Proceedings of 20th International Business Research Conference
4 - 5 April 2013, Dubai, UAE, ISBN: 978-1-922069-22-1
From ‘Need to Know’ to ‘Need to Use’: What Determines
Knowledge Utilisation by Public Managers in the
Governmental Agencies in Québec, Canada
Moktar Lamari, Ph.D, Rejean Landry, Ph.D and Nabil Amara, Ph.D
The purpose of this paper is to conduct an empirical analysis explaining what
makes social science research used in the public health decision-making.
Knowledge-management manuals praise “learning organizations” and reaffirm
the strategic role of the use of new knowledge in any organizational-innovation
initiative and any process to improve the performance of human resources.
Following both Knott and Wildavsky (1980) and Todorova and Durisin (2007),
we assume that knowledge transfer is punctuated with different stages
(reception, assimilation, adoption, implementation). The present research
explains the determinants of success of each of these different stages. The
data used come from a survey carried out in Canada, based on a sample of
224 civil servants. Our findings suggest that social capital, proximity and
interaction mechanisms between researchers and managers are crucial for the
success of social science research utilization. It also suggests that the
determinants of the “need to know” are different from those explaining the
“need to use”. This paper provides new evidences about the knowledge
transfer and highlights that this process is a sequential process made up of
several steps of increasing difficulty, not always easy to overcome.
Keywords: Social science research, knowledge management, Learning organization,
knowledge brokering, policy making
1. Introduction
The on-going reforms in public administrations coincide with a growing interest in
the utilization of the results of scientific research. In Canada, as elsewhere in Western
countries, several public debates and pressure groups have spoken in favour of a better
return on the investment made in social science research (Lomas, 2000; Landry, Lamari
& Amara, 2003; Sabatier, 2007). Moreover, if both federal and provincial governments
“invested considerable time and effort in facilitating dialogue” among on one part,
knowledge brokers, and on the other part, academics and public policy makers, public
investment in policy research and analysis have been erratic over the last decades
(Dobuzinskis, Howlett & Laucock, 2007: 577). At the same time, knowledge
management manuals praise “learning organizations” and reaffirm the strategic role of
new knowledge utilization in any social-innovation initiative and any process to improve
the performance of government policies (Osborne & Gaebler, 1993; Barzelay2001).
In practical terms, numerous managers and decision makers agree that the
utilization of the results of social science research is much easier said than done. While
admitting interest in valuing the results of social science research in the on individual
_________________
Moktar Lamari, Ph.D., (Corresponding author), School of public administration, University of Quebec,
Canada.Email: moktar.lamari@enap.ca
Rejean Landry, Ph.D, Université Laval
Nabil Amara, Ph.D, Université Laval
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work performance, these decision makers underline the complexity of the process of
utilizing results from social science research and are deeply concerned about the
existence of a multitude of difficult obstacles to overcome throughout the process (Oh,
1997; Howlett, 2009). Dobuzinskis et al. (2007:578) outline for that matter that the
nature itself of the pursued objectives could contribute to make collaboration harder,
since “while academics researchers are trained to rationally search for the right answer,
this answer might not fit the context within public policy advisers fell compelled to
operate (…)”. The arguments put forward by both public servants and knowledgemanagement experts are not always based on convincing empirical evidence. Studies
published on the subject remain marked by their descriptive, and sometimes reflective,
nature (Rich, 1997). Today, despite interest in the subject, very little is still known about
the real obstacles and determinants for the utilization of the results of social science
research in public administrations eager for new knowledge and innovation.
The present research proposes to bring empirical responses to this subject. It
aims at identifying the stages and determinants that are likely to facilitate the utilization
of the results of social science research by civil servant and administrative human
capital. To do so, the research is based on the data from a survey carried out among a
representative sample of administrators and decision makers operating in various
Quebec government and public organizations directly involved in the implementation of
Quebec‟s health and welfare policy. Government expenditures in public health make up
almost 40% of total government expenditures.
Using Knott and Wildavsky (1980), we assume that government decision makers
concerned by the utilization of the results of scientific research are faced with a
sequential process made up of several stages of increasing difficulty, not always easy to
overcome. According to these authors, six principal stages punctuate this process. First,
to successfully use the results of scientific research, decision makers begin by
accessing scholarly publications including results useful to their activities (reception
stage). Following which they must find the time to read and identify the principal results
contained in these publications (cognition stage). To proceed further in the utilization
process, and to grasp the practical implications associated with these results, decision
makers have to discuss the results received with their colleagues and partners, not only
to dispel any possible doubts, but especially to confirm their interpretation of the results
in question (discussion stage). Then, they are induced to return to the publications to be
certain of the validity of the sources and the credibility of the research and to decide if
this new knowledge is truly innovative and deserving of being cited as a reference in the
field (reference stage). Once all these stages have been completed, the decision
makers undertake more concrete action for the adoption of this new knowledge and its
adjustment to the contingencies of its utilization context (adoption stage). After this is
done, the decision makers apply this knowledge in the form of decisions and innovative
practices (implementation stage). Obviously, none of these stages can be taken for
granted and each of them has its own challenges and obstacles. However, a 2007 study
from Belkhodja, Amara, Landry and Ouimet showed that numerous research results did
made it through the first stages, but “only to run aground at one of the two last stages of
the utilization process” (p. 405). Likewise, the stages must be completed without
skipping any one of them. The process is progressive and progressively difficult.
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Having dealt with the stages, we now turn our attention to the factors that
influence them. To identify the determinants involved, the present research deduces its
hypotheses and explicative variables from theoretical models dealing with the utilization
of research results (Oh, 1997; Landry, Amara & Lamari, 2001; Huberman, 1994). The
science push model suggests variables dealing with the attributes of research (applied
versus fundamental research) and with the goal associated with the utilization of its
results. The science pull model suggests explicative variables dealing with the attributes
specific to the decision makers concerned (level of schooling, motivation, professional
status, experience, preferences, etc.). The institutional interaction model offers
explicative variables related to organizational contingencies and liaison and knowledgetransfer mechanisms (Glaser, Abelson & Garrison, 1983; Dunn, 1980). The social
interaction model suggests variables dealing with the trust and relational assets linking
the communities of university researchers and managers (Glaser et al., 1983;
Huberman, 1994).
