Incentives for knowledge valorisation: a European benchmark
Incentives for knowledge valorisation: a European benchmark
Linda H. M. van de Burgwal 0 1
Ana Dias 0 1
Eric Claassen 0 1
JEL Classification I 0 1
0 Sovalacc BV , 3029 AK Rotterdam , The Netherlands
1 Athena Institute, VU University Amsterdam , De Boelelaan 1085, 1081 HV Amsterdam , The Netherlands
Studies on incentives to stimulate researcher engagement in knowledge valorisation have primarily focused on incentives for economic output and it remains unclear how universities configure incentives for a broad societal impact of knowledge. Therefore, this article explores the presence and design of incentives for the full range of knowledge valorisation activities by employing a bottom-up, mixed-methods design. In 17 semistructured interviews with representatives from highly ranked European universities, 11 distinct incentives for valorisation activities with an academic, civil society, entrepreneurial or state-governmental orientation were identified. Subsequently, a quantifying survey was conducted (n = 48). Perceived effectiveness did not directly correspond to presence and desirability, indicating that additional motives play a role in implementing incentives, including managing conflict of interest situations. For non-scalable (hour-based) activities broader bandwidths of allocated percentages were employed that were more dependent on case by case attribution of income and for which more conditions to limit conflicts of interest were in place. Interestingly, this study found a negative correlation between the number of such restricting conditions being in place for consultancy and the university's overall success in industry income. A flowchart is presented that university management may use to align the selection and design of their incentives with their motives.
Knowledge valorisation; Incentives paradox; Knowledge transfer; Societal impact
Electronic supplementary material The online version of this article (doi:10.1007/s10961-017-9594-8)
contains supplementary material, which is available to authorized users.
For over 15 years, research institutes have placed the broad societal impact of knowledge
more prominently on the agenda as a means to contribute to the European knowledge
economy (Dale 2010). However, they still have not succeeded in overcoming the European
knowledge paradox (Vilarinho 2015). The inconsistency between excellent scientific
insights and limited innovation outcomes that is described by this paradox is generally
considered to be best addressed by improvements in knowledge transfer and knowledge
valorisation processes (Van Vught 2009). Whereas knowledge transfer highlights the
formal transfer of academic knowledge to parties in the commercial sector for economic
benefit, knowledge valorisation takes a broader scope and looks at the creation of societal
value from knowledge by translating research findings into innovative products, services,
processes and/or business activities (Benneworth and Jongbloed 2010; De Jong 2015;
Hladchenko 2016). This latter definition includes the creation of spin-off companies and
the filing of patent applications on the one hand and the writing of books and the
development of guidelines for policy improvements on the other. Regardless of the broad nature
of valorisation activities and despite best efforts to improve their accompanying processes,
the active engagement of researchers in these processes continues to be a step-limiting
factor (Abreu and Grinevich 2013; Audretsch and Aldridge 2012; Olmos-Penuela et al.
As a first step to support knowledge valorisation, almost all European countries have
adopted a Bayh-Dole-like regime in which universities own the intellectual property (IP)
generated at their institutes and inventive researchers receive a share of the resulting
revenues in turn (Swamidass and Vulasa 2008). Under this regime, universities act as
principals who assign their faculty members the task to not only contribute to academic
knowledge generation, but also to the development of valuable knowledge that can be used
within a broader societal context (Braun and Guston 2003). From the perspective of faculty
members this task is seen as ‘additional’ and sometimes as conflicting with their internal
preference for pure academic science (D’Este and Perkmann 2011; De Jong et al. 2016).
Consequently, academics violate ownership policies by circumventing engagement with
technology transfer offices (Pinto and Ferna´ndez-Esquinas 2016) or by failing to file
invention disclosures and patent applications before publishing their findings (Baldini et al.
2007; Markman et al. 2005; Siegel et al. 2003).
Consistent with the principal-agent theory, the misalignment of objectives between
universities and researchers can be remedied by providing incentives that reward faculty
members for achieving the desired outcomes (Eisenhardt 1988). While there are many
intrinsic motivators that stimulate scientists, which cannot be directly influenced by
university policy makers (Lam 2011), the importance of extrinsic personal rewards in
engaging researchers with knowledge valorisation has been highlighted by numerous
studies (Debackere and Veugelers 2005; Derrick and Bryant 2013; Gala´n-Muros et al.
2015; Geuna and Muscio 2009; Go¨ ktepe-Hulten and Mahagaonkar 2010). As a result,
universities have implemented a wide variety of incentives for researchers to engage in
knowledge transfer activities: revenues from intellectual property rights, royalties,
shareholding, bonuses, promotions, etc. (Belenzon and Schankerman 2009; Gala´n-Muros et al.
