Academic Inventors, Technological Profiles and Patent Value: An Analysis of Academic
Patents owned by Swedish-based firms
Daniel Ljungberg*, Evangelos Bourelos and Maureen McKelvey
Institute for Innovations and Entrepreneurship,
School of Business, Economics and Law,
University of Gothenburg,
Sweden
* Corresponding author: daniel.ljungberg @handels.gu.se
Abstract
This paper analyzes the relationship between academic inventors and firms, focusing on the
relation between academic inventors, the technological profiles of firms and patent value. In
particular, this paper focuses on the value of academic patents as compared to non-academic
patents, owned by large firms based in Sweden. One finding is that academic patents have a
short-term disadvantage, which disappears in the long-term. Our results also indicate that
controlling for whether the patent belongs to a core or non-core technology relative to the firm’s
technological profile neutralizes the premium of non-academic patents. In other words, patents
belonging to firms’ core technologies have significantly higher value, regardless of whether they
are academic or non-academic patents. The above results indicate that the technological profile of
firms is an important characteristic to analyze, when examining the value of academic patents and
the specific role that academics play in industrial invention.
Keywords: Academic patenting; Patent value; Technological profiles
JEL classifications: O31, O34, O39
1
1. Introduction
Academic patents have been used as a main indicator of the contribution of universities to the
knowledge economy, and research has focused upon the relationship between universities,
patents and their impact upon society. This paper analyzes the relationship between academic
inventors and firms, focusing on the relation between academic inventors, the technological
profiles of firms and patent value, by studying firm-owned patents both with and without
academic involvement. Our analysis of patent value explicitly addresses the differentiation of
short-term and long-term value as well as the relation between value and the technological profile
of the firm.
According to a recent review, research on academic patenting has addressed two broad topics,
namely the i) the academic inventor; and ii) the (economic) value of academic patents (Lissoni
2012).1 This paper is related to the second topic, which concerns the (economic) value of
academic patents, sometimes referred to as quality or importance of the patent in the literature.
Much of this literature analyzes patents owned by universities as compared to patents owned by
companies, and so far, most literature addresses data from the USA. The literature examines the
impact of institutions and policy initiatives fostering university patenting as a way of explaining
and investigating the value of university-owned patents, as compared to firm-owned patents
(Henderson et al. 1998, Sampat et al. 2003, Bacchiocchi and Montobbio 2009).
Limited evidence exists for a few European countries, and these studies focus on i) the value of
academic patents in general as compared to corporate patents (Czarnitzki et al. 2011) and ii) the
relation between ownership of academic patents and their value (Crespi et al. 2010, Czarnitzki et
“Academic patent” denotes a patent with one or several inventors affiliated to a university, regardless of assignee.
“University patent” denotes patents owned by a university.
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al. 2011, Czarnitzki et al. 2012, Sterzi 2013).2 Taken as a whole, the question about the relative
value of academic patents remains largely unanswered (Geuna and Rossi 2011, Lissoni 2012).
This paper contributes to the new stream of literature, which studies the relative value of
academic patents owned by firms as compared to non-academic patents owned by the same firms
(Ljungberg and McKelvey 2012).
The purpose of this paper is to analyze the value of academic patents owned by firms based in
Sweden, by comparing firm-owned academic patents, i.e. where at least one inventor working at a
university was involved, with firm-owned non-academic patents, i.e. where no inventors worked
at a university. Using this comparison of two sets of patents owned by firms, we are able to
isolate the impact – and thus examine in more detail – the contribution of academic inventors.
The paper concentrates on two specific issues, which previous literature suggests may affect the
value of firms’ academic patents. First, following Sterzi (2013) and Czarnitzki et al. (2012), we
investigate whether there is a difference in the relative short-term and long-term value of firms’
academic patents. Following previous literature, value is assessed through forward citations3
(Henderson et al. 1998, Sapsalis et al. 2006, Czarnitzki et al. 2011, Sterzi 2013). While short-term
citations have been found to be related to the economic value and importance of patents
(Lanjouw and Schankerman 2004), Sampat et al. (2003) suggest that later, long-term, citations
indicate that these patents are more “science-based”. This, taken together with that academic
inventors conduct more basic research (Trajtenberg et al. 1997), suggests that there should be a
comparative advantage for firms’ academic patents in the long-term.
Patent value has in previous studies mostly been measured as the number of forward citations, but in a few
instances in monetary terms (e.g. Crespi et al. 2010).
3 Value should here therefore be considered in terms of the technological importance of the patent.
2
3
Second, we analyze the value of patents in relation to the technological profiles of firms, by
distinguishing between patents that belong to core and non-core technologies of the firms (cf.
