literature review in research sample

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• 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.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

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.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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.

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.

Open Google Slides Download PowerPoint

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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 .  

Cite this Scribbr article

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

McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved September 18, 2024, from https://www.scribbr.com/dissertation/literature-review/

Is this article helpful?

Shona McCombes

Other students also liked, what is a theoretical framework | guide to organizing, what is a research methodology | steps & tips, how to write a research proposal | examples & templates, what is your plagiarism score.

• UWF Libraries

Literature Review: Conducting & Writing

• Sample Literature Reviews
• Steps for Conducting a Lit Review
• Finding "The Literature"
• Organizing/Writing
• APA Style This link opens in a new window
• Chicago: Notes Bibliography This link opens in a new window
• MLA Style This link opens in a new window

Sample Lit Reviews from Communication Arts

Have an exemplary literature review.

Note: These are sample literature reviews from a class that were given to us by an instructor when APA 6th edition was still in effect. These were excellent papers from her class, but it does not mean they are perfect or contain no errors. Thanks to the students who let us post!

• Literature Review Sample 1
• Literature Review Sample 2
• Literature Review Sample 3

Have you written a stellar literature review you care to share for teaching purposes?

Are you an instructor who has received an exemplary literature review and have permission from the student to post?

Please contact Britt McGowan at [email protected] for inclusion in this guide. All disciplines welcome and encouraged.

• << Previous: MLA Style
• Next: Get Help! >>
• Last Updated: Sep 11, 2024 1:37 PM
• URL: https://libguides.uwf.edu/litreview

What is a Literature Review? How to Write It (with Examples)

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.

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.

Find academic papers related to your research topic faster. Try Research on Paperpal

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. 

Strengthen your literature review with factual insights. Try Research on Paperpal for free!

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. 

Write and Cite as yo u go with Paperpal Research. Start now for free!

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. 

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  

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. 

Whether you’re exploring a new research field or finding new angles to develop an existing topic, sifting through hundreds of papers can take more time than you have to spare. But what if you could find science-backed insights with verified citations in seconds? That’s the power of Paperpal’s new Research feature!  

How to write a literature review faster with Paperpal?  

Paperpal, an AI writing assistant, integrates powerful academic search capabilities within its writing platform. With the Research | Cite feature, you get 100% factual insights, with citations backed by 250M+ verified research articles, directly within your writing interface. It also allows you auto-cite references in 10,000+ styles and save relevant references in your Citation Library. By eliminating the need to switch tabs to find answers to all your research questions, Paperpal saves time and helps you stay focused on your writing.   

Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research | Cite” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 

• Review and Save: Paperpal summarizes the information, while citing sources and listing relevant reads. You can quickly scan the results to identify relevant references and save these directly to your built-in citations library for later access. 
• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references in 10,000+ styles into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

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: 

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 . 

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 22+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• Empirical Research: A Comprehensive Guide for Academics 
• How to Write a Scientific Paper in 10 Steps 
• How Long Should a Chapter Be?
• How to Use Paperpal to Generate Emails & Cover Letters?

6 Tips for Post-Doc Researchers to Take Their Career to the Next Level

Self-plagiarism in research: what it is and how to avoid it, you may also like, how to make a graphical abstract, academic integrity vs academic dishonesty: types & examples, dissertation printing and binding | types & comparison , what is a dissertation preface definition and examples , the ai revolution: authors’ role in upholding academic..., the future of academia: how ai tools are..., how to write a research proposal: (with examples..., how to write your research paper in apa..., how to choose a dissertation topic, how to write a phd research proposal.

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing a Literature Review

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• 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 sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Dissertation
• What is a Literature Review? | Guide, Template, & Examples

What is a Literature Review? | Guide, Template, & Examples

Published on 22 February 2022 by Shona McCombes . Revised on 7 June 2022.

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.

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 summarise sources – it analyses, synthesises, and critically evaluates to give a clear picture of the state of knowledge on the subject.

Instantly correct all language mistakes in your text

Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.

Table of contents

Why write 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, frequently asked questions about literature reviews, introduction.

• Quick Run-through
• Step 1 & 2

When you write a dissertation or thesis, you will 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 scholarly context
• Develop a theoretical framework and methodology for your research
• Position yourself in relation to other researchers and theorists
• Show how your dissertation addresses a gap or contributes to a debate

You might also have to write a literature review as a stand-alone assignment. In this case, the purpose is to evaluate the current state of research and demonstrate your knowledge of scholarly debates around a topic.

The content will look slightly different in each case, but the process of conducting a literature review follows the same steps. We’ve written a step-by-step guide that you can follow below.

The only proofreading tool specialized in correcting academic writing

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Correct my document today

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 objectives and questions .

If you are writing a literature review as a stand-alone assignment, you will have to choose a focus and develop a central question to direct your search. Unlike a dissertation research question, this question has to be answerable without collecting original data. You should be able to answer it based only on a review of existing publications.

Make a list of keywords

Start by creating a list of keywords related to your research topic. 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 if 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 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 use boolean operators to help narrow down your search:

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.

To identify the most important publications on your topic, take note of recurring citations. If the same authors, books or articles keep appearing in your reading, make sure to seek them out.

You probably won’t be able to read absolutely everything that has been written on the topic – you’ll have to evaluate which sources are most relevant to your questions.

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?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• 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 find out how many times an article has been cited on Google Scholar – a high citation count means the article has been influential in the field, and should certainly be included in your literature review.

The scope of your review will depend on your topic and discipline: in the sciences you usually only review recent literature, but in the humanities you might take a long historical perspective (for example, to trace how a concept has changed in meaning over time).

Remember that you can use our template to summarise and evaluate sources you’re thinking about using!

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’s important to keep track of your sources with references to avoid plagiarism . It can be helpful to make an annotated bibliography, where you compile full reference 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.

You can use our free APA Reference Generator for quick, correct, consistent citations.

To begin organising your literature review’s argument and structure, you need to 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 organising the body of a literature review. You should have a rough idea of your strategy before you start writing.

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 summarising sources in order.

Try to analyse 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 organise 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.

If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasise the timeliness of the topic (“many recent studies have focused on the problem of x”) or highlight a gap in the literature (“while there has been much research on x, few researchers have taken y into consideration”).

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, make sure to follow these tips:

• Summarise and synthesise: give an overview of the main points of each source and combine them into a coherent whole.
• Analyse and interpret: don’t just paraphrase other researchers – add your own interpretations, 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 transitions and topic sentences to draw connections, comparisons and contrasts.

In the conclusion, you should summarise the key findings you have taken from the literature and emphasise their significance.

If the literature review is part of your dissertation or thesis, reiterate how your research addresses gaps and contributes new knowledge, or discuss how you have drawn on existing theories and methods to build a framework for your research. This can lead directly into your methodology section.

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 dissertation , thesis, research paper , or proposal .

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise 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  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

Cite this Scribbr article

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

McCombes, S. (2022, June 07). What is a Literature Review? | Guide, Template, & Examples. Scribbr. Retrieved 16 September 2024, from https://www.scribbr.co.uk/thesis-dissertation/literature-review/

Is this article helpful?

Shona McCombes

Other students also liked, how to write a dissertation proposal | a step-by-step guide, what is a theoretical framework | a step-by-step guide, what is a research methodology | steps & tips.

Reference management. Clean and simple.

What is a literature review? [with examples]

What is a literature review?

The purpose of a literature review, how to write a literature review, the format of a literature review, general formatting rules, the length of a literature review, literature review examples, frequently asked questions about literature reviews, related articles.

A literature review is an assessment of the sources in a chosen topic of research.

In a literature review, you’re expected to report on the existing scholarly conversation, without adding new contributions.

If you are currently writing one, you've come to the right place. In the following paragraphs, we will explain:

• the objective of a literature review
• how to write a literature review
• the basic format of a literature review

Tip: It’s not always mandatory to add a literature review in a paper. Theses and dissertations often include them, whereas research papers may not. Make sure to consult with your instructor for exact requirements.

The four main objectives of a literature review are:

• Studying the references of your research area
• Summarizing the main arguments
• Identifying current gaps, stances, and issues
• Presenting all of the above in a text

Ultimately, the main goal of a literature review is to provide the researcher with sufficient knowledge about the topic in question so that they can eventually make an intervention.

The format of a literature review is fairly standard. It includes an:

• introduction that briefly introduces the main topic
• body that includes the main discussion of the key arguments
• conclusion that highlights the gaps and issues of the literature

➡️ Take a look at our guide on how to write a literature review to learn more about how to structure a literature review.

First of all, a literature review should have its own labeled section. You should indicate clearly in the table of contents where the literature can be found, and you should label this section as “Literature Review.”

➡️ For more information on writing a thesis, visit our guide on how to structure a thesis .

There is no set amount of words for a literature review, so the length depends on the research. If you are working with a large amount of sources, it will be long. If your paper does not depend entirely on references, it will be short.

Take a look at these three theses featuring great literature reviews:

• School-Based Speech-Language Pathologist's Perceptions of Sensory Food Aversions in Children [ PDF , see page 20]
• Who's Writing What We Read: Authorship in Criminological Research [ PDF , see page 4]
• A Phenomenological Study of the Lived Experience of Online Instructors of Theological Reflection at Christian Institutions Accredited by the Association of Theological Schools [ PDF , see page 56]

Literature reviews are most commonly found in theses and dissertations. However, you find them in research papers as well.

There is no set amount of words for a literature review, so the length depends on the research. If you are working with a large amount of sources, then it will be long. If your paper does not depend entirely on references, then it will be short.

No. A literature review should have its own independent section. You should indicate clearly in the table of contents where the literature review can be found, and label this section as “Literature Review.”

The main goal of a literature review is to provide the researcher with sufficient knowledge about the topic in question so that they can eventually make an intervention.

The Sheridan Libraries

• Write a Literature Review
• Sheridan Libraries
• Evaluate This link opens in a new window

What Will You Do Differently?

Please help your librarians by filling out this two-minute survey of today's class session..

Professor, this one's for you .

Introduction

Literature reviews take time. here is some general information to know before you start.  .

•  VIDEO -- This video is a great overview of the entire process.  (2020; North Carolina State University Libraries) --The transcript is included --This is for everyone; ignore the mention of "graduate students" --9.5 minutes, and every second is important  
• OVERVIEW -- Read this page from Purdue's OWL. It's not long, and gives some tips to fill in what you just learned from the video.  
• NOT A RESEARCH ARTICLE -- A literature review follows a different style, format, and structure from a research article.  

• Next: Evaluate >>
• Last Updated: Jul 30, 2024 1:42 PM
• URL: https://guides.library.jhu.edu/lit-review

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• CAREER FEATURE
• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

You can also search for this author in PubMed   Google Scholar

Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

doi: https://doi.org/10.1038/d41586-020-03422-x

Interviews have been edited for length and clarity.

Updates & Corrections

Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

Article   Google Scholar  

Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

Article   PubMed   Google Scholar  

Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

Choi, Y. & Lee, S. Y. Nature Rev. Chem . https://doi.org/10.1038/s41570-020-00221-w (2020).

Download references

Related Articles

• Research management

Science-policy advisers shape programmes that solve real-world problems

Career Feature 18 SEP 24

Can AI be used to assess research quality?

Nature Index 18 SEP 24

The grassroots organizations continuing the fight for Ukrainian science

Career Feature 11 SEP 24

The human costs of the research-assessment culture

Career Feature 09 SEP 24

Why I’m committed to breaking the bias in large language models

Career Guide 04 SEP 24

Binning out-of-date chemicals? Somebody think about the carbon!

Correspondence 27 AUG 24

Rise of ChatGPT and other tools raises major questions for research

My identity was stolen by a predatory conference

Correspondence 17 SEP 24

Faculty Positions at the Center for Machine Learning Research (CMLR), Peking University

CMLR's goal is to advance machine learning-related research across a wide range of disciplines.

Beijing, China

Center for Machine Learning Research (CMLR), Peking University

Faculty Positions at Huairou campus of Peking University

The Beijing Laser Accelerator Innovation Center at Peking University invites applications for Applied Physics faculty, including tenured positions.

Peking University (PKU)

Independent Researcher

Dallas, Texas (US)

The University of Texas Southwestern Medical Center (UT Southwestern Medical Center)

Head of the Translational Cancer Research Laboratory

Northwell Health has formed a strategic partnership with the NCI-designated Cancer Center at Cold Spring Harbor Laboratory (CSHL). The institutions...

Manhasset, New York

Tenure-Track Faculty Positions in Human Genetics

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

15 Literature Review Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

Literature reviews are a necessary step in a research process and often required when writing your research proposal . They involve gathering, analyzing, and evaluating existing knowledge about a topic in order to find gaps in the literature where future studies will be needed.

Ideally, once you have completed your literature review, you will be able to identify how your research project can build upon and extend existing knowledge in your area of study.

Generally, for my undergraduate research students, I recommend a narrative review, where themes can be generated in order for the students to develop sufficient understanding of the topic so they can build upon the themes using unique methods or novel research questions.

If you’re in the process of writing a literature review, I have developed a literature review template for you to use – it’s a huge time-saver and walks you through how to write a literature review step-by-step:

Get your time-saving templates here to write your own literature review.

Literature Review Examples

For the following types of literature review, I present an explanation and overview of the type, followed by links to some real-life literature reviews on the topics.

1. Narrative Review Examples

Also known as a traditional literature review, the narrative review provides a broad overview of the studies done on a particular topic.

It often includes both qualitative and quantitative studies and may cover a wide range of years.

The narrative review’s purpose is to identify commonalities, gaps, and contradictions in the literature .

I recommend to my students that they should gather their studies together, take notes on each study, then try to group them by themes that form the basis for the review (see my step-by-step instructions at the end of the article).

Example Study

Title: Communication in healthcare: a narrative review of the literature and practical recommendations

Citation: Vermeir, P., Vandijck, D., Degroote, S., Peleman, R., Verhaeghe, R., Mortier, E., … & Vogelaers, D. (2015). Communication in healthcare: a narrative review of the literature and practical recommendations. International journal of clinical practice , 69 (11), 1257-1267.

Source: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ijcp.12686  

Overview: This narrative review analyzed themes emerging from 69 articles about communication in healthcare contexts. Five key themes were found in the literature: poor communication can lead to various negative outcomes, discontinuity of care, compromise of patient safety, patient dissatisfaction, and inefficient use of resources. After presenting the key themes, the authors recommend that practitioners need to approach healthcare communication in a more structured way, such as by ensuring there is a clear understanding of who is in charge of ensuring effective communication in clinical settings.

Other Examples

• Burnout in United States Healthcare Professionals: A Narrative Review (Reith, 2018) – read here
• Examining the Presence, Consequences, and Reduction of Implicit Bias in Health Care: A Narrative Review (Zestcott, Blair & Stone, 2016) – read here
• A Narrative Review of School-Based Physical Activity for Enhancing Cognition and Learning (Mavilidi et al., 2018) – read here
• A narrative review on burnout experienced by medical students and residents (Dyrbye & Shanafelt, 2015) – read here

2. Systematic Review Examples

This type of literature review is more structured and rigorous than a narrative review. It involves a detailed and comprehensive plan and search strategy derived from a set of specified research questions.

