Hitotsubashi University
Institute of Innovation Research

The Spillover Effectsof Publicly Supported Private R&D:
Analysis of NEDO Follow-up Survey Data

Kazunari Matsushima
Yaichi Aoshima
IIR Working Paper WP#14-04

Jun. 2014

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

The Spillover Effects of Publicly Supported Private R&D: Analysis of NEDO
Follow-up Survey Data

Kazunari Matsushima
Institute of Socio-Arts and Sciences, The University of Tokushima

Yaichi Aoshima
Institute of Innovation Research, Hitotsubashi University

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AbstractInnovation creating economic values has become a vital issue due to severe global competition.
Given such a circumstance, government funding has flowed not only into pure research, but into
applied research and product development linked directly with commercialization as well. Such atendency has been accelerated since “Bayh-Dole Act” was enacted, which made it easier for firms
to appropriate R&D outcomes.
Increased appropriablity that promotes commercialization, however, may prevent technologicaloutcome produced by a government-funded R&D project from being widely utilized in a society.
The project aimed at immediate commercialization may tend to create context-specific knowledge
that can be applied only to the particular product category rather than generalized technologicalknowledge that can be widely available for other products or technological fields. Such a project
may also have strong incentives to keep such technologies in-house. Therefore, the policy sideconfronts dilemma that the more government attempts to encourage private R&D activity withpublic support that are linked directly with market competition, the more the indirect spillover
effects are sacrificed because of increased appropriability. To resolve this dilemma, we must
identify the factors that influence the spillover effects of private R&D projects receiving public
support.
In this paper, we first classified a spillover effect in accordance with three dimensions, spillover
contents, scope of the spillover, and spillover recipient field. Furthermore, spillover contents can bedivided into “technological spillover,” “cognitive spillover” and “social-relations spillover.” And
then, we empirically investigate the factors that influence spillover effects by analyzing dataobtained from 301 private R&D projects supported by NEDO (New Energy and Industrial
Technology Development Organization), Japan’s public management organization promoting
private R&D.
Our findings show that while the project starting at the exploratory phase had positive effects ontechnological spillover both within and outside the firm, and that spillover outside the firm isrestricted when the project is of great strategic importance for a firm. We also found that
information exchanges with other internal divisions had positive effects on not only technicalspillover but on cognitive spillover and social-relations spillover.
Results imply that it is necessary for supporting institutions to confirm that projects are notisolated internally and that there is a system in place to receive assistance and cooperation fromother divisions.

21. Introduction
For many countries, the creation of innovation has become a vital issue as industry maturationis accompanied by a leveling of technology and the global competition engulfing newly developing
countries intensifies. On the other hand, for private firms exposed to daily profit pressure from
capital markets, directing scarce management resources into highly uncertain innovation activities
is no simple task. Consequently the voices at private firms anticipating public funds as a source of
R&D funding are growing. For example, public support for R&D in the private sector in Japan has
risen substantially over the past 20 years, climbing from roughly 2.3 trillion yen in 1991 to about
3.2 trillion yen in 2001 and approximately 3.5 trillion yen in 2011. In recent years in particular,
there has been a tendency to invest public funds in not only pure research but in applied researchand product development linked directly with businesses as well.
Based on the Bayh-Dole Act (Patent and Trademark Law Amendments Act) in the United States,or on similar legislation in other countries, private sector firms are able to retain ownership of therights to technologies that have been created by projects with public supports. Ergo it has become
easier to obtain public support for the development of differentiation technologies that are so
critical for market competition. Furthermore, as governments’ financial positions have become
more constrained with each passing year, the demand that technology development based on publicfunds is not merely the simple promotion of science and technology and that it produce visible
economic outcome also lies behind this tendency.
On the other hand, however, some express doubts that public funds should be spent to assist
commercialization at specific companies. The reason is the danger that the appropriability
enhanced by enforcement of laws such as the Bayh-Dole Act – i.e., the capacity of a firm to retain
the added value it creates for its own benefit – will hinder the spillover of any technologicaloutcomes achieved through the use of public funds. The more public support is directed toward thedevelopment of differentiation technologies linked directly with market competition, the greater the
incentive as well of private firms to keep the outcome in-house. Furthermore, in the case ofcommercialization research aimed at specific application, the outcome can be sufficiently utilized
only by a firm with the accumulated know-how or customer base related to the particular product.
Public support does not accomplish its original role merely by shouldering the costs of private
R&D investment. Public support is justified when it is turned to technology development which,when left to the market, suffers from underinvestment, even though its social importance is high(Nelson, 1959; Arrow, 1962). Furthermore, the significance of public support for private R&D isrecognized when public funds prime the pump and induce additional private investment (Leyden
and Link, 1991; Busom, 2000; Guellec and Van Pottelsberghe, 2003; Almus and Czarnitzki, 2003;

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Duguet, 2004; Gonzalez and Pazo, 2008; Ito and Nakano, 2009) or create demand by boostingsocial recognition of technological fields and industrial areas (David and Hall, 2000; David, Halland Toole, 2000), and when the developed technologies are used broadly in society beyond thescope of the project– in other words, when these spillover effects can be anticipated.
There is a dilemma here that confronts the policy side, however. The dilemma is that the moregovernment attempts to encourage private R&D activities with public support, the more theindirect spillover effects may be sacrificed. In order for public support to encourage technologicaldevelopment, there must be sufficient incentives for the private firms to accept the public support.
And for that, appropriation of the outcome must to some extent be guaranteed. Here also lies thereason why laws such as the Bayh-Dole Act are enacted. Enhanced appropriability, however, may
lead to restraining utilization of the outcome in the society. In particular, as the extent of publicfunds channeled to technological development directly linked to market increases, the tendency for
diffusion of the outcome to be restrained may become stronger. And if that is the case, won’t the
spending of public funds for commercialization research that is likely to produce an economic
outcome at an early stage tarnish the very justification for public support itself? What should bedone to prevent such an outcome? How can encouragement of private R&D be achieved withpublic support without sacrificing social utilization of the outcome? To answer this question, wemust identify the factors that influence the spillover effects derived from private R&D projects
receiving public support. That is the objective of the present study. Specifically, it is to empiricallyclarify the factors that influence project spillover effects by analyzing data obtained from follow-up
surveys for private R&D projects that NEDO (New Energy and Industrial Technology
Development Organization), Japan’s public management organization promoting private R&D,
has supported (referred to below as “NEDO projects”).

