How do researchers generate scientific and societal impacts? Toward an analytical and operational framework

Abstract

Models of research systems increasingly emphasize collaborations between networks of heterogeneous actors, to both produce knowledge and formulate interdisciplinary responses to societal challenges and market needs. In this context, researchers' goals and practices are required to satisfy professional requirements for new scientific findings and societal demand for relevant knowledge. Researchers may need also to find ways to reconcile tensions between these two missions. This article proposes an analytical and operational framework that incorporates individual, organizational and process-context factors to explain distinct configurations of scientific and societal impacts from research. The framework emphasises the role of productive interactions with different nonacademic actors as a mechanism for reconciling the scientific and societal missions of research.

Full text

How do researchers generate scientific and
societal impacts? Toward an analytical and
operational framework
Pablo D’Este
1
, Irene Ramos-Vielba
1,
*, Richard Woolley
1
and
Nabil Amara
2
1
INGENIO (CSIC-UPV), Universitat Polite`cnica de Vale`ncia, Valencia, 46022, Spain and
2
Universite´ Laval Quebec,
Que´bec, G1V 0A6, Canada
*Corresponding author. Email: iramosvi@ingenio.upv.es
Abstract
Models of research systems increasingly emphasize collaborations between networks of heteroge-
neous actors, to both produce knowledge and formulate interdisciplinary responses to societal
challenges and market needs. In this context, researchers’ goals and practices are required to sat-
isfy professional requirements for new scientific findings and societal demand for relevant know-
ledge. Researchers may need also to find ways to reconcile tensions between these two missions.
This article proposes an analytical and operational framework that incorporates individual, organ-
izational and process-context factors to explain distinct configurations of scientific and societal
impacts from research. The framework emphasises the role of productive interactions with differ-
ent nonacademic actors as a mechanism for reconciling the scientific and societal missions of
research.
Key words: scientific impact; societal impact; research quality; analytical framework; productive interactions
1. Introduction
In recent decades, there has been an increased focus on the generation
of both scientific and societal impacts from publicly-funded research
and the demonstration of broader ‘public value’ to society from the
funding of science that goes beyond the importance of scientific
achievements or commercialization goals (Bozeman and Sarewitz
2011;Bornmann 2013). Broadly speaking, scientific impact is associ-
ated with achieving recognition within a professional community of
knowledge producers, while societal impact refers to research contri-
butions to addressing current and/or future social, environmental, eco-
nomic, and other needs outside academia. In the interests of delivering
both scientific and societal impacts, Higher Education Institutions
(HEIs) are being called upon to be more entrepreneurial, more
engaged and more responsive to nonacademic stakeholders’ needs.
Individual researchers are being asked to broaden the set of profes-
sional activities in which they engage and to deliver outcomes that are
of interest to both the academic community and nonacademic stake-
holders (Organ and Cunningham 2011).
In the context of these dual scientific and societal expectations
related to research, evolutions in the scientific community’s orienta-
tions and practices remain relatively poorly specified. Whilst many
scientists are firmly wedded to the principles of curiosity-driven re-
search and academic autonomy, others are oriented toward the
provision of benefits to nonacademic communities. Between these
two extremes are researchers with different mixes of capacities and
willingness to connect the worlds of fundamental research and prac-
tical application, who potentially may play key roles in transforming
scientific ideas into useful inventions (Subramanian et al. 2013).
In some cases, scientific and societal impact can be achieved jointly.
However, this is not automatic; greater scientific impact does not
necessarily imply greater social impact (and/or vice versa) and may
generate significant tensions (Halilem 2010).
This article introduces a framework for investigating the factors
linked to the varied impacts of research. Our analytical framework
proposes three types of factors that influence the generation of scien-
tific and societal impacts. First, key characteristics of individual re-
searchers which are considered antecedents to the generation of
impact. Second, a set of organizational factors that might enable or
limit the generation of impact. Third, a set of preconditions for the
emergence of societal impacts, included as process-context variables.
The inclusion of these process-context variables is inspired by the
concept of ‘productive interactions’ (Molas-Gallart and Tang 2011;
Spaapen and van Drooge 2011).
Our integrated examination of these three types of factors con-
tributes to ongoing discussion on the generation of scientific and so-
cietal impacts from research. Scientists may be required to resolve
V
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Science and Public Policy, 45(6), 2018, 752–763
doi: 10.1093/scipol/scy023
Advance Access Publication Date: 9 March 2018
Article
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tensions between diverging research goals and objectives in order to
generate both scientific and societal impacts from their research. A
fundamental contention of the proposed framework is that comple-
mentarities may arise from researchers’ pursuit of scientific and soci-
etal impacts in collaboration with nonacademic stakeholders and
partners. In this regard, we understand ‘productive interactions’ as a
mechanism contributing to researchers’ resolving of tensions be-
tween the scientific and societal goals of their research, through mu-
tual learning, development of shared understandings and processes
of goal adaptation with nonacademic partners. Thus, productive
interactions, in certain contexts, may create the conditions that con-
tribute to aligning the scientific and societal missions of research.
Our analytical framework is thus designed for the investigation of
research activities at the level of individual researchers working to
produce research impact. Our propositions are expected to shed
new light on the microlevel processes that contribute to research im-
pact, in a system context where funders, evaluators, and other social
stakeholders have explicit expectations that researchers will satisfy
dual scientific and societal missions.
The article is structured as follows. Section 2 reviews the litera-
ture related to the development of our analytical framework. We
summarize the major models of interaction between universities and
other types of organizations, which provides a general heuristic re-
garding the institutional conditions under which individual re-
searchers seek to generate research impact. We then present the
arguments regarding the tension between professional requirements
for scientific advances and societal demands for relevant knowledge.