The key way you’d know a systematic review compared to a narrative review is in the methodology: the systematic review will likely have a very clear criteria for how the studies were collected, and clear explanations of exclusion/inclusion criteria. 

The goal is to gather the maximum amount of valid literature on the topic, filter out invalid or low-quality reviews, and minimize bias. Ideally, this will provide more reliable findings, leading to higher-quality conclusions and recommendations for further research.

You may note from the examples below that the ‘method’ sections in systematic reviews tend to be much more explicit, often noting rigid inclusion/exclusion criteria and exact keywords used in searches.

Title: The importance of food naturalness for consumers: Results of a systematic review  

Citation: Roman, S., Sánchez-Siles, L. M., & Siegrist, M. (2017). The importance of food naturalness for consumers: Results of a systematic review. Trends in food science & technology , 67 , 44-57.

Source: https://www.sciencedirect.com/science/article/pii/S092422441730122X  

Overview: This systematic review included 72 studies of food naturalness to explore trends in the literature about its importance for consumers. Keywords used in the data search included: food, naturalness, natural content, and natural ingredients. Studies were included if they examined consumers’ preference for food naturalness and contained empirical data. The authors found that the literature lacks clarity about how naturalness is defined and measured, but also found that food consumption is significantly influenced by perceived naturalness of goods.

• A systematic review of research on online teaching and learning from 2009 to 2018 (Martin, Sun & Westine, 2020) – read here
• Where Is Current Research on Blockchain Technology? (Yli-Huumo et al., 2016) – read here
• Universities—industry collaboration: A systematic review (Ankrah & Al-Tabbaa, 2015) – read here
• Internet of Things Applications: A Systematic Review (Asghari, Rahmani & Javadi, 2019) – read here

3. Meta-analysis

This is a type of systematic review that uses statistical methods to combine and summarize the results of several studies.

Due to its robust methodology, a meta-analysis is often considered the ‘gold standard’ of secondary research , as it provides a more precise estimate of a treatment effect than any individual study contributing to the pooled analysis.

Furthermore, by aggregating data from a range of studies, a meta-analysis can identify patterns, disagreements, or other interesting relationships that may have been hidden in individual studies.

This helps to enhance the generalizability of findings, making the conclusions drawn from a meta-analysis particularly powerful and informative for policy and practice.

Title: Cholesterol and Alzheimer’s Disease Risk: A Meta-Meta-Analysis

Citation: Sáiz-Vazquez, O., Puente-Martínez, A., Ubillos-Landa, S., Pacheco-Bonrostro, J., & Santabárbara, J. (2020). Cholesterol and Alzheimer’s disease risk: a meta-meta-analysis. Brain sciences, 10(6), 386.

Source: https://doi.org/10.3390/brainsci10060386  

O verview: This study examines the relationship between cholesterol and Alzheimer’s disease (AD). Researchers conducted a systematic search of meta-analyses and reviewed several databases, collecting 100 primary studies and five meta-analyses to analyze the connection between cholesterol and Alzheimer’s disease. They find that the literature compellingly demonstrates that low-density lipoprotein cholesterol (LDL-C) levels significantly influence the development of Alzheimer’s disease.

• The power of feedback revisited: A meta-analysis of educational feedback research (Wisniewski, Zierer & Hattie, 2020) – read here
• How Much Does Education Improve Intelligence? A Meta-Analysis (Ritchie & Tucker-Drob, 2018) – read here
• A meta-analysis of factors related to recycling (Geiger et al., 2019) – read here
• Stress management interventions for police officers and recruits (Patterson, Chung & Swan, 2014) – read here

Other Types of Reviews

• Scoping Review: This type of review is used to map the key concepts underpinning a research area and the main sources and types of evidence available. It can be undertaken as stand-alone projects in their own right, or as a precursor to a systematic review.
• Rapid Review: This type of review accelerates the systematic review process in order to produce information in a timely manner. This is achieved by simplifying or omitting stages of the systematic review process.
• Integrative Review: This review method is more inclusive than others, allowing for the simultaneous inclusion of experimental and non-experimental research. The goal is to more comprehensively understand a particular phenomenon.
• Critical Review: This is similar to a narrative review but requires a robust understanding of both the subject and the existing literature. In a critical review, the reviewer not only summarizes the existing literature, but also evaluates its strengths and weaknesses. This is common in the social sciences and humanities .
• State-of-the-Art Review: This considers the current level of advancement in a field or topic and makes recommendations for future research directions. This type of review is common in technological and scientific fields but can be applied to any discipline.

How to Write a Narrative Review (Tips for Undergrad Students)

Most undergraduate students conducting a capstone research project will be writing narrative reviews. Below is a five-step process for conducting a simple review of the literature for your project.

• Search for Relevant Literature: Use scholarly databases related to your field of study, provided by your university library, along with appropriate search terms to identify key scholarly articles that have been published on your topic.
• Evaluate and Select Sources: Filter the source list by selecting studies that are directly relevant and of sufficient quality, considering factors like credibility , objectivity, accuracy, and validity.
• Analyze and Synthesize: Review each source and summarize the main arguments  in one paragraph (or more, for postgrad). Keep these summaries in a table.
• Identify Themes: With all studies summarized, group studies that share common themes, such as studies that have similar findings or methodologies.
• Write the Review: Write your review based upon the themes or subtopics you have identified. Give a thorough overview of each theme, integrating source data, and conclude with a summary of the current state of knowledge then suggestions for future research based upon your evaluation of what is lacking in the literature.

Literature reviews don’t have to be as scary as they seem. Yes, they are difficult and require a strong degree of comprehension of academic studies. But it can be feasibly done through following a structured approach to data collection and analysis. With my undergraduate research students (who tend to conduct small-scale qualitative studies ), I encourage them to conduct a narrative literature review whereby they can identify key themes in the literature. Within each theme, students can critique key studies and their strengths and limitations , in order to get a lay of the land and come to a point where they can identify ways to contribute new insights to the existing academic conversation on their topic.

Ankrah, S., & Omar, A. T. (2015). Universities–industry collaboration: A systematic review. Scandinavian Journal of Management, 31(3), 387-408.

Asghari, P., Rahmani, A. M., & Javadi, H. H. S. (2019). Internet of Things applications: A systematic review. Computer Networks , 148 , 241-261.

Dyrbye, L., & Shanafelt, T. (2016). A narrative review on burnout experienced by medical students and residents. Medical education , 50 (1), 132-149.

Geiger, J. L., Steg, L., Van Der Werff, E., & Ünal, A. B. (2019). A meta-analysis of factors related to recycling. Journal of environmental psychology , 64 , 78-97.

Martin, F., Sun, T., & Westine, C. D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & education , 159 , 104009.

Mavilidi, M. F., Ruiter, M., Schmidt, M., Okely, A. D., Loyens, S., Chandler, P., & Paas, F. (2018). A narrative review of school-based physical activity for enhancing cognition and learning: The importance of relevancy and integration. Frontiers in psychology , 2079.

Patterson, G. T., Chung, I. W., & Swan, P. W. (2014). Stress management interventions for police officers and recruits: A meta-analysis. Journal of experimental criminology , 10 , 487-513.

Reith, T. P. (2018). Burnout in United States healthcare professionals: a narrative review. Cureus , 10 (12).

Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis. Psychological science , 29 (8), 1358-1369.

Roman, S., Sánchez-Siles, L. M., & Siegrist, M. (2017). The importance of food naturalness for consumers: Results of a systematic review. Trends in food science & technology , 67 , 44-57.

Sáiz-Vazquez, O., Puente-Martínez, A., Ubillos-Landa, S., Pacheco-Bonrostro, J., & Santabárbara, J. (2020). Cholesterol and Alzheimer’s disease risk: a meta-meta-analysis. Brain sciences, 10(6), 386.

Vermeir, P., Vandijck, D., Degroote, S., Peleman, R., Verhaeghe, R., Mortier, E., … & Vogelaers, D. (2015). Communication in healthcare: a narrative review of the literature and practical recommendations. International journal of clinical practice , 69 (11), 1257-1267.

Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology , 10 , 3087.

Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology?—a systematic review. PloS one , 11 (10), e0163477.

Zestcott, C. A., Blair, I. V., & Stone, J. (2016). Examining the presence, consequences, and reduction of implicit bias in health care: a narrative review. Group Processes & Intergroup Relations , 19 (4), 528-542

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

• Privacy Policy

Home » Literature Review – Types Writing Guide and Examples

Literature Review – Types Writing Guide and Examples

Table of Contents

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Objectives – Types, Examples and...

Research Findings – Types Examples and Writing...

Research Topics – Ideas and Examples

Informed Consent in Research – Types, Templates...

Research Paper Conclusion – Writing Guide and...

Research Report – Example, Writing Guide and...

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 5. The Literature Review
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• 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.

• << Previous: Theoretical Framework
• Next: Citation Tracking >>
• Last Updated: Sep 17, 2024 10:59 AM
• URL: https://libguides.usc.edu/writingguide

How To Structure Your Literature Review

3 options to help structure your chapter.

By: Amy Rommelspacher (PhD) | Reviewer: Dr Eunice Rautenbach | November 2020 (Updated May 2023)

Writing the literature review chapter can seem pretty daunting when you’re piecing together your dissertation or thesis. As  we’ve discussed before , a good literature review needs to achieve a few very important objectives – it should:

• Demonstrate your knowledge of the research topic
• Identify the gaps in the literature and show how your research links to these
• Provide the foundation for your conceptual framework (if you have one)
• Inform your own  methodology and research design

To achieve this, your literature review needs a well-thought-out structure . Get the structure of your literature review chapter wrong and you’ll struggle to achieve these objectives. Don’t worry though – in this post, we’ll look at how to structure your literature review for maximum impact (and marks!).

But wait – is this the right time?

Deciding on the structure of your literature review should come towards the end of the literature review process – after you have collected and digested the literature, but before you start writing the chapter. 

In other words, you need to first develop a rich understanding of the literature before you even attempt to map out a structure. There’s no use trying to develop a structure before you’ve fully wrapped your head around the existing research.

Equally importantly, you need to have a structure in place before you start writing , or your literature review will most likely end up a rambling, disjointed mess. 

Importantly, don’t feel that once you’ve defined a structure you can’t iterate on it. It’s perfectly natural to adjust as you engage in the writing process. As we’ve discussed before , writing is a way of developing your thinking, so it’s quite common for your thinking to change – and therefore, for your chapter structure to change – as you write. 

Need a helping hand?

Like any other chapter in your thesis or dissertation, your literature review needs to have a clear, logical structure. At a minimum, it should have three essential components – an  introduction , a  body   and a  conclusion . 

Let’s take a closer look at each of these.

1: The Introduction Section

Just like any good introduction, the introduction section of your literature review should introduce the purpose and layout (organisation) of the chapter. In other words, your introduction needs to give the reader a taste of what’s to come, and how you’re going to lay that out. Essentially, you should provide the reader with a high-level roadmap of your chapter to give them a taste of the journey that lies ahead.

Here’s an example of the layout visualised in a literature review introduction:

Your introduction should also outline your topic (including any tricky terminology or jargon) and provide an explanation of the scope of your literature review – in other words, what you  will   and  won’t   be covering (the delimitations ). This helps ringfence your review and achieve a clear focus . The clearer and narrower your focus, the deeper you can dive into the topic (which is typically where the magic lies). 

Depending on the nature of your project, you could also present your stance or point of view at this stage. In other words, after grappling with the literature you’ll have an opinion about what the trends and concerns are in the field as well as what’s lacking. The introduction section can then present these ideas so that it is clear to examiners that you’re aware of how your research connects with existing knowledge .

2: The Body Section

The body of your literature review is the centre of your work. This is where you’ll present, analyse, evaluate and synthesise the existing research. In other words, this is where you’re going to earn (or lose) the most marks. Therefore, it’s important to carefully think about how you will organise your discussion to present it in a clear way. 

The body of your literature review should do just as the description of this chapter suggests. It should “review” the literature – in other words, identify, analyse, and synthesise it. So, when thinking about structuring your literature review, you need to think about which structural approach will provide the best “review” for your specific type of research and objectives (we’ll get to this shortly).

There are (broadly speaking)  three options  for organising your literature review.

Option 1: Chronological (according to date)

Organising the literature chronologically is one of the simplest ways to structure your literature review. You start with what was published first and work your way through the literature until you reach the work published most recently. Pretty straightforward.

The benefit of this option is that it makes it easy to discuss the developments and debates in the field as they emerged over time. Organising your literature chronologically also allows you to highlight how specific articles or pieces of work might have changed the course of the field – in other words, which research has had the most impact . Therefore, this approach is very useful when your research is aimed at understanding how the topic has unfolded over time and is often used by scholars in the field of history. That said, this approach can be utilised by anyone that wants to explore change over time .

For example , if a student of politics is investigating how the understanding of democracy has evolved over time, they could use the chronological approach to provide a narrative that demonstrates how this understanding has changed through the ages.

Here are some questions you can ask yourself to help you structure your literature review chronologically.

• What is the earliest literature published relating to this topic?
• How has the field changed over time? Why?
• What are the most recent discoveries/theories?

In some ways, chronology plays a part whichever way you decide to structure your literature review, because you will always, to a certain extent, be analysing how the literature has developed. However, with the chronological approach, the emphasis is very firmly on how the discussion has evolved over time , as opposed to how all the literature links together (which we’ll discuss next ).

Option 2: Thematic (grouped by theme)

The thematic approach to structuring a literature review means organising your literature by theme or category – for example, by independent variables (i.e. factors that have an impact on a specific outcome).

As you’ve been collecting and synthesising literature , you’ll likely have started seeing some themes or patterns emerging. You can then use these themes or patterns as a structure for your body discussion. The thematic approach is the most common approach and is useful for structuring literature reviews in most fields.

For example, if you were researching which factors contributed towards people trusting an organisation, you might find themes such as consumers’ perceptions of an organisation’s competence, benevolence and integrity. Structuring your literature review thematically would mean structuring your literature review’s body section to discuss each of these themes, one section at a time.

Here are some questions to ask yourself when structuring your literature review by themes:

• Are there any patterns that have come to light in the literature?
• What are the central themes and categories used by the researchers?
• Do I have enough evidence of these themes?

PS – you can see an example of a thematically structured literature review in our literature review sample walkthrough video here.

Option 3: Methodological

The methodological option is a way of structuring your literature review by the research methodologies used . In other words, organising your discussion based on the angle from which each piece of research was approached – for example, qualitative , quantitative or mixed  methodologies.

Structuring your literature review by methodology can be useful if you are drawing research from a variety of disciplines and are critiquing different methodologies. The point of this approach is to question  how  existing research has been conducted, as opposed to  what  the conclusions and/or findings the research were.

For example, a sociologist might centre their research around critiquing specific fieldwork practices. Their literature review will then be a summary of the fieldwork methodologies used by different studies.

Here are some questions you can ask yourself when structuring your literature review according to methodology:

• Which methodologies have been utilised in this field?
• Which methodology is the most popular (and why)?
• What are the strengths and weaknesses of the various methodologies?
• How can the existing methodologies inform my own methodology?

3: The Conclusion Section

Once you’ve completed the body section of your literature review using one of the structural approaches we discussed above, you’ll need to “wrap up” your literature review and pull all the pieces together to set the direction for the rest of your dissertation or thesis.