Granstrand et al. 1997). Technological profiles categorize the technological competencies of
firms, whereby some technologies are core and central to the firms’ competencies while other
technologies are non-core and thereby more peripheral. Firms may engage in collaboration with
academics for different reasons, and that collaboration could strengthen the existing
competencies of the firm or else aid in building new competencies by exploring new areas
(Ljungberg and McKelvey 2012). The reason for including technological profiles in the analysis is
that there may be an interaction between the technological profile of a patent and its short-term
and long-term value.
Sweden represents an interesting but somewhat special case, due to the country’s institutional
features and its industry structure, which should be explored in later research about academic
patenting in Europe. Firstly, previous research has debated heavily whether or not Sweden has a
problem in commercialization of academic research (Granberg and Jacobsson 2006). However,
the most recent results show that academic patents are granted at similar levels as found in other
countries, if inventor-level data is used instead of university-owned patents (Lissoni et al. 2008).
Moreover Swedish academics in engineering are positive towards commercialization (Bourelos et
al. 2012). Secondly, one preliminary study suggests that Sweden is the only country where firmowned academic patents appear to be less important than university-owned and non-academic
patents (Lissoni and Motobbio 2012). However, Sweden, which retains what is called the
teacher’s exemption (professors’ privilege)4, has too few university-owned academic patents as
compared to USA, Italy and France to provide conclusive evidence (Lissoni et al. 2008), Thirdly,
more than 80 per cent of Swedish academic patents are assigned to firms, and the bulk of these
The teacher’s exemption (professor’s privilege) means that employees of universities and public research
organizations retain IP rights over the results of their research as individual inventors. The individual can choose to
transfer their IP to the university but the university has no claim to IP rights.
4
4
firm-owned academic patents are owned by a few multinational corporations (Lissoni et al. 2008,
Ljungberg 2011), which dominate the Swedish industrial structure.
This paper is structured as follows. Section 2 presents an overview of the value of academic
patents, leading to our hypotheses. Section 3 details the data and methods employed in the paper,
while Section 4 provides the results. Section 5 discusses the findings and conclusions.
2. The value of academic patents owned by firms
In order to comprehend the distinct role of academic inventors and the value of academic
patents, we start by examining the contradictions between basic and applied research that may
affect the value and nature of resulting patents.
Different incentives and structures motivate individuals that pursue scientific or technological
and practical results (Dasgupta and David 1994). Academic inventors are assumed to have a
different orientation than non-academic (firm) inventors. Since academic inventors are trained
and incentivized to conduct more basic research, their patents should reflect inventions resulting
from basic research and have broader applications than inventions stemming from corporate
research (Czarnitzki et al. 2009, Trajtenberg et al. 1997). Firms, on the other hand, tend to be
oriented toward more applied research, focusing on short-term returns. This suggests that
academic patents ought to, on average, be more important or valuable than corporate ones.
Early research on university patenting, in the USA, showed that university-owned patents are on
average more important in terms of receiving more forward citations than firm-owned patents
(Henderson et al. 1998, Sampat et al. 2003, Bacchiocchi and Montobbio 2009). For Europe, the
empirical evidence is less straightforward. Bacchiocchi and Montobbio (2009), in a study of five
European countries, found that university-owned patents are not associated with more citations
5
than corporate patents. Lissoni and Montobbio (2012) found that academic patents in five
European countries tend to be less cited than non-academic ones, but that differences exist
depending on ownership.
Some recent studies have accordingly analyzed academic patents in Europe by examining the
relationship between ownership and patent value. There is some conflicting evidence. Most
notably the survey-based research by Crespi et al. (2010) finds no relation between ownership and
the value of patents as estimated in monetary terms by the inventor(s). However, two recent
studies using forward citations have found that firm-owned academic patents are associated with
short-term value while university-owned patents are associated with long-term value. Czarnitzki
et al. (2012) found that in Germany firm-owned academic patents receive more citations in the
short-term perspective (within five years of the application being filed) while university-owned
patents are related to more long-term citations (later than five years after the application).
Similarly, Sterzi (2013) shows that in the UK firm-owned academic patents are associated with
more citations than university-owned patents in the short-term and medium-term (up to 3 and 6
years after the application, respectively), while this difference disappears for long-term citations
(more than 6 years). The interpretation provided for these findings is that firms seek short-term
or immediate returns, and therefore only engage academics in projects (or seek out academic
inventions) with short-term returns at the expense of more basic long-term inventions.5
Academic patents owned by firms are particularly interesting because these industrial inventions
are ones that, by definition, include the contradicting incentives and structures of both academics
and firms: Academic inventors have an inclination towards more basic and long-term research
Short-term citations have been shown to be related to the importance and economic value of patents (Lanjouw and
Schankerman 2004), while it has been suggested that later citations indicate the degree to which the patent is
“science-based” (Sampat et al. 2003).
