what is suggestions for further studies in research

Suggestions for Future Research

Your dissertation needs to include suggestions for future research. Depending on requirements of your university, suggestions for future research can be either integrated into Research Limitations section or it can be a separate section.

You will need to propose 4-5 suggestions for future studies and these can include the following:

1. Building upon findings of your research . These may relate to findings of your study that you did not anticipate. Moreover, you may suggest future research to address unanswered aspects of your research problem.

2. Addressing limitations of your research . Your research will not be free from limitations and these may relate to formulation of research aim and objectives, application of data collection method, sample size, scope of discussions and analysis etc. You can propose future research suggestions that address the limitations of your study.

3. Constructing the same research in a new context, location and/or culture . It is most likely that you have addressed your research problem within the settings of specific context, location and/or culture. Accordingly, you can propose future studies that can address the same research problem in a different settings, context, location and/or culture.

4. Re-assessing and expanding theory, framework or model you have addressed in your research . Future studies can address the effects of specific event, emergence of a new theory or evidence and/or other recent phenomenon on your research problem.

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FUTURE RESEARCH

Types of future research suggestion.

The Future Research section of your dissertation is often combined with the Research Limitations section of your final, Conclusions chapter. This is because your future research suggestions generally arise out of the research limitations you have identified in your own dissertation. In this article, we discuss six types of future research suggestion. These include: (1) building on a particular finding in your research; (2) addressing a flaw in your research; examining (or testing) a theory (framework or model) either (3) for the first time or (4) in a new context, location and/or culture; (5) re-evaluating and (6) expanding a theory (framework or model). The goal of the article is to help you think about the potential types of future research suggestion that you may want to include in your dissertation.

Before we discuss each of these types of future research suggestion, we should explain why we use the word examining and then put or testing in brackets. This is simply because the word examining may be considered more appropriate when students use a qualitative research design; whereas the word testing fits better with dissertations drawing on a quantitative research design. We also put the words framework or model in brackets after the word theory . We do this because a theory , framework and model are not the same things. In the sections that follow, we discuss six types of future research suggestion.

Addressing research limitations in your dissertation

Building on a particular finding or aspect of your research, examining a conceptual framework (or testing a theoretical model) for the first time, examining a conceptual framework (or testing a theoretical model) in a new context, location and/or culture.

• Expanding a conceptual framework (or testing a theoretical model)

Re-evaluating a conceptual framework (or theoretical model)

In the Research Limitations section of your Conclusions chapter, you will have inevitably detailed the potential flaws (i.e., research limitations) of your dissertation. These may include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Identifying what these research limitations were and proposing future research suggestions that address them is arguably the easiest and quickest ways to complete the Future Research section of your Conclusions chapter.

Often, the findings from your dissertation research will highlight a number of new avenues that could be explored in future studies. These can be grouped into two categories:

Your dissertation will inevitably lead to findings that you did not anticipate from the start. These are useful when making future research suggestions because they can lead to entirely new avenues to explore in future studies. If this was the case, it is worth (a) briefly describing what these unanticipated findings were and (b) suggesting a research strategy that could be used to explore such findings in future.

Sometimes, dissertations manage to address all aspects of the research questions that were set. However, this is seldom the case. Typically, there will be aspects of your research questions that could not be answered. This is not necessarily a flaw in your research strategy, but may simply reflect that fact that the findings did not provide all the answers you hoped for. If this was the case, it is worth (a) briefly describing what aspects of your research questions were not answered and (b) suggesting a research strategy that could be used to explore such aspects in future.

You may want to recommend that future research examines the conceptual framework (or tests the theoretical model) that you developed. This is based on the assumption that the primary goal of your dissertation was to set out a conceptual framework (or build a theoretical model). It is also based on the assumption that whilst such a conceptual framework (or theoretical model) was presented, your dissertation did not attempt to examine (or test) it in the field . The focus of your dissertations was most likely a review of the literature rather than something that involved you conducting primary research.

Whilst it is quite rare for dissertations at the undergraduate and master's level to be primarily theoretical in nature like this, it is not unknown. If this was the case, you should think about how the conceptual framework (or theoretical model) that you have presented could be best examined (or tested) in the field . In understanding the how , you should think about two factors in particular:

What is the context, location and/or culture that would best lend itself to my conceptual framework (or theoretical model) if it were to be examined (or tested) in the field?

What research strategy is most appropriate to examine my conceptual framework (or test my theoretical model)?

If the future research suggestion that you want to make is based on examining your conceptual framework (or testing your theoretical model) in the field , you need to suggest the best scenario for doing so.

More often than not, you will not only have set out a conceptual framework (or theoretical model), as described in the previous section, but you will also have examined (or tested) it in the field . When you do this, focus is typically placed on a specific context, location and/or culture.

If this is the case, the obvious future research suggestion that you could propose would be to examine your conceptual framework (or test the theoretical model) in a new context, location and/or culture. For example, perhaps you focused on consumers (rather than businesses), or Canada (rather than the United Kingdom), or a more individualistic culture like the United States (rather than a more collectivist culture like China).

When you propose a new context, location and/or culture as your future research suggestion, make sure you justify the choice that you make. For example, there may be little value in future studies looking at different cultures if culture is not an important component underlying your conceptual framework (or theoretical model). If you are not sure whether a new context, location or culture is more appropriate, or what new context, location or culture you should select, a review the literature will often help clarify where you focus should be.

Expanding a conceptual framework (or theoretical model)

Assuming that you have set out a conceptual framework (or theoretical model) and examined (or tested) it in the field , another series of future research suggestions comes out of expanding that conceptual framework (or theoretical model).

We talk about a series of future research suggestions because there are so many ways that you can expand on your conceptual framework (or theoretical model). For example, you can do this by:

Examining constructs (or variables) that were included in your conceptual framework (or theoretical model) but were not focused.

Looking at a particular relationship aspect of your conceptual framework (or theoretical model) further.

Adding new constructs (or variables) to the conceptual framework (or theoretical model) you set out (if justified by the literature).

It would be possible to include one or a number of these as future research suggestions. Again, make sure that any suggestions you make have are justified , either by your findings or the literature.

With the dissertation process at the undergraduate and master's level lasting between 3 and 9 months, a lot a can happen in between. For example, a specific event (e.g., 9/11, the economic crisis) or some new theory or evidence that undermines (or questions) the literature (theory) and assumptions underpinning your conceptual framework (or theoretical model). Clearly, there is little you can do about this. However, if this happens, reflecting on it and re-evaluating your conceptual framework (or theoretical model), as well as your findings, is an obvious source of future research suggestions.

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How to Write a Discussion Section | Tips & Examples

Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary : A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasize weaknesses or failures.

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Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that…
• The study demonstrates a correlation between…
• This analysis supports the theory that…
• The data suggest that…

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualizing your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis…
• Contrary to the hypothesized association…
• The results contradict the claims of Smith (2022) that…
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is y .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of…
• The results do not fit with the theory that…
• The experiment provides a new insight into the relationship between…
• These results should be taken into account when considering how to…
• The data contribute a clearer understanding of…
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalizability is limited.
• If you encountered problems when gathering or analyzing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalizability of the results is limited by…
• The reliability of these data is impacted by…
• Due to the lack of data on x , the results cannot confirm…
• The methodological choices were constrained by…
• It is beyond the scope of this study to…

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish…
• Future studies should take into account…
• Avenues for future research include…

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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.

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This final chapter concludes with the four research questions (sections 8.1.1 to 8.1.4) and provides general insights from across the study (section 8.1.5). The contribution to the scientific body of knowledge is summarized in section 8.1.6, which is followed by the second sub-chapter 8.2 suggesting methodological enhancements (section 8.2.1) and content extensions (section 8.2.2) for future research.

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Dissertation Recommendations — How To Write Them

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Recommendations are crucial to your paper because they suggest solutions to your research problems. You can include recommendations in the discussion sections of your writing and briefly in the conclusions of your dissertation , thesis, or research paper . This article discusses dissertation recommendations, their purpose, and how to write one.

Inhaltsverzeichnis

• 1 Dissertation Recommendations — In a Nutshell
• 2 Definition: Dissertation recommendations
• 3 How to write dissertation recommendations
• 4 Dissertation recommendations based on your findings
• 5 Purpose of dissertation recommendations

Dissertation Recommendations — In a Nutshell

• Dissertation recommendations are an important aspect of your research paper.
• They should be specific, measurable, and have the potential of future possibilities.
• Additionally, these recommendations should offer practical insights and suggestions for solving real-life problems.

When making your recommendations, please ensure the following:

• Your recommendations are an extension of your work instead of a basis for self-criticism
• Your research stands independently instead of suggesting recommendations that will complete it
• Your dissertation recommendations offer insights into how future research can build upon it instead of undermining your research

Definition: Dissertation recommendations

Dissertation recommendations are the actionable insights and suggestions presented after you get your research findings. These suggestions are usually based on what you find and help to guide future studies or practical applications. It’s best to place your dissertation recommendations at the conclusion.

How to write dissertation recommendations

When writing your academic paper, you can frame dissertation recommendations using one of the following methods:

Use the problem: In this approach, you should address the issues highlighted in your research.

Offer solutions: You can offer some practical solutions to the problems revealed in your research.

Use a theory: Here, you can base your recommendations on your study’s theoretical approach.