Section 3 draws on a specific literature to formulate some concep-
tual assumptions and empirical building blocks of our integrated
framework. The section is organized in three parts, addressing the
individual antecedents, organizational conditions, and process-
context dimensions of our framework. Section 4 discusses the poten-
tial of our approach to provide support for the development of re-
search policy and highlights some limitations.
2. Broadening the social scope of academic
science
In this section, we review some prominent conceptual models for
how the institutional arrangements of the broader research system
influence the way scientific and societal impacts are generated. We
identify the main arguments shaping the extent to which individual
researchers’ contributions are oriented toward scientific knowledge
production for a professional audience or toward the generation of
societal impacts, or both.
2.1 The production of knowledge and the generation of
impacts from research
There are several models that describe and theorize transformations in
the way knowledge is produced and used to create impact outside of
academia. A common underlying theme in these models is a shift to-
ward multiactor networked processes and activities in research. For
example, post-academic or postindustrial science (Ziman 1996)is
characterized as collective, constrained by limited resources, focused
more on applied research, and shaped reflexively by a specific branch
of the social sciences—science and technology policy. Post-academic
science is characterized by a set of norms (proprietary, localized, au-
thority, commissioned, expert) that contrast with the classic
Mertonian formulation of the normative basis of academic science
(communalism, universality, disinterestedness, originality, skepticism).
The potential for the generation of impacts from research can be
understood as being configured in different ways depending on the
combination of resource restrictions and the degree of policy direc-
tion. Academic capitalism (Slaughter and Leslie 1997), argues that
academic researchers faced with cut-throat competition for funding,
are amenable to targeting their research toward potentially-profit-
making developments, particularly in collaborations with private sec-
tor partners. This strategy is seen as allowing researchers to escape
from competition for limited public funding and to expand university
research budgets. This work highlights the importance of government
and policy support for a more entrepreneurial form of academic sub-
jectivity, common to UK, USA, Canadian, and Australian research
systems (Slaughter and Leslie 1997).
The formulation of postnormal science (Funtowicz and Ravetz
1993) sought to radically redraw the boundaries between research,
stakeholders, and citizens—and, therefore, the contours of impact
generation. The key argument related to postnormal science is that
the seriousness of the challenges faced by science, technology, and so-
ciety requires a more inclusive, interdisciplinary, and perspectival ap-
proach to research and its impacts. Thus, postnormal science elevates
participatory processes to a position of irreducible importance in the
production and use of knowledge. There are some evident commonal-
ities with the ‘new production of knowledge’ (Gibbons et al. 1994),
which proposes an emerging mode for generating research results and
outcomes (Mode 2), which operates in parallel with the traditional
academic (Mertonian) mode of research (Mode 1). Compared to au-
tonomous public research organization centered Mode 1 science,
Mode 2 science is more socially distributed and takes place closer to
the context of knowledge use. Mode 2 science is also more interdiscip-
linary and is networked in its organizational forms, and its results are
evaluated by a range of interested stakeholders, not just other aca-
demics. Mode 2 science involves closer alignment of research with so-
cial, industrial, environmental, and other goals and objectives. Thus,
generation of impact from research is understood to start from a
much more contextualized and problem-oriented perspective.
The triple helix model (Etzkowitz and Leydesdorff 2000) de-
velops an innovation systems approach to the interdependent uni-
versity, government, and industry spheres, into more properly
institutional (Etzkowitz et al. 2000) and evolutionary (Leydesdorff
2000) formulations. Triple helix dynamics lead to the formation of
an ‘overlay of reflexive communication’ among agencies in the three
spheres, including policies and programs designed to stimulate inter-
actions and to support mutual collaboration. A range of hybrid enti-
ties and intermediaries emerge and evolve, embedding a nonlinear
model of impact generation from research. Activities that increase
the modes, motivations and effectiveness of academic engagement
(D’Este and Patel 2007;Landry et al. 2010;Lam 2011) are com-
monly described as ‘third mission activities’ or ‘the entrepreneurial
university’ (Etzkowitz et al. 2000;Molas-Gallart et al. 2002;
Gulbrandsen and Slipersaeter 2007). Recent work has extended the
triple helix model to the quadruple helix through the addition of a
fourth actor—the ‘media-based and culture-based public’
(Carayannis and Campbell 2009). Through its culture and values
and its construction of ‘public reality’, the media-based and culture-
based public influences research systems and ‘innovation culture’.
This extension of the triple helix model allocates an important role
to the public in research processes, and foresees the ability of re-
search systems to adapt and evolve responsively—with a recursive
positive effect on the generation of societal impact.
In the main, these models offer consistent descriptionsof the major
elements involved in transforming systems of knowledge production
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and use. First, knowledge is seen as the outcome of processes involv-
ing a more heterogeneous set of actors than previously was the case.
Second, the objective of knowledge production increasingly is shaped
by stakeholder needs or societal challenges that transcend individual
disciplinary approaches. Third, the importance of science and research
policy, including incentives, priorities, and other forms of ‘directing’
research, is a significant dynamic affecting the evolution of the re-
search system. According to these propositions, individual researchers
are called on to work collaboratively to address both scientific and so-
cietal challenges. The degree to which scientific and societal missions
are relatively aligned or diverge, is likely to vary considerably between
scientific fields. How individual scientists, under these prevailing insti-
tutional conditions and within their specific scientific fields, resolve
the tensions between professional demands for new knowledge out-
puts and heightened societal demands for relevant knowledge remains
an unresolved empirical question that provided the rationale for the
development of our analytical framework. Whilst these models em-
phasize various forms of institutional integration, the impact of these
conditions at the level of individual researcher’s agendas and activities
remains unclear.
