knowledge transfer literature review

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• v.85(4); 2007 Dec

Knowledge Transfer and Exchange: Review and Synthesis of the Literature

Knowledge transfer and exchange (KTE) is as an interactive process involving the interchange of knowledge between research users and researcher producers. Despite many strategies for KTE, it is not clear which ones should be used in which contexts. This article is a review and synthesis of the KTE literature on health care policy. The review examined and summarized KTE's current evidence base for KTE. It found that about 20 percent of the studies reported on a real-world application of a KTE strategy, and fewer had been formally evaluated. At this time there is an inadequate evidence base for doing “evidence-based” KTE for health policy decision making. Either KTE must be reconceptualized, or strategies must be evaluated more rigorously to produce a richer evidence base for future activity.

Knowledge transfer and exchange (KTE) is an interactive interchange of knowledge between research users and researcher producers ( Kiefer et al. 2005 ). The primary purposes of KTE are to increase the likelihood that research evidence will be used in policy and practice decisions and to enable researchers to identify practice and policy-relevant research questions. Even though there are many strategies for KTE, it currently is not clear which ones should be used in which contexts ( Lavis et al. 2003a ). To date, the complete literature on KTE as it pertains to health policy has not been reviewed in a single study. One reason is the challenges in adequately defining KTE across different literatures that tend to use varying terminology in articulating the underlying concept of information and evidence exchange between researchers and health policy decision makers. Accordingly, no summary of the current evidence regarding KTE strategy effectiveness in relation to health policy is available.

Our review was the first part of a larger study designed to find evidence-based KTE practices to inform the design of a specific KTE platform for a series of research projects referred to collectively as the “Alberta Depression Initiative” (ADI). This article reports on the review's findings. Another, separate article reports on the findings of a series of key informant interviews on KTE issues relevant to the ADI research program and also outlines the implications of both the review and interview findings for KTE in research programs like the ADI.

With the growing demands on health care resources and a general culture of accountability, greater emphasis is being placed on generating knowledge that can have a practical impact on the health system ( Lomas 1997 ). To this end, “knowledge transfer” emerged in the 1990s as a process by which research messages were “pushed” by the producers of research to the users of research ( Lavis et al. 2003b ). More recently, “knowledge exchange” emerged as a result of growing evidence that the successful uptake of knowledge requires more than one-way communication, instead requiring genuine interaction among researchers, decision makers, and other stakeholders ( Lavis et al. 2003b ).

While the value of and need for KTE has received wide support, both researchers and decision makers also acknowledge that they are driven by demands that may not be conducive to successful KTE. For researchers, these demands include challenges such as adapting the research cycle to fit real-world timelines, establishing relationships with decision makers, and justifying activities that fit poorly with traditional academic performance expectations ( CHSRF 1999 ). A perceived lack of knowledge of the research process, the traditional academic format of communication, research that is not relevant to practice-based issues, and a lack of timely results are often cited by those charged with making policy decisions as being barriers to using research findings ( CHSRF 1999 ). Both parties also frequently lament the lack of time and resources to participate in KTE.

Noting these challenges, a variety of mechanisms to facilitate KTE have been proposed, such as joint researcher–decision maker workshops, the inclusion of decision makers in the research process as part of interdisciplinary research teams, a collaborative definition of research questions, and the use of intermediaries that understand both roles known as “knowledge brokers” ( CHSRF 1999 ). In addition, interpersonal contact between researchers and decision makers is an oft-cited fundamental ingredient in successful KTE initiatives ( Thompson, Estabrooks, and Degner 2006 ). To date, however, “gold standard” approaches to KTE seem to be based, at best, on anecdotal evidence but mostly on experience and even rhetoric rather than on rigorous evidence. Our primary aim for this review was to examine and summarize the current evidence base for KTE in relation to health policy, resulting in an evidence-based resource for planning KTE processes.

We based our review on adaptations of systematic review methods used commonly for clinical research questions. In our case, we used a review process to address questions at the health policy level. Our methods were intended to be transparent, to include appraisal and validation steps in accordance with the principle of replicability but also to involve comparative and thematic synthesis rather than quantitative analysis, and to use gray literature sources to illuminate contextual issues identified from peer-reviewed studies ( Adair et al. 2006 ; Lavis et al. 2004 ).

Our literature review had four steps: (1) searching for abstracts, (2) selecting articles for inclusion through a relevancy rating process, (3) classifying and rating the selected articles, and (4) synthesizing and validating them. The steps of the review are shown in appendix A . Our initial goals were to ensure a broad capture of a relatively new and poorly defined field and then to identify a final set of the highest-quality and most relevant articles through a consensus screening of abstracts and a selection of articles.

The principal investigator and a medical research librarian developed and ran the search strategy in January 2006. They searched eight databases for English-language abstracts from 1997 to 2005: Medline, EMBASE, Cinahl, PsycINFO, EconLit, the Cochrane Database of Systematic Reviews, sociological abstracts, and social sciences abstracts. The gray literature also was reviewed, including the University of York HTA database, University of Laval KUUC database, New York Academy of Medicine Gray Literature Reports, and ABI Inform (ProQuest dissertations and theses).

We then reviewed reference lists in the identified papers and reports, as well as publication lists of international research centers and researchers known to us to have an interest in KTE. The primary search terms looked for were (the following and variations of) knowledge generation, knowledge translation, knowledge transfer, knowledge uptake, knowledge exchange, knowledge broker, and knowledge mobilization. We also used substitutes for knowledge, such as evidence, information, and data. Our focus in the review was on studies of KTE that could have either an impact on or implications for health care policies at an organizational, regional, provincial, and/or federal level. We attempted to keep the diffusion of innovation literature separate (e.g., Greenhalgh et al. 2004 ; Rogers 1995 ), although of course in some cases, the two literatures overlapped. Our strategy also resulted in some but not extensive overlap with “implementation research,” which has as its focus the set of activities created to carry out a given program (e.g., Fixsen et al. 2005 ). In this sense, the KTE literature can be viewed as a subset of a more broadly defined notion of implementation research that includes activities with practitioners, consumers, and policymakers but also has a greater focus on “transfer” then on information “exchange.”

The initial search yielded 4,250 abstracts. The research team drafted a relevancy criteria statement, tested it on a subset of one hundred abstracts, and discussed with the reviewers the differences in interpretation. They reached a high level of agreement (kappa = 0.78), indicating both clarity and consistency in the researchers' understanding. They talked about the discrepancies and settled on a final relevancy definition that they applied to the abstracts. The team's operational definition was “research conducting/implementing KTE and evaluating KTE between researchers and policy and decision makers.” We specifically excluded publications reflecting exchanges between researchers and clinicians, between providers, or between providers and consumers. One research team member screened the remainder of the identified abstracts and retrieved 150 full articles. A subsequent review of reference lists and KTE websites and research centers found an additional nineteen peer-reviewed articles.

We reviewed these 169 papers and assessed their relevancy, resulting in eighty-one studies that we sorted into implementation studies (i.e., the implementation of specific KTE strategies or evaluations of KTE approaches, n = 18) and nonimplementation papers and reports (i.e., reviews, commentaries, and surveys of relevant stakeholders pertaining to KTE but not reporting on implementation of an actual KTE strategy, n = 63). We then gave these studies a quality rating, using a fifteen-point scale for implementation studies that separately assessed the quality of the literature review, research design, data collection, analysis, and reporting of results, and a ten-point scale for the nonimplementation papers that qualitatively assessed the fit of the paper into the context of the literature, including the date of the paper, journal, and evidence of critical thought (see appendix B ). Two members of the team rated a subset of articles ( n = 20), resulting in a high level of agreement (kappa = 0.78). Discrepancies were discussed, and a consensus was reached in all cases. One member of the research team then rated the remaining papers. In order to limit the pool of studies to those perceived to be of higher quality, we had decided earlier to include only those studies that had an overall score higher than 7/10 or 10/15 on the respective rating scale. The first two subsections of the results focuses on these “higher-rated” studies.

After this, we identified a number of relevant, nonduplicative reports from the gray literature following the preceding strategies, noting that we continued to review all relevant gray literature reports until spring 2007. These reports were not formally rated as the peer-reviewed literature was, and we used them solely to supplement information that did not appear elsewhere. As such, we included information that in our view was (1) a novel addition to the peer-reviewed literature and (2) made a substantial contribution to the knowledge base of KTE as a whole. Key messages from these reports are outlined in the third section of the results.

As indicated, of the eighty-one papers that were quality rated, sixty-three were classified as nonimplementation studies. That is, they were opinion pieces, reviews, or surveys of stakeholders concerning KTE issues. Conversely, slightly more than 20 percent ( n = 18) of the studies reported on a real-world application of a KTE strategy. About 70 percent ( n = 56 of 81) were published from 2003 to 2005, with the remaining ( n = 25) published from 1997 to 2002, suggesting that the field is growing in interest and importance. Overall, thirty-four (of 63) nonimplementation studies were scored 7/10 or greater, and ten (of 18) implementation studies were scored 10/15 or better. Of these “higher-rated” articles ( n = 44), the lead author was located in Canada in 55 percent ( n = 25 of 44) of the studies. The study originated from the United Kingdom or Europe in 23 percent ( n = 10 of 44) of the cases, while 11 percent ( n = 5 of 44) were from the United States, and four studies were from elsewhere. Four reports were identified in the gray literature that, in our view, provide substantial additional information about or insight into KTE. The remaining sections of this article are based on these forty-eight studies or reports, that is, thirty-four of sixty-three nonimplementation studies, ten of eighteen implementation studies, and four gray literature reports.

We also should note the influence of the Canadian Health Services Research Foundation (CHSRF) on our review. Over the last decade, CHSRF has promulgated the use of the terms knowledge transfer and knowledge transfer and exchange . This use, paralleled by many prominent Canadian researchers, some with links to the CHSRF, has resulted in a preponderance of KTE-labeled papers in Canada. As such, on one level, the CHSRF's “marketing” of KTE evidently has been remarkably effective. On another level, however, as is described in the following sections in some detail, without an evaluation of KTE, the CHSRF's efforts may have been premature.

Nonimplementation Studies

The nonimplementation literature identified four major themes: (1) organizing frameworks for applying KTE strategies, (2) barriers and facilitators to KTE, (3) methods and issues for measuring the impact of research studies, and (4) perspectives from different stakeholder groups on what works and what does not work with respect to KTE. These themes were identified on the basis of the highest frequency of appearance in the literature as well as, in our view, the greatest importance for making decisions about the development of KTE strategies and how best to implement them.

Organizing Frameworks for Applying KTE Strategies

We chose five frameworks that had been developed to guide KTE initiatives, which are summarized in table 1 . Dobbins and colleagues (2002) proposed a framework that uses Rogers's Diffusion of Innovations theory (1995) to illustrate the adoption of research into clinical and policy decision making. In a review of the use of research in policymaking, Hanney and colleagues (2003) explored the factors enhancing this use, emphasizing the importance of actions at the interfaces between research producers and users while at the same time highlighting the relevance of “receptor capacity.” Such interaction should occur at various stages in the research process, for example, setting priorities, commissioning research, and communicating findings. Ebener and colleagues (2006) proposed using a knowledge map, or visual association of items, that includes both the knowledge type (i.e., what, how, why, where, and who) and the recipient (i.e., individual, group, organization, or network). This information is similar to components in Lavis and colleagues' (2003a) framework, which recommends five elements to consider when organizing KTE: message, target audience, messenger, knowledge transfer process and support system, and evaluation strategy. In regard to the fifth step, an objective for policymakers would be to inform debate, which is often more realistic than the objective to change decision-making outcomes. Finally, Jacobson, Butterill, and Goering (2003) developed a framework to increase researchers' familiarity with the intended user groups and context.

Organizing Frameworks for KTE Application

Barriers and Facilitators

The barriers and facilitators for KTE are well recognized as a result of dozens of studies and perhaps are the most frequently addressed topic area in the KTE literature on health policy decision making. These factors can be classified on individual and organizational levels and pertain to relationships between researchers and decision makers, modes of communication, time and timing, and context. Table 2 summarizes these factors. Owing to the attention paid to them in the literature, we discuss a number of the studies and concepts here in more detail.

Main KTE Barriers and Facilitators

In Norway, Innvaer and colleagues (2002) systematically reviewed twenty-four surveys of facilitators and barriers to the use of research evidence by health policymakers. The most frequently reported facilitators were personal contact between researchers and policymakers, clear summaries of findings with recommendations for action, good-quality research, and research that included effectiveness data. Other studies have also supported the use of face-to-face encounters as being key to KTE ( Greer 1988 ; Jacobson, Butterill, and Goering 2003 ; Lomas 2000a ; Roos and Shapiro 1999 ; Soumerai and Avorn 1990 ; Stocking 1985 ).

In a participatory evaluation of Manitoba's The Need to Know project, Bowen, Martens, and the Manitoba Need to Know Team (2005) interviewed community partners to identify the characteristics of effective KTE and found that the most important factors were based on relationships. The quality of relationships and the trust developed between the research partners were critical components. The mutual mistrust between policymakers and researchers has been noted elsewhere as a barrier to the use of research ( Choi et al. 2005 ; Trostle, Bronfman, and Langer 1999 ).

In their examination of pharmaceutical policymaking, Willison and MacLeod (1999) suggested that to improve the use of research, researchers must first decide who their audience is. Similar to what Lavis and colleagues (2003a) recommended, Willison and MacLeod emphasized that each audience has different information needs and communication styles and therefore the information must be appropriately tailored. Research should be presented in summary format, in simple language, and with clearly worded recommendations ( Reimer, Sawka, and James 2005 ; Willison and MacLeod 1999 ). Acceptable evidence for decision makers can be less rigorous than that for researchers and includes gray literature (i.e., government publications, consultants' reports, monographs, and conference proceedings) ( Hennink and Stephenson 2005 ; Weatherly, Drummond, and Smith 2002 ). One study noted that decision makers persistently valued experience more than they did research ( Trostle, Bronfman, and Langer 1999 ).

Another frequently recommended facilitator is the inclusion of key individuals, either decision makers or opinion leaders, in the research planning and design stages ( DeRoeck 2004 ; Lomas 2000b ; Ross et al. 2003 ; Vingilis et al. 2003 ; Whitehead et al. 2004 ; Willison and MacLeod 1999 ). Timeliness and the relevance of research also are important ( Dobbins et al. 2001 ; Frenk 1992 ; Hemsley-Brown 2004 ; Hennink and Stephenson 2005 ; Jacobson, Butterill, and Goering 2004 ; Mubyazi and Gonzalez-Block 2005 ; Stewart et al. 2005 ; Trostle, Bronfman, and Langer 1999 ). Since researchers tend to have longer time horizons than decision makers do, Willison and Macleod (1999) suggested that shorter-term objectives be included to address policymakers' needs. British researchers noted the potential for a “sleeper effect,” in which evidence is stored and not used until a more encouraging political climate develops ( Whitehead et al. 2004 ). Similarly, Martens and Roos (2005) referred to the importance of keeping information on hand until a favorable context prevails, a notion also alluded to by Roos and Shapiro (1999) regarding research on the use of prenatal care and the delivery of mental health services in Manitoba.

Bogenschneider and colleagues (2003) suggested that seminar series with different stakeholder groups be used to facilitate the exchange. The EUR–ASSESS project concluded that personal contact with policy staff was more effective than printed material ( Granados et al. 1997 ). This conclusion coincided with reviews by Grimshaw, Eccles, and Tetroe (2004) and Grimshaw and colleagues (2001) , which examined interventions used to influence the uptake of knowledge to change clinical practice. Educational outreach visits and interactive meetings were generally effective, and printed material and didactic meetings were the least effective. Although these reviews reveal important dissemination activities, on the surface there appears to be limited evidence regarding specifically how strategies should be applied to different stakeholder groups.

Although systematic reviews can inform policymaking ( Dobbins et al. 2001 ; Lavis et al. 2004 ; Lavis et al. 2006 ), it also is clear that factors aside from “evidence” (as traditionally defined by researchers) affect decision making. Evidence seldom has a rationally linear impact, given the complexity of the decision-making context ( Whiteford 2001 ). For example, both Frenk (1992) and Lomas (2000b) noted the importance of formal and informal institutional structures for decision making. This can include the distribution of responsibility and accountability as well as the roles of interest groups and policy networks in determining what information will be used according to particular values.

The researcher incentive system in universities also has been cited as a barrier. Fraser (2004) commented that the current professional incentive system (i.e., including publishing in peer-reviewed journals and acquiring grants for academic, as opposed to applied or translational research) is “diametrically opposed” to the needs of potential research users. Researchers, of course, are acutely aware of this challenge and may find themselves asking, with no clear answer, Whose responsibility is KTE? and Who will fund these KTE activities?

Waddell and colleagues (2005) examined the use of research in the context of competing influences on the Canadian policy process. Although policymakers used and valued research evidence, they also described three prevailing influences on the policy process that Waddell and colleagues termed inherent ambiguity , institutional constraints , and competing interests . When describing the process of policymaking, one interviewee commented that “facts and logic aren't deciding factors given that decision makers are faced with an immense amount of competing information on an immense range of subjects.” Institutional constraints include fragmentation across state and local levels of government, as well as across the health, education, social service, and justice sectors.

One recently proposed mechanism to facilitate KTE between researchers and decision makers is a knowledge broker, who is trained specifically in information exchange and has set aside time for the process. Vingilis and colleagues (2003) used the term connector to refer to people who help potential knowledge users determine their knowledge needs and help researchers translate, influence, and initiate KTE. Research on the use of knowledge brokers has been limited, however, and has prompted calls to examine the costs and benefits of this KTE strategy ( Pyra 2003 ) and also the quality of information resulting from knowledge broker–based KTE initiatives ( CHSRF 2000 ).

Finally, the use of health services research in policymaking may be enhanced by a government culture that nurtures an interest in and the value of research ( Bowen, Martens, and the Manitoba Need to Know Team 2005 ; Hennink and Stephenson 2005 ; Roos and Shapiro 1999 ; Whitehead et al. 2004 ).

Learning organizations move beyond employee training into organizational problem solving, innovation, and learning ( Stinson, Pearson, and Lucas 2006 ). This means graduating from simply updating a few practices or implementing initiatives to changing the organization's culture to instill the value of mutual learning ( Dowd 1999 ).

Measuring the Impact of the Research

Research organizations and funders are increasingly recognizing the importance of measuring the impact of health research on policies and practices. Documentary analysis, in-depth interviews, and questionnaires have been used to assess the impacts and outcomes of research knowledge. Several researchers have attempted to measure or score the impact of research on the development of public policy. In an exploratory study examining the role of health services research in Canadian provincial policymaking, Lavis and colleagues (2002) interviewed policymakers in Ontario and Saskatchewan. These interviews highlighted three avenues to the use of citable research: (1) the policymakers read printed material; (2) the policymakers interacted with the researchers; and (3) the researchers were involved with the working groups.

Landry, Amara, and Lamari (2001) looked at the use of social science research by interviewing Canadian university faculty members. They defined the use of research as a six-stage cumulative process from transmission to application, with intermediate stages of cognition, reference, adoption, and influence. Nearly half the respondents indicated that they transmitted findings to practitioners, professionals, and decision makers. However, when moving through the six stages, there was a marked increase in research findings rarely or never used. The most important determinants of utilization were the mechanisms linking researchers to users and the users' context. Knowledge utilization depended more heavily on factors regarding the behavior of the researchers and the users' context, or receptive capacity, than on attributes of the research products themselves ( Landry, Amara, and Lamari 2001 ).

Lavis and colleagues (2003b) devised an assessment tool for funders and research organizations to measure the impact of research. They described the following stages: (1) identify target audiences for research knowledge, (2) select appropriate categories of measures (e.g., producer–push, user–pull, or exchange measures), (3) select measures given resources and constraints, and (4) identify the data sources and/or collect new data, analyzing whether and, if so, how research knowledge was used in decision making. They recommended intermediate outcome measures such as whether a policy changed if resources were available to conduct case studies determining whether knowledge was used in the context of competing influences on decision-making processes.

In relation to this last point, examining how knowledge is used moves beyond whether it was used. Almost three decades ago, research knowledge was identified as being used in one of three ways: instrumental , conceptual , or symbolic ( Weiss 1979 ). An instrumental use is research knowledge that directly shapes policies and results in action; a conceptual use refers to a change in awareness or understanding of certain issues; and a symbolic use merely legitimizes existing policies or positions.

Finally, von Lengerke et al. (2004) suggested that health promotion policy using public health research is associated with the policy's impact if both strong social strategies and the political will to support a given policy are present at the same time. To test this assumption, they analyzed data from a survey of policymakers concerning four prevention and health promotion policies in six European countries. These authors found that research use was positively associated with policy output (i.e., the implementation of programs) and outcome (i.e., effectiveness) in contexts in which political interference was minimal.

Stakeholders' Perspectives

The fourth main theme identified in the nonimplementation studies has to do with perspectives on the relationship between KTE stakeholders and the potential effectiveness of KTE strategies. Table 3 outlines six studies from our review and the perspectives offered. While some of the key points raised can be found elsewhere in the KTE literature (e.g., the importance of relationship building and rapport between the researcher and decision maker), the main issue here is that different stakeholders across different contexts came to similar conclusions.

Stakeholders' Perspectives on KTE Relationships

Implementation Studies

Our review identified eighteen studies in which a specific KTE mechanism was employed or implemented, and ten of these were rated at 10/15 or higher on our quality index. As table 4 shows, there are numerous approaches to KTE. The focus of many of these interventions is on generating two-way communication, which is not surprising given the emphasis on this in the nonimplementation literature. The notion of a phased intervention to build relationships followed by facilitated meetings also arose.

Key KTE Strategies Identified in the Literature

The ten implementation studies synthesized here varied in topic area and context: five focused on health promotion and prevention; two looked at workplace health safety; and the remaining three involved mental health, child health, and cancer pain management, respectively (see table 5 ). The message communicators included researchers, decision makers, and knowledge brokers, and three studies did not specify the communicator. In all these studies, the amount of information available to assess the given KTE strategy varied widely.

Summary of KTE Implementation Studies Identified in the Literature

For example, some studies used more rigorous designs and/or based their assessment on predefined outcome measures. Kothari, Birch, and Charles (2005) used a quasi-experimental study design (i.e., one that had a comparison group) and qualitative methods in determining whether the uptake of information contained in a research report hinged on being involved in developing the report itself. Outcomes measured were the decision maker's understanding of the analysis and intent to use the research findings. Their findings suggested that in the study's time frame, interaction with the report was not associated with the decision makers' greater use. Kramer and Cole's (2003) multiple-case study applied qualitative methods to determine the impact of KTE based on a set of predefined outcome measures. In this study, the mode of communication was a plain-language booklet entitled the “Participative Ergonomic Blueprint.” The “Blueprint” is a facilitator's guide to implementing a successful participative ergonomics program as part of an employer's health and safety program. Finally, the work by Robinson and colleagues (2005) also included a wide range of outcome measures. The challenge here is that the use of multiple linking activities and outcomes makes it difficult for the reader to discern the individual impact of any one KTE strategy. The remaining studies were a mix of posttest studies, case studies, and case reports offering varying levels of evidence with respect to the strategy's effectiveness.

These studies also examined the presence of a formal, planned evaluation of the KTE strategy. From the information reported in these ten studies, only five seemed to have intended to formally evaluate their KTE strategies in advance, and an even smaller set ( n = 3), as alluded to earlier, had clearly defined outcome measures. It is also notable that not a single randomized controlled study of KTE was identified. While some studies reported observations of or reflections on the impact of the KTE strategy, generally these observations were not based on a formal evaluation including a research study design with already identified outcome measures. Of course, the intent of these studies was not necessarily to evaluate the KTE activity; in most cases, the emphasis was on the description of the transfer and exchange of information itself. However, owing to the lack of rigorous evaluation, there is little foundation for transferring findings from these studies to other or even similar contexts.