The conclusion is where you’ll present the key findings of your literature review. In this section, you should emphasise the research that is especially important to your research questions and highlight the gaps that exist in the literature. Based on this, you need to make it clear what you will add to the literature – in other words, justify your own research by showing how it will help fill one or more of the gaps you just identified.

Last but not least, if it’s your intention to develop a conceptual framework for your dissertation or thesis, the conclusion section is a good place to present this.

Example: Thematically Structured Review

In the video below, we unpack a literature review chapter so that you can see an example of a thematically structure review in practice.

Let’s Recap

In this article, we’ve  discussed how to structure your literature review for maximum impact. Here’s a quick recap of what  you need to keep in mind when deciding on your literature review structure:

• Just like other chapters, your literature review needs a clear introduction , body and conclusion .
• The introduction section should provide an overview of what you will discuss in your literature review.
• The body section of your literature review can be organised by chronology , theme or methodology . The right structural approach depends on what you’re trying to achieve with your research.
• The conclusion section should draw together the key findings of your literature review and link them to your research questions.

If you’re ready to get started, be sure to download our free literature review template to fast-track your chapter outline.

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

29 Comments

Great work. This is exactly what I was looking for and helps a lot together with your previous post on literature review. One last thing is missing: a link to a great literature chapter of an journal article (maybe with comments of the different sections in this review chapter). Do you know any great literature review chapters?

I agree with you Marin… A great piece

I agree with Marin. This would be quite helpful if you annotate a nicely structured literature from previously published research articles.

Awesome article for my research.

I thank you immensely for this wonderful guide

It is indeed thought and supportive work for the futurist researcher and students

Very educative and good time to get guide. Thank you

Great work, very insightful. Thank you.

Thanks for this wonderful presentation. My question is that do I put all the variables into a single conceptual framework or each hypothesis will have it own conceptual framework?

Thank you very much, very helpful

This is very educative and precise . Thank you very much for dropping this kind of write up .

Pheeww, so damn helpful, thank you for this informative piece.

I’m doing a research project topic ; stool analysis for parasitic worm (enteric) worm, how do I structure it, thanks.

comprehensive explanation. Help us by pasting the URL of some good “literature review” for better understanding.

great piece. thanks for the awesome explanation. it is really worth sharing. I have a little question, if anyone can help me out, which of the options in the body of literature can be best fit if you are writing an architectural thesis that deals with design?

I am doing a research on nanofluids how can l structure it?

Beautifully clear.nThank you!

Lucid! Thankyou!

Brilliant work, well understood, many thanks

I like how this was so clear with simple language 😊😊 thank you so much 😊 for these information 😊

Insightful. I was struggling to come up with a sensible literature review but this has been really helpful. Thank you!

You have given thought-provoking information about the review of the literature.

Thank you. It has made my own research better and to impart your work to students I teach

I learnt a lot from this teaching. It’s a great piece.

I am doing research on EFL teacher motivation for his/her job. How Can I structure it? Is there any detailed template, additional to this?

You are so cool! I do not think I’ve read through something like this before. So nice to find somebody with some genuine thoughts on this issue. Seriously.. thank you for starting this up. This site is one thing that is required on the internet, someone with a little originality!

I’m asked to do conceptual, theoretical and empirical literature, and i just don’t know how to structure it

Asking questions are actually fastidious thing if you are not understanding anything fully, but this article presents good understanding yet.

thank you SOOO much it is really helpful ..

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• My Library Account
• Articles, Books & More
• Course Reserves
• Site Search
• Advanced Search
• Sac State Library
• Research Guides
• Writing a Literature Review
• Literature Review Examples
• What is a Literature Review?
• Organizing Your Literature Review
• Managing your Citations
• Further Reading on Lit Reviews

Literature Review Samples

Click on the links below for examples of Literature Reviews

• << Previous: Writing a Literature Review
• Next: Organizing Your Literature Review >>
• Last Updated: Jun 6, 2024 9:36 AM
• URL: https://csus.libguides.com/litreview

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

How To Write A Literature Review - A Complete Guide

Table of Contents

A literature review is much more than just another section in your research paper. It forms the very foundation of your research. It is a formal piece of writing where you analyze the existing theoretical framework, principles, and assumptions and use that as a base to shape your approach to the research question.

Curating and drafting a solid literature review section not only lends more credibility to your research paper but also makes your research tighter and better focused. But, writing literature reviews is a difficult task. It requires extensive reading, plus you have to consider market trends and technological and political changes, which tend to change in the blink of an eye.

Now streamline your literature review process with the help of SciSpace Copilot. With this AI research assistant, you can efficiently synthesize and analyze a vast amount of information, identify key themes and trends, and uncover gaps in the existing research. Get real-time explanations, summaries, and answers to your questions for the paper you're reviewing, making navigating and understanding the complex literature landscape easier.

In this comprehensive guide, we will explore everything from the definition of a literature review, its appropriate length, various types of literature reviews, and how to write one.

What is a literature review?

A literature review is a collation of survey, research, critical evaluation, and assessment of the existing literature in a preferred domain.

Eminent researcher and academic Arlene Fink, in her book Conducting Research Literature Reviews , defines it as the following:

“A literature review surveys 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 explored while researching a particular topic, and to demonstrate to your readers how your research fits within a larger field of study.”

Simply put, a literature review can be defined as a critical discussion of relevant pre-existing research around your research question and carving out a definitive place for your study in the existing body of knowledge. Literature reviews can be presented in multiple ways: a section of an article, the whole research paper itself, or a chapter of your thesis.

A literature review does function as a summary of sources, but it also allows you to analyze further, interpret, and examine the stated theories, methods, viewpoints, and, of course, the gaps in the existing content.

As an author, you can discuss and interpret the research question and its various aspects and debate your adopted methods to support the claim.

What is the purpose of a literature review?

A literature review is meant to help your readers understand the relevance of your research question and where it fits within the existing body of knowledge. As a researcher, you should use it to set the context, build your argument, and establish the need for your study.

What is the importance of a literature review?

The literature review is a critical part of research papers because it helps you:

• Gain an in-depth understanding of your research question and the surrounding area
• Convey that you have a thorough understanding of your research area and are up-to-date with the latest changes and advancements
• Establish how your research is connected or builds on the existing body of knowledge and how it could contribute to further research
• Elaborate on the validity and suitability of your theoretical framework and research methodology
• Identify and highlight gaps and shortcomings in the existing body of knowledge and how things need to change
• Convey to readers how your study is different or how it contributes to the research area

How long should a literature review be?

Ideally, the literature review should take up 15%-40% of the total length of your manuscript. So, if you have a 10,000-word research paper, the minimum word count could be 1500.

Your literature review format depends heavily on the kind of manuscript you are writing — an entire chapter in case of doctoral theses, a part of the introductory section in a research article, to a full-fledged review article that examines the previously published research on a topic.

Another determining factor is the type of research you are doing. The literature review section tends to be longer for secondary research projects than primary research projects.

What are the different types of literature reviews?

All literature reviews are not the same. There are a variety of possible approaches that you can take. It all depends on the type of research you are pursuing.

Here are the different types of literature reviews:

Argumentative review

It is called an argumentative review when you carefully present literature that only supports or counters a specific argument or premise to establish a viewpoint.

Integrative review

It is a type of literature review focused on building a comprehensive understanding of a topic by combining available theoretical frameworks and empirical evidence.

Methodological review

This approach delves into the ''how'' and the ''what" of the research question —  you cannot look at the outcome in isolation; you should also review the methodology used.

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 collect, report, and analyze data from the studies included in the review.

Meta-analysis review

Meta-analysis uses statistical methods to summarize the results of independent studies. By combining information from all relevant studies, meta-analysis can provide more precise estimates of the effects than those derived from the individual studies included within a review.

Historical review

Historical literature reviews focus on examining research throughout a period, often starting with the first time an issue, concept, theory, or 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 identify future research's likely directions.

Theoretical Review

This form aims to examine the corpus of theory accumulated regarding an issue, concept, theory, and phenomenon. The theoretical literature review helps to establish what theories exist, the relationships between them, the degree the existing approaches have been investigated, and to develop new hypotheses to be tested.

Scoping Review

The Scoping Review is often used at the beginning of an article, dissertation, or research proposal. It is conducted before the research to highlight gaps in the existing body of knowledge and explains why the project should be greenlit.

State-of-the-Art Review

The State-of-the-Art review is conducted periodically, focusing on the most recent research. It describes what is currently known, understood, or agreed upon regarding the research topic and highlights where there are still disagreements.

Can you use the first person in a literature review?

When writing literature reviews, you should avoid the usage of first-person pronouns. It means that instead of "I argue that" or "we argue that," the appropriate expression would be "this research paper argues that."

Do you need an abstract for a literature review?

Ideally, yes. It is always good to have a condensed summary that is self-contained and independent of the rest of your review. As for how to draft one, you can follow the same fundamental idea when preparing an abstract for a literature review. It should also include:

• The research topic and your motivation behind selecting it
• A one-sentence thesis statement
• An explanation of the kinds of literature featured in the review
• Summary of what you've learned
• Conclusions you drew from the literature you reviewed
• Potential implications and future scope for research

Here's an example of the abstract of a literature review

Is a literature review written in the past tense?

Yes, the literature review should ideally be written in the past tense. You should not use the present or future tense when writing one. The exceptions are when you have statements describing events that happened earlier than the literature you are reviewing or events that are currently occurring; then, you can use the past perfect or present perfect tenses.

How many sources for a literature review?

There are multiple approaches to deciding how many sources to include in a literature review section. The first approach would be to look level you are at as a researcher. For instance, a doctoral thesis might need 60+ sources. In contrast, you might only need to refer to 5-15 sources at the undergraduate level.

The second approach is based on the kind of literature review you are doing — whether it is merely a chapter of your paper or if it is a self-contained paper in itself. When it is just a chapter, sources should equal the total number of pages in your article's body. In the second scenario, you need at least three times as many sources as there are pages in your work.

Quick tips on how to write a literature review

To know how to write a literature review, you must clearly understand its impact and role in establishing your work as substantive research material.

You need to follow the below-mentioned steps, to write a literature review:

• Outline the purpose behind the literature review
• Search relevant literature
• Examine and assess the relevant resources
• Discover connections by drawing deep insights from the resources
• Structure planning to write a good literature review

1. Outline and identify the purpose of  a literature review

As a first step on how to write a literature review, you must know what the research question or topic is and what shape you want your literature review to take. Ensure you understand the research topic inside out, or else seek clarifications. You must be able to the answer below questions before you start:

• How many sources do I need to include?
• What kind of sources should I analyze?
• How much should I critically evaluate each source?
• Should I summarize, synthesize or offer a critique of the sources?
• Do I need to include any background information or definitions?

Additionally, you should know that the narrower your research topic is, the swifter it will be for you to restrict the number of sources to be analyzed.

2. Search relevant literature

Dig deeper into search engines to discover what has already been published around your chosen topic. Make sure you thoroughly go through appropriate reference sources like books, reports, journal articles, government docs, and web-based resources.

You must prepare a list of keywords and their different variations. You can start your search from any library’s catalog, provided you are an active member of that institution. The exact keywords can be extended to widen your research over other databases and academic search engines like:

• Google Scholar
• Microsoft Academic
• Science.gov

Besides, it is not advisable to go through every resource word by word. Alternatively, what you can do is you can start by reading the abstract and then decide whether that source is relevant to your research or not.

Additionally, you must spend surplus time assessing the quality and relevance of resources. It would help if you tried preparing a list of citations to ensure that there lies no repetition of authors, publications, or articles in the literature review.

3. Examine and assess the sources

It is nearly impossible for you to go through every detail in the research article. So rather than trying to fetch every detail, you have to analyze and decide which research sources resemble closest and appear relevant to your chosen domain.

While analyzing the sources, you should look to find out answers to questions like:

• What question or problem has the author been describing and debating?
• What is the definition of critical aspects?
• How well the theories, approach, and methodology have been explained?
• Whether the research theory used some conventional or new innovative approach?
• How relevant are the key findings of the work?
• In what ways does it relate to other sources on the same topic?
• What challenges does this research paper pose to the existing theory
• What are the possible contributions or benefits it adds to the subject domain?

Be always mindful that you refer only to credible and authentic resources. It would be best if you always take references from different publications to validate your theory.

Always keep track of important information or data you can present in your literature review right from the beginning. It will help steer your path from any threats of plagiarism and also make it easier to curate an annotated bibliography or reference section.

4. Discover connections

At this stage, you must start deciding on the argument and structure of your literature review. To accomplish this, you must discover and identify the relations and connections between various resources while drafting your abstract.

A few aspects that you should be aware of while writing a literature review include:

• Rise to prominence: Theories and methods that have gained reputation and supporters over time.
• Constant scrutiny: Concepts or theories that repeatedly went under examination.
• Contradictions and conflicts: Theories, both the supporting and the contradictory ones, for the research topic.
• Knowledge gaps: What exactly does it fail to address, and how to bridge them with further research?
• Influential resources: Significant research projects available that have been upheld as milestones or perhaps, something that can modify the current trends

Once you join the dots between various past research works, it will be easier for you to draw a conclusion and identify your contribution to the existing knowledge base.

5. Structure planning to write a good literature review

There exist different ways towards planning and executing the structure of a literature review. The format of a literature review varies and depends upon the length of the research.

Like any other research paper, the literature review format must contain three sections: introduction, body, and conclusion. The goals and objectives of the research question determine what goes inside these three sections.

Nevertheless, a good literature review can be structured according to the chronological, thematic, methodological, or theoretical framework approach.

Literature review samples

1. Standalone

2. As a section of a research paper

How SciSpace Discover makes literature review a breeze?

SciSpace Discover is a one-stop solution to do an effective literature search and get barrier-free access to scientific knowledge. It is an excellent repository where you can find millions of only peer-reviewed articles and full-text PDF files. Here’s more on how you can use it:

Find the right information

Find what you want quickly and easily with comprehensive search filters that let you narrow down papers according to PDF availability, year of publishing, document type, and affiliated institution. Moreover, you can sort the results based on the publishing date, citation count, and relevance.

Assess credibility of papers quickly

When doing the literature review, it is critical to establish the quality of your sources. They form the foundation of your research. SciSpace Discover helps you assess the quality of a source by providing an overview of its references, citations, and performance metrics.

Get the complete picture in no time

SciSpace Discover’s personalized suggestion engine helps you stay on course and get the complete picture of the topic from one place. Every time you visit an article page, it provides you links to related papers. Besides that, it helps you understand what’s trending, who are the top authors, and who are the leading publishers on a topic.

Make referring sources super easy

To ensure you don't lose track of your sources, you must start noting down your references when doing the literature review. SciSpace Discover makes this step effortless. Click the 'cite' button on an article page, and you will receive preloaded citation text in multiple styles — all you've to do is copy-paste it into your manuscript.

Final tips on how to write a literature review

A massive chunk of time and effort is required to write a good literature review. But, if you go about it systematically, you'll be able to save a ton of time and build a solid foundation for your research.

We hope this guide has helped you answer several key questions you have about writing literature reviews.

