Research Policy 31 (2002) 509–526
Science dependence of technologies: evidence
from inventions and their inventors
Robert J.W. Tijssen
Centre for Science and Technology Studies (CWTS), Leiden University, P.O. Box 9555, 2300 RB Leiden, The Netherlands
Received 26 November 2000; received in revised form 14 December 2000; accepted 19 March 2001
Abstract
Articulating a compelling economic rationale to justify investments in research—by definition furthest removed from direct,
immediate economic benefit—is perhaps one of chief challenges of R&D managers, policy makers and science analysts in
the years ahead. Although several innovation studies and surveys have provided some convincing empirical evidence of
impacts and benefits of research to technical progress, there is still an urgent need for comprehensive models, reliable data
and analytical tools to describe and monitor links between R&D and industrial innovation in more detail.
As for the role of scientific and engineering research in the innovation process, this paper reports on the findings of a novel
methodology to increase our understanding of the contribution of research efforts to successful technical inventions. The
approach is based on a nation-wide mail survey amongst inventors working in the corporate sector and the public research
sector in The Netherlands. The inventors’ inside information regarding their patented inventions—and related technological
innovations on the market—provided a range of quantitative data on the importance of the underpinning research activities.
Statistical models attempting to explain the degree of “science dependence” of the inventions identify a range of relevant
variables, covering the inventor’s own capabilities and previous R&D achievements, external information sources, as well as
the inventor’s R&D environment in general. Some 20% of the private sector innovations turned out to be (partially) based on
public sector research. Furthermore, citations in patents referring to basic research literature were found to be invalid indictors
of a technology’s science dependence. © 2002 Elsevier Science B.V. All rights reserved.
Keywords: Science base of industrial innovation; Patents; Patent citations; Inventors
1. Introduction
Newly emerging R&D-intensive industrial sectors will increasingly be built on a combination of
leading-edge scientific knowledge and sophisticated
technical know-how. The scientific and engineering
research that we conduct today will have an impact on
the economic success of corporations and countries in
5, 10, and even 15 years into the future. However, despite living in such an increasingly knowledge-based
E-mail address: [email protected] (R.J.W. Tijssen).
society, the processes by which scientific and technical
knowledge drive industrial competitiveness and economic impact remain one of the most difficult areas
to assess and understand. Little is known about the
quantitative impact of research activities and outputs
on industrial innovations. This lack of detailed understanding not only undermines the economic rationale
for public and private investments in research—especially the expenditure for risky exploratory academic
research with long-term objectives and uncertain
payoffs—but also the public confidence in the societal
benefits and rates of returns of academic research.
0048-7333/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 0 4 8 - 7 3 3 3 ( 0 1 ) 0 0 1 2 4 - X
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Usually, it is taken for granted that in-house corporate research—basic research included (Rosenberg,
1990)—is a decisive factor in technical change and
innovative industrial applications. Less agreement is
found as to the relative contribution of universities,
public research institutes and government laboratories
as a source of knowledge, discoveries and technical
problem solving. Contributions from external research
(academic or otherwise) are often considered far more
questionable. One of the reasons is the multivaried
way in which the results of scientific research come
to us. Few scientific breakthroughs do find immediate applications and the yields on basic research are
typically realised far into the future. Frequently, the
greatest benefits are the least anticipated and surface
many years later.
Clearly, scores of technological advances have
their origins in basic or applied scientific research, in
many cases based on research efforts that could indeed not have been predicted to have those outcomes.
Only a few of the ‘science dependent’ technologies
reach the stage of full maturity and become adopted
technological innovations that are applied in more
efficient manufacturing processes or introduced into
the marketplace embodied in new or improved products. The surge of science-dependent technologies
that conquered the market over the past 30 years
have resulted in large numbers of new commercial
enterprises that now constitute a major fraction of the
global economy. Lasers, semiconductors, fibre optics,
mobile phones, medical imaging, and biotechnology
all have resulted from fundamental discoveries from
basic research. Many examples exist of outcomes
of government-funded research labs that are (eventually) further developed into new inventions and
technologies, the Internet being the latest high-profile
example.
The role and contribution of public science to technological innovation has been a major concern of S&T
policy since the late 1960s when retrospective studies such as TRACES (Illinois Institute of Technology
Research Institute, 1969; Batelle Columbus Laboratory, 1973) and project hindsight (Sherwin and Isenson, 1967) were carried out in the US. Subsequent
studies in the 1970s, 1980s and 1990s focused on scientific and technical knowledge used in the course of
the innovation processes in companies, attempting to
identify, categorise and quantify the contributions of
the main institutional sources of ideas, instrumentation and technical inputs (e.g. Gibbons and Johnston,
1974; Jaffe, 1988; Brooks, 1994; Mansfield, 1995).
The findings share the conclusion that absorption and
utilisation of new knowledge into new artefacts and
industrial innovations is an extremely complex social process involving a range of corporate sources
and external knowledge and skills where most relationships and two-way interactions between research
and technological development are neither direct nor
obvious.
The EU’s Community Innovation Surveys (CIS)
conducted in the 1990s have provided comparable numerical data on the role of research as an information
source at the aggregate level of entire industrial sectors and countries. Some R&D-intensive sectors were
found to be distinctly science-based, others depend
mainly on engineering research and technical development. The results of the PACE survey by Arundel
et al. (1995) amongst large European firms show that
public sector research is only slightly less important
than other external sources of knowledge. But again
there are significant sectoral differences, varying between sectors such as plastics and fabricate metals
where only 15% of the respondents cases find public research very important, to R&D-intensive sectors
such as pharmaceuticals and aerospace with scores of
30% or more.
Clearly, significant progress has been made in determining the extent to which technical development
within private businesses benefits from various knowledge inputs, including publicly-funded research.
Obtaining comparative measurements on how many
technological innovations were actually made possible by industrial R&D or external research is still an
entirely different matter. Here the old cliché certainly
holds true, what is easy to measure is hard to correlate
and what is easy to correlate is hard to measure. So
far, only Mansfield (1991, 1998) and Beise and Stahl
(1999) have tackled this difficult question. They asked
R&D executives and support staffs of a large number
of companies about the share of product and process innovations that would have not been developed
(without substantial delay) in the absence of public
research.
However, the actual contribution of research to
technical inventions and associated innovations is still
largely unresolved. Which factors are determining
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
this knowledge creation and transfer process? Are
there indeed significant cross-sectoral differences in
“science dependency” of inventions and innovations,
i.e. the extent to which in-house or external scientific
and engineering research is perceived as a significant
contribution to the development of inventions and
innovations? To what extent is an organisation’s
internal R&D environment indicative of that science dependency? How important is the individual
“knowledge-base” of R&D personnel working on
technical inventions?
