What are the types of technology transfer agreements?

Wipo model contracts for academic institutions, technology transfer agreements.

The transfer of knowledge and information about technology can take place in two ways: informally through transfer of knowledge, and formally through technology transfer agreements (contracts).

Informal transfer of knowledge is becoming more and more important in the academic environment as the mobility of researchers and students is greatly contributing to the dissemination of knowledge worldwide. Knowledge can also be transferred through publications, teaching, conferences, courses, presentations, meetings, informal exchanges and personal contacts between scientists, academia and industry.

In the context of formal channels of technology transfer , there is no such thing as a standard contract or agreement. Some universities and research institutions propose standard models as part of their IP policies, but such models are only to be used as a starting point, a support or a tool, and need to be adapted to the specific circumstances and requirements of each case. It is crucial to consult an IP lawyer from the beginning of the negotiation and in particular when signing the agreement.

Businessman and robot shaking hands

There are different types of technology transfer agreements that are frequently used to transfer technology from lab to market .

Technology transfer licensing agreements

Assignments of intellectual property rights, confidentiality agreements, collaborative research agreements, consultancy agreements, sponsored research agreements, material transfer agreements, contract research agreements, academic spin-off agreements, university research-based start-up agreements, joint venture agreements.

In order to support academic institutions in the development and negotiation of technology transfer contracts, WIPO provides model agreements between academic institutions and industry partners. Since licensing is the most frequently used means for technology transfer, the models provide insights into different types of licensing agreements such as know-how licensing, exclusive, software licensing, etc. The models are accompanied by guidelines for customization focusing on challenging issues for technology transfer offices, such as negotiating an audit for royalty rates on the revenues collected by industry partners from sub licensees.

transfer of knowledge contract

01744 20698

  • KNOWLEDGE TRANSFER IN CONTRACTS

transfer of knowledge contract

WHAT KNOWLEDGE AND TO WHO????

In our experience there are two business situations where a client organisation asks for a transfer of our knowledge. The first concerns IT systems implementation. The second is the provision of specialist consultancy services. It is the latter upon which we will focus. We always agree that we will do everything possible to transfer our knowledge and skills.  The knowledge transfer can be defined as ‘ a process of exchange of explicit or tacit knowledge between two parties, during which one party purposefully receives and uses the knowledge provided by another. ’

There are two basic categories of knowledge transfer (1) codification – when the knowledge transfer is based on documents, repositories and knowledge databases and (2) personalisation – involving interaction between people. In the professional work we do it is the personalisation angle that is vital. We often help clients to negotiate complex contracts. Negotiation is a skill and, we argue, that a skill is more difficult to transfer than knowledge. Our knowledge transfer goes through a number of phases. The first is to understand why the negotiation is required in the first place. It is an undoubted fact that many buyers and senior organisational people are negotiating without a modicum of experience. There is regularly the obstacle of a client telling us that no negotiation is possible with a particular supplier. Having agreed that negotiation is not only possible but necessary, we then commence the detailed planning of a strategy and tactics. This involves us conducting rehearsals of the negotiations. We did this for a government department on a contract with a value in excess of £ 1 billion. Our rehearsal of the supplier’s likely tactics were accurate and ensured that the negotiation team were well prepared. The next stage of knowledge transfer is the conduct of the actual negotiation. We are happy to lead the negotiation and we are equally happy to support our client’s negotiation team. The next stage of knowledge transfer is that of reviewing the actual negotiations, identifying where tactics could have been varied, where the approach was successful and where opportunities were missed.

The million $ question is how does the client know that knowledge has been transferred? Is the recipient(s) to be ‘tested’ in a real life scenario? Is the contract going to include a KPI? What happens if the client alleges that they have not acquired the necessary knowledge?

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Channels and processes of knowledge transfer: How does knowledge move between university and industry?

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Gianluca Fabiano, Andrea Marcellusi, Giampiero Favato, Channels and processes of knowledge transfer: How does knowledge move between university and industry?, Science and Public Policy , Volume 47, Issue 2, April 2020, Pages 256–270, https://doi.org/10.1093/scipol/scaa002

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The role of knowledge and technology transfer between academia and the industry has received increasing attention in the analysis of innovation. This article aims to explore the scientific literature concerning knowledge transport mechanisms and describe how the topic was organized by previous studies and terminologies applied. A systematic review was conducted in which the content of recent contributions best fitting these intensions was analysed. The characteristics of knowledge, individuals, organizations, and disciplines were found to be the main determinants in the adoption of transfer mechanisms. These were classified in terms of formalization, relational involvement, direction, and time. On the revealed multi-dimensionality of knowledge transfer and complementarity between transfer activities we framed a new taxonomy distinguishing between channels and processes. Future research may deepen these factors, such as the economic aspects driving the adoption of transfer mechanisms informing decisions on the funding of innovation.

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Knowledge Transfer: What it is & How to Use it Effectively

Knowledge Transfer is a method of sharing information, abilities, and ideas across different areas in your business. Encourage innovation and boost efficiency with this guide.

Last Updated

April 20 2023

Illustration of knowledge transfer

Whether your business is big or small, it’s likely that one of your important daily tasks includes communicating with your team. If you have several departments, it’s even more essential that the right information flows to the right people.

Getting your “wires crossed”, so to speak, can cause organizational delays, major miscommunications, and even prospects falling through the cracks. These issues can be greatly detrimental to your business.

Having a straightforward system for communication and collaboration is the key to avoiding these issues. 

Knowledge transfer systems aid you in streamlining your knowledge which ensures that everyone on your team has the information they need to keep your business running smoothly .

Dilbert cartoon on knowledge transfer

What is Knowledge Transfer?

“Knowledge Transfer” is a practical method for transitioning knowledge from one part of your business to another. 

It is both a theory and a practice – which means that it can be applied to your company culture and to your business systems.

It is more than just communication, though. It involves the circulation of information, ideas, tasks, processes, tools, documents, and so much more.

What Knowledge Transfer is NOT

Knowledge transfer is not the same as “training”. Neither is it simply the circulation of information (facts and data). 

While it does include these things, knowledge transfer has more to do with identifying and harnessing your team members’ adaptable skills and abilities to apply information.

It’s also difficult to transfer personal, experiential knowledge from one person to another. So, knowledge transfer does its best to combine both the practical with the personal in order to shift team behavior and grow their skills. 

Why Knowledge Transfer Matters for Your Business Problem Solving

Have you ever come up with a great idea, just to struggle to figure out how to put it down on paper? 

When it comes to innovation and problem solving, it can be hard to convert abstract concepts into an actual game plan. Beyond that, you need to figure out a way to apply that idea to the task at hand. 

Sharing knowledge is tricky because it involves quantifying and qualifying knowledge that exists in the mind. A knowledge transfer system helps you translate that knowledge into words, visuals, and processes that can then be shared with your team.

A Perfectly Imperfect Approach to Problem Solving

Knowledge transfer matters for your business because it improves innovation, collaboration, and understanding in your business. Rather than relying on facts and data to share information across departments, you’re better able to paint a holistic picture of complicated concepts.

Since we are talking about knowledge – something rather intangible – this is a perfectly imperfect process. You can’t get your team to read your mind… but you can get close.

Uses of Knowledge Transfer

Knowledge transfer can help your business in the following ways:

  • Accelerate the accumulation and dissemination of knowledge across your organization
  • Provide easy and rapid knowledge access to your team
  • Eliminate time and space constraints in communications
  • Stimulate associates to experience the value of sharing knowledge in providing custom-tailored service to customers
  • Respect the dignity of each individual by cultivating an environment that enhances his or her professional development and recognizes each person as a valued member of a service-oriented team

The application of knowledge transfer to your business rings in many other benefits as well, including: improved company culture, improved quality of service, faster business processes, increased efficiency, and better use of business technology and resources.

In fact, one source found that businesses that implemented a knowledge transfer system saw a 50% rise in sales while experiencing a decrease in the cost of training. 

If you’re looking for a way to improve company efficiency, inspire innovation, and reduce costly miscommunications, then it’s worth building a knowledge transfer plan.

How to Do Knowledge Transfer Effectively

So, how does one actually  transfer knowledge?

Since knowledge exists in the mind, the best way to transfer knowledge within an organization is to start with considering how knowledge is transferred from one person to another. 

There are multiple approaches one can take here: writing, telling, or showing. The method you use depends both on how you communicate and how the other person receives information.

Therefore, when transferring knowledge across multiple areas/personnel, you’ll want to employ a variety of approaches and tools.

Knowledge transfer cycle

An effective knowledge transfer strategy combines technology, culture, measurement, and infrastructure in order to share knowledge across multiple areas in your organization.

By employing multiple methods and technologies, you’ll be better able to communicate knowledge to different types of people with different skill sets.

Below we have broken the knowledge transfer process into 5 steps, including the applicable tools for each.

Step 1: Identify & Collect Knowledge

The process all starts with the cultivation of knowledge. This takes place in the culture of your company.

This often looks like:

  • Brainstorming ideas
  • Learning new skills
  • Inviting in experts or consultants
  • Seeking solutions to problems
  • Designing new projects

These result in the “intangible” knowledge that you will next want to collect, document, and share with your team.

To create a strong culture of knowledge generation in your company, you can:

  • Bring up company problems and seek solutions
  • Document those solutions
  • Seek input from team members and outsiders
  • Encourage collaboration and teamwork
  • Mentor and coach staff
  • Train and develop staff

Your goal is to create a factory of ideas and an environment that encourages innovation – where everyone has the opportunity to share their ideas, input, and expertise.

Step 2: Capture & Store Knowledge

When it comes to documenting and sharing knowledge, a lot of businesses believe they have this on lock. 

But proper knowledge capture and knowledge management is more than just having a file cabinet or Google Drive folders. You must have an infrastructure that makes sense for your business and makes access to that knowledge fast and simple. 

Having a knowledge base in place will help you manage both tacit knowledge as well as explicit knowledge that’s being generated in your company. 

This system may include:

  • Visuals and videos
  • Document libraries
  • Knowledge portals
  • CRM systems
  • A dedicated team

With the right knowledge management tools, you make this information readily accessible to anyone on your team that needs it. That means less delay in information changing hands, better organization, and a huge increase in efficiency.

Build a “No-Brainer” Knowledge Base with Helpjuice. Sign Up For a 14-Day Free Trial !

Step 3: Transfer & Share Knowledge

Now that you have the knowledge and have a system for collection, it’s time to circulate that information to other people and/or departments in your organization.

This knowledge transition process is made more efficient and affordable if you use the right technology. 

You’ll want to design a sharing mechanism to facilitate transfer AND create a knowledge transfer plan.

The main components of this include:

  • A clearly outlined process document for how knowledge is to be shared in your company.
  • A document management system (like Google Drive ) that organizes the knowledge and potentially automates knowledge sharing.
  • Communication facilities (like Slack ) that facilitate collaboration and communication.
  • A dedicated person or persons to circulate the knowledge to the appropriate department(s).
  • A follow-up process to confirm that the information was delivered to the right people in the right way at the right time.

