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Research Gap – Types, Examples and How to Identify

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Research Gap

Research Gap


Research gap refers to an area or topic within a field of study that has not yet been extensively researched or is yet to be explored. It is a question, problem or issue that has not been addressed or resolved by previous research.

How to Identify Research Gap

Identifying a research gap is an essential step in conducting research that adds value and contributes to the existing body of knowledge. Research gap requires critical thinking, creativity, and a thorough understanding of the existing literature . It is an iterative process that may require revisiting and refining your research questions and ideas multiple times.

Here are some steps that can help you identify a research gap:

  • Review existing literature: Conduct a thorough review of the existing literature in your research area. This will help you identify what has already been studied and what gaps still exist.
  • Identify a research problem: Identify a specific research problem or question that you want to address.
  • Analyze existing research: Analyze the existing research related to your research problem. This will help you identify areas that have not been studied, inconsistencies in the findings, or limitations of the previous research.
  • Brainstorm potential research ideas : Based on your analysis, brainstorm potential research ideas that address the identified gaps.
  • Consult with experts: Consult with experts in your research area to get their opinions on potential research ideas and to identify any additional gaps that you may have missed.
  • Refine research questions: Refine your research questions and hypotheses based on the identified gaps and potential research ideas.
  • Develop a research proposal: Develop a research proposal that outlines your research questions, objectives, and methods to address the identified research gap.

Types of Research Gap

There are different types of research gaps that can be identified, and each type is associated with a specific situation or problem. Here are the main types of research gaps and their explanations:

Theoretical Gap

This type of research gap refers to a lack of theoretical understanding or knowledge in a particular area. It can occur when there is a discrepancy between existing theories and empirical evidence or when there is no theory that can explain a particular phenomenon. Identifying theoretical gaps can lead to the development of new theories or the refinement of existing ones.

Empirical Gap

An empirical gap occurs when there is a lack of empirical evidence or data in a particular area. It can happen when there is a lack of research on a specific topic or when existing research is inadequate or inconclusive. Identifying empirical gaps can lead to the development of new research studies to collect data or the refinement of existing research methods to improve the quality of data collected.

Methodological Gap

This type of research gap refers to a lack of appropriate research methods or techniques to answer a research question. It can occur when existing methods are inadequate, outdated, or inappropriate for the research question. Identifying methodological gaps can lead to the development of new research methods or the modification of existing ones to better address the research question.

Practical Gap

A practical gap occurs when there is a lack of practical applications or implementation of research findings. It can occur when research findings are not implemented due to financial, political, or social constraints. Identifying practical gaps can lead to the development of strategies for the effective implementation of research findings in practice.

Knowledge Gap

This type of research gap occurs when there is a lack of knowledge or information on a particular topic. It can happen when a new area of research is emerging, or when research is conducted in a different context or population. Identifying knowledge gaps can lead to the development of new research studies or the extension of existing research to fill the gap.

Examples of Research Gap

Here are some examples of research gaps that researchers might identify:

  • Theoretical Gap Example : In the field of psychology, there might be a theoretical gap related to the lack of understanding of the relationship between social media use and mental health. Although there is existing research on the topic, there might be a lack of consensus on the mechanisms that link social media use to mental health outcomes.
  • Empirical Gap Example : In the field of environmental science, there might be an empirical gap related to the lack of data on the long-term effects of climate change on biodiversity in specific regions. Although there might be some studies on the topic, there might be a lack of data on the long-term effects of climate change on specific species or ecosystems.
  • Methodological Gap Example : In the field of education, there might be a methodological gap related to the lack of appropriate research methods to assess the impact of online learning on student outcomes. Although there might be some studies on the topic, existing research methods might not be appropriate to assess the complex relationships between online learning and student outcomes.
  • Practical Gap Example: In the field of healthcare, there might be a practical gap related to the lack of effective strategies to implement evidence-based practices in clinical settings. Although there might be existing research on the effectiveness of certain practices, they might not be implemented in practice due to various barriers, such as financial constraints or lack of resources.
  • Knowledge Gap Example: In the field of anthropology, there might be a knowledge gap related to the lack of understanding of the cultural practices of indigenous communities in certain regions. Although there might be some research on the topic, there might be a lack of knowledge about specific cultural practices or beliefs that are unique to those communities.

Examples of Research Gap In Literature Review, Thesis, and Research Paper might be:

  • Literature review : A literature review on the topic of machine learning and healthcare might identify a research gap in the lack of studies that investigate the use of machine learning for early detection of rare diseases.
  • Thesis : A thesis on the topic of cybersecurity might identify a research gap in the lack of studies that investigate the effectiveness of artificial intelligence in detecting and preventing cyber attacks.
  • Research paper : A research paper on the topic of natural language processing might identify a research gap in the lack of studies that investigate the use of natural language processing techniques for sentiment analysis in non-English languages.

How to Write Research Gap

By following these steps, you can effectively write about research gaps in your paper and clearly articulate the contribution that your study will make to the existing body of knowledge.

Here are some steps to follow when writing about research gaps in your paper:

  • Identify the research question : Before writing about research gaps, you need to identify your research question or problem. This will help you to understand the scope of your research and identify areas where additional research is needed.
  • Review the literature: Conduct a thorough review of the literature related to your research question. This will help you to identify the current state of knowledge in the field and the gaps that exist.
  • Identify the research gap: Based on your review of the literature, identify the specific research gap that your study will address. This could be a theoretical, empirical, methodological, practical, or knowledge gap.
  • Provide evidence: Provide evidence to support your claim that the research gap exists. This could include a summary of the existing literature, a discussion of the limitations of previous studies, or an analysis of the current state of knowledge in the field.
  • Explain the importance: Explain why it is important to fill the research gap. This could include a discussion of the potential implications of filling the gap, the significance of the research for the field, or the potential benefits to society.
  • State your research objectives: State your research objectives, which should be aligned with the research gap you have identified. This will help you to clearly articulate the purpose of your study and how it will address the research gap.

Importance of Research Gap

The importance of research gaps can be summarized as follows:

  • Advancing knowledge: Identifying research gaps is crucial for advancing knowledge in a particular field. By identifying areas where additional research is needed, researchers can fill gaps in the existing body of knowledge and contribute to the development of new theories and practices.
  • Guiding research: Research gaps can guide researchers in designing studies that fill those gaps. By identifying research gaps, researchers can develop research questions and objectives that are aligned with the needs of the field and contribute to the development of new knowledge.
  • Enhancing research quality: By identifying research gaps, researchers can avoid duplicating previous research and instead focus on developing innovative research that fills gaps in the existing body of knowledge. This can lead to more impactful research and higher-quality research outputs.
  • Informing policy and practice: Research gaps can inform policy and practice by highlighting areas where additional research is needed to inform decision-making. By filling research gaps, researchers can provide evidence-based recommendations that have the potential to improve policy and practice in a particular field.

Applications of Research Gap

Here are some potential applications of research gap:

  • Informing research priorities: Research gaps can help guide research funding agencies and researchers to prioritize research areas that require more attention and resources.
  • Identifying practical implications: Identifying gaps in knowledge can help identify practical applications of research that are still unexplored or underdeveloped.
  • Stimulating innovation: Research gaps can encourage innovation and the development of new approaches or methodologies to address unexplored areas.
  • Improving policy-making: Research gaps can inform policy-making decisions by highlighting areas where more research is needed to make informed policy decisions.
  • Enhancing academic discourse: Research gaps can lead to new and constructive debates and discussions within academic communities, leading to more robust and comprehensive research.

Advantages of Research Gap

Here are some of the advantages of research gap:

  • Identifies new research opportunities: Identifying research gaps can help researchers identify areas that require further exploration, which can lead to new research opportunities.
  • Improves the quality of research: By identifying gaps in current research, researchers can focus their efforts on addressing unanswered questions, which can improve the overall quality of research.
  • Enhances the relevance of research: Research that addresses existing gaps can have significant implications for the development of theories, policies, and practices, and can therefore increase the relevance and impact of research.
  • Helps avoid duplication of effort: Identifying existing research can help researchers avoid duplicating efforts, saving time and resources.
  • Helps to refine research questions: Research gaps can help researchers refine their research questions, making them more focused and relevant to the needs of the field.
  • Promotes collaboration: By identifying areas of research that require further investigation, researchers can collaborate with others to conduct research that addresses these gaps, which can lead to more comprehensive and impactful research outcomes.

Disadvantages of Research Gap

While research gaps can be advantageous, there are also some potential disadvantages that should be considered:

  • Difficulty in identifying gaps: Identifying gaps in existing research can be challenging, particularly in fields where there is a large volume of research or where research findings are scattered across different disciplines.
  • Lack of funding: Addressing research gaps may require significant resources, and researchers may struggle to secure funding for their work if it is perceived as too risky or uncertain.
  • Time-consuming: Conducting research to address gaps can be time-consuming, particularly if the research involves collecting new data or developing new methods.
  • Risk of oversimplification: Addressing research gaps may require researchers to simplify complex problems, which can lead to oversimplification and a failure to capture the complexity of the issues.
  • Bias : Identifying research gaps can be influenced by researchers’ personal biases or perspectives, which can lead to a skewed understanding of the field.
  • Potential for disagreement: Identifying research gaps can be subjective, and different researchers may have different views on what constitutes a gap in the field, leading to disagreements and debate.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Identifying Research Gaps to Pursue Innovative Research

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This article is an excerpt from a lecture given by my Ph.D. guide, a researcher in public health. She advised us on how to identify research gaps to pursue innovative research in our fields.

What is a Research Gap?

Today we are talking about the research gap: what is it, how to identify it, and how to make use of it so that you can pursue innovative research. Now, how many of you have ever felt you had discovered a new and exciting research question , only to find that it had already been written about? I have experienced this more times than I can count. Graduate studies come with pressure to add new knowledge to the field. We can contribute to the progress and knowledge of humanity. To do this, we need to first learn to identify research gaps in the existing literature.

A research gap is, simply, a topic or area for which missing or insufficient information limits the ability to reach a conclusion for a question. It should not be confused with a research question, however. For example, if we ask the research question of what the healthiest diet for humans is, we would find many studies and possible answers to this question. On the other hand, if we were to ask the research question of what are the effects of antidepressants on pregnant women, we would not find much-existing data. This is a research gap. When we identify a research gap, we identify a direction for potentially new and exciting research.

peer review

How to Identify Research Gap?

Considering the volume of existing research, identifying research gaps can seem overwhelming or even impossible. I don’t have time to read every paper published on public health. Similarly, you guys don’t have time to read every paper. So how can you identify a research gap?

There are different techniques in various disciplines, but we can reduce most of them down to a few steps, which are:

  • Identify your key motivating issue/question
  • Identify key terms associated with this issue
  • Review the literature, searching for these key terms and identifying relevant publications
  • Review the literature cited by the key publications which you located in the above step
  • Identify issues not addressed by  the literature relating to your critical  motivating issue

It is the last step which we all find the most challenging. It can be difficult to figure out what an article is  not  saying. I like to keep a list of notes of biased or inconsistent information. You could also track what authors write as “directions for future research,” which often can point us towards the existing gaps.

Different Types of Research Gaps

Identifying research gaps is an essential step in conducting research, as it helps researchers to refine their research questions and to focus their research efforts on areas where there is a need for more knowledge or understanding.

1. Knowledge gaps

These are gaps in knowledge or understanding of a subject, where more research is needed to fill the gaps. For example, there may be a lack of understanding of the mechanisms behind a particular disease or how a specific technology works.

2. Conceptual gaps

These are gaps in the conceptual framework or theoretical understanding of a subject. For example, there may be a need for more research to understand the relationship between two concepts or to refine a theoretical framework.

3. Methodological gaps

These are gaps in the methods used to study a particular subject. For example, there may be a need for more research to develop new research methods or to refine existing methods to address specific research questions.

4. Data gaps

These are gaps in the data available on a particular subject. For example, there may be a need for more research to collect data on a specific population or to develop new measures to collect data on a particular construct.

5. Practical gaps

These are gaps in the application of research findings to practical situations. For example, there may be a need for more research to understand how to implement evidence-based practices in real-world settings or to identify barriers to implementing such practices.

Examples of Research Gap

Limited understanding of the underlying mechanisms of a disease:.

Despite significant research on a particular disease, there may be a lack of understanding of the underlying mechanisms of the disease. For example, although much research has been done on Alzheimer’s disease, the exact mechanisms that lead to the disease are not yet fully understood.

Inconsistencies in the findings of previous research:

When previous research on a particular topic has inconsistent findings, there may be a need for further research to clarify or resolve these inconsistencies. For example, previous research on the effectiveness of a particular treatment for a medical condition may have produced inconsistent findings, indicating a need for further research to determine the true effectiveness of the treatment.

Limited research on emerging technologies:

As new technologies emerge, there may be limited research on their applications, benefits, and potential drawbacks. For example, with the increasing use of artificial intelligence in various industries, there is a need for further research on the ethical, legal, and social implications of AI.

How to Deal with Literature Gap?

Once you have identified the literature gaps, it is critical to prioritize. You may find many questions which remain to be answered in the literature. Often one question must be answered before the next can be addressed. In prioritizing the gaps, you have identified, you should consider your funding agency or stakeholders, the needs of the field, and the relevance of your questions to what is currently being studied. Also, consider your own resources and ability to conduct the research you’re considering. Once you have done this, you can narrow your search down to an appropriate question.

Tools to Help Your Search

There are thousands of new articles published every day, and staying up to date on the literature can be overwhelming. You should take advantage of the technology that is available. Some services include  PubCrawler ,  Feedly ,  Google Scholar , and PubMed updates. Stay up to date on social media forums where scholars share new discoveries, such as Twitter. Reference managers such as  Mendeley  can help you keep your references well-organized. I personally have had success using Google Scholar and PubMed to stay current on new developments and track which gaps remain in my personal areas of interest.

The most important thing I want to impress upon you today is that you will struggle to  choose a research topic  that is innovative and exciting if you don’t know the existing literature well. This is why identifying research gaps starts with an extensive and thorough  literature review . But give yourself some boundaries.  You don’t need to read every paper that has ever been written on a topic. You may find yourself thinking you’re on the right track and then suddenly coming across a paper that you had intended to write! It happens to everyone- it happens to me quite often. Don’t give up- keep reading and you’ll find what you’re looking for.

Class dismissed!

How do you identify research gaps? Share your thoughts in the comments section below.

Frequently Asked Questions

A research gap can be identified by looking for a topic or area with missing or insufficient information that limits the ability to reach a conclusion for a question.

Identifying a research gap is important as it provides a direction for potentially new research or helps bridge the gap in existing literature.

Gap in research is a topic or area with missing or insufficient information. A research gap limits the ability to reach a conclusion for a question.

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Thank u for your suggestion.

Very useful tips specially for a beginner

Thank you. This is helpful. I find that I’m overwhelmed with literatures. As I read on a particular topic, and in a particular direction I find that other conflicting issues, topic a and ideas keep popping up, making me more confused.

I am very grateful for your advice. It’s just on point.

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How To Find A Research Gap, Quickly

A step-by-step guide for new researchers

By: Derek Jansen (MBA) | Reviewer: Eunice Rautenbach (DTech) | April 2023

If you’ve got a dissertation, thesis or research project coming up, one of the first (and most important) things you’ll need to do is find a suitable research gap . In this post, we’ll share a straightforward process to help you uncover high-quality, original research gaps in a very time-efficient manner.

Overview: Finding Research Gaps

  • What exactly is a research gap?
  • Research gap vs research topic
  • How to find potential research gaps
  • How to evaluate research gaps (and topics)
  • Key takeaways

What is a research gap?

As a starting point, it’s useful to first define what we mean by research gap, to ensure we’re all on the same page. The term “research gap” gets thrown around quite loosely by students and academics alike, so let’s clear that up.

Simply put, a research gap is any space where there’s a lack of solid, agreed-upon research regarding a specific topic, issue or phenomenon. In other words, there’s a lack of established knowledge and, consequently, a need for further research.

Let’s look at a hypothetical example to illustrate a research gap.

Within the existing research regarding factors affect job satisfaction , there may be a wealth of established and agreed-upon empirical work within a US and UK context , but very little research within Eastern nations such as Japan or Korea . Given that these nations have distinctly different national cultures and workforce compositions compared to the West, it’s plausible that the factors that contribute toward job satisfaction may also be different. Therefore, a research gap emerges for studies that explore this matter.

This example is purely hypothetical (and there’s probably plenty of research covering this already), but it illustrates the core point that a research gap reflects a lack of firmly established knowledge regarding a specific matter . Given this lack, an opportunity exists for researchers (like you) to go on and fill the gap.

So, it’s the same as a research topic?

Not quite – but they are connected. A research gap refers to an area where there’s a lack of settled research , whereas a research topic outlines the focus of a specific study . Despite being different things, these two are related because research gaps are the birthplace of research topics. In other words, by identifying a clear research gap, you have a foundation from which you can build a research topic for your specific study. Your study is unlikely to resolve the entire research gap on it’s own, but it will contribute towards it .

If you’d like to learn more, we’ve got a comprehensive post that covers research gaps (including the different types of research gaps), as well as an explainer video below.

How to find a research gap

Now that we’ve defined what a research gap is, it’s time to get down to the process of finding potential research gaps that you can use as a basis for potential research topics. Importantly, it’s worth noting that this is just one way (of many) to find a research gap (and consequently a topic). We’re not proposing that it’s the only way or best way, but it’s certainly a relatively quick way to identify opportunities.

Step 1: Identify your broad area of interest

The very first step to finding a research gap is to decide on your general area of interest . For example, if you were undertaking a dissertation as part of an MBA degree, you may decide that you’re interested in corporate reputation, HR strategy, or leadership styles. As you can see, these are broad categories – there’s no need to get super specific just yet. Of course, if there is something very specific that you’re interested in, that’s great – but don’t feel pressured to narrow it down too much right now.

Equally important is to make sure that this area of interest is allowed by your university or whichever institution you’ll be proposing your research to. This might sound dead obvious, but you’ll be surprised how many times we’ve seen students run down a path with great excitement, only to later learn that their university wants a very specific area of focus in terms of topic (and their area of interest doesn’t qualify).

Free Webinar: How To Find A Dissertation Research Topic

Step 2: Do an initial literature scan

Once you’ve pinned down your broad area (or areas) of interest, the next step is to head over to Google Scholar to undertake an initial literature scan . If you’re not familiar with this tool, Google Scholar is a great starting point for finding academic literature on pretty much any topic, as it uses Google’s powerful search capabilities to hunt down relevant academic literature. It’s certainly not the be-all and end-all of literature search tools, but it’s a useful starting point .

Within Google Scholar, you’ll want to do a few searches using keywords that are relevant to your area of interest. Sticking with our earlier example, we could use the key phrase “job satisfaction”, or we may want to get a little more specific – perhaps “job satisfaction for millennials” or “job satisfaction in Japan”.

It’s always a good idea to play around with as many keywords/phrases as you can think up.  Take an iterative approach here and see which keywords yield the most relevant results for you. Keep each search open in a new tab, as this will help keep things organised for the next steps.

Once you’ve searched for a few different keywords/phrases, you’ll need to do some refining for each of the searches you undertook. Specifically, you’ll need to filter the results down to the most recent papers . You can do this by selecting the time period in the top left corner (see the example below).

using google scholar to find a research gap

Filtering to the current year is typically a good choice (especially for fast-moving research areas), but in some cases, you may need to filter to the last two years . If you’re undertaking this task in January or February, for example, you’ll likely need to select a two-year period.

Need a helping hand?

research gap business definition

Step 3: Review and shortlist articles that interest you

Once you’ve run a few searches using different keywords and phrases, you’ll need to scan through the results to see what looks most relevant and interesting to you. At this stage, you can just look at the titles and abstracts (the description provided by Google Scholar) – don’t worry about reading the actual article just yet.

Next, select 5 – 10 articles that interest you and open them up. Here, we’re making the assumption that your university has provided you with access to a decent range of academic databases. In some cases, Google Scholar will link you directly to a PDF of the article, but in most cases, you’ll need paid access. If you don’t have this (for example, if you’re still applying to a university), you can look at two options:

Open-access articles – these are free articles which you can access without any journal subscription. A quick Google search (the regular Google) will help you find open-access journals in your area of interest, but you can also have a look at DOAJ and Elsevier Open Access.

DeepDyve – this is a monthly subscription service that allows you to get access to a broad range of journals. At the time of shooting this video, their monthly subscription is around $50 and they do offer a free trial, which may be sufficient for your project.

Step 4: Skim-read your article shortlist

Now, it’s time to dig into your article shortlist and do some reading. But don’t worry, you don’t need to read the articles from start to finish – you just need to focus on a few key sections.

Specifically, you’ll need to pay attention to the following:

  • The abstract (which you’ve probably already read a portion of in Google Scholar)
  • The introduction – this will give you a bit more detail about the context and background of the study, as well as what the researchers were trying to achieve (their research aims)
  • The discussion or conclusion – this will tell you what the researchers found

By skimming through these three sections for each journal article on your shortlist, you’ll gain a reasonable idea of what each study was about, without having to dig into the painful details. Generally, these sections are usually quite short, so it shouldn’t take you too long.

Step 5: Go “FRIN hunting”

This is where the magic happens. Within each of the articles on your shortlist, you’ll want to search for a few very specific phrases , namely:

  • Future research
  • Further research
  • Research opportunities
  • Research directions

All of these terms are commonly found in what we call the “FRIN” section . FRIN stands for “further research is needed”. The FRIN is where the researchers explain what other researchers could do to build on their study, or just on the research area in general. In other words, the FRIN section is where you can find fresh opportunities for novel research . Most empirical studies will either have a dedicated FRIN section or paragraph, or they’ll allude to the FRIN toward the very end of the article. You’ll need to do a little scanning, but it’s usually pretty easy to spot.

It’s worth mentioning that naturally, the FRIN doesn’t hand you a list of research gaps on a platter. It’s not a silver bullet for finding research gaps – but it’s the closest thing to it. Realistically, the FRIN section helps you shortcut the gap-hunting process  by highlighting novel research avenues that are worth exploring.

This probably sounds a little conceptual, so let’s have a look at a few examples:

The impact of overeducation on job outcomes: Evidence from Saudi Arabia (Alzubaidi, 2020)

If you scroll down to the bottom of this article, you’ll see there’s a dedicated section called “Limitations and directions for future research”. Here they talk about the limitations of the study and provide suggestions about how future researchers could improve upon their work and overcome the limitations.

Perceived organizational support and job satisfaction: a moderated mediation model of proactive personality and psychological empowerment (Maan et al, 2020)

In this article, within the limitations section, they provide a wonderfully systematic structure where they discuss each limitation, followed by a proposal as to how future studies can overcome the respective limitation. In doing so, they are providing very specific research opportunities for other researchers.

Medical professionals’ job satisfaction and telemedicine readiness during the COVID-19 pandemic: solutions to improve medical practice in Egypt (El-Mazahy et al, 2023)

In this article, they don’t have a dedicated section discussing the FRIN, but we can deduct it based on the limitations section. For example, they state that an evaluation of the knowledge about telemedicine and technology-related skills would have enabled studying their independent effect on the perception of telemedicine.

Follow this FRIN-seeking process for the articles you shortlisted and map out any potentially interesting research gaps . You may find that you need to look at a larger number of articles to find something interesting, or you might find that your area of interest shifts as you engage in the reading – this is perfectly natural. Take as much time as you need to develop a shortlist of potential research gaps that interest you.

Importantly, once you’ve developed a shortlist of potential research gaps, you need to return to Google Scholar to double-check that there aren’t fresh studies that have already addressed the gap. Remember, if you’re looking at papers from two years ago in a fast-moving field, someone else may have jumped on it . Nevertheless, there could still very well be a unique angle you could take – perhaps a contextual gap (e.g. a specific country, industry, etc.).

Ultimately, the need for originality will depend on your specific university’s requirements and the level of study. For example, if you’re doing an undergraduate research project, the originality requirements likely won’t be as gruelling as say a Masters or PhD project. So, make sure you have a clear understanding of what your university’s expectations are. A good way to do this is to look at past dissertations and theses for your specific programme. You can usually find these in the university library or by asking the faculty.

How to evaluate potential research gaps

Once you’ve developed a shortlist of potential research gaps (and resultant potential research topics) that interest you, you’ll need to systematically evaluate  them  to choose a winner. There are many factors to consider here, but some important ones include the following:

  • Originality and value – is the topic sufficiently novel and will addressing it create value?
  • Data access – will you be able to get access to the sample of interest?
  • Costs – will there be additional costs involved for data collection and/or analysis?
  • Timeframes – will you be able to collect and analyse the data within the timeframe required by your university?
  • Supervisor support – is there a suitable supervisor available to support your project from start to finish?

To help you evaluate your options systematically, we’ve got a topic evaluation worksheet that allows you to score each potential topic against a comprehensive set of criteria. You can access the worksheet completely free of charge here .

Research topic evaluator

Recap: Key Takeaways

We’ve covered quite a lot of ground in this post. Here are the key takeaways:

  • A research gap is any space where there’s a lack of solid, agreed-upon research regarding a specific topic/issue/phenomenon.
  • Unique research topics emerge from research gaps , so it’s essential to first identify high-quality research gaps before you attempt to define a topic.
  • To find potential research gaps, start by seeking out recent journal articles on Google Scholar and pay particular attention to the FRIN section to identify novel opportunities.
  • Once you have a shortlist of prospective research gaps and resultant topic ideas, evaluate them systematically using a comprehensive set of criteria.

If you’d like to get hands-on help finding a research gap and research topic, be sure to check out our private coaching service , where we hold your hand through the research journey, step by step.

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How to find a research gap

Very useful for me, but i am still confusing review of literature review, how to find out topic related previous research.


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Thank you very much for this. It is really a great opportunity for me to learn the research journey.

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Last Updated: Jun 27, 2023 Views: 471327

What is a research gap.

A research gap is a question or a problem that has not been answered by any of the existing studies or research within your field. Sometimes, a research gap exists when there is a concept or new idea that hasn't been studied at all. Sometimes you'll find a research gap if all the existing research is outdated and in need of new/updated research (studies on Internet use in 2001, for example). Or, perhaps a specific population has not been well studied (perhaps there are plenty of studies on teenagers and video games, but not enough studies on toddlers and video games, for example). These are just a few examples, but any research gap you find is an area where more studies and more research need to be conducted. Please view this video clip from our Sage Research Methods database for more helpful information: How Do You Identify Gaps in Literature?

How do I find one?

It will take a lot of research and reading.  You'll need to be very familiar with all the studies that have already been done, and what those studies contributed to the overall body of knowledge about that topic. Make a list of any questions you have about your topic and then do some research to see if those questions have already been answered satisfactorily. If they haven't, perhaps you've discovered a gap!  Here are some strategies you can use to make the most of your time:

  • One useful trick is to look at the “suggestions for future research” or conclusion section of existing studies on your topic. Many times, the authors will identify areas where they think a research gap exists, and what studies they think need to be done in the future.
  • As you are researching, you will most likely come across citations for seminal works in your research field. These are the research studies that you see mentioned again and again in the literature.  In addition to finding those and reading them, you can use a database like Web of Science to follow the research trail and discover all the other articles that have cited these. See the FAQ: I found the perfect article for my paper. How do I find other articles and books that have cited it? on how to do this. One way to quickly track down these seminal works is to use a database like SAGE Navigator, a social sciences literature review tool. It is one of the products available via our SAGE Knowledge database.
  • In the PsycINFO and PsycARTICLES databases, you can select literature review, systematic review, and meta analysis under the Methodology section in the advanced search to quickly locate these. See the FAQ: Where can I find a qualitative or quantitative study? for more information on how to find the Methodology section in these two databases.
  • In CINAHL , you can select Systematic review under the Publication Type field in the advanced search. 
  • In Web of Science , check the box beside Review under the Document Type heading in the “Refine Results” sidebar to the right of the list of search hits.
  • If the database you are searching does not offer a way to filter your results by document type, publication type, or methodology in the advanced search, you can include these phrases (“literature reviews,” meta-analyses, or “systematic reviews”) in your search string.  For example, “video games” AND “literature reviews” could be a possible search that you could try.