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while firms have a predisposition to focus on applied technologies with short-term or immediate
returns.
These two contradicting influences should be reflected in the contrasting value and nature of
firms’ academic and non-academic patents. On the one hand, if firms’ academic patents result
from an invention process predominantly characterized by the nature and orientation of
academic research then we expect them to show different effects in the short-term and the longterm. This follows from prior results suggesting that more science-based patents are related to
later (long-term) citations as compared to corporate ones (Sterzi 2013, Czarnitzki et al. 2012). On
the other hand, if the academic influence is negligible and firms involve academics in patents
leading to immediate returns then the involvement of academic inventors should not influence
the short-term vs long-term value of firm-owned patents. The latter assumption seems the most
plausible, following previous findings that firm-owned academic patents tend to be more cited in
the short-term than university-owned ones (Sterzi 2013, Czarnitzki et al. 2012). However,
Ljungberg and McKelvey (2012), in a study of Swedish-based firms, show descriptive evidence
that firms’ academic patents on average are more likely to cite non-patent literature than nonacademic ones. This indicates that these patents have relatively stronger scientific links (Callaert
et al. 2006), which suggests that firms’ academic patents are to some extent influenced by the
orientation and incentives of the academic inventors. Taking these arguments and the existing
evidence together, we derive the following hypothesis:
Hypothesis 1:
The effect of academic inventors on the value of firm-owned patents is differentiated over time,
with an expected disadvantage in the short-term and an expected advantage in the long-term.
7
Firm-owned academic patents are the outcome of university-industry collaborations, which are
multifaceted by nature (Bishop et al. 2011). In order to assess their relative value, we therefore
need to take into account the specific types of collaborations resulting in academic patents.
Moreover, the firm has different collaborative needs depending on its competencies and
technology base (cf. Santoro and Chakrabarti 2002, Nickerson and Zenger 2004). To take this
into account, we use the concept of technological profiles, as proposed by Granstrand et al.
(1997), which can be used to categorize the technological competencies of firms.
Since firms have high competencies and commit much resources to core technologies, we would
expect that patents belonging to this profile have, on average, higher value than patents in noncore technologies, where the firms have lower competencies and/or commit fewer resources.
Hypothesis 2: Patents belonging to firms’ core technologies have higher value, as compared to
patents in non-core technologies.
Ljungberg and McKelvey (2012) analyzed the value of firm-owned academic patens within firms’
different technological profiles. They found that academic patents in firms’ core technologies
were related to fewer citations as compared to non-academic patents, while academics patents in
non-core technologies were either more cited or not significantly different depending on the
technological profile in question. These findings indicate that, depending on the varying needs
and competencies of the firm, different types of patents will have different types of contribution
from academic inventors. Some are closer to ‘science’, but the most is driven by the (immediate)
needs and competencies of the firm. Thus, controlling for core technologies might explain some
of the variation of the value of academic patents.
8
Hypothesis 3: Controlling for whether patents belong to the core technologies of firms decreases
the effect of academic inventors on patent value.
3. Method
3.1 Sampling and database creation
The sample of patents we assembled for our study originates from the PATSTAT-KITeS
database, containing all European Patent Office (EPO) applications between 1978 and 2009.
From this database, we extracted all EPO applications with at least one inventor based in
Sweden, with an assignee identified as a firm, and with priority year between 1985 and 2009.
Academic patents, i.e. those applications having at least one inventor affiliated to a university,
were identified by matching the original sample of all firm-owned Swedish patent applications
with the Swedish version of the KEINS/APE-INV database. This database contains EPO patent
applications, whose inventors have been matched by name and surname with complete lists of
Swedish university researchers of all ranks from assistant to full professor.6 The database thus
contains the total population of Swedish academic inventors.
Empirically we focus upon large firms, and following the OECD definition we consider firms
with more than 250 employees as large.7 For the firms identified as assignees of Swedish patents,
we gathered firm information by exploring the database Orbis, annual reports and similar
sources, in order to identify the large firm assignees. The database of Swedish patents was
cleaned from patent applications not assigned to firms, which did not fulfill the criterion.