Now, what this process looks like will depend on a variety of factors – from your business structure to the size of your team to your budget available for tools and resources.

Therefore, your best bet is to work with an operations expert in combination with a reliable knowledge base software to create the right system for you.

This will ensure that the knowledge is circulated effectively and efficiently. 

Step 4: Apply Knowledge & Measure Results

The next step is to apply this knowledge and measure the results .

You can use Knowledge Management tools to assess success across multiple key performance indicators (KPIs).

For example, if the knowledge shared was regarding a solution to an important business problem – say, improving follow-up to leads dropping off at one stage of the sales cycle – you will want the appropriate team (in this case, Sales), to apply the solution and the report on the results.

Tools like Hubspot and Pipedrive give you the ability to track the progress of tasks, set benchmarks, and measure your success. This is the best way to know if the knowledge is being put to good use and is paying off.

Whether the results are good, poor, or okay, this should also be recorded and then communicated to the appropriate people. See how the knowledge cycle continues? With this system, you’ll never miss a beat.

Step 5: Create New Knowledge

Assume you discover that a new idea, technology, or solution is paying off. You can then apply this to other areas within your company. If the results are coming up short, on the other hand, this presents a new opportunity to innovate.

Having a knowledge transfer system ensures that your business is never stagnant when it comes to new ideas and problem-solving.

If you want your business to grow, you’ll want to cultivate an environment that encourages the constant pursuit of knowledge.

Create a Knowledge Transfer Plan for Your Business

In Step 4 we mentioned the need to create a solid knowledge transfer plan. While this will differ from business to business, there are basic components worth considering.

Identify the key knowledge holders in your organization . Does the knowledge “trickle down” from the top? Or are the true visionaries the ones in the trenches? Give the right people the opportunity to share the knowledge they have.

Motivate them to share . Encourage your “idea people” and internal experts to share their knowledge. Give them a platform to do that – whether that be through a communication channel like Slack, by giving them the floor during company meetings, or providing some other medium.

Make sharing easy . Have fast and simple tools available for people and departments to share information.

Measure results consistently . Set standards and benchmarks. Monitor progress. Communicate the results to your keep. Be receptive to input and adjust when necessary.

Apply the knowledge . Don’t let your business sleep on the knowledge available. What use is a good idea if it isn’t put into action. Offer incentives your team members to be innovative and take initiative. Encourage taking appropriate risks.

Continue generating knowledge . Bring in industry experts, offer training, hold brainstorm sessions, and otherwise encourage a community that pursues knowledge. If there is a problem, take it to your team to think up a solution. Don’t be a company that says, “We have always done it this way”. Look for different ways to do things.

Ready to improve your company culture, boost innovation, and increase collaboration in your company? 

Hopefully, this guide got your wheels turning when it comes to creating your own effective knowledge transfer strategy.

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Conceptualizing the transfer of knowledge across cases in transdisciplinary research

Carolina adler.

1 Environmental Philosophy Group, and Transdisciplinarity Laboratory (D-USYS TdLab), Institute for Environmental Decisions, ETH Zurich, CHN H 73.1, Universitaetstrasse 16, 8092 Zurich, Switzerland

Gertrude Hirsch Hadorn

2 Environmental Philosophy Group, Institute for Environmental Decisions, ETH Zurich, CHN H 73.2, Universitaetstrasse 16, 8092 Zurich, Switzerland

Thomas Breu

3 Centre for Development and Environment (CDE), University of Bern, Hallerstrasse 10, 3012 Bern, Switzerland

Urs Wiesmann

4 Department of Integrative Geography, Institute of Geography, University of Bern, Hallerstrasse 10, 3012 Bern, Switzerland

Christian Pohl

5 Transdisciplinarity Laboratory (D-USYS TdLab), Institute for Environmental Decisions, ETH Zurich, CHN K 78, Universitaetstrasse 16, 8092 Zurich, Switzerland

Transdisciplinary (TD) research is increasingly suggested as a means of tackling wicked problems by providing knowledge on solutions that serve as pathways towards sustainable development. In contrast to research striving for generalizable findings, TD research produces insights for a particular case and context. TD researchers, who build on other TD projects’ results, need to know under what conditions knowledge gained from their case can be transferred to and applied in another case and context. Knowledge transfer between researchers and stakeholders is extensively discussed in the literature. However, a more profound understanding and management of the challenges related to knowledge transfer across cases, as it applies to TD research, are missing. We specify the challenges of knowledge transfer in TD research by distinguishing TD research for policy from conventional evidence-based policy, which relies on generalizing findings, such as randomized controlled trials. We also compare the functions that cases fulfil in other types of research that include basic, applied and ideographic research. We propose to conceptualize transferability of knowledge across cases as arguments by analogy. Methodologically, this would imply explicit consideration on whether the cases in question are sufficiently similar in relevant aspects while not dissimilar in other additional relevant aspects. On the one hand, this approach calls for explicit material considerations that are needed to learn about which aspects of cases are relevant. On the other hand, formal considerations on how to weigh perceived relevant similarities and dissimilarities of the cases at hand for transferability of knowledge, are needed. Empirical research on how projects in TD research deal with this problem is called for.

Introduction

Research for sustainable development deals with wicked problems in society by generating knowledge on the multiple processes of change, such as global environmental change, where numerous dynamic exchanges in human-environment systems simultaneously exert impacts and feedbacks into said systems. Dealing with impacts of such interacting processes of change requires: (1) a fundamental understanding of components and dynamics within and between systems (systems knowledge), (2) knowledge to clarify and prioritize the values at stake in dealing with these impacts (target knowledge) and (3) knowledge on how we could transform the systems to account for these values, (transformation knowledge) (adapted from ProClim 1997 ). All three forms of knowledge might provide insights relevant to policy in dealing with these impacts as solutions that are consistent with long-term sustainable development. In addition, policy relevant research has to make sure it is sensitive to the local context of problems, as is the case in transdisciplinary (TD) case study research. In this paper, we refer to TD research as joint knowledge production of these three forms of knowledge between researchers of different disciplines and stakeholders from society, the private and the public sector (Hirsch Hadorn et al. 2008 ; Wuelser et al. 2012 ).

If TD researchers want to build on other TD projects’ results, they need to know under what conditions knowledge produced in one case can be transferred to and applied in another case. While knowledge transfer between researchers and stakeholders, or more generally between science and policy, is extensively discussed in the literature, a profound understanding and management of the challenges related to knowledge transfer across cases are missing. Therefore, we call for urgent and concerted consideration to matters of knowledge transfer and application between cases as a methodological challenge that the TD research community needs to address.

In this paper, we propose a conceptual approach and point at the methodological implications for addressing and assessing knowledge transfer across cases in TD research. In “ Framing the problem and current practice ”, we start with a brief sketch of the current practice in TD research in order to highlight that transferability across cases is an issue that needs methodological consideration based on an appropriate conceptualization of the problem. We discuss our problem framing on the challenges of transferring knowledge across cases and distinguish different ways of transfer across cases. We then comment on proposals in the literature as a basis for addressing the methodological gap regarding transferability across cases and propose to conceptualize the problem as argument by analogy. In “ Shortcomings of the conventional approach to evidence-based policy from a TD perspective ”, we show why TD research cannot bypass those challenges of analogical inference by building on generalizable findings from approaches such as randomized controlled trials (RCTs), used in conventional evidence-based policy. In “ The specific challenges of transfer of knowledge across cases for TD research ”, we clarify the specific challenges of transfer for TD research by comparing it to four other ways of investigating cases. We propose to handle transferability of knowledge from TD case study research across cases with reference to whether the cases in question are sufficiently similar in relevant aspects while not dissimilar in additional relevant aspects (“ Methodological implications of conceptualizing transfer of knowledge across cases in TD research as analogical arguments ”). This approach includes on the one hand formal considerations and related criteria for how to weigh perceived similarities and dissimilarities against each other for the cases at hand. Here, TD research can build on existing literature in argument analysis as a starting point. On the other hand, material considerations are needed to learn about which aspects of cases are relevant. Here, empirical research on how projects in TD research deal with this problem is called for. In “ Summary and conclusion ”, we conclude with suggestions to advance case-based methodology in TD research.

Framing the problem and current practice

A common way of relating research with policy processes is through synthesis reviews. Such reviews assess and synthesize scientific findings from multiple and diverse studies to inform policymakers, for instance, as is the case with boundary organizations like the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES). In the IPCC case, scientific evidence informing mitigation and adaptation to climate change is provided through a synthesis of findings derived from models, simulations and observations, ensuring scientific credibility for policymakers who negotiate on targets and measures under the United Nations Framework Convention on Climate Change (UNFCCC). Procedural legitimacy, i.e. accounting for perspectives of policymakers, is ensured via line-by-line approval of the Summary for Policymakers during the IPCC plenaries. Given that efforts to mitigate the effects of climate change appear ineffective, adaptation to the impacts of climate change is gaining urgent importance (Peters et al. 2013 ). However, in assessing scientific findings on adaptation for policymakers, it is not sufficient to focus only on evidence, decoupled from its policy relevance in context (Rose 2014 ). Given the importance of local and context-specific factors for effective adaptation, knowledge on ‘what works’ has to rely on diverse and multiple case studies (Brunner 2010 ). Still, problems of ambiguity and inconsistency arise when numerous and diverse forms of case-specific knowledge are assessed against unspecified or vague criteria to evaluate both the evidence for and the relevance of the knowledge for the problem at hand. Consequently, guidelines issued by the IPCC to its authors to ensure consistency appear inadequate in fulfilling that goal when it comes to the assessment and aggregation of case-specific knowledge (Adler and Hirsch Hadorn 2014 ). Although no assessment reports have been yet issued by IPBES, deliberations on assessment processes reflect similar concerns (Turnhout et al. 2012 ). Key in this debate is how to ensure policy-relevant assessment findings when knowledge is based on context-specific cases with diverse disciplinary perspectives (Turnhout et al. 2012 ).

Another way how research for sustainable development and policy processes inter-relate is through problem-oriented research like policy sciences in the USA (Brunner 2010 ) and TD research in European countries. In TD research, researchers and policy-makers or stakeholders from administration, civil society and the private sector interact at specific stages during the whole research process, from identifying and framing a problem, analysing it, and bringing solutions to fruition. TD research strives for (a) grasping the relevant complexity of a problem, (b) accounting for multiple and diverse values that underpin diverse perceptions of that problem, (c) linking abstract and case-specific insights to build an understanding of the problem and (d) elucidating options for change based on common interest (Pohl and Hirsch Hadorn 2007 ; Wiesmann and Hurni 2011 ).