In short, based on these studies, we did not find an “off the shelf” set of recommendations for developing and implementing KTE strategies. This difficulty is due in part to the relatively small number of implementation studies across fields in health care and also to the even less formal and/or rigorous evaluation of these strategies.

Gray Literature

We included the gray literature in this review to supplement our findings from the peer-reviewed papers. We decided that four reports made a significant contribution to the literature being studied, and they are summarized in table 6 . The fourth report listed is particularly important, as it is the only study in our entire review that used a randomized control trial (RCT) design to assess KTE strategies. 1 Dobbins and colleagues (2007) conducted an RCT to test the effectiveness of KTE strategies in Canadian public health decision making on programs related to the promotion of physical activity and healthy body weight in children. The three progressively more active interventions were access to an online registry of systematic reviews evaluating public health interventions, targeted evidence messages, and knowledge brokering. The targeted messaging was significantly more effective in promoting evidence-informed decision making compared with the website and knowledge-brokering groups. The extent to which decision makers in public health organizations valued research evidence affected the knowledge-brokering activity. Knowledge brokering was more effective in those organizations that placed less value on research evidence and was less effective in those organizations that already recognized the importance of evidence-based decision making.

Summary of Gray Literature Reports

Note: a This study was presented at a conference but not yet published at the time of the review; and it was identified after the initial search.

The bulk of the literature on KTE in regard to health policy pertains to barriers and facilitators to implementation, as well as frameworks that can be used to organize and design KTE strategies, perspectives of KTE from various stakeholder groups, and ways to measure the impact of research on health policy. A smaller subset of the literature pertains to the implementation of KTE strategies for health policy decision making. Overall, these studies have undergone only limited evaluation, and for those that have been evaluated, generalizing findings to other contexts is extremely difficult.

Some of the key messages in the nonimplementation papers, which clearly reiterate Innvaer and colleagues' (2002) findings, are the importance of having personal contacts and building trust through quality relationships over time, in order to have a genuine exchange of information that results in some form of change. It also is clear that no one size fits all, with specific messages needing to be tailored to each audience. That said, what is not as clear from the literature is what works in what contexts and even where the responsibility for KTE rests. In addition, important advances have been made in measuring the impact of research ( Allen Consulting Group 2005 ; Kuruvilla et al. 2006 ). As a recent report from the Canadian Institutes of Health Research ( CIHR 2005 ) pointed out, indicators of the effect of health research are building capacity for training, informing policy, offering health benefits such as life expectancy and quality of life, and providing key economic benefits such as workplace productivity. This broader view of outcomes fits nicely with the perspectives of understanding organizational change and policy development more broadly and, as discussed later, in examining this question by drawing on information across multiple disciplines ( Greenhalgh et al. 2004 ).

Combining these insights with those from the implementation studies, the major finding of this review is that despite the rhetoric and growing perception in health services research circles of the “value” of KTE, there is actually very little evidence that can adequately inform what KTE strategies work in what contexts. Of course, existing studies can provide insight into KTE activity, but in our view, with the current state of the literature, there is insufficient evidence for conducting “evidence-based” KTE for health policy decision making.

This conclusion may explain why researchers and research funders have recently produced informative papers based on years of experience with KTE (e.g., Lomas 2007 ). As most would likely acknowledge, this experience falls short, however, in meeting the criteria as research evidence. We do not mean to suggest that those in the KTE field do not have important insights; rather, we are drawing a parallel to how many health services researchers have been critical of clinicians for drawing conclusions from case series reports, particularly since the advent of the evidence-based medicine movement. Viewed in this light, one call from our findings is the need for the greater application of formal and rigorous research designs to assess and evaluate the success of KTE strategies in specific contexts. Reporting on aspects of KTE such as barriers and facilitators does reveal the relevant issues to those interested in an interchange between researchers and decision makers. But in the end, primary research on KTE itself would be required in order to produce the evidence necessary to decide how best to allocate dedicated KTE resources.

Another of our findings is that KTE, at least as conceptualized to date, simply does not fit with the underlying politics of health policymaking. That is, noting limited rigorous implementation and evaluation of KTE strategies at the policy level, could it be that the concept of KTE in this context has been inappropriately transferred from clinical decision making? For the positivist, cause-and-effect translates in this context into knowledge that is transferred between researcher and decision maker in order to influence change. Of course, many scholars have recognized the many factors resulting in a particular action at the policy level (e.g., Mitton and Patten 2004 ; Whiteford 2001 ) and, indeed, the importance of context in knowledge utilization ( Dobrow et al. 2006 ). But more fundamentally, as Lewis (2007) pointed out, evidence-based decision making has not had the intended effect due to a myriad of reasons, not the least of which is that decisions at the policy level do not fit into neat little boxes that can be informed by technically oriented inputs. As such, it is important to ask whether those on the “KTE path” will ever be able to make significant strides forward. One may argue that KTE strategies need to be evaluated and refined, but the complexities of real-world policymaking and the misalignment between the evidence producers and the decision makers suggest that other literatures and disciplines, and indeed, other ways of thinking need to be given greater weight in these discussions.

Noting these arguments, introducing an evaluation component into KTE exercises, as has been called for elsewhere ( Pyra 2003 ), should be even more important, as it is not only the refinement but, perhaps more fundamentally, the justification of KTE that may be required. For example, it may be more beneficial to conduct an evaluation based on whether and how policy was informed, rather than simply the extent to which research was used ( Eager et al. 2003 ; Lavis et al. 2003a ). If there is to be a demonstrable impact, researchers must learn about the challenges and environment in which decision makers operate and determine how to present the information in a manner appropriate to the real-world environment ( Aaserud et al. 2005 ). But if there is consistently “no impact” through rigorous KTE endeavors across different contexts, it may be that KTE should not continue to be pursued. At this point, we do know, as stated by Lavis and colleagues (2003a, p. 240) in their survey of research organizations mentioned earlier, that “the directors [in funding organizations] were remarkably frank about not evaluating their knowledge transfer activities.”

If KTE activities continue to be pursued, then one way to conceptualize development, implementation, and evaluation is to consider what is under the control of the researchers and what falls under the influence of the policymakers. While this may magnify the perceived gap between these two main stakeholder groups, it can also serve to highlight which aspects of KTE should receive research grant funding and which contextual factors need to be addressed to the decision maker receiving the information. Similarly, much more effort is needed to articulate how knowledge is best transferred from decision makers to researchers and who is responsible for ensuring that this interaction and ultimate exchange takes place. With limited time and the ever-increasing demands on both groups, it is difficult to justify expending resources on ineffective strategies that ultimately are outside the control of either or both parties.

In our view, funding bodies can play a major role here, not only in requiring that detailed KTE plans be incorporated into research grants, but also in funding innovative fellowships such as residency placements in academic and health service delivery organizations and in funding more primary research on the evaluation of specific KTE strategies. On this last point, funders should support more rigorous study designs of KTE strategies, perhaps as a component of larger programs of research that include multiple studies with subsequent designs building directly on previous work. On the other hand, as Greenhalgh and colleagues (2004) point out, controlling for potential confounders in organizational and systems-level research may not be the most prudent way forward. While our study does not speak directly to this debate, we do suggest that at a minimum funders consider allocating resources to KTE endeavors as a pursuit in itself rather than as an “add-on,” as is so often the case at present.

Our review has several limitations. First, our method of including and excluding studies in our review could be criticized. Agreement on inclusion is not the same as the “worthiness” of scientific endeavor but rather merely suggests that individual research team members agree that a given paper “fits” with a relevancy statement outlined for the review. Furthermore, we based our synthesis on studies that, in our view, were of higher quality, but again, we relied on our own values in these judgments. Similarly, the search terms we selected likely reflect our understanding of the topic and our own biases. The fact that more than half the articles retrieved were by Canadian authors may suggest that we have brought a specific lens to this review that excluded other important and related works. Nonetheless, the review was intended to describe how KTE and closely related concepts are currently used, and it did not investigate broader notions such as knowledge diffusion, implementation research, or policy decision making. Nor was our intent to summarize the evolution of related theory. Thus we would argue that the review did address the field of KTE but does not suggest that others who label their work differently have not made important contributions.

Although KTE is not a new concept, it seems to be growing more important. Nonetheless, KTE as a field of research is still in its infancy. It is not hard to find opinion pieces and anecdotal reports about how to use KTE, but the limited reporting of KTE implementation and the even more limited formal evaluation of it leave those wanting to develop their own KTE efforts at a loss for evidence-based strategies. Relationships and institutional knowledge are clearly important themes, as is quality interaction with a few individuals, as opposed to a mass barrage of information to many. Even though no one size fits all, what is needed is more work to inform the application of KTE strategies across contexts to enable evidence-based practice. In our view, the only way to reach this is to conduct primary research on KTE, rather then seeing KTE as an “add-on” to other projects.

Acknowledgments

This study was funded by the Alberta Depression Initiative, which is sponsored by the Government of Alberta (Alberta Health and Wellness) and administered by the Institute of Health Economics. Craig Mitton holds funding from the Michael Smith Foundation for Health Research and the Canada Research Chairs program. Scott Patten is a health scholar with the Alberta Heritage Foundation for Medical Research, and a fellow with the Institute of Health Economics. We are grateful for the assistance of Ms. Diane Lorenzetti, University of Calgary, in helping to develop and run the literature searches. We would also like to thank the editor and three reviewers for their insightful comments.

Steps in Literature Review

Quality Rating Sheets

Ref ID: ________

KTE Empirical Article Quality Rating Sheet

0 – not present or reported anywhere in the article

1 – present but low quality

2 – present and midrange quality

3 – present and high quality

________ 1. Literature Review: Directly related recent literature is reviewed and research gap(s) identified.

________ 2. Research Questions and Design: A priori research questions are stated, and hypotheses, a research purpose statement, and/or a general line of inquiry is outlined. A study design or research approach is articulated.

________ 3. Population and Sampling: The setting, target population, participants, and approach to sampling are outlined in detail.

________ 4. Data Collection and Capture: Key concepts/measures/variables are defined. A systematic approach to data collection is reported. Response or participation rate and/or completeness of information capture is reported.

________ 5. Analysis and Results Reporting: An approach to analysis and a plan to carry out that analysis is specified. Results are clear and comprehensive. Conclusions follow logically from findings.

________ /15 = Total Score

Ref ID: ________ KTE Nomempirical Article Quality Rating Sheet

Source: Adapted from Adair et al. 2006 .

1 Note that this study was not included in the implementation study section, as it was not found in the peer-reviewed literature at the time of our study.

• Aaserud M, Lewin S, Innvaer S, Paulsen E, Dehlgren A, Trommald M, Duley L, Zwarenstein M, Oxman A. Translating Research into Policy and Practice in Developing Countries: A Case Study of Magnesium Sulphate for Pre-eclampsia. BMC Health Services Research. 2005; 5 (68) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Adair CE, Simpson L, Casebeer A, Birdsell J, Hayden K, Lewis S. Performance Measurement in Healthcare—Part I: Concepts and Trends from a State of the Science Review. Healthcare Policy. 2006; 1 (4):85–104. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Allen Consulting Group. Canberra: Commonwealth of Australia; 2005. Measuring the Impact of Publicly Funded Research. Report to the Australian Government Department of Education, Science and Training. [ Google Scholar ]
• Birdsell J, Thornley R, Landry R, Estabrooks C, Mayan M. Edmonton, Alberta: Alberta Heritage Foundation for Medical Research; 2006. The Utilization of Health Research Results in Alberta. [ Google Scholar ]
• Bogenschneider K, Olsen JR, Linney KD, Mills J. Connecting Research and Policymaking: Implications for Theory and Practice from the Family Impact Seminars. Family Relations. 2003; 49 :327–39. [ Google Scholar ]
• Bowen S, Martens P Manitoba Need to Know Team. Demystifying Knowledge Translation: Learning from the Community. Journal of Health Services Research and Policy. 2005; 10 (4):203–11. [ PubMed ] [ Google Scholar ]
• Canadian Health Services Research Foundation (CHSRF) Issues in Linkage and Exchange between Researchers and Decision-Makers: Summary of workshop convened by CHSRF. 1999. May.
• Canadian Health Services Research Foundation (CHSRF) Health Services Research and … Evidence-Based Decision-Making. 2000. [accessed April 29, 2006]. Available at, http://www.chsrf.ca/knowledge_transfer/pdf/EBDM_e.pdf .
• Canadian Institutes of Health Research (CIHR) Developing a CIHR Framework to Measure the Impact of Health Research. 2005. [accessed October 22, 2007]. Available at, http://www.cihr-irsc.gc.ca/e/documents/meeting_synthesis_e.pdf .
• Canadian Institutes of Health Research (CIHR) Ottawa: 2006. Evidence in Action, Acting on Evidence. A Casebook of Health Services and Policy Research Knowledge Translation Stories. Her Majesty the Queen in Right of Canada. [ Google Scholar ]
• Choi B, Pang T, Lin V, Puska P, Sherman G, Goddard M, Ackland M, Sainsbury P, Stachenko S, Morrison H, Clottey C. Can Scientists and Policy Makers Work Together? Journal of Epidemiology and Community Health. 2005; 59 :632–37. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• DeRoeck D. The Importance of Engaging Policy-Makers at the Outset to Guide Research on and Introduction of Vaccines: The Use of Policy-Maker Surveys. Journal of Health Population Nutrition. 2004; 22 (3):322–30. [ PubMed ] [ Google Scholar ]
• Dobbins M, Cockerill R, Barnsley J, Ciliska D. Factors of the Innovation, Organization, Environment, and Individual That Predict the Influence That Five Systematic Reviews Had on Public Health Decisions. International Journal of Technology Assessment in Health Care. 2001; 17 (4):467–78. [ PubMed ] [ Google Scholar ]
• Dobbins M, Ciliska D, Cockerill R, Barnsley J, DiCenso A. A Framework for the Dissemination and Utilization of Research for Healthcare Policy and Practice. Online Journal of Knowledge Synthesis for Nursing. 2002; 9 (7) [ PubMed ] [ Google Scholar ]
• Dobbins M, DeCorby K, Robeson P, Cilisaka D, Thomas H, Hanna S, Manske S, Mercer S, O'Mara L, Cameron R. Ottawa: Canadian Cochrane Colloquium; 2007. The Power of Tailored Messaging: Preliminary Results from Canada's First Trial on Knowledge Brokering. February 12. [ Google Scholar ]
• Dobrow M, Goel V, Lemieux-Charles L, Black N. The Impact of Context on Evidence Utilization: A Framework for Expert Groups Developing Health Policy Recommendations. Social Science and Medicine. 2006; 63 :1811–24. [ PubMed ] [ Google Scholar ]
• Dowd JF. Learning Organizations: An Introduction. Managed Care Quarterly. 1999; 7 :43–50. [ PubMed ] [ Google Scholar ]
• Eager K, Cromwell D, Owen A, Senior K, Gordon R, Green J. Health Services Research and Development in Practice: An Australian Experience. Journal of Health Services and Research Policy. 2003; 8 (S2):7–13. [ PubMed ] [ Google Scholar ]
• Ebener S, Khan A, Shademani R, Compernolle L, Beltran M, Lansang M, Lippman M. Knowledge Mapping as a Technique to Support Knowledge Translation. Bulletin of the World Health Organization. 2006; 84 (8):636–42. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fixsen DL, Naoom SF, Blase KA, Friedman RM, Wallace F. Tampa: Louis de la Parte Florida Mental Health Institute, National Implementation Research Network, University of South Florida; 2005. Implementation Research: A Synthesis of the Literature. FMHI Publication no. 231. [ Google Scholar ]
• Fraser I. Organizational Research with Impact: Working Backwards. Worldviews on Evidence-Based Nursing. 2004; 1 (suppl.1):S52–S59. [ PubMed ] [ Google Scholar ]
• Frenk J. Balancing Relevance and Excellence: Organizational Responses to Link Research with Decision-Making. Social Science and Medicine. 1992; 35 (11):1397–1404. [ PubMed ] [ Google Scholar ]
• Goering P, Butterill D, Jacobson N, Sturtevant D. Linkage and Exchange at the Organizational Level: A Model of Collaboration between Research and Policy. Journal of Health Services and Research Policy. 2003; 8 (S2):14–19. [ PubMed ] [ Google Scholar ]
• Granados A, Jonsson E, Banta HD, Bero L, Bonair A, Cochet C. EUR–ASSESS Project Subgroup Report on Dissemination and Impact. International Journal of Technology Assessment in Health Care. 1997; 13 (2):220–86. [ PubMed ] [ Google Scholar ]
• Greenhalgh T, Robert G, MacFarlane F, Bate P, Kyriakidou O. Diffusion of Innovation in Service Organizations: Systematic Review and Recommendations. The Milbank Quarterly. 2004; 82 (4):581–629. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Greer A. The State of the Art versus the State of the Science: The Diffusion of New Medical Technologies into Practice. International Journal of Technology Assessment in Health Care. 1988; 4 :5–26. [ PubMed ] [ Google Scholar ]
• Grimshaw J, Eccles M, Tetroe J. Implementing Clinical Guidelines: Current Evidence and Future Implications. Journal of Continuing Education in the Health Professions. 2004; 24 (suppl. 1):S31–S37. [ PubMed ] [ Google Scholar ]
• Grimshaw J, Shirran RL, Thomas G, Mowatt C, Fraser L, Bero R, Grilli E, Harvey A, O'Brien MA. Changing Provider Behaviour: An Overview of Systematic Reviews of Interventions. Medical Care. 2001; 39 (suppl. 2):I12–I45. [ Google Scholar ]
• Hanney S, Gonzalez-Block M, Buxton M, Kogan M. The Utilization of Health Research in Policy-Making: Concepts, Examples and Methods of Assessment. Health Research Policy and Systems. 2003; 1 (2) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hemsley-Brown J. Facilitating Research Utilization: A Cross-Sector Review of Research Evidence. International Journal of Public Sector Management. 2004; 17 (6):534–52. [ Google Scholar ]
• Hennink M, Stephenson R. Using Research to Inform Health Policy: Barriers and Strategies in Developing Countries. Journal of Health Communication. 2005; 10 :163–80. [ PubMed ] [ Google Scholar ]
• Innvaer S, Vist G, Trommold M, Oxman A. Health Policy Makers' Perceptions of Their Use of Evidence: A Systematic Review. Journal of Health Services and Research Policy. 2002; 7 :239–44. [ PubMed ] [ Google Scholar ]
• Jacobson N, Butterill D, Goering P. Development of a Framework for Knowledge Translation: Understanding User Context. Journal of Health Services and Research Policy. 2003; 8 :94–99. [ PubMed ] [ Google Scholar ]
• Jacobson N, Butterill D, Goering P. Organizational Factors That Influence University-Based Researchers' Engagement in Knowledge Transfer Activity. Science Communication. 2004; 23 (3):246–59. [ Google Scholar ]
• Jacobson N, Butterill D, Goering P. Consulting as a Strategy for Knowledge Transfer. The Milbank Quarterly. 2005; 83 (2):299–321. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kiefer L, Frank J, Di Ruggerio E, Dobbins M, Manuel D, Gully P, Mowat D. Fostering Evidence-Based Decision-Making in Canada: Examining the Need for a Canadian Population and Public Health Evidence Centre and Research Network. Canadian Journal of Public Health. 2005; 96 :I-1–I-19. [ PubMed ] [ Google Scholar ]
• Kothari A, Birch S, Charles C. “Interaction” and Research Utilisation in Health Policies and Programs: Does It Work? Health Policy. 2005; 71 (1):117–25. [ PubMed ] [ Google Scholar ]
• Kramer DM, Cole DC. Sustained, Intensive Engagement to Promote Health and Safety Knowledge Transfer to and Utilization by Workplaces. Science Communication. 2003; 25 (1):56–82. [ Google Scholar ]
• Kramer DM, Wells RP. Achieving Buy-In: Building Networks to Facilitate Knowledge Transfer. Science Communication. 2005; 26 (4):428–44. [ Google Scholar ]
• Kuruvilla S, Mays N, Pleasant A, Walt G. Describing the Impact of Health Research: A Research Impact Framework. BMC Health Services Research. 2006; 6 :134. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Landry R, Amara N, Lamari M. Utilization of Social Science Research in Canada. Research Policy. 2001; 30 :333–49. [ Google Scholar ]
• Landry R, Lyons R, Amara N, Warner G, Ziam S, Halilem N, Keroack M. Quebec City: Université Laval, Atlantic Health Promotion Research Centre, Dalhousie University; 2006. Knowledge Translation: Planning Tools for Stroke Researchers. [ Google Scholar ]
• Lavis J, Lomas J, Hamid M, Sewankambo NK. Assessing Country-Level Efforts to Link Research to Action. Bulletin of the World Health Organization. 2006; 84 (8):620–28. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lavis J, Posada F, Haines A, Osei E. Use of Research to Inform Public Policymaking. The Lancet. 2004; 364 :1615–21. [ PubMed ] [ Google Scholar ]
• Lavis J, Robertson J, Woodside C, McLeod C, Abelson J. How Can Research Organizations More Effectively Transfer Research Knowledge to Decision Makers? The Milbank Quarterly. 2003a; 81 :221–48. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lavis J, Ross S, Hurley J, Hohenadel J, Stoddart G, Woodward C, Abelson J. Examining the Role of Health Services Research in Public Policymaking. The Milbank Quarterly. 2002; 80 (1):125–54. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lavis J, Ross S, McLeod C, Gildiner A. Measuring the Impact of Health Research. Journal of Health Services Research and Policy. 2003b; 8 :165–70. [ PubMed ] [ Google Scholar ]
• Lewis S. Toward a General Theory of Indifference to Research-Based Evidence. Journal of Health Services Research and Policy. 2007; 12 (3):166–72. [ PubMed ] [ Google Scholar ]
• Loiselle CG, Semenic S, Cote B. Sharing Empirical Knowledge to Improve Breastfeeding Promotion and Support: Description of a Research Dissemination Project. Worldviews on Evidence-Based Nursing. 2005; 2 (1):25–32. [ PubMed ] [ Google Scholar ]
• Lomas J. Hamilton, Ontario: McMaster University Centre for Health Economics and Policy Analysis; 1997. Improving Research Dissemination and Uptake in the Health Sector: Beyond the Sound of One Hand Clapping. Policy Commentary C97-1. [ Google Scholar ]
• Lomas J. Connecting Research and Policy. ISUMA: Canadian Journal of Policy Research. 2000a; 1 (1):140–44. [ Google Scholar ]
• Lomas J. Using “Linkage and Exchange” to Move Research into Policy at a Canadian Foundation. Health Affairs. 2000b; 19 (3):236. [ PubMed ] [ Google Scholar ]
• Lomas J. The In-Between World of Knowledge Brokering. British Medical Journal. 2007; 334 :129–32. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Martens P, Roos N. When Health Services Researchers and Policy Makers Interact: Tales from the Tectonic Plates. Healthcare Policy. 2005; 1 (1):72–84. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mitton C, Patten S. Evidence Based Priority Setting: What Do the Decision Makers Think? Journal of Health Services Research and Policy. 2004; 9 (3):146–52. [ PubMed ] [ Google Scholar ]
• Mubyazi GM, Gonzalez-Block MA. Research Influence on Antimalarial Drug Policy Change in Tanzania: Case Study of Replacing Chloroquine with Sulfadoxine-Pyrimethamine as the First-Line Drug. Malaria Journal. 2005; 20 (4):51. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Philip KL, Backett-Milburn K, Cunningham-Burley S, Davis JB. Practising What We Preach? A Practical Approach to Bringing Research, Policy and Practice Together in Relation to Children and Health Inequalities. Health Education Research. 2003; 18 (5):568–79. [ PubMed ] [ Google Scholar ]
• Pyra K. Knowledge Translation: A Review of the Literature: Prepared for the Nova Scotia Health Research Foundation. 2003. [accessed April 29, 2006]. Available at, http://www.nshrf.ca/AbsPage.aspx?siteid=1&lang=1&id=1059 .
• Rashiq S, Barton P, Harstall C, Schopflocher D, Taenzer P Alberta Ambassador Program Team. The Alberta Ambassador Program: Delivering Health Technology Assessment Results to Rural Practitioners. BMC Medical Education. 2006; 6 (21) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reimer B, Sawka E, James D. Improving Research Transfer in the Addictions Field: A Perspective from Canada. Substance Use and Misuse. 2005; 40 (11):1707–20. [ PubMed ] [ Google Scholar ]
• Robinson K, Elliott SJ, Driedger SM, Eyles J, O'Loughlin J, Riley B, Cameron R, Harvey D on behalf of the CHHDP Strategic and Research Advisory Groups. Using Linking Systems to Build Capacity and Enhance Dissemination in Heart Health Promotion: A Canadian Multiple-Case Study. Health Education Research. 2005; 20 (5):499–513. [ PubMed ] [ Google Scholar ]
• Rogers EM. Diffusion of Innovations. 4th ed. New York: Free Press; 1995. [ Google Scholar ]
• Roos NP, Shapiro E. From Research to Policy: What Have We Learned? Medical Care. 1999; 37 (suppl. 6):JS291–305. [ PubMed ] [ Google Scholar ]
• Ross S, Lavis J, Rodriguez C, Woodside J, Denis JL. Partnership Experiences: Involving Decision-Makers in the Research Process. Journal of Health Services Research and Policy. 2003; 8 (suppl. 2):26–34. [ PubMed ] [ Google Scholar ]
• Sibbald WJ, Kossuth J. The Ontario Health Care Evaluation Network and the Critical Care Research Network as Vehicles for Research Transfer. Medical Decision Making. 1998; 18 :9–16. [ PubMed ] [ Google Scholar ]
• Soumerai S, Avorn J. Principles of Educational Outreach (“Academic Detailing”) to Improve Clinical Decision-Making. Journal of the American Medical Association. 1990; 263 :549–56. [ PubMed ] [ Google Scholar ]
• Stewart R, Wiggins M, Thomas J, Oliver S, Brunton G, Ellison GTH. Exploring the Evidence–Practice Gap: A Workshop Report on Mixed and Participatory Training for HIV Prevention in Southern Africa. Education for Health. 2005; 18 (2):224–35. [ PubMed ] [ Google Scholar ]
• Stinson L, Pearson D, Lucas B. Developing a Learning Culture: Twelve Tips for Individuals, Teams and Organizations. Medical Teacher. 2006; 28 (4):309–12. [ PubMed ] [ Google Scholar ]
• Stocking B. Initiative and Inertia: Case Studies in the NHS. London: Nuffield Trust; 1985. [ Google Scholar ]
• Thompson GN, Estabrooks CA, Degner LF. Clarifying the Concepts in Knowledge Transfer: A Literature Review. Journal of Advanced Nursing. 2006; 53 (6):691–701. [ PubMed ] [ Google Scholar ]
• Trostle J, Bronfman M, Langer A. How Do Researchers Influence Decision-Makers? Case Studies of Mexican Policies. Health Policy and Planning. 1999; 14 (2):103–14. [ PubMed ] [ Google Scholar ]
• Vingilis E, Hartford K, Schrecker T, Mitchell B, Lent B, Bishop J. Integrating Knowledge Generation with Knowledge Diffusion and Utilization. Canadian Journal of Public Health. 2003; 94 (6):468–71. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• von Lengerke T, Rutten A, Vinck J, Abel T, Kannas L, Luschen G, Diaz J, van der Zee J. Research Utilization and the Impact of Health Promotion Policy. Soz. Praventivmed. 2004; 49 :185–97. [ PubMed ] [ Google Scholar ]
• Waddell C, Lavis J, Abelson J, Lomas J, Shepherd C, Bird-Gayson T, Giacomini M, Offord D. Research Use in Children's Mental Health Policy in Canada: Maintaining Vigilance amid Ambiguity. Social Science and Medicine. 2005; 61 :1649–57. [ PubMed ] [ Google Scholar ]
• Weatherly H, Drummond M, Smith D. Using Evidence in the Development of Local Health Policies: Some Evidence from the United Kingdom. International Journal of Technology Assessment in Health Care. 2002; 18 (4):771–81. [ PubMed ] [ Google Scholar ]
• Weiss C. The Many Meanings of Research Utilization. Public Administration Review. 1979; 39 (5):426–31. [ Google Scholar ]
• Whiteford H. Can Research Influence Mental Health Policy? Australian and New Zealand Journal of Psychiatry. 2001; 35 :428–34. [ PubMed ] [ Google Scholar ]
• Whitehead M, Petticrew M, Graham H, Macintyre S, Bambra C, Egan M. Evidence for Public Health Policy on Inequalities: 2: Assembling the Evidence Jigsaw. Journal of Epidemiology in Community Health. 2004; 58 :817–21. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Willison D, MacLeod S. The Role of Research Evidence in Pharmaceutical Policy-Making: Evidence When Necessary but Not Necessarily Evidence. Journal of Evaluation in Clinical Practice. 1999; 5 (2):243–49. [ PubMed ] [ Google Scholar ]