2. Classification of Spillover Effects
The spillover effects of public support for private sector R&D can have various meanings.
Therefore we begin with a definition and classification of spillover effects.
In this paper, we define the spillover effects of public support as “the effects produced on ascope that goes beyond the purpose set by the project.” This is a rather broad definition, which
includes everything other than the outcome and effects aligned with the goals the project had set.
Therefore we must further classify spillover effects in accordance with various dimensions.
The first dimension is the “spillover contents.” These can be divided roughly into
“technological spillover,” “cognitive spillover” and “social-relation spillover.” Technological4

spillover means use of the technological outcome outside the project. Included is direct exploitationof the technological outcome outside the project, such as technology licenses to other companies,
and indirect uses as well, such as other companies developing successor technologies by referringto the technological outcome produced by a project.
“Cognitive spillover” means the heightening of recognition inside and outside companies oftechnologies or products that are the targets for development, or of project activities themselves, as
an outcome of public support being pumped into the projects. The visible outcome of developmentthat are produced with public support, as well as the kind of “endorsement or official go-ahead”
that projects that receive public support represent, are believed to heighten the level of attention
given to the developed technologies or project activities. This heightened recognition not only
stimulates development investment by other firms, it is thought to also produce the effects ofincreasing the potential for commercializing project outcome in-house, and of elevating demand
expectations in the market.
The third classification, “social-relation spillover,” denotes the indirect effects, including thecreation of relationships between firms and personnel training achieved through project activities.
Publicly supported projects include many instances of joint R&D encompassing multiple firms orentities. Such joint research not only raises the efficiency of technology development through theexchange of technical knowledge, but may lead to the construction of long-term relationships
between firms, and to personal relationships between individuals as well. While these are notnecessarily effects that are evident in the short term, if contemplating public investment from a
policy standpoint, they are outcomes that should be taken into consideration as effects that help
reinforce a country’s R&D capabilities over the long term. Public support may also possess apersonnel training aspect in the long-term sense, such as the young researchers who are cultivatedthrough a project’s activities, for example.
The second dimension for spillover effects is the “scope of the spillover.” Various scopes can beconsidered, such as geographical scope, legal scope, and political jurisdiction scope. Here, however,
we will identify scope based on organizational boundaries (the internal and external classificationsof a firm’s organization). The reason is there may be significant differences in the intentions,processes and social effects pertaining to spillover within the entity that holds ownership to theoutcome, and spillover outside that entity.
Much of the existing research refers to the influences beyond firms’ boundaries. If we define
spillover effects as “the effects produced on a scope that goes beyond the purpose set by the
project,” however, the effects need not always be limited to the influences outside the firm. In fact,
the outcomes of projects sometimes are applied internally in forms different from the initial
purpose. In addition, follow-up projects sometimes are established because unexpectedly strong5

outcomes were achieved. Such influences should be also understood separately as spillover effects.
The third dimension for classifying spillover effects (primarily those related to “technologicalspillover”) is the “spillover recipient field.” That refers to whether the project outcome influences
other R&D activities in the same technological field, or in the different technological field. For
example, when a project’s outcomes elicit or are utilized for development in the same technologicalfield at other companies, such outcomes can be classified as external spillover in the same field. In
addition, when additional R&D investment is elicited within a company in the business field whereuse of the technology was envisaged, we can classify this as internal spillover in the same field.
On the other hand, the technology a project has developed sometimes might impact
development in another technological field and different application areas. Normally there is fixedagreement between a project that receives supports and the public institution concerning details of
the technology developed and the areas where that technology can be applied. For example, the“Expected outcomes” are described in the Research Plan, and the envisaged application field(s)identified there. Developed technologies, however, do not always flower and flourish in the field(s)initial considered. For example, the energy-absorbing nylon developed by T Industries, Inc.
through a NEDO project initially was envisaged and developed for application in automotive parts,
but ultimately found its market in sporting goods applications such as badminton rackets. There arecases of so-called “technology diversion” as well, where a developed technology blossoms inside
the firm but in a different field, not in the business field assumed at the outset.
Figure 1 below positions the spillover effects of publicly supported private projects along thethree dimensions described above.

- Figure 1: Classification of spillover effects -3. Existing Research
Existing research dealing with R&D spillover effects has focused primarily on technologicalspillover to analyze the influence produced by spillover on productivity at the inter-firm level orinter-industry level (Griliches, 1992; Nadiri, 1993; van Pottelsberghe, 1997). Research at theinter-industry level identifies the impact produced by spillover on industry productivity by
estimating the size of the spillover effect from the input-output relationship (IO flow) or investment
relationship (investment flow) between industries (Terleckyj, 1974; 1980). This stream of research
had generally found a positive influence of the spillover on productivity.
Research focusing on the spillover effect at the inter-firm level, on the other hand, identifies the6size of the spillover effect by estimating the influence that an increase in R&D activity at othercompanies has on a firm’s own productivity.
In pioneering research, Jaffe (1986) used panel data for U.S. firms to demonstrate that othercompanies’ R&D investments have a positive influence on a firm’s own patent productivity. Jaffe(1988), by analyzing difference panel data, also showed that other companies’ R&D investments
have a positive influence on a company’s R&D intensity and sales growth. To estimate the spillover
effects, he calculated the total amount of R&D investment by other companies that possibly
influence firm’s own R&D, by capturing the similarity of patent portfolios among firms as anindicator, weighting the amount of each other company’s R&D investment by this indicator. Thus
these outcomes suggest that similarity of technological field between firms may facilitate thespillover.
On the other hand, Adams (1990) measured the stock of industry technical knowledge by using
the cumulative number of papers published in scientific periodicals, and showed this has a positive
impact on the productivity growth of each firm. He took the correlation between the distribution offields that the company’s scientists belong to and the distribution of fields of scientific periodicals
in the entire industry, and used this to weight the influence of the stock of industry technologicalknowledge. This suggests that a positive spillover effect is received to the extent the portfolio by
field of the stock of industry technical knowledge is similar to the portfolio by field of scientists
within a company.
Likewise, Anseline et al. (1997) measured the technical knowledge stock based on the
cumulative value of the amount of university research expenditures, and weighted this value by thephysical distance from the university to each firm, and estimated its influence on the number ofcommercialization of high-tech innovation outcome. As a result, the stock of technical knowledge atuniversities exerted a positive and significant influence on the innovation. This suggests that close
physical distance promotes spillover.
There also is research verifying spillover effects that included investigations not at aggregatedlevels such as industries or firms but at the micro-level of research programs as well. For example,
Henderson and Cockburn (1996) used patent data from leading pharmaceuticals manufacturers in
Europe and the United States to demonstrate that patent production within a company in related
research programs, patent production by other companies in the same research program and patentproduction by other companies in related research programs all have a positive and significant
influence on a company’s own patent production. In this regard, for patent production by thespillover source, the indicator used was not the simple number of patents but the number after
subtracting, from the number of newly produced patents, the number of past patents that wereeroded. Furthermore, Okada and Kawara (2004) conducted the research based on Henderson et al.