While assessment procedures of boundary organizations like the IPCC are challenged when aggregating context-specific knowledge on complex cases, TD research is challenged when inferring whether knowledge co-produced for a case is also applicable to another, since both a conceptualization of the problem and a methodology for transfer across cases are missing. Also, it appears that this is not a prominent topic among many other challenges mentioned for conducting TD research (e.g. Jahn and Keil 2015 ; Lang et al. 2012 ; Polk 2014 ). Here, we focus explicitly on outlining key considerations for transferring knowledge developed in one case for application into another case.

We use the term ‘knowledge’ following the customary distinction in TD research between systems knowledge, i.e. a fundamental understanding of components and dynamics within and between systems; target knowledge, i.e. knowledge to clarify and prioritize the values at stake in dealing with impacts; and transformation knowledge, i.e. knowledge on how we could transform the systems to account for these values (adapted from ProClim 1997 ). These forms of knowledge encompass a broad range of information sources such as scientific knowledge from researchers of different disciplines and expertise, know-how and experience of stakeholders and practice experts from the public and private sector, and civil society. In addition to systems, target and transformation knowledge on the problem at hand, i.e. the substance, TD research also develops knowledge about procedures and processes for how to deal with the range of issues in TD case study research, i.e. methods for co-production of knowledge for doing TD research.

We use the terms ‘transfer of knowledge’ as applying substantive knowledge derived in one context (case), or methods that have been used to study that case, to another case or type of problem. The term ‘transferability’ is used to determine whether such a transfer would be appropriate, which is a normative methodological consideration. Considering transferability of knowledge in TD research is important. For instance, when developing policies based on TD research, the interest is not only on whether they will be effective in the case under investigation, but also whether they will be so in another case. Consider the following examples (see Fig.  1 ).

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Object name is 11625_2017_444_Fig1_HTML.jpg

Two ways of transferring knowledge between cases: a between units of the same problem type ( T1 ); and b between units of different problem types ( T2 )

We can think of two situations that depict two types of transfer of knowledge from the researched case to the un-researched case. In the first situation a), we hypothetically wish to learn about climate change based on evidence from numerous case studies. We can make this assessment, for example, by focusing on specific problem types, such as migration. In this case, we have a researched case (Bolivia) and an un-researched case (Tanzania). The question here is, what can we learn (if anything) from migration issues on climate change in Bolivia for Tanzania? Similarly, we can discuss the same situation in another problem, such as droughts, looking to transfer knowledge about droughts and climate change from the known case (the Sahara) to an un-researched region, such as Siberia. In both situations, the transfer of knowledge takes place within each problem type.

In the second instance b), we consider a situation where we want to learn about whether a policy or measure to address migration also applies in addressing droughts in the context of climate change, as is often the case when seeking to mainstream adaptation policies to address multiple adaptation problems. Here we can take knowledge on what we know works for migration in one context and apply this to address drought issues in another context. For example, we could ask: what could we learn, if anything, about how climate change migration issues in Bolivia that could be combined and/or inter-related to issues of climate change and droughts in Siberia?

In both situations, there are assumptions made about the extent of transferable case study knowledge both between units of the same problem type and between units of different problem types, where the question remains: under what conditions can we transfer knowledge between inter and intra-problem types? We concur with Krohn ( 2010 ) in arguing that adequately transferring knowledge across cases, as opposed to generalizing findings, is a crucial yet neglected methodological challenge. This is an important issue to overcome methodologically, given that simply reporting on ‘what works?’ in a given context is not sufficient knowledge in itself for practical application elsewhere, especially if this is devoid of complementary knowledge that also answers: ‘for whom did it work? and how?’ (Pawson 2006 ).

Challenges associated with the transfer of knowledge across diverse and context-specific cases have been the subject of discussion and elaboration in other research communities that have developed various kindred concepts for learning from case studies. Those discussions provide suggestions for structuring key considerations for knowledge transfer across cases in TD research. Here, we mention just some of those. For instance, community-based climate change adaptation uses the concept of scaling out pilots, i.e. isolated localized examples of adaptation, for wider geographical application, while highlighting as a core challenge that local specificities, e.g. success factors in one community, may not be transferable to another community (Gogoi et al. 2014 ). In much in the same way, Burdack et al. ( 2014 ) discuss the applicability of their findings from a case study on water-rights trading for managing water demand and supply to other regions by highlighting the contextual factors that apply in Australia for this intervention to work with the desired effects in that context. In policy sciences, indicators for diffusion of innovations are discussed to supplement the information gained in local or regional case studies. Determining valid indicators requires a systematic investigation of conditions under which a measure may hold or not (Brunner 2014 ; Lasswell 1971 ). Therefore, the community-based approach and policy sciences both consider conditions for or against transferability of transformation knowledge.

Transition management, using local or regional transition experiments to explore the dynamics of transitions in societal systems, takes a broader approach. Core concepts in transition management are deepening, broadening and upscaling of transition experiments used for analyzing and managing both the process and the substance of a successful transition experiment in sustainable development (van den Bosch 2010 , p. 74ff). Deepening is about learning from a project in its context, while scaling-up is about embedding the transition experiment in dominant ways of thinking, doing and organizing. Broadening, i.e. replicating and linking to other contexts and functions, comes to some respect closer to what we mean by transfer in this paper, i.e. applying knowledge to other cases. However, broadening is different in that it stresses variation and recombination of elements. In van den Bosch ( 2010 ), the basic mechanism of broadening is in conducting different experiments in a variety of contexts, either to get the new or different social structures or practices applied in a variety of contexts, or enrich the social structures or practices (van den Bosch 2010 ).

In philosophy of science, there are several systematic analyses and proposals. For instance, Bengtsson and Hertting ( 2014 ) propose that empirical findings are portable from one context to other contexts, if they can be related to ideal-type patterns of action on a more abstract level, and that can function as the vehicle for transfer. Also, in realist evaluations, the ‘context–mechanism–outcome' model (C–M–O) combines the empirical and the conceptual level for considering transferability of knowledge, arguing that the configuration of context, mechanism, and outcome need to be considered in order to judge what works for whom and in under what circumstances (Pawson 2006 , p. 25). Cartwright proposes to use the concept of INUS conditions to analyze conditions for transferability of knowledge to a different case. Transferability is given if all the required supporting factors are in place. A supporting factor conceived as an INUS condition is an “Insufficient but Necessary part of an Unnecessary but Sufficient condition for getting a contribution to the effect you want” (Cartwright and Hardie 2012 , p. 63).

We find in these discussions of kindred approaches that a common feature regarding transferability of knowledge centers on conditions for transferable lessons from one case to another, rather than just the outcomes. Hence, a general answer to the question we pose, ‘under what conditions can co-produced knowledge be transferred to another case?’ seems to be simple: it depends on whether the cases in question are sufficiently similar in relevant aspects, while not dissimilar in other relevant aspects. Therefore, we propose to conceptualize transferring knowledge across cases as arguments by analogy. Arguments by analogy are widely used in everyday life as well as in science. They can serve discovery or justification, or play a programmatic role in the development of a field (Bartha 2013 ). We focus on their justificatory role. Arguments by analogy are non-deductive inferences, which means that they are risky. In order to assess the plausibility or strength of analogical inferences from a source to a target, one has to judge whether source and target are sufficiently similar in the relevant regards and do not show important dissimilarities. However, there are no simple, general and strict rules to answer these questions, since answers have to rely on the substance of the problem and the context, where the problem is addressed. To our knowledge, there is neither much discussion on requirements and strength of analogical inferences for asserting transfer of knowledge across cases in TD research methodology, nor do we see (yet) empirical TD research that provides grounded answers to these questions.

We find that a necessary starting point for investigating transferability of knowledge across cases in TD research is to first account for the perspectives of those involved in a TD research context on issues of transferability. However, we also caution on two challenges for dealing with transferability across cases that TD researchers need to consider. On the one hand, the diversity in contexts and specific case-based results typical of TD research could lead to an ‘ideographic trap’ because each case study is regarded as unique and transferability of knowledge seems impossible or irrelevant (Gallati and Wiesmann 2011 ). On the other hand, knowledge could be transferred to other case studies based on mere assumptions, or on implicit but diverging use of considerations about relevant similarities and dissimilarities. However, inconsistent practice cannot justify and provide assurance for transfer from one case to another. If researchers and policymakers in TD collaborations do not deliberately consider conditions for transferability and eventually find themselves misled in doing so, they risk that the quality of their research on cases is questioned. For instance, as calls for auditability of quality appear to proliferate, inconsistent evidence is perceived as one pertinent quality problem in science for policy (Bilotta et al. 2014 ; Boyd 2013 ; Gluckman 2014 ). With this problem in mind, the question of how to conceive and judge transfer of knowledge across cases, and how transferability of knowledge is to be distinguished from generalizability of findings, requires a closer look.

Shortcomings of the conventional approach to evidence-based policy from a TD perspective

A common critique towards TD case study research is that it does not provide generalizable results, as is the case through other approaches such as randomized controlled trials (RCTs). Along this line, evidence-based policy is an increasingly influential concept, originally developed in the field of health for clinical trials (Dobrow et al. 2004 ; Elphick and Smyth 2004 ) and now also used in research for sustainable development (Bilotta et al. 2014 ; Holmes and Clark 2008 ; Pullin and Knight 2009 ). In evidence-based policy, results from RCTs are considered the gold standard of evidence for policy. RCTs test the significance of statistical relations between variables. Only if a broad range of possibly influential factors in the real world is excluded, can observed frequencies on a few variables under standardized conditions allow for statistical tests for inference on whether some functional or causal relation holds in general. We refer to evidence-based policy as using just the evidence from RCTs as the reliable scientific basis for policy advice, as to the conventional approach of evidence-based policy. RCTs may provide valuable abstract information for structuring a policy problem, if used together with additional information that corrects idealization and accounts for the context of application (see “ Methodological implications of conceptualizing transfer of knowledge across cases in TD research as analogical arguments ”). However, the conventional approach to evidence-based policy ignores the fact that RCTs test abstract relations, assuming that these relations would hold more or less in the same way in concrete contexts. Hence, the expectation that the conventional approach to evidence-based policy will be implemented and bring about the intended effects has not been fulfilled in many cases.

For policy to be implemented in a given context, it is required that “the information is perceived by relevant stakeholders to be not only credible [i.e. based on scientific evidence], but also salient and legitimate” (Cash et al. 2003 , p. 7). Information is salient if at the time given it is considered relevant by policymakers. Information is legitimate if the way it is produced takes account of “stakeholders’ divergent values and beliefs” (Cash et al. 2003 , p. 7). As Cash et al. ( 2003 ) highlight, there are fundamental trade-offs between salience, credibility and legitimacy, since, to some extent, accounting for salience and legitimacy in producing credible results contradicts the methodological requirements of standardized approaches to idealized problems abstracting from many features of the concrete cases investigated, as in RCTs.

