A modified version of this paper is forthcoming in Research Policy

UTILIZATION OF SOCIAL SCIENCERESEARCH KNOWLEDGE IN CANADA

By

Réjean Landry, Nabil Amara and Moktar LaamaryGroupe de recherche sur les interventions gouvernementales (GRIG)

Département de science politiqueUniversité Laval

Québec, Québec, Canada G1K 7P4rejean.landry@pol.ulaval.ca

November 1998

The authors thank the Social Sciencesand Humanities Research Council for financial support.

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ABSTRACT

This paper addresses three questions: What is the extent of use of social science research inCanada? Are there differences between the social sciences disciplines in regard of extent ofuse? What are the determinants of utilization of social science research knowledge in Canada?The paper develops and test an empirical model which derives its dependent and independentvariables from prior studies in knowledge utilization. Instead of limiting utilization to instrumentaluse, the paper defines utilization as a six stages cumulative process. Based on a survey of 1229Canadian social science scholars, the findings of this study show that nearly half of the researchresults lend to some use by practitioners, professionals and decision-makers. Furthermore,comparisons of means of utilization show that the professional social sciences (social work andindustrial relations) lend to higher levels of utilization than the disciplinary social sciences(economics, political science, sociology and anthropology). Multivariate regression analysesshow that the most important determinants of utilization are the mechanisms linking theresearchers to the users, the dissemination efforts, adaptation of research outputs undertakenby the researchers, the users’ context and the publication assets of the researchers. The otherexplanatory factors exert a more mitigated influence on knowledge utilization. The last part ofthe paper derives policy implications from the regression results. Overall, the most importantfinding of this paper is that knowledge utilization depends much more heavily on factorsregarding the behavior of the researchers’ and users’ context than on the attributes of theresearch products.

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Policy makers, practitioners, and even social science scholars assume that users rarelyapply social science knowledge. A review of literature suggests that this dismal picture might beexplained by two factors. First, although theoretical and empirical contributions to knowledgeutilization are accumulating, the impression persists that more stress has been laid on reflexiveand theoretical studies than on empirical analyses. However, excellent exemplar empiricalstudies are more abundant than critics realize. From among these exemplar studies, one mustmention Huberman and Thurler (1991), Lester (1993), Unrau and McDonald (1995), Oh andRich (1996) and Oh (1997). The empirical studies in knowledge utilization are not very visiblebecause they are scattered through the journals of many diverse disciplines. Second, the dismalpicture might also have arisen from a narrow definition of knowledge utilization, which, too often,associates utilization only to instrumental use of knowledge in decision making or professionalpractice.

This paper aims to contribute to the empirical side of knowledge utilization by using amore nuanced definition of utilization. Based on a survey of 1229 Canadian social sciencescholars, it addresses the three following questions: What is the extent of use of social scienceresearch in Canada? Are there differences between the social sciences disciplines in the matterof extent use? What are the determinants of utilization of social science research knowledge inCanada? By using the same set of explanatory variables for different social science disciplines,we will be able to show that the same explanatory variables are not equally important acrossdisciplines in accounting for knowledge utilization. We expect that different scientific disciplineslead to different utilization of knowledge since the contexts in which knowledge is produced andprocessed are influenced by effects of the different contexts in which scientists and usersoperate. This paper explores the influence of such contexts on knowledge utilization. Instead ofconsidering, like most empirical studies in the field, to what extent policy-makers use socialscience research results in the accomplishment of their work, we turn the attention to theactions that the individual researchers undertake to promote the utilization of their researchresults.

The paper is organized as follows. First, it reviews the major theoretical models ofknowledge utilization to derive an integrated model considering a variety of explanatory factorsemployed in different theories of knowledge utilization. Second, it reviews the empirical literatureto discuss definitions and operational measures of knowledge utilization, and definitions andoperational measures of independent variables used to explain utilization. Third, the paperpresents the data collection technique used in the survey administered to social sciencescholars of Canadian universities. Fourth, it introduces a regression model about thedeterminants of knowledge utilization. Finally, the paper presents and discusses the results.

Literature review

Prior theoretical studies of knowledge utilization

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Models of knowledge utilization discussed in the literature on knowledge utilizationfocus on four major alternatives (Weiss (1979; Yin and Moore, 1988; Kline andRosenberg, 1986; Landry, 1990): a science push model, a demand pull model, adissemination model, and an interaction model. While each model supports theimportance of information (research results) in policy making, there are differencesamong them regarding major determinants of knowledge utilization.

The science push model stresses the supply of advances in research findings as themajor determinant of knowledge utilization. It is based on the assumption that the needsof decision-makers will compel them to use research, and high quality knowledge will beautomatically and rapidly captured and used. Thus, utilization follows a linear sequencefrom supply of research advances to utilization by decision makers and practitioners.Prior studies have considered many dimensions of research results influencingutilization: 1) content attributes, notably, efficiency, compatibility, complexity,observability, trialability, validity, reliability, divisibility, applicability, radicalness (Dearingand Meyer, 1994; Edwards, 1991; Lomas, 1993) and; 2) types of research:basic/applied, general/abstract (Machlup, 1980); quantitative/qualitative (Huberman andThurler, 1991); particular/concrete (Rich, 1997); research domains and disciplines (Oh,1997; Rich, 1997). Some empirical studies have found no relation between technicalquality of research results and utilization (Dunn, 1983; Edwards, 1991; Huberman,1987). Two critiques may be addressed to this model: 1) transfer of knowledge to usersis not automatic in a context where no one assumes responsibility for its transfer and, 2)raw research information is not usable knowledge and there is a process fortransforming it into one usable in policy-making (Lomas, 1990). These criticisms havestimulated the emergence of the demand pull model.

