example of a conclusion in research

How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples, frequently asked questions.

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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One of the most common questions we receive at the Writing Center is “what am I supposed to do in my conclusion?” This is a difficult question to answer because there’s no one right answer to what belongs in a conclusion. How you conclude your paper will depend on where you started—and where you traveled. It will also depend on the conventions and expectations of the discipline in which you are writing. For example, while the conclusion to a STEM paper could focus on questions for further study, the conclusion of a literature paper could include a quotation from your central text that can now be understood differently in light of what has been discussed in the paper. You should consult your instructor about expectations for conclusions in a particular discipline.

With that in mind, here are some general guidelines you might find helpful to use as you think about your conclusion.  

Begin with the “what”  

In a short paper—even a research paper—you don’t need to provide an exhaustive summary as part of your conclusion. But you do need to make some kind of transition between your final body paragraph and your concluding paragraph. This may come in the form of a few sentences of summary. Or it may come in the form of a sentence that brings your readers back to your thesis or main idea and reminds your readers where you began and how far you have traveled.

So, for example, in a paper about the relationship between ADHD and rejection sensitivity, Vanessa Roser begins by introducing readers to the fact that researchers have studied the relationship between the two conditions and then provides her explanation of that relationship. Here’s her thesis: “While socialization may indeed be an important factor in RS, I argue that individuals with ADHD may also possess a neurological predisposition to RS that is exacerbated by the differing executive and emotional regulation characteristic of ADHD.”

In her final paragraph, Roser reminds us of where she started by echoing her thesis: “This literature demonstrates that, as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Highlight the “so what”  

At the beginning of your paper, you explain to your readers what’s at stake—why they should care about the argument you’re making. In your conclusion, you can bring readers back to those stakes by reminding them why your argument is important in the first place. You can also draft a few sentences that put those stakes into a new or broader context.

In the conclusion to her paper about ADHD and RS, Roser echoes the stakes she established in her introduction—that research into connections between ADHD and RS has led to contradictory results, raising questions about the “behavioral mediation hypothesis.”

She writes, “as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Leave your readers with the “now what”  

After the “what” and the “so what,” you should leave your reader with some final thoughts. If you have written a strong introduction, your readers will know why you have been arguing what you have been arguing—and why they should care. And if you’ve made a good case for your thesis, then your readers should be in a position to see things in a new way, understand new questions, or be ready for something that they weren’t ready for before they read your paper.

In her conclusion, Roser offers two “now what” statements. First, she explains that it is important to recognize that the flawed behavioral mediation hypothesis “seems to place a degree of fault on the individual. It implies that individuals with ADHD must have elicited such frequent or intense rejection by virtue of their inadequate social skills, erasing the possibility that they may simply possess a natural sensitivity to emotion.” She then highlights the broader implications for treatment of people with ADHD, noting that recognizing the actual connection between rejection sensitivity and ADHD “has profound implications for understanding how individuals with ADHD might best be treated in educational settings, by counselors, family, peers, or even society as a whole.”

To find your own “now what” for your essay’s conclusion, try asking yourself these questions:

• What can my readers now understand, see in a new light, or grapple with that they would not have understood in the same way before reading my paper? Are we a step closer to understanding a larger phenomenon or to understanding why what was at stake is so important?  
• What questions can I now raise that would not have made sense at the beginning of my paper? Questions for further research? Other ways that this topic could be approached?  
• Are there other applications for my research? Could my questions be asked about different data in a different context? Could I use my methods to answer a different question?  
• What action should be taken in light of this argument? What action do I predict will be taken or could lead to a solution?  
• What larger context might my argument be a part of?  

What to avoid in your conclusion  

• a complete restatement of all that you have said in your paper.  
• a substantial counterargument that you do not have space to refute; you should introduce counterarguments before your conclusion.  
• an apology for what you have not said. If you need to explain the scope of your paper, you should do this sooner—but don’t apologize for what you have not discussed in your paper.  
• fake transitions like “in conclusion” that are followed by sentences that aren’t actually conclusions. (“In conclusion, I have now demonstrated that my thesis is correct.”)
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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

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The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points and, if applicable, where you recommend new areas for future research. For most college-level research papers, one or two well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in summarizing key findings and their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides you with important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  the "So What?" question by placing the study within the context of how your research advances past research about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [described in your literature review section] has been filled by your research.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers you the opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The function of your paper's conclusion is to restate the main argument . It reminds the reader of the strengths of your main argument(s) and reiterates the most important evidence supporting those argument(s). Do this by stating clearly the context, background, and necessity of pursuing the research problem you investigated in relation to an issue, controversy, or a gap found in the literature. Make sure, however, that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your essay.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, simple language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., what were the unique or new contributions your study made to the overall research about your topic?].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem and that further investigations should take place.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way.

NOTE : If asked to think introspectively about the topics, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following strategies:

• If your essay deals with a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good place to look is research from your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move from a specific discussion [your research problem] back to a general discussion [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific analysis and discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use your summary of the negative results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to be able to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long.

Failure to match the objectives of your research Often research objectives in the social sciences change while the research is being carried out. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining to read, when an essay is about to end. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper and, as such, the conclusion rarely has citations to sources. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no actual new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you’ve presented, and locate your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights or valuable insight to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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How to Write a Conclusion for a Research Paper

Table of Contents

Writing a conclusion for a research paper is a critical step that often determines the overall impact and impression the paper leaves on the reader. While some may view the conclusion as a mere formality, it is actually an opportunity to wrap up the main points, provide closure, and leave a lasting impression. In this article, we will explore the importance of a well-crafted conclusion and discuss various tips and strategies to help you write an engaging and impactful conclusion for your research paper.

Introduction

Before delving into the specifics of writing a conclusion, it is important to understand why it is such a crucial component of a research paper. The conclusion serves to summarize the main points of the paper and reemphasize their significance. A well-written conclusion can leave the reader satisfied and inspired, while a poorly executed one may undermine the credibility of the entire paper. Therefore, it is essential to give careful thought and attention to crafting an effective conclusion.

When writing a research paper, the conclusion acts as the final destination for the reader. It is the point where all the information, arguments, and evidence presented throughout the paper converge. Just as a traveler reaches the end of a journey, the reader reaches the conclusion to find closure and a sense of fulfillment. This is why the conclusion should not be taken lightly; it is a critical opportunity to leave a lasting impact on the reader.

Moreover, the conclusion is not merely a repetition of the introduction or a summary of the main points. It goes beyond that by providing a deeper understanding of the research findings and their implications. It allows the writer to reflect on the significance of their work and its potential contributions to the field. By doing so, the conclusion elevates the research paper from a mere collection of facts to a thought-provoking piece of scholarship.

In the following sections, we will explore various strategies and techniques for crafting a compelling conclusion. By understanding the importance of the conclusion and learning how to write one effectively, you will be equipped to create impactful research papers.

Structuring the Conclusion

In order to create an effective conclusion, it is important to consider its structure. A well-structured conclusion should begin by restating the thesis statement and summarizing the main points of the paper. It should then move on to provide a concise synthesis of the key findings and arguments, highlighting their implications and relevance. Finally, the conclusion should end with a thought-provoking statement that leaves the reader with a lasting impression.

Additionally, using phrases like "this research demonstrates," "the findings show," or "it is clear that" can help to highlight the significance of your research and emphasize your main conclusions.

Tips for Writing an Engaging Conclusion

Writing an engaging conclusion requires careful consideration and attention to detail. Here are some tips to help you create an impactful conclusion for your research paper:

• Revisit the Introduction: Start your conclusion by referencing your introduction. Remind the reader of the research question or problem you initially posed and show how your research has addressed it.
• Summarize Your Main Points: Provide a concise summary of the main points and arguments presented in your paper. Be sure to restate your thesis statement and highlight the key findings.
• Offer a Fresh Perspective: Use the conclusion as an opportunity to provide a fresh perspective or offer insights that go beyond the main body of the paper. This will leave the reader with something new to consider.
• Leave a Lasting Impression: End your conclusion with a thought-provoking statement or a call to action. This will leave a lasting impression on the reader and encourage further exploration of the research topic.

Addressing Counter Arguments In Conclusion

While crafting your conclusion, you can address any potential counterarguments or limitations of your research. This will demonstrate that you have considered alternative perspectives and have taken them into account in your conclusions. By acknowledging potential counterarguments, you can strengthen the credibility and validity of your research. And by openly discussing limitations, you demonstrate transparency and honesty in your research process.

Language and Tone To Be Used In Conclusion

The language and tone of your conclusion play a crucial role in shaping the overall impression of your research paper. It is important to use clear and concise language that is appropriate for the academic context. Avoid using overly informal or colloquial language that may undermine the credibility of your research. Additionally, consider the tone of your conclusion – it should be professional, confident, and persuasive, while still maintaining a respectful and objective tone.

When it comes to the language used in your conclusion, precision is key. You want to ensure that your ideas are communicated effectively and that there is no room for misinterpretation. Using clear and concise language will not only make your conclusion easier to understand but will also demonstrate your command of the subject matter.

Furthermore, it is important to strike the right balance between formality and accessibility. While academic writing typically requires a more formal tone, you should still aim to make your conclusion accessible to a wider audience. This means avoiding jargon or technical terms that may confuse readers who are not familiar with the subject matter. Instead, opt for language that is clear and straightforward, allowing anyone to grasp the main points of your research.

Another aspect to consider is the tone of your conclusion. The tone should reflect the confidence you have in your research findings and the strength of your argument. By adopting a professional and confident tone, you are more likely to convince your readers of the validity and importance of your research. However, it is crucial to strike a balance and avoid sounding arrogant or dismissive of opposing viewpoints. Maintaining a respectful and objective tone will help you engage with your audience in a more persuasive manner.

Moreover, the tone of your conclusion should align with the overall tone of your research paper. Consistency in tone throughout your paper will create a cohesive and unified piece of writing.

Common Mistakes to Avoid While Writing a Conclusion

When writing a conclusion, there are several common mistakes that researchers often make. By being aware of these pitfalls, you can avoid them and create a more effective conclusion for your research paper. Some common mistakes include:

• Repeating the Introduction: A conclusion should not simply be a reworded version of the introduction. While it is important to revisit the main points, try to present them in a fresh and broader perspective, by foregrounding the implications/impacts of your research.
• Introducing New Information: The conclusion should not introduce any new information or arguments. Instead, it should focus on summarizing and synthesizing the main points presented in the paper.
• Being Vague or General: Avoid using vague or general statements in your conclusion. Instead, be specific and provide concrete examples or evidence to support your main points.
• Ending Abruptly: A conclusion should provide a sense of closure and completeness. Avoid ending your conclusion abruptly or leaving the reader with unanswered questions.

Editing and Revising the Conclusion

Just like the rest of your research paper, the conclusion should go through a thorough editing and revising process. This will help to ensure clarity, coherence, and impact in the conclusion. As you revise your conclusion, consider the following:

• Check for Consistency: Ensure that your conclusion aligns with the main body of the paper and does not introduce any new or contradictory information.
• Eliminate Redundancy: Remove any repetitive or redundant information in your conclusion. Instead, focus on presenting the key points in a concise and engaging manner.
• Proofread for Clarity: Read your conclusion aloud or ask someone else to read it to ensure that it is clear and understandable. Check for any grammatical or spelling errors that may distract the reader.
• Seek Feedback: Consider sharing your conclusion with peers or mentors to get their feedback and insights. This can help you strengthen your conclusion and make it more impactful.

How to Write Conclusion as a Call to Action

Finally, consider using your conclusion as a call to action. Encourage the reader to take further action, such as conducting additional research or considering the implications of your findings. By providing a clear call to action, you can inspire the reader to actively engage with your research and continue the conversation on the topic.

Adapting to Different Research Paper Types

It is important to adapt your conclusion approach based on the type of research paper you are writing. Different research paper types may require different strategies and approaches to writing the conclusion. For example, a scientific research paper may focus more on summarizing the key findings and implications, while a persuasive research paper may emphasize the call to action and the potential impact of the research. Tailor your conclusion to suit the specific goals and requirements of your research paper.

Final Thoughts

A well-crafted conclusion can leave a lasting impression on the reader and enhance the impact of your research. By following the tips and strategies outlined in this article, you can create an engaging and impactful conclusion that effectively summarizes your main points, addresses potential counterarguments, and leaves the reader with a sense of closure and inspiration. Embrace the importance of the conclusion and view it as an opportunity to showcase the significance and relevance of your research.

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• How to conclude an essay | Interactive example

How to Conclude an Essay | Interactive Example

Published on January 24, 2019 by Shona McCombes . Revised on July 23, 2023.

The conclusion is the final paragraph of your essay . A strong conclusion aims to:

• Tie together the essay’s main points
• Show why your argument matters
• Leave the reader with a strong impression

Your conclusion should give a sense of closure and completion to your argument, but also show what new questions or possibilities it has opened up.

This conclusion is taken from our annotated essay example , which discusses the history of the Braille system. Hover over each part to see why it’s effective.

Braille paved the way for dramatic cultural changes in the way blind people were treated and the opportunities available to them. Louis Braille’s innovation was to reimagine existing reading systems from a blind perspective, and the success of this invention required sighted teachers to adapt to their students’ reality instead of the other way around. In this sense, Braille helped drive broader social changes in the status of blindness. New accessibility tools provide practical advantages to those who need them, but they can also change the perspectives and attitudes of those who do not.

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Table of contents

Step 1: return to your thesis, step 2: review your main points, step 3: show why it matters, what shouldn’t go in the conclusion, more examples of essay conclusions, other interesting articles, frequently asked questions about writing an essay conclusion.

To begin your conclusion, signal that the essay is coming to an end by returning to your overall argument.

Don’t just repeat your thesis statement —instead, try to rephrase your argument in a way that shows how it has been developed since the introduction.

Prevent plagiarism. Run a free check.

Next, remind the reader of the main points that you used to support your argument.

Avoid simply summarizing each paragraph or repeating each point in order; try to bring your points together in a way that makes the connections between them clear. The conclusion is your final chance to show how all the paragraphs of your essay add up to a coherent whole.

To wrap up your conclusion, zoom out to a broader view of the topic and consider the implications of your argument. For example:

• Does it contribute a new understanding of your topic?
• Does it raise new questions for future study?
• Does it lead to practical suggestions or predictions?
• Can it be applied to different contexts?
• Can it be connected to a broader debate or theme?

Whatever your essay is about, the conclusion should aim to emphasize the significance of your argument, whether that’s within your academic subject or in the wider world.

Try to end with a strong, decisive sentence, leaving the reader with a lingering sense of interest in your topic.

The easiest way to improve your conclusion is to eliminate these common mistakes.

Don’t include new evidence

Any evidence or analysis that is essential to supporting your thesis statement should appear in the main body of the essay.

The conclusion might include minor pieces of new information—for example, a sentence or two discussing broader implications, or a quotation that nicely summarizes your central point. But it shouldn’t introduce any major new sources or ideas that need further explanation to understand.

Don’t use “concluding phrases”

Avoid using obvious stock phrases to tell the reader what you’re doing:

• “In conclusion…”
• “To sum up…”

These phrases aren’t forbidden, but they can make your writing sound weak. By returning to your main argument, it will quickly become clear that you are concluding the essay—you shouldn’t have to spell it out.

Don’t undermine your argument

Avoid using apologetic phrases that sound uncertain or confused:

• “This is just one approach among many.”
• “There are good arguments on both sides of this issue.”
• “There is no clear answer to this problem.”

Even if your essay has explored different points of view, your own position should be clear. There may be many possible approaches to the topic, but you want to leave the reader convinced that yours is the best one!

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• Literary analysis

This conclusion is taken from an argumentative essay about the internet’s impact on education. It acknowledges the opposing arguments while taking a clear, decisive position.

The internet has had a major positive impact on the world of education; occasional pitfalls aside, its value is evident in numerous applications. The future of teaching lies in the possibilities the internet opens up for communication, research, and interactivity. As the popularity of distance learning shows, students value the flexibility and accessibility offered by digital education, and educators should fully embrace these advantages. The internet’s dangers, real and imaginary, have been documented exhaustively by skeptics, but the internet is here to stay; it is time to focus seriously on its potential for good.

This conclusion is taken from a short expository essay that explains the invention of the printing press and its effects on European society. It focuses on giving a clear, concise overview of what was covered in the essay.

The invention of the printing press was important not only in terms of its immediate cultural and economic effects, but also in terms of its major impact on politics and religion across Europe. In the century following the invention of the printing press, the relatively stationary intellectual atmosphere of the Middle Ages gave way to the social upheavals of the Reformation and the Renaissance. A single technological innovation had contributed to the total reshaping of the continent.

This conclusion is taken from a literary analysis essay about Mary Shelley’s Frankenstein . It summarizes what the essay’s analysis achieved and emphasizes its originality.

By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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Your essay’s conclusion should contain:

• A rephrased version of your overall thesis
• A brief review of the key points you made in the main body
• An indication of why your argument matters

The conclusion may also reflect on the broader implications of your argument, showing how your ideas could applied to other contexts or debates.

For a stronger conclusion paragraph, avoid including:

• Important evidence or analysis that wasn’t mentioned in the main body
• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

The conclusion paragraph of an essay is usually shorter than the introduction . As a rule, it shouldn’t take up more than 10–15% of the text.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, July 23). How to Conclude an Essay | Interactive Example. Scribbr. Retrieved March 12, 2024, from https://www.scribbr.com/academic-essay/conclusion/

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How to write a strong conclusion for your research paper

Last updated

17 February 2024

Reviewed by

Writing a research paper is a chance to share your knowledge and hypothesis. It's an opportunity to demonstrate your many hours of research and prove your ability to write convincingly.

Ideally, by the end of your research paper, you'll have brought your readers on a journey to reach the conclusions you've pre-determined. However, if you don't stick the landing with a good conclusion, you'll risk losing your reader’s trust.

Writing a strong conclusion for your research paper involves a few important steps, including restating the thesis and summing up everything properly.

Find out what to include and what to avoid, so you can effectively demonstrate your understanding of the topic and prove your expertise.

• Why is a good conclusion important?

A good conclusion can cement your paper in the reader’s mind. Making a strong impression in your introduction can draw your readers in, but it's the conclusion that will inspire them.

• What to include in a research paper conclusion

There are a few specifics you should include in your research paper conclusion. Offer your readers some sense of urgency or consequence by pointing out why they should care about the topic you have covered. Discuss any common problems associated with your topic and provide suggestions as to how these problems can be solved or addressed.

The conclusion should include a restatement of your initial thesis. Thesis statements are strengthened after you’ve presented supporting evidence (as you will have done in the paper), so make a point to reintroduce it at the end.

Finally, recap the main points of your research paper, highlighting the key takeaways you want readers to remember. If you've made multiple points throughout the paper, refer to the ones with the strongest supporting evidence.