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Knowledge Transfer in Theory and Practice: A Guide to the Literature

A brief guide to the issues of Knowledge Transfer, taking into account the variety of terminology, methods, and subjects.

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• Published: 13 September 2021

Use and effectiveness of policy briefs as a knowledge transfer tool: a scoping review

• Diana Arnautu 1 &
• Christian Dagenais 1  

Humanities and Social Sciences Communications volume  8 , Article number:  211 ( 2021 ) Cite this article

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There is a significant gap between researchers’ production of evidence and its use by policymakers. Several knowledge transfer strategies have emerged in the past years to promote the use of research. One of those strategies is the policy brief; a short document synthesizing the results of one or multiple studies. This scoping study aims to identify the use and effectiveness of policy briefs as a knowledge transfer strategy. Twenty-two empirical articles were identified, spanning 35 countries. Results show that policy briefs are considered generally useful, credible and easy to understand. The type of audience is an essential component to consider when writing a policy brief. Introducing a policy brief sooner rather than later might have a bigger impact since it is more effective in creating a belief rather than changing one. The credibility of the policy brief’s author is also a factor taken into consideration by decision-makers. Further research needs to be done to evaluate the various forms of uses of policy briefs by decision-makers.

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Introduction

Improving well-being and reducing health-related inequalities is a challenging endeavor for public policymakers. They must consider the nature and significance of the issue, the proposed interventions and their pros and cons such as their impact and acceptability (Lavis et al., 2012 ; Mays et al., 2005 ). Policymakers are members of a government department, legislature or other organization responsible for devising new regulations and laws (Cambridge University Press, 2019 ). They face the challenge of finding the best solutions to multiple health-related crises while being the most time and cost-effective possible. Limited by time and smothered by an overwhelming amount of information, some policymakers are likely to use cognitive shortcuts by selecting the “evidence” most appropriate to their political leanings (Baekgaard et al., 2019 ; Cairney et al., 2019 ; Oliver and Cairney, 2019 ).

To prevent snap decisions in policymaking, it is essential to develop tools to facilitate the dissemination and use of empirical research. Evidence-informed solutions might be an effective way to address these complicated questions since they derive knowledge from accurate and robust evidence instead of beliefs and provide a more holistic view of a problem. Although it may be possible for different stakeholders to agree on certain matters, a consensus is uncommon (Nutley et al., 2013 ). Using research evidence allows policymakers to decrease their bias towards an intervention’s perceived effectiveness. This leads to more confidence among policymakers on what to expect from an intervention as their decisions are guided by evidence (Lavis et al., 2004 ). However, trying to integrate research findings into the policy-making process comes with a whole new set of challenges, both for researchers and policymakers.

Barriers to evidence-informed policy

Barriers to evidence-informed policy can be defined in three categories: the research evidence is not available in an accessible format for the policymaker, the evidence is disregarded for political or ideological reasons and the evidence is not applicable to the political context (Hawkins and Pakhurst, 2016 ; Uzochukwu et al., 2016 ).

The first category of barriers refers to the availability and the type of evidence. The vast amount of information policymakers need to keep up-to-date in specific fields is a particular challenge to this barrier, leading to policymakers being frequently overwhelmed with the amount of information they need to go through regarding each case (Orandi and Locke, 2020 ). Decision-makers have reported a lack of competencies in finding, evaluating, interpreting or using certain evidence such as systematic reviews in their decision-making, leading to difficulty in accessing these reviews and identifying the key messages quickly (Tricco et al., 2015 ). Although policymakers use a broader variety of forms of evidence than previously examined in the literature, scholars have rarely been consulted and research evidence has rarely been seen as directly applicable (Oliver and de Vocht, 2017 ). The lack of awareness on the importance of research evidence and on the ways to access these resources also contribute to the gap between research and policy (Oliver et al., 2014 ; van de Goor et al., 2017 ). Some other frequently reported barriers to evidence use in policymaking were the poor access to timely, quality and relevant research evidence as well as the limited collaboration between policymakers and researchers (Oliver et al., 2014 ; Uzochukwu et al., 2016 ; van de Goor et al., 2017 ). Given the fact that research is only one input amongst all the others that policymakers must consider in their decision, it is no surprise that policymakers may disregard research evidence in favor of other sources of information (Uzochukwu et al., 2016 ).

The second category of barriers refers to policymakers’ ideology regarding research evidence and the presence of biases. Resistance to change and a lack of willingness by some policymakers to use research are two factors present when attempting to bridge the gap. This could be explained by certain sub-cultures of policymaking that grants little importance to evidence-informed solutions or by certain policymakers prioritizing their own opinion when research findings go against their expectations or against current policy (Koon et al., 2013 ; Uzochukwu et al., 2016 ). Policymakers tend to interpret new information based on their past attitudes and beliefs, much like the general population (Baekgaard et al., 2019 ). It also does not seem to persuade policymakers with beliefs opposed to the evidence, rather it increases the effect of attitudes on the interpretation of information by policymakers (Baekgaard et al., 2019 ). This highlights the importance of finding methods to disseminate tailored evidence in a way that policymakers will be open to receive and consider (Cairney and Kwiatkowski, 2017 ).

The third category of barriers refers to the evidence produced not always being tailor-made for application in different contexts (Uzochukwu et al., 2016 ; WHO, 2004 ). Indeed, the political context is an undeniable factor in the use of evidence in policymaking. Political and institutional factors such as the level of state centralization and democratization, the influence of external organizations and donors, the organization of bureaucracies and the social norms and values, can all affect the use of evidence in policy (Liverani et al., 2013 ). The elaboration of new policies implies making choices between different priorities while taking into consideration the limited resources available (Hawkins and Pakhurst, 2016 ). The evidence of research can always be contested or balanced with the potential negative consequences of the intervention in another domain, such as a health-care intervention having larger consequences on the economy. Even if the effectiveness of an intervention can be proved beyond doubt, this given issue might not be a priority for decision-makers, or it might involve unrealistic resources that would rather be granted to other issues. Policymakers need to stay aware of the political priorities identified and the citizens they need to justify their decisions to. In this sense, politics and institutions are not a barrier to the use of research but rather they are the context under which evidence must respond to (Cairney and Kwiatkowski, 2017 ; Hawkins and Parkhurst, 2016 ).

Summaries to prevent information overload

A great deal of research evidence has been developed but not enough of it is being disseminated in effective ways (Oliver and Boaz, 2019 ). Offering a summary of research results in an accessible format could facilitate policy discussion and ultimately improve the use of research and help policymakers with their decisions (Arcury et al., 2017 ; Cairney and Kwiatkowski, 2017 ). In this age of information overload, when too much information is provided, one can have trouble discerning what is most important and make a decision. It is not unlikely that policymakers will, after a brief glance, discard a large amount of information given to them (Beynon et al., 2012 ; Yang et al., 2003 ). Decision-makers oftentimes criticize the length and overly dense contents of research documents (Dagenais and Ridde, 2018 ; Oliver et al., 2014 ). Hence, summaries of research results could increase the odds of decision-makers reading and therefore using the evidence proposed by researchers.

There are different methods to summarize research findings to provide facts and more detail for those involved in decision-making. For example, an infographic is an effective visual representation that explains information simply and quickly by using a combination of text and graphical symbols (Huang and Tan, 2007 ). Another type of research summary is the rapid review, a form of knowledge synthesis tailored and targeted to answer specific questions arising in “real world” policy or program environments (Moore et al., 2016 ; Wilson et al., 2015 ). They are oftentimes commissioned by people who would need scientific results to back up a decision. To produce the information in a timely manner, certain components of the systematic review process need to be simplified or omitted (Khangura et al., 2012 ). One study examining the use of 139 rapid reviews found that 89% of them had been used by commissioning agencies, on average up to three uses per review. Policymakers used those rapid reviews mostly to determine the details of a policy, to identify priorities and solutions for future action and communicate the information to stakeholders. However, rapid reviews might be susceptible to bias as a consequence of streamlining the systematic review process (Tricco et al., 2015 ). Also, policymakers may not always be able to commission a rapid review due to financial constraints.

Policy briefs as a knowledge transfer tool

Another approach to summarizing research, which is more focused on summarizing results for the use of policymakers, is the policy brief. There are multiple definitions to the policy brief (Dagenais et Ridde, 2018 ). However, in this article it will refer to a short document that uses graphics and text to summarize the key elements of one or multiple researches and provides a succinct explanation of a policy issue or problem, together with options and specific recommendations for addressing that issue or problem (Arcury et al., 2017 ; Keepnews, 2016 ).

The objective of a policy brief is to inform policymakers’ decisions or motivate action (Keepnews, 2016 ; Wong et al., 2017 ). Their resolve can be placed on a continuum going from “neutral”, meaning objective and nuanced information, to “interventionist”, which puts forwards solutions to the stated problem (Dagenais and Ridde, 2018 ). However, it is not an advocacy statement nor is it an opinion piece. A policy brief is analytic in nature and aims to remain objective and fact-based, even if the evidence is persuasive (Wong et al., 2017 ). A policy brief should include contextual and structural factors as a way to apply locally what was initially more general evidence (Rajabi, 2012 ).

What is known about format preferences

The format of policy briefs is just as important as the content when it comes to evidence use by policymakers. Decision-makers like concise documents that can be quickly examined and interpreted (Rajabi, 2012 ). Evidence should be understandable and user-friendly, as well as visually appealing and easy to access (Beynon et al., 2012 ; Marquez et al., 2018 ; Oliver et al., 2014 ). Tailoring the message to the targeted audience and ensuring the timing is appropriate are also two important factors in research communication. Indeed, the wording and contextualization of findings can have a noticeable impact on the use of those results (Langer et al., 2016 ). Policymakers also prefer documents written by expert opinions that is both simple and clear. It must be restricted to the information of interest and propose recommendations for action (Dagenais and Ridde, 2018 ; Cairney and Oliver, 2020 ).

In the case of a workshop, sending the policy brief in advance facilitates the use of its information (Dagenais and Ridde, 2018 ). The results tend to be considered further since the information will already have been acknowledged prior to the workshop, leaving enough time during for it to be discussed with other stakeholders. These findings are in line with Langer’s report ( 2016 ), which suggested that interventions using a combination of evidence use mechanisms, such as communication of the evidence and interactions between stakeholders, are associated with an increased probability of being successful.

Why policy briefs were chosen

In the interest of sharing key lessons from research more effectively, it is essential to improve communication tools aimed at decision-making environments (Oliver and Boaz, 2019 ). In recent years, policy briefs have seen an increase in use as a way to inform or influence decision-making (Tessier et al., 2019 ). The policy brief was the chosen scope in this study as it is the most commonly used term referring to information-packaging documents. Indeed, a study of the nomenclature used in information-packaging efforts to support evidence-informed policymaking in low to middle income countries determined that “policy brief” was the most frequently used label (39%) to describe such a document (Adam et al., 2014 ). However, there are many different terms related to such a synthesized document, including the technical note, policy note, evidence brief, evidence summary, research snapshot, etc. (Dagenais and Ridde, 2018 ). Although these different terms were searched, the term “policy briefs” will be used in this paper.

Furthermore, policy briefs are postulated as a less intimidating form of research synthesis for policymakers, as opposed to systematic reviews. They offer key information on a given subject based on a systematic yet limited search of the literature for the most important elements. The policy brief is a first step into evidence, leading to further questioning and reading rather than providing a definitive report of what works (Nutley et al., 2013 ).

How should evidence use be measured?

The idea that evidence should be used to inform decision-making, rather than to determine what should be done, leads to questioning the way that evidence use should be measured (Hawkins and Pakhurst, 2016 ). What constitutes good use of evidence does not necessarily lead to the recommendations being applied. A policymaker might read the evidence but ultimately decide not to apply the recommendations due to taking into consideration a series of other factors such as the interests of other stakeholders and the limited resources available (Oliver and Boaz, 2019 ).

While the evidence may not have been used in decision-making, it was still used to inform (Hawkins and Pakhurst, 2016 ). The term evidence- based policy, implying that decision-making should depend on the body of research found, has been transitioning in the last few years to evidence- informed policy (Oxman et al., 2009 ; Nutley et al., 2019 ). This change reflects a new perspective of looking at research communication processes rather than solely the results and impact of the evidence use on decision-making. It sheds light into the current issues characterizing the know-do gap while also recognizing the political nature of the decision-making process.

Therefore, as a guide to evaluate the use of evidence by decision-makers, the instrumental, conceptual and persuasive use of policy briefs by decision-makers will be used. This approach allows for a more holistic view of evidence use and to determine more specifically in which ways policymakers use research evidence

The instrumental use refers to the direct use of the policy brief in the decision-making process. The conceptual use refers to the use of the policy brief to better understand a problem or a situation. The symbolic, or persuasive use, refers to the use of the policy brief to confirm or justify a decision or a choice, which has already been made (Anderson et al., 1999 ). This framework is based on the idea that good use of evidence should not rely solely on the following decisions taken by policymakers, but also on the manner in which these decisions were taken and how the evidence was identified, interpreted and considered to better inform the parties involved (Hawkins and Pakhurst, 2016 ).

In policy contexts, instrumental use of research is relatively rare while conceptual and strategic use tend to be more common (Boaz et al., 2018 ). However, evidence on the use and effectiveness of policy briefs more specifically as a knowledge transfer tool remains unclear. Previous reviews, such as Petkovic et al., ( 2016 ), have researched the use of systematic review summaries in decision-making and the policy-maker’s perspective towards the summaries in terms of understanding, knowledge and beliefs. Other articles have studied the barriers and facilitators to policymakers using systematic review summaries (Oliver et al., 2014 ; Tricco et al., 2015 ). It remains unknown under which circumstances does a policy brief elicit changes in attitude, knowledge and intention to use. Hence, this study will report what is known about whether policy briefs are considered effective by decision-makers, how policy briefs are used by decision-makers and which components of policy briefs were considered useful.

Therefore, the objectives of this study were to (1) identify evidence about the use of policy briefs and (2) identify which elements of content made for an effective policy brief. The first objective includes the perceived appreciation and the different types of use (instrumental, conceptual, persuasive) and the factors linked to use. The second objective includes the format, the context and the quality of the evidence.

This study used the scoping review method by Arksey and O’Malley ( 2005 ). A scoping study is a synthesis and analysis of a broad range of research material aimed at quickly mapping the key concepts underpinning a wider research area that has not been reviewed comprehensively before and where several different study designs might be relevant (Arksey and O’Malley, 2005 ; Mays et al., 2001 ). This allows to provide a greater conceptual clarity on a specific topic (Davis, et al., 2009 ). A scoping study, as opposed to other kinds of systematic reviews, is less likely to address a specific research question or to assess the quality of included studies. Scoping studies tend to address broader topics where many different study designs might be applicable (Arksey and O’Malley, 2005 ). They do not reject studies based on their research designs.

This method was chosen to assess the breadth of knowledge available on the topic of short documents synthesizing research results and their usage by policymakers. Scoping reviews allow a greater assessment of the extent of the current research literature since the inclusion and exclusion criteria are not exhaustive.

Inclusion and exclusion criteria

The policy brief must have been presented to the target users; policymakers. A policymaker refers to a person responsible for devising regulations and laws. In this paper, the term policymaker will be used along with the term decision-maker, which is characterized more broadly as any entity who, such as health system managers, could benefit from empirical research to make a decision. For this paper, we did not differentiate between types or levels of policymakers. Stakeholders involved in the decision-making process related to a large jurisdiction or organization for which policy briefs were provided were included. As an example, papers were rejected if the participants were making decisions for an individual person or a patient. Articles were accepted if other user types were included as participants, as long as policymakers or decision-makers were included as users. This was decided because many papers included a variety of participants and if the feedback given by policymakers would have been different from other decision-makers, it would have been explained in the article.

Type of document

Articles were included when decision-makers had to assess a short document synthesizing research results. Given that many different terms are used to describe short research syntheses, the articles were identified using terms such as policy briefs, evidence summaries, evidence briefs and plain language summaries. The full list can be found in Table 1 .

Evaluations of systematic reviews were rejected as they are often written using technical language and can be lengthy (Moat et al., 2014 ). Furthermore, past research has evaluated the use and effectiveness of systematic reviews in policy. Given that this paper sought to evaluate short synthesized documents as a technical tool for knowledge transfer, any form of lengthy reports or reviews were excluded.

Rapid reviews were rejected due to their commissioned nature and the large breadth of literature available on their subject. Rapid reviews and commissioned research were excluded because they are different in a fundamental aspect: they are made as a direct response to a request from decision-makers. Since these papers are commissioned, there is already an intended use of these papers by decision-makers, as opposed to the use of non-commissioned papers. The expectations and motivations of these decision-makers in using these research results will be different. For these reasons, rapid reviews and commissioned research were excluded.

Articles were mostly excluded for being only examples of policy briefs, for not testing empirically the effectiveness of a policy brief, for testing another type of knowledge transfer tool (ex: deliberative dialogs) or for not having decision-makers as participants.

Type of study

All empirical studies were included, meaning qualitative, quantitative and mixed-methods. Any type of literature review such as systematic reviews were excluded to avoid duplication of studies and to allow an equal representation of all included studies. This prevented the comparison between the results of a systematic review and the results of one case study. Systematic reviews were however scanned for any study respecting the criteria to be added into the scoping review.

Empirical studies were eligible based on the implementation of a policy brief and the assessment of its use by decision-makers. Outcomes of interest were the use and effectiveness of policy briefs according to decision-makers, as well as the preferred type of content and format of such documents. These results were either reported directly by the decision-makers or through observations by the researchers. Articles were included if the policy brief was reviewed in any sort of way, whether through the participants giving their opinion on the policy brief or any commentary on the way the policy brief had been acknowledged. Articles were not excluded for not assessing a specific type of use. Examples of policy briefs and articles limited to the creation process of a policy brief and articles without any evaluations of the use of policy brief were not included, as no empirical evidence was used to back up what the authors considered made a policy brief effective.

