Research-Methodology

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.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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Future Research – Thesis Guide

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Future Research

Future Research

Definition:

Future research refers to investigations and studies that are yet to be conducted, and are aimed at expanding our understanding of a particular subject or area of interest. Future research is typically based on the current state of knowledge and seeks to address unanswered questions, gaps in knowledge, and new areas of inquiry.

How to Write Future Research in Thesis

Here are some steps to help you write effectively about future research in your thesis :

  • Identify a research gap: Before you start writing about future research, identify the areas that need further investigation. Look for research gaps and inconsistencies in the literature , and note them down.
  • Specify research questions : Once you have identified a research gap, create a list of research questions that you would like to explore in future research. These research questions should be specific, measurable, and relevant to your thesis.
  • Discuss limitations: Be sure to discuss any limitations of your research that may require further exploration. This will help to highlight the need for future research and provide a basis for further investigation.
  • Suggest methodologies: Provide suggestions for methodologies that could be used to explore the research questions you have identified. Discuss the pros and cons of each methodology and how they would be suitable for your research.
  • Explain significance: Explain the significance of the research you have proposed, and how it will contribute to the field. This will help to justify the need for future research and provide a basis for further investigation.
  • Provide a timeline : Provide a timeline for the proposed research , indicating when each stage of the research would be conducted. This will help to give a sense of the practicalities involved in conducting the research.
  • Conclusion : Summarize the key points you have made about future research and emphasize the importance of exploring the research questions you have identified.

Examples of Future Research in Thesis

SomeExamples of Future Research in Thesis are as follows:

Future Research:

Although this study provides valuable insights into the effects of social media on self-esteem, there are several avenues for future research that could build upon our findings. Firstly, our sample consisted solely of college students, so it would be beneficial to extend this research to other age groups and demographics. Additionally, our study focused only on the impact of social media use on self-esteem, but there are likely other factors that influence how social media affects individuals, such as personality traits and social support. Future research could examine these factors in greater depth. Lastly, while our study looked at the short-term effects of social media use on self-esteem, it would be interesting to explore the long-term effects over time. This could involve conducting longitudinal studies that follow individuals over a period of several years to assess changes in self-esteem and social media use.

While this study provides important insights into the relationship between sleep patterns and academic performance among college students, there are several avenues for future research that could further advance our understanding of this topic.

  • This study relied on self-reported sleep patterns, which may be subject to reporting biases. Future research could benefit from using objective measures of sleep, such as actigraphy or polysomnography, to more accurately assess sleep duration and quality.
  • This study focused on academic performance as the outcome variable, but there may be other important outcomes to consider, such as mental health or well-being. Future research could explore the relationship between sleep patterns and these other outcomes.
  • This study only included college students, and it is unclear if these findings generalize to other populations, such as high school students or working adults. Future research could investigate whether the relationship between sleep patterns and academic performance varies across different populations.
  • Fourth, this study did not explore the potential mechanisms underlying the relationship between sleep patterns and academic performance. Future research could investigate the role of factors such as cognitive functioning, motivation, and stress in this relationship.

Overall, there is a need for continued research on the relationship between sleep patterns and academic performance, as this has important implications for the health and well-being of students.

Further research could investigate the long-term effects of mindfulness-based interventions on mental health outcomes among individuals with chronic pain. A longitudinal study could be conducted to examine the sustainability of mindfulness practices in reducing pain-related distress and improving psychological well-being over time. The study could also explore the potential mediating and moderating factors that influence the relationship between mindfulness and mental health outcomes, such as emotional regulation, pain catastrophizing, and social support.

Purpose of Future Research in Thesis

Here are some general purposes of future research that you might consider including in your thesis:

  • To address limitations: Your research may have limitations or unanswered questions that could be addressed by future studies. Identify these limitations and suggest potential areas for further research.
  • To extend the research : You may have found interesting results in your research, but future studies could help to extend or replicate your findings. Identify these areas where future research could help to build on your work.
  • To explore related topics : Your research may have uncovered related topics that were outside the scope of your study. Suggest areas where future research could explore these related topics in more depth.
  • To compare different approaches : Your research may have used a particular methodology or approach, but there may be other approaches that could be compared to your approach. Identify these other approaches and suggest areas where future research could compare and contrast them.
  • To test hypotheses : Your research may have generated hypotheses that could be tested in future studies. Identify these hypotheses and suggest areas where future research could test them.
  • To address practical implications : Your research may have practical implications that could be explored in future studies. Identify these practical implications and suggest areas where future research could investigate how to apply them in practice.

Applications of Future Research

Some examples of applications of future research that you could include in your thesis are:

  • Development of new technologies or methods: If your research involves the development of new technologies or methods, you could discuss potential applications of these innovations in future research or practical settings. For example, if you have developed a new drug delivery system, you could speculate about how it might be used in the treatment of other diseases or conditions.
  • Extension of your research: If your research only scratches the surface of a particular topic, you could suggest potential avenues for future research that could build upon your findings. For example, if you have studied the effects of a particular drug on a specific population, you could suggest future research that explores the drug’s effects on different populations or in combination with other treatments.
  • Investigation of related topics: If your research is part of a larger field or area of inquiry, you could suggest potential research topics that are related to your work. For example, if you have studied the effects of climate change on a particular species, you could suggest future research that explores the impacts of climate change on other species or ecosystems.
  • Testing of hypotheses: If your research has generated hypotheses or theories, you could suggest potential experiments or studies that could test these hypotheses in future research. For example, if you have proposed a new theory about the mechanisms of a particular disease, you could suggest experiments that could test this theory in other populations or in different disease contexts.

Advantage of Future Research

Including future research in a thesis has several advantages:

  • Demonstrates critical thinking: Including future research shows that the author has thought deeply about the topic and recognizes its limitations. It also demonstrates that the author is interested in advancing the field and is not satisfied with only providing a narrow analysis of the issue at hand.
  • Provides a roadmap for future research : Including future research can help guide researchers in the field by suggesting areas that require further investigation. This can help to prevent researchers from repeating the same work and can lead to more efficient use of resources.
  • Shows engagement with the field : By including future research, the author demonstrates their engagement with the field and their understanding of ongoing debates and discussions. This can be especially important for students who are just entering the field and want to show their commitment to ongoing research.
  • I ncreases the impact of the thesis : Including future research can help to increase the impact of the thesis by highlighting its potential implications for future research and practical applications. This can help to generate interest in the work and attract attention from researchers and practitioners in the field.

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  • GETTING STARTED
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Turning a research limitation or future research suggestion into a potential topic idea

As our article, Our top tip for finding a dissertation topic highlighted, the Limitations and Future Research section of journal articles are arguably the quickest and easiest way to find a possible dissertation topic at the undergraduate and master's level. After all, in this section of academic journals, researchers explain the limitations of their own research, as well as potential new lines of inquiry that other researchers could explore. However, the trick is to know how to take the research limitations and/or future research suggestions in these journal articles and turn them into a potential topic idea for your dissertation. In this article, we explain how to achieve this.