Please give these suggestions a try and contact a librarian for additional assistance.

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  • Research Process

What is a Research Gap

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Table of Contents

If you are a young researcher, or even still finishing your studies, you’ll probably notice that your academic environment revolves around certain research topics, probably linked to your department or to the interest of your mentor and direct colleagues. For example, if your department is currently doing research in nanotechnology applied to medicine, it is only natural that you feel compelled to follow this line of research. Hopefully, it’s something you feel familiar with and interested in – although you might take your own twists and turns along your career.

Many scientists end up continuing their academic legacy during their professional careers, writing about their own practical experiences in the field and adapting classic methodologies to a present context. However, each and every researcher dreams about being a pioneer in a subject one day, by discovering a topic that hasn’t been approached before by any other scientist. This is a research gap.

Research gaps are particularly useful for the advance of science, in general. Finding a research gap and having the means to develop a complete and sustained study on it can be very rewarding for the scientist (or team of scientists), not to mention how its new findings can positively impact our whole society.

How to Find a Gap in Research

How many times have you felt that you have finally formulated THAT new and exciting question, only to find out later that it had been addressed before? Probably more times than you can count.

There are some steps you can take to help identify research gaps, since it is impossible to go through all the information and research available nowadays:

  • Select a topic or question that motivates you: Research can take a long time and surely a large amount of physical, intellectual and emotional effort, therefore choose a topic that can keep you motivated throughout the process.
  • Find keywords and related terms to your selected topic: Besides synthesizing the topic to its essential core, this will help you in the next step.
  • Use the identified keywords to search literature: From your findings in the above step, identify relevant publications and cited literature in those publications.
  • Look for topics or issues that are missing or not addressed within (or related to) your main topic.
  • Read systematic reviews: These documents plunge deeply into scholarly literature and identify trends and paradigm shifts in fields of study. Sometimes they reveal areas or topics that need more attention from researchers and scientists.

How to find a Gap in Research

Keeping track of all the new literature being published every day is an impossible mission. Remember that there is technology to make your daily tasks easier, and reviewing literature can be one of them. Some online databases offer up-to-date publication lists with quite effective search features:

  • Elsevier’s Scope
  • Google Scholar

Of course, these tools may be more or less effective depending on knowledge fields. There might be even better ones for your specific topic of research; you can learn about them from more experienced colleagues or mentors.

Find out how FINER research framework can help you formulate your research question.

Literature Gap

The expression “literature gap” is used with the same intention as “research gap.” When there is a gap in the research itself, there will also naturally be a gap in the literature. Nevertheless, it is important to stress out the importance of language or text formulations that can help identify a research/literature gap or, on the other hand, making clear that a research gap is being addressed.

When looking for research gaps across publications you may have noticed sentences like:

…has/have not been… (studied/reported/elucidated) …is required/needed… …the key question is/remains… …it is important to address…

These expressions often indicate gaps; issues or topics related to the main question that still hasn’t been subject to a scientific study. Therefore, it is important to take notice of them: who knows if one of these sentences is hiding your way to fame.

Language Editing Services by Elsevier Author Services:

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How to identify research gaps


Anthony Newman

About this video

Researching is an ongoing task, as it requires you to think of something nobody else has thought of before. This is where the research gap comes into play.

We will explain what a research gap is, provide you with steps on how to identify these research gaps, as well as provide you several tools that can help you identify them.

About the presenter


Senior Publisher, Life Sciences, Elsevier

Anthony Newman is a Senior Publisher with Elsevier and is based in Amsterdam. Each year he presents numerous Author Workshops and other similar trainings worldwide. He is currently responsible for fifteen biochemistry and laboratory medicine journals, he joined Elsevier over thirty years ago and has been Publisher for more than twenty of those years. Before then he was the marketing communications manager for the biochemistry journals of Elsevier.  By training he is a polymer chemist and was active in the surface coating industry before leaving London and moving to Amsterdam in 1987 to join Elsevier.

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Robinson KA, Akinyede O, Dutta T, et al. Framework for Determining Research Gaps During Systematic Review: Evaluation [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Feb.

Cover of Framework for Determining Research Gaps During Systematic Review: Evaluation

Framework for Determining Research Gaps During Systematic Review: Evaluation [Internet].


The identification of gaps from systematic reviews is essential to the practice of “evidence-based research.” Health care research should begin and end with a systematic review. 1 - 3 A comprehensive and explicit consideration of the existing evidence is necessary for the identification and development of an unanswered and answerable question, for the design of a study most likely to answer that question, and for the interpretation of the results of the study. 4

In a systematic review, the consideration of existing evidence often highlights important areas where deficiencies in information limit our ability to make decisions. We define a research gap as a topic or area for which missing or inadequate information limits the ability of reviewers to reach a conclusion for a given question. A research gap may be further developed, such as through stakeholder engagement in prioritization, into research needs. Research needs are those areas where the gaps in the evidence limit decision making by patients, clinicians, and policy makers. A research gap may not be a research need if filling the gap would not be of use to stakeholders that make decisions in health care. The clear and explicit identification of research gaps is a necessary step in developing a research agenda. Evidence reports produced by Evidence-based Practice Centers (EPCs) have always included a future research section. However, in contrast to the explicit and transparent steps taken in the completion of a systematic review, there has not been a systematic process for the identification of research gaps.

In a prior methods project, our EPC set out to identify and pilot test a framework for the identification of research gaps. 5 , 6 We searched the literature, conducted an audit of EPC evidence reports, and sought information from other organizations which conduct evidence synthesis. Despite these efforts, we identified little detail or consistency in the frameworks used to determine research gaps within systematic reviews. In general, we found no widespread use or endorsement of a specific formal process or framework for identifying research gaps using systematic reviews.

We developed a framework to systematically identify research gaps from systematic reviews. This framework facilitates the classification of where the current evidence falls short and why the evidence falls short. The framework included two elements: (1) the characterization the gaps and (2) the identification and classification of the reason(s) for the research gap.

The PICOS structure (Population, Intervention, Comparison, Outcome and Setting) was used in this framework to describe questions or parts of questions inadequately addressed by the evidence synthesized in the systematic review. The issue of timing, sometimes included as PICOTS, was considered separately for Intervention, Comparison, and Outcome. The PICOS elements were the only sort of framework we had identified in an audit of existing methods for the identification of gaps used by EPCs and other related organizations (i.e., health technology assessment organizations). We chose to use this structure as it is one familiar to EPCs, and others, in developing questions.

It is not only important to identify research gaps but also to determine how the evidence falls short, in order to maximally inform researchers, policy makers, and funders on the types of questions that need to be addressed and the types of studies needed to address these questions. Thus, the second element of the framework was the classification of the reasons for the existence of a research gap. For each research gap, the reason(s) that most preclude conclusions from being made in the systematic review is chosen by the review team completing the framework. To leverage work already being completed by review teams, we mapped the reasons for research gaps to concepts from commonly used evidence grading systems. Briefly, these categories of reasons, explained in detail in the prior JHU EPC report 5 , are:

  • Insufficient or imprecise information
  • Biased information
  • Inconsistent or unknown consistency results
  • Not the right information

The framework facilitates a systematic approach to identifying research gaps and the reasons for those gaps. The identification of where the evidence falls short and how the evidence falls short is essential to the development of important research questions and in providing guidance in how to address these questions.

As part of the previous methods product, we developed a worksheet and instructions to facilitate the use of the framework when completing a systematic review (See Appendix A ). Preliminary evaluation of the framework and worksheet was completed by applying the framework to two completed EPC evidence reports. The framework was further refined through peer review. In this current project, we extend our work on this research gaps framework.

Our objective in this project was to complete two types of further evaluation: (1) application of the framework across a larger sample of existing systematic reviews in different topic areas, and (2) implementation of the framework by EPCs. These two objectives were used to evaluate the framework and instructions for usability and to evaluate the application of the framework by others, outside of our EPC, including as part of the process of completing an EPC report. Our overall goal was to produce a revised framework with guidance that could be used by EPCs to explicitly identify research gaps from systematic reviews.

  • Cite this Page Robinson KA, Akinyede O, Dutta T, et al. Framework for Determining Research Gaps During Systematic Review: Evaluation [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Feb. Introduction.
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Business research: definition, types & methods.

10 min read What is business research and why does it matter? Here are some of the ways business research can be helpful to your company, whichever method you choose to carry it out.

What is business research?

Business research helps companies make better business decisions by gathering information. The scope of the term business research is quite broad – it acts as an umbrella that covers every aspect of business, from finances to advertising creative. It can include research methods which help a company better understand its target market. It could focus on customer experience and assess customer satisfaction levels. Or it could involve sizing up the competition through competitor research.

Often when carrying out business research, companies are looking at their own data, sourced from their employees, their customers and their business records. However, business researchers can go beyond their own company in order to collect relevant information and understand patterns that may help leaders make informed decisions. For example, a business may carry out ethnographic research where the participants are studied in the context of their everyday lives, rather than just in their role as consumer, or look at secondary data sources such as open access public records and empirical research carried out in academic studies.

There is also a body of knowledge about business in general that can be mined for business research purposes. For example organizational theory and general studies on consumer behavior.

Free eBook: 2024 global market research trends report

Why is business research important?

We live in a time of high speed technological progress and hyper-connectedness. Customers have an entire market at their fingertips and can easily switch brands if a competitor is offering something better than you are. At the same time, the world of business has evolved to the point of near-saturation. It’s hard to think of a need that hasn’t been addressed by someone’s innovative product or service.

The combination of ease of switching, high consumer awareness and a super-evolved marketplace crowded with companies and their offerings means that businesses must do whatever they can to find and maintain an edge. Business research is one of the most useful weapons in the fight against business obscurity, since it allows companies to gain a deep understanding of buyer behavior and stay up to date at all times with detailed information on their market.

Thanks to the standard of modern business research tools and methods, it’s now possible for business analysts to track the intricate relationships between competitors, financial markets, social trends, geopolitical changes, world events, and more.

Find out how to conduct your own market research and make use of existing market research data with our Ultimate guide to market research

Types of business research

Business research methods vary widely, but they can be grouped into two broad categories – qualitative research and quantitative research .

Qualitative research methods

Qualitative business research deals with non-numerical data such as people’s thoughts, feelings and opinions. It relies heavily on the observations of researchers, who collect data from a relatively small number of participants – often through direct interactions.

Qualitative research interviews take place one-on-one between a researcher and participant. In a business context, the participant might be a customer, a supplier, an employee or other stakeholder. Using open-ended questions , the researcher conducts the interview in either a structured or unstructured format. Structured interviews stick closely to a question list and scripted phrases, while unstructured interviews are more conversational and exploratory. As well as listening to the participant’s responses, the interviewer will observe non-verbal information such as posture, tone of voice and facial expression.

Focus groups

Like the qualitative interview, a focus group is a form of business research that uses direct interaction between the researcher and participants to collect data. In focus groups , a small number of participants (usually around 10) take part in a group discussion led by a researcher who acts as moderator. The researcher asks questions and takes note of the responses, as in a qualitative research interview. Sampling for focus groups is usually purposive rather than random, so that the group members represent varied points of view.

Observational studies

In an observational study, the researcher may not directly interact with participants at all, but will pay attention to practical situations, such as a busy sales floor full of potential customers, or a conference for some relevant business activity. They will hear people speak and watch their interactions , then record relevant data such as behavior patterns that relate to the subject they are interested in. Observational studies can be classified as a type of ethnographic research. They can be used to gain insight about a company’s target audience in their everyday lives, or study employee behaviors in actual business situations.

Ethnographic Research

Ethnographic research is an immersive design of research where one observes peoples’ behavior in their natural environment. Ethnography was most commonly found in the anthropology field and is now practices across a wide range of social sciences.

Ehnography is used to support a designer’s deeper understanding of the design problem – including the relevant domain, audience(s), processes, goals and context(s) of use.

The ethnographic research process is a popular methodology used in the software development lifecycle. It helps create better UI/UX flow based on the real needs of the end-users.

If you truly want to understand your customers’ needs, wants, desires, pain-points “walking a mile” in their shoes enables this. Ethnographic research is this deeply rooted part of research where you truly learn your targe audiences’ problem to craft the perfect solution.

Case study research

A case study is a detailed piece of research that provides in depth knowledge about a specific person, place or organization. In the context of business research, case study research might focus on organizational dynamics or company culture in an actual business setting, and case studies have been used to develop new theories about how businesses operate. Proponents of case study research feel that it adds significant value in making theoretical and empirical advances. However its detractors point out that it can be time consuming and expensive, requiring highly skilled researchers to carry it out.

Quantitative research methods

Quantitative research focuses on countable data that is objective in nature. It relies on finding the patterns and relationships that emerge from mass data – for example by analyzing the material posted on social media platforms, or via surveys of the target audience. Data collected through quantitative methods is empirical in nature and can be analyzed using statistical techniques. Unlike qualitative approaches, a quantitative research method is usually reliant on finding the right sample size, as this will determine whether the results are representative. These are just a few methods – there are many more.

Surveys are one of the most effective ways to conduct business research. They use a highly structured questionnaire which is distributed to participants, typically online (although in the past, face to face and telephone surveys were widely used). The questions are predominantly closed-ended, limiting the range of responses so that they can be grouped and analyzed at scale using statistical tools. However surveys can also be used to get a better understanding of the pain points customers face by providing open field responses where they can express themselves in their own words. Both types of data can be captured on the same questionnaire, which offers efficiency of time and cost to the researcher.

Correlational research

Correlational research looks at the relationship between two entities, neither of which are manipulated by the researcher. For example, this might be the in-store sales of a certain product line and the proportion of female customers subscribed to a mailing list. Using statistical analysis methods, researchers can determine the strength of the correlation and even discover intricate relationships between the two variables. Compared with simple observation and intuition, correlation may identify further information about business activity and its impact, pointing the way towards potential improvements and more revenue.

Experimental research

It may sound like something that is strictly for scientists, but experimental research is used by both businesses and scholars alike. When conducted as part of the business intelligence process, experimental research is used to test different tactics to see which ones are most successful – for example one marketing approach versus another. In the simplest form of experimental research, the researcher identifies a dependent variable and an independent variable. The hypothesis is that the independent variable has no effect on the dependent variable, and the researcher will change the independent one to test this assumption. In a business context, the hypothesis might be that price has no relationship to customer satisfaction. The researcher manipulates the price and observes the C-Sat scores to see if there’s an effect.

The best tools for business research

You can make the business research process much quicker and more efficient by selecting the right tools. Business research methods like surveys and interviews demand tools and technologies that can store vast quantities of data while making them easy to access and navigate. If your system can also carry out statistical analysis, and provide predictive recommendations to help you with your business decisions, so much the better.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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What is Research Gap? And How to Identify it

June 12, 2023 | By Hitesh Bhasin | Filed Under: Marketing

The initial step in conducting a study is to identify previously unknown and unexplored parts of research. When you choose such an area, your research will improve and chances of getting published increase. Finding a research topic is a big task for researchers who are at their primary stage of career. The best way to find the problem is to identify the gap in existing research. The research gap is an unexplored area or under-explored area of research which has further scope for research.

You often find that there are some areas about which not a lot has it been written. This offers significant scope since previous researchers have not earlier explored them. These are called research gaps, and you have an excellent opportunity to fill them.

The research gap is essential from the perspective that it allows that field to progress further with the help of unexplored answers. The research gap can be an opportunity and a starting point for every aspiring researcher to being his research.

Research gap should not be confused with the research question . Both are different concepts.

For example, if a research question is asked what the healthiest diet for humans is, then there would be many studies and multiple possible answers for that question. But if we ask a research question of what are the effects of laxatives on a pregnant woman, we may not find a lot of existing data. This is known as a research gap.

Table of Contents

Researcher class

Following are the three classes of researchers formed considering the gap finding issue:

1. The first group

This class is primarily the class of researchers who act as per their enthusiasm. These class of researchers are proficient in their field, and they have years of experience along with rich knowledge of their field. They have covered almost all the crucial papers of their field of study.

2. The second group

This class is generally encouraged by external factors. For example, a particular college or particular professor is chosen by the researcher. That college or that professor may have a project already in their hand, which he may suggest to you.

This will save a lot of your time, and if you find this appropriate, you will select it for your research. Nevertheless, preliminary research is expected on your part if you are comfortable and are okay with that topic.

3. The third group

External factors like external forces, Professor etc. influences you to select a topic. For example, the environment in which the researcher has grown in and the needs of that external environment.

This will mean that society will force him to focus on the topics in their environment, and the researchers may not have a lot of inclination to find a different topic.

How to identify gaps in research

How to identify gaps in research

There is no scientific way or a well-defined process which will help you to find gaps in existing research. You have to use your imagination, curiosity, creativity with the judgement, which will help you to identify the gaps in the research.

Following are the tips for identifying resource gaps:

1. Inspiration from Literature

You have to read a lot of books and go through articles on the topics that you like. It will help you to provide understanding about the work already been done by researchers in the past. It will also provide you with an opportunity to ask yourself questions which will lead you to gaps in the research. It is recommended to read whatever is being written in your field and recognize it.

You have to question everything rather than being awed by it. Asking yourself the following questions will help you in the process:

  • Significance of this research to my work
  • Will this article help me to formulate any research questions? If yes, then which ones?
  • Does the argument of the author need more clarification?
  • Do I see any different perspective in this article which is not mentioned?
  • What are the questions which are left on answered by the author?
  • The methods of the procedure are used in the research or outdated. How can I update this with more recent procedures?

When you go through different scientific articles, you always have to analyze the introduction section in which the author explains the importance of the research and you have to read carefully the gaps that have been identified by the author previously.

Does the author suggest any further research? If you are advised any further reading, then that could be very inspirational for you to find research gaps.

There are many meta-analyses of the research which are published along with that. You can learn about the trends and developments that have been done for years in your area of expertise.

This will also help you to know the problems that have already been researched in the past. Also, this will help you to understand the trending researches on the related topic.

2. Research advisor

You may face multiple problems about the research topic or in general within your field. The first thing to do in such case is to consult your research advisor. The advisor will help you to articulate your ideas and solve your queries.

He may even identify your mistakes and point you in the right direction. The sooner you find your errors, the better it is. If you have a doubt or query that you think will do you good, then you can always discuss it with your advisor and ask for their opinions.

The immense experience of research advisor can be used for your benefit. The advisor may not have worked on your research problem, but he surely has worked in your field, and he must have studied for his part.

Instead of going through everything that the advisor went through, you can directly take the baton from his hand and carry forward. It will not only save time in researching something that has already been studied, but it will also increase your bonding with the advisor.

3. Digital tools

To update yourself with trending topics in research, it is essential that the researcher is tech-savvy and understands and operates basic internet functions. It can save time researching the same thing but also can cast a wide net.

You will have a variety of options known to yourself because of websites. Apart from that, you will also come to know about emerging trends in your field, key contributors and their publications, even international achievements and previous researches done on your topic.

Sometimes it so happens that you may start work on some topic and that topic would be either discarded or removed from your field.

To be aware of such developments, it is advised that the researcher is tech-savvy. A simple Google search will get you knowledge of thousands of websites and articles in a matter of seconds.

4. Refer influential journals

Many websites of research journals have a separate section known as ‘key concepts’ in which many experts highlight the primary idea of that field. Reading this section will be of immense assistance to generate new ideas.

Also, you should try to look at the reference section of these journals since it can lead you to a significant research gap. The reference section has citations for every sentence, point or claim made in the research.

Sifting through these can help you to understand the topic properly and make you aware of any research gaps which you may not have thought of previously.

5. Keep notes of your queries

It is evident that during your research for research gap, you may encounter a lot of questions, and it is a good habit to note all those questions down and make a list of all your queries.

It would be a good habit to keep track of all those questions as to when and where you encountered them. It can be from an article, a book or while discussing with someone. This will also ensure that there is no purposeful plagiarism in your research.

A chart, picture, graph or any such relevant thing can be used to maintain the record. There are many online apps which will help you to store notes conveniently on your mobile phone, which you can access anytime and anywhere.

6. Research every question

Once you collect a list of questions that can be researched upon, you should start researching each one of them one by one. Make sure that you read about every doubt that you encounter.

Explore if other researchers had the same doubt and what did they do when they have this doubt. If they have answers to that question, you can go ahead with the next questions and avoid duplication of research.

Your research paper is something that you will spend a lot of time on, so ensure that it is something which is in your area of expertise as well as in your area of interest.

Liked this post? Check out the complete series on Market research

Related posts:

  • What is Research Design? Type of Research Designs
  • How to Write Research Proposal? Research Proposal Format
  • 7 Key Differences between Research Method and Research Methodology
  • Qualitative Research: Meaning, and Features of Qualitative Research
  • Research Ethics – Importance and Principles of Ethics in Research
  • 9 Types of Qualitative Research used by Market Researchers
  • Types of interviews in Qualitative Research
  • Qualitative vs. Quantitative Research: What is the Difference?
  • What is a Research Statement and How to Write it
  • What are Ethical Considerations in Research?

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What is Research Gap and How to Identify It?

Define research gap.

The research gap is the space that is visible in the main focus of work with flaws. It is the loophole that is not covered by the past investigations, the current study identifies it! The research gap is simply named as the missing building blocks of the whole research which is demanding to be properly added with the help of the current investigation to justify the existence of the new work done in the same niche.

How can identifying Research Gap Help Your Thesis?

Nothing is useless if you are investigating a topic, it is ideal to promise improvement and ensure the quality of work. The worth of your work will be there by identifying the gaps. It will help you in the following ways:

Increase value of present study

The research gap is for sure help to the current investigation plan as it is going to manage support for the current analysis of work and make your work worth it. If you are saying that something is missing in the past studies then it is sure that if you are covering the concern then you are doing that work. It will help the work look worthwhile and important for the readers.

Helpful for seeing work plan

The gap will help you have a wide area of work, it is going to idealize that you can do better than others. The work plan will add the missing spaces for sure so the work plan will attain the best shape in this regard. You should see the missing concerns as important to link the work of previous investigators with your efforts.

Help out future investigators

You are going to add the missing parts of the puzzle! So it is sure that you are playing an animated role in this regard. You are not doing a time pass or simple chore. Your work is going to be valuable in future to ensure the value of current analysis and investigation. The role of the present work will be important and it will also add weightage to your work for sure.

Ways & Tools To Identify The Research Gaps

Read extensively on the chosen topic.

To find out where the gap lies, first do a thorough reading of the area of the research thesis topic. The websites of prominent journals related to the chosen field are a great place to start. Reading meta-analyses related to the topic also gives you a helpful idea about the existing trends in the research field, and what has been missing.

Look for what the prominent authors in the field have worked on

By finding prominent authors in the field and what they have worked on previously, you can get an idea of what the research has already accomplished. You can also look at what the authors are currently researching to get an idea of the current trends in the literature.

Make use of online tools

Tools such as Essential Science Indicators can help in finding new areas of research in the field. Looking through the upcoming trends in the research area, new avenues for research can be found. Other tools such as Google Scholar make it very easy to search for keywords related to the research topic and find the most cited sources in the area.

Make a map of the literature

After you have read and searched for trends, a map of the main ideas can be made. You can use simple tools of mind mapping and Venn diagrams to organise the main ideas and see where there is a need for further investigations in the field. This visual depiction of ideas makes it easy to find gaps in the existing research.

Seek help from your supervisor

If you are having trouble finding an area of a gap in the chosen field of literature, it is always a good idea to seek help from your supervisor. Getting an outside opinion might help in identifying an area you had not considered.

What To Do, After Finding the Research Gaps?

Find the relevant gap to fill.

The many gaps in literature call for the need to decide which topic to pursue your research. for those who really need topics related like law research paper , It is important to see what is feasible for your situation, which areas will be of the most benefit to study for your field, and what are your abilities as a researcher. These questions will help you filter out the most relevant research gap to address for your research.

Find sources to create a research question based on the chosen gap

Once you have picked the research gap, it is time to search for sources on it. By doing the research you can come to understand the main arguments made regarding the topic, and what evidence exists to support them. Then you can create your research questions and decide your research aim.

Checklist for research gap

  • Read extensively
  • Look for missing parts in the literature
  • Make a map of the existing literature to find new areas
  • Take help from online tools such as Essential Science Indicator

Do’s and Don’ts of Research Gap

What to do and what to avoid in finding a research gap

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research gap business definition

Home Market Research

What is Gap Analysis: Definition, Method, and Template

research gap business definition

It’s an old problem in business: you want to grow your company and put your strategy into action, but you need to know where or how to put your money to make it happen. Sounds familiar? You should do a gap analysis if this is the case.

An organization needs to make the best use of its resources, money, and technology to reach its full potential. A gap analysis can help in this situation.

A gap analysis also called a needs analysis, is important for the performance of any organization. It lets companies see where they are now and where they want to be. With a gap analysis, companies can look at their goals again to see if they are on the right track to achieving them.

So this blog will explain gap analysis, its methods, gap analysis template, and more.

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You will read in detail about the 5 gap analysis tools that your business might need to learn and identify the gaps in your business and excel by analyzing the data collected by following the steps on how to do a gap analysis. Let us first understand the tools needed to conduct a gap analysis.

Content Index

Gap Analysis: Definition

Importance of gap analysis, types of gap analysis, when to use a gap analysis.

  • How to Do: A 5-Step gap analysis template with an example

Gap analysis tools

Gap analysis process using questionpro survey software.

The term “gap” refers to the space between “where we are” (the present state) and where “we want to be” (the target state). Gap analysis assesses the differences between the actual and expected performance in an organization or a business. It can also be called a need analysis, need assessment, or need-gap analysis.

In the 1980s, gap analyses were often used together with duration analyses. It is harder to use and less common than a duration analysis, but it can still be used to determine how exposed you are to different changes in the term structure.

LEARN ABOUT: Level of Analysis

Consider hypothetically, as an organization, and you have manufactured product A. This product has reached the target audience in the market. Product A has all the qualities to excel in the market, including the right features, pricing margin, and demand. Yet the product didn’t perform well in the market for some reason.

Learn more: What is Market Research?

Gap analysis can be performed on:

  • A Strategic Level – to compare the condition or level of your company with that of the industry standards
  • At an Operational Level – To compare your business’s current state or performance with what you had desired.

Here is where a gap analysis process would play a crucial role in understanding internal and external factors, where the product is and what it lacks, where it needs to be to secure its place in the market and give a tough fight to any other competitor offering a similar product.

LEARN ABOUT:  Test Market Demand

Here are some key points that show how gap analysis is important:

  • Gap analysis helps find areas for improvement in your processes, products, or services.
  • It helps to develop good plans to close the gaps between what they want to do and what they do.
  • It aids in setting priorities and effectively allocating resources.
  • Pointing out areas where organizations need to be more compliant helps ensure compliance with rules and regulations.
  • This can help improve performance, increase efficiency, and cut costs.
  • It enhances decision-making and enables companies to make smart choices by providing data-driven insights.
  • By identifying gaps in the market or customer needs, it can find opportunities for growth and expansion.
  • The effectiveness of improvement initiatives over time can be tracked using it to gauge progress.