This paper sets out to address these fundamental
questions concerning the research-related inputs of
technical inventions based on empirical data gathered
at the working bench level of inventors and their
patents, in particular the research base of patented
technical inventions. Clearly, all technical inventions
are based to some extent on research, in the least on
(applied) engineering research of some sort, but sometimes also on inputs from scientific and engineering
research of a more fundamental nature. The inventors
were queried specifically about the role of scientific
research in the development of those patents. The
study covers patented technologies developed in the
private sector as well as in the public sector, including technical inventions that have already reached the
stage of technological innovations. An explanatory
model of science dependency is developed, incorporating both structural and individual factors. Furthermore, new empirical data is supplied dealing with the
critical analytical issue whether or not the references
on patents to the research literature are indeed valid
indicators of a patent’s science dependence. The next
sections of this paper report on the methodology
and present the main findings. The final section discusses methodological and policy implications of this
approach.
511
comparable analysis units suitable for a systematic investigation through comparative analysis of cross-case
findings. The dominant approach is that of reporting
by R&D management at industrial enterprises via a
comprehensive postal questionnaire. The mail survey methodology is relatively cheap and produces
representative volumes of quantifiable information enabling statistical analysis and quantitative indicators.
Patents provide an alternative approach to help
shine a light inside this black box. They are concrete evidence of technical progress and constitute
identifiable and measurable units of inventiveness. 1
Patents are considered important intermediate outcomes of innovation-driven R&D processes in many
technology-intensive manufacturing sectors and
across a range of R&D performing organisations
in both the public and private sector. These public documents provide a large body of information
and adequate quantities of comparable units suitable for detailed statistical analysis. Patent data also
have their share of inherent weaknesses for studying technological developments: not all innovations
are patented (e.g. Arundel and Kabla, 1998), nor are
patents equivalent to innovations. 2 Basically, inventions and patents represent the brilliant ideas; innovations get those ideas out into the market. Moreover,
many patented inventions are never fully developed
or adjusted to market requirements. The knowledge
codified and protected in a single broad “generic”
patent may be used in several innovations, whereas a
complex technical system may cover a range of interrelated and incremental patents. In addition, a single
R&D project or trajectory may result in several interrelated patents covering a spectrum of interrelated
strategic and commercial objectives. For the sake of
analytical simplicity, we henceforth assume that each
patent defines a valuable intermediate concrete outcome of a specific R&D trajectory where the (tacit)
knowledge embodied in the patented invention can be
2. Method and information sources
2.1. Data collection
Only a look inside the “black box” of R&D trajectories enables further identification of factors
contributing to successful inventions and innovation
processes. However, traditional case studies are too
specific and fail to provide an adequate quantity of
1 Inventions are defined as “any patentable product, process, machine, manufacture or composition of matter, or any new and
useful improvement of any of these, such as new uses of known
compounds”.
2 Industrial patenting is obviously only one way of seeking to ensure firms’ ability to appropriate returns to innovation and patenting decisions thus reflect, in part, corporate IPR strategies and
tactical considerations which can be defensive or forward-looking,
or can lay the groundwork for cross-licensing arrangements.
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developed and used for various further applications
and technological innovations.
The inventors listed on patents are the key information source on the history of the R&D process related
to the technical invention. By using recent patents and
addressing their key inventors one can directly tap into
inside information at the project level relevant to the
(sometimes, still on-going) R&D processes aimed at
developing industrial innovations. Furthermore, their
views and detailed empirical data of lab bench activities, interactions and knowledge flows are unknown
to R&D management who usually tend to deal with
external questionnaires concerning innovation activities and outcomes (e.g. Jaffe et al., 2000). As such,
this particular patent and invention-oriented methodology fits into the “object approach” within innovation
studies were (quantitative) data is collected directly
at the level of individual inventions and innovations
through more detailed and tailored surveys and/or using expert opinion (e.g. Palmberg et al., 1999). 3 This
analytical approach enables a more exact comparison
of the different types of inventions and innovations,
their scientific and technical origins and development
trajectories.
Obtaining detailed data on the inventions and innovation is of course context sensitive and usually of
commercial or strategic value. So non-response can
become quite a problem. For example, a recent US
survey dealing directly with inventors barely achieved
a 20% response rate (Erickson, 1999). Moreover, the
response can be subject to positive or negative biases reflecting the respondents’ appreciation of the
resources and underpinning R&D as well as views
on the impact and value of the patent. To address
these potential drawbacks, the inventors were guaranteed absolute confidentiality and were notified in
advance that findings of the survey would be disseminated only in aggregate form at the level of
main institutional categories corresponding to the
3 In contrast, the “subject approach” deals with the various aspects of innovation activity at the institutional level by identifying
innovating firms and institutions and using large-scale mail surveys to gather data. Typically, these surveys—such as CIS—tend
to use indirect proxies for the measurement of technical innovation, such as patents and R&D expenditure. The most advanced
and thorough methodologies for quantitative innovation studies are
now combining the object and subject approach (e.g. Palmberg
et al., 1999).
inventors’ affiliations. 4 There were no prior contacts
with the inventors, patent attorneys or examiners,
and/or the firms and institutions that were granted the
patents.
This patent-inventor survey methodology has two
key strengths compared to the traditional innovation
surveys like CIS: (1) in-depth data on processes,
outputs and outcomes; (2) data on invention-related
research at universities and other public research institutes. The downside of using to patented innovations
is of course the limited scope for analysis and comparison which is restricted to industrial sectors and
organisations where patenting is used as a means for
protection of intellectual property. Patent-based innovation indicators therefore tend to over-emphasise
the technologies developed within the larger firms
active in high-tech industries such as pharmaceutics, chemistry, electronics and instrumentation. They
are less appropriate for measuring research-related
technical progress within SMEs and in sectors such
as aerospace or information technology (especially
software development) that rely on other means
such as secrecy or lead times to secure competitive
advantages.
2.2. Patent sampling and questionnaire
A stratified sample was drawn from all Dutchinvented patents on technical inventions (“utility
patents”) filed through international patent offices in
1998 or 1999, either applied for (EPO, PCT) or granted
(USPTO) in 1998 or 1999. Each of these patents lists
at least one inventor based in The Netherlands. Some
of these patents are held by foreign assignees at the
time of application. Structuring the survey requires
an adequate sampling procedure that guarantees representative sub-samples. In view of the dominance of
4 Confidentiality pledges may often have perverse effects which
cannot be assumed to be related to either increasing or decreasing
sample size. However, in this small-scale low-profile academic
exploratory study there are no obvious personal, commercial or
strategic reasons, or (conflicts of) interests related to either the
subject matter, research topics, or the survey methodology, which
are likely to trigger inventors into deliberately misleading responses
or biased views of the actual events and determinants related to the
science base of the patent. A series of 12 follow-up interviews with
a sample of respondents provided no evidence of such response
biases.
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
some large multinational corporations in the population of Dutch-invented patents (Philips alone accounts
for some 40%), the patents and associated applicants
and assignees were not chosen at random to generate a statistically representative sample of the total
population. Rather, a stratified sampling basis was
designed to include a sufficiently wide range of main
R&D-organisations and institutional sectors involved
in patenting in The Netherlands. The sample includes
a wide variety of main technical areas, including
all R&D-intensive industries (pharmaceutics, chemistry and materials, instruments, electronics). It also
contains equal shares of the three main institutional
categories of R&D performing organisations (i.e.
large multinational enterprises, other private businesses, and the public sector). In addition each of
the above sub-samples contain approximately 50%
patents that include at least one reference to research
paper in their list of relevant documents describing
the contents and background of the invention (see
Section 2.3). The latter sampling criterion ensures
a sufficiently large sample of patents to explore the
statistical relationship between the presence of these
citations on patents and the science dependence of
the corresponding invention (see Section 3.4).