Search strategy

To identify potentially eligible studies, literature searches have been conducted using PsycNET, PubMed, Web of Science and Embase from February 2018 to May 2019 in an iterative process. The search strategy was conceived in collaboration with a specialist in knowledge and information management. The scoping review’s objectives were discussed until four main concepts were identified. Related words to the four main concepts of the scoping study were searched with APA Thesaurus, these concepts being: (1) policy brief, (2) use, (3) knowledge transfer and (4) policymaker. The first term was used to find articles about the kind of summarized paper being evaluated. The second term was used to find articles discussing the ways these papers were used or discussing their effectiveness. Without this search term, many articles were simply mentioning policy briefs without evaluating them. The third term referred to the policy brief’s intent and to the large domain of knowledge transfer to get more precise research results into this field. The fourth search term allowed for the inclusion of the desired participants.

Different keywords for the concept of policy brief (any short document summarizing research results) were found through the literature and were also created using combinations of multiple keywords (e.g., research brief and evidence summary were combined to create research summary). The different concepts were then combined in the databases search engines until a point of saturation was reached and no new pertinent articles were found.

Study selection

Following the removal of duplicates, the articles were selected by analyzing the titles. If they seemed pertinent, the abstracts were then read. The remaining articles were verified by two authors to assess their eligibility, were read in their entirety and possibly eliminated if they did not respect the established criteria.

Data extraction

Once the articles were selected, summary sheets were created to extract data systematically. The factors recorded were the intended audience of the paper, the journal of publication, the objectives of the research, the research questions, a summary of the introduction, the variables researched, the type of research synthesis used in the study and a description of the document, information on sampling (size, response rate, type of participants, participants’ country, sampling method), the type of users reading the document (ex: practitioners, policymakers, consumers), a description of the experimentation, the research design, the main results found and the limits identified in the study.

Data analyses

Based on the extracted information compiled in the summary sheets, the data was taken from those summary sheets and separated into the two objectives of this study, which are (1) the evidence of policy brief use and (2) the elements of content that contributed to their effectiveness. Further themes were outlined based on the results, which formed the main findings. When more than one study had the same finding, the additional sources would be indicated. Similarly, any contradicting findings were also noted.

Literature search

Four-thousand nine-hundred four unique records were retrieved, of which 215 were screened on full text. In total, 22 articles were included in this scoping study. The number of studies in each step of the literature review process are shown in Fig. 1 .

A diagram of the number of records identified, included and excluded in the article.

Study characteristics

The year of publication ranged from 2007 to 2018, with 50% of the articles having been published in the last 5 years.

The studies spanned 35 countries, with the most common being conducted in Canada ( n  = 5), others being conducted in Burkina Faso ( n  = 2), the United States ( n  = 2), Netherlands ( n  = 1), Wales ( n  = 1), Thailand ( n  = 1), Nigeria ( n  = 1), Uganda ( n  = 1), Kenya ( n  = 1) and Israel ( n  = 1). Six studies were conducted in multiple countries Footnote 1 . Of the included studies, 12 took place in a total of 23 low to middle income countries, according to the World Bank Classification ( 2019 ).

Case studies were the most common design (59%), followed by descriptive studies (27%) and randomized controlled trials (14%). Five studies used quantitative research methods, eight were qualitative methods and nine used mixed-methods. For more details, Table 2 presents an overview of the characteristics of selected studies.

Primary objective: use of policy briefs

Appreciation of policy briefs.

The perception of decision-makers regarding policy briefs is a starting point to evaluate if more work should be put into its format to meet the needs of decision-makers or if it should go into communicating the importance of evidence-informed methods to decision-makers.

Of all the eligible studies, 19 (86%) found it useful or had a general appreciation towards policy briefs as a tool for knowledge transfer by decision-makers. Two studies (Kilpatrick et al., 2015 ; Orem et al., 2012 ) did not report about the perceived usefulness or appreciation of such a document and one study (de Goede et al., 2012 ) had policy actors declare they found the document of no importance and neglected it during the policy process. Many participants reported throughout the various studies that taking into consideration the available evidence would help improve decision-making (El-Jardali et al., 2014 ; Marquez et al., 2018 ; Vogel et al., 2013 ).

Types of use of policy briefs

The use of the policy brief in the decision-making process was assessed through its instrumental use, conceptual use and persuasive use.

In regard to instrumental use, many policymakers claimed to have used evidence to inform their decision-making, even sometimes going as far as actively seeking out policy briefs and improving their ability to assess and use research evidence (El-Jardali et al., 2014 ; Jones and Walsh, 2008 ). Policy briefs seem to oftentimes be used as a starting point for deliberations on policies and to facilitate the discussions with policy actors on definitions and solutions to multiple problems (Ellen et al., 2016 ; de Goede et al., 2012 ; Jones and Walsh, 2008 ; Suter and Armitage, 2011 ; Ti et al., 2017 ). Although policy briefs have helped in identifying problems and solutions in their communities, policymakers reported also relying on other sources of information, such as other literature, colleagues and their own knowledge (Goede et al., 2012 ; Suter and Armitage, 2011 ). When it comes to putting recommendations into action, policymakers may be more inclined to report intentions to take into consideration and apply the recommendations when the solutions offered require little effort or co-operation from others (Beynon et al., 2012 ).

Policy briefs are most commonly used conceptually, which is no surprise given that it is the type of use requiring the least commitment. They allow decision-makers to better understand the different facets of a situation, to inform policymaking and raise awareness on certain issues (Campbell et al., 2009 ; El-Jardali et al., 2014 ; Ellen et al., 2016 ; Goede et al., 2012 ; Suter and Armitage, 2011 ). A better comprehension of a situation can also lead to a change of beliefs in certain circumstances. Beynon et al. ( 2012 ) found that reading a policy brief lead to creating evidence-accurate beliefs more commonly amongst those with no prior opinion. The policy brief was not as effective in changing the beliefs of respondents who had an opinion on the issue before reading the brief.

Few studies reported the persuasive use of policy briefs. One study reported policy briefs being used to support prior beliefs such as good timing for specific policies and to allow the progression of information before publication in order to make sure it is aligned with national health policies (de Goede et al., 2012 ). Policy briefs can be seen as an effective tool for advocacy when the objective is to convince other stakeholders of a position using evidence-based research (Ti et al., 2017 ). However, one study had policymakers claim that although research needs to be used more, rarely will they use research to inform policy agendas or to evaluate the impacts of a policy (Campbell et al., 2009 ). Thus, it remains unclear whether policy briefs are often used in a persuasive way.

Factors linked to use

Decision-makers are more inclined to report intentions and actual follow-up actions that require little effort or co-operation from others although globally, women are less likely to claim that they will do follow-up actions than men (Beynon et al., 2012 ). The same study reported that a higher level of self-perceived influence predicts a higher level of influence and those readers are more inclined to act. Furthermore, decision-makers were most likely to use policy briefs if they were directly targeted by the subject of the evidence (Brownson et al., 2011 ).

Dissemination strategies are specific methods of distributing information to key parties with the intention of having the reader process that information. A policy brief could be very well written and have all the necessary information but if it is not properly shared with the intended audience, it might not be read. One effective dissemination strategy appreciated by policymakers is to send the policy briefs a few weeks before a workshop (Mc Sween-Cadieux et al., 2018 ) as well as an individualized email in advance of the policy brief (Ellen et al., 2016 ; Kilpatrick et al., 2015 ). Asking policymakers to be a part of the presentation of the briefs and to arrange a follow-up meeting to receive feedback on the documents was also viewed favorably (Kilpatrick et al., 2015 ).

Secondary objective: elements of content contributing to the effectiveness of policy briefs

Decision-makers often report the language of researchers being too complex, inaccessible, lacking clarity and commonly using overly technical terms (Marquez et al., 2018 ; Mc Sween-Cadieux et al., 2017 ; Rosenbaum et al., 2011 ). They prefer the use of simple and jargon-free language in clear, short sentences (Ellen et al., 2014 ; Jones and Walsh, 2008 ; Kilpatrick et al., 2015 ; Schmidt et al., 2014 ; Vogel et al., 2013 ). Some decision-makers have reported having difficulty understanding the objectives in the policy brief and finding the document too long (Jones and Walsh, 2008 ; Marquez et al., 2018 ; Mc Sween-Cadieux et al., 2017 ). They appreciate the emphasis to be on the advantages of the policy brief and for it to be constructed around a key message to draw the reader and disseminate the critical details. Multiple articles recommended policy briefs not to go over one to two pages, with references to more detailed findings so the reader can investigate further (Dobbins et al., 2007 ; Ellen et al., 2014 ; Kilpatrick et al., 2015 ; Marquez et al., 2018 ; Suter and Armitage, 2011 ).

Furthermore, policy briefs need to be visually engaging. Since policymakers spend on average 30 to 60 min reading information about a particular issue, it is a challenge to present the information in such a way to make them go for the policy brief (Jones and Walsh, 2008 ). Information can be displayed in different ways to be more memorable such as charts, bullets, graphs and photos (Ellen et al., 2014 ; Marquez et al., 2018 ; Mc Sween-Cadieux et al., 2018 ). One research study has reported that an overly esthetic document may seem expensive to produce, which can lead to policymakers wondering why funding was diverted from programs to the production of policy briefs (Schmidt et al., 2014 ). Another study found that “graded-entry” formats, meaning a short interpretation of the main findings and conclusions, combined with a short and contextually framed narrative report, followed by the full systematic review, were associated with a higher score for clarity and accessibility of information compared to systematic reviews alone (Opiyo et al., 2013 ). However, the exact format of the document does not seem to be as important for policymakers as its clarity. Indeed, policymakers do not appear to have a preference between electronic and hard copy formats (Dobbins et al., 2007 ; Kilpatrick et al., 2015 ; Marquez et al., 2018 ). This is also shown by another case study, where policymakers preferred the longest version of a policy brief, one easier to scan, leading to believe that a longer text may not necessarily be the condemnation of a policy brief, as long as it is written in an easily scannable way with small chunks of information dispersed through the document (Ellen et al., 2014 ).

Context-related

There is a preference for local information over global information by decision-makers (Brownson et al., 2011 ; Jones and Walsh, 2008 ; Orem et al., 2012 ). It allows for local council members to identify relevant issues in their communities as well as responses tailored to the socio-political nature of the issue, such as cultural values, historical-political sensitivities and election timing (de Goede et al., 2012 ; Jones and Walsh, 2008 ). Authors of policy briefs, depending on the study, must consider the latest insights as well as the complex power relations underpinning the policy process when writing their recommendations. The issue of the policy brief has a significant impact on whether it can influence the views of decision-makers. To have a better grasp on the relevance of the topic, policymakers want to have the data put into context instead of simply presenting the facts and statistics (Schmidt et al., 2014 ). Furthermore, such research needs to be transmitted in a time-sensitive matter to remain relevant (Ellen et al., 2016 ; Marquez et al., 2018 ; Orem et al., 2012 ; Rosenbaum et al., 2011 ; Uneke et al., 2015 ).

Given the time pressures on policymakers to make rapid and impactful decisions, the use of actionable, evidence-informed recommendations acknowledging the specific situation are much appreciated by policymakers. Decision-makers wish for realistic recommendations on an economic and strategic plan. They dislike a policy brief that is too general and without any propositions of concrete action (Mc Sween-Cadieux et al., 2017 ; de Goede et al., 2012 ). Indeed, many policymakers claim that not concluding with recommendations is the least helpful feature for policy briefs (Moat et al., 2014 ). They prefer that the document provides more guidance on which actions should be taken and the steps to take as well as the possible implementations (Marquez et al., 2018 ; Mc Sween-Cadieux et al., 2017 ). However, it can also be a barrier to use if the content of the policy brief is not in line with the policy-maker’s system belief (de Geode et al., 2012 ).

Quality evidence

Quality, compelling evidence must be provided to facilitate the use of policy brief by decision-makers (Jones and Walsh, 2008 ). Therefore, it is required to know what kind of arguments are needed to promote research in the decision-making process. Although information about the situation and its context is appreciated, policymakers prefer having some guidance on what to do with such information afterwards. Some policymakers have reported a lack of details on the strategies to adopt, the tools to use and the processes required that would otherwise lead to a successful integration of the ideas proposed in the policy brief (Marquez et al., 2018 ; Suter and Armitage, 2011 ). There is a particular interest in detailed information about local applicability or costs, outcome measurements, broader framing of the research (Ellen et al., 2014 ; Rosenbaum et al., 2011 ), clear statements of the implication for practice from health service researchers (Dobbins et al., 2007 ), information about patient safety, effectiveness and cost savings (Kilpatrick et al., 2015 ).

On the other hand, less emphasis should be put on information steering away from important results. One study showed that researchers should more often than not forego acknowledgements, forest plot diagrams, conflicts of interest, methods, risk of bias, study characteristics, interventions that showed no significant effect and statistical information (e.g., confidence interval) (Marquez et al., 2018 ). Surprisingly, policymakers tend to prefer data-centered arguments rather than story-based arguments, the former containing data percentages and the latter containing personal stories (Brownson et al., 2011 ; Schmidt et al., 2014 ), hinting that the use of emotions might not be the most effective method in convincing policymakers to adopt research into their decision-making. However, a certain subjectivity is appreciated. Indeed, policymakers value researchers’ opinions about the policy implications of their findings (Jones and Walsh, 2008 ). Beynon et al. ( 2012 ) found that policy briefs, including an opinion piece acquire significance over time, possibly indicating that the effect of the opinion piece trickles in slowly.

Legitimacy however does not emerge solely from good evidence and arguments, but also from the source of those arguments, more specifically the authors involved. Policymakers specified that they pay attention to the authors of policy briefs and that it influences their acceptance of the evidence and arguments presented (Jones and Walsh, 2008 ). Authoritative messages were considered a key element of an effective policy brief. This is confirmed by Beynon et al. ( 2012 ), who found a clear authority effect on readers’ intentions to send the policy brief to someone else. Readers were more likely to share briefs with a recommendation from an authoritative figure rather than a recommendation from an unnamed researcher. It can be considered an obstacle to the use of the document if the latter is not perceived as coming from a credible source (Goede et al., 2012 ). Authoritative institutions, research groups and experts have been identified as the best mediators between researchers and decision-makers (Jones and Walsh, 2008 ).

The objectives of this study were to identify what the literature has concluded about the use of policy briefs and which elements made for an effective one.

The results showed that policy briefs were considered generally useful, easy to understand and credible, regardless of the group, the issue, the features of the brief or the country tested. Different types of use were assessed, notably the instrumental, conceptual and persuasive use. Many policymakers claimed to use the evidence given in their decision-making process, some even reporting an increased demand for knowledge transfer products by policymakers. This fact and the surge of knowledge transfer literature in the past few years might suggest that policy briefs and other short summaries of research could become a more commonly used tool in the next years for the decision-making process in policy. Given that policymakers oftentimes rely on multiple sources of information and that policy briefs facilitated discussions between different actors, future interventions should aim to combine a policy brief with other mechanisms of evidence use (Langer et al., 2016 ).

One factor linked to a greater use of policy briefs was the dissemination strategies. Arranging a meeting with policymakers following the reading of the document to receive feedback is a good strategy to get the policymakers to read attentively and consider the content of the policy brief (Kilpatrick et al., 2015 ). A greater implication by policymakers seems to encourage the use of the policy brief. This supports the findings of Langer et al. ( 2016 ) concerning interaction as a mechanism to promote evidence use. Indeed, improved attitudes towards evidence were found after holding joint discussions with other decision-makers who were motivated to apply the evidence. Increasing motivation to use research evidence through different techniques such as the framing and tailoring of the evidence, the development of policymakers’ skills in interpreting evidence and better access to the evidence could lead to an increase in evidence-informed decision-making (Langer et al., 2016 ). Instead of working independently, it has been often proposed that researchers and policymakers should work in collaboration to increase the pertinence and promote the use of evidence (Gagliardi et al., 2015 ; Langer et al., 2016 ). The collaboration between policymakers and researchers would allow researchers to better understand policymakers’ needs and the contexts in which the evidence is used, thus providing a well-tailored version of the document for a greater use for those in need of evidence-informed results (Boaz et al., 2018 ; Langer et al., 2016 ). However, multiple barriers are present to the collaboration between researchers and decision-makers, such as differing needs and priorities, a lack of skill or understanding of the process and attitudes towards research (Gagliardi et al., 2015 ). Furthermore, different dilemmas come into play when considering how much academics should engage in policymaking. Although recommendations are often made for researchers to invest time into building alliances with policymakers and getting to know the political context, there is no guarantee that these efforts will lead to the expected results. Influencing policy through evidence advocacy requires engaging in different networks and seeing windows of opportunity, which may blur the line between scientists and policymakers (Cairney and Oliver, 2020 ). To remain neutral, researchers should aim to listen to the needs of policymakers and inform them of new evidence, rather than striving to have policymakers use the evidence in a specific way.

When policymakers considered the policy brief of little importance for their decision-making, it could be partially explained by the fact that the document shared was not aligned with the groups’ belief systems (de Goede et al., 2012 ). Similarly, Beynon et al. ( 2012 ) had found that policy briefs are not as effective in changing opinions in respondents who held previous beliefs rather than forging an opinion on a new topic. Being presented with information opposite of one’s belief can be uncomfortable. This cognitive dissonance can influence the level of acceptance of new information, which can affect its use. To return to a feeling of consistency with their own thoughts, policymakers could easily discard a policy brief opposing their beliefs. The use of policy briefs is, therefore, determined largely by the type of audience and whether they agree with the content. To improve the acceptance, the policy brief should strive to be aligned with the needs of policymakers. This implies that when creating and disseminating the evidence, researchers must consider their audience. Therefore, there is no “one-size-fits-all” and a better solution to improve the use of research is to communicate information based on the type of policymaker (Brownson et al., 2011 ; Jones and Walsh, 2008 ).

These results should lead researchers to first determine who is the targeted audience and how can the format of the policy brief be attractive to them. Different versions of policy briefs can be made according to the different needs, priorities and positions of varying policy actors (Jones and Walsh, 2008 ). Furthermore, people directly targeted by the content of the evidence are more likely to read the policy brief. In the knowledge to action cycle, it seems essential to have a clear picture of who will be reading the policy brief and what kind of information to provide as a way to better reach them.

The lack of recommendations was cited as being the least helpful feature of evidence briefs (Moat et al., 2014 ). This, along with other studies claiming the importance of clear recommendations could lead to believe that policymakers prefer an advocacy brief rather than a neutral brief (Goede et al., 2012 ; Marquez et al., 2018 ; Mc Sween-Cadieux et al., 2017 ). However, this brings the question of impartiality in research (Cairney and Oliver, 2017 ). The purpose of policy briefs and generally of knowledge transfer is to gather the best evidence and to disseminate it in a way to assure that it has an impact. Science is seen as neutral and providing only the facts, yet policymakers ask for precise recommendations and opinions. This seeming contradiction leads to wondering whether researchers should offer their opinion and how much co-production with policymakers should they be involved in to align the results with the policymakers’ agenda (Cairney and Oliver, 2017 ).

The credibility of the messenger is also an important factor in the decision-maker’s use of the document. Briefs were more likely to be shared when associated with an authoritative figure than with an unnamed research fellow. This authority effect may be due to the brief becoming more memorable when associated with an authoritative figure, which leads to a greater likelihood for the policymakers to share that message with other people (Beynon et al., 2012 ). Another possible explanation is the trust associated with authority. The results have shown that policymakers tend to forego the information about conflicts of interest, methodology, risks of bias and statistics. In other words, the details that would show the legitimacy of the data. Instead, they prefer going straight to the results and recommendation. This could lead to believe that policymakers would prefer to read a paper coming from a reputable source that they can already trust, so they can focus on analyzing the content rather than the legitimacy of it. Thus, the partnering between authoritative institutions, researchers and policymakers could help not only to better target the needs of policymakers but also to improve the legitimacy of the message communicated through the brief, in an effort to help policymakers focus more on the information being shared (Jones and Walsh, 2008 ).

Strengths and limitations

The use of all the similar terms related to policy briefs in the search strategy allowed for a wide search net during the literature search process, leading to finding more studies. Another strength was the framework assessing both the types of use and the format of the policy brief preferred by policymakers, which allowed a better understanding of the place policy briefs currently have in policymaking as well as an explanation of different content factors related to its use. As knowledge transfer is becoming a pillar in organizations across the globe, there remains however a gap in the use of research in decision-making. This review will enable researchers to better adapt the content of their research to their audience when writing a policy brief by adjusting the type of information that should be included in the document. One limit of the present scoping study is its susceptibility to a sampling bias. Although the articles assessed for eligibility were verified by two authors, the first records identified through database searching were carried out by a single author. The references of the selected articles were not searched systematically to find additional articles. This scoping study also does not assess the quality of the selected studies and evaluation since its objective is to map the current literature on a given subject.

Although the quality of the chosen articles was not assessed, it is possible to notice a few limits in their method, which can be found in the Table 2 . There is also something to be said about publication bias, meaning that papers with positive results tend to be published in greater proportion than papers failing to prove their hypotheses.

Furthermore, few studies determined the actual use or effect of the policy brief in decision-making but instead assessed self-reported use of the policy brief or other outcomes, such as perceived credibility or relevance of those briefs, since these may affect the likelihood of research use in decision-making. Few studies reported the persuasive use of policy briefs. This could be explained by the reticence of participants to report such information due to the implications that they would use research results only to further their agenda rather than using them to make better decisions, or simply because researchers did not question the participants on such matters. Although the inclusion criteria of this study were fairly large, it is worth noting that the number of selected articles was fairly low, with only 22 studies included. Further research on persuasive research would need to assess researchers’ observations rather than self-reported use by policymakers. Since the current research has shown that policy briefs could be more useful in creating or reinforcing a belief, future studies could assess the actual use of policy briefs in decision-making.

The findings indicate that while policy briefs are generally valued by decision-makers, it is still necessary for these documents to be written with the end reader in mind to meet their needs. Indeed, an appreciation towards having a synthesized research document does not necessarily translate to its use, although it is a good first step given that it shows an open-mindedness of decision-makers to be informed by research. Decision-making is a complex process, of which the policy brief can be one step to better inform the decision-makers on the matter at hand. A policy brief is not a one-size-fits-all solution to all policy-making processes. Evidence can be used to inform but it might not be able to, on its own, fix conflicts between the varying interests, ideas and values circulating the process of policymaking (Hawkins and Pakhurst, 2016 ). Since credibility is an important factor for decision-makers, researchers will have to take into consideration the context, the authors associated with writing policy briefs and the actors that will play a lead role in promoting better communication between the different stakeholders.

Given that the current literature on the use of policy briefs is not too extensive, more research needs to be done on the use of such documents by policymakers. Future studies should look into the ways researchers can take the context into consideration when writing a policy brief. It would also be interesting to search whether different formats are preferred by policymakers intending to use evidence in different ways. Furthermore, there are other types of summarized documents that were excluded in this scoping review such as rapid reviews, or even different formats such as infographics. The use of commissioned summaries could be an interesting avenue to explore, as the demand for these types of documents from policymakers would ensure their use in a significant manner.

Data availability

All data analyzed in this study are cited in this article and available in the public domain.

The studies were conducted in Zambia ( n  = 3), Uganda ( n  = 3), South Africa ( n  = 2), Argentina ( n  = 2), China ( n  = 2), Cameroon ( n  = 2), Cambodia ( n  = 1), Norway ( n  = 2), Ethiopia ( n  = 2), India ( n  = 1), Ghana ( n  = 1), Nicaragua ( n  = 1), Bolivia ( n  = 1), Brazil ( n  = 1), England ( n  = 1), Wales ( n  = 1), Finland ( n  = 1), Germany ( n  = 1), Burkina Faso ( n  = 1), Italy ( n  = 1), Scotland ( n  = 1), Spain ( n  = 1), Mozambique ( n  = 1), Bangladesh ( n  = 1), Nigeria ( n  = 1), Central African Republic ( n  = 1), Sudan ( n  = 1), Colombia ( n  = 1) and Australia ( n  = 1).