Gap analysis is useful for businesses that want to improve performance, get more done, and reach their goals.

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Businesses can use different kinds of gap analysis, depending on their needs and goals. Here are a few of the most popular:

research gap business definition

1. Performance gap analysis

This type of analysis finds the difference between an organization’s or person’s expected performance and their actual performance.

2. Product gap analysis

This analysis finds the difference between the features and capabilities of a product and what the customer wants.

3. Market gap analysis

This analysis examines the gap between the market’s wants and the company’s products or services.

4. Compliance gap analysis

This analysis shows the difference between the required regulatory standards and what an organization does.

5. Strategic gap analysis

This type of analysis helps organizations find the gap between where they are now and where they want to be in the future regarding their strategic objectives and goals.

By choosing the right type of gap analysis, companies can learn important things about their performance, find places to improve, and develop effective plans to reach their desired state.

A gap analysis can be used in many situations, including:

  • Process Improvement: When a business wants to improve its processes or operations, a profit gap analysis can find areas of inefficiency or performance that need fixing.
  • Product Development: this can be used to evaluate customer needs and expectations to ensure a new product meets their needs.
  • Compliance and Risk Management: It can help find regulatory gaps and ensure an organization follows all laws and rules.
  • Organizational Change: When a company’s structure changes, it can help find areas of misalignment or gaps in research skills , knowledge, or resources that need to be addressed.
  • Performance Management: It can help find areas where employees, teams, or departments aren’t performing as well as they could be and develop plans to improve performance and productivity.

LEARN ABOUT: Action Research

In these situations, companies can use it to find areas that need improvement, decide which actions to take first and develop effective strategic planning to reach their goals.

How to Do: A 5-step gap analysis template with an example

A gap analysis template shows employees where your company can improve by showing the difference between reality and target. It’s a terrific method to visualize data and illustrate where your company is suffering and excelling.

In our discussion of the gap analysis template below, we’ll cover the steps of conducting it that can be applied inside a department, your entire firm, or a particular process. The four phases in the template below will help you identify and fix your research problems .

The following steps of conducting a gap analysis template can be followed to analyze and identify loops in your entire business:

Step 1: Identify the area to focus on-

You need to know where to focus. That will be your primary requirement. Whether the issue is financing, product quality or marketing, etc, be specific so that you can focus better.

For example, suppose you want to identify the gaps in your ketchup business. In that case, you need to decide whether to focus on product quality or marketing to identify and eliminate those gaps.

Step 2: Identify what goals you want to achieve-

Now that you know the area to focus on, set your target or goals. Set realistic smart goals and make sure to align them with your business goals and needs. 

For instance, your ketchup manufacturing business aims to produce and sell 162000 units of ketchup in the next year compared to 120000 being sold this year.

Step 3: Know your current state-

Before you go any further, know where you stand currently. By looking into your business reports, you will know your current position in the market, brainstorm and gather as much data as possible on your business’s current performance.

In this case, your ketchup brand currently sells around 100000 units monthly.

Step 4: Determine where you want to be in the future-

Define and determine your parameters, and remember you have set smart goals. By achieving those goals, you will be able to achieve the desired position for your business in the future.

For instance, for your ketchup brand, answer the following question in this step:

Where do you foresee your ketchup manufacturing business in the next year? – The answer can probably be a 35% increase in monthly unit sales.

Step 5: Understand the gaps between the two states-

Now that you have a clear understanding of the attributes of where you stand currently (present state) and where you desire to be in the future (desired state), it is now easy for you to identify what is stopping you from achieving your targets. After you have identified your gaps, make yourself equipped to close those gaps.

You need to understand the drawbacks of your current situation compared to your aspirations. Understand factors such as: What are the efforts being made to achieve success? Or Are attempts being made to improve the quality of the product? Or What are the marketing activities being conducted to hike up the sales volume?

The production needs to ramp up along with a boost in marketing and sales activities. Many teams have to work in unison to sell 135000 units a month compared to the existing 100000 units. Marketing managers need to develop effective strategies for improvement on the basis of the identified strengths, weaknesses, opportunities, and threats in the business.

Learn more: Advanced Analysis with QuestionPro

If you are a business owner, ask yourself 

  • How far have you come from the work you had planned at the beginning of the year?
  • What products or services were you promised to roll out? 
  • Are they already on the floor?
  • Do you have an idea about what worked and what didn’t? And why?

This type of tool can help you compare your business or project’s actual performance against the performance you had planned to achieve. This way, you can figure out what worked for you and what didn’t, what decision you made right and what was not so right!

Learn more: Quantitative Market Research

Here are the 3 gap analysis tools you can use when conducting a gap analysis for your business or organization:

SWOT focuses on Strengths, Weaknesses, Opportunities, and Threats in the internal and external environment analysis , respectively. SWOT analysis helps you determine your current industry or market position.

How to carry out SWOT to analyze Gap?

  • Gather a team of experts from the relevant department so that their expertise will help you identify the problem and the gaps immediately.
  • Create a SWOT analysis matrix.
  • Next, list down all the internal strengths and weaknesses.
  • Note down the opportunities and threats that an external environment might cause.
  • Rearrange each bullet point in order of highest priority at the top and the least important at the bottom.
  • Analyze how you can use your strengths to minimize weaknesses and use the best available opportunities to avoid or eliminate threats.


Fig: SWOT Analysis Matrix

Learn more: Strategic Analysis for business research

  • McKinsey 7s

McKinsey 7s can help you with the following gap analysis purpose:

  • To help you understand the gaps that are evident and that may appear 
  • To help you identify the areas to optimize and boost performance
  • To align the respective processes during a merger or an acquisition, if you have had one recently or are planning to have one.
  • Helps you examine the results of future changes within the business.

Learn more: How to do Market Research for a Business Plan

The 7s refers to the key interrelated elements of an organization. They are:

  • Shared values

These elements are divided into two distinct groups: hard elements (tangible factors that can be controlled) and soft elements (intangible factors that cannot be controlled)

Hard elements are as follows:

  • Strategy – the plan that will help your company gain an advantage over any of your competitors .
  • Structure – the plan or the layout that will define your entire organizational chart structure.
  • Systems – business and technical knowledge your employees already use to complete their daily tasks.

Learn more: Employee Evaluation Survey

Soft elements are as follows:

  • Shared values – these are the set of beliefs or traits that the organization values.
  • Style – A leadership style that defines the organization’s culture.
  • Staff – people who are the backbone or the asset of an organization.
  • Skills – The tool that the employees have to help you succeed.

How to apply McKinsey 7s?

  • Start with gathering a competent team.
  • Look for gaps and weaknesses and align the relationship between the elements.
  • State where the elements will be optimally aligned. When we speak about elements, we are referring to the 7s.
  • Come up with a suitable plan of action to realign the elements.
  • Implement the changes or the solution you have come up with and reduce the gap.

Learn more: Human Resource Surveys

  • Nadler-Tushman’s Congruence Model

This model is based on the principle that business performance is the result of 4 key elements: work, people, structure, and culture .

How to apply this model?

  • First and foremost, gather all the data that point you toward any or all symptoms of poor performance.
  • Specify inputs i.e, whether it’s environment, resources, or history that is causing these poor performances.
  • Identify which outputs are required at the organizational level so that the organization can meet all strategic objectives.
  • Now assess the degree of congruence among all the mentioned components.
  • Strategize and put down a plan of action.

Learn more: Qualitative Market Research

QuestionPro market research survey software is a platform that helps you identify gaps in your business by making available the gap analysis template. A new question type has been added to the QuestionPro survey system to run comparisons between expectation and delivery specifically.

LEARN ABOUT:  Market research industry

In most cases, this realizes itself by asking customers/potential respondents to rate different attributes (Customer Service, On-Time delivery, etc.) on the importance and a satisfaction rating scale . We call this the side-by-side matrix question (the alternative name is a multi-dimensional matrix) — basically two (or more) matrix questions placed next to each other. Follow these simple steps to run a it .

How can you access Gap Analysis?

  • Login » Surveys » Reports » Choice Modelling
  • Select GAP Analysis .

gap analysis

Select the question from the drop-down menu and select the Gap analysis option.

gap analysis

Once you have analyzed the question, you can also download the report in excel, PowerPoint, or even print it.

gap analysis

Learn more: Trend Analysis

Gap analysis is one of the most effective ways to find growth opportunities. It gives your company a strategy based on data and the standards of your industry.

LEARN ABOUT: 12 Best Tools for Researchers

Meeting people’s expectations is never easy, but the analysis helps you make a plan by taking things one step at a time. It is a thorough, step-by-step process that gives you a detailed action plan. You can use it to fix a specific problem or just be proactive about making new strategies.

QuestionPro has useful tools that can help you do a good gap analysis. By using its survey and research tools, businesses can find out where they can improve and develop plans to close the gaps in their performance.

QuestionPro’s easy-to-use interface and customizable features make it a complete solution for companies that want to improve their processes and grow. So sign up now to get your desired outcome!



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Methodological research gap: definition, identification and examples

On this page, types of methodological research gaps.

What is methodological Research gap?

1.1 Definition

Methodological research gap is the missing gap of knowledge on a more appropriate underlying method(s) which can be used in research instead of the previously one. This implies that the researcher or you as a postgraduate student may propose a method in research to address a particular aspect in life or research which is more fitting to realize improved research findings than before.

Remember that in research, there are diverse methods utilized to arrive at valid research findings. That is, from the stage of formulating a research problem up to presentation of research findings, there are several methods that a researcher can adopt to come up with better and more reliable research results. Therefore, in each stage of research process, a researcher such as you and me is expected to identify methodological knowledge gap which should result to more reliable research outcome as aforementioned. This calls for two things;

One, identification of the applicable type of method at each stage in the research process and

Two, devising steps to be followed by the researcher to establish the methodological knowledge gap to be filled in the current study as compared to the one used by your predecessors as per past studies.

To achieve the two objectives above, Table 1.1 below summarizes the common stages in research and the corresponding aspects of methods utilized by the researchers and the examples of those methods and then a detailed explanation on how to identify the specific methodological knowledge gap follow suit.

research gap business definition

From Table 1.1 above, we can identify the different types of methodological knowledge gaps that are found in research and that you as a researcher/ postgraduate student need to be familiar with and also apply.


In research process, the first step is the formulation of a research problem which is the critical matter at hand to the researcher. It is the question that the researcher need to get an answer through scientific investigation or inquiry.

NOTE-that when writing an academic research proposal or dissertation, this is the aspect we indicate mostly in chapter three under research methodology. Yes, down the line as we carry out our research assignment, there are many methods that cut across all the stages. But when we are addressing chapter three of our research proposal, the sub-title on research design refers to the method we consider when identifying the research problem. It is then at this point based on the nature of the research question to be answered that a particular method is selected to answer the question at hand. The relevant methods at this stage are;

2.1.1 Descriptive Research Design

If the researcher adopts a descriptive research design, the aim is to answer research questions of “what” nature which pertain to the respondent who is expected to provide data for the study.

NB: To describe the characteristics of the study variables, the researcher use statistic tools such as mean, standard deviation, variance, minimum and maximum values, frequency and percentages. It is also common in research to use likert scale approach where opinion of the respondent is ranked as SA=Strongly Agree; A=Agree; N=Neutral; D=Disagree and SD=Strongly Disagree or any other connotation is also applicable.


Used when answering research questions of ‘ What’ nature of the respondents.

Used where the researcher want to describe or label characteristics of the study variable.

Used where the researcher does not manipulate the study variables-He/she just does physical observations. E.g. variables such as age , sex , education level e.t.c which are all demographic characteristics and you cannot alter or twist them.

Used where primary data is collected for the study using a questionnaire or interview schedule.

Used where the research problem is clear.

The following are extracts of past studies where the descriptive method was used to enquire on the characteristics of the study variables.

Description of Demographic Characteristics of firms

Table 1: Ownership Structure of firms

research gap business definition

So you see, the researcher is able to describe the characteristics of the study variable, namely ownership structure of firms without any manipulations. That is, the fact remains that if it is sole ownership, it was observed to be 102 out of 223 and the percentage was 46%. Same with description of the other aspects of ownership structure.

Description of Characteristics of Firm Performance (The Variable)

Table 2: Financial Performance of a firms

research gap business definition

So the question is, how do we identify the methodological knowledge gap at this stage of research question? This is explained below

Identification of Methodological Knowledge Gaps

Under descriptive research design , the researcher need to consider the following steps

Step 1: Confirm if the research question is of “WHAT” nature

The researcher need to ensure that the research question at hand is clear and that it is aimed at answering the WHAT kind of perspective

Step 2: Review of past studies

The researcher need to consider similar studies undertaken by other researchers to find out the nature or type of the study variables used in the concept under investigation. The aim being to find out how the study variables were described. For instance, did past studies use mean, standard deviation, variance, minimum and maximum values or they used range to describe the study variable.

Step 3: Assess the appropriateness of the statistics used to describe the characteristics of the study variables.

 At this step, do your own critique on the statistics used to justify or disqualify why they were used to describe the characteristics of the study variables.

Step 4: Develop a Descriptive Based Methodological Knowledge Gap

At step 4, you should pinpoint or rather build up the methodological knowledge gap. This can be achieved by;

-Evaluate if the WHAT question in the past studies was effectively answered or not.

-Find out if the research design adopted is qualitative or not. In other words, is the data source primary or secondary?

-Assessing if past studies used appropriate statistics to describe the study variables in a manner that it is clear as pertain their characteristics being described.

-Find out if the descriptive research design used resulted to the appropriate sampling method or and data collection method in the past studies.

Now, based on those indicators and any other strategy which is appropriate, you as a researcher/or postgraduate student should pinpoint the gaps thereof and suggest a more appropriate method of describing the study variables to be able to answer the research question more correctly.


Researchers in the past literature may have used mean, standard deviation, minimum and maximum values, range and variance to describe the study variables. This can be problematic for apart from mean which is a measure of central tendency, the rest of the statistics are measures of dispersion. That is standard deviation, minimum and maximum values, range and variance are all measures of dispersion and serve the same purpose.

Therefore, in your current study, you can portray the methodological knowledge gap of descriptive nature by using standard deviation only instead of incorporating all the other measures of dispersion. You can then justify your new methodology by arguing that, one; all the other statistics serve the same purpose like standard deviation . Two, use of standard deviation help in avoiding all the other measures of dispersion which eliminates congestion in your write up. Three, the approach/methodology is economical for less time and financial resources are utilized for data to be collected will be for only standard deviation and not for all statistics as in the past studies.

2.1.2 Exploratory Research Design

If the researcher adopts exploratory research design, the aim is to answer research questions of;

Why a research study has been undertaken,

How the research problem has been defined,

What way and why the hypothesis has been formulated,

What data have been collected and

What particular method has been adopted and

Why particular technique of analyzing data has been used and a spectrum of related other enquiries are usually answered.

Used where the research question is not clear.

Used where the area of study is a new one and the researcher is trying to answer questions such as;

What is the problem?

What is the purpose of the study?

What topics could be studied?

Used where generally there is no prior research done or the existing ones do not answer the problem in a more satisfying manner.

Used where there are no set of rules to carry out the research as such, so they are stretchy/more open ended or wide-ranging.

Used where the research is of great importance or value.

Used where there are few theories which can support its findings.

Used where qualitative data is available and can be collected using data collection tools such as interview schedules or questionnaires.

Identification of Methodological knowledge gaps

Under exploratory research design , the researcher is investigating on a new field and past studies are missing. So in this case, it is not easy to identify the methodological knowledge gap for the findings gotten are inconclusive.

However, still the researcher may have a loophole to take advantage of and identify the exploratory based methodological knowledge gap at the initial stages of research process. How can this be done? Very simple;

Illustration 2

A researcher starts with a general idea and uses this research as intermediary to identify issues that can be the focus for future research. If then the researcher(s) from the past exploratory research did not identify a solid rock that further studies in future may be anchored on, then this represents a methodological knowledge gap that can be filled. You as a researcher can pinpoint the weaknesses of the base set by your predecessor researchers and re-do a similar exploratory to provide a better research anchorage in the future.

2.1.3 Explanatory Research Design

Explanatory research design sounds like Exploratory research design as discussed in 2.1.2 above, but they are distinctly dissimilar.

An explanatory research design aims at “exploring a new” on existing phenomena, which is not well explained due to lack of previous research on it. It is a design which is helpful for furtherance of research on the same area or phenomenon for its main purpose is to discover the why and what of a subject under investigation. In short, it is a type of research design which is responsible for finding the  why  of the events through the establishment of cause-effect relationships.

Used where the researcher want to further substantiate an already existing relationship by providing the cause-effect perspective.

Used where there is lack of or there is less information pertaining the relationship at hand.

Used where the researcher has to justify why relationship is of a certain nature.

Used when it is necessary to lay a foundational source of information for the phenomena under investigation.

Identification of Methodological knowledge gaps.

Under explanatory research design , the researcher need to consider the following steps

Step 1: Identify the immediate cause factors.

As a researcher be concerned with the express characteristics and existing social problems, by endeavoring to find out association between direct causes of an outcome.

Step 2: Establish research Problem.

Design an accurate research problem translating to appropriate research objective(s) so as to precisely state the principal areas of investigation to be properly linked.

Step 3: Develop a research hypothesis

In this step, establish a hypothesis with consideration of the most suspected causing study variables, which will be used in hypothesis testing to approve or disapprove the cause of the problem.

Step 4: Data collection.

In this step actual data collection process is undertaken so as to create more information to support the suggested hypothesis.

Step 5: Develop an Explanatory Based Methodological Knowledge Gap

Step 5, is the point where you should pinpoint the methodological knowledge gap. This can be achieved by;

-Evaluating past studies to identify the exiting conceptual framework established by scholars or/researchers.

-Check the level of plausibility of the variables previously used in the aforementioned conceptual framework.

-Critique positively the research findings from past studies pertaining that particular conceptual framework by highlighting the most probable factors further explaining the changes in the outcome variable. And hence suggest your own way of approach.

Illustration 3

Researchers in the past literature may have ignored critical study variables. Therefore, in your current study, you can portray the methodological knowledge gap of explanatory nature by using more critical or specific factors that can explain the changes in outcome variable (ie dependent variable) of the matter at hand. This will represent a methodological knowledge gap to your study.

From economics point of view, the quantity demanded is determined by the changes in price levels. Hence the relationship between the price and quantity demanded of the commodity is negatively related for a normal good. Holding other factors constant. Graphically, the demand curve is up down slopping. This shows that price dictate the changes in quantity demanded.

The conceptual framework is as follows;

research gap business definition

In Economics, it is further proven that price of the commodity may not necessary be the direct cause of change in quantity demanded of a commodity but the Utility or level of satisfaction derived from the product by the consumer may be more predicting than just a decreasing price level. Therefore, an explanatory research design can be adopted to explain the reason as to why demand of a commodity is more tied to the utility derived by the customer and not necessarily the price per se’ . This presents an explanatory methodological knowledge gap for the current study to fill.

The new conceptual framework turns to be as follows;

research gap business definition

In conclusion, you can see utility is the predictor which explains more of the phenomenon of changes in demand amongst consumers than the price of the commodity.

2.1.4 Correlational Research Design

If the researcher adopts a correlational research design, the aim is to answer research questions of “How” nature which pertain to determination of how two variables relate with one another.

Used when answering research questions of the level of strength of a relationship

Used where one wish to determine the direction of a relationship

Used where the researcher does not control the study variables

Used where the research problem is clear

Used where the relationship being tested is at a natural state

NB: A coefficient of between -1 up to +1 is used to describe the strength of the relationship.

The following are extracts of past studies where the correlation method was used to demonstrate the relationship between variables.

research gap business definition

So the question that you have is, how do we identify the correlational methodological knowledge gap at this stage of research question? This is explained in below

Identification of Methodological  Knowledge Gaps

Under correlational research design, the researcher need to consider the following steps.

Step 1: Confirm if the research question is of “HOW” nature

The researcher need to ensure that the research question at hand is clear and that it is aimed at answering the HOW kind of perspective.

The researcher need to consider similar studies undertaken by other researchers to find out the level of correlational strength or weakness between the study variables under investigation. For instance, if the correlation between the two variables is weak, average or strong.

Step 3: Assess the appropriateness of the concept or conceptual framework used

 At this step, do your own critique on the level of correlating between study variables so as to know whether there are high chances of one variable causing the other.

Step 4: Develop a Correlational Based Methodological Knowledge Gap

In step 4, you should pinpoint the methodological knowledge gap. This can be achieved by;

-Evaluating if the HOW question in the past studies was effectively answered or not.

-Find out if the research design adopted is quantitative or not. In other words, is the data source primary or secondary?

-Assessing if past studies used right concept by selecting study variables that are fairly highly correlated (orthogonal).

-Find out if the correlation strength guarantees the researcher to further carry out regression analysis to test the hypothesis thereof.

Now, based on those indicators and any other strategy which is appropriate, you as a researcher/or postgraduate student should pinpoint the gaps thereof.

Illustration 4

Researchers in the past studies may have found no correlation (ie coefficient=0.00) between two variables. This may imply that wrong variables were chosen for the study and hence you as a researcher need to consider other more suitable variables to represent the concept of your focus. This can be achieved if the ones chosen portrays plausibility or logical relationship. This will represent a methodological knowledge gap.

Also, equally, past studies may portray a very high correlation . If that be the case, it means the variables are suffering from a multicollinearity problem and this calls for a replacement of the variables of concern. That is you as a researcher can use less number of the variables previously used by eliminating the ones which are highly correlated or replace the highly correlated variables with others with fair correlation. This again will represent a methodological knowledge gap.

Lastly and not the least, as a researcher, you can consider classification of level of strength or weakness of the correlation between two variables. From past studies, the researchers or scholars could have ignored that perspective. In your case, you can consider the proposition of Cohen of 1988 who suggested that for the purposes of interpreting the magnitude of a correlation, as well as estimating power; r = 0.10, r = 0.30, and r = 0.50 were recommended to be considered small, medium , and large in magnitude, respectively. You can rely on the same argument for sometimes past research findings have similar correlations of 0.10 up to 0.50 which is termed as weak. But you see, this could have been due to use of a small sample. This becomes a methodological knowledge gap for the end results will entail appreciation of the fact that data may be scarce in some cases and this cannot stop one from carrying out a study on a particular field.

2.1.5 Experimental Research Design

An experimental research design, is a scientific method of establishing changes in the variable being studied when another variable is manipulated. The manipulated variable is referred to as the predictor or predator or independent variable.

Used when two or more variables are used in the study

Used where the researcher uses quantitative data

Used where the researcher is in control of one variable, namely the independent variable

Used where cause-effect relationship between or amongst several variables exist

The following are extracts of past studies where the experimental method was used to enquire on the study variables.

research gap business definition

So you see, the researcher is able to portray the cause-effect relationship between the independent variable(s) and the outcome or response variable. This is achieved by manipulating the predictor variable so as to determine the value change in the dependent variable.

Under experimental research design also referred to as causal-effect relationship, the researcher need to consider the following steps so as to develop the gap thereof.

Step 1: Review of past studies

The researcher need to consider similar studies undertaken by other researchers to find out the hypothesis which was tested and the research findings thereof.

Step 2: Re-hypothesize the research problem if in step 1 you realized that the cause-effect relationship was not statistically significant

In experimental research design, the aim is to test the degree of statistical significance one variable referred to as independent variable influence another variable referred to as response/outcome variable. Therefore, if the stated hypothesis was empirically disapproved due to the relationship not being statistically significant, then it means that the concept lacked plausibility or logical sense. This calls for re-defining the research problem and of course re-defining the research hypothesis.

Step 3: Develop Experimental Based Methodological Knowledge Gap

In step 3, you should pinpoint the methodological knowledge gap. This can be achieved by;

-Considering similar conceptual framework from past studies to find out the significant level of the relationship between and or amongst study variables used.

-Investigate further on the sample size used in comparison to the population size . This will guide you to conclude if the sample size was a true representation of the population or not. For if it is not a true representation, this may be the cause of research findings with no statistical significance.

-Interrogate on the perspective of plausibility of the study variables used-if they do not represent logic in their natural/physical relationship, then the regression results will not portray statistically significant relationship.

Illustration 5

Past literature may lack study variables with plausibility or logical relationship-the researcher then can further consider other variables which have a relationship in the natural phenomenon.

Past studies could have used small sample size hence adversely affecting the level of cause-effect relationship. You as a researcher need to consider a large ‘in quotes’ sample size that truly represent the entire population.

2.1.6 Diagnostic Research Design

Diagnostic research is a type of design that aims to examine the original cause of a certain circumstances or occurrence. That is the design can help you to discover more on an issue at hand. For example, the number of “return” customers as compared to new ones could have been reducing for the last three months. The question is, what other causes could be contributing to this trend or what could be the root cause of this? In other words, the diagnostic research design is helpful in identifying where the rains started beating you if it is a case of undesirable results.

It is a research design composed of three research stages, namely;

(1) Problem Inception,

(2) Problem Diagnosis, &

 (3) Problem Solution.

So if the researcher adopts a diagnostic research design, the aim is to answer research questions of;

Origin of the issue – When did the matter crop up? Where do we get more evident on the matter?

Diagnosis of the problem – What is the underlying cause of the issue? In other words, what other factors could be worsening the situation?

Solution for the Matter – What is more practical/logical in solving the matter at hand?

Used where the researcher is more concerned of the specific cause of the problem at hand.

Used where specific data need to be collected to solve an immediate need.

Used where the research problem needs more clarification.

Under diagnostic research design , the researcher need to consider the following steps

Step 1: Identify the immediate cause factors

Step 2: Establish research Problem

Step 3: Develop a hypothesis

In this step 3, establish a hypothesis with consideration of the most suspected causing study variables, which will be used in hypothesis testing to approve or disapprove the cause of the problem.

Step 4: Data collection

Step 5: Develop a Diagnostic Based Methodological Knowledge Gap

In step 5, you should pinpoint the methodological knowledge gap. This can be achieved by;

-Evaluate if past studies had identified any specific factor acting as the key cause of the matter at hand

-Check the level of plausibility of the variables previously used in similar study to the one at hand

-Critique the research findings from past studies and suggest your own way of approach

Illustration 6

Researchers in the past literature may have ignored critical study variables. Therefore, in your current study, you can portray the methodological knowledge gap of diagnostic nature by using more critical or specific factors that can explain the immediate cause of the matter at hand. This will represent a methodological knowledge gap to your study.


Operationalization is the process of assigning a measurable indicator or proxy on a specific fuzzy characteristic to make it possible to measure it through observation. This approach make it possible for the researcher to systematically collect and evaluate phenomena that can't be observed directly.

Fuzzy characteristic is that kind of behavioral element portrayed by the unit of observation which is vague and not easily measurable. In other words it is not definitely expressible in fixed terms and its measurability depends on context or conditions, and therefore a precise meaning is lacking . The idea of operationalization is in addition to the well-known methodologies of measuring of study variables. That is use of measurement scales which are used to qualify or quantify data variables in statistics and they are usually four in number. That is;

Nominal Scale

Ordinal Scale

Interval Scale

Ratio Scale.

NB: That, qualitative data is measured using nominal and ordinal scale while quantitative data is measured using interval and ratio scales.