Would you like to explore SciSpace Discover and kick off your literature search right away? You can get started here .

Frequently Asked Questions (FAQs)

1. how to start a literature review.

• What questions do you want to answer?

• What sources do you need to answer these questions?

• What information do these sources contain?

• How can you use this information to answer your questions?

2. What to include in a literature review?

• A brief background of the problem or issue

• What has previously been done to address the problem or issue

• A description of what you will do in your project

• How this study will contribute to research on the subject

3. Why literature review is important?

The literature review is an important part of any research project because it allows the writer to look at previous studies on a topic and determine existing gaps in the literature, as well as what has already been done. It will also help them to choose the most appropriate method for their own study.

4. How to cite a literature review in APA format?

To cite a literature review in APA style, you need to provide the author's name, the title of the article, and the year of publication. For example: Patel, A. B., & Stokes, G. S. (2012). The relationship between personality and intelligence: A meta-analysis of longitudinal research. Personality and Individual Differences, 53(1), 16-21

5. What are the components of a literature review?

• A brief introduction to the topic, including its background and context. The introduction should also include a rationale for why the study is being conducted and what it will accomplish.

• A description of the methodologies used in the study. This can include information about data collection methods, sample size, and statistical analyses.

• A presentation of the findings in an organized format that helps readers follow along with the author's conclusions.

6. What are common errors in writing literature review?

• Not spending enough time to critically evaluate the relevance of resources, observations and conclusions.

• Totally relying on secondary data while ignoring primary data.

• Letting your personal bias seep into your interpretation of existing literature.

• No detailed explanation of the procedure to discover and identify an appropriate literature review.

7. What are the 5 C's of writing literature review?

• Cite - the sources you utilized and referenced in your research.

• Compare - existing arguments, hypotheses, methodologies, and conclusions found in the knowledge base.

• Contrast - the arguments, topics, methodologies, approaches, and disputes that may be found in the literature.

• Critique - the literature and describe the ideas and opinions you find more convincing and why.

• Connect - the various studies you reviewed in your research.

8. How many sources should a literature review have?

When it is just a chapter, sources should equal the total number of pages in your article's body. if it is a self-contained paper in itself, you need at least three times as many sources as there are pages in your work.

9. Can literature review have diagrams?

• To represent an abstract idea or concept

• To explain the steps of a process or procedure

• To help readers understand the relationships between different concepts

10. How old should sources be in a literature review?

Sources for a literature review should be as current as possible or not older than ten years. The only exception to this rule is if you are reviewing a historical topic and need to use older sources.

11. What are the types of literature review?

• Argumentative review

• Integrative review

• Methodological review

• Systematic review

• Meta-analysis review

• Historical review

• Theoretical review

• Scoping review

• State-of-the-Art review

12. Is a literature review mandatory?

Yes. Literature review is a mandatory part of any research project. It is a critical step in the process that allows you to establish the scope of your research, and provide a background for the rest of your work.

But before you go,

• Six Online Tools for Easy Literature Review
• Evaluating literature review: systematic vs. scoping reviews
• Systematic Approaches to a Successful Literature Review
• Writing Integrative Literature Reviews: Guidelines and Examples

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• PLoS Comput Biol
• v.9(7); 2013 Jul

Ten Simple Rules for Writing a Literature Review

Marco pautasso.

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France

2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .

When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.

Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Rule 1: Define a Topic and Audience

How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:

• interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary),
• an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and
• a well-defined issue (otherwise you could potentially include thousands of publications, which would make the review unhelpful).

Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).

Rule 2: Search and Re-search the Literature

After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:

• keep track of the search items you use (so that your search can be replicated [10] ),
• keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies),
• use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente),
• define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope), and
• do not just look for research papers in the area you wish to review, but also seek previous reviews.

The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,

The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .

• discussing in your review the approaches, limitations, and conclusions of past reviews,
• trying to find a new angle that has not been covered adequately in the previous reviews, and
• incorporating new material that has inevitably accumulated since their appearance.

When searching the literature for pertinent papers and reviews, the usual rules apply:

• be thorough,
• use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
• look at who has cited past relevant papers and book chapters.

Rule 3: Take Notes While Reading

If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.

Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.

Rule 4: Choose the Type of Review You Wish to Write

After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.

There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .

Rule 5: Keep the Review Focused, but Make It of Broad Interest

Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.

While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.

Rule 6: Be Critical and Consistent

Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:

• the major achievements in the reviewed field,
• the main areas of debate, and
• the outstanding research questions.

It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .

How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .

Rule 8: Make Use of Feedback

Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.

Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .

Rule 9: Include Your Own Relevant Research, but Be Objective

In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.

In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.

Rule 10: Be Up-to-Date, but Do Not Forget Older Studies

Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.

Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.

Funding Statement

This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.

Literature Review Guide: Examples of Literature Reviews

• What is a Literature Review?
• How to start?
• Picking your research question and searching
• Search strategies and Databases
• How to organise the review
• Examples of Literature Reviews
• Library summary

All good quality journal articles will include a small Literature Review after the Introduction paragraph.  It may not be called a Literature Review but gives you an idea of how one is created in miniature.

Sample Literature Reviews as part of a articles or Theses

• Hackett, G and Melia, D . The hotel as the holiday/stay destination:trends and innovations. Presented at TRIC Conference, Belfast, Ireland- June 2012 and EuroCHRIE Conference

Links to sample Literature Reviews from other libraries

• Sample literature reviews from University of West Florida

Irish Theses

• Phillips, Martin (2015) European airline performance: a data envelopment analysis with extrapolations based on model outputs. Master of Business Studies thesis, Dublin City University.
• The customers’ perception of servicescape’s influence on their behaviours, in the food retail industry : Dublin Business School 2015
• Coughlan, Ray (2015) What was the role of leadership in the transformation of a failing Irish Insurance business. Masters thesis, Dublin, National College of Ireland.
• << Previous: How to organise the review
• Next: Library summary >>
• Last Updated: Sep 6, 2024 3:37 PM
• URL: https://ait.libguides.com/literaturereview

•   [email protected]

Writing and Presenting Guide

Writing literature reviews, what is a literature review.

"A literature review discusses published information in a particular subject area, and sometimes information in a particular subject area within a certain time period. A literature review can be just a simple summary of the sources, but it usually has an organizational pattern and combines both summary and synthesis. 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. It might give a new interpretation of old material or combine new with old interpretations. Or it might trace the intellectual progression of the field, including major debates. And depending on the situation, the literature review may evaluate the sources and advise the reader on the most pertinent or relevant." Source: The Writing Center at UNC-Chapel Hill. (2013). Literature Reviews. Retrieved from https://writingcenter.unc.edu/handouts/literature-reviews/ This link opens in a new window

Need help writing a literature review?

Check out these resources:

Helpful Books from the Library

Helpful Web Resources

• Literature Reviews (UNC Writing Center) This link opens in a new window
• Learn How to Write a Review of Literature (The Writing Center at the Univ. of Wisconsin) This link opens in
• The Literature Review (Univ. of Toronto) This link opens in a new window
• Write a Literature Review (University Library at Univ. Of California Santa Cruz) This link opens in a new wi
• Literature Reviews (Ithaca) This link opens in a new window
• << Previous: Writing Papers
• Next: APA Annotated Bibliography >>

Literature Review

• Steps for Conducting a Lit Review
• Finding "The Literature"
• Organizing/Writing
• Sample Literature Reviews
• FAMU Writing Center

Sample Lit Reviews from Communication Arts

• Literature Review Sample 1
• Literature Review Sample 2
• Literature Review Sample 3
• << Previous: MLA Style
• Next: FAMU Writing Center >>
• Last Updated: Oct 20, 2022 11:24 AM
• URL: https://library.famu.edu/literaturereview

WTO / Education / 39 Best Literature Review Examples (Guide with Samples)

39 Best Literature Review Examples (Guide with Samples)

A literature review is a compilation of current knowledge on a particular topic derived from the critical evaluation of different scholarly sources such as books, articles, and publications, which is then presented in an organized manner to relate to a specific research problem being investigated.

It highlights the methods, relevant theories, and gaps in existing research on a particular subject. It can be both a summary and synthesis of information on a specific topic. A summary reiterates key information from scholarly sources, while synthesis is a new interpretation or combination of new and old material. 

As a synthesis, it can outline the intellectual progression of knowledge in a particular field or topic, which might involve stating key debates throughout the advancement period.  

Literature Review Examples

Purpose of Literature Review

Literature reviews have different purposes in scholarly articles, research papers , and books, depending on the discipline at hand. First and foremost, reviews are generally meant to showcase the extensive research carried out by an author on a particular topic and their findings, which will form the foundation of the research. It then summarizes the information to show the author’s familiarity with the topic in question.

The review also demonstrates the relationship between the topic being investigated and other topics that were under consideration. Finally, it outlines the gaps in the previous works of other scholars, which create areas of research.

Literature reviews provide a new interpretation of previous scholarly publications and aim to resolve conflicting studies done in the past. In addition, identifying existing gaps in a particular research area illustrates the starting point of the research.

Literature Review vs. Academic Research Paper

A research paper presents new ideas, arguments, and approaches toward a particular topic. The conclusions of a research paper will be based on the analysis and interpretation of raw data collected by the author and an original study. On the other hand, a literature review is based on the findings of other publications. Thus, the review highlights the author’s understanding of a topic based on the previously conducted research. It is part of a research paper.

Where, When, and Why

The need for a literature review in a publication will vary from one situation to the other and the field/discipline of research. These two factors determine what is expected from the lit review. For example, a scientific review will be more analytical on the methods and results of previous research. In contrast, a philosophical review will be more argumentative, highlighting the discrepancies and correspondences between scholars.

It can either be part of a publication or a stand-alone document. As part of a research publication, it is often placed after the introduction to the topic outlining knowledge about a particular topic and critical sources that formed the foundation of the research. As an individual document, it is prepared by students as part of course study to aid the students in familiarizing themselves with different topics in their field of study.

Lit reviews also guide students to help them synthesize theoretical methodologies and frameworks to adopt in academic research . As a publication, literature reviews are used to document existing information about a topic for readers (other scholars) to go through for whatever reasons they may have. Published studies are essentially helpful to new scholars getting into any field of research.

Types of Literature Review

Before looking into how to write a literature review, it is vital to understand the different types. The type will usually depend on the objective approach of the author.

Common types are:  

Argumentative review

An argumentative review is adopted when the research paper or publication is meant to take a contrarian viewpoint on a particular subject. The review analyses an existing argument, philosophical problem, assumption, or conclusion outlined in different studies with an objective to either support or oppose the argument. 

Integrative review

An integrative review integrates secondary data to develop new perspectives and frameworks on a topic. This is more prevalent in research that does not involve primary data. In addition, integrative reviews are more familiar with social sciences.       

Historical review

Historical reviews are used when scholars or authors place a particular idea, concept, theory, or research in a historical context. It examines the idea, theory, or issue from the first time it was discussed and outlines its evolution throughout a given period.  

Methodological review

Methodological reviews look at how a specific theory, concept, results, or findings were developed. Therefore, methodological reviews will analyze the different methods used by different scholars to arrive at conclusions or knowledge about the topic being investigated.

Some of the methods scholars use in different disciplines to obtain information are interviewing, sampling, practical experiments/data collection, research approaches, critical thinking, social experiments, etc.

Methodological reviews are hence used to discuss tested methods of research and ethics that a researcher should be aware of before undertaking their investigations.  

Systematic review

A systematic review is a more detailed and comprehensive review compared to other types of lit reviews. It highlights any existing research evidence associated with a clearly defined research problem or question. The evidence is collected, analyzed, and reported in a summarized but detailed manner. Systematic reviews are popularly presented as a cause-and-effect structure.

Theoretical review

A theoretical review delves into the different theories regarding a particular issue, challenge, concept, or theory. It identifies their inadequacy in explaining the issue or concept at hand. The review then identifies the relationships between the identified theories, and the degree of research done and poses novel hypotheses to be investigated.

Organization of a Literature Review

How an author organizes a literature review will depend on what they aim to achieve. As a consequence, there are multiple ways of organizing it which are discussed below:

Chronological 

A chronological format outlines knowledge on a particular topic based on when the scholarly source of information was published. Starting with the earliest followed up to the most recent chronological order. This format should be used if there is a clear chronological order in the development of the information; therefore, it will not be applicable in some cases. Instead, key turning points, patterns, and events that impacted the direction of the knowledge should be outlined.  

By publication

It can be organized in the scholarly publications reviewed by the author, scholar, or student. The by-publication format should only improve the review and facilitate what the author aims to accomplish. 

Scholars or students can adopt a dominant trend in research, such as history, developmental stages, steps involved in a process, etc.

Methodological

A methodological format is based on the methods used by the researcher. Thus, the order of contents in the lit review will depend on the method they will use to carry out their research, knowledge obtained from the first method appears first, and the rest of the information follows in the same order according to the methods used by the author.  

Literature reviews organized in a thematic format revolve around the subject being investigated in no order. It is, therefore, ordinarily up to the researcher or author to determine how they intend to outline the information. A thematic format will crossover from one period and publication to another, but can sometimes incorporate a chronological order.

Theoretical

Literature reviews organized in a theoretical format have their contents organized in an abstract framework established by the author to discuss different concepts, theories, and concepts and how they relate to the research at hand.

Additional sections

Depending on the objective, other sections do not fit under conventional lit review formats that one may need to add. Below are some of the sections that authors or students can include in the lit review:

• Current situation: The review can have information about the current state of things regarding the topic at hand to facilitate further understanding.
• History: Researchers can summarize the subject under investigation, literature, or concept if the review is not already in chronological format.
• Selection methods: Lit reviews are known to outline the methods or criteria used in selecting the way to present information and scholarly sources referenced in the review.
• Standards: it can also include the standards used in choosing the format to present information in the review and the scholarly literature used in the research.
• Further questions for research: The review can include questions emanating from the review and how the researcher will further explore their research to address the queries raised.

Literature Review Samples

Considerations Before Writing a Literature Review

Preparation is essential when it comes to writing. The objective should be to come up with a review that satisfactorily explores the topic being discussed. The following considerations are steps towards that if incorporated into the writing process:

Authors should seek clarification from mentors or supervisors before commencing the writing process. First, determine what is expected from the lit review. The type and number of sources to be used, the assignment (summarize, synthesize, or critique), and the type of information provided should be clear.

Find models

You should review literature from other authors in the same discipline and evaluate how those authors presented their lit reviews. Previous lit reviews can be used as guides that point authors in the right direction when writing their lit reviews.

Narrow your topic

It is always advantageous to narrow down the research topic to a specific area of research; that way, the number of sources can also be reduced. Even though conducting research will usually involve extensive research on all available materials about a particular topic, having a well-defined topic simplifies the task at hand.

Current sources

Determine if the research project or discipline ought to be based on the most recent findings or information. It is common for knowledge to become obsolete, especially in disciplines where discoveries and new inventions are made fast. If the lit review should be based on current knowledge, limit the sources to the most recent literature. Some disciplines will typically have a limit on how old the sources should be.  

