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Types of Literature Review — A Guide for Researchers
Table of Contents
Researchers often face challenges when choosing the appropriate type of literature review for their study. Regardless of the type of research design and the topic of a research problem , they encounter numerous queries, including:
What is the right type of literature review my study demands?
- How do we gather the data?
- How to conduct one?
- How reliable are the review findings?
- How do we employ them in our research? And the list goes on.
If you’re also dealing with such a hefty questionnaire, this article is of help. Read through this piece of guide to get an exhaustive understanding of the different types of literature reviews and their step-by-step methodologies along with a dash of pros and cons discussed.
Heading from scratch!
What is a Literature Review?
A literature review provides a comprehensive overview of existing knowledge on a particular topic, which is quintessential to any research project. Researchers employ various literature reviews based on their research goals and methodologies. The review process involves assembling, critically evaluating, and synthesizing existing scientific publications relevant to the research question at hand. It serves multiple purposes, including identifying gaps in existing literature, providing theoretical background, and supporting the rationale for a research study.
What is the importance of a Literature review in research?
Literature review in research serves several key purposes, including:
- Background of the study: Provides proper context for the research. It helps researchers understand the historical development, theoretical perspectives, and key debates related to their research topic.
- Identification of research gaps: By reviewing existing literature, researchers can identify gaps or inconsistencies in knowledge, paving the way for new research questions and hypotheses relevant to their study.
- Theoretical framework development: Facilitates the development of theoretical frameworks by cultivating diverse perspectives and empirical findings. It helps researchers refine their conceptualizations and theoretical models.
- Methodological guidance: Offers methodological guidance by highlighting the documented research methods and techniques used in previous studies. It assists researchers in selecting appropriate research designs, data collection methods, and analytical tools.
- Quality assurance and upholding academic integrity: Conducting a thorough literature review demonstrates the rigor and scholarly integrity of the research. It ensures that researchers are aware of relevant studies and can accurately attribute ideas and findings to their original sources.
Types of Literature Review
Literature review plays a crucial role in guiding the research process , from providing the background of the study to research dissemination and contributing to the synthesis of the latest theoretical literature review findings in academia.
However, not all types of literature reviews are the same; they vary in terms of methodology, approach, and purpose. Let's have a look at the various types of literature reviews to gain a deeper understanding of their applications.
1. Narrative Literature Review
A narrative literature review, also known as a traditional literature review, involves analyzing and summarizing existing literature without adhering to a structured methodology. It typically provides a descriptive overview of key concepts, theories, and relevant findings of the research topic.
Unlike other types of literature reviews, narrative reviews reinforce a more traditional approach, emphasizing the interpretation and discussion of the research findings rather than strict adherence to methodological review criteria. It helps researchers explore diverse perspectives and insights based on the research topic and acts as preliminary work for further investigation.
Steps to Conduct a Narrative Literature Review
![what are the kinds of literature review Steps-to-conduct-a-Narrative-Literature-Review](https://typeset.io/resources/content/images/2024/03/Steps-of-writing-a-narrative-review.png)
Source:- https://www.researchgate.net/figure/Steps-of-writing-a-narrative-review_fig1_354466408
Define the research question or topic:
The first step in conducting a narrative literature review is to clearly define the research question or topic of interest. Defining the scope and purpose of the review includes — What specific aspect of the topic do you want to explore? What are the main objectives of the research? Refine your research question based on the specific area you want to explore.
Conduct a thorough literature search
Once the research question is defined, you can conduct a comprehensive literature search. Explore and use relevant databases and search engines like SciSpace Discover to identify credible and pertinent, scholarly articles and publications.
Select relevant studies
Before choosing the right set of studies, it’s vital to determine inclusion (studies that should possess the required factors) and exclusion criteria for the literature and then carefully select papers. For example — Which studies or sources will be included based on relevance, quality, and publication date?
*Important (applies to all the reviews): Inclusion criteria are the factors a study must include (For example: Include only peer-reviewed articles published between 2022-2023, etc.). Exclusion criteria are the factors that wouldn’t be required for your search strategy (Example: exclude irrelevant papers, preprints, written in non-English, etc.)
Critically analyze the literature
Once the relevant studies are shortlisted, evaluate the methodology, findings, and limitations of each source and jot down key themes, patterns, and contradictions. You can use efficient AI tools to conduct a thorough literature review and analyze all the required information.
Synthesize and integrate the findings
Now, you can weave together the reviewed studies, underscoring significant findings such that new frameworks, contrasting viewpoints, and identifying knowledge gaps.
Discussion and conclusion
This is an important step before crafting a narrative review — summarize the main findings of the review and discuss their implications in the relevant field. For example — What are the practical implications for practitioners? What are the directions for future research for them?
Write a cohesive narrative review
Organize the review into coherent sections and structure your review logically, guiding the reader through the research landscape and offering valuable insights. Use clear and concise language to convey key points effectively.
Structure of Narrative Literature Review
A well-structured, narrative analysis or literature review typically includes the following components:
- Introduction: Provides an overview of the topic, objectives of the study, and rationale for the review.
- Background: Highlights relevant background information and establish the context for the review.
- Main Body: Indexes the literature into thematic sections or categories, discussing key findings, methodologies, and theoretical frameworks.
- Discussion: Analyze and synthesize the findings of the reviewed studies, stressing similarities, differences, and any gaps in the literature.
- Conclusion: Summarizes the main findings of the review, identifies implications for future research, and offers concluding remarks.
Pros and Cons of Narrative Literature Review
- Flexibility in methodology and doesn’t necessarily rely on structured methodologies
- Follows traditional approach and provides valuable and contextualized insights
- Suitable for exploring complex or interdisciplinary topics. For example — Climate change and human health, Cybersecurity and privacy in the digital age, and more
- Subjectivity in data selection and interpretation
- Potential for bias in the review process
- Lack of rigor compared to systematic reviews
Example of Well-Executed Narrative Literature Reviews
Paper title: Examining Moral Injury in Clinical Practice: A Narrative Literature Review
![what are the kinds of literature review Narrative-Literature-Reviews](https://typeset.io/resources/content/images/2024/02/Narrative-Literature-Review.png)
Source: SciSpace
You can also chat with the papers using SciSpace ChatPDF to get a thorough understanding of the research papers.
While narrative reviews offer flexibility, academic integrity remains paramount. So, ensure proper citation of all sources and maintain a transparent and factual approach throughout your critical narrative review, itself.
2. Systematic Review
A systematic literature review is one of the comprehensive types of literature review that follows a structured approach to assembling, analyzing, and synthesizing existing research relevant to a particular topic or question. It involves clearly defined criteria for exploring and choosing studies, as well as rigorous methods for evaluating the quality of relevant studies.
It plays a prominent role in evidence-based practice and decision-making across various domains, including healthcare, social sciences, education, health sciences, and more. By systematically investigating available literature, researchers can identify gaps in knowledge, evaluate the strength of evidence, and report future research directions.
Steps to Conduct Systematic Reviews
![what are the kinds of literature review Steps-to-Conduct-Systematic-Reviews](https://typeset.io/resources/content/images/2024/03/Steps-of-Systematic-Literature-Review.png)
Source:- https://www.researchgate.net/figure/Steps-of-Systematic-Literature-Review_fig1_321422320
Here are the key steps involved in conducting a systematic literature review
Formulate a clear and focused research question
Clearly define the research question or objective of the review. It helps to centralize the literature search strategy and determine inclusion criteria for relevant studies.
Develop a thorough literature search strategy
Design a comprehensive search strategy to identify relevant studies. It involves scrutinizing scientific databases and all relevant articles in journals. Plus, seek suggestions from domain experts and review reference lists of relevant review articles.
Screening and selecting studies
Employ predefined inclusion and exclusion criteria to systematically screen the identified studies. This screening process also typically involves multiple reviewers independently assessing the eligibility of each study.
Data extraction
Extract key information from selected studies using standardized forms or protocols. It includes study characteristics, methods, results, and conclusions.
Critical appraisal
Evaluate the methodological quality and potential biases of included studies. Various tools (BMC medical research methodology) and criteria can be implemented for critical evaluation depending on the study design and research quetions .
Data synthesis
Analyze and synthesize review findings from individual studies to draw encompassing conclusions or identify overarching patterns and explore heterogeneity among studies.
Interpretation and conclusion
Interpret the findings about the research question, considering the strengths and limitations of the research evidence. Draw conclusions and implications for further research.
The final step — Report writing
Craft a detailed report of the systematic literature review adhering to the established guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). This ensures transparency and reproducibility of the review process.
By following these steps, a systematic literature review aims to provide a comprehensive and unbiased summary of existing evidence, help make informed decisions, and advance knowledge in the respective domain or field.
Structure of a systematic literature review
A well-structured systematic literature review typically consists of the following sections:
- Introduction: Provides background information on the research topic, outlines the review objectives, and enunciates the scope of the study.
- Methodology: Describes the literature search strategy, selection criteria, data extraction process, and other methods used for data synthesis, extraction, or other data analysis..
- Results: Presents the review findings, including a summary of the incorporated studies and their key findings.
- Discussion: Interprets the findings in light of the review objectives, discusses their implications, and identifies limitations or promising areas for future research.
- Conclusion: Summarizes the main review findings and provides suggestions based on the evidence presented in depth meta analysis.
*Important (applies to all the reviews): Remember, the specific structure of your literature review may vary depending on your topic, research question, and intended audience. However, adhering to a clear and logical hierarchy ensures your review effectively analyses and synthesizes knowledge and contributes valuable insights for readers.
Pros and Cons of Systematic Literature Review
- Adopts rigorous and transparent methodology
- Minimizes bias and enhances the reliability of the study
- Provides evidence-based insights
- Time and resource-intensive
- High dependency on the quality of available literature (literature research strategy should be accurate)
- Potential for publication bias
Example of Well-Executed Systematic Literature Review
Paper title: Systematic Reviews: Understanding the Best Evidence For Clinical Decision-making in Health Care: Pros and Cons.
![what are the kinds of literature review Systematic-Literature-Review](https://typeset.io/resources/content/images/2024/02/Systematic-Literature-Review.png)
Read this detailed article on how to use AI tools to conduct a systematic review for your research!
3. Scoping Literature Review
A scoping literature review is a methodological review type of literature review that adopts an iterative approach to systematically map the existing literature on a particular topic or research area. It involves identifying, selecting, and synthesizing relevant papers to provide an overview of the size and scope of available evidence. Scoping reviews are broader in scope and include a diverse range of study designs and methodologies especially focused on health services research.
The main purpose of a scoping literature review is to examine the extent, range, and nature of existing studies on a topic, thereby identifying gaps in research, inconsistencies, and areas for further investigation. Additionally, scoping reviews can help researchers identify suitable methodologies and formulate clinical recommendations. They also act as the frameworks for future systematic reviews or primary research studies.
Scoping reviews are primarily focused on —
- Emerging or evolving topics — where the research landscape is still growing or budding. Example — Whole Systems Approaches to Diet and Healthy Weight: A Scoping Review of Reviews .
- Broad and complex topics : With a vast amount of existing literature.
- Scenarios where a systematic review is not feasible: Due to limited resources or time constraints.
Steps to Conduct a Scoping Literature Review
While Scoping reviews are not as rigorous as systematic reviews, however, they still follow a structured approach. Here are the steps:
Identify the research question: Define the broad topic you want to explore.
Identify Relevant Studies: Conduct a comprehensive search of relevant literature using appropriate databases, keywords, and search strategies.
Select studies to be included in the review: Based on the inclusion and exclusion criteria, determine the appropriate studies to be included in the review.
Data extraction and charting : Extract relevant information from selected studies, such as year, author, main results, study characteristics, key findings, and methodological approaches. However, it varies depending on the research question.
Collate, summarize, and report the results: Analyze and summarize the extracted data to identify key themes and trends. Then, present the findings of the scoping review in a clear and structured manner, following established guidelines and frameworks .
Structure of a Scoping Literature Review
A scoping literature review typically follows a structured format similar to a systematic review. It includes the following sections:
- Introduction: Introduce the research topic and objectives of the review, providing the historical context, and rationale for the study.
- Methods : Describe the methods used to conduct the review, including search strategies, study selection criteria, and data extraction procedures.
- Results: Present the findings of the review, including key themes, concepts, and patterns identified in the literature review.
- Discussion: Examine the implications of the findings, including strengths, limitations, and areas for further examination.
- Conclusion: Recapitulate the main findings of the review and their implications for future research, policy, or practice.
Pros and Cons of Scoping Literature Review
- Provides a comprehensive overview of existing literature
- Helps to identify gaps and areas for further research
- Suitable for exploring broad or complex research questions
- Doesn’t provide the depth of analysis offered by systematic reviews
- Subject to researcher bias in study selection and data extraction
- Requires careful consideration of literature search strategies and inclusion criteria to ensure comprehensiveness and validity.
In short, a scoping review helps map the literature on developing or emerging topics and identifying gaps. It might be considered as a step before conducting another type of review, such as a systematic review. Basically, acts as a precursor for other literature reviews.
Example of a Well-Executed Scoping Literature Review
Paper title: Health Chatbots in Africa Literature: A Scoping Review
![what are the kinds of literature review Scoping-Literature-Review](https://typeset.io/resources/content/images/2024/02/Scoping-Literature-Review.png)
Check out the key differences between Systematic and Scoping reviews — Evaluating literature review: systematic vs. scoping reviews
4. Integrative Literature Review
Integrative Literature Review (ILR) is a type of literature review that proposes a distinctive way to analyze and synthesize existing literature on a specific topic, providing a thorough understanding of research and identifying potential gaps for future research.
Unlike a systematic review, which emphasizes quantitative studies and follows strict inclusion criteria, an ILR embraces a more pliable approach. It works beyond simply summarizing findings — it critically analyzes, integrates, and interprets research from various methodologies (qualitative, quantitative, mixed methods) to provide a deeper understanding of the research landscape. ILRs provide a holistic and systematic overview of existing research, integrating findings from various methodologies. ILRs are ideal for exploring intricate research issues, examining manifold perspectives, and developing new research questions.
Steps to Conduct an Integrative Literature Review
- Identify the research question: Clearly define the research question or topic of interest as formulating a clear and focused research question is critical to leading the entire review process.
- Literature search strategy: Employ systematic search techniques to locate relevant literature across various databases and sources.
- Evaluate the quality of the included studies : Critically assess the methodology, rigor, and validity of each study by applying inclusion and exclusion criteria to filter and select studies aligned with the research objectives.
- Data Extraction: Extract relevant data from selected studies using a structured approach.
- Synthesize the findings : Thoroughly analyze the selected literature, identify key themes, and synthesize findings to derive noteworthy insights.
- Critical appraisal: Critically evaluate the quality and validity of qualitative research and included studies by using BMC medical research methodology.
- Interpret and present your findings: Discuss the purpose and implications of your analysis, spotlighting key insights and limitations. Organize and present the findings coherently and systematically.
Structure of an Integrative Literature Review
- Introduction : Provide an overview of the research topic and the purpose of the integrative review.
- Methods: Describe the opted literature search strategy, selection criteria, and data extraction process.
- Results: Present the synthesized findings, including key themes, patterns, and contradictions.
- Discussion: Interpret the findings about the research question, emphasizing implications for theory, practice, and prospective research.
- Conclusion: Summarize the main findings, limitations, and contributions of the integrative review.
Pros and Cons of Integrative Literature Review
- Informs evidence-based practice and policy to the relevant stakeholders of the research.
- Contributes to theory development and methodological advancement, especially in the healthcare arena.
- Integrates diverse perspectives and findings
- Time-consuming process due to the extensive literature search and synthesis
- Requires advanced analytical and critical thinking skills
- Potential for bias in study selection and interpretation
- The quality of included studies may vary, affecting the validity of the review
Example of Integrative Literature Reviews
Paper Title: An Integrative Literature Review: The Dual Impact of Technological Tools on Health and Technostress Among Older Workers
![what are the kinds of literature review Integrative-Literature-Review](https://typeset.io/resources/content/images/2024/02/Integrative-Literature-Review.png)
5. Rapid Literature Review
A Rapid Literature Review (RLR) is the fastest type of literature review which makes use of a streamlined approach for synthesizing literature summaries, offering a quicker and more focused alternative to traditional systematic reviews. Despite employing identical research methods, it often simplifies or omits specific steps to expedite the process. It allows researchers to gain valuable insights into current research trends and identify key findings within a shorter timeframe, often ranging from a few days to a few weeks — unlike traditional literature reviews, which may take months or even years to complete.
When to Consider a Rapid Literature Review?
- When time impediments demand a swift summary of existing research
- For emerging topics where the latest literature requires quick evaluation
- To report pilot studies or preliminary research before embarking on a comprehensive systematic review
Steps to Conduct a Rapid Literature Review
- Define the research question or topic of interest. A well-defined question guides the search process and helps researchers focus on relevant studies.
- Determine key databases and sources of relevant literature to ensure comprehensive coverage.
- Develop literature search strategies using appropriate keywords and filters to fetch a pool of potential scientific articles.
- Screen search results based on predefined inclusion and exclusion criteria.
- Extract and summarize relevant information from the above-preferred studies.
- Synthesize findings to identify key themes, patterns, or gaps in the literature.
- Prepare a concise report or a summary of the RLR findings.
Structure of a Rapid Literature Review
An effective structure of an RLR typically includes the following sections:
- Introduction: Briefly introduce the research topic and objectives of the RLR.
- Methodology: Describe the search strategy, inclusion and exclusion criteria, and data extraction process.
- Results: Present a summary of the findings, including key themes or patterns identified.
- Discussion: Interpret the findings, discuss implications, and highlight any limitations or areas for further research
- Conclusion: Summarize the key findings and their implications for practice or future research
Pros and Cons of Rapid Literature Review
- RLRs can be completed quickly, authorizing timely decision-making
- RLRs are a cost-effective approach since they require fewer resources compared to traditional literature reviews
- Offers great accessibility as RLRs provide prompt access to synthesized evidence for stakeholders
- RLRs are flexible as they can be easily adapted for various research contexts and objectives
- RLR reports are limited and restricted, not as in-depth as systematic reviews, and do not provide comprehensive coverage of the literature compared to traditional reviews.
- Susceptible to bias because of the expedited nature of RLRs. It would increase the chance of overlooking relevant studies or biases in the selection process.
- Due to time constraints, RLR findings might not be robust enough as compared to systematic reviews.
Example of a Well-Executed Rapid Literature Review
Paper Title: What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
![what are the kinds of literature review Rapid-Literature-Review](https://typeset.io/resources/content/images/2024/02/Rapid-Literature-Review.png)
A Summary of Literature Review Types
Literature Review Type | Narrative | Systematic | Integrative | Rapid | Scoping |
Approach | The traditional approach lacks a structured methodology | Systematic search, including structured methodology | Combines diverse methodologies for a comprehensive understanding | Quick review within time constraints | Preliminary study of existing literature |
How Exhaustive is the process? | May or may not be comprehensive | Exhaustive and comprehensive search | A comprehensive search for integration | Time-limited search | Determined by time or scope constraints |
Data Synthesis | Narrative | Narrative with tabular accompaniment | Integration of various sources or methodologies | Narrative and tabular | Narrative and tabular |
Purpose | Provides description of meta analysis and conceptualization of the review | Comprehensive evidence synthesis | Holistic understanding | Quick policy or practice guidelines review | Preliminary literature review |
Key characteristics | Storytelling, chronological presentation | Rigorous, traditional and systematic techniques approach | Diverse source or method integration | Time-constrained, systematic approach | Identifies literature size and scope |
Example Use Case | Historical exploration | Effectiveness evaluation | Quantitative, qualitative, and mixed combination | Policy summary | Research literature overview |
Tools and Resources for Conducting Different Types of Literature Reviews
Online scientific databases.
Platforms such as SciSpace , PubMed , Scopus , Elsevier , and Web of Science provide access to a vast array of scholarly literature, facilitating the search and data retrieval process.
Reference management software
Tools like SciSpace Citation Generator , EndNote, Zotero , and Mendeley assist researchers in organizing, annotating, and citing relevant literature, streamlining the review process altogether.
Automate Literature Review with AI tools
Automate the literature review process by using tools like SciSpace literature review which helps you compare and contrast multiple papers all on one screen in an easy-to-read matrix format. You can effortlessly analyze and interpret the review findings tailored to your study. It also supports the review in 75+ languages, making it more manageable even for non-English speakers.
![what are the kinds of literature review what are the kinds of literature review](https://typeset.io/resources/content/images/2024/02/SciSpace-Literature-Review.png)
Goes without saying — literature review plays a pivotal role in academic research to identify the current trends and provide insights to pave the way for future research endeavors. Different types of literature review has their own strengths and limitations, making them suitable for different research designs and contexts. Whether conducting a narrative review, systematic review, scoping review, integrative review, or rapid literature review, researchers must cautiously consider the objectives, resources, and the nature of the research topic.
If you’re currently working on a literature review and still adopting a manual and traditional approach, switch to the automated AI literature review workspace and transform your traditional literature review into a rapid one by extracting all the latest and relevant data for your research!
There you go!
![what are the kinds of literature review what are the kinds of literature review](https://typeset.io/icon.png)
Frequently Asked Questions
Narrative reviews give a general overview of a topic based on the author's knowledge. They may lack clear criteria and can be biased. On the other hand, systematic reviews aim to answer specific research questions by following strict methods. They're thorough but time-consuming.
A systematic review collects and analyzes existing research to provide an overview of a topic, while a meta-analysis statistically combines data from multiple studies to draw conclusions about the overall effect of an intervention or relationship between variables.
A systematic review thoroughly analyzes existing research on a specific topic using strict methods. In contrast, a scoping review offers a broader overview of the literature without evaluating individual studies in depth.
A systematic review thoroughly examines existing research using a rigorous process, while a rapid review provides a quicker summary of evidence, often by simplifying some of the systematic review steps to meet shorter timelines.
A systematic review carefully examines many studies on a single topic using specific guidelines. Conversely, an integrative review blends various types of research to provide a more comprehensive understanding of the topic.
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![what are the kinds of literature review Charles Sturt University](https://library.csu.edu.au/__data/assets/image/0003/1569144/CSU_Logo-Mark_RGB.jpg)
Literature Review: Types of literature reviews
- Traditional or narrative literature reviews
- Scoping Reviews
- Systematic literature reviews
- Annotated bibliography
- Keeping up to date with literature
- Finding a thesis
- Evaluating sources and critical appraisal of literature
- Managing and analysing your literature
- Further reading and resources
Types of literature reviews
![what are the kinds of literature review](https://s3.amazonaws.com/libapps/accounts/26274/images/iStock_000028030602_Illustration.jpg)
The type of literature review you write will depend on your discipline and whether you are a researcher writing your PhD, publishing a study in a journal or completing an assessment task in your undergraduate study.
A literature review for a subject in an undergraduate degree will not be as comprehensive as the literature review required for a PhD thesis.
An undergraduate literature review may be in the form of an annotated bibliography or a narrative review of a small selection of literature, for example ten relevant articles. If you are asked to write a literature review, and you are an undergraduate student, be guided by your subject coordinator or lecturer.
The common types of literature reviews will be explained in the pages of this section.
- Narrative or traditional literature reviews
- Critically Appraised Topic (CAT)
- Scoping reviews
- Annotated bibliographies
These are not the only types of reviews of literature that can be conducted. Often the term "review" and "literature" can be confusing and used in the wrong context. Grant and Booth (2009) attempt to clear up this confusion by discussing 14 review types and the associated methodology, and advantages and disadvantages associated with each review.
Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies . Health Information & Libraries Journal, 26 , 91–108. doi:10.1111/j.1471-1842.2009.00848.x
What's the difference between reviews?
Researchers, academics, and librarians all use various terms to describe different types of literature reviews, and there is often inconsistency in the ways the types are discussed. Here are a couple of simple explanations.
- The image below describes common review types in terms of speed, detail, risk of bias, and comprehensiveness:
![what are the kinds of literature review Description of the differences between review types in image form](https://libapps-au.s3-ap-southeast-2.amazonaws.com/accounts/74130/images/fvets-07-00314-g0001.jpg)
"Schematic of the main differences between the types of literature review" by Brennan, M. L., Arlt, S. P., Belshaw, Z., Buckley, L., Corah, L., Doit, H., Fajt, V. R., Grindlay, D., Moberly, H. K., Morrow, L. D., Stavisky, J., & White, C. (2020). Critically Appraised Topics (CATs) in veterinary medicine: Applying evidence in clinical practice. Frontiers in Veterinary Science, 7 , 314. https://doi.org/10.3389/fvets.2020.00314 is licensed under CC BY 3.0
- The table below lists four of the most common types of review , as adapted from a widely used typology of fourteen types of reviews (Grant & Booth, 2009).
Identifies and reviews published literature on a topic, which may be broad. Typically employs a narrative approach to reporting the review findings. Can include a wide range of related subjects. | 1 - 4 weeks | 1 | |
Assesses what is known about an issue by using a systematic review method to search and appraise research and determine best practice. | 2 - 6 months | 2 | |
Assesses the potential scope of the research literature on a particular topic. Helps determine gaps in the research. (See the page in this guide on .) | 1 - 4 weeks | 1 - 2 | |
Seeks to systematically search for, appraise, and synthesise research evidence so as to aid decision-making and determine best practice. Can vary in approach, and is often specific to the type of study, which include studies of effectiveness, qualitative research, economic evaluation, prevalence, aetiology, or diagnostic test accuracy. | 8 months to 2 years | 2 or more |
Grant, M.J. & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26 (2), 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
See also the Library's Literature Review guide.
Critical Appraised Topic (CAT)
For information on conducting a Critically Appraised Topic or CAT
Callander, J., Anstey, A. V., Ingram, J. R., Limpens, J., Flohr, C., & Spuls, P. I. (2017). How to write a Critically Appraised Topic: evidence to underpin routine clinical practice. British Journal of Dermatology (1951), 177(4), 1007-1013. https://doi.org/10.1111/bjd.15873
Books on Literature Reviews
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- Locations and Hours
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Systematic Reviews
- Types of Literature Reviews
What Makes a Systematic Review Different from Other Types of Reviews?
- Planning Your Systematic Review
- Database Searching
- Creating the Search
- Search Filters and Hedges
- Grey Literature
- Managing and Appraising Results
- Further Resources
Reproduced from Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91–108. doi:10.1111/j.1471-1842.2009.00848.x
Aims to demonstrate writer has extensively researched literature and critically evaluated its quality. Goes beyond mere description to include degree of analysis and conceptual innovation. Typically results in hypothesis or mode | Seeks to identify most significant items in the field | No formal quality assessment. Attempts to evaluate according to contribution | Typically narrative, perhaps conceptual or chronological | Significant component: seeks to identify conceptual contribution to embody existing or derive new theory | |
Generic term: published materials that provide examination of recent or current literature. Can cover wide range of subjects at various levels of completeness and comprehensiveness. May include research findings | May or may not include comprehensive searching | May or may not include quality assessment | Typically narrative | Analysis may be chronological, conceptual, thematic, etc. | |
Mapping review/ systematic map | Map out and categorize existing literature from which to commission further reviews and/or primary research by identifying gaps in research literature | Completeness of searching determined by time/scope constraints | No formal quality assessment | May be graphical and tabular | Characterizes quantity and quality of literature, perhaps by study design and other key features. May identify need for primary or secondary research |
Technique that statistically combines the results of quantitative studies to provide a more precise effect of the results | Aims for exhaustive, comprehensive searching. May use funnel plot to assess completeness | Quality assessment may determine inclusion/ exclusion and/or sensitivity analyses | Graphical and tabular with narrative commentary | Numerical analysis of measures of effect assuming absence of heterogeneity | |
Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies | Requires either very sensitive search to retrieve all studies or separately conceived quantitative and qualitative strategies | Requires either a generic appraisal instrument or separate appraisal processes with corresponding checklists | Typically both components will be presented as narrative and in tables. May also employ graphical means of integrating quantitative and qualitative studies | Analysis may characterise both literatures and look for correlations between characteristics or use gap analysis to identify aspects absent in one literature but missing in the other | |
Generic term: summary of the [medical] literature that attempts to survey the literature and describe its characteristics | May or may not include comprehensive searching (depends whether systematic overview or not) | May or may not include quality assessment (depends whether systematic overview or not) | Synthesis depends on whether systematic or not. Typically narrative but may include tabular features | Analysis may be chronological, conceptual, thematic, etc. | |
Method for integrating or comparing the findings from qualitative studies. It looks for ‘themes’ or ‘constructs’ that lie in or across individual qualitative studies | May employ selective or purposive sampling | Quality assessment typically used to mediate messages not for inclusion/exclusion | Qualitative, narrative synthesis | Thematic analysis, may include conceptual models | |
Assessment of what is already known about a policy or practice issue, by using systematic review methods to search and critically appraise existing research | Completeness of searching determined by time constraints | Time-limited formal quality assessment | Typically narrative and tabular | Quantities of literature and overall quality/direction of effect of literature | |
Preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research evidence (usually including ongoing research) | Completeness of searching determined by time/scope constraints. May include research in progress | No formal quality assessment | Typically tabular with some narrative commentary | Characterizes quantity and quality of literature, perhaps by study design and other key features. Attempts to specify a viable review | |
Tend to address more current matters in contrast to other combined retrospective and current approaches. May offer new perspectives | Aims for comprehensive searching of current literature | No formal quality assessment | Typically narrative, may have tabular accompaniment | Current state of knowledge and priorities for future investigation and research | |
Seeks to systematically search for, appraise and synthesis research evidence, often adhering to guidelines on the conduct of a review | Aims for exhaustive, comprehensive searching | Quality assessment may determine inclusion/exclusion | Typically narrative with tabular accompaniment | What is known; recommendations for practice. What remains unknown; uncertainty around findings, recommendations for future research | |
Combines strengths of critical review with a comprehensive search process. Typically addresses broad questions to produce ‘best evidence synthesis’ | Aims for exhaustive, comprehensive searching | May or may not include quality assessment | Minimal narrative, tabular summary of studies | What is known; recommendations for practice. Limitations | |
Attempt to include elements of systematic review process while stopping short of systematic review. Typically conducted as postgraduate student assignment | May or may not include comprehensive searching | May or may not include quality assessment | Typically narrative with tabular accompaniment | What is known; uncertainty around findings; limitations of methodology | |
Specifically refers to review compiling evidence from multiple reviews into one accessible and usable document. Focuses on broad condition or problem for which there are competing interventions and highlights reviews that address these interventions and their results | Identification of component reviews, but no search for primary studies | Quality assessment of studies within component reviews and/or of reviews themselves | Graphical and tabular with narrative commentary | What is known; recommendations for practice. What remains unknown; recommendations for future research |
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Types of Literature Review
There are many types of literature review. The choice of a specific type depends on your research approach and design. The following types of literature review are the most popular in business studies:
Narrative literature review , also referred to as traditional literature review, critiques literature and summarizes the body of a literature. Narrative review also draws conclusions about the topic and identifies gaps or inconsistencies in a body of knowledge. You need to have a sufficiently focused research question to conduct a narrative literature review
Systematic literature review requires more rigorous and well-defined approach compared to most other types of literature review. Systematic literature review is comprehensive and details the timeframe within which the literature was selected. Systematic literature review can be divided into two categories: meta-analysis and meta-synthesis.