The questionnaire was mailed directly to the scientists, engineers, analysts and technicians that were
listed as (co)inventor of the patent, in most cases the
“primary inventor” first on the list of inventors in
the patent application. 5 Each inventor was asked
to complete a six page standardised questionnaire
consisting mostly of semi-structured questions with
minimal use of prompts for open-ended questions.
The questionnaire covers a range of questions dealing
mostly with key aspects of the underpinning R&D,
the R&D environment of the inventor, and the current development phase and commercial value of
the patented inventions. The inventors were queried
specifically about the extent to which “recent scientific
5
The mail survey was sent to the person highest on the list of
inventors that could be tracked down and presumably able to read a
questionnaire written in Dutch. In five cases, the questionnaire was
sent to either the second or third inventor. The first inventor is not
necessarily the chief inventor or the person most knowledgeable
about the specifics of the invention and its development. In fact,
in two cases, the addressee forwarded the questionnaire to another
co-inventor.
513
research” 6 played a significant role in the development of the selected invention.
2.3. Patent citation data
Patent applicants and patent examiners are obliged
to cite relevant “prior art” that contributes materially to the device, substance, product or process
to be patented or upon which it improves. Given
the accumulate development of technologies, most
of these citations refer to other patents. The patent
examiner-given selection of the key citations is listed
on a patent’s front page (“data sheet”) or search report. However, a significant share of the bibliographic
information on patents contains non-patent literature
citations (NPCs) to scientific and technical publications. These citations provide some indication of the
potential contributions of published research results
to patentable inventions. In this study, the NPCs are
used that were listed on patents granted by the United
States Patent and Trademark Office (USPTO). US
patent law requires the USPTO patent examiners to
establish the novelty and uniqueness of an invention claimed by the patent application. References
to other patents and non-patent literature are added
to restrict or substantiate each claim, or to describe
the invention’s scientific and technical background.
Patents containing more than an average number of
references are often more complex and tend to cover
more legal claims and possible commercial applications. The examiner searches for all related publicly
available documents that are relevant to the invention
but not to bar the patent from being granted.
Patent applicants are subject to “duty of disclosure”,
which obliges them to inform USPTO, as long as the
application is under examination, of any relevant documents known to him which (might) have a bearing
on the patent claims. This legal requirement, and the
fact that in the late 1980s, USPTO has become more
6 The questionnaire contained the following definition of “recent scientific research”: “all exploratory, strategic and/or applied
scientific research—and directly related engineering research—
that was carried out at the earliest 10 years prior to the patent
application”. This broad definition was used in order to include
all obvious types of scientific research which allows for various
(firm- or field-dependent) definitions—some perhaps idiosyncratic
views—amongst inventors about what scientific research actually
constitutes.
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rigorous in enforcing disclosure of prior art, are considered the main reasons why USPTO patents tend to
contain an order of magnitude more NPCs than comparable EPO/PCT patents (see e.g. Michel and Bettels,
2001). The latter patents are not bound to duty of disclosure and tend to deal with a smaller number of
“umbrella” claims per patent focusing on the examination of the most relevant technical information thus
producing a far less NPCs.
As in other large-scale NPC studies, the analysis
will deal with the examiner-given ‘front page’ references NPCs only, primarily because the applicant/
inventor-given references are not available in a
machine-readable format. It should also be noted that
the choice of NPCs by the examiner might involve
some measure of arbitrariness due to accessibility of
documents, level of expertise, and features of the technological area (e.g. Collins and Wyatt, 1988). These
caveats notwithstanding, examiner-given NPCs to
the scientific and technical literature constitute valid
empirical evidence of linkages (causal or otherwise)
between research outputs and innovative applications,
which is specifically suitable for aggregate level
quantitative studies of science–technology interaction
patterns (Schmoch, 1993; Narin, 1994; Tijssen et al.,
2000; Tijssen, 2001). In absence of further data on
the motives and selection mechanisms of examiners
these front page NPCs are considered to be either
significant contributions of scientific and technical
research to the patented invention, or relevant for the
description of the state-of-art in corresponding fields
of science or engineering.
3. Findings
3.1. Patent sample
The postal survey comprised of a total of 171 questionnaires, of which 93 were returned with partial
or complete responses (55% response rate). 7 The
institutional distribution of the respondents covers
the five major R&D-intensive multinational enterprises (MNEs) in The Netherlands (Philips, Akzo
Nobel, Unilever, DSM-Gist, and Shell), a total of 42
7 One patent was deleted from the final statistical analysis due
to insufficient data.
other business enterprises including several small and
medium-sized enterprises (SMEs), five universities,
and six public research laboratories. When looking
at more than one or two technical areas and types
of institutions, one obviously has to deal with the
great variety of patents, and environments in which
R&D activities and patenting take place. An appropriate nation-wide innovation survey should therefore
cover this variety as much as possible in the patent
sample. Table 1 provides a general overview of this
diversity with the present sample broken down by
the main institutional sector of the inventor. It concerns the three background variables of the patents:
(1) technical area of invention; (2) stage of development of the invention (has it evolved into a ‘technological innovation’?), and (3) the patent’s commercial
value.
The findings illustrate the heterogeneity of this
patent sample, where each main institutional sector
seems to have its own distinctive innovation profile.
Chi-squares analyses indicate very significant differences in terms of the patent distributions of institutional sectors across technical areas (χ 2 -value = 22.5,
d.f. = 10, P = 0.01). The five large multinational
corporations tend to concentrate their R&D efforts
in three main technical areas, whereas the patents
of other smaller firms are more equally distributed,
reflecting the wide variety of SMEs in the sample.
More than 50% of the patents held by universities and
public research labs are assigned to the pharmaceutics and medicine sector, which is well known for its
relatively large input from academic research in the
life sciences (e.g. Tijssen et al., 2000). In contrast,
no significant differences were found between sectors
with regard to the invention’s stage of development
(χ 2 -value = 3.87, d.f. = 6, P = 0.69). Some 20% of
the technical inventions were not (yet) developed any
further, 40–50% are under further development, and
25–35% had in effect become technological innovations. This outcome for the public sector may come
as a surprise given their emphasis on research rather
than technical development, let alone commercial exploitation of their inventions. The main explanation
lies in the fact that several of these public sector
patents were actually transferred to external parties
for further development and commercialisation in
return for additional funding of follow-up academic
research.