From a TD perspective, a first criticism relates to inadequate specification and application of criteria. Conventional approaches in evidence-based policy do not consider how and by whom scientific evidence is interpreted for a particular policy problem (Dobrow et al. 2004 ; Holmes and Clark 2008 ; Howick et al. 2013 ). However, policymakers’ perspectives are key for legitimacy, credibility and salience. Taking the IPCC process as an example, interpretation of evidence regarding legitimacy and salience is first done by scientists when writing the Assessment Reports. Interpretation by policymakers follows in the IPCC plenary towards the end of the knowledge production process. From a TD perspective, considerations of legitimacy and salience take place too late in the IPCC process, since perspectives of policymakers need to be accounted for when establishing the evidence. In co-production of knowledge, policymakers are included in the first stage of problem framing, ensuring that the questions addressed by research will be relevant, i.e. salient, and results credible, i.e. evidence appropriate for the particular policy problem (Wiesmann and Hurni 2011 ). Contrary to basing evidence-based policy on RCTs alone, the starting point of basing evidence-based policy on TD research is in establishing both the evidence for scientific information and its salience, based on legitimacy for a particular context (Pohl and Hirsch Hadorn 2007 ; Wiesmann et al. 2008 ). From identifying, framing and structuring the problem, TD research strives to account for perspectives and knowledge requirements of policymakers, since credibility of results and their salience, i.e. relevance needed to account for legitimacy, largely influence their stakes in the particular policy problem.

A second criticism that evidence-based policy based on RCTs faces from a TD perspective is that there are several criteria for quality. For instance, there is broad agreement that evidence has to be assessed differently in basic and applied research. For basic research, it is important to minimise the risk of Type I errors in RCTs, i.e. a false positive (claiming an effect when there is no effect). When scientific evidence is used to inform policy on real-world problems, however, minimizing the risk of Type II errors in RCTs (claiming there is no effect when there is one) becomes more important because of the precautionary principle that prioritizes possible negative impacts on human beings and the environment. Kriebel et al. ( 2001 ) add Type III errors, where scientific evidence produced in well-defined and controlled research environments is used to inform ill-defined ‘wicked problems’. Wicked problems (Rittel and Webber 1973 ) cannot be definitively described, lack clarity on which and whose values are involved and do not allow for a single, definitive and optimal solution. Therefore, Type III errors link back to the question of how and by whom scientific evidence is produced and interpreted for a particular policy. Consequently, accounting for these requirements speaks for TD case study research and against the conventional approach to evidence-based policy.

The specific challenges of transfer of knowledge across cases for TD research

There are many ways for how cases are used in research. If a case is understood as an empirical manifestation of a phenomenon to be investigated (Gerring 2007 , p. 19), then all empirical research can be said to investigate cases in some way. However, depending on the purpose and paradigm of the research, criteria to select cases, their functions and the methods used, differ (Hirsch Hadorn 2017 ). To clarify the specific challenges for transferability of knowledge from TD case study research across cases or problems, we compare it to the perspectives on investigating cases in basic standardized research, grounded theory, applied research and ideographic research (see Fig.  2 ). We conceive these perspectives as ideal–typical simplifications in order to better highlight their specific characteristics (Weber 1962 ), while in research practice, several perspectives may overlap or be combined. For each perspective on investigating cases, we distinguish (a) the empirical level at which characteristics of cases are observed, (b) the conceptual level to structure the information on cases for the purpose in question and (c) how both levels relate. The relations between the empirical and the conceptual level determine what can be learned from investigating cases and respective requirements for transferability. These relations in turn are determined by the underlying paradigm and the purpose of research.

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Functions of cases in TD research compared to other forms of conducting research

Basic research

Basic research can be conducted under a standardized or a grounded theory perspective. Finding general rules in order to explain and predict natural and social processes is rooted in positivism or post-positivism (Guba and Lincoln 2005 ). To find such rules, experiments are designed that test hypotheses by quantitative methods. Such research typically refers to randomly selected cases as empirical instances (be it in the real world or the laboratory) that exhibit certain properties in order to measure and analyze how these properties are distributed and correlated among a standardized set of cases. Results are taken as evidence for or against a general description or explanation of how properties depend on each other and, therefore, are transferable to cases that have not (yet) been investigated. RCTs use this paradigm of generalizable rules. Strictly speaking, treating cases in this way does not align with a ‘case-study’ label or characterization, since the scientific interest is not on the cases, although properties of cases provide the evidence on a statistical level.

Applied research

Applied research is typically conceived as the application of concepts, methods and models from basic research to a specific case (Baumgärtner et al. 2008 ). Strictly speaking, it does not stand on its own but builds on basic research concerning the theoretical level, while its main interest is on the empirical level and on the specific cases themselves. Abstract models and concepts from basic research are adapted and used to describe, predict or manage the concrete problem situation at hand. If an empirical situation is classified as a case for a certain type of problem, then transferability of knowledge across cases can be judged by whether this classification is correct for the cases under consideration.

Grounded theory

Grounded theory (Glaser and Strauss 1967 ) is used in the social sciences to build theory based on qualitative analysis of contrasting cases. It aims at better understanding the heterogeneity of different phenomena or supporting practitioners in dealing with a phenomenon. Cases are used as empirical basis to ground the construction of theories (Walton 1992 ) or ideal-types (Hirsch Hadorn 1997 ). Since comparative analysis is key, a good sample is made up of heterogeneous cases rather than by a large number of cases. To the extent that empirical cases instantiate the relevant features of a theory or ideal-type, this assures classification and consequently transferability across cases of the same type.

The ideographic approach

The ideographic approach (Guba and Lincoln 2005 ) takes empirical cases as subjects of interest in themselves. The purpose of ideographic research is to describe the individual composition of features in single (actual or historical) real-world events or processes in order to understand concrete phenomena and their story, how they came about and what came afterwards. These phenomena are of interest in a specific socio-historical context and the values and beliefs held there; thus the question of transferability is not meaningful for ideographic research.

TD case study research

TD case study research does not fully fit into any one of these four perspectives. Instead, TD research combines features from several of them. The real-world situation under investigation is a subject of interest on its own, like in ideographic research. The purpose is to develop knowledge for use to change the specific situation, like in grounded theory or applied research. However, the problem(s) to be addressed in a concrete context are not simply predefined by general models for further specification, as in applied research, but open to discussion and determined in joint problem structuring. Joint problem structuring includes ideographic elements to understand the specific combination of features of the real-world situation in relation to what is at stake, and for whom. However, to provide a basis for transferability of knowledge across cases, constructing a model of why knowledge works (or not) in this case is also needed. For an example on how knowledge transfer across cases has worked, see Box 1 . While these models may integrate knowledge about general relations, their purpose is not to enable general inferences, since this must not be done on the basis of single-case and small- n studies (Bengtsson and Hertting 2014 ). Instead, models constructed in TD case study research should be used to identify the conditions that speak for or against the effectiveness of knowledge for policy if transferred to another case.

Methodological implications of conceptualizing transfer of knowledge across cases in TD research as analogical arguments

From a methodological standpoint, as discussed in “ Framing the problem and current practice ”, evaluating transferability across cases can be conceived as assessing the plausibility of an argument by analogy. Arguments by analogy refer to relevant similarities of cases in order to justify an inference from one case (the source) to a different case (the target). To specify this vague conceptual idea, we rely on Bartha ( 2013 )’s discussion of analogical arguments and his review of general common sense guidelines for evaluating analogical arguments discussed in argumentation theory. 1

In an argument by analogy, we typically do not have a one-to-one mapping between all elements, properties, relations and functions in the source, on the one hand, and in the target on the other. Therefore, an inference by analogy is a non-deductive inference. While deductive arguments are correct if they conform to some formal schema, this is not possible for non-deductive inferences, since those are risky. Non-deductive inferences can be assessed by how strong or plausible they are. This depends on whether all the relevant information is considered in the premises of the argument. Hence, the plausibility of such an inference results from whether all the relevant similarities and dissimilarities between source and target have been identified, and how perceived similarities and dissimilarities are weighed against each other. Bartha ( 2013 ) lists the following common sense guidelines (G), based on a review of the literature in argumentation theory:

  • (G1) The more the similarities (between two domains), the stronger the analogy.
  • (G2) The more the differences, the weaker the analogy.
  • (G3) The greater the extent of our ignorance about the two domains, the weaker the analogy.
  • (G4) The weaker the conclusion, the more plausible the analogy.
  • (G5) Analogies involving causal relations are more plausible than those not involving causal relations.
  • (G6) Structural analogies are stronger than those based on superficial similarities.
  • (G7) The relevance of the similarities and differences to the conclusion (i.e. to the hypothetical analogy) must be taken into account.
  • (G8) Multiple analogies supporting the same conclusion make the argument stronger.

Clearly, these guidelines are still individually quite vague, also on how to apply them collectively. Hence, they do not work as algorithms that determine the result. However, they can still be useful since they provide guidance for reasoning. This is how Chow characterizes heuristics in general, namely as “satisficing cognitive procedures that can be expressed as rules one reasons in accordance with” (Chow 2015 , p. 1005). Hence, guidelines for weighing similarities and dissimilarities can be used as heuristics to evaluate transfer across cases. Whether the guidelines discussed by Bartha are appropriate for TD research, is the subject of further empirical work.

While these guidelines are useful, they are not sufficient for assessing analogical inferences, since their plausibility also depends on material information. In the context of TD research, the fact that analogical arguments cannot be assessed by referring to some formal schema that would inform about its correctness, is not a weakness but an advantage. In assessing an argument by analogy, one has to clarify which of the many items such as elements, properties, relations or functions are relevant for the inference to be assessed. Items count in evaluating transferability, if similarity or dissimilarity of source and target with respect to these items strengthens or weakens the analogical inference. Learning about relevance of items is not a formal but a material question that depends on empirical information about the specific problem at hand. At this point, accounting for the characteristics of TD research is crucial.

For instance, when assessing transferability of transformation knowledge developed in TD research, one has to consider how this transformation knowledge is embedded in the specific knowledge about the target and about the system (Pohl and Hirsch Hadorn 2007 ). 2 As Barzelay ( 2007 ) highlights, transfer of knowledge from a source to a target “is more complex than ascertaining whether a given practice is effective in source sites, as evaluation researchers might have it; it requires theoretical insight into how observed practices actually mobilize human action and bring about substantively significant effects” (Barzelay 2007 , p. 522). When looking for proposals on how one can learn about which items would count for transfer, we found that several scholars have developed heuristics, i.e. guidelines for how to investigate those items. Barzelay proposes an explanatory heuristic similar to “restrictions and options” (Hirsch Hadorn et al. 2002 ), where researchers can investigate practices in source sites to prepare the ground for what he calls extrapolation of practices from source to target sites. An iteration between implementing changes, observing, and planning new interventions based on the observations is what strategies such as real-world experiments (Gross et al. 2005 ) and adaptive governance (Brunner 2010 ) suggest. Both can be used as strategies to learn from implementation in different contexts about causal relevance of particular aspects, such as conditions of successful transfer of knowledge (Bengtsson and Hertting 2014 ; Gerring 2007 ). Cartwright and Hardie ( 2012 ), elaborate on a framework and principles for knowledge transfer in evidence-based policy that is not conventional evidence-based policy. They suggest thinking of a complex array of factors to be considered for knowledge transfer. Individual factors relevant for knowledge transfer across cases are “an insufficient but non-redundant part of a complex of factors that are unnecessary but together sufficient” (Cartwright 2012 , p. 979). The factors may operate on concrete or abstract levels and may be complemented by additional supporting factors. Some accurate general claim based on RCTs may be part of this complex condition but must not in itself count as sufficient to warrant effectiveness for reasons discussed in previous sections.