2.2 Research quality tensions: the joint pursuit of
scientific and societal impacts
The dual missions to produce new scientific findings and societally
relevant knowledge that characterize the prevailing institutional
conditions of research systems are tied increasingly to assessment
criteria in formal evaluation systems. Research quality at the indi-
vidual or research group level is no longer considered equivalent
simply to the volume of scientific outputs (primarily journal articles)
and their citations counts. Rather, in systems such as the UK
Research Excellence Framework, research quality is conceived as
three dimensional and comprising scientific outputs, societal im-
pacts, and the research environment (REF 2011). Research quality is
viewed (and assessed) more and more as a function of scientific and
societal outcomes and impacts, in keeping with the kinds of scien-
tific research system models described in Section 2.1. Under these
systemic conditions, the researcher’s range of interactions is likely to
expand in both types of partners and types of activities involved
(Felt et al. 2013). Drawing on this discussion, the analytical frame-
work we propose conceives scientific and societal impacts of re-
search as the two components that shape the diverse configurations
of ‘research quality’ (Fig. 1).
This broader definition of what constitutes research quality,
coupled to an expanded set of expected activities, can raise various
problems at the level of the individual researcher’s plans and goals.
The literature proposes several intertwined arguments related to the
difficulties faced by researchers seeking to reconcile the tensions be-
tween the pursuit of new scientific knowledge and societal relevance
of their research activities. One argument refers to the existence of
different (sometimes contradictory) logics behind the achievement
of scientific impact and societal relevance. According to this think-
ing, processes characterizing the creation of relevant scientific know-
ledge, on the one hand, and the production of usable results or the
translation of scientific discoveries to commercial innovations, on
the other hand, diverge (Dasgupta and David 1994;Gittelman and
Kogut 2003). The public funding of research is based on its general
contribution to the advancement of science (fundamental under-
standing) and to the pool of knowledge available to society (busi-
nesses and nonacademic communities) (Salter and Martin 2001).
University academics increasingly are being required to respond to a
broader set of market and societal expectations (Organ and
Cunningham 2011). Primarily research-oriented scientists must
combine their inherent ‘taste’ or preference for science and publica-
tion with the translation of discoveries into product development
inspired by the possibility of obtaining additional benefits (Stern
2004). A mix of new funding opportunities, broader institutional
mandates for universities, and novel research technologies are shap-
ing the patterns of faculty responses to these dual logics (Owen-
Smith and Powell 2001).
The second argument is that the incentive structures governing the
reward system in science have led systematically to the prioritizing of
peer recognition and academic reputation within the scientific com-
munity (Stephan 2010), making the generation of societal impact
from research a secondary consideration from the individual perspec-
tive of career progression in academia. If public sector scientists’ deci-
sions essentially reflect and are shaped by a reputation-based scientific
reward system (Merton 1973), then a fundamental tension exists be-
tween maintaining an individually-directed scientific career trajectory
and seeking, in addition, to contribute to the social relevance and (in
some cases) the resource base of a public research organization. If the
scientist’s decision to engage actively in Knowledge and technology
transfer (KTT) is viewed as a sign of deviance from the dominant re-
ward system in science, then the likelihood that researchers will seek
fully to exploit the societal impact of their research findings could be
reduced (Hessels and Van Lente 2008). Following this reasoning, the
separation between the criteria guiding what is considered by scientific
peers to be outstanding research (reputation) and what is considered
by societal actors to be useful research (relevance), will create signifi-
cant tensions and potential conflicts related to individual scientific car-
eers (Hessels et al. 2009).
The combination of these arguments (contradictory logics and
scientific ouput/impact-based reward systems) highlights the diffi-
culties scientists can face in seeking to generate scientific and societal
impacts from their research. Reconciling high-level academic per-
formance and exploitation of the societal relevance of research is
likely to be contingent on a broad set of factors. These include fac-
tors linked to the institutional and organizational setting in which
research activities are conducted, and to the distinct characteristics
and attitudes of individual scientists. Scientists’ experience is likely
also to contribute to how they shape their research activity to re-
spond to scientific and societal objectives (Cummings and Kiesler
2005). In the following section, we propose a set of factors, inte-
grated in an explanatory framework, to improve our understanding
Figure 1. Two dimensions of research quality.
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of the configurations of scientific impact and societal relevance that
an individual scientist’s work produces.
3. Explaining the diverse balance among
scientific and societal impacts
Research quality may be driven by outstanding scientific contribu-
tions, by timely responses to societal demands, or by idiosyncratic
combinations of both drivers. Here, we specify a range of factors
that likely influence the particular configuration of scientific and so-
cietal impacts generated by individual research activity. We build on
works that focus, in particular, on empirically tested factors, which
facilitate the operationalization of our analytical framework. We
group these factors into three types: (1) individual antecedents; (2)
organizational conditions; and (3) process-context factors.
The proposed analytical framework (Fig. 2) is an individual-level
approach to understanding the distinct configurations of scientific and
social impacts emerging from research activities. We consider scien-
tists’ research agendas to be conditioned by their perception of the
synergies and tensions between scientific and societal missions. Thus,
the framework is based on a set of individual characteristics likely to
influence the particular balance between research goals, in scientists’
research activities. Our analytical framework also suggests that organ-
izational conditions and process-context factors influence the particu-
lar scientific and societal goal mix pursued in individual research
activities. On the one hand, organizational-level factors—that is, the
institutional and organizational setting in which the research is con-
ducted—are both enabling and constraining in relation to the scien-
tist’s choice and pursuit of distinct research goal configurations. On
the other hand, process-context factors—such as, productive inter-
actions with nonacademic actors—might contribute to enhancing the
scientist’s capacity to reconcile the tensions between scientific and so-
cietal goals through the bilateral learning opportunities these inter-
actions produce. The following subsections provide an extended
discussion on and justification for the proposed three sets of factors.
3.1 Individual antecedents
It has been argued that research behavior is influenced strongly by
individual differences (Halilem et al. 2011). A focus on the
individual researcher sheds light on the interplay between the formal
and informal activities undertaken by researchers (Landry et al.