Here are some helpful tips for writing dissertation recommendations that you should incorporate when drafting a research paper:

• Avoid general or vague recommendations
• Be specific and concrete
• Offer measurable insights   Ensure your suggestions are practical and implementable
• Avoid focusing on theoretical concepts or new findings but on future possibilities

“Based on the study’s outcomes, it’s recommended that businesses and organizations develop mental health well-being frameworks to reduce workplace stress. This training should be mandatory for all employees and conducted on a monthly basis.”

Dissertation recommendations based on your findings

After analysing your findings, you can divide your dissertation recommendations into two subheadings as discussed below:

What can be done?

This section highlights the steps you can use when conducting the research. You may also include any steps needed to address the issues highlighted in your research question. For instance, if the study reveals a lack of emotional connection between employees, implementing dynamic awareness training or sit-downs could be recommended.

Is further research needed?

This section highlights the benefits of further studies that will help build on your research findings. For instance, if your research found less data on employee mental well-being, your dissertation recommendations could suggest future studies.

Purpose of dissertation recommendations

Note: Dissertation recommendations have the following purposes:

• Provide guidance and improve the quality of further studies based on your research findings
• Offer insights, call to action, or suggest other studies
• Highlight specific, clear, and realistic suggestions for future studies

When writing your dissertation recommendations, always remember to keep them specific, measurable, and clear. You should also ensure that a comprehensible rationale supports these recommendations. Additionally, your requests should always be directly linked to your research and offer suggestions from that angle.

Note that your suggestions should always focus on future possibilities and not on present new findings or theoretical concepts. This is because future researchers may use your results to draw further conclusions and gather new insights from your work.

Can I include new arguments in the conclusion of a dissertation

Dissertations follow a more formal structure; hence, you can only present new arguments in the conclusion. Use your dissertation’s concluding part as a summary of your points or to provide recommendations.

How is the conclusion different from the discussion sections?

The discussion section describes a detailed account of your findings, while the conclusion answers the research question and highlights some recommendations.

What shouldn't I include in the dissertation recommendations?

Avoid concluding with weak statements like “there are good insights from both ends…”, generic phrases like “in conclusion…” or evidence that you failed to mention in the discussion or results section.

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The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

• Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
• Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
• Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
• Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
• Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

• Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
• Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
• Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE :   If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
• Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Senunyeme, Emmanuel K. Business Research Methods. Powerpoint Presentation. Regent University of Science and Technology; ter Riet, Gerben et al. “All That Glitters Isn't Gold: A Survey on Acknowledgment of Limitations in Biomedical Studies.” PLOS One 8 (November 2013): 1-6.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

• Describe each limitation in detailed but concise terms;
• Explain why each limitation exists;
• Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
• Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
• If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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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.

• How to write a research paper conclusion with Paperpal? 

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.

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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.

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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.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
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• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

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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Private Transit: Existing Services and Emerging Directions (2018)

Chapter: section 6 - conclusions and areas for further research.

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45 Conclusions For public agencies looking to engage most constructively with operators of private transit services, existing practice and regulatory approaches suggest the following strategies: • Work together with private transit providers to create regulations that work for everyone. Several jurisdictions have been successful in taking a cooperative approach to regulation, bringing both public and private players to the table to develop regulatory approach, and helping work toward providing a level of compliance and information sharing that is benefi- cial for all stakeholders. Jurisdictions and organizations around the Bay Area are the furthest along in this regard, with the Shuttle Census (MTC/Bay Area Council), SFMTA’s Commuter Shuttle Pilot Program, and pending microtransit regulations. • Allocate street space to reflect public priorities without stifling private-sector innovation. Develop policy tools like BART’s curb-use decision tree to prioritize public and private goals for access to curb space and rights-of-way (see Section 3), weighing such factors as the level of demand for space, whether proposed private uses supplement or compete with public transit services, and the size and restrictions on ridership of private services. Akin to zoning codes or other land-use regulations, such policies could help clarify and make predictable for all stakeholders what transportation uses are permitted in which locations and do so with a clear public rationale and process. • Update local and state licensing of private transportation services to reflect evolving busi- ness practices and emerging models, for better understanding the size and extent of the private transportation market as it exists today. Most jurisdictions have no way to know how many and what types of private vehicles are working the streets or whether, for instance, large private buses are serving commuters, sightseers, or charter passengers, each of which would have different impacts on the public right-of-way. The SFMTA shuttle and microtransit regu- latory processes can serve as models for managing evolving private transit services. • Use private transit services as an “early warning” to indicate how and where service needs and markets are changing. The presence of private transit services in a corridor can suggest where new or more frequent public transit routes are needed. New residential or commercial development may be creating a need for express routes or connections that didn’t previously exist or could not have been supported. The private market can respond to these signals more quickly and establish the presence of a potential transit market. In areas that are able to sup- port it, however, expanded public transit service is the best long-term solution due to the transparency, service continuity, and civil rights safeguards built into public provision. • Anticipate that conflict may be heightened by reconfiguration of public space, such as geo- metrical changes to the street and the creation of transit-only lanes. Include private tran- sit providers in project planning, and open lines of communication with private providers known to be operating in a corridor before changes start to take place. S E C T I O N 6 Conclusions and Areas for Further Research

46 Private Transit: Existing Services and Emerging Directions • Explore the use of consortium-based services for locations that need group transport but would be unable to support a productive public transit route. For suburban or low-density workplaces that have a critical mass of employees who don’t drive—particularly in sectors that employ many shift workers, such as hospitals, manufacturers, or warehouse and fulfillment operations—but that couldn’t support a public transit route, consortium-based services provide a variety of potential solutions that often build on existing high-capacity transit. • Promote efficiencies in the use of sponsored services by such means as offering priority to private services that support public goals of equity and efficiency, such as being open to more than just sponsored riders and making an effort to avoid deadhead miles by making service available to the general public whenever possible. • Incorporate private operations into emergency planning and response. Private transit services have been enlisted to provide transportation services when public transit was over- whelmed in the wake of several natural disasters, including in Miami after Hurricane Andrew in 1992 and in New Jersey after Hurricanes Irene (2011) and Sandy (2012) (see discussion of flexible route-based services or jitneys, in Section 2). • Ensure that private transit services are a key part of MaaS developments. The adoption of MaaS principles—which center on expanding and integrating the entire range of non-SOV urban mobility options—has the potential to transform cities and transit agencies. MaaS focuses on moving people easily and efficiently by using an ecosystem of services and removing the institutional silos that can hinder movement between modes and providers. Areas for Further Research This study has identified several areas where further research is warranted to continue expanding knowledge about private transit and related services. Several of these depend on greater availability of operational data from private transportation providers. Such areas include the following: • A more comprehensive investigation of the scale and operational characteristics of employer- and property-sponsored shuttles in a variety of urban settings. • Continued study of microtransit as it spreads to more cities and operating environments, including in service partnerships with public agencies. • A greater understanding of the impacts of TNC-based services, both shared- and exclusive- ride, with particular attention to their effects on VMT, traffic congestion, and related safety impacts. • Ongoing study of the outcomes of various regulatory approaches for private transit services, including statutory, administrative, and cooperative approaches and those originating in different parts of the policymaking apparatus. • Exploration of the equity implications of various private transit service types and partnership formats.

TRB's Transit Cooperative Research Program (TCRP) Research Report 196: Private Transit: Existing Services and Emerging Directions provides information about private transit services and ways they are addressing transportation needs in a variety of operating environments. The document contains an overview and taxonomy of private transit services in the United States, a review of their present scope and operating characteristics, and a discussion of ways they may affect the communities in which they operate along with several case studies and other supporting information.

Private transit services—including airport shuttles, shared taxis, private commuter buses, dollar vans and jitneys—have operated for decades in many American cities. Recently, business innovations and technological advances that allow real-time ride-hailing, routing, tracking, and payment have ushered in a new generation of private transit options. These include new types of public-private partnership that are helping to bridge first/last mile gaps in suburban areas.

The report also examines ways that private transit services are interacting with communities and transit agencies, as well as resulting impacts and benefits.

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How to write the part scope for further research?

The part scope for further research is essential in every academic study such as a thesis , dissertation or journal paper . The main purpose of this part is to make the readers aware of the findings emerging from the study, and its shortcomings. The shortcomings of the research gap guide future researchers on a domain that they must consider to save time and avoid repetitive outcomes.

Furthermore, this section also gives guidelines to researchers on other dimensions and critical estimations from which the topic can be explored.

Emphasize the significance of further research

There are no specific rules or guidelines for this part. However, since it is expected to be brief and informative, the following format is recommended.

Start this section by reflecting on the significance of the present study in brief. Answering questions such as:

• Whether the research deviated from its initial objectives?
• What was the original idea behind the research?
• From where was the inspiration drawn?

Answering such questions is important because the reader should connect to the idea of the research.

Limitations of the study

Furthermore, briefly explain the limitations of the study. This step proves significant for scholars who wish to address areas that can enrich the research topic further. The limitations can either be presented separately, in an independent section called “Limitations of the research”, or can be integrated within the future scope. Also, the limitations should be scalable and relatable, i.e. something that other researchers feel can be accomplished under different circumstances. This is also the key to setting recommendations for future studies.