Adam T, Moat KA, Ghaffar A, Lavis JN (2014) Towards a better understanding of the nomenclature used in information-packaging efforts to support evidence-informed policymaking in low-and middle-income countries. Implement Sci 9(1):67

Article   PubMed   PubMed Central   Google Scholar  

Anderson M, Cosby J, Swan B, Moore H, Broekhoven M (1999) The use of research in local health service agencies. Soc Sci Med 49(8):1007–1019

Article   CAS   PubMed   Google Scholar  

Arcury TA, Wiggins MF, Brooke C, Jensen A, Summers P, Mora DC, Quandt SA (2017) Using “policy briefs” to present scientific results of CBPR: farmworkers in North Carolina. Prog Commun Health Partnerships: Res Educ Action 11(2):137

Article   Google Scholar  

Arksey H, O’Malley L (2005) Scoping studies: towards a methodological framework. Int J Soc Res Methodol 8(1):19–32

Baekgaard M, Christensen J, Dahlmann CM, Mathiasen A, Petersen NBG (2019) The role of evidence in politics: motivated reasoning and persuasion among politicians. Br J Polit Sci 49(3):1117–1140

Beynon P, Chapoy C, Gaarder M, Masset, E (2012) What difference does a policy brief make? Full report of an IDS, 3ie, Norad study: Institute of Development Studies and the International Initiative for Impact Evaluation (3ie)

Boaz A, Hanney S, Borst R, O’Shea A, Kok M (2018) How to engage stakeholders in research: design principles to support improvement. Health Res Policy Syst 16(1):60

Brownson RC et al. (2011) Communicating evidence-based information on cancer prevention to state-level policy makers. J Natl Cancer Inst 103(4):306–316

Cairney P, Heikkila T, Wood M (2019) Making policy in a complex world (1st edn.). Cambridge University Press

Cairney P, Kwiatkowski R (2017) How to communicate effectively with policymakers: Combine insights from psychology and policy studies. Palgrave Commun 3(1):37

Cairney P, Oliver K (2017) Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy? Health Res Policy Syst 15:1

Cairney P, Oliver K (2020) How should academics engage in policymaking to achieve impact? Polit Stud Rev 18(2):228–244

Cambridge University Press (2019) Definition of a policymaker. https://dictionary.cambridge.org . Accessed 2019

Campbell DM et al. (2009) Increasing the use of evidence in health policy: Practice and views of policy makers and researchers. Aust N Z Health Policy 6(21):1–11

Google Scholar  

Dagenais C, Ridde V (2018) Policy brief (PB) as a knowledge transfer tool: To “make a splash”, your PB must first be read. Gaceta Sanitaria

Davis K, Drey N, Gould D (2009) What are scoping studies? A review of the nursing literature. Int J Nurs Stud 46(10):1386–1400

Article   PubMed   Google Scholar  

Dobbins M, Jack S, Thomas H, Kothari A (2007) Public Health Decision-Makers’ informational needs and preferences for receiving research evidence. Worldview Evid-Based Nurs 4(3):156–163

El-Jardali F, Lavis J, Moat K, Pantoja T, Ataya N (2014) Capturing lessons learned from evidence-to-policy initiatives through structured reflection. Health Res Policy Syst 12(1):1–15

Ellen M et al. (2014) Health system decision makers’ feedback on summaries and tools supporting the use of systematic reviews: A qualitative study. Evid Policy 10(3):337–359

Ellen ME, Horowitz E, Vaknin S, Lavis JN (2016) Views of health system policymakers on the role of research in health policymaking in Israel. Israel J Health Policy Res 5:24

de Goede J, Putters K, Van Oers H (2012) Utilization of epidemiological research for the development of local public health policy in the Netherlands: a case study approach. Soc Sci Med 74(5):707–714

Gagliardi AR, Berta W, Kothari A, Boyko J, Urquhart R (2015) Integrated knowledge translation (IKT) in health care: a scoping review. Implementation. Science 11(1):38

Hawkins B, Parkhurst J (2016) The ‘good governance’ of evidence in health policy. Evid Policy 12(4):575–592

Huang W, Tan CL (2007) A system for understanding imaged infographics and its applications. In Proceedings of the 2007 ACM symposium on Document engineering, Association for Computing Machinery, Seoul, pp 9–18

Jones N, Walsh C (2008) Policy briefs as a communication tool for development research. ODI Background Note. ODI, London

Keepnews DM (2016) Developing a policy brief. Policy Polit Nurs Pract 17(2):61–65

Khangura S, Konnyu K, Cushman R, Grimshaw J, Moher D (2012) Evidence summaries: the evolution of a rapid review approach. Syst Rev 1:10

Kilpatrick K et al. (2015) The development of evidence briefs to transfer knowledge about advanced practice nursing roles to providers, policymakers and administrators. Nurs Leadership (Toronto, Ont.) 28(1):11–23

Koon AD, Rao KD, Tran NT, Abdul G (2013) Embedding health policy and systems research into decision-making processes in low- and middle-income countries. Health Res Policy Syst 18:9

Langer L, Tripney J, Gough D (2016) The science of using science: researching the use of research evidence in decision-making. Social Science Research Unit, & Evidence for Policy and Practice Information and Co-ordinating Centre

Lavis JN, Posada FB, Haines A, Osei E (2004) Use of research to inform public policymaking. Lancet 364(9445):1615–1621

Lavis JN et al. (2012) Guidance for evidence-informed policies about health systems: linking guidance development to policy development. PLoS Med 9:3

Liverani M, Hawkins B, Parkhurst JO (2013) Political and institutional influences on the use of evidence in public health policy. A systematic review. PLoS ONE 8(10):e77404

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Marquez C, Johnson AM, Jassemi S, Park J, Moore JE, Blaine C, Straus SE (2018) Enhancing the uptake of systematic reviews of effects: What is the best format for health care managers and policy-makers? A mixed-methods study. Implement Sci 13(1):84

Mays N, Roberts E, Popay J (2001) Synthesising research evidence. In: Fulop N, Allen P, Clarke A, Black N (eds) Studying the organisation and delivery of health services: research methods. Routledge, London, pp. 188–220

Mays N, Pope C, Popay J (2005) Systematically reviewing qualitative and quantitative evidence to inform management and policy-making in the health field. J Health Serv Res Policy 10(Suppl 1):6–20

Mc Sween-Cadieux E, Dagenais C, Somé PA, Ridde V (2017) Research dissemination workshops: Observations and implications based on an experience in Burkina Faso. Health Res Policy Syst 15(1):1–12

Mc Sween-Cadieux E, Dagenais C, Ridde V (2018) A deliberative dialogue as a knowledge translation strategy on road traffic injuries in Burkina Faso: a mixed-method evaluation. Health Res Policy Syst 16(1):1–13

Moat KA, Lavis JN, Clancy SJ, El-Jardali F, Pantoja T (2014) Evidence briefs and deliberative dialogues: perceptions and intentions to act on what was learnt. Bull World Health Organiz 20:8

Moore GM, Redman S, Turner T, Haines M (2016) Rapid reviews in health policy: a study of intended use in the New South Wales’ Evidence Check programme. Evid Policy 12(46):505–519

Nutley S, Boaz A, Davies H, Fraser A (2019) New development: What works now? Continuity and change in the use of evidence to improve public policy and service delivery. Public Money Manage 39(4):310–6

Nutley S, Powell AE, Davies HTO (2013) What counts as good evidence, Alliance for Useful Evidence, London

Oliver K, Boaz A (2019) Transforming evidence for policy and practice: creating space for new conversations. Palgrave Commun 5(1):1

Oliver K, Cairney P (2019) The dos and don’ts of influencing policy: a systematic review of advice to academics. Palgrave Commun 5(1):21

Oliver KA, de Vocht F (2017) Defining ‘evidence’ in public health: a survey of policymakers’ uses and preferences. Eur J Public Health 1(27 May):112–7

Oliver K, Innvar S, Lorenc T, Woodman J, Thomas J (2014) A systematic review of barriers to and facilitators of the use of evidence by policymakers. BMC Health Serv Res 14(1):2

Opiyo N, Shepperd S, Musila N, Allen E, Nyamai R, Fretheim A, English M (2013) Comparison of alternative evidence summary and presentation formats in clinical guideline development: a mixed-method study. PLoS ONE 8:1

Article   CAS   Google Scholar  

Orandi BJ, Locke JE (2020) Engaging policymakers to disseminate research. In: Dimick J., Lubitz C. (eds) Health Serv Res. Springer, Cham, pp. 283–288

Chapter   Google Scholar  

Orem JN, Mafigiri DK, Marchal B, Ssengooba F, Macq J, Criel B (2012) Research, evidence and policymaking: The perspectives of policy actors on improving uptake of evidence in health policy development and implementation in Uganda. BMC Public Health 12(1):109

Oxman AD, Lavis JN, Lewin S, Fretheim A (2009) SUPPORT Tools for evidence informed health policymaking (STP) 1: What is evidence-informed policymaking? Health Res Policy Syst 7:S1

Petkovic J, Welch V, Jacob MH, Yoganathan M, Ayala AP, Cunningham H, Tugwell P (2016) The effectiveness of evidence summaries on health policymakers and health system managers use of evidence from systematic reviews: a systematic review. Implement Sci 11(1):1–14

Rajabi F (2012) Evidence-informed Health Policy Making: the role of policy brief. Int J Prevent Med 3(9):596–598

Rosenbaum SE, Glenton C, Wiysonge CS, Abalos E, Mignini L, Young T, Oxman AD (2011) Evidence summaries tailored to health policy-makers in low- and middle-income countries. Bull World Health Organ 89(1):54–61

Schmidt AM, Ranney LM, Goldstein AO (2014) Communicating program outcomes to encourage policymaker support for evidence-based state tobacco control. Int J Environ Res Public Health 11(12):12562–12574

Suter E, Armitage GD (2011) Use of a knowledge synthesis by decision makers and planners to facilitate system level integration in a large Canadian provincial health authority. Int J Integr Care, 11. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3107086 . Accessed 2019

Tessier C, Tessier C, Centre de collaboration nationale sur les politiques publiques et la santé (2019) The policy brief: a tool for knowledge transfer. http://collections.banq.qc.ca/ark:/52327/4002463 . Accessed 2020

Ti L, Hayashi K, Ti L, Kaplan K, Suwannawong P, Kerr T (2017) ”Knowledge translation to advance evidence-based health policy in Thailand. Evid Policy 13(4):723–731

Tricco AC, Antony J, Straus SE (2015) Systematic reviews vs. rapid reviews: What’s the difference? CADTH Rapid Review Summit

Uneke CJ, Ebeh Ezeoha A, Uro-Chukwu H, Ezeonu CT, Ogbu O, Onwe F, Edoga C (2015) ”Promoting evidence to policy link on the control of infectious diseases of poverty in Nigeria: outcome of a multi-stakeholders policy dialogue. Health Promot Perspect 5(2):104–115

Uzochukwu B, Onwujekwe O, Mbachu C, Okwuosa C, Etiaba E, Nyström ME, Gilson L (2016) The challenge of bridging the gap between researchers and policy makers: experiences of a Health Policy Research Group in engaging policy makers to support evidence informed policy making in Nigeria. Glob Health 12(1):1–15

van de Goor I, Hämäläinen RM, Syed A, Lau CJ, Sandu P, Spitters H, Karlsson LE, Dulf D, Valente A, Castellani T, Aro AR (2017) Determinants of evidence use in public health policy making: Results from a study across six EU countries. Health policy 121(3):273–81

Vogel JP, Oxman AD, Glenton C, Rosenbaum S, Lewin S, Gülmezoglu AM, Souza JP (2013) Policymakers' and other stakeholders' perceptions of key considerations for health system decisions and the presentation of evidence to inform those considerations: an international survey. Health Res Policy Syst 11(1):1–9

Wilson MG, Lavis JN, Gauvin FP (2015) Developing a rapid-response program for health system decision-makers in Canada: findings from an issue brief and stakeholder dialogue. Syst Rev 4(25):1–11

Wong SL, Green LA, Bazemore AW, Miller BF (2017) How to write a health policy brief. Fam Syst Health 35(1):21–24

World Bank (2019) Classification of low- and middle- income countries. https://data.worldbank.org/income-level/low-and-middle-income . Accessed 2020

World Health Organization (2004) Knowledge for better health: strengthening health systems. Ministerial Summit on Health Research:16-20

Yang CC, Chen H, Honga K (2003) Visualization of large category map for internet browsing. Decision Supp Syst 35(1):89–102

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Acknowledgements

We thank Julie Desnoyers for her collaboration on developing the search strategy, Stéphanie Lebel for extracting data on the selected articles and Valéry Ridde for peer-reviewing the article. This study was conducted as part of the first author’s doctoral training in industrial-organizational psychology. The candidate received financial support from Équipe RENARD, a research team studying knowledge transfer, which is led by Christian Dagenais and funded by the FRQSC.

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Arnautu, D., Dagenais, C. Use and effectiveness of policy briefs as a knowledge transfer tool: a scoping review. Humanit Soc Sci Commun 8 , 211 (2021). https://doi.org/10.1057/s41599-021-00885-9

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Related links, south african journal of information management, on-line version  issn 1560-683x print version  issn 2078-1865, sajim (online) vol.22 n.1 cape town  2020, http://dx.doi.org/10.4102/sajim.v22i1.1135 .

ORIGINAL RESEARCH

A review of knowledge transfer tools in knowledge-intensive organisations

Alfred H. Mazorodze; Sheryl Buckley

School of Computing, College of Science, Engineering and Technology, University of South Africa, Pretoria, South Africa

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BACKGROUND : Knowledge transfer is very important in knowledge-intensive organisations in both developed and developing countries. A knowledge-intensive organisation is an organisation whose operations depend on specialised knowledge. Knowledge-intensive organisations lose intellectual property when experienced employees retire from their jobs. To avoid knowledge loss, skills and expertise should be transferred from experts to non-experts on time. Knowledge transfer tools allow sharing of tacit knowledge between and amongst staff members. The study provides an analysis and review of the most effective knowledge transfer tools in knowledge-intensive organisations because an organisation's success is based on its ability to transfer knowledge. OBJECTIVES : The study had two main objectives: to identify and review knowledge transfer tools used in knowledge-intensive organisations and to recommend the best knowledge transfer tool that can be used in organisations for the purpose of enhanced competitive advantage. METHOD : A well-structured questionnaire was used to collect quantitative data from the research participants in knowledge-intensive organisations in Namibia. RESULTS : The results indicate that the most effective knowledge transfer tool in knowledge-intensive organisations is a community of practice; 40% of the participants considered the tool effective, and 27% considered it to be very effective. This was followed by the mentoring tool, which was ranked 54% effective and 11% very effective by the participants because it exposes mentees to new ideas and new ways of thinking. Storytelling was ranked 28% effective and 17% very effective because it is a natural learning process. Succession plans were ranked 21% effective and 12% very effective because having succession plans in place on time is essential for organisational success. Coaching and knowledge repositories were ranked below 20% on knowledge transfer effectiveness. From the findings, we conclude that the most effective tool for knowledge transfer in knowledge-intensive organisations is communities of practice (CoP) followed by mentoring, storytelling, succession plans, coaching and finally knowledge repositories. CONCLUSION : The most effective knowledge transfer tool in knowledge-intensive organisations is Communities of Practice, followed by mentoring, storytelling, succession plans and lastly coaching. Communities of Practice are important for knowledge transfer in that they encourage and promote teamwork through discussions and knowledge sharing amongst employees. The study therefore recommends the creation of such Communities of Practice in knowledge-intensive organisations for effective knowledge transfer and sharing.

Keywords : communities of practice; coaching; knowledge repositories; knowledge transfer; mentoring; storytelling; succession planning.

Introduction

Knowledge is an organisation's largest asset, and it must be managed effectively. Knowledge can be obtained either by transmission from the person who has it, by instruction or by extracting it from experience (Kim, Byung & Lee 2012). The European Framework for Knowledge Management (2015) define knowledge as an amalgamation of data and information, where expert opinion and experience are added to result in a valued asset that could be used to improve an organisation's decision-making capabilities. Data is described by Pearlson and Saunders (2004) as discrete and objective facts that are not organised and processed and do not have any specific meaning. Information is data that have been shaped into a form that is meaningful and useful to humans. Turban, Rainer and Potter (2005) stress that knowledge is data and/or information that has been processed and organised to convey understanding, experience and expertise as they apply to a current problem. Thus, knowledge consists of a mixture of information, values, rules and experiences from different sources. The much-needed knowledge resides in the heads of people, and it greatly influences organisational success.

According to Lievre and Tang (2015), knowledge transfer activities help employees to transfer expertise to others on time. During knowledge transfer, people collaborate and exchange important ideas. Some of the knowledge transfer tools or activities commonly used in knowledge-intensive organisations include communities of practice (CoP), succession plans, coaching, storytelling, knowledge repositories, mentoring and job rotation, amongst others (Wenger 2014; Whyte & Classen 2012). Knowledge transfer activities are necessary and can be fixed into the organisational structures and processes. We can therefore infer that knowledge transfer activities allow sharing of important knowledge in organisations, a point that Reiche (2011) also emphasises.

This review only looks at the knowledge transfer tools or activities that might accelerate innovation and boost productivity in knowledge-intensive organisations. It is important to highlight here that the knowledge transfer tools analysed and discussed here are not the end-all. The research was conducted with the following aims and objectives:

to identify and review the knowledge transfer tools used in knowledge-intensive organisations

to analyse and recommend the best knowledge transfer tool that can be used in knowledge-intensive organisations for the purpose of enhanced competitive advantage.

Theoretical framework and literature review

Nonaka and Takeuchi (1995) classify knowledge into two specific categories: 'tacit' and 'explicit'. Tacit knowledge is resident in an individual's mind. Tacit knowledge is deeply rooted in an individual's experiences, ideals, values and emotions (Nonaka & Takeuchi 1995:8). Explicit knowledge refers to knowledge that can be expressed and explained meaningfully in words and numbers (Davenport & Prusak 2000). Explicit knowledge can be communicated to other parties, and it can be processed by humans or machines programmed to perform the tasks. It is vital to understand these two types of knowledge and the mechanisms organisations engage to manage them. Becerra-Fernandez and Sabherwal (2010) made the useful observation that it was difficult to explicate tacit knowledge and then make it accessible for use by others.

Tacit knowledge can be transferred through mentorship and coaching (Davenport & Prusak 2000). With explicit knowledge, knowledge transfer is performed via services and documented processes, through which one appropriates the protocols and eventually takes ownership of this explicit knowledge. Knowledge transfer involves the focussed and purposeful communication of knowledge from the sender to a known receiver (King 2006). Knowledge transfer is considered by Knowledge Management (KM) practitioners as an integral part of every organisation. Knowledge Management practitioners (Grant 1996; Jang & Ko 2014; Leibowitz 2012; Liu 2016; Nonaka & Takeuchi 1994) concur that for knowledge to have an organisational impact, it must be transferred or shared. Kalling (2003) maintains that the organisation's success can be based on its ability to transfer knowledge. In line with this view, Hendriks (2009) contends that knowledge transfer provides opportunities to enhance the organisation's competitive advantage.

Literature review

McDonald and Cater-Steel (2016) describe CoP as an integrated approach for transferring knowledge through formal and/or informal groups. Schiavone (2013) adds that CoP can be formally established or can evolve spontaneously. Wenger (2014) adds that CoP are powerful manifestations of informal learning. Communities of practice are therefore effective knowledge transfer tools, as they permit employees to manage change by clarifying their roles in those organisations. These CoP have the ability to link professionals for knowledge-sharing purposes in knowledge-intensive organisations. A CoP can be Information Technology (IT)-based or non-IT, depending on the characteristic considerations of the community members (Heeyoung & Ilsang 2014). According to Durst and Wilhelm (2012), succession plans identify and develop employees to fill in positions in organisations at the right time. Knowledge-intensive organisations lose the knowledge and skills of experts when they retire from their jobs. Rothwell (2010) argues that the heart of succession plans is the epicentre of KM. Succession plans are an ongoing process that focusses on the knowledge transfer necessitated by an ageing workforce. Knowledge transfer through succession plans represents a proactive step towards employee empowerment, which leads to increased organisational responsiveness.

Coaching is a knowledge transfer tool that focusses on immediate problems and opportunities. Abbott (2014) argues that coaching entails guiding the trainee so as to fuse the operational knowledge that increases organisational performance. Management Mentors (2019) underscore the significance of coaching to perfect employee skills in an organisation. Stuhlmann (2012) defines storytelling as narratives that constitute operational knowledge. Research by Whyte and Classen (2012) proves that storytelling is a vital tool for transferring tacit knowledge, allowing sharing of deeper knowledge, which may boost the organisation's knowledge. An organisation that shares knowledge amongst its staff will certainly benefit from the knowledge-sharing efforts. Leblanc and Hogg (2010) argue that storytelling as a knowledge transfer technique allows organisations to discover tacit knowledge, as it is a natural learning process. Moreover, storytelling can be used to share lessons learnt from projects with coworkers who did not participate in the activity. Thus, storytelling can be used to build a shared understanding amongst employees in an organisation.

As defined by Leibowitz (2012), knowledge repositories are online storehouses of expertise and documentation about a particular domain. Knowledge repositories can be considered online self-help, as they make it easy to find relevant information and resources. Liu (2016) advises that organisations must develop means of documenting organisational knowledge. Relational databases are some of the technologies commonly used in building knowledge repositories for increased organisational efficiency. The American Productivity and Quality Center (APQC) (2010) adds that knowledge repositories support artificial intelligence technologies, including those used for electronic discussion groups, decision support systems, databases, expert systems and best practices. Mentoring is also considered a vital tool for knowledge transfer from experts to non-experts (Young 2013). The main aim of mentoring is to encourage that the individual reflect on the job as a whole (Rooney 2014). In most modern organisations, mentoring creates an organisational culture, which improves organisational performance. Knowledge transfer is highly likely to take place in a decentralised organisational structure, a point which is quite consistent in the literature (Liu 2016). Thus, mentoring provides specialised socialisation and personal support to facilitate knowledge transfer amongst employees.

The passion to share by and amongst the employees is very important for improving organisational performance (Wang & Noe 2010). Knowledge sharing and transfer generates new ideas, increases operational efficiency and helps employees to stay motivated. These knowledge-transferring activities should be linked to a proper organisational strategy that is aligned with the organisational objectives. The strategy should be based on the best possible design for creating, maintaining, transferring and applying organisational knowledge to reach the defined goals as advised by Grant (1996). From the submissions by various authors above, it becomes very clear that knowledge transfer simulates growth and innovation in organisations. Liu (2016) is of the view that knowledge transfer reduces the loss of an organisation's know-how.

Research design and methodology

A survey strategy was used to collect data from purposefully selected research participants in Namibia, a developing country in southern Africa. The participants were drawn from selected public and private knowledge-intensive organisations in Namibia. Quantitative data were collected by using paper-based questionnaires and analysed by using the Microsoft Office Excel 2016 package. Tests for internal consistency were performed to increase the validity and reliability of the findings. The sample had 112 participants from the knowledge-intensive organisations identified, and interestingly, the response rate was 100%.

Results and discussion

The participants responded to the questions asked as shown in Table 1 . The question asked was 'How effective are the following activities for knowledge transfer in your organisation?' The question sought to measure the usefulness of the knowledge transfer tools in the selected public and private organisations.

According to the data shown in Table 1 , the most effective knowledge transfer activity is a CoP, which was classified as very effective (27%) and effective (40%) by the participants. This was followed by mentoring, which was ranked based on a percentage as very effective (11%) and effective (54%). Succession plans were ranked based on a percentage as very effective (12%) and effective (21%). Storytelling was also considered an effective knowledge transfer tool, with 17% of the participants considering the tool as effective and 28% as very effective. Coaching and knowledge repositories were not considered very useful tools for knowledge transfer, and both had less than 20% representation. The following sections present and analyse the information in Table 1 in the form of pie charts for each knowledge transfer tool or activity.

