. In this section, we consider founder- and firm-specificcharacteristics affecting R&D cooperation, and present our hypotheses in turns.
As already discussed, the effects of founder-specific characteristics tend to be ignored in the prior literature. However, founders may play more important roles in
R&D cooperation, especially by start-ups. A number of studies argue that the successof firms depends on founders’ and entrepreneurs’ capabilities. In fact, some studies
find that their capabilities are strongly significant in explaining the post-entry performance of start-ups (e.g., Honjo, 2004; Colombo and Grilli, 2005). In the context of
R&D cooperation, founders’ capabilities may signal the capabilities of their firms topotential partners. In this paper, we consider educational level, work experience, andprior innovation output as founder-specific characteristics affecting R&D cooperation.
First, let us consider founders’ education level as a factor affecting R&D cooperation. According to the competence-based view, the distinctive capabilities of newtechnology-based firms (NTBFs) are closely related to the knowledge and skills of theirfounders (Colombo and Grilli, 2005). Also, founders’ human capital has often been
represented by their education level in a number of previous studies. For example,
Bates (1990) use owner’s education level as a determinant of firm survival. Colombo
and Grilli (2005) use the years of founders’ education as the proxy of their human capital. As already discussed, signaling founders’ capabilities to potential partners may
also be important for start-ups in R&D cooperation. In this sense, founders’ educationlevel can be regarded as a signal of the capabilities of their firms to potential partners
for R&D cooperation. In addition, founders’ education level can also be regarded as
the measure of their network. It is likely that the longer the years of academic ed-

6ucation, the wider is the network with researchers in universities or public researchorganizations. From these respects, we formulate the following hypotheses.
Hypothesis 1a. Firms with highly educated founders are more likely than the others
to cooperate on R&D with external organizations, regardless of the types of partners.
Hypothesis 1b. Firms with highly educated founders are more likely than the others
to cooperate on R&D with academic organizations, including universities and publicresearch organizations.
Further, we consider founders’ innovation output prior to start-up, which is fairly
important as founder-specific characteristics affecting R&D cooperation. Colombo etal. (2006) argue that the synergistic gains from technological alliances depend on thetechnological competencies of NTBFs.4 As discussed earlier, founders are likely to play
more important roles in start-ups that have neither experiences nor track records andhence no reputation. Instead of firms’ prior innovation output, we therefore consider
founders’ prior innovation output as the measure of the technological competencies ofstart-ups. From these reasons, we formulate the following hypothesis.
Hypothesis 2. Firms whose founders had innovation output prior to start-up are more
likely than the others to cooperate on R&D with external organizations, regardless ofthe types of partners.
Furthermore, founders’ academic affiliation may be related to the probabilityof R&D cooperation. Participation in academic associations is expected to enlarge
founders’ networks with researchers in other organizations, especially academic organizations including universities. Therefore, whether or not founders are members ofacademic associations in natural sciences may be closely associated with R&D coop-

4

Narin et al. (1987) also point out that firms’ prior innovation output measured as patents signals
the competencies of the firms to the third party.

7

eration with external organizations, especially academic organizations. From theserespects, we formulate the following hypotheses.
Hypothesis 3a. Firms whose founders are affiliated with academic associations innatural sciences are more likely than the others to cooperate on R&D with externalorganizations, regardless of the types of partners.
Hypothesis 3b. Firms whose founders are affiliated with academic associations innatural sciences are more likely than the others to cooperate on R&D with academicorganizations, including universities and public research organizations.
Founders’ work experience may also be associated with R&D cooperation. If
founders possess prior work experience as an employee in the same industry, they cantake more advantage of their established networks in the industry at start-up than theothers that do not. In addition, if founders have experience of management in otherfirms prior to start-up, they are expected to have managerial skills as well as widebusiness networks with external organizations. In particular, these work experiences
may matter in R&D cooperation with business partners, such as customers and suppliers, rather than with academic organizations, such as universities or public researchorganizations. From these respects, we formulate the following hypotheses.
Hypothesis 4a. Firms whose founders have work experiences are more likely thanthe others to cooperate on R&D with external organizations, regardless of the types ofpartners.
Hypothesis 4b. Firms whose founders have work experiences are more likely thanthe others to cooperate on R&D with business partners.
In addition to founder-specific characteristics, we consider firm-specific characteristics affecting R&D cooperation. As already discussed, it is not easy for small start-ups