2015; Va¨a¨na¨nen 2010).
Despite this wide implementation of incentives, there is still little insight in how
incentive systems can stimulate economic as well as non-economic, societal impact. Most
studies evaluating incentives for knowledge valorisation have focused on those incentives
aiming to improve the economic output of research (Arque´-Castells et al. 2016; Baldini
2010; Di Gregorio and Shane 2003; Friedman and Silberman 2003; Go¨ ktepe-Hulten and
Mahagaonkar 2010; Lach and Schankerman 2004; Macho-Stadler and Pe´rez-Castrillo
2010; Markman et al. 2004; Walter et al. 2013). Well-defined IP policies and licensing
contracts could act as an incentive mechanism by reducing information asymmetries and
by clarifying the expectations from and benefits for researchers (Jensen and Thursby 2003;
Macho-Stadler and Pe´rez-Castrillo 2010). While some studies found a positive correlation
between the presence of written IP policies and patent applications or license agreements
(Baldini 2011; Barjak et al. 2013), others could not confirm this correlation
(Gonza´lezPern´ıa et al. 2013). Regarding the share of revenues that is shared with inventors, some
studies found that researcher engagement increases when researchers benefit from a higher
revenue split (Caldera and Debande 2010; Lach and Schankerman 2004, 2008; Link and
Siegel 2005). A more recent study found that the actual revenue split is less important than
the percentage being above a certain threshold (Arque´-Castells et al. 2016). Another study
found that over-allocation of income to the department (negatively) effects licensing
income (Friedman and Silberman 2003).
Even fewer studies have looked at the differences in the configuration of those
incentives across universities. A recent study benchmarking revenue sharing policies in the UK
looked at how revenues were distributed among researchers, departments and the central
administration but did not differentiate between different types of activities leading to
those revenues (Gazzard and Brown 2012). Consequently, the extent to which incentives
reward the full range of activities that contribute to a societal impact of knowledge is
unclear. Furthermore, only a handful of studies have examined conditions to manage
conflicts of interest that are linked to these incentives. Some studies found that providing
rules on how to manage conflicts of interest is positively correlated with the number of
invention disclosures, patent applications, licensing income and number of licenses (Barjak
et al. 2013; Caldera and Debande 2010), while others found a negative correlation of such
conditions with spin-off formation (Muscio et al. 2016). The nature of these conditions,
however, remains unclear and although there is a general consensus that incentives play a
role in improving engagement from academics, it is not clear how universities implement
incentives in their policies to manage and promote knowledge valorisation (Gala´n-Muros
et al. 2015).
Since knowledge valorisation encompasses many different dimensions, a single focus
on the economic dimension neglects other important impacts of research, such as impact of
knowledge on the general public and societal welfare. Moreover, the lack of insight into
the full scope of incentives and their accompanying conditions contributes to many
uncertainties on which policy provides the best results (Walter et al. 2013). This paper
addresses this knowledge gap and aims to contribute to the improvement of university
policies by gaining insight into how European universities shape their knowledge
valorisation incentives. In order to reach this objective, this study identifies and classifies
incentives and their accompanying conditions and evaluates the relation between the
design of incentives and the ranking of universities. As opposed to earlier studies focusing
on a small set of incentives for economic activities, this study takes an exploratory,
bottomup approach to identify all incentives stimulating engagement in broad valorisation
practices. The results contribute to the knowledge valorisation research field by providing
insight into the nature, abundance and desirability of different incentives and
configurations. Additionally, the study contributes to knowledge valorisation practices by serving as
a benchmark and by presenting a flowchart that universities can use to select and design
incentives in such a way that they best fit their strategic focus.
Although formal incentives for economic activities can mostly be found in institutional
policy documents, e.g. guidelines on royalty sharing (Lach and Schankerman 2008), these
documents generally do not describe less formal incentives that reward non-economic,
societal impact activities nor do they shed light on the actual practice of awarding
incentives. To address and avoid this information gap and in line with previous studies
(Davey et al. 2011; Markman et al. 2004), this study took a mixed-method approach; the
incentives and conditions that were in place were explored by qualitative interviews and
insight in the prevalence of these incentives and conditions was gained via a survey.
In order to identify all incentives that stimulate a broad societal impact of knowledge,
we operationalized the concept of valorisation by using a framework that differentiates
communication of research results to different target groups (Hakala and Ylijoki 2001;
Mostert et al. 2010; van Ark and Klasen 2007). According to this framework knowledge
production, knowledge exchange and knowledge use can be linked to four different target
groups (scientific community, civil society, actors with an entrepreneurial orientation and
state-governmental decision-makers) and correspondingly have an impact on knowledge,
culture, economy and wellbeing, see Table 1.