Operationalization Methodological Knowledge Gaps

How do we identify methodological gap on the basis of measurement or operationalization of a variable/construct? To achieve this objective, you, the researcher need to use the following guidelines as portrayed by these steps below

Step 1: Review Past Studies

In this step, the researcher will aim at finding out how the similar variable was measured by his/her predecessor researchers. Remember that you cannot re-invent the wheel and come up with your own way of measuring a variable. Also, for a variable to be useful in a study, it must be measurable. As we have let you know in our other articles, “ if a variable is not measurable then it does not exist”.

So in this step one, you as a researcher need to find out if a universally authentic method has been used to gauge the variable.

Step 2: Assess the Appropriateness of the Method of Measurement Used

In this step, the researcher need to assess whether the proxy used to gauge the variable is correct. The extent of correctness or fitting will be assessed against some set thresholds such as:-

Contextual environment

Targeted group in the study

Generally accepted/universal method used

Technological advancements

Step 3: Positively critique to highlight areas of improvement in measurement

At this point, pinpoint the weaknesses of using the measurements used by other scholars/researchers on similar variable(s) in the past studies. This should be done with justification without necessarily critiquing in the wrong way . For example, I have witnessed postgraduate students suggesting that the past studies failed to use a certain method in measuring a particular variable.

For example, one will say that the study of X and Z failed to use ROE to measure profitability of the firm for in their case they used market share instead. So this is the reason why the current researcher want to use ROE!

This is wrong approach in creating methodological knowledge gap. The reason being that, may be as per those researchers, the research problem at hand dictated use of a certain proxy or measurement to gauge the study variables thereof. So as per their study, it was appropriate!. So the current researcher has no right to negatively critique or demonize his/her predecessor researcher’s work!

Step 4: Develop an Operational/Measurement Based Methodological Knowledge Gap

The following illustration will take care of the best way to create such gaps.

Illustration 1

Let me assume I am writing a research to investigate on the factors that influence financial performance of firms listed at the stock exchange. The factors I have proposed are X. Y and Z which are the independent variables whereas, financial performance is the criteria variable (Response variable) and is measured using Return on Capital Employed (ROCE)

The corresponding conceptual framework will be as demonstrated by Figure 1.1 below;

research gap business definition

If the research findings portray that there is statistically significant influence of the three factors taken together, namely X, Y and Z on financial performance of those firms listed at the stock market, this currently represents the body of existing knowledge.

From contextual point of view (users of the research report), a methodological knowledge gap based on measurement/operationalization can be established. For instance, suppose another researcher wanted to establish the influence of the same factors, namely X, Y and Z on financial performance with an aim of advising the owners of the companies as far as their Earnings per Share (EPS) is concerned. Then the method used to measure financial performance will change to EPS. This will represent a better way of informing the owners on their expectations as far as their returns are concerned. This is because it is more specific on returns originating from equity invested in the firm other than ROCE which is returns associated with all the investors, hence a general proxy. In other words, we are not saying that the previous way of measuring financial performance, i.e. ROCE was wrong for it fitted the purpose expressing performance of the firm in general.

Therefore, the more appropriate conceptual framework will be as follows as per Figure 1.2;

research gap business definition

So note that although both studies are similar in every other way, the use of a different measurement of the financial performance of the firms to suit a particular purpose, represents a operational/measurement methodological knowledge gap. This is paramount for appropriateness of measurement is key in ensuring that no confusion in defining a variable and also there is clarity on decision making by the management.

Suppose in another study, the researcher investigated on the factors influencing financial performance of firms listed at the security exchange and financial performance was measured in three ways, namely; Return on Equity (ROE), Return On Assets (ROA) and Return On Invest

ment(ROI) as shown in Figure 1.3 below

research gap business definition

From Corporate Finance perspective, the three measures of financial performance are similar/same for ROI=ROA=ROE. This implies that the research findings will be similar and number two, the data for ROA,ROE and ROI is highly correlated and incase of carrying out multicollinearity test, the relationship will prove to be so. Therefore, there arises a need to eliminate some of dependent variables so as to;

One, avoid multicollinearity problem and

Two , avoid waste of resources for data will be required for the three measures of financial performance which represent the same outcome. This represents a methodological knowledge gap based on the measurement adopted by the researcher.

For an improved study, one can incorporate only one measurement of financial performance, such as either ROE or ROI or ROA but not all. See Figure 1.4 below

research gap business definition

NOTE: That, in the three illustrations, we have only focused on the measurement of the dependent variable. This was only made for simplicity of understanding the concept. Otherwise, dissimilar methodologies can be used to measure all study variables as long as one can justify why he/she has gauged a variable in a particular manner. Also, for all the other measuring scale, the right applicability in measuring a characteristics should be considered. If not so, then a researcher can suggest a better method.


At the sampling stage in research process, there exists an opportunity to portray methodological knowledge gap also. How does this occur? As we appreciate that most of the time research data is collected from a sample and not the entire population, this increases the chances of pinpointing weaknesses on the sampling techniques used in similar studies hence suggest a better or more appropriate sampling method. The following steps is of paramount importance to you as a scholar or researcher in identifying sampling methodological knowledge gaps

This step will help you as a researcher to compare the research method and the sampling method(s) used by researchers/scholars in past literature similar to your study. The aim is to assess the matching for each research design has a corresponding appropriate sampling technique that guide towards establishment of the right sample size .

Step 2: Identify the Sample Size used in each Past Study Reviewed

Step 2 is a furtherance of step one, which aims at checking the appropriateness of the sample used. The sample size should always be a true representation of the population. Then it means that if the past studies similar to yours has wrong sample size, it is at this juncture that you should raise a red flag for this implies that the data used in those studies could have resulted to biased research findings which have no validity.

Step 3: Critique Past studies

This is done by suggesting the most appropriate sampling methods(s) to use in your study. Either portray why the sampling method used earlier on is not suitable in the similar current study or why another method is more suitable than those used in similar studies.

Your justification points/strengths may include and not limited to;

i). Type of sampling Method used

You can argue that the sampling method used did not give all the units of observation equal chances of being selected to participate in data collection process hence may be the results were bias. For example, you see there are two types of sampling methods; probabilistic sampling which gives all units of observation equal chance to participate in data collection and this assures us of unbiased and valid research findings. Then there is non-probabilistic sampling techniques which may result to biased research findings although to some extent they can be appropriate based on the context or objective under consideration.

ii.) Mismatch between Research Method and Sampling Method

Each research method used in a study dictates the sampling technique to adopt, the type of data to collect, sample size and other many aspects. Then this calls for interrogation of the matching done by researchers in past studies to see the appropriateness of the matching, which may result to a shortcoming in the process of sampling

iii). Nature of the population

The nature of population may assume manifold aspects such as its characteristics, distribution patterns and so on and so on. This may dictate use of specifically a certain type of sampling. For instance, when data is not equally distributed such as unbalanced data panel.

Step 4 : Develop a Sampling Based Methodological Knowledge Gap

Build your case of the methodological knowledge gap by highlighting issues of your concern in the past studies.

In a study where the population is in form of strata, then stratified random sampling method can be adopted to establish a sample size which is a true representative of the entire population. For example, data to be collected for public hospitals in Kenya. In this case, we can consider only the level five category from the 47 counties. The counties will be representing the stratus and then further random selection can be done to pick the right sample for data collection purposes. In this case, the results are unbiased and validity is assured. Note that this is just a simple approach, for we have left out the key details. So in this case, a random sampling method is more suitable.

In a study where the researcher has an aim of discriminatively looking for certain specific data to meet a specific objective, then a non-probabilistic sampling method can be more appropriate. For instance, when I was doing my PhD, my concern was data for firms listed at the Nairobi Securities Exchange. Since some of my variables would produce contradicting research findings such as cashholdings, I discriminated all financial such as banks and insurance institutions whose liquidity rule and regulations require them to keep a certain level of cash reserves which is almost composed of elements of cashholdings. Hence I went for non-financial firms for convenience purposes. Also some of the firms did not portray some of the study variables I was using in my study so I conveniently selected those firms with all the study variables I had incorporated in my study. This called for use of convenient sampling which is non-probabilistic in nature.

Look at this EXTRACT from my PhD thesis;

(“ The total number of registered organizations at the Nairobi Securities Exchange by 2015 was 63 in total (NSE, 2015). Commercial banks and insurance firms were excluded in this study because they are heavily regulated than non-financial firms and have a unique capital structure (which is similar amongst themselves) from other firms (Berkman & Bradbury, 2001). NSE facilitates its member firms hence it is unique in its operations as compared to the targeted firms for the study. To ensure that the firms used in the study are uniform, such unique firm attributes were put under consideration and discriminatively selected some firms for the study. Therefore, the target population comprised of 47 firms.

Convenience sampling technique was used to collect a more representative sample for the study. Hence, the relevant observation items that enabled achievement of the research objectives were considered and therefore firms with incomplete data were left out. This implies that companies which did not have a full set of data on variables mentioned in the study were excluded. Therefore a sample size of 31 organizations registered at the NSE for the period of eleven years was selected out of the targeted population of 47 firms as shown in Table 3.1 below

research gap business definition

Sometimes, past studies may use sample size which is large but not scientifically determined. That is, as one arrives at a particular sample size, a scientific method should have been used so as to justify the authenticity of the sample size and its usability in the study. Therefore, in the current study, the researcher need to justify why such a “ sample size determination formula” has been used.

For example, the following extract is from a past study where Yamane (Formula) was used to determine the sample size

Sample Size

The appropriate sample for this study was 265 SMEs drawn from a target population of 5311 SMEs operating in Machakos County using Yamane formula (1967). This formula was used to determine the sample size (n) which is the number of subjects to be randomly selected from each category of SME projects in Machakos County.

Sampling Procedure

The procedure of how the 265 firms was selected out of the total population of 5311 was summarized in Table 3.2

research gap business definition

NB: The researcher has to justify why such a formula is used in such a study. Of course there are other sample size determination formulas and if used by the researcher, then again justification is necessary. All these sampling concerns portray sampling methodological knowledge gaps.


After sample size or population has been determined in stage 3, then actual data is collected. This process also just like the other stages of research process presents to you as a researcher another chance to demonstrate the data collection methodological gaps in existence as per past studies.

Remember that data collection is a sensitive procedure and if the appropriate data collection method is not used, then biasness dominates the data collected and the data analysis process will bring forth results which are not admirable. Data collection utilizes three data collection research designs as explained in our research Hub, namely;

Cross sectional Research design- A cross-sectional study design is a type of research design in which data is collected from many unit of observations/respondents at a single point in time.

Longitudinal Research design- A longitudinal study design, is a type of research design whereby researchers repeatedly examine the same unit of observations/respondents to detect any changes that might occur over time (ie at different points in time) without trying to influence those variables.

Time series Research design- Time series designs are a sub-set of longitudinal research design which its analysis focus is on “large series of observations made on the same variable consecutively over different point in time.

The following steps will guide you to establish the data collection methodological knowledge gap

From past studies, identify the data collection research design used to assess its appropriateness in that study(ies). Based on the research problem at hand, one is able to tell the most suitable data collection research design.

Step 2: Assess the nature of population or sample to collect data from

This aspect represents the source of data which can either be primary data or secondary data

If primary data for several variables -then cross sectional research design is useful

If secondary data for several variables -then longitudinal research design is helpful

If secondary data for only one variable-then time series research design is helpful

From past studies, demonstrate that the methods of data collection are unsuitable to your current study. This can be achieved by positively proving that the data collection research design is only suitable in the past similar studies but not the current one. This can be achieved by suggesting the appropriate method of collecting data based on the aforementioned data collection research design which is well fitting.

Step 4: Development of Data Collection Based Methodological Knowledge Gap

At this point show the suitability of the current methods of data collection by justifying that the methods will meet objectives such as;

Right data collection method ensures data validity which translate in to unbiased research findings.

Right data collection method saves time.

Right data collection method translates to reliable data analysis methods etc etc.

Suppose you are carrying a study on the relationship between time taken to coach undergraduate students and academic performance for the last 5 years from 2016 up to 2020.

The data to be collected is primary data and will be collected through questionnaires issued at the year 2020

This is data collection at one data point to represent performance information of students for 5 years

Data is collected from more than one unit of observation

Therefore, cross sectional data collection method will apply.

Suppose you are carrying another study on the relationship between time taken to coach a particular undergraduate student by the name James and his Mathematics performance for the last 5 years from 2016 up to 2020.

One; the data to be collected is secondary data and will be collected through using secondary source such as James academic reports from the year 2016 up to 2020.

This is data collection at 5 different data points, that is 2016; 2017;2018;2019 and 2020.

Two; data is collected for one unit of observation, ie James who is a student.

Therefore, longitudinal data collection method will apply. This will be a new approach of collecting data hence a dissimilar data collection methodological knowledge gap is realized.


This is the last stage in research process whose end result is the research findings which in turn are used for making conclusions. In this stage there are diverse data analysis approaches that are useful to the researcher based on the objectives to be achieved.

Since the methods are several, (visit STATA, E-VIEWs, SPSS program) I will demonstrate the steps to follow when creating  data analysis methodological knowledge.

The following steps matter

Revisit past studies similar to your topic to evaluate the data analysis method utilized and the justifications the authors have provided.

Step 2: Identify both the research question and the specific objectives of the study.

From the past studies you review, you will notice that at least one or two methods of data analysis has been utilized. At this point, equally identify the corresponding research questions and the research specific objectives which obviously should emanate from the research question.

Step 3: Identify the research design used by the researcher in that past study.

With the research question and the specific objectives at hand, further identify the research design used and evaluate its suitability in answering the research question at hand and achieving the study specific objectives. Ask yourself, whether with such a research design, the two aspects were fulfilled to satisfaction.

NB: For your information, the right matching should be for instance, if the research problem was descriptive in nature, the research question will be descriptive and specific objective as well. Then, descriptive research methodology will be used, followed by descriptive research methods and descriptive data analysis. Similarly, the same applies in all other research designs.

Step 4: Benchmark the data analysis method used and the researcher’s research question

Again consider the matching of the research design chosen with the data analysis method used if it is correlated in any way. Of course, you know that the research design chosen dictates the data analysis method used. And the data analysis used is meant to answer the research question. If this is the case as per the past study research findings, then it’s ok. But if not, this becomes a case to raise some questions on how best the data analysis should have been done.

Step 5: Benchmark the data analysis method used and the researcher’s specific objectives 

Repeat step 4 to step 5 and consider the matching of the data analysis method and the extent to which the tool has helped in achieving the set specific objectives of the past study. Of course, you know that the data analysis method chosen dictates the extent to which the specific objectives are achieved. If with the data analysis used, the specific objectives are fully achieved, ok. But if not, this becomes a case to raise some questions on how best such objectives should have been achieved. This calls for identification of areas for further research as indicated in most of the academic papers in chapter five or six of the project paper.

Step 6: Critique the data analysis approach used in the past studies

In step 6, you highlight the appropriate data methodology to use in your current study by pinpointing either weaknesses or strengths of the methods used earlier on by your predecessors in the similar studies you have reviewed.

Show how more suitable or appropriate your methodology is and how it will sufficiently be able to answer the question at hand and meet the specific objectives of the study.

  NB: As you review past studies to interrogate the data analysis method used, you need to be careful of the specific objectives which that particular researcher wanted to achieve.

Step 7: Development of Data Analysis Based Methodological Knowledge Gap

Pegged on the hypotheses set, demonstrate how the data analysis has proven or disapproved the hypotheses.

Assess the level of hypothesis test statistical significance to determine whether to fail to accept the null hypothesis (ie reject the null hypothesis) or fail to reject the null hypothesis (ie accept the null hypothesis).

State the data analysis methodological knowledge gap thereof. So in the following illustrations, we will consider different scenarios with different researchers as portrayed below;

Assume that researcher one carried out a study on the relationship between P and M .

So in this case, the research question he wanted to answer was;

Research Question : Does variable P have a strong association with M ?

The corresponding;

Specific Objective was; To determine the relationship between P and M

research gap business definition

NOTE the following;

The specific objective to be achieved is to establish the relationship between P and M variables.

In the conceptual framework, we do not indicate the role of the variables for we are concerned about the relationship and not which variable influence the other. So no predictor or response variable classification.

The researcher incorporated correlation data analysis method to find out the association between P and M variables. Pearson Product Moment correlation model was used.

Research findings revealed that there exists a strong positive relationship between P and M .

Now, let us assume that illustration one will represent the “existing body of knowledge” that there exists a strong positive correlation between P and M. This assumption will help us understand how to develop data analysis methodology knowledge gaps that can arise from this first case.

So let’s move on….

From past literature, researcher two came across a study by researcher one (refer to researcher one research findings) whereby the research findings showed that there exists a strong positive relationship between P and M .

However, according to researcher two, there exists some data analysis methodological knowledge gap as per the past study undertaken by researcher one if regression analysis is incorporated.

One; researcher one focused on correlation of P and M variables while the current study of researcher two is on the influence of P on M hence need to carry out regression analysis and not correlation analysis

Two; the previous study of researcher one was on determining the level of strength of the relationship between P and M while in the case of researcher two, the focus was to establish the level of significance influence of P on M.

Three; both the research questions and specific objectives of the two researchers were dissimilar .

Point of correction -when creating the methodological gap of whichever nature, its common amongst researchers even I have witnessed my postgraduate students, even others during postgraduate academic defenses state that “ researchers as per past literature failed to use either multiple regression or correlation analysis method and that is why there is a methodological gap. ”

No, you cannot afford to say that because past literature had different research question, different specific objective and of course different hypothesis and so the method used was appropriate as far as that case was concerned. So, no one has a right to negatively critique that researcher(s)!

Therefore, Researcher two further carried out a research on the influence of P on M . This means that, he sought to investigate the level of significance that P has on M .

Now, in this case the research question was,

Research Question: Does variable P have statistically significant influence on M ?

Specific Objective was; to establish the influence of P on M

research gap business definition

1.  The specific objective to be achieved is to establish the influence of variable P on variable M.

2.  In the conceptual framework, we indicate the role of the variables for we are concerned about the influence that one variable (independent variable) has on another variable (dependent variable). So P is taken to be the predictor variable and M is the response variable as indicated in the conceptual framework unlike in the correlation case were there was no classification of the same two variables.

Therefore, from illustration two, there exists some data analysis methodological knowledge gap for researcher one used correlation data analysis method which was and is for sure appropriate for determining the strength of a relationship. But it cannot apply for testing the level of significance of influence of one factor on another. Hence researcher two has to point out that instead of using correlation, simple regression method is more applicable. This way, the knowledge gap is filled and this justifies why researcher two is undertaking a similar study to that of researcher one for there is new knowledge added to the already existing body of knowledge.

Research findings -it was established that there was statistically significant influence of P on M. Therefore, the researcher failed to accept the null hypothesis. Ie rejected the Null Hypothesis so as the accept the Alternative Hypothesis (HA) That variable P has statistically significant influence on P.

Let us assume that both illustration one and two now represent the “existing body of knowledge” that there exists a strong positive correlation between P and M and also P has statistical significant influence on M. This assumption further will help us understand how to develop data analysis methodology knowledge gaps that can arise from these two cases.

So let’s move on again..

From past literature, researcher three came across studies by researcher one and researcher two whereby although both studies focused on P and M variables, there still existed some data analysis methodological knowledge gap as per those past studies.

One; although both researcher one and two focused on the P and M linkage, one was looking at the correlation of P and M while researcher two considered the influence of P on M and as a result, one used correlation analysis and the other simple regression analysis.

Two; although researcher two work was an improved study of researcher one, he only focused on a Bivariate model where by only one independent variable was considered in predicting the changes in

research gap business definition

Three; use of one predictor variable may not be good enough to estimate the changes observed in the response (dependent variable). This is because in normal circumstances, changes that occur on the dependent variable (outcome variable) may not originate from only one predictor/factor. So the simple regression model cannot be the most appropriate estimator of the dependent variable.

Therefore, there exists some data analysis methodological knowledge gap for researcher one used correlation data analysis method which was and is for sure appropriate for determining the strength of a relationship. But it cannot apply for testing the level of significance of influence of one factor on another.

Similarly, the research findings of researcher two of P having statistically significant influence on M was ok. However, use of Bivariate model is not good enough to conclude that P is only influenced by P alone. Hence researcher three has to point out that instead of using simple regression method, multiple regression analysis is far much better for more than one predictor/independent variable is used to predict the outcome variable. This way the knowledge gap is filled and this justifies why researcher three is undertaking a similar study to researcher one and two for there is new knowledge added to the already existing body of knowledge.

NB: That, in Multiple regression data analysis method, Ordinary Least Square (OLS) tool or model is used to analyze the end results.

Therefore, Researcher three carried out a study to determine the factors that influence M. She sought to determine the influence of two variables, namely P and Q which were classified as the independent variables on M.

Research Question:

Does variable P and Q have statistically significant influence on M?

Specific Objective was ;

To establish the influence of P and Q on M

research gap business definition

1.  The specific objective to be achieved is to establish the influence of two variables, P and Q on variable M.

2.  In the conceptual framework, we indicate the role of ALL the variables for we are concerned about the influence that two variables (independent variables) have on another variable (dependent variable). So both P and Q are taken to be the predictor variables and M is the response variable as indicated in the conceptual framework.

3. Although the two independent variables appear separately in the conceptual framework, this does not represent a corresponding two research questions, two research objectives and two hypotheses as witnessed in most academic research proposals/projects. CONCEPTUALLY or THEORETICALLY, the conceptual framework represent only one theory of the joint influence of P and Q on M.

Research findings- it was established that there was statistically significant influence of P and Q on M. Therefore, the researcher failed to accept both null hypotheses. I.e. rejected the Null Hypotheses so as to accept the Alternative Hypothesis (HA) that variable P and Q has statistically significant influence on M.

Similarly, let us assume that the THREE illustrations represent a wider existing body of knowledge so far. This assumption further will help us understand how to develop data analysis methodology knowledge gaps that can arise from these three cases.

So let’s move on further..

Again, from past literature, researcher four came across studies by researcher one, two and three which formed the existing body of knowledge. But still researcher four is able to incorporate new data analysis methodological knowledge gap as per the past studies undertaken by the three researchers so far.

One; some studies considered correlation, others simple regression and others multiple regression analysis methods as witnessed in the case of researcher one to three

Two; some studies used bivariate models with one predictor variable while others like researcher three used multivariate model with two predictor variables

Three; although use of multivariate model with two predictor variables is better and more appropriate in estimating changes in the dependent variable, there still exists some data analysis methodological knowledge gap for researcher three performed multiple regression whereby she ran all the two predictor variables at once either using SPSS or STATA computer program. This approach/method did not give her room to evaluate the prediction power of every incremental predictor variable considered.

Therefore, researcher four carried out a similar study like the rest but incorporate Hierarchical multiple regression model instead of just using multiple regression. This methodology has an option of portraying which predictor variable has more power to estimate the response variable for there is an option in the computer program to command significant change . In addition, use of four predictor variable is more accurate in estimating the dependent variable changes. 

NB: That, in Hierarchical Multiple regression data analysis method, Ordinary Least Square (OLS) tool or model is used to analyze the end results.

Researcher four therefore aimed at interrogating on the factors influencing variable M as it was in the cases of researcher one, two and three. But researcher four feels that there are more factors than just two that influence variable M. For this matter, he proposed four variables, namely; P , Q , R and S as the predictor variables.

Does variable P, Q, R and S have statistically significant influence on M?

Specific Objective was;

To establish the influence of P, Q, R and S on M

research gap business definition

The influence of P, Q, R and S on M is not statistically significant

The conceptual framework appeared as per Figure 1.8 below 

research gap business definition

1. The specific objective to be achieved is to establish the influence of four variables, P, Q, R and S on variable M.

2. In the conceptual framework, we indicate the role of ALL the variables for we are concerned about the influence that the four variables (independent variables) have on another variable (dependent variable). So all variables P, Q, R and S are proposed to be the predictor variables on M which is the response variable as indicated in the conceptual framework.

3. Although the four independent variables appear separately in the conceptual framework, this does not represent corresponding four research questions, four research objectives and four hypotheses as witnessed in most academic research proposals/projects. CONCEPTUALLY or THEORETICALLY, the conceptual framework represent only one theory of the joint influence of P, Q, R and S on M.

Research findings-it was established that there was statistically significant influence of P, Q, R and S taken together on M. Therefore, the researcher failed to accept the null hypothesis i.e. rejected the Null Hypothesis so as to accept the Alternative Hypothesis (HA) that variable P, Q, R and S has statistically significant influence on M.

In conclusion, under methodological knowledge gap perspective, there are many ways of the researcher like you and me to argue any case at hand and succeed. This will further be discussed in oncoming online tutorials.

research gap business definition

  • Library databases
  • Library website

Business Problem Research: Problem Statement Research

Start with a broad topic.

To keep your sanity, it's best to start with a general area of interest. Once you've reviewed the literature on your general area of interest, it'll be easier to create a problem statement from what you've found. Basing your business problem off of the literature is going to save you a lot time and energy further down the road.

Students run into two major problems when they choose a business problem without looking at the literature first . ​

  • There is no or limited literature containing the data or statistics to support your problem statement.
  • The literature may not support your assumption.

Example research topic & search strategies

If you work for a company that has high employee turnover and you'd like to find more information about how to retain employees, these are the steps you could take.

  • Research whether high employee turnover is an actual problem in the industry your company falls under.
  • Determine if there is enough literature to support your topic and that the literature supports your business problem. 
  • Now you can locate articles that support a more specific research topic.

Keep an open mind while reviewing the literature

research gap business definition

Trying to locate data or statistics based on what you'd like see instead of what's available can be tricky. Your preconceived ideas for data or statistics may or may not exist. If they do exist, they may not exist in the way you expect.

Review the literature for gaps and business problems

The easiest way to locate a gap in the literature is to review the literature related to a topic you're interested in. While reviewing the literature, do you notice any themes, industries, or groups that aren't being addressed? Below are instructions for locating a gap in the literature.

  • Quick Answer: How do I find articles on my topic?  
  • Geographic location
  • Business size
  • Demographics (Age, Gender, Ethnicity, Disability, Veterans)  
  • Review relevant studies for opportunities for future research. Many authors will discuss what research could be done based of the work they have done.
  • Include any of these subtopics in to your search to help you limit your results and to locate a gap in the literature.

Look at completed dissertations

Most dissertations will have a section discussing opportunities for further research. Those students have already done the leg work and have insight into the literature. If their idea for further study intrigues you, go out and research to confirm that there is still a gap in research.

  • Quick Answer: How do I find Walden PhD dissertations?
  • Quick Answer: How do I find Walden DBA (Doctor of Business Administration) studies?
  • Quick Answer: How do I find Walden DIT (Doctor of Information Technology) studies?  
  • Quick Answer: How do I find dissertations on a topic?

DBA business problem tutorial

Video: Walden DBA Problem Statement Tutorial (YouTube)

Recorded September 2013 (15 min 20 sec)

  • Previous Page: Ideal Research Path
  • Next Page: Search for Statistics & Data in Articles
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A microscope on small businesses: Spotting opportunities to boost productivity

At a glance.

research gap business definition

  • Micro-, small, and medium-size enterprises (MSMEs) form the backbone of economies. Across the 16 countries we examine, MSMEs account for two-thirds of business employment in advanced economies—and almost four-fifths in emerging economies—as well as half of all value added. They also power dynamism and will play an important role in preserving competitiveness in an era of shifting global production.
  • Boosting MSME productivity relative to large companies could yield significant value. Small business productivity is only half that of large companies, and less in emerging economies. Raising MSMEs to top-quartile levels relative to large companies is equivalent to 5 percent of GDP in advanced economies and 10 percent in emerging economies.
  • Capturing this value requires a fine-grained view. Relative productivity of MSMEs and large companies varies widely across subsector and country. For example, in virtually all countries, eight subsectors out of 24 drive more than 60 percent of the value of narrowing the productivity gap in manufacturing, but the top ones vary by country.
  • A win-win economic fabric can improve productivity for both MSMEs and large enterprises. MSME and large company productivity move in tandem in most subsectors, indicating spillovers if the right conditions are created. For example, automotive MSMEs have gained operational proficiency through systematic interactions with productive original equipment manufacturers, and small software developers have benefited from talent and capital ecosystems seeded by larger companies.
  • All stakeholders have a role to play in developing granular productivity strategies. In subsectors where both small and large companies lag, infrastructure and policy improvements can target both together. Where MSMEs struggle but large enterprises outperform, building networks among them helps. Even where both large and small companies do well, strengthening their interactions could boost productivity.