7using data for Japanese firms, and similarly demonstrated that spillover effects from inside andoutside the firm has a strong influence on productivity.
The principal objective of these researches was to identify the influence of spillovers on
outcome such as productivity while having less intention to clarify the factors and mechanisms
encouraging spillovers. Several factors that promote spillovers are, however, suggested in eachresearch above. The research by Jaffe (1986, 1988), Adams (1990), and Henderson and Cockburn(1996) postulate the influence of technological field similarity and close proximity, while Anseline
et.al. (1997) suggests that geographic proximity promotes spillover.
The influence of geographic proximity has often been identified in research concerningindustrial cluster. There is ample research pointing out that an industrial cluster can lower the costof technical knowledge transfer due to close geographic proximity and the social relationships, andthat this leads to competitiveness (Marshall, 1890; Krugman, 1991; Saxenian, 1994). Jaffe et al.
(1993) is an example of research that demonstrates the influence of such geographic proximity on
spillover using the correlation to patent backward citations and forward citations. Jaffe et al.
clarified that citations by universities and firms within the same state and especially within thesame region accounted for a high proportion of all citations.
The complementary assets and absorptive capacity of firms have been pointed as another factorsthat promote spillover. Complementary assets are supplementary technologies or assets, such as
production facilities and distribution chains needed to put a technology to practical use. The
presence or absence of complementary assets may affect the size of technology spillovers since it
affects the economic value of obtained technical knowledge. In general, the value of technology
realized from other companies depends on absorptive capacity, the capacity that enables a firm tounderstand and use the technology. For example, Cohen and Levinthal (1990) have explained thatfirms make R&D investments even under low appropriability conditions because they expect it toimprove absorptive capacity and facilitate the learning of external technical knowledge. Moreover,
Cockburn and Henderson (1998) show that engaging in exchanges with outside groups, including
collaborative or joint research with public institutions, is critical for increasing absorptive capacity.
Both complementary assets and absorptive capacity are factors residing in recipients of thespillover. On the other hand, there is almost no research that has focused on projects that become
the source of spillovers and clarified the factors stimulating spillovers. The public institutions
providing supports for private R&D, however, certainly want to understand, as much as possible
beforehand, the size of the spillover effect a project will produce or the spillover capacity a project
possesses. The reason is the value of investing public funds not only is created from the realizationof the objectives the project has set, but is affected by the outcome being used broadly beyond theboundary of the project.

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There is a possibility the extent of spillover will be influenced by factors such as the nature of
the technical knowledge that a project generates, the project size, and the attributes of the firm
administering the project. It may also be influenced by the project’s activities, processes, andmanagement. For example, the contents and values of developed technologies might become
widely known and stimulate spillover if the project members engage in active communications
with outside parties, in order to extensively utilize outside knowledge in the solution of technicalproblems. Identifying the factors that impact a project’s spillover effects, including those related tothe project management, becomes critical for achieving effective public support for private R&D.
One existing research that analyzed the factors affecting spillover effects of publicly supported
project is Nagaoka and Tsukada (2011). They have found that the degree of R&D intensity,
inclusion of both pure research and applied research, industry-academic cooperation, the intention
to create technology seeds, and the presence of a PhD, are factors that tend to have a positive effecton spillovers. Because their concern was to comprehend the divergence of the deciding factors for
public support and the deciding factors for spillover effects, however, they did not consider either
the activities or processes of the project as factors stimulating spillover. One distinctiveness of thispaper lies in the attention given to such activities and processes.

4. Factors and Mechanisms Promoting Spillover: Deriving Hypotheses
In Section 2, we classified spillover effects into three categories of “technological spillover,”“cognitive spillover” and “social-relations spillover,” and further positioned spillover effects as
spillovers within a company and spillovers outside a company, and as spillovers in the same fieldand spillovers in different fields. In the following sections, we derive hypotheses concerning thefactors and mechanisms that affect spillover in a form that is aligned with this classification.

4.1 Factors influencing the technological spillover
4.1.1 Nature of knowledge
The first factor thought to affect the technological spillover is the nature of the technology or
knowledge developed by the project.
Much of the existing research has agreed that scientific knowledge tends to spill over (Cohenand Levinthal, 1990; Cockburn and Henderson, 1998; others as well). Scientific knowledge isknowledge related to principles and is not dependent on a specific application environment; it ishighly generalized knowledge that potentially can be expected to be applied across broad fields.
Relatively easily expressed by explicit means such as documents, symbols or others, it tends to9spread widely in the form of scientific articles or patents. The reason much of public support for theprivate R&D is focused on basic research is that the externalities of basic research are substantial,
and when left to the market will not elicit sufficient investment.
On the other hand, unique tacit know-how that is not easily expressed as explicit knowledge
such as written documents, formulas or symbols is contained within the application specific orcontext-specific knowledge pertaining to practical application of products or other goods. While
such tacit knowledge must be obtained in order to realize sufficient benefits from technology, it is
not easily transmitted externally. Furthermore, commercializing technologies and introducing themto the market requires various complementary assets such as other related technology, production
facilities and a sales network. The ability to enjoy the benefits of technological spillovers might belimited to firms possessing such complementary assets. The reason is that context-specificknowledge included in practical and applied technologies is something that is not easily used byoutsiders. We can summarize the above discussion as the following hypothesis.

Hypothesis 1-1:

Spillover beyond the project boundary increases as the extent to which
project’s outcome includes generalized and scientific knowledge increases.

However the above hypothesis might not hold in the case of spillover within the same firm.
When the spillover occurs within the same firm, unique know-how required to take advantage ofpractical and applied research outcome can be shared, and any complementary assets necessary forpractical use also can be used. In the case of internal spillover, there probably is little difference
regardless of whether it is generalized knowledge or context-specific knowledge. Therefore
Hypothesis 1 may hold notably for spillover outside a firm.

Hypothesis 1-2:

Hypothesis 1 holds more notably for spillover outside a firm.

Furthermore, the greater the amount of highly generalized knowledge, the greater the influenceexerted on various technological fields will be. This is because trial and error will be tested invarious fields when future applications are uncertain. In the case of technologies related to practicalapplication, on the other hand, the fields where a technology will be applied are often clearlyrecognized. Therefore the greater the extent to which knowledge is related to practical applications,the greater the tendency is that knowledge is utilized in the same technological or application field,
and vice versa. We can show this argument as Hypothesis 1-3 below.

Hypothesis 1-3:

Spillover to different technological fields increases as the extent to which

10project outcome includes generalized knowledge produced from basic researchincreases.

4.1.2 Information exchange
The second factor that, we conjecture, influences technological spillover is the level of
information exchange with parties outside the project. For technology to spread out, information
for its contents, details, and usefulness must be disseminated and understood by potential
recipients.
As discussed in the previous section, existing research has clarified that geographic proximity
exerts a positive influence on the spillover effect (Jaffe et al., 1993; Anseline et al., 1997). One
thing geographic proximity implies is efficient information exchange that extends beyond theorganization. Being geographically close not only facilitates frequent face-to-face communications,but also promote exchanges of tacit or implicit knowledge through the personal networks built incommunity. That existence of development outcomes are recognized through such informationexchange is of primary importance for promoting spillover. Furthermore, as deep backgroundinformation is exchanged, the expected benefits from a technology may expand, which is also afactor giving impetus to the spillover outside the firm.
Given this discussion, we conjecture that the technological spillover from the projects may
depend on the diversity of the information channels to the outside, maintained by project members,
and the volume of information exchanged through these information channels. We can summarize
this argument as the following hypothesis.