Knowing how to turn a research limitation or future research suggestion into a potential dissertation topic is simply a matter of following a few steps. Since these steps are slightly different depending on whether you are creating a topic idea from a research limitation or a future research suggestion, we have divided this article into two parts:

Using research limitations as a basis to come up with a dissertation topic idea

Using future research suggestions as a basis to come up with a dissertation topic idea

To use research limitations as a basis to come up with a dissertation topic idea, you first need to have read a journal article on a topic that interests you. Having read this journal article, focus on the section at the end of the article, often called Research Limitations (or Discussion/Future Research ), where the authors criticise their own work. Now, follow the four steps below:

  • STEP ONE: Identify the types of research limitation discussed by the authors
  • STEP TWO: Understand the potential relationship between these research limitations and what makes a dissertation topic significant
  • STEP THREE: Choose a research limitation that interests you
  • STEP FOUR: Turn your dissertation topic idea into a purpose statement

STEP ONE Identify the types of research limitation discussed by the authors

Authors of good journal articles will highlight a number of limitations in their work. These include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Within the Research Limitations section, we go into more detail on each of these types of research limitation. Reading these articles will help you to identify what types of research limitation are being discussed by the authors in the journal article you are interested in.

STEP TWO Understand the potential relationship between these research limitations and what makes a dissertation topic significant

Whilst dissertations are rarely "ground-breaking" at the undergraduate or master's level (and are not expected to be), they should still be significant in some way. When coming up with a dissertation topic idea, you need to be able to explain how your idea is significant . Your research may be significant in one or a number of ways. It may:

Capitalise on a recent event

Reflect a break from the past

Target a new audience

Address a flaw in a previous study

Expand a particular field of study

Help an individual, group, organisation, or community

Since this section of the article deals with using research limitations as a basis for coming up with a dissertation topic idea, just two of these perspectives on research significance are relevant: (a) the ability to address a flaw in a previous study ; and (b) the desire to reflect a break from the past . Let's take each in turn:

The ability to address a flaw in a previous study

The journal article you are reading may have: (a) a flaw that the authors identified after the research was completed; and/or (b) a flaw that they had not anticipated in the first instance. When we use the word flaw , we do not mean that the limitation is necessarily disastrous to the study that was carried out. We use the word loosely to highlight that any academic reviewing the journal article you were interested in could identify a particular factor as a limitation.

A flaw identified after the research was completed

When you complete a piece of research, it is easy to look back and recognise flaws. A common problem is the inability to collect sufficient data and/or information. Sometimes this is because your sample size was too small .

For example, imagine you were interested in the career choices of university students at a university with 20,000 students (your population ). You hoped to interview (or survey, observe etc.) 100 people (your sample ), but in the end you only managed to get 30 people to take part in your research. As a result, you are no longer sure whether you collected sufficient data and/or information to answer your research questions with confidence. In other words, you are not sure whether your smaller sample of just 30 people adequately reflects the population of 20,000 students you were interested in studying. Limitations like this are very common.

A flaw that was not anticipated in the first instance

Sometimes researchers use the Research Limitations section of a journal article to reflect on a flaw that they did not anticipate in the first instance. These kinds of flaws may become evident during the research process when the data is being collected and/or analysed. This makes it much more difficult to anticipate such flaws in the first place.

For example, imagine that you had used a survey to examine the career choices of students at a university of 20,000 students (discussed above). For the most part, the survey contained closed questions . These are questions where the potential response to a question is pre-determined (see the example below):

Question What factors influence your choice of career?

Options [tick all that apply] Career prospects Nature of the work Physical working conditions Salary and benefits Other If Other , please state what this is..........

Let's assume that these potential responses are based on your reading of the literature on career choices. However, since we do not want to miss out any options that we have not thought about or that are not in the literature, we include an open question , labelled Other . Respondents can write anything in the space provided.

When we analyse our data, we find that career prospects and salary and benefits were the main factors influencing career choices amongst the university students. However, we also see that a large proportion of respondents had entered factors into the Other option. The idea that people wanted flexible working arrangements was mentioned by most of these respondents. Whilst today we should have included this option in the survey (i.e. flexible working) because it is often mentioned in the literature, if we went back 20 years, this would not have been the case. Therefore, imagining that we were doing this research 20 years ago, we may have missed out an important option in the initial data collection process. Since the literature had not focused on flexible working as an important factor influencing career choices at the time, such a flaw (i.e., missing out flexible working arrangements as an option in the survey) may not have been anticipated when creating the survey.

Whether the flaw you are trying to address was anticipated by the authors of the journal article you are interested in or not, the important point is that addressing such flaws in previous studies is a way that your research can be significant . It can help to justify your choice of dissertation topic.

The desire to reflect a break from the past

Breaking from the past simply means that you want your dissertation to adopt a different approach to the way that previous research was conducted.

The journal article that you are reading will have followed a particular research strategy . The choice of research strategy adopted by the authors is important because it guides the entire dissertation process, from the choice of research design , research methods , data analysis techniques , and so forth [see the section on Research Strategy for an introduction to these components of research strategy]. Since the choice of research strategy is so important in guiding the dissertation process, breaking away from the research strategy adopted by the authors of the journal article you are interested in can make your dissertation significant .

To illustrate this point, let's reflect on the example we just used where we were interested in examining the career choices of students at a university of 20,000 students. This study was guided by a quantitative research design and the use of survey methods . Therefore, a survey was constructed based on the literature, which contained mostly closed questions . For example:

The potential flaw with the study was that potential options, such as flexible working arrangements had been missed out of the list of options . Such options had been missed out (i.e., not anticipated in the first instance) because they were not prominent in the literature. So the question arises: How can this potential flaw be addressed by breaking from the past ?

Let's imagine that instead of using a quantitative research design, we used a mixed methods research design instead. This would involve a combination of qualitative and quantitative research methods .

Rather than relying on the literature alone to come up with the list of options for our survey questions, we could have started the research process with interviews (i.e., a qualitative , primary research phase). By interviewing a sample of the students at the university, we could have first found out all (or most of ) the factors that students thought about when being asked: What factors influence your choice of career? This would ensure that our list of options to be included for this survey question was more comprehensive . The options that were included would not have only been based on a review of the literature on career choices, but also a qualitative , primary research phase.

By using a mixed methods research design instead of a quantitative research design, you could have highlighted how (a) your dissertation broke from the past and (b) why it was significant as a result of this.

Moving forward...

So you need to look at the journal article you are interested in, identify all of the types of flaws in the Limitations and Future Research section, and then try and identify how these research limitations and aspects of research significance are connected in some way. For example, if the authors stated that they had a low response rate , this may indicate that their sample size was too small (or at least lower than they had hoped for). If this was the case, and the authors suggested that this was a major problem (or you feel it could have been a major problem), you could argue that addressing this flaw is one way in which your dissertation topic could be significant . Taking the second example that was presented, if the authors of the journal article you were interested in highlighted problems that could be associated with their choice of research design (e.g., quantitative vs. mixed methods research design), this could illustrate the significance of a study addressing this particular flaw in research design.

National Academies Press: OpenBook

Improving the Health, Safety, and Well-Being of Young Adults: Workshop Summary (2013)

Chapter: 14 future research and other opportunities.

