does research generate new knowledge

How research is creating new knowledge and insight

31 May 2017

Darshan Patel
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The pursuit of knowledge and discovery has always been an intrinsic human characteristic, but when new knowledge is curated and put in the right hands it has the power to bring about high value change to society. Here at the Health Foundation developing and sharing evidence on what works and why is central to our work to bring about better health and health care for people in the UK.

Generating new knowledge and insight

We have a long-standing commitment to research. As I write this, the Health Foundation is currently supporting or working on over 160 research projects. And since 2004 for every £3 of grant funding we have awarded, around £1 has been invested in research and evaluation. All of this work has developed our understanding of how to improve aspects of health and health care .

But how do we go about this? Well, we generate new knowledge and insight through a blend of in-house and externally commissioned analysis and research, innovative researcher-led open calls, by supporting individuals through research Chairs and Fellowships , and through rigorous evaluations of our improvement work , as well as evaluating the work of others .

And we don’t stop there. We try to ensure that our researchers are also well connected to their research colleagues, policy makers and practitioners working in the health service. We hold safe-space networking and knowledge exchange events, share new research widely and support researchers to develop relationships and new connections with policy makers and health care professionals. We also draw on independent expertise to ensure our research is well grounded in policy and practice.

One example of this is our Efficiency Research Programme Advisory Group which provides expertise and guidance on efficiency across the health sector to seven research teams we are funding. This month its chair Professor Peter Smith , explains why efficiency is such an important subject area in health and social care and outlines some of the challenges that researchers face. Our aim in proactively supporting research in these ways is to share new knowledge and insights quickly and in ways that can be applied by those who make decisions or deliver care.

Informing policy and improvement work

We are proud to see all our effort having impact. Over the last few years we have actively contributed to effective policy development nationally and seen an increase in the number of areas where our views and expertise have been used.

Our research has contributed to the Five Year Forward View , formed the basis for the key recommendations on safety measurement in the Berwick Review of Patient Safety in England, and also underpinned funding increases for the NHS in Wales , to name but a few.

Our research and evaluation places a strong emphasis on improving health service delivery and patient care, and our recent work to advise on the national evaluation of complex new models of care is bearing fruit . Our Improvement Analytics Unit is providing rapid feedback on the impact of new care models, allowing those delivering frontline improvements to make real-time course correction.

As we look to a future with a healthier population, we want decision makers to have evidence that is useful and that reflects the nature of the health challenges we face today. We are working with Dr Harry Rutter on ways to overcome the research challenges of evaluating complex systems in population health.

Taking the long view

But often impact can take time. Our research and evaluations into patient safety, and person centred care have been active for more than a decade. When we began working in these areas it was first necessary to convince people of the need for change before action could be taken at scale. Now we’re pleased to see that both patient safety and person centred care are embedded in the understanding of high quality care, and national policy reflects some of the insights and approaches we have pioneered with the NHS. In both areas we have been the chosen partner for system-wide implementation plans, for example the Q initiative , which is connecting people with improvement expertise across the UK, and Realising the Value , which builds on what we know about person centred care.

Our research investment is also helping to build capability and capacity within the research community. Our funding has led to academic career progression, provided stability to enable research teams to grow, and allowed multi-disciplinary teams to carve out a reputation for high-quality research in their particular areas. In some instances our funding has even been leveraged in ways to establish financially sustainable research units.

Plans for the coming year

This year we are going even further to promote research capability , but this time within the NHS. Our exciting new programme, Advancing Applied Analytics, launching in June, will support NHS analysts to develop and test novel analytical applications that have the potential to contribute to improvements in patient care and population health.

And, in six short years our contribution to building a stronger scientific underpinning for quality improvement has culminated in the establishment of a ground-breaking Improvement Research Institute , the first of its kind in Europe. Led by Professor Mary Dixon-Woods , the Institute will strengthen the evidence-base for how to improve health care, growing capacity in research skills in the NHS, academia and beyond. I look forward to seeing how the institute generates new and exciting areas of research and enables wide participation in large scale research programmes.

2017 is going to be particularly exciting as we will be launching a number of researcher-led open calls over the year.

Our Insight 2017 programme is currently open, until 25 July 2017, for ideas to support research that can advance the use of national clinical audits and patient registries to improve healthcare quality.

Later this year we will also be launching a second round of our Behavioural Insights researcher-led open call . Behavioural insights research is gaining widespread traction as a complementary policy lever in tackling the many challenges in improving health and health care. We are thrilled to be a leading funder in this area working alongside experts such as the Behavioural Insights Team . Hannah Burd tells us more this month, explaining how small behaviourally informed changes can lead to significant reductions in inefficiency and waste in health care.

So there you have it, our research in a nutshell.

And, what excites me the most about these 160 or so research projects is the impact that we hope to make in improving health and health care over the next decade. So, keep watching this space and please do get in touch if you would like to know more.

Darshan Patel is a Senior Research Manager at the Health Foundation

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How research is creating new knowledge and insight

The pursuit of knowledge and discovery has always been an intrinsic human characteristic, but when new knowledge is curated and put in the right hands it has the power to bring about high value change to society. I work in the research team at the Health Foundation, an independent charity committed to bringing about better health and health care for people in the UK. Our aim is a healthier population, supported by high quality health care that can be equitably accessed. And one key way in which we go about realising this vision is through supporting and funding innovative research and evaluations, which I explore below.

Darshan Patel 12 Jun 2017

Generating new knowledge and insight

The Health Foundation is currently supporting or working on over 160 research projects.

One example of this is our Efficiency Research Programme ’s advisory group which provides expertise and guidance on efficiency across the health sector to seven research teams we are funding. Here its chair Professor Peter Smith , Emeritus Professor of Health Policy at Imperial College, explains why efficiency is such an important subject area in health and social care and outlines some of the challenges that researchers face. Our aim in proactively supporting research in these ways is to share new knowledge and insights quickly and in ways that can be applied by those who make decisions or deliver care.

Informing policy and improvement work

As we look to a future with a healthier population, we want decision makers to have evidence that is useful and that reflects the nature of the health challenges we face today. We are working with Dr Harry Rutter , London School of Hygiene and Tropical Medicine, on ways to overcome the research challenges of evaluating complex systems in population health.

Taking the long view

But often impact can take time. Our research and evaluations into patient safety, and person centered care have been active for more than a decade. When we began working in these areas it was first necessary to convince people of the need for change before action could be taken at scale. Now we’re pleased to see that both patient safety and person centered care are embedded in the understanding of high quality care, and national policy reflects some of the insights and approaches we have pioneered with the NHS. In both areas we have been the chosen partner for system-wide implementation plans, for example the Q initiative , which is connecting people with improvement expertise across the UK, and Realizing the Value , which builds on what we know about person centered care.

Plans for the coming year

This year we are going even further to promote research capability , but this time within the NHS. Our exciting new program, Advancing Applied Analytics, launching in June, will support NHS analysts to develop and test novel analytical applications that have the potential to contribute to improvements in patient care and population health.

Our exciting new program, Advancing Applied Analytics, launching in June, will support NHS analysts to develop and test novel analytical applications that have the potential to contribute to improvements in patient care and population health.

And, in six short years our contribution to building a stronger scientific underpinning for quality improvement has culminated in the establishment of a ground-breaking Improvement Research Institute , the first of its kind in Europe. Led by Professor Mary Dixon-Woods , University of Cambridge, the Institute will strengthen the evidence-base for how to improve health care, growing capacity in research skills in the NHS, academia and beyond. I look forward to seeing how the institute generates new and exciting areas of research and enables wide participation in large scale research programs.

2017 is going to be particularly exciting as we will be launching a number of researcher-led open calls over the year.

Our Insight 2017 program is currently open, until 25 July 2017, for ideas to support research that can advance the use of national clinical audits and patient registries to improve healthcare quality.

Later this year we will also be launching a second round of our Behavioral Insights researcher-led open call . Behavioral insights research is gaining widespread traction as a complementary policy lever in tackling the many challenges in improving health and health care. We are thrilled to be a leading funder in this area working alongside experts such as the Behavioral Insights Team . Hannah Burd tells us more this month, explaining how small behaviorally informed changes can lead to significant reductions in inefficiency and waste in health care.

So there you have it, our research in a nutshell.

The Health Foundation is an independent charity committed to bringing about better health and health care for people in the UK.

Our aim is a healthier population, supported by high quality health care that can be equitably accessed. From giving grants to those working at the front line to carrying out research and policy analysis, we shine a light on how to make successful change happen. We use what we know works on the ground to inform effective policymaking and vice versa.

We believe good health and health care are key to a flourishing society. Through sharing what we learn, collaborating with others and building people’s skills and knowledge, we aim to make a difference and contribute to a healthier population.

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How research is creating new knowledge and insight - 12th June 2017

What is knowledge and when should it be implemented?

Affiliation.

1 Knowledge Translation Research Network, Ontario Institute for Cancer Research, Toronto, ON, Canada. [email protected]
PMID: 22994990
DOI: 10.1111/j.1365-2753.2012.01899.x

A primary purpose of research is to generate new knowledge. Scientific advances have progressively identified optimal ways to achieve this purpose. Included in this evolution are the notions of evidence-based medicine, decision aids, shared decision making, measurement and evaluation as well as implementation. The importance of including qualitative and quantitative methods in our research is now understood. We have debated the meaning of evidence and how to implement it. However, we have yet to consider how to include in our study findings other types of information such as tacit and experiential knowledge. This key consideration needs to take place before we translate new findings or 'knowledge' into clinical practice. This article critiques assumptions regarding the nature of knowledge and suggests a framework for implementing research findings into practice.

Publication types

Research Support, Non-U.S. Gov't
Biomedical Research
Clinical Medicine
Diffusion of Innovation*
Health Knowledge, Attitudes, Practice*
Problem-Based Learning

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1.2 Ways of Creating Knowledge

What constitutes knowledge.

To have a deep understanding of what research entails, we need to first consider the historical context of ways of creating knowledge and what constitutes knowledge. Remember that “Research is creating new knowledge”. Our knowledge, thoughts, perceptions and actions are influenced by our worldview, which is a collection of attitudes, values, tales, and expectations about the world. 3 One’s view of the world is at the heart of one’s knowledge. There are different methods of acquiring knowledge, including intuition, authority, logical reasoning and the scientific method. 4

Cambridge dictionary defines intuition as the knowledge from an ability to understand or know something immediately based on feelings rather than facts. 1 It is also described as instinctive knowing without the use of cognitive processes or emotionally charged judgments that result from quick, unconscious, and holistic associations. 5 The impression that something is right comes from intuition. Instincts and intuition are sometimes used interchangeably. 4 Justifications like “that feels right to me” are often used to support intuition. However, as there is no means to evaluate the accuracy of the knowledge based on intuition, there is no way to distinguish between accurate and inaccurate knowledge using such an approach. As a result, it is challenging to assess the correctness of intuition in the absence of action. 4 In research, intuition may lead to generating hypotheses, especially in areas with limited or no prior information. Nonetheless, the hypothesis has to be tested before the knowledge is accepted in modern healthcare settings.

Getting knowledge from an authority figure is another common way of acquiring knowledge. 6 Authority refers to a person or organisation having political or administrative power, influence and control. The information generated from such authority is regarded to be true since it was expressed by a social media influencer or an expert in a certain field. 4 This approach entails embracing novel concepts because an authority figure declares them true. 4 It is one of the quickest and simplest ways to learn; therefore, it can often be a good place to start. 6 Some of these authorities are parents, the media, physicians, priests and other religious leaders, the government, and professors. 4 Although we should be able to trust authority figures in an ideal world, there is always a chance that the information they provide may be incorrect or out of context. 4 War crimes such as the Holocaust and the Guatemala Syphilis research, where atrocities against humanity were committed, are only a few instances when people blindly listened to authoritative leaders without scrutinising the information they were given. 4 Information on research topics obtained from authorities could generate new ideas about the concept being investigated. However, these ideas must be subjected to rigorous scientific scrutiny rather than accepted at face value.

Logical reasoning

Logic reasoning or rationalism is any process of knowledge generation that requires the application of reasoning or logic. 4 This approach is predicated on the idea that reason is the primary source of knowledge. 6 It is based on the premise that people can discover the laws that govern the behaviour of natural objects through their efforts. 6 Human behaviour is frequently explained using rationalism. In order to reach sound conclusions utilising this method, premises are provided, and logical principles are followed. However, if any assumptions are wrong, then the conclusion will be invalid. 4 For example, if a student fails to attend a series of compulsory lectures or tutorials, the professor may reason that the student is taking a lackadaisical approach to the subject. However, the assumption that attendance is an indicator of engagement may be untrue and lead to an erroneous conclusion. Perhaps, the student may have been ill or genuinely absent for some other unavoidable reason. This highlights the disadvantage of rationalism, as relying solely on this approach could be misleading, leading to inaccurate conclusions. 4 Thus, while rationalism may be helpful when developing or thinking of a research hypothesis, all research hypotheses need to be tested using the scientific method.

Scientific method

The scientific method is an empirical method for systematically gathering and analysing data to test hypotheses and answer questions. 4 Let’s go back to our example of the professor who concluded that the student who skipped the required classes had a lax attitude. This could possibly be due to some prior interactions with students who had demonstrated a lack of interest in studying the subject. This illustration shows the fallacy of drawing conclusions solely from experience and observation. The amount of experience we have could be constraining, and our sensory perceptions may be misleading. 4 Therefore, it is important to use the scientific method, which allows the researcher to observe, ask questions, test hypotheses, collect data, examine the results and draw conclusions. While researchers often draw on intuition, authority, and logical reason to come up with new questions and ideas, they don’t stop there. 4 In order to test their theories, researchers utilise systematic approaches by making thorough observations under a variety of controlled situations to draw reliable conclusions. 6 Systematic techniques are used in scientific methods, and every technique or design has a set of guidelines or presumptions that make it scientific. 4 Thus, empirical evidence based on observations becomes an item of knowledge. In the following chapters, we will go into greater detail about what the scientific method comprises.