When you conduct meta-analysis you take findings from several studies on the same subject and analyze these using standardized statistical procedures. In meta-analysis patterns and relationships are detected and conclusions are drawn. Meta-analysis is associated with deductive research approach.
Meta-synthesis, on the other hand, is based on non-statistical techniques. This technique integrates, evaluates and interprets findings of multiple qualitative research studies. Meta-synthesis literature review is conducted usually when following inductive research approach.
Scoping literature review , as implied by its name is used to identify the scope or coverage of a body of literature on a given topic. It has been noted that “scoping reviews are useful for examining emerging evidence when it is still unclear what other, more specific questions can be posed and valuably addressed by a more precise systematic review.” [1] The main difference between systematic and scoping types of literature review is that, systematic literature review is conducted to find answer to more specific research questions, whereas scoping literature review is conducted to explore more general research question.
Argumentative literature review , as the name implies, examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. It should be noted that a potential for bias is a major shortcoming associated with argumentative literature review.
Integrative literature review reviews , critiques, and synthesizes secondary data about research topic in an integrated way such that new frameworks and perspectives on the topic are generated. If your research does not involve primary data collection and data analysis, then using integrative literature review will be your only option.
Theoretical literature review focuses on a pool of theory that has accumulated in regard to an issue, concept, theory, phenomena. Theoretical literature reviews play an instrumental role in establishing what theories already exist, the relationships between them, to what degree existing theories have been investigated, and to develop new hypotheses to be tested.
At the earlier parts of the literature review chapter, you need to specify the type of your literature review your chose and justify your choice. Your choice of a specific type of literature review should be based upon your research area, research problem and research methods. Also, you can briefly discuss other most popular types of literature review mentioned above, to illustrate your awareness of them.
[1] Munn, A. et. al. (2018) “Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach” BMC Medical Research Methodology
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- Literature Review: The What, Why and How-to Guide
- Introduction
Literature Review: The What, Why and How-to Guide — Introduction
- Getting Started
- How to Pick a Topic
- Strategies to Find Sources
- Evaluating Sources & Lit. Reviews
- Tips for Writing Literature Reviews
- Writing Literature Review: Useful Sites
- Citation Resources
- Other Academic Writings
What are Literature Reviews?
So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D. The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.
Goals of Literature Reviews
What are the goals of creating a Literature Review? A literature could be written to accomplish different aims:
- To develop a theory or evaluate an existing theory
- To summarize the historical or existing state of a research topic
- Identify a problem in a field of research
Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews . Review of General Psychology , 1 (3), 311-320.
What kinds of sources require a Literature Review?
- A research paper assigned in a course
- A thesis or dissertation
- A grant proposal
- An article intended for publication in a journal
All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.
Types of Literature Reviews
What kinds of literature reviews are written?
Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.
- Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework: 10.1177/08948453211037398
Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.
- Example : The effect of leave policies on increasing fertility: a systematic review: 10.1057/s41599-022-01270-w
Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.
- Example : Employment Instability and Fertility in Europe: A Meta-Analysis: 10.1215/00703370-9164737
Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts . Journal of Advanced Nursing , 53 (3), 311-318.
- Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis: 10.1177/05390184221113735
Literature Reviews in the Health Sciences
- UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
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Literature Reviews
- Types of reviews
- Getting started
Types of reviews and examples
Choosing a review type.
- 1. Define your research question
- 2. Plan your search
- 3. Search the literature
- 4. Organize your results
- 5. Synthesize your findings
- 6. Write the review
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- Meta-analysis
- Systematized
Definition:
"A term used to describe a conventional overview of the literature, particularly when contrasted with a systematic review (Booth et al., 2012, p. 265).
Characteristics:
- Provides examination of recent or current literature on a wide range of subjects
- Varying levels of completeness / comprehensiveness, non-standardized methodology
- May or may not include comprehensive searching, quality assessment or critical appraisal
Mitchell, L. E., & Zajchowski, C. A. (2022). The history of air quality in Utah: A narrative review. Sustainability , 14 (15), 9653. doi.org/10.3390/su14159653
Booth, A., Papaioannou, D., & Sutton, A. (2012). Systematic approaches to a successful literature review. London: SAGE Publications Ltd.
"An assessment of what is already known about a policy or practice issue...using systematic review methods to search and critically appraise existing research" (Grant & Booth, 2009, p. 100).
- Assessment of what is already known about an issue
- Similar to a systematic review but within a time-constrained setting
- Typically employs methodological shortcuts, increasing risk of introducing bias, includes basic level of quality assessment
- Best suited for issues needing quick decisions and solutions (i.e., policy recommendations)
Learn more about the method:
Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: the evolution of a rapid review approach. Systematic reviews, 1 (1), 1-9. https://doi.org/10.1186/2046-4053-1-10
Virginia Commonwealth University Libraries. (2021). Rapid Review Protocol .
Quarmby, S., Santos, G., & Mathias, M. (2019). Air quality strategies and technologies: A rapid review of the international evidence. Sustainability, 11 (10), 2757. https://doi.org/10.3390/su11102757
Grant, M.J. & Booth, A. (2009). A typology of reviews: an analysis of the 14 review types and associated methodologies. Health Information & Libraries Journal , 26(2), 91-108. https://www.doi.org/10.1111/j.1471-1842.2009.00848.x
Developed and refined by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), this review "map[s] out and categorize[s] existing literature on a particular topic, identifying gaps in research literature from which to commission further reviews and/or primary research" (Grant & Booth, 2009, p. 97).
Although mapping reviews are sometimes called scoping reviews, the key difference is that mapping reviews focus on a review question, rather than a topic
Mapping reviews are "best used where a clear target for a more focused evidence product has not yet been identified" (Booth, 2016, p. 14)
Mapping review searches are often quick and are intended to provide a broad overview
Mapping reviews can take different approaches in what types of literature is focused on in the search
Cooper I. D. (2016). What is a "mapping study?". Journal of the Medical Library Association: JMLA , 104 (1), 76–78. https://doi.org/10.3163/1536-5050.104.1.013
Miake-Lye, I. M., Hempel, S., Shanman, R., & Shekelle, P. G. (2016). What is an evidence map? A systematic review of published evidence maps and their definitions, methods, and products. Systematic reviews, 5 (1), 1-21. https://doi.org/10.1186/s13643-016-0204-x
Tainio, M., Andersen, Z. J., Nieuwenhuijsen, M. J., Hu, L., De Nazelle, A., An, R., ... & de Sá, T. H. (2021). Air pollution, physical activity and health: A mapping review of the evidence. Environment international , 147 , 105954. https://doi.org/10.1016/j.envint.2020.105954
Booth, A. (2016). EVIDENT Guidance for Reviewing the Evidence: a compendium of methodological literature and websites . ResearchGate. https://doi.org/10.13140/RG.2.1.1562.9842 .
Grant, M.J. & Booth, A. (2009). A typology of reviews: an analysis of the 14 review types and associated methodologies. Health Information & Libraries Journal , 26(2), 91-108. https://www.doi.org/10.1111/j.1471-1842.2009.00848.x
"A type of review that has as its primary objective the identification of the size and quality of research in a topic area in order to inform subsequent review" (Booth et al., 2012, p. 269).
- Main purpose is to map out and categorize existing literature, identify gaps in literature—great for informing policy-making
- Search comprehensiveness determined by time/scope constraints, could take longer than a systematic review
- No formal quality assessment or critical appraisal
Learn more about the methods :
Arksey, H., & O'Malley, L. (2005) Scoping studies: towards a methodological framework. International Journal of Social Research Methodology , 8 (1), 19-32. https://doi.org/10.1080/1364557032000119616
Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science: IS, 5, 69. https://doi.org/10.1186/1748-5908-5-69
Example :
Rahman, A., Sarkar, A., Yadav, O. P., Achari, G., & Slobodnik, J. (2021). Potential human health risks due to environmental exposure to nano-and microplastics and knowledge gaps: A scoping review. Science of the Total Environment, 757 , 143872. https://doi.org/10.1016/j.scitotenv.2020.143872
A review that "[compiles] evidence from multiple...reviews into one accessible and usable document" (Grant & Booth, 2009, p. 103). While originally intended to be a compilation of Cochrane reviews, it now generally refers to any kind of evidence synthesis.
- Compiles evidence from multiple reviews into one document
- Often defines a broader question than is typical of a traditional systematic review
Choi, G. J., & Kang, H. (2022). The umbrella review: a useful strategy in the rain of evidence. The Korean Journal of Pain , 35 (2), 127–128. https://doi.org/10.3344/kjp.2022.35.2.127
Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: Methodological development, conduct and reporting of an umbrella review approach. International Journal of Evidence-Based Healthcare , 13(3), 132–140. https://doi.org/10.1097/XEB.0000000000000055
Rojas-Rueda, D., Morales-Zamora, E., Alsufyani, W. A., Herbst, C. H., Al Balawi, S. M., Alsukait, R., & Alomran, M. (2021). Environmental risk factors and health: An umbrella review of meta-analyses. International Journal of Environmental Research and Public Dealth , 18 (2), 704. https://doi.org/10.3390/ijerph18020704
A meta-analysis is a "technique that statistically combines the results of quantitative studies to provide a more precise effect of the result" (Grant & Booth, 2009, p. 98).
- Statistical technique for combining results of quantitative studies to provide more precise effect of results
- Aims for exhaustive, comprehensive searching
- Quality assessment may determine inclusion/exclusion criteria
- May be conducted independently or as part of a systematic review
Berman, N. G., & Parker, R. A. (2002). Meta-analysis: Neither quick nor easy. BMC Medical Research Methodology , 2(1), 10. https://doi.org/10.1186/1471-2288-2-10
Hites R. A. (2004). Polybrominated diphenyl ethers in the environment and in people: a meta-analysis of concentrations. Environmental Science & Technology , 38 (4), 945–956. https://doi.org/10.1021/es035082g
A systematic review "seeks to systematically search for, appraise, and [synthesize] research evidence, often adhering to the guidelines on the conduct of a review" provided by discipline-specific organizations, such as the Cochrane Collaboration (Grant & Booth, 2009, p. 102).
- Aims to compile and synthesize all known knowledge on a given topic
- Adheres to strict guidelines, protocols, and frameworks
- Time-intensive and often takes months to a year or more to complete
- The most commonly referred to type of evidence synthesis. Sometimes confused as a blanket term for other types of reviews
Gascon, M., Triguero-Mas, M., Martínez, D., Dadvand, P., Forns, J., Plasència, A., & Nieuwenhuijsen, M. J. (2015). Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. International Journal of Environmental Research and Public Health , 12 (4), 4354–4379. https://doi.org/10.3390/ijerph120404354
"Systematized reviews attempt to include one or more elements of the systematic review process while stopping short of claiming that the resultant output is a systematic review" (Grant & Booth, 2009, p. 102). When a systematic review approach is adapted to produce a more manageable scope, while still retaining the rigor of a systematic review such as risk of bias assessment and the use of a protocol, this is often referred to as a structured review (Huelin et al., 2015).
- Typically conducted by postgraduate or graduate students
- Often assigned by instructors to students who don't have the resources to conduct a full systematic review
Salvo, G., Lashewicz, B. M., Doyle-Baker, P. K., & McCormack, G. R. (2018). Neighbourhood built environment influences on physical activity among adults: A systematized review of qualitative evidence. International Journal of Environmental Research and Public Health , 15 (5), 897. https://doi.org/10.3390/ijerph15050897
Huelin, R., Iheanacho, I., Payne, K., & Sandman, K. (2015). What’s in a name? Systematic and non-systematic literature reviews, and why the distinction matters. https://www.evidera.com/resource/whats-in-a-name-systematic-and-non-systematic-literature-reviews-and-why-the-distinction-matters/
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- Review Decision Tree - Cornell University For more information, check out Cornell's review methodology decision tree.
- LitR-Ex.com - Eight literature review methodologies Learn more about 8 different review types (incl. Systematic Reviews and Scoping Reviews) with practical tips about strengths and weaknesses of different methods.
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- How to Write a Literature Review | Guide, Examples, & Templates
How to Write a Literature Review | Guide, Examples, & Templates
Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.
What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .
There are five key steps to writing a literature review:
- Search for relevant literature
- Evaluate sources
- Identify themes, debates, and gaps
- Outline the structure
- Write your literature review
A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.
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Table of contents
What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.
- Quick Run-through
- Step 1 & 2
When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:
- Demonstrate your familiarity with the topic and its scholarly context
- Develop a theoretical framework and methodology for your research
- Position your work in relation to other researchers and theorists
- Show how your research addresses a gap or contributes to a debate
- Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.
Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.
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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.
- Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
- Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
- Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
- Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)
You can also check out our templates with literature review examples and sample outlines at the links below.
Download Word doc Download Google doc
Before you begin searching for literature, you need a clearly defined topic .
If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .
Make a list of keywords
Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.
- Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
- Body image, self-perception, self-esteem, mental health
- Generation Z, teenagers, adolescents, youth
Search for relevant sources
Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:
- Your university’s library catalogue
- Google Scholar
- Project Muse (humanities and social sciences)
- Medline (life sciences and biomedicine)
- EconLit (economics)
- Inspec (physics, engineering and computer science)
You can also use boolean operators to help narrow down your search.
Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.
You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.
For each publication, ask yourself:
- What question or problem is the author addressing?
- What are the key concepts and how are they defined?
- What are the key theories, models, and methods?
- Does the research use established frameworks or take an innovative approach?
- What are the results and conclusions of the study?
- How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
- What are the strengths and weaknesses of the research?
Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.
You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.
Take notes and cite your sources
As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.
It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.
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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:
- Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
- Themes: what questions or concepts recur across the literature?
- Debates, conflicts and contradictions: where do sources disagree?
- Pivotal publications: are there any influential theories or studies that changed the direction of the field?
- Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?
This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.
- Most research has focused on young women.
- There is an increasing interest in the visual aspects of social media.
- But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.
There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).
Chronological
The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.
Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.
If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.
For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.
Methodological
If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:
- Look at what results have emerged in qualitative versus quantitative research
- Discuss how the topic has been approached by empirical versus theoretical scholarship
- Divide the literature into sociological, historical, and cultural sources
Theoretical
A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.
You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.
Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.
The introduction should clearly establish the focus and purpose of the literature review.
Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.
As you write, you can follow these tips:
- Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
- Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
- Critically evaluate: mention the strengths and weaknesses of your sources
- Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts
In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.
When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !
This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.
Scribbr slides are free to use, customize, and distribute for educational purposes.
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A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .
It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.
There are several reasons to conduct a literature review at the beginning of a research project:
- To familiarize yourself with the current state of knowledge on your topic
- To ensure that you’re not just repeating what others have already done
- To identify gaps in knowledge and unresolved problems that your research can address
- To develop your theoretical framework and methodology
- To provide an overview of the key findings and debates on the topic
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .
A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .
An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .
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Evidence Synthesis, Systematic Review Services : Literature Review Types, Taxonomies
- Develop a Protocol
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Choosing a Literature Review Methodology
Growing interest in evidence-based practice has driven an increase in review methodologies. Your choice of review methodology (or literature review type) will be informed by the intent (purpose, function) of your research project and the time and resources of your team.
- Decision Tree (What Type of Review is Right for You?) Developed by Cornell University Library staff, this "decision-tree" guides the user to a handful of review guides given time and intent.
Types of Evidence Synthesis*
Critical Review - Aims to demonstrate writer has extensively researched literature and critically evaluated its quality. Goes beyond mere description to include degree of analysis and conceptual innovation. Typically results in hypothesis or model.
Mapping Review (Systematic Map) - Map out and categorize existing literature from which to commission further reviews and/or primary research by identifying gaps in research literature.
Meta-Analysis - Technique that statistically combines the results of quantitative studies to provide a more precise effect of the results.
Mixed Studies Review (Mixed Methods Review) - Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies.
Narrative (Literature) Review - Broad, generic term - Refers to an examination and general synthesis of the research literature, often with a wide scope; completeness and comprehensiveness may vary. Does not follow an established protocol.
Overview - Generic term: summary of the [medical] literature that attempts to survey the literature and describe its characteristics.
Qualitative Systematic Review or Qualitative Evidence Synthesis - Method for integrating or comparing the findings from qualitative studies. It looks for ‘themes’ or ‘constructs’ that lie in or across individual qualitative studies.
Rapid Review - Assessment of what is already known about a policy or practice issue, by using systematic review methods to search and critically appraise existing research.
Scoping Review or Evidence Map - Preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research.
State-of-the-art Review - Tend to address more current matters in contrast to other combined retrospective and current approaches. May offer new perspectives on issue or point out area for further research.
Systematic Review - Seeks to systematically search for, appraise and synthesize research evidence, often adhering to guidelines on the conduct of a review. (An emerging subset includes Living Reviews or Living Systematic Reviews - A [review or] systematic review which is continually updated, incorporating relevant new evidence as it becomes available.)
Systematic Search and Review - Combines strengths of critical review with a comprehensive search process. Typically addresses broad questions to produce ‘best evidence synthesis.’
Umbrella Review - Specifically refers to review compiling evidence from multiple reviews into one accessible and usable document. Focuses on broad condition or problem for which there are competing interventions and highlights reviews that address these interventions and their results.
*Apart from some qualifying description for "Narrative (Literature) Review", these definitions are provided in Grant & Booth's "A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies."
Literature Review Types/Typologies, Taxonomies
Grant, M. J., and A. Booth. "A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies." Health Information and Libraries Journal 26.2 (2009): 91-108. DOI: 10.1111/j.1471-1842.2009.00848.x Link
Munn, Zachary, et al. “Systematic Review or Scoping Review? Guidance for Authors When Choosing between a Systematic or Scoping Review Approach.” BMC Medical Research Methodology , vol. 18, no. 1, Nov. 2018, p. 143. DOI: 10.1186/s12874-018-0611-x. Link
Sutton, A., et al. "Meeting the Review Family: Exploring Review Types and Associated Information Retrieval Requirements." Health Information and Libraries Journal 36.3 (2019): 202-22. DOI: 10.1111/hir.12276 Link
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Literature Review – Types Writing Guide and Examples
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Literature Review
Definition:
A literature review is a comprehensive and critical analysis of the existing literature on a particular topic or research question. It involves identifying, evaluating, and synthesizing relevant literature, including scholarly articles, books, and other sources, to provide a summary and critical assessment of what is known about the topic.
Types of Literature Review
Types of Literature Review are as follows:
- Narrative literature review : This type of review involves a comprehensive summary and critical analysis of the available literature on a particular topic or research question. It is often used as an introductory section of a research paper.
- Systematic literature review: This is a rigorous and structured review that follows a pre-defined protocol to identify, evaluate, and synthesize all relevant studies on a specific research question. It is often used in evidence-based practice and systematic reviews.
- Meta-analysis: This is a quantitative review that uses statistical methods to combine data from multiple studies to derive a summary effect size. It provides a more precise estimate of the overall effect than any individual study.
- Scoping review: This is a preliminary review that aims to map the existing literature on a broad topic area to identify research gaps and areas for further investigation.
- Critical literature review : This type of review evaluates the strengths and weaknesses of the existing literature on a particular topic or research question. It aims to provide a critical analysis of the literature and identify areas where further research is needed.
- Conceptual literature review: This review synthesizes and integrates theories and concepts from multiple sources to provide a new perspective on a particular topic. It aims to provide a theoretical framework for understanding a particular research question.
- Rapid literature review: This is a quick review that provides a snapshot of the current state of knowledge on a specific research question or topic. It is often used when time and resources are limited.
- Thematic literature review : This review identifies and analyzes common themes and patterns across a body of literature on a particular topic. It aims to provide a comprehensive overview of the literature and identify key themes and concepts.
- Realist literature review: This review is often used in social science research and aims to identify how and why certain interventions work in certain contexts. It takes into account the context and complexities of real-world situations.
- State-of-the-art literature review : This type of review provides an overview of the current state of knowledge in a particular field, highlighting the most recent and relevant research. It is often used in fields where knowledge is rapidly evolving, such as technology or medicine.
- Integrative literature review: This type of review synthesizes and integrates findings from multiple studies on a particular topic to identify patterns, themes, and gaps in the literature. It aims to provide a comprehensive understanding of the current state of knowledge on a particular topic.
- Umbrella literature review : This review is used to provide a broad overview of a large and diverse body of literature on a particular topic. It aims to identify common themes and patterns across different areas of research.
- Historical literature review: This type of review examines the historical development of research on a particular topic or research question. It aims to provide a historical context for understanding the current state of knowledge on a particular topic.
- Problem-oriented literature review : This review focuses on a specific problem or issue and examines the literature to identify potential solutions or interventions. It aims to provide practical recommendations for addressing a particular problem or issue.
- Mixed-methods literature review : This type of review combines quantitative and qualitative methods to synthesize and analyze the available literature on a particular topic. It aims to provide a more comprehensive understanding of the research question by combining different types of evidence.
Parts of Literature Review
Parts of a literature review are as follows:
Introduction
The introduction of a literature review typically provides background information on the research topic and why it is important. It outlines the objectives of the review, the research question or hypothesis, and the scope of the review.
Literature Search
This section outlines the search strategy and databases used to identify relevant literature. The search terms used, inclusion and exclusion criteria, and any limitations of the search are described.
Literature Analysis
The literature analysis is the main body of the literature review. This section summarizes and synthesizes the literature that is relevant to the research question or hypothesis. The review should be organized thematically, chronologically, or by methodology, depending on the research objectives.
Critical Evaluation
Critical evaluation involves assessing the quality and validity of the literature. This includes evaluating the reliability and validity of the studies reviewed, the methodology used, and the strength of the evidence.
The conclusion of the literature review should summarize the main findings, identify any gaps in the literature, and suggest areas for future research. It should also reiterate the importance of the research question or hypothesis and the contribution of the literature review to the overall research project.
The references list includes all the sources cited in the literature review, and follows a specific referencing style (e.g., APA, MLA, Harvard).
How to write Literature Review
Here are some steps to follow when writing a literature review:
- Define your research question or topic : Before starting your literature review, it is essential to define your research question or topic. This will help you identify relevant literature and determine the scope of your review.
- Conduct a comprehensive search: Use databases and search engines to find relevant literature. Look for peer-reviewed articles, books, and other academic sources that are relevant to your research question or topic.
- Evaluate the sources: Once you have found potential sources, evaluate them critically to determine their relevance, credibility, and quality. Look for recent publications, reputable authors, and reliable sources of data and evidence.
- Organize your sources: Group the sources by theme, method, or research question. This will help you identify similarities and differences among the literature, and provide a structure for your literature review.
- Analyze and synthesize the literature : Analyze each source in depth, identifying the key findings, methodologies, and conclusions. Then, synthesize the information from the sources, identifying patterns and themes in the literature.
- Write the literature review : Start with an introduction that provides an overview of the topic and the purpose of the literature review. Then, organize the literature according to your chosen structure, and analyze and synthesize the sources. Finally, provide a conclusion that summarizes the key findings of the literature review, identifies gaps in knowledge, and suggests areas for future research.
- Edit and proofread: Once you have written your literature review, edit and proofread it carefully to ensure that it is well-organized, clear, and concise.
Examples of Literature Review
Here’s an example of how a literature review can be conducted for a thesis on the topic of “ The Impact of Social Media on Teenagers’ Mental Health”:
- Start by identifying the key terms related to your research topic. In this case, the key terms are “social media,” “teenagers,” and “mental health.”
- Use academic databases like Google Scholar, JSTOR, or PubMed to search for relevant articles, books, and other publications. Use these keywords in your search to narrow down your results.
- Evaluate the sources you find to determine if they are relevant to your research question. You may want to consider the publication date, author’s credentials, and the journal or book publisher.
- Begin reading and taking notes on each source, paying attention to key findings, methodologies used, and any gaps in the research.
- Organize your findings into themes or categories. For example, you might categorize your sources into those that examine the impact of social media on self-esteem, those that explore the effects of cyberbullying, and those that investigate the relationship between social media use and depression.
- Synthesize your findings by summarizing the key themes and highlighting any gaps or inconsistencies in the research. Identify areas where further research is needed.
- Use your literature review to inform your research questions and hypotheses for your thesis.
For example, after conducting a literature review on the impact of social media on teenagers’ mental health, a thesis might look like this:
“Using a mixed-methods approach, this study aims to investigate the relationship between social media use and mental health outcomes in teenagers. Specifically, the study will examine the effects of cyberbullying, social comparison, and excessive social media use on self-esteem, anxiety, and depression. Through an analysis of survey data and qualitative interviews with teenagers, the study will provide insight into the complex relationship between social media use and mental health outcomes, and identify strategies for promoting positive mental health outcomes in young people.”
Reference: Smith, J., Jones, M., & Lee, S. (2019). The effects of social media use on adolescent mental health: A systematic review. Journal of Adolescent Health, 65(2), 154-165. doi:10.1016/j.jadohealth.2019.03.024
Reference Example: Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Title of Journal, volume number(issue number), page range. doi:0000000/000000000000 or URL
Applications of Literature Review
some applications of literature review in different fields:
- Social Sciences: In social sciences, literature reviews are used to identify gaps in existing research, to develop research questions, and to provide a theoretical framework for research. Literature reviews are commonly used in fields such as sociology, psychology, anthropology, and political science.
- Natural Sciences: In natural sciences, literature reviews are used to summarize and evaluate the current state of knowledge in a particular field or subfield. Literature reviews can help researchers identify areas where more research is needed and provide insights into the latest developments in a particular field. Fields such as biology, chemistry, and physics commonly use literature reviews.
- Health Sciences: In health sciences, literature reviews are used to evaluate the effectiveness of treatments, identify best practices, and determine areas where more research is needed. Literature reviews are commonly used in fields such as medicine, nursing, and public health.
- Humanities: In humanities, literature reviews are used to identify gaps in existing knowledge, develop new interpretations of texts or cultural artifacts, and provide a theoretical framework for research. Literature reviews are commonly used in fields such as history, literary studies, and philosophy.
Role of Literature Review in Research
Here are some applications of literature review in research:
- Identifying Research Gaps : Literature review helps researchers identify gaps in existing research and literature related to their research question. This allows them to develop new research questions and hypotheses to fill those gaps.
- Developing Theoretical Framework: Literature review helps researchers develop a theoretical framework for their research. By analyzing and synthesizing existing literature, researchers can identify the key concepts, theories, and models that are relevant to their research.
- Selecting Research Methods : Literature review helps researchers select appropriate research methods and techniques based on previous research. It also helps researchers to identify potential biases or limitations of certain methods and techniques.
- Data Collection and Analysis: Literature review helps researchers in data collection and analysis by providing a foundation for the development of data collection instruments and methods. It also helps researchers to identify relevant data sources and identify potential data analysis techniques.
- Communicating Results: Literature review helps researchers to communicate their results effectively by providing a context for their research. It also helps to justify the significance of their findings in relation to existing research and literature.
Purpose of Literature Review
Some of the specific purposes of a literature review are as follows:
- To provide context: A literature review helps to provide context for your research by situating it within the broader body of literature on the topic.
- To identify gaps and inconsistencies: A literature review helps to identify areas where further research is needed or where there are inconsistencies in the existing literature.
- To synthesize information: A literature review helps to synthesize the information from multiple sources and present a coherent and comprehensive picture of the current state of knowledge on the topic.
- To identify key concepts and theories : A literature review helps to identify key concepts and theories that are relevant to your research question and provide a theoretical framework for your study.
- To inform research design: A literature review can inform the design of your research study by identifying appropriate research methods, data sources, and research questions.
Characteristics of Literature Review
Some Characteristics of Literature Review are as follows:
- Identifying gaps in knowledge: A literature review helps to identify gaps in the existing knowledge and research on a specific topic or research question. By analyzing and synthesizing the literature, you can identify areas where further research is needed and where new insights can be gained.
- Establishing the significance of your research: A literature review helps to establish the significance of your own research by placing it in the context of existing research. By demonstrating the relevance of your research to the existing literature, you can establish its importance and value.
- Informing research design and methodology : A literature review helps to inform research design and methodology by identifying the most appropriate research methods, techniques, and instruments. By reviewing the literature, you can identify the strengths and limitations of different research methods and techniques, and select the most appropriate ones for your own research.
- Supporting arguments and claims: A literature review provides evidence to support arguments and claims made in academic writing. By citing and analyzing the literature, you can provide a solid foundation for your own arguments and claims.
- I dentifying potential collaborators and mentors: A literature review can help identify potential collaborators and mentors by identifying researchers and practitioners who are working on related topics or using similar methods. By building relationships with these individuals, you can gain valuable insights and support for your own research and practice.
- Keeping up-to-date with the latest research : A literature review helps to keep you up-to-date with the latest research on a specific topic or research question. By regularly reviewing the literature, you can stay informed about the latest findings and developments in your field.
Advantages of Literature Review
There are several advantages to conducting a literature review as part of a research project, including:
- Establishing the significance of the research : A literature review helps to establish the significance of the research by demonstrating the gap or problem in the existing literature that the study aims to address.
- Identifying key concepts and theories: A literature review can help to identify key concepts and theories that are relevant to the research question, and provide a theoretical framework for the study.
- Supporting the research methodology : A literature review can inform the research methodology by identifying appropriate research methods, data sources, and research questions.
- Providing a comprehensive overview of the literature : A literature review provides a comprehensive overview of the current state of knowledge on a topic, allowing the researcher to identify key themes, debates, and areas of agreement or disagreement.
- Identifying potential research questions: A literature review can help to identify potential research questions and areas for further investigation.
- Avoiding duplication of research: A literature review can help to avoid duplication of research by identifying what has already been done on a topic, and what remains to be done.
- Enhancing the credibility of the research : A literature review helps to enhance the credibility of the research by demonstrating the researcher’s knowledge of the existing literature and their ability to situate their research within a broader context.
Limitations of Literature Review
Limitations of Literature Review are as follows:
- Limited scope : Literature reviews can only cover the existing literature on a particular topic, which may be limited in scope or depth.
- Publication bias : Literature reviews may be influenced by publication bias, which occurs when researchers are more likely to publish positive results than negative ones. This can lead to an incomplete or biased picture of the literature.
- Quality of sources : The quality of the literature reviewed can vary widely, and not all sources may be reliable or valid.
- Time-limited: Literature reviews can become quickly outdated as new research is published, making it difficult to keep up with the latest developments in a field.
- Subjective interpretation : Literature reviews can be subjective, and the interpretation of the findings can vary depending on the researcher’s perspective or bias.
- Lack of original data : Literature reviews do not generate new data, but rather rely on the analysis of existing studies.
- Risk of plagiarism: It is important to ensure that literature reviews do not inadvertently contain plagiarism, which can occur when researchers use the work of others without proper attribution.
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Literature Reviews
What is a Literature Review?
- Steps for Creating a Literature Review
- Providing Evidence / Critical Analysis
- Challenges when writing a Literature Review
- Systematic Literature Reviews
A literature review is an academic text that surveys, synthesizes, and critically evaluates the existing literature on a specific topic. It is typically required for theses, dissertations, or long reports and serves several key purposes:
- Surveying the Literature : It involves a comprehensive search and examination of relevant academic books, journal articles, and other sources related to the chosen topic.
- Synthesizing Information : The literature review summarizes and organizes the information found in the literature, often identifying patterns, themes, and gaps in the current knowledge.
- Critical Analysis : It critically analyzes the collected information, highlighting limitations, gaps, and areas of controversy, and suggests directions for future research.