Justify the future scope

Furthermore, provide justifications for the reasons why the mentioned areas have not been covered in the current study. Identify the probable bottlenecks other researchers might encounter while considering future research related to the topic. This will help them formulate an achievable or practically applicable plan for their own research, including the scope, aim and methodology.

Suggestions

Finally, the approach of the researcher becomes more direct. To be specific, some direct research suggestions should be given to other scholars for future studies. Be precise so that the reader is confident to undertake future studies in the suggested areas.

Answering the following questions can help:

• What should be explored by others?
• Why is it worth exploring?
• What can be achieved from it?
• Will the suggested study be relevant five to ten years down the line?
• How does it add to the overall body of the literature?

Types of writing a future scope

There are different types of future research scope, based on the kind of writing, such as:

• The future scope is focused solely on study findings.
• The future scope is focused on the theory or theoretical model misused.
• The future scope from lack of literary support.
• The future scope of geographical outreach.
• The future scope of testing methods and statistics.
• The future scope on the complete redesigning of methodology.

Points to keep in mind

The most important aspect of writing the future scope part is to present it in an affirmative way. As identified in the former section, it is crucial to identify if the limitations are methods-based or researcher based. It should be concise and critical to the field of study. Refrain from using a reference in the scope for the future research part.

Make sure the points discussed remain achievable in a proximal time frame. In addition, make sure that they are in relation to the theoretical development of the study in focus.

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Donaldson MS, Mohr JJ; Institute of Medicine (US). Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis. Washington (DC): National Academies Press (US); 2001.

Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis.

• Hardcopy Version at National Academies Press

CONCLUSIONS AND DIRECTIONS FOR FURTHER RESEARCH AND POLICY

• Limitations of This Research

There are limitations to all sampling strategies and to qualitative research, in particular. The strength of this method was that the sample selection used input from a pool of reognized experts in the organization, delivery, and improvement of health care. Even with a pool of recognized experts, it is reasonable to expect that some high performing micro-systems were overlooked. It was also possible that less than high performing micro-systems were included. In fact, a concern was how to ensure that the micro-systems included in the study were high performing or successful micro-systems, and probes were included in the interview to assess what evidence micro-systems might offer to validate statements about their level of performance. We did not, however, seek validation from documents or other written materials. Although the intent of the sampling strategy was to study high performing micro-systems, a very small number of apparently negative cases were useful for comparison. More importantly, as expected, each site had some areas of very strong performance and other areas that were undistinguished, and they formed a natural cross-case comparison group. Although the sites were selected because of expert opinion, the database is limited by being self report. It is possible that the leaders of the micro-systems had an interest in making their micro-system appear to be better than it is, and we did not have any independent verification of their assertions. For this reason, we did not make any judgments about the validity of respondents' assertions and have limited the analysis to descriptive summaries and themes based on the respondents' own words.

TABLE 18 Micro-System Examples of Investment in Improvement

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TABLE 19 Micro-System Examples of Alignment of Role and Training

A second limitation of this study was that the interviews were not tape-recorded to provide a raw data “gold standard” for later reference. For this reason, we went to considerable effort to ensure the quality of note taking as described in the methods section, and we obtained respondents' consent to follow-up with them to clarify notes. Follow-up was necessary in only a few instances. The notes were voluminous and rich in detail.

A third limitation is that for most of the interviews, one respondent represented each of the forty-three micro-systems. A more comprehensive assessment would include interviews with at least one person from each of the key roles within the micro-system, including patients. Such tradeoffs in qualitative analysis between breadth and depth are inevitable, 31 but given that this was an exploratory study, we decided to include as many micro-systems as possible with follow-up in later studies.

Research currently underway will expand on this work by taking a more comprehensive look at individual micro-systems and the outcomes of care provided to determine if high performing micro-systems achieve superior results for patients.

• Directions for Further Research

This research has been exploratory in that it is the first systematic look at health care micro-systems. The power of the research is that it gave a voice to individual micro-systems and provided a way to explore them while creating constructs that may be generalizable to other micro-systems. It has begun the work of defining and characterizing health care micro-systems. The greater value of this analysis will be to go beyond the findings of this research to develop tools to help existing micro-systems improve and to replicate and extend the achievements of these micro-systems.

The basic concept of health care micro-systems—small, organized groups of providers and staff caring for a defined population of patients—is not new. The key components of micro-systems (patients, populations, providers, activities, and information technology) exist in every health care setting. However, current methods for organizing and delivering health care, preparing future health professionals, conducting health services research, and formulating policy have made it difficult to recognize the interdependence and function of the micro-system.

Further analysis of the database would likely yield additional themes. All can be the basis of hypothesis testing for continued work. For example, further work might establish criteria of effectiveness and test whether the features identified as the eight themes are predictive of effectiveness. More refined or additional questions might clarify aspects of the general themes that are critical. More intensive data gathering, for example, of multiple members of the micro-system, including patients could validate results and expand our understanding of these micro-systems.

Two questions were central as we undertook this study: (1) would the term micro-system be meaningful to clinicians in the field? (2) Would they participate and give us detailed enough information to draw inferences? The answers to both questions were clearly: Yes.

Overall, we discovered that the idea of a micro-system was very readily understood by all we interviewed. They had no difficulty in identifying and describing their own micro-systems and, when appropriate because they directed several (such as several intensive care units), differentiating among them in terms of their characteristics.

The study was assisted in its work by an extremely able and distinguished steering group and Subcommittee whose reputations in the field unquestionably enabled us to secure the participation of nearly all who were invited despite our requesting an hour and a half of a busy clinician's time. Many of those interviewed willingly went on for a longer than the allotted 90 minutes and sent us additional materials. Some who were interrupted by urgent clinical business rescheduled time to complete the interviews.

Although this was a selected—not a randomly sampled—group, and there was clearly great enthusiasm and of innovative work going on at the grass-roots level. Many of those interviewed expressed clear ideas about how they were reorganizing practices, their principles for doing so, and their commitment to an ongoing process. Respondents described their early limited successes or outright failures. We heard what had and had not been successful as they tried to disseminate their practices throughout their organizations. We believe there is much that could profitably learned and shared beyond the individual sites that has not been yet been pulled together by a unifying conceptual framework or effective mechanism for deploying what is being learned.

We were struck by two findings in particular: First, the importance of leadership at the macro-system as well as clinical level; and second, the general lack of information infrastructure in these practices. Micro-system leaders repeatedly stressed the importance of executive and governance-level support. This support was singled out repeatedly as a sine qua non to their ability to succeed. It was also apparent that although some steps have been taken to incorporate the explosion of information technologies that are being deployed for managing patient information, free-standing practices as well as much of clinical practice within hospitals have only begun to integrate data systems, use them for real-time clinical practice, or as information tools for improving the quality of care for a patient population. The potential is enormous, but as yet, almost untapped. They appear to be at a threshold of incorporating information technologies into daily practice. The potential created by the development of knowledge servers, decision support tools, consumer informatics 32 continuous electronic patient-clinician communication, and computer-based electronic health records puts most of these micro-systems almost at “time zero” for what will likely be dramatic changes in the integration of information for real-time patient care and a strong baseline for future comparison.

As research on micro-systems moves forward, it will be important to transfer what has been learned from research on teams and organizations to new research that will be conducted on micro-systems. For example, research that will be helpful includes information about the different stages of development and maturity of the organization, creating the organizational environment to support teams, socializing new members (clinicians and staff) to the team, environments that support micro-systems, the characteristics of effective leadership, and how micro-systems can build linkages that result in well-coordinated care within and across organizational boundaries.

• IOM Quality of Care Study

This study was intended to provide more than a database for research, however. It was undertaken to provide an evidence base for the IOM Committee on the Quality of Health Care in America in formulating its conclusions and recommendations. Because that committee was charged with the formulation of recommendations about changes that can lead to threshold improvement in the quality of care in this country, its members believed that it was extremely important to draw not only on their expertise and the literature but also on the best evidence it could find of excellent performance and to do so in a systematic way as exemplified by this study. As that study was not limited by type of health care, the goals of such a project necessitated drawing from a wide range of sites serving a variety of patient populations. It also suggests a sample size that for qualitative analytic methods was quite broad but not unwieldy. The number of sites interviewed—43—served these purposes well. We had several of each “kind” of micro-system (e.g., primary care, critical care) but they varied in location, composition, and in their own approaches to organizing and delivering care, thus providing a very rich database of observation. That report, which is expected to be published in early 2001, will use the responses and analysis described in this technical report to underpin its recommendations about how health care micro-systems, macro-systems, and other organizational forms that have not yet emerged, can improve their performance.