How does scientific method contribute to evidence?

While everyday activities such as cooking, as seen in the opening scenario, may involve research, this type of research may not involve a systematic or controlled approach. Scientific research requires a systematic approach, and it is defined as a systematic inquiry/data-gathering process used to investigate a phenomenon or answer a question. 4 Research is also a way of knowing that involves critical examination of various aspects of a given phenomenon that is under investigation. It requires formulation and understanding of principles that guide practice and the development and testing of new ideas/theories. 7 Research aims to be objective and unbiased and contributes to the advancement of knowledge. Research adds to existing knowledge by offering an understanding or new perspective on a topic, describing the characteristics of individuals or things, or establishing causal links between factors. 8

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Capturing Change in Science, Technology, and Innovation: Improving Indicators to Inform Policy (2014)

Chapter: 5 measuring the three k's: knowledge generation, knowledge networks, and knowledge flows.

5 Measuring the Three K’s: Knowledge Generation, Knowledge Networks, and Knowledge Flows

Knowledge generation can occur formally through directed research and experimental development in academic institutions, firms, and public and nonprofit institutions. Knowledge generation can also occur informally in a working environment through the activities and interactions of actors in an organization or the general economy. People are the critical input for knowledge generation, whether as individual researchers; in research teams; or even in collectives such as organizational subunits, entire organizations, or nation-states. 1 Therefore, indicators of knowledge generation focus on attributes of human capital inputs and related outputs. Knowledge can be acquired by using codified (written) sources such as publications or patents, or in tacit form by hiring people with the needed knowledge or participating in networks where the knowledge is stored ( Chapter 6 focuses on knowledge embodied in people). Knowledge can be both an intermediate input and a final output and can depreciate over time. 2

Knowledge networks link actors, organizations, and technologies in the global economy, revealing new discoveries and transferring knowhow on the development of new techniques, processes, and at times breakthroughs that can be commercialized ( Chapter 4 focuses on innovation). Knowledge networks include research collaborations, coinventorships, coauthorships, and strategic alliances. 3 Knowledge flows transmit across knowledge networks and point to comparative advantage, presence in other markets, and access to foreign technologies. To use acquired knowledge, recipients must have absorptive capacity. 4

Knowledge generation, diffusion, and use, as well as conduits for knowledge flows, are all key elements for economic growth (Romer, 1990). Therefore, it is critically important for the National Center for Science and Engineering Statistics (NCSES) to produce indicators of these varied dimensions of knowledge at the national, international, and subnational levels.

Quite a few data elements, such as research and development (R&D), patents, bibliometrics, and trade in technology, capture knowledge generation, networks, and flows (referred to as “the three K’s”). NCSES has been collecting these data for several decades in order to publish indicators on these topics, drawing on both its own and other data sources, such as the Bureau of Economic Analysis for data on global multinational R&D activities. International R&D is well covered by NCSES’s Business Research and Development and Innovation Survey (BRDIS). While NCSES has good measures of knowledge creation, however, a number of complex issues remain unaddressed, and challenges for measurement remain in the area of knowledge flows.

Therefore, the purpose of this chapter is to discuss the dynamics and outcomes of scientific R&D. To illustrate specific uses of science, technology, and innovation (STI) indicators in this context, the focus is on the policy questions that can be addressed using indicators on the three K’s; however, it should be noted that these indicators have several other uses. Box 5-1 highlights key policy questions relating to the generation and transfer of knowledge. 5 While raw data on R&D expenditures and patent citations are useful for understanding whether the United States is falling behind other countries in R&D expenditures and outcomes, more sophisticated statistics are required to address other

____________________

1 See Phelps and colleagues (2012, p. 7) for a description of repositories of knowledge. Romer (1990, p. S84) makes the following distinction between knowledge as an intermediate and final output: “… knowledge enters into production in two distinct ways. A new design enables the production of a new good that can be used to produce output. A new design also increases the total stock of knowledge and thereby increases the productivity of human capital in the research sector.”

2 See Huang and Diewert (2011) for methods of measuring knowledge depreciation.

3 For an extensive definition of knowledge networks, see Phelps et al. (2012, p. 61, endnote 1).

4 OECD (2013a) gives definitions of knowledge flows and classifications of indicators of knowledge flows in science, technology, and innovation sectors.

5 See Appendix B for the full list of policy questions.

BOX 5-1 Policy Questions Related to Knowledge Generation, Networks, and Flows

What new technologies or fields are emerging from current research?
Is the United States promoting platforms in information and communication technology, biotechnology, and other technologies to enable innovation in applications?
Is the United States falling behind other countries in R&D expenditures and outcomes?
How much are U.S. companies spending to be present in emerging markets? How much R&D are they conducting in these nations?
Is the United States losing or gaining advantage by buying and selling its R&D abroad?
Is the United States benefiting from research conducted in other countries?

issues pertaining to the competitiveness of U.S. companies and the benefits of buying and selling R&D internationally. The focus of this chapter is on the latter set of indicators.

A recent OECD (2012c) study titled Knowledge Networks and Markets in the Life Sciences describes key aspects of the three K’s in which indicators require further development. The following findings are particularly in accord with those presented in this chapter:

Individuals, firms, and countries are not uniformly linked to knowledge networks.
Evidence gaps persist with respect to capturing differences between knowledge production and use (as in the case of R&D), capturing partnerships and their financial dimension, monitoring the combined outward and inward dimensions of knowledge flows, and going beyond intellectual property indicators as measures of knowledge outputs.
Measurement standards need to be adapted if improvements are to be achieved in the interoperability of STI data sources across different domains, such as R&D, patents, other forms of registered intellectual property, scientific publications, innovation survey data, and administrative sources. Solutions need to be developed that address the impact of knowledge flows on the interpretation, relevance, and international comparability of existing STI indicators.

NCSES is poised to make important contributions to the improvement of indicators on the three K’s. Collaborative efforts with other agencies in the United States and abroad should be fruitful for this endeavor.

CODIFIED DEFINITIONS

The internationally accepted definition of “research and experimental development”—more commonly referred to as R&D—comes from OECD (2002, p. 30): “creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.” 6 In BRDIS, NCSES expands on this definition, providing the following guidance (U.S. Department of Commerce, 2011, p. 3):

R&D is planned, creative work aimed at discovering new knowledge or developing new or significantly improved goods and services. This includes (a) activities aimed at acquiring new knowledge or understanding without specific immediate commercial applications or uses (basic research); (b) activities aimed at solving a specific problem or meeting a specific commercial objective (applied research); and (c) systematic use of research and practical experience to produce new or significantly improved goods, services, or processes (development).

The term “research and development” does NOT include expenditures for:

costs for routine product testing, quality control, and technical services unless they are an integral part of an R&D project;
market research;
efficiency surveys or management studies;
literary, artistic, or historical projects, such as films, music, or books and other publications; and
prospecting or exploration for natural resources.

The term “science and technology” (S&T) covers a wide range of activities, including R&D, but is rarely defined in the literature, perhaps because its breadth leads to its being used in different ways in different contexts. The United Nations Educational, Scientific and Cultural Organization (UNESCO) (1984, p. 17) provides a definition of the term that is used for this chapter:

For statistical purposes, Scientific and Technological Activities (STA) can be defined as all systematic activities which are closely concerned with the generation, advancement, dissemination, and application of scientific and technical knowledge in all fields of science and technology, that is the natural sciences, engineering and technology, the medical and the agricultural sciences (NS), as well as the social sciences and humanities (SSH). 7

6 This is the definition used in all OECD, European Union, African Union, and Latin American countries. All elaborate on this definition in their survey instruments as the United States has done to incorporate definitions for basic, applied, and experimental development.

7 Also included in the

Because S&T includes but is not limited to R&D, 8 the focus of this chapter is on indicators of foreign direct investment in R&D and trade in knowledge-intensive services. Measurement of intangible assets also is touched upon, although the panel does not view the development of such measures as more appropriate for NCSES than for the Bureau of Economic Analysis.

MEASURING SCIENCE AND TECHNOLOGY: MAJOR GAPS IN INTERNATIONAL COMPARABILITY

Comparability is a universal challenge for statistics and for indicators based on those statistics. The comparability of data can be affected by the survey techniques used to collect the data and the conversion of the data into statistics through the use of weighting schemes and aggregation techniques. These problems are amplified when statistics are used to create indicators, as the indicators may be a combination of statistics (e.g., an average, a sum, or a ratio) with different comparability problems. In addition to the international or geographic comparison of indicators that describe an aspect of a system (e.g., R&D as a percentage of gross domestic product [GDP]), there are problems with intertemporal and intersectoral comparisons. Users of indicators need to recognize that all statistics and indicators have a margin of error beyond which they should not be pushed. The problem is growing as response rates to official surveys continue to decline.

International comparisons entail fundamental issues such as language (e.g., the Japanese term for “innovation” is actually closer to what most Americans think of as “technology”), and NCSES is to be congratulated for supporting a project with OECD and the European Union (EU) on the cognitive testing of survey questions in multiple languages. Differences in institutions (e.g., the accounting for the European Union Framework program across EU member states) pose problems, as do cultural differences (e.g., the Nordic world has access to “cradle to grave” linked microdata on individuals) and differences in governance structures (e.g., the importance of subnational R&D programs in some countries). These differences can limit comparability and increase the margin of error that should be applied to international comparisons of statistics and indicators.

In the area of S&T indicators, a number of key comparability problems are well known. OECD compiles S&T statistics, monitors the methodology used to produce them, and publishes international comparisons and has documented the problems summarized below.

Research and Development 9

Each country depends for its R&D data on the coverage of national R&D surveys across sectors and industries. In addition, firms and organizations of different sizes are measured, and national classifications for firm sizes differ. Countries also do not necessarily use the same sampling and estimation methods. Because R&D typically involves a few large organizations in a few industries, R&D surveys use various techniques to maintain up-to-date registers of known performers. Analysts have developed ways to avoid double counting of R&D by performers and by companies that contract with those firms or fund R&D activities of third parties. These techniques are not standardized across nations.

R&D expenditure data for the United States are somewhat underestimated for a number of reasons:

R&D performed in the government sector covers only federal government activities. State and local government establishments are excluded from the national figures. 10
In the higher education sector, R&D in the humanities is excluded, as are capital expenditures. 11
R&D expenditures in the private nonprofit sector include only current expenditures. Depreciation is reported in place of gross capital expenditures in the business enterprise sector.

Allocation of R&D by sector poses another challenge to the comparability of data across nations. Using an industry-based definition, the distinction between market and public services is an approximate one. In OECD countries, private education and health services are available to varying degrees, while some transport and postal services remain in the public realm. Allocating R&D by industry presents a challenge as well. Some countries adopt a “principal activity” approach, whereby a firm’s R&D expenditures are assigned to that firm’s principal industrial activity code. Other countries collect information on R&D by “product field,” so the R&D is assigned to the industries of final use, allowing reporting companies to break expenditures down across product fields when more than one applies. Many countries follow a combination of these approaches, as product breakdowns often are not required in short-form surveys.

definition of S&T are “scientific and technological services” and “scientific and technological education and training,” the definitions of which are found in United Nations Educational, Scientific and Cultural Organization (1978).

8 The OECD Frascati Manual (OECD, 2002, p. 19) notes that “R&D (defined similarly by UNESCO and the OECD) is thus to be distinguished from both STET [scientific and technological education and training] and STS [scientific and technological services].” The Frascati definition of R&D includes basic research, applied research, and experimental development, as is clear from NCSES’s presentation of the definition in the BRDIS for use by its respondents.

9 This description draws heavily on OECD (2009, 2011) and Main Science and Technology Indicators (MSTI) (OECD, 2012b).

10 NCSES reports state R&D figures separately.

11 In general, OECD’s reporting of R&D covers R&D both in the natural sciences (including agricultural and medical sciences) and engineering and in the social sciences and humanities. A large number of countries collect data on R&D activities in the business enterprise sector for the natural sciences and engineering only. NCSES does report data on social science R&D.

The Frascati Manual (OECD, 2002) recommends following a main activity approach when classifying statistical units, but recommends subdividing the R&D by units or product fields for firms carrying out significant R&D for several kinds of activities. This applies to all industry groups and, at a minimum, to the R&D industry (International Standard Industrial Classification [ISIC] Rev. 3, Division 73, or North American Industry Classification System [NAICS] 5417 in North America), although not all countries follow this method.

Comparability problems are also caused by the need to preserve the confidentiality of survey respondents (see Chapter 4 ). National statistical practice will prevent publication of the value of a variable if it is based on too few responses. This not only results in suppression of a particular cell in a table, but also requires additional suppression if there are subtotals that could be used to infer the suppressed information. The result is reduced comparability, which can be overcome only by microdata analysis under controlled conditions.

In principle, R&D institutes serving enterprises are classified according to the industry they serve. When this is not done, the percentage of business enterprise expenditure on R&D (BERD) performed by what is most likely a service industry is overestimated compared with estimates for other countries.

Finally, R&D performers recently have been asked in surveys to break down their R&D activities across sites in different national territories or regions. Estimating R&D intensity by region or other subnational unit presents additional challenges. The existence of multinationals headquartered in a given country that conduct R&D and trade in R&D services worldwide makes it difficult to pinpoint where the R&D is funded and performed and where it has impact. For example, the R&D could be funded by a head office in Rome, performed in a research institute in Israel, and have an impact on consumers of the resulting product in the United States.

