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Implications in research: A quick guide

Last updated

11 January 2024

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Implications are a bridge between data and action, giving insight into the effects of the research and what it means. It's a chance for researchers to explain the  why  behind the research. 

When writing a research paper , reviewers will want to see you clearly state the implications of your research. If it's missing, they’ll likely reject your article. 

Let's explore what research implications are, why they matter, and how to include them in your next article or research paper. 

• What are implications in research?

Research implications are the consequences of research findings. They go beyond results and explore your research’s ramifications. 

Researchers can connect their research to the real-world impact by identifying the implications. These can inform further research, shape policy, or spark new solutions to old problems. 

Always clearly state your implications so they’re obvious to the reader. Never leave the reader to guess why your research matters. While it might seem obvious to you, it may not be evident to someone who isn't a subject matter expert. 

For example, you may do important sociological research with political implications. If a policymaker can't understand or connect those implications logically with your research, it reduces your impact.

• What are the key features of implications?

When writing your implications, ensure they have these key features: 

Implications should be clear, concise, and easily understood by a broad audience. You'll want to avoid overly technical language or jargon. Clearly stating your implications increases their impact and accessibility. 

Implications should link to specific results within your research to ensure they’re grounded in reality. You want them to demonstrate an impact on a particular field or research topic . 

Evidence-based

Give your implications a solid foundation of evidence. They need to be rational and based on data from your research, not conjecture. An evidence-based approach to implications will lend credibility and validity to your work.

Implications should take a balanced approach, considering the research's potential positive and negative consequences. A balanced perspective acknowledges the challenges and limitations of research and their impact on stakeholders. 

Future-oriented

Don't confine your implications to their immediate outcomes. You can explore the long-term effects of the research, including the impact on future research, policy decisions, and societal changes. Looking beyond the immediate adds more relevance to your research. 

When your implications capture these key characteristics, your research becomes more meaningful, impactful, and engaging. 

• Types of implications in research

The implications of your research will largely depend on what you are researching. 

However, we can broadly categorize the implications of research into two types: 

Practical: These implications focus on real-world applications and could improve policies and practices.

Theoretical: These implications are broader and might suggest changes to existing theories of models of the world. 

You'll first consider your research's implications in these two broad categories. Will your key findings have a real-world impact? Or are they challenging existing theories? 

Once you've established whether the implications are theoretical or practical, you can break your implication into more specific types. This might include: 

Political implications: How findings influence governance, policies, or political decisions

Social implications: Effects on societal norms, behaviors, or cultural practices

Technological implications: Impact on technological advancements or innovation

Clinical implications: Effects on healthcare, treatments, or medical practices

Commercial or business-relevant implications: Possible strategic paths or actions

Implications for future research: Guidance for future research, such as new avenues of study or refining the study methods

When thinking about the implications of your research, keep them clear and relevant. Consider the limitations and context of your research. 

For example, if your study focuses on a specific population in South America, you may not be able to claim the research has the same impact on the global population. The implication may be that we need further research on other population groups. 

• Understanding recommendations vs. implications

While "recommendations" and "implications" may be interchangeable, they have distinct roles within research.

Recommendations suggest action. They are specific, actionable suggestions you could take based on the research. Recommendations may be a part of the larger implication. 

Implications explain consequences. They are broader statements about how the research impacts specific fields, industries, institutions, or societies. 

Within a paper, you should always identify your implications before making recommendations. 

While every good research paper will include implications of research, it's not always necessary to include recommendations. Some research could have an extraordinary impact without real-world recommendations. 

• How to write implications in research

Including implications of research in your article or journal submission is essential. You need to clearly state your implications to tell the reviewer or reader why your research matters. 

Because implications are so important, writing them can feel overwhelming.

Here’s our step-by-step guide to make the process more manageable:

1. Summarize your key findings

Start by summarizing your research and highlighting the key discoveries or emerging patterns. This summary will become the foundation of your implications. 

2. Identify the implications

Think critically about the potential impact of your key findings. Consider how your research could influence practices, policies, theories, or societal norms. 

Address the positive and negative implications, and acknowledge the limitations and challenges of your research. 

If you still need to figure out the implications of your research, reread your introduction. Your introduction should include why you’re researching the subject and who might be interested in the results. This can help you consider the implications of your final research. 

3. Consider the larger impact

Go beyond the immediate impact and explore the implications on stakeholders outside your research group. You might include policymakers, practitioners, or other researchers.

4. Support with evidence

Cite specific findings from your research that support the implications. Connect them to your original thesis statement. 

You may have included why this research matters in your introduction, but now you'll want to support that implication with evidence from your research. 

Your evidence may result in implications that differ from the expected impact you cited in the introduction of your paper or your thesis statement. 

5. Review for clarity

Review your implications to ensure they are clear, concise, and jargon-free. Double-check that your implications link directly to your research findings and original thesis statement. 

Following these steps communicates your research implications effectively, boosting its long-term impact. 

Where do implications go in your research paper?

Implications often appear in the discussion section of a research paper between the presentation of findings and the conclusion. 

Putting them here allows you to naturally transition from the key findings to why the research matters. You'll be able to convey the larger impact of your research and transition to a conclusion.

• Examples of research implications

Thinking about and writing research implications can be tricky. 

To spark your critical thinking skills and articulate implications for your research, here are a few hypothetical examples of research implications: 

Teaching strategies

A study investigating the effectiveness of a new teaching method might have practical implications for educators. 

The research might suggest modifying current teaching strategies or changing the curriculum’s design. 

There may be an implication for further research into effective teaching methods and their impact on student testing scores. 

Social media impact

A research paper examines the impact of social media on teen mental health. 

Researchers find that spending over an hour on social media daily has significantly worse mental health effects than 15 minutes. 

There could be theoretical implications around the relationship between technology and human behavior. There could also be practical implications in writing responsible social media usage guidelines. 

Disease prevalence

A study analyzes the prevalence of a particular disease in a specific population. 

The researchers find this disease occurs in higher numbers in mountain communities. This could have practical implications on policy for healthcare allocation and resource distribution. 

There may be an implication for further research into why the disease appears in higher numbers at higher altitudes.

These examples demonstrate the considerable range of implications that research can generate.

Clearly articulating the implications of research allows you to enhance the impact and visibility of your work as a researcher. It also enables you to contribute to societal advancements by sharing your knowledge.

The implications of your work could make positive changes in the world around us.

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• Writing Tips

How to Write an “Implications of Research” Section

4-minute read

• 24th October 2022

When writing research papers , theses, journal articles, or dissertations, one cannot ignore the importance of research. You’re not only the writer of your paper but also the researcher ! Moreover, it’s not just about researching your topic, filling your paper with abundant citations, and topping it off with a reference list. You need to dig deep into your research and provide related literature on your topic. You must also discuss the implications of your research.

Interested in learning more about implications of research? Read on! This post will define these implications, why they’re essential, and most importantly, how to write them. If you’re a visual learner, you might enjoy this video .

What Are Implications of Research?

Implications are potential questions from your research that justify further exploration. They state how your research findings could affect policies, theories, and/or practices.

Implications can either be practical or theoretical. The former is the direct impact of your findings on related practices, whereas the latter is the impact on the theories you have chosen in your study.

Example of a practical implication: If you’re researching a teaching method, the implication would be how teachers can use that method based on your findings.

Example of a theoretical implication: You added a new variable to Theory A so that it could cover a broader perspective.

Finally, implications aren’t the same as recommendations, and it’s important to know the difference between them .

Questions you should consider when developing the implications section:

●  What is the significance of your findings?

●  How do the findings of your study fit with or contradict existing research on this topic?

●  Do your results support or challenge existing theories? If they support them, what new information do they contribute? If they challenge them, why do you think that is?

Why Are Implications Important?

You need implications for the following reasons:

● To reflect on what you set out to accomplish in the first place

● To see if there’s a change to the initial perspective, now that you’ve collected the data

● To inform your audience, who might be curious about the impact of your research

How to Write an Implications Section

Usually, you write your research implications in the discussion section of your paper. This is the section before the conclusion when you discuss all the hard work you did. Additionally, you’ll write the implications section before making recommendations for future research.

Implications should begin with what you discovered in your study, which differs from what previous studies found, and then you can discuss the implications of your findings.

Your implications need to be specific, meaning you should show the exact contributions of your research and why they’re essential. They should also begin with a specific sentence structure.

Examples of starting implication sentences:

●  These results build on existing evidence of…

●  These findings suggest that…

●  These results should be considered when…

●  While previous research has focused on x , these results show that y …

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You should write your implications after you’ve stated the results of your research. In other words, summarize your findings and put them into context.

The result : One study found that young learners enjoy short activities when learning a foreign language.

The implications : This result suggests that foreign language teachers use short activities when teaching young learners, as they positively affect learning.

 Example 2

The result : One study found that people who listen to calming music just before going to bed sleep better than those who watch TV.

The implications : These findings suggest that listening to calming music aids sleep quality, whereas watching TV does not.

To summarize, remember these key pointers:

●  Implications are the impact of your findings on the field of study.

●  They serve as a reflection of the research you’ve conducted.              

●  They show the specific contributions of your findings and why the audience should care.

●  They can be practical or theoretical.

●  They aren’t the same as recommendations.

●  You write them in the discussion section of the paper.

●  State the results first, and then state their implications.

Are you currently working on a thesis or dissertation? Once you’ve finished your paper (implications included), our proofreading team can help ensure that your spelling, punctuation, and grammar are perfect. Consider submitting a 500-word document for free.

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What are Implications in Research?

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Manuscripts that do not mention the implications of the study are often desk-rejected by journals. What constitutes the ‘implications’ of research, and why is it important to include research implications in your manuscript?

Research implications: An overview

Once you have laid out the key findings in your paper, you have to discuss how they will likely impact the world. What is the significance of your study to policymakers, the lay person, or other researchers? This speculation, made in good faith, constitutes your study’ implications.

A research paper that does not explain the study’s importance in light of its findings exists in a vacuum. The paper may be relevant to you, the author, and some of your co-workers. But it is unclear how others will benefit from reading it.

How can the findings of your study help create a better world? What can we infer from your conclusion about the current state of research in your field or the quality of methods you employed? These are all important implications of your study.

You cannot predict how your study will influence the world or research in the future. You can only make reasonable speculations. In order to ensure that the implications are reasonable, you have to be mindful of the limitations of your study.

In the research context, only speculations supported by data count as valid implications. If the implications you draw do not logically follow the key findings of your study, they may sound overblown or outright preposterous.

Suppose your study evaluated the effects of a new drug in the adult population. In that case, you could not honestly speculate on how the drug will impact paediatric care. Thus, the implications you draw from your study cannot exceed its scope.

Practical implications

Imagine that your study found a popular type of cognitive therapy to be ineffective in treating insomnia. Your findings imply that psychologists using this type of therapy were not seeing actual results but an expectancy effect. Studies that can potentially impact real-world problems by prompting policy change or change in treatments have practical implications.

It can be helpful to understand the difference between an implication of your study and a recommendation. Suppose your study compares two or more types of therapy, ranks them in the order of effectiveness, and explicitly asks clinicians to follow the most effective type. The suggestion made in the end constitutes a ‘recommendation’ and not an ‘implication’.

Theoretical implications

Are your findings in line with previous research? Did your results validate the methods used in previous research or invalidate them? Has your study discovered a new and helpful way to do experiments? Speculations on how your findings can potentially impact research in your field of study are theoretical implications.

The main difference between practical and theoretical implications is that theoretical implications may not be readily helpful to policymakers or the public.

How to Write Implications in Research

Implications usually form an essential part of the conclusion section of a research paper. As we have mentioned in a previous article, this section starts by summarising your work, but this time emphasises your work’s significance .

While writing the implications, it is helpful to ask, “who will benefit the most from reading my paper?”—policymakers, physicians, the public, or other researchers. Once you know your target population, explain how your findings can help them.

Think about how the findings in your study are similar or dissimilar to the findings of previous studies. Your study may reaffirm or disprove the results of other studies. This is an important implication.

Suggest future directions for research in the subject area in light of your findings or further research to confirm your findings. These are also crucial implications.

Do not try to exaggerate your results, and make sure your tone reflects the strength of your findings. If the implications mentioned in your paper are convincing, it can improve visibility for your work and spur similar studies in your field.

For more information on the importance of implications in research, and guidance on how to include them in your manuscript, visit Elsevier Author Services now!

Differentiating between the abstract and the introduction of a research paper

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Research Implications | Definition, Examples & Tips

Introduction

What are research implications, why discuss research implications, types of implications in research, how do you present research implications.

Every scientific inquiry is built on previous studies and lays the groundwork for future research. The latter is where discussion of research implications lies. Researchers are expected not only to present what their findings suggest about the phenomenon being studied but also what the findings mean in a broader context.

In this article, we'll explore the nature of research implications as a means for contextualizing the findings of qualitative research and the foundation it sets for further research.

Research implications include any kind of discussion of what a particular study means for its research field and in general terms. Researchers write implications to lay out future research studies, make research recommendations based on proposed theoretical developments, and discuss practical and technological implications that can be applied in the real world.

To put it another way, research implications are intended to answer the question "what does this research mean?". Research implications look forward and out. Once findings are presented and discussed, the researcher lays out what the findings mean in a broader context and how they could guide subsequent research.

An aspect of academic writing that's related to implications is the discussion of the study's limitations. These limitations differ from implications in that they explore already acknowledged shortcomings in a study (e.g., a small sample size, an inherent weakness in a chosen methodological approach), but these limitations can also suggest how future research could address these shortcomings. Both the implications and recommendations are often coupled with limitations in a discussion section to explain the significance of the study's contributions to scientific knowledge.

Strictly speaking, there is a fine line between limitations and implications, one that a traditional approach to the scientific method may not adequately explore. Under the scientific method, the product of any research study addresses its research questions or confirms or challenges its expected outcomes. Fulfilling just this task, however, may overlook a more important step in the research process in terms of demonstrating significance.

One of the more famous research examples can provide useful insight. Galileo's experiments with falling objects allowed him to answer questions raised by Aristotle's understanding about gravity affecting objects of different weights. Galileo had something of a hypothesis - objects should fall at the same speed regardless of weight - based on a critique of then-current scientific knowledge - Aristotle's assertion about gravity - that he wanted to test in research. By conducting different experiments using inclines and pendulums (and supposedly one involving falling objects from the Tower of Pisa), he established a new understanding about gravity and its relationship (or lack thereof) to the weight of objects.

Discussion of that experiment focused on how the findings challenged Aristotle's understanding of physics. It did not, however, pose the next logical question: Why would an object like a feather fall at a much slower rate of descent than an object like a hammer if weight was not a factor?

Galileo's experiment and other similar experiments laid the groundwork for experiments on air resistance, most famously the Apollo 15 experiment on the moon where a feather and hammer fell at the same rate in a vacuum, absent any air resistance. The limitation Galileo had at the time was the inability to create a vacuum to test any theories about gravity and air resistance. The implications of his experiments testing Aristotle's claims include the call to further research that could eventually confirm or challenge his understanding of falling objects.

In formal scientific research, particularly in academic settings where peer review is an essential component, contemporary researchers are supposed to do more than simply report their findings. They are expected to engage in critical reflection in placing their research findings in a broader context. The peer review process in research publication often assesses the quality of a research paper by its ability to detail the significance of a given research study. Without an explicit description of the implications in research, readers may not necessarily know what importance the study and its findings holds for them.

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Breaking down the kinds of implications that your research findings might have will be useful in crafting a clearer and more persuasive presentation. More important than saying that the findings are compelling is arguing in what aspects the findings should prove useful.

There are different types of implications, and the type you should emphasize depends on your target audience.

Theoretical implications

When research findings present novel scientific knowledge, it should have an influence on existing theories by affirming, contradicting, or contextualizing them. This can mean the proposal of a brand new theoretical framework or developments to a existing one.