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
515
Table 1
Key characteristics of the patents (% distribution, and number of cases per main institutional sector, n)
Institutional sector of patent assignee/applicant
MNEs
%
Other firms
n
Public sector
%
n
%
n
10
6
8
7
8
3
12
8
54
19
8
3
2
14
5
2
assignee/applicanta
Main technical area of the patent
Chemistry and materials
Electronics and information technology
Pharmaceutics, medicine and biotechnology
Instruments and controls
Machines and transport
Other
39
26
26
4
9
6
6
1
4
1
23
14
19
16
19
7
Development stage of the technical
No development (so far) or discontinued
Further development in progress
Commercial application, market introduction
Other, unknown
20
48
24
8
5
12
6
2
19
40
36
5
8
17
15
2
24
52
24
6
13
6
Current economic value of patentc
New product and/or process
Improved product and/or process
Sales of patent rights
Licenses or cross-licenses
Other, unknown
39
26
4
4
26
9
6
1
1
6
43
23
5
16
14
19
10
2
7
6
19
22
13
19
28
6
7
4
6
9
inventionb
a The main technical areas are aggregates of areas derived from the FhG-ISI/OST classification scheme which is based on the International
Patent Classification codes and used by the French Observatoire des Science et des Techniques (OST, 2000). Two of the responses from
the MNEs remained anonymous as for the company identity and therefore its main technical area.
b Development or application of the invention was performed either by the assignee listed on the patent and/or externally.
c Includes cases with multiple economic benefits derived by the same patent. The benefits are not necessarily accrued by the patent
assignee/applicant but may also involve external parties.
All three institutional groupings show a range of
economic benefits associated with the patented technologies, although some distinctive differences occur between the institution sectors (χ 2 -value = 12.0,
d.f. = 8, P = 0.15). The major value of the patents
held by companies resides in their contributions to
technological innovations, with a 40% share related to
new products/processes and an additional 25% share
for improved products/processes. The universities and
research institutes show a comparatively uniform distribution, covering economic value in terms of product and process innovations but also monetary gain
through licensing or sales of patent rights. This is what
one would expect given the lack of high-priority or
strong incentives in The Netherlands for in-house exploitation of public sector patents. Patent right transfers is occurred in five patents assigned to public sector
organisations—but also for six patents of the smaller
firms, as well as two owned by an MNE.
3.2. Research as input for innovation
The large innovation surveys conducted amongst
large European firms in the mid-1990s (PACE, CIS)
have provided ample evidence that in-house research
of business enterprises are often a key information
source for the development of their technological inventions (Arundel et al., 1995). External R&D is generally considered less significant, especially academic
research. The results from the most recent Community
Innovation Survey (CIS 2) conducted amongst manufacturing firms in The Netherlands indicate that 91%
of the respondents considered external information
sources to be (very) relevant input to technological
innovation processes, whereas external sources are
appreciated by only 54% (CBS, 1998). Only 22%
of the respondents considered academic research
of significance. The corresponding findings on the
science-based firms indicate a stronger reliance on
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Table 2
Information sources and science dependence of inventions (% share of scores per main institutional sector)
Institutional sector of patent assignee/applicant
MNEs
Other firms
Public sector
Important source of idea(s) for invention
Internal knowledge, skills and R&D
External R&D
Other information sources
96
8
24
93
19
29
100
20
32
Dependence of invention on recent researcha
Research was essential
Substantially delayed without research
No direct contribution from research
Contribution of research unknown
33
42
25
0
44
5
49
3
72
0
12
16
a
Research conducted less than 10 years prior to the priority date of the patent.
external R&D: 62% confirm the relevance of external sources, and 34% consider inputs from academic
research to be of value for their innovation processes.
So clearly, research efforts are important for technological innovations, but exactly how significant are
they? Following the categories introduced by Mansfield, the inventors rated the science dependence of
their patents according to three categories: (1) the invention would have been impossible without contributions of “recent” research, i.e. research completed less
than 10 year prior to the development of the invention;
(2) the development of the invention would have been
significantly delayed without recent research, or (3)
no direct contribution from recent research. Inventions
belonging to the first two categories are referred to as
“science dependent inventions” (SD inventions) from
now on. A broad definition of the term “scientific
research” was adopted in the questionnaire, encompassing both in-house industrial research and external
scientific research of either an exploratory, strategic or application-oriented nature (see Footnote 6).
Moreover, no distinction was made between research
that was deemed essential a further the development
process (e.g. theoretical findings, measurements) or
research that was considered crucial for elements of
the invention itself (e.g. technical devices). Additional questions probed for details about the nature of
contributions made by scientific research to the R&D
process leading to the patent. A distinction was made
between three general types of inputs: (a) internal
R&D by the inventor(s) or by others within the organisation; (b) external R&D performed primarily outside
the organisation, and (c) other information sources.
Table 2 presents the data from the inventor survey
confirming the PACE findings: the large majority of
the inventors rely heavily on their own knowledge and
skills and/or in-house R&D as information sources.
Inventors at the smaller firms and in the public sector are somewhat more inclined to incorporate results
from external R&D, although only 20% of the inventors consider this source relevant. Other information
sources are considered to be of somewhat more importance with shares ranging from 24 to 32%. The figures
on the relevance of in-house R&D and external R&D
also fit in quite well with the findings of the CIS survey in The Netherlands (CBS, 1998). However, with
respect to other information sources, CIS data indicate that no less than 80% of the Dutch firms consider
publicly available external sources important for innovation. The reasons for significant discrepancy between inventor-given data and CIS data are not quite
clear (the scope of these ‘other information sources’
is similar in both cases). The most likely explanation is the marked difference between inventors’ assessment, based on the relevance of those sources in
day-to-day R&D practice, versus the overall importance of such in-house facilities as perceived by R&D
management staff who usually fill in CIS questionnaires.
Clearly, the inventors’ in-house R&D capabilities
(i.e. knowledge and skills) represent the essential
knowledge base for industrial R&D and innovation.
As far as working bench research itself is concerned,
78% of the inventors consider their own engineering
research and/or scientific research a (very) important source of input, whereas external research is
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
considered (very) important by only 16% of the inventors. So, being engaged in research activity is an
important element in building, maintaining and exploiting that knowledge base. In the case of these
selected patents, 49% of them could not have been
developed without (recent) scientific or engineering
research. A further 14% of the patents could not
have been developed without considerable delay in
the absence of recent research. 8 As for the scores
in the sub-set of ‘technological innovations’, i.e. the
inventions that lead to concrete applications and/or
were introduced into the market embodied in products or processes, similar fractions are found (46
and 10%). On the whole, recent research was considered essential 33% of the patents held by the five
largest R&D-intensive multinationals (where research
fulfilled an important ancillary role in an additional
42%); 44% of the patents owned by other companies,
and for 72% of the university’-assigned patents. Interestingly, 16% of the inventors working in the public
sector could not indicate the relevance of recent research, most likely because its precise contribution
could not be identified or disentangled from other
inputs and information sources.
On the subject of academic research, it is important to note that 21% of the patents held by Dutch
firms that eventually became technological innovations are based on research (partially) carried out in
Dutch public research system. In nearly half of the
cases this involved research done in collaboration with
Dutch universities. The one-fifth fraction is slightly
higher than results derived from large-scale surveys
amongst firms that were held in the USA (Mansfield,
1998), indicating a 10–15% share of product innovations or process innovations that would not have
been developed without recent public research. However, the 21% share is likely to be upward biased owing to the sampling procedure (see Section 2.2) and
will probably drop to similar levels when a larger and
more representative set of Dutch-invented patents is
examined.
