Hitotsubashi University
Institute of Innovation Research

Collaboration and Innovation Speed:
Evidence from a Prize Data-Set, 1955-2010

Hiroshi Shimizu
Yusuke Hoshino

IIR Working Paper WP#15-04

Jan. 2015

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Collaboration and Innovation Speed: Evidence from a Prize Data-Set, 19552010

Hiroshi Shimizu
Hitotsubashi University
Institute of Innovation Researchshimizu@iir.hit-u.ac.jp
Yusuke HoshinoMusashino University
Faculty of Political Science and Economics
yhoshino@musashino-u.ac.jp
Abstract
The anecdotal evidence has indicated that inter-organizational collaboration
increases R&D productivity by providing access to outside complimentary assets forfirms. Focusing on the length of time from launching R&D project to realizing its R&D
outcomes, we call it innovation speed, this paper examines a prize data-set on industrial
technology, including 434 award-winning R&D projects, and empirically examines therelationship between inter-organizational collaboration and innovation speed andexplores how the relationship varies across different types of collaborations. After
controlling time periods, technological areas, prize categories, and collaboration types,
the data reveal that inter-organizational collaboration among non-business group firms is
associated with shorter innovation speed. The curtailed time periods vary from 19.9% to32.2% according to the models. However, such accelerated time periods are not observedin other collaboration types such as inter-firm collaboration and firm-academic
collaboration.

1

1. Introduction
Traditionally, in-house R&D has been an important source of innovation andcompetitive advantage for firms (Chandler et al., 2001; Mowery, 1983). However, with
the increasing complexity of technology, it has become impossible for firms to internalize
all of the resources needed for in-house R&D (Powell et al., 1996). Thus, the previous
literature on business collaboration has indicated that utilizing external information andresources gives firms an important competitive advantage (Chesbrough, 2003;
Chesbrough et al., 2006; Freeman, 1991; Gulati, 1999). As the literature suggests, anincreasing trend of collaborative inventive activity has been clearly observed in the R&D
100 Awards (Fontana et al., 2009). This trend has been observed in scientificorganizations as well (Wagner and Leydesdorff, 2005).
While organizations collaborate with each other for various reasons and invarious ways, this paper focuses on exploring the effects of collaboration on innovation
speed. One of the reasons behind the collaboration lies in the expectation that a firm cansignificantly reduce time to market. The length of product life cycle has been decreasing(Qualls et al., 1981). Products are rapidly becoming obsolete in many industries.
Supplying new products and marketing them aggressively became important (Gomory,
1989; Rosenaut, 1988). Saving time lowers costs by reducing man-houses and overhead
as well.
While anecdotal accounts have indicated that collaboration actually accelerateinnovation speed, the relationship between collaboration and innovation speed has notbeen empirically examined with systematically collected data. Exploring 434 award
winning R&D projects from a prize data-set on industrial technology, the Okochi Prize,
this paper empirically examines the association between inter-organizational
collaboration and the length of time from launching R&D project to realizing its R&D
outcomes, we call it innovation speed. It explores how the relationship varies dependingof types of collaborations.
The empirical results reveal that inter-organizational collaboration among nonbusiness group firms is associated with shorter innovation speed after controlling time
periods, technological areas, prize categories, and collaboration types. The curtailed time
periods vary from 19.9% to 32.2% according to the models. However, such accelerated
time periods are not observed in other collaboration types such as inter-firm collaborationand firm-academic collaboration.

22. R&D Collaboration and Speed
The utilization of knowledge and resources outside the firm plays an importantrole in establishing competitive advantages (Chesbrough, 2003, 2006; Chesbrough et al.,2006). R&D collaboration can provide entry to a field in which the relevant knowledge
is widely distributed and not easily produced inside a firm or obtained through market
transactions (Chesbrough, 2003; Chesbrough et al., 2006; Powell et al., 1996). Interorganizational collaboration is critical to supplementing the internal resources of anorganization (Dodgson, 1993; Hagedoorn, 1993; Teece, 1986)and can provide access tocomplimentary assets for firms, building technological capability (Teece, 1986). Throughthe collaboration, an organization can share both knowledge and increasing R&D costswith its partners (Ahuja, 2000).
Inter-organizational collaboration in R&D has been discussed from various
angles. For example, one line of discussion in R&D collaboration was conducted within
the boundaries of the firm. Firms’ choices between in-house and external sources of R&D
are dependent on transaction costs, bargaining hazards, and appropriability concerns(Pisano, 1990). Another line of research explored how the increasing trend in interorganizational collaboration in R&D occurred. This latter line of research necessarily
examines the Bayh-Dole Act. The Bayh-Dole Act, which gave universities, small
businesses, and non-profit organizations intellectual property control of their inventions,
resulted from governmental funding; it is considered a primary stimulus of R&D
collaboration in the U.S.(Henderson et al., 1998). However, the effect of the Bayh-Dole
Act is debatable. The Bayh-Dole Act is only one of the factors stimulating university
patenting and licensing activity, which in turn is only one of the important factors behind
the increasing pattern of R&D collaboration; some universities were active in patenting
and licensing before passage of the act (Mowery et al., 2001; Mowery and Sampat, 2001).
Many detailed case studies have explored how collaboration is formed, managed,
and lead to high performance as well. For example, based on the case studies of the
National Cooperative Program for Infertility Research and the TexAQS collaboration, thedevelopment of epistemic and organizational domain creates common incentives tocollaborate and eliminates barriers via effective planning and management (Corley et al.,2006). The case of collaborations between private firms and Lawrence Livermore
National Laboratory revealed that careful selection of projects for collaboration and thedevelopment of a good fit between project characteristics and the specific vehicle
supporting the collaboration leads to the successful completion of the project objectives(Ham and Mowery, 1998). A longitudinal study of 65 biomedical scientists in a New
Zealand university revealed that within-university collaboration and international
3