Micro-, small, and medium-size enterprises (MSMEs) are the lifeblood of economies around the world. They account for more than 90 percent of all businesses, roughly half of value added, and more than two-thirds of business employment. 1 “Micro-, Small and Medium-sized Enterprises Day, 27 June,” United Nations, June 2023.

McKinsey Global Institute

The McKinsey Global Institute was established in 1990. Our mission is to provide a fact base to aid decision making on the economic and business issues most critical to the world’s companies and policy leaders. We benefit from the full range of McKinsey’s regional, sectoral, and functional knowledge, skills, and expertise, but editorial direction and decisions are solely the responsibility of MGI directors and partners.

Our research is currently grouped into five major themes:

  • Productivity and prosperity: Creating and harnessing the world’s assets most productively
  • Resources of the world: Building, powering, and feeding the world sustainably
  • Human potential: Maximizing and achieving the potential of human talent
  • Global connections: Exploring how flows of goods, services, people, capital, and ideas shape economies
  • Technologies and markets of the future: Discussing the next big arenas of value and competition

We aim for independent and fact-based research. None of our work is commissioned or funded by any business, government, or other institution; we share our results publicly free of charge; and we are entirely funded by the partners of McKinsey. While we engage multiple distinguished external advisers to contribute to our work, the analyses presented in our publications are MGI’s alone, and any errors are our own.

You can find out more about MGI and our research at www.mckinsey.com/mgi .

MGI Directors Sven Smit (chair) Chris Bradley Kweilin Ellingrud Sylvain Johansson Olivia White Lola Woetzel

MGI Partners Michael Chui Mekala Krishnan Anu Madgavkar Jan Mischke Jeongmin Seong Tilman Tacke

But small businesses lag behind large companies on productivity. On average, their labor productivity, or value added per worker, is half that of their larger peers. Accelerating productivity growth has always been the sure way to deliver long-term prosperity, and MSMEs can—must—play a crucial role. Their contribution is potentially even more important amid the beginnings of a reconfiguration of global trade patterns. 2 Geopolitics and the geometry of global trade , McKinsey Global Institute, January 2024. Such shifts are unlikely to translate into a meaningful long-term realignment without a competitive network of MSMEs supporting and complementing large companies.

If MSMEs were to narrow the productivity gap with large companies, not only could that breathe new life into economy-wide productivity, employment, and growth, but economies and companies could raise their resilience in an uncertain world. The question is how.

Only by studying MSMEs at the fine-grained level can we understand where and why opportunities exist and plot a path toward higher productivity for all. After all, MSMEs are immensely varied. They range from a self-employed individual, such as a taxi driver or an online game designer; to a microenterprise with one to nine employees, like a laundry or a dental practice; to a small enterprise with up to 50 employees, such as a bakery or local auto repair chain; to a medium-size furniture manufacturing company or software business employing up to 250 people.

Definitions, scope, and data limitations

The data collected for this research are arguably deeper and broader than those collated in the past. Here we present an overview of our approach. (See the technical appendix for more detail on the data sources and analysis undertaken in this research.)

Types of MSMEs studied. We examine a diverse array of MSMEs, from self-employed workers and entrepreneurs to mom-and-pop shops and small family businesses, across 16 countries. One notable exception is smallholder farmers, most of whom can be considered small business owners and constitute a substantial portion of the workforce, particularly in emerging economies. For example, in 2022, the agriculture sector employed 29 percent of the workforce in Indonesia, 33 percent in Kenya, 38 percent in Nigeria, and 43 percent in India. In this research, we focus on the nonfarm sector and do not examine agricultural productivity, which has its own unique dynamics, meriting a separate study.

MSME size category definitions. Enterprise sizes are typically defined by the number of persons employed. We take each country’s national definition of micro-, small, and medium-size enterprises. For example, for European economies in our sample, we used the OECD’s definition of MSMEs. The OECD thresholds are as follows: microenterprises employ nine people or fewer, small enterprises employ between ten and 49 people, medium-size companies between 50 and 249, and large companies 250 or more. However, definitions of enterprise sizes may vary by country. For example, in the United States, large companies are defined as having 500 or more employees. Indonesia and Kenya define businesses with 100 or more employees as large, and Nigeria sets the threshold at 200. India and Indonesia define MSMEs based on their revenue and their investment in plant and equipment as well as employment. While this makes cross-country comparisons inexact, it enables us to use reported data more directly and to limit assumptions.

Scope of data. We gathered data on value added and employment by sector (classified based on economic activity) across corporate size classes (micro, small, medium, and large) from country-level economic and business censuses, MSME and labor surveys, and aggregated databases, such as those of Eurostat, OECD, ILOSTAT, and S&P Global Market Intelligence. Typically, we use 2019 data to exclude potential distortions due to the COVID-19 pandemic. However, for availability reasons, the dates used range from 2016 to 2019 across countries. 1 To verify stability of the data, we examined data from 2009 and 2014 for some countries but did not collect complete longitudinal data due to the significant effort involved. However, the topic of understanding trends in MSME productivity would be a valuable area for future research.

Level of aggregation. We aggregated data at the level of 12 level-one sectors (for example, manufacturing) and 68 level-two subsectors (for example, manufacturing of textiles within the manufacturing sector), as defined by the International Standard Industrial Classification of All Economic Activities (ISIC), Revision 4 or equivalent. For the United States, Brazil, Mexico, and the European economies in our sample, we also collected data for 219 level-three subsectors (for example, manufacturing of carpets and rugs within manufacturing of textiles). The 12 level-one sectors are mining and quarrying; manufacturing; electricity, gas, steam, and air conditioning supply; water supply, sewerage, waste management, and remediation activities; construction; wholesale and retail trade; transportation and storage; accommodation and food services activities; information and communications technology (ICT); professional, scientific, and technical activities; administrative and support service activities; and other service activities. 2 We grouped two sectors—electricity, gas, steam, and air conditioning supply; and water supply, sewerage, waste management, and remediation activities—into one sector: utilities.

Sectoral data for some countries, typically the emerging economies, are not as granular as for the advanced economies. For cross-country comparisons, we used a combination of data sources, including a sector breakdown of employment from ILOSTAT, and distinguished between MSMEs and large companies using national sources. In some cases, we also conducted comparisons at a less granular level by grouping two or three level-two sectors.

Measuring productivity. Productivity is a measure of output relative to input. 3 Investing in productivity , McKinsey Global Institute, March 2024. In macroeconomic terms, it is defined as the value of the goods and services produced divided by the amount of labor, capital, and other resources required for its production. For this report, we focus on labor productivity, measured as value added per worker (in US dollars at purchasing power parity). While the more accurate measure of labor productivity is value added per hour worked—as the number of weekly hours worked varies substantially among countries, from 31 hours in Australia in 2023 to roughly 46 hours in India—we use the per worker metric as it is more commonly available across size categories by country. Due to the lack of comprehensive data at the individual company level for MSMEs, we rely on subsector-level average productivity to make inferences. 4 We focus on national- or sector-level productivity from a growth economics perspective. Organizational productivity research often studies issues related to attrition, disengagement, skills mismatch, or time inefficiency. See, for example, Aaron De Smet, Marino Mugayar-Baldocchi, Angelika Reich, and Bill Schaninger, “ Some employees are destroying value. Others are building it. Do you know the difference? ,” McKinsey Quarterly , September 2023.

Other important limitations. Our research reflects the challenges of working with significant constraints on data availability. For all the countries in our sample, we included data for both formal and informal sectors, although we recognize that data pertaining to the informal sector are often less reliable. Beyond informality, as consistent data were not always available across countries, we had to exclude certain sectors from our analysis. Because of inconsistent data availability, across countries we exclude financial services; real estate; education; human health and social work activities; arts and entertainment; public administration and defense; and activities of households and extraterritorial organizations. These sectors play a substantial role, particularly in advanced economies where they contribute 37 percent of value added, on average, ranging from 26 percent in Poland to 43 percent in the United States. As noted, we also exclude agriculture despite its significant contribution to the economy. These exclusions imply that our findings may not be entirely representative of the entire economy and are limited to the narrower “business” economy. Similarly, from a country perspective, we do not cover some major emerging economies, such as China, and regions, such as the Middle East and North Africa, due to limited data availability. We also include only a selected set of advanced economies in our research. As such, we cannot state definitively the degree to which our conclusions are globally representative. While we derive broad and generalized implications for emerging and advanced economies, these are directional only.

In this research, the McKinsey Global Institute (MGI) has aggregated a richly granular data set of MSME productivity across sectors and subsectors for 16 countries with different income levels accounting for more than 50 percent of global GDP. In this group (listed by per capita GDP in 2021 in purchasing power parity terms) are ten advanced economies: the United States, Germany, Australia, the United Kingdom, Italy, Israel, Japan, Spain, Poland, and Portugal; and six emerging economies: Mexico, Brazil, Indonesia, India, Nigeria, and Kenya. 3 Countries classified as “advanced emerging,” “secondary emerging,” or “frontier” by FTSE Russell have been categorized as emerging economies for this research. For more detail, see FTSE equity country classification September 2023 annual announcement , FTSE Russell, September 2023. At the sector level, in the manufacturing sector, for instance, our data cover 24 level-two subsectors and 95 level-three subsectors. 4 Levels of subsectors are defined by the International Standard Industrial Classification of All Economic Activities (ISIC), Revision 4 or equivalent. See International Standard Industrial Classification of All Economic Activities (ISIC), Rev. 4 , United Nations, 2008. This enables us to explore the details of businesses that are highly diverse in size, economic context, degree of formalization, and, especially, the nature of economic activity in which they engage (see sidebar “Definitions, scope, and data limitations”). Most previous external analysis has tended to study MSMEs in a single country or has compared productivity among countries within a particular sector. 5 Beldina Owalla et al., “Mapping SME productivity research: A systematic review of empirical evidence and future research agenda,” Small Business Economics , volume 58, issue 3, March 2022.

This research focuses on the variation in MSME productivity relative to large companies across sectors, subsectors, and countries, enabled by our rich data set. We use this microscopic, but cross-country, lens to spot potential value and identify how MSMEs can work with other companies in specific business contexts to capture it.

1. Small businesses power the economies of today and tomorrow

MSMEs are ubiquitous and play vital economic roles across countries, albeit with important differences depending on whether they operate in an emerging or advanced economy.

MSMEs fuel economy-wide production and jobs

MSMEs create enormous value for economies around the world. They account for roughly half of global GDP. That share varies significantly among economies (Exhibit 1). In Portugal, Israel, Indonesia, Italy, and Kenya (ordered by decreasing share of value added), the share is larger than 60 percent. In the United States, Nigeria, and India, it is less than 40 percent.

Image description:

Two scatterplot charts appear side by side. The first one shows the share of value added in the business sector attributable to micro-, small, and medium-size enterprises (MSMEs) for different countries. MSMEs contribute about half of the value added in emerging economies (an average of 49 percent) and advanced economies (54 percent).

The second scatterplot shows that small businesses contribute about 70 percent of all employment in the business sector, with an average of 77 percent in emerging countries and 66 percent in advanced economies.

End of image description.

They are also significant employers, accounting for roughly 40 percent of all employment and 70 percent of employment in the business sector, which we define as excluding the farm, government, and finance sectors. That share is as high as 96 percent in Kenya, where MSMEs account for half of all employment.

MSMEs create enormous value for economies around the world.

The business sector plays a larger role in advanced economies. But within the business sector, MSMEs have a greater impact in emerging economies, employing four-fifths of all workers, compared with two-thirds in advanced economies.

MSMEs are also meaningful job creators. 6 Fredrik Heyman, Pehr-Johan Norbäck, and Lars Persson, “Who creates jobs and who creates productivity? Small versus large versus young versus old,” Economics Letters , volume 164, March 2018. In advanced economies, one 2013 study suggested, they contributed more than half of net job growth in businesses. 7 Is small still beautiful? , International Labour Organization, April 2013. In the United States, for example, SMEs have accounted for two out of every three jobs added in the past 25 years. 8 Daniel Wilmoth, “Small business job creation,” US Small Business Administration Office of Advocacy, April 2022. In emerging economies, MSMEs created seven out of ten new formal jobs over the past decade. 9 Small and medium enterprises (SMEs) finance: Improving SMEs access to finance and finding innovative solutions to unlock source of capital , World Bank, October 2019.

MSMEs play a crucial role in production across sectors, but their contribution is more significant in some (Exhibit 2). While there are differences among countries, MSMEs tend to contribute the majority of the value added in four sectors—accommodation and food, construction, professional services, and trade. Although they contribute only about 45 percent of value added in the manufacturing sector, they are the second-largest contributor to small business value after the trade sector. Across all sectors, MSMEs also employ at least half of all business workers.

A bar chart shows MSME contributions to value added and to employment in different sectors of the economy. MSMEs tend to contribute most of the value added in four sectors—accommodation and food, construction, professional services, and trade. Although they contribute only about 45 percent of value added in the manufacturing sector, MSMEs are the second-largest contributor to small business value after the trade sector. Across all sectors, MSMEs also employ at least half of all business workers.

MSMEs drive business dynamism

Many MSMEs grow rapidly into large companies, adding to the vibrancy and dynamism of the economies in which they operate. They promote innovation and competition among companies, encouraging all businesses to continually improve their products, services, and processes, which, in turn, can enhance overall economy-wide productivity and dynamism.

Many large companies of today were MSMEs not long ago. About one in five of today’s very large companies—defined as having a market capitalization of more than $10 billion in the United States and equivalent values in other economies—were MSMEs at some point after 2000 and have since powered their way to large company status.

The share of scaled-up companies varies by country, indicating different levels of MSME dynamism (Exhibit 3). Dynamic MSMEs can stimulate competition among businesses, driving the entire system to become more innovative and efficient, ultimately resulting in increased productivity. 10 The productivity puzzle: A closer look at the United States , McKinsey Global Institute, March 2017. Yet overall, rising productivity—crucially, that of large companies—can create new market opportunities and build business capabilities for smaller enterprises, raising the rate of scaling up.

A bar chart compares the share of 2022 scaled-up companies (large public companies that were MSMEs at some point since 2000) in different advanced and emerging economies. About one in five large companies scaled up from being MSMEs since 2000 but there is variation among countries. Australia has the largest share of scaled-up companies in the sample with 44 percent, and Spain has the smallest with 5 percent. Indonesia has the largest share among emerging economies in the sample, with 31 percent.

Unique factors at the country level can contribute to dynamism. In Australia, high dynamism reflects a resources boom that has expanded growth opportunities for small mining companies. Israel, by contrast, has a small economy, but one of the most technologically advanced in the world. 11 Prableen Bajpai, “An overview of Israel’s economy,” Nasdaq, November 2023. Its dynamism is connected to entrepreneurial ecosystems, a high density of skilled professionals, an ability to tap into global networks, and large-scale lending to MSMEs. 12 Colin Mason and Ross Brown, Entrepreneurial ecosystems and growth oriented entrepreneurship , OECD LEED Programme, November 2013; and Jonathan Friedrich, Amit Noam, and Elie Ofek, “Right up the middle: How Israeli firms go global,” Harvard Business Review , May 2014. Over the past decade, growth in bank credit to SMEs in Israel was higher than to large businesses, at 61 percent versus 16 percent. 13 “Israel,” in Financing SMEs and entrepreneurs 2022: An OECD scoreboard , OECD, March 2022. In India, only about 10 percent of large companies in 2022 were MSMEs at some point after 2000. Indeed, previous MGI research found that India has a “missing middle” of mid-size companies. 14 India’s turning point: An economic agenda to spur growth and jobs , McKinsey Global Institute, August 2020. MSMEs have faced structural barriers, such as the high cost of compliance and finance, that have tended to constrain their growth.

Researchers have found that high-growth businesses in advanced economies tend to be younger and intangibles heavy. Enterprises that tend to rely on profits rather than external financing to fund their growth are also more likely to scale up. 15 Alex Coad and Stjepan Srhoj, “Catching gazelles with a lasso: Big data techniques for the prediction of high-growth firms,” Small Business Economics , volume 55, number 3, October 2020. Our analysis finds that in the information and communications technology (ICT) and mining sectors, one in three enterprises that are large today have grown from being MSMEs in the past two decades (Exhibit 4). These sectors seem to experience a fast pace of innovation and technological disruption as well as higher rates of investment. 16 Felipe Sánchez and Philipp Hartlieb, “Innovation in the mining industry: Technological trends and a case study of the challenges of disruptive innovation,” Mining, Metallurgy & Exploration , volume 37, number 5, October 2020; Critical minerals market review 2023 , International Energy Agency, December 2023; and McKinsey Technology Trends Outlook 2023 , July 2023.

A bar chart shows the share of 2022 scaled-up companies (large public companies that were MSMEs at some point since 2000) in different sectors (mining, information and communications technology (ICT), construction, utilities, manufacturing, trade, and transportation). A set of bars shows the numbers for advanced economies, and another set shows them for emerging economies. Mining and ICT companies scaled up more overall, representing more than 30 percent of all large companies in advanced economies. Construction, utilities, and transportation MSMEs were more dynamic in emerging economies.

MSMEs in the emerging economies in our sample seem to exhibit greater dynamism than in advanced economies in core sectors like construction, utilities, and transportation. Investment in physical infrastructure tends to rise faster in countries that are in the earlier stages of their development. Where such sector growth opportunities have been captured, we see greater business dynamism.

Some emerging economies have powered national growth through the manufacturing and trade sectors as well. In a similar analysis of companies founded after 1950, in China—not included in our sample, as noted—the dynamism of the manufacturing and trade sectors is higher than in the advanced economies on average.

MSMEs can boost national productivity while staying small or by fueling larger companies

In emerging economies, the MSMEs that are so vital to sustaining livelihoods are heavily skewed toward microenterprises. In India, Kenya, and Nigeria, microenterprises employ more than 90 percent of MSME workers, of whom some 90 percent are self-employed own-account workers and contributing family members. They face challenges of particularly low productivity. 17 The low productivity challenges of microenterprises in emerging economies could be linked to informality. According to the World Bank, almost 80 percent of all MSMEs in emerging economies are informal. These businesses typically have limited access to markets, finance, and government support, restricting their productivity. We estimate that informal employment in our sample emerging economies is only one-quarter to one-fifth as productive as formal employment. See Micro-, small and medium-sized enterprises (MSMEs) and their role in achieving the Sustainable Development Goals , United Nations Department of Economic and Social Affairs, 2020; and Guillermo E. Perry et al., Informality: Exit and exclusion , World Bank, 2007.

In emerging economies, the MSMEs that are so vital to sustaining livelihoods are heavily skewed toward microenterprises.

As these emerging economies climb the income ladder, microenterprises may grow their revenue and productivity, but most tend to stay small or medium size. 18 For instance, over the past two decades, as Brazil transitioned from being a lower-middle-income to an upper-middle-income economy, about nine percentage points of tiny microenterprises (with fewer than five employees) advanced into larger micro- and small enterprises (with five to 30 employees), but there was no net movement into higher size categories from 2002 to 2021. As a result, MSMEs as a group continue to contribute larger shares to national output, and in that sense, MSMEs directly lift aggregate productivity growth.

In richer economies, the dynamic is different. Much of employment has shifted away from microenterprises to small and medium-size companies or even to larger ones. Only about half of all MSME workers are employed in microenterprises. As these advanced economies climb the income ladder, beyond a certain point more MSMEs tend to scale up into larger companies, are taken over and merged into them, or simply exit in the process known as creative destruction. As a result, the contribution of large businesses to the national output of the richest economies rises, relative to that of small companies. As such, MSMEs may not increase their share of economies, but they still contribute to business dynamism.

2. Boosting MSME productivity could yield significant value

Despite their central role in economies across the world, MSMEs are only about half as productive as large companies, and narrowing that gap could create significant value. Yet somewhat unexpectedly, this gap is by no means monolithic: relative productivity performance varies enormously across countries and sectors, and even within the same sector among countries.

MSME productivity lags behind that of large companies

The MSME productivity gap—defined as the distance between MSME productivity and that of large companies—varies among countries. For example, in Kenya, MSMEs are just 6 percent as productive as large companies, translating to a hefty 94 percent productivity gap. Among the countries we investigate, MSMEs are relatively most productive in the United Kingdom, at 84 percent of the levels of large companies, translating to a productivity gap of only 16 percent (Exhibit 5). In general, the productivity gap is larger in emerging economies than advanced ones.

A bar chart compares the productivity of small companies (MSMEs) with that of large companies in different countries. The countries are divided into two groups: emerging economies and advanced economies. MSME productivity lags behind that of larger firms across all countries in the samples, with a wider gap in emerging economies. On average, MSMEs in emerging economies are 29 percent as productive as large companies. In advanced economies, MSMEs are 60 percent as productive as large firms. Productivity is measured in value added per worker.

As discussed in the previous chapter, within increasing income levels in emerging economies, MSME productivity rises steeply relative to that of large companies, whereas in advanced economies, the productivity of large companies rises noticeably.

The MSME productivity gap—defined as the distance between MSME productivity and that of large companies—varies among countries.

The size of MSMEs certainly plays a role in their productivity relative to that of large companies. Microenterprises trail large companies by a greater margin than do small and medium-size ones (Exhibit 6), and microenterprises account for much more employment in the emerging economies in our sample.

Yet in our sample advanced economies, only about 15 percent of the differences in MSME productivity among countries can be explained by the mix of micro-, small, and medium-size enterprises. The rest of the variation comes from differences in sector mix as well as how MSMEs in each country fare at a subsector level.

A bar chart shows the same metric as in the previous exhibits, comparing the productivity of small companies (MSMEs) with that of large companies in different countries. The countries are divided into emerging economies and advanced economies. In this case, MSMEs are broken down into three categories: micro-, small, and medium-size enterprises. Microenterprises typically have a smaller productivity ratio relative to large companies than other MSMEs, with the United Kingdom being the only exception in the sample. The countries shown are Kenya, Brazil, and Mexico in the emerging economies group, and Portugal, Poland, Israel, Spain, Australia, Italy, Germany, Japan, the United States, and the United Kingdom in the advanced economies group.

Lack of scale matters more to the MSME productivity gap in some sectors than in others

Considering the broad sectors of our sample advanced economies, the MSME productivity ratio, averaged across economies, ranges from 49 percent in ICT to 104 percent in the administrative services sector. In other words, MSMEs in the ICT sector face the largest gap in productivity relative to large companies in ICT, while MSMEs in administrative services tend to outperform their large peers in productivity. Country-level differences within each sector are greatest in mining and utilities, and smallest in manufacturing and ICT (Exhibit 7).

A dot-plot chart compares the ratio of MSME productivity to large company productivity in different sectors for a sample of advanced economies. The economies are Australia, Germany, Israel, Italy, Japan, Poland, Portugal, Spain, the United Kingdom, and the United States. The sectors are administrative services, accommodation and food services, utilities, trade, transportation, construction, professional services, mining, manufacturing, ICT, and other services. The MSME productivity ratio varies among and within sectors and among countries, but less so in manufacturing and ICT. The only sector where the MSMEs are more productive than large companies for the median of all countries in the sample is administrative services. MSMEs in the ICT sector are the least productive on average when compared to large companies.

Larger scale is generally associated with higher productivity. Yet being small has its advantages, too. Small businesses can be a vehicle for individuals to channel their entrepreneurial ambitions as well as for people who simply own and run a business for a living. 19 Sander Wennekers and Roy Thurik, “Linking entrepreneurship and economic growth,” Small Business Economics , volume 13, August 1999. They shape our social fabric and day-to-day life in important ways and are trusted by citizens. In the United States, for example, MSMEs are considered the most trusted institutions by the general public, more even than the military or the police. 20 Lydia Saad, Historically low faith in U.S. institutions continues , Gallup, July 2023. While small businesses do not have as much time and resources to innovate as large companies, their relative advantage comes from being closer to customers, being less bureaucratic, and reacting nimbly to changing market dynamics. 21 Robert W. Vossen, “Relative strengths and weaknesses of small firms in innovation,” International Small Business Journal , volume 15, issue 3, December 2012; Ming-Jer Chen and Donald C. Hambrick, “Speed, stealth, and selective attack: How small firms differ from large firms in competitive behaviour,” Academy of Management Journal , volume 38, number 2, April 1995. They are able to effectively mobilize local labor and offer flexible work arrangements.

Small businesses also play a crucial role in enabling the productivity of large companies, which tend to focus on core competencies and outsource less essential activities to other businesses, a phenomenon called work fissuring. 22 David Weil, “Understanding the present and future of work in the fissured workplace context,” RSF: The Russell Sage Foundation Journal of the Social Sciences , December 2019. This results in greater concentration of higher-value-added activities in large companies, with smaller businesses taking on lower-value work. Similarly, in many advanced economies, as waves of labor-intensive manufacturing moved to countries with low labor costs—often to MSMEs in those countries—higher-value work remained with larger enterprises.

Moreover, being engaged in higher-value work enables large businesses to build three types of competencies: intangible capital, which comprises both better technology and superior human capital; global connections; and financial capital. Consequently, the MSME productivity ratio tends to be lower, and the productivity gap wider, in sectors where these competencies play a significant role in driving business competitiveness (Exhibit 8).

Three scatterplot charts are used to show that the ratio of MSME productivity to large company productivity is smaller in sectors where three competencies play a significant role in driving business competitiveness: intangible capital, global connections, and financial capital. The first scatterplot shows that the ratio is smaller in intangibles-heavy sectors such as ICT, manufacturing, and professional services. The second scatterplot shows that the ratio is smaller in export-intensive sectors such as manufacturing and mining. The third scatterplot shows that the ratio is smaller in sectors that rely more on traditional financing such as bank loans to secure working capital; for example, manufacturing and transportation.

Intangible capital. In sectors like ICT, manufacturing, and professional services, intangibles drive a larger share of value added and MSMEs have a wider productivity gap. Manufacturing productivity depends on organizational efficiency, the application of technology, and the effective utilization of capital—areas where scale makes a difference. In the mining sector, large companies have an advantage in undertaking explorations because they can invest effectively in acquiring geological information and in developing specialized know-how. In the ICT and professional services sectors, productivity drivers like automation, connectivity, and access to high-skill talent also become more powerful with scale. According to the World Bank Enterprise Surveys conducted between 2013 and 2022 and the OECD ICT Access and Usage by Businesses database, these are areas where MSMEs struggle. 23 World Bank Enterprise Survey; The OECD model survey on ICT usage by businesses , second revision, OECD, 2015. The share of MSMEs that adopt technologies like customer relationship management systems and artificial intelligence is only half the share of large companies. Large companies are twice as likely to provide formal skilling programs and are more active in monitoring performance and awarding performance bonuses. Large enterprises also contributed to 84 percent of research and development spending in the United States in 2015, spending more than five times as much as small businesses. 24 Gary Anderson and Audrey Kindlon, “Indicators of R&D performance by size of company,” National Science Foundation, 2019.

