POST PRINT VERSION.
Accepted by Science Communication on 27 August 2012.
*Note – this is a copy of the final draft version submitted on 5 April 2013 after peer review.
Science Communication (2012) 35(6) 780-809 http://scx.sagepub.com/content/35/6/780
DOI: 10.1177/1075547013491398
Author details:
Corresponding author: Adrian Cherney
*Dr Adrian Cherney
School of Social Science
The University of Queensland
Brisbane, St Lucia 4072 ph + 61 7 3365 3236 fax + 61 7 3365 1544 email: a.cherney@uq.edu.au
Dr Jenny Povey
Professor Brian Head
Professor Paul Boreham
Michele Ferguson
Institute for Social Science Research
The University of Queensland
Brisbane, St Lucia.
Acknowledgements: This project is supported through ARC Linkage project:
LP100100380.

Abstract
Social science disciplines generate diverse forms of research utilization, given the various contexts in which disciplinary knowledge is produced and translated for the fields of policy and practice. We examine this issue from the perspective of academic researchers in the social sciences across education, economics, sociology, political science and psychology. We use survey data from a study of university-based social science researchers in Australia to examine factors that influence perceptions of the policy uptake of social research. Our results show that disciplinary and methodological context matters when it comes to understanding the translation, dissemination and utilization of academic social research.
Keywords : research utilization, translation, research impact, social science, research collaborations.

The need to improve the dissemination and translation of social research for nonacademic audiences, and to increase the impact of academic research has gained increasing attention across a range of academic disciplines. These include social work
(Wilkinson, Gallagher and Smith 2012), education (Rickinson, Sebba & Edwards
2011), economics (Banks 2011), sociology (Cherney and McGee 2011), political science (Rogers 1989), psychology (Meagher, Lyall and Nutley 2008) and public health (Contrandriopoulos et al 2010; Haynes et al 2011; Lavis et al 2003, 2006).
There have been many recent attempts to establish new processes to promote interaction between social scientists and government and community stakeholders, such as the Dutch ‘science shops’ concept that has been supported by the European
Commission and emerged in many countries under various guises (European
Commission 2003).
Interest has also been fuelled by government approaches to the assessment of academic research quality, such as the Excellence in Research Australia (ERA) initiative and the UK government’s Research Excellence Framework. The impact of academic research has also been raised in the United States in the context of how the
National Science Foundation funds research and requires information about relevance for non-science stakeholders (Holbrook 2012; Kamenetzky 2013; Mervis 2011). The issue has gained public attention following calls by some politicians to reduce social science research funding due to its lack of perceived relevance by comparison with medical research (Atran 2013). These issues have coincided with recent attempts in
Australia, Europe and the US to gauge the social and economic impact of university research (Allen Consulting 2005; Holbrook 2012; Kamenetzky 2013; Macintyre
2010; Mervis 2011; Juhlin, Tang and Molas-Gallart 2012; Smith, Ward and House

2011; Wooding et al 2007). The issue of research impact has also been emphasized in wider community and industry concerns that academics need to engage more with end-users, a major criticism being that there is a disjunction between research conducted by academics and its uptake by public or private sector agencies
(Burkhardt & Schoenfeld 2003; Bogenschneider and Corbett, 2010; Rickinson, Sebba and Edwards 2011; Lambert 2003; for specific Australian commentary see Macintyre
2010; Ross 2011; Shergold 2011). Better channels for communication and knowledge translation seem to be essential (Lomas 2000). Closer synergies such as collaborative research partnerships are seen as a way to enhance the impact of academic social research (Huberman, 1990; Lavis et al 2006b; Nutley, Walter and Davies 2007; Orr and Bennett 2012).
It needs to be recognized that different social science disciplines will potentially generate diverse forms of research use. For instance, research uptake will be influenced by discipline-based contextual factors, which will shape knowledge translation activities (Landry, Amara and Lamari 2001b; Levin 2011). These contextual factors include attitudes among academics about the value of conducting applied research and about the importance of investing in processes (e.g. partnerships) that help generate research uptake by end-users. This observation draws attention to two related issues: which factors appear to influence social research use, and how these factors may vary across social science disciplines? There is little empirical work on this topic internationally. Investigations of research utilisation have mainly focused on case studies of specific policy domains (e.g. Kramer and Wells 2005; Wilkinson,
Gallagher and Smith 2012; Weiss and Bucuvalas 1980) and only a few studies have examined research impact across different research disciplines (see Landry, Amara and Lamari 2001b). While case studies on research utilization have been important in

providing insight into the nuances of knowledge translation and impact, their generalizability to other fields is open to debate (Dunn, Dukes and Cahill 1984;
Landry, Amara and Lamari 2001a, 2001b; Seidal 1981). The absence of studies that aim to examine research utilization across the social sciences means that our understanding about disciplinary variations in research translation and uptake is limited. Such studies would help shed light on the potential practices or processes that hinder and facilitate research impact and knowledge transfer.
In this paper we examine the issue of research utilization from the perspective of social science knowledge producers – academic researchers in the social sciences across education, economics, sociology, political science and psychology. We use survey data from a study of university-based social science researchers in Australia to examine perceptions of the policy uptake of social research. Our broader aim is to better understand factors that facilitate the utilization of social research by nonacademic audiences – whom we have termed end-users i
. Importantly we advance understanding in the field of knowledge transfer and use by examining how research utilisation varies across social science disciplines, accounting for any major differences. Our findings also have wider implications for evaluating research impact
. in the social sciences as well as broader lessons about how research is communicated to end-users to improve research uptake.
Conceptualising Research Utilization
Understanding the impact of social science research has been a primary focus of research utilization scholarship which, since the 1970s, has examined the factors and