collaboration are positively related to an article’s quality (He et al., 2009). Faems et al.
(2005) conducted an empirical study of Belgian manufacturing firms, which showed apositive relationship between inter-organizational collaboration and innovative
performance measured by the composition of turnover, which is attributed to both new
and improved products. This demonstrates that the impact of innovative performancediffers depending on the nature of the partner involved. For instance, collaborations withcustomers and suppliers are associated positively with higher levels of turnover stemmingfrom improved products, while collaborations with universities are associated positively
with turnover levels related to new products.
These studies clearly suggest that collaboration plays an important role inincreasing R&D productivity by utilizing external complimentary knowledge. However,
how the collaboration influences innovation speed has not been well, even though the
speed is one of the central motivations in forming inter-organizational collaboration.
Products are rapidly becoming obsolete in many industries. Supplying new
products and marketing them aggressively became important (Gomory, 1989; Rosenaut,1988). Therefore, Firms may license or buy technology from other organization. Firms
may contract R&D organizations to develop new products or form inter-organizational
collaboration. Previous literature suggests that time-saving can enhance product quality(Schmenner, 1988). The speed of innovation generally has been positively correlated witha products’ quality, or the degree to which it satisfies customers’ requirements (Clark andFujimoto, 1991). Referencing updated knowledge can increase quality of invention(Nagaoka, 2007). Saving time lowers costs by reducing man-houses and overhead. A firm
can reduce fixed costs and fully leverage R&D resources by accelerating the pace of R&D
and commercialization (McEvily et al., 2004; Page, 1993). Shortening development time
can allow a firm to spend extra resources to accelerate an innovation in the long run (Clarkand Fujimoto, 1991).
Previous literature has suggested that greater use of external resources is
associated with relatively faster product development (Chesbrough, 2003; Chesbrough etal., 2006; Kessler and Chakrabarti, 1996). It has indicated that relying on outside
knowledge and utilizing external sources can shorten the length of the new products
development process because a firm can focus more on its internal core capabilities (Gold,
1987; Venkatesan, 1992). Of course, it must be noted that external sourcing does notnecessarily reduce innovation speed and development costs since external knowledge istacit and not easily transferable (Kessler et al., 2000).
The extent to which inter-organizational collaboration can enhance R&D
productivity and innovation speed has not been explored well empirically (Faems et al.,4

2005). Despite several case studies discussing inter-organizational collaboration and its
effects, large-scale empirical evidence on the effect of inter-organizational collaboration
on R&D is still scarce.
The primal reason for this dearth of evidence lies in the fact that collaboration
can take various different forms ranging from one based on informal and personal ties toone based on formal organizational contracts (Katz and Martin, 1997). Patents allow
examination of inter-organizational collaboration by exploring co-patenting. Academic
papers published also allow investigation of inter-organizational collaboration by
examining co-authorship. However, co-patenting and co-authorship do not necessarily
cover inter-organizational collaboration because organizations do not always co-patent or
co-author, even though they may jointly carry out R&D. Since several problems
concerning intellectual property rights arise when a firm makes a cross-licensing contract,a firm tends to decide to patent developed technology in its own individual domain rather
than co-patent. Moreover, even if inter-organizational collaboration can be identified bypatenting and co-authoring, the time that organization spent for its R&D cannot.
European community innovation survey has changed its trajectory. Many studies
have begun to investigate external sourcing motivation and its effects based on theEuropean community innovation survey. For example, Investigating the response to the
UK’s version of the second European community innovation survey, Tether (2002) has
shown that co-operation does not necessarily bring innovation straightforwardly (Tether,
2002). It has indicated that firms attempting to introduce higher level innovations tend toengage in co-operative arrangements for innovation. Exploring the France version of thesecond European community innovation survey, Miotti and Sachwald (2003) has
examined co-operative R&D activities and determinants of the choice of partners (Miotti
and Sachwald, 2003). Based on systematically collected data, they examines why cooperate, who does and with whom questions. They have found that R&D co-operation
exhibits a significant positive impact on the propensity to innovate. They have indicatedthat the propensity to co-operate on R&D is higher for firm from sectors with relatively
high R&D intensity. Co-operation with public institutions increases the capability of firms
to conduct research at the technological frontier and to patent. Their analysis has shown
the strategic motivation behind R&D co-operation. And it has examines the effects of cooperative R&D on patenting and product innovation measured by shore of innovative
products in turnover. Using the Dutch firms’ responses to the Community Innovation
Survey, Belderbos, Carree and Lokshin (2004) has examined the impact of R&D cooperation on firm performance (Belderbos et al., 2004b). They have shown that the
impacts of co-operation on firm performance varied according to four types of R&D
5

partners (competitors, suppliers, customers, and universities and research institutes). For
example, while competitor and supplier cooperation improves productivity of firms,
university cooperation and competitor cooperation promotes the growth performance offirms. Customers and universities are important sources for radical innovations.
All of these studies have indicated that the motivation and impact of R&D
cooperation varies across firms, while they have found that the positive impact ofcooperation on firm performance overall. However, such positive impacts would be
realized in different flow of time. The impact of co-operation on the speed of innovation
has been untapped yet. One of the reasons lies in the fact that much of previous literatureon strategic practices related to speed has been based on anecdotal evidences not on
enough systematic hard data (Crawford, 1992; Kessler and Chakrabarti, 1996). It is noteasy to systematically collect data on R&D and commercialization speed.