In sectors where intangibles matter less to competitiveness, the MSME productivity gap tends to be narrower. In such sectors, companies drive productivity through local reach and access to lower-skill labor. Examples are accommodation and food services, administration and support services, trade, and transportation.

Global connections. In sectors like manufacturing and mining where exports drive a larger share of value added, MSMEs have a wider productivity gap with large companies. In trade, however, MSMEs actively participate in cross-border activities, likely driven by commodity brokering in wholesale trade. This translates into a 70 percent share for MSMEs in all trade exports and a higher MSME productivity ratio.

MSMEs are typically less able than larger companies to gain access to global markets and benefit from global procurement. According to the World Bank Enterprise Survey, MSMEs derive just 5.0 percent of their total sales from direct exports, but large enterprises triple that. In emerging economies, on average, MSMEs account for 2.5 percent or less of exports. 25 Trade finance and SMEs: Bridging the gaps in provision , World Trade Organization, 2016. In Indonesia, for instance, only 1.5 percent of small enterprises and 10.0 percent of medium-size enterprises participate in global value chains, compared with more than one-quarter of all large companies. 26 Shujiro Urata, ed., Enhancing SME participation in global value chains: Determinants, challenges, and policy recommendations , Asian Development Bank Institute, March 2021. Moreover, only about one-fifth of purchases of material inputs by MSMEs were of foreign origin, compared with more than one-third for large companies.

Financial capital. Access to finance is the second most cited obstacle for MSMEs in the World Bank Enterprise Survey. In sectors like manufacturing, other services, transportation, construction, and trade, where businesses typically rely more on traditional financing such as bank loans to secure working capital, MSMEs have a wider productivity gap. When the sector as a whole relies less on bank financing—perhaps because it is less necessary, as is the case in ICT—this may create a more level playing field, resulting in relatively smaller productivity gaps.

In addition to these competencies, small businesses may be disproportionately affected by lack of public infrastructure, such as reliable logistics networks, access to basic utilities like uninterrupted power supply, and the availability of 5G. Large businesses often have the ability to establish their operations in areas with robust infrastructure. They also can develop infrastructure themselves, such as investing in power generators and building last-mile connectivity. While this enabler is critical for MSMEs overall, it is difficult to differentiate at the sector level.

Narrowing the productivity gap is equivalent to 5 to 10 percent of GDP

The tremendous variation in MSME productivity ratios across countries indicates potential for improvement. In any given country, overall productivity stands to gain when the ratio of MSME productivity to large company productivity is brought closer to its full potential.

That potential varies by country given different underlying economic conditions. It depends on the industry structure in each business domain, as well as the specific nature of existing bottlenecks to growth, and the extent to which they are addressed to achieve the optimal economic structure. The productivity improvement itself may manifest in various ways. It could stem from some MSMEs increasing their productivity while remaining in their size bracket. Or it could result from a shift in the industry structure in which some small firms transition within the MSME category from micro to small or small to medium, or scale up to become large companies.

Estimating the value of narrowing the productivity gap

To assess the value for each country, we compare the ratio of MSME productivity to large company productivity in the country in each subsector to a benchmark level in the same subsector. We considered three benchmarks—a higher threshold representing the top quartile, a midpoint threshold representing the median, and a lower threshold representing the bottom quartile among all advanced economies. We assumed no change in subsectors in countries that have already achieved the benchmark levels.

As an illustration, the MSME productivity ratio in the manufacturing of food products subsector varies from 46 percent in the United States to 88 percent in the United Kingdom. In addition to the United Kingdom, Israel and Spain are in the top quartile of advanced economies. The value of narrowing the productivity gap in this case is the difference between the actual productivity ratio and the top-quartile threshold of 61 percent.

As MSME productivity improves, the interlinked economics of small and large firms may create feedback loops, altering its overall economic impact. While we recognize that increasing MSME productivity could have multiplier effects on the broader economy, estimating those effects is more challenging. Therefore, we focus only on estimating the first-order effects.

We estimated value only in accommodation and food services, administrative services, construction, ICT, manufacturing, mining, other personal services, professional services, trade, transportation and storage, and utilities. We excluded other sectors, including agriculture, financial services, and real estate, because of inconsistent data that make it difficult to compare across countries. We also excluded self-employed individuals—who are often sustenance workers in emerging economies—in order to be able to compare the remaining MSMEs in emerging economies with those in advanced economies using the same benchmarks. 1 By not considering self-employed workers, who are more prevalent in emerging economies, we establish a lower benchmark for these countries. To be conservative, we chose this approach instead of adjusting the benchmarks for each country based on their per capita GDP.

While meaningful benchmarks would vary based on local conditions, we compare the average ratio of MSME productivity to that of large companies in each country with the top quartile ratio across countries at a subsector level (see sidebar “Estimating the value of narrowing the productivity gap” for an overview of our approach). This exercise is a useful thought experiment to motivate an investigation of the specific drivers of MSME productivity and where to focus.

The gap between the actual productivity ratio and the top quartile level is equivalent to an average of 5 percent of GDP in advanced economies and an average of 10 percent in emerging economies. It ranges from 2 percent in Israel and the United Kingdom to 10 percent in Japan among advanced economies, and from 3 percent in Brazil to 15 percent in Indonesia and Kenya among emerging economies (Exhibit 9). On a per business worker basis, the amount is meaningful, ranging from about $3,000 in Israel to $12,900 in Japan among advanced economies, and from $3,200 in Mexico to $8,800 in Indonesia among emerging economies (all in purchasing power parity terms).

A map is used to locate several countries and indicate their MSME productivity gap with large companies. It measures the difference as a percentage of GDP, between current and top-quartile productivity ratio across subsectors. Narrowing the productivity gap would represent value equivalent to an average of 5 percent of GDP in advanced economies and an average of 10 percent in emerging economies. The potential value ranges from 2 percent in Israel and the United Kingdom to 10 percent in Japan among advanced economies and from 3 percent in Brazil to 15 percent in Indonesia and Kenya among emerging economies.

If we used lower thresholds to set benchmarks, the gap is lower, but still meaningful. For example, comparing the current MSME productivity ratio against the median ratio in each subsector, it is equivalent to 2 percent of GDP in advanced economies on average and 8 percent in emerging economies. Using bottom-quartile benchmarks, it would be about 1 percent of GDP on average in advanced economies and 7 percent in emerging economies.

Among advanced economies, the impact of narrowing the gap is larger in Italy, Japan, Poland, and the United States. In Japan, two-fifths of all MSME value added is in manufacturing and construction where, in many subsectors, MSMEs achieve only the bottom quartile of performance across countries. Similarly, in Italy and Poland, MSMEs in two-fifths of subsectors are in the bottom quartile of performance. In automotive trade, for instance, Poland has the highest productivity gap (73 percent) and Italy the second highest (67 percent) of our sample advanced economies. In the United States, MSMEs in almost half the subsectors are in the bottom quartile of the productivity ratio.

Where the overall gaps are smaller, as in Israel and the United Kingdom, the impact is limited. In these countries, about half the subsectors are already in the top quartile of MSME productivity relative to large companies.

The value is highest in four emerging economies—Kenya, India, Indonesia, and Nigeria—where MSME productivity gaps are the most substantial. In Kenya, the productivity of small businesses is the lowest of all the sample countries, explaining the wide gap. In Indonesia, the productivity of large companies is double that of the figure for other emerging economies, and therefore its MSMEs have further to go.

The sectors that produce the most economic output account for the largest share of GDP from improving their MSME productivity ratios. The three largest are trade, manufacturing, and construction (Exhibit 10). Nevertheless, some sectors in some countries punch above their weight relative to their role in economies. A standout example is ICT, particularly—in order of importance—in India, Nigeria, Brazil, the United Kingdom, Indonesia, and the United States. In these countries, the ICT sector contributes about 8 percent of economic value added on average, but about one-fifth of the value from narrowing the productivity gap. Other examples include transportation and storage in Australia, Kenya, and Israel; administrative services in Portugal, Kenya, and Germany; professional services in Nigeria and India; and accommodation and food services in Germany and the United Kingdom.

A heat map compares the contributions of different sectors of the economy to narrowing productivity gaps for a sample of ten advanced economies and six emerging economies. The heat map is darker for sectors in each county with a large share, with the darkest color indicating a share greater than 30 percent. The lightest color indicates sectors in each country with a share less than 10 percent. Trade, manufacturing, construction, and ICT offer the largest potential, driving more than half the value at stake in most countries.

3. Looking through a microscope to fill the gaps

To move the needle beyond broad-brush solutions, we need to look in detail at variations in relative MSME productivity performance to identify specific opportunities to achieve potential additional value. Consistent with MGI’s micro-to-macro analytical approach, we have looked at MSME productivity through a microscope, homing in on 68 level-two subsectors and 219 level-three subsectors. See the technical appendix for details of each of the 16 countries in our sample.

A granular approach helps prioritize where to act to boost MSME productivity

MSME productivity ratios vary across sectors, but the spread is even wider at the subsector level (Exhibit 11). For instance, in Germany’s sectors, ratios range from 55 percent in manufacturing to about 100 percent in transportation. In subsectors, the range is even wider. The spread is largest in administrative services, where the ratio is about 20 percent in rental and leasing activities and about 120 percent in building services and landscaping activities. There is a wide range in manufacturing, too. Small businesses engaged in the manufacture of tobacco products are only 35 percent as productive as larger counterparts, while those manufacturing basic metals are 85 percent as productive. In transportation, MSMEs engaged in postal and courier activities are less productive than large companies, while in warehousing, they are closely matched.

A dot-plot chart focuses on Germany and compares the ratio of MSME productivity to large company productivity for different subsectors in each of ten broader sectors. The economy sectors are administrative services, accommodation and food services, utilities, trade, transportation, construction, professional services, mining, manufacturing, ICT, and other services. Within German sectors, the ratio of MSME productivity to large company productivity varies significantly, and the spread is even larger at the subsector level. The spread is largest in administrative services, where the ratio is about 20 percent in rental and leasing activities and about 120 percent in building services.

This granular view at the subsector level is important when setting aspirations for, and thinking about ways to boost, MSME productivity. No single country can be considered the north star for all MSME productivity. The truth is that the best-performing MSMEs are found in one country for one type of activity, but in another country for another type of activity.

MSME productivity ratios vary across sectors, but the spread is even wider at the subsector level.

The trade sector illustrates this (Exhibit 12). In automotive trade, Japan’s MSMEs are more vertically integrated with large manufacturers than in many other advanced economies, including the United States (see chapter 5). This enables them to have more efficient logistics that follow just-in-time principles and respond effectively to market fluctuations, making them top-quartile performers. 27 Christina L. Ahmadjian and Joanne E. Oxley, “Vertical relationships, hostages, and supplier performance: Evidence from the Japanese automotive industry,” Journal of Law , Economics & Organization , volume 29, number 3, June 2013. However, in retail and wholesale trade (excluding automotive trade), vertical integration among Japanese MSMEs appears to be weaker, and they fall into the bottom two quartiles of relative performance. In these sectors, the United Kingdom and Germany, respectively, present compelling benchmarks for Japan.

A box-plot chart shows the ratio of MSME productivity to large company productivity for trade subsectors in different advanced economies. The three subsectors shown are automotive trade, retail trade, and wholesale trade. Trade is shown as an example of a larger trend: within subsectors, the productivity ratio varies significantly among countries. For example, Japan’s MSMEs are in the top quartile in automotive trade but lag behind MSMEs in other advanced economies in retail trade and wholesale trade.

Viewing MSMEs at a fine-grained level brings high-value subsectors into sharp focus. Considering manufacturing, for example, in almost all countries eight sizable subsectors (out of 24) account for more than 60 percent of the value from narrowing productivity gaps. 28 The 24 subsectors within the manufacturing sector are manufacturing of food products; beverages; tobacco products; textiles; wearing apparel; leather products; wood products; paper products; recorded media; coke and refined petroleum products; chemical products; pharmaceutical products; rubber and plastics; nonmetallic mineral products; basic metals; fabricated metal products; electronics; electrical equipment; machinery and equipment; automotives; other transport equipment; furniture; repair and installation of machinery and equipment; and other manufacturing, for example, of medical instruments and sports goods. In advanced economies, this ranges between five and 11 subsectors, while in emerging economies, the opportunity is more concentrated in four to eight subsectors.

While the sector overall contributes 18 percent of total value from narrowing productivity gaps in advanced economies and 25 percent in emerging economies, the opportunity is not uniform—the subsectors that offer the largest opportunities differ depending on the country (Exhibit 13). For instance, if we compare Indonesia and Australia, there are important differences. Manufacturing of basic metals, chemicals, rubbers and plastics, and food products are important sources of value in both economies. But in Indonesia, the apparel manufacturing subsector appears to offer meaningful value, whereas in Australia the textiles subsector is a sizable opportunity. For Indonesia, electrical equipment and automotive manufacturing would be higher priorities, but in Australia the comparable subsectors would be machinery and equipment, and fabricated metal.

A stacked bar chart shows the contributions of different subsectors from narrowing productivity gaps in the manufacturing sector for ten advanced economies and six emerging economies. To measure the value of narrowing productivity gaps for each country, we assume that the productivity ratio of MSMEs in the country in each subsector reaches a benchmark level (top quartile among all advanced economies) in the same subsector. The takeaway is that the subsectors that offer the largest opportunities vary significantly by country.

Looking through the microscope also helps to tailor efforts to build MSME competencies

The importance of scale for productivity and the hurdles that stand in the way of MSMEs gaining that scale are well recognized. So, too, are ways to address this issue, such as building national infrastructure and providing access to markets, finance, and technology. But national-level action is only one aspect of the competencies that MSMEs require to thrive and raise their productivity.

Which competencies matter most can vary depending on the type of MSME. For example, drawing on the World Bank Enterprise Survey, we find that more than one-third of MSMEs in the apparel manufacturing subsector report an “inadequately educated workforce” as their biggest obstacle to operations, but less than 15 percent in chemicals manufacturing do so. Because the business needs and hurdles to creating value are somewhat different in each subsector, solutions need to be tailored to local business and industrial contexts.

Because the business needs and hurdles to creating value are somewhat different in each subsector, solutions need to be tailored to local business and industrial contexts.

Take US construction as an example. This sector has one of the highest potentials for adding value because MSMEs perform poorly on productivity relative to large companies, at 46 percent against the top-quartile level of 60 percent in Germany. Large companies in the building construction subsector tend to concentrate on residential and nonresidential construction projects that typically involve larger projects, greater standardization, modular construction methods, and advanced technology and equipment—all of which help to boost productivity. However, MSMEs in the building construction subsector tend to focus on small-scale residential construction and refurbishments. They are subject to comprehensive building codes, regulations, and standards governed by local and state laws—factors that make it challenging for MSMEs to achieve higher productivity. This degree of stratification is not present in all countries in this sector. In the United Kingdom, for example, construction MSMEs receive incentives to participate in projects similar to those undertaken by large companies and are much more productive, relative to large companies, than their counterparts in the United States. Residential construction MSMEs in the United States could potentially diversify by becoming subcontractors to major players, helping them tap into potential additional value.

4. Creating value through networks and interactions

No MSME operates in a vacuum. Its prospects are shaped by its interactions with other companies. These interactions can be mutually beneficial, creating a “win-win” for businesses small and large. When the economic fabric surrounding companies of all sizes enables them to interact productively with one another and grow, the overall economy attains the greatest benefits.

B2B MSMEs tend to be more productive than B2C, suggesting that business interactions matter

Business-to-business (or B2B) companies interact closely with other companies, often larger ones, as part of their supply chains. In five sectors that account for the largest share of GDP from improving their MSME productivity ratio—construction, ICT, manufacturing, trade, and transportation—the productivity gap with large companies is narrower for B2B MSMEs than it is for business-to-customer (B2C) MSMEs that sell primarily to individuals. In fact, the gap is a significant 40 percent narrower on average (Exhibit 14). 29 This is the simple average for nine sample countries for which we have data for 219 level-three subsectors: Brazil and Mexico among emerging economies, and Germany, Italy, Poland, Portugal, Spain, the United Kingdom, and the United States among advanced economies.

A bar chart compares the ratio of MSME productivity to large company productivity for two types of MSMEs: business-to-business (B2B) MSMEs and business-to-consumer (B2C) MSMEs. The types are compared across five sectors: transportation, ICT, manufacturing, construction, and trade. The conclusion is that B2B MSMEs have smaller productivity gaps relative to large companies than B2C MSMEs. The difference is most pronounced in the transportation sector.

The superior performance of B2B MSMEs can be attributed to both a selection bias, because business customers have higher expectations of their providers, and the fact that these MSMEs can benefit from lessons learned in the course of working with larger enterprises. Other research has also noted how large companies have an incentive to help the smaller businesses they work with to become more productive. 30 Sangeeta Bharadwaj Badal, “How large corporations can spur small-business growth,” Gallup Business Journal , January 2013. There can, of course, be situations in which large companies take advantage of MSMEs, leading to less equitable division of benefits. 31 Dougal Jamieson et al., Large businesses and SMEs: Exploring how SMEs interact with large businesses , ORC International, July 2012.

The difference in productivity gaps between B2B and B2C MSMEs is particularly pronounced in the transportation and storage sector, where the productivity ratio of B2B MSMEs that transport commodities (typically via pipelines) is almost double that of B2C MSMEs, which are typically involved in passenger transportation. In the manufacturing sector, B2B MSMEs include manufacturers of iron and steel and of locomotives that, on average, have 60 percent of the productivity of large companies. In comparison, B2C MSMEs in the sector that, for instance, make consumer electronics and jewelry are only 40 percent as productive.

In the trade sector overall, the difference in the productivity gaps of B2B wholesalers and B2C retailers is not large. But in some subsectors, that is not the case. Take the specialized trade subsector where stores sell one type of product rather than a wide variety of products as nonspecialized supermarkets or department stores do. In this subsector, B2B MSMEs are 75 percent as productive as large companies operating in the sector—1.2 times higher than B2C MSMEs, which are only 63 percent as productive. The advantage in terms of absolute productivity is even higher. On average, B2B specialized trade MSMEs are 2.5 times more productive than their B2C counterparts. Interestingly, B2B and B2C MSMEs differ not only on productivity but also on their dynamism. B2B MSMEs are 1.5 times more likely to have scaled up than B2C MSMEs. Twenty percent of large B2B companies were MSMEs two decades ago, against 14 percent of B2Cs.

These gaps between B2B and B2C MSMEs reflect different levels of business competencies to some extent. Our analysis of the World Bank Enterprise Survey indicates that B2B MSMEs have an edge over B2C counterparts on some of the competencies that we discussed earlier, such as the following:

  • B2B MSMEs have a technology and innovation edge. B2B MSMEs are 30 percent more likely than B2C MSMEs to have introduced a process innovation in the past three years. International quality certifications are also 60 percent more common in B2Bs than in B2Cs, perhaps because they are often a requirement when doing businesses with large corporations.
  • B2B MSMEs invest more in building human capital than their B2C counterparts. B2B MSMEs track performance metrics more often and in more detail than B2C MSMEs. They also provide formal training to 60 percent of their employees, compared with about 35 percent of B2C MSMEs. One micro digital marketing agency in the United Kingdom offers employees a 20 percent “development time” commitment—for every ten hours worked in a week, employees can spend two hours on courses of their choosing.
  • B2B MSMEs are more globally connected. B2B MSMEs derive 6 percent of their revenue from direct exports, almost triple the share for B2C MSMEs. B2B e-commerce platforms that facilitate exports of products between small manufacturers and wholesalers or even offshore software services between companies have become increasingly popular. 32 Busting the five biggest B2B e-commerce myths , McKinsey & Company, January 2022. One microenterprise launched in 2000 created a platform to enable a transparent and mutually beneficial system of centralized MSME purchasing across European countries.

Large and small companies perform in tandem, and the right economic fabric can enable both

MSME interactions with other companies matter, but it is arguably a mistake to view those interactions as adversarial, necessitating policies that attempt to create incentives, quotas, or protections that tilt the balance toward either small enterprises or larger ones. 33 The impact of trade policies and agreements on MSMEs’ sustainability , Global Council for the Promotion of International Trade; and “The private sector and the catalytic role of micro, small and medium-sized enterprises,” in Development Co-operation Report 2018: Joining forces to leave no one behind , OECD, 2018. Is this really a zero-sum game? The truth—broadly—is that both MSMEs and large companies can benefit when they are operating within the right economic fabric.

We looked at whether large company productivity moves in tandem with that of smaller businesses in subsectors (Exhibit 15). In accommodation, for instance, the correlation appears strong—the productivity of large and small enterprises moves hand in hand. 34 Of the 68 subsectors analyzed, 46 subsectors showed a correlation of more than 60 percent between large company and MSME productivity, and 56 subsectors showed a correlation of more than 40 percent. In Italy, Mexico, Poland, Spain, and the United States, both large and small companies tend to outperform the average productivity levels of their peers across countries. In Australia, Brazil, Germany, Israel, Portugal, and the United Kingdom, both large and small companies tend to underperform their respective averages.

Four scatterplot charts are used to illustrate that in two-thirds of subsectors, there is a strong correlation between large company and MSME productivity. The accommodation subsector is shown as an example in a scatterplot, and another scatterplot shows the 45 strong-correlation subsectors. In the other one-third of subsectors, the correlation between large company and MSME productivity across countries is weak. The advertising and market research subsector is shown in a scatterplot as an example, and another scatterplot shows all 23 weak-correlation subsectors.

In other subsectors, the correlation is weaker. In advertising and market research, for instance, in Indonesia, Japan, and Nigeria, large companies outperform the average cross-country productivity while small companies underperform, and vice versa in Australia, Germany, Italy, and Spain.

In the vast majority of cases—66 percent, or 45 subsectors—the fortunes of MSMEs and large companies go hand in hand. This interdependent relationship is even more pronounced in manufacturing, where productivity levels of MSMEs and large companies are highly correlated (across countries) in about 80 percent of the 24 subsectors analyzed.

Within each subsector, we categorize countries where both large and small companies perform better than peers as win-win domains. If only one outperforms while the other lags behind, we classify it as either a “large firms outperform” or a “small firms outperform” domain. If both large and small firms lag behind their peers, it is considered a “challenged” domain.

How large is the win-win advantage? In the 45 subsectors where large and small companies are closely intertwined, the overall productivity of the win-win domain is $163,000 (in purchasing power parity terms). That is 1.5 times higher than in the domains where only small businesses or only large businesses outperform. This relationship holds true even for the subsectors in which the correlation is weak.

Other studies corroborate our finding that MSME productivity and large firm productivity are interconnected. One analysis of 26 European countries found that a 1.0 percent rise in MSME productivity is associated with a 0.124 percent increase in the productivity of large firms. While the analysis does not establish a causal relationship, there do appear to be some knowledge spillovers through the sharing of ideas, best practices, and even talent. 35 Andre van Stel, Boris Lokshin, and Nardo de Vries, “The effect of SME productivity increases on large firm productivity in the EU,” KYKLOS , volume 72, number 2, May 2019.

5. Seven examples of win-win domains

Working closely with thriving large companies is one important route to higher MSME productivity, but not the only one. Network effects among small enterprises can help them attain competencies associated with scale. While MSMEs do not have significant market power because they have limited scale, creation of sector-wide infrastructure and boosting interfirm networks and linkages can provide “collective productivity”—the competitive advantage derived from local external economies and joint action—and substitute for direct benefits of scale. 36 Albert Berry, SME competitiveness: The power of networking and subcontracting , Inter-American Development Bank, January 1997.

As countries try to reduce concentration and geopolitical risks, they are aiming to realign their global manufacturing and services footprints, but for this to happen, MSMEs need to raise their productivity game. Without MSMEs getting more productive, it’s hard to imagine a meaningful realignment of global production. Industrial policies that aim to create new manufacturing capabilities also need to focus on MSMEs in those specific ecosystems.

To illustrate examples of how win-win domains have been created in some countries, benefiting both small and large companies, we looked in detail at examples in the largest sectors for MSME value potential (Exhibit 16). Each of these case studies demonstrates how MSMEs have achieved high productivity through network effects.

Six scatterplot charts are used to illustrate seven examples of win-win domains, or domains in which both large companies and MSMEs outperform their counterparts. The examples shown, all in advanced economies, are: in manufacturing, the auto sector in Japan and beverages (wine) in Italy; in trade, wholesale trade in Germany; in construction, examples from both Australia and the United Kingdom; in ICT, software publishing in the United States; and in professional services, R&D in Israel.

In manufacturing, we examine the auto sector in Japan and beverages (wine) in Italy; in trade, the wholesale trade sector in Germany; in construction, examples from both Australia and the United Kingdom; in ICT, US software publishing; and in professional services, Israel’s R&D. Within each of these sectors, both MSMEs and large companies in the highlighted country generally exhibit higher productivity levels compared with their counterparts in other advanced economies. However, this does not necessarily imply that their productivity has increased over time. It is possible that they attained high productivity levels in the past and managed to sustain them over the years.

By looking through the microscope at these examples, a clear message emerges: there is no single path to success, but rather a range of promising possible approaches. A common characteristic of these approaches is their focus on addressing the issue of scale through structural changes, enabling MSMEs to become “collectively large” by creating network efficiencies.

There is no single path to success, but rather a range of promising possible approaches.

1. Japanese auto manufacturing MSMEs benefit from deep integration with large companies

On average, MSMEs in auto manufacturing in Japan have double the productivity of MSMEs in other advanced economies. This is predominantly because medium-size enterprises have close linkages to large companies. Benefits from best practices such as Keiretsu networks and vertical integration trickle down to them. 37 Keiretsu networks are business networks made up of different companies, including manufacturers, supply chain partners, distributors, and sometimes financiers.

With the overall credo of “we are all in this together,” large Japanese OEMs have built deep links with MSMEs, enabling their operational proficiency, and enhancing technological capabilities and access to talent for smaller companies. These deep linkages also extend to financing, with large OEMs often having crossover share investments with their MSME partners. 38 Jeffrey Liker and Thomas Y. Choi, “Building deep supplier relationships,” Harvard Business Review , December 2004.

Toyota is an example of a company that has unusually high integration with its ecosystem partners. Some contractual partnerships with suppliers have lasted for more than 30 years. Toyota has directly involved itself in raising the operational standards of its partners through knowledge transfer, from demand planning and cost reduction to raising management capabilities. In the 2000s, Toyota created three cost-reduction programs for its suppliers, in combination aiming to reduce costs by 60 percent. While many of Toyota’s MSME partners remain reliant on Toyota for more than 70 percent of their revenue, some have developed independently. 39 Stephane Heim, “Capability building and functions of SMEs in business groups: A case study of Toyota’s supply chain,” International Journal of Automotive Technology and Management , volume 13, number 4, October 2013. These MSMEs share some common traits; they often harness their ecosystem partnerships to enhance their technological capabilities and venture into highly specialized production.

2. Italian winemaker MSMEs gain global market access through collective branding and marketing

Italy’s MSME beverage manufacturing sector—particularly its winemakers—is highly fragmented but superproductive. These enterprises are 1.5 times more productive than their counterparts in other advanced economies.

Winemaking typically has some very large players. In the United States, for instance, most wine is made by less than 0.5 percent of makers. But Italy’s wine business is dominated by small, often family-led enterprises. Fragmentation and a plethora of small players are not usually associated with high productivity, but there is a “paradox of scale” in productivity in Italy’s wine business. 40 Mike Veseth, “Italian wine and the paradox of scale: Three case studies,” Wine Economist , July 2023. Why?