Government Budget Appropriations or Outlays for R&D (GBAORD) 12

GBAORD data are assembled by national authorities using statistics collected from budgets. This process entails identifying all the budget items involving R&D and measuring or estimating their R&D content. The series generally cover the federal or central government only. GBAORD is a good reflection of government priorities based on socioeconomic objectives. These statistics often are used for cross-country comparisons, particularly to address such questions as: Is the United States falling behind other countries in R&D expenditures and outcomes? While it is not necessarily the case that high government expenditures foreshadow international preeminence in S&T, it is important to understand whether such expenditures indeed lead to better employment, health, and security outcomes.

However, comparability problems arise because some countries do not include in their GBAORD estimates funding for general support of universities (e.g., the United States) or R&D funded as part of military procurement (e.g., Japan, Israel). Moreover, it currently is not possible for all countries to report, on the basis of budget data, which sectors are responsible for performing the R&D funded by government.

Business Enterprise Expenditures on R&D 13

BERD statistics convey business R&D expenditures. OECD breaks down business R&D expenditure data into 60 manufacturing and service sectors for OECD countries and selected nonmember economies. The reported data are expressed in national currencies (as well as in purchasing power parity U.S. dollars), at both current and constant prices.

When assessing changes in BERD over time, it is necessary to take account of changes in methods and breaks in series, notably in terms of the extension of survey coverage, particularly in the service sector, and the privatization of publicly owned firms. Identifying new and occasional R&D performers is also a challenge, and OECD countries take different approaches to this challenge in their BERD surveys. In addition, not all activities related to foreign affiliates’ R&D are recorded in company transactions. There are intracompany transfers (e.g., intracompany mobility of researchers) with no monetary counterparts that lead to R&D efforts that do not appear in the statistics as R&D spending by foreign affiliates. The increasing internationalization of R&D and other economic activities also makes it difficult to accurately identify inflows of R&D funds to companies and their precise nature (as discussed later in this chapter). For example, there is a growing need to measure international R&D transactions properly and to deal with the problem of nonpriced transfer of R&D within multinational enterprises. All of these issues require expert data manipulation and statistical analysis, thereby presenting challenges to the international comparability of indicators derived from these statistics.

Technology Receipts and Payments 14

Technology receipts and payments, including those for R&D services, show a country’s ability to sell technology abroad and its use of foreign technologies, respectively. Further qualitative and quantitative information is needed to analyze a country’s deficit or surplus because a deficit (surplus) on the technology balance does not necessarily indicate the lack (presence) of competitiveness.

12 This section is based on OECD (2011) and OECD (2012b).

13 This section is based on OECD (2011).

14 This section is based on OECD (2011).

Measurement errors may lead to underestimation or overestimation of technology transfers. Licensing contracts provide payment channels other than technology payments, and payment/receipt flows may be only part of the total price paid and received. Alternatively, national tax and control regulations on technology receipts and payments may bias data on technology flows, notably for international transfers of multinationals. If royalties are less taxable than profits, then they may be preferred to other transfer channels and exceed the value of technology transferred. On the other hand, if limitations are imposed on royalty remittances, then some portion of repatriated profits will represent remuneration of technology transfer.

Each of the above reasons for international incomparability of some S&T measures goes beyond what NCSES can deal with on its own. An OECD Working Party, the National Experts on Science and Technology Indicators (NESTI), has been in place for 50 years to discuss these issues and support collaboration to resolve them. Nonetheless, there are some areas in which NCSES has opportunities to adjust definitions and improve methodologies to obtain more accurate STI indicators. For example, finer-grained size classes for firms would allow a better understanding of the relationship between firm size and innovation (as discussed in Chapter 4 ). In addition, improved measures of business enterprise R&D would shed some light on the question of whether the United States is increasingly depending on knowledge generated in other countries. And better measuring of technology receipts and payments would show which countries are net buyers or sellers of knowledge-intensive services. Recommendations for how NCSES could go about improving these measures appear later in this chapter.

TRADITIONAL INDICATORS OF THE THREE K’S

Patent 15 data and bibliometrics (data on publication counts and citations) can be used to measure new knowledge, knowledge networks, and knowledge flows.

Patent administrative records—including citations, claims, technical classifications, families, 16 and countries where the patents are effective—contain a wealth of information about invention. They also contain detail on inventors and applicants and on the regulatory and administrative processes of the patenting system. 17 Patent information is useful for determining when a new product or process was developed and its linkages to prior inventions and to research that was the foundation for the invention. Observing where patents are registered can also yield clues to how new knowledge is diffused from nation to nation.

Patent data often are used to develop indicators of knowledge generation, flows, and linkages. OECD’s (2008) Compendium of Patent Statistics 2008 gives several examples:

Patent-based statistics can be derived that reflect the inventive performance of countries, regions, and firms.
The inventors’ addresses can be used to monitor linkages, including the internationalization of and international collaboration in S&T activities.
Knowledge networks can be determined by observing cooperation in research and diffusion of technology across industries or countries in specific technological areas.
The market strategy of businesses can be inferred from information contained in the patent file.

At the same time, information derived from patent records must be used with caution (OECD, 2006):

The value distribution of patents is skewed as many patents have no industrial application (and hence are of little value to society), whereas a few are of substantial value.
Many inventions are not patented because they are not patentable, or inventors may protect them using other methods, such as secrecy or lead time.
The propensity to patent differs across countries and industries.
Differences in patent regulations make it difficult to compare counts across countries.
Changes in patent law over the years make it difficult to analyze trends over time.

The panel emphasizes the first point on the above list: patents may be used strategically in some sectors of an economy to deter competition. Andrew Updegrove of

15 “Patents are an exclusive right issued by authorised bodies to inventors to make use of and exploit their inventions for a limited period of time (generally 20 years). Patents are granted to firms, individuals or other entities as long as the invention is novel, non-obvious and industrially applicable. The patent holder has the legal authority to exclude others from commercially exploiting the invention (for a limited time period). In return for the ownership rights, the applicant must disclose information relating to the invention for which protection is sought” (Khan and Dernis, 2006, p. 6).

16 “A patent family is the same invention disclosed by a common inventor(s) and patented in more than one country” (United States Patent and Trademark Office, http://www.uspto.gov/main/glossary/#p [June 2013]). The European Patent Office has the following definition: “A patent family is a set of either patent applications or publications taken in multiple countries to protect a single invention by a common inventor(s) and then patented in more than one country. A first application is made in one country—the priority—and is then extended to other offices” ( http://www.epo.org/searching/essentials/patent-families.html [June 2013]).

17 As administrative records, patent applications and grants are a rich microdata source that do not rely on surveys and do not generate the respondent burden associated with traditional statistical surveys.

Gesmer Updegrove LLP captured this sentiment by saying, “Patents don’t give value; they cause friction” (Updegrove, 2012). Therefore, the notion that substantial patent activity is an indicator of major leaps in S&T capabilities or innovation is not necessarily the case. In some instances, patenting could have a negative impact on knowledge creation and innovation. Thus observed patent activity as an indicator of knowledge generation or innovation should be determined sector by sector.

In his presentation to the panel in February 2012, Stuart Graham, chief economist at the United States Patent and Trademark Office (USPTO), outlined USPTO’s Economic Data Agenda. In the near term, the agency will improve its databases, particularly the Patent Assignment, Trademark Casefile, and Trademark Assignment datasets. Over time, USPTO is also “considering providing a forum that would facilitate the posting of additional matched datasets, papers and findings” and working with other agencies to create “matched datasets to other economically-relevant information.” For NCSES’s activities on STI indicators, particularly those related to producing better measures of knowledge generation, flows, and networks, continued collaboration with USPTO should be beneficial. NCSES already relies on USPTO data for basic measures of patenting activity. However, linking basic research outputs to patents and trademarks (including the human capital and demographic markers that are indicated on the records) and ultimately to outcomes that have significant societal impacts would be of great benefit to users of NCSES indicators. In addition, these linked files would be helpful to researchers who work with the datasets of USPTO, NCSES, and others to understand relationships and rates of return in the STI system.

The panel makes no explicit recommendation here for NCSES to do more than continue to explore wider use of patent indicators and to engage in international cooperation on the development of indicators based on patent records to address user needs. There is no standard method for calculating indicators from patent data, and as noted earlier, analysis of these data without reservation can lead to incorrect inferences and misleading policy decisions. It is important to improve data quality and analytical techniques in this area—an active role for NCSES in collaboration with other agencies and organizations worldwide. As NCSES continues to disseminate patent data as part of its STI indicators program, it would be valuable to users to have clear cautions regarding the use and misuse of these statistics for decision-making purposes.

Bibliometrics

Publication is a major vehicle for disseminating and validating research results. Bibliometric data on publication counts and citations thus are a valuable source for measuring scientific performance, tracking the development of new technologies and research areas, and mapping linkages among researchers. Publication counts are based on science and engineering (S&E) articles, notes, and reviews published in a set of the world’s most influential scientific and technical journals (Ruegg and Feller, 2003, p. 31).

A number of characteristics can be used for categorization of publications and indicator development. Fields are determined by the classification of each journal. Publications are attributed to countries by the author’s institutional affiliation at the time of publication. Indicators of coauthorship appear to be affected by two factors. The first is language, although this has become less of an issue as English has become the language most commonly used internationally by researchers. The second is geographic location, although the effect of information and communication technologies on knowledge flows has undoubtedly lessened its effect. The quality of publications can be measured both by the quality of the journal and by how often it is cited in other publications. Citations can also be used to measure knowledge flows and linkages between different research areas. Coauthorship provides an additional measure of linkages and often is used as an indicator of collaboration patterns.

NCSES currently publishes a number of indicators based on bibliometric data. These include counts of S&E articles, shares of articles with domestic or international coauthors, counts and shares of citations and top-cited articles, and citation rates. These indicators can be used primarily to measure the output of scientific research. For example, counts of articles and citations and shares of world totals show how the United States is faring compared with other countries or regions. These indicators can also be used to measure the extent of collaboration and linkage. An example is the network maps used in the report Knowledge, Networks and Nations: Global Scientific Collaboration in the 21st Century , by the UK Royal Society (The Royal Society, 2011). These network maps are based on authorship of articles and show patterns of collaboration between countries. They are based on numbers of jointly authored research papers, with linkages being displayed when the collaboration between two countries amounts to 5-50 percent of the overall publication output of one of the partners. The OECD (2010) report Measuring Innovation: A New Perspective uses citation data to measure the interrelatedness of different research areas. 18

Bibliometric data potentially can be used to create a number of additional indicators to provide further detail on linkages across research areas or by geographic location. This information can be particularly valuable for mapping the development of new research areas, such as green technologies, or the spread of general-purpose technologies.

There are some limitations to the use of bibliometric analysis for the production of S&T indicators, particularly when used to measure causal relationships, such as socioeconomic impacts of funding basic science. It is also difficult to isolate how much research networks have changed because

18 This report references the citation technique used in Saka et al. (2010).

of a given research funding award granted or the existence of a new collaborative agreement. Impact factors and Hirsh’s (h) index, commonly used by bibliometricians, do not allow for comparisons with counterfactual analysis. Furthermore, measures must be normalized to be helpful for comparing research outputs, or they are no better than “nose-prints”—metaphorically, signs of high window-shopping activity, with no true indication that a substantive purchase has occurred. There are ways for numbers of patents and articles to be inflated by their producers without substantive advances in S&T having been achieved. Bornmann and Marx (2013) state that “… mere citation figures have little meaning without normalization for subject category and publication year…. We need new citation impact indicators that normalize for any factors other than quality that influence citation rates and that take into account the skewed distributions of citations across papers.” Bornmann and Marx describe techniques using percentiles to create normalized indicators, an improvement on impact factors and Hirsh’s (h) index. 19 To its credit, the National Science Board (for which NCSES produces the Science and Engineering Indicators [ SEI ] biennial volumes) is mentioned by Bornmann and Marx as one of the federal agencies that uses percentile ranks of publications. Although this is good practice, it is important to note that these indicators are not appropriate for impact assessment, for which counterfactual evidence is necessary.

RECOMMENDATION 5-1: The National Center for Science and Engineering Statistics should expand its current set of bibliometric indicators to develop additional measures of knowledge flows and networking patterns. Data on both coauthorship and citations should be exploited to a greater extent than is currently the case.

BUSINESS R&D SERVICES AND INTANGIBLE ASSETS

Although NCSES publishes a rich set of data on R&D expenditures and performance, measures of spillover effects still are needed to aid in determining the effects of scientific investment on socioeconomic outcomes. Policy makers would benefit from such measures in addressing such questions as: What is the effect of federal spending on R&D on innovation and economic health, and over what time frame? What is the international balance of trade in R&D services? How much R&D do U.S. multinational companies conduct outside the United States, and how much R&D do foreign multinational companies carry out in the United States? How much are U.S. companies spending to be present in emerging markets? How much R&D are they conducting in these nations?

This section addresses the question of how R&D data can best be exploited, focusing in particular on the measurement of trade in R&D services. BRDIS contains a rich dataset on R&D that is only partially exploited in present indicators. Given the size and complexity of BRDIS, however, a tradeoff is entailed in terms of the time and resources needed to process these data. BRDIS can be exploited by researchers within and outside government, subject to appropriate restrictions to protect respondents, but only if a researcher database is provided with sufficient metadata 20 to define all the variables and the degree of imputation for each.