• Cite this Page Donaldson MS, Mohr JJ; Institute of Medicine (US). Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis. Washington (DC): National Academies Press (US); 2001. CONCLUSIONS AND DIRECTIONS FOR FURTHER RESEARCH AND POLICY.
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Home » Limitations in Research – Types, Examples and Writing Guide

Limitations in Research – Types, Examples and Writing Guide

Table of Contents

Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

• The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
• The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
• The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

• The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
• The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
• The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

• The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
• The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
• The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

• Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
• Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
• Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
• Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
• Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

• To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
• To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
• To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
• To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
• To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

• Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
• Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
• Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
• Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

• Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
• Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
• Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
• Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
• Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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This paper is in the following e-collection/theme issue:

Published on 27.3.2024 in Vol 26 (2024)

Digital Alcohol Interventions Could Be Part of the Societal Response to Harmful Consumption, but We Know Little About Their Long-Term Costs and Health Outcomes

Authors of this article:

• Katarina Ulfsdotter Gunnarsson * , MSc   ; 
• Martin Henriksson * , PhD   ; 
• Marcus Bendtsen * , PhD  

Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden

*all authors contributed equally

Corresponding Author:

Katarina Ulfsdotter Gunnarsson, MSc

Department of Health, Medicine, and Caring Sciences

Linköping University

Linköping, 58183

Phone: 46 13281000

Email: [email protected]

Alcohol consumption causes both physical and psychological harm and is a leading risk factor for noncommunicable diseases. Digital alcohol interventions have been found to support those looking for help by giving them tools for change. However, whether digital interventions can help tackle the long-term societal consequences of harmful alcohol consumption in a cost-effective manner has not been adequately evaluated. In this Viewpoint, we propose that studies of digital alcohol interventions rarely evaluate the consequences of wider dissemination of the intervention under study, and that when they do, they do not take advantage of modeling techniques that allow for appropriately studying consequences over a longer time horizon than the study period when the intervention is tested. We argue that to help decision-makers to prioritize resources for research and dissemination, it is important to model long-term costs and health outcomes. Further, this type of modeling gives important insights into the context in which interventions are studied and highlights where more research is required and where sufficient evidence is available. The viewpoint therefore invites the researcher not only to reflect on which interventions to study but also how to evaluate their long-term consequences.

Beyond the Buzz: Unraveling the Nonexistent Long-Term Outcomes

Consumption of alcohol is in many societies the norm [ 1 ]. In Sweden, for instance, alcohol is consumed regularly by 4 of 5 adults, and approximately 1 of 3 adults are classified as harmful drinkers [ 2 , 3 ]. While evidence suggests that there is no safe limit on alcohol consumption [ 4 ], harmful drinking represents a marked increased risk of negative health and social consequences [ 1 , 4 , 5 ]. In 2016, alcohol was estimated to have contributed to approximately 6.2% of disability-adjusted life years (DALYs) among women and 5.1% of DALYs among men in Sweden, and was attributed to 4.5% and 5.7% of deaths (women and men, respectively) [ 1 ]. A health economic report showed that in 2017, alcohol consumption cost Swedish society €10 billion [ 6 ]. Sweden is not unique in this situation, as many societies worldwide face significant health and economic burdens due to the high prevalence of harmful alcohol consumption.

Reducing the harmful use of alcohol is on the World Health Organization’s list of “best buys” for tackling noncommunicable diseases [ 7 ]. Recommended actions include excise taxes on alcoholic beverages and restrictions on the retail availability of alcohol. Provisioning of psychosocial interventions for persons with harmful alcohol use is also included on the list of recommended actions, and has been operationalized by, for instance, delivering face-to-face interventions in primary health care [ 8 ]. With the ubiquity of internet connectivity in high-income countries, and increasingly in low- and middle-income countries, there is interest to provide digital psychosocial interventions to those who may benefit. Digital interventions—that is, interventions that deliver supportive content through for instance websites, mobile phone apps, text messages, or email—can support those looking for help by giving them tools to increase their motivation, form intentions for change, and give them tools to help support change [ 9 , 10 ]. Digital interventions can scale to large populations and can be designed to be anonymous, which can reduce the stigma of looking for help in face-to-face settings [ 11 - 13 ]. Studies have found that digital alcohol interventions may have positive effects on behavior in a range of different populations [ 14 - 17 ], and evidence from meta-analyses confirms these findings [ 18 - 20 ]. However, whether or not they can help tackle the long-term consequences of harmful alcohol consumption, for instance by reducing the incidence of noncommunicable diseases, and whether or not they can do so in a cost-effective manner, is still uncertain. In contrast, modeling studies have demonstrated that face-to-face interventions hold promise in mitigating health consequences while being cost-effective over the long term [ 21 ]. Thus, limiting our assessment only to the short-term effects of digital interventions means that we cannot compare the societal impacts between modalities.

We propose that studies of digital alcohol interventions rarely evaluate the consequences of wider dissemination of the intervention under study, and that when they do, they do not take advantage of modeling techniques that allow for appropriately studying consequences over a longer time horizon than the study’s period when the intervention is tested. We support our viewpoint with a pragmatic review of the literature ( Multimedia Appendix 1 [ 22 - 27 ]) and make suggestions for how future studies may evaluate the impact of digital alcohol interventions at the societal level—which includes workplace and productivity, social services and nonstatutory care, and the criminal justice sector—and not only consequences related to the health care sector such as costs for treatment. We also invite researchers to reflect on their decision-making when it comes to deciding which intervention research projects should be prioritized; in particular, if formal evaluations of the long-term consequences of dissemination are considered, or even required, in the current decision-making process.

Health economic evaluations of digital alcohol interventions are scarce. When done, the consequences of interventions are evaluated over a short period, the period over which a randomized controlled trial (RCT) is run, ranging from 4 to 12 months. While the reports identified in our pragmatic review ( Multimedia Appendix 1 [ 22 - 27 ]) all concluded that the interventions were cost-effective, they based these findings on short-term follow-up data collected during the trial period—it stands to reason that while there certainly are acute consequences from alcohol consumption, the long-term consequences are substantial and cannot be captured when evaluating such short time horizons [ 28 ]. Thus, current evaluations cannot readily answer the question being asked: What are the consequences of disseminating digital alcohol interventions into society? Considering the growth in interest and resources put into studying digital alcohol interventions, it is unfortunate that the literature cannot provide evidence of the long-term consequences of this investment.

The literature can however provide examples of long-term health economic evaluations outside the scope of digital interventions. Studies of the long-term consequences of nondigital alcohol interventions have been conducted, including the development and use of the Sheffield Alcohol Policy Model [ 29 ], which evaluates public health strategies for alcohol harm reduction. In addition, a review of nondigital brief alcohol interventions found that 14 out of the 23 included studies used modeling techniques that allowed for estimating consequences over a lifetime horizon. The majority of the 14 studies discovered that they either saved costs while improving health or incurred minimal costs compared to the health benefits [ 21 ]. Thus, there exist examples in the literature for evaluating alcohol interventions, which can inspire and be partially adopted by the digital intervention research field. Other literature can be followed to model long-term health economic outcomes, which guides the construction, analysis, and implementation of health economic models [ 30 , 31 ]. In addition, if it is important to model interactions among individuals, their propensity to use support, and relationships between multiple physical and mental health conditions, then agent-based models can be used to capture these complexities and forecast the long-term effects of interventions [ 32 , 33 ].

Importance of Health Economic Research

The importance of health economic evaluations in general, and modeling studies in particular, should not be understated. They provide input to decision-makers faced with difficult prioritization tasks, that is, if resources should be invested in disseminating a specific intervention. In many jurisdictions with a publicly funded health care system, they have long been an integral part of health policy [ 34 ] with a broad acceptance that modeling methods are required [ 35 ], not least in the evaluation of cancer drugs where intermediate outcomes (eg, progression-free survival) are routinely extrapolated to outcomes such as mortality [ 36 ]. It seems prudent that public health care efforts also should provide similar input of long-term outcomes since they often fall under the jurisdiction of public health care systems, however, this is not the case today. Their role can further be extended to also guide the prioritization of research resources. In developing an intervention, one may consider if a hypothetical intervention with a potential effect size even is worth developing and evaluating in an RCT. If such a hypothetical intervention cannot be shown to be cost-effective, then there is no reason to invest resources in its development and evaluation. Researchers are routinely asked by review panels to provide sample size estimates, which are based on anticipated effect sizes. These anticipated effect sizes commonly fall into 2 categories [ 37 - 39 ]: so-called clinical significance, which presents effect sizes large enough that the anticipated sample size is achievable, or minimum relevant effect size, which ensures that even small effect estimates are identified as statistically significant. Interestingly, these anticipated effect sizes are rarely (if ever) put to the test in a health economic evaluation where their long-term costs and health implications can be estimated; thus, a rationale for a study based on a formal evaluation of the long-term consequences of dissemination of the proposed intervention is not produced. We argue that health economic evaluations before RCTs can help reduce research waste and allow both researchers and other decision-makers to prioritize resources more effectively and, indeed, even identify cost-effective research designs [ 40 ].

It should be acknowledged that conducting health economic evaluations is not without barriers. It requires a certain technical skill set by the researcher conducting the evaluation, usually involving statistical software to create models that can extrapolate over time. It also requires epidemiological expertise to decide on input variables, as well as knowledge of the costs and consequences from both the health care and societal perspectives concerning treatment and disease. These are formidable challenges; however, approaching these challenges confers additional benefits. First, it puts into context the intervention being studied, allowing researchers to carefully think about the degree to which interventions will be adopted, their anticipated short- and long-term costs and effects, and how the novel intervention relates to existing interventions. Second, it makes it very clear where evidence is lacking from both an efficacy, epidemiologic, and economic perspective. For instance, when faced with having to decide on the risk of disease given a range of contextual variables, the process of conducting a health economic evaluation highlights where epidemiological studies are required, but also where the evidence is sufficient. Thus, by conducting a health economic evaluation, research prioritizations can become clear beyond the primary objective of the study. These prioritizations can be further refined by conducting sensitivity analyses to show which input variables affect the outcome of the evaluation the most, refining the agenda for which phenomena require further study. Third, by taking our proposed route, one that has been the gold standard for a long time in clinical medicine, decision-makers will be provided with the relevant evidence when prioritizing scarce resources that can be used for the provision of interventions or for further research. Basing evaluations solely on data from 1 RCT will yield a very precise estimate of an irrelevant cost-effectiveness estimate; thus, our advice is to follow the old advice: It is better to be roughly right than precisely wrong.

