Knowledge creation in projects: an interactive research approach for deeper business insight

International Journal of Managing Projects in Business

ISSN : 1753-8378

Article publication date: 3 August 2022

Issue publication date: 1 March 2023

The purpose of this paper is to shed light on different types of knowledge created and how this links to the project design, process, and content.

Design/methodology/approach

In this paper the authors investigate participants' experiences from a three-year interactive research project, designed to trigger reflection among the participants. They apply a knowledge creation perspective on experiences expressed by participants as a result of different research project activities.

The study resulted in five categories of insights with potential for sustainable influence on the participating organizations: an understanding of concepts and theories; an understanding of the impacts of collaborative, reflective work processes; an understanding of the meaning of one's own organizational context; an understanding of the importance of increased organizational self-awareness; and an understanding of the potential for human interaction and communication.

Practical implications

The author’s findings suggest that it is possible to design a project to promote more profound and sustainable effects on a business beyond the explicit purpose of the project. They advise practitioners to make room for iterative reflection; be mindful to create a trustful and open environment in the team; challenge results with opposing views and theories; and make room for sharing experiences and giving feedback.

Originality/value

This study contributes to unraveling key practices which can nurture conditions for knowledge creation in interactive research projects and business projects alike.

  • Practice-based research
  • Collaborative research
  • Knowledge creation
  • Qualitative research
  • Project management

Engström, A. , Johansson, A. , Edh Mirzaei, N. , Sollander, K. and Barry, D. (2023), "Knowledge creation in projects: an interactive research approach for deeper business insight", International Journal of Managing Projects in Business , Vol. 16 No. 1, pp. 22-44. https://doi.org/10.1108/IJMPB-09-2021-0233

Emerald Publishing Limited

Copyright © 2022, Annika Engström, Anette Johansson, Nina Edh Mirzaei, Kristina Sollander and Daved Barry

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode .

1. Introduction

The project form dominates work in large parts of our society, and the term “projectification” is used to explain developments toward the creation of a project society ( Lundin et al. , 2017 ). Projects are seen as efficient ways of organizing people with different areas of expertise to work on a joint task toward common goals, often in contexts that demand collaboration between different competencies, functions, and departments ( Canonico et al. , 2013 ). This is typically the type of context involved in complex product and process development, which has contributed to the view of project-based organizations as key sites for knowledge creation and innovation ( Davies and Hobday, 2005 ). Projects are also often seen as learning spaces ( Nilsen, 2013 ) and used as learning mechanisms ( Scarborough et al. , 2004 ). Understanding how knowledge in projects is created, communicated, and shared in organizations is critical to both project management research and practice, given the strong influence of projects on society.

One arena where knowledge creation is at the very core is academic research projects. Plenty of evidence shows that projects within academic research using interactive, action-oriented, collaborative research forms—thus sharing similarities with typical business context projects—have positive impacts on learning ( Svejvig et al. , 2021 ). Nevertheless, despite the potential this research approach has for addressing complex organizational problems ( Avison et al. , 2018 ) by combining theoretical rigor and practical insights ( Geraldi and Söderlund, 2018 ), it has received little attention in the project research community ( Svejvig et al. , 2021 ).

The co-production of knowledge in research projects ( Lindhult and Axelsson, 2021 ) is a strong tradition in Scandinavian management research ( Gunnarsson et al. , 2015 ), as well as an important part of the sustainable Swedish model for innovation, renewal, and effectiveness in the industry ( Magnusson and Ottosson, 2012 ). In the family of co-productive research approaches (CARs) ( Lindhult and Axelsson, 2021 ), interactive research is developed from action research traditions ( Aagard-Nielsen and Svensson, 2006 ). Actions and changes in behaviors and mindsets are the focus, and shared experiences in joint learning through different phases in the research process are central ( Svensson et al. , 2002 , 2007 ). Interactive research differs from action research in that researchers (the academic system) and practitioners (the practice system) have an equal relationship with and impact on the knowledge created. At the same time, the roles and responsibilities connected with knowledge creation in the respective systems are clearly defined – researchers are responsible for knowledge creation in the academic system, whereas practitioners are responsible for knowledge creation in the practice system ( Aagard Nielsen and Svensson, 2006 ; Cronholm and Goldkuhl, 2003 ).

Interactive research approaches have recently been evaluated and described as powerful in terms of validity for meeting organizational and societal needs and for reaching rigorous research results ( Ellström et al. , 2020 ; Wallo, 2008 ; Wallo et al. , 2012 ; Andersson et al. , 2022 ). However, despite the seemingly common agreement among researchers that interactive research has an impact on learning, research in this context still lacks empirical descriptions and examples of cases and research project designs of this kind ( Lindhult and Axelsson, 2021 ). Interactive research is often designed as projects that include analytic seminars between scholars and practitioners ( Ellström et al. , 2020 ), feedback dialogue meetings with companies, and workshops that include researchers and practitioners ( Svensson et al. , 2002 , 2007 ). There is little understanding of how different types of activities, such as reflective conversations, meetings, and workshops, lead to various kinds of knowledge creation ( Lindhult and Axelsson, 2021 ). Additionally, the increasing demand for academia to collaborate with and contribute to society and the attention to the impact of publications rather than their quantity make robust models important for achieving sustainable effects in interactive, collaborative research projects ( Lindhult and Axelsson, 2021 ; Svejvig et al., 2021 ).

In this paper, we investigate the experiences from a three-year interactive research project with small and medium-sized manufacturing companies in Sweden focusing on innovation capabilities in terms of organizational ambidexterity ( Junni et al. , 2013; Lubatkin et al. , 2006; O’Reilly and Tushman, 2008; Zimmermann et al. , 2015 )—the ability to simultaneously exploit existing and explore new knowledge ( March, 1991 ). The purpose is to shed light on the different types of knowledge created during the project and how that links to the project design, process, and content. We apply a knowledge creation perspective ( Ellström, 2001 , 2010b , 2011 ) to the experiences that the participants expressed as a result of different activities in the project. In doing so, we respond to the challenges raised relating to understanding how knowledge in interactive projects is created and how it is linked to specific activities. Therefore, we contribute to unraveling key practices which can nurture conditions for knowledge creation in both interactive research projects and business projects.

2. The theoretical framework

The theoretical focus of this paper is knowledge creation processes in interactive projects in general, and in interactive research projects particularly. We refer to knowledge creation as an action-oriented learning process and highlight reflection as an influential mechanism.

2.1 Knowledge creation processes in interactive projects

Projects are often viewed upon as learning spaces ( Nilsen, 2013 ) and used as learning mechanisms ( Scarborough et al. , 2004 ), and there is ample evidence that promoting interaction and collaboration are key ingredients in knowledge creation in projects. For example, a study on quality improvement projects by Choo et al. (2007) defines learning behavior as interaction between members and that adhering to a specific method (in this specific case problem-solving steps in the Six Sigma framework) influenced this interaction and subsequently created knowledge. Other examples include the study by Faccin and Balestrin (2018) who identified collaborative practices in R&D projects to be key to ensure complementary exploration and exploitation approaches necessary for both innovation and knowledge creation, and Weck's (2006) study on interfirm R&D projects which concluded that the exchange of complementary specialist knowledge were key success factors in the process of interfirm knowledge creation.

To combine equal relationships and critical thinking, to balance closeness with critical distance, to be proactive without being controlling (the process is owned by the participants), to start from the specific and local but to strive for general explanations, to have knowledge without being an authority, to be able to adapt and improvise while preserving integrity and independence, to be able to combine theory and practice, to be able to act as trailblazers, to think strategically but at the same time respecting ethical considerations, which requires practical wisdom—phronesis—to be part of the development process without being held ransom by it, and to have good knowledge of [the researcher’s] own discipline but at the same time aim for an interdisciplinary understanding. ( Johannisson et al. , 2008 , p. 371, author’s translation)

Face-to-face meetings in person are common in knowledge creation processes in collaborative research projects ( Palm, 2007 ). Workshops of different kinds have become common ways of carrying out project meetings, even though they have various names, such as dialogue conferences ( Gustavsen and Engelstad, 1986 ), interpretive forums ( Mohrman et al. , 2001 ), cooperative inquiries ( Heron and Reason, 2006 ), seminars ( Svensson et al. , 2007 ), meetings or group meetings ( Larsson, 2006 ), jam sessions ( Börjesson and Fredberg, 2004 ), and feedback sessions ( Ellström, 2007 ). Furthermore, these workshops can play different roles in a project. Their goals can be trust building and networking ( van de Ven, 2007 ), knowledge development ( Adler et al. , 2004 ), knowledge creation ( Jacob et al. , 2000 ), knowledge sharing ( Näslund et al. , 2010 ), joint learning ( Larsson, 2006 ), data analysis or interpretation ( Mohrman et al. , 2001 ; Ellström, 2007 ), or testing and validation of results ( Ellram and Tate, 2015 ).

Knowledge creation in interactive research projects depends on a democratic dialogue characterized by reflection and critical joint analysis, in which equally recognized knowledge interests in research and practice have the potential to complement and support each other, yielding more sustainable results ( Aagard Nielsen and Svensson, 2006 ). The role of the participants needs to be negotiated and renegotiated in a process characterized by critical reflection, as position shifts in relation to the phenomenon being studied may be needed ( Sandberg and Wallo, 2013 ). As highlighted in Figure 1 , research in an interactive approach is responsible for academic knowledge development in the research system, whereas more context-specific knowledge is developed in the practice system. Both are equally important for different purposes.

The overlap between the systems, indicated in the center of Figure 1 , illustrates the joint activities in co-production, whereas the arrowed loops show that the roles and the desirable output are different for the two systems ( Svensson et al. , 2015 ).

2.2 Action-oriented perspectives on knowledge creation

There is no consensus on what knowledge creation really is ( Runsten and Werr, 2016 ). Rather, there are many different definitions of knowledge in relation to different philosophical points of view depending on how one sees science ( Chalmers, 2013 ). Knowledge in the cognitive, rational perspective ( Winn and Snyder, 2004 ) is defined as objective information, facts, or methods, which are separated from both situations and actions ( Runsten and Werr, 2016 ). This perspective on knowledge is common in technically oriented action projects focusing on the development of products, processes, or artifacts in co-creation with partners from industry ( Hevner et al. , 2004 ; Susman and Evered, 1978 ; Wieringa and Morali, 2012 ). Knowledge in the situational, contextual perspective ( Lave and Wenger, 1991 ) is regarded as an activity in a social system ( Engeström, 1987 ), in which the individual's ability and influence on the learning process is limited. This perspective on knowledge is common in social science-oriented projects, which focus on the critical analysis of conditions and agents in social systems ( Engeström, 2008 ; Gustavsson, 2007 ).

An alternative to the above perspectives on knowledge is the action-oriented perspective , in which cognition and context are tightly bound ( Ellström, 2001 , 2010a , 2010b ; Granberg and Ohlsson, 2005 ; Ohlsson, 1996 ). The individual's learning is seen neither as purely cognitive and rational nor completely dependent on the social system, without the ability to think rationally. In this action-oriented perspective ( Schön, 1983 ), knowledge is considered an action— knowing in action —in which knowledge in relation to a problem, specific situation, context, or task is created. Knowledge creation here is defined as a learning process for change in mindsets, behaviors, and action patterns ( Ellström, 1992 ). An action-oriented perspective draws upon Dewey's (2002) way of reasoning—we create knowledge while we are acting. When our habits or assumptions are disturbed, we act on impulse and gain experience. These experiences can, depending on the extent to which they reflect intellectually, create potential for knowledge creation, stimulate change in behavior, and lead to the development of new procedures in dealing with life ( Dewey, 2002 ). This perspective is based on interaction, dialogue, and reflection ( Döös and Wilhelmson, 2011 ; Ohlsson, 1996 ). When challenges are dealt within a social context, individuals in groups can jointly form and create an understanding of and insights into common action alternatives ( Granberg and Ohlsson, 2005 ; Ohlsson, 1996 ). Ellström (2011) illustrates this by showing the tension between implicit and explicit action levels; tacit knowledge and routinized actions are based on habits, whereas awareness, transparency, and explicit work processes can increase knowledge- and reflection-based actions with a new and deeper understanding and insight.

There are different ways to categorize different types of knowledge or the content in learning processes. One way is inspired by anthropological emic and etic approaches ( Chilcott and Barry, 2016 ). The emic approach investigates the knowledge of local people within the system and how they think, whereas the etic approach investigates knowledge in the system from an outside perspective. The members of a culture might be too involved in what they are doing to interpret their behavior impartially. Their assumptions can be seen as social representations ( Moscovici, 1981 ), a mindset that is difficult to change. The etic approach functions as a perspective that a researcher or an outsider could have, which sometimes works as feedback or an eye opener for the people within the system. Another way to categorize different types of knowledge is inspired by the Greek episteme ( Gustavsson, 1996 , 2000 ), which means to understand how the world is structured and how it works; techne , which means to create and produce; and fronesis , which means to develop good judgment and to act as a democratic and ethical citizen. These different types of knowledge are closely related to one another and are formed in a dialectical process in which learning is based on what is already known, familiar, and recognizable in the encounter with the unknown. We experience the new based on how we interpret and understand the world. By doing and reflecting, we obtain insights into the larger context ( Gustavsson, 1996 , 2000 ).

2.3 Reflection in knowledge creation processes

A true reflection does not only mean that one has understood but also how the process of understanding occurred—when reflection leads to deeper knowledge. ( Wenestam and Lendahl Rosendahl, 2005 , pp. 82–83)
Usually reflection on knowing-in-action goes together with reflection on the stuff at hand. There is some puzzling, or troubling, or interesting phenomenon with which the individual is trying to deal. As he tries to make sense of it, he also reflects on the understandings which have been implicit in his action, understandings which he surfaces, criticizes, restructures, and embodies in further action. ( Schön, 1983 , p. 50)

Even though knowledge creation seems necessary in organizational research, individuals and groups often prevent development and resist learning by engaging in defensive routines that avoid critical reflection ( Argyris, 1994 , 2010 ). This defense indicates both preparedness and resistance to change and can be seen as energy in learning processes ( Illeris, 2007 ). Defensive behavior ( Aagard Nielsen and Svensson, 2006 ; Adler et al. , 2004 ; Andersson et al. , 2022 ; Argyris, 1990 , 2010 ) and learning difficulties have been discussed by Senge (1990) . An excessive focus on daily activities and implementation based on what seems right, now, rather than development and sticking to long-term strategies, may be one of the reasons for both defensive behavior and learning difficulties. Another possible reason that Senge highlights concerns the overconfidence that we obtain from experience. Learning does not happen automatically, but it requires special arrangements and focus ( Senge, 1990 ). It seems that humans, when most in need of learning, paradoxically hinder it even more ( Argyris, 1990 , 2010 ).

Actions such as defensive behaviors, or theories in use , must be made visible to break them and increase learning ( Argyris, 2010 ). Actively dealing with discrepancies and disturbances stimulates learning ( Engström, 2014 ), which is supported by a climate of psychological safety ( Edmondson, 1999 ) and the ability to learn from failure ( Edmondson, 2011 ). Robust learning includes three important components in relation to leading and analyzing learning activities: steering , challenging, and supporting knowledge creation processes ( Svensson et al. , 2009 ). Steering toward the goal and with certain structures keeps the focus on the content of the learning process. Challenging includes not only disturbances, such as dealing with contradictions, discrepancies, questioning, and uncertainty, but also engagement out of one's comfort zone. Supporting includes active empathetic listening, responding, and confirming someone's thoughts and opinions. Feedback can both challenge and support the learning process. Corrective feedback engages a person or group in dialogue to explore new ways of thinking or doing. Confirmatory feedback aims to support and strengthen a person's or group's pre-existing actions or knowledge ( Egan, 2002 ).

3. Research methodology

To understand the different types of knowledge created in interactive research projects and how they are linked to the project design, process, and content, we studied how the participants in a three-year collaborative research project perceived the learning outcomes. We used a qualitative research approach, in which we focused on the participants' experiences of the project activities. In the following sections, we provide a detailed description of the context in which the study was conducted, the data collection, and the analysis of the participants' experiences.

3.1 Research case

The context of the study presented in this paper is a research project conducted in 2018–2020 in small and medium-sized manufacturing companies. The overall project goal was to strengthen innovation capabilities, in terms of organizational ambidexterity. The project was run by five researchers from different disciplines, and participants from six small and medium-sized manufacturing companies in Sweden. The design of the research project was characterized by an interactive research approach ( Aagard Nielsen and Svensson, 2006 ; Ellström et al. , 2020 ; Svensson et al. , 2002 , 2007 ). Snowball sampling was used to find collaborating companies before the project started. As Yin (2014) notes, this type of participant selection can be beneficial when seeking knowledge within specific areas. The sample-finding phase started from November 2017 to January 2018 and resulted in a group of six manufacturing small and medium-sized enterprises (SMEs). The companies were selected based on their history of working with operations improvements, their exemplary performance records, and their interest in enhancing their organizational ambidexterity capabilities. For these companies' information, please see Table 1 .

Besides using an interactive research approach, the project adopted a problem-driven design and applied an emic approach with ethnographic roots ( Chilcott and Barry, 2016 ), which means that “accounts, descriptions, and analyses expressed in terms of the conceptual schemes and categories regarded as meaningful … by the native members of the culture whose beliefs and behaviours are being studied” ( Lett, 1990 , p. 130) are central to the endeavor. Taking an emic approach in this study meant holding on loosely to the researchers' understandings of the organizations' ambidexterity and innovation capabilities while carefully attending to the participants' framings and practices, rather than using pre-existing operationalizations. During the research process, following Raisch and Birkinshaw (2008) and Czarniawska (2007) , we collected multiple data from three mixed company focus groups, six company focus groups, 18 diaries, 257 survey respondents, and 25 days of shadowing and observations in the companies. Data collection was intertwined with data analysis in different stages and was later followed by feedback sessions with the companies and presentations of preliminary results in common workshops.

The project was planned in an iterative process with four-month cycles, which included the following steps during each cycle ( Figure 2 ): (1) Meetings in a steering group consisting of representatives from each company and all researchers were held, in which previous work was processed and subsequent steps were planned, including the content of the next stages, companies' homework, the data collection needed, and the invitation of guest speakers. (2) Both academic and industrial partners collected data and experimented with new ways of working. (3) Analysis and reflection followed, in which academic and industrial partners met in their own arenas to discuss what was learned. (4) Joint workshops were then arranged, in which partner companies and all academics met to share knowledge and experiences. These workshops were what Ellström et al. (2020) define as analytic seminars; typically, 14–19 participants were present from the industrial partners and the research team.

The project was designed to stimulate reflection among the participants through both discussion and feedback. Reflection within each company was needed before each workshop to fulfill the assigned homework. An example of homework during the projects was reflecting upon their own diaries, which were recorded during one week. The reflections were conducted at both the individual and group levels. Furthermore, the participants reflected on work meetings that took place in their own organizations, and they investigated different work tasks and how they related to the phenomena we studied (ambidexterity). During each workshop, the participants presented their findings and discussed them with both the participants from other industrial partners and the research team. The industrial partners were encouraged to have more than one participant per company to ensure that the ideas, reflections, and insights gained during the workshop could be continuously discussed and worked with later within each company. Several companies started with one or two participants, but ended up with four or more participants toward the end of the project.

Each workshop was held during 24 h, from lunch-to-lunch, starting with a visit to the hosting industrial partner in the morning. Different workshop themes ( Figure 2 ) were decided in the steering group, step by step, for each four-month cycle during the entire process. The workshops were often structured as follows: (1) joint lunch, (2) homework presentations, (3) mixed company group reflections on the presentations, (4) joint dinner, (5) theory input from a researcher or practical examples from a guest speaker, and (6) company-level group reflections on impressions from the workshop and the way forward.

3.2 Data collection—experiences from the project

This study is based on data collected in the form of oral testimonials and presentation materials on two separate occasions during the final phase of the research project: (1) the final workshop and (2) an open webinar. Both events provided meta-reflections on what the participants experienced during the project. This enabled an understanding of the link between the larger context of the interactive research project and its intended and unintended learning outcomes as perceived by the participants. The data consists of recordings and transcriptions of the final workshop and the webinar.

3.2.1 Accounts from the final workshop

The final workshop sought to address the learning outcomes from the project as understood from the perspective of each industrial partner. During the workshop, the participants' discussions took place in small groups, at the company level and in mixed constellations, and in a large group that included all project participants. Five companies were represented, and 19 people, all holding management positions, participated. During the workshop, the following questions were raised: (1) What areas within your organization have received the most innovation focus during the project? (2) Feel free to tell us more about your experiences with the changes you have tested and/or implemented. (3) When it comes to organizational ambidexterity, it is all about balancing the daily execution with the work around renewal in the business. What has facilitated and what has hindered the work on that balance? (4) What is the company's main challenge going forward? Each company presented their answers to the rest of the project team, followed by a joint discussion of thoughts and reflections. The presentation and joint discussion took between 45 and 60 min for each company and were recorded.

3.2.2 Accounts from the open webinar

The purpose of the concluding webinar was to function as an interactive platform for gathering the learning outcomes based on both the researchers' and the practitioners' perspectives and to disseminate the results of the project to a wider, primarily industrial, audience. The free online webinar on the difficult balance between stability and change in small and medium-sized manufacturing companies addressed the issue of working with both innovation and daily activities at the same time. It was a 1.5-h event divided into three parts: (1) a summary of the project background and purpose, (2) presentation of the findings, and (3) reflections by the research team and industrial partners on the findings. The participants representing the industrial partners were asked to prepare answers to three questions: (1) How has your view of innovation ability changed? (2) What exactly has happened in your organization? What focus in your business have you had during the project? (3) What advice do you want to give other companies based on the lessons you learned about innovation ability? One representative from each industrial partner held a presentation based on the above questions, and three selected participants took part in a panel discussion about the learning outcomes from the project. In total, 15 people, including the research team members, presented something at the webinar. The webinar was recorded and made available online afterward.

3.3 Data analysis

All the meetings were recorded and transcribed. Thematic analysis was carried out in two consecutive phases. In the first phase, NVivo software was used for the empirical analysis. The transcribed files were imported into the software, and all text was processed manually by the research team. All parts of the text indicating some sort of learning or change were highlighted and coded in different categories that simply described the content of that aspect (i.e. the first-order concepts). This procedure led to a combination of codes referring to company-specific aspects and very general ones. Once the transcribed files were fully covered, ensuring that no important aspects were left out, the second step of the empirical coding started. Here, the codes were clustered when deemed necessary (i.e. when there were overlaps in the aspects they covered). This was an iterative process that resulted in seven categories, which were the second-order themes: (1) interpretations and definitions of innovation; (2) the role of the project; (3) ownership and company size; (4) strategy, vision, and development; (5) self-image; (6) regional spirit; and (7) examples of changes. From these categories/themes, we managed to derive five aggregated dimensions (business insights) in a final empirical analysis inspired by Gioia et al. (2013) , Aagard Nielsen and Svensson (2006) , Adler et al. (2004) .

In the second phase of analysis, the theoretical examination took place. The five aggregated dimensions were compared to the theoretical framework to understand the outcomes of the project from a knowledge creation perspective and how such insights link to the design, process, and content of projects.

4. Findings

The empirical analysis of the data in NVivo resulted in five categories of insights derived from the participation in the interactive research project, with the potential for sustainable influence on the participating organizations: (1) an understanding of concepts and theories; (2) an understanding of the impacts of collaborative, reflective work processes; (3) an understanding of the meaning of the own organizational context; (4) an understanding of the importance of increased organizational self-awareness; and (5) an understanding of the potential for human interaction and communication.

4.1 Elaborating five categories of business insights

4.1.1 an understanding of concepts and theories.

Throughout the research project, concepts and theories that are related to and that capture innovation and ambidexterity have been constantly addressed. The initial kick-off activity, in which representatives from the companies formed mixed focus groups, concentrated on how the individuals understood innovation, how they defined innovation work, and what made it different from their daily work.

… with innovation, you just don’t know. / … / that’s the whole thing with innovation; you don’t have the methods or the time or the money—that is, you don’t know how it’s going to turn out / … / you’re on thin ice; we don’t know what choices we’ll make. So, making plans is not easily done beforehand.

They also suggested that participating in the project had given them the feeling that working with innovation is “something bigger” that could “lift them,” making them realize that they needed to include more people from the company taking part in the project.

… it’s this that happens, which isn’t planned, that’s really interesting. That’s when innovation happens or that’s when you see new patterns or get ideas. This part of the unplanned is what I think is the most exciting./ … /Researcher A talked about not feeling ashamed about this; it’s really part of being innovative or part of being in an organization, to be there for one another.

4.1.2 An understanding of the impacts of collaborative, reflective work processes

It’s been exciting and interesting to have been part of such a big project; it’s an inspiration to try new ideas and work methods to develop our business./ … /[what’s] most rewarding has been to network with other companies and academics and to benchmark both the good and the bad, the negative and the positive experiences. And what you’ve seen in the project, really regardless of what we manufacture or what we do, is that we all face the same challenges.
… to follow other companies for three years, to see their journey with the things they try, that makes us learn as well and see what we need to do next. It’s an amazing opportunity to get to be close to other companies in this way and to follow them all. It’s been very inspiring and rewarding, and we’ve received many tips and thoughts from the other companies, I think.

In one of the workshops, a manager from a company outside of the project shared some quite provocative thoughts on management. This company had grown fast and yet decided not to have any dedicated managers apart from its CEO. Collaboration, mutual trust, and feedback were brought forward as the company's key success factors. There was an intense and interesting discussion of these issues after the presentation was completed. One of the participating companies referred back to this session and stated that, after this session, the company started with quarterly based co-worker assessments to identify important issues and problems regarding the work situation. This, in turn, led to the closer involvement of manufacturing staff in project start-ups and in upcoming changes in the firm, which meant that problems and issues could be detected earlier. In other words, they learned that interacting and reflecting with more of their employees seemed to lead to better well-being and to better results in their operational work.

4.1.3 An understanding of the meaning of one's own organizational context

Throughout the project, the participants continuously brought up and emphasized the importance of their specific organizational contexts in their innovation capabilities and in the way in which these companies are managed. When they addressed their own contexts, the sizes of their companies (all of them are small to medium-sized companies), how they are owned and managed, the businesses they are in, the needs of their customers, and the region where they are located, it seems as if their ways of reasoning have expanded, and their appreciation and respect for their own specific contexts have developed.

… the pride is considerably larger in such a company, and you have a holistic view and a holistic picture in a different way. In a large company, it’s like, ‘Our department does this,’ but you don’t know the bigger picture … [here] even if you work with the introduction process, you’re fully aware what others do, what the company does and produces, and in what way. It builds on ‘I’m an important part of the whole puzzle.’
… a benefit we’ve seen too is the consensus in our management team. That we are all participating in the project meetings has given us strength. We know what vision we want to reach, and we form new goals and action plans to reach these new ideas, solutions, and everything that we’ve gotten from this project.
… as a small company, you think that everyone knows why; therefore, we don’t really establish why we’re here, but you think that everyone should know why we’re here.
Where are we heading? How shall we work? The entire management team can benefit from answers to these questions. All of us need to be involved in this.
… is it so that in owner-managed companies, SMEs, you’re prepared to take certain risks; no super advanced calculations are being made. You’re telling that you, as the owner, have stood for two and a half years and said, ‘I support this. I know it will cost something; we don’t have any calculations on it.’ If you had known, maybe you would have said no, but now you’re in it and then you just go for it… it’s more based on emotions …. “I’m not fully aware (of the costs). I follow my gut feelings. Then I can have nightmares about it.”