• Steps for writing a research paper conclusion

Many writers find the conclusion the most challenging part of any research project . By following these three steps, you'll be prepared to write a conclusion that is effective and concise.

• Step 1: Restate the problem

Always begin by restating the research problem in the conclusion of a research paper. This serves to remind the reader of your hypothesis and refresh them on the main point of the paper. 

When restating the problem, take care to avoid using exactly the same words you employed earlier in the paper.

• Step 2: Sum up the paper

After you've restated the problem, sum up the paper by revealing your overall findings. The method for this differs slightly, depending on whether you're crafting an argumentative paper or an empirical paper.

Argumentative paper: Restate your thesis and arguments

Argumentative papers involve introducing a thesis statement early on. In crafting the conclusion for an argumentative paper, always restate the thesis, outlining the way you've developed it throughout the entire paper.

It might be appropriate to mention any counterarguments in the conclusion, so you can demonstrate how your thesis is correct or how the data best supports your main points.

Empirical paper: Summarize research findings

Empirical papers break down a series of research questions. In your conclusion, discuss the findings your research revealed, including any information that surprised you.

Be clear about the conclusions you reached, and explain whether or not you expected to arrive at these particular ones.

• Step 3: Discuss the implications of your research

Argumentative papers and empirical papers also differ in this part of a research paper conclusion. Here are some tips on crafting conclusions for argumentative and empirical papers.

Argumentative paper: Powerful closing statement

In an argumentative paper, you'll have spent a great deal of time expressing the opinions you formed after doing a significant amount of research. Make a strong closing statement in your argumentative paper's conclusion to share the significance of your work.

You can outline the next steps through a bold call to action, or restate how powerful your ideas turned out to be.

Empirical paper: Directions for future research

Empirical papers are broader in scope. They usually cover a variety of aspects and can include several points of view.

To write a good conclusion for an empirical paper, suggest the type of research that could be done in the future, including methods for further investigation or outlining ways other researchers might proceed.

If you feel your research had any limitations, even if they were outside your control, you could mention these in your conclusion.

After you finish outlining your conclusion, ask someone to read it and offer feedback. In any research project you're especially close to, it can be hard to identify problem areas. Having a close friend or someone whose opinion you value read the research paper and provide honest feedback can be invaluable. Take note of any suggested edits and consider incorporating them into your paper if they make sense.

• Things to avoid in a research paper conclusion

Keep these aspects to avoid in mind as you're writing your conclusion and refer to them after you've created an outline.

Dry summary

Writing a memorable, succinct conclusion is arguably more important than a strong introduction. Take care to avoid just rephrasing your main points, and don't fall into the trap of repeating dry facts or citations.

You can provide a new perspective for your readers to think about or contextualize your research. Either way, make the conclusion vibrant and interesting, rather than a rote recitation of your research paper’s highlights.

Clichéd or generic phrasing

Your research paper conclusion should feel fresh and inspiring. Avoid generic phrases like "to sum up" or "in conclusion." These phrases tend to be overused, especially in an academic context and might turn your readers off.

The conclusion also isn't the time to introduce colloquial phrases or informal language. Retain a professional, confident tone consistent throughout your paper’s conclusion so it feels exciting and bold.

New data or evidence

While you should present strong data throughout your paper, the conclusion isn't the place to introduce new evidence. This is because readers are engaged in actively learning as they read through the body of your paper.

By the time they reach the conclusion, they will have formed an opinion one way or the other (hopefully in your favor!). Introducing new evidence in the conclusion will only serve to surprise or frustrate your reader.

Ignoring contradictory evidence

If your research reveals contradictory evidence, don't ignore it in the conclusion. This will damage your credibility as an expert and might even serve to highlight the contradictions.

Be as transparent as possible and admit to any shortcomings in your research, but don't dwell on them for too long.

Ambiguous or unclear resolutions

The point of a research paper conclusion is to provide closure and bring all your ideas together. You should wrap up any arguments you introduced in the paper and tie up any loose ends, while demonstrating why your research and data are strong.

Use direct language in your conclusion and avoid ambiguity. Even if some of the data and sources you cite are inconclusive or contradictory, note this in your conclusion to come across as confident and trustworthy.

• Examples of research paper conclusions

Your research paper should provide a compelling close to the paper as a whole, highlighting your research and hard work. While the conclusion should represent your unique style, these examples offer a starting point:

Ultimately, the data we examined all point to the same conclusion: Encouraging a good work-life balance improves employee productivity and benefits the company overall. The research suggests that when employees feel their personal lives are valued and respected by their employers, they are more likely to be productive when at work. In addition, company turnover tends to be reduced when employees have a balance between their personal and professional lives. While additional research is required to establish ways companies can support employees in creating a stronger work-life balance, it's clear the need is there.

Social media is a primary method of communication among young people. As we've seen in the data presented, most young people in high school use a variety of social media applications at least every hour, including Instagram and Facebook. While social media is an avenue for connection with peers, research increasingly suggests that social media use correlates with body image issues. Young girls with lower self-esteem tend to use social media more often than those who don't log onto social media apps every day. As new applications continue to gain popularity, and as more high school students are given smartphones, more research will be required to measure the effects of prolonged social media use.

What are the different kinds of research paper conclusions?

There are no formal types of research paper conclusions. Ultimately, the conclusion depends on the outline of your paper and the type of research you’re presenting. While some experts note that research papers can end with a new perspective or commentary, most papers should conclude with a combination of both. The most important aspect of a good research paper conclusion is that it accurately represents the body of the paper.

Can I present new arguments in my research paper conclusion?

Research paper conclusions are not the place to introduce new data or arguments. The body of your paper is where you should share research and insights, where the reader is actively absorbing the content. By the time a reader reaches the conclusion of the research paper, they should have formed their opinion. Introducing new arguments in the conclusion can take a reader by surprise, and not in a positive way. It might also serve to frustrate readers.

How long should a research paper conclusion be?

There's no set length for a research paper conclusion. However, it's a good idea not to run on too long, since conclusions are supposed to be succinct. A good rule of thumb is to keep your conclusion around 5 to 10 percent of the paper's total length. If your paper is 10 pages, try to keep your conclusion under one page.

What should I include in a research paper conclusion?

A good research paper conclusion should always include a sense of urgency, so the reader can see how and why the topic should matter to them. You can also note some recommended actions to help fix the problem and some obstacles they might encounter. A conclusion should also remind the reader of the thesis statement, along with the main points you covered in the paper. At the end of the conclusion, add a powerful closing statement that helps cement the paper in the mind of the reader.

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How to Write a Conclusion for a Research Paper

Last Updated: June 29, 2023 Approved

This article was co-authored by Christopher Taylor, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article received 42 testimonials and 82% of readers who voted found it helpful, earning it our reader-approved status. This article has been viewed 2,254,955 times.

The conclusion of a research paper needs to summarize the content and purpose of the paper without seeming too wooden or dry. Every basic conclusion must share several key elements, but there are also several tactics you can play around with to craft a more effective conclusion and several you should avoid to prevent yourself from weakening your paper's conclusion. Here are some writing tips to keep in mind when creating a conclusion for your next research paper.

Sample Conclusions

Writing a basic conclusion.

• Do not spend a great amount of time or space restating your topic.
• A good research paper will make the importance of your topic apparent, so you do not need to write an elaborate defense of your topic in the conclusion.
• Usually a single sentence is all you need to restate your topic.
• An example would be if you were writing a paper on the epidemiology of infectious disease, you might say something like "Tuberculosis is a widespread infectious disease that affects millions of people worldwide every year."
• Yet another example from the humanities would be a paper about the Italian Renaissance: "The Italian Renaissance was an explosion of art and ideas centered around artists, writers, and thinkers in Florence."

• A thesis is a narrowed, focused view on the topic at hand.
• This statement should be rephrased from the thesis you included in your introduction. It should not be identical or too similar to the sentence you originally used.
• Try re-wording your thesis statement in a way that complements your summary of the topic of your paper in your first sentence of your conclusion.
• An example of a good thesis statement, going back to the paper on tuberculosis, would be "Tuberculosis is a widespread disease that affects millions of people worldwide every year. Due to the alarming rate of the spread of tuberculosis, particularly in poor countries, medical professionals are implementing new strategies for the diagnosis, treatment, and containment of this disease ."

• A good way to go about this is to re-read the topic sentence of each major paragraph or section in the body of your paper.
• Find a way to briefly restate each point mentioned in each topic sentence in your conclusion. Do not repeat any of the supporting details used within your body paragraphs.
• Under most circumstances, you should avoid writing new information in your conclusion. This is especially true if the information is vital to the argument or research presented in your paper.
• For example, in the TB paper you could summarize the information. "Tuberculosis is a widespread disease that affects millions of people worldwide. Due to the alarming rate of the spread of tuberculosis, particularly in poor countries, medical professionals are implementing new strategies for the diagnosis, treatment, and containment of this disease. In developing countries, such as those in Africa and Southeast Asia, the rate of TB infections is soaring. Crowded conditions, poor sanitation, and lack of access to medical care are all compounding factors in the spread of the disease. Medical experts, such as those from the World Health Organization are now starting campaigns to go into communities in developing countries and provide diagnostic testing and treatments. However, the treatments for TB are very harsh and have many side effects. This leads to patient non-compliance and spread of multi-drug resistant strains of the disease."

• Note that this is not needed for all research papers.
• If you already fully explained what the points in your paper mean or why they are significant, you do not need to go into them in much detail in your conclusion. Simply restating your thesis or the significance of your topic should suffice.
• It is always best practice to address important issues and fully explain your points in the body of your paper. The point of a conclusion to a research paper is to summarize your argument for the reader and, perhaps, to call the reader to action if needed.

• Note that a call for action is not essential to all conclusions. A research paper on literary criticism, for instance, is less likely to need a call for action than a paper on the effect that television has on toddlers and young children.
• A paper that is more likely to call readers to action is one that addresses a public or scientific need. Let's go back to our example of tuberculosis. This is a very serious disease that is spreading quickly and with antibiotic-resistant forms.
• A call to action in this research paper would be a follow-up statement that might be along the lines of "Despite new efforts to diagnose and contain the disease, more research is needed to develop new antibiotics that will treat the most resistant strains of tuberculosis and ease the side effects of current treatments."

• For example, if you are writing a history paper, then you might discuss how the historical topic you discussed matters today. If you are writing about a foreign country, then you might use the conclusion to discuss how the information you shared may help readers understand their own country.

Making Your Conclusion as Effective as Possible

• Since this sort of conclusion is so basic, you must aim to synthesize the information rather than merely summarizing it.
• Instead of merely repeating things you already said, rephrase your thesis and supporting points in a way that ties them all together.
• By doing so, you make your research paper seem like a "complete thought" rather than a collection of random and vaguely related ideas.

• Ask a question in your introduction. In your conclusion, restate the question and provide a direct answer.
• Write an anecdote or story in your introduction but do not share the ending. Instead, write the conclusion to the anecdote in the conclusion of your paper.
• For example, if you wanted to get more creative and put a more humanistic spin on a paper on tuberculosis, you might start your introduction with a story about a person with the disease, and refer to that story in your conclusion. For example, you could say something like this before you re-state your thesis in your conclusion: "Patient X was unable to complete the treatment for tuberculosis due to severe side effects and unfortunately succumbed to the disease."
• Use the same concepts and images introduced in your introduction in your conclusion. The images may or may not appear at other points throughout the research paper.

• Include enough information about your topic to back the statement up but do not get too carried away with excess detail.
• If your research did not provide you with a clear-cut answer to a question posed in your thesis, do not be afraid to indicate as much.
• Restate your initial hypothesis and indicate whether you still believe it or if the research you performed has begun swaying your opinion.
• Indicate that an answer may still exist and that further research could shed more light on the topic at hand.

• This may not be appropriate for all types of research papers. Most research papers, such as one on effective treatment for diseases, will have the information to make the case for a particular argument already in the paper.
• A good example of a paper that might ask a question of the reader in the ending is one about a social issue, such as poverty or government policy.
• Ask a question that will directly get at the heart or purpose of the paper. This question is often the same question, or some version of it, that you may have started with when you began your research.
• Make sure that the question can be answered by the evidence presented in your paper.
• If desired you can briefly summarize the answer after stating the question. You could also leave the question hanging for the reader to answer, though.

• Even without a call to action, you can still make a recommendation to your reader.
• For instance, if you are writing about a topic like third-world poverty, you can various ways for the reader to assist in the problem without necessarily calling for more research.
• Another example would be, in a paper about treatment for drug-resistant tuberculosis, you could suggest donating to the World Health Organization or research foundations that are developing new treatments for the disease.

Avoiding Common Pitfalls

• These sayings usually sound stiff, unnatural, or trite when used in writing.
• Moreover, using a phrase like "in conclusion" to begin your conclusion is a little too straightforward and tends to lead to a weak conclusion. A strong conclusion can stand on its own without being labeled as such.

• Always state the main argument or thesis in the introduction. A research paper is an analytical discussion of an academic topic, not a mystery novel.
• A good, effective research paper will allow your reader to follow your main argument from start to finish.
• This is why it is best practice to start your paper with an introduction that states your main argument and to end the paper with a conclusion that re-states your thesis for re-iteration.

• All significant information should be introduced in the body of the paper.
• Supporting evidence expands the topic of your paper by making it appear more detailed. A conclusion should narrow the topic to a more general point.
• A conclusion should only summarize what you have already stated in the body of your paper.
• You may suggest further research or a call to action, but you should not bring in any new evidence or facts in the conclusion.

• Most often, a shift in tone occurs when a research paper with an academic tone gives an emotional or sentimental conclusion.
• Even if the topic of the paper is of personal significance for you, you should not indicate as much in your paper.
• If you want to give your paper a more humanistic slant, you could start and end your paper with a story or anecdote that would give your topic more personal meaning to the reader.
• This tone should be consistent throughout the paper, however.

• Apologetic statements include phrases like "I may not be an expert" or "This is only my opinion."
• Statements like this can usually be avoided by refraining from writing in the first-person.
• Avoid any statements in the first-person. First-person is generally considered to be informal and does not fit with the formal tone of a research paper.

Community Q&A

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• ↑ http://owl.english.purdue.edu/owl/resource/724/04/
• ↑ http://www.crlsresearchguide.org/18_Writing_Conclusion.asp
• ↑ http://writing.wisc.edu/Handbook/PlanResearchPaper.html#conclusion
• ↑ http://writingcenter.unc.edu/handouts/conclusions/
• ↑ http://writing2.richmond.edu/writing/wweb/conclude.html

About This Article

To write a conclusion for a research paper, start by restating your thesis statement to remind your readers what your main topic is and bring everything full circle. Then, briefly summarize all of the main points you made throughout your paper, which will help remind your readers of everything they learned. You might also want to include a call to action if you think more research or work needs to be done on your topic by writing something like, "Despite efforts to contain the disease, more research is needed to develop antibiotics." Finally, end your conclusion by explaining the broader context of your topic and why your readers should care about it, which will help them understand why your topic is relevant and important. For tips from our Academic co-author, like how to avoid common pitfalls when writing your conclusion, scroll down! Did this summary help you? Yes No

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Conclusions

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This resource outlines the generally accepted structure for introductions, body paragraphs, and conclusions in an academic argument paper. Keep in mind that this resource contains guidelines and not strict rules about organization. Your structure needs to be flexible enough to meet the requirements of your purpose and audience.

Conclusions wrap up what you have been discussing in your paper. After moving from general to specific information in the introduction and body paragraphs, your conclusion should begin pulling back into more general information that restates the main points of your argument. Conclusions may also call for action or overview future possible research. The following outline may help you conclude your paper:

In a general way,

• Restate your topic and why it is important,
• Restate your thesis/claim,
• Address opposing viewpoints and explain why readers should align with your position,
• Call for action or overview future research possibilities.

Remember that once you accomplish these tasks, unless otherwise directed by your instructor, you are finished. Done. Complete. Don't try to bring in new points or end with a whiz bang(!) conclusion or try to solve world hunger in the final sentence of your conclusion. Simplicity is best for a clear, convincing message.

The preacher's maxim is one of the most effective formulas to follow for argument papers:

Tell what you're going to tell them (introduction).

Tell them (body).

Tell them what you told them (conclusion).

OASIS: Writing Center

Writing a paper: conclusions, writing a conclusion.

A conclusion is an important part of the paper; it provides closure for the reader while reminding the reader of the contents and importance of the paper. It accomplishes this by stepping back from the specifics in order to view the bigger picture of the document. In other words, it is reminding the reader of the main argument. For most course papers, it is usually one paragraph that simply and succinctly restates the main ideas and arguments, pulling everything together to help clarify the thesis of the paper. A conclusion does not introduce new ideas; instead, it should clarify the intent and importance of the paper. It can also suggest possible future research on the topic.

An Easy Checklist for Writing a Conclusion

It is important to remind the reader of the thesis of the paper so he is reminded of the argument and solutions you proposed.

Think of the main points as puzzle pieces, and the conclusion is where they all fit together to create a bigger picture. The reader should walk away with the bigger picture in mind.

Make sure that the paper places its findings in the context of real social change.

Make sure the reader has a distinct sense that the paper has come to an end. It is important to not leave the reader hanging. (You don’t want her to have flip-the-page syndrome, where the reader turns the page, expecting the paper to continue. The paper should naturally come to an end.)

No new ideas should be introduced in the conclusion. It is simply a review of the material that is already present in the paper. The only new idea would be the suggesting of a direction for future research.

Conclusion Example

As addressed in my analysis of recent research, the advantages of a later starting time for high school students significantly outweigh the disadvantages. A later starting time would allow teens more time to sleep--something that is important for their physical and mental health--and ultimately improve their academic performance and behavior. The added transportation costs that result from this change can be absorbed through energy savings. The beneficial effects on the students’ academic performance and behavior validate this decision, but its effect on student motivation is still unknown. I would encourage an in-depth look at the reactions of students to such a change. This sort of study would help determine the actual effects of a later start time on the time management and sleep habits of students.

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• How to Write a Thesis or Dissertation Conclusion

How to Write a Dissertation Conclusion | Checklist and Examples

Published on 9 September 2022 by Tegan George and Shona McCombes. Revised on 10 October 2022.

The conclusion is the very last part of your thesis or dissertation . It should be concise and engaging, leaving your reader with a clear understanding of your main findings, as well as the answer to your research question .

In it, you should:

• Clearly state the answer to your main research question
• Summarise and reflect on your research process
• Make recommendations for future work on your topic
• Show what new knowledge you have contributed to your field
• Wrap up your thesis or dissertation

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Table of contents

Discussion vs. conclusion, how long should your conclusion be, step 1: answer your research question, step 2: summarise and reflect on your research, step 3: make future recommendations, step 4: emphasise your contributions to your field, step 5: wrap up your thesis or dissertation, full conclusion example, conclusion checklist, frequently asked questions about conclusion sections.