To conclude, health economic evaluations of digital alcohol interventions lack modeling of long-term societal consequences, leaving a knowledge gap concerning the degree to which they may address the burgeoning societal burdens caused by alcohol consumption. We invite researchers to reflect on their decision-making process when it comes to deciding which studies should be prioritized.

Acknowledgments

This study was conducted under the auspices of the Swedish Research Council for Health, Working Life, and Welfare (2022-00193). The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Conflicts of Interest

MB owns a private company (Alexit AB) that maintains and distributes evidence-based lifestyle interventions to be used by the public and in health care settings. Alexit AB played no role in the study design, data analysis, data interpretation, or writing of this report. KUG and MH declare no competing interests.

A pragmatic review.

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Abbreviations

Edited by T Leung, T de Azevedo Cardoso; submitted 24.11.22; peer-reviewed by S Bonn, W Campbell, R Davis; comments to author 04.03.23; revised version received 19.04.23; accepted 13.02.24; published 27.03.24.

©Katarina Ulfsdotter Gunnarsson, Martin Henriksson, Marcus Bendtsen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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What the Data Says About Pandemic School Closures, Four Years Later

The more time students spent in remote instruction, the further they fell behind. And, experts say, extended closures did little to stop the spread of Covid.

By Sarah Mervosh ,  Claire Cain Miller and Francesca Paris

Four years ago this month, schools nationwide began to shut down, igniting one of the most polarizing and partisan debates of the pandemic.

Some schools, often in Republican-led states and rural areas, reopened by fall 2020. Others, typically in large cities and states led by Democrats, would not fully reopen for another year.

A variety of data — about children’s academic outcomes and about the spread of Covid-19 — has accumulated in the time since. Today, there is broad acknowledgment among many public health and education experts that extended school closures did not significantly stop the spread of Covid, while the academic harms for children have been large and long-lasting.

While poverty and other factors also played a role, remote learning was a key driver of academic declines during the pandemic, research shows — a finding that held true across income levels.

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the COVID-19 Pandemic .” Score changes are measured from 2019 to 2022. In-person means a district offered traditional in-person learning, even if not all students were in-person.

“There’s fairly good consensus that, in general, as a society, we probably kept kids out of school longer than we should have,” said Dr. Sean O’Leary, a pediatric infectious disease specialist who helped write guidance for the American Academy of Pediatrics, which recommended in June 2020 that schools reopen with safety measures in place.

There were no easy decisions at the time. Officials had to weigh the risks of an emerging virus against the academic and mental health consequences of closing schools. And even schools that reopened quickly, by the fall of 2020, have seen lasting effects.

But as experts plan for the next public health emergency, whatever it may be, a growing body of research shows that pandemic school closures came at a steep cost to students.

The longer schools were closed, the more students fell behind.

At the state level, more time spent in remote or hybrid instruction in the 2020-21 school year was associated with larger drops in test scores, according to a New York Times analysis of school closure data and results from the National Assessment of Educational Progress , an authoritative exam administered to a national sample of fourth- and eighth-grade students.

At the school district level, that finding also holds, according to an analysis of test scores from third through eighth grade in thousands of U.S. districts, led by researchers at Stanford and Harvard. In districts where students spent most of the 2020-21 school year learning remotely, they fell more than half a grade behind in math on average, while in districts that spent most of the year in person they lost just over a third of a grade.

( A separate study of nearly 10,000 schools found similar results.)

Such losses can be hard to overcome, without significant interventions. The most recent test scores, from spring 2023, show that students, overall, are not caught up from their pandemic losses , with larger gaps remaining among students that lost the most ground to begin with. Students in districts that were remote or hybrid the longest — at least 90 percent of the 2020-21 school year — still had almost double the ground to make up compared with students in districts that allowed students back for most of the year.

Some time in person was better than no time.

As districts shifted toward in-person learning as the year went on, students that were offered a hybrid schedule (a few hours or days a week in person, with the rest online) did better, on average, than those in places where school was fully remote, but worse than those in places that had school fully in person.

Students in hybrid or remote learning, 2020-21

80% of students

Some schools return online, as Covid-19 cases surge. Vaccinations start for high-priority groups.

Teachers are eligible for the Covid vaccine in more than half of states.

Most districts end the year in-person or hybrid.

Source: Burbio audit of more than 1,200 school districts representing 47 percent of U.S. K-12 enrollment. Note: Learning mode was defined based on the most in-person option available to students.

Income and family background also made a big difference.

A second factor associated with academic declines during the pandemic was a community’s poverty level. Comparing districts with similar remote learning policies, poorer districts had steeper losses.

But in-person learning still mattered: Looking at districts with similar poverty levels, remote learning was associated with greater declines.

A community’s poverty rate and the length of school closures had a “roughly equal” effect on student outcomes, said Sean F. Reardon, a professor of poverty and inequality in education at Stanford, who led a district-level analysis with Thomas J. Kane, an economist at Harvard.

Score changes are measured from 2019 to 2022. Poorest and richest are the top and bottom 20% of districts by percent of students on free/reduced lunch. Mostly in-person and mostly remote are districts that offered traditional in-person learning for more than 90 percent or less than 10 percent of the 2020-21 year.

But the combination — poverty and remote learning — was particularly harmful. For each week spent remote, students in poor districts experienced steeper losses in math than peers in richer districts.

That is notable, because poor districts were also more likely to stay remote for longer .

Some of the country’s largest poor districts are in Democratic-leaning cities that took a more cautious approach to the virus. Poor areas, and Black and Hispanic communities , also suffered higher Covid death rates, making many families and teachers in those districts hesitant to return.

“We wanted to survive,” said Sarah Carpenter, the executive director of Memphis Lift, a parent advocacy group in Memphis, where schools were closed until spring 2021 .

“But I also think, man, looking back, I wish our kids could have gone back to school much quicker,” she added, citing the academic effects.

Other things were also associated with worse student outcomes, including increased anxiety and depression among adults in children’s lives, and the overall restriction of social activity in a community, according to the Stanford and Harvard research .

Even short closures had long-term consequences for children.

While being in school was on average better for academic outcomes, it wasn’t a guarantee. Some districts that opened early, like those in Cherokee County, Ga., a suburb of Atlanta, and Hanover County, Va., lost significant learning and remain behind.

At the same time, many schools are seeing more anxiety and behavioral outbursts among students. And chronic absenteeism from school has surged across demographic groups .

These are signs, experts say, that even short-term closures, and the pandemic more broadly, had lasting effects on the culture of education.

“There was almost, in the Covid era, a sense of, ‘We give up, we’re just trying to keep body and soul together,’ and I think that was corrosive to the higher expectations of schools,” said Margaret Spellings, an education secretary under President George W. Bush who is now chief executive of the Bipartisan Policy Center.

Closing schools did not appear to significantly slow Covid’s spread.

Perhaps the biggest question that hung over school reopenings: Was it safe?

That was largely unknown in the spring of 2020, when schools first shut down. But several experts said that had changed by the fall of 2020, when there were initial signs that children were less likely to become seriously ill, and growing evidence from Europe and parts of the United States that opening schools, with safety measures, did not lead to significantly more transmission.

“Infectious disease leaders have generally agreed that school closures were not an important strategy in stemming the spread of Covid,” said Dr. Jeanne Noble, who directed the Covid response at the U.C.S.F. Parnassus emergency department.

Politically, though, there remains some disagreement about when, exactly, it was safe to reopen school.

Republican governors who pushed to open schools sooner have claimed credit for their approach, while Democrats and teachers’ unions have emphasized their commitment to safety and their investment in helping students recover.

“I do believe it was the right decision,” said Jerry T. Jordan, president of the Philadelphia Federation of Teachers, which resisted returning to school in person over concerns about the availability of vaccines and poor ventilation in school buildings. Philadelphia schools waited to partially reopen until the spring of 2021 , a decision Mr. Jordan believes saved lives.

“It doesn’t matter what is going on in the building and how much people are learning if people are getting the virus and running the potential of dying,” he said.

Pandemic school closures offer lessons for the future.

Though the next health crisis may have different particulars, with different risk calculations, the consequences of closing schools are now well established, experts say.

In the future, infectious disease experts said, they hoped decisions would be guided more by epidemiological data as it emerged, taking into account the trade-offs.

“Could we have used data to better guide our decision making? Yes,” said Dr. Uzma N. Hasan, division chief of pediatric infectious diseases at RWJBarnabas Health in Livingston, N.J. “Fear should not guide our decision making.”

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the Covid-19 Pandemic. ”

The study used estimates of learning loss from the Stanford Education Data Archive . For closure lengths, the study averaged district-level estimates of time spent in remote and hybrid learning compiled by the Covid-19 School Data Hub (C.S.D.H.) and American Enterprise Institute (A.E.I.) . The A.E.I. data defines remote status by whether there was an in-person or hybrid option, even if some students chose to remain virtual. In the C.S.D.H. data set, districts are defined as remote if “all or most” students were virtual.