circumstances which support or undermine the uptake of social research by policy decision-makers and practitioners (Caplan 1979; Larson 1980; Lester and Wilds
1990; Rich 1997; Weiss 1980; Weiss and Bucuvalas 1980). It is a field not only concerned with the behaviours and decisions of those who consume social research but also with investigating how the circumstances and activities of those who produce social research (e.g. academics, research institutes or private think tanks) influence processes of knowledge transfer and uptake (Cherney et al 2012a, 2012b; Florio and
Demartini 1993; Hayes et al 2011; Landry, Amara and Lamari 2001b; Lavis et al
2003; Weber 1986; Weiss and Bucuvalas 1980).
When it comes to measuring research utilization, no single conceptual model has gained unanimous approval (Belkhodja 2012; Rich 1997). One reason for this is the methodological problem associated with specifying the dependent variable of research use, given that it can be defined either as a process or an outcome (Rich
1997). From one perspective, research utilization can be viewed as a definitive endpoint where research has a direct impact on policy or practice. This is often referred to as the problem-solving model, with research seen as inducing changes in policy decision-making (Belkhodja 2012; Weiss 1980). However this view has been criticized as misplaced because it ignores the non-instrumental forms of research impact, such as conceptual or symbolic forms of utilization, which involve research being used to change understanding (i.e. conceptual use) or to confirm and promote pre-existing policy directions or commitments (i.e. symbolic use) (Amara, Ouimet and Landry 2004; Belkhodja 2012; Lavis et al 2003; Weiss 1980). It has also been argued that research utilization rarely follows a linear path from academic knowledge producers to end-users in fields of policy and practice and hence uptake of research evidence may be more diffuse (Juhlin, Tang and Molas-Gallart 2012; Weiss, 1980).

In this study we measure research utilization by adopting a stages or process model, replicating a modified version of the Knott and Wildavsky (1980) research-use
(RU) scale, similar to that used in the study by Landry, Amara and Lamari (2001a,
2001b). This model comprises 6 stages - transmission, cognition, reference, effort, influence and application - and Table 1 provides the descriptions for each stage as presented in our questionnaire to Australian social scientists. The RU scale characterizes research use as both a process and an outcome, in the sense that cognition builds on transmission, reference builds on cognition, effort on reference, influence on effort, and application on influence. The different stages encompass a range of outcomes and reflect an increasing level of knowledge absorption by endusers (Belkhodja 2012). The RU scale does provide the ability to measure the significance of factors that have a bearing on research use, and has been shown to be a reliable scale (Belkhodja 2012; Cherney and McGee, 2011; Cherney et al 2012a,
2012b, Landry, Amara and Lamari 2001a; 2001b; Lester and Wilds 1990; Lester
1993).
INSERT TABLE 1 HERE
Factors Influencing Research Use
Just as there is no agreed conceptual model relating to research utilization, there is no definitive list of variables developed to help predict knowledge use (Lester, 1993).
Singular and multiple perspectives have been proposed. These include, for example, the science-push perspective, which explains advances in research utilization as largely generated by the actions of knowledge producers and the types of products they produce (Belkhodja 2012; Landry, Amara and Lamari 2001a) . Alternatively
Belkhodja et al (2007) adopt an organizational perspective, focusing on multiple

contextual variables to explain knowledge utilization that encompass organizational interests, the needs and behaviors of researchers and end-users, and levels of interaction between researchers and users (also see
Belkhodja 2012)
. Taking account of various individual and contextual factors, variables that influence the utilization of academic social research can be grouped under four broad headings relating to the researchers’ and users’ context, the chosen dissemination activities, and the interactions between academic researchers and potential end-users.
The researchers context relates to a mix of supply-side variables that influence research production. This includes the academic role or position of researchers, such as whether they are in a research-only or a teaching and research position; the types of outputs such as quantitative and qualitative studies; whether research is focused on non-academic users; the importance of different funding sources; success at securing external research grants; and the institutional drivers that influence the motivation to collaborate with external partners (rewards for collaborative research)
(Bogenschneider and Corbett 2010; Contrandriopoulos et al 2010; Cherney et a
2012a, 2012b; Landry, Amara and Lamari 2001a; 2001b; Jacobson, Butterill and
Paula 2004). Disciplinary backgrounds can also be extremely important in influencing research utilization, because these can shape behaviours and views about dissemination and engagement with end-users – matters relevant to the culture of knowledge production within particular research disciplines and the forms of interaction and methods of communication adopted (Bogenschneider and Corbett
2010).
The factors related to end-user contexts encompass judgements on the part of policy-makers and practitioners relating to the value placed on the quality of research evidence, its perceived relevance, and the political and economic feasibility of

adopting research findings. Skills to interpret and apply research findings also matter, as does the level of access to research products such as reports and journals. Added to this are organisational processes such as the value policy-makers and practitioners place on research evidence. Such factors influence the overall demand for academic research within end-user contexts (Belkhodja et al., 2007; Belkhodja 2012;
Contrandriopoulos et al 2010; Nutley, Walter and Davies 2007; Ouimet et al 2009).
Dissemination variables relate to efforts by researchers to tailor research products (e.g. reports) for the needs of users, and to develop communication strategies targeting particular non-academic audiences (Cherney et al 2012b; Huberman 1990;
Mitton et al 2007). The basic argument is that the more researchers invest in dissemination, the more likely research-based knowledge will be adopted (Cherney and McGee 2011; Mitton et al 2007). This includes holding meetings to discuss the scope and results of research projects with specific users or partners, and targeting particular forums such as those where academics report on their research to government committees.
Finally, interaction variables focus on the intensity of the linkages between knowledge producers and potential users or beneficiaries of research. Interactions generally help the process of dissemination but are usually based on informal personal contacts and networks between researchers and end-users. The argument is that the more intensive are these linkages, the more likely research uptake will occur
(Huberman 1990; Landry, Amara and Lamari 2001a; Lomas 2000; Mitton et al 2007).
The data used in this research were drawn from a broader study examining evidencebased policy and practice. The project involves 4 phases: (1) a targeted survey of
Australian social scientists; (2) a targeted survey of policy personnel; (3) interviews