In the demand pull model , the initiative shifts from the researchers to the users whodefine the problems and ask researchers to conduct research that contributes to identifyand assess alternative solutions to specific problems (Weiss, 1979; Yin and Moore,1988; Rich, 1991). This approach to problem solving generates a customer-contractorrelationship where the practitioners and decision-makers behave like customers whodefine what research they want, and where the researchers behave like contractors thatexecute contracts in exchange of payments. This model also follows a linear sequencewhich, in this case, starts with the identification of the research problem by thecustomers. In this model, knowledge utilization is explained only by the needs of theusers: use of knowledge is increased when researchers focus their projects on theneeds of users instead of focussing them only on the advancement of scholarlyknowledge (Frenk, 1992; Chelimsky, 1994; Silverside, 1997; Orlandi, 1996). This modelfalls short to consider that even research geared to solve problems may be pushedaside because it may be in conflict with the organizational interests of the users. Thiscriticism takes us to consider a variant of the demand-pull model, that of organizationalinterests . This model assumes that organizational structures, rules and norms areessential determinants of knowledge utilization (Rich and Oh, 1993), and the principalfactor causing under-utilization of research material lies in political interests of the users,which may be in conflict with the research findings. According to this perspective,

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research results are more likely to be used when they support the interests and thegoals of the organization (Oh, 1997). The demand pull model and its variant can becriticized for 1) focusing largely on instrumental use of research, hence neglecting thatdifferent types of knowledge lead to different uses, 2) laying too much stress on theegotistical interests of the users, and 3) omitting the interaction between producers andusers of research findings, a factor that may increase utilization.

The dissemination model was developed in response to the fact that while examplesof unplanned knowledge transfer existed, knowledge transfer was not automatic. Thismodel suggests that a step should be added to research activities by developingdissemination mechanisms to identify useful knowledge and transfer it to potentialusers. In this model, dissemination is deemed to occur when a potential user becomesaware of the research results. This model explains knowledge utilization with therecourse to two determinants: the content attributes and types of research results andthe dissemination effort. In many cases the products of research never get widelydisseminated and thus have little significant impact (MacLean, 1996). In short, the mainshortcoming of the dissemination model is that the potential users are neither involvedin the selection of the transferable information, nor involved in the production of theresearch results.

The interaction model has been developed to overcome the criticisms of the previousmodels, (Dunn, 1980; Yin and Moore, 1988; Huberman and Thurler, 1991; Nyden andWiewell, 1992; Oh, 1997). It suggests that knowledge utilization depends on variousdisorderly interactions occurring between researchers and users rather than on linearsequences beginning with the needs of the researchers or the needs of the users. Thesupporters of his model can be divided in two camps: 1) the supporters of thecommunication related theories emphasize on the so called “two-communitiesmetaphor”. They assume that a difference between the culture of science and theculture of users leads to a lack of communication between them, and consequently, tolow levels of knowledge utilization (Caplan, 1979; Rich, 1979; Frenk, 1992; Webber,1987; Oh and Rich, 1996). 2) The supporters of the interaction model predict that themore sustained and intense the interaction between researchers and users, the morelikely there will be utilization. Unlike the prior models, this one suggests giving a greaterattention to the relationships between researchers and users at different stages ofknowledge production, dissemination, and utilization.The one-way flow of information and “traditional” dissemination approaches have notproven to be effective in encouraging the adoption and implementation of new researchresults (Westbrook and Boethel, 1998). Scholarly journals are inconvenient since theyneglect to adapt to content, calendar, form, and mode of diffusion to meet theparticularity of the users (Lomas, 1997; Oh and Rich, 1996). The mere reception ofknowledge by the potential user does not imply its “use”. The lack of interactionbetween researchers and their potential audiences has been identified as the mainproblem in under-utilizing research findings (Oh and Rich, 1996; Leung, 1992;Huberman, 1987; Lomas, 1997). According to the interaction model, factors that explainutilization are: types of research outputs, organizational interests of users,dissemination efforts and linkage mechanisms.

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Prior empirical studies on knowledge utilization

Conceptually, utilization is very broad and has been used in relation to virtually everyattribute of knowledge utilization. Operationally, the measurement of utilization has centeredalmost exclusively around the instrumental use of knowledge, that is the contributions ofknowledge to the decisions of practitioners and decision-makers. Seeking to bring in some ofthe non-instrumental aspects of utilization, many studies have incorporated conceptual andsymbolic uses of knowledge (Petz, 1978; Beyer and Trice, 1982).

Instrumental use refers to cases where knowledge is directly used for decision makingand problem solving. As for conceptual use, it refers to cases where knowledge provides newideas, new theories and new hypotheses conducting to new interpretations about the issues andthe facts surrounding the decision-making contexts. Finally, symbolic use of knowledge occurswhen practitioners and decision-makers use knowledge to legitimate their views. According toWeiss (1980), instrumental use is rare and when observed, it would tend to be more frequent inprivate than in public organizations (Dunn, 1980). Caplan (1975) and Weiss (1980) claim thatsymbolic use is more prevalent than the two other modes of use.