Communities of practice

According to the data gathered from the participants and presented in Figure 1 , CoP were found to be effective in organisations by 40% of the participants. This was followed by 27% of the participants who considered CoP as very effective. It emerged that 24% of the participants had no opinion on the effectiveness of CoP; 6% and 3% considered CoP as somewhat effective and not effective, respectively. Considering a CoP 40% effective as adjudged by the participants might mean that the participants to some extent share a common sense of purpose. According to Jang and Ko (2014), knowledge assessment determines the need for a CoP, with its three elements of the domain, the community and the practice. Community of practice objectives should be established and linked to the organisational objectives. Membership penetration and growth should be analysed in a CoP to see how the community has grown. A knowledge transfer activity does not assess whether learning occurs, perhaps that is why some 3% of the participants considered a CoP as not effective for transferring knowledge in an organisation.

Succession planning

Succession plans were considered 21% effective by the participants. Correspondingly, 12% of the participants reflected that succession plans were very effective, whilst the other 37% had no opinion on the effectiveness of succession plans. Moreover, 4% of the participants considered succession plans as somewhat effective, whilst the other 26% considered succession plans not effective at all. When employees retire, organisations face the loss of intellectual and institutional experience, memory and capital for problem-solving. One way of overcoming this challenge is to introduce succession plans to maintain organisational knowledge. Knowledge is the foundation of human capital, and the ability to attract and retain knowledge is an important component of innovation (Young 2013). Succession plans ensure an ongoing availability of talent. The responses from the participants are presented in Figure 2 .

Coaching refers to an interactive process through which managers aim to solve performance problems (Graduate Mentoring Guidebook 2015). From the empirical evidence gathered from the participants, it was found that coaching was very effective and effective by 14% and 5% of the respondents, respectively. The main goal of coaching is to correct inappropriate behaviour, improve performance and impart practical skills. A small percentage of 10% considered coaching as not effective, and 15% considered coaching as somewhat effective. A median percentage (56%) had no opinion on the effectiveness of coaching. According to the Management Mentors (2019), coaching is task oriented and is short term, which is performance driven. The focus of coaching is on immediate problems and opportunities; thus coaching increases productivity by fostering a positive work culture. At individual level, coaching results in the development of self-awareness and greater responsibility. Most importantly, coaching motivates people and facilitates the adoption of a new organisational culture. The responses elicited from the participants are presented in Figure 3 .

Storytelling

Storytelling is used to transfer experts' knowledge to the junior and younger generation of employees (Whyte & Classen 2012). Moreover, storytelling is used to share lessons learnt from projects with coworkers and other peers. In this study, 17% and 28% of the participants considered storytelling as very effective and effective, respectively. It emerged that 30% of the participants considered storytelling as somewhat effective. On the other hand, 6% of the participants did not see storytelling as effective; thus they considered the tool not effective at all. The actual percentages of the responses from the participants are shown in Figure 4 . According to Whyte and Classen (2012), storytelling is a vital tool for transferring tacit knowledge, and it has been found to allow for sharing of deeper information and knowledge. Arguably, storytelling is one of the best ways to transfer tacit knowledge, in the sense that the storyteller is able to transfer knowledge in a way that people understand easily. Most learning processes take place through storytelling; hence, the participants considered the tool so effective.

Knowledge repositories

Leibowitz (2012) defines knowledge repositories as online storehouses of expertise and documentation about a specific domain and discipline. Very few of the participants at the organisations studied had used knowledge repositories, and because of this, they had no opinion on the effectiveness of knowledge repositories (73%). Out of the total participants, 14% considered knowledge repositories as somewhat effective. Only 6% considered knowledge repositories as effective. The individual responses about the effectiveness of knowledge repositories from the participants are shown in Figure 5 . Concisely, knowledge repositories are private databases that manage enterprise information. From an organisational perspective, knowledge repositories make it easy to find relevant information and knowledge. Knowledge repositories help organisations connect people with expertise via discussion forums and online searchable libraries. Some sources, like the Data Mining Techniques (Alton 2011), refer to knowledge repositories as data warehouses. Most importantly, knowledge repositories reduce training time for new staff, and they also help uncover automation opportunities via online self-help. Despite all the visible advantages of knowledge repositories, a great percentage of participants had no opinion on knowledge repositories.

Mentoring was found to be effective at the specific organisations studied. In this study, 54% of the participants considered mentoring as effective because skills have to be transferred from more experienced workers to less experienced workers though mentorship. None of the participants considered mentoring as ineffective. Twenty-two per cent had no opinion on the effectiveness of mentoring, and only 11% of the participants considered mentorship as very effective. The other 13% considered mentoring as somewhat effective. The actual responses from participants are shown in Figure 6 . Mentoring provides professional socialisation to facilitate knowledge transfer in knowledge-intensive organisations. Mentoring in organisations is needed to increase skill levels; hence 54% of the participants identified the tool as effective in knowledge transfer. To the mentees, mentoring improves self-confidence and encourages reflection on their practice (Management Mentors 2019). To the mentors, mentoring provides opportunities for reflecting on their own practice and enhancing peer recognition.

In knowledge-intensive organisations, it has been found that the most effective knowledge transfer tool is CoP; 40% of the participants considered this tool to be effective, and 27% considered it very effective. Communities of practice are important for knowledge transfer in that they encourage and promote teamwork through discussions and knowledge sharing amongst employees (Wenger 2014). This was followed by the mentoring tool, which was ranked 54% effective and 11% very effective by the participants. Mentoring exposes mentees to new ideas and new ways of thinking, which may accelerate and boost organisational effectiveness. Storytelling was ranked 28% effective and 17% very effective because it is a natural teaching and learning tool in organisations, which improves communication. Succession plans were ranked 21% effective and 12% very effective because the loss of organisational memory should not be underestimated. Having succession plans in place on time is essential for organisational success. Coaching and knowledge repositories were ranked below 20% effective. We can therefore conclude that the most effective tool for knowledge transfer in knowledge-intensive organisations is CoP followed by mentoring, storytelling, succession plans, coaching and finally knowledge repositories. The study therefore recommends the creation of CoP in knowledge-intensive organisations for effective knowledge transfer and knowledge sharing. In a CoP, people collaborate regularly to share information, improve their skills and actively work on advancing the general knowledge of the domain.

Acknowledgements

The authors thank all the participants who participated in this study and the language editor who edited this piece of work.

Competing interests

The authors declare that no competing interests exist.

Authors' contributions

A.H.M. collected the data from the knowledge-intensive organisations in Namibia and wrote the article. S.B. assisted by giving direction and the necessary support in writing the research article. Both authors approved the final version of the manuscript.

Ethical consideration

The study received ethical clearance from the university of South Africa's College of Science, Engineering and Technology's Research and Ethics Committee, with reference number: 009/AHM/2017/CSET_SOC.

Funding information

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

Abbott, G.N., 2014, 'Cross-cultural coaching: A paradoxical perspective', in E. Cox, T. Bachkirova & D. Clutterbuck (eds.), The complete handbook of coaching , 2nd edn., pp. 295-317, Sage, London.

Alton, L., 2011, The 7 Most Important Data Mining Techniques , viewed 20 August 2020, from https://www.datasciencecentral.com/profiles/blogs/the-7-most-important-data-mining-techniques

American Productivity & Quality Center (APQC), 2010, Best practice report , Measuring the Impact of Knowledge Management, Arlington, TX.

Becerra-Fernandez, I. & Sabherwal, R., 2010, Knowledge Management systems and processes , Armonk, New York, NY.

Davenport, T.H. & Prusak, L., 2000, Working knowledge: How organizations manage what they know , Harvard Business School Press, Boston, MA.

European Framework for Knowledge Management, 2015, Business Knowledge Management: A study on market prospects, business needs and technological trends: Final report , viewed 15 August 2018, from https://ec.europa.eu/information_society/doc/library/business_knowledge_management.pdf

Graduate Mentoring Guidebook, 2015, viewed 10 May 2015, from http://www.unl.edu/mentoring/introduction

Hendriks, P., 2009, 'Why share knowledge? The influence of ICT on the motivation for knowledge sharing', Knowledge and Process Management 6(2), 91-100.         [  Links  ]

Kalling, T., 2003, 'Organization-internal transfer of knowledge and the role of motivation: A qualitative case study', Knowledge and Process Management 10(2), 115-126.         [  Links  ]

Leblanc, S.M. & Hogg, J., 2010, Storytelling in knowledge management: An effective tool for uncovering tacit knowledge , viewed 20 May 2017, from http://www.tlainc.com/articl222.htm

Leibowitz, J., 2012, Knowledge management handbook: Collaboration and social networking , 2nd edn., CRC Press, Taylor and Francis Group, Boca Raton, FL.

Lievre, P. & Tang, J., 2015, 'SECI and inter-organizational and intercultural knowledge transfer: A case-study of controversies around a project of co-operation between France and China in the health sector', Journal of Knowledge Management 19(5), 1069-1086.         [  Links  ]

Liu, S., 2016, Introduction to knowledge management , viewed 21 May 2017, from http://web.archive.org/web/20160319233812/Introduction_to_Knowledge_Management.html

Management Mentors, 2019, Building strong workforce with mentoring , viewed 22 September 2020, from https://www.mentoringcomplete.com/

McDonald, J. & Cater-Steel, A. (eds.), 2016, Communities of practice: Facilitating social learning in higher education , Springer, Singapore.

Nonaka, I. & Takeuchi, H., 1994, 'A dynamic theory of organizational knowledge creation', Organization Science 5(1), 14-37.         [  Links  ]

Nonaka, I. & Takeuchi, H., 1995, The Knowledge-creating company: How Japanese companies create dynamics of innovation , Oxford University Press, New York, NY.

Pearlson, K.E. & Saunders, C.S., 2004, Managing and Using Information Systems: A Strategic Approach , Wiley, New York, NY.

Rooney, J., 2014, 'Knowledge management and organisational learning', Proceedings of the 11th International Conference on Intellectual Capital , University of Sydney, Sydney.

Rothwell, W.J., 2010, Effective succession planning: Ensuring leadership continuity and building talent from within , 4th edn., Amacom, New York, NY.

Schiavone, F., 2013, Communities of Practice and Vintage Innovation. A Strategic Reaction to Technological Change , Springer, New York, NY. https://doi.org/10.1007/978-3-319-01902-4

Stuhlmann, D.D., 2012, Knowledge management terms , viewed 15 May 2017, from http://home.earthlink.net/~ddstuhlman/defin1.htm .

Turban, E., Rainer, R.K. & Potter, R.E., 2005, Introduction to information technology , Wiley, New York, NY.

Wenger, E., 2014, Learning in landscapes of practice: Boundaries, identity, and knowledgeability in practice-based learning , Routledge, Abingdon.

Young, R., 2013, Knowledge management for the public sector , Asian Productivity Organization, Tokyo.

Received: 30 July 2019 Accepted: 16 July 2020 Published: 09 Oct. 2020

Knowledge transfer and exchange: review and synthesis of the literature

Affiliation.

• 1 University of British Columbia Okanagan, Kelowna, BC. [email protected]
• PMID: 18070335
• PMCID: PMC2690353
• DOI: 10.1111/j.1468-0009.2007.00506.x

Knowledge transfer and exchange (KTE) is as an interactive process involving the interchange of knowledge between research users and researcher producers. Despite many strategies for KTE, it is not clear which ones should be used in which contexts. This article is a review and synthesis of the KTE literature on health care policy. The review examined and summarized KTE's current evidence base for KTE. It found that about 20 percent of the studies reported on a real-world application of a KTE strategy, and fewer had been formally evaluated. At this time there is an inadequate evidence base for doing "evidence-based" KTE for health policy decision making. Either KTE must be reconceptualized, or strategies must be evaluated more rigorously to produce a richer evidence base for future activity.

Publication types

• Meta-Analysis
• Research Support, Non-U.S. Gov't
• Decision Making
• Diffusion of Innovation*
• Evidence-Based Medicine*
• Health Policy*
• Health Services Research
• Information Dissemination

Knowledge investment and search for innovation: evidence from the UK firms

• Open access
• Published: 05 March 2024

Cite this article

You have full access to this open access article

• David B. Audretsch 1 , 2 ,
• Maksim Belitski 3 , 4 , 5 &
• Farzana Chowdhury   ORCID: orcid.org/0000-0001-8526-3544 6  

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Recent research on innovation management and knowledge transfer has demonstrated that industry knowledge collaboration and knowledge spillovers matter for innovation, but so does a firm's Research and Development (R&D). Conditional to a firm's R&D investment, this study makes a theoretical investigation into the role of two knowledge transfer strategies—industry coopetition and industry knowledge spillovers for a firm's innovation. Based on an analysis of a sample of 17,859 UK firms from 2002 to 2014, we demonstrated why and under what conditions firms will (a) invest in internal R&D, (b) engage in coopetition, and (c) access knowledge spillovers to introduce new to firm (incremental innovation) and new to market products (radical innovation). The results of this study demonstrate that firm managers who choose knowledge spillovers versus coopetition are likely to achieve radical vis-à-vis incremental innovation. Benefits from the coopetition can be achieved with low investment in R&D, while R&D is essential in recognizing the knowledge spillover for radical innovation. By deciding whether to deploy its costly R&D and access external knowledge via industry coopetition or spillovers, the firm is also making a concomitant decision about the type of innovative activity it will generate. Thus, a firm strategy for knowledge transfer and investing in knowledge internally is inextricably linked to a firm strategy involving the type of innovative output.

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1 Introduction

Governments in developed and developing countries see innovators as a source of regional growth and employment, technological change, and research commercialization, with multiple examples of support for innovation in the US economy (Andersen et al., 2017 ; Link & Scott, 2010 ). To innovate, firms invest in research and development (R&D) (Goel et al., 2023 ) so that they can secure a competitive advantage (D'aveni & Ravenscraft, 1994 ) and facilitate innovation activity (Laursen & Salter, 2014 ). Increasingly, firms rely on internal knowledge inputs such as investment in Research and Development (R&D) and external knowledge in the form of direct collaboration with incumbent firms (Ketchen et al., 2004 ; Khanna et al., 1998 ) and industrial knowledge spillovers (Grilliches, 1992 ; Audretsch & Feldman, 1996 ; Audretsch & Belitski, 2020a ). We build on Link ( 1978 : 370) in defining "research as the primary search for technical or scientific advancement, and development refers to translating these advancements into product or process innovations." Theoretically and empirically, research and development activities are challenging to separate from the resulting technology. We define coopetition, drawing on Ritala ( 2018 ) and Brandenburger and Nalebuff ( 1996 ), as the simultaneous pursuit of cooperation and competition. Coopetition is likely to enhance firm innovation performance (Gnyawali et al., 2006 ; Bengtsson et al., 2010 ; Huang & Yu, 2011 ), while innovation performance could also suffer due to the intensified tension from coopetition resulting from the intense contradictions inherent in such relationships (Gnyawali et al., 2011 ; Park et al., 2014 ).

Finally, firms aim to access knowledge spillovers as an alternative source of knowledge related to co-location in a city or region with a high investment in R&D by incumbent firms and other entrepreneurs (Audretsch & Belitski, 2013 ). Marshall ( 2009 ) established the concept of knowledge spillovers, further developed by Grilliches ( 1992 ).

A vast body of literature has been produced at the intersection of knowledge transfer from competitors and innovation (Bouncken et al., 2018 ; Ritala & Hurmelinna-Laukkanen, 2013 ) suppliers, customers and universities and innovation (Belitski et al., 2023 ) and knowledge spillovers and innovation (Audretsch & Belitski, 2022 ) with little to no evidence on companies using the combination of open knowledge sources from competitors directly and via knowledge spillovers for innovation (Roper et al., 2013 , 2017 ).

Thus, prior research in knowledge transfer literature has overlooked the two distinct knowledge transfer strategies—knowledge spillovers and knowledge collaboration with competitors leading to a firm's decision on the type of innovation a firm can undertake radical vis-a-vis incremental innovation. The relationship between the source of knowledge transfer and innovation type is often moderated by R&D (Link, 1978 ; Leyden & Link, 2015 ). The assumption of homogeneity in innovation outcomes independent of knowledge inputs is surprising because a very different strand of literature in innovation has explicitly analyzed the heterogeneity of innovation and why not all innovative activities are equally dependent on knowledge spillovers, coopetition, or internal investment in R&D (Link & Scott, 2010 ; Tomlinson & Fai, 2013 ; Park et al., 2014 ; Czakon et al., 2020 ; Link et al., 2022 ). We suggest that the missing condition can be the firm's investment in R&D, and knowledge sourced via localized spillovers can be an essential factor facilitating and limiting innovation type. Furthermore, we argue that along with investment in human capital that contributes to a firm's absorptive capacity, R&D investment is highly beneficial and can increase innovation output directly (Audretsch & Belitski, 2020b ; Bronzini & Piselli, 2016 ) and indirectly by increasing firm's absorptive capacity (Cohen & Levinthal, 1989 ). Estimating the impact of R&D on innovation output is challenging as no unique measure of the output from R&D exists (Leonard, 1971 ; Link, 1978 ; Kobarg et al., 2019 ).

Thus, this paper aims to examine how various sources of knowledge, such as investment in internal R&D, knowledge collaboration with competitors, and industry knowledge spillovers, shape two types of innovation.

In pursuing this aim, we first contribute to the knowledge transfer literature by suggesting that knowledge transfer via spillovers or coopetition and internal investment in R&D have a distinct impact on the choice of innovation output—incremental or radical. Our main finding is that coopetition increases incremental innovation, with the effect is more significant for firms who invest in R&D. In contrast, knowledge spillovers in the industry increase radical innovation for firms who invest in R&D. Coopetition is unlikely to increase radical innovation in firms that do not invest in R&D. In contrast, those who invest in R&D will have on an average higher level of radical innovation at any strength of coopetition. We found that R&D intensity matters for both types of knowledge transfer—industry spillovers and coopetition leading to higher radical innovation.

Second, by exploring both the firm's R&D investment and external knowledge transfer via coopetition or spillovers, we can identify and gain in-depth knowledge regarding  how  certain types of resource acquisition activity influence firms resource endowment (Helfat & Peteraf, 2003 ; Spithoven et al., 2010 ) and  why  a firm decides to engage in a specific type of innovation (Audretsch et al., 2023 ; Hsieh et al., 2018 ).

This study extends the mixed empirical findings originating from the fact that the large body of literature has explicitly ignored the combined effect of R&D investment and coopetition as means of accelerating radical and incremental innovation (Bouncken et al., 2018 ; Nemeh, 2018 ; Nemeh & Yami, 2019 ).

The remainder is as follows—Sect.  2 reviews coopetition and knowledge spillover literature streams. Section  3 illustrates the methodology adopted. Section  4 presents the results, and Sect.  5 discusses these findings with theoretical and managerial implications. Section  6 concludes.

2 Theoretical framework

2.1 heterogeneity in innovation outcomes.

Innovation enhances a firm's competitive advantage through organizational renewal, growth, and profits (Boulding & Christen, 2008 ; Lieberman & Montgomery, 1988 ). We define two innovation types—radical and incremental innovation. Radical innovation is an introduction of a new product to the market that is revolutionary and substantially different from the existing products in the market (Bouncken et al., 2018 ; Pavitt, 1991 ), what scholars often refer to as Schumpeterian innovation (Audretsch & Belitski, 2023a ). Compared to incremental innovation (Koberg et al., 2003 ), which re-introduces existing products and services, radical innovations transform markets and significantly contribute to society (Leifer et al., 2000 ; Marvel & Lumpkin, 2007 ). On the other hand, incremental innovation can allow firms to avoid mistakes made by firms along with costs associated with first-mover innovation; in other words, avoid uncertainties associated with first-mover innovation completely (Arora et al., 2021 ; Cirik & Makadok, 2021 ; Krishnan & Ulrich, 2001 ).

2.2 R&D investment and inter-industry spillover as knowledge transfer

Knowledge resources are essential for creating and sustaining a competitive advantage.

The endowment of organizational knowledge resources is a critical success factor (Spender & Grant, 1996 ). A firm's resource endowments help it build its 'absorptive capacity' and acquire, assimilate, and transform new knowledge (Cohen & Levinthal, 1990 ). Firms can acquire knowledge through various external sources (Cassiman & Veugelers, 2002 ) through coopetition- cooperating with their competitors (Gnyawali et al., 2006 ) and via industry knowledge spillovers (Audretsch & Belitski, 2020a ). Knowledge transfer is unintentionally taking place through knowledge spillovers when firms supply and buy from each other in the industry and invest in R&D; additionally, they can invest in developing and improving internal knowledge sources by investing in developing their R&D (Denicolai et al., 2016 ). Since some of these resources are difficult to imitate, they can play a critical role in firms' ability to innovate (McEvily & Chakravarthy, 2002 ). These resources can also act as an 'isolating mechanism' by enabling firms to gain a competitive advantage, as suggested by Lieberman and Montgomery ( 1988 ).

Innovation requires various interdependent knowledge inputs (Howard et al., 2017 ). R&D can serve as an indication of a firm's investment in its internal knowledge generation activity. However, while investment in R&D internally is distributed in creative works, it is also an investment in firm-specific human capital, which creates greater absorptive capacity (Bianchi, 2001 ). R&D leads to new product creation (innovation) because it facilitates experimentation and improvisation with new knowledge combinations not yet available in the market. It is unlikely that R&D investment will aim to imitate existing products. Such investment brings in new knowledge that helps recognize external (knowledge and market) opportunities and turns them into new market products.

2.3 Coopetition and innovation types

Management scholars argue that firms, despite the risks of coopetition, are increasingly engaging in it, especially in industries with a short product lifecycle (Arranz & Arroyabe, 2008 ; Gnyawali & Park, 2009 ; Nieto & Santamaria, 2007 ). Existing research suggests that coopetition is essential for innovation (Ritala, 2018 ). This is because the coopetition brings together complementary financial and human capital resources necessary for engaging in innovation-related activities (Bianchi, 2001 ; Tether, 2002 ), and by combining their complementary resources and capabilities, firms can enhance their innovation-related activities (Cassiman & Valentini, 2016 ; Khanna et al., 1998 ).

Despite the growing popularity of coopetition, it can present challenges for the participating firms. Existing research suggests that not all firms are ready to coopetition because of the potential involuntary knowledge outflow to competitors (Roper et al., 2017 ). Innovators are less likely to collaborate with competitors when they develop innovation and when considerable investment in R&D and pre-design of the prototype are involved. Innovators are more willing to coopetition during a new product's launch phase. By engaging in the launch phase, firms can reduce the time needed to commercialize new products, access market information (Gnyawali et al., 2006 ), and reduce entry costs (Laursen & Salter, 2014 ). Innovators can also use coopetition to strategically reduce innovation-related competition (Aghion & Howitt, 2008 ).

The positive effects of coopetition relate to learning from competitors (Bouncken & Fredrich, 2016 ; Simonin, 1999 ), but the knowledge gained from the joint activity may not directly translate to innovation. The coopetition process also allows all the members involved to discover their partners' activities, thus making it easier to replicate competitors' processes or innovations (Roper et al., 2013 ) since knowledge transfer occurs through parties involved by information exchange and personal interactions. Employees of both firms within the same industry and markets have diverse educational and work experience, and coopetition brings together a diverse set of knowledge resources and knowledge-sharing opportunities (Nieto & Santamaría, 2007 ). While voluntary and involuntary knowledge transfer between competitors is a positive effect of coopetition, knowledge hoarding can be a problem. These problems can arise when all partners experience a lack of trust, a lack of incentives, and a risk of high opportunism (Bouncken & Fredrich, 2016 ; Khanna et al., 1998 ). Existing research suggests that fear of imitation can also affect motivation to share knowledge within the industry (Cassiman & Veugelers, 2002 ; Oxley & Sampson, 2004 ).