with a selection of academic respondents; and (4) interviews with policy personnel.
Results reported in this paper are drawn from the phase 1 survey.
The academic survey was partially based on existing items or scales (Bogenschneider and Corbett,
2010; Landry, Amara and Lamari, 2001a, 2001b) but with additional items included to gauge the dynamics of research partnerships. Questions were framed around a number of themes relating to seniority, research discipline, academic position, grant success, main orientation of the respondent’s research, experience of working with external partners, methods of dissemination, perceived barriers to research transfer to end-users, benefits resulting from collaborations with external partners, the challenges of research partnerships, and the use and impact of the research produced by respondents.
The survey was first piloted among Fellows of the Academy of the Social
Sciences in Australia (ASSA) in September-October 2010 ii
. Eighty-one surveys were completed, with a response rate of about 17 per cent. There were no significant changes to the survey following the pilot outside of editing some lead-in questions to make them clearer iii
. No scales in the survey were changed. For the main survey, a database was established of Australian academics who had secured at least one
Australian Research Council (ARC) grant (known as Discovery or Linkage grants iv
) between 2001 and 2010 within the field of social and behavioral science v
. The selection of relevant disciplines from which respondents were sampled was based upon the ‘field of research’ codes used by the ARC to categorise the funded projects, and comprised codes relating to anthropology, criminology and law enforcement, human geography, political science, policy and administration, demography, social work, sociology, other studies in human society, psychology, education and economics. Using this database, a web link to the survey was sent via email to 1,950

academic researchers between November 2010 and February 2011. The same reminder email was sent twice during this period and the survey closed in May 2011.
A total of 612 completed surveys were received, which constitutes a response rate of
32 per cent. When the main academic survey was combined with the ASSA pilot, the final total was 693 responses (see also Cherney et al 2012b). In this paper we have drawn on results from the same questions used in the pilot and main survey. This final sample included respondents from the following main disciplines: education, economics, sociology, political science, and psychology. These disciplines comprised the 5 largest discipline clusters in our sample, and will form the primary basis of our analysis. The remaining disciplines have been grouped as ‘other’ (see Table 2) vi
.
INSERT TABLE 2 HERE
Dependent variable
Research utilization was measured using a modified version of the Knott and
Wildavsky (1980) research use scale, which comprises six stages: transmission, cognition, reference, effort, influence, and application. For each of these six stages, respondents were asked to estimate what had become of their research using a 5-point scale ranging from 1 (never), 2 (rarely), 3 (sometimes), 4 (usually), to 5 (always).
Previous research (Cherney et al 2012a) has shown that ‘failure’ in one stage does not preclude academic researchers from progressing to other stages. Data reported in Table 3, illustrating the proportion of academics who pass or fail at each stage of research utilization for each discipline, indicates that academic social researchers do not necessarily have to traverse in sequence each rung of the research utilization ladder to reach the ultimate stage, namely, the substantive application of research findings by end-users (see Table 3). This finding tends to support arguments

relating to the non-linear nature of research transmission, uptake and use. Table 3 illustrates that academic researchers, particularly in political science and psychology, perceive that the uptake of academic research declines in the effort, influence and application stages. In other words there was a decline in the perceived level of influence of academic research during the process of research utilization by external agencies. Later stages of the RU scale (effort, influence, application – see Table 1) can be particularly challenging for academics to influence directly, given that decisions by policy-makers or practitioners to adopt or apply research evidence can be determined by factors (e.g. political considerations) over which academics have little control.
INSERT TABLE 3 HERE
Figure 1 explores the proportion of academic researchers who cumulatively did not pass all six stages of the research utilization scale vii
. The disciplines of political science and psychology have the highest proportion of researchers who did not perceive their research as being adopted by end-users, whereas academics in the field of education had the highest number of academics reporting significant utilization by end-users.
A factor analysis of the items (i.e. 6 stages of utilization – see Table 1), revealed a 1-factor solution and a Cronbach’s alpha coefficient of 0.91 (see Table 4).
This result indicates that these items are measuring one construct and it was decided to use the RU scale as an index to measure research use. A mean index score was calculated for all 6 six stages. The mean score for the research utilization index is presented in Table 5.

INSERT FIGURE 1 HERE
Independent variables
A number of indices were created and included in our model as independent variables. The items used in each index were determined by factor analyses, with each index comprising a 1-factor solution. The Cronbach’s alpha coefficients for these independent variables are presented in Table 4 and detailed descriptions of index compositions are presented in Appendix 1.
INSERT TABLE 4 HERE
Descriptive statistics for each independent variable are presented in Table 5. The disciplines of education and sociology had the highest research utilization index score, while political science had the lowest. A Bonferroni test of significance indicated that there were significant differences between the mean research utilization index scores of political science academics and those academics from education and sociology. A higher proportion of education, sociology, and political science academics reported frequent use of qualitative approaches, whilst, economics and psychology academics more frequently used quantitative approaches. A smaller proportion of education and political science academics were research-only positions as compared to the other three disciplines. On average, academic researchers from our psychology and sociology sample had won a higher number of external grants compared to the other disciplines. Academic researchers from the education discipline accorded higher importance to dissemination activities such as tailoring research and dissemination activities to end-users, compared to the other four disciplines.