This text is divided into three parts. The first puts the principal theories used into
perspective. The second deals with the research methodology by stating the
hypotheses, describing the survey‟s data and justifying the econometric model used.
The third presents and interprets the results obtained. By way of a conclusion, the
principal implications of these results for decision-making are presented.
2. Theory
Studies dealing with the utilization of the results of social science research have
overwhelmingly considered R&D results as neutral products that can be easily
assimilated by interested decision makers (Huberman, 1994). However, Weiss (1979)
had already drawn attention to the intrinsic specificities of the results of social science
research. Weiss demonstrates that these results are greatly distinguishable from those
coming from research in the fields of engineering and natural sciences: “Social science
knowledge does not readily lend itself to conversion into replicable technologies, either
material or social. Perhaps most important, unless a social condition has been
consensually defined as a pressing social problem, and unless the condition has
become fully politicized and debated, and the parameters of potential action agreed
upon, there is little likelihood that policy-making bodies will be receptive to the results of
social science research.” (Weiss, 1979: 444).
Other work has focused on the goals associated with the utilization of the results
of social science research. Three goals are put forward. The first one, described as
instrumental, deals with the valuing of new knowledge in creating new competencies
and human capital. The second, described as conceptual, deals with exploiting new
knowledge to generate new explanations and new perspectives for understanding
problems encountered and policies implemented (Pelz, 1978). The third goal, described
as symbolic, refers to situations in which decision makers resort to new knowledge to
legitimize and justify already pre-identified decisions (Weiss, 1973). Rich (1991)
criticized the symbolic goal by considering it as a catch-all goal that is difficult to
measure or verify.
Moreover, studies on the subject of the use of new social science knowledge
suggest the existence of numerous explanations. These explanations fall within the
scope of four theoretical models (Wiess, 1979; Yin & Moore, 1988). The oldest of these
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Proceedings of 20th International Business Research Conference
4 - 5 April 2013, Dubai, UAE, ISBN: 978-1-922069-22-1
models is the one known as “science push.” This model centred its analyses on the
attributes of the research and the goals of the results of the research (Dearing, Meyer &
Kazmierczak, 1994; Edwards, 1991; Lomas, 1993). Those who adhere to this model
suggest that the results of fundamental research have less chance of being used than
the results of applied research (Machlup, 1993; Oh, 1997; Rich, 1997). They report that
the utilization goals (instrumental goals, conceptual, etc.) can be a factor in resorting to
these research results. The predominance of supply over demand is implicitly assumed
in this model, and it is maintained that “science push” will in the end create its own
demand and value (Landry, Amara & Ouimet, 2007). The linearity of the transfer and
knowledge utilization process is also assumed by letting it be understood that this
process brings together researchers, already aware of their societies‟ needs, and
decision makers who, no matter what they do, can add nothing to the supply process of
new scientific knowledge. Two main criticisms have been made regarding this model.
Obviously, the first criticism deals with the linear, one-way nature of new knowledge
transfer and utilization operations (Yin & Moore, 1988; Rich, 1991; Lomas, 1997).
Landry, Amara and Ouimet (2007) underlines the tensions under which are working
researchers in university : “(...) on the one hand, the traditional vision of universities,
which encourages researchers to publish, competes with the entrepreneurial vision of
universities, which encouragers researchers to consider their publications as knowledge
assets that can be transferred outside the scholarly community” (p.567).
In contrast, the “science pull” model focused on the demand aspect of the
research results (Chelimsky, 1997; Orlandi, 1996; Landry, Amara et Lamari, 2001). This
model assumes that the users of the science are heterogeneous and their need
depends on their own attributes (level of schooling, experience, position held, needs,
preferences, etc.). The model maintains that the use of new knowledge is greater when
researchers take decision makers‟ and potentially concerned organizations‟ attributes
into consideration; “it is assumed that the pull element is triggered by the needs and
demands of research users” (Landry, Amara et Ouimet, 2007:568). As a result, the
decision makers‟ qualifications, their status, their interest in scientific publications are
seen as key determinants for success in the different stages of the research utilization
process. This model was also criticized for its linearity and one-way nature (Huberman
& Turler, 1991).
Continuing further, the institutional interaction model enriches the explanation of
the utilization of new knowledge by using the metaphor of two communities: the
community of scientists and that of users. Through this metaphor, those offering and
those requesting research results are placed face-to-face to accentuate the differences
that separate the two communities in terms of priorities, work methods, preoccupations,
jargon, and even communication culture. This model underlines the strategic
importance of liaison and transfer mechanisms to bring the two communities closer
together (Huberman, 1990). Obviously, decision makers operating in contexts
favourable to interaction and collaboration with researchers have a greater chance of
finding new knowledge that is appropriate to the needs of their actions. Likewise,
organizations having close links and frequent exchanges with the agencies involved in
knowledge production and transfer can more easily incorporate and take advantage of
the knowledge obtained from social science research (Dunn, 1980; Yin & Moore, 1988;
Huberman & Thurler, 1991).
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A fourth model emphasized social interaction to create a new explanatory
framework incorporating variables measuring relational assets and social capital, in
addition to the variables proposed by the three preceding models (Glaser et al., 1983;
Nyden & Wiewell, 1992; Oh & Rich, 1996; Landry et al., 2001). This model suggests
that, in contexts with strong social capital, the utilization of new knowledge is greater.
Social capital takes into account relational assets, reciprocal trust and networks that
facilitate interpersonal relations and the coming together of strategic interests. Coleman
(1990:302) defines social capital by its function: “Social capital is defined by its function.
It is not a single entity but a variety of different entities, with two elements in common:
they all consist of some aspect of social structures, and they facilitate certain actions of
actors within the structure. Like other forms of capital, social capital is productive,
making possible the achievement of certain ends that in its absence would not be
possible. Like physical capital and human capital, social capital is not completely
fungible but may be specific to certain activities. Unlike other forms of capital, social
capital inheres in the structure of relations between actors and among actors.”