- Establishing Context : It places the current research within the broader context of the field, demonstrating how the new research builds on or diverges from previous studies.
Types of Literature Reviews
Literature reviews can take various forms, including:
- Narrative Reviews : These provide a qualitative summary of the literature and are often used to give a broad overview of a topic. They may be less structured and more subjective, focusing on synthesizing the literature to support a particular viewpoint.
- Systematic Reviews : These are more rigorous and structured, following a specific methodology to identify, evaluate, and synthesize all relevant studies on a particular question. They aim to minimize bias and provide a comprehensive summary of the existing evidence.
- Integrative Reviews : Similar to systematic reviews, but they aim to generate new knowledge by integrating findings from different studies to develop new theories or frameworks.
Importance of Literature Reviews
- Foundation for Research : They provide a solid background for new research projects, helping to justify the research question and methodology.
Identifying Gaps : Literature reviews highlight areas where knowledge is lacking, guiding future research efforts.
- Building Credibility : Demonstrating familiarity with existing research enhances the credibility of the researcher and their work.
In summary, a literature review is a critical component of academic research that helps to frame the current state of knowledge, identify gaps, and provide a basis for new research.
The research, the body of current literature, and the particular objectives should all influence the structure of a literature review. It is also critical to remember that creating a literature review is an ongoing process - as one reads and analyzes the literature, one's understanding may change, which could require rearranging the literature review.
Paré, G. and Kitsiou, S. (2017) 'Methods for Literature Reviews' , in: Lau, F. and Kuziemsky, C. (eds.) Handbook of eHealth evaluation: an evidence-based approach . Victoria (BC): University of Victoria.
Perplexity AI (2024) Perplexity AI response to Kathy Neville, 31 July.
Royal Literary Fund (2024) The structure of a literature review. Available at: https://www.rlf.org.uk/resources/the-structure-of-a-literature-review/ (Accessed: 23 July 2024).
Library Services for Undergraduate Research (2024) Literature review: a definition . Available at: https://libguides.wustl.edu/our?p=302677 (Accessed: 31 July 2024).
Further Reading:
Methods for Literature Reviews
Literature Review (The University of Edinburgh)
Literature Reviews (University of Sheffield)
- How to Write a Literature Review Paper? Wee, Bert Van ; Banister, David ISBN: 0144-1647
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Literature Reviews, Introduction to Different Types of
There are many different types of literature reviews, each with its own approach, analysis, and purpose. To confuse matters, these types aren't named consistently. The following are some of the more common types of literature reviews.
These are more rigorous, with some level of appraisal:
- The Systematic Review is important to health care and medical trials, and other subjects where methodology and data are important. Through rigorous review and analysis of literature that meets a specific criteria, the systematic review identifies and compares answers to health care related questions. The systematic review may include meta-analysis and meta-synthesis, which leads us to...
- The Quantitative or Qualitative Meta-analysis Review can both make up the whole or part of systematic review(s). Both are thorough and comprehensive in condensing and making sense of a large body of research. The quantitative meta-analysis reviews quantitative research, is objective, and includes statistical analysis. The qualitative meta-analysis reviews qualitative research, is subjective (or evaluative, or interpretive), and identifies new themes or concepts.
These don't always include a formal assessment or analysis:
- The Literature Review (see our Literature Review video) or Narrative Review often appears as a chapter in a thesis or dissertation. It describes what related research has already been conducted, how it informs the thesis, and how the thesis fits into the research in the field. (See https://student.unsw.edu.au/writing-critical-review for more information.)
- The Critical Review is like a literature review, but requires a more detailed examination of the literature, in order to compare and evaluate a number of perspectives.
- The Scoping Review is often used at the beginning of an article, dissertation or research proposal. It is conducted before the research begins, and sets the stage for this research by highlighting gaps in the literature, and explaining the need for the research about to be conducted, which is presented in the remainder of the article.
- The Conceptual Review groups articles according to concepts, or categories, or themes. It identifies the current 'understanding' of the given research topic, discusses how this understanding was reached, and attempts to determine whether a greater understanding can be suggested. It provides a snapshot of where things are with this particular field of research.
- The State-of-the-Art Review is conducted periodically, with a focus on the most recent research. It describes what is currently known, understood, or agreed upon regarding the research topic, and highlights where are there still disagreements.
Source: Grant, M. J., & Booth, A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal , 26 (2), 91-108. http://doi.org/10.1111/j.1471-1842.2009.00848.x
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- Common Grammar Mistakes
- Writing Concisely
- Avoiding Plagiarism
- Footnotes or Endnotes?
- Further Readings
- Generative AI and Writing
- USC Libraries Tutorials and Other Guides
- Bibliography
A literature review surveys prior research published in books, scholarly articles, and any other sources relevant to a particular issue, area of research, or theory, and by so doing, provides a description, summary, and critical evaluation of these works in relation to the research problem being investigated. Literature reviews are designed to provide an overview of sources you have used in researching a particular topic and to demonstrate to your readers how your research fits within existing scholarship about the topic.
Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . Fourth edition. Thousand Oaks, CA: SAGE, 2014.
Importance of a Good Literature Review
A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories . A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate a research problem. The analytical features of a literature review might:
- Give a new interpretation of old material or combine new with old interpretations,
- Trace the intellectual progression of the field, including major debates,
- Depending on the situation, evaluate the sources and advise the reader on the most pertinent or relevant research, or
- Usually in the conclusion of a literature review, identify where gaps exist in how a problem has been researched to date.
Given this, the purpose of a literature review is to:
- Place each work in the context of its contribution to understanding the research problem being studied.
- Describe the relationship of each work to the others under consideration.
- Identify new ways to interpret prior research.
- Reveal any gaps that exist in the literature.
- Resolve conflicts amongst seemingly contradictory previous studies.
- Identify areas of prior scholarship to prevent duplication of effort.
- Point the way in fulfilling a need for additional research.
- Locate your own research within the context of existing literature [very important].
Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper. 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . Los Angeles, CA: SAGE, 2011; Knopf, Jeffrey W. "Doing a Literature Review." PS: Political Science and Politics 39 (January 2006): 127-132; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012.
Types of Literature Reviews
It is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the primary studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally among scholars that become part of the body of epistemological traditions within the field.
In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are a number of approaches you could adopt depending upon the type of analysis underpinning your study.
Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews [see below].
Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses or research problems. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. This is the most common form of review in the social sciences.
Historical Review Few things rest in isolation from historical precedent. Historical literature reviews focus on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.
Methodological Review A review does not always focus on what someone said [findings], but how they came about saying what they say [method of analysis]. Reviewing methods of analysis provides a framework of understanding at different levels [i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques], how researchers draw upon a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection, and data analysis. This approach helps highlight ethical issues which you should be aware of and consider as you go through your own study.
Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. The goal is to deliberately document, critically evaluate, and summarize scientifically all of the research about a clearly defined research problem . Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" This type of literature review is primarily applied to examining prior research studies in clinical medicine and allied health fields, but it is increasingly being used in the social sciences.
Theoretical Review The purpose of this form is to examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review helps to establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.
NOTE: Most often the literature review will incorporate some combination of types. For example, a review that examines literature supporting or refuting an argument, assumption, or philosophical problem related to the research problem will also need to include writing supported by sources that establish the history of these arguments in the literature.
Baumeister, Roy F. and Mark R. Leary. "Writing Narrative Literature Reviews." Review of General Psychology 1 (September 1997): 311-320; Mark R. Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147; Petticrew, Mark and Helen Roberts. Systematic Reviews in the Social Sciences: A Practical Guide . Malden, MA: Blackwell Publishers, 2006; Torracro, Richard. "Writing Integrative Literature Reviews: Guidelines and Examples." Human Resource Development Review 4 (September 2005): 356-367; Rocco, Tonette S. and Maria S. Plakhotnik. "Literature Reviews, Conceptual Frameworks, and Theoretical Frameworks: Terms, Functions, and Distinctions." Human Ressource Development Review 8 (March 2008): 120-130; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.
Structure and Writing Style
I. Thinking About Your Literature Review
The structure of a literature review should include the following in support of understanding the research problem :
- An overview of the subject, issue, or theory under consideration, along with the objectives of the literature review,
- Division of works under review into themes or categories [e.g. works that support a particular position, those against, and those offering alternative approaches entirely],
- An explanation of how each work is similar to and how it varies from the others,
- Conclusions as to which pieces are best considered in their argument, are most convincing of their opinions, and make the greatest contribution to the understanding and development of their area of research.
The critical evaluation of each work should consider :
- Provenance -- what are the author's credentials? Are the author's arguments supported by evidence [e.g. primary historical material, case studies, narratives, statistics, recent scientific findings]?
- Methodology -- were the techniques used to identify, gather, and analyze the data appropriate to addressing the research problem? Was the sample size appropriate? Were the results effectively interpreted and reported?
- Objectivity -- is the author's perspective even-handed or prejudicial? Is contrary data considered or is certain pertinent information ignored to prove the author's point?
- Persuasiveness -- which of the author's theses are most convincing or least convincing?
- Validity -- are the author's arguments and conclusions convincing? Does the work ultimately contribute in any significant way to an understanding of the subject?
II. Development of the Literature Review
Four Basic Stages of Writing 1. Problem formulation -- which topic or field is being examined and what are its component issues? 2. Literature search -- finding materials relevant to the subject being explored. 3. Data evaluation -- determining which literature makes a significant contribution to the understanding of the topic. 4. Analysis and interpretation -- discussing the findings and conclusions of pertinent literature.
Consider the following issues before writing the literature review: Clarify If your assignment is not specific about what form your literature review should take, seek clarification from your professor by asking these questions: 1. Roughly how many sources would be appropriate to include? 2. What types of sources should I review (books, journal articles, websites; scholarly versus popular sources)? 3. Should I summarize, synthesize, or critique sources by discussing a common theme or issue? 4. Should I evaluate the sources in any way beyond evaluating how they relate to understanding the research problem? 5. Should I provide subheadings and other background information, such as definitions and/or a history? Find Models Use the exercise of reviewing the literature to examine how authors in your discipline or area of interest have composed their literature review sections. Read them to get a sense of the types of themes you might want to look for in your own research or to identify ways to organize your final review. The bibliography or reference section of sources you've already read, such as required readings in the course syllabus, are also excellent entry points into your own research. Narrow the Topic The narrower your topic, the easier it will be to limit the number of sources you need to read in order to obtain a good survey of relevant resources. Your professor will probably not expect you to read everything that's available about the topic, but you'll make the act of reviewing easier if you first limit scope of the research problem. A good strategy is to begin by searching the USC Libraries Catalog for recent books about the topic and review the table of contents for chapters that focuses on specific issues. You can also review the indexes of books to find references to specific issues that can serve as the focus of your research. For example, a book surveying the history of the Israeli-Palestinian conflict may include a chapter on the role Egypt has played in mediating the conflict, or look in the index for the pages where Egypt is mentioned in the text. Consider Whether Your Sources are Current Some disciplines require that you use information that is as current as possible. This is particularly true in disciplines in medicine and the sciences where research conducted becomes obsolete very quickly as new discoveries are made. However, when writing a review in the social sciences, a survey of the history of the literature may be required. In other words, a complete understanding the research problem requires you to deliberately examine how knowledge and perspectives have changed over time. Sort through other current bibliographies or literature reviews in the field to get a sense of what your discipline expects. You can also use this method to explore what is considered by scholars to be a "hot topic" and what is not.
III. Ways to Organize Your Literature Review
Chronology of Events If your review follows the chronological method, you could write about the materials according to when they were published. This approach should only be followed if a clear path of research building on previous research can be identified and that these trends follow a clear chronological order of development. For example, a literature review that focuses on continuing research about the emergence of German economic power after the fall of the Soviet Union. By Publication Order your sources by publication chronology, then, only if the order demonstrates a more important trend. For instance, you could order a review of literature on environmental studies of brown fields if the progression revealed, for example, a change in the soil collection practices of the researchers who wrote and/or conducted the studies. Thematic [“conceptual categories”] A thematic literature review is the most common approach to summarizing prior research in the social and behavioral sciences. Thematic reviews are organized around a topic or issue, rather than the progression of time, although the progression of time may still be incorporated into a thematic review. For example, a review of the Internet’s impact on American presidential politics could focus on the development of online political satire. While the study focuses on one topic, the Internet’s impact on American presidential politics, it would still be organized chronologically reflecting technological developments in media. The difference in this example between a "chronological" and a "thematic" approach is what is emphasized the most: themes related to the role of the Internet in presidential politics. Note that more authentic thematic reviews tend to break away from chronological order. A review organized in this manner would shift between time periods within each section according to the point being made. Methodological A methodological approach focuses on the methods utilized by the researcher. For the Internet in American presidential politics project, one methodological approach would be to look at cultural differences between the portrayal of American presidents on American, British, and French websites. Or the review might focus on the fundraising impact of the Internet on a particular political party. A methodological scope will influence either the types of documents in the review or the way in which these documents are discussed.
Other Sections of Your Literature Review Once you've decided on the organizational method for your literature review, the sections you need to include in the paper should be easy to figure out because they arise from your organizational strategy. In other words, a chronological review would have subsections for each vital time period; a thematic review would have subtopics based upon factors that relate to the theme or issue. However, sometimes you may need to add additional sections that are necessary for your study, but do not fit in the organizational strategy of the body. What other sections you include in the body is up to you. However, only include what is necessary for the reader to locate your study within the larger scholarship about the research problem.
Here are examples of other sections, usually in the form of a single paragraph, you may need to include depending on the type of review you write:
- Current Situation : Information necessary to understand the current topic or focus of the literature review.
- Sources Used : Describes the methods and resources [e.g., databases] you used to identify the literature you reviewed.
- History : The chronological progression of the field, the research literature, or an idea that is necessary to understand the literature review, if the body of the literature review is not already a chronology.
- Selection Methods : Criteria you used to select (and perhaps exclude) sources in your literature review. For instance, you might explain that your review includes only peer-reviewed [i.e., scholarly] sources.
- Standards : Description of the way in which you present your information.
- Questions for Further Research : What questions about the field has the review sparked? How will you further your research as a result of the review?
IV. Writing Your Literature Review
Once you've settled on how to organize your literature review, you're ready to write each section. When writing your review, keep in mind these issues.
Use Evidence A literature review section is, in this sense, just like any other academic research paper. Your interpretation of the available sources must be backed up with evidence [citations] that demonstrates that what you are saying is valid. Be Selective Select only the most important points in each source to highlight in the review. The type of information you choose to mention should relate directly to the research problem, whether it is thematic, methodological, or chronological. Related items that provide additional information, but that are not key to understanding the research problem, can be included in a list of further readings . Use Quotes Sparingly Some short quotes are appropriate if you want to emphasize a point, or if what an author stated cannot be easily paraphrased. Sometimes you may need to quote certain terminology that was coined by the author, is not common knowledge, or taken directly from the study. Do not use extensive quotes as a substitute for using your own words in reviewing the literature. Summarize and Synthesize Remember to summarize and synthesize your sources within each thematic paragraph as well as throughout the review. Recapitulate important features of a research study, but then synthesize it by rephrasing the study's significance and relating it to your own work and the work of others. Keep Your Own Voice While the literature review presents others' ideas, your voice [the writer's] should remain front and center. For example, weave references to other sources into what you are writing but maintain your own voice by starting and ending the paragraph with your own ideas and wording. Use Caution When Paraphrasing When paraphrasing a source that is not your own, be sure to represent the author's information or opinions accurately and in your own words. Even when paraphrasing an author’s work, you still must provide a citation to that work.
V. Common Mistakes to Avoid
These are the most common mistakes made in reviewing social science research literature.
- Sources in your literature review do not clearly relate to the research problem;
- You do not take sufficient time to define and identify the most relevant sources to use in the literature review related to the research problem;
- Relies exclusively on secondary analytical sources rather than including relevant primary research studies or data;
- Uncritically accepts another researcher's findings and interpretations as valid, rather than examining critically all aspects of the research design and analysis;
- Does not describe the search procedures that were used in identifying the literature to review;
- Reports isolated statistical results rather than synthesizing them in chi-squared or meta-analytic methods; and,
- Only includes research that validates assumptions and does not consider contrary findings and alternative interpretations found in the literature.
Cook, Kathleen E. and Elise Murowchick. “Do Literature Review Skills Transfer from One Course to Another?” Psychology Learning and Teaching 13 (March 2014): 3-11; Fink, Arlene. Conducting Research Literature Reviews: From the Internet to Paper . 2nd ed. Thousand Oaks, CA: Sage, 2005; Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1998; Jesson, Jill. Doing Your Literature Review: Traditional and Systematic Techniques . London: SAGE, 2011; Literature Review Handout. Online Writing Center. Liberty University; Literature Reviews. The Writing Center. University of North Carolina; Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: SAGE, 2016; Ridley, Diana. The Literature Review: A Step-by-Step Guide for Students . 2nd ed. Los Angeles, CA: SAGE, 2012; Randolph, Justus J. “A Guide to Writing the Dissertation Literature Review." Practical Assessment, Research, and Evaluation. vol. 14, June 2009; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016; Taylor, Dena. The Literature Review: A Few Tips On Conducting It. University College Writing Centre. University of Toronto; Writing a Literature Review. Academic Skills Centre. University of Canberra.
Writing Tip
Break Out of Your Disciplinary Box!
Thinking interdisciplinarily about a research problem can be a rewarding exercise in applying new ideas, theories, or concepts to an old problem. For example, what might cultural anthropologists say about the continuing conflict in the Middle East? In what ways might geographers view the need for better distribution of social service agencies in large cities than how social workers might study the issue? You don’t want to substitute a thorough review of core research literature in your discipline for studies conducted in other fields of study. However, particularly in the social sciences, thinking about research problems from multiple vectors is a key strategy for finding new solutions to a problem or gaining a new perspective. Consult with a librarian about identifying research databases in other disciplines; almost every field of study has at least one comprehensive database devoted to indexing its research literature.
Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.
Another Writing Tip
Don't Just Review for Content!
While conducting a review of the literature, maximize the time you devote to writing this part of your paper by thinking broadly about what you should be looking for and evaluating. Review not just what scholars are saying, but how are they saying it. Some questions to ask:
- How are they organizing their ideas?
- What methods have they used to study the problem?
- What theories have been used to explain, predict, or understand their research problem?
- What sources have they cited to support their conclusions?
- How have they used non-textual elements [e.g., charts, graphs, figures, etc.] to illustrate key points?
When you begin to write your literature review section, you'll be glad you dug deeper into how the research was designed and constructed because it establishes a means for developing more substantial analysis and interpretation of the research problem.
Hart, Chris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage Publications, 1 998.
Yet Another Writing Tip
When Do I Know I Can Stop Looking and Move On?
Here are several strategies you can utilize to assess whether you've thoroughly reviewed the literature:
- Look for repeating patterns in the research findings . If the same thing is being said, just by different people, then this likely demonstrates that the research problem has hit a conceptual dead end. At this point consider: Does your study extend current research? Does it forge a new path? Or, does is merely add more of the same thing being said?
- Look at sources the authors cite to in their work . If you begin to see the same researchers cited again and again, then this is often an indication that no new ideas have been generated to address the research problem.
- Search Google Scholar to identify who has subsequently cited leading scholars already identified in your literature review [see next sub-tab]. This is called citation tracking and there are a number of sources that can help you identify who has cited whom, particularly scholars from outside of your discipline. Here again, if the same authors are being cited again and again, this may indicate no new literature has been written on the topic.
Onwuegbuzie, Anthony J. and Rebecca Frels. Seven Steps to a Comprehensive Literature Review: A Multimodal and Cultural Approach . Los Angeles, CA: Sage, 2016; Sutton, Anthea. Systematic Approaches to a Successful Literature Review . Los Angeles, CA: Sage Publications, 2016.
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What is a Literature Review? How to Write It (with Examples)
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A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process.
Table of Contents
What is the purpose of literature review , a. habitat loss and species extinction: , b. range shifts and phenological changes: , c. ocean acidification and coral reefs: , d. adaptive strategies and conservation efforts: .
- Choose a Topic and Define the Research Question:
- Decide on the Scope of Your Review:
- Select Databases for Searches:
- Conduct Searches and Keep Track:
- Review the Literature:
- Organize and Write Your Literature Review:
- How to write a literature review faster with Paperpal?
Frequently asked questions
What is a literature review .
A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.
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A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2
1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge.
2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field.
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3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research.
4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered.
5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research.
6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature.
Literature review example
Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic.
Literature Review on Climate Change Impacts on Biodiversity:
Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies:
Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements.
Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources.
The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems.
Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning.
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How to write a good literature review
Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements.
Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review.
- Opening Statement: Begin with a general statement about the broader topic and its significance in the field.
- Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address.
- Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research.
- Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic.
- Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review.
Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria.
- Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement.
- Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic.
- Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature.
- Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps.
- Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review.
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Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research.
- Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective.
- Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field.
- Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice.
- Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field.
- Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community.
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Conducting a literature review
Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1
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Choose a Topic and Define the Research Question:
- Select a topic that is relevant to your field of study.
- Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore?
Decide on the Scope of Your Review:
- Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview?
- Consider the geographical scope. Is your review global, or are you focusing on a specific region?
- Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude?
Select Databases for Searches:
- Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar.
- Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic.
Conduct Searches and Keep Track:
- Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques.
- Record and document your search strategy for transparency and replicability.
- Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references.
Review the Literature:
- Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies.
- Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research.
- Summarize key findings and arguments from each source. Compare and contrast different perspectives.
- Identify areas where there is a consensus in the literature and where there are conflicting opinions.
- Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research?
Organize and Write Your Literature Review:
- Literature review outline should be based on themes, chronological order, or methodological approaches.
- Write a clear and coherent narrative that synthesizes the information gathered.
- Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.).
- Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research.
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A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.
Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.
Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic.
Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods.
Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers. Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved. Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic. Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings. Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject. It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.
The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review: Introduction: Provide an overview of the topic. Define the scope and purpose of the literature review. State the research question or objective. Body: Organize the literature by themes, concepts, or chronology. Critically analyze and evaluate each source. Discuss the strengths and weaknesses of the studies. Highlight any methodological limitations or biases. Identify patterns, connections, or contradictions in the existing research. Conclusion: Summarize the key points discussed in the literature review. Highlight the research gap. Address the research question or objective stated in the introduction. Highlight the contributions of the review and suggest directions for future research.
Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows:
Annotated Bibliography | Literature Review | |
Purpose | List of citations of books, articles, and other sources with a brief description (annotation) of each source. | Comprehensive and critical analysis of existing literature on a specific topic. |
Focus | Summary and evaluation of each source, including its relevance, methodology, and key findings. | Provides an overview of the current state of knowledge on a particular subject and identifies gaps, trends, and patterns in existing literature. |
Structure | Each citation is followed by a concise paragraph (annotation) that describes the source’s content, methodology, and its contribution to the topic. | The literature review is organized thematically or chronologically and involves a synthesis of the findings from different sources to build a narrative or argument. |
Length | Typically 100-200 words | Length of literature review ranges from a few pages to several chapters |
Independence | Each source is treated separately, with less emphasis on synthesizing the information across sources. | The writer synthesizes information from multiple sources to present a cohesive overview of the topic. |
References
- Denney, A. S., & Tewksbury, R. (2013). How to write a literature review. Journal of criminal justice education , 24 (2), 218-234.
- Pan, M. L. (2016). Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis.
- Cantero, C. (2019). How to write a literature review. San José State University Writing Center .
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Choosing a Review Type
For guidance related to choosing a review type, see:
- "What Type of Review is Right for You?" - Decision Tree (PDF) This decision tree, from Cornell University Library, highlights key difference between narrative, systematic, umbrella, scoping and rapid reviews.
- Reviewing the literature: choosing a review design Noble, H., & Smith, J. (2018). Reviewing the literature: Choosing a review design. Evidence Based Nursing, 21(2), 39–41. https://doi.org/10.1136/eb-2018-102895
- What synthesis methodology should I use? A review and analysis of approaches to research synthesis Schick-Makaroff, K., MacDonald, M., Plummer, M., Burgess, J., & Neander, W. (2016). What synthesis methodology should I use? A review and analysis of approaches to research synthesis. AIMS Public Health, 3 (1), 172-215. doi:10.3934/publichealth.2016.1.172 More information less... ABSTRACT: Our purpose is to present a comprehensive overview and assessment of the main approaches to research synthesis. We use "research synthesis" as a broad overarching term to describe various approaches to combining, integrating, and synthesizing research findings.
- Right Review - Decision Support Tool Not sure of the most suitable review method? Answer a few questions and be guided to suitable knowledge synthesis methods. Updated in 2022 and featured in the Journal of Clinical Epidemiology 10.1016/j.jclinepi.2022.03.004
Types of Evidence Synthesis / Literature Reviews
Literature reviews are comprehensive summaries and syntheses of the previous research on a given topic. While narrative reviews are common across all academic disciplines, reviews that focus on appraising and synthesizing research evidence are increasingly important in the health and social sciences.
Most evidence synthesis methods use formal and explicit methods to identify, select and combine results from multiple studies, making evidence synthesis a form of meta-research.
The review purpose, methods used and the results produced vary among different kinds of literature reviews; some of the common types of literature review are detailed below.
Common Types of Literature Reviews 1
Narrative (literature) review.
- A broad term referring to reviews with a wide scope and non-standardized methodology
- Search strategies, comprehensiveness of literature search, time range covered and method of synthesis will vary and do not follow an established protocol
Integrative Review
- A type of literature review based on a systematic, structured literature search
- Often has a broadly defined purpose or review question
- Seeks to generate or refine and theory or hypothesis and/or develop a holistic understanding of a topic of interest
- Relies on diverse sources of data (e.g. empirical, theoretical or methodological literature; qualitative or quantitative studies)
Systematic Review
- Systematically and transparently collects and categorize existing evidence on a question of scientific, policy or management importance
- Follows a research protocol that is established a priori
- Some sub-types of systematic reviews include: SRs of intervention effectiveness, diagnosis, prognosis, etiology, qualitative evidence, economic evidence, and more.
- Time-intensive and often takes months to a year or more to complete
- The most commonly referred to type of evidence synthesis; sometimes confused as a blanket term for other types of reviews
Meta-Analysis
- Statistical technique for combining the findings from disparate quantitative studies
- Uses statistical methods to objectively evaluate, synthesize, and summarize results
- Often conducted as part of a systematic review
Scoping Review
- Systematically and transparently collects and categorizes existing evidence on a broad question of scientific, policy or management importance
- Seeks to identify research gaps, identify key concepts and characteristics of the literature and/or examine how research is conducted on a topic of interest
- Useful when the complexity or heterogeneity of the body of literature does not lend itself to a precise systematic review
- Useful if authors do not have a single, precise review question
- May critically evaluate existing evidence, but does not attempt to synthesize the results in the way a systematic review would
- May take longer than a systematic review
Rapid Review
- Applies a systematic review methodology within a time-constrained setting
- Employs methodological "shortcuts" (e.g., limiting search terms and the scope of the literature search), at the risk of introducing bias
- Useful for addressing issues requiring quick decisions, such as developing policy recommendations
Umbrella Review
- Reviews other systematic reviews on a topic
- Often defines a broader question than is typical of a traditional systematic review
- Most useful when there are competing interventions to consider
1. Adapted from:
Eldermire, E. (2021, November 15). A guide to evidence synthesis: Types of evidence synthesis. Cornell University LibGuides. https://guides.library.cornell.edu/evidence-synthesis/types
Nolfi, D. (2021, October 6). Integrative Review: Systematic vs. Scoping vs. Integrative. Duquesne University LibGuides. https://guides.library.duq.edu/c.php?g=1055475&p=7725920
Delaney, L. (2021, November 24). Systematic reviews: Other review types. UniSA LibGuides. https://guides.library.unisa.edu.au/SystematicReviews/OtherReviewTypes
Further Reading: Exploring Different Types of Literature Reviews
- A typology of reviews: An analysis of 14 review types and associated methodologies Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26 (2), 91-108. doi:10.1111/j.1471-1842.2009.00848.x More information less... ABSTRACT: The expansion of evidence-based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains.
- Clarifying differences between review designs and methods Gough, D., Thomas, J., & Oliver, S. (2012). Clarifying differences between review designs and methods. Systematic Reviews, 1 , 28. doi:10.1186/2046-4053-1-28 More information less... ABSTRACT: This paper argues that the current proliferation of types of systematic reviews creates challenges for the terminology for describing such reviews....It is therefore proposed that the most useful strategy for the field is to develop terminology for the main dimensions of variation.
- Are we talking the same paradigm? Considering methodological choices in health education systematic review Gordon, M. (2016). Are we talking the same paradigm? Considering methodological choices in health education systematic review. Medical Teacher, 38 (7), 746-750. doi:10.3109/0142159X.2016.1147536 More information less... ABSTRACT: Key items discussed are the positivist synthesis methods meta-analysis and content analysis to address questions in the form of "whether and what" education is effective. These can be juxtaposed with the constructivist aligned thematic analysis and meta-ethnography to address questions in the form of "why." The concept of the realist review is also considered. It is proposed that authors of such work should describe their research alignment and the link between question, alignment and evidence synthesis method selected.
- Meeting the review family: Exploring review types and associated information retrieval requirements Sutton, A., Clowes, M., Preston, L., & Booth, A. (2019). Meeting the review family: Exploring review types and associated information retrieval requirements. Health Information & Libraries Journal, 36(3), 202–222. doi: 10.1111/hir.12276
Integrative Reviews
"The integrative review method is an approach that allows for the inclusion of diverse methodologies (i.e. experimental and non-experimental research)." (Whittemore & Knafl, 2005, p. 547).
- The integrative review: Updated methodology Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52 (5), 546–553. doi:10.1111/j.1365-2648.2005.03621.x More information less... ABSTRACT: The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process....An integrative review is a specific review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem....Well-done integrative reviews present the state of the science, contribute to theory development, and have direct applicability to practice and policy.
- Conducting integrative reviews: A guide for novice nursing researchers Dhollande, S., Taylor, A., Meyer, S., & Scott, M. (2021). Conducting integrative reviews: A guide for novice nursing researchers. Journal of Research in Nursing, 26(5), 427–438. https://doi.org/10.1177/1744987121997907
- Rigour in integrative reviews Whittemore, R. (2007). Rigour in integrative reviews. In C. Webb & B. Roe (Eds.), Reviewing Research Evidence for Nursing Practice (pp. 149–156). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470692127.ch11
Scoping Reviews
Scoping reviews are evidence syntheses that are conducted systematically, but begin with a broader scope of question than traditional systematic reviews, allowing the research to 'map' the relevant literature on a given topic.
- Scoping studies: Towards a methodological framework Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8 (1), 19-32. doi:10.1080/1364557032000119616 More information less... ABSTRACT: We distinguish between different types of scoping studies and indicate where these stand in relation to full systematic reviews. We outline a framework for conducting a scoping study based on our recent experiences of reviewing the literature on services for carers for people with mental health problems.
- Scoping studies: Advancing the methodology Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5 (1), 69. doi:10.1186/1748-5908-5-69 More information less... ABSTRACT: We build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework.