2.1 Study population
In line with previous studies, professionals working in technology transfer or similar
offices (e.g. innovation or commercialization offices) were used as a primary source of
information because they are most likely to have an overview of the different incentives in
place in their institution (Davey et al. 2011). The Times Higher Education World
University Rankings (THE Rankings) was used to select European public universities. The
first 200 universities of the THE list 2014–2015 are ranked with a single number allowing
for comparison across rank. Within this set 85 universities were European. Professionals
working with knowledge transfer and valorisation were identified and e-mail addresses
were collected via their institutional websites.
Table 1 Framework for a broad societal impact of knowledge with examples for knowledge production,
exchange and use within each of the domains
orientation with an orientation with an impact
impact on economy on wellbeing
2.2 Semi-structured interviews
Semi-structured interviews were conducted to explore how incentive systems were
implemented in universities across Europe as the aim was to obtain qualitative data to
interpret the design of incentives and the practice of their implementation (Gray 2013).
Interview candidates were approached by email and respondents received the interview
questions beforehand. The total number of interviews was based on saturation of identified
incentives. During the interviews the framework was introduced and the interviewee was
asked to highlight which incentives were in place per domain. The semi-structured design
allowed for probing on the mechanism for attributing rewards and on further conditions
being in place. With the permission of the respondents, the interviews were recorded, fully
transcribed and independently coded by two researchers according to thematic coding (Van
den Nieuwboer et al. 2015). Subsequently, the codes were consensually harmonized to
compile a list of incentives with their respective conditions.
Based upon this overview of incentive systems, a questionnaire was developed to
quantitatively collect data on incentive systems in-place, their desirability, perceived
effectiveness and comments for improvement. The voluntary, open survey was pilot tested after
which the final survey was created and distributed through the online web survey program
Initially, 179 e-mail invitations were sent and delivered to representatives from the 85
institutions. Another 58 invitations were sent but could not be delivered or were responded
to with an out-of-office reply. If in the out-of-office reply referral was made to one or more
colleagues, the invitation was forwarded to these persons, leading to an additional 60
respondents. In sum, 239 initial invitations were sent and delivered to representatives and
not answered with a direct out-of-office reply during the time of the study. Reminders were
sent after two and again after 4 weeks to increase the response rate. Respondents’ IP
addresses were used to identify potential duplicate entries (Eysenbach 2004). Duplicate
database entries by the same IP address or by respondents representing the same institution
were eliminated before analysis. In case of duplicate entries only the most complete
(number of questions answered) or the one filled out by the most senior professional (years
of experience in knowledge valorisation) was included. Respondents who didn’t fill out the
name of their host institution were excluded.
The anonymous survey started with an informed consent page and a time indication of
15 min to complete the survey. Next, respondents were asked to fill out demographic data
and subsequently whether the incentives that were identified during the interviews were in
place, whether the respondents thought they should be in place and what the accompanying
conditions were. Furthermore, respondents were asked per incentive whether they would
recommend any improvements and respondents were allowed to suggest new incentives
that should be in place. Finally, respondents were asked to rank their top three of most
effective incentives (1 being most effective and representing a weight of 3). In this last
question all different types of revenue sharing were combined to avoid a bias of the
respondent’s preference for specific channels.
2.4 Statistical analyses
For sliding scales, the percentages attributed to the first €100.000 was used in the analysis.
When provided, the starting point for negotiations was used when the percentages were
determined on a case by case basis. Percentages for answer possibilities were calculated
based on the total number of respondents that answered that specific question. The
significance threshold was set at .05.
To calculate the effectiveness of incentives, for each incentive the scores were
multiplied with the respective weight. The sum of the weighted scores reflects the total weighted
score of the incentive. The total weighted scores were rescaled to a range from 1 to 100 to
facilitate interpretation (Van den Nieuwboer et al. 2015; Weenen et al. 2013), according to
the following formula (WRI, weighted ranking incentive; HRI, highest rated incentive; n,
number of times; R1, rank 1; R2, rank 2, R3, rank 3):
WRI ¼ PPððnR1 3Þ þ ðnR2 2Þ þ ðnR3 1ÞÞ 100
To analyze the difference between presence and desirability of incentives, the McNemar
test for binary matched-pairs data was used (Fagerland et al. 2013).