Future Research and Other Opportunities

The presentations and discussions summarized throughout this document attest to the research that exists on the health, safety, and well-being of young adults, but many participants also discussed future research in this area. A sound, critically formulated framework for research is needed, said Patrick Tolan of the University of Virginia in his closing remarks. Presentations at the workshop illustrated the complexity of the factors that are relevant to research aimed at understanding and improving the lives, health, safety, and well-being of young adults, including

  • Age considerations, for example, considerations general to young adults, and differences within this group (e.g., age 18 versus 26);
  • The different trajectories that young adults are on, for example, employment, unemployment, 2- or 4-year college education, the military, the justice system, homelessness;
  • Factors such as race, ethnicity, culture, lesbian, gay, bisexual, transgender, and queer/questioning status, and immigration status; and
  • The diversity of opportunities and support structures available to young adults.

Young adults fall into multiple groups on this list. To advance scientific understanding of the nature of problems, of needs, and of risk and response in this age group, Tolan suggested the development of a framework that has a developmental within context orientation.

The speakers at the workshop identified many questions for future research. Although it was not a central focus of the workshop, participants

also highlighted some areas where changes to policies, programs, and systems would be beneficial. These are compiled here to illustrate the range of suggestions made. The suggestions have been grouped by categories to provide a sense of the areas that participants raised as deserving attention, but suggestions may fit appropriately into multiple categories. This list does not represent a prioritized list of research questions or a comprehensive research agenda. The suggestions are identified with the speaker who made them and should not be construed as reflecting consensus from the workshop or endorsement by the National Academies.

UNDERSTANDING THE VARIED EXPERIENCES AND TRAJECTORIES OF YOUNG ADULTS

Researchers, policy makers, and others need to understand more about young adults’ experiences, lives, and trajectories to inform policies, programs, and systems development, said some participants. Specific suggestions included the following:

  • Researchers need to develop a basic developmental and contextual understanding of the young adult years, focusing on youth as active in directing their development. They need to understand the nature of young adults’ needs, the risks they take, and the responses they use to challenges. (Tolan)
  • Longitudinal cohort studies of young adults making the transition from school to work are needed that allow for the analyses of subgroups, including subgroups defined by social class. (Schneider)
  • More research is needed to reveal the experiences of underrepresented racial and ethnic groups and the kinds of signals they get that can affect their future trajectories. (Rivas-Drake)
  • Issues of gender and the problems that disproportionately affect males need renewed attention. (Settersten)
  • Additional research is needed to study the impact of trauma on boys and men of color over the lifespan. (Corbin)
  • Additional research should be done on turning points, on the potential to intervene in young adulthood, and on whether effective existing programs aimed at adults in general are effective specifically among young adults. (Oesterle)
  • Researchers need to look specifically at the “missing half”—those students who do not go on to 4-year colleges, including those who drop out of high school, get only a high school degree, or receive some college education, perhaps earning a certificate or a 2-year degree. (Bonnie)
  • College retention and graduation rates, the debt incurred in pursuing college degrees, and the connection between higher education and workplaces, neighborhoods, and social networks need further study. (Settersten)
  • Increased research into pathways out of the juvenile and adult justice systems could help reduce the risk that previous offenders will reenter those systems. (Mulvey)
  • How research on brain development should impact policy choices, including age lines in policy, should be considered. (Bonnie)

RESILIENCE, PROTECTIVE FACTORS, AND WELL-BEING

In thinking about young adults’ health, safety, and well-being, it is important to examine and emphasize positive factors and areas of success, said some participants. Specific suggestions included the following:

  • The protective factors that buffer young people from negative experiences and processes should be investigated. (Rivas-Drake)
  • More research is needed on community engagement, the life course, and resiliency and protective factors. (Coyne-Beasley)
  • Psychosocial research should look at the development of self-regulatory competence, the ability to function successfully, and the renegotiation of relationships with adults. (Steinberg)
  • The psychosocial needs and social supports of young adults should be assessed so they can be better connected to a trusted adult or community. (Coyne-Beasley)
  • Researchers need to investigate the practices and the relational and emotional health components that enable young adults to emerge from the foster care system as resilient, healthy, and hopeful. (Samuels)
  • Researchers should investigate the role of storytelling in creating self-identity and enhancing well-being. (Clark)

MENTAL HEALTH CARE, MENTAL HEALTH INTERVENTIONS, AND SUBSTANCE ABUSE

Additional research is needed on mental health and substance abuse, particularly in the areas of intervention and service delivery, said some participants. Their specific suggestions included the following:

  • Researchers should investigate how interventions for mental health disorders can appeal to young adults, how to get young adults into treatment, and how to help them stay in treatment. (Davis)
  • Tailored interventions and services need to be tested on a large scale. (Davis)
  • Health care and mental health care systems for juveniles and adults need to be better coordinated. (Davis)
  • Young adults should be prioritized for mental health services. (Copeland)
  • Awareness of the warning signs of schizophrenia, early referral, and intervention can prevent cognitive loss and violence. (Seidman)
  • New interventions for drug and alcohol use are needed for both college and noncollege students. (White)

HEALTH CARE

Enhancing access to health care, ensuring appropriate insurance, improving coordination of care, and providing care that is culturally competent and effective for young adults were emphasized by a number of speakers. Specific suggestions included the following:

  • Young adults need better access to health care, including screening for diseases, for mental health issues, and for risk behaviors, and the provision of necessary services. (Coyne-Beasley)
  • The health care system should engage more multidisciplinary providers, family and community members, and young adults in prevention. (Coyne-Beasley)
  • Health care and mental health care systems for juveniles and for adults need to be better coordinated. (Davis, repeated from above)
  • Health care guidelines and protocols for young adults that are developmentally based need to be developed. (Irwin, Tolan)
  • Future clinicians should have a discipline-specific young adult rotation. (Irwin)
  • The implementation of the ACA should be closely monitored to inform policy makers and advocates about future steps needed. (English)
  • Insurance for young adults needs to be comprehensive rather than focused on catastrophic events. (Neinstein)

FAMILIES, PARENTS, AND RELATIONSHIPS

The influence and roles of families, parents, and relationships in young adults’ lives needs additional study, said some participants. Their suggestions included the following:

  • The roles of race, ethnicity, culture, immigration status, and religion in parenting need further investigation. (Conger)
  • The resources families devote to young adults need to be studied. (Settersten)
  • The effects of the Great Recession on the parenting of young adults needs to be better understood. (Conger)

COMMUNICATIONS, MEDIA, AND DECISION MAKING

Communications, media, and decision making are important areas for future research and policy making to improve young adults’ health, noted some participants. Their suggestions included the following:

  • Studies are needed on the effects of social media on the lives of parents employed full- or part-time. (Clark)
  • Research on marketing to young adults is needed, including subgroups such as underrepresented racial and ethnic groups and rural young adults. (Halpern-Felsher)
  • Policies on marketing need to focus on the images being conveyed, restrict misleading ads, and pursue regulatory efforts such as countermarketing. (Halpern-Felsher)
  • Better development of the science of split-second decision making is needed to inform health-related messages. (Jaccard)

YOUNG ADULTS’ HEALTH, SAFETY, AND WELL-BEING WITHIN SYSTEMS AND ORGANIZATIONS

More research is needed on how young adults are functioning within various systems and organizations and how these systems and organizations could better support young adults’ health, safety, and well-being. Specific suggestions highlighted by some participants included the following:

  • Gaps in health services on college campuses need to be identified and addressed. (Bailie)
  • Researchers should investigate how alcohol abuse in the military can be reduced and prevented. (Hutchinson)
  • The effects of military service and of the military health care system on the health of young adults should be studied. (Hutchinson)
  • How the education levels and physical fitness of young adults can be increased so that more people are eligible to join the military should be studied. (Hutchinson)
  • The success rates for people in the military who receive waivers for physical conditions should be investigated. (Hutchinson)
  • Whether some people who are denied the chance to enlist succeed in the military despite a health condition should be studied. (Hutchinson)
  • Treatments for stress-related disorders among members of the military, including female service members, need to be studied further. (Ritchie)
  • Research is needed on the comparative effectiveness of interventions to prevent and reduce homelessness, including interventions aimed at such subgroups as young adults with mental health problems or homeless young parents. (Courtney)
  • Researchers need to investigate how welfare programs and services for young adults can work together more effectively to provide them with needed skills and supports. (Lower-Basch)
  • The continuity and discontinuity of care afforded to young offenders as they transition in and out of institutions need to be studied. (Mulvey)
  • Much more information is needed on the health status and care of prisoners. For example, what are their rates of sexually transmitted infections, risk behaviors, and mental illness? Their suicide rates, nutrition, immunity, and chronic diseases all need to be monitored, and data are needed on the effectiveness of risk reduction, the minimization of harm, and the continuity of care upon release. (Greifinger)

RESEARCH METHODS AND APPROACHES

The methods used to study young adults’ health, safety, and well-being need careful consideration, emphasized some participants. Their suggestions for research methodology included the following:

  • New studies on young adults are needed, including multiple cohort historical tracking, cross-national comparisons, longitudinal trajectory/pathway studies, collapsed cohort age-graded comparisons, intervention studies, and cost/service system analyses. The reanalysis of existing longitudinal datasets would also be valuable. (Tolan)
  • More randomized trials of health-related interventions should be conducted to evaluate the effects of interventions. (Schneider)
  • Self-reports of health services need to be verified and supplemented through administrative records and other kinds of information. (Schneider)
  • Technologies such as smartphones should be used to learn more about subjective well-being and physical health. (Schneider)
  • The individualization of health-related messages and the integration of behavior-specific and common-cause intervention design principles need to be studied. (Jaccard)
  • Research, policy, and applications need to be linked as much as possible. (Conger)

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Young adults are at a significant and pivotal time of life. They may seek higher education, launch their work lives, develop personal relationships and healthy habits, and pursue other endeavors that help set them on healthy and productive pathways. However, the transition to adulthood also can be a time of increased vulnerability and risk. Young adults may be unemployed and homeless, lack access to health care, suffer from mental health issues or other chronic health conditions, or engage in binge drinking, illicit drug use, or driving under the influence. Young adults are moving out of the services and systems that supported them as children and adolescents, but adult services and systems—for example, the adult health care system, the labor market, and the justice system—may not be well suited to supporting their needs.

Improving the Health, Safety, and Well-Being of Young Adults is the summary of a workshop hosted by the Board on Children, Youth, and Families of the Institute of Medicine (IOM) and the National Research Council (NRC) in May, 2013. More than 250 researchers, practitioners, policy makers, and young adults presented and discussed research on the development, health, safety, and well-being of young adults. This report focuses on the developmental characteristics and attributes of this age group and its placement in the life course; how well young adults function across relevant sectors, including, for example, health and mental health, education, labor, justice, military, and foster care; and how the various sectors that intersect with young adults influence their health and well-being. Improving the Health, Safety, and Well-Being of Young Adults provides an overview of existing research and identifies research gaps and issues that deserve more intensive study. It also is meant to start a conversation aimed at a larger IOM/NRC effort to guide research, practices, and policies affecting young adults.

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Writing a Research Paper Conclusion | Step-by-Step Guide

Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.

  • Restate the problem statement addressed in the paper
  • Summarize your overall arguments or findings
  • Suggest the key takeaways from your paper

Research paper conclusion

The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .

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Table of contents

Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.

The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.

While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.

For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:

Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:

“In conclusion …”

Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.

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future research suggestions

Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.

Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.

Argumentative paper: Restate your thesis and arguments

In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.

Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.

Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.

Empirical paper: Summarize your findings

In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.

Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.

Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.

Argumentative paper: Strong closing statement

An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?

If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.

Empirical paper: Future research directions

In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.

Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .

Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.

Full examples of research paper conclusions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

  • Argumentative paper
  • Empirical paper

While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.

As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.

If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.

  • Do they understand from your conclusion what your research was about?
  • Are they able to summarize the implications of your findings?
  • Can they answer your research question based on your conclusion?

You can also get an expert to proofread and feedback your paper with a paper editing service .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

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future research suggestions

The conclusion of a research paper has several key elements you should make sure to include:

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

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.

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

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He Wants Oil Money Off Campus. She’s Funded by Exxon. They’re Friends.

The two friends, both climate researchers, recently spent hours confronting the choices that will shape their careers, and the world. Their ideas are very different.

Two waist-up portraits, one of Rebecca Grekin, left, wearing a light blue sweater and framed by a tan stone archway in the background, and Yannai Kashtan, right, wearing a dark blue shirt and similarly framed by an arch.

By Hiroko Tabuchi

Photographs by Damon Casarez

Reporting from Stanford University

Two good friends, Rebecca Grekin and Yannai Kashtan, met up one crisp December morning at Stanford University, where they both study and teach. The campus was deserted for the holidays, an emptiness at odds with the school’s image as a place where giants roam, engaged in groundbreaking research on heart transplants, jet aerodynamics, high-performance computing. Work that has changed the world.

Ms. Grekin and Mr. Kashtan are young climate researchers. I had asked them there to explain how they hoped to change the world themselves.

They have very different ideas about how to do that. A big question: What role should money from oil and gas — the very industry that’s the main contributor to global warming— have in funding work like theirs?

“I’m just not convinced we need fossil fuel companies’ help,” said Mr. Kashtan, 25, as we toured the lab where he works, surrounded by sensitive electronic gear used to detect methane. “The forces and the incentives are aligned in the wrong direction. It makes me very cynical.”

For Ms. Grekin, 26, that’s a delicate issue. Her entire academic career, including her Ph.D. work at Stanford, has been funded by Exxon Mobil.

“I know people who are trying to change things from the inside,” she said. “I’ve seen change.”

We spent hours that day — first at her lab, then in his, and then off campus at a hole-in-the-wall Burmese joint — as the two disagreed and agreed in amiable and insistent ways about some of the biggest questions facing the next generation of climate scientists like themselves.

Should universities accept climate funding from the very companies whose products are heating up the planet? Is it better to work for change from within a system, or from outside? How much should the world count on cutting-edge technologies that seem far-fetched today?

And the big one. What is gained or lost when oil producers fund climate solutions?

Some of Ms. Grekin’s research has focused on calculating the true climate impact of food and other things that people consume. In the hallway outside her lab hangs a large poster describing her work. The poster prominently features the ExxonMobil logo.

“They brag about their relationship with Stanford, their association with bright, young, environmentally minded scientists,” Mr. Kashtan said, standing in the hallway. “But the majority of their money is going to things that are pretty explicitly about getting more oil out of the ground.”

