results and future research directions

How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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Home » Future Research – Thesis Guide

Future Research – Thesis Guide

Table of Contents

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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Pharmacokinetics and Drug Interactions in the Elderly and Special Issues in Elderly African-American Populations: Workshop Summary (1997)

Chapter: 3 conclusions and future research directions, conclusions and future research directions.

The ongoing study of pharmacokinetics, pharmacodynamics, and drug interactions in elderly persons is critical for the development of safe and effective therapies and for the prevention of drug toxicities and adverse drug reactions. Aging is associated with an increase in chronic illness and anatomical and physiological changes that affect drug distribution, metabolism, and excretion. Thus, as the number of older Americans increases, it can be expected that polypharmacy in this population will have significant health, social, and economic consequences. Additionally, research should focus on alleviating the disease burden in elderly minority populations.

Box 3.1 summarizes the committee's conclusions regarding future directions for research in this field. The remainder of this chapter provides a more detailed discussion of the committee's conclusions.

RESEARCH NEEDS AND OPPORTUNITIES

Expanding the scientific knowledge base.

Although progress has been made in understanding the aging process, there is still a paucity of data at the intracellular, organ, system, and population levels. The impact of aging on cells and organ systems has commonly been studied in isolation; however, a more integrated approach is needed that will examine the effects of aging on the body. Pharmacokinetic and pharmacodynamic models need to be developed that encompass the entire range of changes occurring at multiple levels throughout the body.

The following list highlights specific areas of research that would add to the body of knowledge and clarify our understanding of the aging process especially with regard to improving pharmacotherapy. This list is by no means comprehensive, as numerous research avenues could yield important information on the impact of pharmacotherapy and drug interactions in the elderly. Areas for future research include the impact of aging, gender, genetics, and ethnicity on physiology and metabolic processes. Specifically,

age-related changes in cellular transport mechanisms and extrahepatic metabolism and transport including the activity of different enzyme isoforms;

biomarkers of drug exposure;

mechanisms that cause variable responses to medications in aging racial and ethnic populations;

age-related hormonal changes affecting drug metabolism or drug sensitivity;

the impact of nutrition on the aging process;

mechanisms underlying diseases prevalent in the elderly (e.g., hypertension, diabetes, osteoporosis, and Parkinson's and Alzheimer's disease);

in vitro models for multiple drug regimens and multiple drug interactions that may be predictive of and correlated with in vivo research;

models for drug interaction related to altered reflex activity and changing homeostatic mechanisms;

the potential beneficial and adverse health effects of nutraceuticals; and

social and psychological aspects of medication use in the elderly (e.g., access to medications, adherence to prescription regimens), with a special emphasis on minority populations.

Addressing Issues in Minority Populations

Many diseases are disproportionately prevalent in elderly African-American and other minority populations. The causes and implications of this excess burden need to be more completely understood and addressed. For example, hypertension and the impact of antihypertensive medications in elderly African Americans have not been fully studied even though the morbidity and mortality is higher in this population than in other segments of the aging population.

Research is needed on multiple levels (molecular, cellular, system, population) to clarify the effect of race and ethnicity on disease prevalence and on variations in the effectiveness of pharmacotherapeutic and other treatment interventions. Such research would be a valuable tool in increasing our understanding of the physiology of aging for all populations and may have implications for pharmacotherapies aimed at various elderly groups. Research on diseases and health conditions that primarily affect minority elderly populations needs to be a priority to alleviate the disease burden experienced by these populations.

Recruitment of Elderly Patients into Clinical Trials

In 1989, the FDA published a guideline for the inclusion of elderly persons in clinical trials (FDA, 1989). However, a number of characteristics of the elderly population may present barriers to conducting clinical trials that are representative of this population. Studies need to include the oldest segment of the population (see Chapter 2 ). In addition, subgroups of the elderly population should be stratified based primarily on their functional status and disease burden and less on their chronological age.

Recruiting minorities for inclusion in studies should be a priority, although it is important to recognize the trends toward multiracial backgrounds and the complexities associated with categorizing race or ethnicity. The workshop speakers presented many innovative ideas about increasing the recruitment of minority populations. The committee supports a number of approaches, including providing transportation, involving the minority community, providing extensive patient education, and decentralizing clinical trials (i.e.., going to patients' homes or to community centers to provide and assess treatment). In addition, collaborative efforts and consortia need to be strengthened between historically minority and other academic institutions. These partnerships will be vital to recruiting minority investigators and to attracting and sustaining minority students in research programs. Further, patient recruitment efforts can draw on the populations available to both institutions.

Obtaining informed consent in elderly populations involves complex issues that need to be addressed including the extent of dementia or cognitive impairment in some elderly patients and their vulnerability to coercion. Informed consent forms have evolved into highly technical legal documents, and a reevaluation of how to best meet their original purpose is needed. Other ethical issues that need to be addressed include studies on vulnerable populations (e.g., nursing home residents) and the confidentiality of patient information.

Research Methodologies and Tools

Trials of acute drug use are well funded; however, there are few long-term studies that examine chronic effects and drug interactions. Inasmuch as elderly persons are living longer and may take the same medications for many years, increased postmarketing surveillance is needed to examine the effects of long-term use of drugs. Incentives to strengthen postmarketing surveillance should be considered. Some of these drugs (e.g., hormone replacement therapy, antidepressants, and lipid-lowering medications) may be used as preventive measures (e.g., treating high cholesterol levels in the absence of cardiovascular disease or prescribing hormone replacement therapy to prevent hip fractures); however, their long-term health effects are not fully known. Further, the pharmacodynamics of many of these medications are only beginning to be investigated.

Research Methodologies

Studying the impact of pharmacotherapy on the elderly population is often difficult from a methodological standpoint. Cross-sectional studies are problematic because confounding variables abound among the elderly, and it is difficult to distinguish the effects of aging from those of disease. Randomized clini-

cal trials often recruit study subjects who represent the younger segments of the elderly population, and who have fewer comorbid conditions, use fewer medications, and may be more compliant in terms of following prescription medication regimens. In addition, many studies use small numbers of patients, frequently with homogenous geographic and ethnic backgrounds. Longitudinal studies are needed that involve large numbers of patients who reflect the diversity of “real world” populations. Furthermore, studies of elderly populations should include observational studies, case-control studies, and cohort studies to take advantage of the realm of methodological approaches that are available. Studies of optimal pharmacotherapies need to consider their cost-effectiveness and delivery. Outcome measures must also be reexamined, and quality-of-life outcomes need to be considered. One workshop speaker recalled the adage that, “adding life to years is at least as important as adding years to life.”

Databases Available for Research

There is a notable lack of adequate databases to research the prevalence and health impact of adverse drug reactions in elderly populations. Prescription information on elderly persons is not currently linked to diagnostic information or health outcomes data. For example, state Medicaid databases are used to reimburse pharmacies for prescriptions, therefore, the data on medication utilization are quite complete and accurate. However, diagnostic information for outpatient care is often incomplete or unavailable, and is not linked to pharmacy utilization databases.

Current changes underway in the health care delivery system may provide opportunities for new databases to be developed, although there are concerns that these changes may instead result in the loss of publicly available data. Trends of interest include the purchase of pharmacy benefit companies by pharmaceutical manufacturers and the increased use of managed care through proprietary health plans paid for by Medicaid and Medicare. Increased use of managed care to provide health care for the elderly offers opportunities for databases to be implemented that would link health outcomes (particularly adverse drug reactions) and prescription information, while paying close attention to patient confidentiality issues. However, these changes may instead be implemented to restructure datasets and require new levels of approval for data use or publishing. It is crucial that the larger issues involving potential censoring or loss of publicly available data on prescription drug use and health outcomes be addressed. The increased privatization of health care services for the elderly may lead to barriers to accessing datasets due to proprietary and competitive interests.

An area of interest to the committee is exploring the feasibility of developing a cooperative national data resource that would expand the researcher's

ability to examine population-based data rather than utilizing a piecemeal approach to data collection. This data resource could include information on diagnosis, medications prescribed, clinical interventions, health outcomes, and other relevant data. Of utmost importance would be maintaining patient confidentiality. This resource could be utilized as a repository to which individual researchers could submit peer-reviewed and approved research questions. Examining the feasibility of developing such a data resource would require the input of patients, health care providers, researchers, ethicists, and other interested persons and groups.

Dissemination of Information

Drug-related information can be complex, and information overload is a common phenomenon among health professionals and patients. Because information regarding drug use and interactions changes rapidly, information systems should be available to health care professionals that can provide up-to-date information that is unbiased, case specific, interactive, and readily accessible. In addition, it is important to develop diverse information dissemination strategies to effectively meet the needs of the heterogeneous elderly population. The information provided should be presented in a manner that can be understood by patients who cannot necessarily process complex information, yet need to make informed decisions and understand their options for treatment. Private- and public-sector initiatives are required to address this critical challenge. Health sciences centers that focus on training medical, nursing, and pharmacy students should be used to conduct independent, unbiased research and continuing education programs for practicing physicians, pharmacists, nurses, and the interested public (Woosley, 1994).

Capacity Building: Researchers and Clinicians

One of the major factors limiting the expansion of research in the area of geriatrics, and particularly geriatric pharmacology and clinical therapeutics, is the small number of health professionals entering this field. This is an area in which, as demographers can attest, the patient base is expanding and will continue to grow. Quality geriatric care depends on the development of multidisciplinary teams (including nursing, physical and occupational therapy, social work) to assess the concomitant problems and implement multiple interventions. However, reimbursements do not adequately cover the time required to handle the complexity of geriatric care. A recent report by the Alliance for Aging Research (1996) found that the United States has less than one fourth the number

of academic physician-scientists needed in geriatrics to teach and conduct research.