At the same time, the panel acknowledges that further exploitation of BRDIS would require additional resources and might also involve a trade-off in terms of the timeliness of the release of key R&D indicators. The time required to process and release R&D statistics increased significantly following the introduction of BRDIS, which is a longer and more complex survey than its predecessor, the Survey of Industrial Research and Development. The panel views timeliness as an important factor in determining the value of R&D and other indicators and encourages NCSES to place high priority on reducing the time lag in the release of BRDIS data.

Trade in R&D Services 21

One important aspect of R&D is R&D services, which are services for the performance of R&D provided by one organization for another. R&D services are for the most part provided by companies and organizations involved in biotechnology; contract research (including physical, engineering, and life sciences firms); and professional, scientific, and technical areas (including social sciences and humanities). These are companies or organizations categorized under NAICS code 5417 (scientific R&D services). Specifying NAICS codes for R&D services (as does BRDIS) is important, since firms in almost any industry can buy or sell R&D services. For example, Boeing can buy services to fill a gap in its R&D program for wing design; Walmart can sell its knowledge, based on R&D, on supply chains; and extraction firms can buy or sell R&D services related to extraction.

Currently, R&D services are captured through the use of a number of indicators published in the SEI . These include R&D by sector and location of performance, funding of R&D

19 “The percentile of a publication is its relative position within the reference set—the higher the percentile rank, the more citations it has received compared with publications in the same subject category and publication year” (Bornmann and Marx, 2013, p. 2).

20 Metadata describe the data and how they were constructed.

21 “Services are the result of a production activity that changes the conditions of the consuming units, or facilitates the exchange of products or financial assets. These types of service may be described as change-effecting services and margin services respectively. Change-effecting services are outputs produced to order and typically consist of changes in the conditions of the consuming units realized by the activities of producers at the demand of the consumers. Change-effecting service are not separate entities over which ownership rights can be established. They cannot be traded separately from their production. By the time their production is completed, they must have been provided to the consumers” (European Commission, 2009, Chapter 6, paragraph 17).

by companies and others, R&D performed abroad by U.S.owned companies, R&D performed in the United States by foreign multinationals (foreign direct investment in R&D), and exports and imports of R&D and testing services. For the SEI , data on R&D performance and funding are taken from BRDIS, while the Bureau of Economic Analysis (BEA) provides the data on foreign direct investment in R&D and on international trade in R&D testing services.

NCSES is expanding its data-linking activities to match BRDIS microdata with BEA survey microdata on U.S. foreign direct investment. The agency also has undertaken fruitful interagency collaboration with BEA to integrate R&D into the system of national accounts.

The panel deliberated on globalization and its impact on the research enterprise in the United States. An immediate policy question was how much R&D, measured in terms of expenditures, was outsourced to countries such as Brazil, China, or India, and whether R&D was performed by foreign affiliates or purchased from other companies. A related question was how much knowledge produced by U.S. R&D is being purchased by other countries, and which countries are leading purchasers. These are important but also complex questions that present a number of difficult challenges for data collection.

The panel thus commissioned a paper on this subject by Sue Okubo (2012). The paper reviews the current work of BEA in this area and compares it with recent NCSES work on BRDIS. 22 Several observations follow from this comparison.

One key observation in Okubo’s paper is the difference between the classifications used by BEA and NCSES and the fact that BEA measures trade in R&D and testing services, whereas NCSES in BRDIS measures R&D services only. While BEA and NCSES are cooperating on survey activity, the panel emphasizes the importance of this cooperation’s leading to comparability of the data produced by these and other agencies (see Recommendation 5-2 later in this section).

The surveys on international transactions administered by BEA and the R&D surveys 23 carried out by NCSES follow different guidance: BEA follows the sixth edition of the International Monetary Fund’s (IMF) (2011) Balance of Payments and International Investment Position Manual , while NCSES follows the Frascati Manual (OECD, 2002a). However, the two approaches are not far apart. The IMF manual includes some R&D and intellectual property elements that are consistent with the Frascati Manual . Therefore, the geographic and ownership scope of BEA’s international transaction surveys and that of the BRDIS are conceptually close. For example, BEA’s international transaction surveys encompass any company with activities in the United States, regardless of ownership. The surveys cover transactions of U.S.-located units of foreign multinational enterprises with entities outside the United States, including transactions with their own foreign parents, and affiliated and unaffiliated trade. Similarly, for the United States, the surveys cover affiliated and unaffiliated trade and transactions by purely domestic companies (no relationship with any multinational enterprise). BRDIS also covers any company with activities in the United States, regardless of ownership, and foreign affiliates of U.S. multinational enterprises.

On the other hand, BRDIS treats foreign parent companies differently from the way they are treated in both BEA’s trade surveys and BEA’s surveys of foreign direct investment. Other differences exist between BRDIS and BEA data on the international balance of payments in R&D trade: BEA’s testing services, which are part of the research, development, and testing measure, may include R&D and non-R&D components, and R&D is treated by NCSES basically as a cost measure, while transactions are treated more like market values. Moris (2009, p. 184) suggests a matrix for use in parsing the data from BEA’s trade surveys and R&D surveys (including BRDIS).

A second key observation in Okubo’s paper relates to the results of the BEA surveys with respect to the sale of R&D and testing services abroad. For 2010, the largest buyers of U.S. R&D and testing services were Bermuda, 24 Ireland, Japan, the Netherlands, and Switzerland, accounting for 6.6 percent of the total trade of $30.9 billion. Such a distribution of trade statistics is rare, as is illustrated by trade in professional, business, and technical (PBT) services. In 2010, the largest buyers of U.S. PBT services were Germany, Ireland, Japan, Switzerland, and the United Kingdom, accounting for 37 percent of total trade; the largest sellers of PBT services to the United States—the countries to which these services were outsourced—were Germany, India, Japan, the Netherlands, Switzerland, and the United Kingdom, which accounted for 40 percent of total U.S. payments for these services (Okubo, 2012). The dominance of the leading countries in the sale and purchase of PBT services is seen in other trade figures, but not in the sale and purchase of R&D and testing services. This difference in the concentration of R&D and testing services merits further analysis.

In summary, the questions that beg to be answered are: Under what circumstances does the R&D activity of multi-

22 In September 2012, NCSES inaugurated a website with two new publications on the International Investment and R&D Data Link project. The site will also house future publications on the BRDIS link (National Science Foundation, 2013b). It should be noted that BEA plans to incorporate R&D as investment in the core economic accounts in 2014.

23 The NCSES surveys referred to include BRDIS and its predecessor, the Survey of Industrial Research and Development.

24 If one were to start with R&D performers only and then look at their R&D exports and imports, legitimate non-R&D performers that only import their R&D from overseas would be eliminated from the analysis. This exercise would require access to the microdata, which are not publicly available. However, NCSES could conduct this analysis and publish the statistics and rankings. There is no escape from accounting and transfer price issues, such as allocated costs that are not related to actual R&D trade. R&D performance data for multinational enterprises are not immune to this issue. Conditioning on performance for trade flows can eliminate unwanted R&D and training data.

national corporations enhance U.S. economic performance, including leadership and strength in S&T? What effect do tax laws have on the location of R&D services? Clearly, the R&D activity of multinational corporations has grown, but the data available with which to analyze and track this activity have limitations. BRDIS includes data on domestic and foreign activities of firms and can provide a more detailed picture of R&D activities than has previously been possible or been fully exploited. Specifically, BRDIS offers more information on R&D service production and flows of R&D services in the United States and in U.S. firms abroad than has heretofore been published. Understanding outsourcing and trade in R&D services is particularly important because the developed economies are dominated by service industries. BRDIS data also can support measures of payments and receipts for R&D services abroad, by leading countries, which is critically important for policy purposes.

RECOMMENDATION 5-2: The National Center for Science and Engineering Statistics (NCSES) should make greater use of data from its Business Research and Development and Innovation Survey to provide indicators of payments and receipts for research and development services purchased from and sold to other countries. For this purpose, NCSES should continue collaboration with the U.S. Bureau of Economic Analysis on the linked dataset.

The panel believes NCSES can provide these estimates and, if necessary, include appropriate questions on BRDIS in 2013 and subsequent years. The 2008, 2009, and 2010 BRDIS did not allow NCSES to collect all of the elements described above, but the 2011 and 2012 questionnaires are more comprehensive in this dimension, collecting data on R&D production, funding, and transactions. Data would be available with which to produce statistics on payments and receipts for R&D services involving U.S. company affiliates at home and abroad and on how those data differ, if at all, from the BEA measures. Similar information on foreign company affiliates from other sources could be used for parallel comparisons. 25 NCSES could consider developing two series—payments and receipts for R&D services—for three to five leading countries. The resulting statistics would show what knowledge creation is being outsourced and which countries are buying U.S. knowledge. This information would enable users to track trends over time and have a better understanding of knowledge flows and the formation of R&D networks.

Over time, this exercise would provide answers to a range of questions: Is the United States losing or gaining advantage by buying and selling its R&D abroad? Is the United States benefiting from research conducted in other countries? What is the United States learning from other countries, and what are other countries learning from the United States? In what technological areas are other countries accelerating development using knowledge sourced in the United States? What is the role of multinational enterprises in transferring R&D capacity from country to country? The data could also be used in regression analysis to answer another important question: What impact does the international flow of R&D have on U.S. economic performance? Users of the data on international flows of R&D services are likely to be interested in seeing how emerging economies are advancing in R&D capacity, in what fields U.S. companies are sourcing or outsourcing R&D and whether it is increasingly being sourced or outsourced in specific countries, and which countries 5-10 years from now may be the hub of new scientific knowledge—possibly countries in Latin America, the Middle East, or sub-Saharan Africa.

Intangible Assets

Until recently, the important role of knowledge-based capital (KBC) was rarely recognized, one exception being Nakamura’s (1999) research on intangibles 26 and the “New Economy.” This situation has changed primarily as a result of the pioneering research of Corrado and colleagues (2005) on intangibles. In their 2006 paper, these authors point out that most knowledge-based investment is excluded from measured GDP and from most productivity and economic growth models. The authors recognize three broad categories of KBC: computerized information (software and databases); innovative property (patents, copyrights, designs, trademarks); and economic competencies (including brand equity, firm-specific human capital, networks joining people and institutions, and organizational know-how that increases enterprise efficiency). Another important form of KBC is human capital that is not firm specific, such as most human capital that is created through education. 27 The World Bank (1997) estimates that for most countries, intangibles, including human capital more broadly defined, represent the majority of a country’s wealth. 28 By all accounts, failing to recognize KBC in any analysis of economic growth or the potential for innovation is a significant omission.

For this reason, a major development in the measurement of KBC occurred when the status of R&D was changed in the 2008 System of National Accounts (SNA) from an expense to an (intangible) capital investment. Efforts are still ongoing both in the United States (see, e.g., U.S. Bureau of Economic Analysis, 2010) and internationally to integrate R&D fully into national accounts. This work requires not only high-

25 See, for example, Eurostat 2010 statistics (Eurostat, 2013). Also see statistics for Germany (Deutsche Bank Research, 2011) and on the Indian engineering R&D offshoring market (NASSCOM and Booz & Company, 2010). These two reports cite private company estimates, as well as published Eurostat statistics.

26 Part of the broad category of KBC; see, e.g., OECD (2012a).

27 Human capital is discussed in Chapter 6.

28 World Bank intangibles include human capital, the country’s infrastructure, social capital, and the returns from net foreign financial assets.

quality data on R&D, but also methods for estimating the depreciation of R&D capital, appropriate R&D deflators, and the estimation of price changes. Although the integration of R&D into the SNA is mainly the responsibility of BEA, NCSES has an important role through its long-standing expertise in the collection of R&D data.

The estimates of Corrado, Hulten, and Sichel for the United States give a sense of the relative importance of various components of KBC as defined above (Corrado et al., 2006). Almost 35 percent of their measured KBC either is currently in GDP (computer software) or is in GDP beginning with estimates for 2013 (mainly scientific R&D). Some data on nonscientific R&D (e.g., social science R&D) are now collected through National Science Foundation (NSF) surveys. Total nonscientific R&D is estimated by Corrado, Hulten, and Sichel to be in excess of 20 percent of total R&D. The largest portion of the unmeasured component, economic competencies, accounts for somewhat less than 40 percent of spending on business intangibles.

More than 70 percent of spending on economic competencies is for firm-specific resources. This spending includes employer-provided worker training and management time devoted to increasing firm productivity. Examples given for management time are time for strategic planning, adaptation, and reorganization. Corrado, Hulten, and Sichel used management consulting industry revenues, trends in compensation, and numbers of individuals in executive occupations to estimate spending in the management time category. Sixty percent of advertising expenditures is allocated to business spending on brand equity intangibles. 29

A number of researchers have estimated KBC for individual countries following the lead of Corrado, Hulten, and Sichel. These individual countries include Australia (Barnes, 2010; Barnes and McClure, 2009), Canada (Baldwin et al., 2008), China (Hulten and Hao, 2012), Finland (Jalava et al., 2007), France and Germany (Delbecque and Bounfour, 2011), Japan (Fukao et al., 2007, 2009, 2012; Miyagawa and Hisa, 2012), the Netherlands (van Rooijen-Horsten et al., 2008), and the United Kingdom (Gil and Haskel, 2008; Marrano et al., 2009). Corrado and colleagues (2012) recently completed KBC estimates for the 27 EU countries and the United States. In addition, the methodology for estimating individual components of KGC has been refined, most notably by Gil and Haskell (2008).