INSERT TABLE 5 HERE
Given that our dependent variable is approximately continuous, a multiple linear regression model was used to estimate the associations between research utilization
(our dependent variable) and a number of explanatory variables, such as benefits and consequences associated with engaging in research with policy-makers and practitioners for each of the five disciplines. As a preliminary check, we examined the correlations between all variables in the model for each discipline separately. The correlations suggested that multicollinearity was unlikely to be a problem. This was confirmed by a relatively low value of the mean Variance Inflation Factor (VIF) for each discipline.
Regression Results
The regression results are presented in Table 6. The results indicate that, for each discipline, a different cluster of variables predict reported levels of research utilization. The discipline of education had the highest number of predictors; four of the variables were positively and significantly related to the reported uptake and use of research produced by academics in the field of education. These variables were: the perceived benefits of collaborative research; importance of tailoring research for endusers; importance of using contacts, seminars and sending reports to policy-makers and practitioners; and the number of external grants. The results show that having to make large efforts investing into research partnerships had a negative but significant relationship on the reported uptake and use of research. In other words, un-

complicated partnership processes were seen to improve the likelihood of research uptake by educational researchers. The other four disciplines had only two to three variables that were significantly associated with research utilization and there does not seem to be a common predictor or pattern across these disciplines compared to the education sample. Nevertheless, there were some noteworthy results pertaining to these other disciplines. For both the disciplines of economics and sociology, holding a teaching and research position was negatively related to the reported use of academic research by policy-makers and practitioners, as was a reliance on qualitative research methods within political science. The emphasis placed on the useability of research was strongly associated with reported levels of research use among political scientists.
Finally, academic respondents in the field of economics indicated that the more policy-makers or practitioners were seen as prioritizing the ‘feasibility’ of research
(i.e. policy-makers or practitioners place greater emphasis on research being economically and politically feasible) the less likely were these academics to report that social research use would occur.
INSERT TABLE 6 HERE
Why is there such a difference between the results pertaining to researchers in the field of education compared to the other four research disciplines? One possibility relates to the overall applied nature of educational research, which favours and promotes engagement with end-users in the government and school sectors. In
Australia and elsewhere there is a long history of research partnerships between academic educational researchers, policy-makers, and practitioners such as teachers

and school principals (Department of Education Training and Youth Affairs 2000;
Bransford et al 2009; Levin and Edelstein 2010; Saha, Biddle and Anderson 1995;
Vanderlinde and van Braak 2010). This orientation can be particularly powerful in socializing academics in the field of education to be mindful of end-user perspectives and needs compared with some other fields of social science. This orientation is possibly reflected in the types of external grants they obtain, which may increase their chance of generating research use. Among our education sample the perceived importance of tailoring research for end-users and its relationship to research utilization reflects awareness that targeted forms of dissemination are needed to improve research absorption among non-academic audiences. The same can be said for the significant relationship found between reported levels of research use and reported involvement in activities focused on interactions with users (i.e. informal contacts, seminars and workshops organized by end-users or sending reports to endusers). Improved forms of interaction help decrease the gap between research produced by academics and users in fields of policy and practice. Ways of reducing the research-policy-practice gap and enhancing the relevance, translation and uptake of academic research has been intensely investigated and debated in the field of education (Burkhardt & Schoenfeld 2003; Levin 2011; Vanderlinde and van Braak
2010).
The above interpretation of educational research does not mean that understandings about research translation and end-user needs are lacking in other social science disciplines, such as sociology, psychology, economics and political science. Results relating to psychology, political science and economics did point to an appreciation among academic researchers in these disciplines that end-user engagement and contexts (e.g. the importance of meetings, and the usability and

feasibility of research) have a bearing on levels of research use. The useability item related to issues of clear communication and timeliness of research results (see appendix 1). The feasibility variable comprised three items: research recommendations are seen as economically and politically feasible and research findings support a current position or practice (see appendix 1). When feasibility is seen as a strong priority for users, our respondents in economics reported that their academic research is less likely to be used. These findings point to some key lessons about research quality: it is not the key priority potentially driving research use, nor is it the single most important factor in determining uptake – contacts, communication and timeliness also matter.
The general pattern of results and the variations we found among the sample point to the potential role that disciplinary processes play in generating research utilization and how they may shape both the behaviors and attitudes of academic researchers. This is perhaps more strongly the case for our education sample. Such disciplinary orientations were also reflected in our descriptive statistics relating to the types of research methods respondents used, with academics in the fields of economics and psychology reporting they more frequently use quantitative methods compared to respondents in sociology, political science and education. Economics and psychology have generally been dominated by a quantitative orientation, such as econometrics in the case of economics and experimental lab studies in the case of psychology. This has strongly influenced the focus of research arising from both fields (Sowey 2002; Griffin and Phoenix 1994). One result worthy of comment relates to the fact that researchers in economics and sociology who occupy teaching and research positions are less likely to report success in generating research utilization than those in research-only positions. Differences in how occupational profiles may