For our problem, i.e. how to assess transferability of knowledge across cases in TD research, what is needed most is guidance for how to answer the following empirical question: which items in a given transdisciplinary case study count for transferability of knowledge across cases? As in the case of guidelines for weighing similarities and dissimilarities (Bartha 2013 ), a structured set of criteria would be helpful. As part of this structure, one might think of distinguishing not only between forms of knowledge (transformation, systems and target knowledge), sources of knowledge (academic disciplines and stakeholders and practice experts), but also between substantive and procedural knowledge most important in TD research. Such criteria could provide an additional heuristic to address the material aspects of relevance in assessing the strength of arguments by analogy when transferring knowledge across cases. In so doing, this additional heuristic could guide the analysis of effectiveness in the proposed solutions through diverse, variable and complex conditions in the given cases.

However, as Crasnov ( 2012 ) has pointed out with reference to political science, relating different sorts of evidence in a mixed methods approach, when judging effectiveness of outcomes in concrete cases, is still a debated issue. A first step to improve this situation would be if researchers would explicitly discuss what knowledge, i.e. lessons from their own case study, could be reasonably transferred to other cases and for which reasons. Efforts to systematize transferability of knowledge across cases would benefit from such empirical information, to provide an evidentiary basis for structuring quality in TD research by means of criteria to be used as heuristics.

Summary and conclusion

There is quite a way to go until a structured approach to knowledge transfer across cases in TD case study research is developed. The problem of transferability of knowledge across diverse and context-specific cases is discussed in various fields. In this paper, we argue that the problem needs to be conceptualized in a way that accounts for the particular requirement of TD research, i.e. an approach that deals with cases where knowledge is co-produced by teams of researchers and stakeholders. Therefore, it is not only different from basic, but also from applied and ideographic research.

In summary, we propose to conceptualize the problem of transferring knowledge across cases as arguments by analogy. Hence, we suggest a consideration to handle transferability of knowledge from TD case study research across cases, regarding whether the cases in question are sufficiently similar in relevant aspects while not dissimilar in further relevant aspects. On the one hand, this approach calls for explicit material considerations needed to learn about which aspects of cases are relevant. What makes appraising transferability of knowledge across cases in TD research special is the fact that relevant aspects include what teams of researchers and stakeholders may take as necessary, sufficient or supporting factors for concrete cases. In addition, lessons learned from TD case studies are not only restricted to the substance or content-related matter, but may also include knowledge about processes employed for knowledge co-production. On the other hand, formal considerations on how to weigh perceived relevant similarities and dissimilarities of the cases at hand for transferability of knowledge are needed. Here, TD research can build on the literature in argument analysis as a starting point.

We have argued that transfer between cases in TD research must be distinguished from generalizing across cases. Transfer across cases is conceptualized as an analogical inference that is assessed regarding its strength or plausibility by investigating the relevant similarities and dissimilarities between the cases at hand and weighing them. Generalizing from cases is conceptualized as a statistical inference that is assessed regarding its inductive risk, through approaches such as RCTs. Not clearly distinguishing between these different types of inference and their preconditions opens a door to unjustified interpretations of results.

However, there are few empirical examples of how these problems are dealt with in practice, even though this is precisely the sort of information that yields insights on how problems of transferability across cases are addressed in context. We assume that the transfer of knowledge from one case to another is often done on implicit assumptions, since a systematic conceptualization and an easy-to-handle method for explicit considerations is missing. Currently we know little about knowledge transfer across cases in TD case study research. For instance, we do not know whether knowledge is transferred (if at all), what kind of knowledge it is (e.g. about facts, about processes), whether researchers and stakeholders differ in what they transfer, and what kind of considerations to transferability they give, if any at all. In an SNF funded project (2016–2018), 3 we analyse the following three research questions: (1) what knowledge do researchers and stakeholders transfer across cases, if at all? (2) What considerations do researchers and stakeholders apply when transferring knowledge across cases? (3) Collectively, what typical considerations for transfer of knowledge across cases exist in TD research? Based on a qualitative analysis (interviews, document analysis and informal exchange) of a heterogeneous sample of 12 TD projects in the field of global environmental change, we will provide some answers to these questions. However, a concerted effort in the TD research community is still needed to fill this empirical gap, by making explicit the considerations taken by knowledge producers on transferability. These results would allow for a grounded exploration of possible methodological advances and enable a systematic structure to emerge for considering effectiveness of policy options based on TD research.

Acknowledgements

The authors would like to acknowledge the funding received by the Swiss National Science Foundation for this work (Interdisciplinary Project No. 162781), as well as the generous feedback obtained from Anne Zimmermann, Justin Jagosh and three anonymous reviewers for improving this manuscript.

Box 1: Lessons on transferability from a long-term transdisciplinary research activity in the Mount Kenya region

The vast region stretching northwards from tropical Mount Kenya to semiarid lowlands is characterized by steep ecological gradients, fast socio-economic transition and rapid land use transformation. Transformations towards sustainable development in this dynamic setting have to deal with wicked problems such as high poverty levels, increasing economic disparities, rapidly degrading environmental functions and increasing upstream–downstream conflicts. The interrelation between these problems and their factual uncertainty, value loads and conflicting interests between multiple stakeholders pose major challenges for sustainability-oriented efforts and supporting research, requiring a transdisciplinary approach.

An interdisciplinary team of Kenyan and Swiss researchers has taken up the challenges of TD research for more sustainable development in this complex setting. In various constellations and under varying project umbrellas the team is engaged in the region since more than three decades. As it is common for engaged transdisciplinary endeavors, that the driving force of research is not to aim at ‘just another case study’ for generalization purposes, but to substantially contribute to more sustainable development in this very context and to the well-being of its half a million people. The contextual orientation implies that in such transdisciplinary endeavors, questions of transferability are subordinate in the research design to questions such as adequate recursive and iterative research approaches at the concrete science-society interfaces.

However, the analyses of the history and development of the long-term transdisciplinary involvement for sustainable development in the Mount Kenya region indicates quite some transfer of springs. The following are examples of such transfers of results and outcomes of various different kinds to other cases be these of the same or of a different type:

  • Transfer of disciplinary insights, e.g. in the fields of climate change or peasants’ adaptation strategies where research in the Mount Kenya region contributed profound case studies to disciplinary development;
  • transfer through replication of approach and studies, e.g. the replication of the integrated transdisciplinary approach in the Kilimanjaro region where similar sociocultural competitions are met;
  • transfer of conceptual and theoretical advancements, e.g. theoretical contributions to the global sustainability debate or contributions to principles of intercultural and transdisciplinary research partnerships derived from the long-standing experiences in the Mount Kenya region;
  • transfer of methodological innovations, e.g. the recognition of typical configurations or patterns of problems and potentials for sustainable development that can be clustered into syndromes or archetypes;
  • transfer of innovation adoption pathways, e.g. conditions for the uptake of an innovative and socially adapted weir for river water regulation in other contexts;
  • transfer of policies, e.g. consolidation of regulations in national legislations developed and tested in the region on camel milk and its marketing as an important component of pastoralist livelihoods; and
  • transfer of governance structures, e.g. the spread of grassroots water users’ associations and their bylaws that were originally initiated in the region watersheds in East Africa and their reflection in water policy reforms at national and transnational level.

From the above examples, two questions arise: first, can transfer outcomes of transdisciplinary endeavors be classified or clustered into distinctive categories and can such categorization help in designing transdisciplinary projects and programmes? Second, what conditions and measures promote successful transfer within the identified transdisciplinary project undertaking? Answering these two questions could significantly increase the impact and relevance of transdisciplinary research and is the core of studying transferability (Kiteme and Wiesmann 2008 , Ehrensperger et al. 2015 ).

1 Bartha, in his treatment of analogy and analogical reasoning, also refers to approaches of analogical reasoning in philosophy of science. For instance, he discusses Mary Hesse’s theory and material criteria for analogical inferences from the model to the target in physics, or Kuhn’s practice of analogical reasoning in his case studies on the history of physics and chemistry, which Kuhn used for the discovery and justification of his theory of scientific revolutions. An extensive treatment of analogical reasoning in model construction and inferences to the target for a broad range of sciences is discussed in Creager et al. ( 2007 ). While it is interesting to see the importance of analogical reasoning in the epistemology of modelling today, it is important to acknowledge that TD research provides a different framework for analogical reasoning for transfer of knowledge across cases.

2 Because transformation, systems and target knowledge are specified in relation to each other, transfer of knowledge has to account for relevant inter-dependencies between the forms of knowledge in the cases at hand. Despite of this fact, each form of knowledge comes with requirements of its own that need to be considered. Target knowledge, for instance, requires debate among those involved for proper specification of the vague goals and principles of sustainable development and agreement on legitimate trade-offs among them to work as concrete targets that can be addressed in a TD project. However, a systematic treatment of transferability of systems, target and transformation knowledge as different forms of knowledge is beyond the scope of this paper and would need elaboration in a separate study.

3 http://p3.snf.ch/Project-162781 .

Handled by Daniel J. Lang, University of Lueneburg, Germany.

Contributor Information

Carolina Adler, Phone: +41 789 228 254, Email: [email protected] .

Gertrude Hirsch Hadorn, Phone: +41 44 632 58 93, Email: [email protected] .

Thomas Breu, Phone: +41 31 631 30 58, Email: [email protected] .

Urs Wiesmann, Phone: +41 31 631 88 69, Email: [email protected] .

Christian Pohl, Phone: +41 44 632 63 10, Email: [email protected] .

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Knowledge Transfer Between Software Teams: Effective Methods and Tips

  • Knowledge Transfer Between Software ...

Modern software development’s landscape requires a flawless interplay of ideas, expertise, and experiences. In these dynamic, collaborative spaces, where swift changes and continuous learning prevail, efficient knowledge absorption and sharing morph into a vital success factor.

But how do software teams share complex technical knowledge? What are the mechanisms for effective knowledge transfer with remote developers? You will find the answers here.

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As a proficient IT outsourcing services company , we confirm the criticality of knowledge transfer in software development. So we prepare this comprehensive guide that presents robust strategies and practical tips for superior knowledge transfer outcomes.

Table of Contents

The Central Role of Knowledge Transfer in Software Development

Before we commence, let’s clarify the essence: what is knowledge transfer in software development? The process involves deliberately and systematically sharing information, expertise, and insights between team members for a seamless exchange of knowledge and the continuity of projects.