An earlier version of this article misstated a job description of Dr. Jeanne Noble. She directed the Covid response at the U.C.S.F. Parnassus emergency department. She did not direct the Covid response for the University of California, San Francisco health system.

How we handle corrections

Sarah Mervosh covers education for The Times, focusing on K-12 schools. More about Sarah Mervosh

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Francesca Paris is a Times reporter working with data and graphics for The Upshot. More about Francesca Paris

Avon Longitudinal Study of Parents and Children

University of bristol and ucl to lead support hub for uk's longitudinal population studies.

Brian Merrill from Pixabay

Press release issued: 27 March 2024

The University of Bristol and UCL will lead the Population Research UK (PRUK) co-ordination hub, part of an existing strategic investment from the UKRI Infrastructure Fund.

The UK is a world leader in population research, bolstered by its unique collection of longitudinal population studies, which follow groups of people over time. Individually, the studies have made substantial contributions to the advancement of social and health sciences and together, they have the power to uncover the drivers behind major societal challenges and provide a potent tool for basic science application.

The collective power of the UK’s longitudinal studies was never more apparent than during the COVID-19 pandemic. Studies across the country coordinated to rapidly capture the epidemiological events affecting their participants, work which was then combined with a lifetime of data to shed light on how our backgrounds and lifelong health shaped our experience of the pandemic. This was just one example of many demonstrating  how important it is to make the most of population-based research in the UK.

The PRUK mission is therefore to further harness the potential of these scientific resources by supporting researchers, policymakers and study management teams to maximise benefits and overcome shared challenges.

The hub is the culmination of previous PRUK activity, which has been funded through the  Economic and Social Research Council  (ESRC) and the  Medical Research Council  (MRC) since 2021. The Hub is a £9 million investment that will play an important role in PRUK providing joined-up thinking across the UK’s longitudinal population studies and commissioning activities that will result in better, easier to use data resources for social, economic and biomedical science. It marks a further commitment to longitudinal population research infrastructure, alongside  ESRC's continuing investments in CLOSER and MRC and ESRC’s joint investment in the UK Longitudinal Linkage Collaboration.

The PRUK co-ordination hub will be directed by Professor Nic Timpson , Principal Investigator of the Children of the 90s cohort at the University of Bristol and Professor Alissa Goodman , Director of the UCL Centre for Longitudinal Studies . Professors Goodman and Timpson bring a wealth of experience in life course research and longitudinal study leadership and management.

PRUK Co-Director, Professor Timpson of the University of Bristol, said: "PRUK will seek to deliver enhanced use of population-based research assets in the UK. Our community model will deliver a combination of connected working, skilled users undertaking open science with complex multi-modal data, and engaged policymaker, public and participant stakeholders. The overall aim is to maximise the sector’s value for the public good."

PRUK Co-Director, Professor Goodman of UCL, added: "The UK is a world-leader in longitudinal population studies and Population Research UK provides us with an incredible opportunity to come together as a community. PRUK will create a forum for the users and creators of longitudinal population studies, and the infrastructure that serves them. Through engagement in the forum and commissioning of services, we will drive innovations in how we work in order to maximise the value and potential of these fantastic resources."

The wider leadership team of the PRUK co-ordinating hub team includes experts from across the UK longitudinal population research infrastructure landscape. Professors Timpson and Goodman are joined in leading the new initiative by Andy Boyd of the UK Longitudinal Linkage Collaboration, Paul Bradshaw of the Scottish Centre for Social Research (ScotCen), Professor Rosie McEachan of Born in Bradford, Chris Orton of Population Data Science at Swansea University Medical School, and Professor Jennifer Symonds of CLOSER.

PRUK will focus on five key areas critical to successful longitudinal population research and data collection, identified in the PRUK prospectus , building on existing provision and commissioning additional activities to address priority needs:

• data discovery
• data access
• data linkage
• training and capacity building
• coordination and advocacy

For more information, please email [email protected]

The X handle for PRUK will be @PopResUK

For more information about UKRI Infrastructure Fund, visit: www.ukri.org/what-we-do/creating-world-class-research-and-innovation-infrastructure/funded-infrastructure-projects/

Further information

About Children of the 90s Based at the University of Bristol, Children of the 90 s, also known as the Avon Longitudinal Study of Parents and Children (ALSPAC), is a long-term health research project that enrolled more than 14,000 pregnant women in 1991 and 1992.  It has been following the health and development of the parents, their children and now their grandchildren in detail ever since.  It receives core funding from the Medical Research Council, the Wellcome Trust and the University of Bristol.

'We were surprised': Intermittent fasting flagged as serious health risk

Intermittent fasting, a trendy method for weight-loss and targeting inflammation, has been flagged as a serious health risk, the American Heart Association announced Monday .

Results of a study presented at the association's conference in Chicago this week revealed that adults following an eight-hour time-restricted eating schedule have a 91% higher chance of death by cardiovascular disease than those eating within the usual timeframe of 12-16 hours per day.

Though it is important to note that these are preliminary findings, said senior study author Victor Wenze Zhong, chair of the Department of Epidemiology and Biostatistics at the Shanghai Jiao Tong University School of Medicine in Shanghai, China.

"Although the study identified an association between an eight-hour eating window and cardiovascular death, this does not mean that time-restricted eating caused cardiovascular death," Zhong said at the event.

Is intermittent fasting healthy? It can be, but 'it's not a magic solution'

How the study was conducted, what else it showed

The independent study lead by Zhong and his team looked at approximately 20,000 adults in the U.S. from 2003 to 2018 using data collected by the Centers for Disease Control and Prevention (CDC) for its National Health and Nutrition Examination Survey . The association tracked dietary patterns in people with an average age of 49 who documented their food intake for at least two days within one year, the association reported.

That data was then compared to CDC mortality data from the same time period.

About half of the participants self-identified as women. Over 73% of the participants self-identified as non-Hispanic white adults, 11% self-identified as Hispanic and 8% self-identified as non-Hispanic Black adults. Data was collected on an additional 6.9% that self-identified as another racial category.

Details of the findings, published by the American Heart Association , include the following:

• People with a pattern of eating less than eight hours per day had a 91% higher risk of death by cardiovascular disease.
• Increased risk of cardiovascular death was also seen in people living with heart disease or cancer.
• Eating between eight and 10 hours per day was associated with a 66% higher risk of death from heart disease or stroke for those with existing cardiovascular diseases.
• Intermittent fasting did not decrease the overall risk of death from any cause.
• For those living with cancer, an eating duration of 16 hours per day or more lowered the risk of cancer mortality.

Further study is needed, experts say

Not all factors that play a role in overall health were considered in this study. Future research seeks to "examine the biological mechanisms that underly the associations between a time-restricted eating schedule and adverse cardiovascular outcomes," the American Heart Association reported . Also needed is insight on whether or not the findings will be similar depending on where participants live in the world.

There is research showing that intermittent fasting could improve "cardiometabolic health measures such as blood pressure, blood glucose and cholesterol levels," according to the American Heart Association.

“We were surprised," Zhong said. "Our research clearly shows that, compared with a typical eating time range of 12-16 hours per day, a shorter eating duration was not associated with living longer."

The most critical piece to this discovery, though, is the increased risk for those already living with heart conditions or cancer.

The findings "encourage a more cautious, personalized approach to dietary recommendations, ensuring that they are aligned with an individual’s health status and the latest scientific evidence,” Christopher Gardner , director of nutrition studies at Stanford University, said of the study.

Gardner noted that the "nutrient quality of the diets" needs to be examined. "Without this information, it cannot be determined if nutrient density might be an alternate explanation to the findings that currently focus on the window of time for eating."

As always, individuals should consult a doctor before considering implementing lifestyle changes.

As noted by the American Heart Association, the news releases and research abstracts are considered preliminary until published in a peer-reviewed scientific journal.

Cardiologists Tell Us What You Really Need to Know About Intermittent Fasting and Heart Health

New research on intermittent fasting made splashy headlines last week. The abstract, presented at an American Heart Association conference, found that intermittent fasting—specifically, the restriction of food consumption to an 8-hour period each day—was associated with a 91% increase in risk of cardiovascular death. (Insert needle-scratch.)

This is shocking intel, not just because the statistic is so staggering, but because it flies in the face of what we’ve previously known to be true about the benefits of intermittent fasting, which would suggest it has the opposite impact on heart health. The practice, in fact, is supposed to be good for your heart.

However, experts say that prior research shouldn’t necessarily be discarded in favor of these new findings and that you don’t need to panic if you get your calories within a restricted window of time each day. Here, they unpack everything we know to date on intermittent fasting’s impact on heart health.

What is intermittent fasting?

Generally speaking, intermittent fasting is a dietary approach where you are in a fasted state for some period of time, says Julia Zumpano, RD , a preventive cardiology nutritionist at Cleveland Clinic. The precise period of time depends on the type of intermittent fasting you’re doing— there are several different types —but one popular approach involves restricting calories to an eight-hour window each day. For example, 12 p.m. to 8 p.m. It doesn’t involve any dietary restrictions beyond eating within a specific time window, and some people find this easier to adhere to than trying to cut out specific foods or counting calories. However, it can result in an overall reduction in caloric intake—some research indicates that people end up cutting out about 300 to 500 calories by intermittent fasting.