Ms. Grekin pushed back on any suggestion that Exxon had influenced her research. The poster was simply being transparent about her funding, she said, which is always appropriate. “You’re supposed to share your funding sources,” she said. “They don’t have anything to do with the research. They just happen to fund graduate school.”

In any case, her work is already being used at 40 universities to cut the climate impact of their sprawling food services, she pointed out. Would that have happened otherwise?

Despite differences like these, Mr. Kashtan and Ms. Grekin are friends. They fill in to teach each other’s classes. They both talk passionately about solutions to climate change, and both co-signed an open letter last year calling on Stanford to establish guidelines for engaging with fossil fuel companies.

future research suggestions

“I know people who are trying to change things from the inside. I’ve seen change.”

Mr. Kashtan says his skepticism about oil-industry motivations was born of his own experience. A physics and chemistry double-major working on his Ph.D., he previously researched a technology called electrofuels that big corporations, including fossil fuel companies, are promoting as a way to fight global warming.

The technology behind electrofuels, also known as e-fuels, sounds equal parts science fiction and magic.

It essentially involves capturing carbon dioxide, the greenhouse gas that is rapidly warming the planet, by sucking it out of the air, then combining it with hydrogen that has been split from water (using renewable energy) to make liquid fuels that can be used in trucks and planes. Start-ups working on e-fuels, including a Stanford spinoff, have raised millions of dollars, typically from the venture capital arms of large oil and gas companies, as well as from airlines.

But Mr. Kashtan has come to believe that deploying e-fuels at scale isn’t just many years away, it also doesn’t make sense from an economic or even energy perspective. For one, he said, capturing carbon dioxide by pulling it out of the atmosphere is itself energy intensive. The rest of the process to produce the fuel, even more so.

Instead, these technologies have become industry-funded red herrings that distract from the critical task of burning less fossil fuels, he said. After all, it is the burning of coal, oil and gas that’s putting the planet-warming gases in the air in the first place.

He’s come to be particularly wary of how well-meaning colleagues, like his friend Ms. Grekin, could play a role in bringing about that delay, for example by amplifying research that emphasizes far-out technological solutions instead of, say, taking steps like curbing emissions.

Technologies like electrofuels aren’t simply “complete wastes of time, talent, and money,” Mr. Kashtan said in his characteristically direct way, “they’re exactly what fossil fuel companies want.”

We were in Mr. Kashtan’s lab, filled with tubes, tanks and ozone scrubbers. The team he’s part of was working on a project to measure air pollution from gas-burning stoves in homes across the world. It wasn’t what he expected to be researching. Since he was a child growing up in Oakland, he’s been interested in the possibilities of technology, not the harms of it.

As a boy he produced a series of YouTube videos earnestly explaining every element of the periodic table. “That’s pure Beryllium metal right there: super toxic, super hard, pretty expensive, and one of my favorite elements,” 12-year-old Yannai says in one clip , decked out in goggles and lab coat.

Ms. Grekin disputed Mr. Kashtan’s notion of new technologies as delay tactics. That approach raised the risk that the world would write off promising innovations prematurely, she said. “Sometimes you don’t know until you do the research,” she said.

“Do we need people focusing on these problems so that we can find either better solutions or and cheaper solutions? Yes. Do we know exactly what those will be? No,” Ms. Grekin said.

“But I see an exception when it comes to climate, because of the timeline,” Mr. Kashtan said. “We’re racing against the clock here.”

“Maybe I’m more optimistic about the future and Yannai, maybe, is less,” Ms. Grekin said.

future research suggestions

“These false solutions are just helping to entrench their interests.”

We were starving and decided to look for lunch. The only option on the all-but-empty campus was a sad Starbucks. So instead we drove to a Burmese restaurant, a local favorite, snagging a table outside so that we could hear each other better.

On the way, Ms. Grekin was apologetic about driving us in her car, a bright yellow Fiat 500 that she’s had for more than a decade, instead of walking or taking a bus. Usually she doesn’t drive, she said. It was just that she’d brought several weeks’ worth of recycling to drop off that day, one of the few permissible excuses for a climate researcher to drive to campus in a car, in her view.

“I came with my entire car full of recycling,” she said.

Ms. Grekin said she also tries to buy very little. “This is from high school. Like, a lot of my clothes are from high school,” she said.

In response, Mr. Kashtan pointed to his own shirt. “This is a hand-me-down,” he said.

Fossil fuel funding for research has become a thorny issue for many universities, and particularly at Stanford’s Doerr School. Founded in 2022 with a $1.1 billion gift by John Doerr , a venture capitalist and billionaire, the school quickly attracted criticism for saying it would work with and accept donations from fossil fuel companies.

A recently issued list of funders of the Doerr School is a who’s who of the fossil fuel industry

In October, a nonprofit group founded by Adam McKay, the writer and director of “Don’t Look Up,” the climate-themed film starring Jennifer Lawrence and Leonardo DiCaprio, criticized the Doerr School in a satirical ad that has since been viewed more than 200,000 times on X, formerly known as Twitter. “The school seeks to come up with ways to combat climate change, so we’re calling on the help of all our friends at Big Oil,” the parody says.

Stanford has been a friend to oil and gas in the past. A researcher at the Stanford Exploration Project, which began in the 1970s, later developed an algorithm for BP that contributed to a 200-million-barrel oil and gas discovery in the Gulf of Mexico.

Today, many of these older programs are atrophying and some are shutting down. A project that worked with oil and gas companies to study the geology of undersea drill sites off the coast of West Africa ended in 2022.

Stanford’s newer fossil fuel funded programs instead tend to focus on climate solutions, like blue hydrogen or carbon storage . Mr. Kashtan questions the climate bona fides of many of those programs.

The Natural Gas Initiative , for example, works with an industry consortium to research ways that natural gas can be part of the climate solution. It is led by a former Chevron strategist, and industry funders get a spot on its board of advisers for a quarter-million dollars a year.

“They’re ultimately about how to drill more efficiently,” he said.

“Exxon did offer me internships that were basically like, ‘Let’s get more oil out of the ground more efficiently,’” Ms. Grekin said. “But I didn’t want to do that,” she said. “So I fought really hard and got an internship that was sustainability-related.”

She feels that her current research, into ways to make heating and air-conditioning systems in commercial buildings more efficient, wouldn’t have been possible without Exxon, which made an entire office building in Houston available to her for experimentation. Her Exxon funding also paid for a recent stint in the Amazon rainforest back in Brazil, where she helped teach a course about sustainable polymers and locally sourced materials.

“The way I see it is, if this money wasn’t coming to me, it could be going toward a new drill, a new rig,” she said.

Can these two friends reach a compromise? They say they did find common ground hammering out proposed guidelines on how Stanford should engage with fossil fuel companies.

The guidelines include a call for eliminating financial sponsorships from any company, trade group or organization that doesn’t have a credible plan for transitioning away from fossil fuels to renewable power, doesn’t provide transparent data, or is otherwise at odds with goals set forth under the Paris accord, the landmark 2015 agreement among the nations of the world to fight climate change.

“In my opinion, all of the fossil fuel companies currently funding Stanford research would be pretty much disqualified,” Mr. Kashtan said. “The only thing that’s going to prompt these companies to shift is either being sued into bankruptcy, or some kind of economic or regulatory pressure, not partnerships with universities.”