A discussion paper by Corrado and colleagues (2012) provides the broadest view of the importance of KBC as it covers the largest number of countries. 30 In their estimates, the United States stands out for two reasons as compared with regional EU country averages: it has the largest share of intangible investment in GDP (11 percent), and it is the only country/region for which intangible investment is a larger share of GDP than tangible investment. In all country/ regional comparisons, however, the rate of growth in intangible investment exceeds that in intangible investment. The authors report three main results. First, capital deepening is the dominant source of economic growth once intangibles are recognized. Second, deepening of intangible capital accounts for one-fifth to one-third of the growth of labor productivity. Finally, the contribution of intangible capital in some large European countries (e.g., Germany, Italy, and Spain) is lower than that in the United Kingdom and the United States. However, there are significant country differences in the distribution of intangibles by broad types: computerized information, innovative property, and economic competencies.

Aizcorbe and colleagues (2009) review various definitions of innovation; propose how measures of innovation like that addressed by Corrado, Hulten, and Sichel could be integrated into a satellite account; and outline future BEA plans. They note that whether advertising and marketing expenditures should be treated as investment is being debated. They question whether cumulating all firms’ advertising expenditures should be registered as increasing aggregate output. In addition, they comment on the difficulty of measuring spending on organizational change. As Corrado, Hulten, and Sichel also recognize, they describe how developing deflators and depreciation rates for most intangibles can be difficult. Their paper calls for cultivation of sources for spending on the development and implementation of new business models, the creation of new artistic originals (see below), the design of new products, and intermediate inputs to innovation. Finally, they hope to work toward better price and depreciation estimates and, in cooperation with the Census Bureau and NSF, the publication of firm and establishment innovation statistics.

Since Corrado, Hulten, and Sichel published their first paper on intangibles in 2005, U.S. government agencies have moved forward to measure and recognize intangibles more comprehensively. As mentioned above, efforts are under way to capitalize R&D and fully integrate it into the SNA. Investment in artistic originals is incorporated into U.S. GDP in 2013 (Aizcorbe et al., 2009). 31 BEA-defined artistic originals include theatrical movies, original songs and recordings, original books, long-lived television programming, and miscellaneous artwork (Soloveichik, 2010a,b,c,d, 2011a,b). For many years, mineral exploration, a relatively small component, has been recognized as investment in U.S. GDP.

Many reports and monographs and at least one book have been produced on KBC. Many of them have been published since 2005. An interim project report from OECD (2012a)

29 More information on how business spending in intangibles was estimated is available in Corrado et al. (2005).

30 The years covered vary in Corrado et al. (2012): the earliest beginning year is 1995, and the latest is 2009. Regions include Scandinavian (Denmark, Finland, and Sweden), Anglosaxon (Ireland and the United Kingdom), Continental (Austria, Belgium, France, Germany, Luxembourg, and the Netherlands), and Mediterranean (Greece, Italy, Portugal, and Spain).

31 See Chapter 7 of this report for more detail on how Aizcorbe and colleagues at BEA are using administrative records and web-based data in the agency’s project to capitalize intangible assets for inclusion in the SNA.

echoes the Corrado and colleagues (2012) conclusion that intangibles have been estimated to account for a substantial share of labor productivity: 20-25 percent across Europe and 27 percent in the United States. In addition, the OECD report notes that there are substantial spillovers from and repeated use of KBC, and that global competitiveness may increasingly be determined by KBC. After offering answers to the question of why business is investing in KBC, the OECD report focuses on policy questions. The policy challenges discussed with respect to KBC are in the areas of taxation, competition, intellectual property rights, personal data, and corporate reporting. Other publications focus on KBC more from an accounting or business perspective. Lev (2001) uses movements in stock market prices to estimate the impact and importance of intangibles. A long report by Stone and colleagues (2008), written from the business/accounting perspective, includes a long list of references. Among its contributions are a summary of efforts to measure firm- and aggregate-level innovation and a taxonomy of possible types of measures—indicator indices, monetary, and accounting. Many authors recognize the complexity of measuring and estimating the contribution of KBC to economic growth.

The potential definition of KBC is far broader then that employed by Corrado, Hulten, and Sichel. Aside from including all formal education, not just employer-provided training, Stone and colleagues (2008) cite two major categories—relational capital and open innovation. Relational capital refers to relationships with external stakeholders, including customers and suppliers. Its value can encompass the complementarity of user needs, such as customers and advertisers using Google for similar purposes. Companies that use open innovation post R&D and commercialization challenges on web-based forums or “marketplaces” that are accessible to communities of scientists, engineers, and entrepreneurs. A component of the Corrado, Hulten, and Sichel definition that is featured less prominently in related research, including that of Stone and colleagues (2008), is general networking. Stone and colleagues comment that general networking is particularly useful for businesses operating in emerging economies. Facebook provides a form of social capital/ networking that by extension has information and business value. Each of these expansions or extensions of the Corrado, Hulten, and Sichel definition of intangibles presents substantial measurement challenges.

As stated by Stone and colleagues (2008, p. II-4), “Intangible assets are not innovations, but they may lead to innovations.” And as stated by Ben Bernanke in the concluding sentence of a 2011 speech, “We will be more likely to promote innovative activity if we are able to measure it more effectively and document its role in economic growth.” The open question, however, is which KBC leads to economic growth and to what degree, and is this part of the challenge of making a direct and quantifiable connection between innovative activity and economic growth? Certainly some components of KBC have been studied extensively to document their role; scientific R&D is the prime example. Other components of KBC have been less well studied; organizational know-how is an example. The importance of KBC as an STI indicator depends on the drawing of connections. However, it is critical to recognize both KBC and tangible capital as factors that may be important indicators of future growth. Although the panel believes work on intangible assets may generate useful STI indicators, it believes NCSES should not seek to produce these statistics on its own, but support and draw on the work of other agencies, particularly BEA, in this area. However, NCSES still has an important role to play through its collection of high-quality R&D data, and it may also be able to contribute with other data sources. This might be the case, for example, if NCSES were to begin collecting data on innovation-related expenditures, as outlined in Chapter 4 .

RECOMMENDATION 5-3: The National Center for Science and Engineering Statistics (NCSES) should continue to report statistics on knowledge-based capital and intangible assets obtained from other agencies as part of its data repository function. In addition, NCSES should seek to use data from the Business Research and Development and Innovation Survey on research and development and potentially also on innovation-related expenditures as valuable inputs to ongoing work in this area.

Indicators of General-Purpose Technologies

“General-purpose technology” (Lipsey et al., 2005) is a term used to describe technologies with the potential to transform the economy and activities across a broad range of sectors and industries (Jovanovic and Rousseau, 2005). Earlier examples are steam, electricity, and internal combustion, while more recent examples include information and communication technologies, biotechnology, nanotechnology, and green technologies. Given their potential importance for innovation and growth, tracking the development of these technologies and their diffusion and application is important to inform policy. In this area, there is one particular policy question that users of STI indicators are eager to have answered: Is the United States promoting platforms in information and communication technology, biotechnology, and other technologies to enable innovation in applications?

Bresnahan and Trajtenberg (1995) outline three characteristics of general-purpose technologies: their pervasiveness across sectors, their development and improvement over time, and their ability to spur innovation in their own and other sectors. These characteristics are useful for guiding the measurement of general-purpose technologies. Tracking knowledge generation in these technologies, their diffusion to other sectors, and the linkages among them is important for understanding innovation and other sources of growth in the economy.

Measuring general-purpose technologies poses two main difficulties. The first is that not all of these technologies can be properly identified as belonging to a particular sector, because they are spread across different industry classifications. The second difficulty arises in identifying the use of these technologies in other sectors. Clearly, the extent of these difficulties varies according to each such technology. Information and communication technology is by far the best covered in statistics in terms of both industry classification and identification of investments in other sectors.

A number of the data sources discussed in this chapter can be used to generate indicators of general-purpose technologies. For example, patents and trademarks can be used to measure the use of such technologies for knowledge creation in sectors other than those in which they were developed, and both patent and bibliometric data can be used to measure the linkages among general-purpose technology sectors. R&D data provide an indicator of knowledge generation in sectors that develop general-purpose technologies, as do broader measures of investment in these technologies. In addition, the BRDIS contains data on the percentage of R&D in energy applications, environmental protection applications, software, medical clinical trials, biotechnology, and nanotechnology. These data can potentially be used to investigate the extent of R&D in these technologies across sectors (thus giving a picture of how “general-purpose” these technologies are).

NCSES currently publishes a number of statistics on general-purpose technologies—particularly for information and communication technology, but increasingly also for green technologies. The panel encourages NCSES to continue this work and also to build on current indicators in this area. In particular, NCSES should examine possibilities for better coverage of the diffusion and uptake of general-purpose technologies in sectors other than those in which they were developed, using both BRDIS and other data sources.

RECOMMENDATION 5-4: The National Center for Science and Engineering Statistics (NCSES) should develop a suite of indicators that can be used to track the development and diffusion of general-purpose technologies, including information and communication technologies, biotechnology, nanotechnology, and green technologies. NCSES should attempt to make greater use of data from the Business Research and Development and Innovation Survey for this purpose while also exploring the use of other sources, such as patent and bibliometric data.

Subnational Issues in Measuring New Knowledge and Knowledge Networks

Compared with the measurement of innovation, the measurement of knowledge production is more clearly connected to geographic location. A number of initiatives by successive administrations have emphasized the ability to locate federal research grants on S&T down to very detailed levels within neighborhoods. Of course, some of this detail is spurious. Establishment data may link to some postal address while the actual economic activity is being carried out over a territory of some size, depending on the industry. Moreover, much of the value derived from these targeted investments comes from the trade of goods and services, which is dispersed geographically.

Still, disaggregating is certainly possible to levels much finer than the states. For example, universities are well-behaved geographic phenomena in that they remain in one place, and their relation to various nested administrative hierarchies is straightforward. Their laboratories and research facilities are similar to those of other establishments; in fact, some of them resemble industrial facilities with loading docks, employees, and so on. The movement of goods and people in the university establishment can be accounted for in the production of scientific work.

Some success appears to have been achieved in gathering data on some of the basic science output tied to spatial units. Geographic identifiers appear in many contexts, including author lists of publications and patent applications. With some care, and some level of error, these outputs can be linked to a location. But difficulties are entailed in measuring the impacts of research investments, particularly with spatial disaggregation. Particularly challenging to measure is the geographic instantiation of a knowledge network and the flows of knowledge from place to place.

As a reference for understanding the national system of R&D, it may be worthwhile to examine the results of a major study conducted in Canada in 2011 (Jenkins et al., 2011). A six-member expert panel carried out a full review of the country’s federal R&D support programs. While one important theme concerned Canada’s balance between tax credits and direct R&D support, the authors’ comprehensive study of the whole system of R&D support programs bears examination for application to the United States. The Canadian panel surveyed more than 60 institutes and programs engaged in supporting business innovation. The distribution was highly skewed, with a few relatively large entities and many small ones. Because each was created under a distinct charter, there is little coherence in the criteria used to evaluate effectiveness, a common problem worldwide. The tendency, as in other countries, is to concentrate on generating more investment in R&D rather than on providing mechanisms for industry to obtain the assistance needed to overcome current problems in operations. Certain gaps also became evident from this comprehensive analysis, leading the Canadian panel to offer recommendations for short-term measures to improve the effectiveness of the country’s innovation system. The Canadian panel notes that the responsibility for fostering innovation cuts across many functions of government and therefore requires a system-wide perspective and whole-of-government priority. That panel’s recommendations include

making encouragement of innovation in the Canadian economy a stated objective of federal procurement policies and programs, restructuring procurement procedures to allow more latitude for innovative solutions to emerge, and reorienting existing federal laboratories to serve sectoral needs. 32

The important message in the present context is that certain aspects of the innovation system emerge from a comprehensive view of the whole. Canada invested the efforts of a distinguished panel in such a process, with clear results for managing its system. That panel’s analysis also raised the question of how to compare existing R&D support programs. Although NCSES, as a statistical office, does not conduct evaluation, it should be in a position to provide information on government programs that would support the evaluation done by others.

In this chapter, the panel has offered four recommendations regarding the development of indicators of knowledge generation, knowledge networks, and knowledge flows. The focus is on techniques that should be used to develop indicators that users want for specific market sectors and that improve the international comparability of the data. The panel also suggests that the production of certain measures is not in NCSES’s purview, and these measures should instead be acquired from other agencies. In the near term, NCSES should give priority to using tools that are readily available at the agency and continuing existing collaborations with other agencies while developing new techniques and cultivating new linkages over time.

32 The report lists the following sectors (p. 3-13): Goods Industries (agriculture, forestry, fishing and hunting; manufacturing; construction; utilities; and oil and gas and mining); Services Industries (transportation and warehousing; information and cultural industries; wholesale trade; retail trade; finance and insurance, real estate and rental and leasing; professional, scientific, and technical services; and other services); and Unclassified Industries.

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Since the 1950s, under congressional mandate, the U.S. National Science Foundation (NSF) - through its National Center for Science and Engineering Statistics (NCSES) and predecessor agencies - has produced regularly updated measures of research and development expenditures, employment and training in science and engineering, and other indicators of the state of U.S. science and technology. A more recent focus has been on measuring innovation in the corporate sector. NCSES collects its own data on science, technology, and innovation (STI) activities and also incorporates data from other agencies to produce indicators that are used for monitoring purposes - including comparisons among sectors, regions, and with other countries - and for identifying trends that may require policy attention and generate research needs. NCSES also provides extensive tabulations and microdata files for in-depth analysis.

Capturing Change in Science, Technology, and Innovation assesses and provides recommendations regarding the need for revised, refocused, and newly developed indicators of STI activities that would enable NCSES to respond to changing policy concerns. This report also identifies and assesses both existing and potential data resources and tools that NCSES could exploit to further develop its indicators program. Finally, the report considers strategic pathways for NCSES to move forward with an improved STI indicators program. The recommendations offered in Capturing Change in Science, Technology, and Innovation are intended to serve as the basis for a strategic program of work that will enhance NCSES's ability to produce indicators that capture change in science, technology, and innovation to inform policy and optimally meet the needs of its user community.