influence research uptake in the social sciences have been found in previous studies
(see Fox and Milbourne 1999 as it relates to research outputs among academic economists). This finding draws attention to the probability that academics within some disciplines have different capacities to devote time and resources to generate research outputs that go beyond traditional academic outlets, and perhaps have different experiences in undertaking research for or with industry partners.
Understanding how academics develop these broader capacities to influence and engage policy-makers and practitioners effectively would be a fruitful avenue for future studies.
What are the broader lessons arising from this study for how research is communicated to end-users to improve research uptake? One is that levels of engagement with end-users and investment in dissemination matter a great deal. Both engagement and dissemination are related to the process of research translation - i.e. the conversion of research findings into forms suitable for their use in solving a problem of a practical nature (Boyd and Menlo 1984: 60). When engagement between academic researchers and policy-makers or practitioners occurs, researchers are better able to learn about the needs of potential end-users and thus tailor (disseminate) their products more effectively. This often requires researchers to communicate research findings that meet tight timeframes, and that meet the idiosyncratic information needs of end-users (Bogenschneider and Corbett, 2010; Cherney et al 2012b). This is not a skill that can come easily to academic researchers whose ways of writing and training in scientific methods, as well as interest in theoretical and esoteric issues, can make it hard for them to communicate in a manner that appeals to the action-orientated and pragmatic concerns of non-academic end-users (Caplan 1979; Dunn 1980).

While our results show that engagement and dissemination are important, the ways they actually occur and the forms of interactions and methods of communication adopted will matter a great deal. For instance, it has been noted by a number of scholars that closer engagement through research collaborations between academics researchers and end-users will not guarantee improved research uptake, given the various political, individual and organizational variables that influence decisions to use academic social research by non-academic end-users (Amara, Ouimet and Landry
2004; Belkhodja 2012; Belkhodja et al 2007; Bogenschneider and Corbett, 2010;
Florio and Demartini 1993; Landry, Lamari and Amara 2003; Oh and Rich 1996;
Ouimet et al 2009; Weber 1986; Weiss and Bucuvalas 1980). Different interests, ideologies and priorities mean that engagement between academics and industry can be a difficult undertaking because motivations for producing and using research may not be the same. This means that engagement in the form of research collaborations between academic researchers and potential end-users would need to be carefully managed and expectations and outcomes clarified (Bammer 2008). Understanding the models of engagement that work best is an important area for future research given that it would provide insight into how academics and policy-makers and practitioners can best manage interactions and communication between them so as to achieve mutual outcomes.
Just as there is no one-size-fits all approach when it comes to academic and end-user engagement, the same applies to the other component of knowledge translation – dissemination. Written forms of dissemination alone will not generate research use (Bogenschneider and Corbett, 2010; Boyd and Menlo 1984; Friedman and Farag 1991; Kramer and Wells 2005; Nutley, Walter and Davies 2007; Rogers
1983). Formatting, style and mode of delivery matter a great deal, as does the context

in which research evidence is communicated. Summaries absent of jargon, toolkits and guides that stipulate practical implications and workshops between researchers and end-users that focus on identifying the most applicable format and content of research products, are central to effective dissemination (Bogenschneider and Corbett,
2010). It is possible that members of certain social science disciplines understand this process much better than do others.
Before concluding it should be noted that relying on the self-reporting of academics about the uptake of their research does have limitations. Respondents were requested to make judgments about processes they may not have directly observed, particularly in regard to the choices and actions of end-users concerning the higher stages of the research utilization scale. There is also a possibility that the data reflect a social desirability bias. That is, the judgement of respondents concerning the utilization of their research can be influenced by how practically relevant they perceive their research to be, which could be inflated in such circumstances. We have not examined the project reference points (e.g. specific project contexts) that underpin why respondents believed their research had an impact, or why they encountered problems in partnership contexts, as this assessment is more suited to qualitative methods.
Qualitative data would prove useful for understanding the dynamics of particular collaborations between academic researchers, policy-makers and practitioners which would provide insight into factors that help predict whether particular types of research partnerships succeed or fail. Also, we did not explore any variations by subdiscipline within our main five disciplines viii
. Each discipline incorporates subdisciplines, which can place different emphasis on applied research. The type of

research, the degree of technicality, theoretical abstraction and practicality of results can lead to different degrees of research utilization within particular social science disciplines.
ix
Exploring these factors would give insight into the overall influence of certain research fields on areas of policy and practice.
Our results show that context matters when it comes to understanding the utilization of academic social research. The variation in factors that influenced reported levels of research utilization across our sample in the disciplines of education, economics, sociology, political science and psychology highlight contextual factors (also see
Belkhodja et al 2007 as it relates to policy-makers use of research)
. The more consistent pattern observed in our education sample, compared to the other disciplines, was explained by reference to the orientation of educational research and the particular culture of engagement that characterizes research in the field of education. This is not to claim that other social science disciplines lack an appreciation of engagement and end-user needs or lack the capacity to partner with policy-makers or practitioners. This is clearly not the case when it comes to such fields as economics and political science, where researchers might engage more with international and national agencies compared to engagement with local partners (such as schools or local authorities) – a pattern more common with research collaboration in education. It is possible that models of research translation and collaborative partnerships with end-users are less well developed or institutionalized across some social science fields. We have noted evidence of variations in the emphasis placed on enhancing the uptake of research across the social sciences. Moreover, engaging endusers and undertaking collaborative research with external partners is not a central theme in mainstream academic training. Understanding how both these factors