Because the percentage attributed to the researcher was not normally distributed for all
incentives (e.g. D(42) = 0.8, p \ .001, Shapiro–Wilk test for patents) and Levene’s test
showed there was no homogeneity of variance (F(6,111) = 4.0, p \ .01), a Kruskal–
Wallis test with posthoc Mann–Whitney tests was conducted to test differences in
attributed percentages. For these posthoc tests a Bonferroni correction was applied to the
significance threshold to correct for the number of tests. Chi square tests were conducted to
analyze the differences in formality per incentive. Adjusted, standardized residuals (ASR)
were calculated to identify which cells (ASR C ?/-2) contributed to statistically
significant omnibus Chi square test results (Sharpe 2015). The same test was used to analyze
differences in income direction and presence of limits and caps. The comments made for
improvement by respondents on the survey were thematically coded and differences
between types of comments were analyzed via a nonparametric binominal test.
The correlation of controlling mechanisms, the researcher-attributed percentage and the
number of formal incentives in place was evaluated with nonparametric Kendall’s tau test.
To evaluate the correlation of controlling mechanisms with rank and industry income of
universities, the number of restricting mechanisms for consultancy was used. For this
incentive all types of restricting conditions were described and of the incentives rewarding
engagement in non-scalable activities it was most often in place. For the
researcherattributed percentage the overall most prevalent incentive—revenue sharing for patents—
Seventeen interviews were conducted and saturation of identified incentives was reached
after nine interviews (see Fig. 1). Of the 239 representatives who received the invitation to
participate during the time of the study, 78 initial responses were collected from 71 unique
IP addresses. This participation rate of 30% is well within the norm (36 ± 19) for
organizational analyses (Baruch and Holtom 2008) and as high as can be expected given the
extensive surveying that knowledge valorisation professionals have been subjected to.
Fig. 1 Saturation of incentives
was reached after nine interviews
After data cleaning, responses from participants representing 48 unique universities were
included, leading to a combined institutional response rate of 56%. Data cleaning included
the elimination of data from 14 respondents who started but did not complete the survey
(i.e. completion rate of 80%); 6 respondents who represented the same institution as
another respondent and 3 respondents who didn’t include the name of their host institution.
There was no selection bias per country sampled (see Supplemental Material, Table A).
Career progression was considered the most effective incentive (relative rank of 100).
At 74 and 58, revenue sharing and attributing university resources were considered
moderately effective. Equity sharing (14), prizes (13) and bonuses (13) were hardly
considered effective (see Fig. 2).
Five incentives were implemented in more than half of the universities: revenue sharing
from patents (98%), revenue sharing from other Intellectual Property Rights (IPRs; 73%),
equity sharing (73%), prizes (64%) and revenue sharing from consultancy (61%, see
Fig. 2). The least common incentive was revenue sharing from contract education, which
was in place in less than 1 out of 4 institutions sampled.
Overall, for all incentives the desirability was at least equal (in the case of revenue
sharing from patents) or higher (for all other incentives) than the actual implementation.
The difference in presence and desirability was statistically significant for three individual
incentives: revenue sharing for university-launched products (p = .03); attributing
university resources (p = .008) and revenue sharing for consultancy (p = .02), see Fig. 2.
The least desired incentives were revenue sharing for contract education (38%), revenue
sharing for contract research (50%), and attributing bonuses (50%). In line with their
relative abundance but in contrast to their limited perceived effectiveness, equity sharing
and attributing prizes were among the most desired incentives (82 and 77%, respectively).
The average percentage attributed to researchers was 42%, with overall the lowest
percentage given for contract research (average = 29%) and the highest for consultancy
(average = 76%), see Fig. 3. The percentage attributed for consultancy activities
statistically significantly differed from other attributed percentages H = 21 (6), p \ .01),
Kruskal–Wallis test, Supplemental Material, Table B. As shown in Fig. 3, the bandwidths
of percentages that are attributed to incentivize activities based on making a margin on
hours (non-scalable activities) are much broader than for activities based on making a
margin on knowledge (scalable activities).
In many cases the percentages were not set in advance (‘informal’ incentives) and the
prevalence of formally set percentages differed per incentive type (p \ .0001, Fisher’s
Fig. 2 The presence and desirability of incentives do not directly correspond to their perceived
effectiveness. The numbers between brackets on the y-axis refer to the relative ranked effectiveness of
the incentive; career progression is considered most effective. * p \ .05; ** p \ .01
Exact Test; see Fig. 4). Especially patents (ASR = 3.7) and other IPRs (ASR = 2.1) were
statistically significant more likely to be organized formally, whereas equity
(ASR = -5.4) was less likely to be organized formally.