- Methodology for JBI scoping reviews Peters, M. D. J., Godfrey, C. M., McInerney, P., Baldini Soares, C., Khalil, H., & Parker, D. (2015). The Joanna Briggs Institute reviewers’ manual: Methodology for JBI scoping reviews [PDF]. Retrieved from The Joanna Briggs Institute website: http://joannabriggs.org/assets/docs/sumari/Reviewers-Manual_Methodology-for-JBI-Scoping-Reviews_2015_v2.pdf More information less... ABSTRACT: Unlike other reviews that address relatively precise questions, such as a systematic review of the effectiveness of a particular intervention based on a precise set of outcomes, scoping reviews can be used to map the key concepts underpinning a research area as well as to clarify working definitions, and/or the conceptual boundaries of a topic. A scoping review may focus on one of these aims or all of them as a set.
Systematic vs. Scoping Reviews: What's the Difference?
YouTube Video 4 minutes, 45 seconds
Rapid Reviews
Rapid reviews are systematic reviews that are undertaken under a tighter timeframe than traditional systematic reviews.
- Evidence summaries: The evolution of a rapid review approach Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: The evolution of a rapid review approach. Systematic Reviews, 1 (1), 10. doi:10.1186/2046-4053-1-10 More information less... ABSTRACT: Rapid reviews have emerged as a streamlined approach to synthesizing evidence - typically for informing emergent decisions faced by decision makers in health care settings. Although there is growing use of rapid review "methods," and proliferation of rapid review products, there is a dearth of published literature on rapid review methodology. This paper outlines our experience with rapidly producing, publishing and disseminating evidence summaries in the context of our Knowledge to Action (KTA) research program.
- What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments Harker, J., & Kleijnen, J. (2012). What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments. International Journal of Evidence‐Based Healthcare, 10 (4), 397-410. doi:10.1111/j.1744-1609.2012.00290.x More information less... ABSTRACT: In recent years, there has been an emergence of "rapid reviews" within Health Technology Assessments; however, there is no known published guidance or agreed methodology within recognised systematic review or Health Technology Assessment guidelines. In order to answer the research question "What is a rapid review and is methodology consistent in rapid reviews of Health Technology Assessments?", a study was undertaken in a sample of rapid review Health Technology Assessments from the Health Technology Assessment database within the Cochrane Library and other specialised Health Technology Assessment databases to investigate similarities and/or differences in rapid review methodology utilised.
- Rapid Review Guidebook Dobbins, M. (2017). Rapid review guidebook. Hamilton, ON: National Collaborating Centre for Methods and Tools.
- NCCMT Summary and Tool for Dobbins' Rapid Review Guidebook National Collaborating Centre for Methods and Tools. (2017). Rapid review guidebook. Hamilton, ON: McMaster University. Retrieved from http://www.nccmt.ca/knowledge-repositories/search/308
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Different Types of Literature Review: Which One Fits Your Research?
By Laura Brown on 13th October 2023
You might not have heard that there are multiple kinds of literature review. However, with the progress in your academic career you will learn these classifications and may need to use different types of them. However, there is nothing to worry if you aren’t aware of them now, as here we are going to discuss this topic in detail.
There are approximately 14 types of literature review on the basis of their specific objectives, methodologies, and the way they approach and analyse existing literature in academic research. Of those 14, there are 4 major types. But before we delve into the details of each one of them and how they are useful in academics, let’s first understand the basics of literature review.
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What is Literature Review?
A literature review is a critical and systematic summary and evaluation of existing research. It is an essential component of academic and research work, providing an overview of the current state of knowledge in a particular field.
In easy words, a literature review is like making a big, organised summary of all the important research and smart books or articles about a particular topic or question. It’s something scholars and researchers do, and it helps everyone see what we already know about that topic. It’s kind of like taking a snapshot of what we understand right now in a certain field.
It serves with some specific purpose in the research.
- Provides a comprehensive understanding of existing research on a topic.
- Identifies gaps, trends, and inconsistencies in the literature.
- Contextualise your own research within the broader academic discourse.
- Supports the development of theoretical frameworks or research hypotheses.
4 Major Types Of Literature Review
The four major types include, Narrative Review, Systematic Review, Meta-Analysis, and Scoping Review. These are known as the major ones because they’re like the “go-to” methods for researchers in academic and research circles. Think of them as the classic tools in the researcher’s toolbox. They’ve earned their reputation because they have a unique style for literature review introduction , clear steps and specific qualities that make them super handy for different research needs.
1. Narrative Review
Narrative reviews present a well-structured narrative that reads like a cohesive story, providing a comprehensive overview of a specific topic. These reviews often incorporate historical context and offer a broad understanding of the subject matter, making them valuable for researchers looking to establish a foundational understanding of their area of interest. They are particularly useful when a historical perspective or a broad context is necessary to comprehend the current state of knowledge in a field.
2. Systematic Review
Systematic reviews are renowned for their methodological rigour. They involve a meticulously structured process that includes the systematic selection of relevant studies, comprehensive data extraction, and a critical synthesis of their findings. This systematic approach is designed to minimise bias and subjectivity, making systematic reviews highly reliable and objective. They are considered the gold standard for evidence-based research as they provide a clear and rigorous assessment of the available evidence on a specific research question.
3. Meta Analysis
Meta analysis is a powerful method for researchers who prefer a quantitative and statistical perspective. It involves the statistical synthesis of data from various studies, allowing researchers to draw more precise and generalisable conclusions by combining data from multiple sources. Meta analyses are especially valuable when the aim is to quantitatively measure the effect size or impact of a particular intervention, treatment, or phenomenon.
4. Scoping Review
Scoping reviews are invaluable tools, especially for researchers in the early stages of exploring a topic. These reviews aim to map the existing literature, identifying gaps and helping clarify research questions. Scoping reviews provide a panoramic view of the available research, which is particularly useful when researchers are embarking on exploratory studies or trying to understand the breadth and depth of a subject before conducting more focused research.
Different Types Of Literature review In Research
There are some more approaches to conduct literature review. Let’s explore these classifications quickly.
5. Critical Review
Critical reviews provide an in-depth evaluation of existing literature, scrutinising sources for their strengths, weaknesses, and relevance. They offer a critical perspective, often highlighting gaps in the research and areas for further investigation.
6. Theoretical Review
Theoretical reviews are centred around exploring and analysing the theoretical frameworks, concepts, and models present in the literature. They aim to contribute to the development and refinement of theoretical perspectives within a specific field.
7. Integrative Review
Integrative reviews synthesise a diverse range of studies, drawing connections between various research findings to create a comprehensive understanding of a topic. These reviews often bridge gaps between different perspectives and provide a holistic overview.
8. Historical Review
Historical reviews focus on the evolution of a topic over time, tracing its development through past research, events, and scholarly contributions. They offer valuable context for understanding the current state of research.
9. Methodological Review
Among the different kinds of literature reviews, methodological reviews delve into the research methods and methodologies employed in existing studies. Researchers assess these approaches for their effectiveness, validity, and relevance to the research question at hand.
10. Cross-Disciplinary Review
Cross-disciplinary reviews explore a topic from multiple academic disciplines, emphasising the diversity of perspectives and insights that each discipline brings. They are particularly useful for interdisciplinary research projects and uncovering connections between seemingly unrelated fields.
11. Descriptive Review
Descriptive reviews provide an organised summary of existing literature without extensive analysis. They offer a straightforward overview of key findings, research methods, and themes present in the reviewed studies.
12. Rapid Review
Rapid reviews expedite the literature review process, focusing on summarising relevant studies quickly. They are often used for time-sensitive projects where efficiency is a priority, without sacrificing quality.
13. Conceptual Review
Conceptual reviews concentrate on clarifying and developing theoretical concepts within a specific field. They address ambiguities or inconsistencies in existing theories, aiming to refine and expand conceptual frameworks.
14. Library Research
Library research reviews rely primarily on library and archival resources to gather and synthesise information. They are often employed in historical or archive-based research projects, utilising library collections and historical documents for in-depth analysis.
Each type of literature review serves distinct purposes and comes with its own set of strengths and weaknesses, allowing researchers to choose the one that best suits their research objectives and questions.
Choosing the Ideal Literature Review Approach in Academics
In order to conduct your research in the right manner, it is important that you choose the correct type of review for your literature. Here are 8 amazing tips we have sorted for you in regard to literature review help so that you can select the best-suited type for your research.
- Clarify Your Research Goals: Begin by defining your research objectives and what you aim to achieve with the literature review. Are you looking to summarise existing knowledge, identify gaps, or analyse specific data?
- Understand Different Review Types: Familiarise yourself with different kinds of literature reviews, including systematic reviews, narrative reviews, meta-analyses, scoping reviews, and integrative reviews. Each serves a different purpose.
- Consider Available Resources: Assess the resources at your disposal, including time, access to databases, and the volume of literature on your topic. Some review types may be more resource-intensive than others.
- Alignment with Research Question: Ensure that the chosen review type aligns with your research question or hypothesis. Some types are better suited for answering specific research questions than others.
- Scope and Depth: Determine the scope and depth of your review. For a broad overview, a narrative review might be suitable, while a systematic review is ideal for an in-depth analysis.
- Consult with Advisors: Seek guidance from your academic advisors or mentors. They can provide valuable insights into which review type best fits your research goals and resources.
- Consider Research Field Standards: Different academic fields have established standards and preferences for different forms of literature review. Familiarise yourself with what is common and accepted in your field.
- Pilot Review: Consider conducting a small-scale pilot review of the literature to test the feasibility and suitability of your chosen review type before committing to a larger project.
Bonus Tip: Crafting an Effective Literature Review
Now, since you have learned all the literature review types and have understood which one to prefer, here are some bonus tips for you to structure a literature review of a dissertation .
- Clearly Define Your Research Question: Start with a well-defined and focused research question to guide your literature review.
- Thorough Search Strategy: Develop a comprehensive search strategy to ensure you capture all relevant literature.
- Critical Evaluation: Assess the quality and credibility of the sources you include in your review.
- Synthesise and Organise: Summarise the key findings and organise the literature into themes or categories.
- Maintain a Systematic Approach: If conducting a systematic review, adhere to a predefined methodology and reporting guidelines.
- Engage in Continuous Review: Regularly update your literature review to incorporate new research and maintain relevance.
Some Useful Tools And Resources For You
Effective literature reviews demand a range of tools and resources to streamline the process.
- Reference management software like EndNote, Zotero, and Mendeley helps organise, store, and cite sources, saving time and ensuring accuracy.
- Academic databases such as PubMed, Google Scholar, and Web of Science provide access to a vast array of scholarly articles, with advanced search and citation tracking features.
- Research guides from universities and libraries offer tips and templates for structuring reviews.
- Research networks like ResearchGate and Academia.edu facilitate collaboration and access to publications. Literature review templates and research workshops provide additional support.
Some Common Mistakes To Avoid
Avoid these common mistakes when crafting literature reviews.
- Unclear research objectives result in unfocused reviews, so start with well-defined questions.
- Biased source selection can compromise objectivity, so include diverse perspectives.
- Never miss on referencing; proper citation and referencing are essential for academic integrity.
- Don’t overlook older literature, which provides foundational insights.
- Be mindful of scope creep, where the review drifts from the research question; stay disciplined to maintain focus and relevance.
While Summing Up On Various Types Of Literature Review
As we conclude this classification of fourteen distinct approaches to conduct literature reviews, it’s clear that the world of research offers a multitude of avenues for understanding, analysing, and contributing to existing knowledge.
Whether you’re a seasoned scholar or a student beginning your academic journey, the choice of review type should align with your research objectives and the nature of your topic. The versatility of these approaches empowers you to tailor your review to the demands of your project.
Remember, your research endeavours have the potential to shape the future of knowledge, so choose wisely and dive into the world of literature reviews with confidence and purpose. Happy reviewing!
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Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.
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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].
Chapter 9 methods for literature reviews.
Guy Paré and Spyros Kitsiou .
9.1. Introduction
Literature reviews play a critical role in scholarship because science remains, first and foremost, a cumulative endeavour ( vom Brocke et al., 2009 ). As in any academic discipline, rigorous knowledge syntheses are becoming indispensable in keeping up with an exponentially growing eHealth literature, assisting practitioners, academics, and graduate students in finding, evaluating, and synthesizing the contents of many empirical and conceptual papers. Among other methods, literature reviews are essential for: (a) identifying what has been written on a subject or topic; (b) determining the extent to which a specific research area reveals any interpretable trends or patterns; (c) aggregating empirical findings related to a narrow research question to support evidence-based practice; (d) generating new frameworks and theories; and (e) identifying topics or questions requiring more investigation ( Paré, Trudel, Jaana, & Kitsiou, 2015 ).
Literature reviews can take two major forms. The most prevalent one is the “literature review” or “background” section within a journal paper or a chapter in a graduate thesis. This section synthesizes the extant literature and usually identifies the gaps in knowledge that the empirical study addresses ( Sylvester, Tate, & Johnstone, 2013 ). It may also provide a theoretical foundation for the proposed study, substantiate the presence of the research problem, justify the research as one that contributes something new to the cumulated knowledge, or validate the methods and approaches for the proposed study ( Hart, 1998 ; Levy & Ellis, 2006 ).
The second form of literature review, which is the focus of this chapter, constitutes an original and valuable work of research in and of itself ( Paré et al., 2015 ). Rather than providing a base for a researcher’s own work, it creates a solid starting point for all members of the community interested in a particular area or topic ( Mulrow, 1987 ). The so-called “review article” is a journal-length paper which has an overarching purpose to synthesize the literature in a field, without collecting or analyzing any primary data ( Green, Johnson, & Adams, 2006 ).
When appropriately conducted, review articles represent powerful information sources for practitioners looking for state-of-the art evidence to guide their decision-making and work practices ( Paré et al., 2015 ). Further, high-quality reviews become frequently cited pieces of work which researchers seek out as a first clear outline of the literature when undertaking empirical studies ( Cooper, 1988 ; Rowe, 2014 ). Scholars who track and gauge the impact of articles have found that review papers are cited and downloaded more often than any other type of published article ( Cronin, Ryan, & Coughlan, 2008 ; Montori, Wilczynski, Morgan, Haynes, & Hedges, 2003 ; Patsopoulos, Analatos, & Ioannidis, 2005 ). The reason for their popularity may be the fact that reading the review enables one to have an overview, if not a detailed knowledge of the area in question, as well as references to the most useful primary sources ( Cronin et al., 2008 ). Although they are not easy to conduct, the commitment to complete a review article provides a tremendous service to one’s academic community ( Paré et al., 2015 ; Petticrew & Roberts, 2006 ). Most, if not all, peer-reviewed journals in the fields of medical informatics publish review articles of some type.
The main objectives of this chapter are fourfold: (a) to provide an overview of the major steps and activities involved in conducting a stand-alone literature review; (b) to describe and contrast the different types of review articles that can contribute to the eHealth knowledge base; (c) to illustrate each review type with one or two examples from the eHealth literature; and (d) to provide a series of recommendations for prospective authors of review articles in this domain.
9.2. Overview of the Literature Review Process and Steps
As explained in Templier and Paré (2015) , there are six generic steps involved in conducting a review article:
- formulating the research question(s) and objective(s),
- searching the extant literature,
- screening for inclusion,
- assessing the quality of primary studies,
- extracting data, and
- analyzing data.
Although these steps are presented here in sequential order, one must keep in mind that the review process can be iterative and that many activities can be initiated during the planning stage and later refined during subsequent phases ( Finfgeld-Connett & Johnson, 2013 ; Kitchenham & Charters, 2007 ).
Formulating the research question(s) and objective(s): As a first step, members of the review team must appropriately justify the need for the review itself ( Petticrew & Roberts, 2006 ), identify the review’s main objective(s) ( Okoli & Schabram, 2010 ), and define the concepts or variables at the heart of their synthesis ( Cooper & Hedges, 2009 ; Webster & Watson, 2002 ). Importantly, they also need to articulate the research question(s) they propose to investigate ( Kitchenham & Charters, 2007 ). In this regard, we concur with Jesson, Matheson, and Lacey (2011) that clearly articulated research questions are key ingredients that guide the entire review methodology; they underscore the type of information that is needed, inform the search for and selection of relevant literature, and guide or orient the subsequent analysis. Searching the extant literature: The next step consists of searching the literature and making decisions about the suitability of material to be considered in the review ( Cooper, 1988 ). There exist three main coverage strategies. First, exhaustive coverage means an effort is made to be as comprehensive as possible in order to ensure that all relevant studies, published and unpublished, are included in the review and, thus, conclusions are based on this all-inclusive knowledge base. The second type of coverage consists of presenting materials that are representative of most other works in a given field or area. Often authors who adopt this strategy will search for relevant articles in a small number of top-tier journals in a field ( Paré et al., 2015 ). In the third strategy, the review team concentrates on prior works that have been central or pivotal to a particular topic. This may include empirical studies or conceptual papers that initiated a line of investigation, changed how problems or questions were framed, introduced new methods or concepts, or engendered important debate ( Cooper, 1988 ). Screening for inclusion: The following step consists of evaluating the applicability of the material identified in the preceding step ( Levy & Ellis, 2006 ; vom Brocke et al., 2009 ). Once a group of potential studies has been identified, members of the review team must screen them to determine their relevance ( Petticrew & Roberts, 2006 ). A set of predetermined rules provides a basis for including or excluding certain studies. This exercise requires a significant investment on the part of researchers, who must ensure enhanced objectivity and avoid biases or mistakes. As discussed later in this chapter, for certain types of reviews there must be at least two independent reviewers involved in the screening process and a procedure to resolve disagreements must also be in place ( Liberati et al., 2009 ; Shea et al., 2009 ). Assessing the quality of primary studies: In addition to screening material for inclusion, members of the review team may need to assess the scientific quality of the selected studies, that is, appraise the rigour of the research design and methods. Such formal assessment, which is usually conducted independently by at least two coders, helps members of the review team refine which studies to include in the final sample, determine whether or not the differences in quality may affect their conclusions, or guide how they analyze the data and interpret the findings ( Petticrew & Roberts, 2006 ). Ascribing quality scores to each primary study or considering through domain-based evaluations which study components have or have not been designed and executed appropriately makes it possible to reflect on the extent to which the selected study addresses possible biases and maximizes validity ( Shea et al., 2009 ). Extracting data: The following step involves gathering or extracting applicable information from each primary study included in the sample and deciding what is relevant to the problem of interest ( Cooper & Hedges, 2009 ). Indeed, the type of data that should be recorded mainly depends on the initial research questions ( Okoli & Schabram, 2010 ). However, important information may also be gathered about how, when, where and by whom the primary study was conducted, the research design and methods, or qualitative/quantitative results ( Cooper & Hedges, 2009 ). Analyzing and synthesizing data : As a final step, members of the review team must collate, summarize, aggregate, organize, and compare the evidence extracted from the included studies. The extracted data must be presented in a meaningful way that suggests a new contribution to the extant literature ( Jesson et al., 2011 ). Webster and Watson (2002) warn researchers that literature reviews should be much more than lists of papers and should provide a coherent lens to make sense of extant knowledge on a given topic. There exist several methods and techniques for synthesizing quantitative (e.g., frequency analysis, meta-analysis) and qualitative (e.g., grounded theory, narrative analysis, meta-ethnography) evidence ( Dixon-Woods, Agarwal, Jones, Young, & Sutton, 2005 ; Thomas & Harden, 2008 ).
9.3. Types of Review Articles and Brief Illustrations
EHealth researchers have at their disposal a number of approaches and methods for making sense out of existing literature, all with the purpose of casting current research findings into historical contexts or explaining contradictions that might exist among a set of primary research studies conducted on a particular topic. Our classification scheme is largely inspired from Paré and colleagues’ (2015) typology. Below we present and illustrate those review types that we feel are central to the growth and development of the eHealth domain.
9.3.1. Narrative Reviews
The narrative review is the “traditional” way of reviewing the extant literature and is skewed towards a qualitative interpretation of prior knowledge ( Sylvester et al., 2013 ). Put simply, a narrative review attempts to summarize or synthesize what has been written on a particular topic but does not seek generalization or cumulative knowledge from what is reviewed ( Davies, 2000 ; Green et al., 2006 ). Instead, the review team often undertakes the task of accumulating and synthesizing the literature to demonstrate the value of a particular point of view ( Baumeister & Leary, 1997 ). As such, reviewers may selectively ignore or limit the attention paid to certain studies in order to make a point. In this rather unsystematic approach, the selection of information from primary articles is subjective, lacks explicit criteria for inclusion and can lead to biased interpretations or inferences ( Green et al., 2006 ). There are several narrative reviews in the particular eHealth domain, as in all fields, which follow such an unstructured approach ( Silva et al., 2015 ; Paul et al., 2015 ).
Despite these criticisms, this type of review can be very useful in gathering together a volume of literature in a specific subject area and synthesizing it. As mentioned above, its primary purpose is to provide the reader with a comprehensive background for understanding current knowledge and highlighting the significance of new research ( Cronin et al., 2008 ). Faculty like to use narrative reviews in the classroom because they are often more up to date than textbooks, provide a single source for students to reference, and expose students to peer-reviewed literature ( Green et al., 2006 ). For researchers, narrative reviews can inspire research ideas by identifying gaps or inconsistencies in a body of knowledge, thus helping researchers to determine research questions or formulate hypotheses. Importantly, narrative reviews can also be used as educational articles to bring practitioners up to date with certain topics of issues ( Green et al., 2006 ).
Recently, there have been several efforts to introduce more rigour in narrative reviews that will elucidate common pitfalls and bring changes into their publication standards. Information systems researchers, among others, have contributed to advancing knowledge on how to structure a “traditional” review. For instance, Levy and Ellis (2006) proposed a generic framework for conducting such reviews. Their model follows the systematic data processing approach comprised of three steps, namely: (a) literature search and screening; (b) data extraction and analysis; and (c) writing the literature review. They provide detailed and very helpful instructions on how to conduct each step of the review process. As another methodological contribution, vom Brocke et al. (2009) offered a series of guidelines for conducting literature reviews, with a particular focus on how to search and extract the relevant body of knowledge. Last, Bandara, Miskon, and Fielt (2011) proposed a structured, predefined and tool-supported method to identify primary studies within a feasible scope, extract relevant content from identified articles, synthesize and analyze the findings, and effectively write and present the results of the literature review. We highly recommend that prospective authors of narrative reviews consult these useful sources before embarking on their work.
Darlow and Wen (2015) provide a good example of a highly structured narrative review in the eHealth field. These authors synthesized published articles that describe the development process of mobile health (m-health) interventions for patients’ cancer care self-management. As in most narrative reviews, the scope of the research questions being investigated is broad: (a) how development of these systems are carried out; (b) which methods are used to investigate these systems; and (c) what conclusions can be drawn as a result of the development of these systems. To provide clear answers to these questions, a literature search was conducted on six electronic databases and Google Scholar . The search was performed using several terms and free text words, combining them in an appropriate manner. Four inclusion and three exclusion criteria were utilized during the screening process. Both authors independently reviewed each of the identified articles to determine eligibility and extract study information. A flow diagram shows the number of studies identified, screened, and included or excluded at each stage of study selection. In terms of contributions, this review provides a series of practical recommendations for m-health intervention development.
9.3.2. Descriptive or Mapping Reviews
The primary goal of a descriptive review is to determine the extent to which a body of knowledge in a particular research topic reveals any interpretable pattern or trend with respect to pre-existing propositions, theories, methodologies or findings ( King & He, 2005 ; Paré et al., 2015 ). In contrast with narrative reviews, descriptive reviews follow a systematic and transparent procedure, including searching, screening and classifying studies ( Petersen, Vakkalanka, & Kuzniarz, 2015 ). Indeed, structured search methods are used to form a representative sample of a larger group of published works ( Paré et al., 2015 ). Further, authors of descriptive reviews extract from each study certain characteristics of interest, such as publication year, research methods, data collection techniques, and direction or strength of research outcomes (e.g., positive, negative, or non-significant) in the form of frequency analysis to produce quantitative results ( Sylvester et al., 2013 ). In essence, each study included in a descriptive review is treated as the unit of analysis and the published literature as a whole provides a database from which the authors attempt to identify any interpretable trends or draw overall conclusions about the merits of existing conceptualizations, propositions, methods or findings ( Paré et al., 2015 ). In doing so, a descriptive review may claim that its findings represent the state of the art in a particular domain ( King & He, 2005 ).
In the fields of health sciences and medical informatics, reviews that focus on examining the range, nature and evolution of a topic area are described by Anderson, Allen, Peckham, and Goodwin (2008) as mapping reviews . Like descriptive reviews, the research questions are generic and usually relate to publication patterns and trends. There is no preconceived plan to systematically review all of the literature although this can be done. Instead, researchers often present studies that are representative of most works published in a particular area and they consider a specific time frame to be mapped.
An example of this approach in the eHealth domain is offered by DeShazo, Lavallie, and Wolf (2009). The purpose of this descriptive or mapping review was to characterize publication trends in the medical informatics literature over a 20-year period (1987 to 2006). To achieve this ambitious objective, the authors performed a bibliometric analysis of medical informatics citations indexed in medline using publication trends, journal frequencies, impact factors, Medical Subject Headings (MeSH) term frequencies, and characteristics of citations. Findings revealed that there were over 77,000 medical informatics articles published during the covered period in numerous journals and that the average annual growth rate was 12%. The MeSH term analysis also suggested a strong interdisciplinary trend. Finally, average impact scores increased over time with two notable growth periods. Overall, patterns in research outputs that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline (DeShazo et al., 2009).
9.3.3. Scoping Reviews
Scoping reviews attempt to provide an initial indication of the potential size and nature of the extant literature on an emergent topic (Arksey & O’Malley, 2005; Daudt, van Mossel, & Scott, 2013 ; Levac, Colquhoun, & O’Brien, 2010). A scoping review may be conducted to examine the extent, range and nature of research activities in a particular area, determine the value of undertaking a full systematic review (discussed next), or identify research gaps in the extant literature ( Paré et al., 2015 ). In line with their main objective, scoping reviews usually conclude with the presentation of a detailed research agenda for future works along with potential implications for both practice and research.
Unlike narrative and descriptive reviews, the whole point of scoping the field is to be as comprehensive as possible, including grey literature (Arksey & O’Malley, 2005). Inclusion and exclusion criteria must be established to help researchers eliminate studies that are not aligned with the research questions. It is also recommended that at least two independent coders review abstracts yielded from the search strategy and then the full articles for study selection ( Daudt et al., 2013 ). The synthesized evidence from content or thematic analysis is relatively easy to present in tabular form (Arksey & O’Malley, 2005; Thomas & Harden, 2008 ).
One of the most highly cited scoping reviews in the eHealth domain was published by Archer, Fevrier-Thomas, Lokker, McKibbon, and Straus (2011) . These authors reviewed the existing literature on personal health record ( phr ) systems including design, functionality, implementation, applications, outcomes, and benefits. Seven databases were searched from 1985 to March 2010. Several search terms relating to phr s were used during this process. Two authors independently screened titles and abstracts to determine inclusion status. A second screen of full-text articles, again by two independent members of the research team, ensured that the studies described phr s. All in all, 130 articles met the criteria and their data were extracted manually into a database. The authors concluded that although there is a large amount of survey, observational, cohort/panel, and anecdotal evidence of phr benefits and satisfaction for patients, more research is needed to evaluate the results of phr implementations. Their in-depth analysis of the literature signalled that there is little solid evidence from randomized controlled trials or other studies through the use of phr s. Hence, they suggested that more research is needed that addresses the current lack of understanding of optimal functionality and usability of these systems, and how they can play a beneficial role in supporting patient self-management ( Archer et al., 2011 ).
9.3.4. Forms of Aggregative Reviews
Healthcare providers, practitioners, and policy-makers are nowadays overwhelmed with large volumes of information, including research-based evidence from numerous clinical trials and evaluation studies, assessing the effectiveness of health information technologies and interventions ( Ammenwerth & de Keizer, 2004 ; Deshazo et al., 2009 ). It is unrealistic to expect that all these disparate actors will have the time, skills, and necessary resources to identify the available evidence in the area of their expertise and consider it when making decisions. Systematic reviews that involve the rigorous application of scientific strategies aimed at limiting subjectivity and bias (i.e., systematic and random errors) can respond to this challenge.
Systematic reviews attempt to aggregate, appraise, and synthesize in a single source all empirical evidence that meet a set of previously specified eligibility criteria in order to answer a clearly formulated and often narrow research question on a particular topic of interest to support evidence-based practice ( Liberati et al., 2009 ). They adhere closely to explicit scientific principles ( Liberati et al., 2009 ) and rigorous methodological guidelines (Higgins & Green, 2008) aimed at reducing random and systematic errors that can lead to deviations from the truth in results or inferences. The use of explicit methods allows systematic reviews to aggregate a large body of research evidence, assess whether effects or relationships are in the same direction and of the same general magnitude, explain possible inconsistencies between study results, and determine the strength of the overall evidence for every outcome of interest based on the quality of included studies and the general consistency among them ( Cook, Mulrow, & Haynes, 1997 ). The main procedures of a systematic review involve:
- Formulating a review question and developing a search strategy based on explicit inclusion criteria for the identification of eligible studies (usually described in the context of a detailed review protocol).
- Searching for eligible studies using multiple databases and information sources, including grey literature sources, without any language restrictions.
- Selecting studies, extracting data, and assessing risk of bias in a duplicate manner using two independent reviewers to avoid random or systematic errors in the process.
- Analyzing data using quantitative or qualitative methods.
- Presenting results in summary of findings tables.
- Interpreting results and drawing conclusions.
Many systematic reviews, but not all, use statistical methods to combine the results of independent studies into a single quantitative estimate or summary effect size. Known as meta-analyses , these reviews use specific data extraction and statistical techniques (e.g., network, frequentist, or Bayesian meta-analyses) to calculate from each study by outcome of interest an effect size along with a confidence interval that reflects the degree of uncertainty behind the point estimate of effect ( Borenstein, Hedges, Higgins, & Rothstein, 2009 ; Deeks, Higgins, & Altman, 2008 ). Subsequently, they use fixed or random-effects analysis models to combine the results of the included studies, assess statistical heterogeneity, and calculate a weighted average of the effect estimates from the different studies, taking into account their sample sizes. The summary effect size is a value that reflects the average magnitude of the intervention effect for a particular outcome of interest or, more generally, the strength of a relationship between two variables across all studies included in the systematic review. By statistically combining data from multiple studies, meta-analyses can create more precise and reliable estimates of intervention effects than those derived from individual studies alone, when these are examined independently as discrete sources of information.
The review by Gurol-Urganci, de Jongh, Vodopivec-Jamsek, Atun, and Car (2013) on the effects of mobile phone messaging reminders for attendance at healthcare appointments is an illustrative example of a high-quality systematic review with meta-analysis. Missed appointments are a major cause of inefficiency in healthcare delivery with substantial monetary costs to health systems. These authors sought to assess whether mobile phone-based appointment reminders delivered through Short Message Service ( sms ) or Multimedia Messaging Service ( mms ) are effective in improving rates of patient attendance and reducing overall costs. To this end, they conducted a comprehensive search on multiple databases using highly sensitive search strategies without language or publication-type restrictions to identify all rct s that are eligible for inclusion. In order to minimize the risk of omitting eligible studies not captured by the original search, they supplemented all electronic searches with manual screening of trial registers and references contained in the included studies. Study selection, data extraction, and risk of bias assessments were performed independently by two coders using standardized methods to ensure consistency and to eliminate potential errors. Findings from eight rct s involving 6,615 participants were pooled into meta-analyses to calculate the magnitude of effects that mobile text message reminders have on the rate of attendance at healthcare appointments compared to no reminders and phone call reminders.