For a detailed account of the KEINS database, constructed in 2004, see Lissoni et al. (2006). This database was
expanded by the authors, during the APE-INV project, by collecting new lists of Swedish university researchers in
2011 and matching these to EPO applications. The methodology used largely corresponds to the one employed for
constructing the KEINS database. Due to the methodology, we have in the present sample not been able to identify
those patents invented by academics that retired or changed profession before 2004.
7 In order to be able to distinguish between the technological profiles of the firms, we sample firms that consistently
and continuously patent, which means that we sample organizations with substantial patent portfolios.
6
9
Moreover, by reviewing the history of the identified firm assignees, the sampled patents were
aggregated at the level of the corporate group. After cleaning the data, the sample consists of
16053 patent applications, including 842 academic patents, owned by 32 firms.
Patent data were extracted from the PATSTAT-KITeS database. In addition, data on patent
citations were extracted from the OECD/EPO patent citations database. This database was
issued by European Patent Office (EPO) together with the OECD, and contains detailed patent
data covering all EPO applications, as well as all patents filed for under the “Patent Cooperation
Treaty” (PCT), between 1978 and 2012.8
3.2 Variables
The dependent variable in our analysis is the number of forward patent citations, following
previous literature (e.g. Henderson et al. 1998, Sapsalis et al. 2006, Czarnitzki et al. 2011, Sterzi
2013).9 While forward citations is a somewhat noisy proxy for patent value (e.g. Harhoff et al.
1999), they are correlated with social, private and market value (Trajtenberg 1990, Harhoff et al.
1999, Hall et al. 2005, Gambardella et al. 2008). To account for the truncation problem of
citations (Hall et al. 2005), we include year dummies as controls in our analysis.
Moreover, the inter-temporal distribution of citations captures different types of value (e.g.
Lanjouw and Schankerman 2004, Sampat et al. 2003). We therefore also distinguish between
Webb et al. (2005) presents an earlier version of the database.
We do not exclude self-citations, neither at the inventor nor owner level, i.e. those citations where the citing and
cited patents have at least one inventor or owner in common. Excluding self-citations is not possible with our data,
as it would require identifying not only all the Swedish inventors, like we did, but also all their co-inventors as well as
all the inventors listed on the citing patents. However, we have manually checked the number of self-citations of a
small sample of patents and found that the number of self-citations is not high. This suggests that including selfcitations in our counts is not likely affect our results in any significant manner. On a more substantive note,
excluding self-citations may be also considered inappropriate, as they may indicate that development of a (patented)
invention is going on within the inventing firm, which is in any case a sign of the invention’s economic potential.
8
9
10
short-term and long-term value, by considering the citations received up to and after 3 years after
the priority year (see Table 1).10
Table 1. Dependent variables
FPC
Patent value: Total number of forward citations
received by June 2012.
FPC<3
Short-term patent value: The number of forward
citations received within the first three years after
filing the application.
FPC>3
Long-term patent value: The number of forward
citations received, by June 2012, after the first three
years after the application.
The first main determinant in our analysis is a dummy variable that classifies the applications as
either academic (1) or non-academic (0). The second determinant is a variable that distinguishes
whether a patent belongs to the firm’s core or non-core technologies, relative to the technological
profile of the firm. Following Granstrand et al. (1997), we constructed the technological profiles
of the firms, defined according to two dimensions of patent classes, namely Patent Share and
Revealed Technology Advantage (RTA).
Granstrand et al. (1997) classify the competencies of firms into a four-field taxonomy, based on
two dimensions of patent classes (see Figure 1). This paper only examines the ‘core’ technologies
and categorize the other three profiles as ‘non-core’ technologies for reasons explained in the
next section. The concept of technological profiles is useful for differentiating technologies based
on the firm’s resource commitment to a technology and its competitive advantage over other
There is some arbitrariness when drawing the line between short-term and long-term citations, with both 3 years
and 5 years being used in the literature (Czarnitzki et al. 2012; Sterzi, 2013). Sterzi (2013) finds that the number of
citations up to 3 years after the priority year in corporate patents is on average about 44% higher than in university
patents. Since our study is limited to corporate patents, we use the 3-year threshold.
10
11
firms in that technology. In this conceptualization, core technologies are those where the firm
commits much of its resources and has high competencies and a strong advantage over other
firms. Accordingly, non-core technologies are those where the firm either commits a low share of
resources (“Niche”), has a competitive disadvantage (“Background”), or both (“Marginal”).