operate across the various social sciences would help identify ways to improve research impact.
Our results provide lessons for academics interested in enhancing the utilization of the research they produce and in better communicating their findings.
The results indicate that there is a premium return for investing in knowledge translation activities and directly engaging with end-users through meetings and dissemination processes and that it is important to tailor research projects and findings to end-user needs. There is of course a risk that a too close relationship with industry partners can potentially compromise the research that academics produce. None-theless, academics who wish to see their research utilized by end-users should not confine their efforts to passive transmission of research, for example by relying upon traditional academic journals for dissemination. However, a significant problem is that University research assessment exercises in some countries (such as the
Australian ERA, the UK REF, and other similar research evaluation initiatives - see
Donovan 2011) which currently focus primarily on research quality indicators (e.g. journal rankings, impact factors and citation counts) can deter academics from investing in alternative outlets beyond standard academic publications such as refereed journals. This is because alternative outputs focused on knowledge translation, such as government reports or practitioner and industry journals may not be counted or valued in the assessment framework (Elton 2000; Martin 2011;
Nightingale & Scott 2007; Shergold 2011).
Finally our analysis shows that when it comes to measuring and demonstrating research impact, no one single model of evaluation will be adequate. Levels of impact will vary across academic disciplines and across problem-types, and there are reasons for this. A one-size-fits-all approach will not adequately measure either the quality or

the social and economic impact of academic social research. While it is quite legitimate for governments to examine whether they are getting value from the research they fund through taxpayers dollars, one fruitful area of investment would be to improve incentives and capacities to translate research to non-academic end-users and work at providing opportunities for academic researchers to engage with industry partners. The results reported in this paper show some of the ways this can be improved.

Appendix I: Independent variables measures
Researchers’ Context
Quantitative studies The quantitative research approach is a single item variable that reflects how often researchers use a quantitative approach such as surveys research, statistical analysis, and
GIS in their research. The results reported are the percentage of respondents who indicated always or usually.
These responses were recorded as 1, while all other responses were recorded as 0.
Qualitative studies
Benefits of collaborative research
Consequences of
The qualitative research approach is a single item variable that reflects how often researchers use a qualitative approach such as interviews, focus groups, ethnography, and observation in their research. The results reported are the percentage of respondents who indicated always or usually. These responses were recorded as 1, while all other responses were recorded as 0.
This Index is based on academic perceptions of the benefits of carrying out research in collaboration with government, industry or community sector partners. This index is comprised of ten dimensions that range on a 6-point scale, ranging from 0 (not applicable), 1 (strongly disagree) to 5
(strongly agree). The ten dimensions are: (1) I have been able to use data that would otherwise be difficult to access;
(2) Research partnerships have provided me with opportunities for my research to have an impact on policy and practice; (3) Research partnerships have helped to increase my industry contacts; (4) My industry contacts have helped with developing future research projects; (5)
Research partnerships enable me to generate extra income for my work unit; (6) Such projects have provided me opportunities to commercialise research outcomes; (7)
Research partnerships have helped me with career advancement; (8) Such projects have required me to be pragmatic and realistic in relation to research outcomes for industry partners (9) Research partnerships have enabled me to publish in a broad range of publication outlets (10) I find projects with external partners more satisfying than fundamental “blue sky” research.
This index is based on problems relating to investing time

investing into research partnerships and resources and accommodating partnership work that academic researchers encounter when carrying out research with partners from government, industry or the community sector. This index is comprised of ten items that range on a
6-point scale, ranging from 0 (not applicable), 1 (strongly disagree) to 5 (strongly agree). The ten dimensions are: (1)
There are inadequate university resources to support research partnerships with end-users; (2) I find there are different research orientations between academics and external partners; (3) You need to invest a lot of time in coordinating the work between different partners; (4)
Confidentiality requirements often restrict what you can report and publish; (5) You can lose ownership of intellectual property; (6) You are subject to delays that impede your ability to publish results in a timely manner;
(7) I am under pressure from my work unit to undertake contract research to meet budget requirements (8) External partners do not appreciate the full costs of research; (9) The ethics process can be time consuming and cumbersome;
(10) The complexity of contractual arrangements can lead to delays in commencing research.
Research Time This is a dummy variable created from the question asking academics to indicate the nature of their position, either research and teaching or research only. The research only was used as the reference group.
Users’ Context
End-users prioritise high quality research
This index is based on academic researcher’s perceptions of what research characteristics end-users prioritise when using academically produced social science research. This index is comprised of seven dimensions that range on a 5point scale, ranging 1 (not a priority) to 5 (high priority).
The seven dimensions are: (1) high quality research; (2) unbiased findings; (3) adds to theoretical knowledge; (4) statistical analysis is high quality; (5) findings can be generalised; (6) offers a new way of thinking; and (7) reputation of researcher.
End-users prioritise the useability of the research
This index is based on academic researcher’s perceptions of what research characteristics end-users prioritise when using academically produced social science research. This index is comprised of four dimensions that range on a 5point scale, ranging 1 (not a priority) to 5 (high priority).

The four dimensions are: (1) findings available when decisions need to be made; (2) findings have direct implications for policy & practice; (3) findings written in a clear style; and (4) report has brief summary of findings.
End-users prioritise the feasibility of the research
This index is based on academic researcher’s perceptions of what research characteristics end-users prioritise when using academically produced social science research. This index is comprised of three dimensions that range on a 5point scale, ranging 1 (not a priority) to 5 (high priority).
The three dimensions are: (1) recommendations are economically feasible; (2) findings support a current position & practice; and (3) recommendations are politically feasible.
Dissemination
Importance of tailoring research when endusers are the focus
This index is based on the importance attributed to various aspects of tailoring research when the focus is on end-users.
This index is comprised of seven dimensions that range on a 6-point scale of adaption, ranging from 0 (does not apply),
1 (very unimportant) to 5 (very important). The seven dimensions are: (1) readability and use of comprehension of my reports and research articles; (2) specific, operational nature of conclusions or recommendations; (3) provision of data that can be analyses by end-users; (4) sensitivity to end-users’ expectations; (5) presentation of reports
(graphics, colour, packaging); (6) on-time presentation of research findings to end-users; (7) attention to
‘deliverables’.
Importance of meetings
& dissemination activities with endusers
This index is based on the importance attributed to organising meetings and dissemination activities for endusers when carrying-out research. This index is comprised of four dimensions that range on a 6-point scale, ranging from 0 (does not apply), 1 (very unimportant) to 5 (very important). The four dimensions are: (1) preparing and conducting meetings in order to plan the subject and scope of projects with end users; (2) regular formal meetings to report on a study’s progress with end-users; (3) formal meetings to discuss findings with end-users; (4) preparing and implementing research dissemination activities for endusers.
Interactions