Italy has created an environment that delivers small players access to branding and marketing. The “Made in Italy” campaign has championed traditional and local production, with a particular focus on the international market. Italy has more than 500 wines that have Protected Designation of Origin or Protected Geographical Indications certifications. Similar designations have delivered success elsewhere, for instance in the cases of Alphonso mangos and basmati rice in India, and Guadarrama beef in Spain. These are stamps of quality in the eyes of consumers and apply to the 42 percent of Italy’s wine production that is exported, enabling small producers to charge premium prices and obviating the need to produce at scale. Where they are located is a key part of marketing. Layered on top of this is that Italy’s MSME winemakers are highly networked with one another through membership of associations or in cooperatives, giving them collectively a louder voice.

3. Construction MSMEs in the United Kingdom profit from better access to new markets and finance

In the United Kingdom, construction sector productivity has stagnated over time. 41 Productivity in the construction industry , UK: 2021, UK Office for National Statistics, October 2021. But small businesses exhibit higher productivity than those in our other sample countries, as policy interventions in the United Kingdom have boosted their ability to respond to burgeoning demand. UK policy makers simplified procurement processes, reduced bidding costs, and accelerated payment timelines for construction projects, enabling MSMEs to compete with large companies for government contracts on a broadly equal footing. 42 “Big opportunities for small firms: Government set to spend £1 in every £3 with small businesses,” UK Government, August 2015. The government has also, more recently, orchestrated demonstrator projects to showcase modern construction methods and to enable small businesses to learn from one another. 43 Transforming UK construction: Demonstrator projects , Innovation Funding Service, UK Government, 2019.

Although the impact of these enablers on productivity growth is not fully evident yet, they seem to have triggered a wave of creative collaborations among MSMEs. For instance, Cara EPS built a digital platform to bring together specialist retrofitter microenterprises, enabling them collectively to undertake substantial contracts leveraging their distinct expertise. 44 Cara EPS: SME Retrofit Consortia , Constructing Excellence, accessed March 4, 2024. MSMEs need to invest in innovation and technology to compete in the same markets as large companies. For ProBuild360, this involved developing capabilities in modern methods of construction and enlisting similar-sized MSMEs not only as suppliers but also as mentors to assist in the adoption of new techniques and materials. This enabled the company to emerge as a key building partner for social housing authorities. 45 Probuild360 , Constructing Excellence, accessed March 4, 2024.

4. Construction MSMEs in Australia gain from subcontracting for larger companies and access to skilled workers

In specialized construction, particularly in the mining sector, Australia’s large players have higher productivity than those in our other sample countries, and MSMEs the second highest among their peers. This is attributable to collaborations between large and smaller players that have developed partly due to the country’s remoteness and climatic extremes, and partly due to effective public policies that encourage partnerships and facilitate a robust system of mutual cooperation.

MSMEs specialize in niche construction projects that are more often subcontracted than in other countries. Australia has one of the highest shares of public–private partnership construction projects in the world. 46 Linda M English, “Public private partnerships in Australia: An overview of their nature, purpose, incidence and oversight,” UNSW Law Journal , volume 29, number 3, November 2006. The government has reduced red tape, cutting the number of regulatory procedures from 14 to ten and the average time it takes to approve permits from 150 days to 112. 47 Reinventing construction: A route to higher productivity , McKinsey Global Institute, February 2017. Skills building has also been a priority. Construction workers go through rigorous certification and licensing processes and benefit from a national system of vocational education and training, formal apprenticeship programs, and industry-led initiatives, such as Construction Skills Queensland. 48 Construction Skills Queensland funding , TAFE Queensland, accessed March 4, 2024.

5. Germany’s wholesale trade MSMEs benefit from vertical integration with European manufacturers and strong logistics infrastructure

Germany’s MSME wholesalers are 1.3 times more productive than the average among advanced economies in our sample and are more productive even when excluding commodity traders. They are able to tap into global markets through the European single market, which is further bolstered by Germany’s central location, contributing to their productivity. They also benefit from Germany’s industry-wide logistics backbone, which is reinforced by a range of benefits conferred by free trade port zones, including tax reductions for imports and reexports, and simplified customs regulations.

German wholesalers are also among the most innovative in Europe. 49 Bernhard Dachs et al., EU wholesale trade: Analysis of the sector and value chains , European Commission, June 2016. These enterprises gain spillover benefits from being part of a larger ecosystem. They often operate as legally independent affiliates or subsidiaries that are vertically integrated with upstream purchasers for retail supermarkets or distributors for large manufacturers for the entire European Union. 50 Matthias Fauth, Benjamin Jung, and Wilhelm Kohler, “German firms in international trade: Evidence from recent microdata,” Journal of Economics and Statistics , volume 243, number 3–4, June 2023. An example is Coffee Friend, a medium-size wholesaler of coffee makers that mediates transactions for several manufacturers based in Europe. 51 Coffee Friend.

6. US software development MSMEs benefit from the network created by industry giants

In the dynamic US software publishing business, MSMEs are 1.7 times more productive on average than those in the same sector in other advanced economies. MSMEs gain from talent and capital ecosystems seeded by successful large companies. Large companies serve as reputational anchors, delivering market access and branding. A virtuous cycle of robust capital ecosystems and the agglomeration of a strong talent pool have enabled the growth of large businesses and continue to support the growth of MSMEs in this sector.

MSMEs in this sector are highly innovative and internationally minded. Small technology firms have patented more per employee than their large counterparts. 52 Robert D. Atkinson, The National Economic Council gets it wrong on the role of big and small firms in U.S. innovation , Information Technology & Innovation Foundation, July 2023. Tech startups are also often seen as “born global” because they create products and services for a global market. 53 A review of micro, small and medium enterprises in the ICT sector , International Telecommunication Union, 2016. Almost half of all US ICT MSMEs were engaged in international trade as long ago as 2007. 54 Small and medium-sized enterprises: Characteristics and performance , United States International Trade Commission, November 2010.

Large companies in the sector are important clients, frequent buyers, and potential partners, and multiple connections mean that MSMEs are able to leverage a larger pool of resources and experience, including talent and capital.

7. Israel stands out for its ability to connect different stakeholders engaged in scientific R&D

The productivity of MSMEs in Israel’s scientific R&D subsector is almost double that of those in other advanced economies in our sample. 55 The scientific R&D subsector includes basic and applied research, and experimental development on natural sciences, engineering and technology, medical sciences, biotechnology, agricultural sciences, social sciences, and humanities. Israel is a unique economy that ranks high among the world’s economies on the quality of its research organizations. The government has long been committed to promoting innovation and R&D, and has helped forge strong links between companies large and small, academia, and venture capitalists.

The productivity of MSMEs in Israel’s scientific R&D subsector is almost double that of those in other advanced economies in our sample.

The close proximity of businesses, research institutions, and venture capital firms in cities such as Jerusalem and Tel Aviv facilitates collaboration and networking. Universities actively encourage researchers to work on projects with commercial potential. Ties between academia and the private sector are strong, encouraged by the government setting up technology transfer offices to facilitate the process of licensing technologies to industry partners and creating startups based on the research undertaken. These close ties are particularly vital because Israel focuses on highly technical (and highly regulated) innovation, such as biotech, health tech, and pharmaceuticals. The Israeli venture capital industry has also thrived since the 1990s with help from governmental programs such as Yozma, which offered incentives to foreign companies willing to back Israeli startups.

6. Delivering a win-win future

Productivity is a hot-button issue for economies navigating particularly turbulent times. Indeed, accelerating productivity growth may be the only route out of current financial stress, reconfiguring global trade patterns, and shifts in companies’ manufacturing and services footprint to build resilience that delivers rising wealth and robust growth in GDP and incomes. 56 The future of wealth and growth hangs in the balance , McKinsey Global Institute, May 2023.

This research indicates that narrowing the MSME productivity gap with large companies can yield considerable value, and that large companies, policy makers, and MSMEs themselves can contribute to capturing that value by acquiring key competencies.

Improving the productivity of small businesses merits immediate attention. It may be a self-resolving issue—a natural progression as employment shifts to larger enterprises as economies develop. As discussed, this progression can play out in different ways. Some MSMEs may scale into larger companies, others may be acquired by larger enterprises, and some may cease operations and make room for new businesses. Indeed, previous MGI research suggests that high-growth emerging economies tend to be the ones where large companies are allowed to scale rapidly. 57 Outperformers: High-growth emerging economies and the companies that propel them , McKinsey Global Institute, September 2018. However, the extent of this natural progression is limited. Even in the most advanced economies, MSMEs continue to contribute the majority of all workers employed by businesses. Change is also likely to be slow. The clear implication is that, overall, MSMEs will continue to play an important role in the long term, and that acting now to boost their productivity growth can make the difference to economy-wide growth.

But given the enormous variation in productivity performance evident at the subsector level and even among enterprises with different business models, getting the conditions right for raising productivity requires a microscopic view to help prioritize, design, and implement solutions.

Three considerations can help shape stakeholder actions

Even with an abundance of initiatives and examples of efforts that stakeholders can make, understanding how to capture the MSME productivity opportunity is a complex exercise. Opportunities vary a great deal on the ground, and there are few one-size-fits-all solutions. Intentional measures targeted at helping small enterprises may even raise questions about how this might affect the overall productivity of economies. We suggest three considerations for stakeholders as they develop their approaches.

Creating a win-win economic fabric is important

The global business landscape is deeply interconnected. The success or failure of large companies can have ripple effects throughout entire economic ecosystems. As such, stakeholders, including policy makers, regulatory bodies, associations, and large companies need to foster the right enabling conditions for the growth and prosperity of all enterprises. These conditions may require measures that go beyond conventional policies focused on MSMEs, such as facilitating access to credit for MSMEs and encouraging training for MSME employees. In addition to such measures, it may involve strategies to build “collective productivity.”

Strengthening networks and interactions between large and small businesses can yield productivity gains in the win-win domains and in domains where large companies outperform their peers but smaller ones lag behind. Where small businesses outperform while larger ones do not, there would be benefit in enabling those small enterprises to evolve into large ones or merge with them to promote business dynamism. When both large and small companies lag behind their peers, more fundamental steps to improve the economic fabric as a whole may be needed; for instance, investing in physical and digital infrastructure, establishing transparent and fair regulatory frameworks that boost competition, reducing trade barriers, and ensuring equal access to financial capital.

Prioritization can pay off

Stakeholders first need to decide which economic domains to focus on to make MSMEs more productive. Failing to prioritize which opportunities to pursue can lead to a dilution of efforts and place a burden on the often-limited resources at hand. Some countries have selected and supported “national champion” sectors, as has happened with beverage manufacturing in Italy, automotive manufacturing in Japan, and R&D in Israel. Such prioritization requires meticulous identification of the nation’s competitive advantages and a keen eye for demand trends, as well as allocating resources toward innovation, facilitating access to capital, and cultivating supportive networks.

A granular and tailored approach matters

Measures designed to help MSMEs improve their performance tend to be broad, but the granular lens of this research reveals that different subsectors have varied needs. Stakeholders may need to design a menu of measures for each prioritized opportunity. 58 Abdulaziz Albaz, Marco Dondi, Tarek Rida, and Jörg Schubert, “ Unlocking growth in small and medium-size enterprises ,” McKinsey & Company, July 2020. In other words, taking a microscopic approach that reflects the dynamics of each subsector and country and that addresses barriers to productivity and scale in that context is warranted.

All stakeholders can boost MSME competencies through a variety of proven approaches

All stakeholders—policy makers, large companies, and MSMEs themselves—can adopt strategies designed to boost productivity, which may involve structural changes that go beyond traditional approaches. Policy makers can provide access to better infrastructure, while large companies can help MSMEs build scale-related competencies. MSMEs can collaborate with others to achieve network efficiencies.

Policy makers can boost access to technology, new markets, and finance

Supportive policy interventions can create advantages of scale for MSMEs and help overall business dynamism. The following three broad contributions stand out:

  • Being intentional in improving technology access and building management skills of businesses. Singapore’s GoBusiness initiative provides financial support for all businesses that adopt technology solutions to improve their business processes, aligned to industry road maps. 59 Productivity solutions grant (PSG) , GoBusiness, Singapore. Governments can also make direct investments in digital infrastructure that help businesses expand their market reach. For example, in India, the Open Network for Digital Commerce aims to build an e-commerce platform, which can particularly assist small retailers reach new consumers because they lack the resources and financial capacity to develop their own platforms. 60 Democratising digital commerce in India: An open network for inclusive, competitive marketplaces , Open Network for Digital Commerce, May 2023. The Help to Grow program in the United Kingdom aims to help small businesses scale up by offering management courses taught by entrepreneurs and industry experts to develop leadership skills and establish business networks. 61 Help to scale-up and grow , Gov.UK, February 2024.
  • Opening up access to new markets. One example is Europe’s “Small Business, Big World” initiative, which offers guidance on customs procedures, trade regulations, and market entry requirements in various countries to enable SMEs to expand their export activities. 62 Small business, big world , European Economic and Social Committee, European Union, May 2012. Canada’s CanExport program supports MSMEs in exploring new export opportunities, enabling them to participate in trade shows, conduct market research, and develop marketing materials for the international market. 63 CanExport funding for exporters, innovators, associations and communities , Trade Commissioner Service, Government of Canada, February 2024.
  • Boosting financial infrastructure that helps underfinanced MSMEs. An open data framework, for instance, can enable financial institutions to use nontraditional data sources for credit underwriting, targeted at a range of underfinanced companies including MSMEs. An Experian study showed that including utility data allowed 20 percent of “thin-file” credit customers with scant documentation to support their credit application to become “thick-file” customers who have higher loan approval rates. 64 Let there be light: The impact of positive energy-utility reporting on consumers , Experian, 2015. For small businesses, this can increase access to financing, provide greater convenience, and improve product options. 65 Financial data unbound: The value of open data for individuals and institutions , McKinsey Global Institute, June 2021. Financial institutions could also benefit from efficiency improvements, better fraud prediction, and reduced friction and cost of data intermediation. Governments can also help businesses improve their working capital management by improving tax-related infrastructure and systems. In Latin America, countries such as Brazil, Chile, Colombia, and Peru have launched initiatives aimed at radically simplifying business registration and tax payment processes. One reform enabled businesses to formally register in a day. 66 Regis Augusto Ely, Daniel de Abreu Pereira Uhr, and Júlia Gallego Ziero Uhr, “The impact of the individual microentrepreneur program on the Brazilian labor market,” Economic Analysis of Law Review , volume 10, number 2, May–August 2019; and Tu empresa en un día , Chilean Subsecretaría de Economía y Empresas de Menor Tamaño, July 26, 2023.

In addition to these interventions, policy makers can also facilitate the availability of globally consistent yet granular data to enable all stakeholders to take a microscopic approach to understanding and thereby improving the productivity of MSMEs.

Large companies can boost the competencies of MSMEs within their value chains

As discussed earlier in this report, networks and linkages between MSMEs and large companies benefit the growth and performance of both. One study of small businesses in New York found that seven out of ten of them increased their revenue within two years of becoming part of a corporate supplier base. 67 Giving small firms the business , Center for an Urban Future, 2011. A 2023 study of Belgian companies found that when MSMEs started supplying superstar companies for the first time, their productivity increased by about 8 percent after four years. They also achieved an increase in sales to businesses other than the new superstar partner. 68 Mary Amiti et al., FDI and superstar spillovers: Evidence from firm-to-firm transactions , National Bureau of Economic Research, working paper number 31128, October 2023.

But it is not a one-way street. As noted earlier, large companies also appear to benefit when their MSME partners and suppliers are more productive. This could be because large companies often depend on MSMEs in most parts of the value chain, from development to supply, production, service delivery, distribution, and sales and post-sales. For example, a large logistics player works with local delivery partners for last-mile delivery, and a small recruiting agency might help a large company fill key positions. Large companies therefore had an incentive to raise MSME capabilities. The following three ways are pivotal:

  • Assisting MSME partners to build digital and R&D capabilities. Unilever’s open innovation platform Foundry connects its different divisions with startups to engage in joint ideation and mentorship opportunities, for instance. 69 Shameen Prashantham, “The two ways for startups and corporations to partner,” Harvard Business Review , January 2019. Google helps small businesses that purchase ad placements from it in gaining a deeper understanding of customer behavior and in improving the utilization and efficiency of ad spaces. 70 Google for Small Businesses. In India, Maruti Suzuki set up a “comprehensive excellence” program for its main MSME suppliers. In 2018–19, 50 percent of the company’s suppliers met the performance standards laid out and reported improved efficiency, more interest from investors, and broader access to procurement and R&D opportunities. 71 Falendra Kumar Sudan, Leveraging the participation of small and medium-sized enterprises in global value chains of the automotive Industry: Insights from Maruti Suzuki India Limited , ADBI working paper number 1167, Asian Development Bank Institute, July 2020. Nestlé’s Nescafe Plan has provided training to small coffee farmers for techniques to increase crop yields. 72 Mark Segal, “Nestle commits over $1 billion to sustainable coffee farming plan,” ESG Today , October 4, 2022.
  • Conferring MSME partners with an ability to build workforce capabilities. One example is Apple, which launched a $50 million fund in collaboration with the International Labour Organization and the International Organization for Migration to provide learning and skills development opportunities for the employees of its suppliers. 73 “Apple launches $50 million Supplier Employee Development Fund,” Apple, March 30, 2022. In India, Walmart launched a Supplier Development Program to train and prepare 50,000 small businesses to better integrate into global supply chains. 74 “Walmart empowers MSMEs to accelerate growth and access new markets,” Walmart, December 9, 2019.
  • Lending weight to the reputation of MSMEs when requesting finance. For example, DuPont leveraged its relationship with a financial institution to secure working capital credit for its MSME suppliers in rural areas, thereby strengthening its supply chain and increasing sales. 75 Partnerships for small enterprise development , United Nations Industrial Development Organization, 2009. Large financial institutions have a particularly important role in providing affordable credit and better product options to MSMEs. Innovative underwriting approaches that use alternative credit data can help; an Experian survey found that 70 percent of small businesses are willing to furnish additional financial information if it will improve the chances of loan approval or reduce borrowing rates. 76 Stefani Wendel, What is alternative credit data? , Experian, September 2018.

MSMEs can collaborate with one another to achieve network efficiencies

Collaboration among MSMEs can help build their capabilities. In Europe, for instance, 30 to 40 percent of SMEs do not belong to any formal network, but can still forge collaborations. 77 OECD SME and Entrepreneurship Outlook 2023 , OECD, June 2023; and Firouze Pourmand Hilmersson and Mikael Hilmersson, “Networking to accelerate the pace of SME innovations,” Journal of Innovation and Knowledge , volume 6, number 1, January–March 2021. That collaboration can even be at the level of individual MSMEs. Innovative companies cooperate on business activities with other organizations more than those that are not innovative. 78 OECD SME and Entrepreneurship Outlook 2023 , OECD, June 2023. One OECD study of SMEs operating in Association of Southeast Asian Nations economies found that they perform better when they are allied with large enterprises, but also when they strike partnerships with other MSMEs. 79 “Productivity, technology and innovation,” in SME Policy Index: ASEAN 2018: Boosting competitiveness and inclusive growth , OECD, 2018. Japan’s Small and Medium Enterprise Agency has shown that SMEs that have partnered with other small enterprises in order to implement technology solutions in their operations have 76 percent more sales per employee than those that haven’t taken this route. 80 White paper on small and medium enterprises in Japan , National Association of Small and Medium Enterprise Promotion Organizations, 2018. For example, a small sheet-metal processor in Japan wanted to incorporate in-house cloud computing into its operations and partnered with two other enterprises in the same sector, but with different specialties. Together, the three companies built a joint order reception system, which enables them to collaborate on a range of projects with the same clients. All three MSMEs improved the digital and management capabilities of their manufacturing operations. 81 White paper on small and medium enterprises in Japan , National Association of Small and Medium Enterprise Promotion Organizations, 2018.

Broader MSME collaborations at the association or group level can help more small businesses raise productivity through knowledge sharing, mentoring, networking, and online platforms. The SME Finance Forum works with more than 240 active member institutions—financial institutions, technology companies, development finance institutions, and relevant large corporations—to facilitate resources to help MSMEs bridge their financial access gap. 82 SME Finance Forum. The DIGITAL SME Alliance in Europe launched a platform for traditional SMEs to access a catalog of digital solutions ranging from videoconferencing to AI modeling. 83 OECD Digital for SMEs Global Initiative (D4SME), OECD, 2022.

Other companies can also facilitate the creation of such MSME networks. For example, IBM, in collaboration with other Fortune 500 companies, launched a Supplier Connection initiative that connects small suppliers to one another and to large businesses to access new opportunities. 84 Freddie Pierce, “IBM launches new Supplier Connection portal,” Supply Chain Digital , May 17, 2020. SABI, an African digital infrastructure provider, fosters connections between MSME merchants, wholesalers, distributors, and manufacturers. It also provides them with enterprise resource planning tools, B2B commerce interfaces, and financial services, enabling MSMEs to reach new customers, improve their cash flow, and streamline their logistics. 85 Sabi.

Raising productivity is, and always has been, the optimal route to healthier incomes and business resilience. In a world beset by uncertainty amid geopolitical tensions and shifts in manufacturing and services footprints, raising the game of the world’s MSMEs—which are so central to jobs, livelihoods, value creation, and economic growth—is a priority. The potential is large, but efforts to capture it need to be thoughtful and very likely targeted. Only by having a granular understanding of MSME productivity can effective action be taken. That action can create a win-win for all companies, small and large.

This report is the latest publication in MGI’s work on productivity and prosperity. The research was led by Anu Madgavkar , an MGI partner in New Jersey; Marco Piccitto , a senior partner and chairman of the MGI Council; Olivia White , a McKinsey senior partner and a director of MGI in San Francisco; María Jesús Ramirez , a consultant in the Silicon Valley office; Jan Mischke , an MGI partner in Zurich; and Kanmani Chockalingam , an MGI fellow in San Francisco. The report was edited by Janet Bush, an MGI executive editor in London, and the exhibits were designed by Juan M Velasco, a senior data visualization editor based in Washington DC.

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  • Published: 29 April 2024

What is context in knowledge translation? Results of a systematic scoping review

  • Tugce Schmitt   ORCID: orcid.org/0000-0001-6893-6428 1 ,
  • Katarzyna Czabanowska 1 &
  • Peter Schröder-Bäck 1  

Health Research Policy and Systems volume  22 , Article number:  52 ( 2024 ) Cite this article

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Knowledge Translation (KT) aims to convey novel ideas to relevant stakeholders, motivating their response or action to improve people’s health. Initially, the KT literature focused on evidence-based medicine, applying findings from laboratory and clinical research to disease diagnosis and treatment. Since the early 2000s, the scope of KT has expanded to include decision-making with health policy implications.

This systematic scoping review aims to assess the evolving knowledge-to-policy concepts, that is, macro-level KT theories, models and frameworks (KT TMFs). While significant attention has been devoted to transferring knowledge to healthcare settings (i.e. implementing health policies, programmes or measures at the meso-level), the definition of 'context' in the realm of health policymaking at the macro-level remains underexplored in the KT literature. This study aims to close the gap.

A total of 32 macro-level KT TMFs were identified, with only a limited subset of them offering detailed insights into contextual factors that matter in health policymaking. Notably, the majority of these studies prompt policy changes in low- and middle-income countries and received support from international organisations, the European Union, development agencies or philanthropic entities.

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Few concepts are used by health researchers as vaguely and yet as widely as Knowledge Translation (KT), a catch-all term that accommodates a broad spectrum of ambitions. Arguably, to truly understand the role of context in KT, we first need to clarify what KT means. The World Health Organization (WHO) defines KT as ‘the synthesis, exchange and application of knowledge by relevant stakeholders to accelerate the benefits of global and local innovation in strengthening health systems and improving people’s health’ [ 1 ]. Here, particular attention should be paid to ‘innovation’, given that without unpacking this term, the meaning of KT would still remain ambiguous. Rogers’ seminal work ‘Diffusion of Innovations’ [ 2 ] defines innovation as an idea, practice or object that is perceived as novel by individuals or groups adopting it. In this context, he argues that the objective novelty of an idea in terms of the amount of time passed after its discovery holds little significance [ 2 ]. Rather, it is the subjective perception of newness by the individual that shapes their response [ 2 ]. In other words, if an idea seems novel to individuals, and thereby relevant stakeholders according to the aforementioned WHO definition, it qualifies as an innovation. From this perspective, it can be stated that a fundamental activity of KT is to communicate ideas that could be perceived as original to the targeted stakeholders, with the aim of motivating their response to improve health outcomes. This leaves us with the question of who exactly these stakeholders might be and what kind of actions would be required from them.

The scope of stakeholders in KT has evolved over time, along with their prompted responses. Initially, during the early phases of KT, the focus primarily revolved around healthcare providers and their clinical decisions, emphasising evidence-based medicine. Nearly 50 years ago, the first scientific article on KT was published, introducing Tier 1 KT, which concentrated on applying laboratory discoveries to disease diagnosis or treatment, also known as bench-to-bedside KT [ 3 ]. The primary motivation behind this initial conceptualisation of KT was to engage healthcare providers as the end-users of specific forms of knowledge, primarily related to randomised controlled trials of pharmaceuticals and evidence-based medicine [ 4 ]. In the early 2000s, the second phase of KT (Tier 2) emerged under the term ‘campus-to-clinic KT’ [ 3 ]. This facet, also known as translational research, was concerned with using evidence from health services research in healthcare provision, both in practice and policy [ 4 ]. Consequently, by including decision-makers as relevant end-users, KT scholars expanded the realm of research-to-action from the clinical environment to policy-relevant decision-making [ 5 ]. Following this trajectory, additional KT schemes (Tier 3–Tier 5) have been introduced into academic discourse, encompassing the dissemination, implementation and broader integration of knowledge into public policies [ 6 , 7 ]. Notably, the latest scheme (Tier 5) is becoming increasingly popular and represents the broadest approach, which describes the translation of knowledge to global communities and aims to involve fundamental, universal change in attitudes, policies and social systems [ 7 ].

In other words, a noticeable shift in KT has occurred with time towards macro-level interventions, named initially as evidence- based policymaking and later corrected to evidence- informed policymaking. In parallel with these significant developments, various alternative terms to KT have emerged, including ‘implementation science’, ‘knowledge transfer’, and ‘dissemination and research use’, often with considerable overlap [ 8 ]. Arguably, among the plethora of alternative terms proposed, implementation science stands out prominently. While initially centred on evidence-based medicine at the meso-level (e.g. implementing medical guidelines), it has since broadened its focus to ‘encompass all aspects of research relevant to the scientific study of methods to promote the uptake of research findings into routine settings in clinical, community and policy contexts’ [ 9 ], closely mirroring the definition to KT. Thus, KT, along with activities under different names that share the same objective, has evolved into an umbrella term over the years, encompassing a wide range of strategies aimed at enhancing the impact of research not only on clinical practice but also on public policies [ 10 ]. Following the adoption of such a comprehensive definition of KT, some researchers have asserted that using evidence in public policies is not merely commendable but essential [ 11 ].