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Open access
Published: 01 February 2010

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

Joseph P Lane 1 &
Jennifer L Flagg 1

Implementation Science volume 5 , Article number: 9 ( 2010 ) Cite this article

31k Accesses

38 Citations

Metrics details

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

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

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

Peer Review reports

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

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

Knowledge Translation in technology-based rehabilitation science and engineering

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

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

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

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

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

Assumptions and definitions regarding knowledge

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

Key assumption

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

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

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

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

Clinicians who recommend assistive technology to clients;

Consumers who apply personal experience when seeking AT;

Manufactures who participate in the design and critique of AT;

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

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

Implementing technology-related knowledge to solve problems

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

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

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

Three states of knowledge

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

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

Discovery State of Knowledge

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

Invention State of Knowledge

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

Innovation State of Knowledge

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

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

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

Three states of knowledge and KTA model

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

Discovery Outputs .

Invention Outputs .

Innovation Outputs .

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

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

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

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

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

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

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

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

Need To Knowledge (NTK) model

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

Six approaches to solving a problem with knowledge

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

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

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

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

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

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

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

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

Novel method of addressing current problem

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

The Need to Knowledge (NTK) model

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

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

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

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

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

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

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

NTK Phase I. Discovery

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

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

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

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

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

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

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

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

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

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

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

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

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

NTK Phase II. Invention

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NTK Phase III. Innovation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The following summarizes the article's key points:

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

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

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

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

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Acknowledgements

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

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Authors' contributions

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

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

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DOI : https://doi.org/10.1186/1748-5908-5-9

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Creating new knowledge in undergraduate research – the challenge of questions, theory and focus in dissertations and projects. A blog for students.

It might seem very daunting and impossible to create new knowledge in undergraduate dissertations and projects, but this is far from the truth. Research is not just about regurgitating the writing of others, it is about engaging with interesting questions and approaches, and developing a new view, a new perspective on established work, taking new steps, asking new questions, asking questions a different way, using new theories, combining across work and approaches in a way that are unusual. Projects and dissertations are actually high level intellectual activities and products and they are evidence of systematic approaches, a lot of hard work organising process, practice, literature, methodology and methods, data, explanations and understanding. They are a unique mix of enthusiasm and hard work, systematic focus and planning and management and flights of fancy brought to fruition, new ideas and new slants, new comments, linking ideas and approaches to established work and creating new interpretations. (Wisker, 2009, 2018)

There are several logical steps to a good dissertation and project and the first is a sound question.

Find a question or a hypothesis which you can actually explore from the literature and in the field, in your own work.

Ensure you find it exciting enough to keep you going over a whole year. Make sure you know what questions you are interested in asking, have some idea where to find the literature, and can work out what methodology and methods will help you to ask and answer your question. You are already planning and making this manageable. When you have linked the interest you have about a specific question with the ways in which you might realistically and proactively ask that question, the next issue is that it needs to be theorised.

You need a good enough question. You also need thorough exploration and discussion of the literature to situate the new work, theories or a theory which helps you develop a perspective on the work you are doing, the question you are asking the an interpretation of the information, the data, then the findings which emerge from asking your question.

What theories will you use to give you a perspective on your work?

You will not just be asking a question and conducting a simple experiment looking at some data, analysing some text, then giving the results; you need to decide what perspectives and theories guide your questioning. There will be theories others have used in the field – you will find these from your supervisor or colleagues, and your reading.

Try them out.

Do they make sense in asking your question or are they not at all useful?

What kinds of perspective on the question and the field do they offer?

This might be a theory about how people learn, or about how we understand our identity, or how people weave ideas and arguments together to persuade others, or about the relationship between the place you grew up in and your sense of identity, or about how the height above sea level affects when water boils. All disciplines have theories, and they enable you to take a view, a perspective, ask a question from a specific angle and ask the why and so what , as well as the how questions.

I recently assessed a very good PhD which looked at how international students responded to supervisor comments and used one main theory – Bakhtin’s dialogism. This is always tough to start with – looking at the theories people use – but his theory is about how students and supervisors engage in a dialogue between them and the work and to and fro about what to look at, what to say, what and how to find, how to interpret, how to situate the new work in the old, what’s really being found, what the contested ideas and arguments are. Bakhtin has something on all of this – he sees it as not a fixed set of questions and answers but a dialogue, a communication between the student and their work, and then as their supervisor starts to help with the work, an expression of the exchange between the supervisor and the student’s work. The student needs to learn from the supervisor’s feedback and so there is another dialogue between that feedback and what the student does with it. This story indicates how that theory about a dialogue really helps us understand how and in what ways the postgraduate student asks questions about what supervisors do to enable students to learn, grow, move on, contribute something new. This student’s view was that Bahktin’s version of a dialogue – supervisor and student knowledge and new expectations in the research and understanding – through feedback make this possible.

I hope I have made this theorising sound straightforward, and indeed a good theory must be simple to understand and usable – as well as representing some complex thinking about the world, ideas, practices, values and so on, and how they relate.

You need to determine what’s new about your work and why it matters , and how you can ask your question, and develop a theoretical perspective on the question and what emerges from asking it on a project leading to a project report or a dissertation. What is developed is your data and findings. It is important that asking the theorised question using the right methodology and methods offers in the end a new perspective, a new finding, something which others will want to know about in their own questions for knowledge and their own constructions of understanding.

These are some of the early logical steps and breakthroughs at the heart of your research for a project or dissertation:

Developing a question
Finding a theory to give you a perspective
Finding the right methodology and methods to help you ask and answer or address your question.

The next major skill is developing the ability to write in a systematic and planned way which expresses the excitement of addressing those questions, exploring and understanding, the contestations, the theorising of what you ask and find or construct.

It is an exciting and focused organised journey filled with hard work and something extraordinary which you find, understand, create, and contribute.

Gina Wisker (2009, 2018) The Undergraduate Research Handbook London: Palgrave Macmillan

Gina Wisker is a lead advisor on our online course: Research and Writing Skills for Dissertations and Projects . Sign up your institution for a free course trial here .

Neil Armstrong: 'Research is creating new knowledge.'

Research is creating new knowledge.

The quote by Neil Armstrong, "Research is creating new knowledge," is a concise yet powerful statement that encapsulates the essence and significance of research. In straightforward terms, this quote implies that research is not merely about gathering existing information but rather about generating fresh insights and uncovering previously unknown truths. It emphasizes the transformative nature of research and highlights its role in facilitating progress and innovation in various fields.At first glance, this quote may seem self-explanatory, as it points out the fundamental purpose of conducting research. However, by introducing an unexpected philosophical concept, we can delve deeper into its meaning and stimulate intriguing discussions. Let us consider the philosophy of constructivism in the context of this quote to illustrate the subtle complexities involved in the process of knowledge creation.Constructivism, in essence, posits that knowledge is actively constructed by individuals based on their experiences, interpretations, and interactions with the world. It suggests that knowledge is not simply passively absorbed from the environment but rather emerges through the active engagement of an individual's cognitive processes. This approach challenges the traditional notion of knowledge as an objective and static entity, and instead portrays it as a dynamic and subjective construct.When we apply the concept of constructivism to Armstrong's quote, a thought-provoking comparison and contrast emerge. Research, as discussed earlier, is undoubtedly a means of creating new knowledge. However, the philosophical lens of constructivism encourages us to ponder the extent to which researchers themselves shape and construct the knowledge they produce. This perspective encourages us to question the role of researchers as active participants in the process of knowledge creation.In the realm of scientific research, for instance, researchers often rely on a hypothesis-driven methodology. They formulate a hypothesis, design experiments, gather data, and analyze the results to draw conclusions. In this process, researchers engage in critical thinking, creative problem-solving, and reasoning to interpret the data and derive meaningful insights. These cognitive processes are inherently subjective and influenced by various factors such as personal biases, cultural backgrounds, and intellectual perspectives.While this subjectivity does not undermine the rigor and credibility of scientific research, it does remind us that the process of knowledge creation is not devoid of human influence. Researchers, as active agents, contribute their unique perspectives, interpretations, and intellectual abilities to generate new knowledge. This understanding calls for a more nuanced appreciation of research as a dynamic interplay between the objective world and the subjective interpretations of researchers.Another intriguing aspect to consider is the collaborative nature of research. In many cases, research efforts are not undertaken by lone individuals but involve teams of researchers from diverse backgrounds and disciplines. The interplay of different perspectives, expertise, and methodologies in collaborative research projects can lead to an even richer creation of new knowledge. Through collective brainstorming, interdisciplinary exchanges, and the synthesis of ideas, collaborative research can produce innovative and multidimensional insights that surpass the boundaries of individual contributions.In conclusion, Neil Armstrong's quote, "Research is creating new knowledge," encapsulates the transformative nature and significance of research. While appearing straightforward at first, a deeper exploration of the philosophical concept of constructivism sheds light on the dynamic and subjective nature of knowledge creation. By acknowledging the role of researchers as active participants who construct knowledge through their unique perspectives and experiences, we gain a more profound understanding of the complexities involved in the research process. Additionally, the collaborative nature of research highlights the value of diverse perspectives and the potential for even greater knowledge creation. Ultimately, this quote serves as a reminder of the boundless possibilities that research offers in expanding our understanding of the world.

Neil Armstrong: 'That's one small step for a man, one giant leap for mankind.'

Neil armstrong: 'mystery creates wonder and wonder is the basis of man's desire to understand.'.

Does a Knowledge Generation Approach to Learning Benefit Students? A Systematic Review of Research on the Science Writing Heuristic Approach

Review Article
Published: 25 August 2020
Volume 33 , pages 535–577, ( 2021 )

Cite this article

Brian Hand ORCID: orcid.org/0000-0002-0574-7491 1 ,
Ying-Chih Chen ORCID: orcid.org/0000-0002-2003-5193 2 &
Jee Kyung Suh 3

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The shifting emphases of new national curricula have placed more attention on knowledge generation approaches to learning. Such approaches are centered on the fundamental sense of generative learning where practices and tools for learning become the focus of the learning environment, rather than on the products of learning. This paper, building on from the previous review by Fiorella and Mayer ( 2015 , 2016 ), focuses on a systematic review of doctoral and master theses of a knowledge generation approach to the learning of science called the science writing heuristic (SWH) approach. The outcomes of examining 81 theses show that students regardless of grade levels and cultural settings were significantly advantage in terms of content knowledge, critical thinking growth, and representational competency. The results also indicate that time in terms of engagement with the approach is critical for achieving student outcomes and for teachers to develop expertise with the approach. Questioning was also noted as being critical. Implications arising from the study are centered on the development and use of writing, the need for interactive dialogical environments, and the importance of questioning as critical elements for success.

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Text Generation Benefits Learning: a Meta-Analytic Review

Learning as a Relationship Between Understanding and Interpretation. The Acquisition of Knowledge in Actionist Practices

Future Research in Learning with, Through and from Scientific Representations

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Hand, B., Chen, YC. & Suh, J.K. Does a Knowledge Generation Approach to Learning Benefit Students? A Systematic Review of Research on the Science Writing Heuristic Approach. Educ Psychol Rev 33 , 535–577 (2021). https://doi.org/10.1007/s10648-020-09550-0

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A practice‐based model to guide nursing science and improve the health and well‐being of patients and caregivers

Sherry s. chesak.

1 Nursing Research Division, Mayo Clinic, Rochester MN, USA

Lori M. Rhudy

Cindy tofthagen.

2 Nursing Research Division, Mayo Clinic, Jacksonville FL, USA

Linda L. Chlan

Associated data.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Aims and Objectives

The purpose of this paper is to describe a model to guide nursing science in a clinical practice‐based setting. Exemplars are provided to highlight the application of this nursing research model, which can be applied to other clinical settings that aim to fill evidence gaps in the literature.

Nurse scientists are well positioned to develop new knowledge aimed at identifying global health solutions to multiple disparities. The generation and application of this knowledge are essential to inform and guide professional nursing practice. While a number of evidence‐based practice models exist to guide the integration of literature findings and other sources of evidence into practice, there is a need for additional models that serve as a guide and focus for the conduct of research in distinct scientific areas in practice‐based settings.

Model development and description.

Mayo Clinic is a large, comprehensive healthcare system with a mission to address unmet patient needs through practice, research and education. PhD‐prepared nurse scientists engage in practice‐based research as an integral component of Mayo Clinic's mission. A practice‐based nursing research model was developed with the intent to advance nursing research in a clinical setting.

The components of the Mayo Clinic Nursing Research model include symptom science, self‐management science and caregiving science. The generation of nursing science is focused on addressing needs of patients with complex health conditions, inclusive of caregivers.

Conclusions

While clinical settings provide rich opportunities for the conduct of research, priorities need to be established in which to focus scientific endeavours. The Mayo Clinic Nursing Research model may be applicable to nurses around the globe who are engaged in the generation of knowledge to guide practice.

Relevance to Clinical Practice

The Mayo Clinic Nursing Research model can be used by nurse scientists embedded in healthcare settings to address clinically relevant questions, advance the generation of new nursing knowledge and ultimately improve the health and well‐being of patients and caregivers.

What does this paper contribute to the wider global clinical community?

There is a need for additional models to guide the conduct of nursing research in clinical settings.
The Mayo Clinic Nursing Research Model was developed as a model to guide the generation of new nursing knowledge in a clinical, practice‐based setting.
The model can be used in a variety of clinical settings for researchers who aim to fill evidence gaps in the literature.