Meta-analyses are regarded as powerful tools for deriving meaningful conclusions. However, there are situations in which it is neither reasonable nor appropriate to pool studies together using meta-analytic methods simply because there is extensive clinical heterogeneity between the included studies or variation in measurement tools, comparisons, or outcomes of interest. In these cases, systematic reviews can use qualitative synthesis methods such as vote counting, content analysis, classification schemes and tabulations, as an alternative approach to narratively synthesize the results of the independent studies included in the review. This form of review is known as qualitative systematic review.
A rigorous example of one such review in the eHealth domain is presented by Mickan, Atherton, Roberts, Heneghan, and Tilson (2014) on the use of handheld computers by healthcare professionals and their impact on access to information and clinical decision-making. In line with the methodological guidelines for systematic reviews, these authors: (a) developed and registered with prospero ( www.crd.york.ac.uk/ prospero / ) an a priori review protocol; (b) conducted comprehensive searches for eligible studies using multiple databases and other supplementary strategies (e.g., forward searches); and (c) subsequently carried out study selection, data extraction, and risk of bias assessments in a duplicate manner to eliminate potential errors in the review process. Heterogeneity between the included studies in terms of reported outcomes and measures precluded the use of meta-analytic methods. To this end, the authors resorted to using narrative analysis and synthesis to describe the effectiveness of handheld computers on accessing information for clinical knowledge, adherence to safety and clinical quality guidelines, and diagnostic decision-making.
In recent years, the number of systematic reviews in the field of health informatics has increased considerably. Systematic reviews with discordant findings can cause great confusion and make it difficult for decision-makers to interpret the review-level evidence ( Moher, 2013 ). Therefore, there is a growing need for appraisal and synthesis of prior systematic reviews to ensure that decision-making is constantly informed by the best available accumulated evidence. Umbrella reviews , also known as overviews of systematic reviews, are tertiary types of evidence synthesis that aim to accomplish this; that is, they aim to compare and contrast findings from multiple systematic reviews and meta-analyses ( Becker & Oxman, 2008 ). Umbrella reviews generally adhere to the same principles and rigorous methodological guidelines used in systematic reviews. However, the unit of analysis in umbrella reviews is the systematic review rather than the primary study ( Becker & Oxman, 2008 ). Unlike systematic reviews that have a narrow focus of inquiry, umbrella reviews focus on broader research topics for which there are several potential interventions ( Smith, Devane, Begley, & Clarke, 2011 ). A recent umbrella review on the effects of home telemonitoring interventions for patients with heart failure critically appraised, compared, and synthesized evidence from 15 systematic reviews to investigate which types of home telemonitoring technologies and forms of interventions are more effective in reducing mortality and hospital admissions ( Kitsiou, Paré, & Jaana, 2015 ).
9.3.5. Realist Reviews
Realist reviews are theory-driven interpretative reviews developed to inform, enhance, or supplement conventional systematic reviews by making sense of heterogeneous evidence about complex interventions applied in diverse contexts in a way that informs policy decision-making ( Greenhalgh, Wong, Westhorp, & Pawson, 2011 ). They originated from criticisms of positivist systematic reviews which centre on their “simplistic” underlying assumptions ( Oates, 2011 ). As explained above, systematic reviews seek to identify causation. Such logic is appropriate for fields like medicine and education where findings of randomized controlled trials can be aggregated to see whether a new treatment or intervention does improve outcomes. However, many argue that it is not possible to establish such direct causal links between interventions and outcomes in fields such as social policy, management, and information systems where for any intervention there is unlikely to be a regular or consistent outcome ( Oates, 2011 ; Pawson, 2006 ; Rousseau, Manning, & Denyer, 2008 ).
To circumvent these limitations, Pawson, Greenhalgh, Harvey, and Walshe (2005) have proposed a new approach for synthesizing knowledge that seeks to unpack the mechanism of how “complex interventions” work in particular contexts. The basic research question — what works? — which is usually associated with systematic reviews changes to: what is it about this intervention that works, for whom, in what circumstances, in what respects and why? Realist reviews have no particular preference for either quantitative or qualitative evidence. As a theory-building approach, a realist review usually starts by articulating likely underlying mechanisms and then scrutinizes available evidence to find out whether and where these mechanisms are applicable ( Shepperd et al., 2009 ). Primary studies found in the extant literature are viewed as case studies which can test and modify the initial theories ( Rousseau et al., 2008 ).
The main objective pursued in the realist review conducted by Otte-Trojel, de Bont, Rundall, and van de Klundert (2014) was to examine how patient portals contribute to health service delivery and patient outcomes. The specific goals were to investigate how outcomes are produced and, most importantly, how variations in outcomes can be explained. The research team started with an exploratory review of background documents and research studies to identify ways in which patient portals may contribute to health service delivery and patient outcomes. The authors identified six main ways which represent “educated guesses” to be tested against the data in the evaluation studies. These studies were identified through a formal and systematic search in four databases between 2003 and 2013. Two members of the research team selected the articles using a pre-established list of inclusion and exclusion criteria and following a two-step procedure. The authors then extracted data from the selected articles and created several tables, one for each outcome category. They organized information to bring forward those mechanisms where patient portals contribute to outcomes and the variation in outcomes across different contexts.
9.3.6. Critical Reviews
Lastly, critical reviews aim to provide a critical evaluation and interpretive analysis of existing literature on a particular topic of interest to reveal strengths, weaknesses, contradictions, controversies, inconsistencies, and/or other important issues with respect to theories, hypotheses, research methods or results ( Baumeister & Leary, 1997 ; Kirkevold, 1997 ). Unlike other review types, critical reviews attempt to take a reflective account of the research that has been done in a particular area of interest, and assess its credibility by using appraisal instruments or critical interpretive methods. In this way, critical reviews attempt to constructively inform other scholars about the weaknesses of prior research and strengthen knowledge development by giving focus and direction to studies for further improvement ( Kirkevold, 1997 ).
Kitsiou, Paré, and Jaana (2013) provide an example of a critical review that assessed the methodological quality of prior systematic reviews of home telemonitoring studies for chronic patients. The authors conducted a comprehensive search on multiple databases to identify eligible reviews and subsequently used a validated instrument to conduct an in-depth quality appraisal. Results indicate that the majority of systematic reviews in this particular area suffer from important methodological flaws and biases that impair their internal validity and limit their usefulness for clinical and decision-making purposes. To this end, they provide a number of recommendations to strengthen knowledge development towards improving the design and execution of future reviews on home telemonitoring.
9.4. Summary
Table 9.1 outlines the main types of literature reviews that were described in the previous sub-sections and summarizes the main characteristics that distinguish one review type from another. It also includes key references to methodological guidelines and useful sources that can be used by eHealth scholars and researchers for planning and developing reviews.
Typology of Literature Reviews (adapted from Paré et al., 2015).
As shown in Table 9.1 , each review type addresses different kinds of research questions or objectives, which subsequently define and dictate the methods and approaches that need to be used to achieve the overarching goal(s) of the review. For example, in the case of narrative reviews, there is greater flexibility in searching and synthesizing articles ( Green et al., 2006 ). Researchers are often relatively free to use a diversity of approaches to search, identify, and select relevant scientific articles, describe their operational characteristics, present how the individual studies fit together, and formulate conclusions. On the other hand, systematic reviews are characterized by their high level of systematicity, rigour, and use of explicit methods, based on an “a priori” review plan that aims to minimize bias in the analysis and synthesis process (Higgins & Green, 2008). Some reviews are exploratory in nature (e.g., scoping/mapping reviews), whereas others may be conducted to discover patterns (e.g., descriptive reviews) or involve a synthesis approach that may include the critical analysis of prior research ( Paré et al., 2015 ). Hence, in order to select the most appropriate type of review, it is critical to know before embarking on a review project, why the research synthesis is conducted and what type of methods are best aligned with the pursued goals.
9.5. Concluding Remarks
In light of the increased use of evidence-based practice and research generating stronger evidence ( Grady et al., 2011 ; Lyden et al., 2013 ), review articles have become essential tools for summarizing, synthesizing, integrating or critically appraising prior knowledge in the eHealth field. As mentioned earlier, when rigorously conducted review articles represent powerful information sources for eHealth scholars and practitioners looking for state-of-the-art evidence. The typology of literature reviews we used herein will allow eHealth researchers, graduate students and practitioners to gain a better understanding of the similarities and differences between review types.
We must stress that this classification scheme does not privilege any specific type of review as being of higher quality than another ( Paré et al., 2015 ). As explained above, each type of review has its own strengths and limitations. Having said that, we realize that the methodological rigour of any review — be it qualitative, quantitative or mixed — is a critical aspect that should be considered seriously by prospective authors. In the present context, the notion of rigour refers to the reliability and validity of the review process described in section 9.2. For one thing, reliability is related to the reproducibility of the review process and steps, which is facilitated by a comprehensive documentation of the literature search process, extraction, coding and analysis performed in the review. Whether the search is comprehensive or not, whether it involves a methodical approach for data extraction and synthesis or not, it is important that the review documents in an explicit and transparent manner the steps and approach that were used in the process of its development. Next, validity characterizes the degree to which the review process was conducted appropriately. It goes beyond documentation and reflects decisions related to the selection of the sources, the search terms used, the period of time covered, the articles selected in the search, and the application of backward and forward searches ( vom Brocke et al., 2009 ). In short, the rigour of any review article is reflected by the explicitness of its methods (i.e., transparency) and the soundness of the approach used. We refer those interested in the concepts of rigour and quality to the work of Templier and Paré (2015) which offers a detailed set of methodological guidelines for conducting and evaluating various types of review articles.
To conclude, our main objective in this chapter was to demystify the various types of literature reviews that are central to the continuous development of the eHealth field. It is our hope that our descriptive account will serve as a valuable source for those conducting, evaluating or using reviews in this important and growing domain.
- Ammenwerth E., de Keizer N. An inventory of evaluation studies of information technology in health care. Trends in evaluation research, 1982-2002. International Journal of Medical Informatics. 2004; 44 (1):44–56. [ PubMed : 15778794 ]
- Anderson S., Allen P., Peckham S., Goodwin N. Asking the right questions: scoping studies in the commissioning of research on the organisation and delivery of health services. Health Research Policy and Systems. 2008; 6 (7):1–12. [ PMC free article : PMC2500008 ] [ PubMed : 18613961 ] [ CrossRef ]
- Archer N., Fevrier-Thomas U., Lokker C., McKibbon K. A., Straus S.E. Personal health records: a scoping review. Journal of American Medical Informatics Association. 2011; 18 (4):515–522. [ PMC free article : PMC3128401 ] [ PubMed : 21672914 ]
- Arksey H., O’Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005; 8 (1):19–32.
- A systematic, tool-supported method for conducting literature reviews in information systems. Paper presented at the Proceedings of the 19th European Conference on Information Systems ( ecis 2011); June 9 to 11; Helsinki, Finland. 2011.
- Baumeister R. F., Leary M.R. Writing narrative literature reviews. Review of General Psychology. 1997; 1 (3):311–320.
- Becker L. A., Oxman A.D. In: Cochrane handbook for systematic reviews of interventions. Higgins J. P. T., Green S., editors. Hoboken, nj : John Wiley & Sons, Ltd; 2008. Overviews of reviews; pp. 607–631.
- Borenstein M., Hedges L., Higgins J., Rothstein H. Introduction to meta-analysis. Hoboken, nj : John Wiley & Sons Inc; 2009.
- Cook D. J., Mulrow C. D., Haynes B. Systematic reviews: Synthesis of best evidence for clinical decisions. Annals of Internal Medicine. 1997; 126 (5):376–380. [ PubMed : 9054282 ]
- Cooper H., Hedges L.V. In: The handbook of research synthesis and meta-analysis. 2nd ed. Cooper H., Hedges L. V., Valentine J. C., editors. New York: Russell Sage Foundation; 2009. Research synthesis as a scientific process; pp. 3–17.
- Cooper H. M. Organizing knowledge syntheses: A taxonomy of literature reviews. Knowledge in Society. 1988; 1 (1):104–126.
- Cronin P., Ryan F., Coughlan M. Undertaking a literature review: a step-by-step approach. British Journal of Nursing. 2008; 17 (1):38–43. [ PubMed : 18399395 ]
- Darlow S., Wen K.Y. Development testing of mobile health interventions for cancer patient self-management: A review. Health Informatics Journal. 2015 (online before print). [ PubMed : 25916831 ] [ CrossRef ]
- Daudt H. M., van Mossel C., Scott S.J. Enhancing the scoping study methodology: a large, inter-professional team’s experience with Arksey and O’Malley’s framework. bmc Medical Research Methodology. 2013; 13 :48. [ PMC free article : PMC3614526 ] [ PubMed : 23522333 ] [ CrossRef ]
- Davies P. The relevance of systematic reviews to educational policy and practice. Oxford Review of Education. 2000; 26 (3-4):365–378.
- Deeks J. J., Higgins J. P. T., Altman D.G. In: Cochrane handbook for systematic reviews of interventions. Higgins J. P. T., Green S., editors. Hoboken, nj : John Wiley & Sons, Ltd; 2008. Analysing data and undertaking meta-analyses; pp. 243–296.
- Deshazo J. P., Lavallie D. L., Wolf F.M. Publication trends in the medical informatics literature: 20 years of “Medical Informatics” in mesh . bmc Medical Informatics and Decision Making. 2009; 9 :7. [ PMC free article : PMC2652453 ] [ PubMed : 19159472 ] [ CrossRef ]
- Dixon-Woods M., Agarwal S., Jones D., Young B., Sutton A. Synthesising qualitative and quantitative evidence: a review of possible methods. Journal of Health Services Research and Policy. 2005; 10 (1):45–53. [ PubMed : 15667704 ]
- Finfgeld-Connett D., Johnson E.D. Literature search strategies for conducting knowledge-building and theory-generating qualitative systematic reviews. Journal of Advanced Nursing. 2013; 69 (1):194–204. [ PMC free article : PMC3424349 ] [ PubMed : 22591030 ]
- Grady B., Myers K. M., Nelson E. L., Belz N., Bennett L., Carnahan L. … Guidelines Working Group. Evidence-based practice for telemental health. Telemedicine Journal and E Health. 2011; 17 (2):131–148. [ PubMed : 21385026 ]
- Green B. N., Johnson C. D., Adams A. Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of Chiropractic Medicine. 2006; 5 (3):101–117. [ PMC free article : PMC2647067 ] [ PubMed : 19674681 ]
- Greenhalgh T., Wong G., Westhorp G., Pawson R. Protocol–realist and meta-narrative evidence synthesis: evolving standards ( rameses ). bmc Medical Research Methodology. 2011; 11 :115. [ PMC free article : PMC3173389 ] [ PubMed : 21843376 ]
- Gurol-Urganci I., de Jongh T., Vodopivec-Jamsek V., Atun R., Car J. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database System Review. 2013; 12 cd 007458. [ PMC free article : PMC6485985 ] [ PubMed : 24310741 ] [ CrossRef ]
- Hart C. Doing a literature review: Releasing the social science research imagination. London: SAGE Publications; 1998.
- Higgins J. P. T., Green S., editors. Cochrane handbook for systematic reviews of interventions: Cochrane book series. Hoboken, nj : Wiley-Blackwell; 2008.
- Jesson J., Matheson L., Lacey F.M. Doing your literature review: traditional and systematic techniques. Los Angeles & London: SAGE Publications; 2011.
- King W. R., He J. Understanding the role and methods of meta-analysis in IS research. Communications of the Association for Information Systems. 2005; 16 :1.
- Kirkevold M. Integrative nursing research — an important strategy to further the development of nursing science and nursing practice. Journal of Advanced Nursing. 1997; 25 (5):977–984. [ PubMed : 9147203 ]
- Kitchenham B., Charters S. ebse Technical Report Version 2.3. Keele & Durham. uk : Keele University & University of Durham; 2007. Guidelines for performing systematic literature reviews in software engineering.
- Kitsiou S., Paré G., Jaana M. Systematic reviews and meta-analyses of home telemonitoring interventions for patients with chronic diseases: a critical assessment of their methodological quality. Journal of Medical Internet Research. 2013; 15 (7):e150. [ PMC free article : PMC3785977 ] [ PubMed : 23880072 ]
- Kitsiou S., Paré G., Jaana M. Effects of home telemonitoring interventions on patients with chronic heart failure: an overview of systematic reviews. Journal of Medical Internet Research. 2015; 17 (3):e63. [ PMC free article : PMC4376138 ] [ PubMed : 25768664 ]
- Levac D., Colquhoun H., O’Brien K. K. Scoping studies: advancing the methodology. Implementation Science. 2010; 5 (1):69. [ PMC free article : PMC2954944 ] [ PubMed : 20854677 ]
- Levy Y., Ellis T.J. A systems approach to conduct an effective literature review in support of information systems research. Informing Science. 2006; 9 :181–211.
- Liberati A., Altman D. G., Tetzlaff J., Mulrow C., Gøtzsche P. C., Ioannidis J. P. A. et al. Moher D. The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. Annals of Internal Medicine. 2009; 151 (4):W-65. [ PubMed : 19622512 ]
- Lyden J. R., Zickmund S. L., Bhargava T. D., Bryce C. L., Conroy M. B., Fischer G. S. et al. McTigue K. M. Implementing health information technology in a patient-centered manner: Patient experiences with an online evidence-based lifestyle intervention. Journal for Healthcare Quality. 2013; 35 (5):47–57. [ PubMed : 24004039 ]
- Mickan S., Atherton H., Roberts N. W., Heneghan C., Tilson J.K. Use of handheld computers in clinical practice: a systematic review. bmc Medical Informatics and Decision Making. 2014; 14 :56. [ PMC free article : PMC4099138 ] [ PubMed : 24998515 ]
- Moher D. The problem of duplicate systematic reviews. British Medical Journal. 2013; 347 (5040) [ PubMed : 23945367 ] [ CrossRef ]
- Montori V. M., Wilczynski N. L., Morgan D., Haynes R. B., Hedges T. Systematic reviews: a cross-sectional study of location and citation counts. bmc Medicine. 2003; 1 :2. [ PMC free article : PMC281591 ] [ PubMed : 14633274 ]
- Mulrow C. D. The medical review article: state of the science. Annals of Internal Medicine. 1987; 106 (3):485–488. [ PubMed : 3813259 ] [ CrossRef ]
- Evidence-based information systems: A decade later. Proceedings of the European Conference on Information Systems ; 2011. Retrieved from http://aisel .aisnet.org/cgi/viewcontent .cgi?article =1221&context =ecis2011 .
- Okoli C., Schabram K. A guide to conducting a systematic literature review of information systems research. ssrn Electronic Journal. 2010
- Otte-Trojel T., de Bont A., Rundall T. G., van de Klundert J. How outcomes are achieved through patient portals: a realist review. Journal of American Medical Informatics Association. 2014; 21 (4):751–757. [ PMC free article : PMC4078283 ] [ PubMed : 24503882 ]
- Paré G., Trudel M.-C., Jaana M., Kitsiou S. Synthesizing information systems knowledge: A typology of literature reviews. Information & Management. 2015; 52 (2):183–199.
- Patsopoulos N. A., Analatos A. A., Ioannidis J.P. A. Relative citation impact of various study designs in the health sciences. Journal of the American Medical Association. 2005; 293 (19):2362–2366. [ PubMed : 15900006 ]
- Paul M. M., Greene C. M., Newton-Dame R., Thorpe L. E., Perlman S. E., McVeigh K. H., Gourevitch M.N. The state of population health surveillance using electronic health records: A narrative review. Population Health Management. 2015; 18 (3):209–216. [ PubMed : 25608033 ]
- Pawson R. Evidence-based policy: a realist perspective. London: SAGE Publications; 2006.
- Pawson R., Greenhalgh T., Harvey G., Walshe K. Realist review—a new method of systematic review designed for complex policy interventions. Journal of Health Services Research & Policy. 2005; 10 (Suppl 1):21–34. [ PubMed : 16053581 ]
- Petersen K., Vakkalanka S., Kuzniarz L. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology. 2015; 64 :1–18.
- Petticrew M., Roberts H. Systematic reviews in the social sciences: A practical guide. Malden, ma : Blackwell Publishing Co; 2006.
- Rousseau D. M., Manning J., Denyer D. Evidence in management and organizational science: Assembling the field’s full weight of scientific knowledge through syntheses. The Academy of Management Annals. 2008; 2 (1):475–515.
- Rowe F. What literature review is not: diversity, boundaries and recommendations. European Journal of Information Systems. 2014; 23 (3):241–255.
- Shea B. J., Hamel C., Wells G. A., Bouter L. M., Kristjansson E., Grimshaw J. et al. Boers M. amstar is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. Journal of Clinical Epidemiology. 2009; 62 (10):1013–1020. [ PubMed : 19230606 ]
- Shepperd S., Lewin S., Straus S., Clarke M., Eccles M. P., Fitzpatrick R. et al. Sheikh A. Can we systematically review studies that evaluate complex interventions? PLoS Medicine. 2009; 6 (8):e1000086. [ PMC free article : PMC2717209 ] [ PubMed : 19668360 ]
- Silva B. M., Rodrigues J. J., de la Torre Díez I., López-Coronado M., Saleem K. Mobile-health: A review of current state in 2015. Journal of Biomedical Informatics. 2015; 56 :265–272. [ PubMed : 26071682 ]
- Smith V., Devane D., Begley C., Clarke M. Methodology in conducting a systematic review of systematic reviews of healthcare interventions. bmc Medical Research Methodology. 2011; 11 (1):15. [ PMC free article : PMC3039637 ] [ PubMed : 21291558 ]
- Sylvester A., Tate M., Johnstone D. Beyond synthesis: re-presenting heterogeneous research literature. Behaviour & Information Technology. 2013; 32 (12):1199–1215.
- Templier M., Paré G. A framework for guiding and evaluating literature reviews. Communications of the Association for Information Systems. 2015; 37 (6):112–137.
- Thomas J., Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. bmc Medical Research Methodology. 2008; 8 (1):45. [ PMC free article : PMC2478656 ] [ PubMed : 18616818 ]
- Reconstructing the giant: on the importance of rigour in documenting the literature search process. Paper presented at the Proceedings of the 17th European Conference on Information Systems ( ecis 2009); Verona, Italy. 2009.
- Webster J., Watson R.T. Analyzing the past to prepare for the future: Writing a literature review. Management Information Systems Quarterly. 2002; 26 (2):11.
- Whitlock E. P., Lin J. S., Chou R., Shekelle P., Robinson K.A. Using existing systematic reviews in complex systematic reviews. Annals of Internal Medicine. 2008; 148 (10):776–782. [ PubMed : 18490690 ]
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- Cite this Page Paré G, Kitsiou S. Chapter 9 Methods for Literature Reviews. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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- Introduction
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- Types of Review Articles and Brief Illustrations
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Systematic Reviews & Evidence Synthesis Methods
Types of reviews.
- Formulate Question
- Find Existing Reviews & Protocols
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- Searching Systematically
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Not sure what type of review you want to conduct?
There are many types of reviews --- narrative reviews , scoping reviews , systematic reviews, integrative reviews, umbrella reviews, rapid reviews and others --- and it's not always straightforward to choose which type of review to conduct. These Review Navigator tools (see below) ask a series of questions to guide you through the various kinds of reviews and to help you determine the best choice for your research needs.
- Which review is right for you? (Univ. of Manitoba)
- What type of review is right for you? (Cornell)
- Review Ready Reckoner - Assessment Tool (RRRsAT)
- A typology of reviews: an analysis of 14 review types and associated methodologies. by Grant & Booth
- Meeting the review family: exploring review types and associated information retrieval requirements | Health Info Libr J, 2019
Label | Description | Search | Appraisal | Synthesis | Analysis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Critical Review | Aims to demonstrate writer has extensively researched literature and critically evaluated its quality. Goes beyond mere description to include degree of analysis and conceptual innovation. Typically results in hypothesis or model | Seeks to identify most significant items in the field | No formal quality assessment. Attempts to evaluate according to contribution | Typically narrative, perhaps conceptual or chronological | Significant component: seeks to identify conceptual contribution to embody existing or derive new theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature Review | Generic term: published materials that provide examination of recent or current literature. Can cover wide range of subjects at various levels of completeness and comprehensiveness. May include research findings | May or may not include comprehensive searching | May or may not include quality assessment | Typically narrative | Analysis may be chronological, conceptual, thematic, etc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mapping review/ systematic map | Map out and categorize existing literature from which to commission further reviews and/or primary research by identifying gaps in research literature | Completeness of searching determined by time/scope constraints | No formal quality assessment | May be graphical and tabular | Characterizes quantity and quality of literature, perhaps by study design and other key features. May identify need for primary or secondary research | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Meta-analysis | Technique that statistically combines the results of quantitative studies to provide a more precise effect of the results | Aims for exhaustive, comprehensive searching. May use funnel plot to assess completeness | Quality assessment may determine inclusion/exclusion and/or sensitivity analyses | Graphical and tabular with narrative commentary | Numerical analysis of measures of effect assuming absence of heterogeneity | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Mixed studies review/mixed methods review | Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies | Requires either very sensitive search to retrieve all studies or separately conceived quantitative and qualitative strategies | Requires either a generic appraisal instrument or separate appraisal processes with corresponding checklists | Typically both components will be presented as narrative and in tables. May also employ graphical means of integrating quantitative and qualitative studies | Analysis may characterise both literatures and look for correlations between characteristics or use gap analysis to identify aspects absent in one literature but missing in the other | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Overview | Generic term: summary of the [medical] literature that attempts to survey the literature and describe its characteristics | May or may not include comprehensive searching (depends whether systematic overview or not) | May or may not include quality assessment (depends whether systematic overview or not) | Synthesis depends on whether systematic or not. Typically narrative but may include tabular features | Analysis may be chronological, conceptual, thematic, etc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Qualitative systematic review/qualitative evidence synthesis | Method for integrating or comparing the findings from qualitative studies. It looks for ‘themes’ or ‘constructs’ that lie in or across individual qualitative studies | May employ selective or purposive sampling | Quality assessment typically used to mediate messages not for inclusion/exclusion | Qualitative, narrative synthesis | Thematic analysis, may include conceptual models | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Rapid review | Assessment of what is already known about a policy or practice issue, by using systematic review methods to search and critically appraise existing research | Completeness of searching determined by time constraints | Time-limited formal quality assessment | Typically narrative and tabular | Quantities of literature and overall quality/direction of effect of literature | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Scoping review | Preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research evidence (usually including ongoing research) | Completeness of searching determined by time/scope constraints. May include research in progress | No formal quality assessment | Typically tabular with some narrative commentary | Characterizes quantity and quality of literature, perhaps by study design and other key features. Attempts to specify a viable review | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
State-of-the-art review | Tend to address more current matters in contrast to other combined retrospective and current approaches. May offer new perspectives on issue or point out area for further research | Aims for comprehensive searching of current literature | No formal quality assessment | Typically narrative, may have tabular accompaniment | Current state of knowledge and priorities for future investigation and research | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Systematic review | Seeks to systematically search for, appraise and synthesis research evidence, often adhering to guidelines on the conduct of a review | Aims for exhaustive, comprehensive searching | Quality assessment may determine inclusion/exclusion | Typically narrative with tabular accompaniment | What is known; recommendations for practice. What remains unknown; uncertainty around findings, recommendations for future research | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Systematic search and review | Combines strengths of critical review with a comprehensive search process. Typically addresses broad questions to produce ‘best evidence synthesis’ | Aims for exhaustive, comprehensive searching | May or may not include quality assessment | Minimal narrative, tabular summary of studies | What is known; recommendations for practice. Limitations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Systematized review | Attempt to include elements of systematic review process while stopping short of systematic review. Typically conducted as postgraduate student assignment | May or may not include comprehensive searching | May or may not include quality assessment |
Reproduced from Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies . Health Info Libr J. 2009 Jun;26(2):91-108. doi: 10.1111/j.1471-1842.2009.00848.x
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Conducting a Literature Review
Review Comparison ChartA selection of the common review types found in the literature is presented and compared in the following table using the SALSA framework developed by Grant and Booth (2009).
Adapted from: Grant, M.J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x Permissions"Review Comparison Chart" from Which review is that? A guide to review types by the University of Melbourne Library is used with permission.
![]() 600 Mt. Pleasant Ave Providence, RI 02908 Social Media Links![]() This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License unless otherwise noted. ©2024 Rhode Island College. All rights reserved. Privacy Policy ![]() Literature Review: Types of Literature Reviews
Types of Literature Reviews
It is important to think of knowledge in a given field as consisting of three layers.
In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews. Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are several approaches to how they can be done, depending upon the type of analysis underpinning your study. Listed below are definitions of types of literature reviews: Argumentative Review This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to make summary claims of the sort found in systematic reviews. Integrative Review Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication. Historical Review Few things rest in isolation from historical precedent. Historical reviews are focused on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomenon emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research. Methodological Review A review does not always focus on what someone said [content], but how they said it [method of analysis]. This approach provides a framework of understanding at different levels (i.e. those of theory, substantive fields, research approaches, and data collection and analysis techniques), enables researchers to draw on a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection and data analysis, and helps highlight many ethical issues which we should be aware of and consider as we go through our study. Systematic Review This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyze data from the studies that are included in the review. Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?" Theoretical Review The purpose of this form is to concretely examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomenon. The theoretical literature review help establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework. * Kennedy, Mary M. "Defining a Literature." Educational Researcher 36 (April 2007): 139-147. All content is from The Literature Review created by Dr. Robert Larabee USC
Systematic Reviews: Methods & Resources
Many organizations have created guidelines to standardize reporting of analytical research. See some of the main ones below. The NIH offers a useful chart of Research Reporting Guidelines , and you can find over 500 on the EQUATOR network
Tools and Guidance
Software tools for systematic reviews
Forest Plot Generators
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Literature Review: The What, Why and How-to Guide: Literature Reviews?
What is a Literature Review?So, what is a literature review . "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available or a set of summaries." - Quote from Taylor, D. (n.d)."The Literature Review: A Few Tips on Conducting it".
What kinds of literature reviews are written?Each field has a particular way to do reviews for academic research literature. In the social sciences and humanities the most common are:
What are the Goals of Creating a Literature Review?
When do you need to write a Literature Review?
In all these cases you need to dedicate a chapter in these works to showcase what has been written about your research topic and to point out how your own research will shed new light into a body of scholarship. Where I can find examples of Literature Reviews? Note: In the humanities, even if they don't use the term "literature review", they may have a dedicated chapter that reviewed the "critical bibliography" or they incorporated that review in the introduction or first chapter of the dissertation, book, or article.
![]() Where to Find Standalone Literature ReviewsLiterature reviews are also written as standalone articles as a way to survey a particular research topic in-depth. This type of literature review looks at a topic from a historical perspective to see how the understanding of the topic has changed over time.