In alignment with the evolution of KT from (bio-)medical sciences to public policies, an increasing number of scholars have offered explanations on how health policies should be developed [ 12 ], indicating a growing focus on exploring the mechanisms of health policymaking in the KT literature. However, unlike in the earlier phases of KT, which aimed to transfer knowledge from the laboratory to healthcare provision, decisions made for public policies may be less technical and more complex than those in clinical settings [ 3 , 13 , 14 ]. Indeed, social scientists point out that scholarly works on evidence use in health policies exhibit theoretical shortcomings as they lack engagement with political science and public administration theories and concepts [ 15 , 16 , 17 , 18 ]; only a few of these works employ policy theories and political concepts to guide data collection and make sense of their findings [ 19 ]. Similarly, contemporary literature that conceptualises KT as an umbrella term for both clinical and public policy decision-making, with calls for a generic ‘research-to-action’ [ 20 ], may fail to recognise the different types of actions required to change clinical practices and influence health policies. In many respects, such calls can even lead to a misconception that evidence-informed policymaking is simply a scaled-up version of evidence-based medicine [ 21 ].

In this study, we systematically review knowledge translation theories, models and frameworks (also known as KT TMFs) that were developed for health policies. Essentially, KT TMFs can be depicted as bridges that connect findings across diverse studies, as they establish a common language and standardise the measurement and assessment of desired policy changes [ 22 ]. This makes them essential for generalising implementation efforts and research findings [ 23 ]. While distinctions between a theory, a model or a framework are not always crystal-clear [ 24 ], the following definitions shed light on how they are interpreted in the context of KT. To start with, theory can be described as a set of analytical principles or statements crafted to structure our observations, enhance our understanding and explain the world [ 24 ]. Within implementation science, theories are encapsulated as either generalised models or frameworks. In other words, they are integrated into broader concepts, allowing researchers to form assumptions that help clarify phenomena and create hypotheses for testing [ 25 ].

Whereas theories in the KT literature are explanatory as well as descriptive, KT models are only descriptive with a more narrowly defined scope of explanation [ 24 ]; hence they have a more specific focus than theories [ 25 ]. KT models are created to facilitate the formulation of specific assumptions regarding a set of parameters or variables, which can subsequently be tested against outcomes using predetermined methods [ 25 ]. By offering simplified representations of complex situations, KT models can describe programme elements expected to produce desired results, or theoretical constructs believed to influence or moderate observed outcomes. In this way, they encompass theories related to change or explanation [ 22 ].

Lastly, frameworks in the KT language define a set of variables and the relations among them in a broad sense [ 25 ]. Frameworks, without the aim of providing explanations, solely describe empirical phenomena, representing a structure, overview, outline, system or plan consisting of various descriptive categories and the relations between them that are presumed to account for a phenomenon [ 24 ]. They portray loosely-structured constellations of theoretical constructs, without necessarily specifying their relationships; they can also offer practical methods for achieving implementation objectives [ 22 ]. Some scholars suggest sub-classifications and categorise a framework as ‘actionable’ if it has the potential to facilitate macro-level policy changes [ 11 ].

Context, which encompasses the entire environment in which policy decisions are made, is not peripheral but central to policymaking, playing a crucial role in its conceptualisation [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. In the KT literature, the term ‘context’ is frequently employed, albeit often with a lack of precision [ 35 ]. It tends to serve as a broad term including various elements within a situation that are relevant to KT in some way but have not been explicitly identified [36]. However, there is a growing interest in delving deeper into what context refers to, as evidenced by increasing research attention [ 31 , 32 , 37 , 38 , 39 , 40 , 41 ]. While the definition of context in the transfer of knowledge to healthcare settings (i.e. implementing health policies, programmes or measures at the meso-level) has been systematically studied [ 36 , 37 , 42 , 43 ], the question of how KT scholars detail context in health policymaking remains unanswered. With our systematic scoping review, we aim to close this gap.

While KT TMFs, emerged from evidence-based medicine, have historically depicted the use of evidence from laboratories or healthcare organisations as the gold standard, we aimed to assess in this study whether and to what extent the evolving face of KT, addressing health policies, succeeded in foregrounding ‘context’. Our objective was thus not to evaluate the quality of these KT TMFs but rather to explore how scholars have incorporated contextual influences into their reasoning. We conducted a systematic scoping review to explore KT TMFs that are relevant to agenda-setting, policy formulation or policy adoption, in line with the aim of this study. Therefore, publications related to policy implementation in healthcare organisations or at the provincial level, as well as those addressing policy evaluation, did not meet our inclusion criteria. Consequently, given our focus on macro-level interventions, we excluded all articles that concentrate on translating clinical research into practice (meso-level interventions) and health knowledge to patients or citizens (micro-level interventions).

Prior systematic scoping reviews in the area of KT TMFs serve as a valuable foundation upon which to build further studies [ 44 , 45 ]. Using established methodologies may ensure a validated approach, allowing for a more nuanced understanding of KT TMFs in the context of existing scholarly work. Our review methodology employed a similar approach to that followed by Strifler et al. in 2018, who conducted a systematic scoping review of KT TMFs in the field of cancer prevention and management, as well as other chronic diseases [ 44 ]. Their search strategy was preferred over others for two primary reasons. First, Strifler et al. investigated KT TMFs altogether, systematically and comprehensively. Second, unlike many other review studies on KT, they focused on macro-level KT and included all relevant keywords useful for the purpose of our study in their Ovid/MEDLINE search query [ 44 ]. For our scoping review, we adapted their search query with the assistance of a specialist librarian. This process involved eliminating terms associated with cancer and chronic diseases, removing time limitation on the published papers, and including an additional language other than English due to authors’ proficiency in German. We included articles published in peer-reviewed journals until November 2022, excluding opinion papers, conference abstracts and study protocols, without any restriction on publication date or place. Our search query is presented in Table  1 .

Following a screening methodology similar to that employed by Votruba et al. [ 11 ], the first author conducted an initial screening of the titles and abstracts of 2918 unique citations. Full texts were selected and scrutinised if they appeared relevant to the topics of agenda-setting, policy formulation or policy adoption. Among these papers, the first author also identified those that conceptualised a KT TMF. Simultaneously, the last author independently screened 2918 titles and abstracts, randomly selecting 20% of them to identify studies related to macro-level KT. Regarding papers that conceptualised a KT TMF, all those initially selected by the first author underwent a thorough examination by the last author as well. In the papers reviewed by these two authors of this study, KT TMFs were typically presented as either Tables or Figures. In cases where these visual representations did not contain sufficient information about ‘context’, the main body of the study was carefully scrutinised by both reviewers to ensure no relevant information was missed. Any unclear cases were discussed and resolved to achieve 100% inter-rater agreement between the first and second reviewers. This strategy resulted in the inclusion of 32 relevant studies. The flow chart outlining our review process is provided in Fig.  1 .

figure 1

Flow chart of the review process

According to the results of our systematic scoping review (Table  2 ), the first KT TMF developed for health policies dates back to 2003, confirming the emergence of a trend that expanded the meaning of the term Knowledge Translation to include policymakers as end-users of evidence during approximately the same period. In their study, Jacobson et al. [ 46 ] present a framework derived from a literature review to enhance understanding of user groups by organising existing knowledge, identifying gaps and emphasising the importance of learning about new contexts. However, despite acknowledging the significance of the user group context, the paper lacks a thorough explanation of the authors’ understanding of this term. The second study in our scoping review provides some details. Recognising a shift from evidence-based medicine to evidence-based health policymaking in the KT literature, the article by Dobrow et al. from 2004 [ 30 ] emphasises the importance of considering contextual factors. They present a conceptual framework for evidence-based decision-making, highlighting the influence of context in KT. Illustrated through examples from colorectal cancer screening policy development, their conceptual framework emphasises the significance of context in the introduction, interpretation and application of evidence. Third, Lehoux et al. [ 47 ] examine the field of Health Technology Assessment (HTA) and its role in informing decision and policymaking in Canada. By developing a conceptual framework for HTA dissemination and use, they touch on the institutional environment and briefly describe contextual factors.

Notably, the first three publications in our scoping review are authored by scholars affiliated with Canada, which is less of a coincidence, given the role of Canadian Institutes of Health Research (CIHR), the federal funding agency for health research: The CIHR Act (Bill C-13) mandates CIHR to ensure that the translation of health knowledge permeates every aspect of its work [ 48 ]. Moreover, it was CIHR that coined the term Knowledge Translation, defining KT as ‘a dynamic and iterative process that includes the synthesis, dissemination, exchange and ethically sound application of knowledge to improve health, provide more effective health services and products, and strengthen the health care system’ [ 49 ] . This comprehensive definition has since been adapted by international organisations (IOs), including WHO. The first document published by WHO that utilised KT to influence health policies dates back to 2005, entitled ‘Bridging the “know-do” gap: Meeting on knowledge translation in global health’, an initiative that was supported by the Canadian Coalition for Global Health Research, the Canadian International Development Agency, the German Agency for Technical Cooperation and the WHO Special Programme on Research and Training in Tropical Diseases [ 1 ]. Following this official recognition by WHO, studies in our scoping review after 2005 indicate a noticeable expansion of KT, encompassing a wider geographical area than Canada.

The article of Ashford et al. from 2006 [ 50 ] discusses the challenge of policy decisions in Kenya in the health field being disconnected from scientific evidence and presents a model for translating knowledge into policy actions through agenda-setting, coalition building and policy learning. However, the framework lacks explicit incorporation of contextual factors influencing health policies. Bauman et al. [ 51 ] propose a six-step framework for successful dissemination of physical activity evidence, illustrated through four case studies from three countries (Canada, USA and Brazil) and a global perspective. They interpret contextual factors as barriers and facilitators to physical activity and public health innovations. Focusing on the USA, Gold [ 52 ] explains factors, processes and actors that shape pathways between research and its use in a summary diagram, including a reference to ‘other influences in process’ for context. Green et al. [ 4 ] examine the gap between health research and its application in public health without focusing on a specific geographical area. Their study comprehensively reviews various concepts of diffusion, dissemination and implementation in public health, proposing ways to blend diffusion theory with other theories. Their ‘utilization-focused surveillance framework’ interprets context as social determinants as structures, economics, politics and culture.

Further, the article by Dhonukshe-Rutten et al. from 2010 [ 53 ] presents a general framework that outlines the process of translating nutritional requirements into policy applications from a European perspective. The framework incorporates scientific evidence, stakeholder interests and the socio-political context. The description of this socio-political context is rather brief, encompassing political and social priorities, legal context, ethical issues and economic implications. Ir et al. [ 54 ] analyse the use of knowledge in shaping policy on health equity funds in Cambodia, with the objective of understanding how KT contributes to the development of health policies that promote equity. Yet no information on context is available in the framework that they suggest. A notable exception among these early KT TMFs until 2010 is the conceptual framework for analysing integration of targeted health interventions into health systems by Atun et al. [ 55 ], in which the authors provide details about the factors that have an influence on the process of bringing evidence to health policies. Focusing on the adoption, diffusion and assimilation of health interventions, their conceptual framework provides a systematic approach for evaluating and informing policies in this field. Compared to the previous studies discussed above, their definition of context for this framework is comprehensive (Table  2 ). Overall, most of the studies containing macro-level KT TMFs published until 2010 either do not fully acknowledge contextual factors or provide generic terms such as cultural, political and economic for brief description (9 out of 10; 90%).

Studies published after 2010 demonstrate a notable geographical shift, with a greater emphasis on low- and middle-income countries (LMICs). By taking the adoption of the directly observed treatment, short-course (DOTS) strategy for tuberculosis control in Mexico as a case study, Bissell et al. [ 56 ] examine policy transfer to Mexico and its relevance to operational research efforts and suggest a model for analysis of health policy transfer. The model interprets context as health system, including political, economic, social, cultural and technological features. Focusing on HIV/AIDS in India, Tran et al. [ 57 ] explore KT by considering various forms of evidence beyond scientific evidence, such as best practices derived from programme experience and disseminated through personal communication. Their proposed framework aims to offer an analytical tool for understanding how evidence-based influence is exerted. In their framework, no information is available on context. Next, Bertone et al. [ 58 ] report on the effectiveness of Communities of Practice (CoPs) in African countries and present a conceptual framework for analysing and assessing transnational CoPs in health policy. The framework organises the key elements of CoPs, linking available resources, knowledge management activities, policy and practice changes, and improvements in health outcomes. Context is only briefly included in this framework.

Some other studies include both European and global perspectives. The publication from Timotijevic et al. from 2013 [ 59 ] introduces an epistemological framework that examines the considerations influencing the policy-making process, with a specific focus on micronutrient requirements in Europe. They present case studies from several European countries, highlighting the relevance of the framework in understanding the policy context related to micronutrients. Context is interpreted in this framework as global trends, data, media, broader consumer beliefs, ethical considerations, and wider social, legal, political, and economic environment. Next, funded by the European Union, the study by Onwujekwe et al. [ 60 ] examines the role of different types of evidence in health policy development in Nigeria. Although they cover the factors related to policy actors in their framework for assessing the role of evidence in policy development, they provide no information on context. Moreover, Redman et al. [ 61 ] present the SPIRIT Action Framework, which aims to enhance the use of research in policymaking. Context is interpreted in this framework as policy influences, i.e. public opinion, media, economic climate, legislative/policy infrastructure, political ideology and priorities, stakeholder interests, expert advice, and resources. From a global perspective, Spicer et al. [ 62 ] explore the contextual factors that influenced the scale-up of donor-funded maternal and newborn health innovations in Ethiopia, India and Nigeria, highlighting the importance of context in assessing and adapting innovations. Their suggested contextual factors influencing government decisions to accept, adopt and finance innovations at scale are relatively comprehensive (Table  2 ).

In terms of publication frequency, the pinnacle of reviewed KT studies was in 2017. Among six studies published in 2017, four lack details about context in their KT conceptualisations and one study touches on context very briefly. Bragge et al. [ 5 ] brought for their study an international terminology working group together to develop a simplified framework of interventions to integrate evidence into health practices, systems, and policies, named as the Aims, Ingredients, Mechanism, Delivery framework, albeit without providing details on contextual factors. Second, Mulvale et al. [ 63 ] present a conceptual framework that explores the impact of policy dialogues on policy development, illustrating how these dialogues can influence different stages of the policy cycle. Similar to the previous one, this study too, lacks information on context. In a systematic review, Sarkies et al. [ 64 ] evaluate the effectiveness of research implementation strategies in promoting evidence-informed policy decisions in healthcare. The study explores the factors associated with effective strategies and their inter-relationship, yet without further information on context. Fourth, Houngbo et al. [ 65 ] focus on the development of a strategy to implement a good governance model for health technology management in the public health sector, drawing from their experience in Benin. They outline a six-phase model that includes preparatory analysis, stakeholder identification and problem analysis, shared analysis and visioning, development of policy instruments for pilot testing, policy development and validation, and policy implementation and evaluation. They provide no information about context in their model. Fifth, Mwendera et al. [ 66 ] present a framework for improving the use of malaria research in policy development in Malawi, which was developed based on case studies exploring the policymaking process, the use of local malaria research, and assessing facilitators and barriers to research utilisation. Contextual setting is considered as Ministry of Health (MoH) with political set up, leadership system within the MoH, government policies and cultural set up. In contrast to these five studies, Ellen et al. [ 67 ] present a relatively comprehensive framework to support evidence-informed policymaking in ageing and health. The framework includes thought-provoking questions to discover contextual factors (Table  2 ).

Continuing the trend, studies published after 2017 focus increasingly on LMICs. In their embedded case study, Ongolo-Zogo et al. [ 68 ] examine the influence of two Knowledge Translation Platforms (KTPs) on policy decisions to achieve the health millennium development goals in Cameroon and Uganda. It explores how these KTPs influenced policy through interactions within policy issue networks, engagement with interest groups, and the promotion of evidence-supported ideas, ultimately shaping the overall policy climate for evidence-informed health system policymaking. Contextual factors are thereby interpreted as institutions (structures, legacies, policy networks), interests, ideas (values, research evidence) and external factors (reports, commitments). Focusing on the ‘Global South’, Plamondon et al. [ 69 ] suggest blending integrated knowledge translation with global health governance as an approach for strengthening leadership for health equity action. In terms of contextual factors, they include some information such as adapting knowledge to local context, consideration of the composition of non-traditional actors, such as civil society and private sector, in governance bodies and guidance for meaningful engagement between actors, particularly in shared governance models. Further, Vincenten et al. [ 70 ] propose a conceptual model to enhance understanding of interlinking factors that influence the evidence implementation process. Their evidence implementation model for public health systems refers to ‘context setting’, albeit without providing further detail.

Similarly, the study by Motani et al. from 2019 [ 71 ] assesses the outcomes and lessons learned from the EVIDENT partnership that focused on knowledge management for evidence-informed decision-making in nutrition and health in Africa. Although they mention ‘contextualising evidence’ in their conceptual framework, information about context is lacking. Focusing on Latin America and the Caribbean, Varallyay et al. [ 72 ] introduce a conceptual framework for evaluating embedded implementation research in various contexts. The framework outlines key stages of evidence-informed decision-making and provides guidance on assessing embeddedness and critical contextual factors. Compared to others, their conceptual framework provides a relatively comprehensive elaboration on contextual factors. In addition, among all the studies reviewed, Leonard et al. [ 73 ] present an exceptionally comprehensive analysis, where they identify the facilitators and barriers to the sustainable implementation of evidence-based health innovations in LMICs. Through a systematic literature review, they scrutinise 79 studies and categorise the identified barriers and facilitators into seven groups: context, innovation, relations and networks, institutions, knowledge, actors, and resources. The first one, context, contains rich information that could be seen in Table  2 .

Continuing from LMICs, Votruba et al. [ 74 ] present in their study the EVITA (EVIdence To Agenda setting) conceptual framework for mental health research-policy interrelationships in LMICs with some information about context, detailed as external influences and political context. In a follow-up study, they offer an updated framework for understanding evidence-based mental health policy agenda-setting [ 75 ]. In their revised framework, context is interpreted as external context and policy sphere, encompassing policy agenda, window of opportunity, political will and key individuals. Lastly, to develop a comprehensive monitoring and evaluation framework for evidence-to-policy networks, Kuchenmüller et al. [ 76 ] present the EVIPNet Europe Theory of Change and interpret contextual factors for evidence-informed policymaking as political, economic, logistic and administrative. Overall, it can be concluded that studies presenting macro-level KT TMFs from 2011 until 2022 focus mainly on LMICs (15 out of 22; close to 70%) and the majority of them were funded by international (development) organisations, the European Commission and global health donor agencies. An overwhelming number of studies among them (19 out of 22; close to 90%) provide either no information on contextual details or these were included only partly with some generic terms in KT TMFs.

Our systematic scoping review suggests that the approach of KT, which has evolved from evidence-based medicine to evidence-informed policymaking, tends to remain closely tied to its clinical origins when developing TMFs. In other words, macro-level KT TMFs place greater emphasis on the (public) health issue at hand rather than considering the broader decision-making context, a viewpoint shared by other scholars as well [ 30 ]. One reason could be that in the early stages of KT TMFs, the emphasis primarily focused on implementing evidence-based practices within clinical settings. At that time, the spotlight was mostly on content, including aspects like clinical studies, checklists and guidelines serving as the evidence base. In those meso-level KT TMFs, a detailed description of context, i.e. the overall environment in which these practices should be implemented, might have been deemed less necessary, given that healthcare organisations, such as hospitals to implement medical guidelines or surgical safety checklists, show similar characteristics globally.

However, as the scope of KT TMFs continues to expand to include the influence on health policies, a deeper understanding of context-specific factors within different jurisdictions and the dynamics of the policy process is becoming increasingly crucial. This is even more important for KT scholars aiming to conceptualise large-scale changes, as described in KT Tier 5, which necessitate a thorough understanding of targeted behaviours within societies. As the complexity of interventions increases due to the growing number of stakeholders either affecting or being affected by them, the interventions are surrounded by a more intricate web of attitudes, incentives, relationships, rules of engagement and spheres of influence [ 7 ]. The persisting emphasis on content over context in the evolving field of KT may oversimplify the complex process of using evidence in policymaking and understanding the society [ 77 ]. Some scholars argue that this common observation in public health can be attributed to the dominance of experts primarily from medical sciences [ 78 , 79 , 80 ]. Our study confirms the potential limitation of not incorporating insights from political science and public policy studies, which can lead to what is often termed a ‘naïve’ conceptualisation of evidence-to-policy schemes [ 15 , 16 , 17 ]. It is therefore strongly encouraged that the emerging macro-level KT concepts draw on political science and public administration if KT scholars intend to effectively communicate new ideas to policymakers, with the aim of prompting their action or response. We summarised our findings into three points.

Firstly, KT scholars may want to identify and pinpoint exactly where a change should occur within the policy process. The main confusion that we observed in the KT literature arises from a lack of understanding of how public policies are made. Notably, the term ‘evidence-informed policymaking’ can refer to any stage of the policy cycle, spanning from agenda-setting to policy formulation, adoption, implementation and evaluation. Understanding these steps will allow researchers to refine their language when advocating for policy changes across various jurisdictions; for instance, the word ‘implementation’ is often inappropriately used in KT literature. As commonly known, at the macro-level, public policies take the form of legislation, law-making and regulation, thereby shaping the practices or policies to be implemented at the meso- and micro-levels [ 81 ]. In other words, the process of using specific knowledge to influence health policies, however evidence-based it might be, falls mostly under the responsibility and jurisdiction of sovereign states. For this reason, macro-level KT TMFs should reflect the importance of understanding the policy context and the complexities associated with policymaking, rather than suggesting flawed or unrealistic top-down ‘implementation’ strategies in countries by foregrounding the content, or the (public) health issue at hand.

Our second observation from this systematic scoping review points towards a selective perception among researchers when reporting on policy interventions. Research on KT does not solely exist due to the perceived gap between scientific evidence and policy but also because of the pressures the organisations or researchers face in being accountable to their funding sources, ensuring the continuity of financial support for their activities and claiming output legitimacy to change public policies [ 8 ]. This situation indirectly compels researchers working to influence health policies in the field to provide ‘evidence-based’ feedback on the success of their projects to donors [ 82 ]. In doing so, researchers may overly emphasise the content of the policy intervention in their reporting to secure further funding, while they underemphasis the contextual factors. These factors, often perceived as a given, might actually be the primary facilitators of their success. Such a lack of transparency regarding the definition of context is particularly visible in the field of global health, where LMICs often rely on external donors. It is important to note that this statement is not intended as a negative critique of their missions or an evaluation of health outcomes in countries following such missions. Rather, it seeks to explain the underlying reason why researchers, particularly those reliant on donors in LMICs, prioritise promoting the concept of KT from a technical standpoint, giving less attention to contextual factors in their reasoning.

Lastly, and connected to the previous point, it is our observation that the majority of macro-level KT TMFs fail to give adequate consideration to both power dynamics in countries (internal vs. external influences) and the actual role that government plays in public policies. Notably, although good policymaking entails an honest effort to use the best available evidence, the belief that this will completely negate the role of power and politics in decision-making is a technocratic illusion [ 83 ]. Among the studies reviewed, the framework put forth by Leonard et al. [ 73 ] offers the most comprehensive understanding of context and includes a broad range of factors (such as political, social, and economic) discovered also in other reviewed studies. Moreover, the framework, developed through an extensive systematic review, offers a more in-depth exploration of these contextual factors than merely listing them as a set of keywords. Indeed, within the domains of political science and public policy, such factors shaping health policies have received considerable scholarly attention for decades. To define what context entails, Walt refers in her book ‘Health Policy: An Introduction to Process and Power’ [ 84 ] to the work of Leichter from 1979 [ 85 ], who provides a scheme for analysing public policy. This includes i) situational factors, which are transient, impermanent, or idiosyncratic; ii) structural factors, which are relatively unchanging elements of the society and polity; iii) cultural factors, which are value commitments of groups; and iv) environmental factors, which are events, structures and values that exist outside the boundaries of a political system and influence decisions within it. His detailed sub-categories for context can be found in Table  3 . This flexible public policy framework may offer KT researchers a valuable approach to understanding contextual factors and provide some guidance to define the keywords to focus on. Scholars can adapt this framework to suit a wide range of KT topics, creating more context-sensitive and comprehensive KT TMFs.

Admittedly, our study has certain limitations. Despite choosing one of the most comprehensive bibliographic databases for our systematic scoping review, which includes materials from biomedicine, allied health fields, biological and physical sciences, humanities, and information science in relation to medicine and healthcare, we acknowledge that we may have missed relevant articles indexed in other databases. Hence, exclusively using Ovid/MEDLINE due to resource constraints may have narrowed the scope and diversity of scholarly literature examined in this study. Second, our review was limited to peer-reviewed publications in English and German. Future studies could extend our findings by examining the extent to which contextual factors are detailed in macro-level KT TMFs published in grey literature and in different languages. Given the abundance of KT reports, working papers or policy briefs published by IOs and development agencies, such an endeavour could enrich our findings and either support or challenge our conclusions. Nonetheless, to our knowledge, this study represents the first systematic review and critical appraisal of emerging knowledge-to-policy concepts, also known as macro-level KT TMFs. It successfully blends insights from both biomedical and public policy disciplines, and could serve as a roadmap for future research.

The translation of knowledge to policymakers involves more than technical skills commonly associated with (bio-)medical sciences, such as creating evidence-based guidelines or clinical checklists. Instead, evidence-informed policymaking reflects an ambition to engage in the political dimensions of states. Therefore, the evolving KT concepts addressing health policies should be seen as a political decision-making process, rather than a purely analytical one, as is the case with evidence-based medicine. To better understand the influence of power dynamics and governance structures in policymaking, we suggest that future macro-level KT TMFs draw on insights from political science and public administration. Collaborative, interdisciplinary research initiatives could be undertaken to bridge the gap between these fields. Technocratic KT TMFs that overlook contextual factors risk propagating misconceptions in academic circles about how health policies are made, as they become increasingly influential over time. Research, the systematic pursuit of knowledge, is neither inherently good nor bad; it can be sought after, used or misused, like any other tool in policymaking. What is needed in the KT discourse is not another generic call for ‘research-to-action’ but rather an understanding of the dividing line between research-to- clinical -action and research-to- political -action.

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Schmitt, T., Czabanowska, K. & Schröder-Bäck, P. What is context in knowledge translation? Results of a systematic scoping review. Health Res Policy Sys 22 , 52 (2024). https://doi.org/10.1186/s12961-024-01143-5

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    1.1 Definition. Methodological research gap is the missing gap of knowledge on a more appropriate underlying method(s) which can be used in research instead of the previously one. This implies that the researcher or you as a postgraduate student may propose a method in research to address a particular aspect in life or research which is more ...

  22. Problem Statement Research

    Business size; Non profit; Demographics (Age, Gender, Ethnicity, Disability, Veterans) Review relevant studies for opportunities for future research. Many authors will discuss what research could be done based of the work they have done. Include any of these subtopics in to your search to help you limit your results and to locate a gap in the ...

  23. The Relevance Gap in Business

    London Business School, United Kingdom The much-discussed "relevance gap" (Starkey & Madan, 2001) between research and practice in management is a major source of concern for business schools, in terms of their legitimacy in the eyes of students, employers, and funding bodies. We frame the relevance gap as

  24. Opportunities for small businesses to boost productivity

    We take each country's national definition of micro-, small, and medium-size enterprises. ... A systematic review of empirical evidence and future research agenda," Small Business Economics, volume 58 ... The gap between the actual productivity ratio and the top quartile level is equivalent to an average of 5 percent of GDP in advanced ...

  25. What is context in knowledge translation? Results of a systematic

    Knowledge Translation (KT) aims to convey novel ideas to relevant stakeholders, motivating their response or action to improve people's health. Initially, the KT literature focused on evidence-based medicine, applying findings from laboratory and clinical research to disease diagnosis and treatment. Since the early 2000s, the scope of KT has expanded to include decision-making with health ...