3. Okochi PrizeAward, prize, and exhibition data are used in the analysis of technologicalinnovation. Patents formally protect intellectual property rights; prizes diffuse
technological knowledge. Because the primary aim of prizes is not to provide pecuniary
gain, applicants do not heavily consider the costs of research and development whenvying for prizes. The certification of new technologies by judges through a prize systemconfers indirect monetary benefits. The announcement of the prize promotes advertisingand generates potential user awareness. Usually firms begin to consider awards after
patenting an invention because the award does not provide any exclusive right oftechnology. Patent and prize are not interchangeable but complementary because
inventors can pursue patents and prizes concurrently (Nicholas, 2011).
Various institutions award a prize. The Nobel Foundation is perhaps the mostwell-known prize-awarding institution. Governmental institutions award citizens for their
excellent technological achievements. For instance, the U.S. Patent and Trademark Officeawards the National Medal of Technology and Innovation (formerly known as the
National Medal of Technology). Academic societies and professional associations also
provide awards in areas of expertise. For example, IEEE (the Institute of Electrical andElectronics Engineers) provides different types of award programs in the field of electricand electronics engineering.
In celebration of Masatoshi Okochi’s achievements, the Okochi Memorial
Foundation was established in 1954. Masatoshi Okochi was a Japanese physicist andentrepreneur. He graduated from Tokyo Imperial University in 1903 and began his career
as a scholar. He became a director of Rikagaku Kenkyūjo (currently, the Institute of6

Physical and Chemical Research, known as RIKEN), which is still one of the largest
natural sciences research institutes in Japan. Okochi promoted empowerment of researchgroup leaders and tried to commercialize their research outcomes. In 1927, he establishedspin-off companies that used the institute’s research outcomes for commercial
applications. The Okochi Memorial Foundation has awarded distinguished achievements
every year since 1954.
This paper investigates the awards given by the Okochi Memorial Foundation to
explore the relationship between inter-organizational collaboration and R&D time
reduction effects for two reasons. First, the Okochi Prize is well-known in manufacturingsectors in Japan. The Okochi Prize competition is regarded as a good opportunity forfirms, government laboratories, and academic institutions to showcase the outcomes of
their R&D. Nominated technology is judged by third party expertise. Therefore, awarded
R&D represents a technological breakthrough. Second, the Okochi Memorial Foundation
reports detailed descriptions of award-winning R&D projects. The description on awardwinning R&D projects reports (1) Background and Target of R&D, (2) R&D process, (3)Description of Technology and its Features (e.g., originality, academic quality, advantages
over competing technologies, economic performance, social contribution, and potentials),and (4) Achievements (e.g., production, sales, and market share). The detailed description
is provided by the award-winning organization and checked by the Okochi Memorial
Foundation. Based on the reports, we can explore the time that an organization spent for
the awarded R&D. The information on R&D duration is significantly important for
exploring the effect of collaboration on innovation speed, which has not been well
documented by other resources.
All of the awarded project is given a certificate and medal/ trophy. Prize money
is given only for the most distinguished R&D projects. However, the amount of monetarygift is one million yen for the Okochi Memorial Grand Technology Prize, which is given
for the best project. 30 thousand yen is awarded for the maximum of seven projects. Suchmonetary gifts is not given for the rest of awarded projects. Since the prize money does
not necessarily large, it does not serve as direct monetary benefits. This prize plays animportant role in providing honorable position and in showcasing the R&D outcomes.
Applicants may nominate themselves. The application and nomination are
judged by a committee composed of industry experts and university professors. The
committee asks for additional information, interviews candidates, and, in some instances,conducts on-site investigations. Even though the committee does not rule out nominationof foreign R&D projects, Japanese firms dominate the Okochi Prizes; nearly all of the
Okochi Memorial award winners are Japanese. The only award-winning foreign7

organization has been TWI (The Welding Institute of the United Kingdom), which was
jointly awarded the prize in 2000 with Hitachi for their Application of Friction Stir
Welding to Rolling Stock Body Shell. The Okochi Prizes emphasize production
technology. Of course, this does not necessarily mean that product innovation is excluded
from the Okochi Prizes; some product innovation certainly originates from process
innovation. Moreover, the award-winning process innovation exists in a marketable form
such as patent licensing, manufacturing facility, and material. The Okochi Memorial
Foundation’s report provides us information such as the name of the award-winning
organization, technological details of the award-winning R&D project, the number ofpatents, academic presentation, sales, and other economic and industrial contributions foreach individual project. Figure 1 shows the number of Okochi Prizes awarded from 1954
to 2010 and their awarded year.
Figure 1: Number of Awarded R&D Projects
20
18
16
14
12
10
8
6
4
2

1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010

0

Source:(Okouchi Memorial Foundation, 1974-2011; Okouchi Memorial Foundation Tomonokai, 19601974) The 1950s includes only from 1954-1959, because the Okochi Prize was established in 1954. The
2000s includes 2010.

Figure 2 displays the time series of award-winning organization categories.
Award-winning organization is specified in the report provided by Okochi Memorial
Foundation. If an award is given to multiple organizations, this figure gives a full countto the award-winning organization. The figure shows that private companies dominate
the awards. This reflects the fact that production or its manufacturing process should exist
8

in a marketable form at the moment of submission of the nomination.
Figure 2: Shares of Okochi Prizes Granted to Different Types of Organizations1
1.2

1

0.8

0.6

0.4

0.2

0

Academic Institution

Company

Governmental Organization

Research Foundation

Source: (Okouchi Memorial Foundation, 1974-2011; Okouchi Memorial Foundation Tomonokai, 19601974)

By classifying award-winning organizations based on Japan’s securities
identification code, Table 1 shows the decennial number of award-winning projects acrossindustrial classes and their total collaboration ratio. If an award is given to multiple
organizations, the figure takes the securities identification code of the principalorganization appearing as the first author in the award report. If an organization is notlisted and is not assigned a securities identification code, a code is assigned based upon
the firm’s business description and technological details as explained in the report. Since
the award winning projects are quite diverse across industries, it merges them into eightcategories. Food, agriculture, constructions, and are included in the Other category. The
top three award winning categories are Electronics/Telecommunication/Information, Iron
and Steel/Non-Ferrous Metal, and Petrochemical/Chemical/ Petroleum /Gas. The top
three industries comprise more than half of each decennial sample throughout all time
periods. It is interesting to note that despite Japan’s competitive advantage in the
Nippon Telegraph and Telephone (NTT) was privatized in 1985. Therefore,technically, it was a nationally owned public corporation before 1985. However, thefigure counts it as a company throughout the periods.
1