Hypothesis 2-1:

Technological spillover outside the project increases to the extent informationexchange with outside parties by project members increases.

The exchange of information outside a project includes not only information exchange outside of
the firm, but information exchange with other departments within the firm and with members of otherprojects.

Both are likely to function as information channels broadening the points of contact forreceiving spillover. It would be natural, however, to think the extent of their effects will differ.
Spillover to other companies outside the firm will be promoted by information exchanges
outside the firm by project members. Nevertheless, given maintenance of confidentiality, it’sdifficult to imagine that information pertaining to critical know-how is included in informal
communications with others outside the project. Although the existence of the technologicaldevelopment will become public as the result of such communications outside a project, theoutlines of the developed technology tend to be well-known to begin with in the case of publicly

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supported projects. This is because projects are obligated to report the details of their development
at the interim and final evaluations stages. Being considered in this way, the influence that
communication with others outside of firm has on spillover might be limited.
If a development project is a joint research project across different firms, on the other hand,exchanges of information that exceed the boundary between the firms probably will occur provided
it is within the range of the rules governing information exchange. Furthermore, spillover of project
outcome within each firm can be expected once the collaborative project has been completed (oreven during the period of the project). When considered in this way, compared with the case of
research by a single firm, joint R&D can be envisaged to engender greater outside spillover.
Because of the information management will be more strictly applied in joint R&D, however, thismight hinder spillover. We summarize these arguments as the following hypothesis.

Hypothesis 2-2:

Spillover of technological outcome outside the firm is higher (or lower) in acase of joint R&D projects across firms.

4.1.3 Strategic importance
The third factor that may affect technological spillover is the strategic position of the project
within firms. Firms worry about leaks of information to other companies the most when applying
for public support. Projects receiving public supports are usually required to report their outcomes
periodically, or at a minimum, part of the outcomes normally will be publicly released. Although
this is necessary to accomplish accountability for public funds use, it is also a drawback for firms
receiving public support. Consequently firms tend to use their own capital to undertake
developments that are of great strategic importance and to rely on public funds for projects withrelatively low importance.
If a firm will accept public support for strategically important technological development aswell for reasons such as financial or cash flow difficulties, however, it presumably will pay
meticulous attention to leaks of information outside the firm. Accordingly, the outside spillover ofproject outcome with a high degree of strategic importance may be restrained.
On the other hand, if a technology development is strategically important for a firm, the project
outcome will likely be readily recognized internally, and the firm probably will seek to use theoutcome on a company-wide basis. Accordingly, contrary to the spillover outside the firm, spillover
within the firm is predicted to become rather greater. We can summarize these arguments as thefollowing two hypotheses.

Hypothesis 3-1:

Spillover of technological outcome outside a firm decreases as the strategic

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importance of a project increases.

Hypothesis 3-2:

Spillover of technological outcome within a firm increases as the strategic
importance of a project increases.

4.2 Influences on cognitive spillover
4.2.1 External acknowledgement
If the technology in question is widely acknowledged and attracts attention externally as theoutcome of public support, then through the process of inducing new investment by other private
firms and stimulating potential demand economic value will be created for society, and thesignificance of public support will be recognized.
For a publicly supported project or a developed technology to be widely acknowledged, it isimportant whether those who report them, such as the press or news media, are interested. The
scale of the capital invested in the project is one factor influencing such interest. If large amountsof public funds are invested in a project, the details must be properly reported and disclosed to the
public to achieve accountability. Furthermore, for news media, a large project can be expected tohave greater value as news than a small project.
Likewise, just as in the case of technological spillover, the exchange of information by project
members with others outside the firm may also promote cognitive spillover. If project members
actively bring attention to the project’s outcome at academic societies and symposia, for example,
or actively disclose details of the development through news media and magazines, this will raise
external recognition and elicit development investment by other companies. We can summarize theabove discussion as the following two hypotheses.

Hypothesis 4 -1:

External recognition increases as the size of a project’s budget increases.

Hypothesis 4-2:

External recognition increases as the level of information exchange withparties outside the firm by project members increases.

4.2.2 Internal acknowledgement
The motivation for project advocates to apply for public support is sometimes found not only intheir expectation of funding that is not accepted internally, but also in their hope of obtaining
in-house recognition by having the “endorsement or official go-ahead” that comes from being
selected as a public project. If the project can attract attention internally and additional investmentby the firm can be elicited, the value of the public support can be vindicated. However obtaining

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public funds may hinder a company-wide commitment to commercialization because the project nomore relies on internal resources. The project may also be organizational isolated for fear of becoming
a window for information leakage to other companies.

Therefore, for a project that has received public support to be recognized internally as animportant project, continuous interaction and information exchange with other internal divisions
may become critical to ensure the project is not isolated from other internal activities. We can show
this as the following hypothesis.

Hypothesis 4-3:

Internal recognition increases as information exchange with other internal
divisions by project members increases.

4.3 Influences on social-relations spillover
One cannot say that public support was of no significance whatsoever because a project was
unable to surmount technological difficulties and failed to achieve the objectives. A project may
provide project members with new activities and experiences that are otherwise not found ininternal projects. Invaluable assets utilized over the long term, including the cultivation andtraining of human resources and the establishment of social relationships with other companies cansometimes be accumulated internally as by-products.
These by-products are more likely accumulated in the case of projects that undertake novel
development jointly with other companies, rather than single-firm projects being conducted as
extensions of existing development. Such projects offer numerous opportunities for direct newexperiences through interaction with people at other companies. We can show this as the following
hypothesis.

Hypothesis 5-1:

Collaborative projects stimulate greater cultivation and training of personnel
and the establishment of relationships with other companies than single-firm
projects.

5. Research Method

5. 1 Outline of research and samples
To investigate the hypotheses discussed above, we use data from a follow-up questionnaire
survey implemented jointly by NEDO and Hitotsubashi University in August 2010 and the datafrom the interview survey implemented afterwards. The follow-up questionnaire survey was a14

survey we conducted for private R&D projects for which NEDO provided financial support, here
called the NEDO projects. In the questionnaire, we posed questions on topics such as the actual
project management conditions, the social environment and market circumstances where theprojects were implemented, and the project’s technological outcome and social effects.
Typically, several private firms together with universities, public institutions, incorporated
associations, and foundations will participate in a single NEDO project. The questionnaires weresent to representatives of each private firm among these that participated in the NEDO projects,
and the representatives were the respondents1. Therefore, the unit of analysis in this paper is not the
project per se but each firm that participated in the project.
The number of questionnaire responses totaled 301, and the response rate was 88%.
Furthermore, following the questionnaire survey, we implemented interviews at 23 companies fromFebruary through October 2011 and from May through August 2013. The technological fields of
the NEDO projects in which the respondent firms participated were classified into electronic
information, new energy, energy conservation, mechanical systems, environment, biotechnology
and medical technology, nanotech and materials, and other. The project budgets were distributed
between 217 million yen and 11,716 million yen. The number of years in which each firm
participated in its project ranged from less than one year to 14 years, while the industries were
diverse and included automobiles, electronic devices, materials, and chemicals.