Ms. Grekin looked taken aback. “I’d like to think that we don’t have to go to those extremes,” she said.

An Exxon spokeswoman said the company was “investing billions of dollars into real solutions.” She added, “Research and healthy debate by students like Rebecca and Yannai are critical to developing solutions that will help us all.”

A spokesman for the Doerr School said, “We are proud of our students for engaging in civil discourse on this topic, and we are listening.”

The conversation stretched on. We ordered more tea. We ended up overstaying our welcome at the Burmese restaurant.

“Maybe I’m naïve,” Ms. Grekin said as we wrapped up the day. She recalled a moment from one of her early Exxon internships, near its sprawling refinery in Baytown, Texas, when she “looked up and there was this huge ball of flame coming out of a flare,” she said, referring to the towering, flaming stacks that are a dramatic feature of refineries. In that moment, she said, she felt her work on sustainability insignificant, her effect on reducing emissions even smaller than what that flare was emitting that very second.

She now thinks differently. “If I can change Exxon by even 1 percent,” she said, “the impact I have might make up for more than that flare.”

Hiroko Tabuchi covers the intersection of business and climate for The Times. She has been a journalist for more than 20 years in Tokyo and New York. More about Hiroko Tabuchi

Learn More About Climate Change

Have questions about climate change? Our F.A.Q. will tackle your climate questions, big and small .

MethaneSAT, a washing-machine-sized satellite , is designed to detect emissions of methane, an invisible yet potent gas that is dangerously heating the world.  Here is how it works .

Two friends, both young climate researchers, recently spent hours confronting the choices that will shape their careers, and the world. Their ideas are very different .

New satellite-based research reveals how land along the East Coast is slumping into the ocean, compounding the danger from global sea level rise . A major culprit: overpumping of groundwater.

The planet needs solar power. Can we build it without harming nature ? Today’s decisions about how and where to set up new energy projects will reverberate for generations.

Did you know the ♻ symbol doesn’t mean something is actually recyclable ? Read on about how we got here, and what can be done.

Nvidia’s Jensen Huang: The incredible future of AI

future research suggestions

Jensen Huang, the CEO of tech titan Nvidia, has a message for the world about artificial intelligence: You ain’t seen nothing yet.

Speaking to a standing room-only audience at the 2024 SIEPR Economic Summit, Huang predicted that in as little as five years AI will be able to pass every test a human takes — not just the legal bar exams that it can complete today, but also highly specialized medical licensing exams.

In about 10 years, he said, the computational capabilities of AI systems will be a million times bigger than they are today. Systems synthetically generating data will have greater capacity to continuously learn, infer, and imagine. Instead of only instantly answering questions, forthcoming AI systems will also have the ability to think critically through problems over longer periods of time.

“In the future, the way you interact with AI will be very different” from what can be done with ChatGPT and other AI models today, said Huang in a keynote question-and-answer session led by John Shoven , a SIEPR senior fellow, emeritus; and the Charles R. Schwab Professor of Economics, emeritus, in Stanford’s School of Humanities and Sciences.

But does this mean AI technology will be able to mimic the human mind? Huang said he wasn’t sure. There needs to be a consensus about what it means to say AI has achieved human intelligence.

In order to have true artificial general intelligence, he said, “you need to know what the definition of success is.”

The gift of pain and suffering

future research suggestions

Having co-founded Nvidia more than 30 years ago, Huang now finds himself at the center of the tech universe. His company, whose market value hit $2 trillion last month (after reaching $1 trillion the previous June), has rocketed thanks to its sophisticated and hugely expensive semiconductor chips and its estimated market share of more than 80 percent in AI chips.

“We sell the world’s first quarter-million-dollar chip,” Huang noted, referring to Nvidia’s powerful graphical processing unit system that weighs 70 pounds, consists of 35,000 parts and has the computing capacity of a data center.

During his Summit appearance, Huang regaled attendees with his insights and now-familiar deadpan humor. Asked about his signature outfit of black leather jacket, black shirt, and black pants, Huang said they are among the few pieces of clothing that don’t make him itch.

When asked his advice for Stanford students aspiring to be successful entrepreneurs, Huang talked about the importance of low expectations and high resilience. Greatness, he said, comes from smart people who have suffered from setbacks. This is why, at Nvidia, he talks openly about pain and suffering “with great glee.”

“For all of you Stanford students,” he said, “I wish upon you ample doses of pain and suffering.”

Watch the full discussion.

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Suggestions for Future Research

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There are always more questions, things to be improved upon, and new ideas to explore. This chapter takes the methods described in Part I of the book and uses their logical progression to make recommendations for areas of research people in the future might be interested in considering.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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  • Studies suggest that belief in a positive future of society can influence how people behave and make decisions in the present.
  • When people believe in a positive collective future, it alters their behaviour towards making that modelled future not just a remote idea but a likely prediction.
  • While it may be hard to imagine a better future, role-playing and simulation to engender future thinking can create lasting impressions and augment our ability to imagine a positive collective future.

Imagine an empty chair representing you in the year 2050. As you sit facing this chair, visualize your future self filled with courage and resilience in the face of climate challenges. Speak to this future self openly, expressing your fears and concerns; allow them to respond with wisdom and reassurance, offering you guidance on how to navigate uncertainties. Realize that within you lies the capacity to overcome fear and embrace hope for a sustainable future.

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This short psychodrama script is based on a method called “the empty chair” , which is used for dealing with difficult emotions from the past, as well as to help prepare for conversations and situations in the future. It could also be a helpful technique for alleviating fear of the future due to climate change in a future-thinking group workshop .

Scenario thinking for strategic foresight

Scenario thinking is a pivotal tool within the field of strategic foresight . It’s employed to anticipate plausible futures on a time horizon beyond 10 years. Scenarios are an aid for decision-making especially in contexts characterized by deep uncertainty.

Going through the process of scenario development prepares decision-makers and their organizations to adeptly navigate the potential risks and opportunities these futures may harbour. For the participants of such a workshop, the exercise also provides a prediction model based on which they can adjust their current beliefs about the future.

How leaders characterize the future

Prediction-making and testing is the basis of how the human brain perceives and learns about the world and this act of pre-living a hypothetical experience enables us to pre-train a response should we once find ourselves in a similar situation. In other words, by creating memories of the future , we enrich the range of what is meaningful, possible and acceptable to our brain, along with pre-living the feelings, actions and decisions that a novel reality may elicit.

Why is future thinking important now?

Many futurists warn about biases , such as availability bias and confirmation bias, when imagining alternative futures. But other than filtering out what’s subjectively implausible, our minds may be unwilling to face the fears and worries stirred up by imagining or merely talking about distant futures.

Even though we are approaching the UN Summit for the Future , many people may not yet be at ease with the skill of futures literacy, which, as promoted by UNESCO, is the capability to use the future effectively . It may be hard for people to imagine distant time horizons, such as 2050, because a typical time horizon for human cognition is 10 years into the future. Particularly if meeting survival needs is the priority, as is the case in developing countries and communities.

future research suggestions

Neuroscientifically, future thinking (also known as prospection) is subserved by several mental processes that enable individuals to simulate, imagine, plan and predict the likelihood of their future. In fact, memory and future imagination are intricately linked through shared neurocognitive mechanisms , and both are influenced by current mental state (mood and emotions), past experiences and a disposition known as future time perspective .