4.1.4 An understanding of the importance of increased organizational self-awareness

… this [project] was supposed to be about innovation, and I felt rather hesitant to do it. It didn’t really fit the vision. We’re a pretty small company… we don't have our own products… so it felt a bit weird, this thing with innovation…‘I didn’t even dare to say the word ‘innovation’ in any context involving our company. To me, innovation was only about one thing, a product that you invent; it’s about patents, research, laboratories, large research groups where you develop a new product for a new market, theories, yes.
… we’ve learned quite a lot from this project. One part of it is that we’re proud that we implement innovations. …We’ve realized that all of us do innovations, that we can make an impact, change, and come up with things. Our views, all the way from the management level to the individual co-worker, have changed.
… I was very hesitant from the beginning whether I should be involved in such a project…I considered myself extremely innovative with lots of ideas. But what I may have forgotten was to include others on that journey. I just started and forced it into the business, so it’s very much managed from the top down.
… it may be the curse of an entrepreneur that you think you can manage everything on your own. But it has turned out that that’s not the case; [throughout the project], we’ve gotten a really good activity going on throughout the organization.…If we see where we are today in comparison to when we started all of this, there’s a big difference, of course. Today, I work much more with strategies and the entire organization.
That others have asked, seen, and investigated [issues and problems in and about their organization] has given us very much. It gives a kind of boost—really, that's fun. We've seen our own innovation capability in a completely different way, and it has sparked a positive spiral of new innovations.

4.1.5 An understanding of the potential in human interaction and communication

… we’ve come to realize that the individual is important, all the way from the top to the bottom…it doesn’t help that we write a new routine for everyone to be involved; we have to have that feeling and build that feeling to get there. That’s something I believe we’ve learned in this project. It’s an important part; it’s the key to be able to move forward.
If I had involved them much earlier, which is what we do now…they’ll tell you, ‘No, you can’t think like that because this and that will happen’, because they work there every day…the best innovations you get, you get when they own it. When they come from the shop floor and start chasing white collar workers, that’s when you’ve started it.
… everyone is equally important, and we can learn a lot from one another. Many organizations have someone who’s very dominant with lots of ideas; with this method, we also let others speak up. There might be many people who are more cautious and a bit quiet but who are very clever and spend a lot of time at work and at home thinking about potential aspects. You want to capture those.
… we created a common platform, a common office where purchase, warehouse, and production management, all the ones who have many daily contacts with one another, sit together so that they can simply just talk to one another over the desk instead of moving to long meetings. So, we’ve shortened the ways of communication.
… when we’ve reached a stage with tangible suggestions, we put them into a sort of plan-do-control-action part, where we work on how to bring the process forward. In this group, when we use this method, we bring out the smartest [ideas] because it’s built on the knowledge of each and everyone in the group. When they’re allowed to participate and have a voice, it creates involvement, and you go from talking to actually doing, and doing creates value, partly building more value but also contributing to the culture in the company and encouraging co-workers to participate in many ways.

4.2 Linking business insights to project design, process and content

By identifying the five categories of business insights stemming from an interactive research project with SMEs we have shed light on the complexities surrounding knowledge creation in projects and associated learning outcomes. The following section focuses on how these five categories link to the design, process, and content of projects and on practices that can nurture conditions for knowledge creation in these types of settings.

First, we see that the category that captures an understanding of concepts and theories (in this particular case, concerning innovation capabilities and ambidexterity) relates to the content development of the research project. The participants not only captured mainstream definitions; they also formed their own understandings and beliefs. The learning outcome here is a change in mindset, as also mentioned by Ellström (1992) . The participants also followed the way of reasoning addressed by Dewey (2002) —to learn while acting without exactly knowing what the outcomes would be. It is obvious that habits and assumptions ( Moscovici, 1981 ) about innovation were disrupted ( Dewey, 2002 ) during the project's collective activities ( Granberg and Ohlsson, 2005 ; Ohlsson, 1996 ), such as the workshops. Reflection led to a new way of viewing innovation conceptually and to the use of an ambidextrous way of thinking in practice ( Sollander and Engström, 2021 ). This implies that the project design and the actual process that the participants followed were essential for the content development.

The second category of business insights captures the impacts of collaborative, reflective work processes. This insight mirrors a deeper understanding and appreciation of what can be gained when reflecting on issues together with others with similar challenges in an open and trusting environment, just as what Edmondson (2011) calls for. The project activities, that is, the way the project was designed and executed, were founded in interaction, dialogue ( Döös and Wilhelmson, 2011 ; Ohlsson, 1996 ), and reflection ( Boud et al. , 2006 ; Dewey, 2002 ; Ellström, 2006 ; Wenestam and Lendahl Rosendahl, 2005 ), and gave the participants both challenges and support ( Svensson et al. , 2009 ) in getting out of their comfort zone.

The third category of business insights captures that, by using emic and etic approaches ( Chilcott and Barry, 2016 ) and feedback processes ( Egan, 2002 ) in the project, participants could be aware of the meaning of their own organizational context in relation to their own and others' challenges and struggles. They all seemed to, throughout the project process, have gained the insight that everyone has their own specific conditions to adhere to. They also realized that these are not necessarily unique. Therefore, it seems that being part of this type of process advanced the participants' ways of using their own contexts as stepping stones to develop their organizations further.

For the fourth category of business insights (an understanding of the importance of increased organizational self-awareness) we see signs of learning outcomes related to a deeper understanding of how the participants view their own company and the roles they play in their organizations. We argue that this is stimulated by the emic approach used in the data collection and the reflective activities in the project ( Chilcott and Barry, 2016 ). This category of insights also indicates how project activities facilitated the avoidance of organizational traps and the participants' own defense behaviors ( Argyris, 1994 , 2010 ).

The fifth category of business insights emphasizes the potential for human interaction and communication offered by the design of these types of projects. We see how the project activities on inclusiveness and learning culture were supported by a climate in the research project of psychological safety ( Edmondson, 1999 ) and the ability to learn from failure ( Edmondson, 2011 ). The project activities also became role models for how the companies organized knowledge creation activities, i.e. the project process, and actively dealt with discrepancies and disturbances as learning potentials ( Engström, 2014 ) within their own organizations.

These five insights originate from the project design, process, and content. The interactive and iterative design, including workshops and homework where the steering group decided the upcoming activities in the project, allowed the participants to investigate and dig deeper into their companies' challenges using the theoretical concepts discussed during workshops. This design ensured practical relevance for the companies, and during the process the companies gained a sense of project ownership which strengthened their engagement and gave them time for both self and organizational reflection. To continue the path of learning, the process was essential for continuously creating a trustful and open environment, which paved the way for critical dialogue, reflection and feedback, all of which are important aspects for learning. The project activities, such as inspirational lectures within the area of innovation, challenging and validating results, and the companies’ own input sharing experiences acted as a final push for the five insights.

5. Discussion

In this project, the participants gained a new understanding of the impacts of collaborative, reflective work processes, along with new knowledge on concepts and theories. This type of knowledge creation corresponds to the knowledge type episteme ( Gustavsson, 1996 , 2000 ). Additionally, we have seen how the participants gained deeper understandings about the meaning of the unique context that their businesses, customers, organizations, and industries constitute together; about their organizational self-awareness; and about the potential for human interaction and communication for new, deeper insights. We connect this with the fact that the interactive cycles gradually made actions and thought patterns visible to the participants, which meant that they developed a reflection-based, deeper understanding, as suggested by Ellström (2011) . This is also in line with the ideas of Granberg and Ohlsson (2005) and Ohlsson (1996) that suggest there is a learning potential in dealing with challenges in a social context. One example of this is a clear shift in how the participants jointly shaped new insights regarding innovation capabilities and ambidexterity, which, in the long term, can strengthen the strategic processes in their organizations.

It is noteworthy that many of the above-mentioned learning outcomes indicate not only the fulfillment of the purpose of the research project (related to innovation capabilities) but also the generation of additional results. Examples include understanding themselves as leaders, as well as insights into the potential of human interaction and communication for deeper insights, which are key life lessons that impact innovation and other business process developments. After the completion of the project, the participants seemed to understand the concept of innovation in a completely different way, on a more general level, and in relation to other phenomena in the organization. A concept they previously barely used in the organizations has become a convenient term to use in their businesses. A shift in the evaluation of both the phenomenon and of themselves seemed to have taken place, or phronesis ( Gustavsson, 1996 , 2000 ). In the project, innovation and unplanned work became linked to one another, and the participants seem to have gained knowledge of how these entities are connected, or episteme ( Gustavsson, 1996 , 2000 ). They also said that they had the opportunity to test and introduce many new methods, or techne ( Gustavsson, 1996 , 2000 ), during the project, which indicates that applied knowledge was activated. This is related to dialectical processes around the known that are challenged by the unknown and that provide new insights, as well as to the fact that the boundaries between theory and practice were blurred ( Ellström, 2011 ). In sum, the interactive approach with the integrated learning cycles catalyzed all three of Aristotle's foundational knowledge types ( Gustavsson, 1996 , 2000 ).

We have managed to tease out several key practices in the project design, process, and content that have had a particular impact on the knowledge created. To start with, the participants' activities and attendance at workshops were consistent during the project, despite the many changes that took place in the management groups (i.e. people leaving for other companies). This indicates that commitment throughout the learning process remained; the companies' sense of commitment and value gained was strong. To achieve this, the project management team carefully aligned the project's research purpose and process with practical relevance ( Geraldi and Söderlund, 2018 ) and fostered an inclusive, psychologically safe environment ( Edmondson, 1999 , 2011 ). Furthermore, the fact that the workshops facilitated reflection ( Döös and Wilhelmson, 2011 ; Ohlsson, 1996 ) and enabled distancing from and the formation of perspectives on everyday problems appeared fundamental. Several participants attested that the resistance ( Argyris, 2010 ) they previously had regarding the ability to be innovative was alleviated by the project's approach and dialogue. Another key practice associated with the project's design was the guidance provided by the iterative process and by steering group decisions on themes and the homework. This seems to have triggered a sense of project ownership and a focus on the companies' own input to the project. The participants described the comments they received from others in the project group as supportive, and various types of input, such as guest lectures, during workshops were considered challenging, according to the three important components of a learning process. Throughout the project, critical dialogue facilitated reflection, which led to deeper insights into the companies' own operations. In all, this supports the notion of steering, supporting, and challenging to create robust knowledge ( Svensson et al. , 2009 ).

6. Conclusions

The purpose of this paper was to shed light on the different types of knowledge created in an interactive research project and to analyze how they are linked to the project design, process, and content. The key features of the project design, process, and content are all connected with state-of-the-art knowledge on how knowledge creation is orchestrated—stimulating psychological safety; steering, supporting, and challenging; and ensuring the alignment of theoretical rigor with practical relevance. In the present study, we confirm that this important knowledge and all three basic types of knowledge that were stimulated— episteme , phronesis, and techne —are indeed transferable to the context of interactive research when using the project form, especially if the goals are to stimulate both intended and unintended learning outcomes, including reflective knowledge and insights.

In this paper we shed light on a key potential of interactive research project management, namely, how to obtain deeper and potentially more sustainable learning effects for the participating partners beyond the explicit project purpose at hand. We have studied how knowledge is created in relation to the project design, process and content.

First, we want to highlight the findings in our study which confirm previous studies. We confirm that Ellström's (2007) ; Ellström et al. (2020) model of interactive research indeed provides conditions for providing valuable insights into the research problem at hand. In our case, we studied how small and medium-sized companies could increase their innovation capabilities while better balancing innovation activities with daily operations. The published results from the project were highly dependent on the reflection, validation, and feedback that took place in our meetings with the participating practitioners. Our results also confirm earlier studies on the productive relationship between the different roles of researchers and practitioners in collaboration ( Aagard Nielsen and Svensson, 2006 ) and the importance of the level of interaction in different phases of the research process ( Svensson et al. , 2002 ; Cronholm and Goldkuhl, 2003 ) as well as the importance of steering , supporting, and challenging to create robust knowledge ( Svensson et al. , 2009 ).

Second, we provide substantial additions to existing knowledge. Our study shows that the interactive and iterative approach with the recurring homework, workshops, and guided reflections contributed not only to joint knowledge creation in a broader sense, but also to deeper insights. We sometimes referred to the metaphor of peeling an onion in our workshops with the companies to show them how we, together, could gain a better understanding of the questions at hand using reflection. Our findings suggest an alteration of Ellström's model with an empowering, expanded view of the taken-for-granted interest of participants in the practice system. We saw that the practitioners were interested not only in practical issues or implications but also in the theoretical underpinnings of their problems; they played a pivotal role in creating theoretical knowledge. The results also complement earlier research by exemplifying and unpacking the key practices of interaction. For example, steering group meetings that assigned homework to the companies fulfilled the steering aspects of the learning process. Inspirational and theoretical lecturers seemed to challenge existing knowledge and mindsets, while feedback meetings and workshops supported knowledge creation and strengthened work with innovation and meta-reflection. A surprising finding was that the interactive and iterative model changed the mindsets of the participating company representatives and increased organizational self-awareness. This, in turn, formed a crucial basis for driving change in work methods and making investments in the organizations, as well as for changing assumptions about customer offerings. While these theoretical contributions primarily belong to the domain of knowledge creation and interaction research, we also contribute more specifically to the field of project management research by illustrating how knowledge creation can take place in practice through examples and rich empirical accounts, thereby contributing to the call by Lindhult and Axelsson (2021) to expand project management research.

Project management scholars can also find practical implications for research in our study. Research seeking to examine the conditions for reflexive knowledge creation and deeper insights can benefit from searching for evidence of the key features of the project design, process, and content, as indicated in the discussion section. Researchers who are eager to design their own interactive, collaborative research projects can hopefully also be inspired by our learning loop design and the transfer of theoretical state-of-the-art knowledge into hands-on practical activities.

We advise practitioners interested in expanding the outcomes of projects beyond the explicit targets to pay careful attention to how they set up their projects. They need to make room for iterative reflection, be mindful of creating a trusting and open environment in the team, challenge results with opposing views and theories, and make room for sharing experiences and giving feedback. In doing so, our study suggests that it is possible to gain deeper insights into complex issues that have the potential to have long-lasting effects on both people and businesses.

There are particularities in a study that are not fully captured and explained. While we cannot tease out any direct cause–effect relationships between specific activities and specific learning outcomes, we can conclude a relationship between the project design, process, and content with the identified learning outcomes. Similarly, while we can verify that learning has taken place, we cannot quantify the learning outcomes in terms of how many participants have gained knowledge. Further research is needed to validate our findings, so we encourage other authors to adopt the presented research process and activities and to focus on the meta-analysis of the impacts that the process has on the outcomes. Preferably, this could take place by assigning a dedicated researcher to follow this process in parallel to the focal problems defined in the project.

research for knowledge creation

Illustration of joint activities, between the two systems involved, in interactive research

research for knowledge creation

The projects iterative process in four-month cycles

Participating companies

Aagard Nielsen , K. and Svensson , L. ( 2006 ), Action and Interactive Research: Beyond Practice and Theory , Shaker Publishing , Maastricht .

Adler , N. , Shani , A.B.R. and Styhre , A. (Eds) ( 2004 ), in , Collaborative Research in Organizations: Foundations for Learning, Change, and Theoretical Development , SAGE , London .

Andersson , S. , Balkmar , D. and Callerstig , A.C. ( 2022 ), “ From glass ceiling to firewalls: detecting and changing gendered organizational norms ”, NORA-nordic Journal of Feminist and Gender Research , Vol.  30 No.  2 , pp. 140 - 153 , doi: 10.1080/08038740.2021.1931438 .

Argyris , C. ( 1990 ), Overcomming Organizational Defenses: Facilitating Organizational Learning , Prentice-Hall , New Jersey .

Argyris , C. ( 1994 ), “ Good communication that blocks learning. Harvard business review ”, July-August .

Argyris , C. ( 2010 ), Organizational Traps. Leadership, Culture, Organizational Design , Oxford University Press , New York .

Argyris , C. and Schön , D.A. ( 1978 ), Organizational Learning: A Theory of Action Perspective , Addison-Wesley , Reading, MA .

Avison , D.E. , Davison , R.M. and Malaurent , J. ( 2018 ), “ Information systems action research: debunking myths and overcoming barriers ”, Information and Management , Vol.  55 No.  2 , pp.  177 - 187 .

Börjesson , S. and Fredberg , T. ( 2004 ), “ Jam sessions for collaborative management research ”, in Adler , N. , Shani , A.B.R. and Styhre , A. (Eds), Collaborative Research in Organizations, Foundations for Learning, Change and Theoretical Development , Sage , Thousand Oaks , pp.  135 - 148 .

Boud , D. , Cressey , P. and Docherty , P. ( 2006 ), Productive Reflection at Work: Learning for Changing Organizations , Routledge , London .

Canonico , P. , Söderlund , J. , De Nito , E. and Mangia , G. ( 2013 ), “ Special issue on organizational mechanisms for effective knowledge creation in projects - guest editorial ”, International Journal of Managing Projects in Business , Vol.  6 No.  2 , pp.  223 - 235 .

Chalmers , A.F. ( 2013 ), What Is This Thing Called Science? , Hackett Publishing , Indianapolis, IN .

Chilcott , M. and Barry , D. ( 2016 ), “ Narrating creativity: developing an emic, first person approach to creativity research ”, International Journal of Narrative Therapy and Community Work , Vol.  3 , pp.  57 - 67 .

Choo , A. , Linderman , K. and Schroder , R. ( 2007 ), “ Method and psychological effects on learning behaviors and knowledge creation in quality improvement projects ”, Management Science , Vol.  53 No.  3 , pp.  437 - 450 .

Cronholm , S. and Goldkuhl , G. ( 2003 ), “ Conceptualising participatory action research—three different practices ”, Electronic Journal of Business Research Methods , Vol.  2 No.  2 , pp.  47 - 58 .

Czarniawska , B. ( 2007 ), “ Narrative inquiry in and about organizations ”, in Clandinin , J. (Ed.), Handbook of Narrative Inquiry: Mapping a Methodology , Sage Publications , pp.  383 - 404 .

Davies , A. and Hobday , M. ( 2005 ), The Business of Projects , Cambridge University Press , Cambridge .

Dewey , J. ( 2002 ), Human Nature and Conduct , Dover Publications , Chelmsford, MA .

Döös , M. and Wilhelmson , L. ( 2011 ), “ Collective Learning: interaction and a shared action arena ”, Journal of Workplace Learning , Vol.  23 No.  8 , pp.  487 - 5000 .

Edmondson , A. ( 1999 ), “ Psychological safety and learning behavior in work teams ”, Administrative Science Quarterly , Vol.  44 No.  2 , pp.  350 - 383 , available at: http://www.jstor.org/stable/2666999 .

Edmondson , A. ( 2011 ), “ Strategies for learning from failure ”, Harvard Business Review , Vol.  89 No.  4 , pp.  48 - 55 .

Egan , G. ( 2002 ), The Skilled Helper. A Problem-Management and Opportunity-Development Approach to Helping , 7th ed. , Brooks/Cole , Pacific Grove, CA .

Ellram , L. and Tate , W.L. ( 2015 ), “ Redefining supply management's contribution in services sourcing ”, Journal of Purchasing and Supply Management , Vol.  21 No.  1 , pp.  64 - 78 .

Ellström , P.E. ( 1992 ), Kompetens, Utbildning Och Lärande I Arbetslivet: Problem, Begrepp Och Teoretiska Perspektiv , Norstedts Juridik AB , Stockholm .

Ellström , P.E. ( 2001 ), “ Integrating learning and work: problems and prospects ”, Human Resource Development Quarterly , Vol.  12 No.  4 , pp.  421 - 430 .

Ellström , P.E. ( 2006 ), “ The meaning and role of reflection in informal learning at work ”, in Boud , D. , Cressey , P. and Docherty , P. (Eds), Productive Reflection at Work , Routledge , New York .

Ellström , P.E. ( 2007 ), “ Knowledge creation through interactive research: a learning perspective ”, Paper presented at the HSS-07 Conference , Jönköping .

Ellström , P.-E. ( 2010a ), “ Organizational learning ”, in McGaw , B. , Peterson , P.L. and Baker , E. (Eds), International Encyclopedia of Education , Elsevier , Amsterdam , Vol.  1 , pp.  47 - 52 .

Ellström , P.-E. ( 2010b ), “ Practice-based innovation: a learning perspective ”, Journal of Workplace Learning , Vol.  22 Nos 1/2 , p. 27 .

Ellström , P.-E. ( 2011 ), “ Informal learning at work: conditions, processes and logics ”, in Malloch , M. , Cairns , L. , Evans , K. and O´Connor , B.N. (Eds), The SAGE Handbook of Workplace Learning , SAGE , Los Angeles .

Ellström , P.E. , Elg , M. , Wallo , A. , Berglund , M. and Kock , H. ( 2020 ), “ Interactive research: concepts, contributions and challenges ”, Journal of Manufacturing Technology Management , Vol.  31 No.  8 , pp.  1517 - 1537 .

Engeström , Y. ( 1987 ), Learning by Expanding: An Activity-Theoretical Approach to Developmental Research , Orienta Konsultit Oy , Helsinki .

Engeström , Y. ( 2008 ), From Team to Knots: Activity-Theoretical Studies of Collaboration and Learning at Work , Cambridge University Press , Cambridge .

Engström , A. ( 2014 ). Lärande Samspel För Effektivitet: En Studie Av Arbetsgrupper I Ett Mindre Industriföretag . ( Fil dr ). Linköpings Universitet , Linköping . ( Linköping Studies in Behavioural Science No 185 ).

Faccin , K. and Balestrin , A. ( 2018 ), “ The dynamics of collaborative practices for knowledge creation in joint R&D projects ”, Journal of Engineering and Technology Management , Vol.  48 , pp.  28 - 43 .

Geraldi , J. and Söderlund , J. ( 2018 ), “ Project studies: what it is, where it is going ”, International Journal of Project Management , Vol.  36 No.  1 , pp.  55 - 70 .

Gioia , D.A. , Corley , K.G. and Hamilton , A.L. ( 2013 ), “ Seeking qualitative rigor in inductive research: notes on the Gioia methodology ”, Organizational Research Methods , Vol.  16 No.  1 , pp. 15 - 31 , doi: 10.1177/1094428112452151 .

Granberg , O. and Ohlsson , J. ( 2005 ), “ Kollektivt lärande i team: om utveckling av kollektiv handlingsrationalitet ”, Pedagogisk Forskning I Sverige , Vol.  10 Nos 3/4 , pp.  227 - 243 .

Gunnarsson , E. , Hansen , H.P. , Nielsen , B.S. and Sriskandarajah , N. ( 2015 ), Action Research for Democracy: New Ideas and Perspectives from Scandinavia , Routledge , London .

Gustavsen , B. and Engelstad , P.H. ( 1986 ), “ The design of conferences and the evolving role of democratic dialogue in changing working life ”, Human Relations , Vol.  39 No.  2 , pp.  101 - 116 .

Gustavsson , B. ( 1996 ), Bildningens Väg , Wahlström & Widstrand , Borås .

Gustavsson , B. ( 2000 ), Kunskapsfilosofi: Tre Kunskapsformer I Historisk Belysning , Fälth & Hässler , Smedjebacken .

Gustavsson , M. ( 2007 ), “ The potential for learning in industrial work ”, Journal of Workplace Learning , Vol.  19 , No.  7 , pp.  453 - 463 .

Heron , J. and Reason , P. ( 2006 ), “ The practice of Co-operative inquiry: research ‘with’ rather than ‘on’ people ”, in Reason , P. and Bradbury , H. (Eds), Handbook of Action Research: The Concise Paperback Edition , Sage , London , pp.  144 - 154 .

Hevner , A.R. , March , S.T. , Park , J. and Ram , S. ( 2004 ), “ Design science in information systems research ”, MISQ , Vol.  28 No.  1 , pp.  75 - 105 .

Illeris , K. ( 2007 ), Lärande , Studentlitteratur , Lund .

Jacob , M. , Hellström , T. , Adler , N. and Norrgren , F. ( 2000 ), “ From sponsorship to partnership in academy‐industry relations ”, R&D Management , Vol.  30 No.  3 , pp.  255 - 262 .

Johannisson , B. , Gunnarsson , E. and Stjernberg , T. ( 2008 ), Gemensamt kunskapande: Den interaktiva forskningens praktik , University Press, Växjö universitet , Göteborg .

Junni , P. , Sarala , R.M. , Taras , V. and Tarba , S.Y. ( 2013 ), “ Organizational ambidexterity and performance: a meta-analysis ”, The Academy of Management Perspectives , Vol.  27 No.  4 , pp. 299 - 312 .

Larsson , A.-C. ( 2006 ), “ Interactive research - methods and conditions for joint analysis ”, in Aagaard Nielsen , K. and Svensson , L. (Eds), Action Research and Interactive Research - beyond Practice and Theory , Shaker Publishing , Maastricht, The Netherlands , pp.  241 - 258 .

Lave , J. and Wenger , E. ( 1991 ), Situated Learning. Legitimate Peripheral Participation , Cambridge University Press , New York .

Lett , J. ( 1990 ), “ Emics and etics: notes on the epistemology of anthropology ”, Emics and Etics: The Insider/outsider Debate , Vol.  7 , pp.  127 - 142 .

Lindhult , E. and Axelsson , K. ( 2021 ), “ The logic and integration of coproductive research approaches ”, International Journal of Managing Projects in Business , Vol.  14 No.  1 , pp. 13 - 35 .

Lubatkin , M.H. , Simsek , Z. , Ling , Y. and Veiga , J.F. ( 2006 ), “ Ambidexterity and performance in small-to medium-sized firms: the pivotal role of top management team behavioral integration ”, Journal of Management , Vol.  32 No.  5 , pp. 646 - 672 .

Lundin , R. , Arvidsson , N. , Brady , T. , Ekstedt , E. , Midler , C. and Sydow , J. (Eds) ( 2017 ), Managing and Working in Project Society - Institutional Challenges of Temporary Organizations , Cambridge University Press .

Magnusson , L. and Ottosson , J. ( 2012 ), Den hållbara svenska modellen , SNS Förlag , Stockholm .

March , J.G. ( 1991 ), “ Exploration and exploitation in organizational learning ”, Organization Science , Vol.  2 No.  1 , pp.  71 - 87 .

Mohrman , S.A. , Gibson , C.B. and Mohrman , A.M. ( 2001 ), “ Doing research that is useful to practice: a model and empirical exploration ”, Academy of Management Journal , Vol.  44 No.  2 , pp.  357 - 375 .

Moscovici , S. ( 1981 ), “ On social representations ”, Social Cognition: Perspectives on Everyday Understanding , Vol.  8 No.  12 , pp.  181 - 209 .

Näslund , D. , Kale , R. and Paulraj , A. ( 2010 ), “ Action research in supply chain management - a framework for relevant and rigorous research ”, Journal of Business Logistics , Vol.  31 No.  2 , pp.  331 - 355 .

Nilsen , E.R. ( 2013 ), “ Organizing for learning and knowledge creation–are we too afraid to kill it? Projects as a learning space ”, International Journal of Managing Projects in Business , Vol.  6 No.  2 , pp. 293 - 309 .

Ohlsson , J. ( 1996 ), Kollektivt Lärande: Lärande I Arbetsgrupper Inom Barnomsorgen. (PhD) , Stockholms universitet , Stockholm .

O’Reilly , C.A. and Tushman , M.L. ( 2008 ), “ Ambidexterity as a dynamic capability: resolving the innovator’s dilemma ”, Research in Organizational Behavior , Vol.  28 , pp. 185 - 206 .

Palm , J. ( 2007 ), Kunskapsbildning mellan träindustri och akademi: en studie av dess förutsättningar och möjligheter , Doctoral dissertation , Linne Univerity , Växjö .

Pettigrew , A.M. ( 1990 ), “ Longitudinal field research on change: theory and practice ”, Organization Science , Vol.  1 No.  3 , pp.  267 - 292 .

Raisch , S. and Birkinshaw , J. ( 2008 ), “ Organizational ambidexterity: antecedents, outcomes, and moderators ”, Journal of Management , Vol.  34 No.  3 , pp.  375 - 409 .

Runsten , P. and Werr , A. ( 2016 ), Kunskapsintegration: Om Kollektiv Intelligens I Organisationer , Studentlitteratur , Lund .