Keep in mind that, in qualitative research , researchers will often contextualize a theory rather than confirm or refute it. This means that a theory or conceptual framework that is applied to an unfamiliar context (e.g., a theory about adolescent development in a study involving graduate students) will undergo some sort of transformation due to the new analysis.

New understandings will likely develop more complex descriptions of theories as they are interpreted and re-interpreted in new contexts. The discussion of theoretical implications here requires researchers to consider how new theoretical developments might be applied to new data in future research.

Practical implications

More applied forums are interested in how a study's findings can be used in the real world. New developments in psychology could yield discussion of applications in psychiatry, while research in physics can lead to technological innovations in engineering and architecture. While some researchers focus on developing theory, others conduct research to generate actionable insights and tangible results for stakeholders.

Education research, for example, may present pathways to a new teaching method or assessment of learining outcomes. Theories about how students passively and actively develop expertise in subject-matter knowledge could eventually prompt scholars and practitioners to change existing pedagogies and materials that account for more novel understandings of teaching and learning.

Exploring the practical dimensions of research findings may touch on political implications such as policy recommendations, marketable technologies, or novel approaches to existing methods or processes. Discussion of implications along these lines is meant to promote further research and activity in the field to support these practical developments.

Methodological implications

Qualitative research methods are always under constant development and innovation. Moreover, applying research methods in new contexts or for novel research inquiries can lead to unanticipated results that might cause a researcher to reflect on and iterate on their methods of data collection and analysis .

Critical reflections on research methods are not meant to assert that the study was conducted without the necessary rigor . However, rigorous and transparent researchers are expected to argue that further iterations of the research that address any methodological gaps can only bolster the persuasiveness of the findings or generate richer insights.

There are many possible avenues for implications in terms of innovating on methodology. Does the nature of your interview questions change when interviewing certain populations? Should you change certain practices when collecting data in an ethnography to establish rapport with research participants ? How does the use of technology influence the collection and analysis of data?

All of these questions are worth discussing, with the answers providing useful guidance to those who want to base their own study design on yours. As a result, it's important to devote some space in your paper or presentation to how you conducted your study and what you would do in future iterations of your study to bolster its research rigor.

Presenting research implications or writing research implications in a research paper is a matter of answering the following question: Why should scholars read or pay attention to your research? Especially in the social sciences, the potential impact of a study is not always a foregone conclusion. In other words, to make the findings as insightful and persuasive to your audience as they are to you, you need to persuade them beyond the presentation of the analysis and the insights generated.

Here are a few main principles to achieve this task. In broad terms, they focus on what the findings mean to you, what it should mean to others, and what those impacts might mean in context.

Establish importance

Academic research writing tends to follow a structure that narrates a study from the researcher's motivation to conduct the research to why the research's findings matter. While there's seldom a strict requirement for sections in a paper or presentation, understanding commonly used patterns in academic writing will point out where the research implications are discussed.

If you look at a typical research paper abstract in a peer-reviewed journal , for example, you might find that the last sentence or two explicitly establishes why the research is useful to motivate readers to look at the paper more deeply. In the body of the paper, this is further explained in detail towards the end of the introduction and discussion sections and in the conclusion section. These areas are where you should focus on detailing the research implications and explaining how you perceive the impact of your study.

It's essential that you use these spaces to highlight why the findings matter to you. As mentioned earlier, this impact should never be assumed to be understood. Rather, you should explain in detail how your initial motivation to conduct the research has been satisfied and how you might use what you have learned from the research in theoretical and practical terms.

Tailor to your audience

Research is partly about sharing expertise and partly about understanding your audience. Scientific knowledge is generated through consensus, and the more that the researcher ensures their implications are understood by their audience, the more it will resonate in the field.

A good strategy for tailoring your research paper to a particular journal is to read its articles for the implications that are explored in the research. Applied journals will focus on more practical implications while more theoretical publications will emphasize theoretical or conceptual frameworks for other scholars to rely on. As a result, there's no need to detail every single possible implication from your study; simply describing those implications that are most relevant to your audience is often sufficient.

Provide useful examples

One of the easier ways to persuade readers of the potential implications of your research is to provide concrete examples that are simple to understand.

Think about a study that interviews children, for example, where the methodological implications dwell on establishing an emotional connection before collecting data. This might include practical considerations such as bringing toys or conducting the interview in a setting familiar to them like their classroom so they are comfortable during data collection. Explicitly detailing this example can guide scholars in useful takeaways for their research design.

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What are Implications and Recommendations in Research? How to Write It, with Examples

Highly cited research articles often contain both implications and recommendations , but there is often some confusion around the difference between implications and recommendations in research. Implications of a study are the impact your research makes in your chosen area; they discuss how the findings of the study may be important to justify further exploration of your research topic. Research recommendations suggest future actions or subsequent steps supported by your research findings. It helps to improve your field of research or cross-disciplinary fields through future research or provides frameworks for decision-makers or policymakers. Recommendations are the action plan you propose based on the outcome.

In this article, we aim to simplify these concepts for researchers by providing key insights on the following:  

• what are implications in research 
• what is recommendation in research 
• differences between implications and recommendations 
• how to write implications in research 
• how to write recommendation in research 
• sample recommendation in research 

Table of Contents

What are implications in research

The implications in research explain what the findings of the study mean to researchers or to certain subgroups or populations beyond the basic interpretation of results. Even if your findings fail to bring radical or disruptive changes to existing ways of doing things, they might have important implications for future research studies. For example, your proposed method for operating remote-controlled robots could be more precise, efficient, or cheaper than existing methods, or the remote-controlled robot could be used in other application areas. This could enable more researchers to study a specific problem or open up new research opportunities.   

Implications in research inform how the findings, drawn from your results, may be important for and impact policy, practice, theory, and subsequent research. Implications may be theoretical or practical. 1  

• Practical implications are potential values of the study with practical or real outcomes . Determining the practical implications of several solutions can aid in identifying optimal solution results. For example, clinical research or research on classroom learning mostly has practical implications in research . If you developed a new teaching method, the implication would be how teachers can use that method based on your findings.  
• Theoretical implications in research constitute additions to existing theories or establish new theories. These types of implications in research characterize the ability of research to influence society in apparent ways. It is, at most, an educated guess (theoretical) about the possible implication of action and need not be as absolute as practical implications in research . If your study supported the tested theory, the theoretical implication would be that the theory can explain the investigated phenomenon. Else, your study may serve as a basis for modifying the theory. Theories may be partially supported as well, implying further study of the theory or necessary modifications are required.  

What are recommendations in research?

Recommendations in research can be considered an important segment of the analysis phase. Recommendations allow you to suggest specific interventions or strategies to address the issues and constraints identified through your study. It responds to key findings arrived at through data collection and analysis. A process of prioritization can help you narrow down important findings for which recommendations are developed.  

Recommendations in research examples

Recommendations in research may vary depending on the purpose or beneficiary as seen in the table below.  

Table: Recommendations in research examples based on purpose and beneficiary  

If you’re wondering how to make recommendations in research . You can use the simple  recommendation in research example below as a handy template.  

Table: Sample recommendation in research template  

Basic differences between implications and recommendations in research

Implications and recommendations in research are two important aspects of a research paper or your thesis or dissertation. Implications discuss the importance of the research findings, while recommendations offer specific actions to solve a problem. So, the basic difference between the two is in their function and the questions asked to achieve it. The following table highlights the main differences between implications and recommendations in research .  

Table: Differences between implications and recommendations in research  

Where do implications go in your research paper

Because the implications and recommendations of the research are based on study findings, both are usually written after the completion of a study. There is no specific section dedicated to implications in research ; they are usually integrated into the discussion section adding evidence as to why the results are meaningful and what they add to the field. Implications can be written after summarizing your main findings and before the recommendations and conclusion.   

Implications can also be presented in the conclusion section after a short summary of the study results.   

How to write implications in research

Implication means something that is inferred. The implications of your research are derived from the importance of your work and how it will impact future research. It is based on how previous studies have advanced your field and how your study can add to that.   

When figuring out how to write implications in research , a good strategy is to separate it into the different types of implications in research , such as social, political, technological, policy-related, or others. As mentioned earlier, the most frequently used are the theoretical and practical implications.   

Next, you need to ask, “Who will benefit the most from reading my paper?” Is it policymakers, physicians, the public, or other researchers? Once you know your target population, explain how your findings can help them.  

The implication section can include a paragraph or two that asserts the practical or managerial implications and links it to the study findings. A discussion can then follow, demonstrating that the findings can be practically implemented or how they will benefit a specific audience. The writer is given a specific degree of freedom when writing research implications , depending on the type of implication in research you want to discuss: practical or theoretical. Each is discussed differently, using different words or in separate sections. The implications can be based on how the findings in your study are similar or dissimilar to that in previous studies. Your study may reaffirm or disprove the results of other studies, which has important implications in research . You can also suggest future research directions in the light of your findings or require further research to confirm your findings, which are all crucial implications. Most importantly, ensure the implications in research are specific and that your tone reflects the strength of your findings without exaggerating your results.   

Implications in research can begin with the following specific sentence structures:  

• These findings suggest that…
• These results build on existing body of evidence of…
• These results should be considered when…
• While previous research focused on x, our results show that y…

What should recommendations in research look like?

Recommendations for future research should be:  

• Directly related to your research question or findings  
• Concrete and specific  
• Supported by a clear reasoning  

The recommendations in research can be based on the following factors:  

1. Beneficiary: A paper’s research contribution may be aimed at single or multiple beneficiaries, based on which recommendations can vary. For instance, if your research is about the quality of care in hospitals, the research recommendation to different beneficiaries might be as follows:  

• Nursing staff: Staff should undergo training to enhance their understanding of what quality of care entails.  
• Health science educators: Educators must design training modules that address quality-related issues in the hospital.  
• Hospital management: Develop policies that will increase staff participation in training related to health science.  

2. Limitations: The best way to figure out what to include in your research recommendations is to understand the limitations of your study. It could be based on factors that you have overlooked or could not consider in your present study. Accordingly, the researcher can recommend that other researchers approach the problem from a different perspective, dimension, or methodology. For example, research into the quality of care in hospitals can be based on quantitative data. The researcher can then recommend a qualitative study of factors influencing the quality of care, or they can suggest investigating the problem from the perspective of patients rather than the healthcare providers.   

3. Theory or Practice: Your recommendations in research could be implementation-oriented or further research-oriented.   

4. Your research: Research recommendations can be based on your topic, research objectives, literature review, and analysis, or evidence collected. For example, if your data points to the role of faculty involvement in developing effective programs, recommendations in research can include developing policies to increase faculty participation. Take a look at the evidence-based recommendation in research example s provided below.   

Table: Example of evidence-based research recommendation  

Avoid making the following mistakes when writing research recommendations :  

• Don’t undermine your own work: Recommendations in research should offer suggestions on how future studies can be built upon the current study as a natural extension of your work and not as an entirely new field of research.  
• Support your study arguments: Ensure that your research findings stand alone on their own merits to showcase the strength of your research paper.   

How to write recommendations in research

When writing research recommendations , your focus should be on highlighting what additional work can be done in that field. It gives direction to researchers, industries, or governments about changes or developments possible in this field. For example, recommendations in research can include practical and obtainable strategies offering suggestions to academia to address problems. It can also be a framework that helps government agencies in developing strategic or long-term plans for timely actions against disasters or aid nation-building.  

There are a few SMART 2 things to remember when writing recommendations in research. Your recommendations must be: 

• S pecific: Clearly state how challenges can be addressed for better outcomes and include an action plan that shows what can be achieved. 
• M easurable: Use verbs denoting measurable outcomes, such as identify, analyze, design, compute, assess, evaluate, revise, plan, etc., to strengthen recommendations in research .   
• A ttainable: Recommendations should offer a solution-oriented approach to problem-solving and must be written in a way that is easy to follow.  
• R elevant: Research recommendations should be reasonable, realistic, and result-based. Make sure to suggest future possibilities for your research field.  
• T imely: Time-based or time-sensitive recommendations in research help divide the action plan into long-term or short-term (immediate) goals. A timeline can also inform potential readers of what developments should occur over time.  

If you are wondering how many words to include in your research recommendation , a general rule of thumb would be to set aside 5% of the total word count for writing research recommendations . Finally, when writing the research implications and recommendations , stick to the facts and avoid overstating or over-generalizing the study findings. Both should be supported by evidence gathered through your data analysis.  

References:  

• Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings.  Psychological bulletin ,  124 (2), 262.
• Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives.  Manag Rev ,  70 (11), 35-36.

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Research Implications & Recommendations

A Plain-Language Explainer With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | May 2024

The research implications and recommendations are closely related but distinctly different concepts that often trip students up. Here, we’ll unpack them using plain language and loads of examples , so that you can approach your project with confidence.

Overview: Implications & Recommendations

• What are research implications ?
• What are research recommendations ?
• Examples of implications and recommendations
• The “ Big 3 ” categories
• How to write the implications and recommendations
• Template sentences for both sections
• Key takeaways

Implications & Recommendations 101

Let’s start with the basics and define our terms.

At the simplest level, research implications refer to the possible effects or outcomes of a study’s findings. More specifically, they answer the question, “ What do these findings mean?” . In other words, the implications section is where you discuss the broader impact of your study’s findings on theory, practice and future research.

This discussion leads us to the recommendations section , which is where you’ll propose specific actions based on your study’s findings and answer the question, “ What should be done next?” . In other words, the recommendations are practical steps that stakeholders can take to address the key issues identified by your study.

In a nutshell, then, the research implications discuss the broader impact and significance of a study’s findings, while recommendations provide specific actions to take, based on those findings. So, while both of these components are deeply rooted in the findings of the study, they serve different functions within the write up.

Need a helping hand?

Examples: Implications & Recommendations

The distinction between research implications and research recommendations might still feel a bit conceptual, so let’s look at one or two practical examples:

Let’s assume that your study finds that interactive learning methods significantly improve student engagement compared to traditional lectures. In this case, one of your recommendations could be that schools incorporate more interactive learning techniques into their curriculums to enhance student engagement.

Let’s imagine that your study finds that patients who receive personalised care plans have better health outcomes than those with standard care plans. One of your recommendations might be that healthcare providers develop and implement personalised care plans for their patients.

Now, these are admittedly quite simplistic examples, but they demonstrate the difference (and connection ) between the research implications and the recommendations. Simply put, the implications are about the impact of the findings, while the recommendations are about proposed actions, based on the findings.

The “Big 3” Categories

Now that we’ve defined our terms, let’s dig a little deeper into the implications – specifically, the different types or categories of research implications that exist.

Broadly speaking, implications can be divided into three categories – theoretical implications, practical implications and implications for future research .

Theoretical implications relate to how your study’s findings contribute to or challenge existing theories. For example, if a study on social behaviour uncovers new patterns, it might suggest that modifications to current psychological theories are necessary.

Practical implications , on the other hand, focus on how your study’s findings can be applied in real-world settings. For example, if your study demonstrated the effectiveness of a new teaching method, this would imply that educators should consider adopting this method to improve learning outcomes.

Practical implications can also involve policy reconsiderations . For example, if a study reveals significant health benefits from a particular diet, an implication might be that public health guidelines be re-evaluated.

Last but not least, there are the implications for future research . As the name suggests, this category of implications highlights the research gaps or new questions raised by your study. For example, if your study finds mixed results regarding a relationship between two variables, it might imply the need for further investigation to clarify these findings.

To recap then, the three types of implications are the theoretical, the practical and the implications on future research. Regardless of the category, these implications feed into and shape the recommendations , laying the foundation for the actions you’ll propose.

How To Write The  Sections

Now that we’ve laid the foundations, it’s time to explore how to write up the implications and recommendations sections respectively.

Let’s start with the “ where ” before digging into the “ how ”. Typically, the implications will feature in the discussion section of your document, while the recommendations will be located in the conclusion . That said, layouts can vary between disciplines and institutions, so be sure to check with your university what their preferences are.