These realities create coordination problems and, sometimes, even
misunderstandings between researchers and decision makers. The inadequacy of
liaison mechanisms generates increasing transaction costs that are difficult to assume
by certain decision makers. Rich and Goldsmith (1982) report that the presence of
these costs leads numerous organizations to initiate their own research, thereby
avoiding the use of university researchers. Moreover, it is for this reason that decision
makers end up preferring reports produced within their organization to university
publications (article, books, etc.). The authors summarize this paradox thusly: "Due to
an emphasis on trust and control, individual managers in the public sector will tend to
use predominantly that research which is produced within their own organizations.
Thus, it is natural to expect that R&D managers will also be R&D users".
3. Methodology
3.1 Hypotheses
The hypotheses of this research have been derived from the theories reviewed
previously. The review of these theories has led to establishing four categories of key
determinants in the process of utilizing social science research by decision makers in
the government and public sectors: i) determinants linked to attributes specific to
decision makers and organizations that are potential users of these results; ii)
determinants linked to the attributes of the research, iii) determinants linked to the
attributes of the foreseen use, and finally iv) determinants linked to the contingencies of
the institutional and social context. It is now the moment to formulate the hypotheses to
be verified for each type of determinant.
3.1.1 What decision makers? Civil servants and decision makers are
heterogeneous and do not all have the same predispositions to successfully complete
the various stages of knowledge utilization. Their diversity can be found in their
professional experience, their level of schooling, their position, their curiosity toward
scientific publications, and their preference for their organization‟s internal publications.
Hypothesis 1. Obviously, the more experience decision makers have in their profession,
the more successful they are in the various stages of the process of utilizing research
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results. Professional experience empowers and facilitates the identification of relevant
sources and useful knowledge.
Hypothesis 2. Everything leads to believe that the more educated civil servants are, the
easier it is for them to be successful in the various stages of the research utilization
process. Useful results are often embedded in complex publications calling for
theoretical pre-requisites and confirmed skills to understand the practical implications of
this research. Compared to less educated decision makers, decision makers holding
graduate degrees are more inclined to stick to scholarly research and would have less
trouble translating it into a decision.
Hypothesis 3. It is easy to assume that decision makers attracted to scientific
publications would have more chance to access and utilize the results of social science
research. This interest in scientific writings is likely to lower costs (linked to adverse
selection) and would likewise make it easier to discriminate between useable and less
useable results.
Hypothesis 4. One might well believe that decision makers that restrict their reading to
their organization‟s internal publications would have less chance of completing the
stages of utilizing new knowledge from university research. In fact, decision makers that
limit themselves to internal reports make little effort to open up to and acquire the
findings of university researchers.
Hypothesis 5. It can also be suggested that the more decision makers are involved in
exchanging and acquiring the results of social science research, the easier it is for them
to find and value new knowledge received.
Hypothesis 6. Along the same lines, the best positioned decision makers in the
administrative hierarchy have a better chance of accessing and valuing new knowledge
than those who are not as well positioned in the hierarchy.
3.1.2 What research? Research is not appreciated in the same way by decision
makers. The attributes that characterize social science research can be decisive in the
process of utilizing results. Two variables are used to take these attributes into account.
The first reflects the applied nature of this research, and the second reflects the
fundamental nature.
Hypothesis 7. According to Weiss (1973), applied research has more chance of
interesting decision makers concerned with the utilization of research results. Research
designed to respond to decision makers‟ expectations would have more chance of
being read, discussed, adopted and valued. In this case, transaction costs are lower
since the results already aim at satisfying pre-identified social needs.
Hypothesis 8. One might well believe that fundamental research (directed uniquely
toward the advancement of theoretical knowledge) would be less solicited by decision
makers involved in the resolution of problems. The results of fundamental research
appear to be more abstract and further from decision makers‟ reality. In general, civil
servants and decision makers tend to prefer results that respond to practical, down-toearth concerns and that bring results directly related to social realities.
3.1.3 What utilization? The attributes of utilization can be determining. Two
types of use are distinguished: i) an instrumental use contributing to the solution of
problems encountered in the implementation of programs and policies, and ii) a
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conceptual use contributing to a better understanding of behaviours and reactions to
implemented policies. Two hypotheses have been retained from this perspective.
Hypothesis 9. The more research results have instrumental usefulness, the more they
are likely to be used by decision makers. The intuition behind this prediction deals with
the concerns of decision makers involved in program management and the resolution of
social problems.
Hypothesis 10. We assume that the probability of completing the different stages of the
utilization of social science research increases in the presence of results offering new
conceptual perspectives. Conceptual insights are particularly popular among decision
makers in charge of program design and public policy planning.
3.1.4 What context? The utilization of research results by decision makers is
also dependent on contextual contingencies. In this paper, several contextual attributes
are considered. The first deals with the intensity of support given to social science
research by government organizations. The second deals with the number of liaison
and knowledge transfer mechanisms between the community of public servants and
that of researchers. The third deals with funding agencies‟ proximity to, and influence
on, government organizations. Finally, the last attribute deals with the importance of
trust linking decision makers to researchers. The following hypotheses have been
retained.
Hypothesis 11. The more involved decision makers are in the support of social science
research projects, the easier it is for them to become acquainted with research results,
to understand them and to apply them. The support for certain research projects by
government decision makers already reflects an interest in research and the
researchers responsible.
Hypothesis 12. The more solid and diversified the liaison and knowledge transfer
mechanisms are, the more likely that the results of social science research will be
valued in decision making. In the contexts that are best equipped in liaison and transfer
infrastructure, research results find their way to decision makers‟ desks much more
easily and would have more likelihood of being converted into concrete action.
Hypothesis 13. The stronger the relationship between government organizations and
agencies specializing in the funding of social science research, the more likely it is that
the results of the research funded by these organizations will be used. This proximity
facilitates the circulation of information and brings the communities concerned closer
together. It is also likely to provide decision makers concerned with first-hand
information about the directions taken by on-going research and about results obtained
or expected.
Hypothesis 14. The stronger the inter-personal relations between researchers and
managers, the more the latter are inclined to acquire, read, discuss, adopt, and utilize
the results produced by these researchers. The existence of relational assets is likely to
bring the two communities closer together, to build up reciprocal trust and to improve
the coordination of efforts leading to better links between research and action.