Firms will choose incremental vis-a-vis radical innovation, with the relationship depending on the firm's investment in R&D. This is because coopetition creates tacit knowledge between competitors. Still, such knowledge may not always be formalized (Quintana-Garcia & Benavides-Velasco, 2004 ) if the recipient firms do not have the capability and creativity to understand and interpret the information (De Dreu et al., 2011 ; Majchrzak et al., 2012 ).

Coopetition is seen as a cost-cutting activity in industries with a high cost of R&D activities to increase organizational capabilities. It is beneficial for partners to be able to innovate product that has already been introduced in the industry by competitors instead of pursuing innovation. Therefore, we hypothesize that a firm will focus on incremental innovation in the absence of or low internal R&D investment. We hypothesize:

Hypothesis 1

Coopetition will have a more significant positive effect on incremental than radical innovation for firms with no investment in internal R&D.

2.4 Coopetition, R&D, and innovation types

The innovation type may originate from different combinations of external knowledge transfer and investment in internal R&D (Semadeni & Anderson, 2010 ). So far, the evidence has been inconclusive. For instance, Mention ( 2011 ) analyzed 1052 innovative firms and discovered that internal knowledge is most important as external collaboration in service firms does not result in innovation and only initiates new components to firm products. Tomlinson and Fai ( 2013 ) analyzed data on 371 UK manufacturing SMEs and found that coopetition has no significant impact on innovation. This argument highlights potential risks of coopetition and possible adverse effects when a firm’s R&D investment is high.

Radical innovation depends on a firm’s investment in internal R&D, industry knowledge spillovers, and knowledge collaboration (Suarez & Lanzolla, 2007 ). These knowledge sources can complement each other and create a competitive advantage for a firm by strengthening the internal knowledge capability and speeding up market entry. Complementarities between knowledge inputs change a firm’s routines and processes (Hill & Rothaermel, 2003 ) and generate new knowledge that can lead to transformative innovation. The recombination of competitors’ knowledge could be more beneficial as it allows experimenting with new ideas (Katila & Ahuja, 2002 ) within existing markets and increases the propensity to innovate (Troilo et al., 2014 ) radically. Even though combined knowledge is essential for innovation, not all firms have the internal capacity to extract knowledge from external sources, in particular via coopetition.

In coopetition, firms manage complementary knowledge to facilitate a collaborative innovation process, with cognitive and technological proximity enhancing knowledge co-creation. Cognitive and technological proximity facilitates the exchange of tacit knowledge between partners (Ritala & Hurmelinna‐Laukkanen, 2013 ), resulting in further knowledge spillovers (Audretsch & Feldman, 1996 ). This results in a positive side of the coopetition-innovation nexus. Howard et al. ( 2017 ), using micro-level data on 717 technology-based firms, demonstrated that knowledge collaboration is more likely between firms with which global trajectories of key technologies are more closely aligned.

Investment in internal knowledge, such as R&D and other investments, bolsters the relationship between external knowledge and innovation (Denicolai et al., 2016 ), stimulating creativity (De Dreu et al., 2011 ) but not always leading to new market products. Legal and strategic agreements with competitors may become a roadblock for the first mover, as co-creating new knowledge may imply value co-creation and capture. Howard et al. ( 2017 ) also demonstrated that when knowledge partners are more active in defending their intellectual property and forming R&D alliances, it generates the interlock, and the firm is more likely to gain access to the partner’s tacit knowledge.

Therefore, enforcement of R&D collaboration agreements and non-disclosure of knowledge when co-creating knowledge with competitors may prevent first-mover advantage. While coopetition increases the risks of unintended knowledge outflows to competitors (Cassiman & Veugelers, 2002 ), such outflows could be legally binding, and an independent market entry strategy may no longer be an option.

As the intensity of coopetition increases, transaction costs, and risks also increase. At the same time, collaborating with competitors creates a delay in independent first market entry, as all collaborators jointly the IP on the novel innovation, reducing the propensity of each partner to innovate and commercialize new products solely, resulting in a negative effect. Knowledge-sharing opportunities with competitors can facilitate the process of innovation (Li et al., 2008 ). Still, it will prevent a firm from commercializing new knowledge independently or individually, limiting the introduction of radical innovation. These results demonstrate potential benefits and risks from coopetition to radical innovation. A link between coopetition and innovation will be diminished by the potential costs, risks, and limitations of coopetition, dissipating the benefits from competitors’ knowledge, eventually flipping the knowledge spillover of the innovation curve downwards (Audretsch & Belitski, 2022 ). We hypothesize:

Hypothesis 2a

The combined effect of internal R&D and coopetition on radical innovation is inverted U-shaped.

2.5 Knowledge spillovers, R&D, and innovation types

Industry knowledge spillovers are an attractive source of external knowledge for innovation from industry. Their unique phenomenon is based on the “non-excludability” of knowledge (Audretsch & Keilbach, 2007 : 1246) created by the third organizations, which is used by pro-active firms who access knowledge spillovers to innovate (Audretsch et al., 2023 ). While the members of competitor firms share common knowledge and other resources (Gong et al., 2013 ), knowledge spillovers allow innovators to use knowledge independently of industry competitors as a positive externality. The main criterion for using the industry knowledge spillover is the firm’s investment in R&D. Existing literature shows that without internal R&D, firms may be unable to integrate knowledge spillovers (Cohen & Levinthal, 1989 ). The firm’s capacity to recognize and adopt external knowledge will facilitate knowledge spillovers and new product creation independently of active collaboration with partners. It may enable rapid market entry before competitors.

Knowledge spillovers are transmitted with the following two mechanisms. Firstly, the mechanism of knowledge spillover works as a conduit to acquire information about new products and processes from external sources (intermediate inputs) and may enable new offerings when combining knowledge from the local market (Bartelsman et al., 1994 ; Keller, 2002 ). Secondly, industry knowledge spillovers result from high investment in R&D in specific sectors, when the number, concentration, and contacts between R&D managers, R&D collaboration, and poaching of employees.

Firms with high investment in R&D will quicker appropriate knowledge spillover and combine knowledge inputs to create radically new products (Audretsch & Belitski, 2020b ). To reap the advantages of co-location with firms who invest in R&D locally, a firm may consider an interval investment in knowledge to better access and absorb the knowledge that spills from other firms in a region. Firms with access to knowledge spillovers are unlikely to choose incremental innovation as they bear the cost of R&D and will aim to create new-to-market products. We hypothesize:

Hypothesis 2b

The combined effect of internal R&D and knowledge spillovers increases radical innovation and has no effect on incremental innovation.

Our conceptual framework is presented in Fig.  1 .

Conceptual framework

3 Data and method

3.1 data matching and sample description.

To test our hypotheses, we used six pooled cross-sectional datasets—Business Structure Database (BSD), known as the Business Registry, and the Community Innovation Survey (UKIS) (Office for National Statistics, 2017 , 2018 , 2019 ). Firstly, we pooled UKIS and BSD data by year and firm ID. Each of these UKIS is conducted every second year by the Office of National Statistics (ONS). Secondly, we used Business Structure Database (BSD) data to match it by year of UKIS. This match was done to minimize the endogeneity issue of a two-way causality. Each wave of the UKIS is selected as a stratified sample of a pool of firms by region, firm size, and industry. The data has a panel element with firms appearing more than once in the survey.

Although we have an unbalanced panel during 2002–2014, our final sample of data available, excluding all missing values, consists of 21,140 observations. For the list of the variables included in this study, please refer to Table 1 , while the correlation matrix is presented in Table 4 in the “ Appendix ”. Our sample consists of 90 industries at 2-digit SIC across 12 UK regions, which were used to calculate knowledge spillovers. Sample description by industry, firm size, and the survey wave is illustrated in Table 2 A, B.

3.2 Measures

Our first dependent variable is radical  innovation , which equals one if a firm develops and introduces a new product or service to the market and zero otherwise (Roper et al., 2017 ; Un et al., 2010 ). Our second dependent variable is incremental innovation, which equals one if a firm has developed a product or service that existed in the market before a firm and is new to the firm, zero otherwise (Kobarg et al., 2019 ). Firms report zero in cases where no innovation project was undertaken, or the project was not completed over the 3-year period the questionnaire referred to. Innovation plans may not have been completed within the 3 years preceding the survey.

We use three explanatory variables related to knowledge transfer from the industry—industry knowledge spillovers and direct collaboration with competitors (coopetition) and knowledge creation in-house—internal R&D. All explanatory variables are lagged one period to address potential lag between knowledge investment and transfer to a firm and innovation outputs (Hall et al., 1984 ) Following Grilliches ( 1992 ) and Keller ( 2002 ), we operationalized  industry spillovers  using the flow of knowledge from the internal R&D expenditure in the industry. These knowledge spillovers are industry-specific and calculated using 2-digit SIC classification and 2-letter postcode for boroughs. Knowledge spillovers include the "spillover pool" (in-house R&D expenditure by firms within the 2-digit SIC and 2-letter postcode). While calculating industry knowledge spillovers we used BERD data and excluded the firm's R&D. The industry spillover is calculated as:

where subscript i indicates a firm; R is a measure of internal R&D within the 2 digit SIC (Bloom et al. 2007); and w ii is an industry weight, w ii  = 1 R f —is the firm's internal R&D; R ir —R&D in industry i and region r (by 2 letter postcodes in the UK); R i_country —R&D expenditure at a country ion the industry r. Knowledge spillover demonstrates the degree of regional specialization of the industry if \(S_{ir}\) ratio increases.

Coopetition is measured by the importance of collaboration with competitors for innovation activities (zero—not important to 3—highly important). Unlike knowledge spillovers, coopetition is not limited to the same region.

Our last explanatory variable is internal R&D as a proxy for technical capital, and we treat these expenditures drawing on Link ( 1978 ) as a direct input into the innovation production process. We operationalized internal R&D over the last 3 years (000s pound sterling) to total sales (000s pound sterling) (Kobarg et al., 2019 ). R&D intensity can measure a firm's absorptive capacity as it facilitates acquiring, assimilating, and transforming new knowledge (Cohen & Levinthal, 1989 , 1990 ; Eisenhardt & Martin, 2000 ).

To demonstrate the effect of knowledge spillover and coopetition on two types of innovation outcomes, we created a variable absorptive capacity low (associated with zero R&D intensity and the first quartile of R&D intensity) and absorptive capacity high, which means that R&D intensity is greater than zero and it's within second, third or fourth quartile). This measure was used in Fig.  2 to calculate the predictive margins of knowledge spillover of innovation and coopetition on innovation at different levels of R&D.

Predictive margins for radical and incremental innovation for industry knowledge spillovers, coopetition, and R&D. Source : Office for National Statistics ( 2017 , 2018 , 2019 )

Firm size and age may directly affect a firm's innovation capabilities (Haltiwanger et al., 2013 ). The log of the number of full-time employees is used as a proxy for firm size and years since the establishment as a proxy for a firm age (Rogers, 2004 ); collaboration is an important channel of knowledge transfer. To control for knowledge collaboration intensity (van Beers & Zand, 2014 ), we use binary variables of regional, national and global knowledge collaboration. Prior research has demonstrated that knowledge is interdependent and that a firm's performance can exhibit a form of external dependency when a firm pursues the development or/and adoption of new technologies (Howard et al., 2017 ).

We control foreign ownership by including the variable 'foreign' (Fitza & Tihanyi, 2017 ). It is coded as one if the company headquarters is located overseas and zero if not. Due to high competition in foreign markets, exporters are more likely to introduce innovation. The variable "exporter" equals one if a firm sells its products in foreign markets and zero otherwise. We add the variable "survival," which indicates whether or not a firm continues its operations in 2017 following the argument that innovators survive longer (Colombelli et al., 2013 ). We control for the legal status of newly established firms (publicly listed company, buyout, spin-off, partnership, not-for-profit), as prior research found that newly emerging organizations are more likely to introduce new products and services and experiments (Bradley et al., 2011 ). Firms that belong to a larger enterprise group could be less likely to innovate (Okhmatovskiy et al., 2020 ).

3.3 Model specification

Using multivariate logistic regression, we estimate two models for radical and incremental innovation (Koberg et al., 2003 ). The following equation represents the logistic (logit) models we used to estimate the likelihood of radical and incremental innovation:

where \({\text{Y}}_{{{\text{i}},{\text{t}}}}\) is a types of innovation (radical or incremental) in the firm i at time t; \({\text{x}}_{{{\text{i}},{\text{t}} - 1}}\) is a vector of our explanatory variables such as R&D intensity, industry spillover, and coopetition and their interaction for a firm i in time t − 1 (Hall et al., 1984 ); \({\text{z}}_{{{\text{i}},{\text{t}}}}\) —is a vector of control exogeneous variables for a firm i in time t. We control for unobserved heterogeneity of a firm i by adding industry and region fixed effects \({\uprho }_{{\text{i}}}\) , and time fixed effects \({\uplambda }_{{\text{t}}}\) . The error term is denoted by \({\text{u}}_{{{\text{i}},{\text{t}}}}\) for firm i, at time t. Our reference year is 2002–2004, our reference region is North-East of England, and our reference industry is mining and agriculture. As part of a robustness check, we applied the weighting of the sample and estimated logistic regression with survey weights by firm size, industry, and region.

We calculated VIF from a Pooled OLS version of the model, and individual and a group value of VIF were less than five (Wooldridge, 2009 ).

Table 3 illustrates the results of the logistic estimation. The results related to the relationship between industry knowledge spillovers and types of innovation outcomes conditional on investment in R&D are presented in columns 3–4 (Table 3 ), and coopetition and innovation outcomes conditional on investment in R&D in columns 1–2 (Table 3 ). Columns 1 and 3 in Table 3 predict the level of radical innovation, and columns 2 and 4 predict the level of incremental innovation; coefficients are reported in odd ratios. The value of the coefficient above unity (1), means an increase in the likelihood, while the value below unity (1) means a decrease in the likelihood. The models' goodness of fit is evaluated by comparing the likelihood ratios across different models.

In Table 3 and our model, we included coopetition in levels and a squared term. Our H1, which states that coopetition has a greater effect on incremental than radical innovation, is supported (columns 1–4, Table 3 ). Interestingly, we found that the relationship between coopetition and radical innovation is not significant for the linear relationship but significant for the non-linear relationship. This finding extends prior research on coopetition and innovation ( \(\beta\)  = 1.241, p  < 0.01 and \(\beta\)  = 0.941, p  < 0.01) (column 1 Table 3 ) (Bouncken et al., 2018 ; Lind & Mehlum, 2010 ) by demonstrating that both innovation types could suffer due to the intensified tension from coopetition (Bengtsson & Kock, 2014 ).

We also find that the relationship between coopetition and incremental innovation is non-linear, expanding the prior research on why companies engage in coopetition ( \(\beta\)  = 2.144, p  < 0.01 and \(\beta\)  = 0.857, p  < 0.01) (column 2 Table 3 ). This finding is important as it demonstrates that knowledge-sharing opportunity with competitors facilitates incremental innovation to a greater extent than radical innovation (Gong et al., 2013 ). More importantly, as the intensity of coopetition increases, the risks and costs related to coopetition grow, leading to a decrease in radical innovation. Our finding is important as we show that coopetition may only work until a certain threshold for radical innovation.

Our hypothesis H2a stated that a combination of investment in R&D and coopetition for radical innovation is inverted U-shaped. We found that the direct effect of investment in R&D is insignificant, so our hypothesis is not supported. Related to our H2a, the odds ratio of the interaction coefficient between R&D investment and coopetition (in levels) is significant and positive (β = 0.621, p  < 0.05) (column 2, Table 3 ). However, the squared term of interaction is insignificant, not supporting H2a. Our estimation demonstrated that R&D investment facilitates the effect of coopetition on incremental innovation, but this does not lead to a negative effect. These findings extend prior research on knowledge transfer from competitors (Ritala & Hurmelinna-Laukkanen, 2013 ), demonstrating the existence of the inflicting point in the relationship between coopetition and radical innovation.

With regards to our hypothesis H2b, we found a positive effect of a firm's R&D on a firm's innovation ( \(\beta\)  = 1.88, p  < 0.01) (column 1, Table 3 ). Economically, we interpret this effect as a one percent increase in internal R&D intensity associated with an increase in the propensity of radical innovation in 1.88 times. The direct effect of knowledge spillovers on two innovation types is insignificant (columns 1–4, Table 3 ).

Our H2b, which states that the combined effect of investment in R&D and knowledge spillover will increase radical innovation, is supported. Additionally, the results suggest that the relationship is more complex than we expected. An increase in industry knowledge spillovers in a region and greater firm's R&D will at some point lead to a negative effect on radical innovation ( \(\beta\)  = 6.111, p  < 0.01) and squared term ( \(\beta\)  = 0.028, p  < 0.05) (column 3, Table 3 ). This demonstrates that the relationship is non-linear.

In order to test non-linearity in the relationship illustrated in Fig.  1 , we draw on Lind and Mehlum's ( 2010 ) technique for non-linearity testing. We use predictive margins to test our hypothesis (Fig.  2 A–D) using the results of estimation in Table 3 for each specification.

Predictive margins in Fig.  2 B clearly demonstrated an inverted U-shape relationship with diminishing returns of coopetition for incremental innovation. Figure  2 B supports our H1, which states that coopetition will have a greater positive effect on on incremental than radical innovation for firms with lack of internal R&D. However, firms that do not invest in R&D and have a high level of coopetition are likely to outperform their counterparts who also invest in R&D and collaborate in the industry with competitors. Additionally, there is a diminishing return for incremental innovation \((\beta\)  = 0.621, p  < 0.01). Figure  2 A also demonstrates the positive effect of coopetition for radical innovation when the level of coopetition is low, and firm invests in R&D. We do not find support for H2a. As expected, we find that firms collaborating with competitors and not investing in R&D will be less likely to introduce new products to the market (Fig.  2 A).

Figure  2 C demonstrates an inverted U-shaped relationship between knowledge spillovers and radical innovation for firms that invest in R&D. This finding partly supports H2b, which predicted the positive effect of knowledge spillover for innovation when the firm's own R&D increases ( \(\beta\)  = 6.111, p  < 0.01) (Roper et al., 2013 , 2017 ). Our results demonstrate limits to the open innovation strategy of firms (Audretsch & Belitski, 2023b ). It also shows that the knowledge spillover may have limits as a positive externality and may reduce radical innovation when industry knowledge spillovers are high. We explain this by excessive and uncontrolled industry spillovers, which may threaten a firm's incentive to invest in R&D and innovate, as the knowledge is easily dissipated. An increase in knowledge spillover consequently decreases the propensity to innovation (Bloom et al., 2013 ). When knowledge of competitors can be easily observed (high knowledge spillovers), free-riding may occur, increasing the cases of reverse engineering, copying, espionage, and poaching of workers (Audretsch & Belitski, 2020a , 2023b ). Figure  2 D shows that changes in knowledge spillovers will not result in subsequent changes in the propensity to achieve incremental innovation independently of R&D investment.

Other interesting findings include the role that firm R&D intensity, firm age, firm employment (size), investment in software, export orientation, and knowledge collaboration play in radical innovation (columns 1 and 3) and incremental innovation (columns 2 and 4, Table 3 ). The firm's software intensity increases the firm's propensity to incremental innovation (column 2, Table 3 ). Firm size (Rogers, 2004 ) and firm age (Haltiwanger et al., 2013 ) reduce radical innovation propensity without effect on incremental innovation. Firm size does not explain the propensity of either innovation type, while younger firms are more likely to innovate radically. An increase in software spending doubles the likelihood of radical innovation.

5 Robustness checks

We performed several robustness checks. We estimated the logistic regression with knowledge spillovers (column 3, 4 Table 3 ) and coopetition (column 1, 2 Table 3 ). Our models also included other controls and time, regional, and industrial fixed effects. By including firm characteristics and interaction terms of R&D and external knowledge, we can observe the change in the significance of the predicted coefficients and decide on the bias size when knowledge spillovers, coopetition, and the other firm's controls are estimated.

Secondly, we calculated knowledge spillovers at the 3-digit SIC level and estimated Eq. (3) with a lower significance of the results. This means that learning from very close competitors (3-digit SIC) vs. distant competitors (2-digit SIC) may reduce innovation and the ability to recombine relevant knowledge (Kobarg et al., 2019 ).

Thirdly, we performed logistic estimations with and without bootstrap and clustered our standard errors by 2-digit industry SIC, correcting for heteroskedasticity across industries. This allows us to check for potential bias in estimation due to autocorrelation in errors between firms when spillovers are calculated at 2-digit SIC 2007. Both estimations provided the same results on the direction of the relationship and the significance levels.

Fourthly, we used weights of the stratified sample provided by the ONS and calculated by the industry and the firm's size using the original UKIS sample. We compared the estimation results with the predicted coefficients between weighted and unweighted models. The results were consistent for the signs, significance, and confidence intervals.

Fifthly, to address the variance structure of the model by standardizing all explanatory and control variables around the mean and performing the estimation. This could decrease the multicollinearity in interactions. All coefficient signs, significance levels, and confidence intervals remained unchanged, supporting H1 and H2b and partly supporting H2a.

6 Discussion

The knowledge transfer and open innovation literature has long focused on challenges confronting a firm's knowledge search and transfer choices and how internal knowledge creation and collaboration with competitors on knowledge influence a firm innovative output. A key finding of this paper is that it is not just the quantity of innovative input that matters, independently, be this a spillover or coopetition, but rather the quality or nature of that innovative input that shapes innovation type.

Extending the innovation literature engages with the depth and breadth of knowledge collaboration (Kobarg et al., 2019 ). Unlike prior research, which pointed out the inverted U-shaped relationship between knowledge collaboration with external partners and innovation performance (Kobarg et al., 2019 ; Audrestch & Belitski, 2022 ), this study argues that it is the type of external knowledge collaboration that matters for the type of innovation and that both knowledge collaboration with competitors (coopetition) and knowledge spillovers are subjected to diminishing marginal returns.

Therefore, we contend that both the coopetition and knowledge spillovers are linked to the type of innovation, and the extent of the knowledge spillover/coopetition should be taken into account under different levels of the firm's own R&D investment as it may generate a very nuanced relationship with innovation types. This is a more nuanced and detailed study compared to the most recent works on limitations to innovation (Saura et al., 2023 ) and the role of coopetition for innovation (Ritala, 2018 ; Belitski et al., 2023 ). Our theoretical argument also complements Denicolai et al. ( 2016 ), Audretsch and Belitski ( 2022 ), and Roper et al. ( 2017 ) prior research, which argued that excessive knowledge transfer reduces innovation across industries. In doing so, this study raises new concerns about how managers and policymakers should approach the "tipping point" of coopetition and knowledge spillover but not go over it, which results in diminishing marginal returns from coopetition (Ritala & Hurmelinna-Laukkanen, 2013 ).

Interestingly, the results of this study burst the myth that coopetition does not influence innovation at all (Tomlinson & Fai, 2013 ) and further the works of Czakon et al. ( 2020 ) and Ritala ( 2018 ), who complement the argument on the positive effect of coopetition on innovation and more complex relationship of an optimal level of coopetition to improve innovation performance (Bouncken et al., 2018 ; Park et al., 2014 ). It extends its works beyond innovation, comparing various mechanisms of industry knowledge sourcing and how it shapes innovation types.