Figure 1. Technological profiles. Adapted from Granstrand et al. (1997)
The technological profiles were calculated based on the firm’s patent portfolio during a five year
window up to a patent’s priority year, to account for knowledge depreciation (e.g. Katila and
Ahuja 2002, Phelps 2010). As for the technological classes, we obtain them from the four-digit
level of the International Patent Classification (IPC4).11
Patent Share (PS): the share of the firm’s total patenting in a given technological class. The
Patent Share of technological class j in firm i is calculated as following:
𝑃𝑆 𝑖 𝑗 =
#𝑃𝑎𝑡𝑒𝑛𝑡𝑠 𝑖𝑗
# 𝐴𝑙𝑙 𝑝𝑎𝑡𝑒𝑛𝑡𝑠 𝑖
We have conducted the same analysis using different levels of patent classifications (IPC3, IPC2, OST7/30), with
consistent results.
11
12
Revealed technology advantage (RTA): the share of the firm in total patenting in a given
technological class, divided by the firm’s aggregate share in all classes. The measure is
calculated as follows:
𝑅𝑇𝐴 𝑖 𝑗 =
𝑃𝑆𝑖𝑗
∑𝑖𝑖=1 #𝑃𝑎𝑡𝑒𝑛𝑡𝑠 𝑖 𝑗
⁄∑𝑖𝑖=1 # 𝐴𝑙𝑙 𝑃𝑎𝑡𝑒𝑛𝑡𝑠 𝑖
The common interpretation of the RTA measure is that if it takes a value over 1, then the firm
has a competitive advantage in that patent class. The original RTA index is, however, asymmetric
and highly skewed. We therefore normalize this index (RTA-1/RTA+1), so that it takes values
between -1 and 1. Negative values indicate a competitive disadvantage of the firm in the
technology, while positive values indicate an advantage.
We construct a dummy variable taking the value 1 if the focal patent is in the firm’s core
technologies and 0 otherwise. Thus, this variable is constructed to indicate whether a patent
belongs to the firms’ core technologies. Core technologies are defined as those patent classes in
the firms’ portfolio, which have Patent Share and RTA values above defined thresholds. The
average Patent Share in our sample is used to draw the first threshold value. Mirroring
Granstrand et al. (1997), we put the threshold for the normalized RTA at 0.33.12 The distribution
of firm-owned patents into core and non-core technologies under these criteria resulted in
approximately 49 per cent core patents and 51 per cent non-core patents.13
Table 2 outlines the independent and control variables.
Although we follow previous studies, there is some arbitrariness in regards to defining the threshold limits. We
have tested our models with different threshold values, within reasonable ranges, with overall consistent results.
13 The distribution of the technological profiles in our data results to negligible numbers in the "background"" and
"niche" technological profiles. We therefore limited our analysis to core and non-core technologies including only
the dummy "Core" in our models.
12
13
Table 2. Independent and control variables
Academic inventor
Dummy, taking the value 1 if the patent has at least one academic
inventor.
Core technology
Dummy variable, taking the value 1 if a patent is part of the firm’s core
technologies and 0 otherwise (Granstrand et al. 1997, Ljungberg and
McKelvey 2012).
Backward patent citations (BPC)
The number of backward patent citations, which are the references added
to the search report to show the prior state of art as perceived as relevant
for the application. Backward citations have been shown to be related to
the value of patents (Harhoff et al. 2003).
Non-patent references (NPR)
Dummy variable, taking the value 1 if the patent includes references to
non-patent literature (NPR). Evidence suggests that NPRs indicate links
between “science” and “technology” (Callaert et al. 2006), and that such
links are related to patent citations (Fleming and Sorenson 2004).
# Inventors
Included to control for the scope of the underlying research project, as a
proxy for its importance for the firm (Sapsalis et al. 2006).
# IPC classes
The number of patent classes according to the 4-digit IPC (international
patent class) to control for patent scope (Lerner 1994). This measure has
been used as an indicator of the complexity of the invention (Harhoff and
Wagner 2009).
Firm dummies
Dummies for each of the 32 firms, to control for the firm fixed effects.14
Priority year dummies
Dummies for the priority year.
Dummies for technological class
(OST7)
Dummies for the technological class of the patent according to the OST7
reclassification of IPC classes.
4. Results
4.1 Descriptive statistics
Table S1 in the Supplementary material presents the distribution of each firm’s patents across the OST7
technological classes.
14
14
This sub-section presents some descriptive statistics, which complement the econometric analysis
in the next sub-section.15 Table 3 shows the average number of citations of firms’ academic and
non-academic patents in the short-term and long-term. On the one hand, non-academic patents
receive on average more citations than academic patents in the short-term (p<0.1). On the other
hand, academic patents receive a higher-than-average number of long-term citations than nonacademic patents, although the difference is not statistically significant.16
Table 3. Forward patent citations (FPCs) by inventorship: Mean citations per patent.