There is a pressing need to develop innovative approaches for recruiting and retaining researchers and clinicians. Programs are needed at many points along the career path, beginning early in the college and postbaccalaureate years to kindle an interest in the field of geriatrics and continuing throughout the professional years to retain the best investigators and clinicians available. Recruitment of minority investigators should be included within broader programs supporting young and mid-career investigators. The options available for approaching this issue include

1- to 2-year postbaccalaureate programs to provide research and clinical experience to young people considering a career in this field;

opportunities for medical, nursing, pharmacy, and other health professional students to have additional exposure to geriatric treatment and research during their education;

collaborative efforts between minority academic institutions and academic health sciences centers to encourage minority students to pursue a research program in this field;

loan-forgiveness programs to assist young researchers with high debt loads from health professional or graduate schools;

new sources of fellowships (e.g., through FDA, pharmaceutical companies, or insurance companies);

increased commitment to funding from fellowship training to first awards to independent grant support;

merit awards at the midcareer level and specialized sabbaticals to retrain midcareer professionals; and

retraining in research methodologies during sabbaticals for midcareer level geriatricians.

Currently there is only a limited understanding of the impact of aging on pharmacokinetics, pharmacodynamics, and drug interactions. Research is needed at the molecular, cellular, organ, system, and population levels for safer and more effective medications to be developed, delivered, and utilized by elderly persons. In addition, attention must be given to understanding and alleviating the disproportionate disease burden in elderly African-American and other minority populations.

The committee's major conclusions are summarized in Box 3.1 at the beginning of this chapter. The committee discussed and reflected only on the workshop presentations and acknowledges that there are numerous research

opportunities in geriatric pharmacology that need to be explored. Research in geriatric pharmacology and clinical therapeutics will require a commitment to fund studies that can further elucidate the relationship between pharmacokinetics and adverse drug interactions in the elderly and the complex individual variability of the aging process. Increasing the knowledge base will enable more effective therapeutic interventions and improved quality of life for the growing population of elderly persons.

Alliance for Aging Research. 1996. Will You Still Treat Me When I'm 65? The National Shortage of Geriatricians . Washington, DC: Alliance for Aging Research.

FDA (Food and Drug Administration). 1989. Guideline for the Study of Drugs Likely to Be Used in the Elderly. Rockville, MD: FDA Center for Drug Evaluation and Research.

Woosley RL. 1994. Centers for education and research in therapeutics. Clinical Pharmacology and Therapeutics 56(6 Part 1):693–697.

Reports in the popular press about the increasing longevity of Americans and the aging of the baby boom generation are constant reminders that the American population is becoming older. Consequently, an issue of growing medical, health policy, and social concern is the appropriate and rational use of medications by the elderly.

Although becoming older does not necessarily correlate with increasing illness, aging is associated with anatomical and physiological changes that affect how medications are metabolized by the body. Furthermore, aging is often related to an increased frequency of chronic illness (often combined with multiple health problems) and an increased use of medications. Thus, a better understanding of the absorption, distribution, metabolism, and excretion of drugs; of the physiologic responses to those medications; as well as of the interactions among multiple medications is crucial for improving the health of older people.

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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

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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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 .

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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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SYSTEMATIC REVIEW article

Knowledge hiding: current research status and future research directions.

• 1 Business School, Huaqiao University, Quanzhou, China
• 2 Department of Management, Kedge Business School, Talence, France
• 3 Business School, Beijing Normal University, Beijing, China

This article provides a review of scientific articles addressing the topic of knowledge hiding in organizations. Based on a descriptive analysis, bibliometric analysis, and content analysis of a sample of 81 articles published in the academic journals in the Web of Science from 2012 to 2020, we identify the main areas and current dynamics of knowledge hiding research. Our results show that the central research themes of knowledge hiding include five clusters: concept and dimensions, antecedents, consequences, theories, and influence mechanisms. Based on our findings, we suggest future research should further develop the concept and dimensions of knowledge hiding; probe deeper into the consequences of knowledge hiding; explore multilateral, cross-level, and collective knowledge hiding; employ innovative theoretical perspectives and research methods to study knowledge hiding; and address how cultural and other contextual factors may shape the knowledge hiding behavior.

Introduction

Knowledge management plays a crucial role in each organization, which can affect the firms' and employees' performance. However, due to the practice of “knowledge hiding,” it is often challenging to achieve satisfactory results in knowledge management ( Connelly and Kelloway, 2003 ). Previous research has pointed out that employees are not willing to share knowledge, due to reasons such as protection and control of knowledge ownership, expertise dominance, and defensive awareness ( Huo et al., 2016 ). About 50% of employees have the intention to withhold, mislead, or conceal knowledge that has been requested by another person ( Peng, 2013 ). This behavior of deliberately not providing the required knowledge to colleagues when requested is called “knowledge hiding” ( Connelly et al., 2012 ), which has become an independent concept that is different from the opposite side of knowledge sharing ( Zhao et al., 2019 ).

Obviously, knowledge hiding is very likely to reduce the efficiency of knowledge exchange among members, hinder the generation of new ideas/thoughts, or even destroy trust ( Connelly et al., 2012 ), increasing the risk of knowledge loss and inhibiting the creativity of individuals and teams ( Cerne et al., 2014 ; Bogilović et al., 2017 ). Along this vein, it makes sense to solve the dilemma of insufficient knowledge sharing through the elimination of knowledge hiding, facilitating knowledge conversion within organizations. As a result, based on a descriptive analysis, bibliometric analysis, and content analysis, we conduct an in-depth analysis of knowledge hiding publications in international Science Citation Index (SCI) and Social Science Citation Index (SSCI) journals. We aim to address these research questions:

1. What is the current publication trend in knowledge hiding?

2. Which themes involving knowledge hiding have been studied by scholars?

3. What are the areas involving knowledge hiding that seem to require future research?

Previous authors have conducted reviews on knowledge hiding (e.g., Xiao and Cooke, 2019 ; Anand et al., 2020 ; de Garcia et al., 2020 ), which are valuable. However, the review of Xiao and Cooke (2019) is based on 52 articles and all of which are written in English or Chinese, and published over the period 1997–2017. Similarly, the review of Anand et al. (2020) is drawing on 52 studies. In their work, de Garcia et al. (2020) have reviewed a total of 57 articles that are published up to April 2018, and their study focuses on distinguishing knowledge hiding and knowledge hoarding from knowledge collection and donation perspectives. Our review differs from these previous works in terms of volume, timeframe, method and the analysis. First, we have combined bibliometric analysis, content analysis and descriptive analysis in this review, which allows for incorporating rich data with less interpretative or subjectivity biases. In contrast to previous reviews, we further overview the concepts and dimensions, antecedents, consequences, theoretical foundations, and influence mechanisms of knowledge hiding. In the meantime, we have included bigger volume of articles in this review. In so doing, we are able to complement the previous reviews, offering a more objective account of evolution of this research topic.

Methodology

Our study has followed the systematic review process ( Pickering and Byrne, 2014 ). Within this process, we employ the principles of Tranfield et al. (2003) , which include (1) setting the scope, (2) conducting the search and data extraction, (3) selecting the studies and analyzing the data, and (4) extracting data and reporting the findings. To ensure the data validity and reliability, we limited our databases by searching the sample of English-written articles from the Web of Science over the period between 1995 and 2020. Further, the main reason for using SCI and SSCI databases is that web of science is “generally considered credible among the scientific community, and [are] commonly used by researchers from a wide range of fields ( de Garcia et al., 2020 , p. 4). Several reviews have used these databases (e.g., Bernatović et al., 2021 ; Vlačić et al., 2021 ).

Retrieval conditions were “Title = knowledge hiding” or “Title = knowledge withholding,” and the time span was “All years (1950–2020).” The database was “Web of Science Core Collection” and the search basis was “Web of Science Category = Unrestricted Category.” In total, we obtained a sample of 233 articles. Subsequent analysis of these 233 articles' abstracts was conducted. In order to ensure data accuracy, we carefully selected studies that fit the definition given by Connelly et al. (2012) and excluded those that belonged to disciplines such as information management. This yielded 81 articles related to knowledge hiding. For these 81 articles, we undertook the reading of full texts, using Excel to record the key findings, theoretical lens, and methodologies. Building upon the content extraction, the authors classified the core clusters in five main themes according to their characteristics: concept and dimensions, antecedents, consequences, theoretical frameworks, and influence mechanism. Figure 1 shows the flow diagram of analysis.

Figure 1 . Flow diagram.

Analysis and Findings

Publication by year.

The analysis of the number of publications per year on knowledge hiding in international journals (see Figure 2 ) shows that scholars started to systematically study knowledge hiding as an organizational behavior in the 2010s. A growing number of studies have addressed knowledge hiding but it dates back only to 2012, when knowledge hiding was first proposed as an independent concept in the work of Connelly et al. (2012) . Knowledge hiding research has gone through two periods: the initial stage (from 2012 to 2018) and the fast development stage (from 2019 to 2020). During the initial stage, publications on knowledge hiding in mainstream international journals were rare, and there were only between one and five articles published per year. Since 2019, there has been a sharp increase in knowledge hiding publications; the number of publications has jumped to more than 30 articles per year (see Figure 2 ).

Figure 2 . Annual distribution of articles on knowledge hiding.

Journal Distribution of Knowledge Hiding Research

From 2012 to 2020, research on knowledge hiding has been published in 43 SCI/SSCI journals (see Table 1 ), with 40 articles (49.38%) published in Journal Citation Reports (JCR) Q1 journals, 19 articles (23.46%) published in JCR Q2 journals, 8 articles (9.88%) published in JCR Q3 journals, and 11 articles (13.58%) published in JCR Q4 journals; 15 articles (18.52%) published in the Chartered Association of Business Schools (ABS3) journals, 10 articles (12.35%) published in ABS4 journals, one article (1.23%) published in Financial Times (FT50) journals; and one article (1.23%) published each in UT Dallas top 100 business school research rankings (UTD24) and ABS4 * journals. The top 10 journals that published most of the knowledge hiding articles are Journal of Knowledge Management, Journal of Organizational Behavior, Management Decision, International Journal of Hospitality Management, European Journal of Work and Organizational Psychology, Knowledge Management Research and Practice, International Journal of Information Management, Asian Business and Management, Leadership and Organization Development Journal , and Journal of Managerial Psychology . The majority of knowledge hiding research has been published in JCR Q1/Q2 journals, and a considerable proportion has been published in ABS3/4 journals.