While your conclusion contains similar elements to your discussion section , they are not the same thing.

Your conclusion should be shorter and more general than your discussion. Instead of repeating literature from your literature review , discussing specific research results , or interpreting your data in detail, concentrate on making broad statements that sum up the most important insights of your research.

As a rule of thumb, your conclusion should not introduce new data, interpretations, or arguments.

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Depending on whether you are writing a thesis or dissertation, your length will vary. Generally, a conclusion should make up around 5–7% of your overall word count.

An empirical scientific study will often have a short conclusion, concisely stating the main findings and recommendations for future research. A humanities topic or systematic review , on the other hand, might require more space to conclude its analysis, tying all the previous sections together in an overall argument.

Your conclusion should begin with the main question that your thesis or dissertation aimed to address. This is your final chance to show that you’ve done what you set out to do, so make sure to formulate a clear, concise answer.

• Don’t repeat a list of all the results that you already discussed
• Do synthesise them into a final takeaway that the reader will remember.

An empirical thesis or dissertation conclusion may begin like this:

A case study –based thesis or dissertation conclusion may begin like this:

In the second example, the research aim is not directly restated, but rather added implicitly to the statement. To avoid repeating yourself, it is helpful to reformulate your aims and questions into an overall statement of what you did and how you did it.

Your conclusion is an opportunity to remind your reader why you took the approach you did, what you expected to find, and how well the results matched your expectations.

To avoid repetition , consider writing more reflectively here, rather than just writing a summary of each preceding section. Consider mentioning the effectiveness of your methodology , or perhaps any new questions or unexpected insights that arose in the process.

You can also mention any limitations of your research, but only if you haven’t already included these in the discussion. Don’t dwell on them at length, though – focus on the positives of your work.

• While x limits the generalisability of the results, this approach provides new insight into y .
• This research clearly illustrates x , but it also raises the question of y .

You may already have made a few recommendations for future research in your discussion section, but the conclusion is a good place to elaborate and look ahead, considering the implications of your findings in both theoretical and practical terms.

• Based on these conclusions, practitioners should consider …
• To better understand the implications of these results, future studies could address …
• Further research is needed to determine the causes of/effects of/relationship between …

When making recommendations for further research, be sure not to undermine your own work. Relatedly, while future studies might confirm, build on, or enrich your conclusions, they shouldn’t be required for your argument to feel complete. Your work should stand alone on its own merits.

Just as you should avoid too much self-criticism, you should also avoid exaggerating the applicability of your research. If you’re making recommendations for policy, business, or other practical implementations, it’s generally best to frame them as ‘shoulds’ rather than ‘musts’. All in all, the purpose of academic research is to inform, explain, and explore – not to demand.

Make sure your reader is left with a strong impression of what your research has contributed to the state of your field.

Some strategies to achieve this include:

• Returning to your problem statement to explain how your research helps solve the problem
• Referring back to the literature review and showing how you have addressed a gap in knowledge
• Discussing how your findings confirm or challenge an existing theory or assumption

Again, avoid simply repeating what you’ve already covered in the discussion in your conclusion. Instead, pick out the most important points and sum them up succinctly, situating your project in a broader context.

The end is near! Once you’ve finished writing your conclusion, it’s time to wrap up your thesis or dissertation with a few final steps:

• It’s a good idea to write your abstract next, while the research is still fresh in your mind.
• Next, make sure your reference list is complete and correctly formatted. To speed up the process, you can use our free APA citation generator .
• Once you’ve added any appendices , you can create a table of contents and title page .
• Finally, read through the whole document again to make sure your thesis is clearly written and free from language errors. You can proofread it yourself , ask a friend, or consider Scribbr’s proofreading and editing service .

Here is an example of how you can write your conclusion section. Notice how it includes everything mentioned above:

V. Conclusion

The current research aimed to identify acoustic speech characteristics which mark the beginning of an exacerbation in COPD patients.

The central questions for this research were as follows: 1. Which acoustic measures extracted from read speech differ between COPD speakers in stable condition and healthy speakers? 2. In what ways does the speech of COPD patients during an exacerbation differ from speech of COPD patients during stable periods?

All recordings were aligned using a script. Subsequently, they were manually annotated to indicate respiratory actions such as inhaling and exhaling. The recordings of 9 stable COPD patients reading aloud were then compared with the recordings of 5 healthy control subjects reading aloud. The results showed a significant effect of condition on the number of in- and exhalations per syllable, the number of non-linguistic in- and exhalations per syllable, and the ratio of voiced and silence intervals. The number of in- and exhalations per syllable and the number of non-linguistic in- and exhalations per syllable were higher for COPD patients than for healthy controls, which confirmed both hypotheses.

However, the higher ratio of voiced and silence intervals for COPD patients compared to healthy controls was not in line with the hypotheses. This unpredicted result might have been caused by the different reading materials or recording procedures for both groups, or by a difference in reading skills. Moreover, there was a trend regarding the effect of condition on the number of syllables per breath group. The number of syllables per breath group was higher for healthy controls than for COPD patients, which was in line with the hypothesis. There was no effect of condition on pitch, intensity, center of gravity, pitch variability, speaking rate, or articulation rate.

This research has shown that the speech of COPD patients in exacerbation differs from the speech of COPD patients in stable condition. This might have potential for the detection of exacerbations. However, sustained vowels rarely occur in spontaneous speech. Therefore, the last two outcome measures might have greater potential for the detection of beginning exacerbations, but further research on the different outcome measures and their potential for the detection of exacerbations is needed due to the limitations of the current study.

Checklist: Conclusion

I have clearly and concisely answered the main research question .

I have summarized my overall argument or key takeaways.

I have mentioned any important limitations of the research.

I have given relevant recommendations .

I have clearly explained what my research has contributed to my field.

I have  not introduced any new data or arguments.

You've written a great conclusion! Use the other checklists to further improve your dissertation.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

For a stronger dissertation conclusion , avoid including:

• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

The conclusion of your thesis or dissertation shouldn’t take up more than 5-7% of your overall word count.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

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• Published: 30 July 2022

Using decision analysis to support implementation planning in research and practice

• Natalie Riva Smith   ORCID: orcid.org/0000-0002-2052-9433 1 ,
• Kathleen E. Knocke 2 &
• Kristen Hassmiller Lich 2  

Implementation Science Communications volume  3 , Article number:  83 ( 2022 ) Cite this article

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The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis.

When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning.

To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.

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Contributions to the literature

We introduce decision analysis for implementation planning as a way to overcome common challenges faced in the planning process, such as uncertainty about how to select interventions or implementation strategies in a given context or reconciling competing objectives among stakeholders.

We discuss the PROACTIVE framework, which describes three broad phases of decision analysis and guides users through explicitly defining the problem of interest, outlining different implementation plans, assessing the potential outcomes of each, and considering those outcomes in context.

We provide key areas for future research to consider on the path towards advancing decision analysis to support implementation planning.

Early implementation involves many choices [ 1 , 2 , 3 ]. These choices involve questions such as what intervention or evidence-based program (EBP) should be pursued for a given health issue of interest, or, if the intervention is already selected, what implementation strategies will best support success. Combined intervention/implementation strategy packages might also be considered. These choices are difficult because there are ever-increasing options for interventions and implementation strategies, and early decisions likely influence subsequent choices or future implementation plans (e.g., adding additional interventions in the future, when resources allow). Insurmountable barriers to implementing an intervention might arise that are unique to a given context, requiring planners to reevaluate their intervention choice, and contextually appropriate implementation strategies are challenging to select [ 4 , 5 ].

In addition to considering the likely effectiveness of different intervention and/or implementation strategy combinations (hereafter referred to as decision “alternatives”), implementors also need to consider the cost implications of each alternative and determine whether they are feasible. Cost considerations—such as how much an intervention and strategy combination might cost, what the timing of those costs is, and who is responsible for those costs—have emerged as key drivers of implementation success [ 6 , 7 , 8 , 9 , 10 , 11 ]. During planning, implementors may ask questions such as “What might be the relative costs of implementation strategy A versus B, compared to the expected consequences of each?” or “How much staff time might be needed to implement this intervention with fidelity?” These questions about the costs and consequences of different alternatives, also called economic evaluations, can be used to help analyze trade-offs between different implementation plans [ 12 , 13 ]. Economic evaluation can also help implementors plan for the financial realities of implementation and facilitate buy-in from investors or stakeholders [ 9 , 14 ].

Additional decision objectives may also be important, with precise objectives being context specific. Sometimes, equity impacts are a priority, other times mitigating potential risks (e.g., harm, failing to be cost-neutral) may be important. One available resource for implementation planning that accounts for the variety of objectives and considerations of implementation is the RE-AIM project planning tool, which includes questions about the expected effects of a program, its required resources, and staff capacity [ 15 ]. Another resource, the Implementation Research Logic Model, guides users to think through potential implementation strategies and scenarios based on known parameters [ 16 ]. While these kinds of planning tools are extremely valuable contributions to implementation science, they are limited in that, for example, they presume a specific intervention is already chosen or provide minimal guidance on how to compare alternative implementation plans.

The complexity of implementation planning also makes existing tools limited. Thinking through implementation planning involves many characteristics that make decisions difficult: long-time horizons (requiring action up-front, though benefits may not be realized until later), the involvement of many different stakeholders with different values and preferences, uncertainty in the possible outcomes under different alternatives, and interconnected decisions [ 17 , 18 , 19 ]. This complexity makes systematic decision-making difficult, particularly because individuals tend to rely on simplifications or heuristics to make decisions in the face of complexity, leading to biased, inconsistent, or even harmful decisions [ 17 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ].

Despite the importance and complexity of implementation planning, it has received relatively little attention in the literature [ 3 , 15 , 28 ] and approaches are needed to help structure the planning process and weigh different alternatives. An ideal approach would be flexible enough to meet a range of planning questions and integrate multiple considerations to help answer complex questions that arise during the planning process. We believe that decision analysis, a widely used and flexible process to systematically approach decision-making, offers just that. In this paper, we discuss the decision analysis approach, with particular attention to its relevance for implementation planning questions.

What is decision analysis?

Decision analysis is a systematic way to assess various aspects of complex problems under uncertain conditions, with the goal of helping decision-makers choose the course of action (i.e., alternative) that best aligns with their objectives, considering their own context [ 17 , 18 , 29 , 30 , 31 , 32 , 33 ]. Applied to implementation planning questions, decision analysis aims to provide structure to the planning process by ensuring that assumptions, possible decision alternatives, available research evidence, and objectives for implementation are laid out systematically and explicitly. Within public health and healthcare, readers may be familiar with patient-level decision analysis (i.e., operationalized in clinical decision aids) that aims to help patients choose treatments that best align with their own care needs and preferences [ 34 ] or, with larger, national-level decision analytic approaches such as those used in the UK to structure healthcare reimbursement decisions [ 35 ]. In the context of this paper, we take a general view and consider decision-makers to be relevant stakeholders engaged with the implementation planning processes (e.g., those who are making adoption decisions, as well as those who can choose to support/resist decisions) [ 36 ] and decision problems to be implementation planning questions (typically about intervention or implementation strategy choices).

We structure our discussion using the PROACTIVE framework as a guide. PROACTIVE is a foundational framework for decision analysis in health introduced by Hunink and colleagues [ 17 ], which draws on work from Keeney and colleagues in operations research [ 18 , 19 ]. PROACTIVE offers a comprehensive overview of established steps in decision analysis while allowing for flexibility and iteration within each [ 17 ]. The framework conceptualizes decision analysis as a process spanning three phases through which (1) the decision problem and overall objectives are defined, (2) different alternatives are compared, and (3) information on each alternative is synthesized [ 17 ]. Overall, this framework provides a clear way to understand the full process of decision analysis, without being overly prescriptive about which specific methods are used throughout.

Throughout our discussion, we use three examples of decision analysis for implementation planning to illustrate how various steps of the PROACTIVE framework can be operationalized (Table 1 ). Examples were selected for their heterogenous approaches and focuses and to showcase the variety of ways that decision analysis could be approached in implementation planning efforts: selecting childhood maltreatment EBPs (Cruden et al.), improving the reach of EBPs for mental health (Zimmerman et al.), and improving rates of colorectal cancer screening (Hassmiller Lich et al.) [ 37 , 38 , 39 ].

Phase 1: PRO—defining problem and objectives

P: defining the problem.

It is critical to understand the precise nature of the problem at hand before making decisions. This often involves understanding the “natural history” of the problem—what is happening over time and what might happen if we take no action? In implementation science, this may be health problems we seek to address (e.g., disparities in blood pressure control) or low uptake of EBPs we have already decided to support (e.g., for depression treatment among veterans). Understanding the determinants of health is also important, including system structure flaws that need to be addressed as well as what barriers or facilitators to implementation exist—both broadly and specific to considered interventions. Here, it is critical that focal problems be understood in the context they will be addressed, asking and answering the question “Why does this problem persist here and now?”.

R: Reframe from other perspectives and understand objectives

Especially with complex issues in implementation that are not constrained to a single industry, discipline, or sector, multiple perspectives are critical. The problem, and what each stakeholder hopes to accomplish, might look quite different. It may take discussion to develop a shared vocabulary and understanding before the core problem, objectives, and the most critical determinants of the problem become clear. Even when prioritized decision objectives overlap, there may remain varied preferences across perspectives. Differences in objectives must be understood and acknowledged, and interconnections and commonalities highlighted to support cross-stakeholder change initiatives.

O: Focus on the unifying objective(s)

The unifying objectives of stakeholders—what a group can unite behind in a change initiative—typically become apparent as the problem is discussed and reframed. Objectives can be competing (e.g., access and system cost), and their priority could differ by stakeholders and across contexts. They might also be different over time (e.g., access matters first, then quality of care). Objectives may include meeting certain benchmarks for standard implementation outcomes such as fidelity or sustainability [ 40 ] or maximizing health benefits. Costs to implement and sustain the intervention are also often relevant; for example, stakeholders may want to keep costs to a minimum or within a prespecified range (potentially because of grant restrictions or a budget). Other objectives such as the potential harms of a given implementation plan or impacts on health equity may be relevant to stakeholders. Still, other objectives might be shaped by organizations’ missions. The goal in this stage of the process is to identify the set of objectives that stakeholders, as a group, agree upon in order to frame the next steps in the decision analysis process.

These first three steps are interconnected—and as such, those involved in decision analysis processes should acknowledge this and ensure these steps are flexible enough to allow for feedback and learning. The importance of stakeholder engagement in these steps cannot be overstated. Without the purposeful and meaningful engagement of relevant stakeholder groups, a problem statement and objectives might be settled on that will later be resisted—threatening sustainability—due to insufficient support or misalignment with the true structure of the system producing problematic outcomes [ 23 ]. Traditional qualitative work (e.g., key informant interviews, focus groups) or quantitative work (e.g., surveys sent to stakeholders) can be leveraged here, and we also suggest that researchers consider methods from the field of systems science that have developed engagement processes specifically designed to facilitate a shared understanding of a problem and objectives through engaging diverse stakeholders in structured systems thinking processes—particularly valuable when motivating problems are complex [ 41 , 42 ].

The case studies each approached these steps somewhat differently. First, Cruden engaged a group of stakeholders to clarify their definition of childhood maltreatment and identify objectives (referred to as criteria in their work) by which to evaluate different EBPs, such as program familiarity, strength of evidence base, or resource availability [ 37 ]. Conversely, Zimmerman et al. began their work with a clear problem and objectives (limited reach of EBPs for mental health, increasing scheduled and completed appointments) and spent time conducting qualitative work with stakeholders to understand the components of the system under study and how perspectives on the system differed between stakeholders [ 38 ]. Finally, Hassmiller Lich had an a priori problem definition and objectives (low rates of colorectal cancer screening, understanding the cost-effectiveness of different interventions from Medicaid’s perspective) [ 39 ].

Phase 2: ACT—comparing different alternatives

A: consider relevant alternatives (interventions and/or implementation strategies).

It is critical to know the range of what alternatives are possible before decisions are made—including doing nothing (a decision itself). Information on possible interventions can come from a variety of sources, including evidence searches or consults with stakeholders and experts. Online repositories such as the National Cancer Institute’s Evidence-Based Cancer Control Programs listing can help identify interventions that align with different focus areas (e.g., HPV vaccination, tobacco control) or populations (e.g., rural adults, schoolchildren) [ 43 ]. Tools such as the CFIR-ERIC matching tool can help narrow the possible universe of implementation strategies based on context- or intervention-specific barriers [ 5 ]. It is also important in this stage to understand what is already in place in the community that can be built on (and not duplicated) and what resources are available to be leveraged.

C: Understand the possible consequences of each alternative

The next stage involves considering the consequences of each alternative—aligned with the objectives of interest. For example, how might the intervention impact health outcomes? What is the cost of proposed implementation strategies? Are there additional consequences that might bolster or undermine intended effects? Who might react to changes the intervention creates—and how will those reactions impact the objectives of interest?

There is a wide range of methods that can be used to understand consequences. As is true with all research, the optimal method(s) will be based on the objectives of implementation planning, available research resources and capacity, and the specific questions that need to be answered. One simple approach is to collate existing literature, reports, or evaluations on the likely consequences under each alternative. Quantitative and computational simulation modeling can be undertaken as well. Decision analysis experts including Briggs et al. [ 44 ] and Marshall et al. [ 45 , 46 ] provide quality overviews of modeling methods and how they align with different questions of interest, along with references for additional reading. The wide range of potential methods available allows those with differing questions, expertise, resources, and/or decision urgency to engage effectively. For example, queuing and discrete event modeling are typically employed when questions focus on service delivery systems or waiting times [ 47 , 48 ]. Agent-based modeling, on the other hand, can simulate interacting individuals in geographically explicit contexts, making it particularly useful when implementation planning depends on social network effects or geographic considerations [ 49 ].

One modeling method we wish to draw particular attention to is system dynamics modeling, which focuses on conceptualizing relationships between variables over time, identifying feedback loops between them, and modeling how the system responds to changes over time [ 50 , 51 ]. These models simulate how accumulating quantities (say, the number of individuals who are up-to-date with colorectal cancer screening) change over time. As part of the broader field of systems science, system dynamics modeling has an explicit focus on and ability to simulate the elements of complexity present in implementation science work [ 52 , 53 , 54 , 55 , 56 , 57 ]. For example, time dynamics and delays during the implementation process are typical (e.g., change takes time, costs accrue quickly while health benefits accrue more slowly, data and system feedback is often delayed in its availability), and feedback loops can exist among relevant implementation considerations (i.e., when something in the system responds to earlier changes, either reinforcing or counteracting earlier changes—in both desired and undesired ways). When these types of characteristics are present, breaking down complex systems into pieces and simplifying assumptions allows for studying individual pathways, but evaluating pathways independently of the broader system can lead to major missed opportunities or even exacerbation of the problem that motivated intervention [ 23 , 58 , 59 ]. These qualities make system dynamics a natural fit for implementation science work [ 38 , 52 , 53 , 60 ].