How to Write a Literature Review (Expert Guide)

Once all pre-writing considerations have been taken into account, it is time to write the document. At this point, you should already be aware of what you wish to accomplish with the literature review, and the steps to writing an exemplary lit review are mentioned below:

Problem formulation

First and foremost, clearly define the topic (research area) to be investigated. For students, this will sometimes be given as an assignment. However, the research could be an academic project, which means that the author has to come up with the problem and define it themselves.

Search for relevant studies

Once the problem is clearly expressed, you should search for studies related to the topic, concept, theory, or idea and questions surrounding the topic. Most stand-alone lit reviews will generally attempt to answer a more concentrated question. On the internet, literature can be searched using keywords related to the research area. In addition to keywords, include vital variables such as synonyms and associated terms. The inclusion of Boolean operators and, or not, is also used to narrow down results to more specific publications.

Familiar sources for publications are:

• Google Scholar
• Library catalogue
• Econ lit (economics)
• Project Muse (humanities and social sciences)
• Inspec (physics, engineering, and computer science )
• Medline (life sciences and biomedicine)

Before selecting relevant studies, go through their abstract and determine if they fit the scope needed in the investigation. Use a list to note down any chosen works. Select landmark sources in the discipline.

Evaluation of sources/data

The next step is the evaluation stage . Evaluation involves a lot of reading. Evaluation can be done in two stages; overall skimming and thorough reading. During the second stage of this step, be critical, ask questions, and take many notes.

Some of the questions authors or researchers should ask themselves are:

• What is the author’s objective? What problem, concept, or theory are they putting across?
• What are the main concepts?
• What are the methodologies used by the author to arrive at the results and conclusions?
• What are the strengths and weaknesses of the results and conclusions?

Use credible sources. Most cited sources are preferred as they indicate their influence in the field. Also, keep track of the citations to be later incorporated.

Identify themes, debates, and gaps

While reading the sources, identify key patterns, themes, debates/arguments, and gaps in each literature. These elements help tie the literature to the topic under investigation. Look for consistent patterns, themes, questions, challenges, methods, and inconsistencies in the same. Consistencies present critical information for consideration, while inconsistencies present opportunities for research areas.

Outline the structure

Formatting is part and parcel of a well-written work. Selecting the structure should start by creating an outline with all the information that will go into the lit review, then consider the different types of structures and select the most suitable. Next, take the basic structure of the introduction, body, and conclusion into consideration and start work from there. 

Analysis and interpretation 

Lastly, perform an in-depth analysis and interpretation of the information obtained from the scholarly research and put it into writing. The summarized, synthesized, and critically evaluated information is then written down in well-structured paragraphs that follow the chosen structure. Transition words are used to draw comparisons, connections, and contrasts.

Format 

Ordinarily, a literature review will have three key components: introduction, body, and conclusion. These components should appear in the document in the following order:

Introduction

An introduction should inform the reader which topic is being studied. It gives the reader an overall idea of the purpose and focus of the document. The introduction lets the reader know beforehand the key things that will be highlighted in the document. Therefore, the introduction should be brief and precise.

The next item is the body, where the primary purpose of the lit review is fulfilled. The body should take critical information from all the sources used and comprehensively present them. This is where the author reports the extensive analysis and interpretation results that they gathered from all the sources they reviewed. The body should be categorized into themes, ideas, and concepts within the main topic.

Lastly, a summary of what the lit review entails should be provided as a conclusion. The critical points obtained from examining the sources should be written down and linked to the primary subject of the review. Key points are those that have the most outstanding contribution to the research.

Studies used should be screened based on provenance (author’s credentials or credibility), methodology, objectivity, persuasiveness, and value related to the topic at hand.

Guidelines for Writing a Literature Review

To improve the delivery of information, there are certain elements that authors can incorporate. They are:

Use evidence

The lit review’s findings, interpretations, and general contents should be based on actual evidence or credible literature. Using citations is evidence of authentic information.

Be selective

There will always be a lot of information available from the reviewed sources. Authors should therefore be selective and discuss the key points that focus on the topic. Not all information must be included in the review.  

Word-for-word quotes are acceptable . This is even more so if a critical point or author-specific terminology or knowledge cannot be paraphrased. Quotes should, however, be used sparingly.

Summarize and synthesize

The information obtained from the sources should be summarized, and the author should use it to synthesize new arguments, concepts, or ideas related to their research.

Keep your voice

The literature review should reflect the author’s voice as it is a review of other people’s works. This can be done by starting and ending the paragraphs with an original voice, ideas, and wordings.

Use caution while paraphrasing

Any paraphrased information should be conveyed accurately and in the author’s words. A citation must always be done, even when paraphrasing has been done.

Proofread before submitting or publishing. Go through the document a few times and make the necessary changes. The review should be within the applicable guidelines. Check for language and any other errors and edit accordingly.

Do’s and Don’ts for a Literature Review 

Every researcher wants to introduce their readers to a particular topic in an informative and engaging manner. Below are tips that can be used to this effect.

The following things should be opted by the researcher when writing a lit review:

• Find a focus: Authors should take a direction, idea, concept, or argument and stick to it. The information conveyed should then be made to align with the chosen point of focus. Thus, the review is not simply a list of analyzed sources, but a detailed summary of how different sources have a focal point (intertwined).
• Well-chosen sources: The quality of the information will, to a great extent, be determined by the quality of sources used. Therefore, take time to select suitable sources and more value will be added to the review.
• Create an annotated bibliography: Creating an annotated bibliography is recommended as one reads their sources. The bibliography keeps track of sources and takes notes. This information can be used when writing the final lit review.
• Synthesize research: Information obtained from the relevant studies should be combined to come up with new or original ideas. You should present a new domain based on previous sources’ knowledge, not just restating the information.
• Argumentative approach: Well-written literature reviews will often argue to support an author’s stance on a particular topic. The author can choose to address how the author’s work is filling a particular gap or support one of the scholar’s arguments and perception towards a particular topic. However, this argumentative approach will not work in all situations; it is usually discipline-specific. 
• Convey it to the reader: It should let the reader know the document’s main idea, concept, or argument. This can be done by including a simple statement that compels the reader to think precisely and know what to expect.
• Break out your disciplinary box: The research will often be multi-disciplinary. Literature reviews should then collect interdisciplinary information from multiple sources as they add novel dynamics to the topic under investigation. It should be noted that this does not imply that the researcher should substitute the literature from the topic’s discipline with that from other disciplines. This is usually an improvement strategy that adds substance to the review.
• Look for repeated patterns: Be attentive to pick out repeated ideas, findings, and concepts from different scholars as they will often illustrate agreed research dead-end or a scholarly conclusion.
• Don’t just review for content: When reviewing the literature, examine the content and other writing and presentation techniques. Look out for unique ways information has been presented, methods used, consistent citations, and non-textual elements such as graphs, and figures used to present information. In addition, the researcher identifies theories used to predict, explain, or understand phenomena within the discipline.
• Search Web of Science and Google Scholar: Conduct citation tracking about the leading scholars already identified in the search process. Scholars cited by multiple scholars outside the principal discipline will generally indicate that there are no new publications on the topic.

The following don’ts should be avoided:

• Do not select studies that are not directly related to the topic being investigated.
• Avoid rushing when identifying and selecting sources to use to research the problem.
• Avoid the use of secondary analytical sources. Instead, opt to use sources with primary research studies or data. Secondary analytical sources will often cite primary analytical sources; research should refer to them instead.
• Do not accept other scholarly findings, theories, or interpretations without critically examining and critiquing them.
• Researchers should not outline the search procedures used to identify scholarly sources for reviewing purposes.
• Avoid including isolated statistical findings without illustrating how they were arrived at using chi-squared or meta-analytic methods.
• Do not review studies that only validate the assumptions, stances, and concepts of your thesis; consider contradicting works with alternative and conflicting stances.

Frequently Asked Questions

It is written by researchers, authors, and students who must study literature to gather knowledge on a particular topic they are interested in.

It should be placed right after the introduction of the dissertation. It places the research in a scholarly context by summarizing existing knowledge on the particular topic.

Researchers and authors are not limited in terms of how many sources they can review. Students will usually have a given number of sources to review as an assignment. However, the number of sources referenced in a lit review will vary from one topic or discipline to the other. Some topics have a vast catalog of available sources, while others have minimal sources, especially emerging issues. It is, however, advised that each key point discussed should have at least 2-3 references/sources. For example, a 10-page lit review will have an average of 30 references.

About This Article

Was this helpful?

Great! Tell us more about your experience

Not up to par help us fix it, keep reading.

11 Perfect Academic Research Statement Examples (with Guide)

Charts , Family , Personal

22 free reward charts for kids (word, pdf, illustrator).

Printable General Color Charts (Word | PDF)

Thank you for your feedback.

Your Voice, Our Progress. Your feedback matters a lot to us.

Scoping Reviews

• What is a Scoping Review?
• Best Practices
• Review Protocol

Eligibility Criteria

Inclusion criteria, exclusion criteria.

• Database Search Strategies
• Study Selection (Screening)
• Data Extraction
• Reference Management

Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. 

Think about criteria that will be used to select articles for your literature review based on your research question.  These are commonly known as  inclusion criteria  and  exclusion criteria .  You may introduce bias into the final review if these are not used thoughtfully. 

According to the PRISMA-SCcR Checklist , item 6 , authors should "specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale."

Inclusion criteria are the elements of an article that must be present in order for it to be eligible for inclusion in a literature review.  

For example, included studies must:

• have compared certain treatments
• be experimental or observational or both
• have been published in a certain timeframe (must have compelling reason)
• be certain publication type(s)
• have recruited a certain population

Exclusion criteria are the elements of an article that disqualify the study from inclusion in a literature review.  

For example, excluded studies: 

• used qualitative methodology
• used a certain study design (e.g, observational)
• are a certain publication type (e.g., systematic reviews)
• were published before a certain year (must have compelling reason)
• used animal models
• was published in a language other than English
• << Previous: Review Protocol
• Next: Database Search Strategies >>
• Last Updated: Sep 12, 2024 4:02 PM
• URL: https://musc.libguides.com/scopingreviews

A systematic review of aspect-based sentiment analysis: domains, methods, and trends

• Open access
• Published: 17 September 2024
• Volume 57 , article number  296 , ( 2024 )

Cite this article

You have full access to this open access article

• Yan Cathy Hua   ORCID: orcid.org/0000-0001-9155-9667 1 ,
• Paul Denny   ORCID: orcid.org/0000-0002-5150-9806 1 ,
• Jörg Wicker   ORCID: orcid.org/0000-0003-0533-3368 1 &
• Katerina Taskova   ORCID: orcid.org/0000-0002-3217-7877 1  

Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a systematic literature review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA. To our knowledge, it is also the first to systematically examine the interrelations among ABSA research and data distribution across domains, as well as trends in solution paradigms and approaches. Our sample includes 727 primary studies screened from 8550 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.

Explore related subjects

• Artificial Intelligence

Avoid common mistakes on your manuscript.

1 Introduction

In the digital era, a vast amount of online opinionated text is generated daily through which people express views and feelings (i.e. sentiment) towards certain subjects, such as user reviews, social media posts, and open-ended survey question responses (Kumar and Gupta 2021 ). Understanding the sentiment of these opinionated text data is essential for gaining insights into people’s preferences and behaviours and supporting decision-making across a wide variety of domains (Sharma and Shekhar 2020 ; Wankhade et al 2022 ; Tubishat et al 2021 ; García-Pablos et al 2018 ; Poria et al 2016 ). The analyses of opinionated text usually aim at answering questions such as “ What subjects were mentioned? ”, “ What did people think of (a specific subject)? ”, and “ How are the subjects and/or opinions distributed across the sample? ” (e.g. (Dragoni et al 2019 ; Krishnakumari and Sivasankar 2018 ; Fukumoto et al 2016 ; Zarindast et al 2021 )). These objectives, along with today’s enormous volume of digital opinionated text, require an automated solution for identifying, extracting and classifying the subjects and their associated opinions from the raw text. Aspect-based sentiment analysis (ABSA) is one such solution.

1.1 Review focus and research questions

This work presents a systematic literature review (SLR) of existing ABSA studies with a large-scale sample and quantitative results. We focus on trends and high-level patterns instead of methodological details that were well covered by the existing surveys mentioned above. We aim to benefit both ABSA newcomers by introducing the basics of the topic, as well as existing ABSA researchers by sharing perspectives and findings that are useful to the ABSA community and can only be obtained beyond the immediate research tasks and technicalities.

We seek to answer the following sets of research questions (RQs):

RQ1. To what extent is ABSA research and its dataset resources dominated by the commercial (especially the product and service review) domain? What proportion of ABSA research focuses on other domains and dataset resources?

RQ2. What are the most common ABSA problem formulations via subtask combinations, and what proportion of ABSA studies only focus on a specific subtask?

RQ3. What is the trend in the ABSA solution approaches over time? Are linguistic and traditional machine-learning approaches still in use?

This review makes a number of unique contributions to the ABSA research field: (1) It is one of the largest scoped SLRs on ABSA, with a main review and a Phase-2 targeted review of a combined 727 primary studies published in 2008–2024, selected from 8550 search results without time constraint. (2) To our knowledge, it is the first SLR that systematically examines the ABSA data resource distribution in relation to research application domains and methodologies; and (3) Our review methodology adopted an innovative automatic filtering process based on PDF-mining, which enhanced screening quality and reliability. Our quantitative results not only revealed trends in nearly two decades of ABSA research literature but also highlighted potential systemic issues that could limit the development of future ABSA research.

1.2 Organisation of this review

In Sect.  2 (“Background”), we introduce ABSA and highlight the motivation and uniqueness of this review. Section  3 (“Methods”) outlines our SLR procedures, and Sect.  4 (“Results”) answers the research questions with the SLR results. We then discuss the key findings and acknowledge limitations in Sects.  5 and 6 (“Discussion” and “Conclusion”).

For those interested in more details, Appendix A provides an in-depth introduction to ABSA and its subtasks. Appendix B describes the full details of our Methods, and additional figures from the Results are provided in Appendix C .

2 Background

2.1 absa: a fine-grained sentiment analysis.

Aspect-based sentiment analysis (ABSA) is a sub-field of Sentiment Analysis (SA), which is a core task of natural language processing (NLP). SA, also known as “opinion mining” (García-Pablos et al 2018 ; Poria et al 2016 ; Liang et al 2022 ; López and Arco 2019 ; Tran et al 2020 ), solves the problem of identifying and classifying given text corpora’s affect or sentiment orientation (Akhtar et al 2020 ; Tubishat et al 2021 ) into polarity categories (e.g. “positive, neutral, negative”) (Brauwers and Frasincar 2023 ; Hu and Liu 2004a ), intensity/strength scores (e.g. from 1 to 5) (Wang et al 2017 ), or other categories. The “ identifying the subjects of opinions ” part of the quest relates to the granularity of SA. Traditional SA mostly focuses on document- or sentence-level sentiment and thus assumes a single subject of opinions (Nazir et al 2022b ; Liu et al 2020 ). In recent decades, the explosion of online opinion text has attracted increasing interest in distilling more targeted insights on specific entities or their aspects within each sentence through finer-grained SA (Nazir et al 2022b ; Liu et al 2020 ; Akhtar et al 2020 ; You et al 2022 ; Ettaleb et al 2022 ). This is the problem ABSA aims to solve.