Non-academic patents
Academic patents
Difference %
z-test P
> |z|
FPC<3
0.93
0.82
-11.83*
0.0860
FPC>3
1.37
1.48
8.03
0.2453
FPC
2.30
2.30
0
0.9682
Figure 2 17 presents the average accumulated number of citations by patent age for both academic
and non-academic patents. As seen in the figure, the mean number of citations for academic
patents fluctuates relatively much across age, during later ages.18
See Tables S2 and S3 in the Supplementary material for further descriptive statistics and correlation matrix.
Since we do not control for age, the descriptive statistics of FPC>3 and FPC require evaluation together with the
upcoming econometric models where we do control for age.
17 Figure 2 is not comparable with the results in Table 3, since in the figure we calculate the average citations per
patent by age for all patents, while we in Table 3 calculate the average of the total number of citations received by all
patents by June 2012.
18 This to some extent indicates that the value of academic patents, on average, has declined after the mid-1990s, as
have been suggested by previous studies (e.g. Czarnitzki et al. 2011).
15
16
15
Average citations/patent by age
6
5
Citations/patent
4
3
Non-Academic
2
Academic
1
0
0
5
10
15
20
25
Patent Age in Years
Figure 2. Mean citations per patent by age
Figure 3 shows that the average number of citations is higher for patents in core technologies, for
all three types of citations (short-term, long-term, and total), supporting the second hypothesis.
While 48.5 per cent of the patents are categorized as belonging to the firms’ core technologies,
these core patents receive approximately 61 per cent of the total number of citations (see Table
S4 in the Supplementary material).
16
Fig.3 Average Citations/patent
5.00
4.50
4.00
Citations/Patent
3.50
3.00
Core
2.50
Non-Core
2.00
1.50
1.00
0.50
0.00
FPC_3
FPC>3
FPC
Figure 3. Mean citations per patent over technological profiles
4.2 Econometric results
Table 4 shows the results of the econometric models. Negative binomial regressions were used
due to overdispersion of the dependent variables.19 In models 1 and 2, we regress short-term
citations (FPC<3), in models 3 and 4 we regress long-term citations (FPC>3) and in models 5
and 6 we regress total citations (FPC). In models 1, 3 and 5 the main independent variable is
“academic inventor”, while in models 2, 4 and 6 we also consider the “Core technology”
dummy.20
The three dependent variables used in our models are count variables.
We have in preliminary models tested the interaction term between the two main determinants and it appears
insignificant. Due to the relatively small amount of academic patents in the sample, the interaction term (of two
dummy variables) is highly correlated (0.62) with the academic inventor variable. Thus, adding the interaction term
does not add any further effect than the highly significant core variable already adds. We did not include the
interaction term in our models because of the multicollinearity problems it introduces and the small value added in
the case of our sample.
19
20
17
“Academic inventor” has a significant negative effect on both short-term and long-term citations
(models 1, 3) before controlling for technological profile. There is, however, a significant
decrease of the effect, from -0.193 on the short-term citations to -0.139 on the long-term
citations, indicating that academic patents perform better in terms of citations in the long-term.21
Therefore, Hypothesis 1 cannot be rejected.
In models 2, 4 and 6, we control for the technological profile of the patent. The variable “Core
technology” is highly significant in all models with a strong positive effect. Thus, Hypothesis 2 is
supported and a patent belonging to the core technological profile of the firm can predict higher
patent value both in the short-term and the long-term.
Interestingly, when we control for technological profile the negative effect of “academic
inventor” remains weakly statistically significant in the short-term (model 2) but disappears in the
long-term (models 4 and 6), supporting Hypothesis 3.22 Hypothesis 3 suggests the importance of
the firm’s technological profile as a control variable and this significant change in the effect of the
“academic inventor” variable stresses the need to control for technological profile when we
estimate the relative value of firm-owned academic patents to avoid biased results.
Table 4. Negative Binomial Regressions: Models 1-6
Short-term citations
Long-term citations
(1)
(2)
(3)
(4)
(5)
(6)
FPC<3
FPC<3
FPC>3
FPC>3
FPC
FPC
+Core
Academic
inventor
Core
technology
+Core
22
+Core
-0.193***
-0.137*
-0.139**
-0.0791
-0.140**
-0.0888
(0.0722)
(0.0720)
0.420***
(0.0581)
(0.0584)
0.431***
(0.0557)
(0.0557)
0.405***
(0.0322)
21
Total
(0.0320)
(0.0273)
The difference in coefficients between model 1 and 3 is significant at the 1 % level, according to a z-test.