T: Identify and estimate trade-offs (preferences and values)

Once an understanding of the potential consequences has been established, the trade-offs between decision alternatives can be examined. For example, one implementation strategy may cost more but improve implementation outcomes more than a cheaper implementation strategy, or one implementation plan might be expected to be less effective overall but reduce health inequities. These kinds of results raise important trade-offs that decision-makers must acknowledge and consider when making decisions. To plan in the face of trade-offs requires an understanding of what decision-makers prefer and value (e.g., is it more important to improve fidelity or feasibility?), all of which may be context-specific [ 33 , 61 ].

A major consideration when incorporating values and preferences into implementation planning is to consider whose values and preferences are being used [ 33 , 62 ]. For example, a clinic could use the values of their patient population, or front-line providers, or administration when weighing trade-offs between alternatives. In some situations, preferences and values may align across stakeholders, and in other cases, they may not. Mixed-methods approaches can help capture how different perspectives and contexts relate to trade-offs in costs and consequences [ 61 ].

One particular method of use here is discrete choice experiments (DCEs), which focus on quantifying the relative importance of different aspects of alternatives [ 63 ]. Applied to implementation planning, DCEs could be used to gather information from stakeholders on which interventions or implementation strategies they prefer [ 63 ], or identify trade-offs between different aspects of implementation plans (e.g., costs, effects, feasibility), allowing for a more comprehensive assessment of the value of different implementation plans.

Cruden et al. identified seven candidate EBPs for child maltreatment prevention from a best practices repository, including three in the final analyses; each intervention was scored on each stakeholder-identified criteria using published literature, and weights were used to capture preferences for different criteria [ 37 ]. In Zimmerman’s work, stakeholders suggested many potential implementation plans, and the team worked to prioritize their top two for further analysis (implicitly incorporating preferences) [ 38 ]. The potential effects of these plans were quantitatively compared using a system dynamics simulation model, developed and calibrated with local data (see Fig.  2 in text for a visual of the system structure) [ 38 ]. Finally, Hassmiller Lich compared four interventions, selected via literature reviews and stakeholder interviews, using a large individual-based simulation model that estimated each intervention’s likely costs (in dollars) and effects (in years of life up-to-date on screening) [ 39 ].

Phase 3: IVE—synthesizing information on each alternative

I: integrate evidence on the likely consequences given identified preferences and values.

It may become clear based on anticipated consequences of alternatives and preferences/values what the “best” decision is for a specific context. Other times, it may not be. In the latter case, using a formal “objective function” that integrates objectives through weighting each component is useful. For example, quantifying the cost per additional patient reached or the net benefit of a decision alternative can integrate costs and health benefits. However, if stakeholders with different perspectives disagree on weights, it may be more useful to present information about expected outcomes for each component (i.e., valued outcome) in a tabular format (often called a “balance sheet,” “values framework,” or “performance matrix” [ 62 , 64 ]).

V: Optimize the expected value

In traditional decision analysis, there are employable rules for making decisions—for example, by choosing the alternative that optimizes an objective or an objective function value or by choosing the alternative that has the smallest chance of poor outcomes. If stakeholders cannot agree on weights for each decision outcome, decision analysts might ask each to select their top 3 alternatives once outcome estimates are available or make their own weights/preferences explicit before seeing outcome estimates. In either case, a process will also be needed for determining which alternative is selected once all stakeholder selections are made (e.g., will each stakeholder get an equal vote, how will ties be broken). These decision rules should be decided through discussion with all involved stakeholders before alternatives’ scores are quantified and analyzed to minimize biases and conflicts.

E: Explore assumptions and evaluate uncertainty

Even when significant investment is made to reflect the local context in the decision analysis process when estimating potential outcomes, uncertainty will always exist. Decision analysis findings can only reflect what is known (not what is unknown), and no model will ever precisely anticipate what could be [ 33 ]. Presenting single estimates of the consequences of alternatives can mask this uncertainty and potentially mislead decision-makers.

Once expected (best-guess) results are estimated, a cornerstone of decision analysis is exploring how consequences and trade-offs change under different assumptions. This can help decision-makers understand the likelihood of different outcomes based on the uncertainty in different aspects of the decision problem. Some uncertainties may be driven by questions about what could happen (e.g., how much an intervention would change health outcomes, or how effective an implementation strategy might be in promoting adherence). Sometimes uncertainty could be in preferences (e.g., how much is one consequence valued compared to another). Various analytic approaches can be used to explore this uncertainty and inform learning and planning. Across these approaches, we note that uncertainty can only be explored insofar as it is a known source of uncertainty, and engaging with diverse evidence bases, data, and stakeholders improves the chances that analytic models better characterize what is known and anticipate potential unknowns.

Optimization analyses help answer questions such as, “Across different implementation plans, what is the optimal course of action given specific constraints (e.g., hours available for specific types of workers, maximum allowable wait times)?” [ 30 ]. Here, “optimal” is defined by decision-makers and often involves minimizing implementation cost or maximizing impact while not violating specific constraints [ 30 ]. Factoring in finite resource constraints can be helpful when considering the risks and barriers related to specific implementation plans. For example, consider a clinic wanting to use grant funds to implement a new intervention, but that cannot afford the salary of a new provider. Given the varying costs/impacts for different providers (e.g., physicians, nurse practitioners) to perform implementation and intervention tasks, the clinic could identify the optimal provider (or provider mix) to undertake required tasks while avoiding hiring an additional provider.

Sensitivity analyses help identify which decision analysis model input values have the greatest leverage on outcomes [ 29 , 65 ]. Single variable sensitivity analyses involve changing single inputs and recording the outcomes (i.e., deterministic sensitivity analyses). The results of these analyses can be displayed in a “tornado plot” such as Fig.  1 , which clearly communicates which parameters have the potential to affect the focal outcome the most. This kind of information can refine implementation planning by helping planners understand where more (or less) attention and effort should be focused to achieve desired outcomes. Multiple inputs can also be manipulated at once to get a sense of how different scenarios impact outcomes (for example, the lowest or highest values for all inputs). Threshold conditions can also be assessed with sensitivity analyses, for example, to learn the conditions under which costs remain below a certain benchmark, or to estimate required resources to ensure a certain level of effectiveness.

Annotated example of a tornado plot displaying results of single-variable sensitivity analyses. Notes : In this figure, variable 1 has the greatest impact on the target outcome as depicted in the figure by the length of the bars and thus may warrant particular attention during the planning process and formal implementation

Probabilistic methods can be used to quantify how uncertainty in inputs translates into holistic decision uncertainty, asking such questions as “Are conclusions about a given intervention’s benefits relative to its costs robust to simultaneous realistic variation in inputs?” [ 29 ]. While a flavor of this type of analysis can be conducted using multivariable sensitivity analyses, this analytic approach typically involves the simulation of many combinations of inputs using probability distributions to generate a diverse, representative range of possible outcomes. This type of analysis informs decision-makers how varied outcomes might be, given plausible uncertainty in model parameters.

Uncertainty analyses can also be used to drive future research directions. Deterministic sensitivity analyses can help decision-makers pinpoint specific model uncertainties that, if addressed through further research, could improve decision-making when current uncertainty renders decision priorities unclear. Probabilistic methods to quantify uncertainty can further inform implementation research efforts through formal Value of Information analyses [ 12 , 66 , 67 , 68 ]. If the overall goal is to reduce the uncertainty in a decision, then these analyses can be used to place a specific value on future research studies that can provide more information (and thus reduce decision uncertainty) [ 12 , 66 , 67 , 68 ].

All case studies integrated their results. Cruden created “summary scores” for each of the three EBPs assessed [ 37 ]. These summary scores were calculated for each stakeholder, as a weighted sum of intervention scores, using weights that stakeholders modified for their own context [ 37 ]. Zimmerman reported visual trends in EBP reach (sessions scheduled, completed) under different implementation plans and used sensitivity analyses and stakeholder review to validate their model [ 38 ]. Other implementation planning work, also in the VA though focused on stroke, has also used system dynamics modeling and reported extensive sensitivity and uncertainty analyses [ 69 , 70 ]. Finally, Hassmiller Lich presented a visual that depicted how the cost of each intervention and the life years up-to-date were related and discussed which of the interventions were likely to be the most cost-effective, as well as the 10-year investment required [ 39 ]. While this model was probabilistic (meaning that each replication would result in a different answer, based on initial simulated values), conducting a full probabilistic analysis was not feasible given the size of the model [ 39 ]. Thus, they report mean values of outcomes over 10 large, full population replications [ 39 ].

Decision analysis provides structure to guide users to clearly articulate and agree upon objectives (e.g., improve outcomes, decrease costs, reduce inequities), uncover diverse decision alternatives, consider the strengths and weaknesses of each alternative in context, and examine potential trade-offs in objectives under each alternative [ 17 ]. These steps are a clear fit with and can add rigor to implementation planning, where implementors typically need to compare different implementation alternatives, understand the potential consequences of those alternatives, and decide what is suitable for their context. Importantly, decision analysis does not prescribe which kinds of consequences should be examined or what is right for a given context. The choice of what consequences to assess, how to value those impacts, and how the valuation leads to a decision is always context-specific and should incorporate the preferences and values of all and often diverse stakeholders [ 17 , 33 , 61 , 71 ]. Additionally, while many of the individual components of decision analysis may be familiar to implementation scientists (e.g., engaging stakeholders, selecting candidate implementation strategies), we believe it is valuable to situate these component pieces within a broader decision analysis framework.

All pieces of the PROACTIVE framework may not always be needed, and the component pieces within the framework may not proceed linearly. After the discussion of objectives, stakeholders might need to reevaluate the problem at hand. Sometimes, it may be that refining the problem definition and objectives provokes enough discussion to uncover a clear decision. Other times, the problem may be clear, and more effort is needed to assess the potential effects under each alternative or understand where major uncertainties are. This drives home that while the process may be constructed around coming to a decision, learning and insight gained throughout the decision analysis process can often be just as valuable [ 17 , 71 ], and once a process is completed for a given decision problem, the investment made can support subsequent decision-making.

Future directions

The value of applying decision analytic approaches to implementation planning has been suggested in other work, though these methods remain underutilized [ 12 , 37 , 38 , 60 , 71 ]. To advance the use of decision analysis in implementation planning and research, we propose key areas for future research and practice based on issues considered in this paper and encourage discussion on how these suggestions can be prioritized by the field.

Embrace model thinking

In addition to the complexity that can arise in the process of making decisions, complexity also exists within systems where implementation occurs (time delays in seeing the impacts of action, feedback loops, nonlinear relationships) [ 52 , 53 , 54 , 55 , 56 ]. Some modeling methods are designed to incorporate this. However, any model will, by definition, be a simplification of reality and require setting boundaries around what is included. Using initial models that represent our current, best understanding of problems and iteratively revising them as our knowledge of the system and problem grows is crucial [ 22 , 71 ]. Models can be leveraged to make assumptions transparent or further improve our understanding of complex problems [ 71 ]. For example, a small model could help identify where we need more data or knowledge—what parameters or structures are uncertain? Why are outcomes so sensitive to a specific component of the system? What happens when we consider different perspectives or constraints, or involve those with other expertise? As we learn from answers to these questions, we can expand the initial model and continue to use it to improve outcomes in complex and changing contexts.

Build the business case for decision analysis approaches

Publications and reports that describe if and how upfront investment planning improves downstream outcomes can help build momentum for future applications of decision analysis. It is possible that decision analysis could reduce long-term costs by helping implementors choose interventions and implementation strategies with the greatest chance of success in a specific context. However, we need to test this assumption in our research and invest in research that evaluates the impact of decision analysis on decision-making and downstream outcomes [ 72 ].

Identify when, how, and for whom decision analytic approaches are useful

As others engage in decision analysis, detailed publications of nuances, objectives, and lessons learned through the process can improve our understanding of how and when pieces of decision analysis are best employed. The application of larger modeling efforts within a decision analysis may be most helpful when many actors are involved or when large sums of money are funds at stake, like in large health systems or at the state or federal level (e.g., Hassmiller Lich et al., Table 1 [ 39 ]). At lower “levels” like in communities or clinics, where even more might be at stake given tighter budgets, investigating how decision analytic approaches can reasonably be deployed should be prioritized. For example, work could evaluate how a structured decision analysis process or a small model that captures the major complexities support planning efforts (e.g., Cruden et al., Table 1 [ 37 ]).

Improve reporting and transparency of cost data

Decision analysis approaches require estimates from published literature, drawing on, for example, studies of implementation effectiveness and work evaluating the costs of implementation strategies. Thoughtfully considering how prior literature can inform future decision-making in different contexts is thus core to any decision analysis, and all inputs used throughout a decision analysis should be interrogated and justified [ 73 ]. One major area of current focus with implementation science that is often missing detail is the costs of implementation [ 74 , 75 , 76 , 77 ]. Recent publications by the “Economics and Cost” action group of the Consortium for Cancer Implementation Science have set out definitions, guidance, methods, and best practices for understanding the costs of implementation and conducting economic evaluations in implementation science [ 36 , 74 , 77 , 78 , 79 , 80 ] and complement ongoing work in the field [ 6 , 8 , 12 , 14 , 61 , 75 , 76 , 81 , 82 , 83 , 84 ].

To address gaps in reporting of cost data, scholars have called for consistent and detailed reporting on intervention costs, intervention adaptations, applied implementation strategies, and accrued implementation costs [ 6 , 9 , 40 , 61 , 85 ]. Refining existing reporting guidelines to help authors standardize what is reported could greatly improve the likelihood that published work could inform decision analytic approaches in the future. For example, the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist guides the reporting of economic evaluation [ 86 , 87 ]. This checklist could be modified to prompt detailed reporting on implementation processes, including actors, timing, purpose, and costs related to each implementation decision or action—quite similar to previous calls for standardized reporting [ 6 ] and existing recommendations for the reporting of implementation strategies [ 40 ].

In addition, efforts should be made to improve the transparency of cost data and data sharing. Cost data is often sensitive or proprietary. Presenting the implementation costs associated with specific sites may allow sites to be identified, raising questions about confidentiality in research. However, incorporating costs into decision analysis depends on transparency and willingness to share data and processes, and the field should consider how to address these issues.

As an example of how we envision reporting and transparency considerations might be operationalized in future published work, Hassmiller Lich et al. specified the different cost components of each intervention they considered, whether costs were one time or recurring, and specific notes on data sources (Fig.  2 ) [ 39 ]. This makes the paper of broader use even though the model was specific to North Carolina; future research can build on this work to better understand the components of costs that might be incurred if similar interventions were implemented in different contexts, even if the specific values might differ.

Illustrative example of costs reported to facilitate transparency and inform decision analytic approaches in other contexts. Notes : Reproduced Table 2 from Hassmiller Lich et al. [ 39 ]. This figure shows the cost estimate inputs required for decision analysis approaches and the variety of potential sources for estimates

Increase collaborative opportunities and training

Demonstrations of decision analysis processes in the peer-reviewed literature can help bolster the evidence base for others to learn from. A focus on implementation planning and decision analysis could also be integrated into existing training programs. In situations where more expertise is needed, decision analysis experts should be engaged (perhaps specifically to help facilitate identifying a clear problem, modeling potential consequences using simulation approaches, or assessing preferences). These experts are often in disciplines like systems science, operations research, health services research, health policy, or explicit decision science fields. Many of these experts are trained to collaborate on interdisciplinary teams and can be complements to collaborators with a deep understanding of implementation complexities and subject matter expertise.

Conclusions

An increased attention to decision analysis can provide a dual benefit to the field of implementation science by lending structure to implementation planning and helping to uncover innovative directions for future research. A key strength of decision analysis is its flexibility to be used in the way that is best suited to a given context, and we hypothesize even a simple analysis executed thoughtfully can be powerful. We encourage implementation scientists to use decision analysis principles in their own work and report on their experiences to help drive the field forward and contribute to better implementation outcomes.

Availability of data and materials

Not applicable.

Abbreviations

Consolidated Framework for Implementation Research

Evidence-based program

Consolidated Health Economic Evaluation Reporting Standards

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Acknowledgements

We would like to acknowledge the Economics and Implementation Science Workgroup for their formative input and internal review process. We would especially thank Meghan O’Leary, Dr. Alex Dopp, and Dr. Enola Proctor for their feedback on the early drafts of the manuscript.

This project was supported by NIH grant numbers T32HD091058 and T32CA057711. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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NRS and KEK contributed to the formative discussions and conception of the manuscript. All authors (NRS, KEK, KHL) drafted the manuscript, edited the manuscript, and approved the final submitted manuscript. The Implementation Science Workgroup leadership provided guidance throughout the drafting process (Drs. Gila Neta, Todd Wagner, and Heather Gold).

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Smith, N.R., Knocke, K.E. & Hassmiller Lich, K. Using decision analysis to support implementation planning in research and practice. Implement Sci Commun 3 , 83 (2022). https://doi.org/10.1186/s43058-022-00330-1

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Social learning: Collaborative learning with large language models

Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be beneficial for them to learn effectively from each other, much like people do in social settings, which would allow LLM-based agents to improve each other’s performance.

To discuss the learning processes of humans, Bandura and Walters described the concept of social learning in 1977, outlining different models of observational learning used by people. One common method of learning from others is through a verbal instruction (e.g., from a teacher) that describes how to engage in a particular behavior. Alternatively, learning can happen through a live model by mimicking a live example of the behavior.

Given the success of LLMs mimicking human communication, in our paper “ Social Learning: Towards Collaborative Learning with Large Language Models ”, we investigate whether LLMs are able to learn from each other using social learning. To this end, we outline a framework for social learning in which LLMs share knowledge with each other in a privacy-aware manner using natural language. We evaluate the effectiveness of our framework on various datasets, and propose quantitative methods that measure privacy in this setting. In contrast to previous approaches to collaborative learning, such as common federated learning approaches that often rely on gradients, in our framework, agents teach each other purely using natural language.

Social learning for LLMs

To extend social learning to language models, we consider the scenario where a student LLM should learn to solve a task from multiple teacher entities that already know that task. In our paper, we evaluate the student’s performance on a variety of tasks, such as spam detection in short text messages (SMS), solving grade school math problems , and answering questions based on a given text.

Language models have shown a remarkable capacity to perform tasks given only a handful of examples–a process called few-shot learning . With this in mind, we provide human-labeled examples of a task that enables the teacher model to teach it to a student. One of the main use cases of social learning arises when these examples cannot be directly shared with the student due, for example, to privacy concerns.

To illustrate this, let’s look at a hypothetical example for a spam detection task. A teacher model is located on device where some users volunteer to mark incoming messages they receive as either “spam” or “not spam”. This is useful data that could help train a student model to differentiate between spam and not spam, but sharing personal messages with other users is a breach of privacy and should be avoided. To prevent this, a social learning process can transfer the knowledge from the teacher model to the student so it learns what spam messages look like without needing to share the user’s personal text messages.