2010) and the influence of the scientist’s allocation of time and other
physical and human resources, to different activities including re-
search, teaching, management, and entrepreneurial activities, among
others (Gulbrandsen and Smeby 2005;Link et al. 2008;Teichler
2010). Researchers’ decisions affect multiple types of activities and
their personal attributes are likely to play an important role in shap-
ing how they meet personal goals and satisfy institutional demands
for scientific and societal impacts. Scientists embody various beliefs
and behaviors which will continuously redefine the orientation of
and boundaries to their work. While the individual characteristics
identified below are not comprehensive, they include individual-
level variables that have been operationalized and validated in prior
empirical research (Table 1) and which contribute to the analytical
and operational features of our analytical framework.
A first set of factors concerns scientist’s motivations for conduct-
ing research and interacting with academic and nonacademic actors.
These factors are highlighted in the literature as relevant antecedents
to the researcher’s disposition to achieve both scientific and societal
impact from research (D’Este and Perkmann 2011). Scientists’ mo-
tivations are configured according to the extent to which they focus
on the pursuit of intrinsic satisfaction or extrinsic goals such as solv-
ing social problems and contribute to society (Roach and
Sauermann 2010;Lam 2011).
Second, a positive attitude toward setting the scientific research
agenda in cooperation with nonacademic actors is likely to influence
awareness of the types of societal demands to which scientists can con-
tribute and the mechanisms for achieving these objectives (Grant 2008;
Olmos-Pe~
nuela et al. 2015). Recent evidence supports the importance
of individual prosocial motivations among scientists for the adoption
of conduct that includes social relevance as a critical research goal and
to explain the engagement of scientists in a broad range of knowledge
transfer activities (D’Este et al. 2016;Iorio et al. 2017).
Third, diverse sets of skills (technical, procedural, managerial)
and intellectual capital (organizational, social, and human capital)
can endow the individual scientist with the capacity required to ad-
dress the academic and nonacademic communities simultaneously
(Fini et al. 2012;Turner et al. 2013). This blend of skills and
Figure 2. An analytical and operational framework.
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capabilities can provide scientists with the ambidexterity required to
reconcile conflicting research goals among research partners from
different institutional settings, for example, academic and nonaca-
demic environments (Ambos et al. 2008).
Finally, scientists’ professional trajectories regarding formal
training within specific disciplinary domains and their patterns of
sectoral and international mobility, have been shown to be import-
ant for a favorable disposition to achieving multiple goals in re-
search activities. For instance, access to extended social capital
networks from experience of working in different sectors, can
strengthen scientists’ ties with nonacademic organizations and pro-
mote differentiated careers patterns (Dietz and Bozeman 2005).
Similarly, international mobility provides access to diverse sources
of funding and types of networks (Ca~
nibano et al. 2008). The link
between varied research background and researcher productivity re-
mains inconclusive, although some studies find a return to produc-
tivity (Franzoni et al. 2014) and social engagement (Lawson et al.
2017) from migration.
In summary, certain individual characteristics are likely to con-
tribute to the ways in which scientists reconcile the multiple de-
mands to which they often are subject, including institutional
conflicts emerging from the combination of science, societal chal-
lenges, and market logics within research endeavors (Pache and
Santos 2013;Sauermann and Stephan 2013). The degree and form
of the tensions associated to the joint satisfaction of scientific and
societal impact goals may depend also on the type of research (i.e.
upstream oriented or close to the end user).
3.2 Organizational conditions
Organizational level characteristics are important for influencing
how individual researchers achieve a scientific and social goals mix
in their research activities, and for shaping the balance between sci-
entific and societal impact. Individual motivations for and attitudes
toward scientific and societal achievements are likely to be moder-
ated by the institutional and organizational setting in which research
activities are conducted, including both the university and the de-
partmental environments. The proposed framework suggests a num-
ber of organizational conditions that positively and/or negatively
influence the achievement of a balanced mix of scientific and soci-
etal impacts.
First, organizational conditions include the existence of a climate
within the scientists’ working environment that is supportive of
more socially-oriented research activities. Peer community practices,
related to public engagement, technology transfer, and commercial-
ization, for example, vary across specializations and disciplines
(Becher and Trowler 2001). Workplace peers are likely to have a
strong influence on the scientist’s behavior based on prevailing
norms regarding the appreciation and esteem attached to engage-
ment with nonacademic audiences (Stuart and Ding 2006;Bercovitz
and Feldman 2008;Clarysse et al. 2011). Faculty who are socially
embedded in departments that support and place value on achieving
societal impact from research (e.g. entrepreneurial activities) can
more easily withstand existing and potential disincentives (Kenney
and Goe 2004;Tartari et al. 2014).
A second organizational feature refers to skill diversity in
the composition of research groups (Wuchty et al. 2007).
Interdisciplinary research groups and organizational environments
are likely to exert a cognitive influence on their researchers’ ability
to process information and knowledge that contributes to original
scientific insights, and to grasp scientific opportunities to contribute
to societal demands. However, interdisciplinary research involves
the integration of multiple forms of expertise through a process
which needs to be carefully managed and balanced, if ‘new epistemic
communities must be constructed and new cultures of evidence pro-
duced’ (Klein 2008: S116). Successful interdisciplinary assimilation
requires high levels of flexibility, negotiation, and compromise
among participants, to favor integration and collaboration along
multiple pathways.
A third organizational feature refers to the existence of a sup-
portive infrastructure regarding physical and human resources to
help identify suitable nonacademic research partners and assist in
the management of intellectual property rights and contracts. The
existence of specialized support units, such as university Technology
Transfer Offices (TTOs), may indirectly influence academics to start
new ventures (Clarysse et al. 2011) and enable links with nonaca-
demic audiences (Landry et al. 2013). Such facilitator and intermedi-
ary roles foster the connection between university scientists and
societal actors who potentially could participate in and benefit from
knowledge exchange processes (Siegel and Wright 2015b).