For the implications section, a common approach is to structure the write-up based on the three categories we looked at earlier – theoretical, practical and future research implications. In practical terms, this discussion will usually follow a fairly formulaic sentence structure – for example:

This research provides new insights into [theoretical aspect], indicating that…

The study’s outcomes highlight the potential benefits of adopting [specific practice] in..

This study raises several questions that warrant further investigation, such as…

Moving onto the recommendations section, you could again structure your recommendations using the three categories. Alternatively, you could structure the discussion per stakeholder group – for example, policymakers, organisations, researchers, etc.

Again, you’ll likely use a fairly formulaic sentence structure for this section. Here are some examples for your inspiration: 

Based on the findings, [specific group] should consider adopting [new method] to improve…

To address the issues identified, it is recommended that legislation should be introduced to…

Researchers should consider examining [specific variable] to build on the current study’s findings.

Remember, you can grab a copy of our tried and tested templates for both the discussion and conclusion sections over on the Grad Coach blog. You can find the links to those, as well as loads of other free resources, in the description 🙂

FAQs: Implications & Recommendations

How do i determine the implications of my study.

To do this, you’ll need to consider how your findings address gaps in the existing literature, how they could influence theory, practice, or policy, and the potential societal or economic impacts.

When thinking about your findings, it’s also a good idea to revisit your introduction chapter, where you would have discussed the potential significance of your study more broadly. This section can help spark some additional ideas about what your findings mean in relation to your original research aims. 

Should I discuss both positive and negative implications?

Absolutely. You’ll need to discuss both the positive and negative implications to provide a balanced view of how your findings affect the field and any limitations or potential downsides.

Can my research implications be speculative?

Yes and no. While implications are somewhat more speculative than recommendations and can suggest potential future outcomes, they should be grounded in your data and analysis. So, be careful to avoid overly speculative claims.

How do I formulate recommendations?

Ideally, you should base your recommendations on the limitations and implications of your study’s findings. So, consider what further research is needed, how policies could be adapted, or how practices could be improved – and make proposals in this respect.

How specific should my recommendations be?

Your recommendations should be as specific as possible, providing clear guidance on what actions or research should be taken next. As mentioned earlier, the implications can be relatively broad, but the recommendations should be very specific and actionable. Ideally, you should apply the SMART framework to your recommendations.

Can I recommend future research in my recommendations?

Absolutely. Highlighting areas where further research is needed is a key aspect of the recommendations section. Naturally, these recommendations should link to the respective section of your implications (i.e., implications for future research).

Wrapping Up: Key Takeaways

We’ve covered quite a bit of ground here, so let’s quickly recap.

• Research implications refer to the possible effects or outcomes of a study’s findings.
• The recommendations section, on the other hand, is where you’ll propose specific actions based on those findings.
• You can structure your implications section based on the three overarching categories – theoretical, practical and future research implications.
• You can carry this structure through to the recommendations as well, or you can group your recommendations by stakeholder.

Remember to grab a copy of our tried and tested free dissertation template, which covers both the implications and recommendations sections. If you’d like 1:1 help with your research project, be sure to check out our private coaching service, where we hold your hand throughout the research journey, step by step.

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What Are Implications in Research? | Examples & Tips

As a researcher, you know you need to provide a background for your study and a clear rationale and to formulate the statement of the problem in a way that leaves no doubt that your work is relevant and important. You also need to guide the reader carefully through your story from beginning to end without leaving any methodological questions unanswered. 

But many authors, when arriving at the end of their paper, run out of steam or lose the thread a bit and struggle with finding an ending for their work. Something can then appear missing, even if the discussion section summarizes the findings clearly, relates them back to the questions raised in the introduction section , and discusses them in the context of earlier works. A tired author who just made it to the end can often not see these missing elements and may finish off their paper with a conclusion section that is more or less a repetition of what has already been stated. After all, what more is there to be said? 

But as sure as the sun will rise again the day after you finally submitted, you will get your paper back from your supervisor or the reviewers with a comment that says, “implications are missing.” For a reader who is not as invested in every little detail of your design and analyses, the main questions that a paper has to answer are “why was this study necessary?” and “why are the findings of this study significant, and for whom, and what are we supposed to do with them now?” The latter are the implications of your work. 

Didn’t I explain the implications in my introduction section?

You will hopefully have already explained why and for whom your study is important. But you now also need to clearly state how you think your actual findings (which might differ from what you expected to find at the beginning) may be relevant and/or can be used in practical or theoretical ways, for future research, or by policymakers. These implications need to be based on your study’s parameters and results, and potential limitations of your methodology or sample should be taken into account to avoid overgeneralization. 

If you make the reader guess what the significance of your work might be or let them assume you don’t think that your work will be important for anyone except yourself and your colleagues who share your enthusiasm because they are working on the same topic, then an editor or reviewer might easily see that as a reason for a desk-reject. To avoid this, in the following, we will give you an overview of the different types of implications that research findings can have, provide some examples for your inspiration, and clarify where your implications should go in your paper. 

Table of Contents:

• Types of Implications in Research

Recommendations Versus Implications 

• Research Implications Examples 
• Where Do the Implications Go in Your paper?

Types of Implications in Research 

Depending on the type of research you are doing (clinical, philosophical, political…) the implications of your findings can likewise be clinical, philosophical, political, social, ethical—you name it. The most important distinction, however, is the one between practical implications and theoretical implications, and what many reviewers immediately notice and flag as an issue is when there is no mention of any kind of practical contribution of the work described in a paper. 

Of course, if you study a mathematical theory, then your findings might simply lead to the debunking of another theory as false, and you might need to do some mental gymnastics if you really wanted to apply that to a real-world problem. But chances are, in that case, your reviewers and readers won’t ask for a real-world implication. In most other cases, however, if you really want to convince your audience that your work deserves attention, publication, prizes, and whatnot, then you need to link whatever you did in the lab or found in the library to real life and highlight how your findings might have a lasting effect on your field (for example, methodologically), common practices (e.g., patient treatment or teaching standards), society at large (maybe the way we communicate), or ethical standards (e.g., in animal research). 

The question is not whether your findings will change the world, but whether they could if they were publicized and implemented—according to the Merriam-Webster online dictionary , the essential meaning of implication is a “possible future effect or result”. This possible result is what you have to identify and describe. And while being creative is certainly allowed, make sure your assumptions stay within realistic expectations, and don’t forget to take the limitations of your methodology or your sample into account. 

If you studied the genetic basis of a disease in some animal model, then make sure you have good reason to draw conclusions about the treatment of the same disease in humans if you don’t want to put off the editor who decides whether to even send your manuscript out for review. Likewise, if you explored the effects of the Covid-19 pandemic on higher education institutions in your country, then make sure the conclusions you draw hold in the context of other countries’ pandemic situations and restrictions and differences across education systems before you claim that they are relevant in a global context. 

Implications, as we already explored, state the importance of your study and how your findings may be relevant for the fine-tuning of certain practices, theoretical models, policymaking, or future research studies. As stated earlier, that does not necessarily mean that you believe your findings will change the world tomorrow, but that you have reason to believe they could have an impact in a specific way. Recommendations, on the other hand, are specific suggestions regarding the best course of action in a certain situation based on your findings. If, for example, you used three different established methods in your field to tackle the same problem, compared the outcomes, and concluded that one of these methods is, in fact, insufficient and should not be used anymore, then that is a recommendation for future research. 

Or if you analyzed how a monetary “Corona support program” in your country affected the local economy and found that most of the money the government provided went to Amazon and not to local businesses, then you can recommend that your government come up with a better plan next time. Such specific recommendations should usually follow the implications, not the other way around, because you always need to identify the implications of your work, but not every study allows the author to make practical suggestions or real-world recommendations.

Research Implications Examples

Clinical implications  .

Let’s say you discovered a new antibiotic that could eliminate a specific pathogen effectively without generating resistance (the main problem with antibiotics). The clinical implications of your findings would then be that infections with this pathogen could be more rapidly treated than before (without you predicting or suggesting any specific action to happen as a result of your findings). A recommendation would be that doctors should start using this new antibiotic, that it should be included in the official treatment guidelines, that it should be covered by the national health insurance of your country, etc.—but depending on how conclusive your findings are or how much more research or development might be needed to get from your findings to the actual medication, such recommendations might be a big stretch. The implications, however, since they state the potential of your findings, are valid in any case and should not be missing from your discussion section, even if your findings are just one small step along the way.

Social implications 

The social implications of the study are defined as the ability or potential of research to impact society in visible ways. One of the obvious fields of research that strives for a social impact through the implementation of evidence that increases the overall quality of people’s lives is psychology. Whether your research explores the new work-life-balance movement and its effect on mental well-being, psychological interventions at schools to compensate for the stress many children are experiencing since the beginning of the Covid-19 pandemic, or how work from home is changing family dynamics, you can most likely draw conclusions that go beyond just your study sample and describe potential (theoretical or practical) effects of your findings in the real world. Be careful, however, that you don’t overgeneralize from your sample or your data to the general population without having solid reasons to do so (and explain those reasons).

Implications for future research

Even if your findings are not going to lead to societal changes, new educational policies, or an overhaul of the national pension system, they might have important implications for future research studies. Maybe you used a new technique that is more precise or more efficient or way cheaper than existing methods and this could enable more labs around the world to study a specific problem. Or maybe you found that a gene that is known to be involved in one disease might also be involved in another disease, which opens up new avenues for research and treatment options. As stated earlier, make sure you don’t confuse recommendations (which you might not be able to make, based on your findings, and don’t necessarily have to) with implications, which are the potential effect that your findings could have—independently of whether you have any influence on that. 

Where Do the Implications Go in Your Paper? 

The implications are part of your discussion section, where you summarize your findings and then put them into context—this context being earlier research but also the potential effect your findings could have in the real world, in whatever scenario you think might be relevant. There is no “implication section” and no rule as to where in the discussion section you need to include these details because the order of information depends on how you structured your methods and your results section and how your findings turned out to prove or disprove your hypotheses. You simply need to work the potential effects of your findings into your discussion section in a logical way.

But the order of information is relevant when it comes to your conclusion at the very end of your discussion section: Here, you start with a very short summary of your study and results, then provide the (theoretical, practical, ethical, social, technological…) implications of your work, and end with a specific recommendation if (and only if) your findings call for that. If you have not paid attention to the importance of your implications while writing your discussion section, then this is your chance to fix that before you finalize and submit your paper and let an editor and reviewers judge the relevance of your work. 

Make sure you do not suddenly come up with practical ideas that look like they were plucked out of the air because someone reminded you to “add some implications” at the last minute. If you don’t know where to start, then go back to your introduction section, look at your rationale and research questions, look at how your findings answered those questions, and ask yourself who else could benefit from knowing what you know now.

Consider Using English Editing Services 

And before you submit your manuscript to your target journal’s editor, be sure to get professional English editing services from Wordvice, including academic editing and manuscript editing , which are tailored to the needs of your paper’s subject area. If you need instant proofreading or paraphrasing while drafting your work, check out our online AI Text Editor , Wordvice AI, which is trained on millions of words of academic writing data and tailored for research writers.

For more advice on how to write all the different parts of your research paper , on how to make a research paper outline if you are struggling with putting everything you did together, or on how to write the best cover letter that will convince an editor to send your manuscript out for review, head over to the Wordvice academic resources pages, where we have dozens of helpful articles and videos on research writing and publications.

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• A Research Guide
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How to Write Implications in Research

• Implications definition
• Recommendations vs implications
• Types of implications in research
• Step-by-step implications writing guide

Research implications examples

What the implications of the research definition?

• Theoretical implications stand for all the new additions to theories that have already been presented in the past. At the same time, one can use a totally new theory that provides a background and a framework for a study.
• Practical implications are about potential consequences that show the practical side of things.

Recommendations VS Implications

• Implied content versus proposed writing. It means that an implication should provide an outcome from your study. The recommendation is always based on the outcome, along with your words as a personal opinion.
• Potential impact a study may have versus a specific act. When you are composing your research paper, your implications have the purpose of discussing how the findings of the study matter. They should tell how your research has an impact on the subject that you address. Now, unlike the implications section of the research paper, recommendations refer to peculiar actions or steps you must take. They should be based on your opinion precisely and talk about what must be done since your research findings confirm that.

What are the types of implications in research?

• Political implications. These are mostly common for Law and Political Sciences students basing implications on a certain study, a speech, or legislative standards. It is a case when implications and recommendations can also be used to achieve an efficient result.
• Technological implications. When dealing with a technological implication, it serves as special implications for future research manuals where you discuss the study with several examples. Do not use a methodology in this section, as it can only be mentioned briefly.
• Findings related to policies. When you have implemented a special policy or you are dealing with a medical or legal finding, you should add it to your policy. Adding an implications section is necessary when it must be highlighted in your research.
• Topical (subject) implications. These are based on your subject and serve as a way to clarify things or as a method to narrow things down by supporting the finding before it is linked to a thesis statement or your main scientific argument.

Step-by-step implications in research writing guide

Step 1: talk about what has been discovered in your research., step 2: name the differences compared to what previous studies have found., step 3: discuss the implications of your findings., step 4: add specific information to showcase your contributions., step 5: match it with your discussion and thesis statement..

Green energy can benefit from the use of vertical turbines versus horizontal turbines due to construction methods and saving costs. 

The use of AI-based apps that contain repetition and grammar-checking will help ESL students and learners with special needs. 

Most studies provide more research on the social emphasis that influences the problem of bullying in the village area. It points out that most people have different cultural behavior where the problem of bullying is approached differently.

If you encounter challenges in terms of precise replication, you can use a CR genetic code to follow the policies used in 1994. Considering the theoretical limitations, it is necessary to provide exact theories and practical steps. It will help to resolve the challenge and compare what has been available back then. It will help to trace the temporal backline. 

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• What Does Implications Mean? | Definition & Examples

What Does Implications Mean? | Definition & Examples

Published on October 25, 2022 by Eoghan Ryan . Revised on March 13, 2023.

Implication is a noun that refers to the act of implying (i.e., suggesting something without explicitly stating it) and to something that is implied or suggested. It’s also used to refer to the act of implicating (i.e., proving someone’s involvement in a crime) and to the state of being implicated.

Implications is often used in academic writing to refer to the possible impact and influence of a study or to what conclusions can be drawn from a particular result.

The implications of this study for further research are discussed in the final chapter.

I resent the implication that my comment was facetious !

Table of contents

Implications vs. effects, frequently asked questions.

“Implications” is often used interchangeably with “ effects .” However, they don’t mean the same thing.

• Implications are the possible conclusions that can be drawn as a result of a cause or action.
• Effects are the consequences or results of a cause or action.

This study examines the effects of long-term stress on memory.

The presidential scandal has major political implications .

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There are numerous synonyms for the multiple meanings of implication .

There are numerous antonyms and near antonyms for the multiple meanings of implication .

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Ryan, E. (2023, March 13). What Does Implications Mean? | Definition & Examples. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/definitions/implications/

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How to Write Implications in a Research Paper

March 4, 2024

In the vast landscape of academic research, the implications section of a research paper stands as a critical juncture where detailed study findings are translated into broad, actionable insights with the power to influence beyond the confines of academic discourse. This integral component of your scholarly work serves not just to extend the reach of your conclusions, but also to underscore their importance across a myriad of domains—ranging from influencing policy decisions and enhancing educational practices to sparking additional research questions and investigations. Developing proficiency in articulating this section is indispensable for scholars dedicated to making a substantial impact within their fields of study.

This guide is meticulously designed to shed light on this process, offering a comprehensive pathway for integrating meaningful implications into your research. It highlights the essential role of this section in amplifying the resonance of your work, aiming to equip researchers with the skills needed to ensure their findings contribute significantly to both academic debates and real-world applications. Through this exploration, authors are encouraged to refine their approach to writing implications, thereby enriching their research endeavors’ academic and practical value.

Unraveling the Mystery: What Are Implications in Academic Studies?