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3.2 Econometric Model
To analyze the determinants of the probability of successfully completing each of
the stages of the process of utilizing social science research, Logit modeling has been
adopted. The following equation identifies the model and its variables.
Log( /1+
EXP +
DIPL +
FONC +
PUB +
EFFOR +
INTER +
BESUTI +
SCIEN +
CONC+
INSTRU +
PROX +
SOU
ECHAN +
CAPRELA
constitutes the error term.
i (i =1.......14) are regression coefficients and
The dependent variable was measured by adapting the knowledge utilization
scale proposed by Knott and Wildavsky (1980). As previously underlined, this scale is
made up of six principal stages: reception, cognition, discussion, reference, adoption
and implementation of the social science research results (Knott & Wildavsky, 1980;
Lester & Wilds, 1990l; Lester, 1993; Landry et al., 2001). Each stage is assumed to be
different from the one preceding it, and the user can only complete one stage at a time.
This is why each stage is considered separately in the Knott and Wildavsky scale
(1980). Obviously, our interest focuses on the determining factors of the probability of
successfully completing each of the stages of the utilization process.
For each stage of the utilization scale, the dependent variable is a binary
measure that has a value of 1 when the users successfully complete the stage and
move on to the next and 0 when they do not successfully complete this stage. The
measures used to render the dependent and independent variables operational are
presented in Table 1.
The dependent variable, presented by [Log( /1- ], is made up of the logarithm
of the ratio of the probability that a user reaches a given stage of the utilization of social
science research. This ratio is determined by the probability of reaching (
over the
probability of not reaching (
The measure used in this logistic regression is
binomial and it takes on the value of 1 if the user successfully completes this stage and
reaches the following stage, and 0 if not.
3.3 Data
The present research relies on the data from a telephone survey carried out
among a sample of managers and professionals directly concerned by social science
research projects funded by the Fonds de financement de la recherche sociale au
Québec (Conseil Québécois de la Recherche Sociale - CQRS) for the period 19952000. The study population includes 629 users of social science research results. Out of
this number, a sample of 327 individuals was retained to reflect the different categories
of potential users and to exclude those having changed their job or address. A total of
224 questionnaires were completed with success. The questionnaire was tested among
ten respondents representative of the target population. The polling company INFRAS
INC. (located in Quebec City) was in charge of administering the survey questionnaire.
The average length of time taken to complete the questionnaire is 21 minutes. The net
rate of response is 66%. The majority of the survey‟s respondents hold a Master‟s
degree (60%). Almost one-quarter of the respondents hold a Bachelor‟s degree, and
only 12% hold a doctoral degree. Almost two-thirds of the respondents are managers or
professionals in the public administrations concerned.
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Decision makers and civil servants who responded to our questionnaire occupy
different functions in various ministerial, community or public organizations. More than
half of the decision makers work within government establishments involved in the
health or social services sector. One out of five respondents works within community
organizations operating in the health sector. The remaining respondents operate in
parapublic organizations or organizations on the periphery of the public sector. The
decision makers questioned have an average of 10 years‟ experience, operate within
organizations with an average of 32 employees and are spread over different fields of
activity in the administrations responsible for public health and social services.
3.4 Variables
The dependent variables used to explain the occurrence of success at each
stage of the process are dichotomous. They have the value of 1 if the respondent
responds to the questions relating to the different stages of utilization by “usually” or
“always” and 0 otherwise. Table 1 describes these variables.
Table 1 about here
The independent variables considered in the model are presented according to
their definition, their average measure and their standard deviation.
Experience: EXP =Number of years of experience in the exercise of functions. The
mean is 10.9 with a standard deviation of 0.5.
Graduate Diploma: DIPL =1 if the highest diploma held is a Master‟s or a Ph.D, 0 if
not. The mean is 0.70 with a standard deviation of 0.03.
Position Held: FONC = 1 if the respondent is a manager or professional, 0 if not. The
mean is 0.66 with a standard deviation of 0.12.
Scientific Publications: PUB = Index including 5 scores (from 1 to 5) measuring the
intensity of interest for the following scientific publications: 1- Books, 2- Book
Chapters, 3- Scientific Articles, 4- Research Reports, 5- Professional Journals.
The mean is 14.5 with a standard deviation of 0.18.
Acquisition Effort: EFFOR = Index including six scores (from 1 to 6) measuring the
intensity of the effort made to acquire research results. These scores include: 1Discussions with researchers about problems to resolve, 2- Discussions with
researchers about possible options to resolve these problems, 3- Discussions with
researchers about the data and methods that are used to analyze these options, 4Discussions with researchers about public and stakeholder reaction to possible
options, 5- Discussions with researchers about the choice and applicability of the
possible options, 6- Discussions with researchers about following up decisions
made. The mean is 17.7 with a standard deviation of 0.04.
Users’ Needs: BESUTI = 1 if, in the user‟s field of activities, researchers are usually or
always focused on users‟ needs, 0 if not. The mean is 0.58 with a standard
deviation of 0.03.
Conceptual Use: CONC = 1 if the user values the information, studies and social
science research reports to understand how practices work, programs and policies
in effect in the user‟s area of intervention (rather agree or completely agree), 0 if
not. The mean is 0.68 with a standard deviation of 0.3.
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Instrumental Use: INSTRU = 1 if the user judges the information, reports and social
science research studies to be important or decisive with regard to the
improvement of practices, programs and policies in the user‟s area of intervention,
0 if not. The mean is 0.66 with a standard deviation of 0.03.
Internal Reports: INTER = 1 if the internal reports are preferred to the results of social
science research, 0 if not. The mean is 0.31 with a standard deviation of 0.09.
Advancement of Science: SCIEN = 1 if the results of social science research are
focused on the advancement of science, 0 if not. The mean is 0.86 with a standard
deviation of 0.02.
Proximity: PROX = Index including 6 scores (from 1 to 6) measuring the frequency of
partnership and exchange contacts related to CQRS projects and publications.