We investigate the effectiveness of this social learning approach by analogy with the established human social learning theory that we discussed above. In these experiments, we use PaLM 2-S models for both the teacher and the student.

Synthetic examples

As a counterpart to the live teaching model described for traditional social learning, we propose a learning method where the teachers generate new synthetic examples for the task and share them with the student. This is motivated by the idea that one can create a new example that is sufficiently different from the original one, but is just as educational. Indeed, we observe that our generated examples are sufficiently different from the real ones to preserve privacy while still enabling performance comparable to that achieved using the original examples.

We evaluate the efficacy of learning through synthetic examples on our task suite. Especially when the number of examples is high enough, e.g., n = 16, we observe no statistically significant difference between sharing original data and teaching with synthesized data via social learning for the majority of tasks, indicating that the privacy improvement does not have to come at the cost of model quality.

The one exception is spam detection, for which teaching with synthesized data yields lower accuracy. This may be because the training procedure of current models makes them biased to only generate non-spam examples. In the paper , we additionally look into aggregation methods for selecting good subsets of examples to use.

Synthetic instruction

Given the success of language models in following instructions, the verbal instruction model can also be naturally adapted to language models by having the teachers generate an instruction for the task. Our experiments show that providing such a generated instruction effectively improves performance over zero-shot prompting, reaching accuracies comparable to few-shot prompting with original examples. However, we did find that the teacher model may fail on certain tasks to provide a good instruction, for example due to a complicated formatting requirement of the output.

For Lambada , GSM8k , and Random Insertion , providing synthetic examples performs better than providing generated instructions, whereas in the other tasks generated instruction obtains a higher accuracy. This observation suggests that the choice of the teaching model depends on the task at hand, similar to how the most effective method for teaching people varies by task.

Memorization of the private examples

We want teachers in social learning to teach the student without revealing specifics from the original data. To quantify how prone this process is to leaking information, we used Secret Sharer , a popular method for quantifying to what extent a model memorizes its training data, and adapted it to the social learning setting. We picked this method since it had previously been used for evaluating memorization in federated learning.

To apply the Secret Sharer method to social learning, we design “canary” data points such that we can concretely measure how much the training process memorized them. These data points are included in the datasets used by teachers to generate new examples. After the social learning process completes, we can then measure how much more confident the student is in the secret data points the teacher used, compared to similar ones that were not shared even with the teachers.

In our analysis, discussed in detail in the paper , we use canary examples that include names and codes. Our results show that the student is only slightly more confident in the canaries the teacher used. In contrast, when the original data points are directly shared with the student, the confidence in the included canaries is much higher than in the held-out set. This supports the conclusion that the teacher does indeed use its data to teach without simply copying it over.

Conclusion and next steps

We introduced a framework for social learning that allows language models with access to private data to transfer knowledge through textual communication while maintaining the privacy of that data. In this framework, we identified sharing examples and sharing instructions as basic models and evaluated them on multiple tasks. Furthermore, we adapted the Secret Sharer metric to our framework, proposing a metric for measuring data leakage.

As next steps, we are looking for ways of improving the teaching process, for example by adding feedback loops and iteration. Furthermore, we want to investigate using social learning for modalities other than text.

Acknowledgements

We would like to acknowledge and thank Matt Sharifi, Sian Gooding, Lukas Zilka, and Blaise Aguera y Arcas, who are all co-authors on the paper. Furthermore, we would like to thank Victor Cărbune, Zachary Garrett, Tautvydas Misiunas, Sofia Neata and John Platt for their feedback, which greatly improved the paper. We’d also like to thank Tom Small for creating the animated figure.

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• Published: 24 September 2022

The neighbourhood built environment and health-related fitness: a narrative systematic review

• Levi Frehlich   ORCID: orcid.org/0000-0002-4099-0657 1 ,
• Chelsea D. Christie 1 ,
• Paul E. Ronksley 1 ,
• Tanvir C. Turin 1 , 2 ,
• Patricia Doyle-Baker 3 , 4 &
• Gavin R. McCormack 1 , 3 , 4 , 5  

International Journal of Behavioral Nutrition and Physical Activity volume  19 , Article number:  124 ( 2022 ) Cite this article

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There is increasing evidence demonstrating the importance of the neighbourhood built environment in supporting physical activity. Physical activity provides numerous health benefits including improvements in health-related fitness (i.e., muscular, cardiorespiratory, motor, and morphological fitness). Emerging evidence also suggests that the neighbourhood built environment is associated with health-related fitness. Our aim was to summarize evidence on the associations between the neighbourhood built environment and components of health-related fitness in adults.

We undertook a systematic review following PRISMA guidelines. Our data sources included electronic searches in MEDLINE, Embase, CINAHL, Web of Science, SPORTDiscus, Environment Complete, ProQuest Dissertations and Theses, and Transport Research International Documentation from inception to March 2021. Our eligibility criteria consisted of observational and experimental studies estimating associations between the neighbourhood built environment and health-related fitness among healthy adults (age ≥ 18 years). Eligible studies included objective or self-reported measures of the neighbourhood built environment and included either objective or self-reported measures of health-related fitness. Data extraction included study design, sample characteristics, measured neighbourhood built environment characteristics, and measured components of health-related fitness. We used individual Joanna Briggs Institute study checklists based on identified study designs. Our primary outcome measure was components of health-related fitness (muscular; cardiorespiratory; motor, and morphological fitness).

Twenty-seven studies (sample sizes = 28 to 419,562; 2002 to 2020) met the eligibility criteria. Neighbourhood destinations were the most consistent built environment correlate across all components of health-related fitness. The greatest number of significant associations was found between the neighbourhood built environment and morphological fitness while the lowest number of associations was found for motor fitness. The neighbourhood built environment was consistently associated with health-related fitness in studies that adjusted for physical activity.

The neighbourhood built environment is associated with health-related fitness in adults and these associations may be independent of physical activity. Longitudinal studies that adjust for physical activity (including resistance training) and sedentary behaviour, and residential self-selection are needed to obtain rigorous causal evidence for the link between the neighbourhood built environment and health-related fitness.

Trial registration

Protocol registration: PROSPERO number CRD42020179807.

Participation in regular physical activity is associated with a reduced risk of developing diabetes [ 1 ], cardiovascular disease [ 2 ], certain cancers [ 3 ] and premature mortality [ 4 ]. Notably, physical activity is also positively associated with health-related fitness [ 5 ]. Health-related fitness reflects physiological attributes that delay the onset of morbidity from diseases that may result from living a physically inactive lifestyle [ 6 ]. Traditional definitions of health-related fitness (i.e., cardiorespiratory endurance, muscular endurance, muscular strength, body composition and flexibility) [ 7 ] have since been updated to be more encompassing [ 8 ]. Current definitions of health-related fitness are multidimensional and include morphologic (e.g., body composition or flexibility) muscular (e.g., grip strength or endurance), cardiorespiratory (e.g., \(\dot{V}{O}_2\ \mathit{\max}\) or sustained cardiorespiratory capacity), motor (e.g., balance or proprioceptive activity), and metabolic (e.g., blood lipid or glucose levels) components [ 6 ]. After controlling for body mass index (BMI) and waist circumference, objective measures of body composition (including the distribution of adipose tissue) have been linked to incident cardiovascular disease [ 9 ]. Findings from a meta-analysis demonstrated that decreases in grip strength were associated with an increased risk of all-cause and cardiovascular mortality [ 10 ]. Associations between lower grip strength in mid-life with functional limitations and disability in older adulthood have also been observed [ 11 ]. Cardiorespiratory fitness, has been shown to be associated with cardiovascular disease risk in adults [ 12 ].

Higher intensity physical activity can improve muscular [ 13 ], cardiorespiratory [ 13 ], and morphological fitness [ 14 ]; however, even lower intensity activities, such as walking, may improve health-related fitness [ 15 ]. Qualitative [ 16 ] and quantitative [ 17 ] evidence consistently demonstrates links between neighbourhood built environment and physical activity. Key built environment features that support physical activity include density (i.e., residential or population), connectivity (i.e., many potential routes, short block sizes, many intersections), and land uses (i.e., recreational and utilitarian destinations) [ 16 , 17 ]. Giles-Corti et al. developed [ 18 ] and later expanded [ 19 ] a framework positing potential pathways by which the local built environment is associated with physical activity and health. The framework highlights important built characteristics including design (e.g., street layout and connectivity), density (e.g., compactness of residential population), transit (e.g., proximity and access), destination proximity (e.g., distance to local destinations), diversity (e.g., mixed residential, commercial, and recreational destinations), desirability (e.g., safety and aesthetics) and distributed features (e.g., resources equitably distributed across different populations) [ 18 , 19 ]. Given the connections between the built environment and physical activity, and physical activity and health-related fitness, neighbourhood built environments may play a vital role in supporting health-related fitness in adults.

Health-related fitness can be influenced by genetic factors, lifestyle behaviours, personal attributes, and physical and social environments [ 8 ]. Notably, some evidence suggests that associations between the built environment and health-related fitness remain after controlling for physical activity [ 20 , 21 , 22 , 23 ]. The persistent relationship may reflect the presence of independent pathways between the built environment and health-related fitness, the existence of other mediators (e.g., sedentary behavior and diet), or inadequate adjustment for physical activity. For example, studies have found the availability of food destinations to be associated with morphological fitness [ 24 ] and sedentary time to be association with functional-related fitness in older adults [ 25 ]. Both sedentary behaviour and diet are associated with built environment [ 26 , 27 , 28 ].

While several studies have found significant associations between some features of the neighbourhood built environment and health-related fitness [ 23 , 29 , 30 , 31 ], this literature has not been systematically synthesized nor critically evaluated. Therefore, the aim of this study is two-fold: (1) to summarize and critically appraise the existing literature on the associations between the neighbourhood built environment and health-related fitness in the adult general population, and; (2) to identify and summarize studies estimating the associations between the neighbourhood built environment and health-related fitness that also control for physical activity.

This systematic review is based on a published study protocol [ 32 ], was registered in the International prospective register of systematic reviews (PROSPERO; ID number: CRD42020179807), and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary material; S 1 ) [ 33 ]. We deviated from the protocol by having only one reviewer (LF) screen all initial titles and abstracts, however, two reviewers (LF and CC) screened the potentially relevant full-texts and collaboratively extracted study data (i.e., through a consensus approach).

Search strategy

Databases were searched from inception to March 2021 with no language or location restrictions. MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCO), Web of Science, SPORTDiscus (EBSCO), and Environment Complete (EBSCO) were search for published evidence (Supplementary material; S 2 ). Our search was supplemented with an exploration of unpublished evidence from ProQuest Dissertations and Theses. Finally, Transport Research International Documentation was also explored for relevant unpublished and published evidence.

Study selection

Citations were collated and uploaded into Covidence (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org ) and duplicates were removed.

Eligibility criteria

Types of studies.

We included observational and experimental studies that reported on quantitative results. Our review excluded qualitative studies and literature reviews.

Participants

We included studies undertaken with healthy adults (≥18 years of age). We excluded studies undertaken with children or adolescents, athletes, or clinical populations.

Exposure(s)

Exposure variables eligible for inclusion were built environment characteristics measured using objective (e.g., Geographical Information Systems or environmental audits) or self-reported (e.g., questionnaire) approaches.

Eligible studies included objective (e.g., researcher-administered field tests or laboratory testing) or self-reported measures (e.g., survey questionnaires) of health-related fitness. Health-related fitness included any measures of muscular, cardiorespiratory, motor, and morphological fitness. We excluded metabolic fitness because compared to the other components of health-related fitness, recent systematic reviews have summarized the associations between the built environment and cardio-metabolic health [ 34 , 35 , 36 , 37 , 38 , 39 ]. Within morphological fitness, outcomes of body composition were included if studies distinguished between fat and fat-free mass (e.g., body fat percentage), but they were excluded if they could not (e.g., BMI and waist-to-hip-ratio).

Data extraction

Data extraction included title, author, year of study, journal, study design, geographical location, sample size, mean age and age range, participant sex/gender distribution, data collection date, study duration, statistical technique, and estimate type(s), whether the built environment was objectively-measured or self-reported, whether the components of health-related fitness were objectively-measured or self-reported, the built environment characteristics measured, the component of health-related fitness measured, built environment exposure, covariates present in the adjusted results, whether adjustment was made for physical activity, and the main study findings.

Assessment of study quality

Study quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools for cross-sectional [ 40 ] (8 items), quasi-experimental [ 41 ] (9-items) or cohort [ 40 ] (12-items) studies. We used three specific study quality tools to accommodate the different studies designs that we expected to encounter in this literature [ 17 , 42 , 43 , 44 ].

Data synthesis

A narrative synthesis was completed by categorizing perceived or objectively measured individual (e.g., street connectivity) or index (e.g., walkability) built environment measures as well as perceived or objectively measured components of health-related fitness (e.g., cardiorespiratory fitness). Using an established framework [ 18 , 19 ], built environment characteristics were grouped into one of seven feature categories (i.e., design, density, transit, destination proximity, diversity, desirability, and distributed). We also added an eighth category – “Composite or Other” features – which included measures that combined individual built environment features into a single index or score (e.g., “walkability”) or where a single built environment variable spanned multiple features (e.g., urban infrastructure improvement). Statistically significant positive, negative, and non-significant associations were summarized.

Study identification

After removal of duplicates, 27,100 records were screened. After reviewing 881 full-text reports, 25 reports were included [ 20 , 21 , 22 , 23 , 24 , 29 , 30 , 31 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ]. Two of the included reports each included two different studies [ 24 , 50 ] within the same paper and the findings of each study were reported separately; thus, 27 studies were included in the final narrative synthesis (Fig.  1 ).

PRISMA flow diagram

Characteristics of included studies

Study design.

Table  1 shows the characteristics of the included studies. The majority ( n  = 21) of studies used cross-sectional designs [ 21 , 23 , 24 , 29 , 30 , 31 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ], with the remainder including cohort [ 20 , 22 , 24 , 50 , 54 ] ( n  = 5) or quasi-experimental [ 45 ] ( n  = 1) designs. Approximately half of studies ( n  = 14) were undertaken in the United States of America (USA) [ 22 , 24 , 29 , 30 , 31 , 46 , 48 , 50 , 51 , 54 , 56 , 57 ] with the remainder undertaken in Japan [ 20 , 49 , 61 ] ( n  = 3), the United Kingdom (UK) [ 47 , 58 , 59 ] ( n  = 3), Canada [ 23 , 55 ] ( n  = 2), France [ 45 , 52 ] ( n  = 2), Brazil [ 53 ] ( n  = 1), China [ 21 ] ( n  = 1), and the Czech Republic [ 60 ] ( n  = 1). Sample sizes across studies ranged from 28 [ 45 ] to 419,562 [ 59 ]. Six studies included older adults (≥ 60 years) only [ 20 , 22 , 45 , 49 , 53 , 61 ]. Six studies included female-only samples [ 45 , 50 , 51 , 54 , 60 ], while the remainder included multi-sexed/gendered samples [ 20 , 21 , 22 , 23 , 24 , 29 , 30 , 31 , 46 , 47 , 48 , 49 , 52 , 53 , 55 , 56 , 57 , 58 , 59 , 61 ].

Built environment measures

Among the 17 studies that included an objective measure of the built environment, neighbourhood geography was either operationalized using ego-centric spatially-defined buffers (or polygons) or distances around or from participants geo-located residential households [ 20 , 29 , 49 , 50 , 51 , 52 , 55 , 58 , 59 , 61 ] ( n  = 11) or by administrative boundaries [ 22 , 24 , 30 , 45 , 50 , 53 ] ( n  = 6). The size of the buffers used ranged from 500m [ 58 ] to 1600 m [ 29 , 49 ], with 800m [ 29 , 49 , 50 , 51 , 59 ] ( n  = 6) being the most commonly used definition. Among the 10 studies that included a measure of self-reported built environment, four used the Neighborhood Environment Walkability Scale (NEWS) [ 21 , 48 , 59 , 60 ], two studies captured perceptions about places in the neighbourhood to be active [ 54 , 57 ], one study used the Physical Activity Neighborhood Environment Scale (PANES) [ 23 ], one study each captured perceived neighbourhood disorder [ 46 ], perceived neighbourhood quality [ 47 ], and perceived safety [ 56 ].

The most common neighbourhood built environment characteristics measured included desirability ( n  = 13) [ 21 , 22 , 24 , 29 , 31 , 47 , 48 , 50 , 51 , 53 , 56 , 58 , 60 ], followed by diversity ( n  = 12) [ 21 , 24 , 29 , 31 , 48 , 49 , 50 , 51 , 54 , 57 , 60 , 61 ], design ( n  = 10) [ 20 , 21 , 24 , 29 , 31 , 48 , 49 , 50 , 51 , 60 ] and composite or other features ( n  = 10) [ 21 , 23 , 30 , 31 , 45 , 46 , 49 , 50 , 57 , 60 ], density ( n  = 9) [ 20 , 21 , 29 , 48 , 49 , 52 , 59 , 60 , 61 ], destination proximity ( n  = 6) [ 20 , 29 , 48 , 54 , 55 , 60 ] and transit ( n  = 2) [ 20 , 49 ] features. No study measured distributed features. The most common built environment elements measured under diversity features included the availability or presence of specific destination types [ 20 , 21 , 24 , 29 , 31 , 48 , 49 , 51 , 52 , 55 , 57 , 59 , 60 , 61 ]. Street connectivity and residential density were the most common built environment elements under design and density , respectively [ 20 , 21 , 24 , 29 , 31 , 48 , 49 , 51 , 52 , 59 , 60 , 61 ]. For desirability features, both greenspace and perceived neighbourhood aesthetics were the most common elements measured [ 21 , 24 , 29 , 31 , 48 , 51 , 52 , 53 , 58 , 60 ]. Desirability also included measures of safety [ 21 , 31 , 48 , 50 , 51 , 53 , 56 , 60 ]. Walkability was the most common element under composite or other features [ 21 , 23 , 30 , 31 , 49 , 60 ].