2.2 ABSA and its subtasks

ABSA involves identifying the sentiments toward specific entities or their attributes, called aspects . These aspects can be explicitly mentioned in the text or implied from the context (“implicit aspects”), and can be grouped into aspect categories (Nazir et al 2022a ; Akhtar et al 2020 ; Maitama et al 2020 ; Xu et al 2020b ; Chauhan et al 2019 ; Akhtar et al 2018 ). Appendix  A.1 presents a more detailed definition of ABSA, including its key components and examples.

A complete ABSA solution as described above traditionally involves a combination of subtasks, with the fundamental ones (Li et al 2022a ; Huan et al 2022 ; Li et al 2020 ; Fei et al 2023b ; Pathan and Prakash 2022 ) being Aspect (term) Extraction (AE), Opinion (term) Extraction (OE), and Aspect-Sentiment Classification (ASC), or in an aggregated form via Aspect-Category Detection (ACD) and Aspect Category Sentiment Analysis (ACSA).

The choice of subtasks in an ABSA solution reflects both the problem formulation and, to a large extent, the technologies and resources available at the time. The solutions to these fundamental ABSA subtasks evolved from pure linguistic and statistical solutions to the dominant machine learning (ML) approaches (Maitama et al 2020 ; Cortis and Davis 2021 ; Liu et al 2020 ; Federici and Dragoni 2016 ), usually with multiple subtask models or modules orchestrated in a pipeline (Li et al 2022b ; Nazir and Rao 2022 ). More recently, the rise of multi-task learning brought an increase in End-to-end (E2E) ABSA solutions that can better capture the inter-task relations via shared learning (Liu et al 2024 ), and many only involve a single model that provides the full ABSA solution via one composite task (Huan et al 2022 ; Li et al 2022b ; Zhang et al 2022b ). The most typical composite ABSA tasks include Aspect-Opinion Pair Extraction (AOPE) (Nazir and Rao 2022 ; Li et al 2022c ; Wu et al 2021 ), Aspect-Polarity Co-Extraction (APCE) (Huan et al 2022 ; He et al 2019 ), Aspect-Sentiment Triplet Extraction (ASTE) (Huan et al 2022 ; Li et al 2022b ; Du et al 2021 ; Fei et al 2023b ), and Aspect-Sentiment Quadruplet Extraction/Prediction (ASQE/ASQP) (Zhang et al 2022a ; Lim and Buntine 2014 ; Zhang et al 2021a , 2024a ). We provide a more detailed introduction to ABSA subtasks in Appendix  A.2 .

2.3 The context- and domain-dependency challenges

The nature and the interconnection of its components and subtasks determine that ABSA is heavily domain- and context-dependent (Nazir et al 2022b ; Chebolu et al 2023 ; Howard et al 2022 ). Domain refers to the ABSA task (training or application) topic domains, and context can be either the “global” context of the document or the “local” context from the text surrounding a target word token or word chunks. At least in English, the same word or phrase could mean different things or bear different sentiments depending on the context and topic domains. For example, “a big fan” could be an electric appliance or a person, depending on the sentence and the domain; “cold” could be positive for ice cream but negative for customer service; and “DPS” (damage per second) could be either a gaming aspect or non-aspect in other domains. Thus, the ability to incorporate relevant context is essential for ABSA solutions; and those with zero or very small context windows, such as n-gram and Markov models, are rare in ABSA literature and can only tackle a limited range of subtasks (e.g. Presannakumar and Mohamed 2021 ).

Moreover, although many language models (e.g. Bidirectional Encoder Representations from Transformers (BERT, Devlin et al 2019 ), Generative pre-trained transformers (GPT, Brown et al 2020 ), recurrent neural network (RNN)-based models) already incorporated local context from the input-sequence and/or general context through pre-trained embeddings, they still performed unsatisfactorily on some ABSA domains and subtasks, especially Implicit AE (IAE), AE with multi-word aspects, AE and ACD on mixed-domain corpora, and context-dependent ASC (Phan and Ogunbona 2020 ; You et al 2022 ; Liang et al 2022 ; Howard et al 2022 ). Many studies showed that ABSA task performance benefits from expanding the feature space beyond the generic and input textual context. This includes incorporating domain-specific dataset/representations and additional input features such as Part-of-Speech (POS) tags, syntactic dependency relations, lexical databases, and domain knowledge graphs or ontologies (Howard et al 2022 ; You et al 2022 ; Liang et al 2022 ). Nonetheless, annotated datasets and domain-specific resources are costly to produce and limited in availability, and domain adaptation, as one solution to this, has been an ongoing challenge for ABSA (Chen and Qian 2022 ; Zhang et al 2022b ; Nazir et al 2022b ; Howard et al 2022 ; Satyarthi and Sharma 2023 ).

The above highlights the critical role of domain-specific datasets and resources in ABSA solution quality, especially for supervised approaches. On the other hand, it suggests the possibility that the prevalence of dataset-reliant solutions in the field, and a skewed ABSA dataset domain distribution, could systemically hinder ABSA solution performance and generalisability (Chen and Qian 2022 ; Fei et al 2023a ), thus confining ABSA research and solutions close to the resource-rich domains and languages. This idea underpins this literature review’s motivation and research questions.

2.4 Review rationale

This review is motivated by the following rationales:

First, the shift towards ML, especially supervised and/or DL solutions for ABSA, highlights the importance of dataset resources. In particular, annotated large benchmark datasets are crucial for the quality and development of ABSA research. Meanwhile, the finer granularity of ABSA also brings the persistent challenge of domain dependency described in Sect.  2.3 . The diversity of ABSA datasets and their domains can have a direct and systematic impact on research and applications.

The early seminal works in ABSA were motivated by commercial applications and focused on product and service reviews (Liu et al 2020 ; Rana and Cheah 2016 ; Do et al 2019 ), such as Ganu et al ( 2009 ), Hu and Liu ( 2004b ), and Pontiki et al ( 2014 , 2015 , 2016 ) that laid influential foundations with widely-used product and service review ABSA benchmark datasets (Rana and Cheah 2016 ; Do et al 2019 ). Nevertheless, the need for mining insights from opinions far exceeds this single domain. Many other areas, especially the public sector, also have an abundance of opinionated text data and can benefit from ABSA, such as helping policy-makers understand public attitudes and reactions towards events or changes (Sharma and Shekhar 2020 ), improving healthcare services and treatments via patient experience and concerns in clinical visits, symptoms, drug efficacy and side-effects (Cavalcanti and Prudêncio 2017 ; Gui and He 2021 ), and guiding educators in meeting teacher and learner needs and improving their experience (Wankhade et al 2022 ; Tubishat et al 2021 ; García-Pablos et al 2018 ; Poria et al 2016 ). While the more general SA research has been applied to “nearly every domain” (Nazir et al 2022b , p. 1), this does not seem to be the case for ABSA. Chebolu et al ( 2023 ) reviewed 62 public ABSA datasets released between 2004 and 2020 covering “over 25 domains” (Chebolu et al 2023 , p. 1). However, 53 out of these 62 datasets were reviews of restaurants, hotels, and digital products; only five were not related to commercial products or services, and merely one was on the public sector domain (university reviews).

The above-mentioned evidence raises questions: Will this dataset domain homogeneity be found with a larger sample of primary studies? Does this domain skewness reflect the concentration of ABSA research focus or merely the lack of dataset diversity? This motivated our RQ1 (“ To what extent is ABSA research and its dataset resources dominated by the commercial (especially the product and service review) domain? What proportion of ABSA research focuses on other domains and dataset resources? ”) Answers to these questions could inform and shape future ABSA research through individual research decisions and community resource collaboration.

Second, there are many good survey papers on ABSA, most focused on introducing the methodological details of common ABSA subtasks and solutions (e.g. Maitama et al 2020 ; Sabeeh and Dewang 2019 ; Rana and Cheah 2016 ; Soni and Rambola 2022 ; Ganganwar and Rajalakshmi 2019 ; Zhou et al 2019 ) or specific approaches such as DL methods for ABSA (e.g. Liu et al 2020 ; Do et al 2019 ; Wang et al 2021a ; Chen and Fnu 2022 ; Mughal et al 2024 ; Zhang et al 2022c ; Satyarthi and Sharma 2023 ). We list these surveys in Appendix A.3 as additional resources for the reader. Nonetheless, many of these reviews only explored each subtask and/or technique individually and often by iterating through reviewed studies, and few examined their combinations or changes over time and with quantitative evidence. For example, although the above-listed reviews (Liu et al 2020 ; Do et al 2019 ; Wang et al 2021a ; Chen and Fnu 2022 ) reported the rise of DL approaches in ABSA similar to that of NLP as a whole, it is unclear whether ABSA research was also increasingly dominated by the attention mechanism from the Transformer architecture (Vaswani et al 2017 ) and pre-trained large language models since 2018 (Manning 2022 ), and if linguistic and traditional ML approaches were still active. In addition, most of these surveys used a smaller and selected sample that could not support conclusions on trends. As the field matures, we believe it is necessary and important to examine trends and matters outside the problem solution itself, so as to inform research decisions, identify issues, and call for necessary community awareness and actions. We thus proposed RQ2 (“ What are the most common ABSA problem formulations via subtask combinations, and what proportion of ABSA studies only focus on a specific sub-task? ”) and RQ3 (“ What is the trend in the ABSA solution approaches over time? Are linguistic and traditional machine-learning approaches still in use? ”).

In order to identify patterns and trends for our RQs, a sufficiently sized representative sample and systematic approach are required. We chose to conduct an SLR, as this type of review aims to answer specific research questions from all available primary research evidence following well-defined review protocols (Kitchenham and Charters 2007 ). Moreover, none of the existing SLRs on ABSA share the same focus and RQs as ours: Among the 192 survey/review papers obtained from four major digital database searches detailed in Sect.  3 , only eight were SLRs on ABSA, within which four focused on non-English language(s) (Alyami et al 2022 ; Obiedat et al 2021 ; Hoti et al 2022 ; Rani and Kumar 2019 ), two on specific domains (software development, social media) (Cortis and Davis 2021 ; Lin et al 2022 ), one on a single subtask (Maitama et al 2020 ), and one mentioned ABSA subtasks as a side-note under the main topic of SA (Ligthart et al 2021 ).

In summary, this review aims to address gaps in the ABSA literature. The high-level nature of our research questions is best answered through a large-scale SLR to provide solid evidence. The next section presents our SLR approach and sample.

Following the guidance of Kitchenham and Charters ( 2007 ), we conducted this SLR with pre-planned scope, criteria, and procedures highlighted below. The complete SLR methods and process are detailed in Appendix B .

3.1 Main procedures

For the main SLR sample, we sourced the primary studies in October 2022 from four major peer-reviewed digital databases: ACM Digital Library, IEEE Xplore, Science Direct, and SpringerLink. First, we manually searched and extracted 4191 database results without publication-year constraints. Appendix B.1 provides more details of the search strategies and results. Next, we applied the inclusion and exclusion criteria listed in Table  1 via automatic Footnote 1 and manual screening steps and identified 519 in-scope peer-reviewed research publications for the review. The complete screening process, including that of the automatic screening, is described in Appendix B.2 . We then manually reviewed the in-scope primary studies and recorded data following a planned scheme. Lastly, we checked, cleaned, and processed the extracted data and performed quantitative analysis against our RQs.

3.2 Main SLR sample summary

Figure  1 shows the number of total reviewed vs. included studies across all publication years for the 4191 SLR search results. The search results include studies published between 1995 and 2023 ( \(\textrm{N}=1\) ), although all of the pre-2008 ones (2 from the 90s, 8 from 2003–2006, 17 from 2007) were not ABSA-focused and were excluded during automatic screening. The earliest in-scope ABSA study in the sample was published in 2008, followed by a very sparse period until 2013. The numbers of extracted and in-scope publications have both grown noticeably since 2014, a likely result of the emergence of deep learning approaches, especially sequence models such as RNNs (Manning 2022 ; Sutskever et al 2014 ). We also present a breakdown of the included studies by publication year and type in Figure  9 in Appendix C .

Number of studies by publication year: total reviewed ( \(\textrm{N}=4191\) ) vs. included ( \(\textrm{N}=519\) )

3.3 Note on “domain” mapping

In order to answer RQ1, we made the distinction between “research application domain” (“research domain” in short) and “dataset domain”, and manually examined and classified each study and its datasets into domain categories.

We considered each study’s research domain to be “non-specific” unless the study mentioned a specific application domain or use case as its motivation. For the dataset domain, we examined each dataset used by our sample, standardised its name, and recorded the domain from which it was drawn/selected based on the description provided by the author or the dataset source webpage. Datasets without a specific domain (e.g. Twitter tweets crawled without a specific domain filter) were labelled as “non-specific”.

We then manually grouped the research and dataset domains into 19 common categories used for analysis. More details and examples on domain mapping are available in Appendix  B.3 .

3.4 Phase-2 targeted review on in-context learning

Additionally, generative “foundation models” (Bommasani et al 2022 ), defined as models with billions of parameters pre-trained on enormous general-purpose data and adaptable to diverse downstream NLP tasks, have become ubiquitous after our SLR data collection (e.g. ChatGPT OpenAI 2023 , released in November 2022). We use the term “foundation models” to distinguish them from the earlier pre-trained Large Language Models (LLMs) such as BERT (Devlin et al 2019 ), BART (Lewis et al 2020 ), and T5 (Raffel et al 2020 ), which have relatively fewer parameters and typically require fine-tuning for task adaptation (Zhang et al 2022c ). These generative foundation models brought a new paradigm of “In-context Learning” (ICL) (Brown et al 2020 , p. 4), where task adaptation can occur solely via conditioning the model on the text input instructions (“prompts”) with zero (“zero-shot ICL”) or few (“few-shot ICL”) examples and no model parameter changes (Brown et al 2020 ; Dong et al 2024 ). To capture and analyse this new development while balancing feasibility and currency, we conducted a Phase-2 targeted review in July 2024.

This Phase-2 targeted review focuses solely on the ICL implementations of pre-trained generative models for ABSA tasks, excluding those involving fine-tuning to draw a distinction from other non-ICL deep-learning approaches covered in the SLR. To extend the SLR sample beyond the original extraction time, we conducted a new database search Footnote 2 in July 2024 for studies published from 2022 onwards and removed the ones already included in the SLR sample. The new search results were screened using the SLR criteria described in Table  1 and then combined with the 519 SLR final samples. We then applied an additional filtering condition “Gen-LLM” to all the in-scope ABSA primary studies, which further selected publications with at least one occurrence of any of the following keywords outside the Reference section: “generative”, “in-context”, “in context learning”, “genai”, “bart”, “t5”, “flan-t5”, “gpt”, “chatgpt”, “llama”, and “mistral”. With the help of our automatic screening pipeline detailed in Appendix  B.2 , we were able to efficiently auto-screen the new search results and re-screen the previous SLR sample for the ”Gen-LLM” keywords in less than one hour.

In total, the new search yielded 271 additional in-scope ABSA primary studies from 4359 search results. After applying the “Gen-LLM” filtering condition to the combined 790 in-scope ABSA primary studies, we obtained 208 Phase-2 samples for manual review, which comprised 91 studies from the new search and 117 from the earlier SLR sample, ranging from 2008 to 2024. The Phase-2 targeted review results are presented in Sect.  4.5 . Unless specified otherwise, the results below only refer to those of the SLR.