Table 1 . Top publishing journals on knowledge hiding.

Publishing Activity by Authors, Authors' Institutions, and Locations

Knowledge hiding has attracted considerable attention from researchers and practitioners. As shown in Table 2 , Matej Cerne published the most articles (eight) on knowledge hiding followed by Škerlavaj and Connelly, with seven and six articles respectively. The most active institutions in the research field of knowledge hiding were University of Ljubljana (eight publications), followed by BI Norwegian Business School, McMaster University and Tongji University, each with seven publications. Table 3 lists the locations of authors' institutions, with the top four being China, Pakistan, Canada and United Arab Emirates.

Table 2 . Top publishing authors and institutions on knowledge hiding.

Table 3 . Publishing activity by authors' institution location.

Publishing Activity by Data Sources

Our analysis shows that previous data on knowledge hiding have tended to be collected in one single location, such as China, Pakistan, United Arab Emirates, Saudi Arabia, United States, and so on (see Table 4 ). Eight publications used data that were collected from multi-countries and regions (e.g., North America, Germany and Austria, Europe, Slovenia, Croatia, Serbia, Bosnia and Herzegovina, Montenegro and Macedonia). The top three locations from which researchers have collected knowledge hiding data were China (29 publications), Pakistan (13 publications) and United Arab Emirates (5 publications).

Table 4 . Locations from which researchers have collected knowledge hiding data.

Highly Cited Publications

Citations can show the research focus of scholars and reveal their main theoretical lens. Highly cited articles are often regarded as important references in the field. Table 5 presents the top 15 highly cited publications on knowledge hiding.

Table 5 . Top 15 articles on knowledge hiding by the number of citations.

Further, through a co-citation analysis, co-authorship analysis, keyword and co-occurrence analysis, and content analysis, we find that most research on knowledge hiding focuses on the concept and dimensions of the topic. For instance, as one of the highly cited publications, it is important to acknowledge that Connelly et al. (2012) take the lead in defining the concept of knowledge hiding and propose evasive hiding, playing dumb, and rationalized hiding as three dimensions of knowledge hiding. Based on the work of Connelly et al. (2012) ; Zhao et al. (2016) further examine the interpersonal antecedents of the three dimensions of knowledge hiding. Hernaus et al. (2019) distinguish the three dimensions of knowledge hiding and address how individual competitiveness may lead to knowledge hiding. Connelly and Zweig (2015) point out that the three dimensions of knowledge hiding are not equally and always harmful, where under certain circumstances, some knowledge hiding can be beneficial. Among the highly cited publications, scholars also focus on the antecedents of knowledge hiding, paying particular attention to workplace stressors, psychological ownership, and territoriality of knowledge. For example, Zhao et al. (2016) ; Škerlavaj et al. (2018) , and Khalid et al. (2018) have examined the influence mechanisms of workplace stressors, such as workplace ostracism, abusive supervision, and interpersonal injustice, on knowledge hiding. Peng (2013) ; Huo et al. (2016) , and Singh (2019) emphasize the predictive effect of psychological ownership and territoriality of knowledge on knowledge hiding. Serenko and Bontis (2016) ; Hernaus et al. (2019) , and Malik et al. (2019) also investigate the antecedents of knowledge hiding with different focuses (e.g., intra-organizational knowledge hiding, the individual-level and job-related factors within academia, organizational politics). These studies represent the two most important research directions of knowledge hiding.

Following, among the highly cited publications, we find that individual and team creativity, interpersonal relationships, and retaliation show the key consequences of knowledge hiding. The main contributions in the field include the work of Cerne et al. (2014) , who point out that “when employees hide knowledge, they trigger a reciprocal distrust loop in which coworkers are unwilling to share knowledge with them” (p. 172). In recent years, Connelly and Zweig (2015) , and Serenko and Bontis (2016) also prove that knowledge hiding can lead to retaliation. Cerne et al. (2017) and Malik et al. (2019) examine the destructive effect of knowledge hiding on individual creativity. Bogilović et al. (2017 ) and Fong et al. (2018) analyze the impacts of individual-level knowledge hiding on team-level creativity. These studies represent the mainstream consequences of knowledge hiding.

Additionally, we identify that the research focus on knowledge hiding has moved from the individual level to a multilevel influence mechanism. For example, Huo et al. (2016) ; Cerne et al. (2017) ; Fong et al. (2018) , and Hernaus et al. (2019) explore the moderating effect of team-level task interdependence on the relationship between individual-level variables and knowledge hiding. In addition, team-level cultural factors (e.g., mastery climate, workplace ethics) and organizational justice are variables that scholars have examined when exploring the multilevel influence mechanism of knowledge hiding ( Huo et al., 2016 ; Cerne et al., 2017 ; Khalid et al., 2018 ).

Major Research Clusters and Topics

Using CiteSpace4.0 software, we conducted the descriptive analysis, bibliometric analysis, and content analysis of the 81 knowledge hiding articles that are published in the international journals from 2012 to 2020. In order to clearly demonstrate the current status of knowledge hiding research, we structure our findings into the following five clusters (see Figure 3 ).

Figure 3 . Research framework of knowledge hiding. Source: extended and developed from Connelly et al. (2012) and Xiao and Cooke (2019) .

Concept and Dimensions

The bibliometric analysis suggests that keywords related to the concept of knowledge hiding include knowledge sharing, knowledge withholding, and knowledge management process. Based on these keywords and the results of our content analysis, we extract “concept and dimensions” as the first cluster that reflects the research interests in knowledge hiding.

The concept of knowledge hiding was first defined as the act of deliberately not providing knowledge or providing knowledge that is not what the seeker needs when facing a colleague's request ( Connelly et al., 2012 ). These were the first authors to discuss the linkages and differences between knowledge hiding and related concepts, such as knowledge sharing/non-sharing ( Anand et al., 2020 ), knowledge withholding ( Webster et al., 2008 ), knowledge hoarding ( Xiao and Cooke, 2019 ; de Garcia et al., 2020 ), counterproductive/deviant behavior ( Connelly and Zweig, 2015 ; Serenko and Bontis, 2016 ), workplace deception ( Connelly et al., 2012 ), and incivility ( Zhao et al., 2016 ). Later, scholars further proposed concepts such as knowledge sharing hostility ( Stenius et al., 2016 ), disengagement from knowledge sharing ( Zhao et al., 2016 ), knowledge contribution loafing ( Fang, 2017 ), and knowledge manipulation ( Bogilović et al., 2017 ). In recent years, scholars have tried to differentiate knowledge hiding from other related concepts (e.g., employee silence and knowledge protection) ( Bari et al., 2020 ).

In order to distinguish these different concepts, we compare relevant concepts through questioning whether knowledge seeking exists, the degree of knowledge sharing, and the intentionality of the behavior (see Figure 4 ). In general, scholars have widely accepted the definition of knowledge hiding given by Connelly et al. (2012) . The mainstream view believes that knowledge hiding is an important aspect of knowledge withholding, and it is not the opposite of knowledge sharing ( Connelly et al., 2012 ; Serenko and Bontis, 2016 ; Zhao et al., 2016 ). Consequently, one cannot simply equate knowledge hiding with non-sharing or a lack of knowledge sharing. In addition to subjective intention, the reasons that individuals do not share knowledge with others can be related to a lack of relevant knowledge or the inability to share the knowledge. It is worth pointing out that there are different opinions in boundaries between knowledge hiding and concepts such as knowledge non-sharing, counterproductive knowledge behavior, and knowledge protection. Hence, there still exists some confusion and cross-use of related concepts in the knowledge hiding research. In addition, the existing literature has seldom defined knowledge hiding from the indigenous/cross-cultural perspective.

Figure 4 . Comparison between knowledge hiding and related concepts. Source: extended and developed from Connelly et al. (2012) and de Garcia et al. (2020) .

Connelly et al. (2012) have developed three dimensions of knowledge hiding and an employee self-evaluation scale with 12 items, with each dimension measuring four items. Among them, evasive hiding means that the hider provides invalid knowledge or pretends to agree to help, but lacks follow-up action. An example item is “I agreed to help him/her but never really intended to.” Playing dumb refers to pretending to be ignorant of the relevant knowledge or not understanding the knowledge seeker's question, with a sample item “I pretended I did not know what he/she was talking about.” Rationalized hiding means that the hider explains the reasons for not providing required knowledge, such as the necessity to keep it confidential or offering that knowledge sharing is not allowed by the superiors. An example item is “I explained that the information is confidential and available only to people on a particular project.” Most scholars believe that rationalized hiding is different in nature from evasive hiding and playing dumb, because rationalized hiding does not involve deception, but the evasive hiding and playing dumb do have a high degree of deception.

The scale of Connelly et al. (2012) has been proved to have high reliability and validity in a series of empirical studies. In general, scholars use this scale and its original items directly, making some contextual adaptation of expressions only according to the particular research needs. There are other knowledge hiding scales, such as Peng's ( Peng, 2013 ) three-item counterproductive knowledge behavior scale and knowledge withholding behavior scales developed by Lin and Huang (2010) ; Tsay et al. (2014) , and Serenko and Bontis (2016) . Anand et al. (2020) have advocated that knowledge hiding is composed of unintentional hiding (driven by contingent situation), motivational hiding (driven by performance and competition), controlled hiding (driven by psychological ownership), victimized hiding (driven by hostility and abuse), and favored hiding (driven by identity and norms). Jha and Varkkey (2018) identify the four strategies adopted by supervisors to hide knowledge from subordinates, namely, playing innocent, misleading, rationalized hiding, and counter-questioning.

Antecedents

The antecedents of knowledge hiding include the Big Five personality traits, abusive supervision, negative workplace gossip, and career insecurity. Combined with the research framework of knowledge hiding (see Figure 3 ), the second cluster as antecedents is popular among scholars. Inspired by the work of Connelly et al. (2012) and Xiao and Cooke (2019) , we review knowledge hiding antecedents from four aspects: knowledge characteristics, individual factors, team and interpersonal factors, and organizational factors.