Importance of using contacts, seminars and sending reports to policy-makers and practitioners
This index is based on the importance attributed to using methods such as informal contacts, seminars and reports for presenting research to policy-makers and public practitioners. This index is based on six items measured on a 6-point scale, ranging from 0 (does not apply), 1 (very unimportant) to 5 (very important). The six items are: (1) informal contacts with policy personnel of government agencies; (2) informal contacts with public or community sector practitioners; (3) participation in seminars and workshops organised by government policy agencies; (4) participation in seminars and workshops organised by practitioners within public or community sectors; (5) sending reports to government policy agencies; (6) sending reports to practitioners within public or community sectors.
Number of external grants
This index is the sum of all the research grants (i.e. ARC discovery, ARC linkage, other external competitive grants) academics have received.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Rol e of the funding source: This project was finically supported through the
Australian Research Council Linkage project LP100100380. This project has received cash and in-kind support from the following industry partners: Australian Productivity
Commission; Australian Bureau of Statistics; Queensland Health; Queensland Dept of
Communities; Queensland Dept of Employment; Queensland Dept of Premier and
Cabinet; Victorian Dept of Planning and Community Development; Victorian Dept of
Education & Early Childhood; and the Victorian Dept of Human Services.

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Table 1. Research Utilization Scale
Variable
Transmission I transmit my research results to end-users
Cognition
Reference
Effort
My research reports have been read and understood by end-users
My work has been cited in reports and strategies by end-users
Efforts were made to adopt the results of my research by end-users
Influence My research results have influenced the choices and decisions of end-users
Application My research has been applied by end-users
*end-users were defined as comprising policy-makers within government, or practitioners/managers within public or community sectors or private sector organisations
Table 2. Disciplines n %
Education
Economics
Sociology
Political Science
156
102
90
78
22.5
14.7
13.1
11.2
Psychology
Other disciplines
110
157
15.9
22.6
Total 693 100.0
*Other comprised a variety of fields including social work, geography, criminology, anthropology, demography and archaeology.
Table 3. Proportion of academics who pass or fail each stage of the research utilization stages across disciplines
Research
Utilization stages
Education Economics Sociology
Pass
%
Fail
%
Pass
%
Fail
%
Pass
%
Fail
%
Political
Science
Pass
%
Fail
%
Psychology
Pass
%
Fail
%
Transmission 96 4 82 18 91 9 83 17 75 25
Cognition
Reference
Effort
Influence
Application
95
87
82
85
86
5
13
18
15
14
85
77
75
69
74
15
23
25
31
26
89
87
72
73
73
11
13
28
27
27
87
83
60
68
60
13
17
40
32
40
75
66
68
66
68
25
34
32
34
32

Figure 1. Proportion of academic researchers who did not pass all six stages of the research utilization scale per discipline
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
46%
45%
41%
32%
25%
Political
Science
Psychology Economics Sociology Education
Table 4. Internal reliability coefficients (Cronbach’s alpha) for variables
Name of variable
RU Index
Researchers’ Context
Benefits of collaborative research
Consequences of investing in research partnerships
User’s Context
End-users prioritise high quality research
End-users prioritise the useability of the research
End-users prioritise the feasibility of the research
Dissemination
Importance of tailoring research when end-users are the focus
Importance of meetings & dissemination activities with end-users
Interactions
Importance of using contacts, seminars and sending reports to policy-makers and practitioners
559
612
693
612
693
693
693
Number of cases
Number of items in a scale
Cronbach alpha
3
7
7
4
6
10
10
0.91
0.93
0.89
0.78
0.78
0.69
0.94
693
693
4
6
0.95
0.88

Table 5. Means and standard deviations a academic research across 5 disciplines
Research Utilization Index
Researchers’ Context
Quantitative studies
Range Education Economics Sociology
Political
Science
Psychology
Min Max M SE M SE M SE M SE M SE
1 5 3.64 0.05 3.38 0.08 3.63 0.08 3.21 0.09 3.38 0.09
0 1 0.41 0.04 0.85 0.04 0.43 0.05 0.17 0.04 0.91 0.03
Qualitative studies
Benefits of collaborative research
Consequences of investing in research partnerships
Research Time (teaching and research position)
0
0
0
1
5
5
0.84
3.38
0.03
0.06
0.13
3.10
0.03
0.11
0.74
3.25
0.05
0.10
0.71
2.77
0.05
0.15
0.26
2.97
0.04
0.13
0 1 0.81 0.03 0.55 0.05 0.51 0.05 0.76 0.05 0.60 0.05
Research Time (research only position)
Number of external grants
User’s Context
End-users prioritise high quality research
0
0 51 7.81 0.57 8.38 0.87 9.49 0.96 5.76 0.61 10.57 0.87
1
End-users prioritise the useability of the research 1
1 0.19 0.03 0.45 0.05 0.49 0.05 0.24 0.05 0.40 0.05
5
5
3.91
4.64
0.05
0.03
3.60
4.53
0.07
0.05
3.69
4.62
0.07
0.05
3.65
4.55
0.07
0.06
3.63
4.52
0.07
0.05
5 3.90 0.06 3.74 0.08 3.78 0.08 3.85 0.09 3.82 0.07 End-users prioritise the feasibility of the research 1
Dissemination
Importance of tailoring research when end-users are the focus
Importance of meetings & dissemination activities with end-users
0
0
Interactions
Importance of using contacts, seminars and sending reports to policy-makers and practitioners
0 a.
Standard deviations only reported for continuous measures.
5
5
5
3.70
4.24
4.23
3.78
0.06
0.04
0.05
0.06
3.39
3.68
3.47
3.21
0.08
0.11
0.13
0.10
3.50
3.96
3.91
3.68
0.09
0.06
0.11
0.09
3.27
3.46
3.34
3.44
0.12
0.15
0.15
0.10
3.38
3.62
3.46
3.07
0.09
0.14
0.15
0.13