Depression, burnout and eco-anxiety cause negative future bias

The psychological terrain of individuals heavily influences their ability to envision a positive future. Research underscores a significant correlation between a negative orientation towards one's past – manifested as dominant negative memories, rumination and regret – and a pessimistic outlook on the future.

A negative future orientation, conceptualized as a sense of future hopelessness, negative expectations or lack of thinking about the future, impedes one's capacity to imagine and work toward a positive future. Similarly, depression, burnout, and apathy reduce the ability to imagine the future . Furthermore, the rise of eco-anxiety, fuelled by concerns about environmental degradation, and solastalgia (a form of existential distress caused by environmental change), can hinder the belief in one's ability to act positively for the future.

Engaging in future thinking may lead to the emergence of such fears and lock our imaginations in visions of collective dystopia or, worse, no imaginable visions of the future at all.

How leaders view the next 10 years in terms of risks

Belief in a positive collective future is a powerful motivator

Yet, studies suggest that belief in a positive future of society can influence how people behave and make decisions in the present. Data from more than 6,000 participants in 24 countries suggests that belief in economic development and scientific progress, as well as in the benefits of building a more caring and moral community in the future, predicts individual engagement in public, private and financial pro-environmental action.

When people believe in a positive collective future, it alters their behaviour towards making that modelled future not just a remote idea but a likely prediction. Indeed, episodic future thoughts can ultimately influence our decisions and actions in favor of goal pursuit .

The links between future thinking and decision-making are multiple, from reducing psychological and temporal distance to the future to increasing estimated probability of a future outcome. Episodic future thinking and collective future thinking may promote the pursuit of a common goal by shaping the feeling that imagined events will (or will not) happen in the future – referred to in studies as belief in future occurrence .

Role-playing and simulation for future thinking

Although research in collective future thinking is in its infancy , it is marked by intense interest from both social psychologists and futurists. One of the first notable studies suggests that hope for peace is lower in younger than in older generations in populations marked by decades of conflict. Interestingly, the degree of participants’ hope for peace increased after a three-minute virtual ageing experience where they saw their aged hands and were guided by the experimenter to imagine they were 80 years old and reminisce on their past.

This example emphasizes that while it may be hard to imagine a future better than today (given current predictions about climate change), certain interventions based on role-playing and simulation can create lasting impressions and augment our ability to imagine a positive collective future.

Workshop participants could, for example, imagine stepping into the shoes of their future selves or explaining their choice today to their grown-up (grand)child 25 years from now. Such immersive experiences can create a deep connection with the future, fostering empathy and a sense of responsibility towards upcoming generations; or help make peace with fear of the future and empower action in the now.

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Treatment of Tuberculosis: Guidelines. 4th edition. Geneva: World Health Organization; 2010.

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Treatment of Tuberculosis: Guidelines. 4th edition.

A5 suggestions for future research.

At many points during the process of revising the third edition, future research needs were identified. There was often no evidence available to allow the formulation of recommendations for specific issues; sometimes the only available evidence was judged to be of low quality. While seven questions are the focus of this fourth edition, additional questions emerged.

Gaps in the evidence and additional questions are summarized below as suggestions for future research. A few may be amenable to systematic reviews, but others will require clinical trials, large cohort studies, epidemiological studies, or behavioural research. The suggestions are listed (in no order of priority) under each of the seven questions.

1. Duration of rifampicin in HIV-negative TB patients

  • For various forms of drug resistance (other than MDR), and balanced against tolerability and costs, what is the minimum number of effective drugs for the intensive and continuation phases?
  • In particular countries followed prospectively, what is the cost and impact of changing from a 6HE to a 4HR continuation phase?
  • In new cases of TB meningitis (and other forms of extrapulmonary TB), what is the optimal duration of treatment?

2. Intermittent dosing

  • In new pulmonary TB patients, does daily treatment throughout the course of therapy, compared with a twice weekly or 3 times weekly intermittent regimen throughout the course of therapy, reduce relapse, failure and acquired drug resistance?
  • What is the impact of drug resistance on outcomes after intermittent regimens?
  • What is the impact on adherence of three times weekly dosing, for example when provided by a family member after a daily intensive phase?
  • How important is earlier culture conversion (associated with a daily intensive phase) to patient outcomes and TB transmission?

3. Treatment of new patients where isoniazid resistance is high

  • In new patients with smear-positive pulmonary TB, does HRE in the continuation phase for 4 months reduce failure, relapse and acquired drug resistance compared with HR?
  • What is the efficacy of ethambutol in preventing acquisition of rifampicin resistance in patients with pretreatment isoniazid resistance?
  • What other efficacious and tolerable regimens exist for isoniazid-resistant TB?
  • What is the incidence of ocular toxicity due to ethambutol?
  • Do new patients with isoniazid resistance respond differently from previously treated patients with the same drug resistance profile?

4. HIV-related TB

  • In new HIV-positive patients with smear-positive pulmonary TB who are given ART, does a 9-month rifampicin-based TB treatment regimen, compared with a 6-month rifampicin-based regimen, increase treatment success rate and reduce recurrent TB at the end of treatment and for the first 12 months after successful completion of treatment?
  • In patients on ART, what is the impact of three times weekly treatment compared with daily treatment during the continuation phase on mortality, failure, relapse and acquired drug resistance?

5. Sputum monitoring

  • Using data from Union Study A and C, determine how well positive bacteriological results at various months of treatment predict failure and relapse, compared with negative bacteriological results.
  • Reanalyse data from the British Medical Research Council to study patients who are smear-positive during 2 consecutive months. Of those patients who are smear-positive at 2 months, how many are still smear-positive at 3 months? Of these, how many are culture-positive? What is the ability to predict treatment outcomes? (Repeat with other pairs of months.)
  • How well can sputum monitoring predict pretreatment or acquired MDR-TB?
  • How useful is the monitoring of smear conversion as an indicator of TB control programme performance?
  • How often does persistent smear-positivity at the second month of treatment trigger patient or programmatic interventions?

6. Extension of treatment

  • For preventing relapse, which patients stand to benefit most from treatment extension? Other than positive smear microscopy, what risk factors (such as patient weight) predict poor outcomes, and can feasibly be ascertained in resource-limited settings?
  • Should the intensive phase or the continuation phase be extended, by how long, and with which drugs?
  • In adults with pulmonary TB, does performing smear microscopy at the end of the intensive phase and, if sputum is smear-positive, extending this phase reduce failure, relapse or acquired drug resistance?

7. Previously treated patients

  • In new smear-positive pulmonary TB patients who have failed first-line TB treatment does an empirical MDR-TB regimen, compared with the standard WHO retreatment regimen with first-line drugs, increase treatment success rate and reduce failure at the end of this second course of TB treatment?
  • What is the level of MDR in subgroups of previously treated patients (failed first vs. subsequent course of therapy; returned after defaulting; relapsed)?

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  1. How to Write Recommendations in Research

    Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable. Relatedly, when making these recommendations, avoid: Undermining your own work, but rather offer suggestions on how future studies can build upon it.

  2. Types of future research suggestion

    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 ...