Knowledge characteristic is one of the first antecedents popular among scholars. Due to the complex nature of knowledge, Connelly et al. (2012) point out that such complexity affects the willingness of individuals to provide help when facing colleagues' knowledge requests. Simply, it often requires more time and energy to generate complex knowledge that knowledge owners tend to keep the knowledge for themselves. Hernaus et al. (2019) argue that people are more likely to hide tacit knowledge rather than explicit knowledge. In addition, the task relevance and the value of knowledge have a positive relation with knowledge hiding ( Connelly et al., 2012 ; Huo et al., 2016 ).

Individual factors mainly include personality traits and psychological factors such as emotion and cognition. In terms of personality traits, scholars focus mainly on the influence of the Big Five personality traits, in particular neuroticism. For example, Pan and Zhang (2018) reveal that employees with high conscientiousness and low neuroticism are less likely to hide knowledge, while people with high neuroticism are more likely to hide knowledge ( Anaza and Nowlin, 2017 ). Pan et al. (2018) verify the effects of a “dark triad of personality” (Machiavellianism, narcissism, and psychopathy) on different dimensions of knowledge hiding. Fang (2017) and Aljawarneh and Atan (2018) examine the relationship between anxiety and knowledge hiding and the relationship between cynicism and knowledge hiding.

When it comes to the cognitive perception, prior research has focused mainly on the individual's self-efficacy, territoriality and psychological ownership, psychological safety, psychological contract breach, perceived pressure or job insecurity, perceived workplace status, and career prospects. Tsay et al. (2014) ; Jha and Varkkey (2018) , and Hernaus et al. (2019) argue that individuals' confidence in their knowledge and perception of their competitiveness affect their willingness to share knowledge. Peng (2013) ; Huo et al. (2016) ; Kang (2016) ; Singh (2019) ; Khalid et al. (2020) , and Zhai et al. (2020) believe individuals' perceived exclusivity of knowledge, knowledge power, and knowledge privacy are the primary factors that determine how much knowledge they are willing to share with colleagues. He et al. (2020) ; Lin et al. (2020) , and Wu (2020) explore the formation mechanism of knowledge hiding from the perspectives of psychological safety and perceived threats. Pradhan et al. (2019) ; Ghani et al. (2020a) , and Jahanzeb et al. (2020a) emphasize the negative impacts of employee psychological contract breaches on knowledge sharing in the organizations. Jha and Varkkey (2018) ; Škerlavaj et al. (2018) , and Feng and Wang (2019) examine the impacts of workplace stressors, such as time pressure and job insecurity, on knowledge hiding.

Prior studies have also investigated knowledge hiding from employee and supervisor perspectives. In their work, Butt (2019) and Butt and Ahmad (2019) show that concerns about career prospects are important individual-level reasons for supervisors to hide knowledge from subordinates. Liu et al. (2020) find that perceived workplace status affects knowledge hiding through two opposing mechanisms: perception of knowledge sharing obligation and perception of being envied. The goal orientation has also attracted some scholars' attention in recent years when studying knowledge hiding behavior. Research by Zhu et al. (2019) shows that performance-driven goal orientation has a positive relationship with employees' knowledge hiding behaviors, which allows employees to achieve the competitive goal of surpassing colleagues. Nadeem et al. (2021) argue that shared goals are negatively related to knowledge hiding. Moh'd et al. (2021) analyze the relationship between achievement goal orientation (e.g., learning goals, performance display/performance-avoidance goal orientation) and knowledge hiding. Some scholars highlight that individual motivational factors (such as expected results/rewards and perceived knowledge sharing costs) affect knowledge hiding ( Lin and Huang, 2010 ; Shen et al., 2019 ). Although emotion and cognition have been regarded as the two core elements that drive individual behavior (e.g., Lee and Allen, 2002 ), studies on how emotional/affective factors influence knowledge hiding are still underdeveloped. We believe only Zhao and Xia (2019) have studied the negative emotional state of nursing staff as the antecedent of their knowledge hiding behavior.

Team-level and interpersonal factors reflect leadership, interpersonal relationships, and their respective interactions. When considering leadership, scholars pay the most attention to abusive leadership, followed by ethical leadership. Khalid et al. (2018) point out that knowledge hiding is not necessarily an employee's intention to directly harm other organization members, but a negative reaction of employees to abusive supervision. Further, as indicated by displaced aggression theory, when employees encounter abusive leaders, they are more likely to retaliate by targeting innocent victims, namely, their colleagues but not the leaders. Based on the reactance theory, Feng and Wang (2019) point out that when employees experience frustration resulting from the abuse of their supervisors, they will take revenge in a direct or indirect way so that they can maintain a sense of freedom. However, because of their supervisors' supreme power and status in organizations, employees usually do not directly retaliate against supervisors so as not to cause stronger hostility and reciprocal retaliation. Ethical leadership can also influence employees' behavior intentionally or unintentionally through the role model effect. Abdullah et al. (2019) ; Anser et al. (2020) , and Men et al. (2020) argue a significant but negative correlation between ethical leadership and subordinates' knowledge hiding behavior. Interestingly, the study by Xia et al. (2019) describes an inverted U–shaped curve relationship between knowledge leadership and employee knowledge hiding. Through a multilevel model, Lin et al. (2020) find that individual-focused empowering leadership can improve the supervisor-subordinate relationship and therefore inhibit knowledge hiding, whereas differentiated empowering leadership can cause group relational conflict and then lead to knowledge hiding. Based on social exchange theories, Abdillah et al. (2020) argue that altruistic leaders' humility, patience, understanding, sympathy, and compassion will be perceived by employees as uniquely socio-emotional resources, which can enhance the positive emotion of employees, improve the quality of the exchange between supervisors and subordinates (obtaining the trust and respect of the subordinates), and encourage employees be willing to make extra efforts for the organization and eliminate selfish behaviors that harm the interests of the organization, thus effectively preventing employee knowledge hiding behaviors.

From the perspective of interpersonal abuse, prior research shows that employees who encounter interpersonal unfair treatment are less willing to share their personal knowledge assets with others ( Abubakar et al., 2019 ), whereas fair interpersonal interaction is significantly negatively correlated with the three dimensions of knowledge hiding ( Ghani et al., 2020b ). Among these, the factor of passive-aggressiveness in the workplace attracts more attention from scholars. Aljawarneh and Atan (2018) find that incivility in the workplace can drive employees to feel cynical and thus hide knowledge as a countermeasure. Zhao et al. (2016) and Riaz et al. (2019) point out that, as a typical workplace passive-aggressiveness, workplace ostracism would significantly increase employees' deceptive knowledge hiding (e.g., evasive hiding and playing dumb). Similarly, research by Yao et al. (2020a , b ) shows that negative interpersonal experiences, such as workplace bullying and negative workplace gossip, accelerate the exhaustion of employee resources, such as emotions, time, energy, and organizational identity, leading them to hide knowledge. Anand et al. (2020) also find that hostility and abusive colleagues/supervisors drive employees to hide knowledge.

Concerning the impacts of interpersonal relationship on knowledge hiding, current research has focused on exploring the effects of supervisor-subordinate relationships. Scholars first divide supervisor-subordinate relationships into formal work-related relationships (contractual relationship, Leader-Member Exchange) and informal non-work-related relationships (Chinese personal guanxi relationships, Supervisor-Subordinate Guanxi) ( He et al., 2020 ), or into economic LMX and social LMX ( Babič et al., 2019 ), and then explore their impacts on employees' knowledge hiding behaviors. Previous research reveals that LMX negatively affects evasive hiding and playing dumb ( Zhao et al., 2019 ). However, this reciprocal social exchange is more likely to reduce the level of knowledge hiding within the team, especially when the relationship between individuals and their supervisors has social LMX characteristics ( Cerne et al., 2014 ). Furthermore, upward LMX social comparison leads to envy among team members, so it is a potential interpersonal antecedent of knowledge hiding among colleagues ( Weng et al., 2020 ). It is worth noting that team prosocial motivation and social LMX (but not economic LMX) have an interaction effect on knowledge hiding ( Babič et al., 2019 ). Lin and Huang (2010) ; Butt (2019) ; Butt and Ahmad (2019) ; Semerci (2019) examine the influences of interpersonal factors such as trust, reciprocity, relationship recognition, lack of interpersonal relationship, relationship conflict, and interpersonal competition. Interestingly, Lin and Huang (2010) point out that emotional bonds such as trust and reciprocity among team members can make individuals give up hiding too much knowledge to avoid retaliation from others. In addition, task conflicts and relationship conflicts have additive effects on knowledge hiding ( Semerci, 2019 ).

At the organizational level, scholars have explored the roles of organizational culture, knowledge management policies and systems, organizational politics, organizational justice, organizational recognition, and a competitive performance environment on employees' conduct of knowledge hiding. First, the knowledge sharing culture has been proved to be closely related to the extent to which the knowledge hiding behavior can be accepted and adopted by the members of the organization ( Connelly et al., 2012 ). For example, Anaza and Nowlin (2017) point out that the lack of incentives for knowledge sharing and the lack of supervisor feedback on subordinates' knowledge sharing will lead employees to hide knowledge. Jha and Varkkey (2018) highlight that a lack of organizational recognition of knowledge sharing and workload increase due to knowledge sharing increase employee knowledge hiding.

Social norms, organization policies, and management systems have also been found to have a profound impact on employees' tendency to hide knowledge. For instance, Butt and Ahmad (2019) argue that knowledge hiding is deeply embedded in many local companies and is regarded as a common code of conduct in the United Arab Emirates. Serenko and Bontis (2016) find that organizational knowledge management systems and policies have a significant direct impact on employee knowledge hiding, whereas injustice prompts employees to spontaneously engage in knowledge hiding behavior. Malik et al. (2019) propose that perceived organizational politics positively predict knowledge hiding. Abubakar et al. (2019) find that distributional, procedural, and interactional injustice increase the level of knowledge hiding among employees. Research by Jahanzeb et al. (2020b) confirms that employees who encounter organizational unfairness consider knowledge hiding as a means to rationalize the cognitive separation between oneself and the organization in order to maintain one's dignity. Finally, some scholars have examined the impact of a competitive working environment. For example, Anaza and Nowlin (2017) explain how internal competition can lead to knowledge hiding. Similar findings can be found in the work of Anand et al. (2020) , who argue that organizational internal performance and competitive factors drive employees to hide knowledge.