The discourse on implications within academic studies serves as a vital link, enabling the extension of research findings beyond the confines of theoretical exploration to practical application and societal advancement. This discourse broadens the applicability and understanding of your research, fostering a dialogue that stretches beyond academic boundaries. Whether influencing policy decisions, guiding directions for future research, or suggesting innovations within industry practices, this section is where the transformative potential of your study comes to fruition. Here, abstract concepts are distilled into tangible insights, catalyzing real-world change and innovation.

For instance, investigations into renewable energy sources do more than expand our knowledge of alternative fuels; they directly impact environmental policies and practices toward sustainable development. In this context, learning how to write implications in a research paper involves more than narrating the outcomes; it’s about demonstrating how these outcomes can address pivotal global issues, such as climate change, and lead to actionable solutions. This part of the paper is an opportunity for authors to illustrate the expansive influence of their work, highlighting its capacity to inform decisions and inspire future innovations.

The Importance of Writing Implications

The articulation of implications is central to disseminating and applying research findings. Through this narrative, the research transcends the confines of academia and enters the realm of societal contribution. Writing implications demands a profound understanding of your research and its potential intersections with the world at large. It is an exercise in foresight, envisioning the ripple effects of your findings across various domains. For researchers, this is an opportunity to advocate for the significance of their work, drawing connections between their findings and broader societal or disciplinary advancements. This section of your research paper is where you argue for the relevance of your study, convincing stakeholders of the necessity to act upon or consider your findings. For example, research implications in educational psychology might inform teacher training programs, curriculum development, and learning interventions, directly impacting educational practices and student outcomes. By effectively communicating these implications, researchers contribute to their field’s body of knowledge and engage in a larger conversation about progress, innovation, and societal betterment.

A Step-by-Step Guide to Crafting Implications

The process to write implications in a research paper is pivotal, offering a pathway from empirical findings to broader scholarly and societal contributions. This guide is dedicated to unraveling the complexity of this task, ensuring researchers can communicate the significance of their work with clarity and impact. Starting with the identification of key findings, it’s crucial to isolate those insights that fundamentally advance understanding within the field. This distinction between primary and secondary outcomes lays the groundwork for significant implications and is directly tied to the study’s core inquiries. Researchers can articulate their work’s broader effects by learning the data and situating findings within the existing body of knowledge. This comprehensive approach ensures that the implications section of a research paper not only highlights the study’s contributions but also charts a course for future inquiry and application.

Step 1: Identify the Key Findings

The initial step in establishing a foundation for meaningful implications involves a discerning review of your study’s outcomes to highlight the key findings. This task transcends simple result summarization, requiring a discerning evaluation to identify insights with a profound potential to impact the field. These findings must squarely address the research question, providing clear, significant insights that lay the groundwork for in-depth exploration and further studies. This critical separation between primary and secondary outcomes anchors the implications in the most consequential elements of the study. Scholars concentrate on these pivotal findings and ensure their work’s implications possess the necessary depth and specificity to enrich the field substantially. This precision in identifying key findings underpins the relevance of the implications. It sets the stage for impactful contributions that resonate well beyond the confines of academic discourse, enhancing the study’s overall significance and utility.

Step 2: Analyze the Findings

The subsequent phase involves an exhaustive analysis to illuminate the deeper meaning and wider scope of the key findings. This step significantly broadens the comprehension of the outcomes by delving into the data against the backdrop of existing literature and theoretical underpinnings. Such a thorough analysis does more than position the study within the scholarly debate; it uncovers ways the research either adds new insights or contests established beliefs. Researchers can utilize diverse analytical techniques to delve into their data’s subtleties, constructing a solid base for drawing out implications that affirm the study’s relevance and value beyond academic circles. This meticulous approach to analysis ensures that the identified implications are deeply rooted in a comprehensive understanding of the research findings and poised to make a lasting impact.

Step 3: Identify the Implications

In this phase, researchers are tasked with conceptualizing the broader effects of their findings on various domains such as practice, policy, theoretical development, and subsequent inquiries. This requires expansive thinking about how the study’s outcomes could be applied or what new questions they raise. It’s a matter of pondering the potential influence on policy formulation, suggesting enhancements in professional practices, or filling gaps in theoretical knowledge. Addressing implications comprehensively guarantees the study’s resonance extends beyond scholarly limits, illustrating its capacity to instigate change and motivate further exploration. When researchers articulate these implications, they not only shed light on the practical and academic importance of their findings but also on the transformative power inherent in their work.

Step 4: Connect the Implications to the Research Question

Ensuring the implications are directly linked to the research question is critical for maintaining the study’s coherence and relevance. This alignment affirms that the implications emerge naturally from the investigation’s efforts to address its core question, reinforcing the findings’ significance. By establishing this connection, the clarity of the study’s contributions is enhanced, firmly to write implications in a research paper itself. This pivotal step acts as a bridge, merging a detailed analysis of the findings with their broader impact and application, thereby solidifying the study’s importance in both academic and practical realms. It demonstrates how meticulously drawn implications can inform, influence, and inspire, underlining the study’s contribution to advancing knowledge and practice.

Step 5: Provide Recommendations

The process culminates in translating the identified implications into actionable recommendations, transforming theoretical insights into pragmatic suggestions for future endeavors, policy formation, and professional practice. This crucial step makes the study actionable, offering specific, research-based recommendations to direct practical application or guide subsequent research. By converting implications into clear recommendations, the study’s influence is magnified, serving as a guidepost for forthcoming work, policy refinement, or alterations in practice. This transformation of implications into recommendations not only extends the research’s reach but also ensures its findings make a concrete contribution to the field and society at large, embodying the study’s ultimate goal of fostering tangible progress and understanding.

Recommendations in Research: Examples to Guide You

Providing examples of recommendations in research helps to illustrate how to transform theoretical implications into practical, actionable steps. For instance, a study on the effects of digital learning tools in elementary education might lead to recommendations for integrating specific types of technology in the classroom. These recommendations could include developing training programs for teachers to effectively implement these tools, underscoring the study’s direct applicability to educational practices.

Similarly, research findings in environmental science regarding the impact of urban green spaces on mental health can lead to recommendations for city planning and public health policy. Such a study might suggest that municipalities increase their investment in urban parks and green corridors, providing a clear link between the research findings and policy implications. These examples demonstrate how researchers can articulate their study’s implications in ways that prompt real-world change, showcasing the potential for research to inform policy, influence practice, and guide future investigations. By providing clear, evidence-based recommendations, researchers can ensure that their findings contribute meaningfully to their field and society.

Pro Tips for Writing Impactful Implications

To write implications in a research paper requires precision, foresight, and a deep understanding of your research’s potential effects on the field and beyond. To achieve this, start by ensuring that each implication is directly tied to your findings, avoiding broad or unfounded claims. Each statement should be grounded in your research data, providing a clear and credible link between your study’s results and the suggested implications.

Moreover, it’s vital to consider the audience of your research paper. Tailor your implications to speak directly to the concerns and interests of your readers, whether they are fellow researchers, practitioners, or policymakers. This targeted approach increases the relevance of your work and enhances its potential impact. Additionally, integrating your implications seamlessly into the narrative of your research paper helps maintain the reader’s engagement, leading them naturally from your findings to the broader significance of your work. This narrative cohesion ensures that your research is informative and compelling, encouraging readers to consider the practical applications of your study and its contributions to advancing knowledge and practice in your field.

By adhering to these guidelines, you can ensure that your efforts to write implications in a research paper are both effective and impactful, bridging the gap between academic research and tangible societal benefits.

The implications section is pivotal as the conduit between scholarly insights and their wider application. Here, the depth and breadth of your findings resonate, reaching beyond academic circles into policy development, professional practices, and the genesis of new inquiries. The ability of cogent implications to elevate a paper is profound; they underscore the significance of your conclusions and chart paths for application and further inquiry. In crafting your paper, accord the implications section the focus and thoroughness it warrants. Developing the knack for presenting implications is an evolving process enriched by deep reflection and careful effort. This stage invites you to consider the far-reaching effects of your work, fostering a dialogue that extends your contribution beyond theoretical bounds.

Embrace this task with an eagerness for discovery and a zeal for contributing meaningfully. Engaging in this endeavor ensures your work transcends scholarly boundaries, providing insights ripe for practical use and future exploration. Let crafting implications illuminate the vast potential of your study, underscoring its role in shaping understanding, influencing policy, and steering professional practice.

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Search catalog, critical thinking and academic research: implications.

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Think through the Implications

According to Linda Elder and Richard Paul, "An implication is that to which our thinking is leading us. When you say things, you imply certain other things. For example, if you make a promise, you imply that you will keep it" ( The Aspiring Thinker's Guide to Critical Thinking , 2009, p. 28). In academic research, you should consider the implications of what your sources are saying. You should also consider the implications of your own arguments.

Research involves critical interaction with information sources. Don't just accept an argument because it seems correct on the surface or because it matches your point of view. Instead, think through the implications of the argument. What would it mean to accept this argument? What effects would it have? What are the implications and/or consequences of agreeing or disagreeing with this argument?

Critical Questions

• What are the implications of my argument or interpretation of this source?
• What would happen if I agreed or disagreed with this source?
• What implications or consequences haven't been considered?
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• Last Updated: Jul 10, 2023 11:50 AM
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Implications or Recommendations in Research: What's the Difference?

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High-quality research articles that get many citations contain both implications and recommendations. Implications are the impact your research makes, whereas recommendations are specific actions that can then be taken based on your findings, such as for more research or for policymaking.

Updated on August 23, 2022

That seems clear enough, but the two are commonly confused.

This confusion is especially true if you come from a so-called high-context culture in which information is often implied based on the situation, as in many Asian cultures. High-context cultures are different from low-context cultures where information is more direct and explicit (as in North America and many European cultures).

Let's set these two straight in a low-context way; i.e., we'll be specific and direct! This is the best way to be in English academic writing because you're writing for the world.

Implications and recommendations in a research article

The standard format of STEM research articles is what's called IMRaD:

• Introduction
• Discussion/conclusions

Some journals call for a separate conclusions section, while others have the conclusions as the last part of the discussion. You'll write these four (or five) sections in the same sequence, though, no matter the journal.

The discussion section is typically where you restate your results and how well they confirmed your hypotheses. Give readers the answer to the questions for which they're looking to you for an answer.

At this point, many researchers assume their paper is finished. After all, aren't the results the most important part? As you might have guessed, no, you're not quite done yet.

The discussion/conclusions section is where to say what happened and what should now happen

The discussion/conclusions section of every good scientific article should contain the implications and recommendations.

The implications, first of all, are the impact your results have on your specific field. A high-impact, highly cited article will also broaden the scope here and provide implications to other fields. This is what makes research cross-disciplinary.

Recommendations, however, are suggestions to improve your field based on your results.

These two aspects help the reader understand your broader content: How and why your work is important to the world. They also tell the reader what can be changed in the future based on your results.

These aspects are what editors are looking for when selecting papers for peer review.

Implications and recommendations are, thus, written at the end of the discussion section, and before the concluding paragraph. They help to “wrap up” your paper. Once your reader understands what you found, the next logical step is what those results mean and what should come next.

Then they can take the baton, in the form of your work, and run with it. That gets you cited and extends your impact!

The order of implications and recommendations also matters. Both are written after you've summarized your main findings in the discussion section. Then, those results are interpreted based on ongoing work in the field. After this, the implications are stated, followed by the recommendations.

Writing an academic research paper is a bit like running a race. Finish strong, with your most important conclusion (recommendation) at the end. Leave readers with an understanding of your work's importance. Avoid generic, obvious phrases like "more research is needed to fully address this issue." Be specific.

The main differences between implications and recommendations (table)

Now let's dig a bit deeper into actually how to write these parts.

What are implications?

Research implications tell us how and why your results are important for the field at large. They help answer the question of “what does it mean?” Implications tell us how your work contributes to your field and what it adds to it. They're used when you want to tell your peers why your research is important for ongoing theory, practice, policymaking, and for future research.

Crucially, your implications must be evidence-based. This means they must be derived from the results in the paper.

Implications are written after you've summarized your main findings in the discussion section. They come before the recommendations and before the concluding paragraph. There is no specific section dedicated to implications. They must be integrated into your discussion so that the reader understands why the results are meaningful and what they add to the field.

A good strategy is to separate your implications into types. Implications can be social, political, technological, related to policies, or others, depending on your topic. The most frequently used types are theoretical and practical. Theoretical implications relate to how your findings connect to other theories or ideas in your field, while practical implications are related to what we can do with the results.

Key features of implications

• State the impact your research makes
• Helps us understand why your results are important
• Must be evidence-based
• Written in the discussion, before recommendations
• Can be theoretical, practical, or other (social, political, etc.)

Examples of implications

Let's take a look at some examples of research results below with their implications.

The result : one study found that learning items over time improves memory more than cramming material in a bunch of information at once .

The implications : This result suggests memory is better when studying is spread out over time, which could be due to memory consolidation processes.

The result : an intervention study found that mindfulness helps improve mental health if you have anxiety.

The implications : This result has implications for the role of executive functions on anxiety.

The result : a study found that musical learning helps language learning in children .

The implications : these findings suggest that language and music may work together to aid development.

What are recommendations?

As noted above, explaining how your results contribute to the real world is an important part of a successful article.

Likewise, stating how your findings can be used to improve something in future research is equally important. This brings us to the recommendations.

Research recommendations are suggestions and solutions you give for certain situations based on your results. Once the reader understands what your results mean with the implications, the next question they need to know is "what's next?"

Recommendations are calls to action on ways certain things in the field can be improved in the future based on your results. Recommendations are used when you want to convey that something different should be done based on what your analyses revealed.

Similar to implications, recommendations are also evidence-based. This means that your recommendations to the field must be drawn directly from your results.

The goal of the recommendations is to make clear, specific, and realistic suggestions to future researchers before they conduct a similar experiment. No matter what area your research is in, there will always be further research to do. Try to think about what would be helpful for other researchers to know before starting their work.

Recommendations are also written in the discussion section. They come after the implications and before the concluding paragraphs. Similar to the implications, there is usually no specific section dedicated to the recommendations. However, depending on how many solutions you want to suggest to the field, they may be written as a subsection.

Key features of recommendations

• Statements about what can be done differently in the field based on your findings
• Must be realistic and specific
• Written in the discussion, after implications and before conclusions
• Related to both your field and, preferably, a wider context to the research

Examples of recommendations

Here are some research results and their recommendations.

A meta-analysis found that actively recalling material from your memory is better than simply re-reading it .

• The recommendation: Based on these findings, teachers and other educators should encourage students to practice active recall strategies.

A medical intervention found that daily exercise helps prevent cardiovascular disease .

• The recommendation: Based on these results, physicians are recommended to encourage patients to exercise and walk regularly. Also recommended is to encourage more walking through public health offices in communities.

A study found that many research articles do not contain the sample sizes needed to statistically confirm their findings .

The recommendation: To improve the current state of the field, researchers should consider doing power analysis based on their experiment's design.

What else is important about implications and recommendations?

When writing recommendations and implications, be careful not to overstate the impact of your results. It can be tempting for researchers to inflate the importance of their findings and make grandiose statements about what their work means.

Remember that implications and recommendations must be coming directly from your results. Therefore, they must be straightforward, realistic, and plausible.

Another good thing to remember is to make sure the implications and recommendations are stated clearly and separately. Do not attach them to the endings of other paragraphs just to add them in. Use similar example phrases as those listed in the table when starting your sentences to clearly indicate when it's an implication and when it's a recommendation.

When your peers, or brand-new readers, read your paper, they shouldn't have to hunt through your discussion to find the implications and recommendations. They should be clear, visible, and understandable on their own.

That'll get you cited more, and you'll make a greater contribution to your area of science while extending the life and impact of your work.

The AJE Team

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What are the Academic Implications of a Research Study?

Gain knowledge about the distinction between academic limitations and recommendations to successfully incorporate them into your research.