These scores include the following items: 1- I was a partner in CQRS projects, 2- I
was a participant in CQRS projects, 3- I attended CQRS conferences, 4- I consult
CQRS newsletters, 5- I consult CQRS reports, 6- I consult the CQRS Website.
The mean is 16 with a standard deviation of 0.33.
Financial Support: SOU = Index including 5 scores (from 1 to 5) measuring the
importance of the different types of support given to CQRS projects by the
practitioners‟ community. The items considered are: 1- Creation of opportune time
for the dissemination of research results, 2- Time off given to practitioners,
professionals or managers to participate in training, exchange, reflection activities,
3- Preparation of training programs or knowledge updating projects integrating
research results or resulting expertise, 4- Development of training or intervention
tools based on the research or resulting expertise, 5- Material (rooms, access to
general services) or financial contribution to support knowledge transfer projects.
The mean is 15.1 with a standard deviation of 0.32.
Exchange Mechanisms: ECHAN = Index including 11 scores measuring the
importance of liaison and transfer mechanisms existing between the social science
research community and that of the users (each score goes from 1 to 5). These
scores reflect the following items: 1- Articles in professional or stakeholder
journals, 2- papers at stakeholders‟ conferences, 3- Interest groups and monitoring
committees bringing together users and university researchers, 4- Presentations,
seminars or conferences in institutional or community settings, 5- Presentations to
seminars bringing together students in professional development training,
members of research teams, stakeholders and managers, 6- Preparation of media
events or articles for the print media, 7- Preparation of videos and films, 8- Reports
sent by email, 9- Reports sent by regular mail, 10- Distribution of summaries or
highlights of research, 11- Use of Internet sites to make research projects and their
results known. The mean is 30.5 with a standard deviation of 0.51.
Social Relations Capital: CAPRELA = Index including 5 scores measuring the
intensity of relations with researchers. These scores (from 1 to 5) refer to the
following items: 1- I know university researchers personally, 2- I know university
researchers involved in my field of activity personally, 3 – I have a long experience
collaborating with university researchers, 4- I have a long experience in utilizing
university research results, 5- I have a long experience participating in universityresearch conferences and symposiums. The mean is 17.9 with a standard
deviation of 0.29.
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The internal coherence of the indices with several items was verified using Cronbach‟s
alpha. The coefficients obtained show that the indices are valid and coherent for use as
variables of the explicative model retained.
Table 2 about here
4. Findings
Table 3 presents the results of the six logistic regressions used to explain the
probability of success in each of the stages of the utilization scale. The stages involved
are those of i) the reception of results, ii) the understanding of the results received
(cognition), iii) the discussion of these results, iv) citing these results as references, v)
the adoption of these results and vi) the implementation of the results of social science
research in decision making.
The Logit model adopted explains the probabilities of success at each of these
stages and gives satisfactory results. The percentage of correctly predicted cases
varies from 70.33% to 87% according to the regressions presented in Table 3. In
addition, this model offers satisfactory R^2s (the Nagelkerke R2) varying from 0.19, for
the understanding stage, to 0.44 for the reference stage. For all models, the probability
ratio enables the rejection of the null hypothesis (stipulating that all the coefficients are
equal to zero) to 0.001. Depending on the stages, four to seven variables had
statistically significant coefficients (with bilateral test).
The interpretation of the results will be done in two stages. Firstly, the
interpretation approaches Table 3 vertically. From this angle, the interpretation focuses
on why the users successfully complete each of the stages and finish the utilization
process. This reading deals with the determinants of success at each stage of the
utilization process. Secondly, the interpretation of the results will be made from a crosssectional perspective highlighting the impact of the individuals‟ attributes, social science
research, possible utilizations, and the context for success at each stage of the
utilization process. This perspective enables the verification of the significance of the
results in relation to the theoretical models used to analyze the utilization of new
knowledge. The comments will deal with the coefficients of regression and interpret the
importance of the impact exerted by the explicative variable on the probability of
success at the stage in question. For the binary variables, the exponential of the
coefficient is calculated to assess, in a percentage, the impact of a marginal variation of
a given factor on the variation of the probability of successfully completing (or not) the
stage of the utilization process concerned.
We will begin by highlighting the determinants linked to success in the different
stages of the utilization process. Altogether, 62% of the respondents say they ususally
or always receive the results of social science research. This percentage is quite
revealing about the difficulties pertaining to the reception of the research results. In
practical terms, one out of three practitioners does not manage to have access to the
results of social science research that is useful to their decision making. As Column 2 of
Table 3 indicates, the probability of successfully completing the reception stage
significantly increases with the users‟ increase in interest in scientific publications, in the
improvement of close ties with the agencies funding social science research, and with
the increase in relational capital linking users to social science researchers. On the
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other hand, this probability significantly decreases among users who concentrate their
interest on organizations‟ internal publications. Users favouring internal reports have
almost 30% less chance than the others of receiving the results of social science
research. Curiously, the level of schooling, experience and position held by the decision
makers do not demonstrate any significant statistical impact on the probability of
accessing the results of social science research.
Our survey also shows that 85% of the respondents say they usually or always
read and understand the results of social science research received. This result
suggests that the decision makers have the means at their disposal to read the
publications and the competencies necessary to understand their contents. Column 3‟s
results show that the probability of successfully completing the reading and
understanding stage increases with experience (years of service), with the increase in
close ties with the agencies funding the social science research, with the increasing
number of liaison mechanisms and mechanisms for transferring scientific writings, and
with the development of personal relations between public servants and researchers.
Contrary to our expectations, those with a Master‟s or doctoral degree have no
advantage over the others when it comes to reading and assimilating the results of
social science research. Obviously, the attributes of the research and the usefulness of
the research results do not significantly influence the probability of success at the
cognition stage of research results.
The results show that only 39% of the respondents say that they usually or
always discuss research results with their colleagues. Paradoxically, this stage‟s rate of
success appears relatively low. Column 4‟s results show that the probability of
successfully completing the discussion stage of the utilization process of social science
research (that received and read) increases according to professional experience, the
applied nature of the social science research, the scientific interest in the research
results in question, and the conceptual richness provided by these results. Likewise, this
probability is higher in contexts characterized by the users‟ strong support for the social
science research projects and a diversification of the transfer mechanisms and of the
relational capital linking users and researchers. It is important to point out that the
research and contextual attributes play a determining role at this stage.