In general, income resulting from scalable activities was more often paid out in private
and less often within the university context (e.g. on a personal account for work-related
expenses or to be used in further research) than income resulting from non-scalable
activities, see Table 2. This prevalence of researchers being rewarded in private was
(statistically significant) low for contract research (36%, ASR = -2.9) and high for
patents (84%, ASR = 2.0), p = .03, FET. For contract research a payout on a personal
account for work-related expenses was a possibility in 55% of the cases (ASR = 2.1) but
for patents this was only 16% (ASR = -2.2), p = .03, FET. In turn, the use of revenues
for further research was highly likely for contract research (64%, ASR = 2.6) and contract
education (75%, ASR = 2.0) and less likely for patents (19%, ASR = -2.0), p = .003,
FET (see Table 2). The percentage of times in which the researcher could decide how the
revenues were attributed averaged at 46%, with no statistically significant differences
Restrictions to the incentives were found in the form of limits on the maximum amount
of time a researcher can spend on these activities and caps on the maximum amount of
money a researcher can earn with them. Time limits were only described for non-scalable
activities and the prevalence of such limits differed statistically significantly between
incentives (p \ .001, FET, see Table 2). Whereas a time limit was in place in the majority
Fig. 3 The bandwidths of percentage attributed to the researcher is much broader for activities that make a
margin on hours (non-scalable activities) than for activities that make a margin on hours (scalable activities).
The average attributed percentage is significantly higher for consultancy than for all other incentives. The
figure shows the percentages that are attributed for the first €100.000 in case of a sliding scale or, when
provided, the starting point for negotiations when the percentages were determined on a case by case basis.
Awards/prizes, resources sharing and bonuses are excluded because they are not based on percentages
Fig. 4 Incentives based upon activities that make a margin on knowledge are more often formally
organized with fixed percentages or sliding scales. Incentives based upon activities that make a margin on
hours more often rely on an informal organization
of universities with incentives for consultancy (88%, ASR = 3.6), only 20% of institutions
with incentives for contract research had a time limit in place for these activities
(ASR = -4.3). Time limits were also prominent for contract education (80%) but this
Table 2 Income resulting from non-scalable activities is more often directed to the researcher within the
university context than income resulting from scalable activities
difference was non-significant (ASR = 0.8). Caps were found for all revenue-sharing
incentives and although in general more institutions implemented this cap for non-scalable
activities than for scalable activities these differences were statistically non-significant
(p = .2, FET, Table 2). Other limiting conditions were the need to disclose income, the
need to disclose activities, a limitation on transfer of university intellectual property or use
of university assets and the need to ask for permission for specific consultancy activities.
Comments for improvements were categorized as relating to rules and regulations, or to
rewards. The majority of comments (25) on rules and regulations argued for more rules and
included more clarity (8 comments), increased control on conflicts of interest or prizing (10
comments), uniformity of rules within the institution or country (3 comments) or improved
enforcement (4 comments), (86%, p \ .001). Only four comments argued for limiting the
rules and regulations, advocating a more laissez-faire approach to the activities. There was
no statistically significant difference between comments arguing for more rewards (11;
more revenues or resources attributed, faster return to inventor, inclusion of contributors
next to inventors, inclusion of career progression, broader use within organization) and
those arguing for less rewards (4; setting a sliding scale, reducing attributed percentage,
narrowing types of activities that are rewarded), p = .12.
The researcher-attributed percentage of patent revenues did not correlate with the
position of the university on the THE ranking nor with the industry income ranking of the
university (r = -.01, p = .91 and r = .01, p = .39, Kendall’s tau, respectively; see
Fig. 5a, b). The same holds true for the correlation between these two positions on the THE
ranking and the number of formal incentives in place at the surveyed institutions (r = .15,
p = .23 and r = .03, p = .85, Kendall’s tau for position and industry income, respectively;
see Fig. 5c, d). Results show that there is statistically significant correlation at the
Fig. 5 Negative correlation between the number of restricting conditions in place for consultancy activities
and the position on the Industry Income Ranking from the THE Ranking 2014 (f, 100 = best). No such
correlations were shown for the percentage of patent revenues shared with the researcher and the number of
formal incentives in place (b, d). Furthermore, the number of restricting conditions, the percentage of patent
revenues shared with the researcher and the number of formal incentives in place did not correlate with the
final position on THE ranking (1 = best, a, c and e, respectively)
a = 10% level between the number of restricting conditions in place for consultancy and
the position on the THE ranking in terms of Industry Income (see Fig. 5f; 100 = best;
r = -.30, p = .06, Kendall’s tau) but not between these conditions and the final position
on the THE ranking (Fig. 5e; 1 = best; r = .11, p = .50, Kendall’s tau).