Social Sciences
Hard science and health sciences:
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Intraosseous meningiomas are a rare subtype of meningiomas representing approximately 2% of all cases. They can confound a diagnosis of other bone lesions including metastatic tumors. We present a case of a patient with prostate cancer who on staging workup was suspected to have a skull metastasis. Both bone scan and CT Head demonstrated a lesion in the right frontal calvarium. Surgical resection and pathology revealed an intraosseous meningioma. The patient was restaged as having localized prostate cancer and the was offered curative treatment for his malignancy. The case highlights the importance of obtaining tissue diagnosis in cases of radiographic isolated oligometastatic disease in patients with a known primary malignancy. Keywords: Carcinoma prostate; Intraosseous meningioma; Oligometastatic. © 2024 The Authors. Published by Elsevier Inc. on behalf of University of Washington. PubMed Disclaimer The suspected skull metastasis as… The suspected skull metastasis as seen on a 99m-Tc bone scan. (A) Axial, (B) Sagittal and… (A) Axial, (B) Sagittal and (C) Coronal CT scan of the suspected skull… Gross features of the surgical… Gross features of the surgical specimen post gross total-resection. Microscopic features of the intraosseous… Microscopic features of the intraosseous meningioma. Post-op CT showing titanium plate… Post-op CT showing titanium plate over the cranial defect.
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Financial fraud detection through the application of machine learning techniques: a literature review
Humanities and Social Sciences Communications volume 11 , Article number: 1130 ( 2024 ) Cite this article Metrics details
Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham methods were applied, and 104 articles published between 2012 and 2023 were examined. These articles were selected based on predefined inclusion and exclusion criteria and were obtained from databases such as Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect. These selected articles, along with the contributions of authors, sources, countries, trends, and datasets used in the experiments, were used to detect financial fraud and its existing types. Machine learning models and metrics were used to assess performance. The analysis indicated a trend toward using real datasets. Notably, credit card fraud detection models are the most widely used for detecting credit card loan fraud. The information obtained by different authors was acquired from the stock exchanges of China, Canada, the United States, Taiwan, and Tehran, among other countries. Furthermore, the usage of synthetic data has been low (less than 7% of the employed datasets). Among the leading contributors to the studies, China, India, Saudi Arabia, and Canada remain prominent, whereas Latin American countries have few related publications. Similar content being viewed by others![]() Feature generation and contribution comparison for electronic fraud detection![]() A synthetic data set to benchmark anti-money laundering methods![]() A numeric-based machine learning design for detecting organized retail fraud in digital marketplacesIntroduction. Financial fraud represents a highly significant problem, resulting in grave consequences across business sectors and impacting people’s daily lives (Singh et al., 2022 ). Its occurrence leads to reduced confidence in the economy, resulting in destabilization and direct economic repercussions for stakeholders (Reurink, 2018 ). Abdallah et al. ( 2016 ) define fraud as a criminal act aimed at obtaining money unlawfully. There are diverse types of fraud, such as asset misappropriation, expense reimbursement, and financial statement manipulation. Scholars have classified fraud into three categories: banking, corporate, and insurance (Ali et al., 2022 ; Nicholls et al., 2021 ; West and Bhattacharya, 2016 ). The problem becomes evident in the case of financial fraud, evidenced by the 2022 figures of the PricewaterhouseCoopers survey report revealing that 56% of companies globally have fallen victim to some form of fraud. In Latin America, 32% of companies have experienced fraud (PricewaterhouseCoopers, 2022 ). These alarming statistics align with the findings from Klynveld Peat Marwick Goerdeler (KPMG), indicating that 83% of the surveyed executives reported being targeted by cyber-attacks in the past 12 months. Furthermore, 71% had encountered some type of internal or external fraud (KPMG, 2022 ). These survey results reveal the higher risks of financial fraud faced by companies in Latin America, the United States, and Canada. In this context, traditional approaches, and techniques, as well as manual methods, have lost relevance and effectiveness because they cannot effectively address the complexity and scale of the information involved in detecting financial fraud. As previously mentioned, despite the interest of organizations in detecting financial fraud using machine learning (ML), current knowledge in this field remains limited. After an initial research phase, specialized literature shows that most researchers have directed their efforts toward the analysis of credit card fraud using a supervised approach (Femila Roseline et al., 2022 ; Madhurya et al., 2022 ; Plakandaras et al., 2022 ; Saragih et al., 2019 ). In the studies of Ali et al. ( 2022 ), Hilal et al. ( 2022 ), and Ramírez-Alpízar et al. ( 2020 ), ML techniques employing the supervised approach were found to be the most widely used method for detecting financial fraud, compared to the unsupervised, deep learning, reinforcement, and semi-supervised approaches, among others. Moreover, scholars such as Whiting et al. ( 2012 ) have compared the performance of data mining models for detecting fraudulent financial statements using data from quarterly and annual financial indexes of public companies from the COMPUSTAT database. Reurink ( 2018 ) has analyzed financial fraud resulting from false financial reports, scams, and misleading financial sales in the context of the financial market. Just like Wadhwa et al. ( 2020 ), he presented a wide variety of data mining methods, approaches, and techniques used in fraud detection, in addition to research addressing online banking fraud (Zhou et al., 2018 ; Moreira et al., 2022 ; Srokosz et al., 2023 ) and financial statement fraud (S. Chen, 2016 ; Ramírez-Alpízar et al., 2020 ). The abovementioned research works show that the accuracy of ML techniques in developing models for detecting financial fraud has increased (Al-Hashedi and Magalingam, 2021 ). The effectiveness of financial fraud detection and prevention depends on the effective selection of appropriate ML techniques to identify new threats and minimize false fraud alarm warnings, responding to the negative impact of financial fraud on organizations (Ahmed et al., 2016 ). The use of ML techniques has made it possible to identify patterns and anomalies in large financial data sets. However, developments in detection tools, inaccurate classification, detection methods, privacy, computer performance, and disproportionate misclassification costs continue to hinder the accurate and timely detection of financial fraud (Dantas et al., 2022 ; Mongwe and Malan, 2020 ; Nicholls et al., 2021 ; West and Bhattacharya, 2016 ). Recently, several studies have reviewed financial statement fraud detection methods in data mining and ML (Gupta and Mehta, 2021 ; Shahana et al., 2023 ); however, the present study is different from these past works in the area. These authors established the types of financial fraud and the different data mining techniques and approaches used to detect financial statement fraud. In contrast, our study explains the trends in the use of ML approaches and techniques to detect financial fraud, and it presents the more frequently used datasets in the literature for conducting experiments. Fraud detection mechanisms using machine learning techniques help detect unusual transactions and prevent cybercrime (Polak et al., 2020 ). Although each of these approaches uses different methods in their experimentation, a systematic literature review (SLR) shows that the application of each algorithm mirrors performance metrics to determine the accuracy with which it predicts that a financial transaction is fraud. Such metrics include Accuracy, Precision, F1 Score, Recall, and Sensitivity, among others. The research presented uses a rigorous and well-structured methodology to expand current knowledge on financial fraud detection using machine learning (ML) techniques. Through the use of a systematic literature review that follows adaptations of PRISMA guidelines and Kitchenham’s methodology, the study ensures a carefully planned and transparent review process. The sources of information consulted include research articles published in reputable academic databases such as Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect, ensuring that the review covers the most relevant and quality scientific literature in the field of financial fraud and machine learning. Moreover, the study includes a bibliometric analysis using VOSviewer software, which allows identifying trends and patterns within the literature both quantitatively and visually. Based on the 104 articles reviewed, which cover the period 2012–2023, we manage to describe the types of fraud, the models applied, the ML techniques used, the datasets employed, and the metrics of performance reported. These contribute to filling the existing gaps in the literature by providing a comprehensive and up-to-date synthesis of the evidence on the use of machine learning techniques for financial fraud detection, thus laying the groundwork for future research and practical applications in this field. Our responses to the initial research questions raised are four main contributions that justify this research. Thus, this study contributes to the literature on financial fraud detection by examining the relationship between the current literature on financial fraud detection and ML based on the scholars, articles, countries, journals, and trends in the area. Fraud has been classified as internal and external, with a focus on credit card loan fraud investigations and insurance fraud. The different ML techniques and their models applied to experiments were grouped. The most widely used datasets in financial fraud detection using ML are analyzed according to the 86 articles that contained experiments, highlighting that most of them involve real data. This paper is useful for researchers because it studies and presents the metrics used in supervised and unsupervised learning experiments, providing a clear view of their application in the different models. Therefore, this study is relevant because it presents in a consolidated and updated manner new contributions derived from experiment results regarding the use of ML, which helps address the problem when financial fraud occurs. The research work is organized as follows: the section “Methods” comprehensively describes the research method and the questions addressed in the study. Section “Results of the data synthesis” presents the findings encompassing authors, articles, sources, countries, trends, financial fraud types, and datasets with their characteristics to which the detection models using ML techniques were applied, with the results of their metrics. Finally, the section “Discussion and conclusion” highlights the conclusions, including future lines of research in the field. The study focuses on SLR, which provides a comprehensive view of the great developments in financial fraud detection. Considering the purpose, scientific guidelines were followed in the literature review of the PRISMA and Kitchenham methods, which were adapted by the authors (Ashtiani and Raahemi, 2022 ; Kitchenham and Brereton, 2013 ; Kitchenham and Stuart, 2007 ; Kumbure et al., 2022 ; Moher et al., 2009 ; Roehrs et al., 2017 ; Saputra et al., 2023 ; Wohlin, 2014 ). The method used in the SLR was developed with carefully planned and executed activities: (a) planning of the review, (b) definition of research questions, (c) description of the search strategy, (d) consultation concerning the search strategy, (e) selection of the inclusion/exclusion criteria and data selection, (f) description of the quality assessment, (g) investigation of the study topics, (h) description of data extraction, and (i) synthesis of the data. Each of the activities conducted in this study is explained below. Planning of the reviewThe research purpose was established in accordance with the indicated research goals and questions. The analysis focused on research articles published between 2012 and 2023, particularly those using ML methods for financial fraud detection. Accordingly, the SLR procedure presented by Kitchenham and Stuart ( 2007 ) and Moher et al. ( 2009 ) was implemented following a series of steps adapted and modified by Ashtiani and Raahemi ( 2022 ) and Kumbure et al. ( 2022 ), as depicted in Fig. 1 . Thus, it was possible to ensure a rigorous and objective analysis of the available literature in our field of interest. ![]() Description of the general process used to review the literature in the study area. Authors’ own elaboration. The procedures implemented in this review process are discussed in the following subsections. Definition of research questionsIn SLR, research questions are key and decisive for the success of the study (Kitchenham and Stuart, 2007 ). Therefore, analyzing the existing literature on financial fraud detection through ML techniques and its characteristics, problems, challenges, solutions, and research trends is crucial. Table 1 describes the research questions to provide a structured framework for the study. Within the proposed systematic review, the questions were fine-tuned, achieving a better classification and thematic analysis. The research questions were categorized into two groups: general questions (GQ) and specific questions (SQ). GQs provide an overview of the current state of the art, that is, a general framework for future research. Meanwhile, SQs focus on specific matters emerging from the application areas of the topic, thereby improving the filtering process of the study. Description of the search strategyThe search strategy was designed to identify a set of studies addressing the research questions posed. This strategy was to be implemented in two stages. In the first stage, a manual search was conducted by selecting a set of test documents through a defined database. Following the strategy proposed by Wohlin ( 2014 ), a snowball search was conducted. This approach involved choosing from a set of initial references (e.g., relevant articles or books addressing the subject matter) and searching for new related references relevant to the study based on these. In the second stage, an automated search was performed using the technique described by Kitchenham and Brereton ( 2013 ), which included preparing a list of the main search terms to be applied in the queries in each database, as indicated in subsection “Search queries”. Manual searchIn the study’s initial stage, nine journal articles were selected from the test set of papers (Ahmed et al., 2016 ; Ali et al., 2022 ; Bakumenko and Elragal, 2022 ; Gupta and Mehta, 2021 ; Hilal et al., 2022 ; Nicholls et al., 2021 ; Nonnenmacher and Marx Gómez, 2021 ; Ramírez-Alpízar et al., 2020 ; West and Bhattacharya, 2016 ). The manual literature search helped identify articles related to financial fraud detection through ML techniques, which were used as an initial set and were part of the final analysis. In the subsequent stage, a backward and forward snowball search was conducted. This approach involved using the initial set to select the relevant articles. The backward snowball search process comprised reviewing article titles, including those meeting the inclusion and exclusion criteria. In the forward snowball search, the analysis was performed in the Scopus database to identify studies citing one or more of the articles in the initial set. This filtering method helped identify studies meeting the inclusion and exclusion criteria, eliminate duplicates from the previous set, and analyze articles answering the questions posed, which were retained in the final study set. Automated searchThe research work mainly aimed to obtain a reliable set of relevant studies to minimize bias and increase the validity of the results. To this end, a manual search for articles meeting the inclusion and exclusion criteria was conducted by assessing the abstracts and other sections of articles. We decided to implement an automated search strategy using five databases: Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect, known for their impartiality in the representation of research works, with inclusion and exclusion criteria already defined, thereby complementing the search. Thus, 104 related articles meeting the criteria established in the final set were identified. Search queriesStudies from 2012 onward were reviewed with keywords such as “financial fraud” and “machine learning” to identify model-based approaches and associated techniques. Table 2 presents a summary of the queries used in each data source. Inclusion and exclusion criteria and study selectionThe study established inclusion and exclusion criteria, a key process to select the most relevant articles. The exclusion criteria were documents published between 2012 and 2023 (until March), such as conference reviews, book chapters, editorials, and reviews. Further, the availability of the full text of the article was considered. We decided to exclude articles published before 2012 for the following reasons: (i) They were over 11 years old; (ii) Relevant publications prior to 2012 were scarce; and (iii) Sufficient number of articles were available between 2012 and 2023. For the inclusion and exclusion criteria, appropriate filtering tools were applied to each data source during the search stage. This enabled the automated selection of the most relevant and appropriate studies based on the research goal. Data processing strategiesIn the data processing strategy used, databases were selected following strict inclusion and exclusion criteria to ensure the quality and relevance of the information collected (Table 3 ). Various databases initially identified the following number of relevant articles: Scopus (28), Taylor & Francis (80), SAGE (71), ScienceDirect (663), and IEEE Xplore (5132). This initial step provides a broad overview of the available literature in the field of financial fraud detection using ML models. Subsequently, a data removal phase was carried out so as to ensure data integrity, such that the following number of articles (given in parentheses) were removed from each database: Scopus (0), Taylor & Francis (63), SAGE (57), ScienceDirect (636), and IEEE Xplore (5114). This rigorous process ensures the integrity of the data collected and avoids redundancy. The final step consisted of obtaining the consolidated number of articles included after the selection and exclusion of duplicates: Scopus (28), Taylor & Francis (17), SAGE (14), ScienceDirect (27), and IEEE Xplore (18). This methodological strategy ensured the relevance of the articles that carried out a complete analysis in the field of financial fraud detection using ML models. Quality assessmentOnce the inclusion and exclusion criteria were applied, the remaining articles were assessed for quality. The evaluation criteria used included the purpose of the research; contextualization; literature review; and related works, methods, conclusions, and results. To minimize the empirical obstacles associated with full-text filtering, a set of questions proposed by Roehrs et al. ( 2017 ) (see Table 4 ) was used to validate whether the selected articles met the previously established quality criteria. Research topicsIn conducting the literature review to understand the current state of published research on the topic, a data orientation process was addressed, including preprocessing techniques and ML models and their metrics. Accordingly, four research topics were defined based on the research goals. They are presented in Table 5 . Data extractionFor data extraction, the necessary attributes were first defined and the information pertaining to the study goals was summarized. Next, the relevant information was identified and obtained through a detailed reading of the full text of each article. The information was then stored in a Microsoft Excel spreadsheet. Data were collected on the attributes specified in Table 6 . In Table 6 , the “Study” column corresponds to the identifiers of the research topics in Quality Assessment, and the “Subject” column refers to the category to which the different attributes belong. The names of the attributes and a brief description are presented in the last two columns of the table, including additional columns with relevant information. Data synthesisData synthesis included analyzing and summarizing the information observed in the selected articles to address the research questions. To perform this task, a synthesis was conducted following the guidelines proposed by Moher et al. ( 2009 ) based on qualitative data. Further, a descriptive analysis was performed to obtain answers to the research questions. Consequently, a qualitative approach to data evidence was followed. Results of the data synthesisIn this section, the 104 finally selected articles have been considered. The data were synthesized to address the five research questions mentioned. General questions (GQ) GQ1: Which were the most relevant authors, articles, sources, countries, and trends in the literature review on financial fraud detection based on the application of machine learning (ML) models? The literature on financial fraud detection applying ML models has been studied by a large number of authors. However, some authors stood out in terms of the number of published papers and number of citations. Specifically, the most significant authors with two publications are Ahmed M. (with 318 citations), Ileberi E. (82 citations), Ali A. (20 citations), Chen S. (84 citations), and Domashova J and Kripak E. (each with 6 citations). Other relevant authors with one publication and who have been cited several times are Abdallah A. (with 333 citations), Abbasimehr H. (18 citations), Abd Razak S. (13 citations), Achakzai M. A. K. (5 citations), and Abosaq H. (2 citations). The aforementioned authors have contributed significantly to the development of research in financial fraud detection using ML models (Fig. 2 ). ![]() Shows the analysis of the connections between authors based on co-authorship of publications. Produced with VOSviewer. Collectively, the researchers have contributed a solid knowledge base and have laid the foundation for future research in financial fraud detection using ML models. Although other researchers contributed to the field, such as Khan, S. and Mishra, B., both with 7 citations, among others, some have been more prominent in terms of the number of papers published. Their collective works have enriched the field and have promoted a greater understanding of the challenges and opportunities in this area. As depicted in Fig. 3 , clusters 2 (green) and 4 (yellow) present the most relevant research articles on financial fraud detection using ML models. Cluster 2, comprising 9 articles with 357 citations and 32 links, is highlighted because of the significant impact of the articles by Sahin, Huang, and Kim. These articles have the highest number of citations and are deemed to be useful starting points for those intending to dive into this research field. Cluster 4, constituting 6 articles with 158 citations and 27 links, includes the works of Dutta and Kim, who have also been cited considerably. ![]() Depicts the connections between articles based on their bibliographic references. Produced with VOSviewer. Articles in clusters 1 (red) and 3 (dark blue) could be valuable sources of information; however, they were observed to have a lower number of citations and links than those in clusters 2 and 4, such as that of Nian K. (62 citations and 4 links) and Olszewski (92 citations and 4 links). However, some articles in these clusters have had a substantial number of citations. In Cluster 10 (pink), the article by Reurink A. is prominent, with 38 citations. This is followed by the article by Ashtiani M.N. with 10 citations. In Cluster 11 (light green), the article by Hájek P. has 129 citations. In Cluster 12 (grayish blue), the articles by Blaszczynski J. and Elshaar S. have the greatest number of citations, indicating their influence in the field of financial fraud detection. In Cluster 13 (light brown), the article by Pourhabibi T. has the greatest number of citations at 102, suggesting that he has been relevant in the research on financial fraud detection. Finally, in Cluster 14 (purple), the articles by Seera M. have 63 citations and 2 links. The article by Ileberi E. has 11 citations and 1 link. Both articles have a small number of citations, indicating a lower influence on the topic. In conclusion, clusters 2, 4, and 11 are the most relevant in this literature review. The articles by Sahin, Huang, Kim, Dutta, and Pumsirirat are the most influential ones in the research on financial fraud detection through the application of ML models. The information presented in Fig. 4 is the result of a clustering analysis of the articles resulting from the literature review on financial fraud detection by implementing ML models. In total, 48 items were identified and grouped into 12 clusters. The links between the items were 100, with a total link strength of 123. ![]() Shows the relationship between different scientific journals based on bibliographic links. Produced with VOSviewer. The following is a description of each cluster with its respective number of items, links, and total link strength (the number of times a link appears between two items and its strength): Cluster 1 (6 articles—red): This cluster includes journals such as Computers and Security , Journal of Network and Computer Applications , and Journal of Advances in Information Technology . The total number of links is 27, and the total link strength is 32. Cluster 2 (6 articles—dark green): This cluster includes articles from Technological Forecasting and Social Change , Journal of Open Innovation: Technology, Market, and Complexity , and Global Business Review . The total number of links is 18, and the total link strength is 19. Cluster 3 (5 articles—dark blue): This cluster includes articles from the International Journal of Advanced Computer Science and Applications , Decision Support Systems , and Sustainability . The total number of links is 19, and the total link strength is 20. Cluster 4 (4 articles—dark yellow): This cluster includes articles from Expert Systems with Applications and Applied Artificial Intelligence . The total number of links is 26, and the total link strength is 45. Cluster 5 (4 articles—purple): This cluster includes articles from Future Generation Computer Systems and the International Journal of Accounting Information Systems . The total number of links is 15, and the total link strength is 16. Cluster 6 (4 articles—dark blue): This cluster includes articles from IEEE Access and Applied Intelligence . The total number of links is 18, and the total link strength is 26. Cluster 7 (4 articles—orange): This cluster includes articles from Knowledge-Based Systems and Mathematics . The total number of links is 23, and the total link strength is 29. Cluster 8 (4 articles—brown): This cluster includes articles from the Journal of King Saud University—Computer and Information Sciences and the Journal of Finance and Data Science . The total number of links is 13, and the total link strength is 13. Cluster 9 (4 articles—light purple): This cluster includes articles from the International Journal of Digital Accounting Research and Information Processing and Management . The total number of links is 2, and the total link strength is 2. The clusters represent groups of related articles published in different academic journals. Each cluster has a specific number of articles, links, and total link strength. These findings provide an overview of the distribution and connectedness of articles in the literature on financial fraud detection using ML models. Further, clustering helps identify patterns and common thematic areas in the research, which may be useful for future researchers seeking to explore this field. Clusters 1, 4, and 7 indicate a greater number of stronger articles and links. These clusters encompass articles from Computers and Security , Expert Systems with Applications , and Knowledge-Based Systems , which are important sources for the SLR on financial fraud detection through the implementation of ML models. The analysis presented indicates the number of documents related to research in different countries and territories. In this case, a list of 50 countries/territories and the number of documents related to the research conducted in each of them is presented. China leads with the highest paper count at 18, followed by India at 13 and Saudi Arabia and Canada at 9 each. Canada, Malaysia, Pakistan, South Africa, the United Kingdom, France, Germany, and Russia have similar research outputs with 4–9 papers. Sweden and Romania have 1 or 2 research papers, indicating limited scientific research output. The presence of little-known countries such as Armenia, Costa Rica, and Slovenia suggests ongoing research in places less common in the academic world. From that point on, the number of papers has gradually decreased. The production of papers is geographically distributed across countries from different continents and regions. However, more research exists on the subject from countries with developed and transition economies, which allows for a greater capacity to conduct research and produce papers. Figure 5 , sourced from Scopus’s “Analyze search results” option, depicts countries with their respective number of published papers on the topic of financial fraud detection through ML models. ![]() Represents the number of scientific publications in the study area classified by country. Produced with VOSviewer. The above shows the diversity of countries involved in the research, where China leads the number of studies with 18 papers, followed by India with 13 and Saudi Arabia and Canada each with 9 papers. The other countries show little production, with less than 7 publications, which indicates an emerging topic of interest for the survival of companies that must prevent and detect different financial frauds using ML techniques. The most relevant keywords in the review of literature on financial fraud detection implementing ML models include the following: In Cluster 1, the most relevant keywords are “decision trees” (13 repetitions), “support vector machine (SVM)” (11 repetitions), “machine-learning” (10 repetitions), and “credit card fraud detection” (9 repetitions). A special focus has been placed on the topic of artificial intelligence (ML), in addition to algorithms and/or supervised learning models such as decision trees, support vector machines, and credit card fraud detection. In Cluster 2, the most relevant keywords are “crime” (46 repetitions), “fraud detection” (43 repetitions), and “learning systems” (13 repetitions). These terms reflect a broader focus on financial fraud detection, where the aspects of crime in general, fraud detection, and learning systems used for this purpose have been addressed. In Cluster 3, the most relevant keywords are “Finance” (19 repetitions), “Data Mining” (18 repetitions), and “Financial Fraud” (12 repetitions). These keywords indicate a focus on the financial industry, where data mining is used to reveal patterns and trends related to financial fraud. In Cluster 4, the most relevant keywords are “Machine Learning” (45 repetitions), “Anomaly Detection” (16 repetitions), and “Deep Learning” (11 repetitions). They reflect an emphasis on the use of traditional ML and deep learning techniques for anomaly detection and financial fraud detection. In general, the different clusters indicate the most relevant keywords in the SLR on financial fraud detection through ML models. Each cluster presents a specific set of keywords reflecting the most relevant trends and approaches in this field of research (Fig. 6 ). ![]() Shows the relationships between keywords based on their co-occurrence in the literature reviewed. Produced with VOSviewer. GQ2: What types of financial fraud have been identified in ML studies? Financial fraud is generated by weaknesses in companies’ control mechanisms, which are analyzed based on the variables that allow them to materialize. These include opportunity, motivation, self-fulfillment, capacity, and pressure. Some of these are comprehensively analyzed by Donald Cressey through the fraud theory approach. The lack of modern controls has led organizations to use ML in response to this major problem. According to the findings of the Global Economic Crime and Fraud Survey 2022–2023, which gathered insights from 1,028 respondents across 36 countries worldwide, instances of fraud within these companies have caused a financial loss of approximately 10 million dollars (PricewaterhouseCoopers, 2022 ). Referring to the concept of fraud, as outlined in international studies (Estupiñán Gaitán, 2015 ; Márquez Arcila, 2019 ; Montes Salazar, 2019 ) and the guidelines of the American Institute of Certified Public Accountants, it is an illegal, intentional act in which there is a victim (someone who loses a financial resource) and a victimizer (someone who obtains a financial resource from the victim). Thus, the proposed classification includes corporate fraud and/or fraud in organizations, considering that the purpose is to misappropriate the capital resources of an entity or individual: cash, bank accounts, loans, bonds, stocks, real estate, and precious metals, among others. In this SLR study, we have considered fraud classifications by authors of 86 articles, which encompass experiments. We have excluded the 18 SLR articles from our analysis. The types presented in Table 7 follow the holistic view of the authors of the research for a better understanding of the subject of financial fraud, considering whether it is internal or external fraud. Table 7 highlights the diverse types of frauds, and the research works on them. According to the classification, external frauds correspond to those performed by stakeholders outside the company. This study’s findings show that 54% of the analyzed articles investigate external fraud, among which the most important studies are on credit card loan fraud, followed by insurance fraud, using supervised and unsupervised ML techniques for their detection. In research works (Kumar et al., 2022 ) analyzing credit card fraud, attention is drawn to the importance of prevention through the behavioral analysis of customers who acquire a bank loan and identifying applicants for bad loans through ML models. The datasets used in these fraud studies have covered transactions performed by credit card holders (Alarfaj et al., 2022 ; Baker et al., 2022 ; Hamza et al., 2023 ; Madhurya et al., 2022 ; Ounacer et al., 2018 ; Sahin et al., 2013 ), while other research works have covered master credit card money transactions in different countries (Wu et al., 2023 ) and fraudulent transactions gathered from 2014 to 2016 by the international auditing firm Mazars (Smith and Valverde, 2021 ). The second major type of external fraud is insurance fraud, which is classified as fraud in health insurance programs involving practices such as document forgery, fraudulent billing, and false medical prescriptions (Sathya and Balakumar, 2022 ; Van Capelleveen et al., 2016 ) and automobile insurance fraud involving fraudulent actions between policyholders and repair shops, who mutually rely on each other to obtain benefits (Aslam et al., 2022 ; Nian et al., 2016 ; Subudhi and Panigrahi, 2020 ); as a result of the issues they face, insurance companies have developed robust models using ML. As regards internal fraud, caused by an individual within the company, 46% of studies have analyzed this type, with financial statement fraud, money laundering fraud, and tax fraud standing out. The studies show that the investigations are based on information reported by the US Securities and Exchange Commission (SEC) and the stock exchanges of China, Canada, Tehran, and Taiwan, among others. To a considerable extent, the information taken is from the real sector, and very few studies have