8

to be successful in innovation, and thus R&D cooperation with external organizations
may be an essential strategy for them to obtain complementary assets, share costs andrisks, and improve performance. As pointed out by some studies, however, the benefits
from R&D cooperation may depend on the absorptive capacity of firms, suggesting
the importance of sufficient size and R&D capacity in R&D cooperation (e.g., Cohenand Levinthal, 1989). In fact, this view has been supported by a number of empiricalstudies, including Fontana et al. (2006) and Lopez (2008).5 From these reasons, weformulate the following hypotheses.
Hypothesis 5. Larger firms are more likely to cooperate on R&D with externalorganizations, regardless of the types of partners.
Hypothesis 6. Firms investing more in R&D are more likely to cooperate on R&D
with external organizations, regardless of the types of partners.
Moreover, there may be some differences in the probability to cooperate on R&D
between independent firms and subsidiaries or affiliated firms. For example, Mohnen
and Hoareau (2003) find that members of a corporate group tend to cooperate in
R&D. As pointed out by Mohnen and Hoareau (2003, p.135), firms that belong to large
corporate groups might be able to tap information from universities/government labs or
establish contacts with them more easily through this network. In this respect, whether
the firm is independent or a subsidiary or affiliated firm may be a key determinant of
R&D cooperation, especially with universities or public research organizations. Fromthese respects, we formulate the following hypotheses.
Hypothesis 7a. Independent firms are less likely to cooperate on R&D with externalorganizations, than subsidiaries and affiliated firms.
Hypothesis 7b. Independent firms are less likely to cooperate on R&D with aca5

For evidence on the effects of firm size and R&D capacity, see Table 1.

9demic organizations, including universities and public research organizations, than subsidiaries and affiliated firms.
Based on these hypotheses, in the following sections, we investigate the determinants of R&D cooperation using data on Japanese high-tech start-ups.

4.

Data

4.1.

Data sources

The sample used in the analysis comes from an original survey conducted in 2008. To
the best of our knowledge, there exists no data source of R&D activities of Japanesestart-ups. To construct the sample of the start-ups for our research project, we collected data by postal questionnaire. We sent questionnaires to 13,852 firms in Japanesemanufacturing and software industries which were incorporated between January 2007
and August 2008. The list of firms for the survey was obtained from a database compiled by Tokyo Shoko Research (TSR), a major credit investigation company in Japan.
In the questionnaire, we asked the founders about firm-specific characteristics including
R&D activities as well as founders’ personal attributes.6
The number of effective responses was 1,514 (approximately 11%). Based on theiranswers, we regarded 1,060 firms among them as “real” start-ups that had started
their businesses during 2007 and 2008. Then, we identified 672 R&D-oriented firms
whose founders conducted R&D or that employed R&D personnel when starting theirbusinesses or afterwards.7 As a result, we obtain 495 firms in the final sample becauseof missing values for some variables.
While we compile data on founder- and firm-specific characteristics including R&D
6

Because the firms may have multiple founders, we asked about the number of co-founders. In fact,
our sample includes some firms with multiple founders. If a firm has multiple founders, the founder
who is the president was asked to answer about himself or herself.
7
The ratio of R&D-oriented firms to all the respondents appears to be fairly high. This may bepartly attributed to the fact that our survey was targeted to relatively R&D-intensive industries forthe purpose of our study.

10cooperation from the survey, we use another data source to collect data on industryspecific characteristics. Data on the appropriability of innovation output and technological opportunities are taken from the Report on the Japanese National Innovation
Survey 2003, compiled by the National Institute of Science and Technology Policy (NISTEP) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT).

4.2.

R&D cooperation

In the questionnaire survey, we asked the founders whether or not they engage in R&D
cooperation with universities, public research organizations, and business partners,
respectively.8 Table 2 provides the summary statistics for R&D cooperation by thetype of partners. As shown here, 60 of 495 firms (approximately 12%) engage in R&D
cooperation with universities or public research organizations. Table 2 also shows that140 firms (approximately 28%) cooperate on R&D with business partners.
With respect to the sub-samples by industries in Table 2, the propensity for R&D
cooperation is the highest in the chemical and precision machinery industries, regardlessof the types of partners. As a whole, start-ups in the manufacturing sector are more
likely to cooperate on R&D than those in the software sector. Further, the propensity
for R&D cooperation tends to vary according to the types of partners even in the same
industry. These statistics suggest that the propensity for R&D cooperation differs
among industries and according to the types of partners.

4.3.

Determinants of R&D cooperation

In this paper, we investigate the determinants of R&D cooperation using a regression
model, and test the hypotheses developed in Section 3. Based on the questionnaire
survey, the dependent variables for R&D cooperation are defined as two dummies,
8
Based on our original survey, we cannot clearly tell if R&D cooperation with academic organizations
or business partners is really new. The possibilities remain that some founders of the firms in our sample
had conducted R&D cooperation with external partners before start-up or started up new businesses
to conduct R&D cooperation.