9automobile industry, the Okochi Prize has rarely been awarded in the category oftransportation equipment. While machinery was one of the dominant industrial areas in
the Okochi Prize in the 1950s, that industry has been steadily decreasing in its share.
An inter-organizational collaboration occurs when multiple organizations win
the prize as co-applicants and recipients. It is safe to hold this assumption because the
Okochi Prizes are given based on self-nomination. The collaboration ratios do not show
significant difference across industries. They range roughly from 11 to 36 percentages.
As previous literature has pointed out that collaboration has played an important role for
its R&D, the highest collaborating industrial category is Pharmaceuticals (Cockburn andHenderson, 1998, 2001; Zucker et al., 2002).
Table 1: Award-Winning Industries and Collaboration Ratio
Collaboration
1950s

1960s

1970s

1980s

1990s

2000s

Total
Ratio

Electronics/Telecommunication/
17

42

53

44

50

38

244

29.79

Iron and Steel/Non-Ferrous Metal

14

32

25

33

35

24

163

11.43

Machinery/Precision Machinery

27

18

13

9

6

4

77

24.14

7

34

34

19

15

9

118

21.19

Pharmaceuticals

2

2

8

6

6

5

29

36.36

Pulp/Fiber/Glass/Textile

5

10

6

6

4

4

35

17.18

Transportation

2

8

11

12

10

4

47

17.21

Others

2

5

1

2

4

1

15

20

Information

Petrochemical/Chemical/
Petroleum /Electricity/ Gas

Source: (Okouchi Memorial Foundation, 1974-2011; Okouchi Memorial Foundation Tomonokai, 19601974)

The following figure shows the shares of different types of inter-organizational
collaborations receiving an award from the Okochi Prize Committee. Since the numberof Okochi Prize winning R&D projects hovers around 10 every year, the figure shows afive-year moving average of percentage of collaborative R&D for the Okochi Prizes. A
reasonable assumption is that Japanese firms tend to collaborate with group firms such as“keiretsu” firms or “zaibatsu” firms. 2 Actually, it is not rare to observe interOn “keiretsu” and “zaibatsu,” see Miwa, Y., Ramseyer, J.M., 2006. The Fable of theKeiretsu: Urban Legends of the Japanese Economy. University of Chicago Press ;[Bristol : University Presses Marketing, distributor], Chicago, Ill, Morikawa, H., 1992.
2

10

organizational collaboration among group firms. For example, Fujitsu won the prizes in1981 and 1982 with Fujitsu Laboratories, which was separated from Fujitsu and became
independent in 1968. Matsushita Electric Industrial Co. won the prizes with Matsushita
Electronics in 1986. Hitachi won the prize with Hitachi High-Technologies and Hitachi
High-Tech Fielding in 2007. Denso and Toyota Boshoku, both of which are automobile
parts manufacturers and Toyota’s suppliers, won the prize together in 2001.
However, the number of award-winning group-firm collaborations is notnecessarily great compared to the number of inter-organizational collaborations among
non-group firms. From 1954 to 2010, a total of 728 R&D projects were awarded. The
number of award-winning inter-organizational collaborations was 151 during this period.
The inter-organizational collaboration among non-group organizations was 114 out of151. Thus, inter-organizational collaboration among group-firms is not a dominantpattern among Okochi Prize winners. This patter holds across award-winning industries.
Figure 3: Inter-Organizational Collaborative R&D (Award-Winning Year)
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

Collaboration

Non-Group Collaboration

Group Collaboration

Source: (Okouchi Memorial Foundation, 1974-2011; Okouchi Memorial Foundation Tomonokai, 19601974)

Zaibatsu: the Rise and Fall of Family Enterprise Groups in Japan. University of Tokyo
Press, Tokyo.
11

4. Estimation Strategy
This paper examines how different types of inter-organizational collaboration
lead to different speed of innovation. In the Okochi Memorial Foundation’s reports, theaward-winning organization reports when it began its R&D project. Because thisinformation is rarely obtained at the large scale, this is one of the strengths of the Okochi
Prize data when estimating the relationship between collaboration and innovation speed.
From 1954 to 2010, the Okochi Memorial Foundation awarded 728 R&D
projects. We identify the R&D start year for 434 award-winning R&D projects out of a
total 728 projects. The reports do not necessarily always indicate precise R&D start year
because the details are not firmly established. For many cases, the R&D start year isprecisely indicated in the report; for others, the date is merely given as a time span. The
R&D start year is not stated at all for some cases. Thus, this paper uses other source of
information in addition to that provided in the Okochi Memorial Foundation’s report. The
Institute of Innovation Research at Hitotsubashi University has conducted detailed casestudies on Okochi award-winning R&D projects by interviewing the concerned managers,
scientists and engineers. The Institute has amassed more than 50 case studies since itbegan the Okochi Prizes case study project in 2008. This paper employs the R&D startyear precisely articulated by the Okochi Memorial Foundation’s reports or by the casestudies. If the R&D start year was reported in an ambiguous way such as “the beginningof 1970s,” this paper does not include that data in the sample. The number of observations
for this dataset on the innovation speed is 434. The average innovation speed is 10.862
years.
This paper set the award-winning year to calculate innovation speed as the end
year for two reasons. The first is that many of the reports do not provide the specific year
in which the R&D project was completed. It is difficult to determine when the R&D
project was completely finished because R&D is a long consecutive process in which
technology is usually incrementally and sometimes radically being developed. The
second is the fact that the award is given to the project whose results exist in a marketableform such as patent licensing, manufacturing facility, and material. An organization
usually sends its application to the Okochi Memorial Foundation just after the industrial
and commercial impacts are released. This paper regards innovation not as justdevelopment of new technology but as new technological development bringing
significant industrial and economic impact. Therefore, this paper measures the period
between the beginning of the R&D project and the year of the award in order to investigate
the innovation speed from R&D to industrial and commercial success.
This paper estimates the correlation between collaboration and the log12