5.2 Variables and models
5.2.1 Dependent variable
The spillover effect was measured separately for “technological spillover,” “cognitive spillover”
and “social-relations spillover” as discussed below, in accordance with the classification shown in
Section 2.

Technological spillover
For measurement of the technological spillover, we first used the number of citations of thepatents the project had obtained (which we call NEDO patents). Although partial, “the total numberof citations” that NEDO patents receive may show the level of use of the technological outcome byusers outside the project. Next, to separate the spillover outside the firm from in-house spillover,
we separated the total number of citations into citations by the company itself and citations byother companies, which we respectively labeled “number of internal citations” and “ number of1

Respondents were the representatives within each firm that participated in the NEDO projects who were the mostfamiliar with the projects in question, including not only their own company but the facts in terms of theirrelationships with the other organizations. While bias is unavoidable because the respondents were individuals,
we believe that, as information obtained from individuals, the responses are highly reliable.

15

external citations.”

Furthermore, to distinguish the spillover recipient fields, the difference (distance) of thetechnological fields between a NEDO patent and patents citing a NEDO patent was captured by
calculating the cosine similarity of the International Patent Classification (IPC) codes (considered
to the subclass level) assigned to the two patents. Cosine similarity takes a value from 0 to 1. Whenthe cosine similarity is one, this means the IPC codes assigned to the two patents are completely
identical and indicates the patents are in the same field, while oppositely, when the cosine
similarity is 0 (zero) the IPC codes assigned to the two patents are different and indicates thepatents are in different fields. Next, we labeled the number of citations weighted using this cosine
similarity the “influence in the same technological field.” As the number of times the NEDO
patent(s) are cited in other patents classified in the same field technologically rises, this “influencein the same technological field” increases. Conversely, we labeled the number of citations weighted
using the value “1 - similarity” the “influence on different technological fields.” As the number oftimes a NEDO patent is cited in patents affiliated with a different field increases, the value of the“influence on different technological fields” increases.
Both the “influence in the same technological field” and the “influence on different
technological fields” have an influence within the firm and an influence outside the firm. The“influence in the same field within the firm” is the influence on the field limited to citations within
the firm, while the “influence in the same field outside the firm” is the influence in the same
technological field calculated using only citations outside the firm. Values were similarly calculated
for “influence on different technological fields” as well.
For measurement of the technological spillover, in addition to the indicators using the numberof citations of these patents, we made complementary use of the subjective evaluations by the
respondents as well. Specifically, we asked respondents to indicate the extent to which thedeveloped technology was diverted and used in applications different from the originally intended
purpose, by using a five-point scale for the three categories “development and product technology,”“evaluation and testing technology” and “scientific knowledge.” We set the mean value of the
responses for these three categories as the “technology diversion within the firm” (α = 0.83). The
patent citation cannot capture the actual status of technology use. Consequently we decided torequest a subjective evaluation, at least concerning the internal spillovers the respondents
understood.

Cognitive spillover
We measured cognitive spillover within firms and outside of firms, respectively. First, forcognitive spillover within firms, we received responses on a five-point scale on the level to which

16the legitimacy of the development activity is ensured within firms, which we set as the variable“development legitimacy within the firm.” On the other hand, we captured cognitive spillover
outside of firms based on two variables. One was responses on a five-point scale on the level of
improvement of recognition for the project outside the firm, which we set as the variable “external
recognition.” The second was responses on a five-point scale for the 3 question items “stimulated
technological development by other domestic organizations,” “stimulated technologicaldevelopment by other overseas organizations” and “invigorated application markets,” from which
we prepared a synthetic variable named “impact on society” by using the values of the three
answers as a mean value (α = 0.79).

Social-relations spillover
We measured social-relations spillover based by two indicators. We first received responses on a
five-point scale on the extent of creation of networks outside the firm through the project activity,
which we used as the variable named “network.” Second, we asked questions on the effect instimulating human resources development within the firm, and used the responses for the variablenamed “human resource development (HRD).”

5.2.2 Explanatory variables
The explanatory variables are divided broadly, according to the presented hypothesis, into avariable that captures the nature of the generated knowledge, a variable that captures the exchange
of information with others outside the project, and a variable that captures the strategic intent of the
firm.

Nature of generated knowledge
We measures the nature (generalizability) of the generated knowledge by two variables. One is a“exploratory phase” variable; this is a dummy variable indicating the project being in the
exploratory phase at the start. Some projects commenced from the exploratory phase and, throughthe development activity, proceeded to the practical application phase. Even so, those begun fromthe exploratory phase have a greater possibility of creating highly generalized knowledge thanprojects starting from the application phase. The second variable is a dummy variable to showwhether commercialization was intended at outset of the project, named “intention forcommercialization”. Because NEDO projects are under the jurisdiction of the Ministry of Economy,
Trade and Industry, their objective is to promote industrial technology, and in principle all projects
are expected to ultimately seek a path to commercialization. But in reality, basic research with noexpectation for immediate commercialization also is conducted. In such cases, the respondents

17

answered “initially no intention for commercialization.” Of the sample for this survey, 63% of the
responding firms answered that commercialization was intended, so for the remaining 37%,
commercialization presumably was not the initial intent. Projects that don’t intend
commercialization tend to pursue basic research, and that the technological knowledge produced bytheir efforts is more highly generalized in nature.

Information exchange
For the information exchange outside the project, we measured both the interaction with people
outside the firm and interaction with other divisions within the firm. For interaction outside thefirm, we received responses on a five-point scale for the five items “information exchanges withindividuals in other firms,” “implementation of technology surveys by other firms andorganizations,” “implementation of cost

analysis by other firms and organizations,”“implementation of market research by other firms and organizations” and “implementation ofpatent survey by other firms and organizations,” respectively, and then set the mean of the fivevalues as the variable “information exchange outside the firm” (α = 0.79). The reason we included
not only communication by project members but also the implementation of various surveys byoutside organizations as a component of the variable is the belief that external organizations
provide an information channel to the outside, through which information on the actual status anddetails of development are transmitted.
For interaction with other divisions within the firm, we similarly received responses on a
five-point scale for five items, these being “information exchange with individuals in otherdivisions within the firm,” “implementation of technology surveys by other divisions within thefirm,” “implementation of cost analysis by other divisions within the firm,” “implementation ofmarket research by other divisions within the firm” and “implementation of patent survey by otherdivisions within the firm,” and then set the mean value as the variable “information exchange withother internal divisions”(α = 0.80).
In addition to these, we captured whether the projects were “joint research” by using a dummy
variable. As shown by the hypothesis, the purpose is to examine whether joint research promotes
spillovers through additional information channels, or instead hinders spillovers because ofagreements governing strict information management.