11

taking the value of one if firms engage in R&D cooperation with universities or publicresearch organizations (C U N IV ), or with business partners (C F IRM ), and zerootherwise. The definitions of the variables are presented in Table 3. Table 3 also shows
the variables for the determinants of R&D cooperation, which will be discussed below
in details. In addition, Table 4 shows the descriptive statistics of the independent
variables, and provides their mean values for sub-samples, by the type of partners.
Founder-specific characteristics
With respect to founder-specific characteristics, the variables for education level, work
experience, and prior innovation output are included in the model. First, we use
dummy variables to examine the effects of founders’ education level; university education (U EDU ), graduate school education (GEDU ), or others (reference variable).
Table 4 indicates that 48% (11%) of the founders achieved bachelor degree (masteror doctor degrees). As shown in Table 4, the means of GEDU are clearly different
between the sub-samples of the firms that engaged in R&D cooperation and the othersfor C U N IV , suggesting that the firms whose founders had graduate school education
tend to conduct R&D cooperation with universities or public research organizations.
Second, the variables for founders’ prior innovation output are also included in themodel. In this paper, prior innovation output is defined as two dummies, taking thevalue of one for firms whose founders achieved product/process innovations (IN N OV )and patent applications (P AT ) before start-up, and zero otherwise, respectively. As
shown in Table 4, founders have prior experience of product/process innovations andpatent applications at start-up in 33% and 20% of firms, respectively. A clear findingin Table 4 is that there are considerable differences in the means of IN N OV and P AT
between the firms that engaged in R&D cooperation and the others, regardless of thetypes of partners. Table 4 indicates that 60% and 44% of the sample firms that takethe value of one for IN N OV and P AT engage in R&D cooperation with universities

12

or public research organizations, and that 50% and 30% of the firms that take the valueof one for these variables cooperate on R&D with business partners, respectively.
Third, the dummy variable for the firms whose founders are affiliated with academic associations in natural sciences (ACAD) is included in the model. As shown in
Table 4, 13% of the founders in our sample are affiliated with academic associationsin natural sciences. Table 4 indicates further that 42% of the firms whose founders
are affiliated with academic associations cooperate on R&D with universities or publicresearch organizations, while only 9% of the other firms cooperate on R&D with them.
However, there are no significant differences between the firms that cooperated withbusiness partners and the others.
Fourth, we include two dummy variables for founders’ work experiences in themodel. One is a dummy taking the value of one for firms whose founders possess priorexperience as an employee in the same industry at start-up (JEXP ), and the otheris a dummy taking the value of one for firms whose founders have prior experience
as a manager in other firms at start-up (M EXP ). Table 4 shows that 87% (37%) of
the founders in our sample used to be employees (managers) of other firms. Table 4
also suggests that there are no significant differences in the means of these variables
between the firms that engaged in R&D cooperation with universities or public researchorganizations and the others, while the founders who have prior work experiences tend
to engaged in R&D cooperation with business partners compared to the others.
Finally, we include the variable for founders’ age at start-up (AGE) as a control
variable in the model. In the sample, the minimum and maximum ages of founders are
20 and 80 at start-up, respectively. The natural logarithm of founders’ age at start-up
is used in the regressions. As shown in Table 4, there are no significant differences in themeans between the firms that engaged in R&D cooperation and the others, regardless13

of the types of partners.
Firm-specific characteristics
As firm-specific characteristics affecting R&D cooperation, the variable for firm size
(SIZE), measured as the natural logarithm of the number of employees at start-up,
is included in the model. The median value of the number of employees in the sample
is 2, indicating that the sample consists of small start-ups. As shown in Table 4, itappears that the propensity for R&D cooperation does not vary according to firm size.
The variable for R&D expenditures (RD) is also used as an independent variable.9 As
shown in Table 4, the means of this variable exhibit considerable differences between
the firms that engaged in R&D cooperation and the others, regardless of the types ofpartners.
Moreover, a dummy variable for independent firms (IN D), as compared to subsidiaries or affiliated firms, is used as an independent variable in the model. As shownin Table 4, 83% of the sample firms are independent firms. According to the meansof this variable in Table 4, independent firms are less likely to cooperate with universities or public research organizations, but there are no significant differences in thepropensity for R&D cooperation with business partners between independent firms andsubsidiaries or affiliated firms.
In addition, we include two control variables for the reasons of choosing its businessfield and location. In the questionnaire, we asked the founders about the most important reason to choose the current business and the location of start-up. In this paper,
we construct a dummy variable for business choice (DBU SI), taking the value of one
if the most important reason to choose the current business is to make the best useof unique capabilities and technologies, and zero otherwise. We use another dummy
9Instead of R&D expenditure, we also used R&D intensity, defined as the number of R&D personnel
divided by the total number of employers and employees. Because of missing values for R&D personnel,
the sample size was reduced considerably in the model with R&D intensity. In fact, the effect of R&D
intensity was not significant, and, hence, we do not use R&D intensity in the model.