The difference in coefficients between the model 2 and 4 is significant at the 1 % level, according to a z-test.
18
BPC
NPR
#Inventors
#IPC classes
Firm
dummies
Priority year
0.0334***
0.0368***
0.0188***
0.0222***
0.0216***
0.0252***
(0.00756)
(0.00726)
(0.00629)
(0.00626)
(0.00535)
(0.00525)
0.258***
0.250***
0.121***
0.112***
0.190***
0.182***
(0.0357)
(0.0353)
(0.0361)
(0.0361)
(0.0302)
(0.0301)
0.0763***
0.0711***
0.0674***
0.0610***
0.0725***
0.0671***
(0.0106)
(0.0105)
(0.0107)
(0.0107)
(0.00904)
(0.00901)
0.147***
0.161***
0.135***
0.152***
0.136***
0.151***
(0.0139)
(0.0140)
(0.0128)
(0.0129)
(0.0113)
(0.0113)
included***
included***
included***
included***
included***
included***
included***
included***
included***
included***
included***
included***
OST7
included***
included**
included***
included**
included***
included***
Constant
-0.940***
-1.217***
0.835***
0.578***
0.943***
0.692***
(0.167)
(0.172)
(0.128)
(0.130)
(0.118)
(0.118)
Observations
16,053
16,053
16,053
16,053
16,053
16,053
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
5. Conclusions
This paper has examined issues related to the value of academic patents in Sweden, through
comparisons between short-term and long-term patent value and in relation to the technological
profile of firms. This paper is limited to academic patents owned by firms, and their value is
assessed relative to the non-academic patents owned by the same firms. The results strongly
suggest that academic patents – at least the subset owned by firms – must be better understood
from the perspective of the firms’ technological development and profiles.
The findings provide support for all three hypotheses tested in this paper. The first hypothesis
suggests that the value of academic patents should be different in the short-term and long-term,
with more positive effects in the long-term. While previous studies, focusing on the relation
between ownership and value of academic patents, indicate that firms might seek academic
inventions with more immediate and short-term returns (Czarnitzki et al. 2012, Sterzi 2013), our
results, focusing on the firm-owned patents, suggest that firms’ academic patents have lower
short-term value but similar long-term value as compared to the non-academic patents.
19
The second hypothesis addresses the technological profile of firms, and especially the
categorization of patents into core and non-core technologies. This hypothesis proposes that any
patents that are part of the firms’ core technologies ought to have higher value than any patent in
non-core technologies. This hypothesis was also supported, and suggests that the value of the
patent is heavily dependent on the type of the patent relative to the technological profile of the
firm.
The third hypothesis suggests that controlling for the technological profile of the firm decreases
the effect of academic inventors on patent value. If we assume that academic patents across
different technological profiles indicate different types of collaboration between the firms and the
academics, then our results indicate that academic involvement per se is not adequate in order to
evaluate the patent value. Instead, the patent value has to be assessed under the prism of the
specific technological profile the patent belongs to and, taking the analysis one step further, the
particular collaboration between the academic inventor and the firm. This suggests that there is a
strong need to think about the firms, their strategies and technological profiles when examining
the value and impact of (academic) patents (cf. Ljungberg and McKelvey 2012).
This paper contributes to a stream of literature studying academics as inventors, using the
KEINS/APE-INV database. This database provides opportunities to develop new types of
comparisons and insights, since it makes it possible to identify academic patents at the inventor
level, i.e. in terms of university employees as inventors, instead of starting from ownership
(Lissoni et al. 2008, Ljungberg and McKelvey 2012, Lissoni and Montobbio 2012). The results
from this stream of literature suggest that, at least for Europe, it is much more appropriate to
analyze academics as inventors (at the individual level) and focus less upon universities as owners
of patents.
20
This paper contributes to this stream of literature by focusing on the firm perspective, studying
the relative value of academic patents owned by firms as compared to non-academic patents
owned by the same firms. Moreover, we also add a new explanatory variable, namely categorizing
the patents as core or non-core relative to the technological profile of the firm, developing a
specific indicator of technological profiles (cf. Granstrand et al. 1997, Ljungberg and McKelvey
2012) for this purpose. This explanatory variable might be an important control in order to
robustly assess the value of (academic) patents.
It would be interesting for future research to investigate the same topic and apply the same
methodology to other European countries, i.e. to focus more explicitly on the academic patents
owned by firms. The existing research on academic patents from the firm perspective, including
the question about the value of firm-owned academic patents, is currently sparse and the existing
evidence is ambiguous. Moreover, this would allow for cross-country comparisons of results and
for checking the generalizability of our findings.