  3. Research Recommendations

    Limitations and future research: This section discusses any limitations of the study and suggests areas for future research that could build on the findings of the current project. How to Write Research Recommendations. Writing research recommendations involves providing specific suggestions or advice to a researcher on how to conduct their study.

  4. 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 ...

  5. Future Research

    Suggest methodologies: Provide suggestions for methodologies that could be used to explore the research questions you have identified. Discuss the pros and cons of each methodology and how they would be suitable for your research. ... Provides a roadmap for future research: Including future research can help guide researchers in the field by ...

  6. Conclusions and recommendations for future research

    The initially stated overarching aim of this research was to identify the contextual factors and mechanisms that are regularly associated with effective and cost-effective public involvement in research. While recognising the limitations of our analysis, we believe we have largely achieved this in our revised theory of public involvement in research set out in Chapter 8. We have developed and ...

  7. Discussion and Conclusions

    Future research needs recommendations are valuable inputs for researchers, funders, and advocates making decisions about avenues for future scientific exploration. We performed an empirical evaluation of the published literature to appreciate the variability in the presentation of information on future research needs. We found that most systematic reviews, meta-analyses, or economic analyses ...

  8. What are Implications and Recommendations in Research? How to Write It

    Research recommendations suggest future actions or subsequent steps supported by your research findings. It helps to improve your field of research or cross-disciplinary fields through future research or provides frameworks for decision-makers or policymakers. Recommendations are the action plan you propose based on the outcome.

  9. Using future research suggestions as a basis to come up with a ...

    Using future research suggestions as a basis to come up with a dissertation topic idea. To use future research suggestions as a basis to come up with a dissertation topic idea, you need to have read a journal article on a topic that interests you. Having read this journal article, focus on the section at the end of the article, often called Future Research (or Discussion/Research Limitations ...

  10. Recommendations for Research in the Future and Final Comments

    A study's limitations can highlight areas where more research can be conducted in the future. The focus of this chapter then shifts to providing several general recommendations for the responsible conduct of research to avoid the reproducibility crisis that is currently challenging the practice of research in psychology and medicine.

  11. Turning a research limitation or future research suggestion ...

    Turning a research limitation or future research suggestion into a potential topic idea. As our article, Our top tip for finding a dissertation topic highlighted, the Limitations and Future Research section of journal articles are arguably the quickest and easiest way to find a possible dissertation topic at the undergraduate and master's level. After all, in this section of academic journals ...

  12. 14 Future Research and Other Opportunities

    Read chapter 14 Future Research and Other Opportunities: Young adults are at a significant and pivotal time of life. ... and well-being need careful consideration, emphasized some participants. Their suggestions for research methodology included the following: New studies on young adults are needed, including multiple cohort historical tracking ...

  13. Ideas for writing the "future research directions" section (pt.I)

    Explain how future research can overcome these limitations to build on the current findings. Longitudinal Studies: If your study had a cross-sectional design, suggest the need for longitudinal ...

  14. Limitations and Future Research Directions

    Research is often conducted progressively. Acknowledging limitations helps to define what is yet to be investigated and can provide avenues for future research. This chapter presents the limitations of this research and suggests ideas for future research directions. Download chapter PDF. Research is often conducted progressively.

  15. Recommendations for future research

    In particular, further evidence may be required on how to provide accessible information and education, and how to deliver accessible vaccination services. However, although these issues were raised in the present work, we did not conduct a systematic review on these topics and, as such, cannot make definitive recommendations for future research.

  16. Suggestions for Future Research

    Abstract. There are always more questions, things to be improved upon, and new ideas to explore. This chapter takes the methods described in Part III of the book and uses their logical progression to make recommendations for areas of research people in the future might be interested in considering. Download chapter PDF.

  17. Writing a Research Paper Conclusion

    Empirical paper: Future research directions. In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research. Whatever the scope of your own research, there will always be room for further investigation of related topics, and you ...

  18. Groupthink: An examination of theoretical issues, implications, and

    The examination allowed for the presentation of both implications and research suggestions designed to refocus research efforts on the model as originally proposed by Janis. ... M. D. (1997). Groupthink: An examination of theoretical issues, implications, and future research suggestions. Small Group Research, 28(1), 72-93. https:// https ...

  19. Suggestions for Future Research

    The last chapter starts with a brief summary of the goal of the book and the future research problems faced by OKP researchers. With respect to the research objectives as stated in Chapter 1, the ...

  20. Two Young Climate Scientists. Two Visions of the Solution

    She's Funded by Exxon. They're Friends. The two friends, both climate researchers, recently spent hours confronting the choices that will shape their careers, and the world. Their ideas are ...

  21. Recommendations

    Research design considerations for the FRN should be offered as suggestions only to avoid appearing overly prescriptive. The workgroup recommended separating the presentation of two elements of potential future research: methods issues and specific topics. Methods issues tend to transcend specific topics. They should be ranked separately.

  22. Nvidia's Jensen Huang: The incredible future of AI

    Jensen Huang, the CEO of tech titan Nvidia, has a message for the world about artificial intelligence: You ain't seen nothing yet. Speaking to a standing room-only audience at the 2024 SIEPR Economic Summit, Huang predicted that in as little as five years AI will be able to pass every test a human takes — not just the legal bar exams that it can complete today, but also highly specialized ...

  23. Suggestions for Future Research

    Abstract. There are always more questions, things to be improved upon, and new ideas to explore. This chapter takes the methods described in Part I of the book and uses their logical progression to make recommendations for areas of research people in the future might be interested in considering. Download chapter PDF.

  24. NTRS

    This document will start with the background of the joint efforts and methods of research and collaboration, followed by a detailed discussion of the current state of wildland fire efforts. In contrast to the current state, the vision for the future state reveals specific areas of improvement with suggestions for potential emerging technologies.

  25. A generative AI reset: Rewiring to turn potential into value in 2024

    It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...

  26. Implications and suggestions for future research

    Suggestions for future research. This study has provided original insights into the use of evidence by health-care managers in organisational technology adoption. Whereas we investigated in detail the individual and collective sensemaking processes as managers sourced and applied evidence during the innovation journey, future research can ...

  27. How future thinking aids intergenerational decision-making

    Engaging in future thinking may lead to the emergence of such fears and lock our imaginations in visions of collective dystopia or, worse, no imaginable visions of the future at all. How leaders view the next 10 years in terms of risks. Image: World Economic Forum. Belief in a positive collective future is a powerful motivator.

  28. 2024 University Research Symposium

    Join the Research Leadership Academy and the Office of Research and Innovation for the University Research Symposium. This year's symposium will focus on 21st Century Threats to Health | Bright Ideas to Real World Solutions. This forum aims to foster connections among researchers and scholars, creating a space to explore collaborative opportunities, forge new partnerships and collectively ...

  29. Suggestions for future research

    Gaps in the evidence and additional questions are summarized below as suggestions for future research. A few may be amenable to systematic reviews, but others will require clinical trials, large cohort studies, epidemiological studies, or behavioural research. The suggestions are listed (in no order of priority) under each of the seven questions.

  30. Remarks of President Joe Biden -- State of the Union Address As

    The United States Capitol Good evening. Mr. Speaker. Madam Vice President. Members of Congress. My Fellow Americans. In January 1941, President Franklin Roosevelt came to this chamber to speak to ...