This section presents the SLR results corresponding to each of the RQs:

4.1 Results for RQ1

To answer RQ1, we examined the distribution of reviewed studies by their research (application) domains, dataset domains, and the relationship between the two. From the 519 reviewed studies, we recorded 218 datasets, 19 domain categories (15 research domains and 17 dataset domains), and obtained 1179 distinct “study-dataset” pairs and 630 unique “study & dataset-domain” combinations. The key results are summarised below and presented in Table  2 and Fig.  2 . We also list the datasets used by more than one reviewed study in the Appendix Table  15 .

Distribution of unique “study–dataset” pairs ( \(\textrm{N}=1179\) , with 519 studies and 218 datasets) by research (application) domains (left) and dataset domains (right). Note (1) The top flow visualises a mismatch between the two domains: the majority of studies without a specific research domain used datasets from the product/service review domain. (2) The disproportionately small number of samples in both domains that were neither “non-specific” nor “product/service review”

In summary, our results answer RQ1 by showing that: (1) The majority (65.32%) of the reviewed studies were not for any specific application domain and only 24.28% targeted “product/service review”. (2) The dataset resources used in the sample were mostly domain-specific (84.44%) and dominated by the “product/service review” datasets (70.95%). (3) Both the research effort and dataset resources were scant in the non-commercial domains, especially the main public sector areas, with fewer than 13 studies across 14 years in each of the healthcare, policy, and education domains, where about half of the used datasets were created from scratch for the study.

Beyond RQ1, (1) and (2) above also suggest a significant mismatch between the research and dataset domains as visualised in Fig.  2 . Further, when filtering out datasets used by less than 10 studies, we discovered an alarming lack of dataset diversity as only 12 datasets remained, of which 10 were product/service reviews. When examining the three-way relationship among research domain, dataset domain, and dataset name, we further identified an over-representation (78.20%) of the four SemEval restaurant and laptop review benchmark datasets. This is illustrated in Fig.  4 .

4.1.1 Detailed results for RQ1

For research (application) domains indicated by the stated research use case or motivation, the majority (65.32%, \(\textrm{N}=339\) ) of the 519 reviewed studies have a “non-specific” research domain, followed by just a quarter (24.28%, \(\textrm{N}=126\) ) in the “product/service review” category. However, the number of studies in the rest of the research domains is magnitudes smaller in comparison, with only 12 studies (2.31%) in the third largest category “student feedback/education review” since 2008, followed by 8 in Politics/policy-reaction (1.54%), and only 7 in Healthcare/medicine (1.35%). Figure  3 revealed further insights from the trend of research domain categories with five or more reviewed studies. Interestingly, “product/service review” has been a persistently major category over time, and has only been consistently taken over by “non-specific” since 2015. The sharp increase of domain-“non-specific” studies since 2018 could be partly driven by the rise of pre-trained language models such as BERT and the greater sequence processing power from the Transformer architecture and the attention mechanism (Manning 2022 ), as more researchers explore the technicalities of ABSA solutions.

Number of in-scope studies by research (application) domain and publication year ( \(\textrm{N}=518\) ). This graph excludes the one 2023 study (extracted in October 2022) to avoid trend confusion

As to the dataset domains, Table  2 suggests that among the 630 unique “study & dataset-domain” pairs, the majority (70.95%, \(\textrm{N}=447\) ) are in the “product/service review” category, followed by 15.56% ( \(\textrm{N}=98\) ) in “Non-specific”. The third place is shared by two magnitude-smaller categories: “student feedback/ education review” (3.02%, \(\textrm{N}=19\) ) and “video/movie review” (3.02%, \(\textrm{N}=19\) ). The numbers of studies with datasets from the Healthcare/medicine (1.43%, \(\textrm{N}=9\) ) and Politics/policy-reaction (0.79%, \(\textrm{N}=5\) ) domains were again single-digit. Moreover, nearly half of the unique datasets in the public domains were created by the authors for the first time: 5/9 in Healthcare/medicine, 2/4 in Politics/policy-reaction, and 8/12 in Student feedback/ Education review.

Furthermore, to understand the dataset diversity across samples and domains, we grouped the 1179 unique “study-dataset” pairs by “research-domain, dataset-domain, dataset-name” combinations and zoomed into the 757 entries with ten or more study counts each. As shown in Table  3 and illustrated in Fig.  4 , among these 757 unique combinations, 95.77% ( \(\textrm{N}=725\) ) are in the “non-specific” research domain, of which 90.48% ( \(\textrm{N}=656\) ) used “product/service review” datasets. Most interestingly, these 757 entries only involve 12 distinct datasets of which 10 were product and service reviews, and 78.20% ( \(\textrm{N}=592\) ) are taken up by the four SemEval datasets from the early pioneer work (Pontiki et al 2014 , 2015 , 2016 ) mentioned in Sect.  2.4 : SemEval 2014 Restaurant, SemEval 2014 Laptop (these two alone account for 50.33% of all 757 entries), SemEval 2016 Restaurant, and SemEval 2015 Restaurant. This finding echos (Xing et al 2020 ; Chebolu et al 2023 ): “The SemEval challenge datasets... are the most extensively used corpora for aspect-based sentiment analysis” (Chebolu et al 2023 , p.4). Meanwhile, the top dataset used under “product/service review” research and dataset domains is the original product review dataset created by the researchers. Chebolu et al ( 2023 ) and Wikipedia ( 2023 ) provides a detailed introduction to the SemEval datasets.

Number of studies per each research (application) domain (left), dataset domain (middle), and dataset (right) combination, filtered by datasets used by 10 or more in-scope studies ( \(\textrm{N}=757\) ). The three-way relationship highlights that not only did the majority of the sample studies with “non-specific” research domain use datasets from the ‘product/service review‘ domain, but their datasets were also dominated by only four SemEval datasets on two types of product and service reviews

It is noteworthy that among the 519 reviewed studies, 20 focused on cross-domain or domain-agnostic ABSA, and 19 of them did not have a specific research application domain. However, while all 20 studies used multiple datasets, 17 solely involved the “product/service review” domain category by using reviews of restaurants and different products, and 14 used at least one SemEval dataset. The only three studies that went beyond the “product/service review” dataset domain added in movie reviews, singer reviews, and generic tweets.

4.2 Results for RQ2

RQ2. What are the most common ABSA problem formulations via subtask combinations, and what proportion of ABSA studies only focus on a specific sub-task?

For RQ2, we examined the 13 recorded subtasks and 805 unique “study-subtask” pairs to identify the most explored ABSA subtasks and subtask combinations across the 519 reviewed studies. As shown in Fig.  5 a, 32.37% ( \(\textrm{N}=168\) ) of the studies developed full-ABSA solutions through the combination of AE and ASC, and a similar proportion (30.83%, \(\textrm{N}=160\) ) focused on ASC alone, usually formulating the research problem as contextualised sentiment analysis with given aspects and the full input text. Only 15.22% ( \(\textrm{N}=79\) ) of the studies solely explored the AE problem. This is consistent with the number of studies by individual subtasks shown in Fig.  5 b, where ASC is the most explored subtask, followed by AE and ACD.

Moreover, Fig.  6 reveals a small but noticeable rise in composite subtask ASTE since 2020 ( \(\textrm{N}=1\) , 5 and 10 in 2017, 2021, 2022) and a decline in ASC and AE around the same period. This could signify a problem formulation shift driven by deep-learning, especially multi-task learning methods for E2E ABSA. Our Phase-2 targeted review findings in Sect.  4.5 add more insights into this.

Number of studies by ABSA subtask

Distribution of unique “Study–ABSA subtask” pairs by publication year ( \(\textrm{N}=805\) ). This graph excludes the one 2023 study (extracted in October 2022) to avoid trend confusion

4.3 Results for RQ3

To answer RQ3, we examined the 519 in-scope studies along two dimensions, which we call “paradigm” and “approach”. We use “ paradigm ” to indicate whether a study employed techniques along the supervised-unsupervised dimension and other types, such as reinforcement learning. We classify non-machine-learning approaches under the “unsupervised” paradigm, as our focus is on dataset and resource dependency. By “ approach ”, we refer to the more specific type of techniques, such as deep learning (DL), traditional machine learning (traditional ML), linguistic rules (“rules” for short), syntactic features and relations (“syntactics” for short), lexicon lists or databases (“lexicon” for short), and ontology or knowledge-driven approaches (“ontology” for short).

Overall, the results suggest that our samples are dominated by fully- (60.89%) and partially-supervised (5.40%) ML methods that are more reliant on annotated datasets and prone to their impact. As to ABSA solution approaches, the sample shows that DL methods have rapidly overtaken traditional ML methods since 2017, particularly with the prevalent RNN family (55.91%) and its combination with the fast-surging attention mechanism (26.52%). Meanwhile, traditional ML and linguistic approaches have remained a small but steady force even in the most recent years. Context engineering through introducing linguistic and knowledge features to DL and traditional ML approaches was very common. More detailed results and richer findings are presented below.

4.3.1 Paradigms

Table  4 lists the number of studies per each of the main paradigms. Among the 519 reviewed studies, 66.28% ( \(\textrm{N}=344\) ) is taken up by those using somewhat- (i.e. fully-, semi- and weakly-) supervised paradigms that have varied levels of dependency on labelled datasets, where the fully-supervised ones alone account for 60.89% ( \(\textrm{N}=316\) ). Only 19.65% ( \(\textrm{N}=102\) ) of the studies do not require labelled data, which are mostly unsupervised (18.69%, \(\textrm{N}=97\) ). In addition, hybrid studies are the third largest group (14.07%, \(\textrm{N}=73\) ).

We further analysed the approaches under each paradigm and focused on three for more details: deep learning (DL), traditional machine learning (ML), and Linguistic and Statistical Approaches. The results are detailed below and presented in Fig.  7 and Tables  5 ,  6 .

Number of studies using DL and traditional ML approaches

4.4 Approaches

As shown in Fig.  7 a and Table  5 , among the 519 reviewed studies, 60.31% ( \(\textrm{N}=313\) ) employed DL approaches, and 30.83% ( \(\textrm{N}=160\) ) are DL-only. The DL-only approach is particularly prominent among fully-supervised (47.15%, \(\textrm{N}=149\) ) and semi-supervised (31.82%, \(\textrm{N}=7\) ) studies. Supplementing DL with syntactical features is also the second most popular approach in fully-supervised studies (16.77%, \(\textrm{N}=53\) ).

DL Approaches

Figure  7 a suggests that the 313 studies involving DL approaches are dominated by Recurrent Neural Network (RNN)-based solutions (55.91%, \(\textrm{N}=175\) ), of which nearly half used a combination of RNN and the attention mechanism (26.52%, \(\textrm{N}=83\) ), followed by attention-only (19.17%, \(\textrm{N}=60\) ) and RNN-only (9.90%, \(\textrm{N}=31\) ) models. The RNN family mainly consists of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU). These neural-networks are featured by sequential processing that captures temporal dependencies of text tokens, and can thus incorporate surrounding text as context for prediction (Liu et al 2020 ; Satyarthi and Sharma 2023 ). On the other hand, the sequential nature poses challenges with parallelisation and the exploding and vanishing gradient problems associated with long sequences (Vaswani et al 2017 ; Liu et al 2020 ). Although LSTM and GRU can mitigate these issues somewhat through cell state and memory controls, efficiency and long-dependency challenges still hinder their performance (Vaswani et al 2017 ; Liu et al 2020 ; Satyarthi and Sharma 2023 ). The attention mechanism complements RNNs by dynamically updating weights across the input sequence based on each element’s relevance to the current task, and thus guides the model to focus on the most relevant elements (Vaswani et al 2017 ).

In addition, convolutional and graph-neural approaches [e.g. convolutional neural networks (CNN), graph neural networks (GNN), graph convolutional networks (GCN)] also play smaller but noticeable roles in DL-based ABSA studies. While CNN was commonly used as an alternative to the sequence models such as RNNs (Liu et al 2020 ; J et al 2021 ; Zhang et al 2020 ), the graph-based networks (GNN, GCN) were mainly used to model the non-linear relationships such as external conceptual knowledge (e.g. Liang et al 2022 ) and syntactic dependency structures (e.g. Fei et al 2023a , b ; Li et al 2022c ) that are not well captured by the sequential networks like RNNs and the flat structure of the attention modules. As a result, they inject richer context into the overall learning process (Du et al 2021 ; Xu et al 2020a ; Wang et al 2022 ).

Figure  8 depicts the trend of the main approaches across the publication years. We excluded the one study pre-published for 2023 to avoid confusing trends. It is clear that DL approaches have risen sharply and taken dominance since 2017, mainly driven by the rapid growth in RNN- and attention-based studies. This coincides with the appearance of the Transformer architecture in 2017 (Vaswani et al 2017 ) and the resulting pre-trained models such as BERT (Devlin et al 2019 ) that were a popular embedding choice to be used alongside RNNs in DL and hybrid approaches (e.g., Li et al 2021 ; Zhang et al 2022a ). GNN/GCN-based approaches remain small in number but have noticeable growth since 2020 ( \(\textrm{N}=2\) , 2, 16, 24 in each of 2019–2022, respectively), suggesting an increased effort to dynamically integrate relational context into the learning process within the DL framework.

Distribution of studies per method by publication year ( \(\textrm{N}=1017\) with 519 unique studies). This graph excludes the one 2023 study (extracted in October 2022) to avoid trend confusion

Traditional ML approaches

Interestingly, as shown in Fig.  8 traditional ML approaches remain a steady force over the decades despite the rapid rise of DL methods. Table  5 and Fig.  7 b provide some insight into this: Among the 519 reviewed studies, while 60.31% employed DL approaches as mentioned in the previous sub-section, over half (54.53%, \(\textrm{N}=283\) ) also included traditional ML approaches, with the top 3 being Support Vector Machine (SVM; 20.14%, \(\textrm{N}=57\) ), Conditional Random Field (CRF; 14.49%, \(\textrm{N}=41\) ), and Latent Dirichlet allocation (LDA; 12.72%, \(\textrm{N}=36\) ). Table  5 suggests that among the major paradigms, traditional ML were often used in combination with DL approaches for fully-supervised studies (7.28%, \(\textrm{N}=23\) ), and along with linguistic rules, syntactic features, and/or lexicons and ontology in hybrid studies (27.40%, \(\textrm{N}=20\) ). Across all paradigms, traditional ML-only approaches are relatively rare (max \(\textrm{N}=7\) ).