Consequences

Based on the highly cited publications and the keyword analysis, we find that consequences, performance, behavior , and employee/team creativity are some keywords that reflect the outcome of knowledge hiding. Therefore, we use the term consequences to summarize the third cluster concerning the knowledge hiding research.

Current research focuses mainly on the individual- and team-level consequences of knowledge hiding. A small number of studies examine the individual-level consequences of knowledge hiding between supervisors and subordinates. In terms of individual-level results, the existing research has examined the effects of knowledge hiding on individual job performance, psychological status and attitude, workplace behavior, and supervisor-subordinate/coworker relationships. For instance, most studies have found that knowledge hiding among colleagues and between supervisors and subordinates can reduce task performance, organizational citizenship behavior (OCB), and creativity ( Connelly et al., 2012 ; Cerne et al., 2014 ; Arain et al., 2019 , 2020a , b ; Jahanzeb et al., 2019 ; Malik et al., 2019 ; Singh, 2019 ; Zhu et al., 2019 ).

However, there are some mixing findings. For example, Wang et al. (2019) argue that perceived colleague knowledge hiding does not reduce the performance of salespersons. Instead, it encourages them to work harder to improve their sales performance. Burmeister et al. (2019) find that knowledge hiding (playing dumb, in contrast to evasive hiding and rationalized hiding) has opposite effects on OCB, and knowledge hiders experience different emotions. Khoreva and Wechtler (2020) point out that evasive hiding is negatively related to in-role performance, and playing dumb is positively related to it. In addition, both evasive hiding and rationalized hiding will hinder innovation performance. Regarding psychological status and attitudes, research suggests that knowledge hiding increases employees' moral disengagement ( Arain et al., 2020a ) and decreases their psychological safety, well-being, job satisfaction, and sense of thriving ( Jiang et al., 2019 ; Offergelt et al., 2019 ; Khoreva and Wechtler, 2020 ). Furthermore, knowledge hiding can trigger knowledge seekers' deviant behaviors, turnover intention, upward silence, and non-engagement in knowledge sharing ( Connelly and Zweig, 2015 ; Offergelt et al., 2019 ; Singh, 2019 ; Arain et al., 2020a ).

Concerning interpersonal relationships, studies reveal that knowledge hiding among colleagues or between supervisors and subordinates can damage workplace relationships, which can even lead to a trust crisis ( Connelly et al., 2012 ; Cerne et al., 2014 ; Arain et al., 2020b ). In particular, Connelly et al. (2012) , Cerne et al. (2014) , and Connelly and Zweig (2015) highlight that knowledge hiding can result in a vicious circle of rejecting knowledge sharing. Studies also find that knowledge hiding has significant negative effects on team performance ( Zhang and Min, 2019 ), team creativity ( Fong et al., 2018 ; Bari et al., 2019 ), team viability ( Wang et al., 2019 ), team learning, and absorptive capability ( Fong et al., 2018 ; Zhang and Min, 2019 ).

In summary, scholars have made advancements on the impacts of knowledge hiding on the individual level, but research on its impacts on team and organizational levels is still at a nascent stage. Few scholars have recently analyzed the “boomerang effect” or “negative reinforcement cycle” of knowledge hiding—the impact of knowledge hiding on the hiders' psychological status, job performance, and creativity (e.g., Cerne et al., 2014 ; Jiang et al., 2019 )—and its double-edged sword effect ( Wang et al., 2019 ), which has opened up a new avenue for research.

Theoretical Perspectives

The fourth cluster concentrates on theories that are popular among scholars that they use to conduct knowledge hiding research. The theories applied in the field of knowledge hiding are mainly from two domains—managerial theory and psychological theory—and include theories such as “exchange” (represented by social exchange theory), “resources” [represented by Conservation of Resources (COR) Theory], “learning” (represented by social learning theory), “cognition” (represented by social cognitive theory), “ownership” (represented by psychological ownership theory), “goal orientation” (represented by achievement goal theory), “personality traits,” “job characteristics,” social identity theory, displaced aggression theory, and justice theory (see Table 6 ). Although scholars have introduced other theories to study knowledge hiding, the effectiveness of this theoretical development needs to be enhanced. For example, how to theorize individual emotions has not yet been made systematic and thus needs to be further explored in future research. Furthermore, we find that theories that are mostly used to examine the motivation/antecedents of knowledge hiding or the direct/indirect (mediating) influence of antecedent variables on knowledge hiding are less used to illustrate the consequences of knowledge hiding and the boundary conditions.

Table 6 . Theoretical perspectives used in knowledge hiding research.

Influence Mechanisms

There are findings on the mediating roles of antecedent variables that affect knowledge hiding. Emotional and cognitive factors (e.g., leadership, workplace stressors, interpersonal relationships, personality traits, and psychological ownership) can induce knowledge hiding. In terms of leadership, Abdullah et al. (2019) point out that ethical leadership inhibits employees' knowledge hiding by enhancing their relational social capital. Anser et al. (2020) find that the ethical behavior of ethical leaders can enhance the perception of “meaningful work” for service industries employees, thereby reducing the possibility of engaging in knowledge hiding behaviors. Khalid et al. (2018) find that perception of interpersonal justice mediates the relationship between abusive supervision and knowledge hiding. Feng and Wang (2019) believe that abusive supervision indirectly affects knowledge hiding through job insecurity. Pradhan et al. (2019) show that psychological contract breaching and the attacks toward supervisors play a partial mediating role in the process in which abusive supervision affects knowledge hiding. Ghani et al. (2020a) further point out that abusive supervision can easily lead to psychological contract breach, thus leading employees to attack their colleagues and deliberately hide knowledge from them. In addition, Lin et al. (2020) find that individual-focused empowering leadership enhances the psychological safety of subordinates, thereby reducing their knowledge hiding, whereas differentiated empowering leadership causes group relational conflicts, thereby increasing subordinate knowledge hiding. Abdillah et al. (2020) study the dual mediating mechanisms of altruistic leadership, which inhibits and prevents employees from knowledge hiding, pointing out that the positive emotions induced by altruistic leadership and LMX have important effects.

Regarding workplace stressors and interpersonal relationships, Aljawarneh and Atan (2018) find that cynicism mediates the relationship between tolerance of workplace incivility and knowledge hiding. Riaz et al. (2019) find that workplace ostracism has a significant impact on evasive hiding and playing dumb, and that work strain plays a mediating role. Yao et al. (2020a , b ) have shown that relational identification and interpersonal trust play a chain-mediating role in the relationship between negative workplace gossip and knowledge hiding. At the same time, emotional exhaustion and organizational identification play a chain-mediating role in the relationship between workplace bullying and knowledge hiding. Jahanzeb et al. (2020b) believe that the experience of injustice causes employees to be psychologically separated from the organization and thus employees will show more knowledge hiding behaviors. Zhao et al. (2019) demonstrate that organizational identification mediates the negative impact of LMX on evasive hiding and playing dumb. Weng et al. (2020) point out that employees' upward LMX social comparison with their colleagues leads to envy of and knowledge hiding toward their colleagues. He et al. (2020) discover that psychological safety fully mediates the influence of LMX on knowledge hiding and partially mediates the influence of supervisor-subordinate guanxi on knowledge hiding.

Another aspect is shown through personality traits. Wang et al. (2014) find that perceived social identity mediates the relationship between the Big Five personality traits and knowledge hiding. Pan et al. (2018) examine the positive relationship between the “dark triad of personality” (Machiavellianism, narcissism, and psychopathy) and knowledge hiding, as well as the mediating effect of transactional psychological contracts on this relationship. Zhao and Xia (2019) point out that the negative affect states of nurses staff can “activate” their moral disengagement mechanism, allowing them to redefine their knowledge hiding behaviors as reasonable and acceptable, and thus exacerbating their knowledge hiding tendency. The final aspect is psychological ownership. Research by Peng (2013) and Huo et al. (2016) show that employees' psychological ownership of knowledge enhances their territorial awareness, which in turn causes them to hide knowledge from colleagues. Liu et al. (2020) confirm that the influence of workplace status on employee knowledge hiding is carried out through two opposite mechanisms: perceived knowledge sharing responsibility and envy. The former negatively mediates the relationship between the two, and the latter positively mediates it.

Some scholars have also studied the mediating effect of knowledge hiding. For instance, scholars examine the process through which knowledge hiding impairs individual or team creativity and innovation performance. Cerne et al. (2014) find that the knowledge hiding makes hiders reduce their own creativity, and colleague distrust plays a mediating role. Arain et al. (2019) show that supervisor knowledge hiding can reduce subordinates' self-efficacy and thus reduce their innovation. Khoreva and Wechtler (2020) point out that playing dumb and rationalized hiding can indirectly influence employee innovation performance through the mediating effect of well-being. Fong et al. (2018) confirm that a decrease in absorptive capacity is the key mediator in the relationship between knowledge hiding and team creativity. Zhang and Min (2019) state that team learning partially mediates the relationship between knowledge hiding and project team performance.

Moreover, researchers have studied the process through which knowledge hiding affects employees' subsequent interpersonal behaviors. For instance, Burmeister et al. (2019) find that guilt and shame play opposite mediating roles in the relationship between individual knowledge hiding and its subsequent interpersonal-oriented OCB. Arain et al. (2020b) point out that supervisor knowledge hiding negatively influences subordinates' OCB toward their supervisors, and subordinate distrust in their supervisors plays a mediating role. Supervisor knowledge hiding can also activate employee moral disengagement, prompting them to reduce OCB toward their supervisors and increase silence behaviors ( Arain et al., 2020a ). Jiang et al. (2019) suggest that knowledge hiding makes the hiders feel the insecurity of self-expression and interpersonal risk, thereby reducing their psychological safety and endangering their ability to thrive at work. Despite these advancements, it is necessary to develop a robust framework that integrates multipath models based on different innovative theoretical perspectives.