Furthermore, the data reveal that 64% of the decision makers say they usually or
always cite the results of social science research that they have received and read. The
probability of successfully completing the reference stage of research results is high
when users hold a Ph.D or Master‟s degree, when the research provides conceptual
insight, when the research projects benefit from material support offered by potential
users, and when researchers and users have close personal ties. The decision makers
holding a graduate degree have almost three times more likelihood of successfully
completing the stage verifying the pertinence of the references of the research results
than the others. The probability of successfully completing this stage drops 30% when
the decision makers favour the organizations‟ internal publications. The results offering
conceptual insight have three times more chance of being cited than the other results
(results with conceptual, symbolic, etc. usefulness). Along the same lines, the
probability of successfully completing this stage significantly decreases when the
research results received fall within the scope of a fundamental perspective mainly
aimed at the advancement of science. Compared to the other results, the results of
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fundamental social science research (solely directed towards the advancement of
science) have 50% less chance of being cited by public decision makers.
The results show that two out of three respondents (67%) say they usually or
always make an effort to adopt the results of social science research that they have
received, read and discussed. In practical terms, one-third of decision makers say that
they do not make an effort to favour incorporating the results of social science research
in their decisions or professional activities. Column 6‟s results show that the probability
of successfully completing the adoption stage of the utilization process is higher among
users who demonstrate a strong interest in scientific publications, who are already
actively involved in the acquisition of new social science knowledge, who are interested
in the instrumental import of this new knowledge, who are closely tied to social science
research projects, and who have good relations with researchers. On the contrary, this
probability significantly decreases when the users‟ interests are centred more on
knowledge supplied in internal reports. It is important to point out that, compared to the
other results, research results providing obvious instrumental usefulness are twice as
likely to see the users make an effort to adopt them.
Finally, we arrive at the final stage of the utilization process. Our results show
that 54% of the respondents say that the results of social science research received are
usually or always used to influence their decisions. As a result, the final stage of the
utilization process is successfully completed by about one out of two decision makers.
The probability of successfully completing the implementation stage is higher when the
users are professionals or managers. Compared to the others, this category of users is
twice as likely to incorporate research results in their decisions and activities. Likewise,
the success of this stage is greater when decision makers demonstrate an interest in
scientific publications, when they make an effort to acquire new social science
knowledge, when they have close ties to CQRS, and when they have an important
relational asset with the researchers. The interest demonstrated for internal reports
once again provides a negative effect on the utilization process. Our analyses show that
the attributes of social science research results have no real impact on successfully
completing this final stage of the utilization process.
Several theoretical implications can be drawn from the empirical results
obtained. With regard to users’ attributes, all the variables had, at one stage or
another, a statistically significant explicative capacity. The results suggest that the
coefficients relating to degrees and positions held by the users were only significant in
the action-taking stage. Obviously, the users that are best positioned in the decisional
process would find it easier to implement new knowledge received. The coefficients
relating to user experience and effort used to acquire results were only shown to be
significant in two stages. More than the others, the most experienced users appear to
be the most predisposed to understand and discuss the results of social science
research. Likewise, the users most involved in an effort to acquire research results are
more likely to adopt new social science knowledge and put it into practice.
In addition, it should be noted that the coefficient relating to the interest shown
in scientific publications is positively and statistically significant in three stages. The
users who are most interested in scientific publications are more likely to receive the
results, to consent to effort being made to use them and to implement them. The
coefficient relating to the variable measuring the importance given to internal reports is
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negative and statistically significant in four stages of the utilization process. This result
suggests that users who consider internal reports to be more interesting for their
activities have much less chance of receiving the results of social science research, of
citing these results as useable references, of consenting to effort being made to use
them and, when all is said and done, of using these results in their activities and
decisions.
Important precisions to note; the variables reflecting users‟ individual attributes
are more numerous to influence the implementation of research results. The probability
of success at this stage is greater among users occupying positions of professionals or
managers, among users attracted to reading scientific writings and publications and
among those making more effort to acquire research results from researchers in their
field. All in all, the evidence presented by our study corroborates the principal intuitions
suggested by the science pull theoretical model. However, this comment should be
qualified by stating that the impact of users‟ attributes is not systematic and varies
considerably in intensity depending on the stage of the utilization process.
Of the factors reflecting research attributes, most of the variables considered in
the model had statistically significant coefficients in the stages relating to discussion of
the research results, to citing these results as references and to adoption. These
variables had no effect on the reception of results, understanding results or their
implementation in decision making. This evidence suggests that the probability of
discussing the results of social science research is greater when these results have a
conceptual impact capable of enlightening decision makers about situations and issues
under study, when they respond to users‟ decision-making needs and when they enable
the advancement of scientific knowledge. This leads us to say that users of the results
of social science research do not limit their discussions to results responding directly to
the needs of their activity. These users do not exclude the fundamental results from
their subjects of conversation. This evidence puts the work of Knorr (1977) into a new
perspective. This work shows that users are more interested in research results that are
useful for immediate action. In the light of this evidence, it is possible to conclude that
research attributes exercise an undeniable impact on the utilization process. Our study
does not contradict the premises of the science push model that suggests research
offers sufficiently heterogeneous results to make them attractive to the user community.
On the other hand, it is important to add that the determinants suggested by the science
push model do not explain everything.
In fact, the present study confirms the importance of contextual attributes in the
utilization process. In this process, the relational capital is determining in all the stages
studied. Decision makers having good relations with university researchers have more
chance of receiving the results of social science research, understanding them,
discussing them, citing them as references, consenting to making an effort to use them,
and taking advantage of them in their decision making. Thus, the institutional contexts
that are rich in social capital appear to be more favourable to using the results of social
science research.
The coefficient relating to the variable measuring the proximity of CQRS is
positive and statistically significant in the prediction of success in four stages of the
utilization process: reception stage, understanding stage, adoption stage, and
implementing these results in decision making. Thus, decision makers with close ties to
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CQRS (participants in organized conferences/symposiums, readers of CQRS
newsletters, visitors to the CQRS Website, etc.) are more inclined to successfully
complete the majority of the stages of the utilization process.