Finally, organizational-level barriers to interactions with stake-
holders can limit the capacity of scientists to achieve an appropriate
balance between scientific and societal impacts. These barriers are
generally cognitive (due to the different cultures of academics and
nonacademics) or transaction-related (due to administrative obs-
tacles and negotiation conflicts) (Bruneel et al. 2010;Tartari et al.
2012;Ramos-Vielba et al. 2016). The organizational conditions are
likely to influence the extent to which scientists perceive these bar-
riers as insurmountable obstacles. Scientists’ perceptions of barriers
will differ depending on the support provided by their organiza-
tional setting, among other factors.
In summary, the set of organizational conditions identified here
are likely to act either as enabling or limiting for academic re-
searchers seeking to generate both scientific outputs and societal im-
pacts. This set of conditions is not exhaustive; rather, it identifies a
range of factors derived from the literature for which there is sub-
stantial empirical evidence (Table 1).
3.3 Process-context factors: productive interactions
There is increasing consensus among scholars in the fields of soci-
ology of science and research evaluation that research projects that
generate significant societal impact require an iterative process of
interaction between scientists and stakeholders, involving bidirec-
tional flows of knowledge and expertise (Nowotny et al. 2001;
Smits and Kuhlmann 2004). We draw on the concept of ‘productive
interactions’ (Molas-Gallart and Tang 2011;Spaapen and van
Drooge 2011;de Jong et al. 2014) to argue that interactions between
researchers and stakeholders are vital not only to achieve societal
impacts from research activities, but also to resolve tensions between
scientific and societal goals in research activities.
In this sense, interactions between science and society may be a
precondition for future societal impact, which it is impossible to
grasp or anticipate in a precise manner at a particular point in time,
since the effects are remote from the initial phase of research and
only weakly attributable to specific actions. Thus, while the attribu-
tion of societal impact from specific research activities represents a
phenomenal challenge for research evaluation, the focus on ‘pro-
ductive interactions’ provides a workable approach to appreciative
foresight: that is, interactions between researchers and stakeholders,
which ‘can be seen as necessary interim steps in the process that
leads to societal impact’ (Spaapen and van Drooge 2011: 214).
However, not every type of interaction between scientific re-
searchers and stakeholders will be equally likely to produce societal
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Table 1. Individual antecedents and organizational conditions.
(I) Individual antecedents
Components Description Methods Selected criteria Areas of research Sources
1. Motivations for research
and knowledge transfer
Reputational (ribbon) and intrinsic
(puzzle) rather than financial (gold)
Mixed (interviews and
survey)
Motivating factors to engage
in industrial links activities
Biosciences, Computer sci-
ence, Physical sciences
Lam (2011: 1360)
Range of incentives for different chan-
nels of engagement with industry
Quantitative (survey and
public records)
Ranking incentive items for
interactions with industry
Engineering and Physical
sciences
D’Este and Perkmann
(2011: 323)
List of nine motives for entering a
training program that aims to fos-
ter knowledge transfer into
practice
Quantitative (survey) Ranking motive items for
entering the training
program
Young researchers from all
fields
Arzen
sek et al. (2014:
192)
Recognition within the scientific
community, personal financial
gain, additional funding for gradu-
ate students and laboratory
equipment
Qualitative (semi-structured,
in-person interviews)
Categories of stakeholders:
(1) TTO directors and uni-
versity administrators, (2)
academic scientists, (3)
managers/entrepreneurs
Five major public and private
research universities in
Arizona and North
Carolina
Siegel et al. (2003:
116–17)
2. Research agenda setting Inspired by prosocial motivation as the
desire to benefit other people
Quantitative (survey) Items for prosocial motiv-
ation as a personality trait
Firefighting and fundraising Grant (2008: 51)
Openness to nonacademics’ inputs along
stages of research process
Quantitative (survey) Researchers’ open behavior
during research processes
All fields Olmos-Pe~
nuela et al.
(2015: 392)
3. Ambidexterity Diverse skills contribute to develop a
favorable entrepreneurial attitude
Mixed (structural equation
modelling)
Technical / procedural / man-
agerial skills
New technology-based firms Fini et al. (2012: 394)
Intellectual capital resources: organiza-
tional/social/human
Literature review Initiative, cooperation, multi-
tasking, brokering
–Turner et al. (2013:
327)
The ability to which academic scien-
tists can simultaneously achieve re-
search publication and research
commercialization at the individual
level
Quantitative (survey) Multiplicative interaction of
publication and commer-
cialization involvement at
the individual level
Life, science, engineering,
and medical research
Chang et al. (2016:9)
4. Professional trajectory Intersectoral jobs relates to
publication and patent
productivity
Quantitative (curriculum
vitae (CV) analysis and
patents)
Diverse work experience in a
career and new social
networks
A wide range of fields Dietz and Bozeman
(2005: 353)
Links between mobility and research
performance
Quantitative (CV analysis) Researchers’ stays in a differ-
ent organization
Physics, biology, philosophy
and philology
Ca~
nibano et al.
(2008: 21)
High share of senior researchers: pre-
dominance of more experienced re-
searchers with an established
university career over young re-
searchers at the beginning of a uni-
versity career
Quantitative (two distinct
surveys)
Factors that enhance/inhibit
the establishment of uni-
versity–firm interactions
Researchers from all fields
and manufacturing firms
Schartinger et al.