8
The three categories of the “science dependence” variable do
not present gradations on a single scale because the intermediate
category “substantially delayed without research” introduces the
time dimension that is not present in the other categories. For further analytical purposes, this category was merged with “research
was essential” into a composite category “direct contribution of
research” versus “no direct contribution of research”.
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3.3. Modelling the science dependence
of inventions and innovations
The inventor’s stock of knowledge and know-how
are obviously essential elements of an inventor’s
“R&D knowledge base” 9 when developing successful research-based inventions. Also, being directly
engaged in scientific or engineering research is more
than likely to be of some significance. However, is
research merely one of many useful sources of background information, or is it truly essential for those
patented discoveries and inventions? Is the inventor’s
knowledge base directly associated with the science
dependence of his or her inventions? How large is the
contribution of this knowledge base? And to what extent do macro level (“structural”) factors, such as the
industrial sector or R&D missions of the organisation,
impact on the science dependence? These individual
and organisational factors clearly determine the nature
and scope of technical inventions, but do they provide
for an adequate explanatory model of a technology’s
science dependence?
As indicated above, the industrial sector matters.
Several of the above mentioned innovation studies
indicate significant sectoral differences in terms of
science dependence. Moreover, recent patent citation studies have shown that the science relatedness
of patented innovations is not randomly distributed
across industrial sectors but tends to concentrate quite
strongly in science-based industrial sectors such as
pharmaceutics and biotechnology (e.g. Knoll et al.,
1998; Malo and Geuna, 2000). Furthermore, patents
assigned to public research institutes and universities
are by definition bound to be more science dependent
compared to those originating from business companies, especially from the SME’s in the traditional
manufacturing sectors. The analytical question is to
what extent these structural factors, like technological domain and institutional sector, may help explain
the science dependence of inventions. Or are they
9 This concept “R&D knowledge base” consists of two main
categories: (1) information sources and (2) the institutional environment in which the R&D is conducted. The former category
encompasses all tacit and codified knowledge with a direct or indirect bearing on research activities and technical development that
are accessible to the inventor. The latter refers to the corporate
R&D unit, university department, or other organisational entity in
which the inventor operates during R&D activities.
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Table 3
Factors contributing to the science dependence of technical inventions, multiple regression analysis on the ‘science dependence’ scores
provided by the inventors (n = 81)
Independent variables
Beta coefficients (S.E.)
Model 1
Model 2
Information sources
Inventor(s) knowledge and skills
Technical inventions or research by the inventor(s)
Bibliographic information sources (patents, research papers, etc.)
External technical inventions (outside the organisation)
External research (outside the organisation)
Internal interaction (co-workers, colleagues, clients, suppliers, etc.)
External interaction (conferences, workshops, trade fairs, etc.)
−0.29
0.28
0.22
−0.18
0.15
0.13
−0.05
(0.12)
(0.12)
(0.14)
(0.12)
(0.14)
(0.11)
(0.17)
−0.25
0.24
0.10
−0.09
0.09
0.12
−0.08
(0.12)
(0.12)
(0.15)
(0.13)
(0.14)
(0.12)
(0.13)
Research environment
Outsourcing of basic research
Resources for basic research
International R&D cooperation
Public/private domestic R&D cooperation
Resources for applied research
−0.19
0.17
0.12
0.08
−0.05
(0.19)
(0.17)
(0.17)
(0.19)
(0.17)
−0.11
0.12
0.06
0.09
−0.06
(0.20)
(0.18)
(0.17)
(0.19)
(0.17)
Main institutional sector (of the patent assignee/applicant)
Main technical area (of the technical invention)
Fit of regression model
Multiple r
r2
simply underlying variables that are already covered
by general characteristics, the R&D environment at
the organisational level?
Two formal models were defined to examine these
issues and test these hypotheses. The first model incorporates the two main factors underlying the inventor’s
knowledge base: (1) ‘information sources’ and (2)
‘R&D environment’. The second model examines the
possible added value of structural factors. This extended model includes two factors defining the general
R&D background, i.e. ‘main technical area’ and ‘main
institutional sector’. These four factors are captured
by a list of 13 variables presented in Table 3. In this
analysis, the set of institutional sectors was extended
to five categories: (1) MNEs; (2) SMEs; (3) large enterprises and private R&D labs (LEs); (4) universities,
and (5) publicly-funded research institutes. As for information sources, each inventor was asked to indicate
the different types of input relevant to the development
process leading to the patented invention. With regard
to the research environment, respondents identified
the research-related main features in their working environment (i.e. within their research unit/department),
0.23 (0.12)
0.15 (0.12)
0.55
0.30
0.60
0.36
more specifically whether of not these aspects had
become less/more important in previous years. The
original three categories of the dependent variable
“science dependence” listed in Table 2 are reduced to
two categories: (1) no direct contribution from scientific research, and (2) direct contribution from scientific research—where this research is regarded either
“essential” for the invention, or instrumental in preventing “substantially delay” in the technical development. 10 Each quantitative variable defines a nominal
measurement scale (i.e. an arbitrary categorical value).
The explanatory power of the two specified models is determined by executing a regression analysis
model. Multiple regression analysis was performed
with the CATREG procedure supported by the SPSS
Statistics Package (Windows version 8.0), allowing for
variables with categories based on nominal measurement levels. Each regression analysis yields a weighting of the relative importance of the various variables
incorporated in the model. Table 3 contains the
10 The 11 cases with “contribution of research unknown” were
deleted from the analysis leaving a total of 81 valid cases.
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
goodness-of-fit of both models along with list of standardised beta coefficients and standard deviations of
each variable. No direct causality can be derived from
these coefficients; they simple indicate that the extent
to which science dependence of patents relates to each
of these key variables taking into account the other
variables. Positive coefficients relate to SD inventions.
The results of the first “knowledge-base” model
suggest that inventor’s information sources are much
more important than the research environment in explaining the science dependence of inventions. The
highest positive coefficient relates to the information source ‘related inventions and research’ (0.28),
which is to be expected. Moreover, the inventor’s own
knowledge and skills shows a significant negative coefficient (−0.29). In other words, inventions tend to
be more science dependent if the required knowledge
and competences are outside the immediate reach of
the inventor and hence requires additional (external)
research. Furthermore, external knowledge sources
such as ‘bibliographic information sources’ (0.22) and
‘external technical inventions’ (−0.18) also appears
relatively important. Hence, SD inventions tend to be
characterised by R&D activities of inventor and the
use of bibliographic information sources. The highest
coefficients within the group of variables describing
the research environment concern the negative coefficient for ‘outsourcing of basic research’ (−0.19) and
its counterpart ‘resources for basic research’ (0.17).
Although most of the coefficients are fairly weak,
overall it seems fair to conclude that SD inventions
tend to correlate with the presence of previous R&D
and inventions produced by the inventor, the use of
bibliographic data as an information source, and access to resources for basic research. In other words, a
typical (private sector) research environment in which
inventors are using modern information technology
facilities to keep abreast of developments world-wide
and retrieve information that is of considerable use
for their own R&D activities.