Table 6. Multiple linear regression equations predicting utilization of academic research
Quantitative studies
Qualitative studies
Benefits of collaborative research
Consequence s of investing in research partnerships
Teach/resear ch
Number of external grants
End-users prioritise high quality research
End-users prioritise the useability of the research
End-users prioritise the feasibility of the research
Importance of tailoring research when endusers are the focus
Education
β
0.12
-0.10
0.22
-0.14
0.03
0.02
-0.03
0.17
-0.08
0.37
***
**
***
***
SE β
(0.13)
(0.07)
(0.12)
(0.09)
(0.12)
(0.07)
Economics
β
(0.10) 0.24
0.16
(0.06) 0.13
-0.03
-0.34
(0.01) 0.01
0.18
-0.00
-0.25
(0.13) 0.11
**
* (0.09) 0.27
**
SE β
(0.19)
(0.20)
(0.10)
(0.11)
(0.15) -0.25
(0.01)
(0.17)
(0.11)
(0.11)
Sociology
β
0.25
0.28
0.15
0.05
-0.00
0.21
-0.07
-0.09
*
**
Importance of meetings
& disseminatio n activities with endusers
Importance
-0.04 (0.10) -0.01
0.13
* (0.07) 0.07
(0.11) 0.04
(0.11) 0.14 of using contacts, seminars and sending reports to policymakers and practitioners
Constant 1.06
* (0.61) 2.64
*** (0.79) 0.80
Observations 155
Adjusted R 2 0.284
Standard errors in parentheses
* p < 0.10, ** p < 0.05, *** p < 0.01
98
0.343
86
0.262
SE β
(0.16)
(0.18)
(0.10)
(0.10)
(0.15)
(0.01)
(0.13)
(0.17)
(0.11)
(0.16)
(0.09)
(0.11)
(0.82)
Political Science
β
-0.04
-0.35
0.13
-0.07
-0.16
-0.01
0.15
0.44
-0.17
0.01
0.10
0.15
0.72
75
**
**
0.349
SE β
(0.20)
Psychology
β
0.13
(0.17) -0.00
(0.08)
(0.09)
(0.19)
(0.02)
(0.15)
(0.18)
(0.11)
(0.08)
(0.09)
(0.13)
(0.76)
0.14
-0.05
0.05
0.02
0.15
0.26
-0.13
0.05
0.17
0.09
0.49
101
* (0.09)
**
*
0.513
SE β
(0.23)
(0.17)
(0.09)
(0.13)
(0.01)
(0.10)
(0.16)
(0.10)
(0.09)
(0.09)
(0.09)
(0.76)
36

i
Throughout this this paper we use the term “end-user” in a generic sense to refer to nonacademic audiences e.g. policy personnel, or practitioners/ managers within the public, private or community sectors. ii Fellows are recognised for their outstanding contributions to the social sciences in Australia and abroad. See http://www.assa.edu.au/. iii We have reported here the combined results from the same questions used in the pilot and main survey.
iv Australian Research Council (ARC) grants are national competitive grants and funds a significant proportion of research activity in Australian Universities. Discovery grants fund fundamental research that may not have an immediate applied focus, but it is assumed to have some broader community benefit. Linkage grants fund research collaborations between academic chief investigators and industry partners (including government agencies). Industry partners are required to make a cash and in-kind contribution to the project (see http://www.arc.gov.au/ncgp/default.htm). These grants emphasis track recorded with 40% of
ARC Discovery assessment based on track record. v
The reason for targeting academics who had secured research grants was to ensure the project captured experienced academics who were likely to have had a history of research collaborations, since one aim was to understand the impact and dynamics of such partnerships. vi
Respondents were asked to identify their main disciplinary background from a predetermined list. Due to space restrictions and the length of the survey respondents were not asked to identify their sub-discipline. vii
Figure 1 and table 3 were calculated differently. Figure 1 is calculated by assigning a value of 1 when respondents replied always, usually, or sometimes to a particular stage (which means they progressed across a stage), with all other responses assigned the value of 0, which means they failed to move up the scale. Figure 1 refers to how many respondents did not in total progress across all 6 stages, thus giving a cumulative count across each discipline. This exclusion criteria has been adopted in existing studies that have used the research use scale
(e.g. Cherney & McGee; Landry, Amara and Lamari 2001b) and is based on a linear understanding of research utilization. Table 3 did not have this same restriction from one stage to the next, i.e. if you don’t pass stage 1 you can’t progress to stage 2 and represents a more non-linear representation of research utilization. Hence both figure 1 and table 3 are based in different assumptions. viii
We are unable to explore the impact of sub-disciplines because we only asked respondents to identity their main research discipline in the academic survey. ix We would like to thank one of the referees for drawing our attention to this issue.
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