14

variable for location choice (DLOC), taking the value of one if the most importantreason to choose the location is to obtain easy access to necessary information andtechnologies, and zero otherwise.
Industry-specific characteristics
Furthermore, the variables for industry-specific characteristics are included as control
variables in the model. Following Okamuro (2009), the variables for the appropriability(AP P ROP ) and technological opportunities (T ECHOP P ) are used to control fordifferences in the technological environments among industries. AP P ROP is definedas the extent to which the innovative outcomes can be appropriated by the innovatorsthemselves. T ECHOP P denotes the availability of useful information for innovation.10
Furthermore, interchangeably to these variables, industry dummies at the two-digit
level are included in the model to control for industry-specific characteristics.

5.

Estimation results

We examine the determinants of R&D cooperation in Japanese high-tech start-ups,
by estimating the regression model. Since, as already mentioned, R&D cooperation ismeasured by binary variables, we adopt probit models to estimate the determinants of
R&D cooperation. In Tables 5 and 6, we show the estimation results for the determinants of R&D cooperation with universities or public research organizations and withbusiness partners, respectively.
With respect to the founder-specific characteristics, Tables 5 and 6 demonstratethat the variable for graduate school education (GEDU ) has a positive and significant effect on R&D cooperation with universities or public research organizations(C U N IV ), while this variable has no significant effect on R&D cooperation withbusiness partners (C F IRM ). According to Tables 5 and 6, on the other hand, the10

For more details on the construction and measurement of these variables, see Okamuro (2009).

15

variable for university education (U EDU ) does not have any significant effect on R&D
cooperation, regardless of the types of partners. These results indicate that firms withhighly educated founders are more likely to cooperate on R&D with universities or
public research organizations, which supports Hypothesis 1b.
In Tables 5 and 6, the variables for founders’ prior innovation output, IN N OV and
P AT , have both positive and significant effects on the probability of R&D cooperation,
regardless of whether C U N IV or C F IRM are used as dependent variables. These
results suggest that start-ups whose founders possess sufficient research capabilities are
more likely to cooperate on R&D with external organizations, partly because researchcapabilities of founders act as a signal inducing those of firms to potential partners.
Moreover, as shown in Table 5, the dummy variable for founders’ academic affiliation (ACAD) has a significantly positive effect on C U N IV . In Table 6, however,
ACAD does not have any significant effect on C F IRM . The effects of founders’ work
experiences (JEXP and M EXP ) are not significant in any models of Table 5, andthe coefficients of JEXP on C F IRM are positive and significant in Table 6. Thesefindings suggest that founders’ own academic and business networks are important indetermining R&D cooperation with academic and business partners, respectively. As
for the variable for founders’ age (AGE), we do not find any significant association
with C U N IV and C F IRM in Tables 5 and 6.11
With respect to the variables for firm-specific characteristics, the coefficients offirm size (SIZE) are not significant in any models of Tables 5 and 6. As alreadydiscussed, some previous studies found positive and significant effects of firm size on
R&D cooperation. While most of the studies have used data on relatively large firms,
as repeatedly explained, we employ data on small start-ups. Therefore, our findings,
which are not consistent with those of previous studies, may imply that the size effect on
11
The presence of co-founders may also have influence on R&D cooperation. Therefore, we estimatedthe model using the variable for the number of founders. However, we do not report the estimation
results with this variable, since we did not find any significant results for this variable.

16

R&D cooperation is negligible within small firms, including start-ups, while it appears
significant among relatively large firms.
In contrast, the coefficients of R&D expenditures (RD) are positive and significant
after controlling for firm size in all models of Tables 5 and 6. These results suggestthat firms investing more in R&D tend to cooperate on R&D with external organizations, regardless of the types of partners. Our findings are consistent with Cohen andLevinthal (1989), which indicate that the firms investing more in R&D are more likely
to engage in R&D cooperation than the others because of their sufficient absorptive
capacity. The variable for the independent firms (IN D) has a negative and statisticallysignificant effect in Table 5, but no significant effect in Table 6. This suggests that independent firms are less likely to cooperate on R&D with universities or public researchorganizations, compared to subsidiaries or affiliated firms, which supports Hypothesis
7b. Our findings imply that subsidiaries and affiliated firms have wider networks thanindependent firms through parent or group companies, and therefore, they have more
opportunities to engage in R&D cooperation.
Moreover, the coefficients of the variables for business and location choices (DBU SI
and DLOC) are overall not significant in Tables 5 and 6, although these variables were
expected to have significant effects on R&D cooperation. As for industry-specific characteristics, Table 5 indicates that higher appropriability (AP P ROP ) leads to R&D
cooperation with universities or public research organizations, while this variable does
not have any significant effect regarding R&D cooperation with business partners asshown in Table 6. On the other hand, the variable for technological opportunities(T ECHOP P ) does not have any significant effect on R&D cooperation, regardless ofthe types of partners.
In order to check the robustness of these estimation results, we additionally estimate the determinants of R&D cooperation with bivariate probit model, assuming thatthe error terms of the probit models with C U N IV and C F IRM are significantly cor-17