Strategic intent
With regard to the strategic position of projects within a firm, we used the responses to thequestion “Is the technology development by this project indispensable for the long-term strategy ofyour firm” as a dummy variable (Yes = 1, No = 0).

18

5.2.3 Control variables
We included several control variables in the analysis. One is the project’s technological
performance. If a project produces excellent technological outcome, that fact alone is likely to
attract attention, and will encourage both internal use and use outside the firm because the outcome
will be useful for individuals outside the firm as well. We used a five-point scale to ask about“technological performance”. The second control variable is the project size. The larger the size ofa project, the higher the number of patents generated tends to be, which can be expected to increase
the number of citations as well. We measured project size using two variables – “budget size” and“number of project members”. The former variable “budget size” can also be construed as an
explanatory variable that prods cognitive spillover, while the latter variable “number of project
members” partially highlights information exchange outside the project because it indirectly shows
the number of information channels to the outside. Finally, because the number of patent citations
can be predicted to increase over the passage of time, as a control variable we introduced a “timelag” to show the length of time since the end of a project. Descriptive statistics for each variable,
and the correlation matrix are shown in Table 1.

- Table 1: Descriptive statistics and correlation table-

5.2.4 Analytical model
We conducted a regression analysis based on the above variables. For the analysis using thepatent citation data as the explained variable, we adopted the tobit model. The reason is there werenumerous samples where the number of citations was zero (0), including cases where no patent wasapplied for. For cases where the dependent variable was on a five-point scale, we used the Ordered
Logit model. In addition, when the dependent variable was a composite variable, we made anestimate based on OLS.

6. Results of Analyses

6.1 Influence on technological spillover
Table 2 shows summary of the results.

- Table 2: Multiple regression analysis results-

19

First, it indicates that more spillover occurs both internally and externally for projects starting at
the exploratory phase. Significant relationships were found for all the dependent variables using thepatent citation. Although Hypothesis 1-2 suggests the generality of knowledge did not overly
influence internal spillover, according to the results, a project staring at the exploratory phase show
both higher internal and external spillovers.

Likewise, Hypothesis 1-3, stating that spillover todifferent fields increases as project outcome include more generalized knowledge, also is notsupported. Whether in a different field or in the same field, outcome of the projects starting from
exploratory phase spill over more than others. Whereas the hypotheses were partially rejected, ourhypotheses stating positive relationships between the generalizability of knowledge produced andspillovers are supported overall.
One concrete example of a NEDO project showing this is development of hydrogen fuel cells.
This was a project that covered from technology to improve the efficiency of hydrogen production, to
product development and evaluation technology related to the practical application of hydrogen gas
sensors and vessels for hydrogen gas transport. The objective of the project was narrowly definedfrom the outset, and application of the technology was clearly limited to hydrogen use. Because
practical application was envisaged and development was limited to this use, the technologies
developed did not spill over to others even within the firm.
On the other hand “intention for commercialization,” another indicator showing the nature ofknowledge created, is at odds with the hypothesis and does not have a significant influence on
spillover. For technological diversion within the firm, there are rather positive influences. The“intention for commercialization” not only indicates the nature of the knowledge, but may also
capture the size of the firm’s commitment to commercialization. It may therefore promote internal
use of the technological outcome. Likewise, because it is necessary for a product or technology to
be completed to some degree for substantive redirection of the application of the technology withina firm, the more a project has made clear its “intention for commercialization,” the more likely willbe the redirection of the technology’s application within the firm.
Next, the results in Table 2 show that the exchange of information by project members withothers outside the project has a positive impact on technological spillover. However, only
information exchanges with other internal divisions had an influence ; the exchange of information

outside the firm did not have a significant effect on technological spillover. Important confidential
information tend to be controlled so that they are not leaked to outside, meaningful information
might not be exchanged during ordinary communication with outside parties. Similarly, in the caseof joint research, spillovers beyond project boundaries may not occur because strong controls will
be exercised on confidential matters between the joint research parties.
On the other hand, “information exchange with other internal divisions” can be accomplished20

without being entangled in confidentiality problems.
The example of diversion of desulfurization catalyst technology for light oil to kerosene at “C”
Oil Co., Ltd. is an example of a NEDO project where the exchange of information with anotherdivision within a firm promoted internal technological spillover. In this development, thedeveloped technology was widely known internally and its commercialization was accelerated
because the developers were frequently in communication with the headquarters R&D divisionfrom an early stage. Moreover, the project members eventually enjoyed frequent interaction withindividuals in the Technology Division as well as the Mass Production Division at the refinery, andtheir being questioned directly about the possibility of the technology’s application to kerosene
served as an opportunity, and the diversion of the desulfurization catalyst technology todesulfurization of kerosene was successful.
Furthermore, we can understand from Table 2 that the more strategically important a project isfor a firm, the more spillover outside the firm will be restrained but, on the other hand, the more
in-house spillover will be facilitated. On the point of internal spillover within a firm,“indispensable for the long-term strategy” had an significantly positive influence on “technology
diversion”. For citations outside the firm, on the other hand, a significantly negative influence canbe noted, whether in the same or a different field. This is consistent with Hypothesis 3-1 andHypothesis 3-2.

6.2 Cognitive spillover and social-relations spillover
For cognitive spillover, the project size didn’t have significantly positive influence on both
“impact on society” and “external recognition,” which is not consistent with Hypothesis 4-1. On
the other hand, information exchanges with other divisions wihtin the project had a positive impact
on both “impact on society” and “external recognition” though information exchanges outside thefirm did not have any significant influence on either of the two. Moreover, “impact on society” wasrelated to “technological performance” and “intention for commercialization.” This isunderstandable because a project has an impact on society to the extent it achieves superior
technological outcome. Likewise, we can assent to the idea that the impact on society will grow as
a project’s intention for commercialization increases. To the extent the developed technology isrealized as a visible product or service, it will stimulate behaviors of other companies since it
spread as a strong threat or opportunity.
For social-relations spillover, being joint research has a positive and significant effect on both
personnel training and the building of relationships with other companies, consistent with
Hypothesis 5-1. Moreover, results show that, while “information exchange outside the firm” have apositive impact on the establishment of relations with other companies, “information exchange

21

with other internal divisions” stimulate personnel development.