Health-related fitness measures

With the exception of two studies [ 23 , 57 ], all health-related fitness measurements were objectively measured [ 20 , 21 , 22 , 24 , 29 , 30 , 31 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 58 , 59 , 60 , 61 ]. There was a total of eight studies that included at least one measure of muscular fitness [ 20 , 21 , 22 , 23 , 31 , 46 , 49 , 61 ]. Six studies included grip strength [ 20 , 21 , 22 , 46 , 49 , 61 ], while a timed curl-up test [ 21 ], a maximal repetition curl up test (up to 75 repetitions) [ 31 ], timed push-up [ 31 ], a 5-repetion sit-to-stand test [ 61 ], and self-reported muscular strength were each reported in individual studies [ 23 ]. Among the 12 studies measuring cardiorespiratory fitness, six used an estimation of \(\dot{V}{O}_2\ \mathit{\max}\)  [ 29 , 30 , 31 , 50 , 55 , 57 ], three included habitual walking speed [ 22 , 49 , 61 ], two included timed distance tests [ 45 , 54 ], and one included self-reported cardiorespiratory fitness [ 23 ]. Among the four studies measuring motor fitness, three used the Timed Up-and-Go test [ 49 , 53 , 61 ] and two used a timed one-foot standing test [ 21 , 49 ]. There was a total of 16 studies that included at least one measure of morphological fitness [ 20 , 21 , 23 , 24 , 31 , 45 , 47 , 48 , 50 , 51 , 52 , 54 , 56 , 58 , 59 , 60 ]. Fourteen studies included measurements of body composition [ 20 , 21 , 24 , 31 , 47 , 48 , 50 , 51 , 52 , 54 , 56 , 58 , 59 , 60 ], two studies used Sit-and-Reach tests [ 21 , 45 ], and one reported perceived flexibility [ 23 ].

Study quality

Most studies ( n  = 17) were assessed to have the highest methodological quality score possible for their respective study design (cross-sectional, cohort or quasi-experiments). Cross-sectional studies of lower methodological quality tended to inadequately describe the sample design and setting and the reliability or validity of the built environment measures, and/or they did not control for confounders [ 31 , 47 , 48 , 55 , 56 , 60 ]. Quasi-experiments of lower quality tended to provide unclear descriptions of their follow-up data collection [ 45 ]. Lower quality cohort studies tended to inadequately describe the follow-up data collection or the reliability or validity of the built environment measures, and/or they did not control for confounders [ 20 , 24 , 54 ].

Adjustment for physical activity

Out of the 27 studies, eleven (40.7%) adjusted for physical activity. Physical activity was adjusted for in four of the eight studies that assessed muscular fitness [ 20 , 21 , 23 , 61 ], in five of the twelve studies that assessed cardiorespiratory fitness [ 23 , 29 , 30 , 50 , 61 ], in two of the four studies that assessed motor fitness [ 21 , 61 ], and in the eight of the sixteen studies that assessed morphological fitness [ 20 , 21 , 23 , 47 , 50 , 52 , 56 , 59 ].

Associations between the neighbourhood built environment and health-related fitness

Muscular fitness.

Table  2 summarizes the associations between neighbourhood built features and muscular fitness. Excluding distributed features, all other built features were examined in relation to muscular fitness. Across these features, all but two studies found either positive or null associations with muscular fitness.

Self-reported street connectivity was positively associated with curl-up performance in a cross-sectional study of Chinese women [ 21 ] while topography (i.e., slope steepness) was positively associated with grip strength in a cohort of Japanese males [ 20 ]. No studies found neighbourhood safety to be associated with muscular fitness [ 21 , 31 ]. Brown et al. [ 22 ] found positive associations between neighbourhood architecture and grip strength, while Sun et al. [ 21 ] found positive associations between self-reported neighbourhood aesthetics and curl-up performance in males. In a cross-section of older Japanese adults, having more utilitarian destinations (men and women), recreational facilities (men and women), and medical facilities (men only) in the neighbourhood was associated with better performance in the Sit-to-Stand test [ 61 ]. Moreover, among women, a greater number of neighbourhood utilitarian destinations and medical facilities was positively associated with grip strength [ 61 ]. Among a cohort of older Japanese women, neighbourhood bus stop density was negatively associated with grip strength [ 20 ]. Composite features were also associated with muscular fitness. In a cross-sectional sample of adults from the USA, neighbourhood physical disorder (vandalism/graffiti, rubbish/litter, vacant/deserted homes, and crime) was negatively associated with grip strength, although the study did not adjust for physical activity [ 46 ]. In a cross-sectional study of Canadian adults, self-reported neighbourhood walkability was positively associated with perceived muscle strength [ 23 ].

Adjusting for physical activity, there were five positive, one negative and ten null associations between built environment features and muscular fitness. Although attenuated, after adjustment for self-reported frequency of achieving sufficient MVPA (≥30 minutes/day) in the past week and self-reported days of resistance training in a usual week, perceived overall neighbourhood walkability was still positively associated perceived muscular fitness in a Canadian population [ 23 ]. After adjusting for self-reported physically activity habit (yes/no), land slope remained positively associated and bus stop density negatively associated, with objectively measured grip strength in Japanese adults [ 20 ]. In another Japanese sample, the number of neighbourhood destinations were positively associated with objectively measured grip strength after adjusting for self-reported total (i.e., occupation, household, and leisure) physical activity [ 61 ]. In a sample of Chinese adults, after adjustment for self-reported total MVPA (i.e., weekly MET-minutes), for men perceived neighbourhood aesthetics and for women street connectivity, were positively associated with curl-up performance [ 21 ].

Cardiorespiratory fitness

Table  3 summarizes the associations between the neighbourhood built environment and cardiorespiratory fitness. Excluding distributed features, all other built features were examined in relation to cardiorespiratory fitness. Among these, transit features were not associated with cardiorespiratory fitness, while the other features were found to have positive or null associations with cardiorespiratory fitness.

A cross-sectional analysis of American adults, found that intersection density was positively associated with maximal metabolic equivalent of task (MET) values [ 29 ]. In older Japanese adults, population density was positively associated with an increased walking speed [ 61 ]. No studies found neighbourhood safety to be associated with cardiorespiratory fitness [ 31 , 49 , 50 ]. A cross-sectional study found that a front facing architecture type (including porches, stoops, and buildings built above grade) was positively associated with gait speed in a cohort of older Hispanic Americans [ 22 ]. Further, Hoehner et al. [ 29 ] found positive cross-sectional associations between a greater proportion of vegetation in the neighbourhood and maximal METs. In cross-sectional associations, the number of private exercise facilities, and community centres were positively associated with maximal METs and habitual walking speed, in samples of American [ 29 ] and Japanese [ 61 ] adults, respectively. In three separate cross-sectional samples, distance to dance studios and baseball diamonds was positively associated with \(\dot{V}{O}_2\ \mathit{\max}\) in Canadian adults [ 55 ], perception of places to walk in the neighbourhood was positively correlated with 1-mile walk scores in American women [ 54 ], and perceptions of convenient neighbourhood facilities was positively associated with estimated \(\dot{V}{O}_2\ \mathit{\max}\) in American adults [ 57 ]. Composite built environment associations with cardiorespiratory fitness included an intervention of older French women, where an improved urban environment consisting of a pedestrian circuit, improved roadway accessibility and rehabilitation of a central square, was positively associated with 6-minute walk scores [ 45 ]. In a cross-section of American adults, more walkable neighbourhoods, and non-auto commuting neighbourhoods, were positively associated with maximal METs for males and females, and males only, respectively [ 30 ]. In a cross-section of Canadian adults, self-reported neighbourhood walkability was positively associated with perceived cardiorespiratory fitness [ 23 ].

Among studies that adjusted for physical activity, there were seven positive and nine null associations between built environment features and cardiorespiratory fitness. Although attenuated, after adjusting for self-reported weekly MET-minutes of outdoor physical activity, traditional core neighbourhoods remained positively associated with maximal metabolic equivalents obtained through a treadmill test in American adults [ 19 ]. In another sample of American adults, after adjustment for self-reported weekly MET-minutes of MVPA, associations between and intersection density and maximal MET were no longer statistically significant; however, associations between greenspace (positive), the number of exercise facilities in the neighbourhood (positive), and distance to the closest city center (negative) remained significant [ 29 ]. Moreover, after adjustment for self-reported MVPA (≥30 minutes/day) in the past week and self-reported days of resistance training in a usual week, perceived overall neighbourhood walkability remained positively associated with self-reported cardiorespiratory fitness in a sample of Canadian adults [ 23 ]. Further, in a sample of Japanese older adults, population density and the number of community centers in the neighbourhood remained positively associated with walking speed after adjusting for total (i.e. occupational, household and leisure) self-reported physical activity measured using the Physical Activity Scale for the Elderly [ 61 ].

Motor fitness

Table  4 summarizes the associations between the neighbourhood built environment and motor fitness. Excluding distributed and destination features proximity, all other built features were examined in relation to motor fitness. Across these features, transit , desirability , and composite or other features were not found to be associated with motor fitness while design , density , and diversity were found to be positively or not associated with motor fitness.

A cross-sectional study of older Japanese males, population density within a 1600 m neighbourhood buffer, and intersection density within an 800 m neighbourhood buffer was positively associated with timed one-legged stance scores (with eyes open) [ 49 ]. Although the study did not adjust for physical activity. There were no associations between safety [ 21 , 53 ] or aesthetics [ 21 , 53 ] of the neighbourhood built environment and motor fitness. In the same sample of older Japanese males, availability of destinations within the 1600 m neighbourhood buffer were positively associated with timed one-legged stance scores (with eyes open) [ 49 ]. There were no associations between composite built environment measures and motor fitness [ 21 , 49 ].

Associations between the built environment and motor fitness were not statistically significant after adjustment for physical activity [ 21 , 61 ].

Morphological fitness

Table  5 summarizes the associations between the neighbourhood built environment and morphological fitness. Excluding distributed features, all other built features were examined in relation to morphological fitness. Among these features, for morphological fitness negative associations were found for transit , null associations found for destinations , negative and null associations found for design , and negative, null, and positive associations found for density, diversity , desirability , and composite or other features .

A cohort study of American adults found intersection density negatively associated with changes in visceral adipose tissue [ 24 ]. A cross-sectional study of French adults found residential density negatively associated with both fat mass index and percent fat mass in males [ 52 ]. A cross-sectional study in the UK found a curvilinear relationship between residential density and body fat [ 59 ]. Specifically, residential density was positively associated with body fat ≤1800 units per km [ 2 ] then negatively associated with body fat > 1800 units per km 2 [ 59 ]. Perceptions of neighbourhood safety were negatively associated with visceral adipose tissue in a cross-section of African American females [ 56 ]. A cross-sectional study of Chinese adults found that perceived pedestrian and traffic safety was negatively associated with sit-and-reach scores in males [ 21 ]. Lee et al. [ 24 ] found that greenspace was positively associated with change in visceral adipose tissue in a cohort of American adults. Conversely, in a cross-sectional sample of UK adults, residential greenness was negatively associated with body fat [ 58 ].

A cross-sectional study of American university students found perceptions of access to destinations was negatively associated with body fat percentage in males [ 48 ]. A cross-sectional study of ethnic minority American women found objectively measured neighbourhood amenities were negatively associated with body fat percentage [ 51 ]. Lee et al. [ 24 ] found that total food stores, full-service restaurants, fast food restaurants, supermarkets, and convenience stores was negatively associated with a change in visceral adipose tissue. Bus stop density was negatively associated with skeletal mass index in a cohort of Japanese males [ 20 ]. Perceptions of neighbourhood access to services and land use mix diversity were negatively associated with sit-and-reach scores in Chinese males [ 21 ].

For composite features, an intervention including older French women found an improved urban environment consisting of a pedestrian circuit, improved roadway accessibility and rehabilitation of a central square, to be positively associated with sit-and-reach test scores [ 45 ]. In cross-sectional analyses of three different cohorts, Ellaway et al. [ 47 ] found that an index of perceived neighbourhood problems (vandalism, litter, crime, youth disorderly conduct, and foul odor) was positively associated with change in body fat percentage over time. In Canadian adults, McCormack, et al. [ 23 ] found that perceptions of neighbourhood walkability and a park quality score were positively associated with perceived flexibility.

Adjusting for physical activity there were five positive, four negative and fourteen null associations with morphological fitness. After adjusting for the self-reported number of days per week performing vigorous exercise (≥20 minutes continuous), body fat percent remained positively associated with perceived neighbourhood problems [ 47 ]. Further, after adjusting for different levels of activity in varying occupations, residential density was inversely associated with fat mass index and percent fat mass in males [ 52 ]. Moreover, after adjustment for self-reported weekly MVPA (≥30 minutes/day) and self-reported days of resistance training in a usual week, perceived overall neighbourhood walkability remained positively associated with self-reported flexibility among Canadian adults [ 23 ]. Among Japanese older males, bus stop density was negatively associated with skeletal muscle index after adjusting self-reported physically active habit [ 20 ]. Adjusting for physical activity measured via an active living index (i.e., frequency and duration of physical activities minus frequency and duration of sedentary behavior), neighbourhood safety was positively associated with visceral and total adipose tissue in premenopausal women [ 56 ]. In in a large UK sample, after adjustment for self-reported physical activity (weekly MET hours), population density was found to have a non-linear association with objectively measured whole body fat [ 59 ]. Among Chinese males, perceived neighbourhood destinations and safety was negatively associated with sit and reach performance, after adjustment for self-reported MVPA in weekly MET minutes [ 21 ].

We found 27 different studies that estimated the relationship between the neighbourhood built environment and health-related fitness. The reviewed evidence suggests that specific built environment features are more often than not to have either a positive or no association with health-related fitness. Moreover, this evidence suggests that associations between the built environment and health-related fitness persist, albeit attenuated, after controlling for physical activity. Using the updated built environment framework by Giles-Corti et al. [ 19 ] we found specific built characteristics associated with design , density , diversity , and desirability features to be the most commonly studied; while no studies examined built characteristics associated with distributed features.

The most common component of health-related fitness investigated was morphological fitness, with an emphasis on body composition. The negative associations between the built environment and body composition found in our review tend to support findings from previous reviews summarizing evidence related to built environment and weight outcomes [ 42 , 43 , 62 ]. Our findings suggest that having multiple, easily accessible destinations within a neighbourhood may favorably influence body composition. This result is congruent with longitudinal findings suggesting that having multiple, easily accessible destinations within a neighbourhood is linked to favorable changes in physical activity behaviour [ 17 , 63 ].

The second most common association between the neighbourhood built environment and health-related fitness category was with cardiorespiratory fitness, and in general, measurements of estimated maximal aerobic capacity. Given the link between physical activity and cardiorespiratory fitness, our findings tend to support those that have been found previously between the built environment and physical activity [ 17 , 63 ]. Similar to associations between the built environment and morphological fitness, having multiple destinations within a neighbourhood that are easily accessible was associated with favorable cardiorespiratory fitness. There are multiple lines of evidence, including cross-sectional [ 17 , 44 ], longitudinal [ 63 ], and natural experiments [ 63 ], indicating favorable changes in physical activity behaviour with improvements in neighbourhood destinations.

Overall, the results of our review indicate that physical activity likely mediates, at least partially, associations between the neighbourhood built environment and health-related fitness. There are numerous explanations as to the mechanisms explaining how the built environment might be positively associated with health-related fitness. For example, carrying heavy loads in the hands is related to forearm musculature activity [ 64 ] and muscular fitness, therefore, in areas with a higher land-use mix, residents may walk to complete daily errands and carry items back to their residence, which may slow impairments to activities of daily living [ 65 ]. Recreational facilities located within walking distance of home, where resistance or aerobic training might be performed, may explain positive associations between the neighbourhood built environment and cardiorespiratory and muscular fitness. Increases in motor fitness has been shown through proprioceptive exercises such as wobble boards or unstable activities [ 66 ]. Speculatively, neighbourhoods with high population density, street connectivity, and land use mix, may provide opportunities to manoeuvre around obstacles (i.e., people, benches, traffic bollards etc.), which may emulate some movements undertaken during structured proprioceptive exercises. Among older adults, more frequent falls, which are associated with motor fitness [ 67 ], have been found in peripheral areas compared with city areas [ 68 ]. There is also consistent evidence demonstrating associations between neighbourhood walkability and walking [ 69 , 70 ], which subsequently could result in improved cardiovascular [ 71 ], and morphological fitness [ 72 ].

However, other pathways may exist linking built environment with fitness that are not mediated by physical activity. For example, traffic density, which is associated with the built environment (e.g., air pollution) [ 19 ], can have detrimental effects on cardiorespiratory fitness [ 73 , 74 ]. Diet, which is associated with morphological fitness [ 75 ], is also associated with the built environment (e.g., proximity and availability of fast food restaurants, supermarkets, and convenience stores) [ 62 , 76 ].

Our findings suggest that the built environment may have effects on health-related fitness independent of physical activity. However, studies adjusting for physical activity did so using self-reported physical activity, which may not accurately capture the total volume nor intensities of physical activity undertaken. Moreover, among these studies few included measures of transport-related physical activity that may be more strongly associated with the built environment [ 77 ].

Strengths and weaknesses

A strength of our review is the overall breadth of included exposures, outcomes, and study designs. Capturing multiple components of health-related fitness allowed for a broader scope of the literature to be evaluated and to better theorize the multiple ways in which the built environment might impact health-related fitness. However, our broader research objective may have contributed to heterogeneous sample of studies included in our review which together with their dissimilar sample designs and methods, limited our ability to conduct a meta-analysis.

Limitations common in the literature exploring the relationships between physical activity and the neighbourhood built environment were also present in studies included this review. The lack of control for residential exposure time [ 78 ] and residential self-selection [ 79 ] was pronounced in our summary. In our review, we only found two of studies that controlled for length of residential exposure time [ 49 , 50 ]. The lack of control for residential self-selection is also an important variable in neighbourhood built environment research; however, we found no studies controlling for this potential confounder. This confounder is potential important because individual who undertake physical activity for the main purpose of improving or maintaining their health-related fitness may choose to reside in neighbourhoods that have built features that support desired physical activities (e.g., access to parks, pathways, recreational facilities). Speculatively, not adjusting for residential self-selection could lead to over-estimates of the association between the built environment and health-related fitness, especially in cross-sectional studies [ 79 ]. Further, our study quality tools assessed the quality of reporting limiting our ability to assess bias. Moreover, as many of the identified studies were cross-sectional in design assessment of causality is limited.

Future directions

Evidence suggests that the built environment, through its potential influence on physical activity, is associated with a range of health outcomes such as cardiovascular disease, overweight and obesity, and type 2 diabetes [ 36 ]. Findings from our review suggest that health-related fitness is another important factor that should be considered when exploring the role of the built environment in supporting health, especially given its relationships both with physical activity [ 5 ] and chronic disease [ 6 ]. Future research is needed to examine the causal pathways between the built environment and health-related fitness, not only via physical activity but also other potential mediators (e.g., sedentary behaviour, air pollution). To generate rigorous evidence for informing urban design and public health policy and interventions, this future research should include longitudinal, experimental, and quasi-experimental study designs that incorporate objective measures of the built environment, health-related fitness, and physical activity (and other mediators).