related. The estimation results with the bivariate probit model are shown in Table 7,
which are overall consistent with those in Tables 5 and 6. The assumption that theerror terms of both probit models are correlated, ρ 6= 0, is not supported in Table 7.
In Table 8, we summarize the results on our hypotheses. As shown in Table 8,
our hypotheses are generally supported in the empirical analysis. With respect to thefounder-specific characteristics, founders’ education level, work experience, and priorinnovation output are found to have positive effects on R&D cooperation. In particular,
the estimation results show that the founders’ research capabilities and networks are
fairly important factors in determining R&D cooperation, regardless of the types ofpartners. Our findings suggest that the start-ups whose founders possess sufficientresearch capabilities and network are more likely to engage in R&D cooperation withexternal organizations, suggesting that founders’ personal attributes act as a signal of
the research capabilities of their firms to potential partners.

6.

Conclusions

This paper has explored the determinants of R&D cooperation in Japanese high-techstart-ups. Using a sample from an original survey conducted in 2008, we have examinedthe effects of founder-, firm-, and industry-specific characteristics on R&D cooperationby the type of partners. Our findings indicate that founder-specific characteristics, suchas educational background, academic affiliation and prior innovation output, are fairly
important in determining R&D cooperation with universities or public research organizations. We also found that the roles of founders’ work experience and prior innovation
output are significant in determining R&D cooperation with business partners. With
respect to firm-specific characteristics, it was found that the firms investing more in
R&D tend to conduct R&D cooperation, regardless of the type of partners. It was also
found that independent firms are less likely to cooperate on R&D with universities or18

public research organizations compared to subsidiaries or affiliated firms.
However, this paper may include some limitations, which should be addressed infuture research. While we found the significant roles of founder-specific characteristics
in determining R&D cooperation of start-ups, the mechanism through which thesecharacteristics affect R&D cooperation was not necessarily examined in this paper.
For example, we cannot clarify how founders’ educational background affects R&D
cooperation, although we regarded this variable as the measure of their capabilities andnetwork. Moreover, we did not address the dynamic aspect of R&D cooperation. We
use cross-section data from our recent survey and thus cannot observe the developmentof R&D cooperation. It may be necessary to construct a panel dataset with repeatedsurveys in order to understand the dynamic process of R&D cooperation.
Despite of these limitations, this paper provides new evidence and implications.
We addressed R&D cooperation of start-ups, while most of the previous studies haveexamined R&D cooperation of large established firms. Further, we paid special attention to the roles of founders in R&D cooperation, which has been largely ignored in
the prior literature. Our findings suggest that founders’ human capital play a criticalrole in determining R&D cooperation of high-tech start-ups. From the viewpoint ofpublic policy, this paper implies that policy makers should pay attention to founders’ attributes in supporting start-ups and providing them with the opportunities for businessand research matching among firms, universities, and public research organizations. In
these respects, we contribute to the literature on R&D cooperation and provide some
implications both for academics and practitioners.

19

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22

23

All types of firms

Vertical, comp, uni/ins
Uni/ins

Lopez, 2008

Miotti and Sachwald, 2003
Mohnen and Hoareau, 2003

Firm (size[+]), Industry

Firm (size[+], rd[+]), Industry

Firm (size[+], rdint[+], patent[+], subsidy[+]), Industry, Country
Firm (size[+], rdint[+], spill[+], risk[+]), Industry
Firm (size[+], rd[+], subsidy[+]), Industry
Firm (size[+], rd[+], patent[+], group[+],subsidy[+]), Industry, Country
Firm (size[+], rd[+], patent[+]), Industry

Firm (size[+], rdint[+], spill[+]), Industry
Firm (size[+], patent[+], vc[+]), Founder, Industry
Firm (size[+], rdint[+]), Industry, Region

Major significant determinants
Firm (size[+], rd[+]), Industry

325 Belgian firms∗

1275 UK firms∗

2378 French firms∗
9191 French, German,
Irish and Spanish firms
724 Japanese firms

6026 Spanish firms∗

558 EU firms (SMEs)

Sample1652
Spanishfirms(SMEs, large firms)
2194 Dutch firms∗
522 Italian firms (Startups)
1800 German firms

IV probit

Binary choice (notspecified)
Logit

Simultaneous equations
Logit
Trivariate probit

Multivariate probit
Hazard, panel probit
Count data hurdlemodel
Negative binomial

Econometric model
Logit

Note:
1. Whether the firm engaged in R&D cooperation with other organizations is (partly) used as dependent variables in the above papers.
2. Firm, Founder, Industry, Region and Country represent that the variables are at the firm, industry, region and country levels, respectively.
3. Partner (comp: competitor, cust: customer, sup: supplier, uni: university, ins: public research institute, consul: consultant).
4. Determinants (size: firm size, rd[int]: R&D [intensity], spill: incoming spillover, patent: patent holding or application, vc: venture capital financing,subsidy: public subsidy or support, risk: the importance of cost and risk, group: group affiliation).
5. ∗ indicates that the data set is from each country’s version of the European Community Innovation Survey.