transformed Speed using the regression model and controlling award type, industrial area,
and time period. Collaboration variables are classified into six types. One isCollaboration that simply takes the value one if the award is given to multiple
organizations. This variable is set to examine how the general inter-organizational
collaboration is associated with the innovation speed. The second type is Inter-firm
collaboration, which takes on the value one if the all co-recipients of the prize are private
firm. Third, User-customer collaboration examines if the inter-firm collaboration isconducted in the relation between supplier and assembler for example. It explores
assumption that user-customer collaboration tends to narrowly focus on industrial
application and takes less time, compared to horizontal collaboration, which tends todevelop more basic technology, takes longer innovation speed. Fourth, if the collaboration
among firms and academic/governmental research institutions is awarded, the variable
Industry-academic collaboration takes the value one. It examines an assumption thatindustry-academic collaboration takes longer time compared to inter-firm collaboration
because a firm tends to collaboration with academic in the area where research is basic
and still not close to market.
The fifth type is Non-group firm collaboration, which takes on the value one if
the co-recipients of the prize are firms and not from the same business group organization.
Business group has been defined in a variety of ways (Colpan et al., 2010). Japanesefirms traditionally form a business group called keiretsu and zaibatsu in which firms
develop tight interlocking relationships and shareholdings (Gilson and Roe, 1993; Miwa
and Ramseyer, 2006; Morikawa, 1992). The nature of collaboration varied among prize
recipients; in some instances, collaboration occurred within the business group, but inother instances, it occurred outside of the business group. Therefore, following thedefinition of business group, which is a group of independently managed firms withintertwined activities reinforced by governance mechanisms such as presidents’ councils,
partial cross-ownership, and personnel exchanges (Lincoln and Shimotani, 2010), thispaper explores how collaboration within the same business group and the collaboration
among the non-group organizations are each associated with the innovation speed. The
last variable for collaboration is the Number of organizations involved in the awardwinning R&D projects.
Collaboration is not the single determinant of the innovation speed. Various
factors such as the amount of R&D investment, the nature of R&D, the level ofcompetition, technological areas, and the technological capabilities influence the speedof innovation. The amount of R&D investment is one of the critical factors determining
the innovation speed. However, it is certainly difficult to collect R&D investment for13

individual R&D projects for a large sample size. This paper includes four control
variables for the investigation of collaboration and innovation speed.
First, introducing a variable, Paper, it controls to what extent the R&D project
was basic rather than close to industrial application by exploring whether the project
published academic paper or not. If an academic paper was published from the R&D
project, we assume that the project had begun from basic research or the project had beenscience-based, both of which tend to take longer time period.
Second, it controls award classifications by introducing Okochi Memorial Grand
Technology Prize. The Okochi Memorial Grand Technology Prize is awarded for the best
R&D projects among the applicants each year. It is reasonable to assume that a project
bringing distinguished R&D outcome takes longer time period.
Third, it controls R&D start year in a decennial manner. It is reasonable toassume that the macroeconomic conditions a firm confronts have influenced its R&D
project setting. For instance, if a firm confronts favorable economic condition for longterm R&D investment, it may launch challenging R&D project. Furthermore, the productlife cycle of the industry may have influenced a firm’s R&D project choice.
Fourth, it controls industrial areas of the award-winning R&D project. As it isexplained above, this study identifies the award-winning R&D project’s industrial
category based on its securities code category. It uses the lead organization’s industrial
category when multiple organizations share the prize. If the lead organization is anacademic/governmental organization, this paper employs the second lead organization’s
securities code. If all of the co-recipients are academic/governmental institutions, it
examines the description about the award-winning technology. The categories are merged
into eight categories as Table 1 indicates. Table 2 and 3 provide descriptive statistics andcorrelations for all variables

14

Table 2 : Descriptive Statistics of Variables
Variable

Obs

Mean

Std. Dev.

Min

Max

lnSpeed

434

2.226

0.584

0

3.807

Collaboration

434

0.207

0.406

0

1

Inter-firm collaboration

434

0.141

0.348

0

1

User-customer collaboration

434

0.124

0.330

0

1

Non-group firm collaboration

434

0.071

0.258

0

1

Number of organizations

434

1.272

0.592

1

4

Industry-academic collaboration

434

0.069

0.254

0

1

Paper

434

0.578

0.494

0

1

Okochi Memorial Grand Technology Prize

434

0.101

0.302

0

1

Before 1960

434

0.242

0.423

0

1

1960s

434

0.224

0.417

0

1

1970s

434

0.253

0.435

0

1

1980s

434

0.194

0.396

0

1

1990s

434

0.078

0.269

0

1

2000s

434

0.009

0.096

0

1

Transportation

434

0.069

0.254

0

1

Pulp/Fiber/Glass/Textile

434

0.046

0.210

0

1

Pharmaceuticals

434

0.041

0.200

0

1

Petrochemical/Chemical/ Petroleum /Gas

434

0.177

0.382

0

1

Machinery/Precision Machinery

434

0.083

0.276

0

1

Iron and Steel/Non-Ferrous Metal

434

0.235

0.425

0

1

Electronics/Telecommunication/ Information

434

0.323

0.468

0

1

Others

434

0.083

0

1

0.007

15

Table 3 : Correlation Matrix of Variables
1

2

3

4

5

6

7

8

9

10

11

1

1

lnSpeed2

Collaboration-0.0402

1

3

Inter-firm collaboration-0.0434

0.7906

1

4

User-customer collaboration-0.06450.737

0.9121

1

5

Non-group firm collaboration-0.11410.54220.68580.73571

6Number of Organizations-0.05310.89840.6882

0.66450.4472

1

7

Industry-academic collaboration-0.0115

0.5328-0.0841-0.0477-0.0756

0.5196

1

8

Paper

0.26130.01090.0231-0.0033-0.0531-0.0414-0.0064

1

9

Okochi Memorial Grand Technology Prize

0.03770.05410.01790.03530.02540.03920.05890.08581

10

Before 1960

0.0046

0.0561-0.01170.01520.03130.086

0.1006-0.57450.0598

1

11

1960s

-0.08360.053

0.0536

0.06590.10890.05260.0064-0.2139-0.0703-0.3031

1

12

1970s

0.0375-0.0367-0.0527-0.0431-0.0999-0.04390.0292

0.34740.0149-0.3292-0.3126

13

1980s

0.0692-0.1067-0.0303-0.061-0.0679-0.097-0.1335

0.32380.0287-0.2768-0.262814

1990s-0.03290.02010.0301-0.006-0.0143-0.018-0.01180.1795-0.0695-0.1647-0.156415

2000s-0.0204

0.0696

0.09970.10970.16050.0372-0.02630.08240.0475-0.0545-0.0517

16

Electronics/Telecommunication/ Information-0.1228-0.0490.0046-0.0361-0.0766-0.0838-0.0909