(2001: 262)
(continued)
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(II) Organizational conditions
Components Description Methods Selected criteria Areas of research Sources
1. Supportive climate Influence of university departments on
entrepreneurial competencies
Quantitative (archive, survey
and start-ups)
View on importance attached
by departments
Electrical engineering, com-
puter science
Kenney and Goe
(2004: 701)
Work context effects on transition to
for-profit science
Quantitative (case–cohort
data)
Coworker transitions/entre-
preneur coauthors
Life sciences/biotech firms Stuart and Ding
(2006: 116–19)
Absorption of localized social norms in
work environments
Quantitative (administration
records)
Institution patents/chair and
cohort prior disclosures
Medical schools departments Bercovitz and
Feldman (2008: 77)
Social environment: proximity to other
academics who started spin-offs
Quantitative (survey and
public records)
Spin-offs created by a univer-
sity in the same year
Engineering and physical
sciences
Clarysse et al. (2011:
1089)
2. Interdisciplinarity Higher productivity of team work in
knowledge creation
Quantitative (Web of Science
papers and patents)
Citations: team- versus solo-
authored work
Sciences a engineering, SS,
arts and humanities
Wuchty et al. (2007:
1037)
Principles for interdisciplinary research
performance and evaluation
Literature review (quantita-
tive and qualitative
studies)
Engaging in mutual learning
and joint activities
–Klein (2008: S122)
Integration of disciplines within a re-
search environment
Quantitative (survey) Collaboration between scien-
tists from different discip-
lines with the goal of
producing new knowledge
All fields van Rijnsoever and
Hessels (2011)
3. Infrastructure facilities Knowledge and technology transfer
organizations as crucial nodes con-
necting suppliers and users of
knowledge
Quantitative (websites and
survey)
Help firms to specify their
needs related to research
–Landry et al. (2013:
437)
Department support is decisive to
develop nonacademic competences
Qualitative case studies
(longitudinal)
Departments with critical
mass of industry networks
Biotechnology and
engineering
Rasmussen et al.
(2014: 100)
Intermediary role of university TTOs
between suppliers of innovation and
societal actors
Literature review (theoret-
ical/empirical)
Proper incentives scheme in
place/sufficient rewards
–Siegel and Wright
(2015a: 5 and 11)
Supportive infrastructure available for
the researcher that wishes to move
research findings into the commer-
cial realm
Qualitative (longitudinal
multiple case studies)
TTOs, education in patenting
or help in evaluating busi-
ness plans, support struc-
tures located outside the
academic organization
Life sciences (stem cell re-
search projects)
Nilsson et al. (2010:
619)
4. Barriers and obstacles Collaboration mitigates orientation-
related and transaction-related barriers
Quantitative (survey and
public records)
Working on collaborative
projects with universities
Engineering & Physical sci-
ences / firms partners
Bruneel et al. (2010:
863)
Experiences shape perceptions of bar-
riers to industry collaboration
Quantitative (survey and
public records)
Academic’s industrial collab-
orators/channels
Engineering and Physical
sciences
Tartari et al. (2012:
664)
Interactions between risk to scientific au-
tonomy and risk to scientific credibility
Quantitative (survey) Cooperation with nonaca-
demic public organizations
All fields Ramos-Vielba et al.
(2016: 565)
Obstacles reflecting costs of realizing
KTT activities from an institute’s
point of view
Quantitative (survey) KTT activities with private
enterprises
Engineering, natural sciences,
mathematics and physics,
medicine, and economics
and business administration
Arvanitis et al. (2008:
1872–73)
Table 1. (continued)
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impact in the short, medium, or long run. Similarly, not every scien-
tist who interacts with stakeholders will generate research outcomes
with societal impact. Drawing on studies by Abreu et al.(2009),
Spaapen and van Drooge (2011),Perkmann et al. (2015) and others,
we suggest that there are three components of interactions between
scientists and stakeholders that are particularly critical for generat-
ing societal impact: (1) variety of the stakeholders; (2) breadth and
depth of the interaction modes; and (3) presence of interactive
learning.
First, regarding the variety of stakeholders, there are likely to be
as many different interpretations of what constitutes valid societal
impact as there are potential beneficiaries. Businesses, government
agencies, nonprofit organizations, civic societies, hospitals, patient
groups, will have distinct expectations about what constitutes rele-
vant societal impact from scientific research. Thus, the variety of
stakeholders with whom researchers interact is likely to enhance
awareness and understanding of the distinct expectations of scien-
tific research and to increase the potential to address a wider range
of unmet societal needs. Moreover, greater awareness and under-
standing of stakeholders’ views of what constitutes valid and legit-
imate research goals will increase the scientist’s capacity to reconcile
conflicting institutional logics among partners in the context of re-
search activities.
The second component refers to the conduits or mechanisms of
interaction. Interactions between scientists and stakeholders can
take multiple forms. They can be formal or informal, depending on
whether the interactions are meditated through contractual arrange-
ments; they can be transactional (e.g. one-off, market-based inter-
actions) or relational (frequent and personal-based interactions);
they can be targeted or open-ended depending on the specific terms
of the research agenda, etc. Understanding the range of interaction
modes between scientists and nonacademic actors is critical since
they will affect the type of societal impact. For instance, while
technology transfer may require some form of market-based formal-
ized contract, responding to highly-contextualized demands from
potential beneficiaries may involve only informal and interpersonal
interactions. We consider that it is essential to account for the specif-
icities of the different modes of interaction and their complementar-
ities to understand the potential for societal impact from research.
Finally, productive interactions can enhance the learning opportu-
nities for the parties involved. Scientists are likely to acquire a better
understanding of the potential applications of the findings from their
research activities and improved capacity to identify new puzzles to
inspire blue-sky and applied research. Similarly, stakeholders can
learn how to participate in and influence the definition of research tar-
gets to ensure that their demands are met while obtaining a better
understanding of whether a particular piece of scientific research will
achieve their objectives. If interactions set in motion bidirectional
flows of knowledge and lead to learning opportunities for the parties
involved, the capacity of the scientist to resolve the tensions between
the scientific and societal goals of their research activities are likely to
be greatly enhanced. Table 2 presents a broad set of items which we
suggest could be tools to capture these three process-context compo-
nents to conduct empirical research and contribute to the operational-
ization of the concept of productive interactions.