Though examining utilization with such a typology may provide insights into knowledgeutilization, such an operationalization falls short of the conceptual complexity inherent to theidea of knowledge utilization. As such, viewing utilization in terms of only three aspects providesan incomplete picture. Furthermore, as pointed out by Rich (1991), conceptual and symbolicuses can be considered as catchall categories that limit their validity. Hence, other scales andindices have been designed to measure knowledge utilization. The most important are theLarsen information utilization scale (Larsen, 1982), the Hall stages of concern scale (Hall et al.,1979), the Hall levels of use scale (Hall et al., 1975), the Johnson evaluation utilization scale(Johnson, 1980), the Pelz and Horsley research utilization index (Pelz and Horsley, 1981), andthe van de Vall and Bolas overall policy impact scale (van de Vall and Bolas, 1982). Althoughthey represent a step in the right direction, these scales are too focussed on instrumental use,too focussed on particular uses (i.e., evaluation), or defined in terms of perceptions instead ofobservable behavior. The Knott and Wildavsky scale (1980) does not carry these shortcomings.It is a scale that is frequently cited in the literature. Moreover, Lester and Wilds (1990) andLester (1993) have used this scale to derive an index based on seven cumulative stages ofutilization by state agency officials. Given that our study surveyed what became of the researchresults of researchers, we have been led to adopt a slightly modified version of the Knott andWildavsky scale where the stages of adoption and implementation have been joined in a singlestage called application. Therefore, the scale used in this study includes the six followingstages: transmission, cognition, reference, effort, influence and application. Table 1 presents thestages of knowledge use such as presented in the questionnaire sent to the respondents of thesurvey.

Table 1 here

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Let us now turn to the explanatory variables considered in prior studies in knowledgeutilization. The literature has paid attention to six broad categories of independent variables: 1)the types of the research outputs; 2) the researchers’ context; 3) the users’ context; 4) theusers’ needs; 5) the dissemination efforts and; 6) the linkages between researchers and users.These broad categories of variables include a large number of individual variables. Hence, itvaries from 21 explanatory variables in Sabatier (1978), to 110 in Rothman (1980), to 15 in theempirical studies of Lester and Wilds (1990) and Lester (1993), and to 47 in Huberman andThurler (1991) and Huberman (1994). However and interestingly, there is a large overlappingbetween the explanatory variables identified by the theoretical models and the empirical studies.Let us review in turn the independent variables identified in the literature and the hypothesesassociated to them.

Types of products

In the science push model of knowledge utilization, use is explained by theadvancements to knowledge brought by the research products. The quantitative studies are theflagships of advancements to knowledge in the social sciences. Therefore, one may predict thatquantitative studies are more likely than qualitative studies to explain use of social scienceknowledge. However, in a transaction costs interpretation à la Williamson (1975, 1985, 1995),we would assume that the greater the difficulties to read and understand the quantitativeresearch reports, the higher the costs incurred by the users and, consequently, the less likelythe use of research. Therefore, the impact of the types of research products on utilization isindeterminate.

Adaptation of products

This explanatory variable derives from the dissemination model. Huberman and Thurler(1991) have developed valid and interesting indicators of adaptations of research products forusers. Adaptation includes factors such as efforts to make reports more readable and moreeasy to understand, efforts to make conclusions and recommendations more specific, moreoperational, efforts to focus on variables amenable to interventions by users, efforts to makereports more appealing. The prediction is that when researchers invest resources to adapt theirproducts so as to facilitate their appropriation by users, it increases the use of social scienceknowledge. This prediction is central in the dissemination model. In terms of transaction costeconomics, it means that the higher the costs supported by researchers to adapt their products,the lower the costs supported by the practitioners and professionals and, as a consequence, thehigher the use of social science knowledge.

Dissemination efforts

Dissemination efforts derive also from the explanatory variables of the disseminationmodel. Researchers engage resources into dissemination efforts when they hold meetings todiscuss the subject and scope of their projects with users, to discuss results with users and todisseminate results to users. One may easily deduce that the more resources researchersengage in dissemination activities, the higher the use of social science research. Once more,

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increase in the use of knowledge is obtained because the costs of the users are reduced at theexpense of an increase in the costs supported by the researchers.

Linkage mechanisms

This variable derives from the explanatory variables of the interaction model. Hubermanand Thurler (1991) have devised one of the most interesting sets of indicators of mechanismslinking researchers and users. The mechanisms considered include informal personal contacts,participation in committees, transmission of reports to non-academic organizations. The moreresources the researchers invest in these types of linkage mechanisms, the higher the use ofsocial science research. As with dissemination efforts, one may point out that knowledgeutilization increases as the transaction costs incurred by the researchers increase while those ofthe users decrease.

Users’ context

The influence of the context of the users derives from the explanatory variables of thedemand pull model and its variant, the organizational interests model. To identify the contextualfactors of the users that influence knowledge utilization is a paramount task. However, priorempirical studies point to the following points: use of knowledge increases as users considerresearch pertinent, as research coincides with their needs, as users' attitudes give credibility toresearch, as results reach users at the right time. Although these factors are not entirely underthe control of the researchers, we might suggest that researchers could invest resources thatwould make the users’ context more receptive to research. Again, these investments increasethe costs that researchers incur in transacting with users.

Researchers’ context

This last category of contextual variables derives from the science push model. As in thecase of the users’ context, the contextual factors surrounding the researchers that are likely toinfluence utilization of knowledge are not easy to identify. There is no theory to assist us in thisdedicated task. In this study, we have taken into account five contextual factors. The first is thatof publications. We hypothesize that as the number of publications increases, researchers have,as by-products, more research results available for use by practitioners and professionals. Asdid many prior studies, we also predict that use of knowledge is increased when the researchersfocus their projects on the needs of users instead of focussing them only on the advancement ofscholarly knowledge. Finally, we hypothesize that the sources of funding influence the use ofknowledge. More precisely, we predict that research projects based on university internal fundsare less likely to lead to knowledge utilization than projects funded by sources external touniversities. The assumption underlying this hypothesis is that the researchers who rely onexternal sources of funds are more outward looking and are therefore more sensitive to theneeds of users located outside the academic milieu. These hypotheses are summarized inTable 3.