0.15-0.0522-0.1827-0.0271

17

Iron and Steel/Non-Ferrous Metal

0.0205-0.0557

0.026

0.038

0.0784-0.0343-0.1082

0.07710.0839-0.0340.015718

Machinery/Precision Machinery-0.07480.1553-0.0495-0.0374-0.01850.17240.3133-0.1831-0.0180.2202-0.081119

Petrochemical/Chemical/ Petroleum /Electricity/ Gas

0.15230.04510.0551

0.06250.0117

0.0618-0.0077-0.11640.00390.0897

0.098320

Pharmaceuticals

0.0631-0.004-0.0256-0.01630.0244-0.0267

0.0268-0.0571

0.03540.05550.066721

Pulp/Fiber/Glass/Textile

0.1898-0.0209-0.0841-0.0784-0.0577-0.0175

0.08

0.084

0.0067-0.0096-0.0284

22

Transportation-0.1531-0.0274

0.02050.03490.0655-0.0331-0.0743-0.0248-0.0614-0.0691-0.0372

23

Others-0.0067-0.0102-0.023-0.0164-0.04470.00020.0138-0.0404-0.0056

16

Table 3 : Correlation Matrix of Variables (Continued)
12

13

14

15

16

17

18

19

12

1970s

1

13

1980s

-0.2854

1

14

1990s

-0.1699-0.1428

1

15

2000s

-0.0562-0.0472-0.02811

16Electronics/Telecommunication/ Information

0.02850.11110.129-0.0151

17

Iron and Steel/Non-Ferrous Metal

0.0268-0.03770.0002

0.1171-0.3825

1

18

Machinery/Precision Machinery-0.0216-0.0839-0.0566-0.029-0.2075-0.16671-0.021-0.0901-0.113-0.0448-0.3205-0.2574-0.13971

Petrochemical/Chemical/ Petroleum /Electricity/
19

20

21

22

23

Gas
20

Pharmaceuticals-0.0523-0.0242-0.0641-0.0212-0.1517-0.1218-0.0661-0.10211

21

Pulp/Fiber/Glass/Textile

0.0913-0.0434-0.0176-0.0201-0.1435-0.1153-0.0626-0.0966-0.0457

1

22

Transportation-0.0335

0.09640.0896-0.0263-0.188-0.151-0.082-0.1266-0.0599-0.0567

1

23

Others

-0.0603

0.0694-0.047-0.0156-0.1113-0.0894-0.0485-0.0749-0.0354-0.0335-0.0439171

5. Estimation Results
Table 4 shows the estimation results on the association between interorganizational collaboration and innovation speed. The coefficients of control variables
show consistent with the basic assumptions. First, the estimation results generally match
those described by qualitative case studies on R&D industrial specificity. As the estimatedresults show, the awarded R&D projects in the pharmaceutical industry take longer toachieve industrial and economic success than the projects in other industries. The study
by DiMasi et al. (2003) estimated that it takes 142 months (11.8 years) for new drug R&D
in the U.S. This figure is clearly close to the average period of our sample, which is 10.862
years. However, 11.8 years, estimated by Dimasi et al. (2003), includes only R&D period;
it is necessary to include the period of achieving industrial and economic success in the
Okochi Prize context. Furthermore, it takes longer to come through all of thegovernmental safety tests and permit approval processes in the pharmaceutical industryof Japan, compared to the other developed economies (Shiaw, 2010). Second, theestimation results show that it takes longer time period if the R&D project publishes anacademic paper. It suggests that science-based R&D project takes longer innovation speed.
The key point of this paper is to empirically examine the association between
collaboration and the innovation speed. First of all, Collaboration does not any significant
association with innovation speed at statistically significant level. Both Inter-firm
collaboration and User-customer collaboration do not show any association neither, eventhough their coefficient is negative as our assumptions expect. However, Non-group firm
collaboration shows negative association with innovation speed at statistically significant
level for all models. The coefficients of the variable Non-group inter-firm collaboration
are -0.225, -0.198, -0.322, -0.235, -0.253, -0.253 in each model. These estimations clearly
indicates that the collaboration among non-group firms are negatively and significantly
associated from 19.8% in model 6 up to 32.2% in model 7 with innovation speed
reduction, after controlling for other factors. Since the average innovation speed in thesample is 10.862 years, the actual time reduction is from 2.15 up to 3.5years.
While non-group inter-firm collaboration is negatively associated the innovation
speed, other collaboration types do not reveal such time reduction at a statisticallysignificant level. It suggests since utilization of outside resources and knowledge can takevarious forms, ranging from offering general advice and insights to active participation
in a specific piece of research or formal research consortium (Katz and Martin, 1997),
performance of collaboration varies a good deal according to nature of collaboration,
collaborators’ aim, and partners (competitors, suppliers, customers, and universities and18research institutes) (Belderbos et al., 2004a). The estimated results might reflect the factthat the nature of industry-academic collaboration is different from inter-firm
collaboration, as we expected. The primal aim of an academic institution does not lie inindustrializing findings. Moreover, industry-academic collaboration tends to target more
fundamental R&D and is less commercially oriented than firm-firm collaboration.
One of the key findings of this paper is the only collaboration type that is
associated with shorter innovation speed is non-group inter-firm collaboration. It implies
that inter-organizational collaborations among the same business group firms do not havea statistically significant association with the innovation speed.
The estimation raises the question of which form of non-group firm collaboration
reduces the time period from R&D to the award year. Since this paper examines the time
reduction effect of inter-organizational collaboration on the overall period from the R&D
start year to the awarded year, we cannot empirically discern in which process time
reduction plays a role. One might argue that the time reduction effect can arise in theprocess of marketing and distribution, as well as in the process of R&D. If amanufacturing firm collaborates with a retail industry, the time to reach the productmarket may lessen. However, there is no instance of inter-organizational collaboration
between a manufacturing firm and a marketing or retail firm in the sample. All of theaward-winning firms have been in the manufacturing sectors in the Okochi Prize.
Moreover, since the Okochi Prize aims to award the development of significant
manufacturing technology, it is quite reasonable to suppose that the time period reduction
effect takes place in the research and development of process innovation in particular.
Then, how is non-group inter-firm collaboration associated with high innovation
speed? There can be several interpretations on this finding. One is that because Japanesebusiness group firms are as well-coordinated and closely-tied as a single individual firm,
there is no significant difference between the innovation speeds. Another is thatcomplimentary knowledge between collaborating organizations, which is considered ameans of increasing R&D productivity, is not greater in the same business group firms ofJapan. One can suppose that if a firm requires innovating in the very short period of time,it needs to explore outside resources beyond its business group. These points require moredetailed study in the areas of business group studies. However, the estimations are quiteconsistent with the previous literature on collaboration indicating that collaborations
provide mutual access to complimentary resources, if we look the individual cases in the
Okochi Prize data-set carefully (Rothaermel and Deeds, 2004; Rothaermel and Hill,2005). Furthermore, the R&D tends to target to develop clearly well-defined industrial
application in non-group inter-firm collaboration, while group firm collaborations tend to19