7. Discussion
7.1 Two dilemmas faced by public support for private R&D
First, the results in this paper showed that, while spillover of technological outcome outside thefirm becomes greater to the extent the project undertakes basic research activities, the spillover ofoutcome is restricted when a project is of great strategic importance for a firm. This result implies
the public institutions providing supports will confront difficult decisions.
In recent years, as government fiscal conditions have become increasingly strained, there has
been a tendency to demand visible outcome from public support for private R&D. This hasconsequently led to a push for innovations that will be linked to economic outcome, not the mere
promotion of science and technology, being raised as a policy goal. Considered from this viewpoint,
supporting project activities that are strategically important for firms with a significant
commitment for commercialization would be the preferred approach. Conversely, support for
projects that are in the exploratory phase should be limited. In fact, as our previous study indicated(Aoshima et.al.,2013), whereas there is a significant positive relationship between the level ofstrategic importance for a firm and commercialization of developed technology, there is asignificant negative relationship between commercialization and the extent to which a project is in
the exploratory phase when it commences2. If what is demanded is the generation of short-term
economic value, then from this outcome as well, projects that are strategically important for firms
and projects that have advanced to the commercialization phase should be the targets for public
support. Under such an approach, however, spillover to society outside the firm boundary could not
be expected; and the very justification for providing public support would be shaken. In otherwords, if considered from this aspect, the issue becomes the difficulty of compatibility between the
pursuit of commercialization and the pursuit of spillover effects to society. Once this is understood,
however, facilitating steps to make both goals as compatible as possible might be feasible. For
example, when supporting projects with great strategic importance for firms, goals that would beconsidered might include the intention to announce and release the outcome widely to stimulate
spillover of the outcome as much as possible, or putting together a well-balanced portfolio of
projects in the exploratory phase and projects in the commercialization phase.
Second, the results showed that the exchange of information with outside parties that went2

For the relationship to commercialization of the results, see Aoshima, Matsushima, and Eto (2013).

22

beyond the project, and information exchange with other divisions within the firm in particular,
induced not only technical spillover but also cognitive spillover and social-relations spillover. Thus,
management mechanisms for promoting information exchanges with outside parties become
critical for stimulating the spillover. From this point of view, it may be necessary for public
institutions supporting the project to confirm that the projects are not isolated internally receiving
assistance and cooperation from other divisions.
Supporting institutions face one more dilemma, however. As indicated by our previous study,
information exchanges with other internal divisions tend to decrease significantly as NEDO
accounts for a larger share of the project budget.

That is, information exchanges with outside

parties decrease as the amount of public support increases, which weakens the spillover effect ofthe project outcome.
From a firm’s viewpoint, projects that receive public support can become a window that leaks
information to the outside. Publicly supported projects must regularly announce and disclose theiroutcomes. Of course, while this makes management of information disclosed outside the project
prudent, it is understandable that other projects within a firm will tend to avoid interaction with apublicly supported project because disclosure at a level that will enable evaluators to understandthe development details and their value is required. Moreover, the fact that a project is isolated
from a firm’s internal resource allocation process as a result of receiving public support also will
work in the direction of curtailing information exchange with other divisions. R&D projects at
firms regularly engage in activities to emphasize the significance and validity of their activities, inorder to garner their budgets. Details of their research will be transmitted to others during thisprocess. Projects being managed with 100% public support can dispense with such activity.
Because they do not rely on internal resources, they have no need to be complained by anyone, andno need to persuade anyone. This may promote the informational isolation of such projects within afirm, however.
With their understanding of the existence of such a dilemma, what public institutions should
consider is not only providing financial supports but also preparing, as much as possible, devices toensure projects are not isolated internally.

7.2 Contributions and limitations of the study
One contribution of the present study is to empirically clarify the factors and mechanisms that
stimulate spillover at the project level. Another contribution could be that we have empiricallyinvestigated the fact that, even with a tendency to demand greater economic outcome from public
support to private R&D, there is a contradictory aspect to the pursuit of social value and promoting
commercialization by private firms. Nevertheless, there are several limitations as well.

23

The first is the limitations concerning the measurement of spillover. In the present study, we
adopted the method of quantitatively understanding spillover based on patent citations, andsupplementing this with subjective responses by means of a questionnaire. Pion patent citation data,
however, enables us to capture only a part of the spillover. Firms that were stimulated bydevelopment at other firms might attempt development using proprietary technology, without citing
others’ patents. Moreover, even when the commercialization of technologies developed withpublic supports significantly fosters complementary product markets or supplier industries, inwhich we can identify a clear spillover effect, we cannot sufficiently capture this from patents.
Furthermore, although distinctiveness of the present study is to distinguish technical spillover inthe same field from that into a different field, we were unable to observe any difference in thefactors arising from differences in the spillover fields. We believe this is because, while the numberof patent citations have a large influence, the similarity of the citation source and citation recipient
is not sufficiently reflected in the indicator.
In this paper we classified spillover effects systematically, and also referred to not only thetechnological spillover that has been noted in existing research but to cognitive spillover andsocial-relations spillover as well, and analyzed factors having an influence on these. However ameasurement of cognitive and social-relations spillover relies on subjective ratings from the sidethat carried out the projects, an approach that must be deemed inadequate. Supplementing this witha survey of other companies and individuals that have been recipients of spillover is perhaps
necessary.
A second limitation is insufficient compatibility with existing research. Our study, as factors
stimulating spillover, mainly noted the internal factors residing in source projects such as the natureof the knowledge generated, the exchange of information with parties outside the project, and thestrategic importance for the firm. On the other hand, we have not dealt with factors at the industry
level or inter-firm level that have been clarified by existing research, including geographic
proximity, complementary assets, absorptive capacity, the industry’s stock of knowledge, or otherfactors on the side of spillover recipient firm. We need to undertake an analysis that encompasses
both the factors that have been dealt with by existing research, and the factors at spillover source
firms that are addressed by the present study.

24

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26

Spillover anddiffusion ofExternaltechnologicaloutcome outside
a firmPump-priming

Internal

effect of R&D
investment withina firmSame field

Unexpected
application anddiffusion ofExternaltechnologicalrecognition

outcome outside

Construction ofrelationships
between firms

a firmInternal
Technology

recognition

Personnel training

diversion within a(Development

effect within afirm

legitimacy within

firm

a firm)Different field

Technological spillover

Cognitive
spilloverFigure 1: Classification of spillover effects

27

Social-relations
spillover- Table 1: Descriptive statistics and correlation tableMean

S. D.

N

1 Total number of citations

7.76

24.41

301

2 Influence in the same field

4.15

13.50

301

.960**

3 Influence on different fields

3.61

12.06

301

.950**.824**

4 Number of internal citations

2.12

9.47

301

.851**.740**.894**

0.99

4.58

301

.861**.839**.803**.902**

1.13

5.70

301

.722**.555**.840**.938**.695**

7 Technology diversion

3.25

1.22

269

.076

.070

.074

.091

.117

.062

8 Number of external citations

3.76

10.75

301

.891**.925**.768**.629**.701**.481**

.088

2.10

6.86

301

.771**.873**.583**.499**.588**.357**.066.946**

1.67

4.80

301

.892**.822**.885**.694**.729**.567**.101.887**.689**

Influence in the same field
5(Internal)
Influence on different fields
6(Internal)