As indicated, the fit of the regression model accounts for only 30% of the relational information in
the data. Incorporating the two macro level variables
(‘main technical area’ and “main institutional sector”)
into the “knowledge-base and R&D environment”
model may provide a better fit and hence a clearer
view of the relative contributions of these variables
within the broader context in which the invention
519
was developed. The science dependence of inventions
is indeed explained somewhat better by this model
(6%-points more variance is accounted for). Both
structural variables account for a significant share
of the variance in the data, in particular the institutional sector of the patent assignee/application, which
proves to be an additional decisive factor in explaining science dependence in addition to the inventor’s
own basic knowledge and skills, and previous inventions and research activities. The role of specific
elements in the R&D environment is now much less
pronounced, suggesting that science dependence of
inventions is basically a derivative of institutional sector and the inventor’s R&D capacities and previous
achievements. Other internal and external factors do
impact on the outcome of R&D processes but seem
to exert only a subtle effect.
Considering the large amount of unexplained variance this extended model is still at best a crude proxy
of the various inputs and background variables in this
process. Other, unknown factors seem to play a major
role in explaining the science dependence of the inventions. The residual variance might be explained in
part by inaccurate measurements and conceptual ambiguities, but it seems more likely that the model fails
to account for the role of specific social, cognitive and
organisational factor (e.g. teamwork or not, the specific research topic involved, or the presence of adequate technical facilities and instruments) that affect
the inventor’s R&D-possibilities and the direction of
invention trajectories. Moreover, comparatively little
is known about the micro level interaction between
the various inputs and constraints shaping the intricate
R&D process leading to a discovery or invention, especially with regard the contribution and “lock-in” effects related to the R&D-environment or to “network
knowledge” (i.e. information and advise derived from
informal interactions with people in the inventor’s personal R&D network).
In the main, these findings confirm the generally
accepted notion that the R&D intensity for an industrial sector is a common determinant of an invention’s
science dependence. SD inventions tend to be found
in relatively high numbers in certain R&D-intensive
technical areas in conjunction with certain types of
science-based assignees. Patents of public research institutes or universities in the pharmaceutics area obviously come to mind as the prime example of such
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Table 4
Institutional and technical sectors of science-dependent inventions in The Netherlands (% of all research-dependent patents)a
Main institutional sector (main technical area)
Pharmaceutics, medicine, biotechnology
Chemistry and materials
Electronics and information technology
Instruments and controls
Machines and transport
Other
Total
MNEs (%)
LEs (%)
SMEs (%)
Research
institute (%)
University (%)
9
4
2
2
4
9
13
8
2
2
15
8
4
2
4
2
2
2
2
6
21
32
13
21
13
Total (%)
43
17
15
13
9
2
a
Patented inventions where recent scientific research was crucial, or where the invention could not have been developed without
substantial delay in the absence of that research.
cases, as illustrated in Table 4 where these patents
account for 21% of all SD inventions. The contribution of the universities also includes a few patents
dealing with (medical) instrumentation. The second
largest concentration of SD inventions concerns the
13% share of other large enterprises (LEs) in the chemicals area, followed by a 9%-share of these firms in the
pharmaceutics area (biotechnology firms mainly) and
a 9%-share of the large multinationals in that same
area (DSM Gist and Akzo Nobel). Although this patent
distribution is partially specific for the situation in The
Netherlands (especially with regard to the MNEs), it
will also reflect general characteristics of industrial
sectors in terms of their R&D intensity, notably in
the science-based sector Pharmaceutics, medicine and
biotechnology.
Fig. 1 displays a map of the underlying co-occurrence pattern of the categories defining the four key
Fig. 1. Graphical overview of interrelationships between the research dependence of inventions and key background variables.
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
contributing variables. This display is derived from the
analysis of interrelationships between these variables
based on a descriptive statistical analysis technique
HOMALS (supported by the SPSS Statistics Package, Windows version 8.0). The distance between the
spatial co-ordinates of each pair of categories in the
two-dimensional space is linearly related to their degree of co-occurrence in the data. 11 Visual inspection
of this graph reveals a complex relational structure
amongst the variables. The upper left-hand quadrant
contains the SD inventions in conjunction with two
science-related categories: the institutional sector ‘research institutes’, and the science-intensive technical
area ‘pharmaceutics, medicine, and biotechnology’.
Not surprisingly, we find the other science-related
categories ‘basic research’ and ‘applied research’,
‘universities’ in close vicinity in the lower left quadrant. The top of the graph features the technical
area ‘ICT and electronics’ accompanied by the institutional sector ‘large enterprises’. On the lower
right-hand side of the graph represents the group
of research independent patents, which are mainly
granted to SMEs active in ‘machines and transport’,
‘instruments and controls’ and ‘other technical areas’.
Between these two extremes, we find the MNEs. It is
interesting to see that the imaginary axis defined by
the science dependence of patents is almost orthogonal to the axis representing the absence/presence
of (applied/basic) research, confirming the results in
Table 3 that the presence of research activities within
organisations is certainly not the most discriminative
indicator of patents’ science dependence. The technical area ‘chemistry and materials’ occupies a central
position in this graph at the intersection of the imaginary and geometrical axes, thus indicating a relatively
large degree of heterogeneity of this particular technical area in terms of its dependence on scientific and
engineering research.
11 The HOMALS technique is based on the mathematical decomposition of the relational information (‘variance’) within a data
array according to the eigenfactor–eigenvalue method. The first
dimension of the graph corresponds to the statistically most important factor, accounting for the largest share of the variance. The
second factor—mathematically independent of the first factor—
accounts for the second largest share, etc. The first factor accounts
for 46% of the variance, the second factor 30%. In total, the
two-dimensional space and the corresponding location of the categories account for 76% of all relational information in the table.
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The models described above incorporate a range of
variables related to the R&D process and its organisational and technical environment. Note that the actual
outcome of this process in terms of the immediate applicability of the invention or the patent’s commercial
value was not included in the model due to lack of
sufficient data on factors describing systemic features
of the commercialisation process. Modelling these
processes and economic pay-offs of research is usually quite difficult, even though a range of valuation
methods have been applied to that affect in the 1980s
and 1990s (e.g. Averch, 1994). Valuation of patents
in this study, in terms of their short-term return on
investment, may prove equally difficult since the actual gains may take several years to fully materialise.
The data from this survey do at least seem to confirm
the notion that research effort and pay-offs in terms
of valuable patents seem unrelated. Returning to the
background data on the patent briefly described in
Table 1, additional correlation analysis reveals that
the science dependence of these patented inventions
is indeed not correlated with the perceived economic
value of the patent (r = −0.03), nor with the current
development state of the invention (r = 0.02). Judging by these tentative empirical findings, the science
dependence of a technology is indeed no guarantee
for a commercially successful innovation.
3.4. Science dependence and non-patent
literature citations
Some of the crucial research contributions to inventions are bound to be captured by the inventor’s
or the patent office examiner’s list of references, especially in those cases where basic scientific research
and prior research papers contributed directly to the
inventive steps. Several analysts studying the relationships between science and technology have used
these citations as an external indicator of contributions made by scientific research. Citation studies of
patents have shown that industrial researchers in some
fields make extensive use of the current scientific
journal literature, especially in science-based industries such as biotechnology and pharmaceuticals when
new knowledge and techniques emerge (Narin and
Noma, 1985). Narin and co-workers have used these
particular citations, listed on the title page of patents
issued by the US Patent Office (USPTO), as a proxy
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measure to determine the dependence of technologies
on government-funded academic research (e.g. Narin
et al., 1997). Others are less convinced that these
‘non-patent citations’ (NPCs) indicate such causal
links and prefer to view them in terms of partial indicators of “science relatedness” or “science–technology
interaction” (e.g. Schmoch, 1993; Tijssen et al.,
2000). Recent studies have indeed raised critical
questions concerning the validity of these citations to
describe this sequential process of knowledge flows
and causality of science–technology linkages (e.g.