target to develop basic technology.
For example, in 2003, Tokyo Electric Power Company and NGK Insulators
jointly won the Special Production Award for the development and commercial viabilityof the sodium-sulfur battery for electric energy storage. The award report indicates thatTokyo Electric Power Company and NGK Insulators jointly carried out R&D andindustrialized the technology based on NGK Insulators mass production technology,
which Tokyo Electric Power Company did not have. In 2000, Toyota Motor and KOGI
Corporation, a company operating in steel roll casting, machinery, and environmental
equipment businesses, won the Special Production Award for the development of hightoughness alloy cast iron shortening manufacturing time and reducing cost of stamping
die for automotive body. KOGI developed the high toughness alloy cast by jointly
collaborating with its potential user and borrowing knowledge of automobile
manufacturing.
These non-group inter-firm collaboration cases show that the industrial
applications and their mutual access to complimentary resources are well-defined, whilegroup-firm collaborations tends to be a collaboration between a parental firm and its
subsidiary firm, many of which used to be a part of the parental firm. The one good
example of the group-firm collaborations is collaboration between Fujitsu and Fujitsu
Laboratories Ltd. Fujitsu Laboratories Ltd was spun off from Fujitsu in 1968. Based on
the joint R&D, they won the prizes in 1981, 1982, 1990, 2005, and 2010. Even though
they exist as an independent firm, it is reasonable to assume that their decision making is
generally based on organizational hierarchy serving as a single firm. Toyota Motors andToyota Motor Kyushu is another good example of group-firm collaborations. Toyota
Motor jointly developed a new vehicle assembly system with Toyota Motor Kyushu,
which spun off from Toyota Motor in 1991 and manufactured high-priced product line,
and won the production award in 1993. Since they virtually functions as a single firm, it
is possible to assume that access to complimentary assets is virtually limited in theboundary of the single firm.
One might argue against the finding that the innovation speed reported by thejoint prize winners tends to be shorter than the actual R&D time period. There is no formal
definition of the R&D start in the Okochi Memorial Foundation reports; determination iscompletely subjective and left to the discretion of the applicants. It is not always easy for
an award-winning organization to identify when the R&D project was launched. This isparticularly true when the R&D is quite evolutionary and based on incremental
technological change. On the other hand, it is possible to suppose that the reported R&D
start year tends to be when organizations agreed to or began R&D collaboration if the20

award is shared by multiple organizations. However, when organizations agree to R&D
collaboration, it is rare for them to start R&D and develop technology from scratch. It
would appear that organizations have already begun R&D and developed technologicalknowledge before they agree to the collaboration. Thus, one might suppose that theinnovation speed reduction effect observed by the estimation results reflects not the actual
negative association between collaboration and innovation speed but the possibility thatthe inter-organizational R&D project tends to report its innovation speed as shorter.
However, such innovation speed reduction is observed only in the collaboration among
non-group firms. Thus, it can plausibly be argued that the inter-organizational
collaboration among non-group firms is negatively associated with the innovation speed.

21

Table 4: Estimation Results(1)
Collaboration

(2)

(3)

(4)

(5)

(6)

(7)

-0.0874

0.0928(0.0650)(0.104)

Inter-firm collaboration,-0.0709

0.0780(0.0747)(0.102)User-customer collaboration

(8)

(9)

-0.111(0.0787)

Group firm collaboration

Non-group firm collaboration-0.225**-0.299**-0.198*-0.322**-0.235**-0.253**(0.102)(0.140)(0.115)(0.141)(0.102)(0.124)

Number of organizations-0.02630.0161(0.0497)(0.0618)

Industry-academic collaboration

Paper

Okochi Memorial Grand Technology Prize

1960s

1970s

1980s

-0.221-0.135-0.156(0.145)(0.109)(0.135)0.502***

0.494***

0.496***

0.494***

0.491***

0.497***

0.501***

0.505***

0.505***(0.0753)(0.0750)(0.0749)(0.0746)(0.0747)(0.0748)(0.0752)(0.0751)(0.0751)-0.0453-0.0500-0.0476-0.0474-0.0474-0.0460-0.0417-0.0410-0.0408(0.0864)(0.0864)(0.0863)(0.0860)(0.0861)(0.0861)(0.0862)(0.0861)(0.0862)-0.244***-0.243***-0.243***-0.237***-0.236***-0.238***-0.238***-0.239***-0.239***(0.0801)(0.0802)(0.0801)(0.0799)(0.0800)(0.0800)(0.0799)(0.0799)(0.0800)-0.349***-0.342***-0.345***-0.353***-0.353***-0.356***-0.360***-0.361***-0.361***(0.0952)(0.0950)(0.0949)(0.0947)(0.0948)(0.0949)(0.0949)(0.0949)(0.0950)-0.264**-0.252**-0.258**-0.262**-0.262**-0.267***-0.279***-0.280***-0.280***(0.103)(0.102)(0.102)(0.102)(0.102)(0.102)(0.103)(0.103)(0.103)22