This study provides a benchmark of incentives for knowledge valorisation in top European
universities and shows that the presence and desirability of incentives are not related to
their perceived effectiveness. This supports the idea that different motives may play a role
when establishing incentives, such as managing conflict of interest situations, distributing
income or fostering a university culture of knowledge valorisation. Furthermore, this study
highlights that differences between scalable knowledge-based and non-scalable hour-based
activities are reflected in their corresponding incentives. For non-scalable activities the
bandwidths of allocated percentages are broader, incentives are more often informally
regulated, more often rely on the income being directed to the university context rather
than paying revenues out in private and there are more restricting conditions in place.
Interestingly, this study also supports the idea that increasing restricting conditions may
limit a university’s success in terms of industry income.
4.1 Rationales for implementing incentives
Valorisation professionals considered career progression to be the most effective incentive
for academics to engage in knowledge valorisation activities, which is consistent with
previous research highlighting the effectiveness of this incentive [e.g. (Lam 2011; Renault
2006)]. Attributing prizes or awards and sharing equity were considered least effective,
which is in marked contrast to their high desirability. For prizes, this contrast may be
explained by their contribution to institutional logics; i.e. the socially constructed patterns
of practices, norms, values and rules that determine which activities are considered
legitimate and desirable (Thornton and Ocasio 2008). As illustrated by quote #1 in Table 3,
prizes may signal the value the institution places in these types of activities (Siegel et al.
2003), reduce the tension between research and knowledge valorisation (Olmos-Penuela
et al. 2015; Sauermann and Stephan 2013) and lead to a crowding-in effect by rewarding
researchers for a performance they would most likely also deliver without these incentives
(Derrick and Bryant 2013; Korff et al. 2014). Finally, prizes may increase awareness of
activities by colleague scientists and as such improve appreciation of such activities
(Besley 2015; Frey and Neckermann 2008). A similar argument could explain the contrast
between perceived effectiveness and desirability of equity sharing. Equity sharing may
facilitate engagement of researchers in spin-off companies by serving as a mechanism to
distribute the resulting revenues rather than as a motivating mechanism (Gazzard and
Brown 2012; Grimaldi et al. 2011). In this capacity, equity sharing also contributes to
prolonged involvement of researchers in the further development of the technology or
knowledge in question (Jensen and Thursby 2003).
Revenue sharing, although generally considered quite effective, was not so desirable for
contract research and contract education. One reason to not incentivize academics for
contract research can be found in the idea that contract research is a benefit in itself; it
allows researcher to do more research, which is in line with previous research on personal
drivers (D’Este and Perkmann 2011; Derrick and Bryant 2013; Go¨ ktepe-Hulten and
Table 3 Quotes from the interviews reflecting discussed insights
‘‘That could, for example, be prizes or other ways to express the relevance, or to express the
appreciation on the institutional level.’’—respondent 1
They are benefiting in their academic career by having the industry sponsorship and working in their
lab. And that’s all they want: to work and to get paid and in that sense that is an incentive for them.—
I am not sure if it will be a good idea to provide a direct financial incentive to researchers to go for one
type of funding and not for other types of funding.—respondent 2
You are able or not to invite people to your lab from outside, you are able to travel, you are able to [do]
any number of such things. And the more means you have, it may help you (…) with your effort in
your field. So of course, it is not just a question of a personal incentive. It is given the fact that the (…)
science related goals of these people are a driving force. If you provide more means for them to
achieve that, they will really like that.—respondent 9
I think it is not easy to implement and that is why is not done. So it is difficult but I think that you could
certainly try to be a little tougher in some form of a management where we would allocate more
secretarial help, somewhat bigger budgets, or more lab space and so on, depending on some
measurement of performance. On the other hand that could also create some form of tension,
complicate internal politics here, some atmosphere that might also be counterproductive. (…) At this
point we don’t quite take from those who don’t do to those who do. And there I think we could do
If they make courses for continuing education outside of their normal teaching, probably there is some
kind of financial incentive. (…) They do a course and they get paid for it. And some do it. There are
some personal incentives to do something like that in terms of freedom to do it.—respondent 4
Mahagaonkar 2010) and illustrated by quote #2. Another reason can be found in the
possible negative effects such an incentive could have in diverting efforts to attract
research funding or to provide education in this specific direction, as illustrated in quote #3.
This statement finds its basis in the theory of ‘multiple tasking’ where activities that are
rewarded are given more attention to the neglect of activities that are not part of incentive
schemes, such as curriculum-based education and administration tasks (Fehr and Schmidt
2004; Prendergast 1999).
While sharing university resources was considered moderately effective and was
described as being highly desirable (see quote #4), this incentive was rarely implemented.