Thus, this study contributes to knowledge transfer and open innovation literature that calls for exploring the extent to which firms engage in different types of collaboration strategies for innovation, which subsequently lead to different types of innovation (Audretsch & Belitski, 2023b ). In doing so, we offer a framework that helps to explain what types of investments in knowledge and knowledge collaborations are likely to yield innovation and when they lead to incremental innovation. Unlike prior work on the limits to open innovation (Saura et al., 2023 ), where the collaboration intensity is observed, this study examined various combinations between R&D and innovation inputs—R&D coopetition with external partners and knowledge spillovers to unpack its joint effect on manager's choice of innovation type.

The results of the study offer several insights. Firstly, we learned why knowledge collaborations sometimes have different outcomes. Besides, we learned that collaborative relationships and innovative outcomes are not linear. Secondly, at the core of our investigation is the possibility that not all R&D investment and collaboration lead to innovation (Semadini & Anderson, 2010 ; Suarez & Lanzolla, 2007 ), and there is an optimum level of R&D investment and coopetition where firms get the maximum return from these investments. Thirdly, we demonstrated that this issue is important but needs to be explored related to types of innovation and firms' investment decisions. We call for subsequent studies to examine how spillovers and knowledge collaboration with different external partners occur in industrial clusters and across different locations within the same industry.

6.1 Managerial implications

First, managers need to be aware that co-location with their competitors in the industry facilitates innovation if firms invest in R&D (Brandenburger & Nalebuff, 1996 ; Raza-Ullah & Kostis, 2020 ). High knowledge spillovers may increase unintended knowledge outflows, decreasing this propensity to innovate for both knowledge recipient and knowledge provider in the market if the level of industry spillovers is greater than 0.4 (a tipping point for spillover).

Second, investment in R&D is important when a firm is co-located with competitors and the level of knowledge spillovers is less than 0.4. In contrast, reducing R&D expenditure can be a desirable strategy when the intensity (depth) of coopetition increases significantly. Firms will be worse off if they slightly modify new products and continue R&D.

Third, to increase returns to coopetition, firm managers need to figure out how to secure first-mover advantage and form an R&D alliance when interlocked partners co-create knowledge together and, exchange tacit knowledge and protect intellectual property (Howard et al., 2017 ). Prior research considered the interlocks as a mechanism to decrease competitive uncertainties or means of improving resource dependences by minimizing patent litigations and R&D alliances (Howard et al., 2017 ); this, however, may have an important implication for the first-mover advantage and be able to develop and commercialize new to market products before competitors.

6.2 Policy implications

Policymakers must regulate the breadth and depth of coopetition using legal means, as excessive coopetition limits innovation. The same is true with knowledge spillovers; however, the degree of spillover regulation may be limited as passive collaboration is more challenging to track and measure. That said, the government may have more powers to enhance knowledge spillovers but fewer powers to limit them, preventing unintended knowledge outflows. On the contrary, the government has less power to initiate coopetition while more control over cartels and how this coopetition develops. The role of legal and strategic protection mechanisms needs to be further discussed when studying coopetition. Joint patents could be a good option. At the same time, it is unlikely to facilitate a first-mover advantage for every single firm in the partnership; it is likely to control intellectual property rights and prevent free-riding between competitors. Managing knowledge spillovers for innovation is challenging as part of the localized knowledge created by incumbent firms may be transferred to competitors involuntarily (Audretsch & Keilbach, 2007 ) and, while the government may still want competitors to continue collaborating and recombining ideas (Bouncken et al., 2018 ; Simonin, 1999 ).

7 Conclusion

An implication of Cohen and Levinthal ( 1989 ) and the ensuing mountain of research is that while the sources of knowledge accessed externally and by internal investment in R&D are very different, the resulting innovation is different. Just as all paths may lead to Rome, an implicit implication from our study is that the two very different paths to knowledge—coopetition or industry spillovers may have different effects on each innovation type, with R&D moderating this relationship.

The point of this paper is to suggest that when it comes to knowledge, not all roads lead to the same place. Knowledge transferred via industry knowledge spillovers is more conducive to both innovation types. By contrast, knowledge generated by the firm itself is more conducive to radical innovation. Thus, while Cohen and Levinthal ( 1989 ) identified that R&D has two faces, this paper suggests that those two faces of R&D tend to generate very different types of innovative output and differently moderate the relationship between two sources of knowledge—spillovers and collaboration. By deciding whether to deploy its costly R&D and access external knowledge, the firm is, therefore, also making a concomitant decision about the type of innovative activity. In addition, this study contributes to the knowledge spillover of innovation literature in explaining the returns from knowledge collaboration and industry spillovers.

Firm managers deciding on incremental innovation and consequently investing in R&D would waste resources. Our results also demonstrate that benefits from coopetition can be achieved when internal R&D is in place but is moderate, as an increase in R&D may revert knowledge spillover of innovation (Audretsch & Belitski, 2022 ).

This study has several limitations. Our first limitation is that the panel element is small, meaning seventy percent of firms only appear twice in the data. At the same time, this limits the research design, specifically the ability to get close to causal inferences, pooling the two distinct data sources together and including all firms that have ever reported their innovation activity.

Our second limitation is that fitting controls and region/time/industry fixed effects may not fully handle a model's 'endogeneity' issue. While we performed various sensitivity checks on the specification and estimator in this repeated cross-sectional firm-level setting, future research should address several outstanding endogeneity problems. First, simultaneity (some technological/policy shock affecting both R&D activity and innovative activity); reverse causation (innovative activity is likely associated with the firm's R&D and vice versa, which varies by the firm over time; firm-level unobserved factors (e.g., management strategy, workforce quality outside qualifications, firm ownership and legal status and other firm characteristics also vary with time). Future researchers may restrict the analysis to the panel component, allowing them to run regressions with firm fixed effects.

Aghion, P., & Howitt, P. W. (2008). The economics of growth . MIT Press.

Google Scholar  

Andersen, M. S., Bray, J. W., & Link, A. N. (2017). On the failure of scientific research: An analysis of SBIR projects funded by the US National Institutes of Health. Scientometrics, 112 (1), 431–442.

Article   Google Scholar  

Arora, A., Belenzon, S., & Dionisi, B. 2021.  First-mover advantage and the private value of public science  (No. w28533). National Bureau of Economic Research.

Arranz, N., & de Arroyabe, J. C. F. (2008). The choice of partners in R&D co-operation: An empirical analysis of Spanish firms. Technovation, 28 (1–2), 88–100.

Audretsch, B. D., & Belitski, M. (2023b). The limits to open innovation and its impact on innovation performance. Technovation, 119 , 102519.

Audretsch, D. B., & Belitski, M. (2013). The missing pillar: The creativity theory of knowledge spillover entrepreneurship. Small Business Economics, 41 , 819–836.

Audretsch, D. B., & Belitski, M. (2020a). The limits to collaboration across four of the most innovative UK industries. British Journal of Management, 31 (4), 830–855.

Audretsch, D. B., & Belitski, M. (2020b). The role of R&D and knowledge spillovers in innovation and productivity. European Economic Review, 123 , 103391.

Audretsch, D. B., & Belitski, M. (2022). The knowledge spillover of innovation. Industrial and Corporate Change, 31 (6), 1329–1357.

Audretsch, D. B., & Belitski, M. (2023a). Geography of knowledge collaboration and innovation in Schumpeterian firms. Regional Studies . https://doi.org/10.1080/00343404.2023.2222137

Audretsch, D. B., Belitski, M., Caiazza, R., Chowdhury, F., & Menter, M. (2023). Entrepreneurial growth, value creation and new technologies. The Journal of Technology Transfer, 256 , 1–17.

Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. The American Economic Review, 86 (3), 630–640.

Audretsch, D. B., & Keilbach, M. (2007). The theory of knowledge spillover entrepreneurship. Journal of Management Studies, 44 (7), 1242–1254.

Bartelsman, E. J., Caballero, R. J., & Lyons, R. K. (1994). Customer-and supplier-driven externalities. The American Economic Review, 84 (4), 1075–1084.

Belitski, M., Delgado-Márquez, B. L., & Pedauga, L. E. (2023). Your innovation or mine? The effects of partner diversity on product and process innovation. Journal of Product Innovation Management . https://doi.org/10.1111/jpim.12696

Bengtsson, M., Eriksson, J., & Wincent, J. (2010). Coopetition dynamics—An outline for further inquiry. Competitiveness Review, 20 (2), 194–214.

Bengtsson, M., & Kock, S. (2014). Coopetition—Quo vadis? Past accomplishments and future challenges. Industrial Marketing Management, 43 (2), 180–188.

Bianchi, T. (2001). With and without co-operation: Two alternative strategies in the food-processing industry in the Italian South. Entrepreneurship and Regional Development, 13 , 117–145.

Bloom, N., Schankerman, M., & Van Reenen, J. (2013). Identifying technology spillovers and product market rivalry. Econometrica, 81 (4), 1347–1393.

Article   MathSciNet   Google Scholar  

Boulding, W., & Christen, M. (2008). Disentangling pioneering cost advantages and disadvantages. Marketing Science, 27 (4), 699–716.

Bouncken, R. B., & Fredrich, V. (2016). Learning in coopetition: Alliance orientation, network size, and firm types. Journal of Business Research, 69 (5), 1753–1758.

Bouncken, R. B., Fredrich, V., Ritala, P., & Kraus, S. (2018). Coopetition in new product development alliances: Advantages and tensions for incremental and radical innovation. British Journal of Management, 29 (3), 391–410.

Bouncken, R. B., & Kraus, S. (2013). Innovation in knowledge-intensive industries: The double-edged sword of coopetition. Journal of Business Research, 66 (10), 2060–2070.

Bradley, S. W., Aldrich, H., Shepherd, D. A., & Wiklund, J. (2011). Resources, environmental change, and survival: Asymmetric paths of young independent and subsidiary organizations. Strategic Management Journal, 32 (5), 486–509.

Brandenburger, A. M., & Nalebuff, B. J. (1996). Coopetition . Doubleday.

Bronzini, R., & Piselli, P. (2016). The impact of R&D subsidies on firm innovation. Research Policy, 45 (2), 442–457.

Cassiman, B., & Valentini, G. (2016). Open innovation: Are inbound and outbound knowledge flows really complementary? Strategic Management Journal, 37 (6), 1034–1046.

Cassiman, B., & Veugelers, R. (2002). R&D co-operation and spillovers: Some empirical evidence from Belgium. American Economic Review, 92 , 1169–1184.

Cirik, K., & Makadok, R. (2021). First-mover advantages versus first-mover benefits: What’s the difference and why does it matter? Academy of Management Review (ja).

Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic Journal, 99 (397), 569–596.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 745 , 128–152.

Colombelli, A., Haned, N., & Le Bas, C. (2013). On firm growth and innovation: Some new empirical perspectives using French CIS (1992–2004). Structural Change and Economic Dynamics, 26 , 14–26.

Czakon, W., Niemand, T., Gast, J., Kraus, S., & Frühstück, L. (2020). Designing coopetition for radical innovation: An experimental study of managers’ preferences for developing self-driving electric cars. Technological Forecasting and Social Change, 155 , 119992.

D’aveni, R. A., & Ravenscraft, D. J. (1994). Economies of integration versus bureaucracy costs: Does vertical integration improve performance? Academy of Management Journal, 37 (5), 1167–1206.

De Dreu, C. K., Nijstad, B. A., Bechtoldt, M. N., & Baas, M. (2011). Group creativity and innovation: A motivated information processing perspective. Psychology of Aesthetics, Creativity, and the Arts, 5 (1), 81.

Denicolai, S., Ramirez, M., & Tidd, J. (2016). Overcoming the false dichotomy between internal R&D and external knowledge acquisition: Absorptive capacity dynamics over time. Technological Forecasting and Social Change, 104 , 57–65.

Dougherty, D., & Heller, T. (1994). The illegitimacy of successful product innovation in established firms. Organization Science, 5 (2), 200–218.

Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21 (10–11), 1105–1121.

Estrada, I., Faems, D., & de Faria, P. (2016). Coopetition and product innovation performance: The role of internal knowledge sharing mechanisms and formal knowledge protection mechanisms. Industrial Marketing Management, 53 , 56–65.

Fitza, M., & Tihanyi, L. (2017). How much does ownership form matter? Strategic Management Journal, 38 , 2726–2743.

Gnyawali, D. R., He, J., & Madhavan, R. (2006). Impact of coopetition on firm competitive behavior: An empirical examination. Journal of Management, 32 (4), 507–530.

Gnyawali, D. R., & Park, B. J. (2009). Coopetition and technological innovation in small and medium-sized enterprises: A multilevel conceptual model. Journal of Small Business Management, 47 (3), 308–330.

Gnyawali, D. R., & Park, B. J. R. (2011). Coopetition between giants: Collaboration with competitors for technological innovation. Research Policy, 40 (5), 650–663.

Goel, R. K., Göktepe-Hultén, D., & Nelson, M. A. (2023). Drivers of international research spending. Annals of Science and Technology Policy, 7 (1), 1–80.

Gong, Y., Kim, T. Y., Lee, D. R., & Zhu, J. (2013). A multilevel model of team goal orientation, information exchange, and creativity. Academy of Management Journal, 56 (3), 827–851.

Griliches, Z. (1992). The search for R&D spillovers. The Scandinavian Journal of Economics, 94 (Supplement), S29-47.

Hall, B. H., Griliches, Z., & Hausman, J. A. (1984). Patents and R&D: Is there a lag? (No. w1454). National Bureau of Economic Research.

Haltiwanger, J., Jarmin, R. S., & Miranda, J. (2013). Who creates jobs? Small versus large versus young. Review of Economics and Statistics, 95 (2), 347–361.

Helfat, C. E., & Peteraf, M. A. (2003). The dynamic resource-based view: Capability lifecycles. Strategic Management Journal, 24 (10), 997–1010.

Hill, C. W. L., & Rothaermel, F. T. (2003). The performance of incumbent firms in the face of radical technological innovation. Academy of Management Review, 28 , 257–274.

Howard, M., Withers, M., & Tihanyi, L. (2017). Knowledge dependence and the formation of interlocks. Academy of Management Journal, 60 , 1986–2013.

Hsieh, W. L., Ganotakis, P., Kafouros, M., & Wang, C. (2018). Foreign and domestic collaboration, product innovation novelty, and firm growth. Journal of Product Innovation Management, 35 (4), 652–672.

Huang, K. F., & Yu, C. M. J. (2011). The effect of competitive and non-competitive R&D collaboration on firm innovation. Journal of Technology Transfer, 36 (4), 383–403.

Katila, R., & Ahuja, G. (2002). Something old, something new: A longitudinal study of search behavior and new product introduction. Academy of Management Journal, 45 (6), 1183–1194.

Keller, W. (2002). Trade and the transmission of technology. Journal of Economic Growth, 7 , 5–24.

Ketchen, D. J., Jr., Snow, C. C., & Hoover, V. L. (2004). Research on competitive dynamics: Recent accomplishments and future challenges. Journal of Management, 30 (6), 779–804.

Khanna, T., Gulati, R., & Nohria, N. (1998). The dynamics of learning alliances: Competition, co-operation, and relative scope. Strategic Management Journal, 19 (3), 193–210.

Kobarg, S., Stumpf-Wollersheim, J., & Welpe, I. M. (2019). More is not always better: Effects of collaboration breadth and depth on radical and incremental innovation performance at the project level. Research Policy, 48 (1), 1–10.

Koberg, C. S., Detienne, D. R., & Heppard, K. A. (2003). An empirical test of environmental, organizational, and process factors affecting incremental and radical innovation. The Journal of High Technology Management Research, 14 (1), 21–45.

Krishnan, V., & Ulrich, K. T. (2001). Product development decisions: A review of the literature. Management Science, 47 (1), 1–21.

Laursen, K., & Salter, A. J. (2014). The paradox of openness: Appropriability, external search and collaboration. Research Policy, 43 (5), 867–878.

Leifer, R., McDermott, C. M., O’Connor, G. C., Peters, L. S., Rice, M. P., & Veryzer, R. W. (2000). Radical Innovation . Harvard Business School Press.

Leiponen, A., & Helfat, C. E. (2010). Innovation objectives, knowledge sources, and the benefits of breadth. Strategic Management Journal, 31 , 224–236.

Leonard, W. N. (1971). Research and development in industrial growth. Journal of Political Economy, 79 (2), 232–256.

Leyden, D. P., & Link, A. N. (2015). Toward a theory of the entrepreneurial process. Small Business Economics, 44 , 475–484.

Li, D., Eden, L., Hitt, M. A., & Ireland, R. D. (2008). Friends, acquaintances, or strangers? Partner selection in R&D alliances. Academy of Management Journal, 51 (2), 315–334.

Article   CAS   Google Scholar  

Lieberman, M. B., & Asaba, S. (2006). Why do firms imitate each other? Academy of Management Review, 31 (2), 366–385.

Lieberman, M. B., & Montgomery, D. B. (1988). First-mover advantages. Strategic Management Journal, 9 (S1), 41–58.

Lind, J. T., & Mehlum, H. (2010). With or without U? The appropriate test for a U-shaped relationship. Oxford Bulletin of Economics and Statistics, 72 (1), 109–118.

Link, A. N. (1978). Rates of induced technology from Investments in research and development. Southern Economic Journal, 45 , 370–379.

Link, A. N., & Scott, J. T. (2010). Government as entrepreneur: Evaluating the commercialization success of SBIR projects. Research Policy, 39 (5), 589–601.

Link, A. N., Swann, C. A., & van Hasselt, M. (2022). An assessment of the US Small Business Innovation Research (SBIR) program: A study of project failure. Science and Public Policy, 49 (6), 972–978.

Majchrzak, A., More, P. H., & Faraj, S. (2012). Transcending knowledge differences in cross-functional teams. Organization Science, 23 (4), 951–970.

Marshall, A. (2009). Principles of economics: Unabridged (8th ed.). Cosimo Inc.

Marvel, M. R., & Lumpkin, G. T. (2007). Technology entrepreneurs’ human capital and its effects on innovation radicalness. Entrepreneurship Theory and Practice, 31 (6), 807–828.

McEvily, S. K., & Chakravarthy, B. (2002). The persistence of knowledge-based advantage: An empirical test for product performance and technological knowledge. Strategic Management Journal, 23 (4), 285–305.

Mention, A. L. (2011). Co-operation and coopetition as open innovation practices in the service sector: Which influence on innovation novelty? Technovation, 31 (1), 44–53.

Nemeh, A. (2018). Building a first-mover advantage from coopetition . Routledge companion to coopetition strategies (pp. 298–306).

Nemeh, A., & Yami, S. (2019). Orchestrating resources for FMA in coopetitive NPD. RandD Management, 49 (1), 64–85.

Nieto, M. J., & Santamaría, L. (2007). The importance of diverse collaborative networks for the novelty of product innovation. Technovation, 27 (6–7), 367–377.

Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & Van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, 36 , 1016–1034.

Office for National Statistics. (2017). Business structure database, 1997–2017: Secure access [data collection] (9th ed.). UK Data Service. SN: 6697. https://doi.org/10.5255/UKDA-SN-6697-9

Office for National Statistics. (2018). UK innovation survey, 1994–2016: Secure access [data collection] (6th ed.). UK Data Service. SN: 6699. https://doi.org/10.5255/UKDA-SN-6699-6

Office for National Statistics. (2019). Business expenditure on research and development, 1995–2017: Secure access [data collection] (8th ed.). UK Data Service. SN: 6690. https://doi.org/10.5255/UKDA-SN-6690-8

Okhmatovskiy, I., Suhomlinova, O., & Tihanyi, L. (2020). Legacy of the state: Prior shared experience and survival of spin-offs from restructured state enterprises. Journal of Management, 46 (4), 503–529.

Oxley, J. E., & Sampson, R. C. (2004). The scope and governance of international R&D alliances. Strategic Management Journal, 25 (8–9), 723–749.

Park, B., Srivastava, M. K., & Gnyawali, D. R. (2014). Walking the tight rope of coopetition: Impact of competition and cooperation intensities and balance on firm innovation performance. Industrial Marketing Management, 43 (2), 210–221.

Pavitt, K. (1991). Key characteristics of the large innovating firm. British Journal of Management, 2 (1), 41.

Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14 (3), 179–191.

Quintana-Garcia, C., & Benavides-Velasco, C. A. (2004). Cooperation, competition, and innovative capability: A panel data of European dedicated biotechnology firms. Technovation, 24 (12), 927–938.

Raza-Ullah, T., & Kostis, A. (2020). Do trust and distrust in coopetition matter to performance? European Management Journal, 38 (3), 367–376.

Ritala, P. (2018). Coopetition and market performance.  The Routledge Companion to Coopetition Strategies , 317–325.

Ritala, P., & Hurmelinna-Laukkanen, P. (2009). What’s in it for me? Creating and appropriating value in innovation-related coopetition. Technovation, 29 , 819–828.

Ritala, P., & Hurmelinna-Laukkanen, P. (2013). Incremental and radical innovation in coopetition: The role of absorptive capacity and appropriability. Journal of Product Innovation Management, 30 (1), 154–169.

Rogers, M. (2004). Networks, firm size and innovation. Small Business Economics, 22 (2), 141–153.

Roper, S., Love, J. H., & Bonner, K. (2017). Firms’ knowledge search and local knowledge externalities in innovation performance. Research Policy, 46 (1), 43–56.

Roper, S., Vahter, P., & Love, J. H. (2013). Externalities of openness in innovation. Research Policy, 42 (9), 1544–1554.

Saura, J. R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2023). Exploring the boundaries of open innovation: Evidence from social media mining. Technovation, 119 , 102447.

Semadeni, M., & Anderson, B. S. (2010). The follower’s dilemma: Innovation and imitation in the professional services industry. Academy of Management Journal, 53 (5), 1175–1193.

Simonin, B. L. (1999). Ambiguity and the process of knowledge transfer in strategic alliances. Strategic Management Journal, 20 (7), 595–623.

Spender, J. C., & Grant, R. M. (1996). Knowledge and the firm: overview. Strategic Management Journal, 17 (S2), 5–9.

Spithoven, A., Clarysse, B., & Knockaert, M. (2010). Building absorptive capacity to organise inbound open innovation in traditional industries. Technovation, 30 (2), 130–141.

Suarez, F. F., & Lanzolla, G. (2007). The role of environmental dynamics in building a first mover advantage theory. Academy of Management Review, 32 (2), 377–392.

Ter Wal, A. L., & Boschma, R. A. (2009). Applying social network analysis in economic geography: Framing some key analytic issues. The Annals of Regional Science, 43 (3), 739–756.

Tether, B. S. (2002). Who co-operates for innovation, and why. An empirical analysis. Research Policy, 31 , 947–967.

Tomlinson, P. R., & Fai, F. M. (2013). The nature of SME co-operation and innovation: A multi-scalar and multi-dimensional analysis. International Journal of Production Economics, 141 (1), 316–326.

Troilo, G., De Luca, L. M., & Atuahene-Gima, K. (2014). More innovation with less? A strategic contingency view of slack resources, information search, and radical innovation. Journal of Product Innovation Management, 31 (2), 259–277.

Un, C. A., Cuervo-Cazurra, A., & Asakawa, K. (2010). R&D collaborations and product innovation. Journal of Product Innovation Management, 27 (5), 673–689.

Van Beers, C., & Zand, F. (2014). R&D co-operation, partner diversity, and innovation performance: An empirical analysis. Journal of Product Innovation Management, 31 (2), 292–312.

Wooldridge, J. M. (2009). On estimating firm-level production functions using proxy variables to control for unobservables. Economics Letters, 104 (3), 112–114.

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Audretsch, D.B., Belitski, M. & Chowdhury, F. Knowledge investment and search for innovation: evidence from the UK firms. J Technol Transf (2024). https://doi.org/10.1007/s10961-023-10045-7

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