Sandberg , F. and Wallo , A. ( 2013 ), “ The interactive researcher as a virtual participant: a Habermasian interpretation ”, Action Research , Vol.  11 No.  2 , pp. 194 - 212 .

Scarborough , H. , Bresnen , M. , Edelman , L.F. , Laurent , S. , Newell , S. and Swan , J. ( 2004 ), “ The processes of project-based learning: an exploratory study ”, Management Learning , Vol.  35 No.  4 , pp.  491 - 506 .

Schön , D. ( 1983 ), The Reflective Practitioner: How Professional Think in Action , Basic Books , New York .

Senge , P.M. ( 1990 ), The Fifth Discipline: The Art and Practice of the Learning Organization , Bantam Doubleday , New York .

Sollander , K. and Engström , A. ( 2021 ), “ Unplanned Managerial Work: An Ambidextrous Learning Potential ”, Studies in Continuing Education , pp.  1 - 19 , doi: 10.1080/0158037X.2021.1874903 .

Susman , G.I. and Evered , G.I. ( 1978 ), “ An assessment of the scientific merits of action research ”, Administrative Science Quarterly , Vol.  23 No.  4 , pp.  582 - 603 .

Svejvig , P. , Sankaran , S. and Lindhult , E. ( 2021 ), “ Guest editorial ”, International Journal of Managing Projects in Business , Vol.  14 No.  1 , pp.  1 - 12 , doi: 10.1108/IJMPB-02-2021-313 .

Svensson , L. , Brulin , G. and Ellström , P.-E. ( 2015 ), “ Interactive research and ongoing evaluation as joint learning processes ”, in Sustainable Development in Organizations , pp. 346 - 362 .

Svensson , L. , Brulin , G. , Ellström , P.-E. and Widegren , Ö. (Eds) ( 2002 ), Interaktiv Forskning - Förutveckling Av Teori Och Praktik , Arbetslivsinstitutet , Stockholm .

Svensson , L. , Ellström , P.E. and Brulin , G. ( 2007 ), “ Introduction–on interactive research ”, International Journal of Action Research , Vol.  3 No.  3 , pp.  233 - 249 .

Svensson , L. , Brulin , G. , Jansson , S. and Sjöberg , K. ( 2009 ), Lärande Utvärdering Genom Följeforskning , Studenlitteratur , Lund .

van de Ven , A.H. ( 2007 ), Engaged Scholarship A Guide for Organizational and Social Research , Oxford University Press , New York, NY .

Wallo , A. ( 2008 ), The Leader as a Facilitator of Learning at Work: A Study of Learning-Oriented Leadership in Two Industrial Firms , Doctoral dissertation, Linköping University Electronic Press .

Wallo , A. , Kock , H. and Nilsson , P. ( 2012 ), “ Accelerating and braking in times of economic crisis: organisational learning in a top management team ”, European Journal of Training and Development , Vol.  36 No.  9 , pp.  930 - 944 .

Weck , M. ( 2006 ), “ Knowledge creation and exploitation in collaborative R&D projects: lessons learned on success factors ”, Knowledge and Process Management , Vol.  13 No.  4 , pp.  252 - 263 .

Wenestam , C.G. and Lendahl Rosendahl , B. ( 2005 ), Lärande I Vuxenlivet , Studentlitteratur , Lund .

Westlander , G. ( 2008 ), Forskarroller I Interaktivt Utvecklingsarbete: Om Samverkansprocesser För Ergonomiska Förbättringar , Linköping University Electronic Press , Linköping .

Wieringa , R. and Morali , A. ( 2012 ), “ Technical action research as a validation method in information systems design science ”, Design Science Research in Information Systems: Advances in Theory and Practice LNCS , Vol.  7286 , pp.  220 - 238 .

Winn , W. and Snyder , D. ( 2004 ), “ Cognitive perspectives in psychology ”, in Jonassen , D.H. (Ed.), Handbook of Research on Educational Communications and Technology , Lawrence Erlbaum Associates Publishers , New Jersey .

Yin , R.K. ( 2014 ), Case Study Research Design and Methods , 5th ed. , SAGE , Los Angeles .

Zimmermann , A. , Raisch , S. and Birkinshaw , J. ( 2015 ), “ How is ambidexterity initiated? The emergent charter definition process ”, Organization Science , pp. 1119 - 1139 .

Acknowledgements

This work was conducted within Innovate and supported by Swedish Knowledge Foundation (grant number KK20170312).

Corresponding author

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Building upon a knowledge translation framework, pitfalls in knowledge creation, tailored communication: a conceptual framework, core components on how the content is said, core components on how the content is communicated, methodology, conflicts of interest.

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From research to evidence-informed decision making: a systematic approach

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Charlotte C Poot, Rianne M van der Kleij, Evelyn A Brakema, Debbie Vermond, Siân Williams, Liza Cragg, Jos M van den Broek, Niels H Chavannes, From research to evidence-informed decision making: a systematic approach, Journal of Public Health , Volume 40, Issue suppl_1, March 2018, Pages i3–i12, https://doi.org/10.1093/pubmed/fdx153

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Knowledge creation forms an integral part of the knowledge-to-action framework aimed at bridging the gap between research and evidence-informed decision making. Although principles of science communication, data visualisation and user-centred design largely impact the effectiveness of communication, their role in knowledge creation is still limited. Hence, this article aims to provide researchers a systematic approach on how knowledge creation can be put into practice.

A systematic two-phased approach towards knowledge creation was formulated and executed. First, during a preparation phase the purpose and audience of the knowledge were defined. Subsequently, a developmental phase facilitated how the content is ‘said’ (language) and communicated (channel). This developmental phase proceeded via two pathways: a translational cycle and design cycle, during which core translational and design components were incorporated. The entire approach was demonstrated by a case study.

The case study demonstrated how the phases in this systematic approach can be operationalised. It furthermore illustrated how created knowledge can be delivered.

The proposed approach offers researchers a systematic, practical and easy-to-implement tool to facilitate effective knowledge creation towards decision-makers in healthcare. Through the integration of core components of knowledge creation evidence-informed decision making will ultimately be optimized.

Knowledge translation (KT) aims to fill the evidential gap between knowledge and practice; a process that is considered by the World Health Organization (WHO) to be one of the most important public health challenges of this century. 1 The knowledge gap has often been referred to as the knowledge-to-action (KtA) gap. This term implies a broader application of knowledge, involving decision-makers, health practitioners, patients and the public.

Using this broader definition of KT, Graham et al . 2 developed a KtA framework that conceptualizes the process of KT. This framework, comprises two distinct but related components. The ‘Action Cycle’ represents the activities that are needed to apply evidence-based knowledge to practice. This includes tailoring interventions to the local context and identifying and evaluating barriers and facilitators to implementation. The ‘Knowledge Creation funnel’, on the other hand, refers to the simultaneous process of the generation of the tools and key messages that aid in the Action Cycle. These are created by distiling and tailoring core messages from research knowledge to the needs of the knowledge user. In its broadest definition, knowledge users include, policy-makers, health practitioners and the general public. This article will focus on KT to decision-makers (In this article decision-makers include managerial decision-makers (e.g. managers in hospital, community organisations and private business.) as well as policy decision-makers at the national, provincial, district and local levels. 7 ) as they are in the best position to influence health decisions and benefit public health through evidence-informed decision making.

Even though the action process and the knowledge creation process must form part of any KT model, it remains ambiguous how these processes should be executed. Large inconsistencies can especially be identified in the knowledge creation process due to a lacking systematic approach on how to put the process into practice. 3 This article strives to provide a systematic approach on how the knowledge creation process can be put into practice. More specifically it focuses on what, based on the literature, are the core components of the knowledge creation process that every researcher engaging with KT should consider. The use of a case study will demonstrate how the systematic approach can be used by researchers to effectively establish evidence-informed decision making.

With the focus shifting from knowledge dissemination to KT, the role of reciprocity between decision-makers and researchers in facilitating evidence-informed decision making has become widely acknowledged. 4 Whereas the traditional and more linear model—‘the science push model’—underlines the supply of evidence to inform evidence-informed decision making, the interaction model reflects the need of reciprocity and partnership building. The latter, suggests that the more sustained the interaction between researchers and policy-makers is, the larger the impact of evidence-informed decision making becomes. 5 – 9 This interactive KT model is, however, a complex, time-consuming step that is hampered by political instability, high turn-over of policy-making staff 6 and perceived cultural differences between researchers and policy-makers. 10 , 11 Consequently, the traditional, linear approach remains the most common used approach.

Despite the less complex nature of the traditional science push model, in practice, researchers and policy-makers rarely speak the same language. Evidence provided to decision-makers is generally considered to be too complex, too detailed, too technical or lacking in timeliness. 6 , 12 , 13 Aside from these substantive elements, inattentiveness to design and structure of a research report can also trouble the communication from researchers to decision-makers. 14

In order to avoid these pitfalls, it is paramount to tailor knowledge to the level of understanding, needs and demands of the target audience. Guided by Lavis’ extension of Lasswell’s communication model effective communication depends on tailoring what is being said (content), how it is being said (language), how it is communicated (channel) to whom (audience) and with what purpose (intended effect). 7 , 15 Although the ‘who’, the ‘what’ and the ‘to whom’ are often taken into consideration, the ‘how’ is often overlooked in communication to decision-makers. 7 , 16 Strikingly, it is precisely this ‘how’ aspect of the communication process that might be crucial in influencing evidence-informed decision making. Drawn from the literature on the field of science communication, visual communication and user-centred design, we formulated a number of core components approaching this ‘how’ aspect (Fig. 1 ). These components can be divided in translational components and design components, determining how the content is said, or how the content is communicated, respectively.

Conceptual framework adopted from Lasswell’s communication model and its extension by Lavis et al. Each step in the sequence represents further interpretations of the framework when communicating evidence-based research (content) to decision-makers (audience) with the purpose to influence evidence-informed decision making. Core components on knowledge creation provide elaborate interpretation of how the content is said and communicated.

Conceptual framework adopted from Lasswell’s communication model and its extension by Lavis et al. Each step in the sequence represents further interpretations of the framework when communicating evidence-based research (content) to decision-makers (audience) with the purpose to influence evidence-informed decision making. Core components on knowledge creation provide elaborate interpretation of how the content is said and communicated.

The first core component, the translational component, entails that content should be target-audience appropriate and packaged in a mode of communication that is familiar to the target audience. Information should be concise and understandable, adapted in terms of length and complexity of grammar. 6 , 14 In addition, messages that are meant to prompt action should be expressed as an actionable message. This can be established by integrating concepts of applicability (i.e. feasibility of an intervention), and transferability (i.e. likelihood that the intervention will equally benefit health in this specific setting). 17

Knowledge should moreover be represented in a form that facilitates understanding. 18 Representation forms include common used textual statements, compelling narratives (storytelling) or the visualisation of data into graphs or infographics. Visualisation of data is an effective means of representing complex ideas of information in a format that is quickly understood. 18 – 20 Storytelling, an increasingly used tool in public health communication, 21 , 22 provides context to the situation by anchoring a problem in the real world. 14 , 23 The power of storytelling lies therefore, beside the transfer of explicit knowledge, in the transfer of tacit knowledge.

Apart from the choice on language and representation one should tailor how content is being communicated to the target audience. Simply communicating information in a form and language tailored to the needs and demands of decision-makers does not sufficiently influence evidence-informed decision making. 24 , 25 The majority of research evidence is consumed by decision-makers via a written channel. 12 , 13 Therefore, elements of design, including navigation, organisation, 25 , 26 design aesthetics 27 and semiotics should also be taken into account. 14 , 28

This means that navigation between information should be intuitive and information should be presented in an orderly way. 28 Online repositories such as the ‘HealthCOMpass’ 29 and ‘Science for Environment Policy’ 30 are generally effective in transferring knowledge by presenting information in separate self-contained ‘chunks’ of information, enabling decision-makers to access the information in the order they choose. Furthermore, it is important to create and incorporate design aesthetics. An appealing exterior can be accomplished through the use of complementing colours, a polished house style, simple typography and the appropriate use of visual aids. 31 , 32 Ultimately, visual aids can become more meaningful via the utilisation of semiotics. Semiotics refers to the interpretation of a visual into the meaning that goes with it. Pictograms can be ideal to communicate a subject as they derive their meaning from an iconic relation with what they refer to and are understood universally. 33 , 34

This article takes one approach in how researchers can communicate knowledge to decision-makers with the purpose of influencing evidence-based decision making. It should be noted that this article does not attempt to cover all aspects of KT to decision-makers. Rather, takes a starting point in how to create knowledge (tools and key messages) in such a way that it fits the needs and demands of decision-makers. In the following section, using a case study we provide an approach on how core components of knowledge creation can be integrated in an easy-to-implement tool.

A case study

The knowledge gap is apparent in all areas of public health. However, it may be even more evident in low- and middle- income countries (LMICs). 35 – 37 LMICs are generally characterized by suboptimal primary care standards, general poor health and significant challenges in implementing clinically and cost-effective interventions. 35 , 36 , 38 , 39 There is a growing recognition of the need to improve the translation of evidence into practice in these LMICs and to adapt evidence-based interventions proven to be effective in developed settings to the local context. 35 , 40 , 41 The FRESH AIR study, aimed at addressing the need to prevent, diagnose and treat non-communicable lung diseases (NCLDs) in LMICs is considered an ideal case study. Exploring barriers and facilitators to the implementation of evidence-based interventions in low-resources settings and tailoring them to the context are key elements to reach the FRESH AIR aim. Due to this implementation design, KT and creation were included as an integral part of the FRESH AIR study. The protocol has been published elsewhere. 42 This case study elucidates one of the methods FRESH AIR is using to create knowledge tailored to decision-makers.

A systematic approach to knowledge creation

The approach to knowledge creation was guided by Lasswell’s adapted communication model and consisted of two phases: a preparation and a developmental phase. Both are schematically depicted in Fig. 2 . Creating a knowledge platform requires the developer to think and approach the subject matter from multiple angles, making use of scientific and analytical knowledge as well as editorial reasoning. Due to the complexity of this iterative process, one should therefore keep in mind that Fig. 2 is a simplification of the developmental process.

Methodological approach towards knowledge creation Integration of Lasswell’s adapted communication model with Graham’s knowledge-to-action framework. Separate phases provide a step-by-step approach towards knowledge creation.

Methodological approach towards knowledge creation Integration of Lasswell’s adapted communication model with Graham’s knowledge-to-action framework. Separate phases provide a step-by-step approach towards knowledge creation.

Preparation and developmental phase

During the preparation phase the purpose of the knowledge platform was defined through the formulation of the main objectives. A main audience was defined to specify the ‘to whom’ aspect. Both the objective and the audience were decisive in ‘what’ was to be communicated. Subsequently, the ‘what’ led to the development of a framework of the knowledge platform, comprising all topics the knowledge platform should address.

The second phase, the developmental phase, provides an approach towards the ‘how’ aspect of the communication model. The approach to ‘how it is said’ and ‘how it is communicated’ were guided by two separate pathways, respectively, the translational cycle and the design cycle. The translational cycle involves the translation of scientific data and information into tailored content. Whereas, the design cycle is the incorporation of core components on navigation, organisation, design aesthetics and semiotics. For conceptual and illustrative purposes, we made a clear distinction between the approaches. In practice however, the two approaches are complex and intertwined with each other.

Translational cycle

During the translational cycle research findings (non-translated knowledge) generated during the FRESH AIR project were passed through a number of consecutive steps. Through the integration of the translational core components this resulted in the generation of content tailored to decision-makers (translated knowledge). As individual studies rarely provide sufficient evidence for decision making, evidence was also synthesised from other sources. 43

Evidence acquired per topic (Step 1) was synthesised and critically appraised (Step 2). Critical appraisal, defined as the examination of research evidence on the level of evidence and relevance, is an important step within the translational process. 44 Critical appraisal was performed using a flow-chart like tool. The flow-chart integrated multiple appraisal tools on grey literature with the Scottish Intercollegiate Guidelines Network (SIGN) grading system on scientific evidence to create a tool that can be applied to all types of evidence. 45 , 46 The level of evidence and relevance was categorized into five categories. Scientific evidence that was based on meta-analysis, rigorous systematic reviews or RCT with very low risk of bias according to the SIGN grading system (Grade A), was extracted (Step 3). In the case of disputable evidence (Grade E or D) due to either a high risk of bias, low level of evidence or evidence-based on non-analytical studies such as expert opinion or a case report, an annotation was added.

Based on the extracted data key message were formulated (Step 4) and data was aggregated into explorative or explanatory overview charts, infographics, visuals, textual statements or narratives (Step 5). Before incorporation into the knowledge platform the product was run through a set of criteria to determine whether all core translational components were sufficiently integrated (see checklist in Fig. 2 ). When the translated knowledge product scored insufficiently, it re-entered the translational cycle.

Design cycle

Parallel to the translational cycle the communication channel was designed. Core components on navigation, organisation, design aesthetics and semiotics were integrated into so called ‘proof of concepts’ (trial products) which were subsequently tested on the experience of the user (user-experience analysis). ‘Proof of concepts’ allow for iterative amendments during several moments of evaluation, thereby warranting feasibility and sustainability early on. 47 , 48

Study deliverables

The following section presents how the systematic knowledge creation approach was put into practice within the FRESH AIR project.

Preparation phase

In the FRESH AIR knowledge dissemination strategy several objectives of the knowledge platform have been formulated. The first objective is to inform decision-makers and other stakeholders about the prevalence of NCLD diseases, risk factors and present feasible context-specific solutions. The second objective is to share materials that assist in the implementation of these context-specific solutions. Since purpose and audience determine the knowledge that is to be communicated, two separate channels were created, each serving one of the above mentioned objectives. A public website serves the first objective whereas a linked knowledge base serves the second. A knowledge base offers access to a large range of documents, including scientific publications, translated policy briefs, protocols and educational materials. Since the knowledge base complements the website as a source of information the following section will focus on the development of the public website.

Developmental phase

The translational- and design cycle served as a template to develop the public website. Figure 3 depicts an example of how the translational cycle was operationalised. After retrieval of evidence (Step 1) and critical appraisal of evidence (Step 2), relevant data was extracted (Step 3). This was then used to formulate key messages and create visualisations (Steps 4 and 5). Correct interpretation of the visualisations was supported by adding a simplification of the key messages (Step 5).

From evidence to visual representation of data in five steps. A case study example providing interpretation of the different steps of the translational cycle.

From evidence to visual representation of data in five steps. A case study example providing interpretation of the different steps of the translational cycle.

Figure 4 illustrates a concept of the home-page of the FRESH AIR public website, demonstrating the integration of the core components of knowledge creation. As the development of the website is an on-going project and has not yet been delivered, intermediate results are presented and complemented by future ideas.

Core components of knowledge creation integrated into the homepage of the website.

Core components of knowledge creation integrated into the homepage of the website.

Future plans

Novel knowledge is continuously generated during the FRESH AIR project. Hence, core components of knowledge creation will be integrated in several additional ways. Information will be presented in various forms. Global prevalence of disease will be expressed in a bubble chart. Bubble charts are explorative rather than explanatory, allowing comparison between settings and different measures.

Furthermore, storytelling will be used to trigger action or share knowledge by presenting successful implementation stories. Excessive detail will be avoided to permit the reader to be able to imagine a comparable solution within their own situation. Composite stories will be created from interview narratives derived from the qualitative FRESH AIR data.

Warranting sustainability and outreach

Elements regarding sustainability, outreach and dissemination will furthermore be taken into account. Sustainability will be warranted by basing the website on a WordPress platform. This free content-management software does not require programming skills and allows for content management independent of a web designer. Outreach to a non-academic audience, including decision-makers, will be maximized through the integration of several social media channels and hyperlinks to leading health institutions.

In this paper we presented a systematic approach towards knowledge creation- the tailoring of research knowledge to decision-makers to facilitate evidence-informed decision making. We elaborated on the knowledge creation cycle, an integral part of the KtA framework by Graham et al. 49 Guided by Lasswell’s widely known communication model, we formulated an approach that incorporates how content should be communicated—an overlooked but essential component. The approach integrates two core components: (i) the translation of knowledge towards the audience and (ii) the design of knowledge created. Through a case study we demonstrated how these two core components can be put into practice.

This systematic approach is, to our knowledge, the first to provide a practical approach to knowledge creation. A systematic approach to knowledge creation was urgently needed for two reasons. Firstly, the vast amount of literature covering the question on how to communicate scientific evidence to a target audience, indicates a lack of an overall effective approach. 31 , 50 , 51 Secondly, the European Commission increasingly emphasizes to include strategies on knowledge dissemination to a non-academic audience in project proposals. 52 Consequently, researchers are expected to engage in knowledge creation; a skill that they have generally not been trained in.

Whereas decision-makers have been equipped with multiple tools to assist in using research evidence for evidence-informed decision making, 53 , 54 researchers have hardly been provided with any. The SUPPORT tool, developed for decision-makers and researchers presents a variety of activities on KT, but does not provide a practical approach on how these activities can be operationalised. 16 , 55 Our approach complements herein, as it provides researchers engaging in knowledge creation with a simple, easy-to-implement tool that does not require advanced training.

As previously noted, this paper only covers a small portion of the broad and complex process knowledge translation entails. While we have proposed a strategy to warrant that researcher and policy makers ‘speak the same language’, our approach should not be considered a stand-alone solution, but one embedded within the KtA cycle. As suggested by Graham et al. , knowledge has to go through a number of phases before it can shape practice. These phases include adaptation to the local context, assessing barriers to implementation and monitoring knowledge use. 49 , 56 Furthermore, researchers should build capacity for implementation by formulating, implementing and evaluating capacity building plans.

Even though our approach was developed towards communicating research evidence to decision-makers, it may be widely applicable as the approach integrates essential and universal components of science communication, data visualisation and user-centred design. Regardless of the specific audience, the questions concerning ‘how something is said’ and ‘how it is communicated’ should always be given full attention in the process of communicating research-evidence.

To conclude, this approach offers researchers a tool to facilitate effective knowledge creation towards decision-makers in healthcare. The tool complements existing approaches; it is systematic, practical and designed to be easily implemented by researchers engaging in KT. However, it should not be considered a stand-alone communication tool, but rather a tool within the communication process of KT. Nonetheless, through the integration of core components on knowledge creation an approach has been established that may be widely applicable to similar projects, ultimately optimising evidence-informed decision making.

This work was funded by a research grant from European Union's Horizon 2020 research and innovation programme under Grant agreement no. 680997, TRIAL ID NTR5759.

The authors declare no conflict of interest. All authors have contributed to writing and revision of the article.

Bridging the ‘Know-Do’ Gap. Meeting on Knowledge Translation in Global Health. 10–12 October 2005, World Health Organization, Geneva, Switzerland; 2006 .

Graham I , Logan J , Harrison M et al.  . Lost in knowledge translation: time for a map? J Contin Educ Health Prof 2006 ; 26 : 13 – 24 .

Google Scholar

Field B , Booth A , Ilott I et al.  . Using the Knowledge to Action Framework in practice: a citation analysis and systematic review . Implement Sci 2014 ; 9 : 172 .

Brownson RC , Jones E . Bridging the gap: translating research into policy and practice . Prev Med 2009 ; 49 : 313 – 5 .

Landry R , Amara N , Lamari M . Utilization of social science research knowledge in Canada . Res Policy 2001 ; 30 : 333 – 49 .

Innvaer S , Vist G , Trommald M et al.  . Health policy-makers’ perceptions of their use of evidence: a systematic review . J Health Serv Res Policy 2002 ; 7 : 239 – 44 .

Lavis JN , Robertson D , Woodside JM et al.  . How can research organizations more effectively transfer research knowledge to decision makers? Milbank Q 2003 ; 81 : 221 – 48 , 171-2.

Choi BC , Pang T , Lin V et al.  . Can scientists and policy makers work together? J Epidemiol Community Health 2005 ; 59 : 632 – 7 .

Golden-Biddle K , Reay T , Petz S et al.  . Toward a communicative perspective of collaborating in research: the case of the researcher-decision-maker partnership . J Health Serv Res Policy 2003 ; 8 ( Suppl 2 ): 20 – 5 .

Cherney A , Head B , Boreham P et al.  . Perspectives of academic social scientists on knowledge transfer and research collaborations: a cross-sectional survey of Australian academics . Evid Policy 2012 ; 8 : 433 – 53 .

Cvitanovic C , McDonald J , Hobday AJ . From science to action: principles for undertaking environmental research that enables knowledge exchange and evidence-based decision-making . J Environ Manage 2016 ; 183 : 864 – 74 .

Shanley P , López C . Out of the loop: why research rarely reaches policy makers and the public and what can be done . Biotropica 2009 ; 41 : 535 – 44 .

Sorian R , Baugh T . Power of information: closing the gap between research and policy.

Stamatakis KA , McBride TD , Brownson RC . Communicating prevention messages to policy makers: the role of stories in promoting physical activity . J Phys Act Health 2010 ; 7 ( Suppl 1 ): S99 – 107 .

Lasswell HD . The structure and function of communication in society. In: Bryson L (ed) . The Communication of Ideas . New York : Harper , 1948 : 37 – 51 .

Google Preview

Grimshaw JM , Eccles MP , Lavis JN et al.  . Knowledge translation of research findings . Implement Sci 2012 ; 7 : 50 .

Armstrong R , Waters E , Dobbins M et al.  . Knowledge translation strategies to improve the use of evidence in public health decision making in local government: intervention design and implementation plan . Implement Sci 2013 ; 8 : 121 .

Kirk A . Data Visualisation: A Handbook for Data Driven Design , 1st edn. London : Sage Publications , 2016 .

Polman JL , Gebre EH . Towards critical appraisal of infographics as scientific inscriptions . J Res Sci Teach 2015 ; 52 : 868 – 93 .

diSessa AA , Sherin BL . Meta-representation: an introduction . J Math Behavior 2000 ; 19 : 385 – 98 .

Zwald M , Jernigan J , Payne G et al.  . Developing stories from the field to highlight policy, systems, and environmental approaches in obesity prevention . Prev Chronic Dis 2013 ; 10 : 120141 .

Slater MD , Rouner D. Value-affirmative and value-protective processing of alcohol education messages that include statistical evidence or anecdotes . Commun Res 1996 ; 23 : 210 – 35 .

Denning S . Chapter 1: Telling the Right Story. The Leader’s Guide to Storytelling: Mastering the Art and Discipline of Business Narrative , 2nd edn. San-Francisco : Wiley , 2005 .

Dobbins M , Hanna SE , Ciliska D et al.  . A randomized controlled trial evaluating the impact of knowledge translation and exchange strategies . Implement Sci 2009 ; 4 : 61 .

Coleman R , Lieber P , Mendelson AL et al.  . Public life and the internet: if you build a better website, will citizens become engaged? New Media Soc 2008 ; 10 : 179 – 201 .

Shackel B . Usability—context, framework, definition, design and evaluation . Interact Comput 2009 ; 21 : 339 – 46 .

Pandir M , Knight J . Homepage aesthetics: the search for preference factors and the challenges of subjectivity . Interact Comput 2006 ; 18 : 1351 – 70 .

Pavlas D , Lum H , Salas E . The influence of aesthetic and usability web design elements on viewing patterns and user response: an eye-tracking study . Proc Hum Factors Ergon Soc Annu Meet 2010 ; 54 : 1244 – 8 .

Collaborative HCC . HealthCOMpass: From the Health Communication Capacity Collaborative . [cited 2017 15-05-2017]. http://www.thehealthcompass.org/ .

Commission E. Science for Environment Policy . [updated 04-05-2017; cited 2017 15-05-2017]. http://ec.europa.eu/environment/integration/research/newsalert/index_en.htm .

Schneider F , van Osch L , de Vries H . Identifying factors for optimal development of health-related websites: a delphi study among experts and potential future users . J Med Internet Res 2012 ; 14 ( 1 ): e18 .