The neighbourhood built environment appears to be associated with all components of health-related fitness (i.e., muscular, cardiorespiratory, motor, and morphological fitness). Somewhat expectedly, our findings of the built environment-health-related fitness relationship tend to mirror the built environment-physical activity evidence in that a more supportive neighbourhood built environments can support higher levels of physical activity [ 17 , 63 ]. However, while physical activity might be an important mediator between the built environment and health-related fitness, our findings suggest there are potentially behaviours or factors other than physical activity that might explain some of the association between the neighbourhood built environment and health-related fitness. The relationship between the neighbourhood built environment and health-related fitness may be a promising area to improve public health. However, to make firm policy, practice, and design recommendations, future research on the associations between the neighbourhood environment and health-related fitness that controls for important confounders is needed (e.g., objectively-measured physical activity, resistance training, sedentary behaviour, diet, neighbourhood exposure, and residential self-selection).

Built environment definitions were adapted from Giles-Corti et al. [ 18 ] and Giles-Corti et al. [ 19 ]

Health-related fitness definitions were adapted from Caspersen et al. [ 80 ], Shephard [ 8 ], and Vanhees et al. [ 6 ]

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Body mass index

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Joanna Briggs Institute

Metabolic equivalent of task

United States of America

United Kingdom

Neighborhood Environment Walkability Scale

Physical Activity Neighborhood Environment Scale

Moderate-to-vigorous physical activity

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LF and GRM conceived and designed the study. LF ran the searches. LF screened titles and abstracts. LF and CDC screened full texts. LF and CDC extracted data from included studies. LF drafted the manuscript. LF, CDC, PER, TCT, PD, GRM contributed to interpretation of the data, revisions, and the final manuscript. The author(s) read and approved the final manuscript.

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PRISMA checklist. S2. Full search strategies

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Frehlich, L., Christie, C.D., Ronksley, P.E. et al. The neighbourhood built environment and health-related fitness: a narrative systematic review. Int J Behav Nutr Phys Act 19 , 124 (2022). https://doi.org/10.1186/s12966-022-01359-0

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Key factors influencing farmers’ adoption of sustainable innovations: a systematic literature review and research agenda

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Despite the benefits of sustainable innovations in the agricultural sector being widely recognized, their adoption rate remains below the level designated by the 2030 Sustainable Development Goals. To understand the reasons behind this phenomenon, the current systematic literature review (SLR) provides a comprehensive overview of factors affecting farmers’ innovation adoption behavior in developed countries. A total of 44 studies, published since 2010, were identified, analyzed, and summarized. The analysis revealed that specific innovation characteristics foster the innovation adoption process, together with individual psychological and socio-demographic features. It emerged that the path to adopting sustainable innovations can be driven by environmental values; for example, when comparing organic and conventional farming, organic farmers have a stronger environmental view and are more likely to take less into account economic gains. On the contrary, complexity of innovation, a high degree of innovation aversion, and a low perceived control over innovation are among the core barriers to the innovation adoption. Findings provide important insights on potential research avenues that could further depict farmers’ adoption dynamics of sustainable innovations.

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Introduction

The adoption of sustainable agricultural innovations offers a promising alternative for mitigating the environmental impacts stemming from agricultural practices (Foguesatto et al. 2020 ). In recent years, there has been a growing recognition of the urgency to adopt more sustainable strategies in the agricultural sector, driven by a desire to assess their positive environmental effects (D’Amato et al. 2021 ). MacRae et al. ( 1990 ) asserted that the achievement of sustainability in agriculture relies on the pursuit of specific agricultural practices that aim to curtail the long-term repercussions of human activities on natural resources. Among the various options available, organic farming, precision farming, regenerative agriculture, and agroecology undoubtedly stand out as effective approaches (Ferreira et al. 2022 ; Ndaba et al. 2022 ; Sachet et al. 2021 ; Newton et al. 2020 ). These offer innovative solutions to tackle the challenges of agricultural sustainability, safeguarding the environment while ensuring the production of wholesome, high-quality food. By embracing these modalities, farmers can promote ecological resilience, conserve natural resources, and respond to the pressing need for sustainable agricultural systems. Organic farming, in particular, is widely regarded as the most sustainable and responsive method in the primary sector (Ferreira et al. 2022 ; Canaj et al. 2021 ). It emphasizes the adoption of natural techniques and the exclusion of pesticides and chemical fertilizers, thereby promoting soil health and preserving biodiversity (Chiriacò et al. 2017 ; Lee and Yun 2015 ).

Even if organic farming has its roots in the 1990s (Kuepper 2010 ; Joachim 2006 ), it still represents a true innovation in the agricultural system by incorporating cutting-edge concepts of technology and research (Padel, 1994 ). This approach places ecological balance as a priority and strives to minimize the environmental impact of agricultural practices through the utilization of organic inputs and sustainable farming methods (Canaj et al. 2021 ).

Nevertheless, there are still several obstacles that slow down the implementation of sustainable practices that are useful for the ecological transition (Manta et al. 2022 ). Among them, development and technology transfer capacity and an attitude of resistance of farmers themselves to innovations appear to be the most relevant obstacles (Niggli et al. 2017 ).

It follows that, despite the recognition of the sustainable practice benefits, the adoption rate of sustainable innovations remains below the level designated by the Sustainable Development Goals (SDGs) identified by the United Nations for 2030, as many farmers are reluctant to adopt innovations (D’Amato et al. 2021 ; Foguesatto et al. 2020 ; Zeweld et al. 2017 , 2018 ). To understand where the obstacle to the innovative adoption may lie, several studies and reviews have been proposed to deepen the analysis of the innovation adoption process among farmers and the multiple factors that may influence their behavior (Guerin 2001 ). It turned out that psychological factors play a strategic role in influencing the process of innovation adoption and diffusion among farmers (Caffaro and Cavallo 2019 ; Zulfiqar and Thapa 2018 ; Price and Leviston 2014 ), as well as socio-economic factors, including farmer’s age, income, and education (Akimowicz et al. 2021 ; Serebrennikov et al. 2020 ; Caffaro and Cavallo 2019 ), and some contextual factors, such as the size of the farm, and the environmental and political context in which it operates (Piñeiro et al. 2020 ; Foguesatto et al. 2020 ; Hernandez-Vivanco et al. 2018 ; Bravo-Monroy et al. 2016 ). Nevertheless, psychological, socio-demographic, and contextual determinants that influence the farmer’s innovation adoption process are often analyzed separately, producing in some cases conflicting results.

The presence of poorly defined and sometimes contradictory results in empirical studies may stem from the tendency to primarily rely on a variance-based approach, whereas a configurational approach might be more suitable.

The variance-based approach aims to identify the most influential factors on a particular phenomenon or outcome by analyzing correlation or regression coefficients. In contrast, the configurational approach examines complex patterns and configurations of variables that lead to specific results. Indeed, this approach recognizes that the combinations of variables or conditions can produce unique or different outcomes compared to individual variables (Furnari et al. 2021 ). The configurational approach argues that the different combinations of attributes lead to adoption, thereby explaining the existence of multiple pathways and potentially resolving contradictory findings (David et al. 2021 ; Fürstenau et al. 2021 ). Therefore, if this is indeed the case, employing a variance-based approach to analyze innovation adoption becomes futile, as it fails to fully illuminate the phenomenon under investigation. Concentrating solely on measuring correlations or cause-and-effect relationships between individual variables may prove inadequate in achieving a comprehensive understanding (Meyer et al. 1993 ). Innovation adoption, being a complex phenomenon, necessitates a thorough examination of the interactions between various variables and conditions. This approach acknowledges nonlinearity, where variables that are found to determine the development of a given phenomenon in one situation might yield different results in another situation (Fiss et al. 2013 ).

In this view, the current SLR uses a configurational theorization: past findings may be contradictory because they acknowledge that a given cause would affect all farmers in the same way, irrespective of its combination with other factors. When this assumption is challenged, it is necessary to revisit and reanalyze previous findings to provide a better account of the paths that lead to adoption.

The configurational approach acknowledges the concept of equifinality, understanding that different paths can lead to success in various contexts. For instance, a company might thrive through business innovation or a niche strategy, whereas the same approaches could result in failure for another organization. This approach recognizes the significance of disorder, diversity, and nonlinear relationships in shaping outcomes (Meyer et al. 1993 ).

Bearing in mind this scenario, the current SLR challenges the idea that there can be only one path to success during the innovation process, rather it sets out to identify all the factors that might come into play, aware that these may interact with each other in a multitude of ways and for a multitude of reasons. In doing so, the variables found in various studies will be categorized around two major reasons driving adoption: desirability and feasibility (Gatewood et al. 1995 ). Both dimensions are relational so that they result from interplay of agency (what farms can do and what innovations can do) and structure (what regulation or routines allow to do). It is assumed that if an innovation is seen as strongly desirable, farmer may make it feasible, by actively seeking to financial or technical aids. And conversely, if it is not deemed feasible, the desirability will be curtailed.

The results of this review could have both theoretical and policy implications. From a theoretical perspective, understanding the factors influencing the farmer’s adoption of innovations would enrich the current knowledge, providing a valuable addition to the available literature. In terms of policy implications, a clear picture of the factors underlying the dynamics of farmer’s adoption of product and process innovations could be useful in better targeting policy measures tailored to encourage sustainable innovations in the agricultural sector.

Research procedure

Review protocol.

Writing an SLR entails the use of a protocol that systematically describes all the steps to be followed, from the definition of the research question to the careful analysis of the selected manuscripts. The study was conducted based on the following research question: “What are the main factors influencing the sustainable innovation adoption process by farmers?” Subsequently, a six-step selection process was followed starting in March 2021, according to the preferred reporting items for systematic reviews and meta-analyses approach-PRISMA (Page et al. 2021 ). Initially, the search results were manually tabulated in a spreadsheet, and duplicates were removed. Then, filters concerning the type of manuscript, the years and place of publication, and the English language were applied to select the documents, and later the titles and abstracts of the studies have been read. After this step, full texts were analyzed, and further exclusions were made when necessary. Furthermore, a quality assessment of the selected studies was performed in order to critically evaluate scientific studies to determine the quality, reliability, and validity of their results. The main objective of quality assessment is in fact to assess whether a study has been conducted in a rigorous manner and whether the results obtained are reliable and can be considered valid (Tummers et al. 2019 ). The development of a meta-analysis was excluded because, to make a correlation between variables, it is necessary to use homogeneous samples and results (Pati and Lorusso 2018 ). Indeed, it synthesizes econometrically the data from various sources to obtain a global estimation of the effect or association between the variables of interest. Instead, this review investigated studies of different nature (qualitative and quantitative) and studies using samples with different numbers of participants and diverse selection modes (mainly random convenience samples). More in depth, an integrative approach was chosen to explore the investigated studies due to their heterogeneity in designs and outcomes preventing quantitative analysis (Torraco 2005 ). When different studies exhibit significant differences in methodology, samples, outcome measures, or other aspects, conducting a traditional quantitative analysis such as a meta-analysis can be challenging or inappropriate. Instead, the integrative approach aims to obtain a comprehensive view of the results from the included studies, seeking to identify patterns, trends, or common themes that emerge through the analysis of both qualitative and quantitative evidence.

For each study, information about the year and country in which the research was conducted was identified, and information about the methodology used and the main outcomes obtained was extracted. Finally, the factors influencing the innovation process were extrapolated.

The procedure was carried out by three researchers simultaneously, following the suggestion of Pati and Lorusso ( 2018 ), and the final outcomes were the results of a common agreement. The SLR was completed in December 2022.

Study selection criteria

Given the aim of the systematic literature review, a Boolean algorithm was applied as follows:

((organic OR sustainable OR green) AND (innovation OR practices OR product) AND (agricult* OR farm* OR entrepr* OR producer OR food) AND (factors affecting OR risk OR driver OR barrier OR attitude OR behavior OR adopt* OR motives))

The Boolean algorithm was launched on the Scopus and Web of Science platforms. Specifically, key terms were searched in the titles, keywords and abstracts of manuscripts contained in the Scopus database and searched in “topic” for the Web of Science database.

The output led to many studies (8167 in Scopus and 5784 in Web of Science). The two databases Scopus and Web of Science were chosen due to their thoroughness and reliability (Page et al. 2021 ). First, before implementing a manual selection screening, a time constraint was inserted (only papers published after 2009) and only English language studies published in journals were used. It was chosen to investigate this time frame because in the field of social sciences it is important to cover at least a minimum of 10 years for a SLR (Paul and Criado 2020 ); additionally, this time spam coincides with the recent growth of sustainable innovations in agriculture. Furthermore, book, general reports, and conference proceedings were also excluded as lacking peer review and with more limited availability (Alves et al. 2016 ). Finally, only studies reporting results from the primary data collection were included in the analysis, as they are suitable for collecting useful information (e.g., sample number, country, methodology, and factors that influenced farmers) to achieve the intended goal.

After applying these filters, the number of papers was reduced to 5746 on Scopus and 2853 on Web of Science (applying the “Advanced Search” window). Of these, the title and abstract were read to make an initial sorting, excluding studies involving consumer behavior, performed in developing countries, and studies not examining factors influencing farmers during the innovation process. In addition, only studies investigating process and product innovations were selected. This resulted in a total of 89 reports assessed for eligibility. The current study explores the adoption of product and process innovations by farmers, as both are essential for a company’s long-term competitiveness (Damanpour and Gopalakrishnan 2001 ). These innovations drive significant structural changes on farms, necessary for achieving sustainability in the medium and long term (Gaziulusoy 2010 ). Process innovation, closely linked to product innovation, improves resource efficiency, promotes sustainable product design, and enhances product quality and range (Li et al. 2017 ; Damanpour 2010 ). Product and process innovations are interconnected and play a crucial role in driving agricultural development and competitiveness (Xie et al. 2019 ). Therefore, to have all the factors that may intervene in the process of innovation adoption is important to analyze both product innovations and process innovations. Furthermore, Zanello et al. ( 2016 ) pointed out that innovation is costly and risky; thus, pioneering innovation is mainly concentrated in few rich countries. Innovation requires appropriate institutions and policies to drive incentives and facilitate the process, as well as strong local capabilities to identify the right technology and appropriate transfer mechanism and to absorb and make adaptations based on local economic, social, technical, and environmental conditions (Fu and Gong 2011 ). Therefore, only innovation adoption in developed countries was analyzed, as these countries seem to possess the necessary scientific and technical knowledge to drive incentives and facilitate the process and the possibility to acquire the appropriate technology and transfer mechanism (Zanello et al. 2016 ).

Following this initial screening, the authors read the full manuscripts and applied the selection criteria, which brought the number of studies to 44. The applied procedure is fully shown in Fig. 1 .

PRISMA flow diagram

Year of publication of the investigated studies

Figure 2 shows the years in which the studies analyzed in current SLR were published. Many of them were performed in recent years, highlighting the growing importance of the topic among scholars.

Number of publications per year

Countries included in the reviewed studies

The 44 studies in the review were carried out in 15 different countries. Most of the studies were conducted in Italy (9 studies), the USA and Germany (both with 8 studies), followed by the Netherlands and the UK with 7 studies. Four studies per country were conducted in Spain, France, and Australia, followed by Greece and Belgium with three studies, Switzerland, Hungary, and Denmark with two studies, and finally Poland with one study. The main geographical area of data collection was Europe. Six studies applied a multi-country sample, which is why the number of countries investigated is greater than the total number of papers selected. For more details, please see the Appendix (Table 4 ).

Theoretical backgrounds of the studies

The main theories and models applied by the investigated studies were also detected. It was found that most of the studies were not built on a theory or model, but their reasoning was only supported by previous empirical studies. Indeed, only 19 studies referred to a theoretical strand. There have been many theories used (e.g., the theory of planned behavior, the reasoned action theory, the theory of technology acceptance and diffusion of innovation, the AKAP sequence, and the classification of internal and external barriers and risk management strategies), but always referred in some way to the psychological sphere of the entrepreneur.

Research methodologies of the studies

The methodologies applied by the studies were quite heterogeneous: 25 studies were developed through quantitative research approaches, 12 of the studies presented a qualitative methodology, and 7 studies were based on mixed-method approaches. In particular, the first category included semi-structured questionnaires and the use of national databases, the second involved face-to-face interviews, and the last one consisted of exploratory questionnaires and workshops, or focus groups, or application of experimental economics mechanisms. Almost all studies relied on convenience samples and data collection methods were generally, briefly described.

Quality assessment procedure and outcomes

The 44 studies included in the review were assessed for overall quality. This assessment was carried out by applying the eight quality criteria presented by van Dinter et al. ( 2021 ) (Table 1 ). Each study was evaluated based on the satisfaction of eight different requirements. A score equal to 1 was given when the criterion was fully met, a score equal to 0.5 in the case of incomplete information, and a score of 0 if the criterion was not met in the study. The quality assessment was developed independently by two scholars and all discrepancies were deeply discussed to reach a common final judgment. The concluding scores obtained ranged from the lowest score of 4.5 to the highest of 8. The mean was 6.21 and the median 6. 37.3% of the documents examined received a score below 6, 29.5% were between 6 and 6.5, and 33.2% were between 7 and 8.

Narrative summary of the studies

This section outlines the factors affecting the innovation process found within the analyzed documents.

From each study, the factors affecting the adoption of innovations in agriculture were extrapolated and then, were grouped into the two components of desirability and feasibility (Table 2 ). Indeed, the intention to innovate, which is considered an important antecedent to the implementation of innovation itself (Krueger NFand Carsrud 1993 ), is related to attitudes regarding perceived desirability and feasibility (Gatewood et al. 1995 ). Since intentions have been shown to be a good predictor of behavior (Ajzen 1991 ), understanding the identity and nature of antecedent factors that influence entrepreneurial intentions is of crucial importance to the study of entrepreneurial innovation behavior (Shane and Venkataraman 2000 ). However, the relationship between these factors is quite complex (Krueger and Kickul 2006 ).

Therefore, considering this complexity and drawing on configurational theory, factors influencing the adoption process of sustainable innovations were grouped within these two dimensions to understand what might influence one and/or the other. For comprehensive information regarding the sources of the different factors identified and general details about the selected papers, please refer to Tables 4 and 5 available in the Appendix.

This review underlined that the perceived advantages of using an innovation is among the most influential factors. Indeed, innovation characteristics such as “relative advantage” and “compatibility” significantly increased the probability of adopting sustainable innovations among farmers. If potential adopters do not perceive any relative advantage in the innovation and good compatibility of the innovation with the farm, they generally do not consider it further (Tey and Brindal 2012 ). Following this reasoning, farmers often have conflicting goals (Dessart et al. 2019 ), as they want to introduce sustainable innovations while protecting their production activity (Gosling and Williams 2010 ). Therefore, sustainable innovation is adopted only where farmers expect it to help them achieve their economic goals tolerance (Pannell et al. 2006 ). These results agree also with Ferlie et al. ( 2001 ) and Rogers ( 1995 ) as they also found the power of the variables described above. On the contrary, some authors argued that the implementation of sustainable innovations is negatively correlated with economic goals, and positively correlated with pro-environmental attitudes (Greiner 2015 ; Greiner and Gregg 2011 ; Kallas et al. 2010 ). This result may be surprising in that some sustainable practices yield more than conventional ones (Dessart et al. 2019 ). However, it is possible to assume that if farmers have a strong environmental vision, they may disregard economic gains. Thus, a path to adoption may be guided by environmental values so that farmers are willing to change their routines to adopt. These environmental values are stronger among organic farmers (Siepmann and Nicholas 2018 ).