Influence in the same field
9(External)
Influence on different fields
10(External)

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

11 Development legitimacy

3.52

0.83

269

.118

.102

.120*

.102

.128*

.072

.233**

.073

.025

.126*

12 External recognition

3.57

0.81

269

.069

.076

.053

.035

.034

.031

.201**

.056

.058

.040

.609**

13 Impact on society

2.83

0.91

269

.102

.107

.084

.118

.085

.125*

.289**

.109

.123*

.063

.268**.295**

14 HRD

3.83

0.66

269

.198**.177**.196**.185**.167**.172**.187**.158**.126*.167**.340**.320**.262**

15 Network

3.87

0.73

268

.127*

.116

.124*

.119

.093

.120*

.171**

.127*

.107

.125*

.185**.250**.240**.371**

16 Joint research

0.47

0.50

270

-.034

-.069

.008

-.018

-.058

.011

-.070

-.059

-.078

-.018

-.105

-.081

-.064

.122*

.177**

0.10

0.30

271

-.054

-.041

-.062

-.018

.009

-.034

.176**

-.077

-.054

-.091

.114

.112

.030

.027

-.044

.067

0.63

0.48

271

.057

.071

.035

.045

.073

.020

.277**

.076

.077

.056

.038

.132*

.175**

.022

-.100

-.193**

.145*

0.81

0.40

273

.094

.077

.096

.068

.043

.073

-.053

.082

.052

.102

-.028

-.113

-.053

.053

.039

.023

-.155*-.128*

3.17

0.88

270

.156*

.128*

.166**

.128*

.110

.123*

.250**

.111

.071

.144*

.231**.152*.276**.253**

.128*

-.040

.093

.296**-.134*

2.68

0.95

270

.123*

.090

.144*

.143*

.138*

.127*

.182**

.035

-.003

.082

.155*

.117

.247**.161**.179**

.033

-.024

.130*

-.200** .423**

22 Budget size (￥ 1M)

3660.722987.53

245

.107

.146*

.056

.033

.032

.031

-.055

.162*

.178**

.083

.028

.040

.148*

.069

.036

-.121

-.015

.035

.056

23 Number of members

23.29

182.33

270

-.021

-.021

-.019

-.015

-.016

-.013

.046

-.022

-.019

-.021

.044

-.035

.064

.023

.099

.060

-.009

.058

.021

.092

.071

-.051

24 Time lag

10.75

2.72

272

.087

.109

.054

.027

.093

-.007

.006

.122*

.111

.100

.011

.012

-.012

.137*

.141*

.056

-.107

-.308**.074-.247**-.131*

-.017

.051

25 Technological performance

3.80

0.79

269

.113

.128*

.083

.127*

.146*

.097

.241**

.095

.097

.069

.335**.381**.289**.206**.254**-.102.162**.159**

-.104

.146*

.155*

.138*

.026

Indispensable for the long17
term strategy
Intention for
18
commercialization
19 Exploratory phase
Information exchange with20
other internal divisions
Information exchange
21
outside the firm

Note: *p<0.05, **p<0.01.

28

.044

24

.115

-.061

- Table 2: Multiple regression analysis resultsTechnological spillover
Total

Internal

Total number of Influence in the Influence on
citationssame field
different fields
tobit
Constant
Budget size (￥ 1M)
Number of members
Time lagTechnological Performance
Joint research
Indispensable for the longterm strategy
Intention forcommercialization
Exploratory Phase
Information exchange withother internal divisions
Information exchange
outside the firm
R-Squared (Pseudo)
Log Likelihood
N

tobit

Nu mber ofinternal
citations

tobit

tobit

External

Influence in the
Influence onsame field
different fields
tobit

tobit

Technology
diversion

Nu mber ofexternal
citations

OLS

tobit

tobit

tobit

Influence in the
Influence onsame field
different fields-112.015 ***-57.618 ***-73.065 ***-124.393 ***-45.500 ***-126.363 ***.201-59.883 ***-42.336 ***-30.874 ***(21.806)( 11.400)( 14.172)(29.338)( 10.640)(31.399)(.597)( 12.876)(9.666)(6.275)

.001

-.001

.000

.000

.002 *.001 **.000 *.001 ***.001 ***.000 *(.000)(.001)( .000)(.000)(.001)(.000)( .001)(.000)(.000)(.000)

-.139

-.087

-.063

-.278

-.140

-.055

.000

-.013

-.009

-.007

( .202)( .107)(.125)(.248)(.093)(.208)(.000)(.026)(.019)(.012)

3.657 ***

1.889 ***

2.311 ***

2.474 **

1.015 ***

1.960 **

.075 ***

2.023 ***

1.367 ***

1.015 ***(.935)(.486)(.599)(1.065)( .380)(.945)(.028)(.536)( .402)(.257)

2.775

1.967

.454

5.785

2.090

5.339

.191 **

1.332

.895

.250

(2.955)( 1.551)( 1.885)( 3.569)(1.275)(3.291)(.094)(1.708)( 1.285)(.813)

4.071

.637

3.117

3.054

-.299

3.263

-.042

-.451

-1.167.696( 4.671)( 2.429)(3.001)(5.335)( 1.916)( 4.815)( .149)(2.710)(2.035)(1.298)-14.043-5.849-7.937

7.505

4.502

.350

(9.022)(4.626)(5.716)( 8.545)(3.051)( 8.373)

7.103

4.579

2.806

4.640

2.092

-.987

(5.271)( 2.774).652 **-12.592 **-7.634 *-6.682 **(.253)( 6.204)(4.501)(3.050).663 ***

2.458

2.777

1.059

( 2.276)(1.435)(3.367)(6.213)(2.218)(5.432)(.165)(2.987)16.035 **8.341 **

10.275 **17.887 **7.029 **

29.621 **.150

11.410 ***8.000 **

(6.426)(3.368)(4.169)(8.130)(3.042)(.186)(4.052)(3.070)10.472 ***4.823 ***

8.155 ***

( 11.676)

5.441 ***
( 1.930)

8.579 **

2.753 **

8.759 **

( 3.190)( 1.647)( 2.127)(3.778)(1.344)(3.573)( .093)(1.828)( 1.369)(.902)-1.832( 2.823)-1.510( 1.469)-.684( 1.784)

2.292( 3.190)

1.103(1.161)1.853(2.878).147 *(.087)-2.351( 1.628)-1.798( 1.210)-.696(.773)

.179

.053

.055

.064

239

-393.732
239-338.696
239-317.244
239

.036

.043

.043

.043

.063

.063

-537.877
239-459.903
239-441.614
239-219.716
239-169.327
239-173.049
239

Note: *p<0.1, **p<0.05, ***p<0.01. U pper row is the unstandardized coefficient; lower row is the standard errors.

29.222 **

5.125 ***

3.289 **

3.121 ***

- Table 2: Multiple regression analysis results (continued)-30