obtained synthetic information based on the application of different learning models. The following is a summary of the financial information obtained by the researchers to apply AI models and techniques: Stock market financial reports : Fraud in the Canadian securities industry (Lokanan and Sharma, 2022 ), companies listed on the Chinese stock exchanges (Achakzai and Juan, 2022 ; Y. Chen and Wu, 2022 ; Xiuguo and Shengyong, 2022 ), companies with shares according to the SEC (Hajek and Henriques, 2017 ; Papík and Papíková, 2022 ), companies listed on the Tehran Stock Exchange (Kootanaee et al. 2021 ), companies in the Taiwan Economic Journal Data Bank (TEJ) stock market (S. Chen, 2016 ; S. Chen et al., 2014 ), analysis of SEC accounting and auditing publications (Whiting et al., 2012 ) Wrong financial reporting to manipulate stock prices (Chullamonthon and Tangamchit, 2023 ; Khan et al., 2022 ; Zhao and Bai, 2022 ) Financial data of 2318 companies with the highest number of financial frauds (mechanical equipment, medical biology, media, and chemical industries; Shou et al., 2023 ), fraudulent financial restatements (Dutta et al., 2017 ) Data from 950 companies in the Middle East and North Africa region (Ali et al., 2023 ), analyzing outliers in sampling risk and inefficiency of general ledger financial auditing (Bakumenko and Elragal, 2022 ), fraudulent intent errors by top management of public companies (Y. J. Kim et al., 2016 ), reporting of general ledger journal entries from an enterprise resource planning system (Zupan et al., 2020 ) Synthetic financial dataset for fraud detection (Alwadain et al., 2023 ). Studies have analyzed situations involving fraudulent financial statements. In these cases, instances of fraud have already occurred, leading to the creation of financial reports that contain statements with outliers that can be deemed fraudulent intent or errors in financial figures. This raises a reasonable doubt about whether an intent exists with regard to the reporting of unrealistic figures. Notably, once there are parties responsible for the financial information presented to stakeholders, such as organization owners, managers, administrators, accountants, or auditors, it is unlikely for it to be unintentional (an error). In this context, transparency and explainability are essential so as to ensure fairness in decisions, thus avoiding bias and discrimination based on prejudiced data (Rakowski et al., 2021 ). Because of its significance, the information reported in financial statements is vital for investigations. Studies have indicated substantial amounts of data extracted from the financial reports of regulatory bodies such as stock exchanges and auditing firms. These entities use the data to establish the existence of fraud and its types through predictive models that use ML techniques. Thus, they require financial data such as dates, the third party affected, user, debit or credit amount, and type of document, among other aspects involving an accounting record. This information aids in identifying the possible impact in terms of lower profits and the perpetrator and/or perpetrators to gather sufficient evidence and file criminal proceedings for the financial damage caused. Moreover, investigations concerning money laundering fraud and/or money laundering, the second most investigated internal fraud type, encompass the reports of natural and legal persons exposed by the Financial Action Task Force in countries such as the Kingdom of Saudi Arabia (Alsuwailem et al., 2022 ), transactions from April to September 2018 from Taiwan’s “T” bank and the account watch list of the National Police Agency of the Ministry of Interior (Ti et al., 2022 ), money laundering frauds in Middle East banks (Lokanan, 2022 ), transactions of financial institutions in Mexico from January 2020 (Rocha-Salazar et al., 2021 ), and synthetic data of simulated banking transactions (Usman et al., 2023 ). Concerns regarding the entry of proceeds from money laundering into an organization have been articulated in relation to the financial damage it causes to the country. At the macroeconomic level, these activities negatively affect financial stability, distorting the prices of goods and services. Moreover, such activities disrupt markets, making it difficult to make efficient financial decisions. At the microeconomic level, legitimate businesses face unfair competition with companies using illegal money, which may lead to higher unemployment levels. Furthermore, money laundering has a social impact because it affects the security and welfare of society. Thus, some research works (Alsuwailem et al., 2022 ) have indicated the need to implement ML models for promoting anti-money laundering measures. For instance, in Saudi Arabia, money from illicit drug trafficking, corruption, counterfeiting, and product piracy have entered the country. The measures to be taken are categorized according to the three stages of money laundering: placement, layering (also known as concealment), and integration. These include new legal regulations against money laundering, staff training, customer identification and validation, reporting of suspicious activities, and documentation and storage of relevant data (Bolgorian et al., 2023 ). Regarding the 7.5% incidence of internal fraud, specifically categorized as tax fraud resulting from tax evasion, the studies have analyzed tax returns on income and/or profits of legal persons and/or individuals from the Serbian tax administration during 2016–2017 (Savić et al., 2022 ). Studies have encompassed periodic value-added tax (VAT) returns, together with the anonymous list of clients for the tax year 2014 obtained from the Belgian tax administration (Vanhoeyveld et al., 2020 ) and income tax and VAT taxpayers registered and provided by the State Revenue Committee of the Republic of Armenia in 2018 (Baghdasaryan et al., 2022 ). These studies hold great relevance for tax administrations using different strategies to minimize the impact of fraud resulting from tax evasion. Tax evasion reduces the government’s ability to collect revenue, directly affecting government finances and causing budget deficits, thereby increasing public debt. GQ3: Which ML models were implemented to detect financial fraud in the datasets? Given that ML is a key tool to extract meaningful information and make informed decisions, this study analyzes the most widely used ML techniques in the field of financial fraud detection. It takes as reference 86 experimental articles, excluding 18 SLR articles. In these articles, the most commonly used trends and approaches in the implementation of ML techniques in financial fraud detection were identified. For the analysis, the pattern of frequency of use of ML models was observed. Several of them have been prominent because of their popularity and implementation in detecting financial fraud (Fig. 7 ). Some of the most widely used models include long-short term memory (LSTM) with 7 mentions, autoencoder with 10 mentions, XGBoost with 13 mentions, k -nearest neighbors (KNN) with 14 mentions, artificial neural network (ANN) with 17 mentions, NB with 19 mentions, SVM with 29 mentions, DT with 29 mentions, LR with 32 mentions, and RF with 34 mentions. ![]() Illustrates the most common machine learning models in financial fraud detection. Authors’ own elaboration. The LSTM model is a recurrent neural network used for sequence processing, especially for tasks concerning natural language processing (Chullamonthon and Tangamchit, 2023 ; Esenogho et al., 2022 ; Femila Roseline et al., 2022 ). Moreover, autoencoders are models used for data compression and decompression. These models are useful in dimensionality reduction applications (Misra et al., 2020 ; Srokosz et al., 2023 ). XGBoost is a library combining multiple weak DT models, offering a scalable and efficient solution in classification and regression tasks (Dalal et al., 2022 ; Udeze et al., 2022 ). KNN and ANN are widely used models in various ML applications. KNN is based on neighbor closeness, and ANN is inspired by human brain functioning. NB is a probabilistic algorithm commonly used in text classification and data mining (Ashtiani and Raahemi, 2022 ; Lei et al., 2022 ; Shahana et al., 2023 ). SVM, DT, LR, and RF, the most commonly mentioned models, are used in a wide range of classification and regression applications. These models are prominent because of their effectiveness and applicability to different scenarios, such as credit card loan fraud (external fraud) and financial statement fraud (internal fraud). The most frequently used ML techniques are supervised learning (56.73%); unsupervised learning (18.29%), a combination of supervised and unsupervised learning (15.38%), a combination of supervised and deep learning (2.88%), and mathematical approach, supervised, and semi-supervised learning (0.96%). Figure 8 presents the ML techniques in the literature reviewed and indicates the number of times each type of technique is applied. Some articles applied several ML methods, in which the algorithms are mainly classified according to the learning method. In this case, there are four main types: supervised, semi-supervised, unsupervised, and deep learning. ![]() Shows the different experimental approaches used in the study. Authors’ own elaboration. Supervised learning is the most widely used technique, with 56.73% of citations in financial fraud studies. In this approach, labeled training data are used, where the expected outputs are known and a model is built that can make higher-accuracy predictions on new unlabeled data. Common examples of supervised learning techniques include the models of LR, SVM, DT, RF, KNM, NB, and ANN. Moreover, unsupervised learning constitutes 18.27% of the mentions. The technique focuses on discovering patterns in the data without knowing data with labels and/or types for training. Some of these include DBSCAN, autoencoder, and isolation forest (IF). The combination of supervised, unsupervised, and semi-supervised learning is used with a frequency of 1.92%. This technique and/or approach combines elements of supervised and unsupervised learning, using both labeled and unlabeled data to train the models. It is also used when labeled data are scarce or expensive to obtain; thus, the aim is to take advantage of unlabeled information to improve model performance. Finally, supervised and deep learning represents 2.88% of the mentions. It is based on deep neural networks with multiple neurons and hidden layers to learn complex data representations. It has achieved remarkable developments in areas such as image processing, voice recognition, and machine translation. Specific questions (SQ) SQ1: What datasets were used by implementing ML models for financial fraud detection? First, the data structure and fraud types may vary with the collection of datasets. The performance of fraud detection models may be affected by variations in the number of instances and attributes selected. Therefore, investigating the datasets and their characteristics is relevant, as data differ in terms of data type (number, text) and the data source from which they were obtained (synthetic and/or real), as can be observed in Fig. 9 . ![]() Depicts the datasets used in the research on financial fraud detection. Authors’ own elaboration. Credit card fraud detectionThe dataset was created by the Machine Learning group at Université Libre de Bruxelles. It encompasses anonymized credit card transactions labeled as fraudulent or genuine. The transactions were performed in September 2013 over two days by European cardholders; a record of only 492 frauds out of 284,807 transactions is highly unbalanced because the positive types (frauds) represent only 0.172% of all transactions (Machine Learning Group, 2018 ). The characteristics of the set encompass numerical variables resulting from a principal component analysis (PCA) transformation. For confidentiality, the original features of the data have not been disclosed. Features V1, V2…, V28 have been the main components obtained through PCA. The only features that have not transformed with PCA include “Time,” which denotes the seconds elapsed between each transaction. “Amount” denotes the transaction amount. The “Class” feature is the response variable, taking 1 as the value in case of fraud and 0 (no fraud) otherwise. This dataset has been used by 15 authors in their papers, who have applied different financial fraud detection techniques (Alarfaj et al., 2022 ; Baker et al., 2022 ; Fanai and Abbasimehr, 2023 ; Fang et al., 2019 ; Femila Roseline et al., 2022 ; Hwang and Kim, 2020 ; Ileberi et al., 2021 , 2022 ; Khan et al., 2022 ; Misra et al., 2020 ; Ounacer et al., 2022 ). Statlog (German credit data)The dataset was proposed by Professor Hofmann to the UC Irvine ML repository on November 16, 1994, for facilitating credit rating (Hofmann, 1994 ). It mainly aims to determine whether a person presents a favorable or unfavorable credit risk (binary rating). The set is multivariate, which implies that it contains many attributes used in credit rating. These attributes include information on existing current account status, credit duration, credit history, and credit purpose and amount, among others. In total, there are 20 attributes describing several characteristics of individuals and contains 1000 instances; it has been widely used in research related to credit rating (Esenogho et al., 2022 ; Fanai and Abbasimehr, 2023 ; Lee et al., 2018 ; Pumsirirat and Yan, 2018 ; Seera et al., 2021 ). Stalog (Australian credit approval)The dataset belongs to the UC Irvine ML repository and was created by Ross Quinlan in 1997. It focuses on credit card applications within the financial field (Quinlan, 1997 ). It has a total of 690 instances and 14 attributes of which 6 are numeric of type integer/actual and 8 are categorical; consequently, its data characteristics are multivariate—that is, it contains multiple variables and/or attributes. Several studies have used the ensemble data (Lee et al., 2018 ; Pumsirirat and Yan, 2018 ; Seera et al., 2021 ; Singh et al., 2022 ). China Stock Market and Accounting ResearchThe China Stock Market and Accounting Research (CSMAR) Database contains financial reports and violations of CSMAR. It provides information on China’s stock markets and the financial statements of listed companies; the data were collected between 1998 and 2016 from publicly funded companies (CSMAR, 2022 ). It includes fraudulent and non-fraudulent companies committing several types of fraud, such as showing higher profits and/or earnings, fictitious assets, false records, and other irregularities in financial reporting. The set comprises 35,574 samples, including 337 annual fraud samples of companies in the Chinese stock market. This is selected as a data source to illustrate the financial statement information of listed companies in three studies (Achakzai and Juan, 2022 ; Y. Chen and Wu, 2022 ; Shou et al., 2023 ). Synthetic financial datasets for fraud detectionIt was generated by the PaySim mobile money simulator using aggregated data from a private dataset deriving from one month of financial records from a mobile money service in an African country (López-Rojas, 2017 ). The original records were provided by a multinational company offering mobile financial services in more than 14 countries worldwide. The dataset has been used in numerous studies (Alwadain et al., 2023 ; Hwang and Kim, 2020 ; Moreira et al., 2022 ). The synthetic dataset provided is a scaled-down version, representing a quarter of the original dataset. It was made available for Kaggle. It constitutes 6,362,620 samples, with 8213 fraudulent transaction samples and 6,354,407 non-fraudulent transactions. It includes several attributes related to mobile money transactions: transaction type (cash-in, cash-out, debit, payment, and transfer); transaction amount in local currency; customer information (customer conducting the transaction and transaction recipient); initial balances before and after the transaction; and fraudulent behavior indicators (isFraud and isFlaggedFraud). These attributes indicate a binary classification. Default of credit card clientsIt was created by I-Cheng Yeh and introduced on January 25, 2016, and is available in the UC Irvine ML repository (Yeh, 2016 ). The dataset, which is used for classification tasks, focuses on the case of defaulted payments of credit card customers in Taiwan in the business area. Moreover, it is a multivariate dataset with 30,000 instances and 24 attributes. They include attributes such as the amount of credit granted, payment history, and statement records spanning April through September 2005. This data source is selected in studies such as those by Esenogho et al. ( 2022 ), Pumsirirat and Yan ( 2018 ), and Seera et al. ( 2021 ). Synthetic data from a financial payment systemEdgar Lopez Rojas created the dataset in 2017. The synthetic data were generated in the BankSim payment simulator. It is based on a sample of transactional data provided by a bank in Spain (López-Rojas, 2017 ). It includes the following characteristics: step, customer ID, age, gender, zip code, merchant ID, zip code of merchant, category of purchase, amount of purchase, and fraud status. It comprises 594,643 transactions, of which ~1.2% (7200) were labeled as fraud and the rest (587,443) were labeled as genuine, and it was processed as a binary classification problem. The dataset has been used in several investigations (Esenogho et al., 2022 ; Pumsirirat and Yan, 2018 ; Seera et al., 2021 ). This dataset is a financial and economic information and research database (Compustat, 2022 ). It contains characteristics related to various aspects of companies, such as asset quality, revenues earned, administrative and sales expenses, and sales growth, among others. COMPUSTAT collects and stores detailed information on listed companies in the United States and Canada. The set includes information on 61 characteristics and consists of 228 companies, of which half showed fraud in their information while the other half did not present fraud (binary classification), and it is used in studies (Dutta et al., 2017 ; Whiting et al., 2012 ). Insurance Company Benchmark (COIL 2000)This dataset is used in the CoIL 2000 challenge, available at the UC Irvine Machine Learning Repository, created by Peter Van Der Putten. It consists of 9822 instances and 86 attributes containing information about customers of an insurance company and includes data on product use and sociodemographic data (Putten, 2000 ). It is characterized as multivariate and is used to perform regression/classification tasks by studies using the dataset (Huang et al., 2018 ; Sathya and Balakumar, 2022 ). Bitcoin network transactional metadataThis dataset contains Bitcoin transaction metadata from 2011 to 2013. It was created by Omer Shafiq (Kaggle handle: OmerShafiq) and introduced to the Kaggle online community in 2019. The set comprises 11 attributes and 30,000 instances related to Bitcoin transactions, bitcoin flows, connections between transactions, average ratings, and malicious transactions (Omershafiq, 2019 ). It is efficient for investigating and analyzing anomalies and fraud detection in Bitcoin transactions (Ashfaq et al., 2022 ). SQ2: What were the metrics used to assess the performance of ML models to detect financial fraud? Based on previous studies (Nicholls et al., 2021 ; Shahana et al., 2023 ), the performance of the metrics used in ML models is the last step in determining whether the results align with the problem at hand. The metrics demonstrate the ability to do a specific task, such as classification, regression, or clustering quality, as they allow comparing the performance of models. Many evaluation metrics have been used in previous studies, such as precision, sensitivity, recall, accuracy, and area under the curve. These metrics can be calculated using the confusion matrix. Figure 10 compares the target and true values with the predicted ones based on the study by Torrano et al. ( 2018 ). ![]() Presents the confusion matrix generated during the evaluation of the financial fraud detection models. Authors’ own elaboration. According to previous studies (Shahana et al., 2023 ; Zhao and Bai, 2022 ), true positive (TP) projects a positive value (fraud) that matches the true value; true negative (TN) accurately predicts a negative outcome (no fraud); false positive (FP) denotes the predicted positive whose true value is negative (no fraud); and false negative (FN) represents the predicted negative whose true value is positive (fraud). FP and FN represent the misclassification cost, also known as classification model prediction error. The metrics used to evaluate the effectiveness of supervised ML techniques are as follows. The accuracy metric is the most commonly used (Ramírez-Alpízar et al., 2020 ). It is defined as the total number or proportion of correct predictions/samples over the total number of records analyzed. Further, it is a method of evaluating the performance of a binary classification model distinguishing between true and false. In Eq. ( 1 ), it calculates the accuracy metric. The sensitivity metric known as recall (TP or TPR rate) is the ratio of successfully identified fraudulent predictions to the total number of fraudulent samples. Equation ( 2 ) calculates the sensitivity metric. The specificity metric (TN rate or TNR) is the percentage of non-fraudulent samples properly designated as non-fraudulent. It is represented in Eq. ( 3 ). Accuracy is the ratio of correctly classified fraudulent predictions to the total number of fraudulent predictions. Equation ( 4 ) calculates the precision metric. F1-score is a metric that combines accuracy and recall using a weighted harmonic mean (Bakumenko and Elragal, 2022 ). It is presented in Eq. ( 5 ). Type I error (FP or FPR rate) is the number of legitimate predictions mistakenly labeled as fraudulent as a percentage of all legitimate predictions. The metric is defined in Eq. ( 6 ). Type II error (FN or FNR rate) is the proportion of fraudulent samples incorrectly designated as non-fraudulent. Type I and II errors make up the overall error rate. It is defined in Eq. ( 7 ). The area under the curve (AUC), or area under the receiver operating characteristic curve, represents a graphic of TPR versus FPR (Y. Chen and Wu, 2022 ). AUC values range from 0 to 1; the more accurate an ML model, the higher its AUC value. It is a metric that represents the model’s performance when differentiating between two classes. Following the guidelines in previous studies (Amrutha et al., 2023 ; García-Ordás et al., 2023 ; Palacio, 2019 ), some metrics used to evaluate the effectiveness of unsupervised ML techniques will be defined. The silhouette coefficient identifies the most appropriate number of clusters; a higher coefficient means better quality with this number of clusters. Equation ( 8 ) calculates the metric. where x denotes the average of the distances of observation j with respect to the rest of the observations of the cluster to which j belongs. Furthermore, y denotes the minimum distance to a different cluster. The silhouette score takes values between −1 and 1. Based on the study by Viera et al. ( 2023 ), 1 (correct) represents the assignment of observation j to a good cluster, zero (0) indicates that observation j is between two distinct groups, and −1 (incorrect) indicates that the assignment of j to the cluster is a bad clustering. The rand index is the similarity measure between two clusters considering all pairs and including those assigned to the same cluster in both the predictions and the true cluster. Equation ( 9 ) calculates the index. The Davies–Bouldin metric is a score used to evaluate clustering algorithms. It is defined as the mean value of the samples, represented in Eq. ( 10 ). where k denotes the number of groups \({c}_{i},{c}_{j}\) , k represents the centroids of cluster i and j , respectively, with \(d\left({c}_{i},{c}_{i}\right)\) as the distance between them, while \({\alpha }_{i}\) and \({\alpha }_{j}\) corresponds to the average distance of all elements in clusters i and j and the distance to their respective \({c}_{i}\) and \({c}_{j}\) centroids (Viera et al., 2023 ). The Fowlkes–Mallows index is defined as the geometric mean between precision and recall, represented in Eq. ( 11 ). The cophenetic correlation coefficient is a clustering method to produce a dendrogram (tree diagram). Equation ( 12 ) indicates the metric. where \(x(i,j)=|{x}_{i}-{x}_{j}|\) represents the Euclidean distance between the i th and j th points of \(x\) . While \(t(i,j)\) is the height of the node at which the two points, \({t}_{i}\) and \({t}_{j}\) , of the dendrogram meet and \(\bar{x}\) and \(\bar{t}\) are the mean value of \(x(i,j)\) and \(t(i,j).\) Discussion and conclusionResearch on the detection of financial fraud by applying ML techniques is a significant topic. On the one hand, fraud directly affects the business world and, on the other hand, detecting it early involves great challenges; this has led to designing tools using AI, such as ML techniques. This study is an SLR using adaptations of the PRISMA and Kitchenham methods to critically analyze and synthesize the study results. Research articles published in Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect were explored. The results were presented in two parts. The first one included a bibliometric study with the open-source software VOSviewer, followed by a discussion of the SLR results. The bibliometric analysis presented the results of the authors, articles, sources, countries, and most important trends in the literature on financial fraud detection by applying ML, as well as an analysis of fraud types, ML models, and datasets. From the 104 articles dating from 2012 to 2023, several types of fraudulent activities are described, as well as external (e.g., credit cards, insurance) and internal (e.g., financial statements, money laundering) frauds, and a brief report on fraud, in general, is provided. Further, it was possible to extract supervised and unsupervised ML techniques, with the 10 most used models as RF in supervised techniques and autoencoder as an unsupervised technique. During the literature review on the detection of financial fraud using machine learning models, it became evident that several authors have made significant contributions. However, some stand out more in terms of the number of publications and citations. Some of the most notable ones, Ahmed M. with 318 citations, Ileberi E. with 82, and Chen S. with 84, have made important advances in the field. Others, such as Abdallah A., with only one publication, but with 333 citations, have also made a considerable impact. And although researchers such as Khan S. and Mishra B. have fewer citations, the combined work of all these authors has established a robust knowledge base, providing a deeper understanding of the challenges and opportunities present in financial fraud detection through machine learning techniques. Consistent with the analysis of the article clusters, clusters 2, 4 and 11 emerge as the most influential in this field with topics of interdisciplinary interest (artificial intelligence/machine learning, accounting, finance), among academics and auditing firms. The SLR evidences that authors in these domains often cooperate when it comes to publication, in turn, studies by (Huang et al., 2018 ; J. Kim et al., 2019 ; Sahin et al., 2013 ; Dutta et al., 2017 ) are highly cited articles. Similarly, the leading countries in the research area include China, which has the largest number of published articles, followed by India and Saudi Arabia. The production of articles on the subject was found to be geographically distributed among countries whose economies are developing and are in transition, which indicates a greater capacity for the production of papers and research. In comparison to Ashtiani and Raahemi’s ( 2022 ) study highlighting the United States, leading with the largest number of papers (18) in the area, followed by China (8) and Greece (7), Al-Hashedi and Magalingam’s ( 2021 ) posit that India is the top producer of articles with 24, followed by China (14) and the United States (9). The journals that have accepted the publication of these studies are specifically in the accounting and computer science domain. There is much literature on computers and security, expert systems with applications, and knowledge-based systems on financial fraud detection through ML models, as supported by Al-Hashedi and Magalingam ( 2021 ) and Ali et al. ( 2022 ). The keywords highlighted in the studies include crime, fraud detection, and ML. These words indicate a central focus on the financial industry, where learning and/or data mining systems help discover patterns or anomalies in financial data, in addition to attractive trends and approaches in the research field. The literature has indicated articles investigating fraud types, particularly credit card loan fraud and insurance fraud, which are of great interest to the scientific community (Al-Hashedi and Magalingam, 2021 ; Ali et al., 2022 ; West and Bhattacharya, 2016 ). This study has classified the different types of fraud into internal and external, and sub-classifications have been derived. In both types, ML techniques have been used to detect financial fraud—supervised (59 articles), unsupervised (19 articles), supervised and unsupervised (16 articles), and deep learning (3 articles), among others. Most of the studies analyzed have developed binary classification models, that is, fraud or non-fraud. Supervised learning techniques require labeled data, and the most frequently used models are LR, RF, and SVM, among others. In the experiments, the prevalence of metrics such as accuracy, precision, sensitivity, and F1-score are highlighted. For unsupervised learning as a technique, the data do not have a label and focus on discovering new patterns with algorithms such as DBSCAN, autoencoder, and IF, among others. The evaluation with internal metrics was not made in detail. Few studies using semi-supervised learning and deep learning techniques have been highlighted because of the fact that they are novel. Further, it is found in the trend through the keywords, as the research works address the subject of ML, learning algorithms, deep learning, SVM, fraudulent transactions, and anomaly detection, but it is evident that there is little research on unsupervised learning and deep learning. The scarce use of these techniques may be because of the complexity of the models and the high consumption of computational resources. In the analysis of the 86 experiment articles, few articles were found that used unsupervised techniques. Also, a large part of the datasets used is labeled, which requires further experimentation with models and unlabeled real-world datasets (Ounacer et al., 2018 ; Pumsirirat and Yan, 2018 ; Rubio et al., 2020 ; Van Capelleveen et al., 2016 ; Vanini et al., 2023 ). Meanwhile, labeled data are costly because an expert is required for their construction. Thus, more attention has been given to data origin, preprocessing, and feature extraction before training an ML model to increase detection accuracy. Accordingly, it should be emphasized that deep learning models require a thorough design and adjustment compared with previous models. They are quite sensitive to the architecture structure and choice of hyperparameters. Further, the data quality and quantity required is relatively high, so it should be considered in the design stage. The studies show that the datasets for the experiments were taken from the stock exchanges of China, Canada, the United States, Taiwan, and Tehran, among others. The researchers used ML models to detect financial fraud in credit card loans, highlighting the use of the “Credit Card Fraud Detection” dataset, mentioned 15 times. Also, the performance of ML models can be affected because of the selected set by the number of selected attributes and instances. From the analysis, it was observed that most of the articles use real datasets obtained from existing databases, historical records, or other collection methods, and few studies use synthetic datasets (four articles), which are those generated by modeling or simulation techniques and try to mimic a real dataset. Still, the integration of real and synthetic datasets enables a comprehensive approach to the problem by providing a basis and complementary information for conclusions and comparisons with other studies on the performance of ML models. Specifically, the datasets used in recent studies and/or articles, spanning from 2012 to 2023, reveal concern related to obsolete data approximately from 1994, which, because of their age, do not provide effective and accurate results in the current context as a result of the new fraud modalities created day after day, with characteristics and behavior patterns that have evolved significantly over time. The literature review and bibliometric analyses on financial fraud detection using machine learning and its various techniques conducted between 2012 and 2023 show a remarkable evolution in this field. Authors, including Ahmed M., Ileberi E., and Chen S. have made important contributions with a high number of citations. There has been fundamental interdisciplinary collaboration between areas such as artificial intelligence, accounting, finance, and information security, highlighting widely cited studies such as Huang et al. ( 2018 ), J. Kim et al. ( 2019 ), Sahin et al. ( 2013 ), and Dutta et al. ( 2017 ). Countries such as China, India and Saudi Arabia leading in publications can be seen, which reflects the global effort of emerging economies. Supervised learning techniques such as Random Forest, and unsupervised ones, like Autoencoder, are the most widely used. Furthermore, the effort and enthusiasm for the use of deep learning, despite its complexity and high computational resource requirements, are evident. Research mainly uses real datasets such as those from the Chinese, Canadian, US, Taiwanese, and Tehran stock exchanges, with the “Credit Card Fraud Detection” dataset being the most important one. The journals that publish these studies belong both to the accounting area and to computer science, with extensive literature in Computers and Security, Expert Systems with Applications, and Knowledge-Based Systems. While it is true that the accuracy of fraud detection depends on the quality of the data and preprocessing with various algorithms, the need for robust and updated approaches to face new fraud modalities is particularly highlighted. Limitations and scope for future researchThe study had limitations that affected the scope and interpretation of the results. Although a systematic review was performed, the lack of quantitative support in the data collected is acknowledged. From the 104 articles identified in the SLR, 18 correspond to systematic reviews, which limits the availability of studies with specific details or experiments. This affected the depth of the analysis and the comprehensiveness of the results obtained. The literature review reveals a predominant emphasis on the banking sector, especially in relation to credit card fraud and insurance fraud. The narrow focus leads to a lack of diversity in the types of fraud studied, excluding internal fraud types such as embezzlement, racketeering, smurfing, defalcation, collusion, signature forgery, and manipulation of accounting documents, among others. The underrepresentation of these other fraud types compromises the generalization of the findings and the applicability of ML models to contexts beyond the banking sector. The datasets analyzed show a significant deficiency in the representation of fraud types. It can be observed that most of these datasets originated from the main stock exchanges and, additionally, the information used to carry out the experiments is old. This scenario indicates the inclusion of non-contemporary fraud types in the analysis. The limited availability of information on the performance metrics of the unsupervised learning models made it difficult to count the evaluation metrics used to predict financial fraud. The field of financial fraud detection using ML models offers promising prospects for future research. An area of potential improvement is experimentation with advanced techniques, such as reinforcement learning or deep neural network architectures, to improve the accuracy and efficiency of models, including unsupervised learning. This approach could enable the development of more sophisticated systems capable of identifying complex fraud patterns and dynamically