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Table 2 here.

Research Design

Studies in the field of knowledge utilization are based on two designs : the input/outputdesign and the process design. In the former, respondents are asked to identify how theresults of a specific study affect a specific decision. In the latter, respondents are askedto identify how research results in general influence the decision-making situations ofthe users. Assuming that a specific decision can be attributed to the use of a specificscientific information is rather simplistic because research results generate manyeffects, not a single effect (Mandell and Sauter, 1984), and because decisions do notdepend on a single piece of research but on a series of research results convergingtoward one direction (Booth, 1990; Pollard, 1987; Rich, 1997). This study is based on aprocess design view.

Prior studies have employed three major approaches to assess dissemination, use andimpact of scientific information on users (Dunn, 1983; Rich and Oh, 1993; Rich, 1997):1) citation analysis, 2) case studies, and 3) surveys. In addition to carrying theshortcomings of the input/output design, citation analysis lay all the attention on onestage of use: citation at the expense of the other stages of use defined in the variousscales of knowledge utilization. Case studies may offer the advantage of observing boththe researchers and the users, however, they lack external validity. Surveys may lead toobservation of a large number of individuals, at all stages of knowledge production andutilization. This approach has both, internal and external validity, and is widely applied inthe studies of knowledge utilization. This study is based on a survey approach.

Data

The data used in this study were collected using a mail survey during the winter of 1998.The respondents were faculty members of 55 Canadian universities in departments ofanthropology, economics, industrial relations, political science, social work and sociology. Weidentified the respondents with the web sites. We contacted by telephone these fewdepartments which did not have web sites. This procedure generated a population of 3252persons. A questionnaire of 17 questions was sent to this population on January 12th 1998. Atfollow up letter was sent a week later. A total of 1388 questionnaires were returned to us, thus

resulting in a gross return rate of 42%. However, 159 of these questionnaires were not usablefor the following reasons : potential respondents had retired (17), potential respondents werenot faculty members but administrative personnel (42), wrong addresses (46), respondentswere in a sabbatical year outside Canada (36), respondents had health problems (1), andrefusal to participate in the survey (17). Therefore, 1229 of the questionnaires obtained wereusable, this indicating a net return rate of 38%. This return rate can be considered as quitegood. Such a data set composed of faculty members of diverse social science disciplines is

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especially appropriate to study the factors explaining utilization of social science knowledgeproduced in Canadian universities.

Extent and differences of uses of social science results

As shown in Table 3 on the stages of knowledge utilization, our results indicate thatnearly 50% of the Canadian scholars in social sciences reported that they usually or alwaystransmit their research findings to practitioners, professionals and decision-makers. At the otherextreme, 20% of the respondents never transmit their findings to users, whereas 15% considerthat this question does not apply to their situation. As one moves though the six stages fromtransmission to application, one can observe an increase in the research findings that are rarelyor never used and, conversely, a decrease in the research results that are usually and alwaysused. Still, there are 12% of the respondents who reported that their research results usually ledto applications and 3% who indicated that their findings always resulted in applications by users.On the whole, it suggests that about 25% of the social science research results never or rarelylead to application, whereas 16% of the social science research usually or always leads toapplication. It is a much better performance than what assume most social science scholars andmost decision makers and practitioners. This estimate suggests that a very significantproportion of Canadian social scientists is engaged in scholarly research leading to utilization bypractitioners and decision-makers. These results do not consider possible differences inutilization from across research fields. This is the question that we will now consider in turningattention to differences across disciplines.

Table 3 here

To compare the level of utilization of knowledge in the various social science disciplines,we have used a one way Anova, more specifically the Duncan’s multiple range test, whichcompares the means for groups in homogeneous subsets. This test is appropriate to group thedifferent disciplines into homogeneous subsets, that are disciplines between which thedifferences of means are not statistically significant, and hence to compare the means of thedifferent subsets. The null hypothesis tested is the equality of means for the variable knowledgeutilization between the different social science disciplines. An index including the six followingstages of knowledge utilization was developed: transmission, cognition, reference, effort,influence, application. Each stage is presumed to be more important than the previous one andthe entire scale is cumulative in the sense that all the stages of knowledge use are importantindicators and build on each other (Knott and Wildavsky, 1980; Lester and Wilds, 1990; Lester,1993). Cognition builds on transmission, reference on cognition, effort on reference, influenceon effort, and application on influence. Therefore, each successive stage needs to be weighedmore heavily as one moves from one stage to the next. The index was created on the followingbases: the respondents were asked to indicate on a 0 to 5 scale (where 0=does not apply, 1=always and 5 = never) how accurately each stage described the utilization of their research forthe last five years (See Table 1 for the exact wording of the question describing the content ofeach stage). Each response was then multiplied by the scale score for each stage ( by 1 forstage 1, by 2 for stage 2, and so on) to produce a summary scale score out a possible scale of105. Therefore, the means of utilization can range from 0 to 105.