What has prior research said about intermittent fasting’s impact on heart health?

Prior, well-controlled, short-term studies have shown that intermittent fasting can result in improvements in body weight , insulin resistance , blood pressure , and some markers of inflammation , a.k.a. “a number of surrogates for heart health that tend to be associated with improved cardiovascular outcomes in the long term,” says Sean Heffron, MD , preventive cardiologist and director of fitness-focused cardiology at the Center for the Prevention of Cardiovascular Disease at NYU Langone Heart.

Longer-term data is scarce in humans due to the general limitations of diet and nutrition studies (more on that to follow), but we do have longer-term data based on animal studies. “Lots of these studies show that life-long adherence to limited energy intake is associated with greater lifespan,” Dr. Heffron says.

What did this new research find?

This new study aimed to look at long-term data in humans to fill the gaps in our understanding of intermittent fasting’s impact on health. It included data collected as part of the National Health And Nutrition Examination Survey , which asked 20,000 adults of varying ages, sexes, and ethnicities what they ate during an average period of eight years.

During that time period, participants reported what they had eaten in two separate interviews less than two weeks apart. If they reported having eaten within a restricted eight-hour window, researchers generalized that eating pattern to their lives overall, essentially labeling them as intermittent fasts.

Researchers then looked at who had died subsequent to filling out the surveys to see whether or not their engagement in this form of intermittent fasting was associated with overall death, cardiovascular death, and death from cancer, says Dr. Heffron. “They found that there were associations, meaning that restricting food consumption over a very short period was associated with worse outcomes,” he says.

In other words, the study noted that those who consumed food in windows of time shorter than 8 hours had a greater risk of cardiovascular death than those who ate food in windows of time longer than 8 hours—hence the shocking headline.

What are the limitations of this new research?

There are many, which even the study’s authors acknowledge. To begin with, this research was presented in an abstract rather than as a full study. It has not yet been published in a peer-reviewed journal.

An abstract, Zumpano explains, is just the preliminary part of a study in which researchers share their methods and hypotheses or conclusions. Still, they don’t provide any of the details or specifics of the study. “It’s basically a one-sheet page describing what they wanted to do,” she says. “So it’s kind of like looking at the front of a food label without being able to look at the back. You don’t know the nutrition facts or have the ingredients—you’re just looking at the marketing on the front.” Generally, she says, this format is one of the biggest limitations of these findings. “We can't come to conclusions until we have all of the information when the full study comes out,” she says.

Another limitation is that the abstract was based on an observational study, which means its details were self-reported. “We know observational studies aren’t necessarily always accurate,” she says. People can’t always recall what they ate, for example, or they might even fib about it. And given that participants only contributed a 24-hour dietary recall twice throughout the entire survey period, this analysis does not include a lot of data from which to draw conclusions, says Dana Hunnes, RD, PhD , senior dietitian at UCLA Medical Center and author of Recipe for Survival: What You Can Do to Live a Healthier and More Environmentally Friendly Life .

Additionally, several factors are not accounted for in the analysis. Perhaps most glaringly, it does not seem to include any details whatsoever on the content of participants’ diets. “We don’t know what these 20,000 individuals are eating as it’s not described anywhere,” says Dr. Hunnes. “They could be eating nothing but hamburgers, hot dogs, fries, ice cream, and other highly processed, high-calorie foods.” Other lifestyle factors that impact health, such as exercise habits, were not included in the analysis either.

And finally, the study did not demonstrate a causal effect, says Dr. Hunnes, meaning that you can’t conclude from it that intermittent fasting caused the increased risk of cardiovascular death but rather that the two were linked. It also offers no insight into why this might be the case.

For all of these reasons, Dr. Heffron ultimately says, rather definitively, that this is “not a noteworthy study.”

What research needs to be done to investigate this finding further?

Perhaps some of the above gaps in knowledge will be filled in when the full study is released. Still, even then, Dr. Heffron says a considerable amount of further work would be required to validate the results of this analysis. “You’d need to do the appropriate statistical analyses from the cohort that these data are derived from and then look for validation within other independent cohorts,” he says. “And if that pans out, then you would think about potential short-term-duration interventions, which are possible in nutrition.”

Generally speaking, Dr. Hunnes says a significant amount of additional research is needed on intermittent fasting beyond this analysis, especially regarding its impacts on heart health. “In fact, much more information in general is needed with regard to lifestyle and heart health,” she says.

She says this intel can be difficult to collect as there are limitations to nearly all nutrition studies. “There are so many variables and factors that are difficult to account for and are confounding,” she says. “If we were to conduct a long-term nutrition study where all variables could be accounted for—ie. 1,000 people live in a hotel or dormitory and are given access only to foods provided by the study, and were monitored in their daily activities 100 percent of the time, and all other variables are known—we could make grand conclusions.”

Dr. Heffron agrees. “The gold standard in health outcomes research is a randomized controlled trial, and it’s very difficult to randomize anybody to a nutritional intervention,” he says. “Modifying even one small aspect of a diet for a long-term study, which is what’s necessary to find out if someone dies or not as a result of that modification, is very difficult.”

TL;DR: What conclusions should we draw about the impacts of intermittent fasting on heart health? In the short term, we know that intermittent fasting can help people lose weight and reduce certain risk factors for heart disease. We don’t yet know how intermittent fasting affects human health in the long run.

If the data on intermittent fasting is inconclusive, what diets are shown to be beneficial to heart health?

Even though Dr. Heffron believes this abstract’s data should more or less be taken with a grain of salt, he says it’s okay to abandon intermittent fasting if you’re troubled by the new intel. In fact, he rarely recommends it per se anyway, despite the fact that there is evidence to suggest that intermittent fasting is an effective way to restrict overall calories and, in turn, mitigate major risk factors for heart disease. This is because he prefers to focus his counseling on encouraging behaviors patients will be able to maintain for decades, as those are better associated with improved heart health outcomes.

Such behaviors include better food choices, and Zumpano agrees that emphasis should be placed more on what you eat than when you eat it. “Focus on including heart-healthy foods and decreasing the things that we know can negatively impact your heart, like processed meats, processed foods, fast foods, junk foods, and sugary foods,” she says.

If you are looking for specific dietary guidelines, Dr. Hunnes says we know that some diets are generally good for heart health. “For example, we know from long-term epidemiological studies that the Mediterranean diet is, generally speaking, a heart-protective diet and that populations that eat this type of diet live longer with fewer cardiovascular problems, tend to have lower body weight, lower body-fat percentage, and die less from heart disease,” she says. “We similarly know that the DASH diet (dietary approaches to stop hypertension), which is similar to a Mediterranean diet, also helps protect against heart disease and death from heart disease. And we know that a whole-food, plant-based diet that limits processed foods is protective against heart disease and death from heart disease.”

This doesn’t mean that intermittent fasting has no role in a heart-healthy diet, but rather that more research is needed to understand its impact, says Dr. Hunnes. “We would need to tweak those diets to find out if intermittent fasting makes any of them better or worse for heart disease outcomes,” she says.

Zumpano also notes that, in any case, there isn’t a one-size-fits-all approach to diet. So, even if it is determined that intermittent fasting is harmful or helpful to heart health in the long term, those findings may not be applicable to all demographics. “If you’re questioning what the best diet is for you, I would recommend seeing a dietician,” she says. “We’re able to look at your risk factors, medical history, family history, labs, etcetera, and can pinpoint the best plan for you.”

Where is customer care in 2024?

Customer care leaders are facing their greatest challenge in decades. They must prepare their organizations for an AI-enabled future while simultaneously meeting tough commercial targets and rising customer expectations. Our latest global survey suggests that many companies are struggling on all these fronts.

About the authors

This article is a collaborative effort by Eric Buesing , Maximilian Haug, Paul Hurst, Vivian Lai, Subhrajyoti Mukhopadhyay, and Julian Raabe , representing views from McKinsey’s Operations Practice.

Major disruptions are always painful, and the transition from a care paradigm dominated by human agents to one steered by AI technologies may be the biggest disruption in the history of customer service. Can organizations find a route to hyperefficient, digitized customer care while retaining the personal contact and responsiveness that customers require?

Right now, many customer care leaders feel trapped in no-man’s-land. Technology has enabled them to evolve their operations significantly, and the traditional call center environment is rapidly becoming a thing of the past. Yet when these digitally enabled models underperform—and they often do—companies need to master entirely new approaches to performance improvement alongside their traditional tool kits.

Customer care in the spotlight

The key findings in this article are based on McKinsey’s fourth global survey of customer care executives. This survey was our largest yet, gathering the views of more than 340 leaders at the director, senior director, vice president, and C-suite levels. Respondents came from companies with annual revenues of $100 million to $10 billion-plus, representing every major industry segment.

The majority of respondents said that the companies they worked for were headquartered in North America (just over 50 percent) or Western Europe (almost 25 percent), with 10 percent headquartered in India and 4 percent in China. Most respondents said their organizations operated in multiple regions: 75 percent reported operating in North America, 58 percent in Europe, 57 percent in Asia–Pacifc, 39 percent in the Middle East and Africa, and 37 percent in Latin America. We plan to expand future research to include more organizations headquartered outside North America and Western Europe.