If you intend to write a research paper, you should be aware that you must provide a background story that will lead to the rationale behind the research, providing context and assisting in the formulation of the issue statement, aiming to leave no doubt about your work, demonstrating its relevance and importance, and stating all possible methodological questions.

However, it is not uncommon for researchers to lose momentum at the end and struggle to find the correct conclusion for their research. Despite the fact that the discussion properly explains the findings, connects them to the issues raised in the introduction, and investigates them in the context of past research, something may appear to be lacking. This frequently leads to the conclusion of a research that is similar to one that has already been expressed.

This results in a lack of academic implications when readers or reviewers fail to recognize the significance of your research. To avoid this, continue reading this article to learn more about the academic implications .

What are the academic implications?

Implications are the consequences of your research; you must describe exactly why you assume your actual results are relevant and/or might be employed in future research. Most importantly, your implications must be supported by evidence. 

These implications must be based on the details and outcomes of your research, and any limitations of your approach or sample should be recognized in order to avoid gross generalization.

Depending on the type of research you perform, the implications of your findings can be clinical, philosophical, political, social, or ethical. It is crucial to understand that the most essential distinction is between practical implications, theoretical implications and implications for future research.

Practical Implications

The term “practical” literally means “real outcomes.” The reality that would occur if certain circumstances were met is referred to as practical implication. Determining the practical implications of several solutions can aid in determining which ones deliver the intended results.

For example, when doing clinical research, these outcomes are more likely to be practical. Assume you’re doing a trial for a medicine that treats infection without generating organism resistance; the consequences in this situation are that illnesses can be treated more promptly than in the past.

Theoretical Implications

In contrast, the theoretical implication is a newly discovered addition(s) to current theories or establishing elements for new theories. Theory’s role in research is to propose fascinating and potentially promising subjects to focus on.

The ability or possibility of research to affect society in apparent ways is characterized by theoretical implications. For example, research on human relationships and how COVID affects them can theorize that humans are less likely to interact now they’ve been through COVID. 

However, be cautious not to extrapolate your sampling or data to the broader population unless you have compelling reasons for it.

Implications that require future research

If you discover that your findings do not result in social reforms, innovative pedagogical policies or medical changes, they may have vital implications for future research projects. 

This implies that if you discover anything that may have an impact on other research, you should discuss the implications. Just be careful not to mistake an implication as a recommendation.

Implications vs. Recommendations

In a research paper, an implication is a conclusion that can be deduced from the research findings and the significance of these findings; this does not immediately imply that you think your findings will change everything by tomorrow, but that you have reason to expect they could have an impact in a particular way.

Recommendations, on the other hand, are precise ideas based on your findings about the best way to proceed in a certain scenario. For example, if you discovered a better approach to deal with a certain type of data (that may be utilized in other topics), you would recommend they discard the previous method.

Simply defined, an implication is an implicit conclusion of your research, whereas a recommendation is what you recommend based on the facts. 

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About Jessica Abbadia

Jessica Abbadia is a lawyer that has been working in Digital Marketing since 2020, improving organic performance for apps and websites in various regions through ASO and SEO. Currently developing scientific and intellectual knowledge for the community's benefit. Jessica is an animal rights activist who enjoys reading and drinking strong coffee.

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Research impact: a narrative review

Trisha greenhalgh.

Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Woodstock Rd, Oxford, OX2 6GG UK

James Raftery

Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK

Steve Hanney

Health Economics Research Group (HERG), Institute of Environment, Health and Societies, Brunel University London, ᅟ, UB8 3PH UK

Matthew Glover

Impact occurs when research generates benefits (health, economic, cultural) in addition to building the academic knowledge base. Its mechanisms are complex and reflect the multiple ways in which knowledge is generated and utilised. Much progress has been made in measuring both the outcomes of research and the processes and activities through which these are achieved, though the measurement of impact is not without its critics. We review the strengths and limitations of six established approaches (Payback, Research Impact Framework, Canadian Academy of Health Sciences, monetisation, societal impact assessment, UK Research Excellence Framework) plus recently developed and largely untested ones (including metrics and electronic databases). We conclude that (1) different approaches to impact assessment are appropriate in different circumstances; (2) the most robust and sophisticated approaches are labour-intensive and not always feasible or affordable; (3) whilst most metrics tend to capture direct and proximate impacts, more indirect and diffuse elements of the research-impact link can and should be measured; and (4) research on research impact is a rapidly developing field with new methodologies on the horizon.

This paper addresses the question: ‘What is research impact and how might we measure it?’. It has two main aims, first, to introduce the general reader to a new and somewhat specialised literature on the science of research impact assessment and, second, to contribute to the development of theory and the taxonomy of method in this complex and rapidly growing field of inquiry. Summarising evidence from previous systematic and narrative reviews [ 1 – 7 ], including new reviews from our own team [ 1 , 5 ], we consider definitions of impact and its conceptual and philosophical basis before reviewing the strengths and limitations of different approaches to its assessment. We conclude by suggesting where future research on research impact might be directed.

Research impact has many definitions (Box 1). Its measurement is important considering that researchers are increasingly expected to be accountable and produce value for money, especially when their work is funded from the public purse [ 8 ]. Further, funders seek to demonstrate the benefits from their research spending [ 9 ] and there is pressure to reduce waste in research [ 10 ]. By highlighting how (and how effectively) resources are being used, impact assessment can inform strategic planning by both funding bodies and research institutions [ 1 , 11 ].

We draw in particular on a recent meta-synthesis of studies of research impact funded by the UK Health Technology Assessment Programme (HTA review) covering literature mainly published between 2005 and 2014 [ 1 ]. The HTA review was based on a systematic search of eight databases (including grey literature) plus hand searching and reference checking, and identified over 20 different impact models and frameworks and 110 studies describing their empirical applications (as single or multiple case studies), although only a handful had proven robust and flexible across a range of examples. The material presented in this summary paper, based on much more extensive work, is inevitably somewhat eclectic. Four of the six approaches we selected as ‘established’ were the ones most widely used in the 110 published empirical studies. Additionally, we included the Societal Impact Assessment despite it being less widely used since it has recently been the subject of a major EU-funded workstream (across a range of fields) and the UK Research Excellence Framework (REF; on which empirical work post-dated our review) because of the size and uniqueness of the dataset and its significant (?) international interest. The approaches we selected as showing promise for the future were chosen more subjectively on the grounds that there is currently considerable academic and/or policy interest in them.

Different approaches to assessing research impact make different assumptions about the nature of research knowledge, the purpose of research, the definition of research quality, the role of values in research and its implementation, the mechanisms by which impact is achieved, and the implications for how impact is measured (Table  1 ). Short-term proximate impacts are easier to attribute, but benefits from complementary assets (such as the development of research infrastructure, political support or key partnerships [ 8 ]) may accumulate in the longer term but are more difficult – and sometimes impossible – to fully capture.

Philosophical assumptions underpinning approaches to research impact

Knowledge is intertwined with politics and persuasion. If stakeholders agree on what the problem is and what a solution would look like, the research-impact link will tend to turn on the strength of research evidence in favour of each potential decision option, as depicted in column 2 of Table  1 [ 12 ]. However, in many fields – for example, public policymaking, social sciences, applied public health and the study of how knowledge is distributed and negotiated in multi-stakeholder collaborations – the links between research and impact are complex, indirect and hard to attribute (for an example, see Kogan and Henkel’s rich ethnographic study of the Rothschild experiment in the 1970s, which sought – and failed – to rationalize the links between research and policy [ 13 ]). In policymaking, research evidence is rather more often used conceptually (for general enlightenment) or symbolically (to justify a chosen course of action) than instrumentally (feeding directly into a particular policy decision) [ 12 , 14 ], as shown empirically by Amara et al.’s large quantitative survey of how US government agencies drew on university research [ 15 ]. Social science research is more likely to illuminate the complexity of a phenomenon than produce a simple, ‘implementable’ solution that can be driven into practice by incorporation into a guideline or protocol [ 16 , 17 ], as was shown by Dopson and Fitzgerald’s detailed ethnographic case studies of the implementation of evidence-based healthcare in healthcare organisations [ 18 ]. In such situations, the research-impact relationship may be productively explored using approaches that emphasise the fluidity of knowledge and the multiple ways in which it may be generated, assigned more or less credibility and value, and utilised (columns 3 to 6 in Table  1 ) [ 12 , 19 ].

Many approaches to assessing research impact combine a logic model (to depict input-activities-output-impact links) with a ‘case study’ description to capture the often complex processes and interactions through which knowledge is produced (perhaps collaboratively and/or with end-user input to study design), interpreted and shared (for example, through engagement activities, audience targeting and the use of champions, boundary spanners and knowledge brokers [ 20 – 24 ]). A nuanced narrative may be essential to depict the non-linear links between upstream research and distal outcomes and/or help explain why research findings were not taken up and implemented despite investment in knowledge translation efforts [ 4 , 6 ].

Below, we describe six approaches that have proved robust and useful for measuring research impact and some additional ones introduced more recently. Table  2 lists examples of applications of the main approaches reviewed in this paper.

Examples of applications of research impact assessment frameworks

Established approaches to measuring research impact

The payback framework.

Developed by Buxton and Hanney in 1996 [ 25 ], the Payback Framework (Fig.  1 ) remains the most widely used approach. It was used by 27 of the 110 empirical application studies in the recent HTA review [ 1 ]. Despite its name, it does not measure impact in monetary terms. It consists of two elements: a logic model of the seven stages of research from conceptualisation to impact, and five categories to classify the paybacks – knowledge (e.g. academic publications), benefits to future research (e.g. training new researchers), benefits to policy (e.g. information base for clinical policies), benefits to health and the health system (including cost savings and greater equity), and broader economic benefits (e.g. commercial spin-outs). Two interfaces for interaction between researchers and potential users of research (‘project specification, selection and commissioning’ and ‘dissemination’) and various feedback loops connecting the stages are seen as crucial.

The Payback Framework developed by Buxton and Hanney (reproduced under Creative Commons Licence from Hanney et al [ 70 ])

The elements and categories in the Payback Framework were designed to capture the diverse ways in which impact may arise, notably the bidirectional interactions between researchers and users at all stages in the research process from agenda setting to dissemination and implementation. The Payback Framework encourages an assessment of the knowledge base at the time a piece of research is commissioned – data that might help with issues of attribution (did research A cause impact B?) and/or reveal a counterfactual (what other work was occurring in the relevant field at the time?).

Applying the Payback Framework through case studies is labour intensive: researcher interviews are combined with document analysis and verification of claimed impacts to prepare a detailed case study containing both qualitative and quantitative information. Not all research groups or funders will be sufficiently well resourced to produce this level of detail for every project – nor is it always necessary to do so. Some authors have adapted the Payback Framework methodology to reduce the workload of impact assessment (for example, a recent European Commission evaluation populated the categories mainly by analysis of published documents [ 26 ]); nevertheless, it is not known how or to what extent such changes would compromise the data. Impacts may be short or long term [ 27 ], so (as with any approach) the time window covered by data collection will be critical.

Another potential limitation of the Payback Framework is that it is generally project-focused (commencing with a particular funded study) and is therefore less able to explore the impact of the sum total of activities of a research group that attracted funding from a number of sources. As Meagher et al. concluded in their study of ESRC-funded responsive mode psychology projects, “ In most cases it was extremely difficult to attribute with certainty a particular impact to a particular project’s research findings. It was often more feasible to attach an impact to a particular researcher’s full body of research, as it seemed to be the depth and credibility of an ongoing body of research that registered with users ” [ 28 ] (p. 170).

Similarly, the impact of programmes of research may be greater than the sum of their parts due to economic and intellectual synergies, and therefore project-focused impact models may systematically underestimate impact. Application of the Payback Framework may include supplementary approaches such as targeted stakeholder interviews to fully capture the synergies of programme-level funding [ 29 , 30 ].

Research Impact Framework

The Research Impact Framework was the second most widely used approach in the HTA review of impact assessment, accounting for seven out of 110 applications [ 1 ], but in these studies it was mostly used in combination with other frameworks (especially Payback) rather than as a stand-alone approach. It was originally developed by and for academics who were interested in measuring and monitoring the impact of their own research. As such, it is a ‘light touch’ checklist intended for use by individual researchers who seek to identify and select impacts from their work “ without requiring specialist skill in the field of research impact assessment ” [ 31 ] (p. 136). The checklist, designed to prompt reflection and discussion, includes research-related impacts, policy and practice impacts, service (including health) impacts, and an additional ‘societal impact’ category with seven sub-categories. In a pilot study, its authors found that participating researchers engaged readily with the Research Impact Framework and were able to use it to identify and reflect on different kinds of impact from their research [ 31 , 32 ]. Because of its (intentional) trade-off between comprehensiveness and practicality, it generally produces a less thorough assessment than the Payback Framework and was not designed to be used in formal impact assessment studies by third parties.

Canadian Academy of Health Sciences (CAHS) Framework

The most widely used adaptation of the Payback Framework is the CAHS Framework (Fig.  2 ), which informed six of the 110 application studies in the HTA review [ 33 ]. Its architects claim to have shaped the Payback Framework into a ‘systems approach’ that takes greater account of the various non-linear influences at play in contemporary health research systems. CAHS was constructed collaboratively by a panel of international experts (academics, policymakers, university heads), endorsed by 28 stakeholder bodies across Canada (including research funders, policymakers, professional organisations and government) and refined through public consultation [ 33 ]. The authors emphasise that the consensus-building process that generated the model was as important as the model itself.

Simplified Canadian Academy of Health Sciences (CAHS) Framework (reproduced with permission of Canadian Academy of Health Sciences [ 33 ])

CAHS encourages a careful assessment of context and the subsequent consideration of impacts under five categories: advancing knowledge (measures of research quality, activity, outreach and structure), capacity-building (developing researchers and research infrastructure), informing decision-making (decisions about health and healthcare, including public health and social care, decisions about future research investment, and decisions by public and citizens), health impacts (including health status, determinants of health – including individual risk factors and environmental and social determinants – and health system changes), and economic and social benefits (including commercialization, cultural outcomes, socioeconomic implications and public understanding of science).

For each category, a menu of metrics and measures (66 in total) is offered, and users are encouraged to draw on these flexibly to suit their circumstances. By choosing appropriate sets of indicators, CAHS can be used to track impacts within any of the four ‘pillars’ of health research (basic biomedical, applied clinical, health services and systems, and population health – or within domains that cut across these pillars) and at various levels (individual, institutional, regional, national or international).

Despite their differences, Payback and CAHS have much in common, especially in how they define impact and their proposed categories for assessing it. Whilst CAHS appears broader in scope and emphasises ‘complex system’ elements, both frameworks are designed as a pragmatic and flexible adaptation of the research-into-practice logic model. One key difference is that CAHS’ category ‘decision-making’ incorporates both policy-level decisions and the behaviour of individual clinicians, whereas Payback collects data separately on individual clinical decisions on the grounds that, if they are measurable, decisions by clinicians to change behaviour feed indirectly into the improved health category.

As with Payback (but perhaps even more so, since CAHS is in many ways more comprehensive), the application of CAHS is a complex and specialist task that is likely to be highly labour-intensive and hence prohibitively expensive in some circumstances.

Monetisation models

A significant innovation in recent years has been the development of logic models to monetise (that is, express in terms of currency) both the health and the non-health returns from research. Of the 110 empirical applications of impact assessment approaches in our HTA review, six used monetization. Such models tend to operate at a much higher level of aggregation than Payback or CAHS – typically seeking to track all the outputs of a research council [ 34 , 35 ], national research into a broad disease area (e.g. cardiovascular disease, cancer) [ 36 – 38 ], or even an entire national medical research budget [ 39 ].