1. INTRODUCTION

Nursing is the largest profession in health care, with continued growth expected over the next several years (Grady & Hinshaw, 2017 ). Nursing science plays a critical role in addressing health challenges, generating new knowledge and translating evidence to practice to improve patient outcomes (Grady, 2017 ; Powell, 2015 ). Furthermore, nursing science integrates biobehavioural approaches to better understand patients' needs and preferences, develop individualised symptom management interventions (Trego, 2017 ), advance interventions to promote self‐management of chronic conditions and thus promote well‐being and quality of life (Grady, 2017 ; Powell, 2015 ). Patients' healthcare needs are becoming increasingly more complex, giving rise to the need for practice‐based research. The clinical practice setting provides an opportunity to conduct research, by which patients' and caregivers needs and outcomes may be addressed and improved.

The purpose of this paper is to present the Mayo Clinic Nursing Research (MCNR) model (Figure (Figure1)—a 1 )—a model developed to guide and focus nursing science generation in a practice‐based setting with an emphasis on promoting the health and well‐being of patients and caregivers with complex needs. The components of the model are described, and exemplars of the generation of practice‐based nursing knowledge are presented.

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Mayo clinic nursing research model [Color figure can be viewed at wileyonlinelibrary.com ]

2. BACKGROUND

Over a century ago, Florence Nightingale recognised not only the need for formal training for nurses but also the power of the nurse to improve patient outcomes (Nightingale, 1992 ). This is still true in today's healthcare environment. Nurses can help fill a critical need not only for the education and training of healthcare workers, but also for the design and testing of solutions to common health problems (National Institutes of Health, 2015 ). As noted by Dr. Patricia Grady, director emeritus of the National Institute of Nursing Research (NINR), ‘…nurse scientists can use their expertise in clinical research and their understanding of the relationship between behaviour and biology to further expand the reach and impact of nursing science in the larger community’ (National Institute of Nursing Research, 2016 , p. 6). However, recommended models for the structure and organisation of nursing research in clinical settings are scarce.

PhD‐prepared nurse scientists (sometimes referred to as nurse researchers) design and implement research studies to improve health‐related outcomes. Although most nurse scientists are employed in academic settings such as schools/colleges of nursing, there is an emerging trend for nurse scientists to have full‐time appointments in practice settings (Robichaud‐Ekstrand, 2016 ). The nurse scientist role has wide variability in how it is operationalised but can be described in three ways. First, in academic settings, Boyer's model of scholarship includes discovery, integration, application and teaching to frame the discussion of discovery and practice in nursing (Boyer, 1990 ; Hickey et al., 2019 ). Academic service partnerships have emerged as strategies to close the academic‐practice gap by connecting clinical practice with academia in order to meet mutually beneficial goals (Sadeghnezhad et al., 2018 ). Examples of programmes in academic‐service partnerships include preparation of new graduate nurses, patient safety initiatives, transitions‐in‐care programmes, advancement of evidence‐based nursing and opportunities for clinical research (Sadeghnezhad et al., 2018 ). While such programmes inform the advancement of nursing research as a component of evidence‐based practice in clinical settings, they are less informative in guiding the generation of knowledge among nurse scientists embedded in clinical settings.

In a second approach, a nurse scientist supports evidence‐based practice, quality improvement, the conduct of research by clinical nurses and, if applicable, ANCC Magnet Recognition Program® activities (Kowalski, 2020 ). A third approach similarly involves embedding nurse scientists in clinical practice settings but the role is focused on the conduct and facilitation of nursing‑oriented research, rather than simply providing support for research conducted by others (Chan et al., 2010 ). This third approach is used in the setting in which this model was developed.

Evidence‐based practice models such as the Iowa Model and the Johns Hopkins Nursing Evidence‐Based Practice Model have been adopted to guide translation of evidence to practice but they have limited utility in describing the infrastructure, focus and outcomes of nursing research in a clinical setting. The Iowa Model Revised: Evidence‐Based Practice to Promote Excellence in Health Care uses an algorithm to guide evidence‐based practice processes from identification of a trigger to integrating and sustaining a practice change (Buckwalter et al., 2017 ). The conduct of research is included in the Iowa Model as a strategy to be used when insufficient evidence exists to recommend a practice change. The Johns Hopkins Nursing Evidence‐Based Practice Model (Dang & Dearholt, 2018 ) includes a patient‐centred approach and incorporates a continuum of Inquiry–Practice/Learning–Practice Improvement as a method to ensure that best practices are applied to patient care. However, the model is centred on an evidence‐based practice approach, which differs from research in that research involves systematic investigation of phenomena to discover new information or reach new understandings and conclusions to generate new knowledge (Cohen et al., 2015 ; Hickey et al., 2019 ). The Joanna Briggs Institute (JBI) (Joanna Briggs Institute, 2016 ), based in the Faculty of Health and Medical Sciences at the University of Adelaide, South Australia, aims to promote evidence‐based decision‐making by promoting the use of the best available evidence. JBI, through its JBI Collaboration, works with universities and hospitals around the world to synthesise, transfer and implement evidence that is culturally relevant and applicable across diverse healthcare settings internationally.

The NINR sets strategic funding and training priorities that advance nursing science to enhance the health and well‐being of individuals across diverse populations (National Institute of Nursing Research, 2016 ). Current research priorities established by the NINR include four scientific foci: symptom science, wellness, self‐management of chronic conditions, and end‐of‐life and palliative care (National Institute of Nursing Research, 2016 ). In addition, all areas of NINR's research programmes place an emphasis on promoting innovation and developing the nurse scientists of the 21st century (National Institute of Nursing Research, 2016 ). Recognising that symptoms are the primary reason patients seek care, the NINR developed the symptom science model to advance research. The symptom science model describes an analytical sequence beginning with a sequelae or cluster of symptoms, which are then characterised into a phenotype with biological correlates, followed by the application of research methods that can be used to identify targets for therapeutic and clinical interventions (Cashion & Grady, 2015 ).

Nurse scientists are well positioned to develop new knowledge aimed at identifying global health solutions to social, economic, psychological and biological disparities. The generation and application of this knowledge are essential to provide the best available evidence to inform and guide professional nursing practice. While a number of evidence‐based practice models exist to guide the integration of literature findings and other sources of evidence into practice, there is a need for additional models that serve as a guide and focus for the conduct of research in distinct scientific areas in practice‐based settings. Therefore, the project team identified a need for the development of a model articulating the goals and strategies to advance nursing research within their institution, and which would have broad applicability to other institutions and nurse scientists embedded in the clinical practice.

Mayo Clinic is a large academic medical centre that incorporates practice, education and research into its mission, which has been emulated in the Department of Nursing and the Division of Nursing Research for over three decades. Today, the Mayo Clinic Nursing Research Division is an enterprise‐wide unit providing infrastructure and support for nursing research at its sites in Mayo Clinic. A cadre of PhD‐prepared nurse scientists lead independent programmes of research and provide consultation to all staff in research‐related matters, including scientific review of research protocols. In addition, small cadres of registered nurses providing direct patient care conduct research studies under the mentorship of a nurse scientist. These clinical nurse scholars identify clinically relevant questions that are investigated by an independent research study (Chlan et al., 2019 ). Details of this programme are described elsewhere (Chlan et al., 2019 ; National Institute of Nursing Research, 2016 ).

The project team developed a model of nursing research to guide the foci for nurse scientists' research at the institution and to generate new nursing knowledge based on needs that arise from the practice setting. The model was also intended to encompass strategic priorities established both by the institution and the field of nursing science. No ethics approval was required for this project.

The team started the process of model development by conducting a literature review regarding (1) existing models of nursing research and evidence‐based practice, (2) nursing science, (3) the nurse scientist role, (4) national and international nursing research strategic priorities and (5) research strategies to transform health care. In addition, the team sought input from multidisciplinary stakeholders at the institution regarding their perception of the current and potential future contributions of nursing science to the practice. Finally, organisational resources describing the research environment were used to inform the model. Thus, it is a model rooted in practice, rather than a theory‐based model.

4.1. The Mayo Clinic nursing research model

The MCNR model is focused on three primary areas across multiple diseases, illnesses, and healthcare settings: symptom science, self‐management science and caregiving science. With a focus in these areas, nurse scientists leverage team science, big data, innovation and technology to move knowledge generation quicker along the discovery, translation and application continuum to meet the needs of patients and caregivers.

The following assumptions informed the development of the model. First, nursing research is vital for the generation of new knowledge to improve the health and well‐being of patients and their caregivers. Second, the health and well‐being of individuals with complex conditions are enhanced by developing and testing patient‐centred interventions through research that focuses on the science of symptom assessment and management, self‐management and caregiving. The MCNR model was developed to guide how this vision will be implemented in a clinical setting with programmes of nursing research aligned to inform and transform health care.

4.2. Patients and caregivers as the focal point of the model

At the centre of the model (Figure (Figure1) 1 ) are the patient and caregiver with complex needs—medical, physical or psychosocial—around which all other elements in the model centre. The nurse scientist focuses on a better understanding of those needs and the testing of interventions used to address them, with the definitive goal of improving patients' and caregivers' health and well‐being. For the purposes of this model, health is defined from a holistic, phenomenological perspective of optimal overall physical, mental, spiritual, social and role functioning (Saylor, 2004 ; Watson, 2008 ); and well‐being is designated as individuals' perceptions, judgements and expectations regarding their health (Saylor, 2004 ; Sullivan, 2003 ). These foci are consistent with the patient‐centred model of care in which patients are viewed as a whole and their individual viewpoints and characteristics are taken into consideration when making decisions regarding care (Zhao et al., 2016 ). It is also congruent with the mission and values of Mayo Clinic (Mayo Clinic, 2021 ), as well as the profession of nursing (Spurlock, 2019 ).

4.3. MCNR model scientific foci

The generation of symptom science, self‐management science and caregiving science are the scientific foci that promote the health and well‐being of patients and caregivers in a practice‐based, patient‐centred clinical setting. It is through the conduct of scientific investigation in these three main areas, described below, that nursing research seeks solutions to unmet, complex health needs of patients and caregivers.

Symptom science seeks to transform the practice using biological, clinical and/or behavioural approaches to investigate symptoms aiming to individualise care and assess patient‐reported outcomes such as quality of life and well‐being (Grady, 2017 ). Self‐management science is based on a complex set of cognitive and behavioural self‐regulation responses that individuals engage in to manage chronic illnesses or factors that increase the risk for illness (Araújo‐Soares et al., 2019 ). Research to support self‐management includes developing and evaluating a broad range of interventions often focused on providing education and guidance for managing specific illnesses, partnering with healthcare providers and coping with challenges of living with chronic illness (Allegrante et al., 2019 ).

Caregiving science is research that explores effective approaches to reduce burden on and promote the health and well‐being of professional and lay caregivers (Grady, 2017 ). Research that examines methods to include caregivers in the care process and to design and test interventions that include them has the potential to significantly contribute to improved patient outcomes and patient‐centred care (Littleton‐Kearney & Grady, 2018 ).

4.4. Leveraging team science, big data, innovation, and technology

In addition to cutting‐edge research methods, nurse scientists leverage team science, big data, innovation and technology as tools, resources and methods to seek solutions to unmet health needs of patients and caregivers (Brennan & Bakken, 2015 ; Conn, 2019 ; Grady & Gough, 2018 ). Within the MCNR model, these four resources and methodologies contribute to the advancement of nursing science in the areas of symptom, self‐management, and caregiving. Team science leverages the strengths and expertise of professionals trained in different disciplines or nursing specialties through a collaborative effort to address a scientific challenge (Bennett & Gadlin, 2012 ). Team‐based research initiatives can be uni‐ or multidisciplinary groups, and teams can be large or small (Conn, 2019 ). In team science, multiple stakeholders contribute unique perspectives on the topic at hand and are deeply engaged in the project (Bennett et al., 2018 ). The World Health Organisation has acknowledged the importance of team‐based research through implementation of nursing collaborating centres, which focus on collaborative research of global or regional importance (National Institutes of Health, 2015 ).

Big data science allows researchers to analyse large and complex volumes of information that are newly available at unprecedented rates from sources such as electronic health records, large databases, sensor‐enabled equipment, imaging techniques, smart devices and high‐throughput genetic sequencing methods (Fernandes et al., 2012 ). Through the application of big data research methods, including artificial intelligence, researchers can discover new ways of understanding and addressing the needs of the patient (Fernandes et al., 2012 ). For example, big data methodologies can be implemented to maximise the utility of patient‐reported outcome data in order to capture the patients' perspectives on how their disease, and the treatment of their disease, is impacting their lives. These data can be used to inform clinical decision‐making, predict long‐term outcomes and identify future innovations in health technologies and other interventions (Calvert et al., 2015 ). This patient‐centric approach ultimately allows healthcare providers to have a better understanding of how individuals are living with and managing their illness, and to make more informed decisions regarding personalised interventions that will have a measurable impact on the patient experience (Brennan & Bakken, 2015 ).

Innovation is defined as a creative, fast‐moving endeavour that involves scientific methods and improvisation to design unique solutions that change the world (Mayo Clinic Center for Innovation, 2020 ). Innovative research uses novel theoretical concepts, methodologies and interventions to challenge current clinical practice paradigms. Innovations in health care can be seen in product innovation for the introduction of new types of goods and services, and in process innovation, which is centred on enhancing internal processes for the production of high‐quality care (Arshad et al., 2018 ; Govindasamy & Wattal, 2018 ; Thune & Mina, 2016 ).

Technology in medical research involves innovations that impact health or healthcare delivery (Healthcare News & Insights, 2020 ; Martins & Del Sasso, 2008 ). Biotechnology, machine learning, pharmaceuticals, information technology, remote monitoring and medical devices are examples of technology. Other technologies include software and applications for self‐management and symptom tracking. Technologies can maximise efficiency and access to health care, such as digital solutions to connect patients to the appropriate provider (National Institute of Mental Health, 2020 ).