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The results of the Duncan’s test are reported in Table 4. The means of knowledgeutilization ranges from 40.2 for political science to 64.8 for social work. The Duncan’s test showsthat there are four homogeneous subsets of disciplines. As shown in Table 4, there is nosignificant statistical difference between political science, anthropology and sociology withrespect to the level of utilization of their research results. Likewise, there is no significantstatistical difference in the level of utilization of research results between anthropology,sociology and economics. As for industrial relations, it is the only discipline in a subset where,with a mean of 56,8, knowledge utilization is higher than in the two previous subsets ofdisciplines. Finally, with a mean of 64.8, social work constitutes the subset within which theresearch results are the most frequently used by practitioners and decision-makers. The resultsshown suggest that the professional social sciences, namely industrial relations and social work,stand apart as the two disciplines whose research results lend to higher use by practitioners anddecision-makers. Conversely, the means of utilization of the knowledge produced by thedisciplinary social sciences, that are political science, sociology, economics and anthropology,are much lower. How can one explain these differences in knowledge utilization? This is thequestion that we will now consider with regression models.

Table 4 here

Regression Models

The utilization of social science knowledge is examined by using six categories ofexplanatory variables: types of products, adaptation of products to users, dissemination efforts,linkages with users, perceptions of users’ context and researchers’ context. The dependentvariable refers to the different stages of utilization defined in the Knott-Wildasky scale ofknowledge utilization (1980). To study the impact of the explanatory variables with such aquantitative dependent variable, we have developed the following ordinary least squares model:

KU = a0 + b1QUANP + b2QUALP + b3ADAPP + b4DISSE + b5LINKA + b6USERC +b7PUBA + b8KNOWF + b9USERF + b10FUNDI + b11FUNDE + e

Where,

b1, … b11 are coefficients

e are residuals

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The dependent variable measure:

Following Lester (1993), the dependent variable of this study is an index derived fromthe Knott and Wildavsky scale (1980). As already mentioned, this scale includes the sixcumulative following stages of knowledge utilization: transmission, cognition, reference, effort,influence, application generating, as described previously, a scale score ranging from 0 to 105.An item analysis on the components of this additive scale was performed by computing theCronbach’s �. This coefficient provides a reliability coefficient for multiple item scales such asthose included in the scale of knowledge utilization. The Cronbach’s � is shown in Annex 1 forall the multiple item scales variables used in the regression model. The values of the �coefficients reported in Annex 1 indicate that all the multiple item scales employed in this studyare reliable.

The independent variables measures

The independent variables included in the explanatory model are measured as follows:

Types of products:

QUANP : Research products based on data analyzed using correlation ormultivariate techniques. (1=correlation or multivariate techniques areusually or always used in research projects and 0= otherwise).

QUALP : Research products based on case studies using qualitative data. (1=casestudies using qualitative data are usually or always used in researchprojects and 0=otherwise).

Dissemination

ADAPP: adaptation of products measured as an index of importance accorded bythe researcher in adapting his research products for users. This index iscomposed of five cumulative dimensions which range on a five-pointscale of adaptation. Therefore, the index ranges from 0 to 25. The scaleof adaptation ranges from 0 to 5 where 0= does not apply, 1 = negligibleadaptation to 5 = decisive adaptation. The five dimensions are: 1)readability and use of comprehension of my reports and research articles;2) specific, operational nature of conclusion or recommendation; 3) focuson variables that can be manipulated by users; 4) sensitivity to users’sensibilities; 5) appeal of reports (graphics, color, humor, packaging).

DISSE: Dissemination effort measured by an index of importance accorded by theresearch to three types of activities of dissemination during the last five

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years. The scale of importance of these activities ranges from 0 to 5where 0 = does not apply, 1 = negligible importance and 5 = decisiveimportance. The three categories of activities included are: 1) preparingand conducting meetings in order to plan the subject and scope ofprojects with users; 2) formal meetings to report on a study’s progress orto discuss preliminary results with users; 3) preparation and effectuationof research results dissemination activities among users. This indexranges from 0 to 15.

Interactions

LINKA: Intensity of linkages with users is measured as an index indicating theimportance accorded by the researchers to different linkage mechanisms.The scale of importance of the mechanisms ranges from 0 to 5 where 0 =does not apply, 1 = negligible importance to 5 = decisive importance. Theeight linkage mechanisms considered are the following: 1) informalcontacts with personnel and experts of government agencies; 2) informalcontacts with personnel and experts of private organizations; 3)participation in committees, seminars and workshops organized bygovernment agencies; 4) participation in committees, seminars andworkshops organized by private organizations; 5) sending reports toministries and government agencies; 6) sending reports to privateorganizations; 7) publication of articles in newspapers; 8) participation inradio or television programs. Therefore, this index ranges from 0 to 40.

Users’ context

USERC: Perception of users’ context measured as an index indicating the opinionsof the researcher regarding six statements. The opinions are measuredon a scale ranging from 0 to 5 where 0= does not apply, 1 = disagreecompletely with the statement and 5 = completely agree with thestatement. The six statements considered are as follows: 1) my researchis considered pertinent by practitioners and professionals; 2) my researchcoincides with the needs and expectations of practitioners andprofessionals; 3) there is a target public of practitioners comprising areceptive audience for the dissemination and use of my research results;4) practitioners and professionals attribute credibility to my researchresults; 5) my research is oriented to take into account the needs ofusers; 6) my research findings have reached users at just the rightmoment to be used. Therefore, this index ranges from 0 to 30.

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Researchers’ context

PUBA: Publication assets measured as the total number of articles, chapters ofbooks and books published during the last five years. The books weremultiplied by 5.

KNOWF: Opinion of the researcher regarding the extent to which his projects arefocussed on the advancement of scholarly knowledge. Frequency offocus is measured on a scale ranging from 0 to 5 where 0 = does notapply, 1 = never focussed on advancement of scholarly knowledge and 5= always.

USERF: Opinion of the researcher regarding the extent to which his projects arefocussed on users’ needs. Frequency of focus is measured on a scaleranging from 0 to 5 where 0 = does not apply, 1 = never focussed onusers' needs and 5 = always focussed on users’ needs.