Monetisation models express returns in various ways, including as cost savings, the money value of net health gains via cost per quality-adjusted life year (QALY) using the willingness-to-pay or opportunity cost established by NICE or similar bodies [ 40 ], and internal rates of return (return on investment as an annual percentage yield). These models draw largely from the economic evaluation literature and differ principally in terms of which costs and benefits (health and non-health) they include and in the valuation of seemingly non-monetary components of the estimation. A national research call, for example, may fund several programmes of work in different universities and industry partnerships, subsequently producing net health gains (monetised as the value of QALYs or disability-adjusted life-years), cost savings to the health service (and to patients), commercialisation (patents, spin-outs, intellectual property), leveraging of research funds from other sources, and so on.

A major challenge in monetisation studies is that, in order to produce a quantitative measure of economic impact or rate of return, a number of simplifying assumptions must be made, especially in relation to the appropriate time lag between research and impact and what proportion of a particular benefit should be attributed to the funded research programme as opposed to all the other factors involved (e.g. social trends, emergence of new interventions, other research programmes occurring in parallel). Methods are being developed to address some of these issues [ 27 ]; however, whilst the estimates produced in monetised models are quantitative, those figures depend on subjective, qualitative judgements.

A key debate in the literature on monetisation of research impact addresses the level of aggregation. First applied to major research budgets in a ‘top-down’ or macro approach [ 39 ], whereby total health gains are apportioned to a particular research investment, the principles of monetisation are increasingly being used in a ‘bottom-up’ [ 34 , 36 – 38 ] manner to collect data on specific project or programme research outputs. The benefits of new treatments and their usage in clinical practice can be built up to estimate returns from a body of research. By including only research-driven interventions and using cost-effectiveness or cost-utility data to estimate incremental benefits, this method goes some way to dealing with the issue of attribution. Some impact assessment models combine a monetisation component alongside an assessment of processes and/or non-monetised impacts, such as environmental impacts and an expanded knowledge base [ 41 ].

Societal impact assessment

Societal impact assessment, used in social sciences and public health, emphasises impacts beyond health and is built on constructivist and performative philosophical assumptions (columns 3 and 6 in Table  1 ). Some form of societal impact assessment was used in three of the 110 empirical studies identified in our HTA review. Its protagonists distinguish the social relevance of knowledge from its monetised impacts, arguing that the intrinsic value of knowledge may be less significant than the varied and changing social configurations that enable its production, transformation and use [ 42 ].

An early approach to measuring societal impact was developed by Spaapen and Sylvain in the early 1990s [ 43 ], and subsequently refined by the Royal Netherlands Academy of Arts and Science [ 44 ]. An important component is self-evaluation by a research team of the relationships, interactions and interdependencies that link it to other elements of the research ecosystem (e.g. nature and strength of links with clinicians, policymakers and industry), as well as external peer review of these links. Spaapen et al. subsequently conducted a research programme, Evaluating Research in Context (ERiC) [ 45 ], which produced the Sci-Quest model [ 46 ]. Later, they collaborated with researchers (who had led a major UK ESRC-funded study on societal impact [ 47 ]) to produce the EU-funded SIAMPI (Social Impact Assessment Methods through the study of Productive Interactions) Framework [ 48 ].

Sci-Quest was described by its authors as a ‘fourth-generation’ approach to impact assessment – the previous three generations having been characterised, respectively, by measurement (e.g. an unenhanced logic model), description (e.g. the narrative accompanying a logic model) and judgement (e.g. an assessment of whether the impact was socially useful or not). Fourth-generation impact assessment, they suggest, is fundamentally a social, political and value-oriented activity and involves reflexivity on the part of researchers to identify and evaluate their own research goals and key relationships [ 46 ].

Sci-Quest methodology requires a detailed assessment of the research programme in context and the development of bespoke metrics (both qualitative and quantitative) to assess its interactions, outputs and outcomes, which are presented in a unique Research Embedment and Performance Profile, visualised in a radar chart. SIAMPI uses a mixed-methods case study approach to map three categories of productive interaction: direct personal contacts, indirect contacts such as publications, and financial or material links. These approaches have theoretical elegance, and some detailed empirical analyses were published as part of the SIAMPI final report [ 48 ]. However, neither approach has had significant uptake elsewhere in health research – perhaps because both are complex, resource-intensive and do not allow easy comparison across projects or programmes.

Whilst extending impact to include broader societal categories is appealing, the range of societal impacts described in different publications, and the weights assigned to them, vary widely; much depends on the researchers’ own subjective ratings. An attempt to capture societal impact (the Research Quality Framework) in Australia in the mid-2000s was planned but later abandoned following a change of government [ 49 ].

UK Research Excellence Framework

The 2014 REF – an extensive exercise to assess UK universities’ research performance – allocated 20 % of the total score to research impact [ 50 ]. Each institution submitted an impact template describing its strategy and infrastructure for achieving impact, along with several four-page impact case studies, each of which described a programme of research, claimed impacts and supporting evidence. These narratives, which were required to follow a linear and time-bound structure (describing research undertaken between 1993 and 2013, followed by a description of impact occurring between 2008 and 2013) were peer-reviewed by an intersectoral assessment panel representing academia and research users (industry and policymakers) [ 50 ]. Other countries are looking to emulate the REF model [ 51 ].

An independent evaluation of the REF impact assessment process by RAND Europe (based on focus groups, interviews, survey and documentary analysis) concluded that panel members perceived it as fair and robust and valued the intersectoral discussions, though many felt the somewhat crude scoring system (in which most case studies were awarded 3, 3.5 or 4 points) lacked granularity [ 52 ]. The 6679 non-redacted impact case studies submitted to the REF (1594 in medically-related fields) were placed in the public domain ( http://results.ref.ac.uk ) and provide a unique dataset for further analysis.

In its review of the REF, the members of Main Panel A, which covered biomedical and health research, noted that “ International MPA [Main Panel A] members cautioned against attempts to ‘metricise’ the evaluation of the many superb and well-told narrations describing the evolution of basic discovery to health, economic and societal impact ” [ 50 ].

Approaches with potential for the future

The approaches in this section, most of which have been recently developed, have not been widely tested but may hold promise for the future.

Electronic databases

Research funders increasingly require principal investigators to provide an annual return of impact data on an online third-party database. In the UK, for example, Researchfish® (formerly MRC e-Val but now described as a ‘federated system’ with over 100 participating organisations) allows funders to connect outputs to awards, thereby allowing aggregation of all outputs and impacts from an entire funding stream. The software contains 11 categories: publications, collaborations, further funding, next destination (career progression), engagement activities, influence on policy and practice, research materials, intellectual property, development of products or interventions, impacts on the private sector, and awards and recognition.

Provided that researchers complete the annual return consistently and accurately, such databases may overcome some of the limitations of one-off, resource-intensive case study approaches. However, the design (and business model) of Researchfish® is such that the only funding streams captured are from organisations prepared to pay the membership fee, thereby potentially distorting the picture of whose input accounts for a research team’s outputs.

Researchfish® collects data both ‘top-down’ (from funders) and ‘bottom-up’ (from individual research teams). A comparable US model is the High Impacts Tracking System, a web-based software tool developed by the National Institute of Environmental Health Sciences; it imports data from existing National Institutes of Health databases of grant information as well as the texts of progress reports and notes of programme managers [ 53 ].

Whilst electronic databases are increasingly mainstreamed in national research policy (Researchfish® was used, for example, to populate the Framework on Economic Impacts described by the UK Department of Business, Innovation and Skills [ 54 ]), we were unable to identify any published independent evaluations of their use.

Realist evaluation

Realist evaluation, designed to address the question “what works for whom in what circumstances”, rests on the assumption that different research inputs and processes in different contexts may generate different outcomes (column 4 in Table  1 ) [ 55 ]. A new approach, developed to assess and summarise impact in the national evaluation of UK Collaborations for Leadership in Applied Health Research and Care, is shown in Fig.  3 [ 56 ]. Whilst considered useful in that evaluation, it was resource-intensive to apply.

Realist model of research-service links and impacts in CLAHRCs (reproduced under UK non-commercial government licence from [ 56 ])

Contribution mapping

Kok and Schuit describe the research ecosystem as a complex and unstable network of people and technologies [ 57 ]. They depict the achievement of impact as shifting and stabilising the network’s configuration by mobilising people and resources (including knowledge in material forms, such as guidelines or software) and enrolling them in changing ‘actor scenarios’. In this model, the focus is shifted from attribution to contribution – that is, on the activities and alignment efforts of different actors (linked to the research and, more distantly, unlinked to it) in the three phases of the research process (formulation, production and extension; Fig.  4 ). Contribution mapping, which can be thought of as a variation on the Dutch approaches to societal impact assessment described above, uses in-depth case study methods but differs from more mainstream approaches in its philosophical and theoretical basis (column 6 in Table  1 ), in its focus on processes and activities, and in its goal of producing an account of how the network of actors and artefacts shifts and stabilises (or not). Its empirical application to date has been limited.

Kok and Schuit’s ‘contribution mapping’ model (reproduced under Creative Commons Attribution Licence 4.0 from [ 57 ])

The SPIRIT Action Framework

The SPIRIT Action Framework, recently published by Australia’s Sax Institute [ 58 ], retains a logic model structure but places more emphasis on engagement and capacity-building activities in organisations and acknowledges the messiness of, and multiple influences on, the policy process (Fig.  5 ). Unusually, the ‘logic model’ focuses not on the research but on the receiving organisation’s need for research. We understand that it is currently being empirically tested but evaluations have not yet been published.

The SPIRIT Action Framework (reproduced under Creative Commons Attribution Licence from [ 58 ] Fig.  1 , p. 151)

Participatory research impact model

Community-based participatory research is predicated on a critical philosophy that emphasises social justice and the value of knowledge in liberating the disadvantaged from oppression (column 5 in Table  1 ) [ 59 ]. Cacari-Stone et al.’s model depicts the complex and contingent relationship between a community-campus partnership and the policymaking process [ 60 ]. Research impact is depicted in synergistic terms as progressive strengthening of the partnership and its consequent ability to influence policy decisions. The paper introducing the model includes a detailed account of its application (Table  2 ), but beyond those, it has not yet been empirically tested.

This review of research impact assessment, which has sought to supplement rather than duplicate more extended overviews [ 1 – 7 ], prompts four main conclusions.

First, one size does not fit all. Different approaches to measuring research impact are designed for different purposes. Logic models can be very useful for tracking the impacts of a funding stream from award to quantitised (and perhaps monetised) impacts. However, when exploring less directly attributable aspects of the research-impact link, narrative accounts of how these links emerged and developed are invariably needed.

Second, the perfect is the enemy of the good. Producing detailed and validated case studies with a full assessment of context and all major claims independently verified, takes work and skill. There is a trade-off between the quality, completeness and timeliness of the data informing an impact assessment, on the one hand, and the cost and feasibility of generating such data on the other. It is no accident that some of the most theoretically elegant approaches to impact assessment have (ironically) had limited influence on the assessment of impact in practice.

Third, warnings from critics that focusing on short-term, proximal impacts (however accurately measured) could create a perverse incentive against more complex and/or politically sensitive research whose impacts are likely to be indirect and hard to measure [ 61 – 63 ] should be taken seriously. However, as the science of how to measure intervening processes and activities advances, it may be possible to use such metrics creatively to support and incentivise the development of complementary assets of various kinds.

Fourth, change is afoot. Driven by both technological advances and the mounting economic pressures on the research community, labour-intensive impact models that require manual assessment of documents, researcher interviews and a bespoke narrative may be overtaken in the future by more automated approaches. The potential for ‘big data’ linkage (for example, supplementing Researchfish® entries with bibliometrics on research citations) may be considerable, though its benefits are currently speculative (and the risks unknown).

Conclusions

As the studies presented in this review illustrate, research on research impact is a rapidly growing interdisciplinary field, spanning evidence-based medicine (via sub-fields such as knowledge translation and implementation science), health services research, economics, informatics, sociology of science and higher education studies. One priority for research in this field is an assessment of how far the newer approaches that rely on regular updating of electronic databases are able to provide the breadth of understanding about the nature of the impacts, and how they arise, that can come for the more established and more ‘manual’ approaches. Future research should also address the topical question of whether research impact tools could be used to help target resources and reduce waste in research (for example, to decide whether to commission a new clinical trial or a meta-analysis of existing trials); we note, for example, the efforts of the UK National Institute for Health Research in this regard [ 64 ].

Once methods for assessing research impact have been developed, it is likely that they will be used. As the range of approaches grows, the challenge is to ensure that the most appropriate one is selected for each of the many different circumstances in which (and the different purposes for which) people may seek to measure impact. It is also worth noting that existing empirical studies have been undertaken primarily in high-income countries and relate to health research systems in North America, Europe and Australasia. The extent to which these frameworks are transferable to low- or middle-income countries or to the Asian setting should be explored further.

Box 1: Definitions of research impact

Impact is the effect research has beyond academia and consists of “ ….benefits to one or more areas of the economy, society, culture, public policy and services, health, production, environment, international development or quality of life, whether locally, regionally, nationally or internationally ” (paragraph 62) and as “ …manifested in a wide variety of ways including, but not limited to: the many types of beneficiary (individuals, organisations, communities, regions and other entities); impacts on products, processes, behaviours, policies, practices; and avoidance of harm or the waste of resources. ” (paragraph 63) UK 2014 Research Excellence Framework [ 65 ]

“ ‘Health impacts’ can be defined as changes in the healthy functioning of individuals (physical, psychological, and social aspects of their health), changes to health services, or changes to the broader determinants of health. ‘Social impacts’ are changes that are broader than simply those to health noted above, and include changes to working systems, ethical understanding of health interventions, or population interactions. ‘Economic impacts’ can be regarded as the benefits from commercialization, the net monetary value of improved health, and the benefits from performing health research. ” Canadian Academy of Health Sciences [ 33 ] (p. 51)

Academic impact is “ The demonstrable contribution that excellent research makes to academic advances, across and within disciplines, including significant advances in understanding, methods, theory and application. ” Economic and societal impact is “ fostering global economic performance, and specifically the economic competitiveness of the UK , increasing the effectiveness of public services and policy, [and] enhancing quality of life, health and creative output. ” Research Councils UK Pathways to Impact ( http://www.rcuk.ac.uk/innovation/impacts/ )

“ A research impact is a recorded or otherwise auditable occasion of influence from academic research on another actor or organization. […] It is not the same thing as a change in outputs or activities as a result of that influence, still less a change in social outcomes. Changes in organizational outputs and social outcomes are always attributable to multiple forces and influences. Consequently, verified causal links from one author or piece of work to output changes or to social outcomes cannot realistically be made or measured in the current state of knowledge. […] However, secondary impacts from research can sometimes be traced at a much more aggregate level, and some macro-evaluations of the economic net benefits of university research are feasible. Improving our knowledge of primary impacts as occasions of influence is the best route to expanding what can be achieved here. ” London School of Economics Impact Handbook for Social Scientists [ 66 ]

Acknowledgements

This paper is largely but not entirely based on a systematic review funded by the NIHR HTA Programme, grant number 14/72/01, with additional material from TG’s dissertation from the MBA in Higher Education Management at UCL Institute of Education, supervised by Sir Peter Scott. We thank Amanda Young for project management support to the original HTA review and Alison Price for assistance with database searches.

Competing interests

TG was Deputy Chair of the 2014 Research Excellence Framework Main Panel A from 2012 to 2014, for which she received an honorarium for days worked (in common with all others on REF panels). SH received grants from various health research funding bodies to help develop and test the Payback Framework. JR is a member of the NIHR HTA Editorial Board, on paid secondment. He was principal investigator in a study funded by the NIHR HTA programme which reviewed methods for measuring the impact of the health research programmes and was director of the NIHR Evaluation, Trials and Studies Coordinating Centre to 2012. MG declares no conflict of interest.

All authors have completed the unified competing interest form at http://www.spp.pt/UserFiles/file/APP_2015/Declaracao_ICMJE_nao_editavel.pdf (available on request from the corresponding author) and declare (1) no financial support for the submitted work from anyone other than their employer; (2) no financial relationships with commercial entities that might have an interest in the submitted work; (3) no spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; and (4) no non-financial interests that may be relevant to the submitted work.