Our proposed analytical framework (Fig. 2) suggests that the
three components of productive interactions potentially contribute
to resolving critical tensions faced by individual scientists when
seeking to meet demands for scientific and societal impact from their
research activities. We argue that these three components of pro-
ductive interactions can shape scientists’ cognition, skills, and atti-
tudes and, potentially, contribute to reducing the tension associated
with reconciling scientific and societal demands.
In relation to cognition, we argue that productive interactions
can enhance scientists’ awareness about the potential practical appli-
cations of their research activity and their capacity to identify
Table 2. Process-context factors: productive interactions as preconditions.
Components Description Sources
1. Variety of
stakeholders
Knowledge exchanges with a wide range of end users:
small and medium-sized enterprises, big firms, government agencies, nonprofit organizations,
hospitals, civic societies, patient groups, international organizations, and others
Abreu et al. (2009)
Hughes et al. (2011)
Hughes and Kitson (2012)
2. Modes of
interaction
a. Formal interactions (signed contract):
Consultancy, contracted/joint research, teaching/training, personnel exchanges, creative/cul-
tural products, guidelines/protocols/norms, renting equipment/materials, testing prototypes
Commercialization activities: IPRs, patents, licensing, utility models, know-how, spin-offs
b. Informal interactions (no-signed contract):
Advisory work, dissemination activities, lectures for the community, specific training, nona-
cademic professional networks, presentations at nonacademic fora, nonacademic actors in
both teaching and curriculum design.
Communication activities: use of both analog and digital social media) means to disseminate
research results to nonacademic actors
Molas-Gallart et al. (2002)
D’Este and Patel (2007)
Landry et al. (2010)
Olmos-Pe~
nuela et al. (2013)
Lupton (2014)
Perkmann et al. (2015)
Sugimoto et al. (2017)
3. Bidirectional
learning
a. Flows of knowledge and expertise
a.1. Benefits from interactions (formal and informal) to research activities: specific inputs,
relevant information sources, external resources, new ideas, approaches and perspectives,
validation, legitimacy, recognition
a.2. Benefits from interactions (formal and informal) to nonacademic actors: concrete im-
provements (organizational practices, problems solutions, products and services), new
chances (better understanding, business opportunities, training, networks), confirmation of
choices (reputation, support, validation)
b. Transmission (processing for final use):
Facilitating practices (formats, language, ways and manners) specifically for end users
Amara et al. (2004)
Gulbrandsen and Smeby (2005)
Perkmann and Walsh (2008)
Landry and Amara (2009)
Spaapen and van Drooge (2011)
Levin et al. (2011)
Dowling (2015)
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previously undetected research problems. This helps them to over-
come conflicts associated with the need or desire to satisfy scientific
and societal goals through their research activities. On the one hand,
it allows the scientist a better understanding of what nonacademic
actors believe constitutes valuable and relevant research (Olmos-
Pe~
nuela et al. 2015). On the other hand, productive interactions can
help scientists to weigh the knowledge benefits for the advance of
their research activities, which will attenuate their concern that
interaction with stakeholders might undermine scientific achieve-
ment or damage career progression (Perkmann and Walsh 2009).
Regarding skills, we consider participation in productive inter-
actions as helping scientists to develop coordination and networking
capabilities. On the one hand, it increases their capacity to manage
and coordinate research portfolios to realize the synergies between
research projects addressing different goals and with different beliefs
about desirable research outcomes (Wallace and Rafols 2015). On
the other hand, productive interactions contribute to a better appre-
ciation of the priorities and incentives of other actors in setting the
terms and objectives of research activities and enable networking
and brokerage capabilities to align different institutional logics (e.g.
science and market logics) (Pache and Santos 2013).
Finally, in relation to attitudes, productive interactions can con-
tribute to the formation and shaping of scientists’ motivations to as-
sume certain research roles and academic identities. In particular,
productive interactions may be conducive to prosocial research be-
havior, since scientists may derive intrinsic satisfaction from re-
search activities that show a positive impact on the wellbeing of a
third party (D’Este et al. 2016). Productive interactions also can be
conducive to the formation of hybrid identities in academic settings;
the scientist might assume a role that reconciles inherent contradic-
tions between the practices and norms of an academic identity and
the active participation in technology transfer activities (Jain et al.
2009). According to Felt et al. (2013), many researchers engage in a
range of interactions, including governance and valorization activ-
ities, which are insufficiently recognized by the existing academic re-
ward system. We suggest that it is precisely these ‘productive’ types
of interactions that enable researchers to resolve the tensions be-
tween demands to produce scientific and societal impact from their
work and to accommodate these, sometimes divergent, goals within
their overall research portfolio.
To summarize, the capacity of individual scientists to generate
both scientific and societal impact is enhanced in the presence of inter-
actions which involve potential beneficiaries of research, and which
exhibit bilateral learning opportunities. In this sense, we suggest that
productive interactions are likely to play a moderating role in our ana-
lytical framework, by enhancing the connection between the individ-
ual level characteristics (discussed previously) and the capacity to
balancing scientific and societal impacts from research activities.
4. Discussion
The nature of the relationship between the scientific and societal im-
pacts of research is an open research question and an item on science
and research policy agendas. To date, there is little understanding of
the complementary or substitutive effects that might exist between
the generation of scientific and societal impacts. This limits the basis
for informed policy discussion. In this article, we proposed an ana-
lytical and operational basis for understanding how scientific/soci-
etal impacts emerge from research and suggested a set of factors that
might contribute to the reconciling of potential tensions between
these two types of research impact at the individual researcher level.
The proposed framework aims to capture a range of interrelated
aspects conducive to the generation of scientific and societal im-
pacts, in the coproduction of diverse configurations of ‘research
quality’. The proposed framework is designed to provide insights
into the three types of factors affecting joint pursuit of the scientific
and societal impacts of academic research at the individual level: in-
dividual antecedents, organizational conditions, and process-context
factors. We consider the process-context factors as crucial for inves-
tigating and understanding the inclusion of societal demands in re-
search outcomes. We argue that ‘productive’ types of interactions
with nonacademic partners and organizations may enable re-
searchers to resolve potential tensions between pursuit of the scien-
tific and societal impacts from their work, or to accommodate these
dual missions within their research portfolios.