FUNDI: Degree of importance of university funding for the realization of researchprojects during the last five years. The degree of importance ranges from0 to 5 where 0 = does not apply, 1 = negligible importance and 5 =decisive importance.

FUNDE: Degree of importance of external funding for the realization of theresearch projects during the last five years. The degree of importanceranges from 0 to 5 where 0 = does not apply, 1 = negligible importanceand 5 = decisive importance. Four sources of external funding wereconsidered: 1) research councils (i.e. : Social Sciences and HumanitiesResearch Council of Canada); 2) not for profit organizations; 3) industry;4) ministries and government agencies. The index of degree ofimportance of external funding ranges from 0 to 20.

Results

Regression results are summarized in Table 5. It can be seen that, in the comprehensivemodel that includes all the social science disciplines, use of quantitative methodologies,adaptation of research results, dissemination efforts, linkage mechanisms, users’ context,publication assets and external funding are significantly and positively related to utilization ofsocial science research results. The four other variables included in the model, namely,qualitative research products, focus on advancement of scholarly knowledge, focus on users’needs and funding from internal university sources were found not related to utilization ofknowledge. The total amount of variance in knowledge utilization explained by this model isshown by the adjusted R2 to be .60. In a second step, we have employed the same explanatoryvariables to show that the same variables are not equally important in all the social sciencedisciplines in accounting for knowledge utilization.

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The next four columns report the regression results of the four disciplinary socialsciences. Factors regarding users’ context and publication assets explain use of knowledge ineconomics, political science, sociology and anthropology. Efforts made by researchers in matterof dissemination explain utilization in economics, political science, and sociology, but not inanthropology. Adaptation of research products for the needs of users is significantly andpositively associated to knowledge utilization in economics, political science and anthropology,but not in sociology. Funding of research projects from sources external to universities issignificantly and positively related to utilization of knowledge for economics and sociology, butnot for political science and anthropology. As for the intensity of the linkage mechanismsexisting between researchers and users, it is significantly and positively related to use ofknowledge in political science and anthropology, but not in economics and sociology. The focusof research projects on the advancement of scholarly knowledge is not significantly related toutilization of knowledge in economics, political science and anthropology, but it is significantlyand negatively related to utilization in anthropology. As can be seen in Table 5, the type ofproducts provided, that is research results based on quantitative or qualitative methodologies, isnot significantly associated to utilization except in political science and anthropology wherequantitative products are significantly and positively associated to use of results. As for fundingof research projects by sources internal to universities, it is significantly and positivelyassociated to utilization of knowledge in economics and sociology, but negatively associated toutilization in political science and not significantly associated to utilization in anthropology.Contrary to our expectations, projects focussed on users’ needs are not significantly associatedto utilization of knowledge.

Let us now turn our attention to the professional social sciences. Social work is thesocial science discipline which ranked the highest on the scale of knowledge utilization. As canbe seen in Table 5, the intensity of the linkage mechanisms and the users’ context aresignificantly and positively related to use of research in social work whereas, funding ofresearch projects from sources internal to universities is significantly and negatively related toutilization. The six other explanatory factors are not significantly associated to utilization in thecase of social work. The total amount of variance in knowledge utilization in social work isshown by the adjusted R2 to be .14. It suggests that the explanatory variables included in thismodel do not capture adequately variations occurring in social work regarding what becomes ofthe research results in this professional discipline. As for the second professional social sciencediscipline, it can be seen in the last right hand column that adaptation of products, disseminationefforts and users’ context are significantly and positively related to utilization, whereas researchresults based on quantitative methodologies as well as on qualitative methodologies, researchfocussed on the advancement of scholarly knowledge and research funded by universityinternal sources are significantly but negatively associated to use of research in industrialrelations. The intensity of linkage mechanisms and the publication assets do not explainutilization of research in industrial relations. The total amount of variance in utilization explainedin industrial relations is shown by the adjusted R2 to be .69.

Table 5 here

Discussion and policy implications

This paper derived its dependent variable and independent variables from prior studies onknowledge utilization. It has tried to examine the question of knowledge utilization of social

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science research results by using a scale of utilization derived from Knott and Wildvasky. Basedon the responses of 1229 Canadian social science scholars, the findings of this study show that,on the whole, nearly half of the social science knowledge usually or always passes over the firststage of utilization to be transmitted to practitioners, professionals and decision makers,whereas 20% of the social science research results do not even go through this first stage ofutilization. These simple descriptive findings suggest that social science research results aremore extensively used than commonly assumed. Furthermore, comparisons of the means ofutilization between the various social sciences show that the results of the disciplinary socialsciences, namely economics, political science, sociology and anthropology lend to lower levelsof use than the research results of the professional social sciences, that are industrial relationsand social work. Although not unexpected, this finding invites us to try to explain thesedifferences. This question has been examined by using regression models.

The results of the regression models clearly show that the determinants of knowledge utilizationincluded in the science push model, the demand pull model and the dissemination model do notconstitute good predictors of knowledge utilization. Furthermore, the findings of this study alsoshow that the theory of the two communities is a ‘vue de l’esprit’ that does not describe theactual behavior of the social science scholars. Clearly, this paper shows that the determinantsassociated to the interaction model provide the best predictors of knowledge utilization in socialsciences. Let us discuss in turn the implications of the findings for each of the categories ofvariables.