Although especially relevant for researchers with spin-off companies (Fini et al. 2009) and
for teachers with a high teaching load (Arvanitis et al. 2008), objections to its
implementation were found in its zero-sum nature: university resources are limited and to reward
one researcher or research group would mean to simultaneously ‘punish’ another leading to
potential conflicts within the university (see quote #5).
4.2 Incentivizing scalable versus non-scalable activities
This study found an abundance of incentives that rely on revenue sharing and
correspondingly reward economic outcomes rather than efforts to establish a broad societal
impact of knowledge. Revenue sharing incentives reduce the principal’s risks of moral
hazard and information asymmetry but increase the risk for researchers because the
outcome of their efforts is mediated by factors beyond their influence, such as the efficiency of
technology transfer professionals and the willingness of industry to adopt specific
technologies (Belenzon and Schankerman 2009).
The average attributed percentage for successful patent exploitation in the top public
universities in Europe is lower (38%) than was previously found in the US (51%) and in
the UK (56%) (Gazzard and Brown 2012; Lach and Schankerman 2004). More
importantly, this study shows that the bandwidths of percentages that are attributed to researchers
across universities are especially broad for non-scalable activities. Additionally, incentives
for these type of activities are less often formally organized. This informal organisation
provides researchers with opportunities to take control on the outcomes and could serve as
an incentive in itself, as explained by quote #6.
In line with previous studies, this study found no correlation between the percentage of
patent revenues attributed to the researchers and the university’s ranking on the Times
Higher Education list (Friedman and Silberman 2003; Lach and Schankerman 2004).
While previous studies demonstrated a correlation between the researcher-attributed
percentage and licensing income (Di Gregorio and Shane 2003; Friedman and Silberman
2003; Lach and Schankerman 2004), this study found no correlation between this
percentage and the broader category industry income. This study does suggest that the
presence of a bureaucratic atmosphere might be counterproductive in stimulating
knowledge valorisation, as shown by the negative correlation between industry income and the
number of restrictive conditions for consultancy activities. This is in contrast to some
(Barjak et al. 2013; Caldera and Debande 2010) but not all (Muscio et al. 2016) previous
studies looking at the relation between restricting conditions and indicators for economic
impact. Despite this negative correlation, valorisation professionals expressed their
preference for a bureaucratic atmosphere as shown by the statistically significant majority of
the comments made for improvement referring to implementing more or better reinforcing
rules and regulations. As such, these professionals emphasized the role incentives play in
managing conflict of interest situations, which may at the same time limit the very process
they aim to improve.
4.3 Implications, limitations and future research
Although researchers are incentivized to engage in knowledge valorisation efforts, they
simultaneously incur opportunity costs for time they cannot spend on the ‘‘knowledge for
knowledge’’ activities that currently primarily determine their career progression. Some
alignment between academic and valorisation tasks can be found in contract research and
contract education, but incentives for these activities are underrepresented in top European
universities. Other incentives that have the potential to alleviate opportunity costs are
incentives with a discretionary component that may (in part) reward effort rather than
outcome, such as considering knowledge valorisation activities in career progression
decisions, attributing awards or prizes, attributing bonuses and sharing university resources
(see Fig. 6). Although this study sheds some light on these discretionary incentives, in
order to be able to incentivize efforts into the civil society and state-governmental domains
as well as those in the economic domain, future research should look at ways to more
objectively identify performances that contribute to an impact in these domains (see
The large differences in attributed percentages between institutions and the lack of
correlation with institutional quality and industry income could tempt universities to
employ incentive mechanisms as differentiating recruitment tools and lead to football-like
migration of excellent academics (Friedman and Silberman 2003). This might be especially
Fig. 6 Flowchart to select and design incentives. Areas for further research include a ways to more
objectively evaluate efforts and performance in non-economic domains, b the effect of formality on how
researchers organize scalable and non-scalable activities c how limiting conditions effect researchers’
perception of procedural justice. N No; Y Yes
beneficial to attract younger researchers who have not yet achieved a tenured position,
since they are more likely to move between public research organisations (Crespi et al.
2006), and have higher expectations of success and financial gains from knowledge
valorisation activities than more experienced researchers (Hayter 2015). Scientists that might
also be attracted by this type of recruitment tool are those experienced with entrepreneurial
behaviour (Renault 2006). Obviously, these effects are mediated by the awareness of
researchers on current incentive schemes as well as the perceived effectiveness of support
organisations in bringing academic knowledge to the market successfully (Arque´-Castells
et al. 2016; Go¨ktepe-Hulten and Mahagaonkar 2010). Although an early study on mobility
between public research institutions did not find an influence of different percentages
(Crespi et al. 2006), more recent anecdotal evidence suggests that differential incentives
are used to recruit researchers to universities (Derrick and Bryant 2013). Consequently, this
behaviour could serve a reinforcing cycle in which competitive incentive schemes attract
high quality researchers with well-established relationships with societal partners who in
turn attract further (industrial) research funding (Derrick and Bryant 2013). As such, this
benchmark could lead to an observer bias after the fact with changes in performance,
behaviour, and rules and regulations on the basis of increased insight into corresponding
mechanisms at different universities.