In addition, the support for research funded by CQRS (through creating time to
deliver the results from CQRS research, participation in training activities and
development of intervention tools incorporating the results of CQRS research, or
making rooms or various forms of support available for this research) seems to be a key
determinant in the discussion and citation stages. The coefficient relating to the
exchange mechanisms variable appeared positive and statistically positive only in the
regressions predicting the occurrence of understanding and discussion of the results of
social science research. Decision makers with exchange ties to researchers (exchanges
during scientific meetings, exchange of regular mail and emails, exchanges of reports,
etc.) are noticeable through their greater ease in understanding and discussing
research results compared to the others.
All in all, the evidence obtained shows that contextual and institutional
determinants play a key role in the use of research results. Social capital appears to be
an important condition for success in the majority of stages of the utilization process.
The premises suggested by the theoretical model of social interaction were validated
and, henceforth, it is no longer sufficient to be limited to the determinants suggested by
the science pull and science push linear models. Taking into consideration social and
institutional contingencies is fundamental in the process of utilizing the results of social
science research.
5. Conclusion and Implications
The results of this research have permitted to identify the principal determinants
that explain why only a limited number of decision makers succeed in accessing and
using the results of social science research. The Logit model advocated to explain the
success of each of these stages presented a prediction rate and percentages of
explained variance that were very satisfactory. Depending on the stages, four to seven
coefficients were revealed as statistically significant. The results obtained are useful
enough for decision makers concerned with placing greater value in contributing to the
promotion of social science research.
The principal obvious fact presented in this research concerns the diversity of the
factors in play in the knowledge utilization process. Depending on the stages, the
impact of these factors is more or less significant. Thus, the most curious users who are
interested in scientific publications are more inclined to receive relevant results in their
activity, to make an effort to use them and to succeed in implementing them to influence
decision making. The most experienced users are more inclined to understand the
results of social science research and are more liable to discuss these results with their
counterparts and colleagues. Users with a Master‟s or doctoral degree tend to cite
research results more as a useful reference when decision making. Users occupying
positions of professionals or managers are conspicuous only in the final implementation
stage. The position and decisional power of practitioners play a key role in the use.
Curiously, users who prefer organizations‟ internal reports to publications from the
university community are less likely to receive the results of social science research,
less likely to refer to scientific work and less predisposed to consent to efforts being
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made to adopt and use these results in their practices.
Moreover, our study shows that the probability of discussing the results of social
science research is greater when these results include a conceptual usefulness (to
understand problems encountered), conform to users‟ needs and contain useful
teachings for the advancement of science. On the other hand, users tend not to cite
results having a fundamental use and focusing uniquely on the advancement of
science. The results of social science research with an instrumental use value more
easily interest practitioners and lead them to put more effort into the utilization of these
results in the exercise of their functions.
Another major contribution of this research deals with the impact of social
interactions and close ties between researchers and administrators. All things being
equal otherwise, administrators maintaining interpersonal relations with university
researchers have more chance of receiving the results of social science research, of
reading them, of understanding them, of discussing them, of citing them as references,
and of making an effort to adopt them and implement them in decision making.
Moreover, contexts with a strong social capital appear to be favourable for the utilization
of the results of social science research. That leads us to conclude that the investment
in creating social capital (by consolidating relational assets, installing a climate of trust
and credibility between researchers and decision makers, etc.) constitutes a key factor
in the process of valuing the results of social science research through public policies
and decision making.
Along the same lines, the proximity of grants for social science research
appeared as a significant determinant to explain success in most of the stages of the
utilization process. Decision makers who have been partners of social science research
projects funded by CQRS, have taken part in conferences organized by CQRS and
regularly consult CQRS newsletters, reports and Website are more likely to successfully
complete the majority of the stages of the knowledge utilization process. In addition,
practitioners‟ support for research programs (through finding time to disseminate CQRS
research results, participating in training activities and developing intervention tools
incorporating the results of CQRS research, or making rooms and various forms of
support available to social science research projects) appears to be a facilitating factor
for the discussion of the results of social science research and for citing these results as
relevant references.
The diversification of relation and transfer mechanisms linking researchers to users
appeared to be a considerable factor for success in the understanding and discussion of
social science research. Decision makers fostering exchange relations with researchers
(at scientific meetings, through correspondence, through emails, exchanging reports,
etc.) have greater facility to understand and discuss the results of social science
research.
The results obtained can suggest several useful implications for programs aiming
at intensifying the utilization of research results in decision making and favouring
innovation in public and para-public organizations. Three categories of implications
emerge.
The first category of implications concerns the administration community concerned with
the utilization of the results of social science research. The principal implication has to
do with access to publications including the results of social science research. In fact,
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there would be cause to limit the obvious crowding-out effect that the internal
publications of organizations exert on scientific publications (scientific journals, books,
analyses, etc.). To counter these damaging effects and increase the chances of use, it
is advisable to encourage practitioners and public organizations to diversify their
sources of new social science knowledge. It is also advisable to enrich the internal
reports with relevant literature from the research community. In this way, research
results find their way to the decision maker‟s desk in a form other than a book, scholarly
article or collection of articles from a conference. In the elaboration of analyses, public
organizations wishing to intensify innovation should encourage their staff to redouble
their efforts to keep informed of new advances in knowledge and of the practical
repercussions of research published on organizational intervention and innovation.
Incentives must also be created to encourage practitioners to have more and more
exchanges, to make contacts and to generate interactions with university researchers.
The public servants and practitioners operating in administrative services deserve to be
part of the design, implementation and evaluation of social science research programs.
The second category of implications concerns the research community. This work
brings good news to social science researchers regarding what is in store for their
research results among practitioners. Obviously, social science research results that
have been published or sent to decision makers do not go unheeded. These results, at
least the ones that find their way to decision makers, arouse interest and are read and
understood to a great extent. Users discuss these results regardless of whether the
research is fundamental or applied. This being said, it is still true that social science
research results already having an instrumental appeal are more likely to generate
adoption efforts from decision makers. With this in mind, it is important to encourage
and motivate researchers involved in the dissemination and delivery of the results of
social science research. Institutions promoting research and social innovation have an
interest in offering more and more incentives for the delivery and increased funding of
fundamental and applied research in social science.