Regarding the moderating role of contextual factors on knowledge hiding, the existing research mainly explores the contingency influence of individual differences, job characteristics, team characteristics, and team/organizational climate. In terms of individual differences, some scholars find that organizational psychological ownership can effectively reduce the knowledge hiding resulting from territoriality ( Peng, 2013 ). Furthermore, psychological ownership significantly moderates the inverted U-shaped relationship between knowledge leadership and knowledge hiding. This curved relationship is more obvious among employees with high psychological ownership ( Xia et al., 2019 ). High psychological ownership can also minimize the impact of abusive supervision on knowledge hiding ( Ghani et al., 2020a ). Other scholars explore the boundary effect of positive traits, such as individualism/collectivist values ( Semerci, 2019 ), positive affectivity ( Jahanzeb et al., 2020a ), benevolence or tolerance ( Jahanzeb et al., 2020b ), prosocial motivations ( Škerlavaj et al., 2018 ), harmonious work enthusiasm ( Anser et al., 2020 ), professional commitment ( Malik et al., 2019 ), trust-related affect/cognition ( Nadeem et al., 2021 ), social skills ( Wang et al., 2019 ), and cultural intelligence ( Bogilović et al., 2017 ). In addition to these studies, scholars examine the impacts of negative traits on knowledge hiding, such as negative reciprocity ( Zhao et al., 2016 ; Jahanzeb et al., 2019 ), instrumental thinking ( Abdullah et al., 2019 ), hostile attribution bias ( Khalid et al., 2020 ), moral disengagement ( Zhao et al., 2016 ), and cynicism ( Jiang et al., 2019 ).

In relation to job characteristics, task interdependence has attracted a lot of attention. Huo et al. (2016) point out that task interdependence can reduce the territorial awareness and knowledge hiding caused by psychological ownership. Hernaus et al. (2019) find that task interdependence can help reduce the probability of employees' evasive knowledge hiding due to maintaining their competitiveness. Fong et al. (2018) show that task interdependence moderates the relationship between knowledge hiding and team absorptive capacity. Weng et al. (2020) suggest that the interdependence of cooperative and competitive goals has opposite moderating effects on the relationship between upward LMX social comparison and knowledge hiding. In addition, Pan and Zhang (2018) also analyze the influence of work autonomy on the intensity of the relationship between neuroticism and knowledge hiding.

Regarding the team/organizational climate, research shows that in an environment that values information exchange and cooperation, the negative influence of knowledge hiding will be greatly weakened. Accordingly, Cerne et al. (2014) study the boundary effect of the team achievement-motivation climate (e.g., performance climate and mastery climate) on the relationship between knowledge hiding and the decrease in the hider's creativity. They discover that the negative effect of knowledge hiding on the hider's creativity is reduced in a mastery climate. Furthermore, Cerne et al. (2017) find the moderating effects of mastery climate, task interdependence, and autonomy on the relationship between knowledge hiding and innovative work behavior. Bari et al. (2019) obtain similar findings which point out that a perceived mastery climate reduces the negative impact of evasive hiding and playing dumb on team creativity. Feng and Wang (2019) find that the interaction between abusive supervision and a mastery climate is negatively related to knowledge hiding, and the interaction between abusive supervision and a performance climate is positively related to knowledge hiding. On the one hand, when the organization pays more attention to individual performance feedback, performance-prove goal orientation can positively predict knowledge hiding. On the other hand, when the organization pays more attention to group performance feedback, performance-prove goal orientation is negatively correlated with knowledge hiding ( Zhu et al., 2019 ). Compared to individual rewards, team-based rewards are more likely to reduce the distrust caused by knowledge hiding, promoting the team to work hard to achieve a common goal, forming a relatively stable team structure, and improving team viability ( Wang et al., 2019 ). Yao et al. (2020a , b ) reveal the buffering effect of a forgiveness climate on the relationship between negative workplace gossip/workplace bullying and knowledge hiding. Khalid et al. (2018) clarify the role of Islamic work ethics in moderating the relationship between abusive supervision and knowledge hiding. Among these findings, the existing research on the moderating effects still focuses more on the first stage of the antecedents–knowledge hiding–consequences linkage, but there is a lack of systematic development of the moderation mechanism in the second stage.

Future Research Directions

Based on a descriptive analysis, bibliometric analysis, and content analysis, we find that research on knowledge hiding focuses mainly on five clusters. Despite the ongoing progress, several research gaps are worth further addressing.

(1) Comprehensive studies on the concept and dimensions of knowledge hiding are needed to provide a robust conceptual framework. Although the definition and three-dimensional view of knowledge hiding by Connelly et al. (2012) are widely adopted by many scholars, more research is needed to carry out in-depth comparative analysis to clarify the connections and differences between knowledge hiding and similar concepts (e.g., knowledge non-sharing, knowledge sharing hostility, knowledge contribution loafing, counterproductive knowledge behavior, knowledge hoarding, knowledge protection, employee silence, etc.). Further, more studies should continue exploring the dimensions of knowledge hiding. There is a lack of focus on knowledge hiders' psychological motivation and respective knowledge hiding strategies. For example, research on proactive, reactive, and passive knowledge hiding could enrich the field research. In addition, more studies should further explore the unique reasons and consequences of a rationalized hiding behavior. There is a need to verify the ethical aspect of rationalized hiding, when knowledge hiding is used to protect confidential information or the interests of third parties ( Zhao et al., 2019 ).

(2) Future studies need to further explore the consequences of knowledge hiding. Based on a systematic review (see Figure 3 ), we find that previous studies have focused mainly on the antecedents of knowledge hiding. Although some studies have addressed the impacts of knowledge characteristics, individual factors, team-level and interpersonal factors, and organizational-level factors on knowledge hiding, more work is needed to provide comprehensive studies on the generating mechanisms and the respective coping strategies of knowledge hiding. Prior studies have shown that knowledge hiding has impacts on individual-level outcomes (e.g., individual creativity, in-role and extra-role performance, and coworker relationships) and team-level outcomes (e.g., team creativity). However, there is a lack of research on organizational-level outcomes. Moreover, prior studies focus mainly on the impacts of knowledge hiding on the knowledge seekers and the whole team, but seldom has the research discussed the potential effects of knowledge hiding on the knowledge hiders themselves. Therefore, future research should devote more attention to the negative effects of knowledge hiding on the knowledge hiders, the team, and the organization, and also explore the consequences of different dimensions of knowledge hiding. For example, more studies could address the research gap as to whether knowledge hiding may stimulate self-reflection and prompt moral and psychological compensation for the knowledge hiders. To enrich the multilevel mediating and moderating variables, future studies could explore the boundary conditions of knowledge hiding and their respective knowledge management strategies. In short, it is necessary to increase research on the consequences of knowledge hiding to enrich the antecedents–knowledge hiding–consequences research path.

(3) More studies on multilateral, cross-level, and collective knowledge hiding are needed, and it is appropriate to introduce new paradigms for knowledge hiding research. Existing research on knowledge hiding highlights mainly two parties: the hider (A) and the seeker (B) (i.e., B seeks knowledge from A, while A hides knowledge from B). Most studies address knowledge hiding among colleagues at the horizontal level. In recent years, some scholars have started to show interest in knowledge hiding at the vertical level, that is, the top-down knowledge hiding of superiors from subordinates. However, the research on the antecedents and the generating mechanisms of knowledge hiding at the vertical level is still in the stage of exploration. There is a lack of research on bottom-up knowledge hiding (of the subordinates from their superiors). Therefore, it is necessary to study knowledge hiding adopted by people from different hierarchies (e.g., bottom, mid, and high levels) in the organizations, comparing the differences between top-down and bottom-up knowledge hiding, so as to identify regular patterns of cross-level knowledge flow within the organizations. Future research could also examine whether the knowledge hiding of top managers could trigger a trickle-down effect, referring to the fact that the behaviors of the top leaders will affect employees in the formal vertical power chain, given that knowledge hiding can be a multi-participant phenomenon. Therefore, future research could examine the contagious effects of knowledge hiding (e.g., B seeks knowledge from A, but A hides knowledge from B; B then feels lost and hides knowledge from other colleagues), diffusion effects (e.g., B seeks knowledge from A while A hides knowledge from B; A asks C to hide knowledge from B as well), bystander effects (e.g., B seeks knowledge from A, while A hides knowledge from B; C witnesses A's knowledge hiding and is influenced by it, so C also hides knowledge from B and other colleagues), and collective knowledge hiding.

(4) Future scholars should innovate theoretical perspectives and integrate multidisciplinary theories into knowledge hiding research. At present, knowledge hiding research is based mainly on theories such as social exchange, social cognition, social capital, social learning, conservation of resources, territoriality, and psychological ownership. To enrich the field research, it is necessary to diversify the theories. For example, future studies could explore the influence of social exchange relations (e.g., relative LMX) on knowledge hiding, comparing the influence of social LMX and economic LMX on employee willingness to hide knowledge. Future scholars could also conduct multi-interdisciplinary research studies. The research on how an individual's previous workplace behavior affects his or her subsequent workplace behavior has attracted great interest from scholars and mainstream journals in organizational behavior in recent years. Given that knowledge hiding is a typical morality-related behavior, future research could introduce novel and original theoretical viewpoints. For example, a moral balance model and a moral cleansing effect in disciplines such as moral psychology and cognitive psychology, can be used to explore how an individual's previous knowledge hiding behavior influences subsequent behavior in the workplace. Furthermore, knowledge hiding is considered as an emotion-driven behavior. Therefore, scholars could consider employing Lazarus's cognitive–motivational–relational (CMR) theory of emotion ( Lazarus, 1991 ) to better understand the psychological process behind knowledge hiding. Moreover, there is a lack of research on the relationship between individual affect/emotion and knowledge hiding. Therefore, scholars could employ theories, such as affective events theory and self-conscious moral emotion theory, to analyze the subsequent behavior of the hiders and seekers who are driven by affect/emotion.