Veugelers and Cassiman,
2005

Comp, cust, sup, uni,
consul
Uni

Uni/ins

Fontana et al., 2006

Tether, 2002

Comp, cust, sup, ins

Fritsch and Lukas, 2001

Uni

Comp, cust, sup, uni/ins
All types of firms

Belderbos et al., 2004
Colombo et al., 2006

Motohashi, 2005

Partner type
All types of firms

Paper
Bayona et al., 2001

Table 1: Review of the previous studies on the determinants of R&D cooperation.

Table 2: Summary statistics on R&D cooperation by industry.

Industry
Food and beverage
TextilesWood
Furniture
Paper and pulp
Publishing and printing
ChemicalsPetroleum and coal
Plastics
Rubber
Leather
Stone, clay and glass
Iron and steel
Fablicated metals
General machinery
Electrical machinery
Communications machineryTransportation machinery
Precision machinery
Miscellaneous manufacturing
SoftwareFull sample

N (A)
39
19
5
2
5
10
25
2
12
2
4
8
9
18
40
29
17
11
12
41
185
495

Universities or PROs
Yes (B)
B/A (%)
3
7.7
2
10.5
1
20.0
0
0.0
0
0.0
0
0.0
10
40.0
1
50.0
2
16.7
0
0.0
0
0.0
1
12.5
2
22.2
3
16.7
4
10.0
5
17.2
3
17.6
1
9.1
5
41.7
1
2.4
16
8.6
60
12.1

Note:
1. PROs means public research organizations.
2. N indicates the number of observations.
3. ‘Yes’ represents the number of firms engaged in R&D cooperation.

24

Business partners
Yes (C) C/A (%)
9
23.1
7
36.8
1
20.0
0
0.0
2
40.0
1
10.0
13
52.0
0
0.0
7
58.3
0
0.0
1
25.0
1
12.5
2
22.2
5
27.8
12
30.0
13
44.8
8
47.1
3
27.3
6
50.0
12
29.3
37
20.0
140
28.3

25

Variable
Definition(Dependent variable)C U N IV
Dummy variable: 1 if the firm engages in R&D cooperation with universities or public research organizations,0 otherwise.
Dummy variable: 1 if the firm engages in R&D cooperation with business partners, 0 otherwise.
C F IRM(Independent variable)
Founder-specific characteristics
U EDU
Dummy variable: 1 if the founder had university education, 0 otherwise.
GEDU
Dummy variable: 1 if the founder had graduate school education, 0 otherwise.
IN N OV
Dummy variable: 1 if the firm has prior experience of product/process innovations at start-up, 0 otherwise.
P AT
Dummy variable: 1 if the founder has prior experience of patent applications at start-up, 0 otherwise.
ACAD
Dummy variable: 1 if the founder is members of academic associations in natural science, 0 otherwise.
JEXP
Dummy variable: 1 if the founder had prior experience as an employee in the same industry at start-up, 0
otherwise.
M EXP
Dummy variable: 1 if the founder had prior experience as a manager in other firms at start-up, 0 otherwise.
AGE
Natural logarithm of the founder’s age at start-up.
Firm-specific characteristics
SIZE
Natural logarithm of the number of employees at start-up.
RD
Natural logarithm of R&D expenditures at start-up.
IN D
Dummy variable: 1 if the firm is founded as an independent firm, 0 if a subsidiary or an affiliated firm.
DBU SI
Dummy variable: 1 if the most important reason to choose the current business is to make the best use of
unique capabilities and technologies, 0 otherwise.
DLOC
Dummy variable: 1 if the most important reason to choose the location is to obtain easy access to necessary
information and technologies, 0 otherwise.
Industry-specific characteristics
AP P ROP
Degree of appropriability.
T ECHOP P Degree of technological opportunities.

Table 3: Definitions of variables.

26

0.500
0.283
0.600
0.441
0.417
0.817
0.383
3.899
1.171
4.586
0.717
0.283
0.217
1.287
0.920

1
1
1
1
1
1
1
4.382
5.557
10.463
1
1
1
1.834
1.120

Max.

1.202
0.906

1.004
2.347
0.844
0.195
0.306

0.476
0.080
0.294
0.163
0.094
0.871
0.363
3.799

1.238
0.89

1.175
3.719
0.807
0.271
0.286

0.514
0.093
0.500
0.298
0.150
0.900
0.407
3.846

1.202
0.914

0.965
2.184
0.837
0.180
0.299

0.465
0.110
0.265
0.155
0.127
0.851
0.349
3.797

Means for subsamples
C U N IVC F IRM
1 (N : 60) 0 (N : 435)
1 (N : 140) 0 (N : 355)

Note:
1. Number of observations for full sample is 495 (489 for P AT ).
2. N indicates the number of observations for subsamples.
3. S.D. means standard deviation.