The third category of implications concerns intermediation and scientific brokerage
agencies, the «relationnist» (organisation, individual, expert, etc.) connecting the social
science research community to the administrative decision-making community. The
obvious facts laid out by our study show that the liaison activities and the transfer
mechanisms exert a considerable impact on the success of acquiring, understanding
and using research results. A return on the investments made by these agencies is
made possible through valuing the results of social science research. The decision
makers concerned with the activities of using research results have an interest in
encouraging initiatives and exchange and interaction infrastructures between
researchers and decision makers. The results obtained regarding relational and social
determinants are also very interesting. These results suggest that investing in the
creation of collaboration and proximity networks can be very beneficial to the
organizations concerned by new knowledge utilization. These results show that creating
exchange and meeting opportunities between the user and researcher communities
promotes collaboration and limits negative prejudices having to do with the
incompatibility and differences between the two communities.
Finally, it is important to note that the variables and measures used in the present study
have permitted to operationalize numerous theoretical constructs come across in the
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most recent writings dealing with the impact of social science research results on social
and organizational innovation. The measurement instruments used in this research can
constitute a useful reference for planners in search of metric indicators able to
benchmark and measure the performance of public programs aiming to strengthen
organizational innovation through a greater valuing of new knowledge and research
results.
Table 3 about here
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Table 1
Dependent variables descritpion (six stages of knowledge utilization)
Dependent variables
Mean (standard
deviation)
0.62 (0.48)
RECEPTION
1= Reception of social research knowledge;
0 if not
COGNITION
1= Cognition of social research new knowledge;
0 if not
0.84 (0.35)
DISCUSSION
1= Discussion of social research new knowledge;
0 if not
0.39 (0.48)
REFERENCE
1= Reference of social research new knowledge;
0 if not.
0.64 (0.48)
ADOPTION
1= Adoption of social research new knowledge;
0 if not
0.67 (0.47)
IMPLEMENTATION
1= Implementation of social research new knowledge;
0 if not.
0.54 (0.48)
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Table 2
Internal coherence of the indices with several items (Cronbach’s alpha)
Indices
N
Number of items
Proximity to CQRS (PROX)
214
6
Acquisition efforts (ACQUI)
214
6
Exchange mechanismes (ECHAN)
214
11
Financial support (SOU)
214
5
Social Relations Capital (CAPRELA)
212
5

0.71
0.87
0.8
0.85
0.88
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Table 3
Determinants of social research utilisation (six stages) : Logit regressions
Dependent variables
Cognition
Discussion
Reference
Adoption
Implementation
β
β
β
β
β
β
-0.007(0.26)
0.005 (0.40)
0.29 (0.37)
0.9 (0.5) *
0.29 (0.39)
-0.35 (0.18)**
0.04 (0.02)**
0.14 (0.5)
0.20 (0.52)
0.04 (0.9)
0.04 (0,05)
0.03 (0.26)
0.05 (0.02)**
0.37 (0.4)
0.05 (0.4)
0.01 (0.07)
0.46 (0.4)
-0.22 (0.19)
0.03 (0.03)
0.98 (0.42)**
0.22 (0.45)
0.11 (0.07)
0.012 (0.044)
- 0.52 (0.2)**
0.03 (0.03)
0.04 (0.4)
0.1 (0.46)
0.18 (0.8)**
0.12 (0.04)***
-0.43 (0.21)**
0.03 (0.02)
0.05 (0.4)
0.69 (0.4)**
0.12 (0.07)*
0.87 (0.04)**
-0.35 (0.19)**
Research attributes
User‟s needs (BESUTI)
Advancement of science (SCIEN)
0.29 (0.37)
0.27 (0.53)
0.39 (0.5)
0.15 (0.7)
0.61 (0.3)**
0.95 (0.6)*
0.44 (0.43)
-0.76 (0.4)**
0.32 (0.4)
0.23 (0.66)
0.52 (0.38)
0.14 (0.61)
Utilization attributes
Conceptual utilization (CONC)
Instrumental utilization (INSTRU)
0.23 (0.51)
0.10 (0.39)
0.24 (0.6)
0.51 (0.5)
0.92 (0.5)**
0.40 (0.43)
1.2 (0.6)**
-0.06 (0.45)
0.5 (0.6)
0.7 (0.4)*
0.023 (0.55)
0.09 (0.41)
0.12 (0.04)***
0.31 (0.04)
0.015 (0.029)
0.09 (0.05)**
0.1 (0.06)*
0.49 (0.67)
0.04 (0.02)**
0.1 (0.05)*
0.04 (0.04)
0.08 (0.05)*
0.06 (0.03)**
0.18 (0.05)***
0.01 (0.05)
0.11 (0.05)**
0.01 (0.03)
0.27 (0.06)***
0.12 (0.05)**
0.04 (0.05)
0.03 (0.03)
0.16 (0.6)***
0.08 (0.04)**
0.06 (0.53)
0.002 (0.03)
0.17 (0.05)***
-1.65 (1.44)
-2.2 (1.96)
-6.6 (1.7)***
-5.6 (1.82)**
-7.5 (2.5)***
-5.4 (1.7)**
Nagelkerke- R^ 2
0.20
0.19
0.36
0.44
0.42
0.37
% of correctly predicted cases
70.88
87.03
79.23
83.33
78.14
70.33
182
165
183
185
182
182
Individual attributes
Experience (EXP)
Graduate Diploma (DIPL)
Position (FONC)
Scientific publications (PUB)
Acquisition efforts (EFFOR)
Internal reports (INTER)
Contextual attributes
Proximity to CQRS (PROX)
CQRS Financial support (SOU)
Exchange mechanisms (ECHAN)
Social relations capital
(CAPRELA)
Constant
N
Reception
Statistical significant level: *p<0.10 ; **p<0.05 ; ***p <0.01. (standard errors are between parentheses).
24