1990s-0.356***-0.351***-0.357***-0.361***-0.363***-0.363***-0.371***-0.368***-0.368***(0.126)(0.126)(0.126)(0.125)(0.125)(0.125)(0.125)(0.125)(0.125)-0.497*-0.493*-0.481*-0.429-0.425-0.437-0.435-0.440-0.438(0.282)(0.282)(0.282)(0.283)(0.283)(0.283)(0.283)(0.283)(0.283)-0.0840-0.0790-0.0806-0.0719-0.0719-0.0751-0.0802-0.0805-0.0798(0.168)(0.169)(0.168)(0.168)(0.168)(0.168)(0.168)(0.168)(0.168)0.0401

0.04840.04900.06600.06940.0616

0.05950.05410.0550(0.171)(0.171)(0.171)(0.170)(0.170)(0.171)(0.171)(0.170)(0.171)-0.0254-0.0455-0.0466-0.0337-0.0294-0.02660.00126-0.00213-0.00156(0.185)(0.184)(0.184)(0.184)(0.184)(0.184)(0.185)(0.185)(0.185)

0.265

0.268

0.269

0.277

0.275

0.276

0.270

0.271

0.271

(0.173)(0.173)(0.173)(0.172)(0.172)(0.172)(0.172)(0.172)(0.172)

0.226

0.227

0.226

0.246

-0.425

0.240

0.252

0.245

0.248

(0.201)(0.201)(0.201)(0.200)0.252(0.201)(0.200)(0.200)(0.201)

0.489**

0.487**

0.482**

0.495**

0.503**

0.492**

0.509**

0.500**

0.502**(0.206)(0.206)(0.206)(0.205)(0.205)(0.205)(0.205)(0.205)(0.205)-0.239-0.233-0.229-0.207-0.202-0.212-0.211-0.216-0.215(0.189)(0.190)(0.189)(0.189)(0.189)(0.190)(0.189)(0.189)(0.190)2.143***

2.132***

2.137***

2.130***

2.124***

2.164***

2.136***

2.144***

2.125***(0.165)(0.165)(0.165)(0.164)(0.165)(0.177)(0.165)(0.165)(0.180)Observations

434

434

434

434

434

434

434

434

434

R-squared

0.201

0.199

0.201

0.207

0.208

0.207

0.211

0.210

0.210

2000s

Electronics/Telecommunication/ Information

Iron and Steel/Non-Ferrous Metal

Machinery/Precision Machinery

Petrochemical/Chemical/ Petroleum /Electricity/ Gas

Pulp/Fiber/Glass/Textile

Pharmaceuticals

Transportation

Constant

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

23

6. Conclusions
This paper examines the impact of inter-organizational collaboration on theinnovation speed by exploring the prize data on significant manufacturing technology. By
investigating the Okochi Prizes data-set, it empirically examines the relationship between
inter-organizational collaboration and its innovation speed.
If we do not introduce the types of collaboration, the estimated results do notshow any significant relation between collaboration and innovation speed. However, after
controlling for technological areas, prize categories, and collaboration types, theestimated results show that non-group inter-firm collaboration have association with from
19.8% up to 32.2% faster innovation speed. However, such accelerated innovation speeds
are not observed in other collaboration types such as user-customer collaboration andfirm-academic collaborations. The collaboration can influence not only the innovation
speed but also other things such as costs, nature of technology, and economic andindustrial impact. However, since the timing of market entry plays an important role inestablishing competitive advantages, and the reduction of the innovation speed can cut
R&D costs, faster innovation can be of significance for a firm.
In conclusion, we mention some limitations of this paper that future researchshould address more explicitly. First, as the previous literature often points out, interorganizational collaboration would reduce R&D time by facilitating the use ofcomplimentary knowledge. However, it must be noted that the association between nongroup inter-firm collaboration and shorter innovation speed this paper has observed is notnecessarily causal relationship. It is highly possible to assume that a firm select R&D
projects whose target is well-defined and outcome can be realized in a relatively shorter
period for non-group inter-firm collaboration rather than such collaboration has
innovation speed reduction effects, even though the time reduction effects are often in the
anecdotal evidence. Further empirical research aimed at discerning the time reduction
effects from this endogeneity bias will surely advance our understanding of collaborationand innovation speed.
Second, we do not control for the amount of R&D investment. It is obviously
assumed that the increase in the R&D investment can reduce the innovation speed. One
might suppose that if multiple organizations worked together, the total investment in the
R&D project would be bigger. If so, one can argue that inter-organizational collaboration
can be regarded as the proxy of R&D investment. Other things being equal, bigger R&D
investment can bring faster innovation speed. However, if inter-organizational
collaboration is positively associated with the amount of R&D investment, and if theincrease in the amount of R&D investment results in shorter innovation speed, we should24

observe shorter innovation speed in all types of inter-organizational collaborations.
However, such association is seen only in non-group inter-firm collaboration. Of course,
since the joint effort between industry and academia tends to target fundamental and basic
research themes, it is reasonable to suppose that this type of collaboration tends to belengthier in nature. However, if the inter-organizational collaboration is positively
associated with the amount of R&D investment, we should observe the negative
coefficient not only in the collaboration among non-group firms, but also in collaboration
among the same business group firms. If we could, however, control the R&D investmentin the award-winning projects, we could control the effect of collaboration more precisely.
All of these limitations are immanent to the prize data that this paper has explored.
However while these limitations exist, the prize data has provide detailed information on
the R&D process that allows empirically investigating the relationship between interorganizational collaboration and innovation speed.
Acknowledgements
This research was supported by the Japan Society for the Promotion of Science, Scientific
Research (A), 23242054.
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