Meyer, 2000a). Having both the inventors’ ratings
and data on NPCs at our disposal provides a unique
opportunity to test those citations in terms of their
value as an indicator of an invention’s science dependence. Low correlation measures would invalidate the
simplistic ‘linear model’ and suggest more complex
interactive models of science–technology relationships (e.g. Rip, 1992; Meyer, 2000b). Note that these
particular citations reflect only some of the contributions of science to technology, notably the codified
part of the knowledge transfer reflecting inputs of
(basic) research as a source of ideas, analytical methods and data. Other important sources such as skills,
techniques, and instrumentation are usually more embedded in the research process and therefore not very
visible through the scientific literature and citation
links (Le Pair, 1988).
Ideally one would expect to find at least one citation
referring to a research paper if the patent has benefited
noticeably from scientific research efforts. However,
only 17 of the 49 patented RD inventions listed NPCs.
Apparently, the science dependence of a technical invention does not automatically lead to large numbers
of NPCs on the front page of the patent. At this point
it should be taken into account that large differences
occur between USPTO patents and EPO/PCT patents
with regard to the propensity for including these citations to research papers on the patent examiner reports (see Section 2.3). Nevertheless, SD inventions
and science-based patents in either patenting system
are likely to contain more NPCs than non-SD inventions. The scores on the NPC variable were therefore split into four categories (i.e. 0; 1 or 2; 3–9;
and 10 or more citations). Once again, the variable
‘science dependence’ was reduced to two categories.
The Chi-square test indicates no statistically significant differences between NPC distributions between
the two RD categories (χ 2 -value = 3.23, d.f. = 3,
P = 0.36, n = 75). Clearly, the presence of one or
more NPCs on patents is not a good indicator of the
invention’s science dependence as perceived by the inventors themselves, thus confirming the questionable
validity of these citations as causal measures of knowledge flows from the science base to the technology
domain.
As noted, this outcome may still mask differences
between technical areas and the patent examination
objectives and procedures of USPTO and EPO. With
regard to EPO patents, the results of the Chi-square
test yield a very insignificant χ 2 -value of 0.88 (d.f. =
2, P = 0.65, n = 20), whereas USPTO patents
show the expected slightly better, but still statistically
insignificant, correspondence between both variables
(Chi-square value = 2.91, d.f. = 3, P = 0.41, n =
75). Not surprisingly, we find a very significant relationship between the NPC variable and the main technical areas (χ 2 -value = 48.8, d.f. = 15, P = 0.000,
n = 82), where the technical area “pharmaceutics,
medicine and biotechnology” accounts for 7 of the 8
patents listing 10 or more NPCs.
In conclusion, the presence of NPCs on patents is
not related to the science dependence of a invention
itself, but should rather be seen as a reflection of the
main technical area involved and the common practice amongst USPTO patent applicants (enforced by
USPTO regulations) to add such references to either
substantiate or restrict the knowledge claims related
to the science-based invention. In those cases where
NPCs are included, one would at least expect to find
a positive correlation with the “science relatedness”
of an inventor’s knowledge base and research environment. This is indeed borne out by the findings:
the presence of these citations in patents is significantly correlated with the contributions from external
research (ρ = 0.35), and the presence of resources
for basic research (ρ = 0.32). Not surprisingly, the
highest positive correlation (ρ = 0.54) goes to the
use of databases for searching papers and patents, a
key source for retrieving information on research articles dealing with new developments and associated
knowledge domains. Concluding, these findings reject
the strong hypothesis of these NPCs as indicators of
causal relationships. However, the weaker hypotheses
of “science relatedness” and “USPTO enforced citation practices” both seem to hold.
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4. Discussion and conclusions
4.1. Inventor survey methodology
The inventor’s perspective was sought to obtain a
more accurate micro level “working bench” view of
the development process leading to patented inventions. The prime aim was to examine what actually
happens in innovation practice, rather than eliciting
general information provided by senior R&D management. As such, patents appear to provide a fruitful
entry-point on specificities of the technical inventions,
and a rich source of insider information on the background and context of their innovation-related R&D
efforts. While this analytical method does not associate
itself with particular class of patents, or with specific
types of innovative products of processes, nor yield
a precise measure of science dependence of innovations, it does provide a more systematic and detailed
account of the knowledge base and science dependence of technical inventions. The success of this first
exploratory study in terms of response rate amongst
inventors and the quality of their information presents
an encouraging vindication of the research design and
methodology adopted. Note that this first study deals
only with patented inventions and discoveries, thus
discarding those innovations that are not protected by
patents. However, using one of their patents as a point
of entry, inventors could also be queried about their
other important but ‘hidden’ non-patented inventions
and innovations.
Any assessment of the impacts of R&D on the innovation process is affected by the methodological limits
of known methods. Some final critical comments are
therefore in order with respect to this particular case
study, which is also inevitably restricted in its scope
and explanatory power, not least because the complex
and interrelated nature of causal and intermediate
factors determining science dependence which defies any sort of simplistic ‘one-method one-model’
approach. A general methodological problem is the
fact that this kind of expert opinion based ‘object
approach’ will always contain organisational-, sectorand nation-specific characteristics. Moreover, this particular small-scale study applies a stratified sampling
approach designed to cast a wide net across research
performing organisations, firms and industrial sectors
in The Netherlands, but does not provide a detailed
523
picture of science dependence across wide range
of modern technologies that were (co)developed by
Dutch inventors. Consequently, the relatively small
sample size is an impediment to a comprehensive
understanding of the diversity of science–technology
relationships within The Netherlands, let alone for
generalisation to other comparable countries.
Mail surveys amongst inventors can provide a
nuanced understanding and quantitative analysis simultaneously, but one cannot rely on them to capture
the complexity of the inventive process and all relevant impacts of related R&D. Clearly, further understanding is required of both the knowledge creation
processes and the information sources underlying
technical inventions in relation to the variables incorporated in this study. Ideally, one needs a substantial
sample of follow-up interviews to verify the questionnaire responses and fully understand results within the
context of the case study. When querying inventors
one has to keep in mind that these insider accounts
also have inherent limitations with regard to the reliability and time-dependence. Expert opinions always
contain a subjective element and need verification
and objectification. Moreover, confidentiality issues
and corporate policies regarding participation in such
surveys may affect responses. Also, scientists and
engineers do not tend to view their daily activities in
terms of distinctive categories derived from innovation
surveys and micro-studies of science and technology.
Many of the skills, competencies and other inputs to
technical inventions are likely to be taken for granted
to the practitioner. In addition, the inventor’s views
of relevant inputs and the technical and commercial
success of an invention may be filtered by the extent
of his/her contribution, as well as limited perception
of its further development into an innovation and
subsequent utilisation and market introduction.