Ludden GD , van Rompay TJ , Kelders SM , van Gemert-Pijnen JE . How to increase reach and adherence of web-based interventions: a design research viewpoint . J Med Internet Res 2015 ; 17 : e172 .

Gaines E . Media Literacy and Semiotics . Palgrave: Macmillan, US, 2010 10-03-2017]. http://www.rasaneh.org/Images/News/AtachFile/18-7-1391/FILE634853701396680000.pdf .

van den Broek J , Koetsenruijter W , de Jong J et al.  . Chapter 5 Semiotics: The Meaning of What We See. Visual Language: Perspectives for Both Makers and Users , 1st edn. The Hague, The Netherlands : Eleven International Publishing , 2012 : 71 – 4 .

Siddiqi K , Newell JN . Putting evidence into practice in low-resource settings . Bull World Health Organ 2005 ; 83 : 882 .

Haines A , Kuruvilla S , Borchert M . Bridging the implementation gap between knowledge and action for health . Bull World Health Organ 2004 ; 82 : 724 – 31 ; discussion 32.

Berwick DM . Disseminating innovations in health care . J Am Med Assoc 2003 ; 289 : 1969 – 75 .

Tomoaia-Cotisel A , Scammon DL , Waitzman NJ et al.  . Context matters: the experience of 14 research teams in systematically reporting contextual factors important for practice change . Ann Fam Med 2013 ; 11 ( Suppl 1 ): S115 – 23 .

VanDevanter N , Kumar P , Nguyen N et al.  . Application of the Consolidated Framework for Implementation Research to assess factors that may influence implementation of tobacco use treatment guidelines in the Viet Nam public health care delivery system . Implement Sci 2017 ; 12 : 27 .

Siron S , Dagenais C , Ridde V . What research tells us about knowledge transfer strategies to improve public health in low-income countries: a scoping review . Int J Public Health 2015 ; 60 : 849 – 63 .

Pinnock H , Barwick M , Carpenter CR et al.  . Standards for Reporting Implementation Studies (StaRI) statement . Br Med J 2017 ; 356 : i6795 .

Cragg L , Williams S , Chavannes NH . FRESH AIR: an implementation research project funded through Horizon 2020 exploring the prevention, diagnosis and treatment of chronic respiratory diseases in low-resource settings . NPJ Prim Care Respir Med 2016 ; 26 : 16035 .

Burls A . What is Critical Appraisal? London : Hayward Medical Communications , 2009 .

Harbour R , Miller J . A new system for grading recommendations in evidence based guidelines . Br Med J 2001 ; 323 : 334 – 6 .

Tyndall J . How low can you go? Toward a hierarchy of grey literature. Alice Springs, Australia, 2008 .

Abras C , Maloney-Krichmar D , Preece J . User-Centered Design. Berkshire Encyclopedia of Human-Computer Interaction: When Science Fiction Becomes Science Fact . Thousand Oaks : Sage Publications , 2005 : 763 – 8 .

IDEO . Field Guide to Human-Centered Design . 2015 . IDEO.org (20 April 2017, date last accessed).

Graham ID , Logan J , Harrison MB et al.  . Lost in knowledge translation: time for a map? J Contin Educ Health Prof 2006 ; 26 : 13 – 24 .

Castro-Sanchez E , Spanoudakis E , Holmes AH . Readability of ebola information on websites of Public Health Agencies, United States, United Kingdom, Canada, Australia, and Europe . Emerg Infect Dis 2015 ; 21 : 1217 – 9 .

Stout PA , Villegas J , Kim H . Enhancing learning through use of interactive tools on health-related websites . Health Educ Res 2001 ; 16 : 721 – 33 .

Sutton C . Sharing Knowledge: EC-funded Projects on Scientific Information in the Digital Age—Conclusions of a Strategic Workshop. In: Innovation GfRa-D (ed) . Luxembourg : Publications Office of the European Union , 2011 .

Lavis JN . Research, public policymaking, and knowledge-translation processes: Canadian efforts to build bridges . J Contin Educ Health Prof 2006 ; 26 : 37 – 45 .

Lomas J . The in-between world of knowledge brokering . Br Med J 2007 ; 334 : 129 – 32 .

Lavis JN , Oxman AD , Lewin S et al.  . SUPPORT Tools for evidence-informed health Policymaking (STP). Introduction . Health Res Policy Syst 2009 ; 7 : I1 .

Grol R , Grimshaw J . From best evidence to best practice: effective implementation of change in patients’ care . Lancet 2003 ; 362 : 1225 – 30 .

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Translating three states of knowledge–discovery, invention, and innovation

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Knowledge Translation (KT) has historically focused on the proper use of knowledge in healthcare delivery. A knowledge base has been created through empirical research and resides in scholarly literature. Some knowledge is amenable to direct application by stakeholders who are engaged during or after the research process, as shown by the Knowledge to Action (KTA) model. Other knowledge requires multiple transformations before achieving utility for end users. For example, conceptual knowledge generated through science or engineering may become embodied as a technology-based invention through development methods. The invention may then be integrated within an innovative device or service through production methods. To what extent is KT relevant to these transformations? How might the KTA model accommodate these additional development and production activities while preserving the KT concepts?

Stakeholders adopt and use knowledge that has perceived utility, such as a solution to a problem. Achieving a technology-based solution involves three methods that generate knowledge in three states, analogous to the three classic states of matter. Research activity generates discoveries that are intangible and highly malleable like a gas; development activity transforms discoveries into inventions that are moderately tangible yet still malleable like a liquid; and production activity transforms inventions into innovations that are tangible and immutable like a solid. The paper demonstrates how the KTA model can accommodate all three types of activity and address all three states of knowledge. Linking the three activities in one model also illustrates the importance of engaging the relevant stakeholders prior to initiating any knowledge-related activities.

Science and engineering focused on technology-based devices or services change the state of knowledge through three successive activities. Achieving knowledge implementation requires methods that accommodate these three activities and knowledge states. Accomplishing beneficial societal impacts from technology-based knowledge involves the successful progression through all three activities, and the effective communication of each successive knowledge state to the relevant stakeholders. The KTA model appears suitable for structuring and linking these processes.

Peer Review reports

Knowledge translation (KT) represents a process for improving communication between the producers and consumers of knowledge to increase the application of research-based knowledge in practical forms. Moving knowledge into practice benefits a society by improving the quality of life for its members, and enhancing the economic competitiveness for its goods and services. The biomedical fields and medical professions initiated this KT movement [ 1 , 2 ]. They are able to analyze repositories of highly structured documentation on medical, surgical, and pharmacological interventions. Randomized controlled trials permit systematic reviews to establish evidence-based practices for consideration by stakeholders for the purpose of knowledge utilization. This is the thrust of the 'bench to bedside' initiatives in federally sponsored research programs [ 3 ].

The Canadian Institutes for Health Research (CIHR) has led efforts to structure the KT process [ 4 ]. Their Knowledge to Action (KTA) model describes how to match findings from completed research activity to the needs of knowledge users ( i.e ., end of grant KT), or by involving these stakeholders in ongoing research activity ( i.e ., integrated KT). It is important to note that the KTA model presumes a need to generate new knowledge and to do so through empirical methods.

Knowledge Translation in technology-based rehabilitation science and engineering

The KT concept is now diffusing into other fields. Rehabilitation and the allied health professions are among the recent adopters of KT [ 5 ]. Rehabilitation is an applied human services context involving multiple medical, science, and engineering disciplines working in clinical, educational, vocational, or community settings. Their collective goal is to maximize the quality of life for persons with disabilities, regardless of their age, demographics, or diagnosis.

A person's functional status and goals drive the appropriate rehabilitation interventions. Functional impairments in a person's mobility, sensory systems, or cognitive abilities are viewed as gaps between the person's current capabilities and their optimal ability to perform desired activities. The field of rehabilitation employs clinical, home, or community-based interventions to restore, sustain, or supplement a person's functional capabilities. These rehabilitation interventions often involve technology-based devices or services. These devices and services were defined by Federal law in 1988 twenty years ago as 'assistive technology' [ 6 ].

The existence of assistive technology (AT) devices and services as interventions must be taken into account when considering how knowledge is translated and applied in the rehabilitation field. Publications from a major international KT conference recognized that the commercialization of technology-based devices and services represent a 'special case' of KT [ 7 ]. The commercialization process is far more complex than an exchange of conceptual knowledge between scholars, as it involves instrumental, conceptual and strategic use, the government, industrial and academic sectors, at least six stakeholder groups and three different methodologies. As Dr. Michael Gibbons stated in a KT keynote presentation:

'The once clear lines of demarcation between government, industry, and the universities, between science of the university and the technology of industry, between basic research, applied research, and product development, between careers in academe and those in industry no longer apply' [ 8 ].

From this perspective, no organization, investigator, or project is singularly responsible for completing the entire process of knowledge transformation. In fact, the concept of 'open innovation' is practiced by corporations to advance their interests through internal and external knowledge flows, and is equally relevant to knowledge exchanges between any source and their various stakeholders [ 9 ]. The government and academic sectors can facilitate the application of knowledge by embracing cross-sector collaboration via open innovation.

Assumptions and definitions regarding knowledge

The KT literature notes that adopting new knowledge typically involves a measure of adaptation to fit the user's context [ 10 ]. For an applied field like rehabilitation and for the context of assistive technology devices and services, multiple stakeholders qualify as users, and some in turn become producers of knowledge in different forms for other users. The adoption of knowledge for technology-related projects clearly requires some adaptation of the assumptions and definitions underlying KT and its models. This article explores the feasibility of adapting the CIHR's KTA model in particular.

Key assumption

Existing KT models are predicated on the goal of putting knowledge generated through academic research into practice. The application of research-based knowledge is expected to help solve a problem. A recent thematic analysis if 28 KT models [ 11 ] substantiated the focus on knowledge creation through research methods. These KT models–including the KTA model–represent knowledge creation and application as some form of academic research activity either underway or completed. With that assumption in place, the KTA model suggests one can either involve stakeholders after research activity is completed (end of grant KT), or involve stakeholders during the design and conduct of the research activity (integrated KT).

Knowledge Translation models and methods treat knowledge as existing in one state. This is the intangible conceptual state captured in the peer-reviewed literature generated by research activity conducted in the academic sector. However, knowledge exists in other states and may require transformation into other states to enable uptake and use by stakeholders. Knowledge in applied fields, such as those developing and producing technology-based devices and services, should be defined in a broader manner to include the various states of knowledge.

And just who are the stakeholders in the commercialization of technology-related knowledge? As one example, rehabilitation professionals involved with AT commercialization may collaborate with six different stakeholder groups:

Scholars who cite and integrate prior research findings in new studies;

Clinicians who recommend assistive technology to clients;

Consumers who apply personal experience when seeking AT;

Manufactures who participate in the design and critique of AT;

Resource Brokers who permit the adoption of new AT, or recommend intellectual property protection;

Policy Makers who set third-party reimbursement levels, or establish parameters of sponsored research programs [ 12 ].

Implementing technology-related knowledge to solve problems

When knowledge is translated into action, the state of knowledge itself is transformed and it is important to ask: What are the knowledge states arising in this transformation process, and can KT accommodate those other states within its models?

Not all solutions to problems require the creation of new knowledge through research; nor does the direct application of conceptual knowledge always solve a problem. This is particularly true for technology-related knowledge that is defined by the application of knowledge in a tangible form. Funding agencies and investigators alike expect any technology-related solution to a problem to involve embodying knowledge in a tangible form.

Instances where existing technology cannot provide the desired function may prompt research activity to discover new capabilities. Or they may prompt a search for relevant discoveries from prior research that are extant in the literature. Such existing technology-related knowledge may be applied to solve a problem using methods other than research. For example, a project may employ development methods to transform conceptual knowledge into a tangible form–a prototype that proves that a conceptual application is feasible in a practical form. As another example, a project may employ production methods to transform the 'proof of concept' prototype into a device or service ready for application and use in the commercial marketplace. These technology development, transfer, and commercialization activities are not research, but instead are successive transformations of the research knowledge into other states. Their relevance to health and quality of life require expanding the underlying definition of knowledge. By differentiating the various states of knowledge that arise during the transformation process, KT may be able to accommodate methods beyond research within its models. This expansion and accommodation will help KT meet its goal of providing more effective technology-based health services and products [ 13 ].

Three states of knowledge

Three methods of activity generate three different states of knowledge. Research activity generates knowledge in one state, while development activity and production activity generate knowledge in different states. The three states of knowledge represent a progression with the former states necessary for the latter to exist. The concept of open innovation recognizes the necessity of inter-sector collaboration in accomplishing the full range of transformations, with each state of knowledge dependent on the others.

The three states of knowledge are analogous to the three classic states of matter. This analogy will help clarify why the implementation of science in practice remains a challenging issue. Classically speaking, matter exists as gas, liquid, or solid (although plasma and a dozen additional states are now known). The three analogous states of knowledge are as follows.

Discovery State of Knowledge

The technology-based solution to a specific problem may require the creation of new knowledge. Once a gap in knowledge is identified, the new knowledge can be recognized as a 'discovery.' A key attribute of a discovery is novelty, because it is the first articulation of something not previously known or demonstrated. Discoveries depend upon the scientific method to ensure validity and reliability. Despite presumed objectivity, their novelty may generate resistance if they contradict widely held beliefs [ 14 ]. Consequently, discoveries must be documented in a manner that permits independent replication. Lacking tangible form, discoveries are described in detailed manuscripts, which are submitted for peer-review for quality assurance. Those deemed valid are accepted for dissemination through journal articles or conference presentations. The publication system ensures the discovery is documented, attributed, and indexed for reference by others as a contribution to the global knowledge base. Publication ensures public disclosure and passively promotes awareness and use among stakeholders. Discoveries are malleable, subject to revision, rejection, or dispersion. As such, research-based discoveries are analogous to the gas state of matter.

Invention State of Knowledge

Conceptual discoveries may become embodied in a tangible, yet provisional form–a proof of the concept's viability [ 15 ]. This second state of knowledge is called invention. An invention is something not previously demonstrated to be possible in practice. A key attribute of invention is feasibility. Feasibility combines with novelty; however, the invention and discovery do not have to occur together. One may apply independent prior discoveries to test the feasibility of a technology-based solution. This state change from discovery to invention requires the use of development models and methods that are distinct from those of research. Of course, the two activities may operate in tandem as suggested by the phrase 'research and development.' The output from this development activity is a proof-of-concept prototype. The prototype is a work in progress–a patchwork of elements, components, and external support systems, all combined to demonstrate feasibility. The demonstration of feasibility suggests potential functional applications that form the basis for intellectual property claims through the patenting process. The inventions are more tangible than discoveries, just as liquids are more tangible than gases, although inventions may still be shaped or formed in many different ways.

Innovation State of Knowledge

Inventions may be further refined until they reach some final form, such as a functional device or service, capable of mass production, distribution, and support. This refinement is done with commercial intent, which is a perspective that academics are not trained to embrace. Dr. Chesbrough clearly defines this separate state:

'By innovation I mean something quite different from invention. To me innovation means invention implemented and taken to market.' [ 9 ]

The key attribute of knowledge embodied as an innovation is utility, in addition to the novelty and feasibility of the prior knowledge states. A technology-based solution may be feasible and novel in a laboratory setting, but utility is only achieved when the solution addresses the economic and operational constraints of the target user's problem in the context of the marketplace. Market utility means something of value, which is available to society in a consumable form. Transforming a prototype invention into an innovation requires yet another set of models and methods–those of new product development. Production methods ensure that the innovations final form is designed to meet constraints of functionality, physical dimensions, and cost. Accomplishing production activity requires a precise understanding of the intended market and the requirements of the customers for that device or service. The final form must be specified in exacting detail, as the raw materials and components must be ordered in economically advantageous quantities, while the tooling and assembly work must be planned to operate efficiently. Only then will the device or service be competitive in the commercial marketplace. The high level of specification and planning locks the innovation in a final form that can no longer be modified without substantial cost in materials and tooling. The innovation state of knowledge is equivalent to the solid state of matter. An innovation remains in the marketplace until replaced by another innovation offering greater utility. Such a replacement will have recapitulated the same sequential transformation of technology-related knowledge from research discovery, through development invention, and on out to production innovation.

Three states of knowledge and KTA model

Differentiating between research-based discoveries, development-based inventions, and production-based innovations is a critical first step to generating operational versions of the KTA model pertaining to the context of technology transfer and commercialization. In fact, a study describing an operational version of the KTA model [ 16 ] gave rise to the idea of modifying the KTA model to accommodate the development and production phases of commercialization (see Figures 1 , 2 , and 3 ).

figure 1

Discovery Outputs .

figure 2

Invention Outputs .

figure 3

Innovation Outputs .

Specifically, the KTA's knowledge creation funnel representing research activity can be replicated to incorporate the development and production activities necessary to achieve invention and innovation outputs. Similarly, the KTA model's action cycle can be replicated to represent the different approaches necessary to effectively communicate the unique nature of discoveries, inventions, and innovations.

Adapting models is one thing. Ensuring fidelity to the concepts underlying the model is something else. The extant literature coupled with new research activity form the foundation for KT. These primary and secondary resources fuel the KT processes of quality assessment (rigor), synthesis (evidence), and tailored communication (relevance). What are the corollary concepts for technology-related projects? Rigorous quality assessments rely on the three methodologies (research, development, and production), each applied within their own context. Given the narrow focus of the eventual goal, decision making relies on the synthesis of primary evidence collected from the full range of stakeholders. Relevance is paramount for knowledge input and output, again focused on the eventual goal of a device or service in the marketplace.

The context of technology-related rehabilitation devices and services, has now adapted the assumptions and descriptions underlying the KTA model in the following ways: solving problems may involve technology-related knowledge drawn from the states of discovery, invention, and/or innovation; discovery represents novelty, invention requires both novelty and feasibility, while innovation embodies novelty, feasibility, and utility; and modelling the research, development, and production phases of activity is necessary to adapt the concepts and processes KT for incorporation into technology-related practices.

'Implementation science' exists as a topic of discussion because the methods used to create new knowledge are not designed to facilitate effective communication to a range of stakeholders, nor are they intended to ensure actual use by these stakeholders in practice. The implementation of scientific findings requires additional efforts. Traditionally passive dissemination and utilization strategies are used for scholarship, with the primary audience being others academics who read the journals and who attend the conferences for their own professional advancement. The shared culture and language that facilitates communication within this relatively closed system acts as a barrier for communication to other stakeholders. KT ensures that the knowledge producer works with the knowledge consumers. With input from knowledge consumers, the knowledge producers appraise the quality of research outputs, synthesize the work with other relevant sources, and translate the source format and language describing the conceptual discovery into formats and language most appropriate for effective communication to the outside stakeholders [ 17 , 7 ].

Both techniques are expected to lead to the direct application of discoveries by stakeholders. For technology-related discoveries, stakeholder use may require further research activity to expand the discovery or development activity to generate inventions. Stakeholder use may even continue through production activity to generate innovations. These downstream outcomes create opportunities for knowledge in the innovation state to have beneficial impacts on the quality of life for end users. The KT approach has both costs and benefits to the investigator. It can increase the likelihood of achieving the intended outcomes and impacts, and accelerate the timeframes involved in doing so. It also exacts significant additional costs, including the commitment of additional time, effort, and resources on the part of the knowledge producer. This is not a role for which academics are traditionally trained or rewarded, but these costs are no more discretionary than those required to ensure rigor in the research process itself.

Federal agencies allocate funds to university-based scholars for the purpose of generating discoveries through research methods. However, many federal agencies also allocate funds to university and corporate laboratories to generate development-based inventions, and to manufacturers for production-based innovations relevant to the federal agency's mission. All parties recognize the value of transforming technology-related knowledge into devices and services.

For applied research fields, such as such as technology-based devices and services, it is important to look beyond the first state of knowledge–discovery. The subsequent states of invention and innovation help frame how knowledge can be applied to solve problems related to quality of life. Given their contributions to the desired impact, the downstream roles of development and production activity should be considered from the inception point of any technology-related project.

Recall that the KTA model assumes on-going or completed research activity as the starting point. Even this point is fairly far along in the process. Before one can initiate research an agency identified a priority, wrote and circulated a request for proposals, applicants wrote and submitted proposals, a peer-review process occurred, and funding was awarded and disbursed according to some timeframe. Only then does research activity commence via project implementation. The stakeholders involved in these prior actions have done much to pre-ordain the problem as amenable to research-based knowledge applied by stakeholders.

Need To Knowledge (NTK) model

By suspending the inherent assumption that the discovery outputs of research activity are the only outputs in need of translation, stakeholders are freed to consider how to solve problems with technology-related knowledge in the form of invention or innovation outputs. Six approaches to solving problems have been developed using various combinations of research, development, and production activities. It is important to note that quality appraisal and synthesis activities, which are key components of many KT models, are not described in these approaches. As portrayed in the discussion section of this paper, comparable activities are performed before research activity begins. Specifically, problem/solution definition carried out in collaboration with stakeholders and a series of preliminary assessments are designed to ensure rigor and relevance of the work. These steps obviate the need for additional quality appraisal and synthesis at the completion of research. Further, quality appraisal and synthesis activities occur throughout the NTK model using techniques appropriate for invention and innovation outputs.

Six approaches to solving a problem with knowledge

Need to research to KT–Identify needs (problems) and potential solutions. Generate a new discovery (solution) and communicate its value to target stakeholders.

Need to research and development to KT–A new discovery, based on unmet needs, transformed into an invention, then offered to stakeholders for future innovation.

Need to research, development, and production to KT–A new discovery, based on unmet needs, transformed into an invention, and then specified as a device or service innovation, with its utility communicated to stakeholders.

Need to development and production to KT–An invention based on unmet needs and prior discoveries, transformed into an innovative device or service, with its utility communicated to stakeholders.

Need to production to KT–An innovation in the form of a device or service, based on unmet needs and prior research and development activity, distributed to stakeholders.

Need to KT–All the necessary research, development, and production work has already been done based on defined unmet needs. This option revisits the communication of the completed work to ensure it is offered in the appropriate forms and methods to the pertinent stakeholders for their future implementation.

Regardless of the chosen approach, all projects should integrate KT activities into their processes from their inception–a 'prior to grant' approach, rather than an end of grant or integrated approach to KT. As demonstrated in the preceding approaches, a 'prior to grant' approach starts with a defined need, such as a societal problem deemed worthy of government intervention. Appropriate due diligence then verifies that technology-related knowledge could solve the problem. Integration of stakeholders into the definition of problems and solutions ensures that future outputs in the form of discoveries, inventions, or innovations would have receptive stakeholders who are aware and ready for implementation. Using predefined needs to determine what knowledge to produce is the foundation of and reason for the title of the Need to Knowledge (NTK) model. This model does not assume that knowledge exists and must be put into action, but rather that needs exist, and knowledge may contribute to a solution.

If a funding agency requires projects to achieve fairly specific deliverables, a principal investigator could propose a scope that is bounded at the front end by any preceding activity as foundational knowledge, and bounded at the back end by ensuing activity to complete the continuum from problem input to solution impact. Any relevant prior research discoveries would find immediate application in ensuing development and/or production activities. Any ongoing research discoveries could be applied to the specific problem under study, while still being incorporated as contributions to the global knowledge base.

Novel method of addressing current problem

The authors generated an operational KT model by expanding the KTA model's framework to integrate the three states of knowledge and the methods used to transform knowledge from one state to another. Each state of knowledge involves its own unique set of adaptations to the KTA model, both down through the 'knowledge creation funnel,' and out around the 'action cycle.' Taken together, the three iterations comprise the Need to Knowledge (NTK) model. The following section describes the key elements of the NTK model's structure in terms of stages, gates and steps.

The Need to Knowledge (NTK) model

A 'prior to grant' perspective does not presume a requirement for research activity. Instead, it presumes that the application of technology-related knowledge in some state and through some activity may be a valid solution to a social problem. Thus, the definition of the need precedes the validation of a knowledge-based solution. The solution is expected to take the form of a technology-based device or service available to stakeholders in the marketplace. The solution follows from the problem definition. The NTK model expands the application of the KTA model from an exclusive focus on research methods to considering the methods most appropriate to solving the problem. For technology-related knowledge these include the methods applied in device or service development and those of industrial or commercial production. The methods for knowledge application and knowledge implementation deserve parity with the empirical methods for knowledge generation - at least within the applied contexts referenced here.

The NTK model represents the entire continuum of required activities, from problem statement through solution delivery. These activities are expected to be accomplished by some combination of stakeholders over time. Although presented here as a linear model, the collective activities may be recursive, iterative, or even disjointed. In this example, the model is applied to assistive technology for persons with disabilities. It may be equally applicable to all forms of technology-related innovations in fields such as medical, consumer products, housing, transportation, and alternative energy.

As previously described, the NTK model contains three phases, each named for the state of knowledge generated by the primary activity in that phase: discovery, invention, and innovation.

The three phases are cumulative in that successive knowledge states arise out of the preceding states. Iterations are possible. Invention state knowledge may reveal a need for additional discovery state knowledge. However, a project must stay focused on the goal, and not be drawn into a discovery/invention loop. The project's knowledge must progress to the innovations state to achieve the intended beneficial impact on a target audience.

Each phase contains three activity stages and three associated decision gates. The activity stages specify what the project needs to accomplish at that point. Some of the activities help the project progress sequentially. Other activities help the project prepare to address barriers encountered later in the process, or to obviate those downstream barriers entirely. KT recognizes the importance of tailoring the knowledge message to the language, culture, and values of each stakeholder group. The KT process itself can be tailored to the current knowledge state.

In the NTK model, each phase of activity ends with the subject knowledge in a different state than when the phase began. At the end of each phase, the project conducts KT activities tailored to that state of knowledge. The project should ensure that any knowledge is disclosed properly and with forethought for the subsequent consequences. KT is an opportunity to initiate active communication with the appropriate stakeholders regarding discoveries, inventions, or innovations, even while project work continues. In cases where the project terminates at the earlier knowledge states of discovery or invention, the KT process is a means for engaging stakeholders. This can be done by identifying lessons learned, sharing results from preliminary assessments and other forms of synthesis, such as a business case or technical report, and recommending opportunities for future endeavors. The stakeholders' experience may be more appropriate to continue the project through related methods to achieve the intended beneficial impact. Offering the aforementioned information in formats readily absorbed by the stakeholder group helps to ensure that the project will indeed move forward.

The NTK model is predicated on the three different states of knowledge involved in a technology-related project. An operational-level model needs to explicitly address these differences to ensure that the subject knowledge is effectively communicated to the relevant stakeholder groups, as it is successively transformed into different states. The following narrative explains how KT can be implemented within the NTK model.

NTK Phase I. Discovery

Phase I conducts research activity to achieve the discovery state of knowledge. It involves three stages and three decision gates. Figure 1 adapts the KTA model to show the NTK model's discovery phase. It shows stages one, two, and three in the discovery creation funnel, and shows the appropriate activities to communicate a research-based discovery in the action cycle:

Stage one: Define problem and solution/gate one. Initiate project scoping?

Stage two: Project Scoping/gate two. Need for research-based discovery?

Stage three: Conduct research to generate discovery/gate three. Justification to generate a business case?

The CIHR's KTA model was designed for use with extramurally funded ongoing or concluded research projects. The KTA model may proceed from knowledge creation to problem application, or proceed from problem identification to knowledge creation. This is entirely appropriate for a model accommodating both inquiry- and need-driven research. The KTA model accommodates unanticipated or serendipitous opportunities to create and apply research.

In contrast, the NTK model contends that when both the sponsor and the investigator intend to solve a problem with a technology-related solution, the process should begin with the definition of the problem and the solution in stage one, and the identification of the appropriate method for effective intervention in stage two. In these instances, stages one and two are critical to ensure that government agencies are funding technology-related projects with actual relevance to society, and to ensure that an investigator's efforts are focused to generate beneficial impacts downstream.