Full sample
Mean S.D. Min.
Founder-specific characteristics
U EDU
0.479 0.500
0GEDU
0.106 0.307
0IN N OV
0.331 0.471
0P AT
0.196 0.398
0ACAD
0.134 0.340
0JEXP
0.865 0.342
0M EXP
0.366 0.482
0AGE
3.811 0.254 2.996
Firm-specific characteristics
SIZE
1.025 0.986
0RD
2.618 2.923
0IN D
0.828 0.378
0DBU SI
0.206 0.405
0DLOC
0.295 0.456
0Industry-specific characteristics
AP P ROP
1.212 0.211 0.869
T ECHOP P 0.907 0.150 0.559

Table 4: Summary statistics for independent variables.

Table 5: Estimation results using probit model: Universities or public research organizations.

Variable
Model 1
Founder-specific characteristics
U EDU
0.259
(0.190)GEDU
0.674∗∗(0.275)IN N OV
0.410∗∗(0.176)
P AT
0.668∗∗∗(0.210)JEXP−0.339(0.226)
M EXP−0.036(0.182)AGE
0.010(0.008)Firm-specific characteristics
SIZE−0.038(0.089)RD
0.081∗∗∗(0.029)IN D−0.464∗∗(0.222)
DBU SI
0.140(0.202)
DLOC−0.067(0.200)Industry-specific characteristics
AP P ROP
0.752∗∗(0.371)
T ECHOP P
0.702(0.616)
Constant term
−3.310∗∗∗(0.866)
Industry dummies
No
Number of obs.
495
Pseudo R2
0.219
Log likelihood−142.757ACAD

C U N IV
Model 2Model 3Model 40.183
(0.193)0.651∗∗(0.278)0.145(0.200)0.617∗∗(0.285)0.229(0.197)0.617∗∗(0.278)0.377∗∗(0.184)

0.462∗∗(0.193)0.626∗∗∗(0.214)−0.263(0.228)−0.060(0.181)
0.011(0.008)0.687∗∗∗(0.220)−0.318(0.242)−0.035(0.188)0.005(0.009)0.522∗∗(0.206)
0.632∗∗∗(0.225)−0.213(0.247)0.090(0.188)0.008(0.008)−0.059(0.090)0.084∗∗∗(0.028)−0.552∗∗(0.229)0.173(0.203)−0.088(0.199)−0.059(0.092)0.076∗∗(0.030)−0.564∗∗(0.2335)
0.150(0.210)−0.098(0.209)−0.087(0.094)0.084∗∗∗(0.029)−0.653∗∗∗(0.243)0.175(0.212)−0.113(0.210)0.623∗(0.374)0.690(0.616)−3.145∗∗∗(0.865)
No
489
0.217
−141.040−1.458∗∗∗(0.522)
Yes
495
0.243
−138.369−1.540∗∗∗(0.521)
Yes
489
0.245
−135.915

Note:
1. Standard errors are in parentheses.
2. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

27

Table 6: Estimation results using probit model: Business partners.

Variable
Model 1
Founder-specific characteristics
U EDU
0.040
(0.134)
GEDU−0.229(0.243)IN N OV
0.476∗∗∗(0.135)P AT
ACAD−0.126(0.203)
JEXP
0.408∗∗(0.195)M EXP
0.065(0.136)AGE
0.002(0.006)
Firm-specific characteristics
SIZE
0.065
(0.069)RD
0.095∗∗∗(0.023)IN D
0.044(0.179)DBU SI
0.176∗(0.157)
DLOC−0.017(0.145)
Industry-specific characteristics
AP P ROP
0.263(0.289)T ECHOP P−0.511(0.449)Constant term
−1.449∗∗(0.685)
Industry dummies
No
Number of obs.
495
Pseudo R2
0.093
Log likelihood−267.299

C F IRM
Model 2Model 3Model 40.002
(0.135)−0.330(0.247)0.012(0.138)−0.357(0.250)0.047(0.137)−0.272(0.246)0.452∗∗∗(0.139)0.469∗∗∗(0.164)0.095(0.206)0.438∗∗(0.197)0.084(0.137)−0.001(0.006)−0.191(0.210)0.412∗∗(0.203)0.068(0.140)−0.003(0.006)0.434∗∗(0.171)−0.179(0.213)0.442∗∗(0.206)0.088(0.140)−0.005(0.006)0.100(0.069)0.108∗∗∗(0.023)0.042(0.182)0.186(0.157)−0.025(0.144)0.067(0.071)0.086∗∗∗(0.023)0.023(0.188)0.222(0.160)−0.028(0.149)0.106(0.072)0.100∗∗∗(0.023)0.032(0.191)0.226(0.161)−0.025(0.149)0.243(0.290)−0.654(0.444)−1.179∗(0.678)
No
489
0.091
−266.909−1.560∗∗∗(0.409)
Yes
495
0.120
−259.322−1.498∗∗∗(0.410)
Yes
489
0.118
−259.050