Authors’ contributions

JR was principal investigator on the original systematic literature review and led the research and writing for the HTA report (see Acknowledgements), to which all authors contributed by bringing different areas of expertise to an interdisciplinary synthesis. TG wrote the initial draft of this paper and all co-authors contributed to its refinement. All authors have read and approved the final draft.

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Significance of the Study – Examples and Writing Guide

Table of Contents

Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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New Research: What Are the Welfare Implications of Lobbying?

What Are the Welfare Implications of Lobbying?  Even amid the rising protectionism and trade wars of recent years, tariff levels are still at historically low levels. As a result, most trade agreements in recent years have not focused on reducing tariffs. Rather, they have focused on domestic policy reforms within countries, such as environmental regulations or product standards. Such “deep” agreements are controversial, however, given their exposure to lobbying by special interests. An international agreement to reduce carbon emissions, for instance, might seek to set stricter regulations on car emission standards across multiple countries – a move that car manufacturers around the world might seek to influence through political pressure.

In a recent paper published in the American Economic Review , Giovanni Maggi – the Howard H. Leach Professor of Economics and International Affairs at Yale University – and his coauthor, Ralph Ossa (University of Zurich), use a novel theoretical model to examine the role of lobbying on international regulatory agreements and the resulting welfare effects. Their analysis distinguishes between deep agreements focused on "product" standards, defined as restrictions on the characteristics of products that can be sold in a given country, and "process" standards, defined as restrictions on production processes that can take place on domestic soil. They find that international agreements on product standards provide interest groups with strong incentives to lobby together across countries, which often leads to excessive deregulation and decreased welfare – whereas international agreements on process standards often lead to tighter regulations and enhanced welfare.

Deep agreements involve imposing constraints on domestic regulatory policies. One of the main controversies concerns lobbying by big corporations, who have been very active in influencing these agreements. The concern is that these corporations may be distorting these agreements in a direction that is detrimental to public welfare. — Giovanni Maggi,  Howard H. Leach Professor of Economics & International Affairs

Results at a Glance

• International regulatory agreements negotiated under lobbying pressures can have highly distinct welfare implications, depending on whether the agreements are focused on product standards (restrictions on the characteristics of products that can be sold in a country) or process standards (restrictions on production processes that can take place on domestic soil).
• When a country loosens its product standards, it typically benefits both domestic and foreign producers. Loosening process standards, on the other hand, benefits domestic producers while hurting foreign producers.
• International agreements on product standards tend to be influenced more heavily by interest groups than agreements on process standards, since interest groups will lobby together when their incentives are aligned.
• As a result, international regulatory cooperation on product standards leads to greater deregulation and more negative welfare outcomes than cooperation on process standards.

Background and Context

With tariffs at historically low levels, international agreements increasingly focus on domestic regulatory policies such as product standards and process standards. These “deep agreements” are controversial, however, largely because they are more exposed to the domestic political process and thus lobbying by special interests.

Recent international regulatory negotiations, including the Comprehensive Economic and Trade Agreement (CETA) between the European Union (EU) and Canada and the Transatlantic Trade and Investment Partnership (TTIP) between the United States and the EU, have triggered large scale protests. The main concern is that deep agreements can get hijacked by lobbyists because business groups exert disproportionate influence on regulatory cooperation bodies, undermining consumer safety and endangering the environment.

While the academic literature has extensively analyzed agreements that focus on tariffs and other border policies (“shallow agreements”), little attention has been paid to the political economy of international regulatory agreements and the impact that such agreements have on welfare. In this paper, Maggi and his coauthor set out to fill this gap.

Journal Publication

The Political Economy of International Regulatory Cooperation Authors: Giovanni Maggi, Ralph Ossa

Study & Main Findings

Maggi and Ossa construct a novel model of regulatory cooperation that examines how interest groups influence international regulatory cooperation, and the welfare effects of such agreements. As noted above, their theoretical framework distinguishes between two types of deep agreements: those that focus on product standards (or restrictions on the characteristics of products that can be sold in a country) and those that focus on process standards (or restrictions on production processes that can take place on domestic soil, such as environmental standards imposed on factories and workplace safety standards). In the model, agreements are motivated by lobbying pressures as well as welfare considerations, such as pollution mitigation.

The researchers find that international regulatory negotiations focused on product standards tend to be influenced more heavily by producer interest groups than negotiations focused on process standards. This is because international cooperation on product standards tends to align producers’ interests across countries, whereas cooperation on process standards does not. When a country loosens its product standards, domestic producers clearly benefit, but foreign producers also benefit, because a lower domestic price leads to stronger demand at home, and this puts upward pressure on the world price of the product.

Given these considerations, international cooperation on product standards incentivizes global producers to lobby together. As a result, international cooperation on product standards tends to lower standards, increasing global producers’ profits while decreasing welfare – making it easier for all kinds of products to be sold, but potentially reducing consumer safety or harming the environment.

By contrast, the researchers find that cooperation on process standards typically pits domestic and foreign producers’ interests against one another. This is because loosening process standards in one country benefits domestic producers at the expense of foreign producers. Intuitively, loosening process standards increases the domestic supply of the product, and this puts downward pressure on the world price of the product. For this reason, international negotiations focused on process standards trigger “counter-lobbying,” whereby producers in a given country push for tighter standards in other countries. As a result, international cooperation on process standards tends to tighten standards and increase global welfare.

The Way Forward

Maggi and Ossa’s study sheds light on how political interests interact across countries in shaping international regulatory cooperation, providing new insights on the welfare impacts of politically pressured deep agreements. While their focus on product and process standards highlights several important policy areas, future research on other salient features of deep integration – including foreign investment and intellectual property rights – will lead to a broader understanding of the welfare impacts of international cooperation.

The authors' findings underscore that controversies surrounding deep agreements warrant serious attention and necessitate a deeper understanding of their welfare effects. Moreover, investigating alternative strategies to mitigate the potentially detrimental welfare impacts of lobbying is a critical area for future research, with important implications for policy design.

“In a perfect world, the lobbies would be kept out of the negotiation room when governments work on coordinating their regulatory policies," said Maggi. "If there was a way to do that, it would be fantastic – but it is wishful thinking. Nonetheless, raising the concern and understanding when the concern is more justified or less justified is very important.”

Research summary by Odile Mukiza . Originally published as an EGC Research Summary , August 2024

Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Volume 30, Number 10—October 2024

Epidemiologic quantities for monkeypox virus clade i from historical data with implications for current outbreaks, democratic republic of the congo.

Main Article

Figure . Estimates of key epidemiologic parameters for monkeypox virus clade I from historical data with implications for current outbreaks, Democratic Republic of the Congo. A) Cumulative density function of the incubation period, estimated from data on 15 cases reported in a previous study ( 7 ). B) Cumulative density function of the serial interval, estimated from data on 32 transmission links associated with household outbreaks ( 8 , 9 ), and on data on 11 transmission links associated with a hospital outbreak ( 10 ). C) Cumulative density function of the estimated generation time, based on the same data reported for the serial interval and on estimates of the incubation period. D) Estimates of R t in the Kamituga Health Zone, obtained from the time-series of hospitalized cases (suspected, probable, and confirmed) (L.M. Masirika et al., unpub. data, https://www.medrxiv.org/content/10.1101/2024.05.10.24307057v1 ) and using the 2 estimates of the generation times. Lines indicate mean estimates; shaded areas indicate 95% credible intervals. R t , time-varying reproduction number.

• World Health Organization . Mpox—Democratic Republic of the Congo [ cited 2024 Jun 25 ]. https://www.who.int/emergencies/disease-outbreak-news/item/2024-DON522
• World Health Organization . Mpox (monkeypox) outbreak 2022 [ cited 2024 Jun 25 ]. https://www.who.int/emergencies/situations/monkeypox-oubreak-2022
• Kibungu  EM , Vakaniaki  EH , Kinganda-Lusamaki  E , Kalonji-Mukendi  T , Pukuta  E , Hoff  NA , et al. ; International Mpox Research Consortium . Clade I–associated mpox cases associated with sexual contact, the Democratic Republic of the Congo. Emerg Infect Dis . 2024 ; 30 : 172 – 6 . DOI PubMed Google Scholar
• Katoto  PD , Muttamba  W , Bahizire  E , Malembaka  EB , Bosa  HK , Kazadi  DM , et al. Shifting transmission patterns of human mpox in South Kivu, DR Congo. Lancet Infect Dis . 2024 ; 24 : e354 – 5 . DOI PubMed Google Scholar
• Vakaniaki  EH , Kacita  C , Kinganda-Lusamaki  E , O’Toole  Á , Wawina-Bokalanga  T , Mukadi-Bamuleka  D , et al. Sustained human outbreak of a new MPXV clade I lineage in eastern Democratic Republic of the Congo. Nat Med . 2024 ; Epub ahead of print . DOI PubMed Google Scholar
• Masirika  LM , Udahemuka  JC , Schuele  L , Ndishimye  P , Otani  S , Mbiribindi  JB , et al. Ongoing mpox outbreak in Kamituga, South Kivu province, associated with monkeypox virus of a novel Clade I sub-lineage, Democratic Republic of the Congo, 2024. Euro Surveill . 2024 ; 29 : 2400106 . DOI PubMed Google Scholar
• Nolen  LD , Osadebe  L , Katomba  J , Likofata  J , Mukadi  D , Monroe  B , et al. Extended human-to-human transmission during a monkeypox outbreak in the Democratic Republic of the Congo. Emerg Infect Dis . 2016 ; 22 : 1014 – 21 . DOI PubMed Google Scholar
• Formenty  P , Muntasir  MO , Damon  I , Chowdhary  V , Opoka  ML , Monimart  C , et al. Human monkeypox outbreak caused by novel virus belonging to Congo Basin clade, Sudan, 2005. Emerg Infect Dis . 2010 ; 16 : 1539 – 45 . DOI PubMed Google Scholar
• Besombes  C , Mbrenga  F , Malaka  C , Gonofio  E , Schaeffer  L , Konamna  X , et al. Investigation of a mpox outbreak in Central African Republic, 2021-2022. One Health . 2023 ; 16 : 100523 . DOI PubMed Google Scholar
• Learned  LA , Reynolds  MG , Wassa  DW , Li  Y , Olson  VA , Karem  K , et al. Extended interhuman transmission of monkeypox in a hospital community in the Republic of the Congo, 2003. Am J Trop Med Hyg . 2005 ; 73 : 428 – 34 . DOI PubMed Google Scholar
• Miura  F , van Ewijk  CE , Backer  JA , Xiridou  M , Franz  E , Op de Coul  E , et al. Estimated incubation period for monkeypox cases confirmed in the Netherlands, May 2022. Euro Surveill . 2022 ; 27 : 2200448 . DOI PubMed Google Scholar
• Guzzetta  G , Mammone  A , Ferraro  F , Caraglia  A , Rapiti  A , Marziano  V , et al. Early estimates of monkeypox incubation period, generation time, and reproduction number, Italy, May–June 2022. Emerg Infect Dis . 2022 ; 28 : 2078 – 81 . DOI PubMed Google Scholar
• Monkeypox in the Democratic Republic of the Congo: epidemiological situation report sitrep no. 014 (06–12 May 2024 ) [in French] [ cited 2024 Jun 25 ]. https://reliefweb.int/report/democratic-republic-congo/la-variole-simienne-monkeypox-en-republique-democratique-du-congo-rapport-de-la-situation-epidemiologique-sitrep-no014-06-12-mai-2024
• Cori  A , Ferguson  NM , Fraser  C , Cauchemez  S . A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol . 2013 ; 178 : 1505 – 12 . DOI PubMed Google Scholar
• Thompson  RN , Stockwin  JE , van Gaalen  RD , Polonsky  JA , Kamvar  ZN , Demarsh  PA , et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics . 2019 ; 29 : 100356 . DOI PubMed Google Scholar
• Wallinga  J , Lipsitch  M . How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci . 2007 ; 274 : 599 – 604 . DOI PubMed Google Scholar
• World Health Organization . 2022–24 mpox (monkeypox) outbreak: global trends. Literature summary & epidemic parameters [ cited 2024 Jun 25 ]. https://worldhealthorg.shinyapps.io/mpx_global/#6_Literature_summary__epidemic_parameters
• Brosius  I , Van Dijck  C , Coppens  J , Vandenhove  L , Bangwen  E , Vanroye  F , et al. ; ITM MPOX Study Group . Presymptomatic viral shedding in high-risk mpox contacts: A prospective cohort study. J Med Virol . 2023 ; 95 : e28769 . DOI PubMed Google Scholar
• Miura  F , Backer  JA , van Rijckevorsel  G , Bavalia  R , Raven  S , Petrignani  M , et al. ; Dutch Mpox Response Team . Time scales of human mpox transmission in the Netherlands. J Infect Dis . 2024 ; 229 : 800 – 4 . DOI PubMed Google Scholar
• Fine  PEM , Jezek  Z , Grab  B , Dixon  H . The transmission potential of monkeypox virus in human populations. Int J Epidemiol . 1988 ; 17 : 643 – 50 . DOI PubMed Google Scholar
• Sun  YQ , Chen  JJ , Liu  MC , Zhang  YY , Wang  T , Che  TL , et al. Mapping global zoonotic niche and interregional transmission risk of monkeypox: a retrospective observational study. Global Health . 2023 ; 19 : 58 . DOI PubMed Google Scholar
• Charniga  K , McCollum  AM , Hughes  CM , Monroe  B , Kabamba  J , Lushima  RS , et al. Updating reproduction number estimates for mpox in the Democratic Republic of Congo using surveillance data. Am J Trop Med Hyg . 2024 ; 110 : 561 – 8 . DOI PubMed Google Scholar
• Johnson  PLF , Bergstrom  CT , Regoes  RR , Longini  IM , Halloran  ME , Antia  R . Evolutionary consequences of delaying intervention for monkeypox. Lancet . 2022 ; 400 : 1191 – 3 . DOI PubMed Google Scholar

1 These senior authors contributed equally to this article.

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Research Spotlight: Durable Benefit from PARP Inhibitors in Metastatic Prostate Cancer in Routine Practice: Biomarker Associations and Implications for Optimal Clinical NGS Testing

Triner D, Graf RP, Madison RW, et al. Durable benefit from poly(ADP-ribose) inhibitors in metastatic prostate cancer in routine practice: biomarker associations and implications for optimal clinical next-generation sequencing testing. ESMO Open. 2024; 9(9). https://doi.org/10.1016/j.esmoop.2024.103684

Background:

Randomized clinical trials have demonstrated the efficacy of poly ADP-ribose polymerase inhibitors (PARPi) in patients with metastatic castration-resistant prostate cancer (mCRPC) and BRCA1/2 alterations. However, questions remain about the efficacy of PARPi for alterations in other homologous recombination DNA repair (HRR) genes, as well as the use of tissue biopsy or liquid biopsy for the detection of HRR alterations.

Study details:

In this study, researchers leveraged de-identified, patient-level data from the Flatiron Health-Foundation Medicine Clinico-Genomic Database to describe routine practice outcomes of patients with mCRPC undergoing PARPi therapy who received comprehensive genomic profiling by liquid biopsy or tissue biopsy. Patients were grouped by specific HRR mutation status or an HRD signature (HRDsig) based on a proprietary algorithm that utilizes genome-wide copy number features to predict genomic scarring consistent with homologous repair deficiency (HRD). Those with BRCA alterations were further subcategorized into BRCA homozygous loss and all other BRCA alterations.

The analysis included 445 patients, with 214 receiving tissue biopsies and 231 receiving liquid biopsies. Of that, 38.2% of patients had BRCA alterations and experienced more favorable outcomes to PARPi compared to those with other HRR mutations or no mutations. Of those with BRCA alterations, those with homozygous loss of BRCA1/2 genes (3.1% of patients) had a more favorable time to next treatment, time to treatment discontinuation, and routine clinical practice overall survival compared to those with other BRCA alterations. Homozygous BRCA loss was also highly associated with HRDsig positivity, and those who were HRDsig positive had more favorable outcomes than those who were HRDsig negative.