The types of research methods used to produce research results have been shown to be muchless important than expected. Recourse to quantitative methodologies influence knowledgeutilization positively in political science and anthropology and negatively in industrial relations,whereas the recourse to qualitative methods is never significantly related to utilization except inindustrial relations where it is significantly and negatively related to utilization of research.However, and most importantly, the fact that neither the recourse to quantitative or qualitativemethodologies influences knowledge utilization in economics, sociology and social work impliesthat the types of research methods adopted by the researchers of these disciplines do not affectutilization of knowledge in these disciplines. On the whole, these mitigated results suggest thatthe types of research methods adopted by the researchers constitute neither a very goodpredictor of utilization nor a powerful lever of intervention to foster utilization of social scienceresearch results.

The story about dissemination is quite different. Dissemination efforts undertaken by theresearchers represent a good predictor of utilization in all the social sciences except inanthropology and social work. Likewise, adaptation of research products for users influencespositively use in economics, political science, anthropology and industrial relations. Clearly,dissemination influences positively utilization. As a consequence, increasing disseminationcould increase utilization. Dissemination efforts and adaptation of products represent factorsthat are under the control of the researchers. These activities can be considered as costsincurred by the researchers to make transactions with users. The higher the disseminationefforts of the research results, the more important the adaptations of the research products, thehigher the transaction costs supported by the researchers. This interpretation suggests thatknowledge utilization of the social science research results could be increased by creatingincentives targeting dissemination. It could be achieved by compensating and even rewardingthe researchers for the transaction costs incurred by their dissemination activities. Thesecompensations or rewards could be based on assessments of prior record of the researchers inmatter of dissemination and product adaptation. The forms used to prepare applications forresearch grants could be modified so as to include a question where the researchers could be

17

asked to describe their activities of the five preceding years in matter of dissemination andproduct adaptation.

The mechanisms linking the researchers and the users have also been shown to be goodpredictors of knowledge utilization in political science, anthropology and social work. Clearly,paying more attention to the linkage mechanisms could increase utilization of social scienceknowledge. Once more, the higher the resources invested in the linkage mechanisms, thehigher the costs incurred by the researchers when they transact with users. This interpretationsuggests that devising incentive schemes, which would compensate researchers who investresources in linkage mechanisms, could increase utilization of social sciences.

As for the users’ context has been found to influence positively utilization in all the socialscience disciplines. This result carries important theoretical and practical implications. Factorsregarding the users’ context are contingent to the particular situations of the users and, as aconsequence, are difficult to include in a deductive theory of knowledge utilization. In itself thisresult suggests that it will be difficult to improve the existing interaction theory by addingdeterminants into a theory composed of elements related together in a deductive manner.Furthermore, the fact that many of the factors included in the users’ context are neither underthe control of the researchers nor under the control of the university research granting agenciesimplies that the margins for intervention are thin. The main course of action that can be figuredout consists in investing resources in actions that would make the users’ context more receptiveto social science research. We suggest that it could be achieved at low costs by symbolicinterventions that would stress the fact that social science research results are used moreextensively than assumed, thus inducing the non users to pay more attention to social sciencesresearch results.

The researchers’ context has also been shown to influence utilization of knowledge. Wepredicted that the higher the number of publications, the higher the use of knowledge. Theargument behind this prediction was that the researchers with the greater number ofpublications were more likely to have a greater number of by-products available for use bypractitioners, professionals and decision-makers. We have found that the publication assetsinfluence utilization in economics, political science, sociology and anthropology but not in socialwork and industrial relations. This result suggests that the scientific credibility brought by a greatnumber of publications explain utilization in the disciplinary social science disciplines.Furthermore, in examining the focus of the research projects, we have been astonished to findthat research projects focussed on users’ needs were not more likely to lend to utilization thanthe projects focussed on the advancement of scholarly knowledge. Focus on the users’ needsinfluences positively utilization only in sociology but in no other social sciences. The fact that thefocus of the projects, either on users’ needs or on the advancement of scholarly knowledge, didnot explain utilization in economics, political science, anthropology and social work implies thatthis factor does not affect utilization. Therefore, policy interventions should not attempt toinfluence focus in attempting to induce the researchers to shift the focus of their projects fromthe advancement of scholarly knowledge to the users’ needs. Finally, the results show, asexpected, that research projects based on internal university funding are less likely to lend touse than projects based on funding from sources external to universities. This result suggeststhat universities that support research projects with their internal funds should have lowexpectations with respect to utilization of the results of such projects.

Overall, the most important finding of this paper is that knowledge utilization depends muchmore heavily on factors related to the behavior of the researchers and users’ context than onthe attributes of the research products. The results of this study have shown that utilization of

18

social science research results is far more complex than predicted by the existing theories,including the interaction theory. Instead, the findings show that utilization is affected bydissemination activities and linkage mechanisms but also by contingent factors that are difficultto integrate into the development of a more comprehensive deductive interaction theory ofknowledge utilization and that are difficult to manipulate by public policy. Therefore, additionaltheoretical research is needed to refine the interaction theory of knowledge utilization and,likewise, more empirical quantitative studies are needed to better identify the factors explainingwhy researchers succeed or fail to pass over each of the different cumulative stages ofknowledge utilization.

19

Table 1Stages of knowledge utilization

Stage 1 Transmission :

I transmitted my research results to the practitioners andprofessionals concerned

Stage 2 Cognition :

My research reports were read and understood by the practitionersand professionals concerned

Stage 3 Reference :

My work has been cited as a reference in the reports, studies, andstrategies of action elaborated by practitioners and professionals

Stage 4 Effort :

Efforts were made to adopt the results of my research bypractitioners and professionals

Stage 5 Influence :

My research results influenced the choice and decision ofpractitioners and professionals

Stage 6 Application :

My research results gave rise to applications and extension by thepractitioners and professionals concerned

Adapted from Knott and Wildawsky (1980)

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Table 2

Predictions regarding the impact of the independent variables on knowledge utilization

Independent variables Dependent variable

(Knowledge utilization)

Types of products :

� Quantitative studies

� Qualitative studies

?