The NTK model's discovery phase starts with stage one. The problem is defined before any research is initiated or even considered as a viable solution. Stage one defines a problem, articulates solutions, and establishes the overall goal. Stage two defines the project's potential contribution to the overall goal. One might assume a problem exists and propose a reasonable solution, or have anecdotal information about a problem/solution set within some bounded context. Neither is sufficient to justify the investment of public funds in a protracted process of knowledge creation and application. Both funders and grantees should be confident that the due diligence was performed in stage two to ensure that the project is novel, can be accomplished, fits within prior and ensuing work, and has a high likelihood of generating beneficial impacts through technology-related devices or services.

If stages one and two define and justify a requirement to generate new knowledge through research, stage three commences to do so. This is a key point of intersection between the NTK model's discovery phase and the KTA model's knowledge creation process. At that point, both models are engaged in the creation of new knowledge (discovery) while considering its subsequent application. As both of these models transition from the knowledge creation process to the action cycle, and from the discovery phase to invention phase, they both address a problem with conceptual knowledge. The critical difference between the KTA and NTK models is that the preliminary work performed in the NTK model's stages one and two provide a validated context for the application of the knowledge. These stages obviate the search for a problem context by starting with a problem and then designing a project to generate or apply knowledge as a solution.

The NTK discovery phase adapts the descriptions in KTA action cycle blocks to fit this focused context by revising the text to fit the discovery state of knowledge. As the NTK discovery phase action cycle moves in a clockwise direction, the stage one and stage two work provides invaluable information for communicating the discovery to the target audience, as well as to the other stakeholders who have potential uses for the discovery.

Customizing the form and content of a vehicle for communicating a discovery to each stakeholder group is central to the KT process. The customizing includes the language, culture, and value systems of each group, as well as the organizational level targeted ( e.g ., individual, organization, sector) [ 18 ]. The customizing should also consider the three types of knowledge use that may be pursued by individual stakeholders ( e.g ., instrumental, conceptual, symbolic/strategic) [ 19 ].

Creating a framework at this level of detail is very important for projects expected to result in technology-related devices or services. To achieve success, most if not all of the various stakeholder groups must recognize the value in the underlying knowledge. Various groups may have more or less appreciation for each of the three states of knowledge, but in the end they all must demonstrate support for the project's goal. The level of support among the stakeholders is an important input for the decision-makers involved in the decision gates that follow each stage of activity. If they determine that one or more stakeholder groups will either ignore or actively oppose the new device or service, internal decision-makers may terminate the project, or external decision-makers may withhold additional support.

Getting a new device or service introduced into the marketplace requires that all nine decision gates result in a decision to proceed. Each decision to proceed only leads to the next decision gate, while decisions to terminate a project or simply cease involvement stop progress toward the goal, but still call for KT activity. The NTK discovery phase is foundational work. This foundation may be built from the identification of previous knowledge discoveries, or it may require the creation of new knowledge. Nevertheless, the foundation alone is not sufficient to achieve the goal. The NTK discovery phase only encompasses one-third of the total number of stages. Decision gate three following stage three is a very important decision to move from discovery to invention. This decision has tremendous implications for time, effort, and resources. The decision-makers in the sponsor and project organizations should also be mindful of the importance of shifting the project's primary methodology from research to development.

As stated earlier, the conduct of research activity is optional within the NTK model. Decision gate two determines if the project initiates stage three research activity. The analyses conducted in stages one and two may determine that a technology-related solution does not require the discovery of new knowledge. The knowledge may already reside in the published literature, in which case the project moves directly to knowledge application under development methods. Or, the knowledge may reside in application in another field of use. In that case, the tools of technology transfer may be appropriate to apply as part of the development process. In either case, if the solution to the problem does not require research activity, the project could move directly from decision gate two to stage four within the invention phase.

NTK Phase II. Invention

Phase II conducts development activity to achieve the invention state of knowledge. Figure 2 again adapts the KTA model to show the NTK model's invention phase. Figure 2 shows stages four, five, and six in the invention creation funnel, and shows the appropriate activities to communicate a development-based invention in the action cycle:

Stage four: Build business case and plan development/gate four. Implement plan?

Stage five: Implement development plan/gate five. Proceed to testing?

Stage six: Testing and validation/gate six. Plan for production?

The conceptual technology-related discovery generated or identified in phase I can now be transformed into knowledge in the invention state. The invention phase represents knowledge as a tangible asset with value. The phrase 'intellectual property' recognizes knowledge as such an asset. The patent and trademark system exists to identify and protect ownership of any intellectual property. The patent review considers both novelty and feasibility–the two attributes we define here as representing the invention state of knowledge. Novelty was established during the discovery phase, and now the project demonstrates its feasibility by designing and testing the knowledge in a prototype form.

A patent provides the invention owner with the legal rights to practice its use in applications yet to be determined. Beyond the patent reviewer's subjective decision that the invention is useful, the patent review process does not consider the objective market utility of the invention. This limitation supports this paper's distinction between an invention that must have a 'useful purpose' and be operational [ 20 ], and an innovation that must have commercial viability. For this reason, projects intended to result in an innovation must conduct preliminary work to verify not only the eventual utility of the intended device or service, but also its marketability. Stages four through six, described in the following paragraphs, ensure that these conditions are met.

Stage four, build business case and scope development plan, is a check to ensure that the next block of effort will likely meet the requirements of external partners–particularly the manufacturers and service deliverers. Researchers are not trained to consider the economic consequences of their actions, but the business case requirement ensures that the appropriate knowledge is gathered, synthesized, and analyzed in consideration of the external stakeholder partners. With this analysis in place, the investigator and their funding source can make an informed decision to implement the development plan or pursue another line of activity (decision gate four).

Stage five, implement development plan, follows from a decision to proceed. Development implementation involves building models or components that perform in practice the function envisioned in concept. These early stage models are called 'alpha' prototypes, as they are the preliminary versions. The alpha prototypes or their components are subjected to trial and measurement for the purpose of further refinement. User input is gained through focus groups to identify both essential and optional features and functions. The alpha prototypes represent successive approximations of the envisioned device or service, culminating with the beta prototype.

The next decision (gate five) is whether or not the beta prototype shows sufficient promise as a future device or service to warrant more comprehensive testing and validation. A decision to proceed requires a commitment for additional investment. The data and insights gained from the alpha version's technical, market, and user assessments are considered high quality primary source information, as it was generated through standard development methods. This information is synthesized, along with the investor's own considerations and constraints, to help formulate a decision to stop or to proceed.

Stage six, testing and validation of a beta prototype, is not an ad hoc process. There are formal protocols designed to pass the scrutiny of independent agencies. The methods involve sufficient rigor to ensure that the results reflect the actual functional capabilities of the prototype. Given the focus on the goal, the testing may require adherence to government or industry standards. Knowledge in the discovery state is not subjected to such scrutiny, yet careful calibration of performance may be necessary to win participation by external stakeholders including clinicians, manufacturers, or policy makers. Testing may involve both laboratory and field settings. The laboratory testing is a variation of research activity. Formal testing may require access to skilled technicians, fairly expensive instrumentation, and perhaps even controlled conditions. Both laboratory and field testing will involve human subjects representing the likely or potential users of the device or service. The testing and validation typically reveals additional opportunities to refine and improve the prototype device, particularly through feedback obtained from human subjects. Additional testing may be required to confirm that any changes have not detracted from established performance parameters.

These three stages and their underlying steps apply development methodologies to build and test prototypes representing the intended technology-based device or service. This work is conducted within the framework of a business case, in recognition of the role of private sector manufacturers in the subsequent transformation. The stages and steps draw heavily from the standard practices established by industry for new product development. This ensures the process rigor and user relevance, along with the quality of evidence generated at each step. The Product Development Manager's Association (PDMA) has extensively described many of these practices in a series of reference publications [ 21 , 22 ].

Being mindful of the eventual goal for a device or service in the marketplace helps investigators–whether in academia or industry–make sound decisions in this interim invention phase that preserve the asset's future value to others. Development work that might satisfy intellectual interests as an end in itself, may not satisfy the requirements of external stakeholders who will be responsible for investing the time and resources to transform an invention into an innovation for the marketplace. The business case provides a template for defining the required development work, some of which may appear superfluous to those not trained to anticipate the downstream requirements of the innovation phase. The business case guides the investigator's allocation of time and resources, and ensures the results are relevant to the goal.

Even in technology-related fields, an investigator's efforts may not lead to an invention with commercial potential. There may be ancillary benefits that satisfy academic incentives, such as funding and publications, but these inputs and outputs are not the goal. A recent analysis of research and development activity within the field of rehabilitation engineering showed that most projects do not achieve the intended outcomes [ 23 ]. Most development projects that did not progress from invention to innovation had not adequately addressed the requirements of the external stakeholders on which the eventual outcome depended.

With the completion of stage six, testing and validation, the tangible device or service has progressed from alpha, through beta, and on to a pre-production prototype. If the investigator has not yet claimed the underlying intellectual property, this pre-production version provides all the details necessary. If a patent application was filed previously, it can be amended to include any refinements. The invention phase closes with one of two final actions. If the investigator's role had been set to end upon completion of the invention phase, the activities related to KT for knowledge in the invention state should be initiated.

However, if the investigator had planned to continue their involvement in the project throughout the innovation phase, then they must consider decision gate six, to go or not go forward to production planning. The testing and validation may have revealed new information regarding the viability of the product or service or its market potential, and the investigator must carefully consider their decision to either terminate or continue the project. In either case, they should initiate KT for the invention state output of the subject knowledge. This is a critical step because the investigator will likely need a corporate collaborator to implement the innovation phase. The knowledge generated through standard development methods, and organized within the framework of the evolving business plan, gives the external partner the right information in the right form for their consideration. To the extent the project investigator has practiced KT, a corporation can make a sound and informed decision regarding future involvement. It is better to enlist a partner that is committed for the long-term than to convince a partner in the short-term who decides to withdraw in the future.

The NTK invention phase represents a substantial increase in project expenditures ( i.e ., so-called 'sunk costs') that include the time, effort, and resources applied to the previous stages. In its embodied state as a proof of concept, the prototype is considered property with value as an asset. This pre-production form has assumed the knowledge state analogous to a liquid. It is less malleable than a discovery (gas) and more malleable than a finished product or service (solid). The translation process is different for knowledge in this liquid state, so the means, message, and method must be different from those used to communicate the discovery in its conceptual (gas) state.

The three stages (four through six) of the invention phase transform conceptual discoveries into embodied inventions. The action cycle works with knowledge in this more refined and less flexible state, so it begins with a more focused message to the relevant knowledge users. Depending on their roles, these stakeholders may be able to put knowledge about the prototype device or service directly into use, or they may be involved in the ensuing innovation phase of activity.

The invention phase is only the middle third of a triad of activity. If the gate six decision is to terminate the project, then widely disclosing the prototype might be the only option for generating stakeholder awareness. A decision to continue the project reaffirms the original goal of a new or improved technology-based device or service in the marketplace. In that case, the intellectual property must be protected as an asset, as well as protected from improper or untimely disclosure. The investigator and related stakeholders must balance the desire to communicate the invention, with the need to preserve the invention's value for the innovation phase. This is often where a conflict arises between academia's drive to publish and industry's drive to maintain secrecy.

NTK Phase III. Innovation

Phase III conducts production activity to achieve the innovation state of knowledge. Figure 3 further adapts the KTA model to show the NTK model's innovation phase. Unlike Figures 1 and 2 , the three stages and decision gates in the innovation phase are distributed across both the innovation creation funnel and the action cycle. This is because a successful device or service innovation requires continuous and iterative interactions between the producers and the consumers–between the investigators and the stakeholders:

Stage seven: Production planning and preparation/gate seven. Go to launch?

Stage eight: Launch innovation/gate eight. Shift from launch to maintenance?

Stage nine: Post-launch assessment/gate nine. Continue, terminate, replace?

The transformation from an invention state prototype to an innovation state device or service is not typically the domain of scholars. Scholars in the academic sector are trained and supported to generate discoveries through research methods. Executives in the industrial sector are trained and supported to generate innovations through production methods. Both scholars and executives lay partial claim to the shared territory of development, although the term has different meanings to each sector. Scholars speak of development in their academic context of refining a theory, testing a hypothesis, or generating additional evidence for a position. Executives speak of development in their production context, testing and validating pre-production prototypes and their underlying technology-based capabilities.

Some scholars do function as entrepreneurs or collaborate with industry as consultants, just as some executives participate in the academic process. These exceptions prove the rule of having experts lead in their areas of expertise. Accomplishing the project's goal is highly dependent on an external manufacturer's decision to collaborate in the innovation phase. Scholars do not produce and deliver devices or services to the marketplace, nor do policy makers or clinicians. The innovation phase is typically directed by executives working for manufacturers. In this third phase of the overall process, the executives base their decisions on the foundational work completed in the discovery and invention phases. The preparatory work in stages one through six needed to build a convincing argument for proceeding in terms that the manufacturer can understand and accurately value–a business case. After all, communicating effectively in language and formats best understood by the audience is a core attribute of KT.

In the hands of a qualified, competent, and financially sound corporation, the production planning and preparation proceeds smoothly. Such manufacturers are experienced in executing the great number of steps in the high level of detail involved. The innovation phase transforms the knowledge from a semi-malleable state to a solid state. In stage seven, the specifications created for tooling, materials, logistics, and support essentially 'freeze' the design into a form that can be replicated in great numbers at an affordable cost. These steps are detailed within the Product Development Managers Association (PDMA) materials on new product development, so they are not described here [ 21 , 22 ].

Even after all of the effort expended in stage seven, the project leaders need the discipline and objectivity to decide whether or not to introduce the device or service into the marketplace (decision gate seven). A private sector heuristic is to ignore the sunk cost–the go or no go decisions should be made without considering the prior investment. A project should cease if it does not look promising despite all of the prior efforts to demonstrate its worth. This decision requires a particular perspective based on two factors. First, these private sector decision-makers are stewards of resources belonging to the corporate entity or its shareholders. Recipients of government funding may not share that perspective. Second, private sector organizations typically have multiple projects so they can act without emotional or professional attachment to any one option. In contrast, recipients of government funding may be operating as independent investigators or as part of a small team, without options for expending the available resources. The latter may proceed with the project launch simply because there is no other option for expending the resources and supporting themselves in the process. Individual project managers in a corporation may advocate for their own projects but they are operating within a hierarchy.

The gate seven decision is typically made at the highest executive level by people who are best positioned to act in the interest of the corporation. This is not the same as acting in the best interest of society. The rationale for keeping many technology-related projects in the academic sector is that corporations lack the profit motive to participate. Unfortunately, those projects still need corporate buy-in to eventually become available in the marketplace. The NTK model's early interest in establishing the business case is based on this pragmatic situation. If the business case calls for government subsidy then that is an issue to be resolved sooner rather than later.

As shown in Figure 3 , the stage seven activity begins in the innovation creation funnel but then continues on into the action cycle. The production methods require high levels of stakeholder interaction regarding test marketing to hone the form and content of messages used to communicate the innovation's objective utility to potential customers. The results of all of this limited release, test marketing and internal review lead to decision gate seven–go to launch?

A decision to proceed initiates stage eight, product launch. This entails a mass production process by the manufacturer. The accompanying marketing, promotion, and advertising are focused on the essence of KT–achieving stakeholder awareness, interest, adoption, and use of the device or service being promoted. The activity involved is widely understood due to the success of our mass marketing and media culture. Decision gate eight shifts efforts from launch to maintenance levels. A corporation cannot sustain the expenses involved in a launch indefinitely, and those efforts may artificially inflate evidence of awareness, interest, and use. Moving from launch to maintenance permits the corporation to consider the market viability of the device or service on its own merits.

Stage nine is the post-launch assessment. The corporation must now decide if the device or service is sustainable, and whether it should be integrated into its core product mix. This assessment continues for the innovation's life cycle as the device or service tracks through the marketplace's curve of introduction, growth, and maturation.

The assessment is not limited to the phase III innovation activity, but will likely involve a summative-level evaluation of the entire NTK model process. The assessment asks, 'how well did the project perform at accomplishing the goal?' The answer will feed into the decision gate nine, where a decision is made to terminate the production activity, or to repeat the entire three phase process to generate a new or improved version of the device or service. Even for successful products, manufacturers will eventually decide to repeat the entire process. They know that competing companies will create similar devices or services to compete for market share. Therefore, the best chance of staying ahead in such a competitive environment is to initiate work on the next generation device or service. This practice is known in industry as continuous quality improvement.

The KT process moves knowledge into application. Existing KT models focus on knowledge as conceptual discoveries generated through research methods. However, projects intended to move technology-related knowledge into application apply two additional methods: development methods that transform conceptual discoveries into tangible inventions, and production methods that transform inventions into device or service innovations. These three states of knowledge outputs are described as analogous to the three classic states of matter: gas, liquid, and solid. The analogy suggests that transforming knowledge into each state, and then translating knowledge outputs from each state, must consider multiple methods.

The paper demonstrates how the widely cited KTA model can be adapted to accommodate all three states of knowledge. The resulting NTK model begins by identifying a problem (need) and then defining a technology-related solution (knowledge). This deliberately focused approach is necessary to ensure the novelty, feasibility, and utility of the eventual solution. The stage/gate model describes the progression through the three states of knowledge, and the KT activities most appropriate for communicating each knowledge state to the relevant stakeholders.

The NTK model is offered as an operational framework for technology-related projects, where the intended application requires these knowledge transformations to reach the marketplace as a device or service. Additional material related to this paper–including the NTK model in detailed electronic form–can be found at http://www.kt4tt.buffalo.edu .

The following summarizes the article's key points:

Technology-related knowledge exists in three states analogous to the three states of matter: research discoveries are the gas state, development inventions are the liquid state, and production innovations are the solid state.

Applying technology-related knowledge as solutions to societal problems requires careful consideration of the relevant state of knowledge in the project, and the methods applied to transform the knowledge from one state to the next.

Knowledge translation models can be expanded to accommodate all three knowledge creation methods, and to effectively communicate all three states of knowledge to the target stakeholders.

The resulting operational model may be applied to any project intending to create and apply technology-related innovations to benefit society.

Canadian Institutes of Health Research: About Knowledge Translation. [ http://www.cihr-irsc.gc.ca/e/29418.html ]

World Health Organization: Bridging the 'Know-Do' Gap: Meeting on Knowledge Translation in Global Health: 1-12 October 2005; Geneva. 2006, Switzerland: World Health Organization

Google Scholar  

National Institutes of Health Roadmap for Medical Research. [ http://nihroadmap.nih.gov/overview.asp ]

Graham ID, Logan J, Harrison MB, Straus SE, Tetroe J, Caswell W, Robinson N: Lost in knowledge translation: Time for a map?. The Journal of Continuing Education in the Health Professions. 2006, 26: 13-24. 10.1002/chp.47.

Article   PubMed   Google Scholar  

Sherwood AM, Melia RP: Knowledge translation: A mandate for Federal research agencies. Journal of Rehabilitation Research and Development. 2007, 44: vii-x.

PubMed   Google Scholar  

U.S. Department of Education: The Rehabilitation Act. [ http://www.ed.gov/policy/speced/reg/narrative.html ]

Speeding up the spread: Putting KT research into practice and developing an integrated KT collaborative research grade background paper KT08. [ http://www.ahfmr.ab.ca/download.php/fdb47de28f52562a0452b42534d33b39 ]

Gibbons M: Why is knowledge translation important? Grounding the conversation. Focus Technical Brief. 2008, 21: 1-9.

Chesbrough H: Open Innovation: The New Imperative for Creating and Profiting from Technology. 2003, Boston: Harvard University Press

Hall GE, Hord SM: Implementing Change: Patterns, Principles, and Potholes. 2006, Boston: Allyn & Bacon, Second

Ward V, House A, Hamer S: Developing a framework for transferring knowledge into action: a thematic analysis of the literature. Journal of Health Services Research and Policy. 2009, 14: 156-164. 10.1258/jhsrp.2009.008120.

Article   PubMed   PubMed Central   Google Scholar  

Lane JP: State of the science in technology transfer. At the confluence of academic research and business development- Merging technology transfer with knowledge translation to deliver value. Assistive Technology Outcomes and Benefits.

Straus SE, Tetroe J, Graham I: Defining knowledge translation. Canadian Medical Association Journal. 181: 165-168. 10.1503/cmaj.081229.

Weiss RA: The discovery of endogenous retroviruses. Retrovirology. 2006, 3: 67-10.1186/1742-4690-3-67.

Madhavan R, Rajiv Grover: From embedded knowledge to embodied knowledge: New product development as knowledge management. The Journal of Marketing. 1998, 62: 1-12. 10.2307/1252283.

Article   Google Scholar  

Tugwell PS, Santesso NA, O'Connor AM, Wilson AJ, Effective Consumer Investigative Group: Knowledge translation for effective consumers. Physical Therapy. 2007, 87: 1728-1738. 10.2522/ptj.20070056.

Sudsawad P: Knowledge Translation: Introduction to models, strategies, and measures. 2007, Austin: Southwest Educational Development Laboratory, National Center for the Dissemination of Disability Research

Carlisle PR: Transferring, translating and transforming: An integrative boundary framework for managing knowledge across boundaries. Organization Science. 2004, 15: 555-568. 10.1287/orsc.1040.0094.

Amara N, Ouimet M, Landry R: New evidence on instrumental, conceptual, and symbolic utilization of university research in government agencies. Science Communication. 2004, 26: 75-106. 10.1177/1075547004267491.

General Information Concerning Patents: What Can Be Patented. [ http://www.uspto.gov/web/offices/pac/doc/general/index.html#whatpat ]

Kahn KB, Castellion G, Griffin A: The PDMA handbook of new product development. 2005, Hoboken: John Wiley & Sons, Inc, Second

Belliveau P, Griffin A, Somermeyer S: The PDMA toolbook (I, II, III) for new product development. 2002, New York: John Wiley & Sons, Inc, 2004, 2007

Lane JP: Delivering on the D in R&D: Recommendations for Increasing Transfer Outcomes from Development Projects. Journal of Assistive Technology Outcomes and Benefits. 2008, 2: 1-35.

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This is a publication of the Center on Knowledge Translation for Technology Transfer, which is funded by the National Institute on Disability and Rehabilitation Research of the U.S. Department of Education under grant number H133A080050. The opinions contained in this presentation are those of the grantee and do not necessarily reflect those of the U.S. Department of Education.

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JPL organized the framework, conceived the links between knowledge translation and technology transfer, suggested the states of knowledge, and linked discovery, invention, and innovation in the model. JLF conducted a review of academic and industry literature and applied the results to the stages and steps within the creation and action segments of the three model phases. Both authors have read and approved the final version of this manuscript.

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Lane, J.P., Flagg, J.L. Translating three states of knowledge–discovery, invention, and innovation. Implementation Sci 5 , 9 (2010). https://doi.org/10.1186/1748-5908-5-9

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The Dynamic of Knowledge Creation in Joint Industry-Academia Research Projects: Return from Recent Action-Research Experiences in the Domain of Logistics and Supply Chain Management

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The chapter focuses on knowledge processes in joint industry-academia research projects. Our experience of knowledge creation in joint industry-academia research projects in the domain of logistics and supply chain management (SCM) has led us to deepen the study of industry-academia interactions more specially the knowledge processes at work in such projects. With this perspective, we adopted an action research approach to launch and conduct two research projects in collaboration with a global manufacturing company. The chapter reviews the knowledge management (KM) literature on knowledge processes, presents the action research approach, and reports the results from the two action-research joint industry-academia research projects with knowledge creation objectives in logistics and SCM. The analysis of the projects reveals that the knowledge creation dynamic results from three intertwined, interactive, and iterative processes: knowledge transfer, knowledge sharing and knowledge generation. This outlines a framework of industry-academia knowledge processes dynamic. The analysis also underlines factors influencing the dynamic, among them action-research methodological choices and tactics. The chapter concludes on the value of action research to boost knowledge creation in joint industry-academia research projects and questions adopting a KM approach in this type of projects that could be part of the KM strategies of partners.

  • knowledge processes
  • knowledge creation
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  • supply chain management

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Nathalie fabbe-costes *.

  • Aix Marseille Univ, CRET-LOG, Aix-en-Provence, France

*Address all correspondence to: [email protected]

1. Introduction

Joint industry-academia research projects are promoted by governments and funding agencies, and more and more companies and research centers are engaged in this type of project. The objective is to undertake research projects mixing participants from one company (or a consortium of companies) and from a single (or multiple) academic research center(s). These research projects are supposed to benefit to every participant: boosting research and development (R&D) and innovation in companies and stimulating impactful academic research. Such projects also aim at facilitating knowledge sharing between academics and practitioners as well as knowledge creation/generation thanks to industry-academia interactions. An educational ambition is sometimes explicitly included in such projects with the objective of enhancing the competences of the parties involved in the research project through dialogue, co-working, and mutual learning.

Knowledge creation is often an expected but challenging output of such projects [ 1 ]. Mots partners involved in this kind of projects expect to learn from the others. The project management often lead partners to share knowledge and the interactions during the projects sometimes end with knowledge generation. Anticipated or not, explicitly managed or not, there are knowledge management (KM) processes in joint industry-academia research projects. Even if there is no deliberate KM in the management of these projects, KM is a key issue in joint industry-academia research projects since they pose the question of who the existing and new knowledge belongs to and how can the partners use it and create value from it. More generally, these projects could (or should) have an explicit place in the KM strategies of the partners. The literature studying joint industry-academia research projects assumes these projects should end with knowledge creation, that can even be a co-creation (e.g. [ 1 , 2 ]) or co-production (e.g. [ 3 , 4 ]). However, the dynamic of this knowledge creation remains a black box. In line with the need for further research at a micro-level [ 2 ], the first objective of this research is to open the knowledge creation black box and study the knowledge processes at work.

There are many ways of conducting joint industry-academia research projects. Some projects, broken into work packages done separately, do not end with close industry-academia collaboration. Our experience of joint industry-academia research projects in management sciences, more precisely in the domain of logistics and supply chain management (SCM), shows that industry-academia interactions are fundamental to create knowledge valuable from a managerial and an academic perspective [ 5 ]. The key role of industry-academia interactions [ 2 , 3 ] and dialogue [ 1 ] is now clearly recognized and appears critical to enhance the impact of industry-academia collaboration [ 4 ]. Therefore, it seems important to adopt research approaches that demand or at least favor these interactions that, according to [ 1 ] and [ 2 ], support knowledge co-creation. However, despite the importance of research approaches [ 5 , 6 ], literature studying joint industry-academia research projects does not discuss much the role of research approaches in knowledge creation.

Indeed, many scholars consider that mutually productive form of collaboration between research and practice are the more likely to be both relevant to contemporary practice and the source of new meaningful knowledge as well as increased research impacts [ 4 ]. Action research refers to a class of research approaches focused on knowledge creation aiming at performing collaboratively embedded action and research. However, to our knowledge, little is known about the dynamic of the KM processes at work in such projects. If recent papers studying university-industry collaborations at a micro-level adopt action research (such as [ 1 , 2 , 4 ]), to our knowledge, none provide any in-depth analysis of the contribution of action research to the knowledge creation dynamic. This is the second objective of this research targeting at academic and professional outputs, in line with a recent call in KM literature [ 6 ].

To address the above mentioned gaps, we adopted an action research approach to deepen our understanding of the knowledge processes in joint industry-academia research projects with an explicit knowledge creation ambition. Under the umbrella of this methodological choice that leads industry and academia to interact with each other, we launched successively two research projects with a global manufacturing company. Each project addresses research questions related to core contemporary logistics and SCM issues of strategic importance for the company. Therefore, the two projects have a double objective: 1) to do the collaborative research works decided with the partner; 2) to analyze industry-academia interactions during the projects, especially the knowledge processes at work and their dynamic in terms of knowledge creation.

The research contributions are at the conceptual, methodological, and practical level. The research provides a conceptual basis to study the knowledge processes at work in joint industry-academia research projects. It also discusses action research as a valuable class of research approaches in joint industry-academia research projects. The research opens the knowledge creation black box and provides an in-depth insight of the knowledge processes and their interactions. The research proposes a framework of knowledge creation dynamic that can inspire joint industry-academia research partners in the management of their collaborative projects and KM strategies.