Homozygous BRCA loss proved more difficult to detect in liquid biopsy compared to tissue biopsy (1% vs. 3.1%, respectively). Liquid biopsies were able to detect a similar prevalence of homozygous BRCA loss compared to tissue biopsies when the liquid biopsy had a high (20% or greater) circulating tumor DNA (ctDNA) tumor fraction, a determination of the amount of tumor DNA circulating in each blood sample.

Why this matters:

Patients with mCRPC who have BRCA alterations, particularly those with homozygous BRCA loss, were more likely to have favorable outcomes and durable benefits from PARPi in clinical practice than those in other biomarker-defined subgroup, mirroring results from prospective trials.

While homozygous BRCA loss can be detected with validated liquid biopsies, many patients with mCRPC do not have a high enough ctDNA tumor fraction to rule out the presence of BRCA loss using liquid biopsies. When clinically feasible, tumor tissue comprehensive genomic profiling should be prioritized to optimize the identification of homozygous BRCA loss. However, when tumor testing is not feasible, sufficient ctDNA tumor fraction levels for detection (greater than 20%) are enriched at clinical time points when tumors are progressing, making liquid biopsy a potentially useful tool to leverage at times of disease progression to guide treatment decision making for PARPi.

View the full publication in ESMO Open .  

Related Articles

Research spotlight: circulating tumor dna assessment for treatment monitoring adds value to psa in metastatic castration resistant prostate cancer, development of a pan-cancer algorithm to predict homologous recombination deficiency and sensitivity to parpi therapy.

Peer Reviewed

GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

Bibliography

Antkare, I. (2020). Ike Antkare, his publications, and those of his disciples. In M. Biagioli & A. Lippman (Eds.), Gaming the metrics (pp. 177–200). The MIT Press. https://doi.org/10.7551/mitpress/11087.003.0018

Barbour, R. S. (2001). Checklists for improving rigour in qualitative research: A case of the tail wagging the dog? BMJ , 322 (7294), 1115–1117. https://doi.org/10.1136/bmj.322.7294.1115

Bom, H.-S. H. (2023). Exploring the opportunities and challenges of ChatGPT in academic writing: A roundtable discussion. Nuclear Medicine and Molecular Imaging , 57 (4), 165–167. https://doi.org/10.1007/s13139-023-00809-2

Cabanac, G., & Labbé, C. (2021). Prevalence of nonsensical algorithmically generated papers in the scientific literature. Journal of the Association for Information Science and Technology , 72 (12), 1461–1476. https://doi.org/10.1002/asi.24495

Cabanac, G., Labbé, C., & Magazinov, A. (2021). Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals . arXiv. https://doi.org/10.48550/arXiv.2107.06751

Carrion, M. L. (2018). “You need to do your research”: Vaccines, contestable science, and maternal epistemology. Public Understanding of Science , 27 (3), 310–324. https://doi.org/10.1177/0963662517728024

Centre for Digital Humanities Uppsala (2023). CDHUppsala/word-rain [Computer software]. https://github.com/CDHUppsala/word-rain

Chinn, S., & Hasell, A. (2023). Support for “doing your own research” is associated with COVID-19 misperceptions and scientific mistrust. Harvard Kennedy School (HSK) Misinformation Review, 4 (3). https://doi.org/10.37016/mr-2020-117

Cholewiak, S. A., Ipeirotis, P., Silva, V., & Kannawadi, A. (2023). SCHOLARLY: Simple access to Google Scholar authors and citation using Python (1.5.0) [Computer software]. https://doi.org/10.5281/zenodo.5764801

Dadkhah, M., Lagzian, M., & Borchardt, G. (2017). Questionable papers in citation databases as an issue for literature review. Journal of Cell Communication and Signaling , 11 (2), 181–185. https://doi.org/10.1007/s12079-016-0370-6

Dadkhah, M., Oermann, M. H., Hegedüs, M., Raman, R., & Dávid, L. D. (2023). Detection of fake papers in the era of artificial intelligence. Diagnosis , 10 (4), 390–397. https://doi.org/10.1515/dx-2023-0090

DeGeurin, M. (2024, March 19). AI-generated nonsense is leaking into scientific journals. Popular Science. https://www.popsci.com/technology/ai-generated-text-scientific-journals/

Dunlap, R. E., & Brulle, R. J. (2020). Sources and amplifiers of climate change denial. In D.C. Holmes & L. M. Richardson (Eds.), Research handbook on communicating climate change (pp. 49–61). Edward Elgar Publishing. https://doi.org/10.4337/9781789900408.00013

Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. In J. Tiedemann & N. Tahmasebi (Eds.), Proceedings of the 21st Nordic Conference on Computational Linguistics (pp. 271–276). Association for Computational Linguistics. https://aclanthology.org/W17-0237

Google Scholar Help. (n.d.). Inclusion guidelines for webmasters . https://scholar.google.com/intl/en/scholar/inclusion.html

Gu, J., Wang, X., Li, C., Zhao, J., Fu, W., Liang, G., & Qiu, J. (2022). AI-enabled image fraud in scientific publications. Patterns , 3 (7), 100511. https://doi.org/10.1016/j.patter.2022.100511

Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods , 11 (2), 181–217.   https://doi.org/10.1002/jrsm.1378

Haider, J., & Åström, F. (2017). Dimensions of trust in scholarly communication: Problematizing peer review in the aftermath of John Bohannon’s “Sting” in science. Journal of the Association for Information Science and Technology , 68 (2), 450–467. https://doi.org/10.1002/asi.23669

Huang, J., & Tan, M. (2023). The role of ChatGPT in scientific communication: Writing better scientific review articles. American Journal of Cancer Research , 13 (4), 1148–1154. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164801/

Jones, N. (2024). How journals are fighting back against a wave of questionable images. Nature , 626 (8000), 697–698. https://doi.org/10.1038/d41586-024-00372-6

Kitamura, F. C. (2023). ChatGPT is shaping the future of medical writing but still requires human judgment. Radiology , 307 (2), e230171. https://doi.org/10.1148/radiol.230171

Littell, J. H., Abel, K. M., Biggs, M. A., Blum, R. W., Foster, D. G., Haddad, L. B., Major, B., Munk-Olsen, T., Polis, C. B., Robinson, G. E., Rocca, C. H., Russo, N. F., Steinberg, J. R., Stewart, D. E., Stotland, N. L., Upadhyay, U. D., & Ditzhuijzen, J. van. (2024). Correcting the scientific record on abortion and mental health outcomes. BMJ , 384 , e076518. https://doi.org/10.1136/bmj-2023-076518

Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74 (5), 570–581. https://doi.org/10.1002/asi.24750

Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M., & Delgado López-Cózar, E. (2016). Back to the past: On the shoulders of an academic search engine giant. Scientometrics , 107 , 1477–1487. https://doi.org/10.1007/s11192-016-1917-2

Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A multidisciplinary comparison of coverage via citations. Scientometrics , 126 (1), 871–906. https://doi.org/10.1007/s11192-020-03690-4

Simon, F. M., Altay, S., & Mercier, H. (2023). Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown. Harvard Kennedy School (HKS) Misinformation Review, 4 (5). https://doi.org/10.37016/mr-2020-127

Skeppstedt, M., Ahltorp, M., Kucher, K., & Lindström, M. (2024). From word clouds to Word Rain: Revisiting the classic word cloud to visualize climate change texts. Information Visualization , 23 (3), 217–238. https://doi.org/10.1177/14738716241236188

Swedish Research Council. (2017). Good research practice. Vetenskapsrådet.

Stokel-Walker, C. (2024, May 1.). AI Chatbots Have Thoroughly Infiltrated Scientific Publishing . Scientific American. https://www.scientificamerican.com/article/chatbots-have-thoroughly-infiltrated-scientific-publishing/

Subbaraman, N. (2024, May 14). Flood of fake science forces multiple journal closures: Wiley to shutter 19 more journals, some tainted by fraud. The Wall Street Journal . https://www.wsj.com/science/academic-studies-research-paper-mills-journals-publishing-f5a3d4bc

The pandas development team. (2024). pandas-dev/pandas: Pandas (v2.2.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.10957263

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science , 379 (6630), 313–313. https://doi.org/10.1126/science.adg7879

Tripodi, F. B., Garcia, L. C., & Marwick, A. E. (2023). ‘Do your own research’: Affordance activation and disinformation spread. Information, Communication & Society , 27 (6), 1212–1228. https://doi.org/10.1080/1369118X.2023.2245869

Vikramaditya, N. (2020). Nv7-GitHub/googlesearch [Computer software]. https://github.com/Nv7-GitHub/googlesearch

This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

Smith Experts Explain Google Antitrust Implications

Amid back-to-back court cases, Google has been facing unprecedented federal government allegations of illegally monopolizing the marketplace – for its system for monetizing advertising and as an internet search monopolist (as ruled by Judge Amit P. Mehta in early August).

Advertising Antitrust

In the current case, the DOJ alleges Google unlawfully maintained a monopoly over the digital advertising market through its dominance in ad-tech software used to buy and sell online ads. “The optimal remedy would aim to restore competition in the ad-tech market,” says Clinical Professor of Finance David Kass , a former antitrust economist with the Federal Trade Commission. “Potential remedies include (1) structural remedies (divestitures) including breaking up Google's ad-tech business, and separating its ad buying and selling businesses, (2) behavioral remedies (conduct restrictions, data access requirements), and (3) regulatory oversight (monitor compliance).”

For investors in Google, the eventual remedy “may have a significant negative impact on revenues and profits and add to the volatility in its stock price until this case is resolved,” Kass says. “However, investors in Google's competitors may benefit if the case leads to more innovation and growth opportunities and it could also benefit advertisers by lowering their costs. This case could lead to a substantial reduction in Google's control over the ad-tech market with competitors gaining market share.”

Search Antitrust

Google in the previous case was ruled to have a monopoly in the market for general search services and general search text ads. And Google’s distribution agreements – its contracts with other companies, such as Apple, making Google their default search engine – are exclusive and have anticompetitive effects. In other words, “they serve to keep competitors out of the market for search services and search text ads,” says Research Professor Kislaya Prasad , academic director of Smith’s Center for Global Business .

Google, now entering a remedy phase , faces potential outcomes ranging from restrictions on deals it has with the likes of Apple to the forming of separate companies for products like Google’s Chrome browser and its Android operating system.

“It is important to keep sight of Judge Mehta’s decision and the behavior that was found to be problematic,” Prasad says. “Google never really had a satisfactory answer for why it was willing to pay vast sums of money through revenue sharing agreements – in the vicinity of $20 billion – to make Google the default search engine on browsers and mobile devices,” Prasad says. “Perhaps the most problematic such payment was to Apple, because Android and iPhone are the main competitors for mobile devices, and this does seem like a payment to prevent Apple from creating its own search engine.”

A Deeper Dive

In the following Q&A, Prasad and Smith Associate Professor of Marketing Bobby Zhou , who co-authored a recent study on antitrust regulation of digital markets, further examine the implications for Google, its competitors and consumers.

Should or how best can Google’s search be fixed to restore competition?

Zhou: Ending exclusive agreements with device manufacturers and browsers such as Apple and Firefox to set Google to be the default search option. Terminating these agreements would allow other search engines to compete more fairly. Breaking Up Google’s business units, specifically, separates Google’s search business from its other services, such as advertising and YouTube. This would reduce the company’s ability to leverage its dominance in search services to benefit its other businesses. Implementing mechanisms that give consumers more choice in selecting their default search engine, such as prompts during device setup, can reduce Google’s dominance.

Prasad: I think a likely outcome is that courts forbid problematic distribution agreements. Browsers would then be able to offer other search services or give consumers a choice of search provider. Companies would still be free to offer Google, and even to make it the default. However, companies like Apple have more of an incentive to develop their own search engine when they are no longer getting revenue from Google in exchange for the search data originating from its devices that Google is getting. Other options – such as splitting up Google – seem unnecessarily extreme. Some have suggested that Android or Chrome could be spun off, and there is some talk that the Department of Justice is going to propose this. Even if there is merit to splitting up Google, I don’t see this as being necessitated by the logic of the case. At the other extreme, forcing a choice of search engines would be good, but doesn’t seem to go far enough if this is not also accompanied by restrictions on revenue sharing.

What is most ideal for consumers amid the possibilities?

Zhou: Ending exclusive agreements that make Google the default search engine on devices and browsers might benefit consumers the most. First, consumers would have more options to choose from, rather than being automatically directed to Google. This can lead to a more personalized and satisfying search experience. Second, breaking the default status can reduce Google’s ability to leverage its dominance in search to benefit its other services, leading to a more balanced and fair market (especially regarding the ad market).

Prasad: Although Google has been a remarkable innovative company, more competition for search services would be desirable. Consider for example, a consumer with a privacy focus must jump through many hoops to install a search engine such as DuckDuckGo, which focuses on user privacy. Moreover, Google’s contracts with browsers and mobile providers makes it in their interest to keep Google as the default. If users can more easily access different browsers, and these browsers can improve search quality by scaling, then users can be better matched to the browser that is best for them. If it is indeed the case that Google is making monopoly profits from general search text ads, then advertisers would also benefit from more competition in the search services market.

To what extent and how does this case compare to the  antitrust suit against Microsoft two decades ago – especially in terms of setting a precedent for the broader tech industry?

Zhou: This case is likely to have a far greater impact on all major technology platforms. Two decades ago, Microsoft was primarily considered as a productivity tool and software service provider. Its Internet Explorer (IE) did not really integrate well with Office Suites or any other major functions of the Windows system. It was the size (market share) of its operating system and its bundling of IE that resulted in the antitrust lawsuit. However, Google's search is deeply woven with its online advertising business, its online content services (YouTube, YouTube Music, Google News, etc.) and even its office productivity suite (Google Drive, Google Docs, etc.). It is the complementarity across all of Google's services around search that makes Google's dominance in search so much more defensible and harder to disentangle. Furthermore, network effects have long been established within each one of the major technology platforms such as Apple, Meta, X, Android. This implies that all these platforms might draw similar scrutiny like that on Google moving forward. By comparison, none of the other firms were like Microsoft 20 years ago, so its legal impact as a precedent was relatively limited.

Prasad: There are many similarities. Most crucially, a key issue in both cases is the use of contracts whose effect is to exclude competitors. However, there is a more subtle issue at work here. As technology advances court cases serve to adapt laws – in this instance the Sherman Act – to suit the circumstances. This sets the standard for what behavior is, and is not, acceptable within the new technological paradigm. In that sense, the final resolution of the case will be a signal to other companies. It will affect their behavior and may also embolden the DOJ to prosecute more cases.

Further key implications?

Zhou: I want to highlight one unique aspect of Google: It is in the business of connecting consumers with relevant information. As a result, it can capture so much of consumers' attention (time), which is currently the most prized asset. As long as consumers are able to find the relevant information reasonably quickly on Google, given this habit, it will be challenging to use the argument of consumer welfare being negatively affected by Google's search dominance to break it up. Regulators need to evaluate this case holistically, looking across all the main services Google provides, to make a compelling case of a clean break up (separation) of Google (different business units).

Prasad: Google has said it will appeal whatever judgment may come. However, the finding that Google is a monopolist will embolden other companies to take Google to court. This has already happened with Yelp. Although they previously did not have success, they have now gone to court again with a complaint that Google unfairly disadvantages search services like Yelp in its results. Other such lawsuits may be forthcoming. A possible consequence – undesirable, in my view – is that Google is so distracted by lawsuits that it turns away from what it does best, and what has made it one of the world’s most valuable companies. Which is, to innovate and come up with remarkable products and services.

• Kislaya Prasad
• Smith Brain Trust
• Center for Global Business
• Decision, Operations and Information Technologies

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