?

Adaptation of products

� Adaptation of products to users +

Dissemination efforts

� Dissemination efforts of researchers +

Linkage mechanisms

� Intensity of linkages with users +

Users' context

� Perceptions of users' context +

Researchers' context

� Publication assets

� Focus on advancement of knowledge

� Focus on users' needs

� Importance of university internal funding forprojects

� Importance of external funding for projects

+

-

+

-

+

21

Table 3

Knowledge utilization scales

Stages ofutilization

Does not applyand missing

data

0 and M.D.

%*

Never

1

%

Rarely

2

%

Sometimes

3

%

Usually

4

%

Always

5

%

Average on1 to 5 scale

(s.d.)

Transmission

Cognition

Reference

Adoption

Influence

Application

15,4

20,2

20,5

27,3

28,8

31,3

4,8

2,2

6,4

8,4

8,2

10,1

9,2

7,3

14,3

13,7

16,8

14,7

23,0

22,9

29,4

26,7

30,8

28,2

26,5

32,3

20,2

17,6

11,9

12,3

21,1

1

5,1

9,2

6,3

3,5

3,4

3,2

(1.55)

3,1

(1.56)

2,7

(1.49)

2,4

(1.55)

2,2

(1.43)

2,2

(1.47)

*The percentages are computed on the rows.

22

Table 4Means of knowledge utilization for groups of social science disciplines in homogeneous

subsets (Duncan’s Test)

Disciplines Subset for alpha =.05Number of

observations1 2 3 4

Politicalscience

288 40.20

Anthropology 119 46.53 46.53

Sociology 265 46.92 46.92

Economics 281 49.17

Industrialrelations

36 56.86

Social work 122 64.88

Significance* .089 .510 1.000 1.000

* When the significance test is above the threshold alpha = .05, the null hypothesis cannot berejected.

23

Table 5 : Regression equations predicting utilization of social science knowledge

Disciplinary social sciences Professional socialsciences

Alldiscipline

s

Economics

Politicalscience

Sociology Anthro-pology

Socialwork

Industrialrelations

Independent variables

Intercept -14.545(-4.26) ***

-19.650(-2.89) ***

-13.445(-1.71) **

-12.262(-1.40) *

-19.606(-1.71) **

6.072(.31)

-4.383(-.18)

Quantitative products(QUANP)

2.173(1,54) *

2.311(.92)

5.803(1.47) *

.562(.18)

7.695(1.80) **

-1.150(-.25)

-20.376(-2.26) **

Qualitative products(QUALP)

.642(.48)

-.868(-.27)

2.615(.85)

.151(.05)

4.220(1.08)

-1.752(-.40)

-14.976(-1.57) *

Adaptation ofproducts(ADAPP)

.654(3.39) ***

.825(2.08) **

.600(1.38) *

.011(.02)

1.003(2.11) **

.647(.97)

2.055(1.51) *

Dissemination efforts(DISSE)

1.301(6.06) ***

2.003(4.26) ***

2.019(3.66) ***

1.525(3.59) ***

.171(.31)

.089(.10)

1.967(1.37) *

Linkage mechanisms(LINKA)

.408(3.30) ***

.044(.17)

.696(2.19) **

.314(1.01)

.434(1.40) *

.608(1.91) **

-.554(-.82)

Users' context(USERC)

1.569(12.75) ***

1.533(5.91) ***

1.319(4.98) ***

1.814(6.59) ***

1.817(6.51) ***

1.526(2.15) **

2.498(2.07) **

Publication assets(PUBA)

.208(4.83) ***

.190(1.78) **

.216(2.44) ***

.252(2.87) ***

.550(3.56) ***

.129(.87)

.178(.85)

Focus onadvancement of

scholarly knowledge(KNOWF)

-.691(-.36)

2.208(.54)

1.913(.37)

-9.172(-1.68) **

-4.64(-.73)

-.316(-.07)

-13.763(-1.46) *

Focus on users'needs

(USERF)

1.589(1.02)

.438(.12)

-5.272(-1.17)

2,500(.740)

4.048(1.04)

2.158(.45)

-4.308(-.58)

University funding(FUNDI)

-.347(-1.16)

.783(1.29) *

-1.562(-2.19) **

1.054(1.46) *

-.205(-.22)

-1.231(-1.34) *

-2.566(-1.87) **

External funding(FUNDE)

.936(3.47) ***

.898(1.66) **

.420(.67)

.971(1.62) *

.370(.49)

.112(.13)

1.504(1.01)

N 896 223 202 197 92 93 30

Adjusted R 2 .605 .604 .587 .539 .761 .147 .693

F 125.96 31.94 27.12 21.97 27.62 2.45 7.16

Figures between parentheses indicate T ratios.

*, ** and *** indicate that variable is significant at 10 %, 5 % and 1 % level respectively.

24

Annex 1

Internal reliability coefficients (Cronbach’s alpha) for variables including multipleitem scales

Variable names Number of cases Number of items inscales

�

Knowledge utilization(KU)

1229 6 .89

Adaptation of products(ADAPP)

1158 5 .74

Dissemination efforts(DISSE)

1229 3 .85

Linkage mechanisms(LINKA)

1084 8 .81

Users’ context(USERC)

1072 6 .91

Publication assets(PUBA)

1229 3 .56

External funding(FUNDE)

1041 3 .55

25

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