The psychological and socio-demographic characteristics of adopters significantly influence their willingness to embrace innovations. According to Piñeiro et al. ( 2020 ) and Chams and García-Blandón ( 2019 ), farmers with higher levels of education, a proactive approach to staying informed about potential innovations, a positive attitude towards sustainability, adherence to social norms, and a sense of control over adopting new practices are more likely to engage in the adoption process of innovations within the agri-food sector. These factors are crucial in shaping the strategic role of individuals in embracing innovative practices.

Similarly, Pierpaoli et al. ( 2013 ) by a selection of 20 studies identified the acquisition of good information and education level as the socio-demographic factors most influencing the adoption of innovations. On the contrary, in accordance with previous studies (e.g., Gupta et al. 2020 ; Kernecker et al. 2019 ; Lawrence and Tar 2018 ; Eastwood et al. 2017 ), it was found that the complexity of innovation, a high degree of innovation aversion, and the low perceived control over innovation are strong barriers to innovation adoption as they make the adopter insecure (e.g., Bechini et al. 2020 ; Aubert et al. 2012 ). At the same time, low education, poor advice support (Lindblom et al. 2017 ), and unfavorable working conditions (Caffaro et al. 2019 ; Bijttebier et al. 2018 ) turn out to be rather common hindering factors. Finally, the change of work routine and the increase in the market cost of the product also negatively influence the innovation process (Ghadge et al. 2020 ; Al-Rahmi et al. 2019 ) as it reduces the farmer’s certainty in implementing innovation. In fact, resistance to change in work routines and the personality of the entrepreneur are related (Creissen et al. 2021 ; Dessart et al. 2019 ). Farmers who are not predisposed to change in general may be particularly against change in general (Bonke and Musshoff 2020 ; George and Zhou 2001 ), as changing routines triggers a high perception of risk in them (Bakker et al. 2021 ; Trujillo-Barrera et al. 2016 ). However, high-risk perception can be mitigated by a good risk tolerance (Trujillo-Barrera et al. 2016 ; Arbuckle et al. 2013 ). Despite this, a lot of farmers reject the change (Barreiro-Hurle et al. 2018 ; Hellerstein et al. 2013 ).

Regarding the age and gender of farmers, the current review has returned conflicting results, and therefore it is still quite problematic to understand the real impact of these personal features on the innovation adoption process. Indeed, different studies have produced contrasting findings regarding the relationship between age and innovation adoption among farmers. While some studies suggest that older farmers are more inclined to embrace innovation (García-Cortijo et al. 2019 ; Vezina et al. 2017 ), others highlight the significance of younger farmers in driving agricultural innovation (Jack et al. 2022 ; Bianchi et al. 2022 ). Similarly, the influence of sex on innovation adoption varies across studies, with both women and men being identified as active adopters in certain contexts (Aznar-Sánchez et al. 2020 ; Thorsøe et al. 2019 ). Finally, contextual factors are also influential. Indeed, this review showed that the possibility of having a comparison with other peers and technical and/or financial support significantly increases the likelihood that the innovation will be adopted (see, among others, De Steur et al.  2020 ; Bordbar 2014 ). One of the reasons for this is that policies can enhance the farmers’ confidence and incentivize the adoption of sustainable practices. By offering tangible guidelines and support, policies can help farmers mitigate income volatility and foster a greater sense of security in embracing sustainable approaches. For example, through direct payments decoupled from production decreed by the European CAP, the risk tolerance of European farmers has increased (Koundouri et al. 2009 ). In addition, the ability to have advisory services can improve the farmers’ awareness, their environmental concerns, and the significance they place on preserving natural environment (Cullen et al. 2018 ). Likewise, long-term planning and teamwork have been found to help the implementation of innovation. These findings are consistent with Pierpaoli et al. ( 2013 ) who state that the observability of innovation results, good work design, and perceived ease of use were ranked as determinants to be considered. Finally, farm size and years of experience have also been found to affect the innovation process, although in literature there are conflicting opinions. For example, regarding the size of the farm, Dalla Corte et al. ( 2015 ), Rosenbusch et al. ( 2011 ), and Cohen ( 2010 ) found that a smaller farm size gives the company a greater agility to change routines for innovation implementation. This agrees with Bonney et al. ( 2007 ) who state that small- and medium-sized farms have always adapt and innovate to remain competitive in the market. In contrast, Muzira and Bondai ( 2020 ), Serebrennikov et al. ( 2020 ), and Borgen and Aarset ( 2016 ) found that larger farms are better able to administer the innovation process because they have more funds, more workers, and strategic planning of the work to be done. About years of experience in the industry, García-Cortijo et al. ( 2019 ) and Vezina et al. ( 2017 ) found that older farms are more likely to adopt new practices or products because the farmer at their helm possess the experience that can guide them on the new path. Conversely, Gütschow et al. ( 2021 ) and Rosenbusch et al. ( 2011 ), in their study of SMEs, pointed out that younger farms benefit more from innovation because mature farms have already routines that are difficult to change in terms of organizations, cost, and time than new farms that have yet to consolidate their routines.

Type of innovation

The analyzed manuscripts were classified into four innovation categories: organic farming, precision farming, regenerative agriculture, and agroecology. Nine articles were found that investigated the adoption of precision farming by farmers. These studies highlighted the increasing interest in this practice and analyzed its positive effects on agricultural productivity and resource efficiency. Organic farming was examined in ten articles, indicating a growing interest in this sustainable cultivation method. The results of these studies highlighted the benefits of organic farming in terms of soil conservation, biodiversity promotion, and improved food quality. Regenerative agriculture was discussed in thirteen articles, demonstrating the growing attention towards this practice aimed at restoring and enhancing the health of agricultural ecosystems. Studies on agroecology, on the other hand, amounted to twelve, revealing significant interest in integrating ecological and social principles into agricultural practices.

The analysis of the selected studies in this SLR has shown that there are no particular factors influencing the adoption of one type of innovation over another. However, an attempt was made to summarize key factors that could help farmers adopt specific practices for each type of innovation. Table 3 below summarizes the results.

It has been found that the adoption of sustainable agricultural practices is facilitated by some common elements that play a crucial role in promoting change. Education serves as a fundamental starting point by providing in-depth information about specific practices and their benefits. This type of training enables farmers to understand the scientific rationale and positive impacts of sustainable practices. Similarly, technical support is equally important as farmers require hands-on assistance in implementing practices correctly. Expert consultants can provide personalized advice, helping farmers overcome technical challenges and optimize crop management strategies. Furthermore, economic incentives play a key role in motivating farmers to transition to sustainable practices. Subsidies, favorable financing, or tax incentives can reduce initial costs and mitigate financial risks associated with the transition. These incentives provide a financial boost and are an important encouragement for farmers to adopt sustainable practices. The networking structure is another important factor. Interaction and collaboration among farmers, experts, organizations, and research institutions facilitate the exchange of knowledge and mutual learning. Sharing best practices, experiences, and challenges allows farmers to benefit from the expertise of others and adopt more effective approaches. In addition to these elements, adaptation and reinvention are necessary. Sustainable practices require a flexible approach tailored to the specificities of individual farms and different regions. Farmers need to be willing to experiment with new methods, integrate traditional knowledge with innovations, and modify their existing practices to achieve more sustainable outcomes.

Lastly, continuous education is essential in this context. The agricultural sector is constantly evolving, with new scientific discoveries, technologies, and practices emerging regularly. Farmers need to stay informed about the latest trends and update their skills to adopt the best available solutions.

Research agenda

The results of the present SLR highlighted that scholars have detected several core factors influencing the adoption of sustainable innovations among farmers, including innovation characteristics, various socio-demographic and psychological features of farmers, and some contextual elements in which farms operate. Nevertheless, it is clear that this topic has not been completely probed, and there exist some under-explored research areas that require further investigation. Firstly, new studies should aim to investigate farmers’ innovation adoption detecting contextual, psychographic, and socio-demographic characteristics together with innovation-specific features, providing a detailed picture of the factor enabling/hindering innovation adoption, and offering practical insights tailored to distinct typologies of innovation.

Secondly, from a methodological point of view, studies on the subject should be based on established theories or models. Indeed, in the current review, only 19 out of 44 analyzed studies referred to a theoretical model or theory. The literature points to multiple models and theories, and it would be wise to always choose the most appropriate one with respect to the intended goal. Third, future research should aim to achieve greater external and internal validity by involving larger/representative samples of farmers (since almost all studies have relied on limited convenience samples) and applying robust and transparent (and thus replicable) data collection methodologies typical of experimental studies. However, it should be kept in mind that this is quite difficult to do practically, and that the accuracy and reliability of the work also depend heavily on the theoretical models used and from the context of reference. Configurational theory suggests that there is no single path to pursue in implementing sustainable innovations, as there are infinite combinations of factors. In this view, researchers can reduce the possibility of reaching erroneous conclusions by formulating a priori hypotheses that can be pursued in multiple ways and by assessing the sensitivity of study conclusions to bias of varying degrees.

Concerning internal validity, scholars should apply validated data collection methods based on efficient designs and robust data quality control. Finally, data collection procedures (including all variables collected and exact protocols applied) should be fully disclosed in future studies, allowing researchers to replicate the analysis and effectively extend previous results.

Analyzing in detail the results of this SLR, some general considerations can be performed.

Firstly, a low number of studies have considered the socio-demographic characteristics of farmers as factors affecting the adoption of sustainable innovations. Moreover, when these factors were examined, the results were inconsistent. For example, Nastis et al. ( 2019 ) investigated the role of older farmers and, consequently, their years of experience on the farm as an important factor affecting the individuals’ innovation adoption strategies. On the contrary, Barnes et al. ( 2019 ) found that younger farmers are more likely to adopt innovations due to a greater adaptive capacity to new technologies. Similarly, regarding the sex at birth variable, Aznar-Sánchez et al. ( 2020 ) revealed that being female encourages the adoption of innovative practices since women are more predisposed to collaboration with the farm team and/or other farms and companies of the sector. On the other hand, Thorsøe et al. ( 2019 ) found that men are more favorable to the adoption of innovations. Furthermore, many of the studies detected in the current review highlighted that educational background is a significant predictor of innovation adoption (Nastis et al. 2019 ; Mishra et al. 2018 ; Sassenrath et al. 2010 ) as it contributes to increasing self-confidence. In contrast with these findings, Barnes et al. ( 2019 ) found no effect on educational status. These conflicting results do not allow to depict a clear picture of the socio-demographic characteristics of the adopter. Therefore, to overcome these limitations of the literature, future research must further investigate whether age, gender, and educational status affect the process of innovation adoption into the farm. This finding could be of significant interest for policymakers to build specific incentives to foster the adoption of innovations in the agricultural sector.

Moreover, as emphasized in our results, few studies have studied the role of contextual factors in affecting sustainable innovation adoption. This is an important shortcoming in the literature as today it is essential for farms to develop open innovation strategies to be effectively competitive in the current, dynamic marketplace. Open innovation can lead to a balance between productivity and sustainability (Chesbrough 2003 ). Indeed, it has been shown that to facilitate the adoption of sustainable innovative practices, companies must collaborate and integrate their knowledge with external sources (Stefan and Bengtsson 2017 ). Thus, future research should consider the importance of cooperation, as a strategic element for farmers’ innovation adoption, both within the team and with other farms and organizations operating in the same field of interest, by emphasizing the potential benefits of cooperation and its different avenues.

Furthermore, regarding the types of innovation identified in this systematic literature review, it can be stated that all four agricultural approaches have demonstrated significant importance in terms of reducing environmental impact. For each of these approaches, it has been possible to provide a brief guide on the aspects to consider in order to assist farmers in innovating. These factors include education and information on sustainable approaches, financial support, access to specific resources and technical assistance for each practice, as well as collaboration and knowledge exchange among farmers.

Finally, we underline that it was surprising to note that only 10 articles analyzed innovation in organic agriculture. Organic agriculture has been at the forefront of the agricultural revolution in recent years. It is considered a priority by the European Union, primarily due to the significant impact of the agri-food sector in terms of CO 2 emissions and soil pollution. As a result, its implementation is among the objectives of the European Green Deal, which aims to gradually lead the continent towards climate neutrality. Therefore, it is crucial to promote further research on innovation in organic agriculture in order to effectively address environmental challenges and achieve sustainability goals.

Organic agriculture requires a constant commitment from farmers. It represents what the literature on innovations might define as a “radical innovation” (Dosi 1988 ): an almost total break with the knowledge networks of the productive paradigm, replaced by completely new and revolutionary techniques (Morgan and Murdoch 2000 ). The process of transitioning to sustainability requires farmers to set aside much of the knowledge they have acquired in intensive production and acquire new knowledge (Morgan and Murdoch 2000 ). In this process, the development of open innovation becomes essential. It is not surprising, therefore, that researchers have found that a lack of knowledge is one of the main obstacles to farmers’ sustainable conversion (Padel 1994 ). Hence, it would be interesting to explore whether, in addition to stronger environmental values, there are further differences between farmers who are open to innovation and those who are not, concerning socio-demographic characteristics or teamwork skills, for example. Consequently, it is suggested that prospective studies analyze this topic in greater detail to better define possible differences in the adoption process of sustainable innovations.

Conclusions

The current review provided a twofold result: first, the different factors that have an important role in explaining the adoption of product and process innovations by farmers were detected. Second, the methodological gaps among the available studies were highlighted to provide actionable directions for future studies.

Furthermore, findings confirmed that innovation adoption is influenced by multiple factors of various natures that interact with each other during the adoption process and therefore cannot be considered individually. Subsequently, suggestions were formulated for the implementation of more internally and externally robust studies, resulting from a detailed analysis of existing methodological gaps in the investigated documents.

However, despite the relevance of the results, some limitations of the present SLR need to be highlighted. The first limitation deals with the nature of the review. Indeed, although the procedure is systematic, it must be assumed that, having to replicate this work, another group of researchers may give importance to the details that were overlooked in this SLR. A further limitation of this study lies in choosing to use only scientific articles and excluding all other documents (such as books and gray literature). Additionally, it is possible that valuable studies were published on platforms other than Scopus and Web of Science, despite their recognized value and dissemination in the international scientific community. Potentially interesting research may also not have been filtered as it might not have carried the specific search key terms in the text. In addition, the current review included only research performed in developed countries totally overlooking insights from developing nations. However, it should be remembered that these are characteristics of most SLRs and depend, as Paul and Criado ( 2020 ) pointed out, on the subjective component intrinsic to the literature review process. Finally, the nature of the studies analyzed did not allow a thorough investigation of the interactions among the various factors influencing the innovation process. In this study we simply investigate which variables influence farmers’ choices; nevertheless, it would be greatly valuable to investigate the relations among factors in further studies. In addition, future research might use different theoretical models and methodologies or might investigate one specific agricultural sector (or compare results across sectors), where farmers are more/less prone to innovations.

Despite these limitations, theoretical, methodological, and practical implications can be drawn from the work. From a theoretical point of view, these findings try to overcome the existing gaps of the literature, which is rather fragmented and incomplete especially regarding the organic farming sector, by providing a complete set of determinants useful to create a general picture of the factors affecting farmers’ innovations adoption. Relatedly, it is important to note that, as Greenhalgh et al. ( 2004 ) pointed out, many factors may simultaneously intervene in affecting farmers’ behavior; therefore, both socio-demographic, psychological, and contextual factors need to be considered complementary to each other. Current findings could indeed be a useful guideline for scholars who intend to approach new research in the domain of farmers’ innovation adoption behavior. Notwithstanding, however, there are several alternative robust theoretical models available. At the methodological level, the current SLR provides several practical insights on possible patterns scholars can follow to perform new empirical studies with higher levels of internal and external validity.

Finally, policymakers can take several actions to promote the adoption of sustainable practices. This includes investing in agricultural education to provide targeted training programs that raise awareness, develop technical skills, and enhance understanding of the challenges and opportunities associated with sustainable practices. It is also crucial to provide farmers with adequate technical support by establishing mechanisms that grant access to specialized consultants and industry professionals. Financial incentives, such as grants and tax incentives, can help reduce costs and barriers related to the adoption of sustainable practices. Moreover, policymakers should encourage knowledge sharing and collaboration among farmers to facilitate mutual learning and the dissemination of practical information. Implementing awareness and outreach policies through communication campaigns and promoting successful models of sustainable agriculture can increase awareness among farmers and the general public about the benefits of sustainable practices in addressing environmental and social challenges.

At a practical level, current results provide insights contributing to the ongoing policy debate on the most effective measures to foster sustainable innovation adoption among farmers in developed countries. Truly understanding the factors influencing farmer decision-making would allow for the development of more appropriate and effective agri-environmental policies, as policy interventions based on incomplete information may be insufficient to reduce the negative environmental externalities of agriculture. This may be the case with European CAP, which, relying primarily on traditional policy instruments that do not deepen farmer decision-making understanding, has had a mixed record in achieving environmental goals (Eurostat 2018 ). Therefore, excluding some factors can lead to unrealistic ex-ante policy assessments. Current outcomes could help policy institutions to better target specific interventions to farmers’ individual characteristics and farms’ needs to promote a wider diffusion of sustainable innovations and thus achieve the United Nations Sustainable Development Goals 2030 (FAO 2016 ).

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Open access funding provided by Università degli Studi di Palermo within the CRUI-CARE Agreement. This research is a part of the BioDegraPack project, funded by the Italian Ministry of Agricultural, Food, Forestry and Tourism Policies (MIPAAF).

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Giuseppina Rizzo, Giuseppina Migliore & Giorgio Schifani

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Conceptualization: Giuseppina Migliore and Riccardo Vecchio; methodology: Riccardo Vecchio; investigation: Giuseppina Rizzo, Giuseppina Migliore, and Giorgio Schifani; writing original draft: Giuseppina Rizzo; writing—review and editing: Giuseppina Migliore and Riccardo Vecchio; data curation: Giuseppina Rizzo; visualization: Giorgio Schifani; supervision: Riccardo Vecchio

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Rizzo, G., Migliore, G., Schifani, G. et al. Key factors influencing farmers’ adoption of sustainable innovations: a systematic literature review and research agenda. Org. Agr. (2023). https://doi.org/10.1007/s13165-023-00440-7

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Received : 11 May 2023

Accepted : 24 July 2023

Published : 16 August 2023

DOI : https://doi.org/10.1007/s13165-023-00440-7

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