The chapter is organized as follows. Section 2 presents the context of joint industry-academia research projects and why it is of interest to deepen the study of KM processes in this context. A review of the KM literature focused on knowledge processes clarifies the objectives of the chapter in terms of KM: deepen the study of knowledge processes dynamics, especially knowledge creation/generation and knowledge sharing/transfer. Section 3 builds upon our experience of joint industry-academia research projects to justify the choice of an action research approach to deepen the study of KM processes in such projects. An analysis of action research approaches highlights differences and commonality. Section 4 presents and analyzes two action research projects and the knowledge processes at work in these projects. A framework of industry-academia knowledge creation dynamic and factors influencing it derive from the reflective/reflexive analysis. Conclusion underlines the value of action research in joint industry-academia research projects to boost knowledge creation. It also questions the adoption of a deliberate KM in the management of these projects that could be a brick of the partners’ KM strategies.

2. Knowledge processes in joint industry-academia research projects

Subsection 2.1. presents the context of joint industry-academia research projects and why it is of interest to deepen the study of knowledge processes in this context. Subsection 2.2. examines the KM literature about knowledge processes and focuses on two of them: knowledge creation/generation and knowledge sharing/transfer. It clarifies in highlight boxes the conceptual and theoretical basis of the chapter and its expected outputs. Considering the context of joint industry-academia research projects, subsection 2.3. concludes with additional points.

2.1 Joint industry-academia research projects and KM activities

For political, economic, and pragmatic reasons, joint industry-academia research is developing. As mentioned in [ 5 ], “most funded calls for research put pressure on researchers to conduct collaborative research with companies and to produce more value for industry and society. Companies are looking for external expertise (an alternative from consultancy); they seek to diversify the partners who participate in their open innovation processes and expect to gain useful knowledge from researchers. Academics, on the other hand, are looking for ‘problems’ with practical relevance that fit with their research interests, or theoretical challenges linked to practice issues, combined with funding… that could lead to ‘something new’ for theory, with good potential for publication or dissemination”.

Even when knowledge creation is not a goal per se in industry-academia research projects, these projects are propitious to knowledge exchanges/sharing between partners as well as to knowledge generation/creation. This explains why knowledge creation and protection are often explicitly stated points in university-industry research agreements and contracts [ 7 ].

Different types of knowledge processes are generally taken into consideration and call for specific treatments in research industry-academia agreements with regards to intellectual property. Whether knowledge creation is one of the expected outputs of a project, or, considering that interactions during the project might generate knowledge, further use of this “common knowledge” always explicitly makes part of research contracts. Even if it is generally the first to be mentioned and experienced during any collaborative research project, the question and status of knowledge sharing is less clear. To cover these exchanges, most agreements include a confidentiality section and try ex ante to identify the “prior knowledge” of partners to protect it.

Most partners engaged in such projects are interested by learning from others and to benefit from their knowledge. However, there is sometimes an asymmetry in the willingness to share knowledge. Industry sometimes imagines that it is possible to solve problem and/or innovate thanks to academic knowledge transfer and use, or that academics can work independently and bring solutions or innovation without interacting much with practitioners. Academics sometimes look for practice experiences to feed their research process without caring much about counterparts for practice. Collaborative research projects are challenging for both parties and the management of knowledge in these projects appears to be a key issue, although the literature does not talk much about this question.

Since our PhD dissertation and the beginning of our academic carrier, we have been doing research in collaboration with private companies and/or public organizations. Our ambition was twofold. On the one hand, we wanted to help them to solve logistics and SCM problems or to foresee their future and strategize, as well as to develop their logistics and SCM knowledge, competences, and capabilities. On the other hand, these collaborative projects were aiming at developing our knowledge base and creating significant knowledge in logistics and SCM. An in-depth analysis of our experience in joint industry-academia research projects [ 5 ] revealed the importance of industry-academia interactions to create knowledge, highlighted the role of industry-academia dialogue and co-construction, and proposed guidelines for improving dialogue and co-construction during such projects as well as quality of outputs for both parties.

This work suggested to launched new joint industry-academia research projects to deepen the knowledge processes at work, especially those ending with knowledge creation. Based on this new round of experience engaged in early 2018, the objective of this chapter is to try to better understand knowledge processes in joint industry-academia research projects aiming at producing knowledge with both managerial and academic relevance and value, i.e., being useful for companies and society, as well as being valuable from an academic point of view.

2.2 KM processes: review of the literature and research objectives in the context of joint industry-academia research projects

Knowledge Management (KM), as an area of management studies, emerged in the 1990s. Since the beginning, the study of KM processes (also called KM activities [ 8 ]) is a core topic in KM research. There is no consensus about the number and nature of KM processes. For [ 9 ], the “four major processes consist of the process of creating the knowledge (including knowledge maintenance and updating), the process of storing and retrieving the knowledge, the process of transferring (sharing) the knowledge, and the process of applying the knowledge”. Behind the semantic heterogeneity of the terms to describe KM processes/activities, an analysis of 160 KM frameworks around the globe identifies “six broad categories of knowledge management activities which could be regarded in KM research and KM practice as general accepted basic KM activities” [ 10 ]. These categories are (ranked by frequency of presence in the studied frameworks): Share – that includes Transfer –, Create – that includes Generate –, Use – that includes Apply –, Store, Identify and Acquire knowledge.

Since we study joint industry-academia research projects explicitly aiming at creating new knowledge, “ Create/Generate ” is an expected core KM process in these projects. Since our intent is to favor interactions in joint industry-academia research projects, “ Share/Transfer ” is therefore an inevitable and somehow explicitly desired KM process in such projects, notably when partners want to learn from each other. Bearing in mind the overall list of KM processes, the research focuses on these two processes.

2.2.1 Knowledge creation/generation

Knowledge creation and knowledge generation are often interchangeably used in the KM literature. They are generally included in the same category namely knowledge creation (see [ 10 ]).

Most KM papers mention knowledge creation as one of the core activities/processes of KM. According to [ 11 ], “knowledge creation is often considered as the initial stage of the knowledge flow process”, also called “spiral of knowledge creation” [ 12 ]. Even if authors (e.g. [ 12 ]) insist on the dynamic and dialectical nature of the knowledge-creating process and on the importance of its context, knowledge creation implicitly refers in the KM literature to a deliberate production process of new knowledge. Knowledge creation, “driven by curiosity or in response to a problem, refers to the deliberate and purposeful collation of observations, data, or facts to generate new or novel ways of understanding a particular phenomenon” [ 13 ]. Here, knowledge generation appears to be a sub-process of knowledge creation, the process that ends with new knowledge.

Knowledge generation is an KM process more recently studied compared to knowledge creation [ 14 ]. In the literature focused on knowledge generation, it is viewed as a complex and rather emergent phenomenon. More precisely in [ 14 ], knowledge is viewed as constructed in practice and in context, held within individuals and collectives through nets of interaction, at once forms and is formed by activity. Knowledge generation is a knowledge process as such reflecting the emergent and construct character of organizational knowledge [ 15 ], and “the value of knowledge for organizations and their members is increasingly linked with its construction within rapidly changing, often ambiguous and very specific contexts as well as in social settings” [ 14 ].

This overview of the create/share process in the KM literature suggests that knowledge creation can be viewed as a result or a process . The knowledge creation process can be viewed as a deliberate and purposeful production process and/or a dynamic, complex, never-ending dialectic spiral. Knowledge generation appears like an emergent, uncertain, and complex process producing “sticky” knowledge [ 16 ]. Behind the difference between knowledge creation and generation lies the ontological question of the nature of knowledge. KM literature balances between an instrumental and positivistic view of knowledge, and a systemic and constructionist view [ 15 ], assuming its distributed, localized, paradoxical, and dialectical nature.

According to our experience of joint industry-academia research projects, it is worth considering separately knowledge creation and knowledge generation. In this chapter, knowledge creation refers to the result that can be a mix of expected – thus “deliberate” – and emergent “unexpected” knowledge. Knowledge generation refers to the process that results in knowledge creation. This process can combine deliberate and/or emergent aspects. We will keep in mind the two ontological perspectives about the nature of the knowledge as well as the importance of the context of/for this KM process.

2.2.2 Knowledge sharing/transfer

Knowledge sharing is one of the most researched topics in the field of KM [ 11 ]. It is one of the most studied KM activity/process, one question being why and how people/organizations share or do not share knowledge. However, the KM literature addresses very different ways of “sharing” knowledge clearly mentioned by the words – transfer, distribution, communication, diffusion, dissemination – used in the “share” category in [ 10 ].

Many KM papers (e.g. [ 13 ]) adopt a classic sender-receiver communication approach of knowledge exchanges that can be mono directional or bi-directional. In this view, explicit knowledge (viewed like and object) can be transferred to an identified individual receiver or disseminated broadly to multi-individuals. Dynamic interactions (such as conversation, dialogue, sharing) call for another approach.

Knowledge transfer is an important research topic in KM. It has been studied within firms and in inter-organizational contexts such as mergers, alliances, partnerships, or open innovation/research projects. A transfer begins when both a need and the knowledge to meet that need coexist. The use of the “transfer” metaphor reflects a structural view of knowledge and the possible movement of knowledge [ 16 ], in general from an “expert” individual or organization to a “novice” one. The underlying assumption is that knowledge can be transferred through a communication channel and reused by the receiver.

According to contemporary epistemological approaches in knowledge management, “the notion of transfer is an insufficient and perhaps inappropriate objective for the development of knowledge” [ 14 ], in particular because of the stickiness of knowledge which nature is socially constructed, practice-based, context dependent, and tacitly held.

Knowledge sharing refers to situations where partners both have knowledge and find interesting to engage mutual exchanges of knowledge. Sharing is viewed as a gradual process generally including discussion and dialogue. Knowledge sharing implicitly “recognizes the complexity and elusiveness of knowledge, its situatedness, plurality, and entwinement with human understanding and interaction” [ 14 ].

Knowledge sharing is a dynamic context-dependent process [ 12 , 15 ]. Therefore, the context of the process (time, space, conditions, participants, objectives, agenda, etc.) is of importance. In line with [ 12 ] and the notion of “ Ba ” (a common place or space for creating knowledge), it is possible to improve the conditions of the interactions and stimulate knowledge sharing.

According to our experience of joint industry-academia research projects, it is worth considering separately knowledge transfer and knowledge sharing. In this chapter, knowledge transfer refers to the transmission of knowledge while knowledge sharing refers to a more dynamic , interactive, and situated mutual exchange of knowledge. We bear in mind the importance of the context and of the “ Ba” for knowledge sharing. Again, the ontological view of knowledge seems a key point in delineating between knowledge transfer and sharing.

2.2.3 Relationships between knowledge processes

Heisig [ 10 ] mentions that KM activities/processes mutually complement each other and therefore require co-ordination. The unified model of dynamic knowledge creation in [ 12 ] also suggests the complementary nature of knowledge transfer, knowledge sharing and knowledge generation. Nonetheless, the KM literature does not develop much the relationships between knowledge processes that are often studied separately and viewed as sequential.

Our research intents to study what knowledge processes are at work in joint industry-academia research projects and to unveil the knowledge creation dynamic. Therefore, the objective is to study the relationships/interactions between knowledge transfer, sharing, generation ending with knowledge creation.

2.3 Additional considerations from the KM literature review of value in our context

The context of our study and the review of the KM literature focused on knowledge processes suggest concluding Section 2 with two additional considerations.

2.3.1 The nature of knowledge: Bridging “schools”

The KM literature, in particular some literature reviews or conceptual papers, mentions there are divergent streams of KM research linked to important questions about the knowledge definitions (and their implications), and the nature (ontology) of knowledge and KM.

Knowledge can be viewed [ 9 ] as a state of mind, an object, a process, a capability, with impact for example on how it can be observed, measured, etc. Debates about the definition and nature of knowledge has led to knowledge typologies, taxonomies, and lists of paradoxes (see “dichotomies” in [ 10 ]).

As mentioned in subsection 2.2., there are different ontological views of knowledge, leading to different epistemological approaches. A positivist approach views knowledge as an object, independent of the context, a resource that can be transferred, used. An interactionist, constructionist or constructivist approach considers that knowledge is sticky, cannot be dissociated from its context and that it is a dynamic phenomenon related to learning. The nature of knowledge led to debates (see [ 17 ]) and, according to [ 18 ], to fundamental errors in KM. There are different knowledge “perspectives” that although competing can be combined.

McIver et al. [ 19 ] bridges two theoretical schools of thought: the commodity or possession perspective (viewing knowledge as a resource or even an object) and the community or knowing perspective (a dynamic phenomenon that manifests itself in the very act of knowing something). The process of knowing highlights “the difference between knowledge which implies something that can be located and is independent and knowing which implies a process or action of knowers which is inseparable from them”. Adopting a practice perspective, [ 19 ] proposes a multidimensional view of “knowledge-in-practice” combining two dimensions: tacitness and learnability .

Bridging the epistemology of possession and of practice, [ 20 ] draws from a pragmatist approach a distinction between knowledge , what is possessed, and knowing , what is part of action and is about relation. They do not see knowledge and knowing as competing, but as complementary and mutually enabling, and see the interplay of knowledge and knowing as a potentially generative phenomenon. “For human groups, the source of new knowledge and knowing lies in the use of knowledge as a tool of knowing within situated interaction with the social and physical world” [ 20 ]. Cook and Brown [ 20 ] emphasizes the importance of interactions and dialogue: “a conversation’s back-and-forth not only dynamically affords the exchange of knowledge, it can also afford the generation of new knowledge, since each remark can yield new meaning as it is resituated in the evolving context of the conversation”.

According to our research objectives, it is worth not choosing a knowledge view and questioning the relevance of articulating/bridging different knowledge views. The above proposals seem fruitful in the context of joint industry-academia research projects. They bolster the question of studying knowledge processes relationships/interactions.

2.3.2 KM enablers or barriers

The KM literature includes studies looking at success factors for KM and KM enablers or barriers.

Some papers address success factors at a general KM level embracing all knowledge processes. Heisig [ 10 ] identified four categories of context factors which are critical for the success of KM activities: 1) Human-oriented factors: culture – people – leadership. 2) Organization: process and structure. 3) Technology: infrastructure and applications. 4) Management process: strategy, goals, and measurement. Based these categories, a systematic literature review of KM literature [ 8 ] lists every KM practice improving the performance of KM processes/activities that could be useful to analyze problems or suggest solutions.

Other papers address enablers or barriers to specific knowledge processes. As examples, [ 12 ] identifies factors facilitating the process of dynamic knowledge creation, and [ 16 ] proposes a taxonomy of barriers to intrafirm knowledge transfer.

Even if the study of enablers or barriers to knowledge processes and dynamics is not the core output of our study, we keep in mind these results that could be referred to or expanded in our context.

3. Methodology of the study: action research in joint industry-academia research projects

Sub-Section 3.1. summarizes the return from experience done in [ 5 ] to justify the choice of action research to conduct joint industry-academia research projects aiming at creating knowledge. Sub-Section 3.2. presents different action research approaches stressing differences and commonality.

3.1 The importance of dialogue and co-construction in joint industry-academia to create knowledge

As mentioned before, knowledge creation, although a key aspect of joint industry-academia research projects, is not always a “common” objective nor a common proof of success. In general, academic partners are knowledge-creation oriented. They are often “charged with generating and sourcing scientific knowledge, translating this knowledge into commercial potential, and/or contributing to their community of knowledge” [ 7 ]. Sometimes companies are more focused on the immediate transfer and use of available knowledge to obtain, in action, quick results. In such case, explicit knowledge creation with academics is often out of their scope. Conversely, academics, who look for knowledge creation and expect partitioners to share their knowledge, do not always consider the need to provide a counterpart for action. Unless they have experience of interactive knowledge creation, both partners often have a narrow view of what knowledge is (or can be), of the added value of an interactive work on and about knowledge, and of what can be its “value” both from a practical point of view and an academic point of view.

As mentioned before, the in-depth analysis of our experience of knowledge creation in joint industry-academia research projects in logistics and SCM [ 5 ] points out “the importance of industry-academia interactions, especially dialogue and co-construction, at each stage of a research project to create valuable logistics and SCM knowledge, both from a managerial and an academic point of view”.

With reference to the KM literature reviewed in Section 2, some points can be raised that call for specific research choices to deepen the study of the dynamic of knowledge creation in such projects.

Our previous study revealed the variety of logistics and SCM knowledge creation (in terms of result). Every project combines knowledge expected since the beginning of the project (and deliberately researched) and “surprises” emerging from knowledge generation .

The analysis of the projects that produced the most valuable knowledge from both points of view (academia and industry), highlights the importance of dialogue and co-construction . In launching new project, attention should therefore be paid to the willingness of the partner(s) to dialogue, and to the project context. As mentioned in [ 5 ] “despite the positive image projected by those who promote collaboration between scholars and practitioners with the aim of creating knowledge, collaborating with industry is not so easy and many academics experience difficulties related to the conflicting logics behind this type of collaboration”.

The crucial role of industry-academia interactions suggests adopting research method and agenda that give more space to in-depth conversations, not only dedicated to coordination in the project management but, more importantly, to share knowledge, have time to confront viewpoints at each stage of the research project and work together to co-construct. It is therefore important to take care of the “ Ba ” [ 12 ] during the project.

Even if the analysis of our previous projects [ 5 ] did not deepen knowledge processes dynamics, objectives ( ex-ante ) for industry and research, and outputs for practice and academia, reveal a mix of knowledge (viewed a resource, an object) and knowing as part of action, with (from our perspective) a twofold level of “action”. Action refers to: 1) logistics and SC management and 2) collaborative research project management; both being of value for partners. Further projects should therefore explicitly ambition to develop both knowledges about and for both levels of action.

This is even more so important that at each stage of any project, there are from both partners demands for knowledge transfer and moments when there is intensive knowledge sharing . Their relationships with knowledge generation and creation being difficult to track back. To better understand the dynamic between knowledge processes further research projects should keep traces of exchanges and productions of knowledge.

These results suggest adopting the guidelines presented in [ 5 ] and deepening the study adopting in vivo research to better understand the dynamic of knowledge processes in joint industry-academia research projects aiming at creating knowledge. When launching new joint industry-academia research projects from the early 2018, we chose action research as the main research approach.

3.2 Action research to conduct joint industry-academia research projects

“Action research is an orientation to knowledge creation that arises in a context of practice and requires researchers to work with practitioners” [ 21 ]. Action research aims at contributing both to practical concerns and creating scientifically acceptable knowledge through the development of mutually productive forms of collaboration between research and practice.

3.2.1 Different action research approaches

Under the umbrella of action research there are many ways of conducting research projects, of organizing interactions between scholars and practitioners, and of defining their respective roles. Different approaches are promoted such as collaborative management research, interactive research, action learning research, participatory action research, or action research for transformation (ART).

In management sciences, action research approaches emphasize knowledge creation through some form of co-operation between researchers and practitioners where research is conducted jointly by the researchers and the practitioners during the entire research process, from formulation of the initial problem to dissemination of results [ 21 , 22 ]. In action research, knowledge is assessed by its practical consequences and not only by its explanatory power.

Interactive research [ 23 , 24 , 25 , 26 ] explicitly includes an educative ambition, called “the third task” in [ 23 ]. Interactive research “focusses on creating opportunities for researchers and practitioners to engage in joint learning and knowledge creation” [ 25 ]. It is therefore about research, development, and learning. The educational task aims at enhancing the competences of the parties (partitioners, scholars, and students) involved in the research project through dialogue, co-working, and learning. In interactive research, knowledge creation results from the interactions of two cyclical systems [ 23 , 24 , 25 , 26 ]: the research system and the practice system. These two activity systems may be seen as two interlocked, collective, and interactive learning cycles that produce successive versions of common conceptualizations of the research object and common understanding of the ongoing change process that could be viewed as significant both from the perspective of practice and from the perspective of research [ 23 , 25 ]. Interactive research insists on the distinction between “on-stage-performance” at the workplace and “back-stage-reflections” [ 23 , 25 ].

Action learning research [ 27 ] focuses on knowledge in action and considers that there is no learning without action and no action without learning. “It does not impose expert knowledge but, rather, creates collaborative environments where research experts and local stakeholders share and work with different kinds of knowledge and share the resulting intellectual property”. Therefore, action learning research insists on the direct experience of solving problems and demands reflective practice. In action learning research, researchers and managers are connected to the “real” world and problems, immersed in the setting, are actor and agent of change and create knowledge through cycles of action and reflection . Action learning research “involves the theoretical positioning and analysis of the action, using appropriate theoretical perspectives and frames with a view to identifying emergent theory and contributing to actionable knowledge” [ 27 ]. “Participants are in a group and committed to action and learning and to the generation of actionable knowledge. They are facilitated in meeting on equal terms to discuss and report on progress. Integral to this method is an awareness of self, of one’s companions and of the external world” [ 27 ].

Participatory action research [ 22 ] and action research for transformation (ART) [ 28 ] aim at solving complex societal problems including the people. They broaden repertoires of learning to produce more inclusive knowledge forms and works with people in a way that they become active. They help stakeholders to learn while addressing the challenges they care about. ART is critically engaged “with the production of knowledge for sustainability through more action-oriented transformations research”: co-producing a better world for all. It privileges experiential learning with reflection on action for desired futures. Thus, by engaging and empowering people, ART “can direct the inexhaustible resource of human creativity at all levels – individuals to society – toward addressing our global problems” [ 28 ]. For the researchers, “transformational work requires intimate engagement and self- awareness, which brings the whole person to the work; it is not just about changing something ‘out there’, but it is also about both changing ourselves and our mental models, and our relationships between the out there and the in here” [ 28 ].

The above types of action research show different stakeholders involved in the projects as well as varying degrees of engagement and of willingness to empower them and to transform organizations and society. Even if “action research with partitioners always includes partitioners as partners in the work of knowledge creation”, every project is happening along a spectrum [ 21 ]. On one end there is “as minimum as necessary” consultations between partners, on the other end partners are “co-researchers” who co-design the work and may take it in new directions [ 21 , 22 ]. The spectrum not only concerns generating knowledge but also educating people, empowering stakeholders, and transforming organizations and society.

3.2.2 Action research commonality

Despite the variety of research forms within the class of action research approaches, some common features can be highlighted: action research privileges praxis and pragmatism, includes experiential learning, calls for reflection on action, brings “intelligent collaboration directly into knowledge creation processes” [ 28 ]. It leads academic researchers to dialogue with practitioners (and even people) and, to a certain extent, to co-produce action and research, knowledge creation being a mean and an end .

Because “the scientific value of action and collaborative research is still a matter of debate within the social science research community” [ 23 ], the quality of action research is a hot topic. For [ 27 ], quality in action learning research relies in 1) Action learning research engagement with real-life issues, 2) The collaborative nature of action learning research, 3) The reflective character of action learning research, 4) Workable outcomes and actionable knowledge. More generally, in [ 21 ], quality in action research 1) proceeds from a praxis of participation, 2) is guided by practitioners’ concerns for practicality, 3) is inclusive of stakeholders’ ways of knowing, 4) and helps to build capacity for ongoing change efforts. Seven criteria can be used to assess quality of an action research project/paper [ 21 ]: articulation of objectives, partnership and participation, contribution to action research theory/practice, methods, and process, actionability, reflexivity, significance.

Action research is challenging, and many academics experience difficulties related to the conflicting logics behind this type of collaborating research. Some faciliatory points are frequently mentioned. Early dialogue and negotiation between the parties involved in the research process is useful to express the different expectations on the planned research process. In the joint definition phase, a written initial agreement can help clarifying partners roles and ambitions vis-à-vis research, practice, and society. It seems necessary to respect and preserve the differences between the “spheres” of research and practice [ 23 ]. The quality of the “relational space” is important [ 28 ] and it is necessary to use research tactics and methods creating an interplay between research-oriented and practice-oriented activities over time [ 22 ], (e.g., join seminars [ 23 ]). It is also necessary to make distinction and alternate between performing “on-stage” and engaging in critical analysis and reflection “back-stage” [ 23 , 25 ]. To facilitate this learning loop, it is importance to produce intermediary documents to share and disseminate knowledge [ 28 ]. To favor reflexivity, the disciplined use of field notes, journal keeping, and formal documentation are critical for capturing the dynamics of the reflective process [ 27 ]. To make it possible to build common understanding and take intelligent action, the “conceptual space” is critical [ 28 ]. Importantly, there is no valuable knowledge creation from a scientific point of view without robust research methods [ 21 ].

From this respect, action research is an approach toward designing the whole research process consistent with the use of different types of research methods [ 25 ]. Bradbury et al. [ 21 , 25 , 27 ] list many possible qualitative and quantitative methods that can be included and combined in an action research design.

This synthesis of action research clearly shows that this approach seeks knowledge creation thanks to a dialogue between theory and practice and favors industry-academia interactions and co-construction. It is therefore a relevant approach to foster industry-academia knowledge processes in joint research projects and to deepen the study of knowledge transfer, sharing and generation and of their interactions. However, despite the clear focus of action research on knowledge creation, very few studies are focused of the knowledge processes dynamic in such research projects. Our research intents to fill this gap.

4. Results from two action research in joint industry-academia research projects

Sub-Section 4.1. specifies the context and the methodological choices made to conduct the two action research projects analyzed in this chapter. Sub-Section 4.2 presents the projects and reports the knowledge processes at work. Combining projects analysis, sub-Section 4.3 outlines a framework of knowledge creation dynamic unveiling knowledge processes interactions and identifies some important factors influencing it.

4.1 Context and methodological choices to conduct the research projects

Considering that action research is a “macro design” and that “an obvious challenge for interactive research is to clarify and strengthen its methodological basis” [ 25 ], it is important to explicit the context and the methodological choices for the two projects analyzed in this chapter. It is important to remind that the projects have a double objective: 1) undertaking collaborative projects that fit with our research program and with companies’ key strategic issues; 2) deepening our understanding of the knowledge processes dynamic leading to knowledge creation.

The company that gave us the opportunity to launch the two research projects is a global manufacturer with prior experience in joint industry-academia research projects. Since its product and service offerings rely on international supply chains, the company is more and more dependent on multi-tiers networks of suppliers and retailers and the quality of the supply chain (SC) execution is crucial. In this company, SCM is considered as a strategic dynamic capability to be developed to succeed in its volatile, uncertain, complex, and ambiguous (VUCA) business environment.

The company contacted us to develop an industry-academy partnership to benefit from the logistics and SCM knowledge of our research center and to boost collaboratively R&D and research. For both projects, we gave a list of research topics aligned with our research program and considered as “gaps” in the academic literature. The company selected the topics fitting the best with its strategic priorities.

Since the beginning, the overall idea was to develop at least one collaborative research project including a 3-years PhD student participating in action and working under a co-supervision. As the academic supervisor, I was expected to be an active member of the research team, participating in the knowledge processes of the project. This was therefore an opportunity to build upon the guidelines from [ 5 ], to adopt action research, and to develop “generative learning” [ 13 ] to explore, extend and develop results from [ 5 ], especially to better understand knowledge processes dynamic leading to knowledge creation.

The collaboration began in early 2018 and the first project (P1) was officially launched in November 2018. The second project (P2) was discussed in early 2020 and was officially launched in October 2020. P1 and P2 have a similar ambition (share and generate knowledge, improve SC performance, develop competences and capabilities) but there are differences in terms of topic, conceptual basis, SC scope, research planning and supervisor in the company (both being logistics and SCM managers with expertise and seniority in the industry and the company).

In both projects, the industry-academia dialogue began at an early stage to refine the research topic, co-construct the project, and clarify the objectives for practice and research. In both projects, a formal 3-years contract agreement (aligned with PhD requirements) has been negotiated, each planning being in three main phases.