Any obstruction in the seamless flow of this vital resource amongst the employees invariably weakens the quality of the products being developed. This compromise in quality directly inflicts monetary losses not just on outsourcing companies but also on their clients. 

However, by cultivating a culture of robust knowledge circulation, all entities involved in the software development process will reap enormous benefits. Here’s how:

  • Efficiency in Time and Capital Management: Sharing knowledge can act as a bulwark against excessive work, helping swiftly resolve familiar issues. Furthermore, if a key employee exits the scene, a well-oiled knowledge management mechanism ensures there’s no need to reconstruct knowledge from ground zero, thus saving time and money.
  • Creation of a Robust Knowledge Base: Companies, over some time, accumulate invaluable insights, best practices, and proprietary know-how. Effective knowledge transfer equips teammates to repurpose these proven strategies, eliminating the need for constant rethinking.
  • Risk Mitigation: Effective knowledge sharing between teams mitigates the risk of missed deadlines. Teams armed with comprehensive knowledge can accurately assess their tasks and the requisite time for completion. Knowledge transfer also serves as a channel for developing collaboration during challenging tasks within outsourcing, reducing the risk of failures .
  • Promotion of Continuous Improvement: The democratization of knowledge within a team empowers each member to optimize their results, creating an environment suitable for professional development. Making learning accessible to all members elevates overall performance and stokes a culture of continuous improvement.
  • Enhancement of Employee Satisfaction: Knowledge transfer facilitates employee skill development, paving the way for career advancement. Moreover, it instills a sense of achievement within employees as they witness their personal growth, thereby boosting job satisfaction.

Transfer of knowledge isn’t just power in software engineering – the currency drives growth, innovation, and success. Thus, every organization has a duty to prioritize knowledge dissemination, making it as much a part of its business strategy as any other key component.

Methods to Foster Efficient Knowledge Transfer Among Development Teams

Software engineering teams face unique challenges that require innovative solutions. Several techniques have proven invaluable to combat these challenges and facilitate the knowledge transfer process.

Documentation and Knowledge Repositories

Compiling detailed documentation and maintaining knowledge repositories is a non-negotiable aspect of effective knowledge transfer. A centralized, easily accessible information store allows team members to find answers swiftly and independently. Documented knowledge—from technical specifications to guidelines and best practices—provides a tangible, reliable source of information for existing and future team members.

Pair Programming and Code Reviews

Undoubtedly, pair programming and code reviews serve as dynamic mediums of knowledge transfer. They offer the immediate benefits of identifying and rectifying errors and serve as platforms for junior developers to learn from their more experienced colleagues. These practices enforce shared coding conventions and improve overall code quality, fostering a culture of knowledge sharing and continuous improvement.

Mentoring and Onboarding Programs

Implementing mentoring and onboarding programs enhances knowledge transfer significantly. These programs provide new team members with an understanding of project intricacies and operational procedures, reducing the learning curve. They also provide an avenue for continuous skill development and knowledge sharing, cultivating a nurturing and inclusive environment.

Game Days (Hackathons)

Hackathons, or “Game Days,” stimulate knowledge transfer through friendly competition. By offering a space for developers to collaborate, experiment, and learn in a low-risk, high-energy environment, these events spark creative thinking and encourage problem-solving. Moreover, these create opportunities to share novel ideas and techniques within the team.

Cross-Team Collaboration and Communication

Promoting cross-team collaboration and open communication is critical to effective knowledge transfer. It eradicates information silos and promotes a free flow of ideas, encouraging innovation and diversity in thought. When teams communicate freely, they share not only knowledge but also context, fostering a comprehensive understanding of the project at hand.

Face-to-face Q&A sessions

Face-to-face Q&A sessions offer a platform for clarifying doubts, sharing experiences, and imparting knowledge more personally and interactively. They serve as forums where senior team members can share their insights and experiences, fostering a culture of openness and shared learning.

Brown Bag Lunches (BBLs)

Brown Bag Lunches are informal meetings where employees bring their own lunch and gather to discuss various topics. They foster an informal atmosphere conducive to open discussion and the free exchange of ideas. These sessions can prove instrumental in encouraging knowledge transfer across different domains and disciplines within a team.

Continuous Learning and Skill Development

This is a critical factor in ensuring the effective transfer of knowledge in any field and includes regular workshops, training sessions, or even online courses. By equipping team members with new skills, they can share this knowledge with others, thereby enhancing team capability and promoting a culture of lifelong learning.

Tips for Successful Knowledge Transfer

A well-rounded, multi-pronged approach to knowledge transfer is integral to software development. Processes must be planned, executed, and evaluated carefully to ensure the highest efficiency. As we delve into the main steps of knowledge transfer, it’s crucial to emphasize the distinction between two fundamental types of knowledge: explicit and tacit.

  • Explicit knowledge represents the concrete, codified piece of information that is documented, organized, and hence, readily transferable. This encompasses coding best practices and standards, programming patterns, techniques, and related documentation. Explicit knowledge is the tangible collection of insights providing a development practice roadmap.
  • Conversely, tacit knowledge is abstract, emerging from developers’ experiences, and encompasses unrecorded insights, intuitive capabilities, and learned competencies. This reservoir of experience, crucial for nuanced decision-making in software development, constitutes 51% of an average employee’s workplace knowledge, per the Panopto Workplace Knowledge and Productivity Report .

The interplay between these two types of knowledge—explicit and tacit—forms the crux of software development, combining the structured guidance of documented information with the flexibility and adaptability derived from personal experience.

Tacit and Explicit Knowledge Holders

Now we are ready to share some essential tips for successful knowledge transfer between in-house and dedicated teams . While specifics may vary, consider these universal components:

Identify Key Knowledge Areas

To optimize knowledge transfer, it is paramount first to identify and classify the areas of knowledge that hold the most value. That could include crucial project details, proprietary software techniques, company best practices, and insights gathered over time. Having a clear understanding of what knowledge needs to be transferred aids in developing an effective knowledge transfer plan.

Create Structured Knowledge Transfer (KT) Plans

Once key knowledge areas are identified, creating a knowledge transfer (kt) plan is vital. Such a plan should outline who will share knowledge, who will receive it, and how it will be shared. A well-structured kt planning also includes a timeline and clearly defined objectives, providing a roadmap for successful knowledge transfer. 

Foster a Learning Environment

The importance of cultivating a learning environment within an organization cannot be understated. Promoting a sense of curiosity, intellectual flexibility, and an eagerness to learn is the bedrock for fostering an environment ripe for sharing knowledge. An organizational culture infused with these values actively promotes the transfer of experience and innovative thinking.

Use a Variety of Knowledge Transfer Methods

Utilizing a blend of knowledge transfer methods and knowledge transfer software ensures a broader and deeper reach. As discussed earlier, methods could range from documentation and mentoring programs to more interactive techniques like Q&A sessions, hackathons, or Brown Bag Lunches. A diverse approach accommodates various learning styles and preferences, increasing the effectiveness of the knowledge transfer process.

Arrange for Employee Training

Formal training programs enhance your team’s skill set, ultimately benefiting the organization. Investing in employees’ continuous learning and development empowers them to become more effective knowledge contributors and receivers. 

Evaluate and Improve Knowledge Transfer Processes

Lastly, periodic evaluation of the knowledge transfer process is essential for its success. Feedback should be gathered from both the contributors and receivers of knowledge to understand the current process’s effectiveness and identify improvement areas. Foster an atmosphere of continual improvement, discarding the “this is how we’ve always done it” mentality. 

What is a KT session?

A KT (Knowledge Transfer) session is more than a simple meeting. It is a well-structured seminar or workshop designed to relay crucial knowledge or skills from one individual or group to another within the organization. This structured exchange is the engine that powers the knowledge transfer process.

An existing employee knowledge transfer checklist can be highly beneficial for this assessment. Feedback from team members, particularly around their experience with knowledge transfer sessions, is also invaluable in understanding the current scenario.

Bottom Line

Knowledge transfer is not just an operational necessity—it’s a strategic imperative that directly influences an organization’s adaptability, innovation, and success. It enables it to preserve its intellectual capital, maintain continuity, and sustain its competitive edge in an increasingly complex and fast-paced digital landscape.

In asserting our authority on this matter, it is essential to note Relevant’s extensive experience and knowledge transfer best practices. We’ve successfully executed over 200 projects across diverse sectors, including fintech , retail , travel , and construction . 

Our robust and meticulously devised process of knowledge transfer in software development outsourcing serves as a foundation, preventing project disturbances and preserving critical knowledge within the company, even in the face of employee turnover. Therefore, clients who have entrusted us with their IT projects have reaped substantial benefits through our partnership.

Disappointed with your previous vendor and seeking a new software development partner for the project transition? Contact us . We stand ready to offer the assistance you require.

Effective knowledge transfer, or KT (meaning in software development), drives innovation, efficiency, and job satisfaction. It helps to eliminate redundant work, enhances the quality of products, reduces risk, fosters continuous improvement, and heightens employee satisfaction.

You must examine several aspects to evaluate the current state of knowledge transfer in your company. These include the ease of access to important information, a structured knowledge transfer plan, the use of knowledge transfer software, and implementing knowledge transfer best practices.

Employee training is pivotal in effectuating successful knowledge transfer. By arming employees with vital skills and knowledge, we equip them to disseminate this expertise amongst their peers. Through these sessions, senior employees pass their wisdom to the less experienced, fostering an environment of continuous learning and knowledge exchange. Training sessions serve dual purposes – enhancing individual proficiencies while amplifying knowledge dissemination.

Consider a knowledge transfer plan as a strategic blueprint. It governs the passage of critical information from one sector of an organization to another, frequently from departing employees to their successors or colleagues.

The method is two-fold to measure the effectiveness of a knowledge transfer plan. First, tracking key metrics such as the count of knowledge transfer sessions, the degree of employee engagement, and the subsequent boost in efficiency or reduction in errors is vital. Second, one must collate feedback from participants in the knowledge transfer process. This dual approach offers a comprehensive view of what’s functioning optimally and what areas demand improvement in your knowledge transfer plan in software development.

Knowledge transfer, as the heartbeat of software development outsourcing, bridges in-house and outsourced teams by seamlessly sharing essential information and expertise. It elevates collaboration and strengthens the development process, irrespective of your outsourcing destination’s geographical location.

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NATO Ally Pledges All Its Artillery to Ukraine in Boost for Kyiv

Denmark has pledged its "entire artillery" stocks to Ukraine, the country's leader has said, as Kyiv issued renewed pleas for all-important military aid in the face of Russian gains in the east of the country.

"They are asking us for ammunition now, artillery now," Danish Prime Minister Mette Frederiksen, said during the Munich Security Conference in Germany. "From the Danish side, we decided to donate our entire artillery."

Artillery has dominated the nearly two years of war between Russia and Ukraine, but Kyiv has depleted its ammunition stockpiles and fires far fewer rounds than Russia each day. It has been firing around a fifth o f the shells Moscow's artillery batteries have been burning through.