The following limitations of the study should be noted. Our analysis is limited to firms that own
academic patents. Future research in settings that allow for sampling also a control group of a
substantial number of firms not owning academic patents would be fruitful, in order to further
control for firms’ resources and strategies and thus eliminate any possible endogeneity. Specific
data on firms’ R&D expenses and resource allocation could also increase the power of the
control variables in the models.
Acknowledgements
The authors recognize support from the European Science Foundation ESF APE-INV project; University of
Gothenburg; and the companies supporting the professorship in Innovation and Industrial Management as well as
the earlier EU project KEINS. We acknowledge useful comments from participants at the Schumpeter 2012
21
conference and the 2012 ESF Leuven workshop. We also acknowledge Yitchak Haberfield, who has provided
valuable comments on the econometrics.
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Supplementary material
S1. Distribution of patents across firms and technological classes
OST7 (class)
Firm id
1
2
3
4
5
6
7
Total
1
24
15
0
0
34
82
209
364
2
818
162
68
2
237
149
6
1.442
3
3
12
216
17
145
12
1
406
4
11
3
1
0
27
13
0
55
5
9
67
27
49
52
4
0
208
6
6
176
504
344
31
0
1
1.062
7
0
11
38
33
1
0
0
83
8
5.043
250
4
0
9
11
10
5.327
9
15
35
2
0
21
353
8
434
10
0
0
0
0
17
31
8
56
11
76
8
0
0
0
1
1
86
12
0
0
3
0
128
14
0
145
13
10
67
0
0
1
0
0
78
14
4
145
1
0
0
1
1
152
15
88
1
0
0
5
3
0
97
16
6
11
6
58
2
0
0
83
17
0
59
38
165
8
2
1
273
18
75
0
0
0
0
1
0
76
19
4
3
2
13
0
0
0
22
20
49
122
7
0
26
141
3
348
21
16
8
362
0
42
357
61
846
22
5
483
22
13
133
8
35
699
23
15
29
4
0
19
316
6
389
24
0
0
65
0
0
94
0
159
25
25
218
8
0
9
56
2
318
26
5
16
15
0
7
112
6
161
26
27
650
41
2
0
3
1
10
707
28
354
22
0
0
0
2
0
378
29
41
58
22
0
81
852
39
1.093
30
5
115
12
0
3
3
0
138
31
3
3
2
0
11
64
5
88
32
12
9
0
6
240
13
0
280
Total
7.372
2.149
1.431
700
1.292
2.696
413
16.053
S2. Mean and standard deviation for the dependent, independent and control variables
Mean
Std. Dev.
Min
Max
FPC<3
0,918
1,851
0
44
FPC>3
1,378
2,821
0
77
FPC
2,296
3,953
0
83
Academic Inventor
0,052
-
0
1
Core Technology
0,485
-
0
1
BPC
4,021
2,276
0
27
NPR
0,251
0,434
0
1
#Inventors
2,1
1,364
1
16
#IPC classes
-
-
1
10
Firm id
-
-
1
32
Priority year
-
-
1989
2009
Technological class
-
-
1
7
(OST7)
S3. Correlations for the dependent, independent and control variables
FPC<3
FPC>3
FPCtotal
Academic
Core
Inventor
Technol
BPC
1
FPC>3
0,4065
1
FPC
0,7582
0,9039
#Inventors
#IPC
classes
ogy
FPC<3
NPR
1
27
Firm id
Priority
year
OST7
Academic
-0,0118
0,0084
0,0005
1
0,1473
0,1055
0,1443
-0,0454
1
BPC
0,0206
-0,0241
-0,0075
-0,0275
-0,0588
1
NPR
0,1187
0,0516
0,0923
0,0733
0,1142
-0,2098
1
#Inventors
0,0773
0,0512
0,0727
0,1628
0,0249
0,0498
0,0294
1
#IPC classes
0,1082
0,1556
0,1617
0,037
-0,0373
0,0472
0,0653
0,1002
1
Firm id
-0,063
-0,039
-0,0573
-0,0869
-0,0014
0,1085
-0,1938
-0,1064
-0,0363
1
Priority year
-0,0322
-0,3147
-0,2396
-0,0545
0,0312
0,1116
0,0352
0,0242
-0,1517
0,0338
1
OST7
-0,111
-0,0291
-0,0727
-0,0646
-0,1504
0,1688
-0,2418
-0,0059
0,0728
0,3004
-0,0539
Inventor
Core
Technology
S4. Distribution of patents and citations over technological profiles
Core Patents
Non-Core patents
Ratio %
48,54
51,46
Citations Ratio %
60,93
39,07
28
1