Note:
1. Standard errors are in parentheses.
2. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

28

Table 7: Estimation results using bivariate probit model
Model 1(i)
(ii)
VariableC U N IVC F IRM
Founder-specific characteristics
U EDU
0.267
0.041
(0.191)(0.134)
GEDU
0.682∗∗−0.232(0.276)(0.243)IN N OV
0.417∗∗
0.476∗∗∗(0.176)(0.135)P AT
0.664∗∗∗−0.130(0.210)(0.203)
JEXP−0.3420.407∗∗(0.225)(0.195)
M EXP−0.0360.067(0.182)(0.136)
AGE
0.010
0.001
(0.008)(0.006)
Firm-specific characteristics
SIZE−0.0370.066(0.089)(0.069)RD
0.080∗∗∗
0.095∗∗∗(0.029)(0.023)IN D−0.461∗∗
0.043(0.222)(0.179)DBU SI
0.138
0.177(0.201)(0.157)
DLOC−0.067−0.017(0.200)(0.145)
Industry-specific characteristics
AP P ROP
0.746∗∗
0.263(0.371)(0.289)T ECHOP P
0.691−0.514(0.616)(0.449)Constant term
−3.301∗∗∗−1.442∗∗(0.866)(0.684)
Industry dummies
No
No
ρ
0.080
(0.110)
Number of obs.
495
Log likelihood−409.795
LR χ2 (ρ=0)
0.521
ACAD

Model 2(iii)
(iv)
C U N IVC F IRM

Model 3(v)
(vi)
C U N IVC F IRM

Model 4(vii)
(viii)C U N IVC F IRM

0.193(0.193)0.659∗∗(0.278)−0.001(0.135)−0.333(0.247)0.235(0.198)0.622∗∗(0.279)0.378∗∗(0.184)0.155(0.200)0.625∗∗(0.285)0.012(0.138)−0.360(0.250)0.464∗∗(0.193)0.621∗∗∗(0.215)−0.266(0.227)0.062(0.181)
0.012(0.008)0.470∗∗∗(0.164)0.099(0.206)0.437∗∗(0.197)0.086(0.137)−0.001(0.006)0.685∗∗∗(0.220)−0.318(0.242)−0.035(0.188)0.005(0.009)−0.192(0.210)0.412∗∗(0.203)0.069(0.140)−0.003(0.006)

0.522∗∗(0.206)0.627∗∗∗(0.225)−0.210(0.246)0.088(0.188)0.008(0.008)0.433∗∗(0.171)−0.182(0.213)0.442∗∗(0.206)0.088(0.140)−0.005(0.006)−0.058(0.090)0.084∗∗∗(0.028)−0.546∗∗(0.228)0.171(0.203)−0.090(0.200)0.101(0.069)0.108∗∗∗(0.023)0.042(0.182)0.187(0.157)−0.024(0.144)−0.058(0.092)0.076∗∗(0.030)−0.563∗∗(0.233)0.148(0.210)−0.098(0.209)0.067(0.071)0.086∗∗∗(0.023)0.023(0.186)0.223(0.160)−0.027(0.149)−0.087(0.094)0.084∗∗∗(0.029)−0.651∗∗∗(0.243)0.174(0.212)−0.116(0.210)0.106∗(0.072)0.100∗∗∗(0.023)0.031(0.191)0.226(0.161)−0.024(0.149)

0.611
0.243
(0.374)(0.290)0.666−0.658(0.616)(0.444)−3.121∗∗∗−1.169∗(0.864)(0.678)NoNo
0.105(0.112)489−407.516
0.866−1.463∗∗∗ −1.558∗∗∗(0.5022)(0.409)
Yes
Yes
0.040
(0.115)495−397.630
0.123

Note:
1. Standard errors are in parentheses.
2. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

29

0.048
(0.137)−0.274(0.247)0.452∗∗∗(0.139)−1.553∗∗∗ −1.497∗∗∗(0.521)(0.409)
Yes
Yes
0.076
(0.117)489−394.756
0.418

Table 8: Summary of the estimation results.
VariableC U N IV C F IRM
Founder-specific characteristics
U EDU
GEDU
+
IN N OV
+
+
P AT
+
+
ACAD
+
JEXP
+
M EXP
AGE
Firm-specific characteristics
SIZE
RD
+
+
IN D
−
DBU SI
DLOC
Industry-specific characteristics
AP P ROPT ECHOP P

30

Hypotheses supported

Hypothesis.1b
Hypothesis.2
Hypothesis.2
Hypothesis.3b
Hypothesis.4b
Control

Hypothesis.6
Hypothesis.7b
Control
Control
Control
Control