4.5. Discovery‐translation‐application continuum

Research conducted at Mayo Clinic occurs along a continuum to address unmet patient needs. The process by which new information makes its way into practice along this continuum is through discovery, translation and application, depicted in the outermost ring of the model in Figure Figure1. 1 . Discovery uses scientific methods to seek solutions to improve the health and well‐being of patients with complex conditions; translation is the development and testing of possible solutions; and application is the dissemination, integration, and evaluation of solutions into practice (Ammerman et al., 2014 ).

Nursing research contributes to innovation at all points along the discovery‐translation‐application continuum, continually advancing science, transforming patient care and improving outcomes (Grady, 2017 ). Guided by the MCNR model, nurse scientists discover answers to puzzling clinical questions that can be translated and applied directly to clinical practice to improve patient care as rapidly and as safely as possible. There are at least seven implementation science models or frameworks available to guide translation of findings to practice. Systematic reviews show variability in their scope and application so selection of an implementation framework according to the context of change is key (Dintrans et al., 2019 ; Moullin et al., 2015 ). In our setting, translation is achieved through clinical partnerships where the department's evidence‐based practice model is used to guide implementation. As depicted in the model in circular form (Figure (Figure1), 1 ), this process is iterative rather than linear. Discoveries are made through observation, discussion or other forms of data. These discoveries, seen through the nursing lens, may have broader applications to be considered. Further, empirical evidence is needed prior to implementing new discoveries into practice. During implementation, new discoveries and applications may come to light.

5. EXEMPLARS OF THE MAYO CLINIC NURSING RESEARCH MODEL

The overall purpose of the MCNR model is to provide a coordinated focus and consistent approach that guides and prioritises practice‐based nursing research. Nurse scientists use the model in their own focused areas of research as well as to guide nurses in the conduct of research that arises from their practice. Outlined below are exemplars of how the MCNR model guides the conduct of practice‐based research among nurse scientists at Mayo Clinic. Examples of how the model has informed research are presented. Not all aspects of the model are evident in each exemplar.

The first nursing research exemplar, within the domain of symptom science (second ring of the MCNR model), aims to address unmet needs of critically ill patients (centre of model) related to comfort‐promoting interventions. Under the mentorship of a PhD‐prepared nurse scientist, this descriptive, cross‐sectional study is being conducted by two practising ICU nurses who first identified in their own clinical setting the problems of: (1) numerous sources of discomfort among ICU patients; (2) the absence of objective assessment of these discomforts as distinct from objective assessment of pain; and (3) the inability to intervene appropriately with effective comfort‐promoting interventions. Next, they identified the distinction between discomfort and pain. They are currently assessing, describing and quantifying the contributing sources of discomfort experienced by nonmechanically ventilated ICU patients using the Discomforts Perceived by ICU Patients instrument, a modified version of the French instrument Inconforts des Patients de REAnimation (IPREA) questionnaire (Baumstarck et al., 2019 ). The end‐product of this study will be the discovery of new knowledge (outer ring of model) to inform ICU nursing practice regarding discomfort‐producing stimuli. Future areas of investigation would include developing and testing interventions (translation of possible solutions through clinical trials), of which those that are found to be effective would then be directly applied in the setting of ICU clinical nursing practice contributing to symptom science for critically ill patients.

An exemplar within the domain of caregiving science (second ring of MCNR model) is a multidisciplinary trial co‐led by a nurse scientist and physician (team science—third ring of model). The investigators noted that patients with advanced cancer or those nearing the end‐of‐life experience significant, unique distress related to their disease, treatment and impending mortality. In addition, they noted a lack of evidence on best methods to manage psychosocial distress in patients and caregivers with complex needs (centre of model). Thus, they designed a study to determine the feasibility of a modified version of the Resilient Living Program (The Resilient Option, 2020 ) that is tailored to the needs of patients with advanced cancer and their adult caregivers. Outcomes of the study include feasibility of participant recruitment, acceptability of the intervention and self‐reports of resilience, quality of life, stress, anxiety, sleep, fatigue and caregiver role overload. Findings from this study will lead to the discovery (outer ring of model) of best practices for integrating a resilience training programme within the care of patients with complex needs (centre of model), and their caregivers. Future studies will examine the outcomes of revised training programmes that are more effectively tailored to the unique needs of these populations.

Recognising the emotional distress their patients endure, a group of nurses working on the bone marrow transplant (BMT) unit expressed interest in specific nursing interventions to support their patients' emotional well‐being. Although they knew from their clinical experience that hospitalisation for BMT is quite stressful, they wanted to have a better understanding of when the most distressing times were for the patients, and what aspects of undergoing BMT were the most stressful. A review of the literature did not identify the specific information they were seeking. In collaboration with a nurse scientist and social workers on the unit, they implemented a descriptive study aimed at answering their questions. The study is in progress, and when finished, the results will inform both nursing and social work practice. This is an example of how clinical nurses identified a need centred around the health and well‐being of complex patients (centre of the MCNR model), focused on symptom science (second ring of the model), and used team science (third ring of the model) to discover new information (outer ring of the model) from which nursing interventions can be developed and tested.

The final nursing research exemplar is within the domains of symptom science and self‐management science (second ring of the MCNR model) to address the unmet needs of complex critically ill patients (centre of model). As of this writing, a randomised controlled clinical trial is testing the efficacy of self‐administered versus intensive care unit (ICU) nurse‐administered sedative therapy for anxiety in critically ill patients receiving mechanical ventilatory support (1R01 {"type":"entrez-nucleotide","attrs":{"text":"HL130881","term_id":"1051909465","term_text":"HL130881"}} HL130881 ). Primary outcomes of the study include anxiety, duration of mechanical ventilation, delirium, level of arousal, alertness and sedative exposure. Post‐ICU outcomes are also being examined and include functional status, depression and health‐related quality of life. Findings from this clinical trial will be applied to the practice setting (outer ring of the model) to implement patient‐centred interventions that improve not only ICU outcomes but also quality of life during the trajectory of recovery from critical illness and injury.

6. DISCUSSION

The MCNR model guides nursing research across settings and prioritises inquiry on symptom science, self‐management science and caregiving science. The model is unique in that it specifically focuses on generation of nursing knowledge through the focus and conduct of research in a practice‐based clinical setting. Few such models have been found in the literature; those that are available focus on advancing bedside nurses' involvement in research (Brewer et al., 2009 ; Stutzman et al., 2016 ). Robust programmes of nursing research remain relatively uncommon in clinical settings (Robichaud‐Ekstrand, 2016 ). Availability of time and resources needed to facilitate clinical research are often constrained. Even in large academic medical centres with institutional commitment, the contributions of nursing research often go unrecognised, even from within the nursing profession. The MCNR model can be used to communicate the scope and focus of nursing research, from which studies can be developed to address significant problems impacted by nursing practice.

In creating the MCNR model, we sought to demonstrate the unique contributions of nursing research at our institution and develop a framework to guide the overall direction of nursing research. This model may have limited application in nonclinical settings; however, other institutions may glean information to develop similar models tailored to their settings. Adaptation of the model to fit a specific organisational context and available resources may be necessary. Although the model is implemented in a setting rich in human and other resources to guide nursing science, it could easily be used in settings with more limited resources to help frame the scope and function of nursing science. However, this model was primarily developed for use in clinical settings in which some resources for the conduct of research exist. Unfortunately, there are still many settings where the resources needed to facilitate nursing research are sparse or non‐existent.

The MCNR model can also be integrated with existing models of nursing research. The National Institutes of Health Symptom Science Model is one example of a complementary model that can be used in tandem with the MCNR. The Symptom Science Model provides a guide for researchers to study complex symptoms experienced by individuals and incorporates the components of phenotypic characterisation, biomarker discovery and clinical application, with an overall goal of symptom reduction and improvement (Cashion et al., 2016 ). These methodologic components can be used to advance the care of patients with complex needs in the context of the institutional priorities and infrastructure described in the model. The MCNR model can be applied in several ways to advance scientific knowledge in the areas of symptoms, self‐management and caregiving. The model incorporates advancements in biological sciences, technology and big data methods to meet the needs of patients in a holistic way using nursing's unique body of knowledge (Henly et al., 2015 ). While nurse scientists may not have extensive expertise in all areas, collaborating with other scientists and clinicians who have complementary expertise ensures that investigations incorporate the best science and technology from other fields to inform nursing knowledge and practice.

As nurse scientists are increasingly employed in clinical settings, it will become more important to evaluate and publish outcomes of models, including this one. Nursing research within our institution is evolving to best meet the needs of patients. The MCNR model is a step in the process to define our direction and differentiate our areas of expertise from those of other disciplines.

The model is not without limitations. The MCNR Model was developed by nurse scientists within the Division of Nursing Research to serve as a guide and focus for our conduct of research, and to communicate our work with others. It is a reflection of the current foci of nursing research at a single institution and, as noted earlier, may need to be adapted to meet the needs of other institutions. It is intended to serve as a starting point for the infrastructure needed to generate research ideas and to serve as a guide to focus the conduct of research in distinct scientific areas in practice‐based settings. It is not intended to constrain research foci that are outside of this model. The model may be of lower utility in settings where nurse scientists are not available. It will be revisited periodically by the research team and stakeholders to ensure that it reflects the current focus of nursing research throughout the institution.

7. CONCLUSION

Nurse scientists embedded in healthcare settings are uniquely positioned to inform translation of research findings to practice. As health care evolves and the needs of patients and caregivers become more complex, the importance of studying symptoms, self‐management and caregiving is becoming increasingly critical. Nurse scientists leverage team science, big data, innovation and technology to move knowledge generation along the continuum of discovery, translation and application. The MCNR model can be used to advance generation of new nursing knowledge to improve the health and well‐being of patients and caregivers.

8. RELEVANCE TO CLINICAL PRACTICE

The MCNR model can be used by nurse scientists embedded in healthcare settings to address clinically relevant questions and ultimately improve the overall physical, mental, spiritual, social and role functioning of patients and caregivers, as well as to enhance individuals' perceptions, judgements and expectations regarding their health. The model provides a structure for addressing nursing science priorities through the discovery, translation and application continuum, and advancing the generation of new nursing knowledge.

CONFLICT OF INTEREST

The authors report no conflicts of interest with this manuscript.

AUTHOR CONTRIBUTIONS

Conception and design of the work, drafting of the article, critical revisions of the article and final approval of the version to be published: All authors.

DATA AVAILABILITY STATEMENT

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Research is creating new knowledge. The quote by Neil Armstrong, "Research is creating new knowledge," is a concise yet powerful statement that encapsulates the essence and significance of research. In straightforward terms, this quote implies that research is not merely about gathering existing information but rather about generating fresh ...
(PDF) Generation of Knowledge through Research
to adopt and/or generate knowledge and use it for the purpose of im proving production and. quality of lives. Scientific research is widely used in all pure sciences and social sciences for this ...
Does a Knowledge Generation Approach to Learning Benefit ...
The shifting emphases of new national curricula have placed more attention on knowledge generation approaches to learning. Such approaches are centered on the fundamental sense of generative learning where practices and tools for learning become the focus of the learning environment, rather than on the products of learning. This paper, building on from the previous review by Fiorella and Mayer ...
A practice‐based model to guide nursing science and improve the health
The project team developed a model of nursing research to guide the foci for nurse scientists' research at the institution and to generate new nursing knowledge based on needs that arise from the practice setting. The model was also intended to encompass strategic priorities established both by the institution and the field of nursing science.
Knowledge Generation
Introduction, overview, and applications. Steven Simske, in Meta-Analytics, 2019. 1.7.6 Data mining and knowledge discovery. The distinction between data mining and knowledge discovery is largely one of timing. Data mining is the process by which substantial amounts of data are organized, normalized, tabulated, and categorized; in short, it is analyzing large databases in order to generate ...

How research is creating new knowledge and insight

Generating new knowledge and insight

Informing policy and improvement work

Taking the long view

Plans for the coming year

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How research is creating new knowledge and insight

Generating new knowledge and insight

Informing policy and improvement work

Taking the long view

Plans for the coming year

Darshan Patel

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What is knowledge and when should it be implemented?

Publication types

1.2 Ways of Creating Knowledge

Logical reasoning

Scientific method

How does scientific method contribute to evidence?

Capturing Change in Science, Technology, and Innovation: Improving Indicators to Inform Policy (2014)

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

Knowledge Translation in technology-based rehabilitation science and engineering

Assumptions and definitions regarding knowledge

Key assumption

Implementing technology-related knowledge to solve problems

Three states of knowledge

Discovery State of Knowledge

Invention State of Knowledge

Innovation State of Knowledge

Three states of knowledge and KTA model

Need To Knowledge (NTK) model

Six approaches to solving a problem with knowledge

Novel method of addressing current problem

The Need to Knowledge (NTK) model

NTK Phase I. Discovery

NTK Phase II. Invention

NTK Phase III. Innovation

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Implementation Science

Creating new knowledge in undergraduate research – the challenge of questions, theory and focus in dissertations and projects. A blog for students.

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Neil Armstrong: 'Research is creating new knowledge.'

Neil Armstrong: 'That's one small step for a man, one giant leap for mankind.'

Does a Knowledge Generation Approach to Learning Benefit Students? A Systematic Review of Research on the Science Writing Heuristic Approach

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References for the Studies Included in the Analysis

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A practice‐based model to guide nursing science and improve the health and well‐being of patients and caregivers

Lori M. Rhudy

Linda L. Chlan

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What does this paper contribute to the wider global clinical community?

1. INTRODUCTION