Ambiguities in definitions of concepts describing
interactions, knowledge flows, or properties of tangible and tacit knowledge inputs, may also lead to misunderstandings and unintended biases in the answers.
In this particular case, both key concepts—‘research’
and ‘science dependence’—can be quite slippery notions. It is relatively easy to capture the use of codified
research results through the formal scientific and technical literature and for researchers to recall their use of
this kind of input into the development of technologies,
but the use of tacit “embedded” research knowledge,
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either from the inventors own background and experience (graduate training, attending conferences, etc.) or
“network knowledge” provided by a colleague, is less
likely to be remembered and show up as a significant
research contribution in such a survey. As a result, the
construct ‘recent scientific research’ that was used in
this study probably defines a too conservative conceptual framework to capture all relevant research-based
inputs into the development process.
4.2. Implications for modelling
science–technology relationships
What then are the overall implications of the results
emerging from the models and analyses used in this
particular study? The outcome confirms that several,
more or less equally influential factors seem to be
determining the knowledge creation and transfer processes leading to successful technical inventions. The
type of organisation and internal R&D environment
are clearly the most significant determinants in the
science dependence of its patented inventions. Internal
sources are being used heavily for the development of
invention ideas, where inventors often cite their own
research and previous patents as important elements
in the inventive process. Both in-house research and
external knowledge sources (science included) appear
to be essential assets for securing strategic positions
in leading-edge technologies, where the individual
knowledge base of R&D personnel seems to be particularly important in the materialisation of creativity,
suggesting that the research activity of the staff is
important for building up and maintaining innovation
power.
As for the contribution of external research vis-à-vis
in-house research underpinning these Dutch inventions, the results of this study seem to correspond with
the outcomes of the Community Innovation Survey
held amongst firms in The Netherlands. However,
a host of interdependent country-specific factors related to innovative capacity may all impact on how,
when and why internal and external research is used
in the development of technical inventions. 12 The
12 Notably, industrial structure, firm sizes and the presence of
large R&D-intensive multinational corporations, the quality and
utility of academic research, university/industry relationships, domestic policies and programmes concerning private R&D, and the
commercialisation of public research.
outcome of the Communication Innovation Surveys
held amongst many thousands of companies in the
EU Member States suggests significant differences
between the various countries in terms of the main
sources of information used for innovation processes
(e.g. Bosworth et al., 1996; Eurostat, 2000). Hence,
caution is warranted in straightforward generalisation
of the findings of this study to other countries.
Although the statistical model of the various inputs and background variables explicitly includes the
inventor’s R&D environment, it obviously cannot
provide a good account of all relevant in-house factors in terms of innovation-related functions of the
organisation (research, technical, commerce), nor of
the intricate interactions between the external and
internal environments (e.g. Forrest, 1991). There is
neither a single set of motivating factors characterising groups or individuals that produce technical
inventions and technological innovations, nor a single
organisational pattern of R&D teams, nor a typical
R&D trajectory within an organisation or industrial
sector. Moreover, research efforts may serve a variety
of purposes, where the resulting learning process and
outcomes may or may not prove to be relevant or
even crucial for the development of a technical invention. As a result, science–technology interactions
are a complex phenomenon where plausible theory
and convincing explanatory models are lacking, and
the measurement of relevant dimensions is still at
an early stage. Despite the many admirable efforts
in recent years, such as the renowned Kline and
Rosenberg’s chain-link model (Kline and Rosenberg,
1986), we still have no comprehensive conceptual
framework nor theoretical scheme capable of determining all relevant (non)causal interrelationships
between science and technology. The problem is even
more complicated by the fact that science–technology
relationships are changing fast in many countries and
industrial sectors. The possibilities for statistically
modelling and measuring of science–technology relationships and knowledge flows is therefore severely
limited by the lack of the satisfactory understanding
of the working of the whole mechanism and the availability of relevant empirical data. Not surprisingly
then, the fit of the models used in this study leaves
the majority of the variance in the data unexplained.
However, in view of the above inherent constraints,
this outcome should not be seen as insurmountable
R.J.W. Tijssen / Research Policy 31 (2002) 509–526
obstacle for further advances in modelling techniques
and development of valid indicators. In contrast, their
shortcomings should stimulate efforts to seek new
information sources and develop an appropriate mix
of quantitative and qualitative indicators and improve
modelling of science–technology linkages.
Fortunately, an abundance of empirical data does
exist in the form of patent citations to the research literature, especially on USPTO patents, although one
should keep in mind that the citations examined in
this study refer only to papers in international science
journals that tend to publish basic research findings.
One may assume that patents listing these citations are
indeed likely to be more basic-research related compared to those without these citations. However, patent
documents lacking such citations cannot be regarded
as significantly less science-dependent, especially in
terms of (in)direct benefits derived from applied scientific research or engineering research. Unfortunately,
the results of this study strongly suggest that these citations are invalid micro level measures of causal links
between science and technology and should therefore
be used with extreme care in innovation studies, especially as proxies of science dependence.
The survey-based approach used in this study seems
to hold some promise as a way forward. Although
these first findings are subject to several restrictions
owing to the fact that direct science–technology relationships are hard to identify and quantify, the data
from the inventors do shed more light on some relevant
features of knowledge bases underpinning the contributions made by scientific and engineering research.
The conceptual framework and statistical model used
in this study identifies certain general features of these
science–technology relations, although it tends to focus only those elements that are relatively easy to
identify, categorise and quantify. Clearly, many relevant aspects of the science dependence of modern
technologies have yet to be clarified and taken into account. Nevertheless, the (weak) exploratory model and
patent-based indicators provide a useful entry-point
for highlighting various substantial issues of relevance
that deserve further attention and address questions
and issues raised by analysts and policy makers.
The final overall conclusion to be drawn is that
the present scope for interpretation and generalisation
from this study on Dutch inventions and inventors to
a wider view of science–technology linkages and a
525
broader R&D-innovation linkage theory is obviously
still quite limited. The ability of the approach in terms
of its robustness to generate and support trustworthy
micro narratives, and its scalability and exportability, is therefore an open question. As a consequence,
this study should primarily be understood as an exploratory effort contributing to a better modelling and
understanding of the tangible linkages between technology and science. As such, it should been seen as
step in an on-going knowledge-creation process towards a more comprehensive quantitative model of
science-based technical change and innovation that has
produced significant empirical results in the 1980s and
1990s which are now shaping debate and measures in
domestic and international S&T policy (e.g. OECD,
2000).
Acknowledgements
The author would like to thank Ton Nederhof, Renald Buter, Erik van Wijk, Suze van de Luijt, and
Christine Alkemade for their assistance during various stages of the research project. This undertaking
of course would have been impossible without the
support and data kindly supplied by all the Dutch
inventors participating in the mail survey. The helpful comments of the three anonymous referees, and
comments by Barry Bozeman (Georgia Tech) and his
PhD students, on an earlier version of this paper, are
all gratefully acknowledged. This work was funded
by The Netherlands Ministry of Education, Culture
and Science.
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