At the very beginning of every project, me and the PhD student opened a “research diary” [ 29 ] to report on the research process (project traceability). The diary and the research documentation and data produced all along the project are used to develop, in parallel of each project, a meta-level analysis thanks to reflective and reflexive inquiry [ 21 , 27 , 30 , 31 ]. Reflexivity mixed with “contemplative activities” [ 32 ], in turn, leads me to write a lot of reflective notes about industry-academia interactions, dialogue and co-construction as well as knowledge processes and dynamic in these projects, pointing out problems, questions, ideas, and even emotional reactions. The lessons learned during the beginning of P1 (2018–2019) were reused in P2, and from the moment when the projects overlapped (2020), there are learning interactions between the projects that are not independent.

The action research (AR) choices for both projects combine aspects from the approaches presented in subsection 3.2. The research and practice spheres [ 23 , 24 , 25 , 26 ] were clearly identified, and partners agree their identity must be preserved (interactive AR), but a collaborative action learning [ 27 ] sphere was considered necessary to have more day-to-day dialogue and co-construction (action learning research). The educational task [ 23 ] (interactive AR) linked to the participation of a PhD student in each project was crucial to create this collaborative sphere. In P1, the PhD student mainly works in the company and devotes 70% of time in action, in P2, the PhD student mainly works in the research center and devotes 50% of time in action.

Compared to existing literature in supply chain knowledge management research, our methodological choices fill a gap. As stated in [ 33 ]: “the low occurrence of the face-to-face mode identifies a significant literature gap for a qualitative topic such as knowledge management in the supply chain”. More generally, it fills this gap in the broader logistics and SCM literature that, in the search for more scientific rigor, seems to have “lost its connection with practice” [ 34 ].

The overall analysis of the projects, separately and combined, uses the conceptual basis in the highlight boxes in subsections 2.2 and 2.3.

4.2 Knowledge processes and their relationships in P1 and P2

This subsection presents the two action-research joint industry-academia research projects with clear knowledge creation objectives in logistics and SCM. We adopt a narrative form to report industry-academia knowledge processes at work. From the data and in line with the choices made in Section 2, we identified: knowledge transfers from academics to partitioners (coded Ta), knowledge transfers from partitioners to academics (Ti), interactive knowledge sharing (S), research knowledge generation (Gr) – with reference to theory and elaborated during “back-stage-reflections” –, practice knowledge generation (Gp) – related to “on-stage-performance” at the workplace –, and knowledge generation combining both (Gr + p). It is important to note that in action research [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] both academics and partitioners can participate to Gr, Gp and Gr + p. Among the many interactions and knowledge processes episodes, we focused on patterns ending with knowledge creation with value for partners and being attested by some “production” (P) (some being public academic publications – work-in-progress papers, conference papers, articles –, others being for internal use in the company and in formats that best suit knowledge transfer and dissemination (in line with ART [ 28 ])).

Lessons from P1.

For P1, the overall question is how to improve tracking and tracing systems to develop SC visibility and to create more value to SC stakeholders, the SC scope being the downstream SC. In the beginning of P1, the company clearly wanted to benefit from our 20-years research experience on traceability and tracking/tracing systems. The first knowledge process was (Ta) with a conference about “total traceability”, given in the company, starting-up the industry-academia dialogue. The project has been discussed, designed, and written on this prior academic conceptual basis and many knowledge exchanges (Ta + Ti) during the negotiating and contracting period.

In P1, knowledge sharing (S) quickly began “on-stage” with the participation of the PhD student in many R&D projects focused on track and trace issues (diagnosis of existing systems, usefulness of available new technologies, changes in systems and/or in the logistics operations, etc.) and triadic supervision meetings. The first PhD-year included intensive professional and academic learning. (S) about R&D projects raised a question: “why improving track and trace systems?”. The answer was vague: “to have visibility”. A sequence of knowledge transfer (Ta + Ti) + (S) led to 3 research works based on qualitative methods.

The first one explored SC maps and SC mapping activities in the company to specify SC visibility needs. Academics asked for Ti and collected data with reflexive interviews with practitioners (Gp). Data analysis (Gr) produced intermediary (P) with restitution (Ta). The results – with “surprises” – were discussed during a focus group with (S) that led to (Gr + p) and (P). The unexpected results of this work led to another “back-stage” pure theoretical reflection by academics (Gr) with (P).

The objective of the second research was to deepened knowledge about the concept of SC visibility. A literature review (Gr) combined with individual reflexive thinking from the experience of people in the company (Gp + Ti) led to analysis (Gr) and (P). (Ta) of the results had important consequence for action (Gp). It reveals SC visibility as the core co-constructed “conceptual space” of the research.

The third work complemented the conceptual space with a synthesis (Gr) of the concept of value with (P). Discussions with (S) led to (Gp + r). The overall analysis for the PhD, linking track and trace, SC visibility and value, is in progress (P1 finished end of 2021 and the PhD is to be defended in 2022).

Lessons from P2.

P2, which scope is the end-to-end SC, questions the relevance of improving both SC robustness and resilience to face risk, uncertainties, and crisis and how to do so. The topic was proposed by academics just before the beginning of the covid-19 pandemic. It has been quickly accepted considering the need for both partners to learn from this special crisis. P2 project was mainly based on an academic literature review (Gr) with (P). A kick-off industry-academia meeting launched the project: conceptual basis for the research has been proposed (Ta) and interactive questions and answers resulted in (S). Discussions show the need to stabilize a common conceptual basis to favor dialogue and co-construction of “useful” knowledge.

The pandemic context (covid “waves”) put pressure on practitioners and researchers and imposed the agenda and method for the first data collection. Qualitative interviews were the opportunity to (S) about the concepts and to foster (Ti + Gp) to collect experience of covid first wave. Back-stage analysis by academics (Gr) produced intermediary (P). Another industry-academia meeting with intensive co-preparation with (S), mixed (Ta + Ti + S + Gr + p), leading to refined results (P).

Because of the pandemic, it had been difficult up-to-now to develop the interactions in the practice sphere with the PhD student. However, the research and practice spheres could benefit from frequent online meeting with (S) leading to (Ta + Ti) but could not end yet with (Gr + p). However, the PhD student could participate in crisis working groups which is a first step toward more engaged and collaborative action research.

4.3 Combined lessons from the two projects

4.3.1 knowledge creation dynamic: about km processes and role of action research.

The analysis of P1 and P2 confirms there are different knowledge processes at work that combine and end with knowledge creation ( Figure 1a ). It is valuable to distinguish transfer from sharing and generation from creation (the result). Iterative transfers (Ta + Ti) are very different from sharing (S) in an interactional practice and/or research space.

research for knowledge creation

Knowledge creation dynamic. a. Knowledge processes interplay. b. Knowledge generation variety. T (knowledge transfer): collaborative research leads to a T dynamic (succession of exchanges Ta, Ti and Ta + Ti). T–>S (knowledge sharing): T calls for conversation, dialog, turning into S. S: co-working in action and/or research leads to a S dynamic. S–>T: S stimulates T (one-to-one or to-many – dissemination). T–>G (knowledge generation): T (specifically Ti collected by academics or Ta) provides basis for G (especially Ti–>Gr; Ta–>Gp). S–>G: S (specially by I + A in the P + R sphere – see Figure 1b ) stimulates G. S–>G leads to more “surprises” than T–>G. G: action research leads to a G dynamic combining three G spheres and G actors ( Figure 1b ) ending with Gp, Gr, Gp + r, the later leading to the greatest “surprises” in terms of C (knowledge creation). G–>T: in action research there is a systematic T of any G (communication, dissemination). G–>S: G sometimes demands discussion, dialog to deepen reflection.

(Ta) was the first knowledge process at work in the two projects, clearly expected by the company. It was necessary to trigger the research process and stimulated others knowledge processes.

The overall analysis of knowledge processes sequences in P1 and P2 leading to knowledge creation (with P) unveils the interplay between knowledge processes. Figure 1 outlines a framework of knowledge creation dynamic.

The results not only deepen KM studies but also AR studies. Our research refines the analysis of research and practice spheres interplaying [ 30 , 31 , 32 , 33 ].

Compared to previous projects [ 5 ], the action research approach proved to boost knowledge creation thanks to industry-academia co-working in action (in our cases for the PhD student) and in research . Knowledge creation benefits from the combination of knowledge and knowing [ 19 , 20 ], and from a more balanced industry-academia relationship [ 27 ]: knowledge of academics or practitioners, as well as knowledge generated in the research and/or action sphere are equally valuable, and benefit from being blended. Nevertheless, action research confirms to be time-consuming (academics and managers need time to get used to each other, learning takes time, knowledge creation dynamic is time-costing) with important methodological challenges.

4.3.2 Facilitators, barriers to KM processes and their dynamic

The in vivo test of guidelines adopted from [ 5 ] and of action research confirms they can be considered as valuable in the context joint industry-academy research projects. Even if our objective was not focused on facilitators and barriers, during our reflective and reflexive analysis we identified factors worth noting.

The role of the SC expert leader and industry supervisors reveals very important, especially their support since the beginning and all along the projects, and the animation with the rest of the company (promoting the project, boosting participation of people in the projects, fostering intra-organizational interactions, and contributing to expand knowledge transfer, sharing and generation in the company and SC partners).

Prior experience of partners in joint industry-academia research projects is another important point as well as their learning orientation and culture, including experiential learning [ 28 ], with cognitive (noticing, pay attention), affective (feeling, be astonished) and behavioral (acting, tell about it) capabilities [ 32 ]. Their efforts to learn from experience and draw progress upon it had a direct impact: experience during P1 clearly served P2 (especially concerning the care to build the conceptual and relational spaces of the project).

The overall context of the projects plays a key role. It can boost the willingness to create knowledge (example in P2), or constraints interactions, dialogue, and co-construction and knowledge processes (example the covid pandemic for P1 and P2).

Because the PhD student is a cornerstone of such projects with impact on the knowledge processes dynamic, the relationships between the co-supervisors and the frequency of the triadic interactions (PhD student and co-supervisors) are crucial. They impact the research process and the PhD student learning process.

The PhD student’s vision of its role in the process is also very important. With regards to the participatory nature of the projects, the question of how he/she sees its knowledge power has a strong influence on (Ti), (S), and (Gr + p).

The conceptual space is a key resource in such projects [ 29 ]. Without a common and clear conceptual basis, it is difficult to dialogue and co-produce knowledge. The co-construction of the conceptual space is a key issue that, in P1 and P2, benefited from a rich state-of-the art from academics (Gr + Ta + S).

The projects confirmed the importance of the interactional space and “ Ba” for dialogue and co-construction. There are key enabling persons, tactics (example industry-academia meetings), or methods (example focus groups) that stimulate, develop, and maintain their quality. The covid pandemic showed the sensitiveness of this space and the need to maintain it. Remote online meeting using video conferencing systems changed the interactions, but the frequency of industry-academia exchanges increased, and the audience could be developed (example in P2 industry-academic meetings), stimulating Ta + Ti + S (example sharing papers, news, data that would not have been shared in “normal” circumstances).

In both projects, the knowledge processes dynamic is undoubtedly stimulated by intermediary productions all along the project process, whatever their form, audience, and degree of achievement.

In such projects, the knowledge creation needs to alternate on-stage/back-stage [ 23 , 25 ] work and give time to be reflective and reflexive [ 21 ]. The “iterative cycle of action and reflection” [ 27 ] by academic and/or practitioners is core to the dynamic.

Such projects demand to be able to mobilize – sometimes in an opportunistic way – a wide range of methods or tactics to adapt to an ever-changing context.

5. Conclusion

This chapter combines our experience in running joint industry-academia research projects in the domain of logistics and SCM, a review of the KM literature focused on knowledge processes, an analysis of action research approaches, and the reflective/reflexive experience from two ongoing action research joint industry-academia research projects with a company. Considering the contexts of the two projects, action research appears like an adequate way of producing knowledge in volatile, uncertain, complex, and ambiguous (VUCA) contexts, to address global challenges.

The research has several theoretical contributions and managerial implications. It provides a rigorous conceptual basis to study four distinct KM processes: knowledge creation, generation, sharing and transfer. The in-depth analysis of the dynamic of knowledge creation confirms the complementary nature of these KM processes and gives insights about the interactions/relationships between them. This confirms the importance of adopting a holistic perspective, not reduced to a unique KM process, and the relevance of articulating/bridging different knowledge views. From a methodological point of view, the micro-KM processes identified and used to code knowledge creation episodes (Ta, Ti, S, Gr, Gp, Gr + p) can be reused in another research. The framework proposed in Figure 1 is an important grid of reading for academic and practitioners. It reveals the knowledge creation dynamic at a micro-level: the interplay of KM processes as well as of industry and academia actors, the interlocked nature of research and practice spheres. The research also confirms the value of action research as a class of research approaches for joint industry-academia projects but highlight some challenging points. It stresses how important are: the conceptual and interactional spaces, the robustness of research methods, the discussion about intermediary productions, and the efforts of key persons to maintain the interplay of actors, even if it is time-consuming. The research also suggests taking care of the iterative on-stage/back-stage work necessary to articulate action and reflection to create knowledge.

The results presented in this chapter not only complement KM studies, deepening the study of knowledge processes and of their interactions, but also action research studies, combining different approaches and reporting from in vivo experiences. It also bridges KM and AR studies showing that action research boost knowledge creation in joint industry-academia research projects.

Beyond the understanding of knowledge processes in joint industry-academia research projects, the results suggest another issue. The KM literature as well as the logistics and SCM literature stress the difference between doing activities because you have to and doing them consciously to create value . In line with [ 8 ] which “defines firm’s KM practices as the conscious organizational and managerial practices intended to achieve organizational goals through efficient and effective management of the firm’s knowledge resources”, an overall question can be raised: could/should knowledge processes be consciously managed in joint industry-academia research projects? Could/should these projects explicitly include a deliberate KM strategy? Would a conscious approach of KM foster knowledge processes and their dynamic? Since joint industry-academia research projects make part of the partners’ knowledge strategy – although more implicitly than explicitly – another question could be raised. Should joint industry-academia research project be consciously considered by research partners as making part of their KM strategy?

Acknowledgments

I am grateful for Renault Group, involved in the two projects. The company provides us space for fructuous collaboration engaging with formal agreements with Aix-Marseille University (P1: PVM-2018-394; P2: PVM-2020-196) and partly funding the research projects P1 and P2.

P1 and P2 are supported by Aix-Marseille University that accepted the PhD projects linked to the above conventions and gave a grant to the P2 PhD student. The P1 PhD student benefits from an ANRT grant (CIFRE n°2018/1125).

These projects would not have been possible without the commitment the SC expert leader Aimé-Frédéric Rosenzweig, the two supervisors in the company: Jean-François Lomellini (P1) and Thierry Koscielniak (P2), and of the two PhD students involved in the projects: Lucie Lechaptois (P1) and Yasmina Ziad (P2).

Conflict of interest

No conflict of interest.

  • 1. Marijan D, Gotlied A. Industry-academia research collaboration in software engineering: The Certus model. Information and Software Technology. 2021; 132 :1-12. DOI: 10.1016/j.infsof.2020.106473
  • 2. Jones SE, Coates N. A micro-level view on knowledge co-creation through university-industry collaboration in a multi-national corporation. Journal of Management Development. 2020; 39 :723-738. DOI: 10.1108/JMD-08-2019-0365
  • 3. Rossi F, Rosli A, Yip N. Academic engagement as knowledge co-production and implications for impact: Evidence from Knowledge Transfer Partnerships. Journal of Business Research. 2017; 80 :1-9. DOI: 10.1016/j.jbusres.2017.06.019
  • 4. Sannö A, Öberg AE, Flores-Garcia E, Jackson M. Increasing the impact of industry–academia collaboration through co-production. Technology Innovation Management Review. 2019; 9 :37-47 Available from: https://www.timreview.ca/article/1232
  • 5. Fabbe-Costes N. Logistics knowledge creation in joint industry-academia research projects: The importance of dialogue and co-construction. Knowledge Management Research & Practice. 2018; 16 :464-476. DOI: 10.1080/14778238.2018.1486788
  • 6. Serenko A. A structured literature review of scientometric research of the knowledge management discipline: A 2021 update. Journal of Knowledge Management. 2021; 25 :1889-1925. DOI: 10.1108/JKM-09-2020-0730
  • 7. Bercovitz JE, Tyler BB. Who I am and how I contract: The effect of contractors’ roles on the evolution of contract structure in university-industry research agreements. Organization Science. 2014; 25 :1840-1859. DOI: 10.1287/orsc.2014.0917
  • 8. Inkinen H. Review of empirical research on knowledge management practices and firm performance. Journal of Knowledge Management. 2016; 20 :230-257. DOI: 10.1108/JKM-09-2015-0336
  • 9. Alavi M, Leidner DE. Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly. 2001; 25 :107-136. DOI: 10.2307/3250961
  • 10. Heisig P. Harmonisation of knowledge management – Comparing 160 KM frameworks around the globe. Journal of Knowledge Management. 2009; 13 :4-31. DOI: 10.1108/13673270910971798
  • 11. Ribière V, Walter C. 10 years of KM theory and practices. Knowledge Management Research & Practice. 2013; 11 :4-9. DOI: 10.1057/kmrp.2012.64
  • 12. Nonaka I, Toyama R, Konno N. SECI, Ba and leadership: A unified model of dynamic knowledge creation. Long Range Planning. 2000; 33 :5-34. DOI: 10.1016/S0024-6301(99)00115-6
  • 13. Beesley LGA, Cooper C. Defining knowledge management (KM) activities: Towards consensus. Journal of Knowledge Management. 2008; 12 :48-62. DOI: 10.1108/13673270810875859
  • 14. Leybourne S, Kennedy M. Learning to improvise, or improvising to learn: knowledge generation and ‘innovative practice’ in project environments. Knowledge and Process Management. 2008; 12 :48-62. DOI: 10.1002/kpm.1457
  • 15. Tsoukas H. The firm as a distributed knowledge system: A constructionist approach. Strategic Management Journal. 1996; 17 :11-25. DOI: 10.1002/smj.4250171104
  • 16. Szulanski G. Exploring internal stickiness: Impediments to the transfer of best practice within a firm. Strategic Management Journal. 1996; 17 :27-43. DOI: 10.1002/smj.4250171105
  • 17. Gourlay S. Conceptualizing knowledge creation: A critique of Nonaka’s theory. Journal of Management Studies. 2006; 43 :1415-1436. DOI: 10.1111/j.1467-6486.2006.00637.x
  • 18. Fahey L, Prusak L. The eleven deadliest sins of knowledge management. California Management Review. 1998; 40 :265-276. DOI: 10.2307/41165954
  • 19. McIver D, Lengnick-Hall CA, Lengnick-Hall ML, Ramachandran I. Integrating knowledge and knowing: A framework for understanding knowledge-in-practice. Human Resource Management Review. 2012; 22 :86-99. DOI: 10.1016/j.hrmr.2011.11.003
  • 20. Cook SDN, Brown JS. Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing. Organization Science. 1999; 10 :381-400. DOI: jstor.org/stable/2640362
  • 21. Bradbury HH. What is good action research? Why the resurgent interest? Action Research. 2010; 8 :93-102. DOI: 10.1177/1476750310362435
  • 22. Baskerville R. Investigating information systems with action research. Communications of the Association for Information Systems. 1999; 2 (1):19. DOI: 10.17705/1CAIS.00219
  • 23. Ellström PE. Knowledge creation through interactive research: A learning perspective. In: Paper presented at the HSS-07 Conference; 8-11 May 2007; Jönköping, Sweden
  • 24. Säfsten K, Bäckstrand J. Co-creation of knowledge – Key aspects for relevance in collaborative research projects. In: Paper presented at the 23rd EurOMA International Conference; 17-22 June 2016; Trondheim, Norway
  • 25. Ellström PE, Elg M, Andreas Wallo A, Berglund M, Kock H. Interactive research: Concepts, contributions and challenges. Journal of Manufacturing Technology Management. 2020; 31 :1517. DOI: 10.1108/JMTM-09-2018-0304
  • 26. Berglund M, Ulrika Harlin U, Säfsten K. Interactive research in production start-up – Application and outcomes. Journal of Manufacturing Technology Management. 2020; 31 :1561-1581. DOI: 10.1108/JMTM-11-2018-0380
  • 27. Coghlan D, Coughlan P. Notes toward a philosophy of action learning research. Action Learning: Research and Practice. 2020; 7 :193-203. DOI: 10.1080/14767333.2010.488330
  • 28. Bradbury H, Waddell S, O’Brien K, Apgar M, Teehankee B, Fazey I. A call to action research for transformations: The times demand it. Action Research. 2019; 17 :3-10. DOI: 10.1177/1476750319829633
  • 29. Nadin S, Cassel C. The use of a research diary as a tool for reflexive practice. Some reflections from management research. Qualitative Research in Accounting & Management. 2006; 3 :208-217. DOI: 10.1108/11766090610705407
  • 30. Cunliffe AL. Reflexive inquiry in organizational research: Questions and possibilities. Human Relations. 2003; 56 :983-1003. DOI: 10.1177/00187267030568004
  • 31. Hibbert P, Coupland C, MacIntosh R. Reflexivity: Recursion and relationality in organizational research processes. Qualitative Research in Organizations and Management: An International Journal. 2010; 5 :47-62. DOI: 10.1108/17465641011042026
  • 32. Bartunek JM. Contemplation and organization studies: Why contemplative activities are so crucial for our academic lives. Organization Studies. 2019; 40 :1463-1479. DOI: 10.1177/0170840619867717
  • 33. Cerchione R, Esposito E. A systematic review of supply chain knowledge management research: State of the art and research opportunities. International Journal of Production Economics. 2016; 182 :276-292. DOI: 10.1016/j.ijpe.2016.09.006
  • 34. Touboulic A, Walker H. A relational, transformative and engaged approach to sustainable supply chain management: The potential of action research. Human Relations. 2015; 69 :301-343. DOI: 10.1177/0018726715583364

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What is the Co-Creation of New Knowledge? A Content Analysis and Proposed Definition for Health Interventions

Tania pearce.

1 School of Health, University of New England, Armidale, NSW 2351, Australia; ua.ude.enu@2elpamm

Myfanwy Maple

Anthony shakeshaft.

2 National Drug and Alcohol Research Centre, University of New South Wales, Randwick Campus, 22–32 King Street, Randwick, NSW 2031, Australia; [email protected]

Sarah Wayland

3 C43A, Jeffrey Miller Admin Building, Cumberland Campus, The University of Sydney, Lidcombe, NSW 2141, Australia; [email protected]

Kathy McKay

4 Department of Health Services Research, University of Liverpool, Liverpool L69 3BX, UK; ku.shn.trop-ivat@yakcmk

5 Tavistock and Portman NHS Foundation Trust, University of Liverpool, Liverpool L69 3BX, UK

Co-creation of new knowledge has the potential to speed up the discovery and application of new knowledge into practice. However, the progress of co-creation is hindered by a lack of definitional clarity and inconsistent use of terminology. The aim of this paper is to propose a new standardised definition of co-creation of new knowledge for health interventions based on the existing co-creation literature. The authors completed a systematic search of electronic databases and Google Scholar using 10 of the most frequently used co-creation-related keywords to identify relevant studies. Qualitative content analysis was performed, and two reviewers independently tested the categorisation of papers. Of the 6571 papers retrieved, 42 papers met the inclusion criteria. Examination of the current literature on co-creation demonstrated how the variability of co-creation-related terms can be reduced to four collaborative processes: co-ideation, co-design, co-implementation and co-evaluation. Based on these four processes, a new definition of co-creation of new knowledge for health interventions is proposed. The analysis revealed the need to address the conceptual ambiguity of the definition of “co-creation of new knowledge”. The proposed new definition may help to resolve the current definitional issues relating to co-creation, allowing researchers and policymakers to progress the development of co-creation of new knowledge in research and practice.

1. Introduction

Researchers, practitioners and policy makers have a strong interest in increasing the speed and efficiency with which research findings contribute to improved public health outcomes. The most frequently cited translational models that facilitate research findings into practice are: RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance); “T” models (Translation Research Continuum); and KTA (Knowledge to Action) [ 1 ]. RE-AIM is an evaluation framework that measures the impact of health interventions on improvements to public health outcomes [ 1 ]. The “T” model framework follows a six-stage linear process where research moves from a discovery or “basic” stage (T0) through to the uptake of research findings into clinical or public health practice (T5) [ 2 , 3 ]. The KTA takes a systems approach where new knowledge is created and applied into practice through two interconnected processes: knowledge creation and action research [ 1 ]. Although these translational models have been widely applied, there is no empirical evidence on whether their application actually improves the uptake of research findings into practice. There is also a paucity of empirical evidence on how these translational models, for example those used in health interventions, are interpreted or used by different stakeholders at different points in the processes that they describe [ 1 ]. Beyond translational models, Community-Based Participatory Research [ 4 ] or Action Research [ 5 ] are alternate practices involving multiple stakeholders; however, there is generally no requirement for a collaborative commitment throughout the whole process from the design phase, and there are often power dynamics that remain unresolved. The result can be that the researchers drive the initial concepts and then leave post intervention trial, taking the knowledge gained with them [ 6 ], thus limiting ongoing benefit to those for whom the intervention was being designed.

Despite conceptual appeal, translational models may be of limited practical benefit for a number of reasons. First, a pre-condition for translating research into practice is that research findings should be readily available, of good methodological quality, and provide useful and useable evidence for those working at multiple levels (community to policy). It has been identified that a substantial proportion (40%–89%) of published research does not meet these criteria: (1) the papers did not include sufficient detail to allow their results to be useful or replicable; (2) they did not take into account existing evidence on the same questions; or, (3) they contained readily avoidable design flaws [ 7 ]. Second, numerous systematic reviews across a wide range of content areas have identified a minority of studies (ranging from an estimated 5% to 25%) report on methodologically sound evaluations of interventions aimed at identifying best evidence-based practice, meaning there is relatively little published research readily able to be translated into improved services or policies [ 8 , 9 , 10 , 11 , 12 , 13 ]. Third, there is traditionally minimal collaboration between academics, service providers, communities and policymakers in determining the most important research questions and the most appropriate evaluation methods, meaning published research findings are often of limited practical value. Fourth, despite the methodological benefits of well-controlled evaluation designs, such as Randomised Controlled Trials (RCTs), and their perceived desirability among researchers and academic journal editors, typically the evidence they generate has high internal validity and low external validity (generalisation), meaning the practical applicability of the results of such highly controlled trials is usually variable [ 14 , 15 ]. Fifth, dissemination strategies often fail to convey the importance of research evidence that has established the benefits and costs of interventions, which limits the demand for using translational models to transfer that evidence into practice [ 16 ].

Given these limitations, there is scope to develop additional approaches to improve the speed and efficiency with which research findings contribute to improved health interventions and outcomes. These alternatives do not need to replace existing translational models but would complement them. The common principle across current translational models is a focus on reducing the time gap between discovering new knowledge through research and the uptake of that new knowledge into practice (that is, into the delivery of services, programs or treatments or its integration into policy). A complementary framework, which conceptualises the generation of new knowledge as occurring alongside the delivery of health interventions in organisations, addresses the aforementioned limitations. An example of how this framework could be applied in practice includes mental health organisations involved in the delivery of health interventions such as suicide prevention programs. Health interventions are defined as those interventions creating change in services, treatment or policies, resulting in better health outcomes [ 17 ]. In this scenario, the service providers and researchers collaborate to embed the collection of data (research evaluation) into the routine delivery of services. The collection of data therefore occurs alongside the delivery of the health intervention targeting suicide prevention. The framework would then rely on identified parties co-creating the evidence and the outcome and, thus, the knowledge obtained.