To make matters worse, executives say that most of the challenges highlighted in our last survey  are still present today (see sidebar, “Customer care in the spotlight”). Those challenges include rising call volumes, high levels of employee attrition, and persistent talent shortages. Meanwhile, some of the largest consumer-facing technology organizations in the world have become exceptional at digitally enabled customer care, which is lifting customer expectations everywhere, piling further pressure onto customer care staff and leadership at other companies.

Our survey reveals three major themes that are top of mind for customer care leaders. First, their priorities are shifting, from an overwhelming focus on customer experience to a multidimensional approach that also emphasizes revenue goals and technology transformation. Second, they are working hard to build future-ready AI-enabled ecosystems for their operations. Finally, they are boosting their capabilities by investing in employee upskilling programs and building stronger outsourcing relationships.

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Reprioritizing core operations.

When we began monitoring the sentiment of customer care leaders in 2016, their priorities were clear. Customer experience came first, followed at a distance by operational improvement, technology transformation, and revenue generation—in that order.

Over the past seven years, those priorities have converged (Exhibit 1). Revenue generation, which was mentioned by about one in 20 customer care leaders in our first survey, has been rising steadily in importance ever since. It is now a priority for a third of customer care leaders. But over the past two years, technology enhancements and operational improvements have seen the fastest increases. The expectation that customer care functions can do it all and do it well has never been higher.

Leaders also understand that they need to engage with their customers to delight them. Currently, only 11 percent of respondents say reducing contact volume is important to them, a 20-percentage-point drop over 12 months. Indeed, 57 percent of leaders expect call volumes to increase by as much as one-fifth over the next one or two years.

Separate research suggests that these leaders are right to stay focused on direct personal interaction, even when many of their customers are young digital natives. In a recent McKinsey survey of 3,500 consumers, respondents of all ages said that live phone conversations were among their most preferred methods of contacting companies for help and support. That finding held true even among 18- to 28-year-old Gen Z consumers, a cohort that favors text and social messaging for interpersonal communications.

There’s also evidence that younger consumers are getting tired of the digital self-service paradigm. One financial-services company reports that its Gen Z customers are 30 to 40 percent more likely to call than millennials, and they use the phone as often as baby boomers. Premium-segment customers of all ages also prefer the phone, with many saying that live phone support is part of the premium service they are paying for.

These findings don’t point to a future of phone-only customers, however. While customers of all generations prioritize support from a real person, they also want the flexibility to use different channels according to their needs. Digital-chat services have achieved a high level of acceptance across generations, and email remains important, especially for older consumers (Exhibit 2).

The need to excel in service across multiple channels creates extra challenges for customer care leaders, especially when budgets are tight. And 37 percent of respondents in our survey say that cost is still a key priority. This tension is driving companies to look for ways to control the customer care costs that go beyond call volume reduction, with automation and outsourcing the most frequently cited levers.

Creating a future-ready AI ecosystem

The tensions in modern customer care are clearly seen in companies’ approaches to advanced digital technologies. Our survey demonstrates that digital has already become a decisive differentiator. Among respondents who report that their operations are delivering better-than-expected performance, more than half have high levels of digital integration. Banking, telecommunications, and travel and logistics are among the leading industries in this regard.

Those high performers are in the minority, however. Only 8 percent of respondents from North America report greater-than-expected satisfaction with their customer performance. In Africa, Europe, and the Middle East, the figure is 5 percent. Among organizations reporting that performance was in line with or lower than expected, more than 80 percent also say their levels of digital integration are partial or low.

Leaders agree that they need to get digital right. More than half of the respondents to our survey expect the share of inbound contacts that take place through digital channels to exceed 40 percent in the next three years.

Artificial intelligence will play a decisive role in future customer care ecosystems. Respondents to our survey are already deploying AI tools in a variety of applications, including chatbots and automated email response systems, training and support for call center agents, back-office analytics, and decision making.

Over the past 12 months, the availability of powerful generative AI (gen AI) tools, especially large language models (LLMs) that can parse and respond to unstructured text or speech, has opened new possibilities for technology in customer care. More than 80 percent of respondents are already investing in gen AI, or expect to do so in the coming months, with leaders highlighting a wide range of potential applications.

One European subsidiary of a global bank replaced its well-established rules-based customer chatbot with a new system based on gen AI technology. Seven weeks after launch, the AI chatbot was 20 percent more effective at successfully answering customer queries than the old tool. The bank has already identified a road map of improvements that could double its performance in the coming months.

Early adopters are extremely ambitious about the potential of gen AI. The executive in charge of customer care at one major global organization told us that they expect 100 percent of customer interactions to be AI-enabled in the coming years, using a combination of technologies including new virtual assistants, agent-assist tools, and AI-powered voice analytics.

For most companies, however, the gen AI customer care revolution is still in its early stages. Leaders highlight multiple issues that are making it hard for them to integrate these technologies into their existing processes and workflows. The issues include technical challenges regarding deployment and scaling; concerns about safety, security, and governance; and difficulties in defining the desired outcomes from, or business case for, gen AI investments (Exhibit 3).

Learn more about Customer Care

Rethinking skills.

Today, customer care organizations lack many of the critical skills they need to deliver excellent service and navigate the transition to a digitally mediated, AI-enabled world. In part, that’s because customer care leaders have been running to stand still. Record levels of staff attrition following the COVID-19 pandemic meant that supervisors spent much of their time interviewing and bringing new staff up to speed. They spent less time mentoring their established teams, a problem exacerbated by the introduction of hybrid and remote working arrangements. Some agents and team leaders have spent years working with little interaction or coaching from their managers.

Staff turnover has now slowed, and two in three leaders in our latest survey say upskilling and reskilling are critical priorities. Companies highlight a range of benefits that accrue from effective upskilling and reskilling programs, including improvements to employee morale, increased productivity, and faster adoption of new technologies and working methods. Meanwhile, technology is changing upskilling programs. Twenty-one percent of leaders tell us that they are already using AI-based tools to train and support their customer care staff.

AI-based agent support systems are already becoming a key tool for companies seeking to offer extremely effective personal service to demanding customers. These systems can help agents resolve complex queries the first time, simultaneously reducing care costs and boosting customer experience.

One global construction equipment company, for example, uses a gen AI system to help its call center staff navigate thousands of pages of technical-support documentation. The system selects the appropriate steps to resolve a customer’s problem in seconds, based on free text questions entered by the agent and background information such as the serial numbers of vehicles and parts. The tool has cut average call resolution times from around 125 minutes to a few seconds, and it is currently saving customers €150,000 to €300,000 per day in reduced asset downtime.

Elsewhere, companies are using AI to transform the way they manage and support their customer care agents. New AI-based tools can optimize call volume forecasting, for example. This approach helped one company improve forecast accuracy by seven percentage points, while halving the work required to manage team capacities and schedules. The change improved customer service levels by more than 10 percent, while cutting staffing and overtime costs by more than 5 percent.

Companies are also looking outside their organizations for innovative ways to fill capability gaps. Outsourcing, once viewed primarily as a way to reduce costs, is increasingly seen as an effective source of additional skilled capacity and innovation capabilities. Fifty-five percent of the companies in our survey currently outsource part of their customer care operations, and 47 percent of those organizations expect to increase their outsourcing over the next two years.

Outsourcing relationships are becoming deeper too, with respondents telling us that they are now using their business process outsourcing for a range of activities that extends far beyond traditional call and email handling. They include content management and digital-marketing services, payments handling, and the development of AI-based customer care tools. Following the blueprint established by major players in the industrial products, medical device, software, and e-commerce sectors, some companies are now working with outsourcing partners to set up global innovation hubs that will drive the development of next-generation customer care technologies.

Our survey suggests that customer care organizations are running at two different speeds. In the fast lane, top performers have seized the opportunities presented by advances in digital technologies. With ruthless prioritization, they are investing capital to drive efficiency and service excellence across the customer journey. The best have already reshaped their organizations around highly integrated digital platforms. One high-performing company with more than 5,000 service agents is on track to deliver 75 digital-experience improvements this year, for example.

Other companies are still in the slow lane, struggling to fit a patchwork of digital point solutions into legacy care ecosystems. Unsure where to put their dollars, they are trapped in a cycle of continual system adaptation with no clear destination or road map.

In 2024, both types of organizations may need to shift their positions on the road. Gen AI is raising the bar for performance, productivity, and personalization in customer care, and tomorrow’s fully AI-enabled care organizations will operate very differently from those of today. It’s time for companies to look at their care ecosystems with fresh eyes. They should formulate an independent perspective on the changing expectations of their customers and the role of advanced AI in their organization. The future of customer care is calling. Leaders should answer with a bold vision and an aggressive time line for change.

Eric Buesing is a partner in McKinsey’s Charlotte office, where Paul Hurst is an associate partner; Maximilian Haug is an associate partner in the Boston office; Vivian Lai is a consultant in the New York office; Subhrajyoti Mukhopadhyay is an expert in the Chicago office; and Julian Raabe  is a partner in the Munich office.

The authors wish to thank Jorge Amar, Brian Blackader, Marcela Guaqueta, Suryansha Gupta, and Josh Wolff for their contributions to this article.

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