(5) Research designs need more diversification. Most of the prior studies focus on the individuals, and few research studies focus on both individual and team effects. Knowledge hiding is a complex organizational behavior that concerns individual, team/interpersonal, and organizational levels. Therefore, future research could introduce data tracking technologies, such as big data analysis, to study and compare the dynamic and static (long-term and short-term) effects of multilevel knowledge hiding. Moreover, it is necessary to diversify research methods in the field. Most existing research uses one-wave or multistage surveys, employee self-evaluation, and empirical tests, with few studies using case studies and interviews. These research methods may suffer from a lack of reliability of data sources. Future research could integrate multiple methodologies (e.g., combining case studies, experimental research, surveys, and objective data mining) to verify data, which could improve the internal and external validity of the research and enhance the robustness of conclusions. In particular, it is necessary to focus on the combination of experimental and empirical research, making full use of the strengths of each method to validate the research. Researchers could carry out preliminary tests on relevant hypotheses through experimental research and then supplement them with surveys for secondary verification.

(6) Future research should integrate more cultural, sectoral, and organizational factors to enrich the findings. As discussed in the findings, most of the knowledge hiding data were collected in China and Pakistan. It is necessary to develop the diversity of knowledge hiding data in terms of country of origin. In addition, there is a lack of cross-country academic collaboration. Collaborating across borders could help to generate new ideas and allow for collecting data from different sources. Meanwhile, it would be very interesting to promote cross-country studies to identify the different definitions, perceptions, implementations, and patterns of knowledge hiding, whilst paying more attention to the relationship between cultural dimensions and knowledge hiding. Apart from cross-cultural and cross-country variables, future research could also investigate industry characteristics (such as knowledge-intensive and non-knowledge-intensive industries and masculine and feminine industries), team standards/norms (such as team moral norms), and firm size (small medium enterprises vs. multinational companies) so as to identify the boundary conditions of individual knowledge hiding behavior. Through conducting sector-specific and cross-sector comparison for knowledge hiding, we would be able to adjust knowledge management methods.

Conclusions

This article provides a systematic review of knowledge hiding. It contributes to the identification of publication patterns on knowledge hiding between 2012 and 2020. Further, we have highlighted the most influential studies, mapped the research gaps, and provided the potential research directions in the field.

This study is not without limitations. We use SCI and SSCI web of science as the databases. Using this literature search method excludes book chapters, reports, unpublished dissertations, with/without peer reviewed conference proceedings, newsletters, government documents, and working papers. Consequently, this review may not have captured the full range of scholarly literature on knowledge hiding. In the future, to reduce the publication bias ( Kepes et al., 2012 ), it would be interesting to include other databases to search literatures, for instance, the work published in the Emerging Sources Citation Index (ESCI) journals can be considered. Second, the research on knowledge hiding is emerging, and some scholars may argue that it is not yet mature enough to review the research field. In our opinion, it is only with such a complete literature review that a clear picture of knowledge hiding research can be developed so that scholars can better define research problems, innovate the research theories and methods, and enrich the field research with a robust framework.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

PH, CJ, ZX, and CS designed and supervised the study. PH collected the data. PH and ZX analyzed the data. PH, CJ, and CS wrote the manuscript. All authors contributed equally to this manuscript, reviewed, and approved this manuscript for publication.

Funding was provided by Huaqiao University's Academic Project Supported by the Fundamental Research Funds for the Central Universities (20SKGC-QT02) and the National Natural Science Foundation of China (72172048).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: knowledge hiding, systematic literature review, future research directions, content analysis, bibliometric analysis, descriptive analysis

Citation: He P, Jiang C, Xu Z and Shen C (2021) Knowledge Hiding: Current Research Status and Future Research Directions. Front. Psychol. 12:748237. doi: 10.3389/fpsyg.2021.748237

Received: 27 July 2021; Accepted: 05 October 2021; Published: 29 October 2021.

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*Correspondence: Zhixing Xu, xuzhixing@bnu.edu.cn ; Chuangang Shen, psychshen@hqu.edu.cn

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• Published: 10 April 2024

Current and future directions for research on hallucinations and delusions

• Reshanne R. Reeder   ORCID: orcid.org/0000-0002-8525-5285 1  

Scientific Reports volume  14 , Article number:  8328 ( 2024 ) Cite this article

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Hallucinations and delusions can be symptoms of psychiatric illness, but more often—though less commonly known—are actually part of a healthy range of experiences found throughout the general population. The studies in this Special Collection paint a picture of the wide range of hallucinatory and delusional experiences across diverse populations, as well as comparative perspectives between clinical and non-clinical samples. In this editorial, I make three related points that are exemplified in the articles published here. First, that hallucinations and delusions are part of a normal distribution of human diversity; their mere presence does not indicate psychosis or psychiatric illness. Second, that the ubiquity of hallucinatory and delusional experiences across clinical and non-clinical populations suggests common cognitive and neural mechanisms. Finally, despite these commonalities, it is important to understand the difference between psychiatric symptoms and healthy experience. In summary, I conclude that it is important to investigate both common mechanisms and distinguishing factors to comprehensively elucidate these oft-misunderstood experiences. This Special Collection provides a showcase of the cutting-edge research that encompasses these objectives.

Hallucinations and delusions are two cornerstones of psychosis, a collection of symptoms that can occur across a range of psychiatric disorders and stems from a reduced ability to disentangle reality from fantasy 1 , 2 . Although it may seem that a Special Collection called Hallucinations and Delusions would mainly report studies of psychosis, these experiences are surprisingly common throughout the general population. So although psychosis is defined by hallucinatory and delusional experiences, the presence of hallucinations and delusions are not indicative of a psychotic disorder. Together, the studies in this Special Collection paint a picture of the wide range of hallucinatory and delusional experiences across diverse populations, as well as comparative perspectives between clinical and non-clinical samples. In this editorial, I would like to make three related points that are exemplified in the articles published here:

Hallucinations and delusions are part of a normal distribution of human diversity; their mere presence does not indicate psychosis or psychiatric illness.

The ubiquity of hallucinatory and delusional experiences across clinical and non-clinical populations suggests common cognitive and neural mechanisms.

Despite the aforementioned commonalities, it is important to understand the difference between psychiatric symptoms and healthy experience.

A normal distribution of human diversity

Hallucinations and delusions are pat of a normal distribution of human diversity; their mere presence does not indicate psychosis or psychiatric illness.  Experimentally-induced hallucinations are safe, controlled, and have no negative consequences 3 ; confabulating memories sometimes is perfectly normal 4 ; and despite the insistence of some individuals, simply having conspiracy beliefs is not a sign of pathology 5 .

The study by Shenyan et al. 3 demonstrates that hallucinations can be reliably induced in non-clinical samples using experimental techniques called Ganzfeld 6 and Ganzflicker 7 , 8 . Prolonged, unstructured (Ganzfeld) or repetitive (Ganzflicker) stimulation to the visual system can elicit both simple and complex hallucinatory experiences, such as geometric patterns, illusory colors, or even meaningful real-world objects and scenes. The authors, for the first time, quantified the onset and frequency of Ganzfeld- and Ganzflicker-induced hallucinations, and included participant drawings to reveal diverse, individualized visual experiences.

In another study highlighting psychosis characteristics in the general population, Stephan-Otto et al. 4 found that hallucination proneness is associated with false memories of novel words during a word recall task. The researchers asked participants to complete a series of questionnaires, memorize lists of high- and low-frequency words, then tested their recall while collecting fMRI data. Behaviorally, hallucination scores correlated with response bias for both high- and low-frequency words. Interestingly, neuroimaging data revealed a significant association between specifically verbal hallucination proneness and activation of brain areas related to language during false recognition of novel words. This points to individual differences in susceptibility to confabulated inner speech in the general population.

Finally, conspiracy beliefs are generally related to psychopathological characteristics (e.g., paranoia, anxiety) and erratic behavior such as volatility in decision-making 9 ; however, in a paper published in this Collection, Suthaharan and Corlett 5 found that these negative effects are reduced in individuals who have a strong social network around their beliefs. This interestingly suggests that social and environmental factors contribute to the (positive or negative) impact of conspiracy beliefs on mental health and behavior. In contrast to the stereotype of a ‘crazy conspiracy theorist’, the authors conclude that conspiracy beliefs are not inherently pathological.

Common cognitive and neural mechanisms

The ubiquity of hallucinations and delusions across clinical and non-clinical populations suggests common cognitive and neural mechanisms.  We are developing techniques to experimentally induce hallucinations, which could potentially elucidate how they can develop into psychotic experiences 3 , 7 , 8 . In the study by Shenyan et al. 3 , the authors purport that a (cortically hierarchical) low-level hallucinatory mechanism may be responsible for simple hallucinations, whereas top-down processes (such as mental imagery or beliefs) may contribute to complex hallucinations. These same mechanisms are proposed to be involved in different kinds of hallucinatory experiences in clinical populations, as well 10 . Further, Stephan-Otto and colleagues 4 propose common mechanisms for reality monitoring of inner speech in both clinical and non-clinical populations. These techniques could therefore be used to better understand the cognitive and neural mechanisms that contribute to hallucinatory experience across diverse populations.

Comparative studies in this Collection highlight similarities in the content and organization of psychotic symptoms across different populations. For example, Fleming and colleagues 11 modeled a data-driven profile of symptoms experienced in first-episode psychosis (FEP) and persistent psychotic illness. Individuals with FEP are those that have only recently begun to experience symptoms of psychosis, which may or may not progress into persistent psychotic illness, specifically schizophrenia or schizoaffective disorder. Interestingly, the multidimensional profile of symptoms is quite similar between the two groups: auditory hallucinations tended to cluster with delusions that the patient was under another agent’s control; religious and grandiose delusions both clustered with thought disorder symptoms; and other reality and perceptual distortions formed a separate cluster of symptoms to these. Therefore, the content and organization of psychotic experiences in this clinical population is not indicative or predictive of illness severity or longevity, and supports the proposition that these experiences are part of the normal distribution of diverse thinking.