Our proposed analytical framework offers two advantages. First,
the development of a conceptual and structured approach to individ-
ual researcher’s involvement in the generation of scientific and soci-
etal impacts advances our micro-level understanding of how both
types of impact emerge from research systems. Second, in starting
from the individual level, the framework is designed to gather data
that can be analyzed and interpreted at various levels and aggrega-
tions. The different levels of aggregation—scientific field, coauthor-
ship network, research group, department or research center—
provide multiple lenses of varying granularity to study the configur-
ation of the scientific and societal impacts generated by research
activity.
The framework allows identification of conditions likely to gen-
erate socially relevant research, as the precursor to its societal im-
pact. Societal impact is more likely in a context of interactions with
potential beneficiaries, which elicit learning opportunities for all the
parties involved in the research activities. The three components of
productive interactions—variety of stakeholders, modes of inter-
actions, and bilateral learning—can shape scientists’ cognition,
skills, and attitudes, potentially contributing to a better reconcili-
ation of scientific and societal goals in scientific research.
It is important to recognize that engaging in interactions with
nonacademic actors does not lead necessarily to positive (or any) ef-
fects or impacts. There is some research that finds a negative impact
of university external engagement on academic research activities
(e.g. Louis et al. 2001;Czarnitzki and Toole 2010). However, our
emphasis on a particular type of interactions (i.e. productive inter-
actions) and its interplay with individual and organizational level
characteristics in our framework, allows us to identify factors linked
to a diversity of scenarios, where interactions with nonacademic
actors may contribute in different degrees to the balancing of scien-
tific and societal goals from research activities.
Our operational framework captures the heterogeneous configur-
ations of individual characteristics and organizational factors that ap-
pear to lead to scientific and societal impacts. Scientific and societal
impacts can be analyzed also as outcomes of a portfolio of more or
less diverse activities undertaken by researchers (Cruickshank 2013),
since scientific research leads to major contributions that can be bene-
ficial for society through many different pathways. The acknowledg-
ment of multiple forms of knowledge mobilization/translation may be
not properly captured through a single ‘impact builds on research’ for-
mula (Smith et al. 2011;Mounier 2015). A wide range of science–so-
ciety interactions, for example, complex societal valuation processes
or social responsibility as a form of engagement can influence scien-
tists’ motivations and their awareness of other stakeholders’ expect-
ations. Therefore, the production of high quality research should be
understood as achieved through a variety of combinations of actors,
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motivations, and ingredients, which may be oriented toward scientific
or societal missions or both.
From a policy point of view, our framework is designed to pro-
vide new insights into what constitutes research quality. This is im-
portant not least for improving the framing of assessment processes,
in order to take full account of research impact. Assessment should
avoid a simplistic dichotomy between scientific impact and societal
impact, which does not correspond to diverse configurations of re-
search process outcomes. Funding priorities in science have become
contingent upon the production of demonstrable socioeconomic
benefits, particularly in a political environment shaped by the global
financial crisis, budget ‘austerity’, and concerns over reducing the
public deficit (Holmwood 2011;Bornmann 2013). Evaluation crite-
ria increasingly are being linked to societal benefits such an intended
research product or final outcome, in specific use contexts (Carew
and Wickson 2010). However, the diversity of research makes it dif-
ficult to evaluate different types of impact in a rigorous, objective,
consistent and complete way, across disciplines and science institu-
tions (Martin 2011). We would prefer a versatile and comprehensive
approach to research activity, which integrates the generation of sci-
entific outputs and societal impacts as interrelated and potentially
mutually reinforcing dimensions of research quality, as elaborated
in this article. We believe better information regarding the configur-
ations of scientific and societal impacts generated by individual re-
searcher’s activities is particularly timely in the current policy
context. The assumption that all researchers should pursue a uni-
form strategy in relation to the production of scientific and societal
impact from their research would be based on weak or false impres-
sions that such impacts are necessarily linked. This would have po-
tentially destructive implications for research system diversity.
Our approach has three main limitations. First, the selection of
literature and empirical factors addressed in the framework is lim-
ited, and it risks missing or underplaying certain factors in the lit-
erature. Second, there is a possible bias toward the literature on
entrepreneurship and toward quantitative methods and the UK
and US research contexts. Our framework may be biased to a de-
gree by the expectations about researchers and research systems
contained in this literature. Finally, the explanatory capacity of
the proposed analytical framework will depend, among other fac-
tors, on how well we can operationalize the concept of ‘productive
interactions’ to capture the process-context factor included. This
raises the issue of measurement theory and other technical chal-
lenges, which will have a significant impact on the effectiveness of
the framework.
Future research should focus on operationalizing and using the
framework for empirical studies of research impact. It is envisaged
that empirical results highlighting extant heterogeneity in the config-
urations of scientific and societal impacts generated by research
activities, which also capture the specific combinations of factors
that influence these diverse outcomes, would be a useful contribu-
tion to work on the outcomes and impacts of scientific research.
Acknowledgement
This research was supported by the Spanish Ministry of Economy, Industry
and Competitiveness through the State Plan of Scientific and Technical
Research and Innovation (EXTRA project, grant CSO2013-48053-R) and by
the Oslo Institute for Research on the Impact of Science (OSIRIS, grant
256240) funded by the Research Council of Norway. A previous version of
this article was presented at the scientific workshop on ‘Excellence policies in
Science’, hosted by the Rathenau Instituut and the Centre for Science and
Technology Studies at Leiden, 2–3 June 2016. The authors would like to
thank the participants in the workshop for their comments, particularly
Leonie van Drooge for her insightful advice. Comments from Jordi Molas-
Gallart were also helpful for the development of the article.
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