Artillery and its associated ammunition have featured high on Ukraine's wish list of aid from its Western backers. Analysts and Ukrainian officials have suggested the shortages have constrained Kyiv's plans for its operations along the front lines.

But sending shells has depleted NATO stockpiles, and nations within the alliance have committed to upping ammunition production.

 Mette Frederiksen and Volodymyr Zelensky

In January, the alliance said it had inked a contract worth $1.2 billion to produce artillery rounds, refilling NATO states' stockpiles while maintaining the flow of aid to Ukraine. The alliance is planning on buying around 220,000 155mm artillery shells, a high-demand ammunition.

Earlier this month, the U.S. Army said it was hoping to dramatically increase its output of 155mm shells, allowing the U.S. military to "restock ourselves and also restock our allies."

"There is still ammunition in stock in Europe," Frederiksen said. "This is not only a question about production, because we have weapons, we have ammunition, we have air defense [systems] that we don't have to use ourself at the moment, that we should deliver to Ukraine."

Kyiv's presidential office said on Saturday that Ukrainian President Volodymyr Zelensky had met with Frederiksen, and thanked the Danish leader for the more than dozen packages of defense assistance Copenhagen had committed to the embattled nation.

The two leaders "paid special attention to further defense support for our country, bilateral cooperation and cooperation with other countries to provide Ukraine with the necessary weapons," Kyiv said.

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"Ukraine needs ammunition and the necessary equipment to defend its freedom," Czech President Petr Pavel added during the Munich conference. "We must encourage investment in the European defense industry and increase its capacity," he said.

Zelensky, also in Munich, said Ukrainian operations were only limited by its access to weaponry and ammunition.

"Keeping Ukraine in an artificial deficit of weapons, particularly in deficit of artillery and long-range capabilities, allows [Russian President Vladimir] Putin to adapt to the current intensity of the war," Zelensky said in an address.

On Saturday, Ukraine said its forces were pulling back from the decimated front-line city of Avdiivka after more than four months of bitter fighting. Russia launched its offensive on Avdiivka in October, and it quickly became one of the bloodiest battles of the war.

The city spent a decade on the front lines of fighting in Ukraine, and Kyiv had built up its defenses ahead of the outbreak of all-out war in February 2022. But Russia had slowly gained territory around the Donetsk settlement, albeit at a high cost to its soldiers and its supplies of military equipment.

Ukraine's army chief, Colonel General Oleksandr Syrskyi, said on Saturday that Kyiv's forces had retreated from Avdiivka to "avoid encirclement" and save the lives of its fighters.

"Our soldiers honorably fulfilled their military duty, did everything possible to destroy the best Russian military units, [and] inflicted significant losses on the enemy in manpower and equipment," Syrskyi said in a statement.

Russia's Defense Ministry said on Saturday that "isolated formations" of Ukrainian soldiers had managed to leave Avdiivka. Moscow's forces are attempting to "clear the city" and the settlement's northeast coking plant, the Kremlin said.

Uncommon Knowledge

Newsweek is committed to challenging conventional wisdom and finding connections in the search for common ground.

About the writer

Ellie Cook is a Newsweek security and defense reporter based in London, U.K. Her work focuses largely on the Russia-Ukraine war, the U.S. military, weapons systems and emerging technology. She joined Newsweek in January 2023, having previously worked as a reporter at the Daily Express, and is a graduate of International Journalism at City, University of London.

Languages: English, Spanish.

You can reach Ellie via email at [email protected] .

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Justice Department Transfers Approximately $500,000 in Forfeited Russian Funds to Estonia for Benefit of Ukraine

Deputy Attorney General Lisa Monaco and Estonian Secretary General Tõnis Saar announced today at the Munich Security Conference the transfer of nearly $500,000 in forfeited Russian funds for the purpose of providing aid to Ukraine. The funds were forfeited by the United States following the breakup of an illegal procurement network attempting to import into Russia a high-precision, U.S.-origin machine tool with uses in the defense and nuclear proliferation sectors. Additionally, on Wednesday, Feb. 14, a citizen of Latvia charged criminally in connection with the procurement scheme pleaded guilty to violating U.S. export laws and regulations.

This transfer is the first of its kind from the United States to a foreign ally for the express purpose of assisting Ukraine, and the second time the Justice Department’s Task Force KleptoCapture has made confiscated Russian assets available for Ukraine—having provided $5.4 million in forfeited funds last year to the State Department for the support of Ukrainian war veterans. The confiscated funds are being transferred to Estonia since under current authorities, the facts of this case do not allow for a direct transfer to Ukraine. Estonia will use the funds for a project to expedite damage assessments and critical repairs to the Ukrainian electrical distribution and transmission system, which have been purposefully targeted by Russian forces.

“Today’s announcement demonstrates the unwavering resolve of the United States and our Estonian partners to cut off President Putin's access to the western technologies he relies on to wage an illegal war against Ukraine,” said Deputy Attorney General Lisa Monaco, who signed the transfer agreement on behalf of the United States. “This step for justice and restoration blazes a new trail toward combating Russia’s ongoing brutality. The Department of Justice will continue pursuing creative solutions to ensure the Ukrainian people can respond and rebuild.”

“Preventing cross-border crime has been and will be an even greater priority in the future,” said Secretary General Tõnis Saar of the Estonian Ministry of Justice.“Effective prosecution of sanctioned crimes is a very important part of this. In my opinion, this agreement provides additional motivation to deal with sanctions violations even more. The reason is very simple, the goal here is not only to detect, prosecute and ensure justice, but to direct illegal income to the victim, i.e. Ukraine. I hope that this will become the new normality for sanctioned crimes in other countries in the future.”

“Since the start of Russia’s full-scale invasion of Ukraine, the Department of Justice, together with its U.S. and overseas partners, has leveraged every tool available to cut off the Kremlin from the resources it needs to prosecute its war of aggression. These efforts are yielding results,” said Assistant Attorney General Matthew G. Olsen of the Justice Department’s National Security Division. “Today, we demonstrate once again our commitment to holding Russia to account and to aiding the people of Ukraine as they bravely resist and rebuild.”

“I commend the investigators who prevented this sensitive piece of Connecticut-manufactured equipment from crossing the Russian border, and our team of prosecutors who are not only bringing the individuals and entities involved to justice but have worked to seize and forfeit the funds involved in its purchase,” said U.S. Attorney Vanessa Roberts Avery for the District of Connecticut. “We thank our law enforcement partners and the Government of the Republic of Estonia for helping us achieve our mission to chase down the assets of those who violate our laws and to ensure proper compensation to their victims.”

“This agreement between the United States and Estonia not only reinforces our strong partnership, it fortifies the commitments of both countries to stand up to Russian aggression,” said Executive Associate Director Katrina W. Berger of Homeland Security Investigations (HSI). “This transfer stems from a joint investigation into the attempted illegal shipment of military materials to aid the Russian war against Ukraine. HSI will continue to ensure the safety of the homeland of this great nation, and when necessary, that of our allies.”

“The Putin regime has purposefully targeted critical and civil infrastructure in Ukraine to weaken morale, cripple the Ukrainian economy, and use winter as a weapon of war. The funds we are providing to Estonia today will be used to dramatically reduce the time needed to evaluate and prioritize urgent repairs to Ukraine’s electrical infrastructure, all in effort to literally keep the lights on,” said Task Force KleptoCapture Co-Director Michael Khoo.

This action demonstrates that the Department and its international partners will seek and develop novel solutions to ensure that the profits of Russian criminal networks are redirected for the support of the Ukrainian people.

The agreement with Estonia showcases the joint commitments of the United States and Estonia to both enforce the export control regimes that deprive the Russian war machine of critical technologies and supplies and use the confiscated criminal proceeds to sustain Ukraine as it resists illegal Russian aggression.

In March 2023, an investigation into the attempted smuggling of a dual-use export-controlled item to Russia resulted in the forfeiture of $484,696, representing funds wired into the United States to purchase the item. The item, known as a jig grinder, is a high-precision grinding machine system that requires a license for export or reexport to Russia because of its applications in nuclear proliferation and defense programs. The jig grinder was intercepted before it could reach Russia.

In addition to the forfeiture, U.S. authorities, with the active support of the Estonian Prosecutor General’s Office and the Estonian Tax and Customs Board, charged multiple individuals and companies who were part of the smuggling network. The transfer of the forfeited funds to Estonia is in recognition of the crucial assistance received from the Estonian authorities.

Among those criminally charged in the smuggling case, Latvian national Vadims Ananics, 47, was arrested in Latvia on Oct. 18, 2022, and pleaded guilty earlier this week in federal court in Connecticut. Ananics admitted to his role in the scheme to violate U.S. export laws and regulations by attempting to smuggle a dual-use export-controlled item to Russia.

According to court documents and statements made in court, Ananics was the general manager of CNC Weld, a Latvia-based corporation. Beginning in 2018, Ananics conspired with others, including individuals in Russia and a Russia-based company, to violate U.S. export laws and regulations to smuggle a 500 Series CPWZ Precision jig grinder that was manufactured in Connecticut to Russia.

In August 2019, to finalize the purchase of the jig grinder, Ananics and others traveled to Bridgeport, Connecticut, where he informed the sellers that the jig grinder was being purchased for the benefit of CNC Weld. Only after the jig grinder was exported from the United States did Ananics inform the sellers that CNC Weld was not the end user.

Ananics pleaded guilty to one count of conspiracy to violate the Export Control Reform Act, an offense that carries a maximum penalty of five years in prison. A sentencing date has not yet been scheduled.

U.S. authorities, working with Latvian authorities, intercepted the jig grinder in Riga, Latvia, before it was to be shipped to Russia. In March 2023, $484,696 in funds involved in the purchase of the jig grinder were subsequently forfeited .

In turn, Estonia has, in consultation with the United States, agreed to use the transferred funds to finance a drone-based program to assess the damage Russian aggression has done to Ukraine’s electrical distribution and transmission infrastructure.

In April 2023, an additional €312,192.44 (approximately $342,000) was ordered forfeited as part of a criminal sentence imposed on one of the shell companies involved in the jig-grinder smuggling network. The funds are currently held in Latvia pending final enforcement of the U.S. forfeiture order.

HSI Field Offices in New Haven, Connecticut; Portland Oregon; and the Hague, Netherlands; the U.S. Department of Commerce’s Office of Export Enforcement in Boston; and the FBI handled the investigation. In addition to the assistance by Estonian authorities, the Prosecutor General’s Office of the Republic of Latvia, the Latvian Tax and Customs Police, and the Latvian State Police provided valuable assistance.

Assistant U.S. Attorneys Rahul Kale, Konstantin Lantsman, Stephanie Levick, and David Nelson for the District of Connecticut and Trial Attorneys Brendan Geary and Matthew Anzaldi of the National Security Division’s Counterintelligence and Export Control Section are handling the investigation and the Ananics prosecution. The Justice Department’s Office of International Affairs provided valuable assistance. The international sharing agreement was prepared with the support of the Criminal Division’s Money Laundering and Asset Recovery Section, the Treasury Executive Office for Asset Forfeiture, and the State Department.