Sheffield and colleagues 12 investigated the relationship between cognitive biases, delusional thinking, and game-based decision-making between individuals with schizophrenia spectrum conditions and healthy controls. Interestingly, across multiple self-report measures and various games, both groups showed similar relationships between beliefs and behavior: specifically, volatile decision-making (e.g., changing strategies multiple times) was positively correlated with paranoid thinking, and hasty decision-making was related to having unusual beliefs (e.g., mind reading, alien abduction), in both individuals with schizophrenia spectrum conditions and healthy controls. These findings further support the idea that the same cognitive mechanisms contribute to individual differences in both clinical and non-clinical populations.

Psychiatric versus healthy experience. Despite potential common mechanisms for these experiences, and their prevalence across clinical and non-clinical populations, it is important to distinguish psychiatric symptoms from healthy experience . Perhaps the single most important difference between clinically-relevant and clinically-irrelevant experience is its impact on quality of life.

Environmental and social factors importantly contribute to this impact: conspiracy beliefs under a ‘sacred canopy’ (i.e., a social support buffer) can benefit an individual to a similar extent as being part of a religious or social organization; but these beliefs can exacerbate psychopathological characteristics if social support is lacking 5 . As another example, individuals who purposefully seek hallucinatory experiences are able to choose the environment, onset, and duration of the event; and can attribute the experience to an explicable source, such as Ganzflicker 3 . Controlling an unusual experience in this way can neutralize its potential negative effects. On the other hand, individuals who experience clinical hallucinations have little to no control over their experiences and cannot easily attribute them to a known source. The onset, duration, and environment of the experience are unpredictable, and can be embarrassing (e.g., at a social event) or even dangerous (e.g., while driving). All of these factors can lead to an extreme negative reaction to hallucinations and a debilitating impact on quality of life. So although much of the behavioral and cognitive bases of these divergent experiences are ubiquitous, this does not mean that these experiences should be treated the same for both clinical and non-clinical populations. The critical questions, then, concern how and why such experiences might develop into psychiatric illness.

In summary, hallucinations and delusions are part of a healthy range of diverse experiences found throughout the general population, but they can develop into more severe symptoms of psychiatric illness. It is important to investigate both common mechanisms (that contribute to our understanding of their cognitive and neural bases) and distinguishing factors (that separate clinically-relevant from clinically irrelevant symptoms) to comprehensively elucidate these often misunderstood experiences. This Special Collection provides a showcase of the cutting-edge research that encompasses these objectives.

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Retirement planning – a systematic review of literature and future research directions

• Published: 28 October 2023

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• Kavita Karan Ingale   ORCID: orcid.org/0000-0003-3570-4211 1 &
• Ratna Achuta Paluri 2  

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Rising life expectancy and an aging population across nations are leading to an increased need for long-term financial savings and a focus on the financial well-being of retired individuals amidst changing policy framework. This study is a systematic review based on a scientific way of producing high-quality evidence based on 191 articles from the Scopus and Web of Science databases. It adopts the Theory, Context, Characteristics, and Method (TCCM) framework to analyze literature. This study provides collective insights into financial decision-making for retirement savings and identifies constructs for operationalizing and measuring financial behavior for retirement planning. Further, it indicates the need for an interdisciplinary approach. Though cognitive areas were studied extensively, the non-cognitive areas received little attention. Qualitative research design is gaining prominence in research over other methods, with the sparse application of mixed methods design. The study’s TCCM framework explicates several areas for further research. Furthermore, it guides the practice and policy by integrating empirical evidence and concomitant findings. Coherent synthesis of the extant literature reconciles the highly fragmented field of retirement planning. No research reports prospective areas for further analysis based on the TCCM framework on retirement planning, which highlights the uniqueness of the study.

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

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

• Hardcopy Version at National Academies Press

CONCLUSIONS AND DIRECTIONS FOR FURTHER RESEARCH AND POLICY

• Limitations of This Research

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

TABLE 18 Micro-System Examples of Investment in Improvement

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

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

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

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

• Directions for Further Research

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

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

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

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

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

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

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

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

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

• IOM Quality of Care Study

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

• Cite this Page Donaldson MS, Mohr JJ; Institute of Medicine (US). Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis. Washington (DC): National Academies Press (US); 2001. CONCLUSIONS AND DIRECTIONS FOR FURTHER RESEARCH AND POLICY.
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Research aims to improve data quality in manufacturing, seeking 'golden data'

In an era when artificial intelligence and machine learning are key to advancing technology, researchers want to ensure they’re fueled for success with high-quality data.

• Jordi Shelton

11 Apr 2024

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If artificial intelligence (AI) was a car, data would be the fuel. But what if there was no way to ensure that fuel wasn’t full of waste? How would this fuel be filtered, and how would that information reach consumers?

Ran Jin, associate professor in the Grado Department of Industrial and Systems Engineering , is determined to fuel AI models in the Manufacturing Industrial Internet with high-quality data. His research, funded by a grant from the National Science Foundation, aims to tackle three primary objectives: 

• What is good-quality data? How is it defined?
• What is the root cause of bad data? Can it be prevented?
• Is there a “golden” data set that can be shared with other manufacturing processes and centers?

“The performance of machine learning and AI models pretty much depends on data quality. At this moment, there is no systematic way to define and evaluate data quality," Jin said. “We want to know: If we can define data quality and determine what causes poor data quality, can we improve it? If we can improve it, how can we share these data sets to further improve AI development? Then we plan to generate a golden data set which can be used across different platforms or systems.”

Food for thought

Jin drew an analogy to cooking a turkey to explain the project's approach to improving data quality. Just as defining quantitative measures for a delicious turkey can lead to a better understanding of the potential reasons behind common cooking issues, this project aims to quantitatively define and evaluate data quality.

“Turkey is a pretty normal, ordinary dish, but it can be tricky to cook,” Jin said. “If we evaluate how delicious a good turkey is, we must define a bunch of measures – whether it’s juicy, crispy, salty, or has certain flavors like smokiness. That’s essentially the first step.”

When translating these factors to data, this might include how fresh, relevant, or complete the data is.

Once the markers for an ideal turkey are defined, the second step is to understand what didn’t work. “Why did the turkey dry out? Why is the flavor off? It might be because we left it in the oven too long, or the seasonings weren’t measured correctly: These are all potential root causes behind poor turkey turnout.” Jin said.

Finally, Jin said the goal of his research is like sharing the perfect turkey recipe. Sharing this widely ensures a crispy, juicy, flavorful turkey every time – without unnecessary steps, ingredients, or waste. In a data quality setting, this translates to simplified, relevant, and useful data that can consistently inform machines and manufacturing systems to make good decisions.

“We want to determine how we can improve the turkey itself and optimize a recipe that works effectively and produces a delicious, perfectly cooked turkey that can be shared on the Internet with other cooks,” said Jin. “With respect to data, we want to create a data set that can be effectively shared for AI development purposes, which has several merits such as representativeness, privacy protection, and effectiveness for AI model improvement.”

Mapping outcomes

Despite the advances in manufacturing AI methodologies over the past decade, including significant strides in deep learning and neural networks, Jin points out that data generation and quality have become the major roadblocks in modeling and decision-making performance.

"More and more, people are realizing the bottleneck for overall modeling and decision-making performance is on the data generation and quality side," Jin said. “A typical phrase we use is ‘put garbage in and get garbage out.’”

As AI becomes more advanced and widely used, ensuring access to high-quality data is crucial. Just as bad data quality can become a major barrier for improving decision-making in AI models, good data quality can pave the way for future advancements.

“In terms of broader impacts, data quality is the foundation for all types of research,” Jin said. “While we’re focusing specifically on electronics manufacturing in our research, this can be applied broadly to many different industries, like aerospace or biomanufacturing.”

Breaking down the Manufacturing Industrial Internet

The Manufacturing Industrial Internet, as Jin describes, is key to collecting data from various manufacturing processes for adaptive computation for manufacturing improvements. This is different and more sophisticated than the internet that connects our phones and laptops to the web. The Manufacturing Industrial Internet connects everything in a manufacturing environment and is driven by AI instead of humans in decision making. Additionally, it enables machines to communicate with other machines in a factory and a supply chain. The interconnected system can optimize quality, reduce cost and waste, and increase productivity and flexibility of product designs.

“The key functionality of the Manufacturing Industrial Internet is to collect data from different manufacturing processes and systems and use that information to provide real-time — or close to real-time — decision making and control,” Jin said. 

While the internet system we use regularly is not the same as the Manufacturing Industrial Internet, there are similarities between the two systems.

“Just like humans use an online social network to communicate with each other for better collaboration, the Manufacturing Industrial Internet acts like a social network of AI agents that communicate and collaborate with each other autonomously for different objectives,” Jin said.  “Another example includes the Internet of Vehicles system connects vehicles on the road and transportation infrastructures or the smart grid — the system in which generators and distributors connect and talk with each other to make decisions and enable control collectively for better overall performance.”

The future of AI success: data quality

The Manufacturing Industrial Internet and AI have large quantities of data at their disposal. Jin hopes his research will better enable other data scientists to understand what is and is not useful.

“We have massive, passively connected data with very low cost from Manufacturing Industrial Internet. The question is: should we use all data or should we just use a smaller but more meaningful data subset?” Jin said. “The latter has several benefits, including lower computational workload and storage space, and it ensures better AI performance.”

By enhancing data quality, the project not only aims to improve the efficiency and effectiveness of manufacturing processes but also sets the stage for broader applications across various industries, underscoring the profound impact of high-quality data on the future of manufacturing and beyond.

"I strongly believe this project will set the foundation to evaluate data. We cannot emphasize enough how important data valuation and quality is,” Jin said. “Data is fuel for AI, and that is incredibly important for our future economy."

Chelsea Seeber

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