Some research has focused on “how to” co-create, especially in health and community settings [ 18 ]; however, there remains a lack of consensus on the meaning and use of the term co-creation of new knowledge. Many terms are used interchangeably and with ill-defined or no definition as to the meaning behind the terms. A review of the existing literature showed co-creation (also referred to as co-design and co-production) is conceptualised and operationalised in many different ways even within the same field. In health, for instance, the current trend is to depict co-creation as a model of participatory research [ 19 , 20 ]. Others define co-creation as the fusion of two concepts (community-based participatory research and integrated knowledge translation [ 21 ], while some researchers [ 22 , 23 ] base their understanding on a model devised by Sanders and Stappers. In the latter example, co-design is described as a collection of activities ranging from ideation to planning and evaluation. Despite the lack of consensus, two specific definitions of co-creation have been proposed to resolve some of this conceptual ambiguity: (1) “a process whereby researchers and stakeholders jointly contribute to the ideation, planning, implementation and evaluation of new services and systems as a possible means to optimise the impact of research findings ” [ 22 ]; and, (2) “ the collaborative generation of knowledge by academics working alongside stakeholders from other sectors” [ 20 ]. Although both definitions share the concept of equitable collaboration between stakeholders, neither definition appears to adequately capture the concept of co-creation as simultaneously focusing on both program or policy delivery and the generation of new knowledge. The first definition focuses on the former (see italicised text), and the second focuses on the latter (see italicised text).

The lack of a universally accepted definition creates unnecessary ambiguity [ 24 , 25 , 26 , 27 ]. Researchers are not able to effectively search electronic databases and retrieve relevant studies, which inhibits the development of a coherent, critical mass of adequately homogenous co-creation research. It then becomes far more difficult for service providers and policymakers to engage in co-creation activities because they are being asked to engage in a process that either lacks clarity or is highly variable across different researchers and disciplines. A number of factors are likely to be maintaining the current conceptual ambiguity of co-creation. For example, ‘co-creation’ is used widely in different fields of practice, such as business management, technology, tourism, marketing and, more recently, health. This has generated a range of specific applications of the concept that, at least on face value, may have implied that it is a different concept applied in different contexts, rather than the same concept adapted to different contexts [ 28 ]. As a result, these perceived conceptual differences are exacerbated by different fields of practice attributing different levels of importance to the component processes within co-creation, such as co-ideation and co-evaluation. It must also be remembered the concept of the co-creation of new knowledge is at a relatively early stage of evolution, which means it will initially be characterised by a diverse set of co-created related terms which will solidify into more standardised and accepted lexicon over time [ 29 ].

With recent attention to these collaborative practices and scholars calling for a consensus on the use and meaning of co-related terms [ 30 ], the objective of this paper is to act as a starting point for debate and discussion on standardising the concept of co-creation of new knowledge. The aim of this paper is to propose a definition that is likely to have utility for those working in health interventions to standardize language to better inform others of the processes used. To achieve our aim, three steps were undertaken. First, the identification of contemporary studies that use a co-creation-related term. Second, the use of qualitative content analysis to assess the use of co-creation-related terms and examine patterns in their manifest attributes and meanings. Third, the use of the results of the data analysis to form a foundation for a new proposed definition of co-creation of new knowledge.

2. Materials and Methods

We conducted a qualitative content analysis of existing definitions and/or descriptions of any collaborative activities to formulate a standardised definition of “co-creation of knowledge”. Content analysis is a method used for analysing information and interpreting its meaning using a systematic coding approach to identify trends, patterns and relationships in data [ 31 ]. It allows researchers to reliably perform an inductive analysis through systematic examination and constant comparative evaluation of meanings and context [ 32 ]. In using this approach, the authors followed the three phases of data analysis described by Elo and Kyngäs [ 33 ]: (1) preparation (unit of analysis and data collection); (2) organisation (coding and abstraction); and (3) reporting (synthesis of results), detailed below.

2.1. Preparation Phase

2.1.1. unit of analysis and data collection method.

In preparation for the collection of data, published papers containing any description and/or definitions of collaborative activities (e.g., co-design, co-production etc.) were chosen as the unit of analysis [ 34 ]. The method of data collection involved searching for papers containing original or secondary definitions or descriptions of co-creation-related terms. As attention to these practices has grown considerably in recent years [ 35 ] and to ensure the literature we were obtaining is contemporary, we used a five-year range in our search. Each step is clearly described below.

2.1.2. Sampling Strategy

In the absence of a standardised list of co-creation-related keywords and/or established index headings (e.g., Medical Subject Headings (MeSH)) to identify papers, author (TP) performed a snowball search of Google and Google Scholar to compile a list of terms. Initially, the Google search focused on two keywords commonly appearing in the literature: “co-creation” and “co-production”. As this paper focused on the specific use of co-creation terminology, broader concepts such as “collaboration”, “mode 2”, “participatory action research” and “partnership” were excluded from the snowball search. No limits were placed on either publication date or publication type. This helped ensure the maximum number of differing terms were being identified. Scanning the titles and abstracts of the records retrieved resulted in an exhaustive list of co-creation-related synonyms. This process identified 22 unique keywords ( Table 1 ).

Co-creation-related keywords.

2.1.3. Constructing a Controlled List of Search Terms

The list of 22 keywords was reduced to those used most frequently, given the high probability that less frequently used terms, such as “co-development”, would be used simultaneously with more popular terms, such as “co-creation” and “co-production”. Using advanced search methods in two electronic databases—PubMed (Medline) and ProQuest (multidisciplinary databases)—the 22 keywords were ranked by frequency. As shown in Table 1 , this process identified the 10 most frequently used keywords (>50 records retrieved).

2.1.4. Search Protocol and Screening of Records

As shown in Figure 1 , the 10 most frequently mentioned keywords were used to identify and review potentially relevant papers in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant search [ 36 ]. Seven electronic academic databases were searched (Emerald, EBSCO (including CINAHL), Informit, ProQuest, PubMed, Scopus and Web of Science), as was Google Scholar to capture grey literature. The eligibility criteria included: (1) papers containing clear definitions or descriptions of co-creation-related processes or activities; (2) papers focused on the delivery of services, programs, policies or products; and (3) empirical and non-empirical papers published in either peer reviewed or grey literature. Both English and non-English citations (with English abstracts) were eligible. As earlier test searches of co-creation-related keywords with no date limitations retrieved a large amount of irrelevant material, the date range was limited to the period from 1 January 2014 to 1 November 2018. This time period allowed the retrieval of a representative sample of differing definitions and descriptions of contemporary co-creation-related terms. Author (TP) completed the search on the 3 November 2018. Search results were imported into Endnote X8, and duplicate citations were removed using the Endnotes’ duplicate identification tool. Rigorous manual checks for any remaining duplicates were also undertaken. The literature search, as shown in Figure 1 , resulted in the identification of 12,094 articles, of which 5523 were duplicates, leaving 6571 papers for review. After exclusion of 6197 papers, the full-text versions of the eligible papers (n = 374) were exported from Endnote X8 into NVivo 11 Pro QSR. Author (TP) systematically checked and re-checked the 374 papers for evidence of definitions or descriptions of co-creation-related activities. At this stage, no detailed assessment of the definition was made, rather any identification of a process description resulted in the papers being included. Of these, 42 papers containing clear definitions or descriptions of co-created activities were identified. It is important to note that 69 additional papers also contained descriptions of co-created processes; however, the descriptions in these papers were too ambiguous to allow them to be categorised. For instance, some papers on co-design did not offer a clear and complete description of the co-design process. Instead, the authors of those papers focused on detailed reporting on the outcomes of the study while only providing a general descriptive overview of the level of involvement by the participants engaged in the process of co-design [ 18 , 37 , 38 ]. This lack of clarity around the co-design process and what was involved and the level of contribution by participants made it difficult for us to accurately categorise these papers.

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Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

All papers including manifest process descriptions met the eligibility criteria for full assessment, as demonstrated in Figure 1 .

2.2. Organization Phase

As only 42 papers met the inclusion criteria, author (TP) performed a manual process of inductive analysis. First, the data were analysed by the lead author through open coding (careful reading, highlighting key phrases and segments of text relating to descriptions or definitions of co-creation-related activities). Second, during the abstraction process, a coding scheme of four subcategories was formed through comparison of similar descriptions and meanings of co-creation-related terms. From this process, regardless of the terminology used in the papers, descriptions of co-related activities were aligned with one of four categories: “ co-ideation ”, “ co-design ”, “ co-implementation ” and “ co-evaluation ”. For example, if a paper referred to a process as “co-production” but was actually describing an activity involving design, then it was classed as “co-design”. If a paper on “co-production” described a mixture of collaborative processes (e.g., co-implementation and co-evaluation), then the paper was categorised under both processes.

3. Reporting and Results Phase

3.1. variability of terms and description of co-creation activities.

The iterative comparison of 42 papers demonstrated the wide variability of co-creation-related terms being used in the literature. For instance, co-ideation was referred to using nine terms, with seven terms used to refer to each of the categories co-design, co-implementation and co-evaluation. In all, the literature used 18 unique terms to describe the four activities of co-ideation, co-design, co-implementation and co-evaluation. Of the 92 mentions of a co-creation-related activity identified in the 42 included papers, most terms were related to co-design and co-ideation (n = 36 and n = 26 respectively), followed by co-implementation (n = 17). Synonyms used to describe activities relating to co-evaluation were the least used terms (n = 12). The use of variable terms is evidence of a lack of standardisation in the use of co-creation-related terms across the four identified industries within which this concept appears. Business and marketing used co-creation-related terms most frequently (n = 28), followed by health and welfare sectors (n = 23), community-based (n = 21) and public policy (n = 20) sectors.

3.2. Trustworthiness

The trustworthiness of the study was established using Lincoln and Guba’s (1985) [ 39 ] four criteria: credibility, dependability, confirmability and transferability. Credibility was reached through the use of the constant comparison method to ensure consistency in the categorisation of data and critical peer debriefing with co-researchers [ 40 ]. Dependability was established by having clear documentation of the data collection process and development of the coding frame and the use of correlation coefficient (ICC) where two co-authors (SW and KM) blind to the literature on co-creation categorised a random selection of 40% of the included papers into one or more of the four categories identified in Figure 1 . Reliability between coders was calculated in Statistical Package for the Social Sciences (IBM SPSS 25) (IBM, Armonk, NY, USA), with coding scores demonstrating a good result (intraclass correlation coefficient 0.726; 95% confidence interval 0.624–0.810), as did the interrater reliability of the examination component (intraclass correlation coefficient 0.888; 95% confidence interval. 0.833–0.927). Confirmability was achieved through feedback from two of the co-authors who have experience as community practitioners, while transferability was achieved by searching and including literature from a broad cross section of disciplines.

3.3. Results

The results of our findings are presented in the following 3 tables:

Table 2 summarises the range of co-creation terms used in differing fields of practice appearing in papers published between 2014 and 2018. The identified terms and their descriptions were assessed and categorised according to four primary collaborative processes. In Table 3 , examples of the key phrases and similar descriptions and meanings used by authors to define and describe attributes of co-creation are included. These attributes and meanings assigned by researchers and practitioners were then extracted during the organisational phase and used to formulate the operational definitions shown in Table 4 .

Use of co-creation-related terms in papers with a manifest description of their co-creation process, published in 2014–2018 (n = 42 papers).

Examples of latent and manifest content used to inform a new definition of “Co-Creation of Knowledge”.

Co-Creation of New Knowledge—Terminology and Operational Definitions.

* RCT: Randomised Controlled Trial; SWD: Step Wedge Design; MBD: Multiple Base Design; WHO-QoL:World Health Organization Quality of Life.

4. Discussion

This study identified 42 papers published between 2014 and 2018 that provided a manifest definition and/or description of co-creation-related terms. Among these 42 papers, a co-creation-related term was mentioned 92 times. The range of terms varied widely: for example, co-ideation was described using nine terms. These 92 appearances of a co-creation-related term were readily collapsed into the 4 processes proposed in the standardised definition of the co-creation of new knowledge: co-ideation (26 times); co-design (36 times); co-implementation (17 times); and co-evaluation (12 times). Blinded coders (SW and KM) replicated the classification process, achieving good agreement between themselves and the first author in the classification of studies with a clear definition and/or description of co-creation. During the coding trial, there was considerable variation between coders when assessing papers with latent definitions. This event prevented the coding of papers with ambiguous definitions or descriptions of co-creation-related activities. It also reinforced the importance of establishing unambiguous definitions to optimise the consistent application of the concept of co-creation of new knowledge regardless of the user (researchers, service providers and public health policy practitioners).

Given the current variability and the potential to improve these existing definitions, this paper proposes a standardised definition for the co-creation of new knowledge based on the inductive analysis of the existing literature and input from co-researchers as community practitioners. Specifically, through this process using the content analysis model proposed by Elo and Kyngäs [ 33 ], we have achieved our aim of defining co-creation of new knowledge as:

The generation of new knowledge that is derived from the application of rigorous research methods that are embedded into the delivery of a program or policy (by researchers and a range of actors including service providers, service users, community organisations and policymakers) through four collaborative processes : (1) generating an idea (co-ideation); (2) designing the program or policy and the research methods (co-design); (3) implementing the program or policy according to the agreed research methods (co-implementation), and (4) the collection, analysis and interpretation of data (co-evaluation).

This definition comprises three core principles (indicated by the italicised text in the definition). Principle 1: new knowledge derives from the application of rigorous research methods. While specifying that new knowledge must derive from rigorous research methods may be tautological, these concepts are used separately to emphasise that co-creation of new knowledge is an under-utilised way of applying accepted scientific methods, not an alternative to them. This means commonly used frameworks, such as continuous quality improvement or participatory action, would only achieve co-creation of new knowledge if the methods used were sufficiently rigorous [ 88 ]. Principle 2: research methods are embedded into the delivery of a program or policy as a way to ensure the new knowledge has an immediate practical application, such as quantifying the impact of a program or policy, or the economic efficiency with which it is delivered. Principle 3: as summarised in Table 2 , co-creation comprises four collaborative processes. Common across all of the included papers was the use of the prefix ‘co-’ representing collaboration and mutual engagement. Within each collaborative process, the level of participation and partnership between researchers and service providers may vary depending on the activity being undertaken [ 89 ] and the way the data are being collected. The new knowledge, however, would only be defined as being co-created if it comprised all four processes. Evidence suggests having input from all stakeholders across the entire co-creation process will result in stronger partnerships and a greater commitment by all stakeholders to use the knowledge produced [ 90 ].

4.1. Implications for Service Delivery and Policy Implementation

For service delivery and policy implementation, the benefits of using a standardised definition for the co-creation of new knowledge are threefold. First, it will allow service providers, policymakers and researchers to more easily differentiate between what is co-created knowledge and what is not. Currently, as shown in Table 2 , the literature on co-creation is heterogeneous, and co-creation-related terms are applied without any clear consistency in their meaning. Second, improved clarity, both in the definition of co-creation of new knowledge and in key stakeholders’ understanding about it, is a necessary (although insufficient) step in facilitating a more frequent evaluation of programs and policies that will provide governments, funders and services with more immediate, more relevant and more high-quality evidence about which policies and programs are most effective and are good value for money. This contrasts with the current focus on translational models for utilising research evidence in practice which, as argued in the introduction, are of limited practical benefit to service providers and policymakers. Third, greater clarity about co-creation as a concept and an approach will assist in developing new and innovative ways of embedding research into practice because the processes required for embedded research are clearly specified. The new, applied and timely research evidence generated by greater use of co-creation processes will, in turn, build sustainability in the delivery of cost-effective programs and policies. Good quality evidence provides an unambiguous, transparent rationale that can be used to defend the provision of programs and policies when their existence is challenged by threats, such as funding cuts and organisational restructures. More frequent embedding of research into practice is also likely to encourage a greater focus from all stakeholders on improving outcomes for clients and target populations using rigorous and appropriate methods of data collection [ 91 ].

4.2. Implications for Future Research

There are four key ways in which the concept of co-creation of new knowledge can be developed. First, there is a need to develop a measure of co-creation of new knowledge (based on the definition) to capture the extent to which studies that claim to use a co-creation approach actually do so. The psychometric properties of such a measure would need to be established, including inter-and intra-rater reliability and validity (including content, construct and face validity). Similarly to the development of a measure for the extent to which co-creation is used in relevant papers, a measure of the extent and quality of collaboration between researchers and practitioners would be useful, given the three principles for co-creation proposed by Greenhalgh [ 20 ] emphasise the centrality of collaboration in co-creation of new knowledge. This concept has been applied elsewhere, such as in Pretty’s participation typology used by researchers to assess levels of community participation ranging from no participation to self-mobilisation [ 91 , 92 ]. The extent to which existing measures might be applicable to the co-creation of new knowledge, however, is unknown. Establishing a new co-creation measure will be important where evaluations suggest a program or policy is ineffective, because it would help clarify whether the apparent lack of effectiveness is a consequence of the program or policy itself, of inadequate application of the co-creation process (using a measure of co-creation) or of an under-developed partnership between the key stakeholders (using a measure of participation). Second, identifying when it is appropriate to use a co-creation process is important because these processes will not be applicable to all types of research [ 93 ]. As a general principle, co-creation processes are likely to be most well aligned with research that seeks to produce actionable or usable knowledge [ 93 ]. Third, adaptation of high-quality evaluation designs and measures that could be used in the co-creation of new knowledge would usefully allow for the lack of strict controls in service delivery. Service delivery providers exist in unstable environments, with a changing client base and funding pressures. The co-existence of researchers and service providers calls for evaluation designs that are adaptable to the needs of all stakeholders that are typically able to be achieved in the context of the routine delivery of services or the implementation of public policy [ 88 ]. Fourth, given researchers have very different key performance indicators (KPIs) than service providers and policymakers, establishing common KPIs, such as demonstrating the benefits and costs of programs or policies as they are implemented and using standardised co-creation of new knowledge processes, would encourage greater collaboration and strengthen the focus on outcomes. Further, maximising the value of co-creation of new knowledge will come from understanding the perspectives of the end-user (consumers, citizens, patients, governments, service providers and philanthropists) on the feasibility of co-creation to achieve social policy objectives and funding goals. Standardised terminology will also assist in future theory development and testing where these processes are used and clearly defined.

4.3. Strengths and Limitations

The study has four key limitations. First, this paper examined nearly five years of published co-creation literature. Limiting the search for papers by publication date was based on evidence that the current definitions and processes would be informed by earlier research findings. Furthermore, as the searches were conducted using multiple electronic databases, covering a broad spectrum of disciplines, the risk of bias to a specific discipline was reduced. Second, the outcome of the intraclass correlation co-efficient may have been compromised by the small sample size, as a number of samples above 30 is recommended [ 94 ]. The third limitation is that publications may have been misclassified, although the strength of agreement between coders in categorising the manifest papers suggests that this is unlikely. Fourth, the independent review of 40% of papers may be insufficient to establish that the definition of ‘co-creation of new knowledge’ can be applied consistently in the field. The adequacy of the proposed definition is based on a combination of descriptions of previously applied research processes and the knowledge and experience of field practitioners. The test conducted by the independent reviewers demonstrated general consensus with good agreement. A useful next step for research would be to explore with stakeholders and policymakers this issue in real time or, prospectively, to understand what they think co-creation might be defined as and then apply this to the existing published research. The findings of this paper have already been shared with organisations involved in co-creation activities, namely, those from the field of mental health and suicide prevention.

5. Conclusions

Although co-creation of new knowledge is presented as an alternative model for translating research, its use in industry is hindered by its conceptual immaturity. Evidence of a lack of definitional consistency is seen in the wide variability of terms used by industry professionals to describe co-creation. It is important for practitioners to understand such variability exists, as this could prevent double-work or excess use of limited resources when developing new community-based and targeted health initiatives. In this paper, a new standardised co-creation definition has been proposed, which has been developed from the existing activities and processes identified in the contemporary literature. This new definition will help to address the lack of clarity, initiate debate around building an evidence base on co-creation and demonstrate how the definition can be consistently applied. The practical novelty of this theoretical work is clear, as it allows practitioners and other healthcare workers and researchers to start with the same understandings and strategies when developing new healthcare interventions, making such development clearer and more straightforward. Also, by including in the definition the key principle of embedding research methods in the delivery of services may help to ensure a greater investment by practitioners in the research process and its outcomes. Advancement of co-creation of new knowledge as a concept will depend upon the future development of measures of co-creation to ensure its reliability and validity and the alignment of common key performance indicators to encourage greater collaboration between stakeholders. Future collaborations between researchers, service providers and consumers, building targeted health intervention using the four processes identified in the proposed model of co-creation of new knowledge, will likely reduce the timeframe between development of new interventions and community benefit. Using the three core principles proposed will clarify commitment and roles of all players in any health intervention developments.

Acknowledgments

The authors thank Alice Knight for her constructive comments and testing of the coding framework, Rebecca Sanders for the co-efficient calculation and Lyndal Bugeja, Kirsten McCaffrey and Katherine McGill for their valuable suggestions regarding the manuscript.

Author Contributions

Conceptualization, T.P., M.M. and A.S.; methodology, T.P.; formal analysis, T.P.; validation, T.P., S.W. and K.M.; writing—original draft, T.P.; writing—review and editing, T.P., M.M., A.S., S.W., K.M. All authors have read and agreed to the published version of the manuscript.

This research was supported by an Australian Government Research Training Program (RTP) Scholarship.

Conflicts of Interest

The authors declare no conflict of interest.

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  • Science and Technology

Research reveals depths of racial and ethnic bias in health care

William Brangham

William Brangham William Brangham

Karina Cuevas Karina Cuevas

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We’ve long known about racial and ethnic bias in health care, but now we’re getting some first-hand knowledge of how pervasive it is through interviews with health care workers in the largest study of its kind. William Brangham breaks down the study’s findings with one of its co-authors Dr. Laurie Zepheryn. It's part of our ongoing coverage of Race Matters.

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Notice: Transcripts are machine and human generated and lightly edited for accuracy. They may contain errors.

Geoff Bennett:

We have long known about racial and ethnic bias in health care, but now we're getting some firsthand knowledge of how pervasive it is from people within that system through the largest study of its kind. The report was based on interviews with doctors, nurses, dentists and mental health workers.

William Brangham breaks down the study's findings, part of our ongoing coverage of Race Matters.

William Brangham:

In this study from The Commonwealth Fund, nearly half of health care workers in the U.S. say racism against patients is a major problem, and equal numbers report that they have personally witnessed discrimination against patients in their workplace.

Employees at facilities that mostly serve Black or Latino patients reported higher instances of discrimination.

To expand on the study's findings and why it matters, I'm joined by one of its co-authors. Dr. Laurie Zephyrin is senior vice president for advancing health equity at The Commonwealth Fund.

Dr. Zephyrin, so good to have you on the "NewsHour."

So half of health care workers say racism is a major problem, they have seen it in their own workplaces. I'm curious why you chose to look at this issue from this perspective.

Dr. Laurie Zephyrin, The Commonwealth Fund:

Thank you, and thanks for having me.

Previous research really tells us that racism and discrimination impact health care outcomes, especially for people of color. This study goes a step further, really spotlighting the voice of health care workers who have witnessed racism and discrimination and also experienced it themselves.

In terms of why health workers, health care workers, understanding what health care workers are experiencing and what they need from their employers and colleagues to address discrimination is really critical to successful and sustainable change. Health care workers are a key part of the health care system, and they can be a part of the solution.

We do know that the perspective of patients and providers are incredibly important, but for this study, we decided to focus on health care workers because they're on the ground. They impact the day-to-day care of people. And health care workers are living and breathing in the health care system every day.

They really experience the realities of what it is to provide health care firsthand.

One of the more striking disparities in this was the perspective of Black health care workers. And I'm going to put this graphic up.

While half of all health care workers said doctors are more accepting of white patients advocating for themselves compared to Black patients, it was 70 percent of Black workers who said this. I mean, that kind of perspective just has to really leap out at you.

Dr. Laurie Zephyrin:

Yes, it does. It does leap out at you.

Where you come from is important. Diverse experiences are incredibly important. The data are clear just in general on the importance of a culturally diverse work force. It has a really profound impact on the health care system, on the patients served. I'm sure you have seen the data about diverse work force. It can address cultural needs, language needs, improve communication, improve patient status satisfaction.

And there also may be more awareness of the impacts of discrimination and bias because of lived experience.

There was also similar disparities when it came to language differences, with over 70 percent of Latino workers saying that non-English-speaking patients just don't get the same kind of care as English-speaking patients.

Do these disparities, do you believe, actually impact patient outcomes?

There are data that support the linkage between discrimination and impact on quality of care.

So we do know that there are significant disparities and inequities and outcomes, whether we're talking about maternal mortality and the crisis we're experiencing in this country or we're talking about inequities in life expectancy. We do know that where you live, work, play impacts your outcomes, right?

And there's impacts of discrimination and racism on the social determinants of health. So we certainly have data that support this linkage. And to your point earlier, for people that have lived experience, whether it's race, ethnicity, language, other aspects of culture, there just may be more of an understanding, more of a recognition, more of a sensitivity to witnessing and discrimination within the health care system.

There was also an interesting generational divide, with older health care workers not seeing quite as striking a level of crisis as younger workers did.

What do you attribute that to?

Yes, we didn't ask why in the study. And so you don't know what you don't know.

But a few things come to mind in terms of why younger people, younger health care workers are seeing more. This could reflect a generational shift in health care workers being more equity-oriented and younger workers who recognize equity as a key component of health care outcomes.

So we need more research to clarify these generational differences. And more research could be potentially important to inform efforts to really prevent younger health care workers from leaving the profession.

On that issue, you talked with workers about what they would like to see done to make things better. What were the sort of general principles they articulated?

Creating a safe reporting environment was one that came up as crucial.

So, the study found that witnessing discrimination creates stress and also that helped care workers fear retaliation. So having a safe reporting environment that not only supports reporting, but also helps with reconciliation, is really important as well.

I think education also remains crucial to engendering reform, and training is going to be very critical, not just anti-bias training, but also training recognizing that discrimination can be a game changer in health care, that it can impact quality of health care outcomes, and also be able to recognize the signs of discrimination.

All right, Dr. Laurie Zephyrin at The Commonwealth Fund, thank you so much for being here.

Thank you. Thanks so much for having me.

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William Brangham is a correspondent and producer for PBS NewsHour in Washington, D.C. He joined the flagship PBS program in 2015, after spending two years with PBS NewsHour Weekend in New York City.

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EDA and Civic Roundtable Collaborate on UC Hub

New tool will facilitate information exchange among university centers.

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For nearly 45 years, the Economic Development Administration’s (EDA) University Center program has empowered leading colleges and universities to become champions of regional economic ecosystems. These centers provide small businesses and local governments with research-informed technical assistance leading to job creation, business expansion, and the development of highly-skilled talent pools.

EDA’s 73 University Centers (UC) have positioned themselves as critical players supporting technology transfer and commercialization, innovation, and high-growth entrepreneurship—including University of Oregon’s Resource Assistance for Rural Environments program , and Washington State University’s Manufacturing Roundtables , among many other outstanding examples.

To better address the growing needs of this dynamic network, EDA’s Research and National Technical Assistance (RNTA) program is announcing an award of $250,461 to Civic Roundtable to create the UC Hub, an innovative project designed to maximize the impact of the UC program.

The UC Hub will provide a digital platform for connectivity and cooperation between University Centers and regional stakeholders, allowing these economic development leaders to share and access resources such as whitepapers and datasets; interact with each other through a facilitated discussion forum; and identify opportunities for collaboration. Built on the Civic Roundtable platform , the UC Hub will finally provide a centralized location for participant interaction.

“Our University Center program is vital to the transformation of economic development knowledge from theory to praxis and we anticipate the UC Hub will invigorate and accelerate this process,” said Assistant Secretary of Commerce for Economic Development Alejandra Y. Castillo. “EDA is dedicated to boosting regional economics through place-based, locally-led strategies, and our nation’s University Centers play a crucial role in connecting data, resources, and expertise to making those strategies work best for everyone.”

“EDA’s University Center program has a documented, decades-long history of successes,” explained Austin Boral, Co-founder of Civic Roundtable. “However, promising practice knowledge is not being effectively shared across participant institutions, leading to lost opportunity. The UC Hub will help solve this issue.”

The UC Hub will debut in mid-2024. Receive updates on this, and other EDA programs, by subscribing to EDA’s Impact newsletter .

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