The investigation was coordinated through the Justice Department’s Task Force KleptoCapture, an interagency law enforcement task force dedicated to enforcing the sweeping sanctions, export controls, and economic countermeasures that the United States, along with its foreign allies and partners, has imposed in response to Russia’s unprovoked military invasion of Ukraine. Announced by the Attorney General on March 2, 2022, and under the leadership of the Office of the Deputy Attorney General, the task force will continue to leverage all of the department’s tools and authorities to combat efforts to evade or undermine the collective actions taken by the U.S. government in response to Russian military aggression.

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transfer of knowledge contract

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Mbappe has already signed his contract with Real Madrid -report

According to reports from MARCA and AS.

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French striker Kylian Mbappe signed his contract with Real Madrid two weeks ago and is set to join the club as soon as the summer transfer window opens, according to a report published today on MARCA and later confirmed by AS.

Mbappe and his current club Paris Saint-Germain announced last Thursday that they will be parting ways this summer and it seems clear that the striker will be a Real Madrid player.

Mbappe will sign a five-year deal with Los Blancos and his salary will be in the €15M-€20M range, per those same reports. Real Madrid’s offer was not as high as it was back in 2022 as the club felt they had leverage in the negotiations, but Mbappe ultimately decided to accept the offer and join the club, if the reports are accurate.

Most Real Madrid fans will likely remain skeptic until they see an official announcement from the club, but it looks like this time it might be real.

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Mike Conley agrees to 2-year contract extension with Timberwolves, AP source says

MINNEAPOLIS — The Minnesota Timberwolves and point guard Mike Conley agreed Monday to a two-year contract extension worth about $21 million that would last through his 19th season in the NBA , according to a person with knowledge of the deal.

The person spoke to The Associated Press on condition of anonymity because the contract had not yet been finalized. The league is on a brief hiatus for the All-Star break.

The 36-year-old Conley is in his first full season with Minnesota, after arriving a little over a year ago in a trade with Utah just before the deadline. He’s currently making a little more than $24 million this season on a deal that was set to expire this summer, and this move is another signal that the club is all-in on trying to chase a title with the current core, even if it comes with a hefty luxury tax hit.

The four-time winner of the NBA Sportsmanship Award has provided the Timberwolves an invaluable dose of leadership, maturity and unselfishness for coach Chris Finch and the staff, plus career-best 44.2% shooting from 3-point range. That rate ranks ninth in the league.

“Last year I was coming in here almost deer in the headlights. I was trying to figure it out. It was fast and fast movement and trying to really mesh with everybody’s games as well. And now, I understand everybody on the team,” Conley said. “I understand Finch and what he wants to accomplish.”

His assist-to-turnover ratio (6.08) is the second-best in the NBA. His reunion with former Jazz teammate Rudy Gobert has also helped unlock the big man’s fullest offensive potential after a rocky debut season.

Most of all, Conley has stayed healthy, playing in 50 of 57 games for the Western Conference-leading Wolves (39-16). He has sat out a few times for rest, but mostly chafed at the idea when broached by the coaching staff.

“I don’t know how long I’m going to play, so every time I go out I’ve got to play as hard as I can, play as good as I can, give everything I’ve got, and hopefully it’s enough to win,” Conley said.

The Wolves return from the break Friday and host Milwaukee.

AP NBA: https://apnews.com/hub/NBA

transfer of knowledge contract

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  1. Technology Transfer Agreements

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    The information to be reviewed to affect the obligations of such knowledge transfer include, (i) copies of procedures and operations manuals, (ii) relevant system, software and/or hardware information, (iii) a list of third party suppliers of goods and services which are to be transferred to DIR or Service Provider, (iv) key support contact deta...

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    Definition of Knowledge Transfer (KT Plan) DEFINITION: Knowledge transfer is the methodical replication of the expertise, wisdom, and tacit knowledge of critical professionals into the heads and hands of their coworkers.

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    Sample 1 Transfer of Knowledge. Provisions regarding transfer of technical skill and knowledge to PRDE is not applicable to this Agreement. Contractor certifies that this provision is not applicable as the services provided hereunder are non- recurring services.

  5. KNOWLEDGE TRANSFER IN CONTRACTS

    The knowledge transfer can be defined as ' a process of exchange of explicit or tacit knowledge between two parties, during which one party purposefully receives and uses the knowledge provided by another. '

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    Contract Transaction-specific investment Trust Personal relationship Knowledge transfer quantity and credibility 1. Introduction When faced with increasingly uncertain market demands and intense competition, firms need to acquire valuable knowledge from all possible sources ( Frazier et al., 2009, Hau and Evangelista, 2007 ).

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    About This Guide This guide will introduce employees to knowledge transfer and help you and your team understand: What is knowledge What is knowledge transfer Why knowledge transfer is important When knowledge transfer occurs The knowledge transfer process

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    Knowledge transfer is a non-linear (bidirectional) process that may also proceed unidirectionally (as those linear in Knowledge sharing). According to Tangaraja and colleagues (2016), the essential peculiarity of Knowledge transfer is that it is distinguished by the strategy used. [19]

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    The absence of critical strategies that ensure knowledge transfer occurs between transitioning employees has cost contract organizations as much as $1 million to replace 10 experienced employees (Memon et al., 2014). The general business problem is that inadequate knowledge transfer between employees adversely affects an organization's

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    Knowledge transfer is a broad concept encompassing many different channels of interaction between science and industry, including research contracts, joint patenting, and academic spin-offs.

  11. Knowledge Transfer Plan

    Explicit knowledge is precisely what lends itself to transfer strategies, such as formal documents, guidelines, and other described codified processes. They are easier to share and quantifiable.Tacit & implicit knowledge, which can be in the form of, for example, coaching, is harder to pin down because it's a more intuitive type of knowledge.It is intangible, ranging from insights learned ...

  12. Trends and Best Practices in Knowledge Transfer

    In this on-demand webinar, APQC's Lauren Trees explains why hybrid work, rapid change, and new technology are driving so many knowledge management programs to prioritize knowledge transfer. She then shares 5 steps for excellent knowledge transfer, based on APQC's research on successful knowledge transfer programs. This is a copy of the presentation slides; click here for the webinar recording.

  13. Knowledge Transfer and Best Practices Sample Clauses

    At a minimum, such knowledge transfer processes will include Supplier meeting with Gap and designated Gap Authorized Users at least once every twelve (12) months, or more frequently as Gap may request, to (a) explain how the Gap IT Environment operates in connection with the provision of the Services; (b) explain how the Services are provided; a...

  14. Evaluating Knowledge Transfer Policies and Practices: Conceptual

    2.2 Channels of Knowledge Transfer . The public research sector has three main roles that are supported by government policy. The first is to create trained and educated citizens, the second is to push the frontiers of knowledge by undertaking cutting edge research, and the third is to support economic activity through several channels for transferring knowledge from universities and public ...

  15. Channels and processes of knowledge transfer: How does knowledge move

    The length of the agreement has also been employed by Bonaccorsi and Piccaluga (1994) to analyse U-I interorganizational relations. The proposed taxonomy consisted of six main categories involving increasing level of university's resource involvement, length and formalization of the agreement. ... Knowledge transfer between universities and ...

  16. Knowledge Transfer: What it is & How to Use it Effectively

    Knowledge Transfer is a method of sharing information, abilities, and ideas across different areas in your business. Encourage innovation and boost efficiency with this guide. 6m read Written by Josh Brown Last Updated April 20 2023

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    Knowledge contracts have both explicit (written) and implicit (psychological) forms, focus is here on the written component which can lead to the development of psychological contract as expectations of employees met in terms of knowledge transfer and knowledge integration. stated and implied expectations of the psychological contract.

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    Knowledge transfer between researchers and stakeholders is extensively discussed in the literature. However, a more profound understanding and management of the challenges related to knowledge transfer across cases, as it applies to TD research, are missing. ... For instance, there is broad agreement that evidence has to be assessed differently ...

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    Processes must be planned, executed, and evaluated carefully to ensure the highest efficiency. As we delve into the main steps of knowledge transfer, it's crucial to emphasize the distinction between two fundamental types of knowledge: explicit and tacit. The interplay between these two types of knowledge—explicit and tacit—forms the crux ...

  20. PDF CONTRACTING FOR KNOWLEDGE TRANSFER ABSTRACT

    The study draws conclusions on the role of contracts for knowledge transfer and more widely on the dialectic nature of knowledge in business networks. INTRODUCTION Within the recent management and organizational literature there is a growing interest in contracts as repositories of knowledge and platforms for learning process (Mayer and Argyres ...

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    Despite economic perils of government shutdowns, foreclosures, bankruptcies, and employee layoffs, some contract leaders consistently fail to implement knowledge transfer strategies that could improve production and profitability and maintain operational readiness when employees transition in and out of the organization. The conceptual framework for this descriptive research study was Nonaka ...

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    of knowledge transfer: what is the relationship between knowledge transfer in contract manufacturing and the business relationships of the organizations participating in the knowledge transfer. To be able to investigate the contract manufacturing projects we conducted empirical research using the case study method.

  23. NATO Ally Pledges All Its Artillery to Ukraine in Boost for Kyiv

    In January, the alliance said it had inked a contract worth $1.2 billion to produce artillery rounds, refilling NATO states' stockpiles while maintaining the flow of aid to Ukraine.

  24. Office of Public Affairs

    This transfer is the first of its kind from the United States to a foreign ally for the express purpose of assisting Ukraine, and the second time the Justice Department's Task Force KleptoCapture has made confiscated Russian assets available for Ukraine—having provided $5.4 million in forfeited funds last year to the State Department for ...

  25. Transfer: Mbappe to become Real Madrid's highest-paid player

    It is now understood that Mbappe has already signed a pre-contract agreement with Madrid until 2029. Mbappe has accepted a lower salary compared to what he is currently earning with PSG, as Real ...

  26. Mbappe has already signed his contract with Real Madrid -report

    French striker Kylian Mbappe signed his contract with Real Madrid two weeks ago and is set to join the club as soon as the summer transfer window opens, according to a report published today on ...

  27. Knowledge Transfer & Training Sample Clauses

    Related to Knowledge Transfer & Training. Knowledge Transfer 7.1 Three (3) months prior to the Expiry Date of the Agreement (or where the Agreement is terminated within the timescale notified by the Department) the Provider will upon request:. TECHNOLOGY/KNOWLEDGE TRANSFER ACTIVITIES The goal of this task is to develop a plan to make the knowledge gained, experimental results, and lessons ...

  28. Mike Conley agrees to 2-year contract extension with Timberwolves, AP

    The Minnesota Timberwolves and point guard Mike Conley have agreed to a two-year contract extension worth about $21 million that would last through his 19th season in the NBA, according to a ...