Next to revenue splits, the formality of the incentives might be a factor to take into
consideration. But to further elucidate this relationship, more insight into how researchers
organize their scalable and non-scalable activities around these incentives is needed.
Previous studies suggest that star scientists may have a different approach to informal
conditions than less experienced academic entrepreneurs (Markman et al. 2008). From the
university perspective, a reason for a more informal approach to non-scalable activities can
be found in the possibilities to limit the impact these activities have on the other tasks of
academics, which was also suggested by the presence of numerous limiting conditions.
Consequently, the risks of information asymmetry and moral hazard might differ between
scalable and non-scalable activities and the principal and agent might differentially benefit
from a more formal or informal organization for each (see Fig. 6b).
Although the direction of the causality cannot be determined in a survey study, the
negative correlation between restrictive conditions and industry income could argue for a
decoupling of incentives and control mechanisms. Academics are expected to behave
entrepreneurially, not only via protecting IP and starting spin-off companies but also in
their research and education activities. As such, rationales for restrictive conditions that
emphasize the fear of academics neglecting their main tasks of teaching and research
(Kalar and Antoncic 2015; Philpott et al. 2011) cannot fully explain the implementation of
restricting conditions on contract research and contract education. On the contrary,
controlling conditions based upon this rationale counteract themselves by restricting the same
entrepreneurial actions that the incentives they accompany aim to support (Renault 2006).
The detrimental effect of such restricting conditions was even found in the core of the
academic enterprise, where academics showed less effort for publishing in high-impact
journals when newly introduced incentives were perceived as controlling, thus highlighting
that controlling mechanisms can crowd out intrinsic motivation (Andersen and Pallesen
2008). While we do not argue that the possible risks of conflict of interest should be
ignored, the results do suggest that a less bureaucratic and more laissez-faire approach
might actually benefit the societal impact of knowledge and perhaps academics should be
given some slack and more support when engaging with societal stakeholders rather than
be inhibited by limiting conditions. Previous studies have shown that perceived procedural
justice might be an important mediating factor in determining efforts to engage in
valorisation activities and subsequent likelihood of commercial success (Arque´-Castells et al.
2016; Muscio et al. 2016) and future studies looking into restricting conditions are
encouraged to take this concept into account (see Fig. 6c).
This study focused on 48 of the 85 best European universities as listed by the THE
Ranking. The reason for this approach is twofold. First, from a pragmatic standpoint, only
for the top European universities their exact rank was disclosed by the THE Ranking,
allowing for comparisons between universities. More importantly, research excellence is
described as a necessary condition for knowledge valorisation success (Debackere and
Veugelers 2005) and by focusing on the best European universities, those universities that
were more likely to meet this condition were selected. Accordingly, the sample of the
current study represents a good example for other universities but it also needs to be noted
that contextual factors for these universities might differ significantly from other
By shedding light on the presence, desirability and perceived effectiveness of
incentives that are in place this study serves as a valuable policy tool but it also has
some limitations. In many universities, the organisation of knowledge valorisation is
diffuse with technology transfer offices, department heads, deans and communication
offices sharing responsibility for the broad range of activities researchers engage in.
Consequently, valorisation professionals might not have a complete perspective on all
incentives that are in place across the university. However, given their close interaction
with researchers, their involvement with numerous valorisation projects and the fact that
they are closely involved with the majority of valorisation activities, they are the most
adept party within universities to shed light on these issues. This approach also harbours
its own weakness; valorisation professionals are often asked to participate in academic
research and survey fatigue might be causing limited response rates, ultimately leading to
biased results. Future studies might benefit from a more in-depth approach for which the
current results can serve as a theoretical basis. One such approach would be to question
the researchers, rather than support staff or their managers which incentives they are
most motivated by.
For all incentives, universities are suggested to reconsider the reasons for implementing
them since for each reason, a different balance between the type of incentives and their
design is best suited. Consequently, incentives may not only play a role in rewarding
societally engaged academics and motivating academics that are still reluctant to engage in
knowledge valorisation but also provide organizational legitimacy to universities within
the changing research and innovation context (Braun and Guston 2003). In this sense, a
proper design of incentives may on the long run influence the issue of responsiveness
within the broader research community.
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