adjusting to the changing strategies of criminals, who are constantly innovating new fraud methods. Moreover, it is suggested that the applicability of fraud detection systems in contexts other than banking be analyzed by adopting the anomaly approach, which would make it possible to move forward in the detection of fraud in real-time and minimize risks in organizations. It is also proposed that a dataset be created, containing real context information, which is freely accessible and includes new fraud methods to provide the scientific community with an updated dataset. Data availabilityThe datasets generated and/or analyzed in this study are available in the Harvard Dataverse repository https://doi.org/10.7910/DVN/CM8NVY . Abdallah A, Maarof MA, Zainal A (2016) Fraud detection system: a survey. J Netw Comput Appl 68:90–113. https://doi.org/10.1016/j.jnca.2016.04.007 Article Google Scholar Achakzai MAK, Juan P (2022) Using machine learning meta-classifiers to detect financial frauds. Financ Res Lett 48:102915. https://doi.org/10.1016/j.frl.2022.102915 Ahmed M, Mahmood AN, Islam MdR (2016) A survey of anomaly detection techniques in financial domain. Future Gener Comput Syst 55:278–288. https://doi.org/10.1016/j.future.2015.01.001 Al Ali A, Khedr AM, El-Bannany M, Kanakkayil S (2023) A powerful predicting model for financial statement fraud based on optimized XGBoost ensemble learning technique. Appl Sci 13(4):2272. https://doi.org/10.3390/app13042272 Article CAS Google Scholar Alarfaj FK, Malik I, Khan HU, Almusallam N, Ramzan M, Ahmed M (2022) Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. IEEE Access 10:39700–39715. https://doi.org/10.1109/ACCESS.2022.3166891 Al-Hashedi KG, Magalingam P (2021) Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019. Comput Sci Rev 40:100402. https://doi.org/10.1016/j.cosrev.2021.100402 Ali A, Abd Razak S, Othman SH, Eisa TAE, Al-Dhaqm A, Nasser Tusneem ME, Elshafie H, Saif A (2022) Financial fraud detection based on machine learning: a systematic literature review. Appl Sci (Switz). https://doi.org/10.3390/app12199637 Alsuwailem AAS, Salem E, Saudagar AKJ (2022) Performance of different machine learning algorithms in detecting financial fraud. Comput Econ. https://doi.org/10.1007/s10614-022-10314-x Alwadain A, Ali RF, Muneer A (2023) Estimating financial fraud through transaction-level features and machine learning. Mathematics 11(5):1184. https://doi.org/10.3390/math11051184 Amrutha E, Arivazhagan S, Jebarani WSL (2023) Deep clustering network for steganographer detection using latent features extracted from a novel convolutional autoencoder. Neural Process Lett 55(3):2953–2964. https://doi.org/10.1007/s11063-022-10992-6 Arévalo F, Barucca P, Téllez-León I-E, Rodríguez W, Gage G, Morales R (2022) Identifying clusters of anomalous payments in the salvadorian payment system. Lat Am J Cent Bank. 3(1):100050. https://doi.org/10.1016/j.latcb.2022.100050 Ashfaq T, Khalid R, Yahaya A, Aslam S, Alsafari S, Hameed I (2022) A machine learning and blockchain bases efficient fraud detection mechanism. Sensors 22(19):7162. https://doi.org/10.3390/s22197162 Article ADS PubMed PubMed Central Google Scholar Ashtiani MN, Raahemi B (2022) Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review. IEEE Access 10:72504–72525. https://doi.org/10.1109/ACCESS.2021.3096799 Aslam F, Hunjra A, Ftiti Z, Louhichi W, Shams T (2022) Insurance fraud detection: evidence from artificial intelligence and machine learning. Res Int Bus Financ. https://doi.org/10.1016/j.ribaf.2022.101744 Baghdasaryan V, Davtyan H, Sarikyan A, Navasardyan Z (2022) Improving tax audit efficiency using machine learning: the role of taxpayer’s network data in fraud detection. Appl Artif Intell 36(1). https://doi.org/10.1080/08839514.2021.2012002 Baker MR, Mahmood ZN, Shaker EH (2022) Ensemble learning with supervised machine learning models to predict credit card fraud transactions. Rev Intell Artif. https://doi.org/10.18280/ria.360401 Bakumenko A, Elragal A (2022) Detecting anomalies in financial data using machine learning algorithms. Systems. https://doi.org/10.3390/systems10050130 Bekirev AS, Klimov VV, Kuzin MV, Shchukin BA (2015) Payment card fraud detection using neural network committee and clustering. Optical Mem. Neural Netw 24(3):193–200. https://doi.org/10.3103/S1060992X15030030 Benchaji I, Douzi S, Ouahidi BEl (2021) Credit card fraud detection model based on LSTM recurrent neural networks. J Adv Inf Technol 12(2):113–118. https://doi.org/10.12720/jait.12.2.113-118 Błaszczyński J, de Almeida Filho AT, Matuszyk A, Szeląg M, Słowiński R (2021) Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Syst Appl 163:113740. https://doi.org/10.1016/j.eswa.2020.113740 Bolgorian M, Mayeli A, Ronizi NG (2023) CEO compensation and money laundering risk. J Econ Criminol 1:100007. https://doi.org/10.1016/j.jeconc.2023.100007 Chen S (2016) Detection of fraudulent financial statements using the hybrid data mining approach. SpringerPlus 5(1):89. https://doi.org/10.1186/s40064-016-1707-6 Article PubMed PubMed Central Google Scholar Chen S, Goo Y-JJ, Shen Z-D (2014) A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements. Sci World J 2014:1–9. https://doi.org/10.1155/2014/968712 Chen Y, Wu Z (2022) Financial fraud detection of listed companies in China: a machine learning approach. Sustainability 15(1):105. https://doi.org/10.3390/su15010105 Chullamonthon P, Tangamchit P (2023) Ensemble of supervised and unsupervised deep neural networks for stock price manipulation detection. Expert Syst Appl 220:119698. https://doi.org/10.1016/j.eswa.2023.119698 Compustat (2022) Compustat. S&P Global Market Intelligence. https://www.marketplace.spglobal.com/en/datasets?cq_cmp=9778467255&cq_plac=&cq_net=g&cq_pos=&cq_plt=gp&utm_source=google&utm_medium=cpc&utm_campaign=DMS_Marketplace_Search_Google&utm_term=&utm_content=586436401424&_bt=586436401424&_bk=&_bm=&_bn=g&_bg=133704002389&gclid=Cj0KCQjw4s-kBhDqARIsAN-ipH3TguUoVohfDZgD65fjvKomc6BBgJ3uA9zP95m6u4vOs5yG7_L7w2UaAnnvEALw_wcB CSMAR (2022) China Stock Market & Accounting Research (CSMAR). Wharton University of Pennsylvania. https://wrds-www.wharton.upenn.edu/pages/about/data-vendors/china-stock-market-accounting-research-csmar/ Dalal S, Seth B, Radulescu M, Secara C, Tolea C (2022) Predicting fraud in financial payment services through optimized hyper-parameter-tuned XGBoost model. Mathematics 10(24):4679. https://doi.org/10.3390/math10244679 Dantas RM, Firdaus R, Jaleel F, Neves Mata P, Mata MN, Li G (2022) Systemic acquired critique of credit card deception exposure through machine learning. J Open Innov: Technol Mark Complex 8(4):192. https://doi.org/10.3390/joitmc8040192 Domashova J, Kripak E (2021) Identification of non-typical international transactions on bank cards of individuals using machine learning methods. Procedia Comput Sci 190:178–183. https://doi.org/10.1016/j.procs.2021.06.023 Domashova J, Kripak E (2022) Development of a generalized algorithm for identifying atypical bank transactions using machine learning methods. Procedia Comput Sci 213:101–109. https://doi.org/10.1016/j.procs.2022.11.044 Dutta I, Dutta S, Raahemi B (2017) Detecting financial restatements using data mining techniques. Expert Syst Appl 90:374–393. https://doi.org/10.1016/j.eswa.2017.08.030 Elshaar S, Sadaoui S (2020) Semi-supervised Classification of Fraud Data in Commercial Auctions. Appl Artif Intell 34(1):47–63. https://doi.org/10.1080/08839514.2019.1691341 Esenogho E, Mienye ID, Swart TG, Aruleba K, Obaido G (2022) A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access 10:16400–16407. https://doi.org/10.1109/ACCESS.2022.3148298 Eshghi A, Kargari M (2019) Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty. Expert Syst Appl 121:382–392. https://doi.org/10.1016/j.eswa.2018.11.039 Estupiñán Gaitán R (2015) Control interno y fraudes: análisis de informe COSO I, II y III con base en los ciclos transaccionales, Tercera edición (Niebel BW (ed)). Ecoe Ediciones Fanai H, Abbasimehr H (2023) A novel combined approach based on deep autoencoder and deep classifiers for credit card fraud detection. Expert Syst Appl 217:119562. https://doi.org/10.1016/j.eswa.2023.119562 Fang Y, Zhang Y, Huang C (2019) Credit card fraud detection based on machine learning. Comput Mater Contin 61(1):185–195. https://doi.org/10.32604/cmc.2019.06144 Femila Roseline J, Naidu G, Samuthira Pandi V, Alamelu alias Rajasree S, Mageswari N (2022) Autonomous credit card fraud detection using machine learning approach✰. Comput Electr Eng 102:108132. https://doi.org/10.1016/j.compeleceng.2022.108132 García-Ordás MT, Alaiz-Moretón H, Casteleiro-Roca J-L, Jove E, Benítez-Andrades JA, García-Rodríguez I, Quintián H, Calvo-Rolle JL (2023) Clustering techniques selection for a hybrid regression model: a case study based on a solar thermal system. Cybern Syst 54(3):286–305. https://doi.org/10.1080/01969722.2022.2030006 Gupta S, Mehta SK (2021) Data mining-based financial statement fraud detection: systematic literature review and meta-analysis to estimate data sample mapping of fraudulent companies against non-fraudulent companies. Global Bus Rev https://doi.org/10.1177/0972150920984857 Hajek P, Henriques R (2017) Mining corporate annual reports for intelligent detection of financial statement fraud—a comparative study of machine learning methods. Knowl-Based Syst 128:139–152. https://doi.org/10.1016/j.knosys.2017.05.001 Hamza C, Lylia A, Nadine C, Nicolas C (2023) Semi-supervised method to detect fraudulent transactions and identify fraud types while minimizing mounting costs. Int J Adv Comput Sci Appl 14(2). https://doi.org/10.14569/IJACSA.2023.0140298 Hilal W, Gadsden SA, Yawney J (2022) Financial fraud: a review of anomaly detection techniques and recent advances. Expert Syst Appl 193:116429. https://doi.org/10.1016/j.eswa.2021.116429 Hofmann H (1994) Statlog (German credit data). UCI Machine Learning Repository. https://doi.org/10.24432/C5NC77 Huang D, Mu D, Yang L, Cai X (2018) CoDetect: financial fraud detection with anomaly feature detection. IEEE Access 6:19161–19174. https://doi.org/10.1109/ACCESS.2018.2816564 Hwang J, Kim K (2020) An efficient domain-adaptation method using GAN for fraud detection. Int J Adv Comput Sci Appl 11(11). https://doi.org/10.14569/IJACSA.2020.0111113 Ileberi E, Sun Y, Wang Z (2021) Performance evaluation of machine learning methods for credit card fraud detection using SMOTE and AdaBoost. IEEE Access 9:165286–165294. https://doi.org/10.1109/ACCESS.2021.3134330 Ileberi E, Sun Y, Wang Z (2022) A machine learning based credit card fraud detection using the GA algorithm for feature selection. J Big Data 9(1):24. https://doi.org/10.1186/s40537-022-00573-8 Khan S, Alourani A, Mishra B, Ali A, Kamal M (2022) Developing a credit card fraud detection model using machine learning approaches. Int J Adv Comput Sci Appl 13(3). https://doi.org/10.14569/IJACSA.2022.0130350 Kim J, Kim H-J, Kim H (2019) Fraud detection for job placement using hierarchical clusters-based deep neural networks. Appl Intell 49(8):2842–2861. https://doi.org/10.1007/s10489-019-01419-2 Kim YJ, Baik B, Cho S (2016) Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Syst Appl 62:32–43. https://doi.org/10.1016/j.eswa.2016.06.016 Kitchenham B, Brereton P (2013) A systematic review of systematic review process research in software engineering. Inf Softw Technol 55(12):2049–2075. https://doi.org/10.1016/j.infsof.2013.07.010 Kitchenham B, Stuart C (2007) Guidelines for performing systematic literature reviews in software engineering. https://www.researchgate.net/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering Kootanaee AJ, Aghajan AAP, Shirvani MH (2021) A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements. J Optim Ind Eng 14(2):183–201. https://doi.org/10.22094/JOIE.2020.1877455.1685 KPMG (2022) Una triple amenaza en las Américas. KMPG. https://kpmg.com/co/es/home/insights/2022/01/kpmg-fraud-outlook-survey.html Kumar S, Ahmed R, Bharany S, Shuaib M, Ahmad T, Tag Eldin E, Rehman AU, Shafiq M (2022) Exploitation of machine learning algorithms for detecting financial crimes based on customers’ behavior. Sustainability 14(21):13875. https://doi.org/10.3390/su142113875 Kumbure MM, Lohrmann C, Luukka P, Porras J (2022) Machine learning techniques and data for stock market forecasting: a literature review. Expert Syst Appl 197:116659. https://doi.org/10.1016/j.eswa.2022.116659 Lee H, Choi E, Kim I, Choi D, Go W, Lee K, Yim H, Lee T (2018) Feature selection practice for unsupervised learning of credit card fraud detection. J Theor Appl Inf Technol 96(2):408–417 Google Scholar Lei X, Mohamad UH, Sarlan A, Shutaywi M, Daradkeh YI, Mohammed HO (2022) Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms. Inf Process Manag 59(5):103036. https://doi.org/10.1016/j.ipm.2022.103036 Lokanan M, Tran V, Vuong NH (2019) Detecting anomalies in financial statements using machine learning algorithm. Asian J Account Res 4(2):181–201. https://doi.org/10.1108/AJAR-09-2018-0032 Lokanan ME, Sharma K (2022) Fraud prediction using machine learning: The case of investment advisors in Canada. Mach Learn Appl 8:100269. https://doi.org/10.1016/j.mlwa.2022.100269 Lokanan ME (2022) Predicting money laundering using machine learning and artificial neural networks algorithms in banks. J Appl Secur Res 1–25. https://doi.org/10.1080/19361610.2022.2114744 López-Rojas E (2017) Synthetic financial datasets for fraud detection. Kaggle. https://www.kaggle.com/datasets/ealaxi/paysim1 Machine Learning Group (2018) Credit card fraud detection. Kaggle. https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud Madhurya MJ, Gururaj HL, Soundarya BC, Vidyashree KP, Rajendra AB (2022) Exploratory analysis of credit card fraud detection using machine learning techniques. Glob Transit Proc 3(1):31–37. https://doi.org/10.1016/j.gltp.2022.04.006 Malik EF, Khaw KW, Belaton B, Wong WP, Chew X (2022) Credit card fraud detection using a new hybrid machine learning architecture. Mathematics 10(9):1480. https://doi.org/10.3390/math10091480 Márquez Arcila RH (2019) Auditoría forense. Ecoe Ediciones Misra S, Thakur S, Ghosh M, Saha SK (2020) An autoencoder based model for detecting fraudulent credit card transaction. Procedia Comput Sci 167:254–262. https://doi.org/10.1016/j.procs.2020.03.219 Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097 Mongwe W, Malan K (2020) A survey of automated financial statement fraud detection with relevance to the South African context. S Afr Comput J 32(1). https://doi.org/10.18489/sacj.v32i1.777 Montes Salazar CA (2019) Riesgos de fraude en una auditoría de estados financieros (1.a ed.). Alfaomega. ISBN: 9789587782639. https://www.alfaomegacloud.com/reader/riesgos-de-fraude-en-una-auditoria-de-estados-financieros?location=3 Moreira MÂL, Junior C, de SR, Silva DF, de L, de Castro Junior MAP, Costa IP, de A, Gomes CFS, dos Santos M (2022) Exploratory analysis and implementation of machine learning techniques for predictive assessment of fraud in banking systems. Procedia Comput Sci 214:117–124. https://doi.org/10.1016/j.procs.2022.11.156 Narsimha B, Raghavendran CV, Rajyalakshmi P, Reddy GK, Bhargavi M, Naresh P (2022) Cyber defense in the age of artificial intelligence and machine learning for financial fraud detection application. Int J Electr Electron Res 10(2):87–92. https://doi.org/10.37391/ijeer.100206 Nian K, Zhang H, Tayal A, Coleman T, Li Y (2016) Auto insurance fraud detection using unsupervised spectral ranking for anomaly. J Financ Data Sci 2(1):58–75. https://doi.org/10.1016/j.jfds.2016.03.001 Nicholls J, Kuppa A, Le-Khac N-A (2021) Financial cybercrime: a comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. IEEE Access 9:163965–163986. https://doi.org/10.1109/ACCESS.2021.3134076 Nonnenmacher J, Marx Gómez J (2021) Unsupervised anomaly detection for internal auditing: Literature review and research agenda. Int J Digit Account Res 1–22. https://doi.org/10.4192/1577-8517-v21_1 Olszewski D (2014) Fraud detection using self-organizing map visualizing the user profiles. Knowl Based Syst 70:324–334. https://doi.org/10.1016/j.knosys.2014.07.008 Omershafiq (2019) Bitcoin network transactional metadata. Kaggle. https://www.kaggle.com/datasets/omershafiq/bitcoin-network-transactional-metadata Ounacer S, Ait El Bour H, Oubrahim Y, Ghoumari MY, Azzouazi M (2018) Using isolation forest in anomaly detection: the case of credit card transactions. Period Eng Nat Sci 6(2):394. https://doi.org/10.21533/pen.v6i2.533 Palacio SM (2019) Abnormal pattern prediction: detecting fraudulent insurance property claims with semi-supervised machine-learning. Data Sci J 18(1):35. https://doi.org/10.5334/dsj-2019-035 Papík M, Papíková L (2022) Detecting accounting fraud in companies reporting under US GAAP through data mining. Int J Account Inf Syst 45:100559. https://doi.org/10.1016/j.accinf.2022.100559 Plakandaras V, Gogas P, Papadimitriou T, Tsamardinos I (2022) Credit card fraud detection with automated machine learning systems. Appl Artif Intell 36(1). https://doi.org/10.1080/08839514.2022.2086354 Polak P, Nelischer C, Guo H, Robertson DC (2020) Intelligent” finance and treasury management: what we can expect. AI Soc 35(3):715–726. https://doi.org/10.1007/s00146-019-00919-6 PricewaterhouseCoopers (2022) Encuesta Global de Crimen y Fraude Económico de PwC Colombia 2022 – 2023. https://www.pwc.com/co/es/publicaciones/encuesta-crimen-fraude-economico.html Pumsirirat A, Yan L (2018) Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine. Int J Adv Comput Sci Appl 9(1). https://doi.org/10.14569/IJACSA.2018.090103 Putten P (2000) Insurance Company Benchmark (COIL 2000). UCI Machine Learning Repository. https://doi.org/10.24432/C5630S Quinlan R (1997) Statlog (Australian credit approval). UCI Machine Learning Repository. https://doi.org/10.24432/C59012 Rakowski R, Polak P, Kowalikova P (2021) Ethical aspects of the impact of AI: the status of humans in the era of artificial intelligence. Society 58(3):196–203. https://doi.org/10.1007/s12115-021-00586-8 Ramírez-Alpízar A, Jenkins M, Martínez A, Quesada-López C (2020a) Use of data mining and machine learning techniques for fraud detection in financial statements: a systematic mapping study. Rev Ibér Sist Tecnol Inf Lousada No. E28:97–109 Reurink A (2018) Financial fraud: a literature review. J Econ Surv 32(5):1292–1325. https://doi.org/10.1111/joes.12294 Rocha-Salazar J-J, Segovia-Vargas M-J, Camacho-Miñano M-M (2021) Money laundering and terrorism financing detection using neural networks and an abnormality indicator. Expert Syst Appl 169:114470. https://doi.org/10.1016/j.eswa.2020.114470 Roehrs A, da Costa CA, Righi R, da R, de Oliveira KSF (2017) Personal health records: a systematic literature review. J Med Internet Res 19(1):e13. https://doi.org/10.2196/jmir.5876 Rubio J, Barucca P, Gage G, Arroyo J, Morales-Resendiz R (2020) Classifying payment patterns with artificial neural networks: an autoencoder approach. Lat Am J Cent Bank 1(1–4):100013. https://doi.org/10.1016/j.latcb.2020.100013 Sahin Y, Bulkan S, Duman E (2013) A cost-sensitive decision tree approach for fraud detection. Expert Syst Appl 40(15):5916–5923. https://doi.org/10.1016/j.eswa.2013.05.021 Saputra M, Santosa PI, Permanasari AE (2023) Consumer behaviour and acceptance in fintech adoption: a systematic literature review. Acta Inform Pragensia 12(2):468–489. https://doi.org/10.18267/j.aip.222 Saragih MG, Chin J, Setyawasih R, Nguyen PT, Shankar K (2019) Machine learning methods for analysis fraud credit card transaction. Int J Eng Adv Technol 8(6S):870–874. https://doi.org/10.35940/ijeat.F1164.0886S19 Sathya M, Balakumar B (2022) Insurance fraud detection using novel machine learning technique. Int J Intell Syst Appl Eng 10(3):374–381 Savić M, Atanasijević J, Jakovetić D, Krejić N (2022) Tax evasion risk management using a hybrid unsupervised outlier detection method. Expert Syst Appl 193:116409. https://doi.org/10.1016/j.eswa.2021.116409 Seera M, Lim CP, Kumar A, Dhamotharan L, Tan KH (2021) An intelligent payment card fraud detection system. Ann Oper Res. https://doi.org/10.1007/s10479-021-04149-2 Shahana T, Lavanya V, Bhat AR (2023) State of the art in financial statement fraud detection: a systematic review. Technol Forecast Soc Change 192:122527. https://doi.org/10.1016/j.techfore.2023.122527 Shou M, Bao X, Yu J (2023) An optimal weighted machine learning model for detecting financial fraud. Appl Econ Lett 30(4):410–415. https://doi.org/10.1080/13504851.2021.1989367 Singh A, Jain A, Biable SE (2022) Financial fraud detection approach based on firefly optimization algorithm and support vector machine. Appl Comput Intell Soft Comput 2022:1–10. https://doi.org/10.1155/2022/1468015 Smith Q-J, Valverde R (2021) A perceptron based neural network data analytics architecture for the detection of fraud in credit card transactions in financial legacy systems. WSEAS Trans Syst Control 16:358–374. https://doi.org/10.37394/23203.2021.16.31 Sofy MA, Khafagy MH, Badry RM (2023) An intelligent Arabic model for recruitment fraud detection using machine learning. J Adv Informat Technol. https://doi.org/10.12720/jait.14.1.102-111 Srokosz M, Bobyk A, Ksiezopolski B, Wydra M (2023) Machine-learning-based scoring system for antifraud CISIRTs in banking environment. Electronics 12(1):251. https://doi.org/10.3390/electronics12010251 Subudhi S, Panigrahi S (2020) Use of optimized fuzzy C -Means clustering and supervised classifiers for automobile insurance fraud detection. J King Saud Univ— Comput Inf Sci 32(5):568–575. https://doi.org/10.1016/j.jksuci.2017.09.010 Ti Y-W, Hsin Y-Y, Dai T-S, Huang M-C, Liu L-C (2022) Feature generation and contribution comparison for electronic fraud detection. Sci Rep 12(1):18042. https://doi.org/10.1038/s41598-022-22130-2 Article ADS CAS PubMed PubMed Central Google Scholar Tingfei H, Guangquan C, Kuihua H (2020) Using variational auto encoding in credit card fraud detection. IEEE Access 8:149841–149853. https://doi.org/10.1109/ACCESS.2020.3015600 Torrano C, Recuero P, Ramirez F, Hernández S, Torres J (2018) Machine learning aplicado a la ciberseguridad: técnicas y ejemplos en detección de amenazas. Zeroxword Computing Udeze CL, Eteng IE, Ibor AE (2022) Application of machine learning and resampling techniques to credit card fraud detection. J Niger Soc Phys Sci 769. https://doi.org/10.46481/jnsps.2022.769 Usman A, Naveed N, Munawar S (2023) Intelligent anti-money laundering fraud control using graph-based machine learning model for the financial domain. J Cases Inf Technol 25(1):1–20. https://doi.org/10.4018/JCIT.316665 Van Capelleveen G, Poel M, Mueller RM, Thornton D, Van Hillegersberg J (2016) Outlier detection in healthcare fraud: a case study in the Medicaid dental domain. Int J Account Inf Syst 21:18–31. https://doi.org/10.1016/j.accinf.2016.04.001 Vanhoeyveld J, Martens D, Peeters B (2020) Value-added tax fraud detection with scalable anomaly detection techniques. Appl Soft Comput 86:105895. https://doi.org/10.1016/j.asoc.2019.105895 Vanini P, Rossi S, Zvizdic E, Domenig T (2023) Online payment fraud: from anomaly detection to risk management. Financ Innov 9(1):66. https://doi.org/10.1186/s40854-023-00470-w Vanneschi L, Horn DM, Castelli M, Popovič A (2018) An artificial intelligence system for predicting customer default in e-commerce. Expert Syst Appl 104:1–21. https://doi.org/10.1016/j.eswa.2018.03.025 Viera J, Aguilar J, Rodríguez-Moreno M, Quintero-Gull C (2023) Analysis of the behavior pattern of energy consumption through online clustering techniques. Energies 16(4):1649. https://doi.org/10.3390/en16041649 Wadhwa VK, Saini AK, Kumar SS (2020) Financial fraud prediction models: a review of research evidence. Int J Sci Technol Res 9(1):677–680 West J, Bhattacharya M (2016) Intelligent financial fraud detection: a comprehensive review. Comput Secur 57:47–66. https://doi.org/10.1016/j.cose.2015.09.005 Whiting DG, Hansen JV, McDonald JB, Albrecht C, Albrecht WS (2012) Machine learning methods for detecting patterns of management fraud. Comput Intell 28(4):505–527. https://doi.org/10.1111/j.1467-8640.2012.00425.x Article MathSciNet Google Scholar Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th international conference on evaluation and assessment in software engineering. pp. 1–10 Wu B, Lv X, Alghamdi A, Abosaq H, Alrizq M (2023) Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2. Inf Process Manag 60(2):103231. https://doi.org/10.1016/j.ipm.2022.103231 Article PubMed Google Scholar Xiong T, Ma Z, Li Z, Dai J (2022) The analysis of influence mechanism for internet financial fraud identification and user behavior based on machine learning approaches. Int J Syst Assur Eng Manag 13(S3):996–1007. https://doi.org/10.1007/s13198-021-01181-0 Xiuguo W, Shengyong D (2022) An analysis on financial statement fraud detection for Chinese listed companies using deep learning. IEEE Access 10:22516–22532. https://doi.org/10.1109/ACCESS.2022.3153478 Yeh I-C (2016) Default of credit card clients. UCI Machine Learning Repository. https://doi.org/10.24432/C55S3H Zhang Z, Zhou X, Zhang X, Wang L, Wang P (2018) A model based on convolutional neural network for online transaction fraud detection. Secur Commun. Netw. 2018:1–9. https://doi.org/10.1155/2018/5680264 Zhao Z, Bai T (2022) Financial fraud detection and prediction in listed companies using SMOTE and machine learning algorithms. Entropy 24(8):1157. https://doi.org/10.3390/e24081157 Zhou H, Chai H, Qiu M (2018) Fraud detection within bankcard enrollment on mobile device based payment using machine learning. Front Inf Technol Electron Eng 19(12):1537–1545. https://doi.org/10.1631/FITEE.1800580 Zupan M, Budimir V, Letinic S (2020) Journal entry anomaly detection model. Intell Syst Account Financ Manag 27(4):197–209. https://doi.org/10.1002/isaf.1485 Download references AcknowledgementsWe would like to express our gratitude to the Universidad Cooperativa de Colombia, Ibagué campus, Espinal. This research work was supported by Universidad Cooperativa de Colombia and derived from research project INV3456 entitled “Detection of anomalies in financial data in social economy organizations through machine learning techniques” associated with the PLANAUDI, AQUA and SINERGIA UCC group, from the Research Center of the Public Accounting and Systems Engineering program of the UCC Ibagué campus. Author informationAuthors and affiliations. School of Public Accounting, Universidad Cooperativa de Colombia, 730001, Ibagué-Espinal campus, Ibagué, Colombia Ludivia Hernandez Aros & John Johver Moreno Hernandez School of Systems Engineering, Universidad Cooperativa de Colombia, 730001, Ibagué-Espinal campus, Ibagué, Colombia Luisa Ximena Bustamante Molano & Fernando Gutierrez-Portela School of Business Administration, Universidad Cooperativa de Colombia, 730001, Ibagué-Espinal campus, Ibagué, Colombia Mario Samuel Rodríguez Barrero You can also search for this author in PubMed Google Scholar ContributionsAll authors contributed to the creation and design of the study. Corresponding authorCorrespondence to Ludivia Hernandez Aros . Ethics declarationsCompeting interests. The authors declare no competing interests. Ethical approval and consent to participateThe authors declare that they have no human participants, human data, or human tissue. Consent to publishThe authors have no data from any individual person on any form. 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If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . Reprints and permissions About this articleCite this article. Hernandez Aros, L., Bustamante Molano, L.X., Gutierrez-Portela, F. et al. Financial fraud detection through the application of machine learning techniques: a literature review. Humanit Soc Sci Commun 11 , 1130 (2024). https://doi.org/10.1057/s41599-024-03606-0 Download citation Received : 15 November 2023 Accepted : 13 August 2024 Published : 03 September 2024 DOI : https://doi.org/10.1057/s41599-024-03606-0 Share this articleAnyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. 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1. Narrative Literature Review. A narrative literature review, also known as a traditional literature review, involves analyzing and summarizing existing literature without adhering to a structured methodology. It typically provides a descriptive overview of key concepts, theories, and relevant findings of the research topic.
Narrative or traditional literature reviews. Critically Appraised Topic (CAT) Scoping reviews. Systematic literature reviews. Annotated bibliographies. These are not the only types of reviews of literature that can be conducted. Often the term "review" and "literature" can be confusing and used in the wrong context.
Rapid review. Assessment of what is already known about a policy or practice issue, by using systematic review methods to search and critically appraise existing research. Completeness of searching determined by time constraints. Time-limited formal quality assessment. Typically narrative and tabular.
Systematic literature review requires more rigorous and well-defined approach compared to most other types of literature review. Systematic literature review is comprehensive and details the timeframe within which the literature was selected. Systematic literature review can be divided into two categories: meta-analysis and meta-synthesis.
What kinds of literature reviews are written? Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified.
Types of reviews and examples. Definition: "A term used to describe a conventional overview of the literature, particularly when contrasted with a systematic review (Booth et al., 2012, p. 265). Characteristics: Example: Mitchell, L. E., & Zajchowski, C. A. (2022). The history of air quality in Utah: A narrative review.
Examples of literature reviews. Step 1 - Search for relevant literature. Step 2 - Evaluate and select sources. Step 3 - Identify themes, debates, and gaps. Step 4 - Outline your literature review's structure. Step 5 - Write your literature review.
Narrative (Literature) Review - Broad, generic term - Refers to an examination and general synthesis of the research literature, often with a wide scope; completeness and comprehensiveness may vary. Does not follow an established protocol. ... Literature Review Types/Typologies, Taxonomies. Grant, M. J., and A. Booth. "A Typology of Reviews: An ...
Types of Literature Review are as follows: Narrative literature review: This type of review involves a comprehensive summary and critical analysis of the available literature on a particular topic or research question. It is often used as an introductory section of a research paper. Systematic literature review: This is a rigorous and ...
A literature review is an academic text that surveys, synthesizes, and critically evaluates the existing literature on a specific topic. It is typically required for theses, dissertations, or long reports and serves several key purposes: ... Types of Literature Reviews.
The State-of-the-Art Review is conducted periodically, with a focus on the most recent research. It describes what is currently known, understood, or agreed upon regarding the research topic, and highlights where are there still disagreements. Source: Grant, M. J., & Booth, A. (2009). A typology of reviews: an analysis of 14 review types and ...
A literature review may consist of simply a summary of key sources, but in the social sciences, a literature review usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories.A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that ...
However, evaluating different types of literature reviews can be challenging. Therefore, some guidelines for eventuating literature review articles across approaches are suggested as a starting point to help editors, reviewers, authors, and readers evaluating literature reviews (summarized in Table 4). These depart from the different stages of ...
A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship ...
The review purpose, methods used and the results produced vary among different kinds of literature reviews; some of the common types of literature review are detailed below. Common Types of Literature Reviews 1 Narrative (Literature) Review. A broad term referring to reviews with a wide scope and non-standardized methodology
Different Types Of Literature review In Research. There are some more approaches to conduct literature review. Let's explore these classifications quickly. 5. Critical Review. Critical reviews provide an in-depth evaluation of existing literature, scrutinising sources for their strengths, weaknesses, and relevance.
9.3. Types of Review Articles and Brief Illustrations. EHealth researchers have at their disposal a number of approaches and methods for making sense out of existing literature, all with the purpose of casting current research findings into historical contexts or explaining contradictions that might exist among a set of primary research studies conducted on a particular topic.
There are many types of reviews --- narrative reviews, scoping reviews, systematic reviews, integrative reviews, umbrella reviews, rapid reviews and others --- and it's not always straightforward to choose which type of review to conduct.These Review Navigator tools (see below) ask a series of questions to guide you through the various kinds of reviews and to help you determine the best choice ...
There are many types of literature reviews. The purposes of a literature review will vary, and the sources used in one will depend on the discipline and the review's topic. Literature reviews may have differences that include: Purpose: The reason or objective of the review. One review may be to see how much has been published on a topic (a ...
The literature review is an opportunity to discover and craft your scholarly identity through the kinds of questions you engage, the discussions you enter, the critiques you launch, and the research you advance.
Quantities of literature and overall quality/direction of effect of literature. Scoping review: Preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research evidence (usually including ongoing research). Completeness of searching determined by time/scope constraints.
Listed below are definitions of types of literature reviews: Argumentative Review. This form examines literature selectively in order to support or refute an argument, deeply embedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint.
Gold-standard guideline on how to perform and write-up a systematic review and/or meta-analysis of the outcomes reported in multiple clinical trials of therapeutic interventions. AHRQ's Methods Guide for Effectiveness and Comparative Effectiveness Reviews. Synthesis without meta-analysis (SWiM) in systematic reviews.
Most literature reviews are embedded in articles, books, and dissertations. In most research articles, there are set as a specific section, usually titled, "literature review", so they are hard to miss.But, sometimes, they are part of the narrative of the introduction of a book or article. This section is easily recognized since the author is engaging with other academics and experts by ...
A literature review may consist of simply a summary of key sources, but it usually has an organizational pattern and combines both summary and synthesis, often within specific conceptual categories.A summary is a recap of the important information of the source, but a synthesis is a re-organization, or a reshuffling, of that information in a way that informs how you are planning to investigate ...
Thus, the purpose of this review is to provide an overview of recent literature detailing the different types of WBV, the various parameters that contribute to WBV efficacy, and the evidence of WBV in metabolic disease. A systematic search was conducted using Medline, Embase, Cochrane, CINAHL, and PubMed.
Intraosseous meningiomas are a rare subtype of meningiomas representing approximately 2% of all cases. They can confound a diagnosis of other bone lesions including metastatic tumors. We present a case of a patient with prostate cancer who on staging workup was suspected to have a skull metastasis. …
The literature review and bibliometric analyses on financial fraud detection using machine learning and its various techniques conducted between 2012 and 2023 show a remarkable evolution in this ...
Introduction Inguinal hernioplasty (IH) is one of the most frequently performed surgical procedures globally. Today, a variety of surgical techniques and prosthesis types are available for this procedure. Methods At our center, we performed 200 inguinal hernioplasties using the dynamic self-adjusting prosthesis (protesi autoregolantesi dinamica, PAD) from May 1, 2022, to May 31, 2023.