Linguistic and statistical approaches

While Table  5 illustrates the prevalence of fusing ML approaches with linguistic and statistical features or modules, there were 67 studies (12.91% out of the total 519) on pure linguistic or statistical approaches. As shown in Table  6 , although small in number, these non-ML approaches have persisted over time. The most popular combination (34.33%, \(\textrm{N}=23\) ) was rules built on syntactic features (e.g. POS tags and dependency parse trees) and used along lexicon resources (e.g. domain-specific aspect lists, SentiWordNet, Footnote 3 MPQA Footnote 4 ). A typical example is using POS tags and/or lexicon resources to narrow the scope of aspect or opinion term candidates, applying further rules based on POS tags or dependency relations for AE or OE, and/or using lexicon resources for candidate pruning, categorisation, or sentiment labelling (e.g. Asghar et al 2019 ; Dragoni et al 2019 ; Nawaz et al 2020 ). The second top combination (23.88%, \(\textrm{N}=16\) ) is the above-mentioned one plus ontology (e.g. domain-specific ontology, ConceptNet, Footnote 5 WordNet Footnote 6 ) to bring in external knowledge of concepts and relations (e.g. Federici and Dragoni 2016 ; Marstawi et al 2017 ). Pure statistical methods were relatively rare (5.97%, \(\textrm{N}=4\) ), and mainly included frequency-based methods such as N-gram and TF-IDF, and other statistical modelling methods that were not commonly seen in the ML field.

4.5 ICL and generative approach in ABSA - Phase-2 targeted review results

ICL is a subgenre of the DL approach. However, we discuss the relevant results in this separate subsection due to the Phase-2 review’s more focused sample and finer granularity. Despite the trending popularity of the ICL approach in NLP research and applications since 2022 (Dong et al 2024 ), our results suggest that the ABSA research community is just beginning to explore it with caution. Among the 208 ABSA studies from 2008 to 2024 containing at least one occurrence of the “Gen-LLM” keywords, only five (all published in 2024) applied ICL to both composite and traditional ABSA tasks. All of these studies were exploring the performance of foundation models via ICL against other approaches, rather than focusing on an ICL ABSA solution. Table  7 summarises the models, ABSA tasks, and key findings from these studies. Overall, four of the five studies found that zero-shot and even 5-shot ICL on foundation models (mainly GPTs) could not reach the performance of fine-tuned or fully trained DL models, especially those leveraging pre-trained LLMs to fine-tune a contextual-embedding.

In addition, we identified an emerging trend by examining the Phase-2 review non-ICL samples: Those employing fine-tuned generative LLMs mostly formulated the ABSA tasks as Sequence-to-Sequence (Seq2Seq) text generation problems, with a particular focus on composite tasks such as ASTE and ASQE. As shown in Table  8 , within the 208 samples, a total of 18 studies (all from the new search) published in 2022–2024 applied pre-trained generative LLMs with fine-tuning. The majority of these studies used models based on T5 ( \(\textrm{N}=9\) ) and BART ( \(\textrm{N}=5\) ) with the full Transformer (Vaswani et al 2017 ) encoder-decoder architecture, followed by encoder-only ( \(\textrm{N}=3\) , BERT and RoBERTa Liu et al 2019 ) and decoder-only ( \(\textrm{N}=1\) , GPT-2 Radford et al 2019 ) models. All but two of these 18 studies were on composite ABSA tasks, mainly ASTE and ASQE. Moreover, two studies (Yu et al 2023b ; Zhang et al 2024b ) also leveraged the generative capability of these LLMs to augment training data to enrich the fine-tuned embedding.

Compared with this Seq2Seq generation approach, the common applications of pre-trained LLMs in earlier studies from the main SLR sample often formulate the ABSA task as a classification problem (Zhang et al 2022c ). These studies mostly use encoder-only LLMs for their pre-trained representations to fine-tune a contextual embedding (Zhang et al 2022c ), which is then connected to other context-injection or relationship-learning modules and a classifier output layer. For instance, Zhang et al ( 2022a ) employed pre-trained BERT with BiLSTM, a feed-forward neural network (FFNN), and CRF. Li et al ( 2021 ) used pre-trained BERT as an encoder and a decoder featuring a GRU. In contrast, the Seq2Seq generative approach can be illustrated by the signature “Generative Aspect-based Sentiment analysis (GAS)” proposed by Zhang et al ( 2021b ), which leveraged the LLM’s pre-trained and fine-tuned encoder module for context-aware embedding and used the fine-tuned decoder module to generate text representations of the label sets (e.g., triplets) or as annotations next to the original input text (Zhang et al 2021b , 2022c ).

5 Discussion

This review was motivated by the literature gap in capturing trends in ABSA research to answer higher-level questions beyond technical details, and the concern that the domain-dependent nature could predispose ABSA research to systemic hindrance from a combination of resource-reliant approaches and skewed resource domain distribution. By systematically reviewing the two waves of 727 in-scope primary studies published between 2008 and 2024, our quantitative analysis results identified trends in ABSA solution approaches, confirmed the above-mentioned concern, and provided detailed insights into the relevant issues. In this section, we examine the primary findings, share ideas for future research, and reflect on the limitations of this review.

5.1 Significant findings and trends

5.1.1 the out-of-sync research and dataset domains.

Under RQ1, we examined the distributions of and relationships between our sample’s research (application) domains and dataset domains. The results showed strong skewness in both types of domains and a significant mismatch between them: While the majority (65.32%, \(\textrm{N}=339\) ) of the 519 studies did not aim for a specific research domain, a greater proportion (70.95%, \(\textrm{N}=447\) ) used datasets from the “product/service review” domain. A closer inspection of the link between the two domains revealed a clear mismatch: Among the 757 unique “research-domain, dataset-domain, dataset-name” combinations with ten or more studies: 90.48% ( \(\textrm{N}=656\) ) of the studies in the “non-specific” research domain (95.77%, \(\textrm{N}=725\) ) used datasets from the “product/service review” domain. This suggests that the lack of non-commercial-domain datasets could have forced generic technical studies to use benchmark datasets from a single popular domain. Given ABSA problem’s domain-dependent nature, this could have indirectly hindered the solution development and evaluation across domains.

The results also showed that the other important and prevalent ABSA application domains such as education, medicine/healthcare, and public policy, were clearly under-researched and under-resourced. Among the reviewed samples from these three public-sector domains, about half of their datasets were created for the studies by their authors, indicating a lack of public dataset resources, hence the cost and challenge of developing ABSA research in these areas. As a likely consequence, even the most researched domain among these three had only 12 studies (2.31% out of 519) since 2008. The dataset resource scarcity in these public sector domains deserves more research community attention and support, especially given these domains’ overall low research resources vs. the high cost and domain knowledge required for quality data annotation. In particular, for domains such as “Student feedback/education review” that often face strict data privacy and consent restrictions, it is crucial that the ABSA research community focus on creating ethical and open-access datasets to leverage community resources.

5.1.2 The dominance and limitations of the SemEval datasets

The results under RQ1 also revealed further issues with dataset diversity, even within the dominant “product/service review” domain. Out of the 757 unique “research-domain, dataset-domain, dataset-name” combinations with ten or more studies, 78.20% ( \(\textrm{N}=592\) ) are taken up by the four popular SemEval datasets: The SemEval 2014 Restaurant and Laptop datasets alone account for 50.33% of all 757 entries, and the other two (SemEval 2015 and 2016 Restaurant).

The level of dominance of the SemEval datasets is alerting, not only because of their narrow domain range, but also for the inheritance and impact of the SemEval datasets’ limitations. Several studies (e.g. Chebolu et al 2023 ; Xing et al 2020 ; Jiang et al 2019 ; Fei et al 2023a ) suggest that these datasets fail to capture sufficient complexity and granularity of the real-world ABSA scenarios, as they primarily only include single-aspect or multi-aspect-but-same-polarity sentences, and thus mainly reflect sentence-level ABSA tasks and ignored subtasks such as multi-aspect multi-sentiment ABSA. The experiment results from Xing et al ( 2020 ), Jiang et al ( 2019 ) and Fei et al ( 2023a ) consistently showed that all 35 ABSA models (including those that were state-of-the-art at the time) (9 in Xing et al 2020 , 16 in Jiang et al 2019 , 10 in Fei et al 2023a ) that were trained and performed well on the SemEval 2014 ABSA datasets showed various extents of performance drop (by up to 69.73% in Xing et al 2020 ) when tested on same-source datasets created for more complex ABSA subtasks and robustness challenges. Given that the SemEval datasets are heavily used as both training data and “benchmark” to measure ABSA solution performance, their limitations and prevalence are likely to form a self-reinforcing loop that confines ABSA research. To break free from this dataset-performance self-reinforcing loop, it is critical that the ABSA research community be aware of this issue, and develop and adopt datasets and practices that are robustness-oriented, such as the automatic data-generation framework and the resulting Aspect Robustness Test Set (ARTS) developed by Xing et al ( 2020 ) for probing model robustness in distinguishing target and non-target aspects, and the Multi-Aspect Multi-Sentiment (MAMS) dataset created by Jiang et al ( 2019 ) to reflect more realistic challenges and complexities in aspect-term sentiment analysis (ATSA) and aspect-category sentiment analysis (ACSA) tasks.

5.1.3 The reliance on labelled-datasets

The domain and dataset issues discussed above would not be as problematic if most ABSA studies employed methods that are dataset-agnostic. However, our results under RQ3 show the opposite. Only 19.65% ( \(\textrm{N}=102\) , with 97 being unsupervised) of the 519 reviewed studies do not require labelled data, whereas 66.28% ( \(\textrm{N}=344\) ) are somewhat-supervised, and fully-supervised studies alone account for 60.89% ( \(\textrm{N}=316\) ).

As demonstrated in Sect.  2.3 , the domain can directly affect whether a chunk of text is considered an aspect or the relevant sentiment term, and plays a crucial role in contextual inferences such as implicit aspect extraction and multi-aspect multi-sentiment pairing. The domain knowledge reflected via ABSA labelled datasets can further shape the linguistic rules, lexicons, and knowledge graphs for non-machine-learning approaches; and define the underpinning feature space, representations, and acquired relationships and inferences for trained machine-learning models. When applying a solution built on datasets from a domain that is very remote from or much narrower than the intended application domain, it is predictable that the solution performance would be capped at subpar and even fail at more context-heavy tasks (Phan and Ogunbona 2020 ; You et al 2022 ; Liang et al 2022 ; Howard et al 2022 ; Chen and Qian 2022 ; Zhang et al 2022b ; Nazir et al 2022b ). Thus, domain transfer is crucially necessary for balancing the uneven ABSA research and resource distributions across domains. However, our finding that 17 out of the 20 reviewed cross-domain or domain-agnostic ABSA studies solely used datasets from the “product/service review” domain raised questions about these approaches’ generalisability and robustness in other domains, as well as whether such dataset choices became another reinforcement of concentrating research effort and benchmarks within this one dominant domain.

The rapid rise of deep learning (DL) in ABSA research could further add to the challenge of overcoming the negative impact of this domain mismatch and dataset limitations via the non-linear multi-layer dissemination of bias in the representation and learned relations, thus making problem-tracking and solution-targeting difficult. In reality, of the 519 reviewed studies, 60.31% ( \(\textrm{N}=313\) ) employed DL approaches, and nearly half (47.15%, \(\textrm{N}=149\) ) of the fully-supervised studies and 30.83% ( \(\textrm{N}=160\) ) of all reviewed studies were DL-only.

Moreover, RNN-based solutions dominate the DL approaches (55.91%, \(\textrm{N}=175\) ), mainly with the RNN-attention combination (26.52%, \(\textrm{N}=83\) ) and RNN-only (9.90%, \(\textrm{N}=31\) ) models. RNN and its variants such as LSTM, BiLSTM, and GRU are known for their limitations in capturing long-distance relations due to their sequential nature and the subsequent memory constraints (Vaswani et al 2017 ; Liu et al 2020 ). Although the addition of the attention mechanism enhances the model’s focus on more important features such as aspect terms (Vaswani et al 2017 ; Liu et al 2020 ), traditional attention weights calculation struggles with multi-word aspects or multi-aspect sentences (Liu et al 2020 ; Fan et al 2018 ). In addition, whilst 16.77% ( \(N=53\) ) of the fully-supervised studies introduced syntactical features to their DL solutions, additional features also increased the input size. According to Prather et al ( 2020 ), sequential models, even the state-of-the-art LLMs, showed impaired performance as the input grew longer and could not always benefit from additional features.

5.1.4 The potential of generative LLMs and foundation models

Lastly, the Phase-2 targeted review highlights the ABSA community’s caution toward the direct adoption of generative foundation models, with only five out of 208 recent studies testing the ICL approach and most yielding subpar results compared to other methods. However, most of these studies only tested zero-shot instructions with simple model settings. It is worth further exploring the potential of foundational models and ICL in ABSA by focusing more on instruction and example engineering, model parameter optimisation, and task re-formulation (Dong et al 2024 ).

On the other hand, the fine-tuning of smaller generative LLMs has seen increasing adoption through the “ABSA as Seq2Seq text generation” approach, demonstrating promising task performance. Although this generative approach can incorporate data augmentation and self-training to reduce reliance on labelled datasets, the cost of fine-tuning, the need for labelled base data, and the domain-transfer problem remain significant challenges (Zhang et al 2022c ). In this context, the task adaptability and multi-domain pre-trained knowledge of foundation models could provide potential solutions.

As Zhang et al ( 2022c ) noted, progress in applying pre-trained LLMs and foundation models to ABSA could be impeded by dataset resources constraints. To match the parameter size of these models, more diverse, complex, and larger datasets are required for effective fine-tuning or comprehensive testing. In low-resource domains where dataset resources are already limited, this requirement could further complicate the adoption of these technologies (Satyarthi and Sharma 2023 ).

5.2 Ideas for future research

Overall, by adopting a “systematic perspective, i.e., model, data, and training” (Fei et al 2023a ,  p.28) combined with a quantitative approach, we identified high-level trends unveiling the development and direction of ABSA research, and found clear evidence of large-scale issues that affect the majority of the existing ABSA research. The skewed domain distributions of resources and benchmarks could also restrict the choice of new studies. On the other hand, this evidence also highlights areas that need more attention and exploration, including: ABSA solutions and resource development for the less-studied domains (e.g. education and public health), low-resource and/or data-agnostic ABSA, domain adaptation, alternative training schemes such as adversarial (e.g. Fei et al 2023a ; Chen et al 2021 ) and reinforcement learning (e.g. Vasanthi et al 2022 ; Wang et al 2021b ), and more effective feature and knowledge injection. Future research could contribute to addressing these issues by focusing on ethically producing and sharing more diverse and challenging datasets in minority domains such as education and public health, improving data synthesis and augmentation techniques, exploring methods that are less data-dependent and resource-intensive, and leveraging the rapid advancements in pre-trained LLMs and foundation models.

In addition, our results also revealed emerging trends and new ideas. The relatively recent growth of end-to-end models and composite ABSA subtasks provide opportunities for further exploration and evaluation. The fact that hybrid approaches with non-machine-learning techniques and non-textual features remain steady forces in the field after nearly three decades suggests valuable characteristics that are worth re-examining under the light of new paradigms and techniques. Moreover, the small number of Phase-2 samples using ICL and fine-tuning generative LLM approaches may suggest that we have only captured early adopters. More thorough exploration of these approaches and continued tracking of their development alongside other methods are necessary to understand how the ABSA community can leverage the resources and capabilities embedded within LLMs and foundation models.

Lastly, it is crucial that the community invest in solution robustness, especially for machine-learning approaches (Xing et al 2020 ; Jiang et al 2019 ; Fei et al 2023a ). This could mean critical examination of the choice of evaluation metrics, tasks, and benchmarks, and being conscious of their limitations vs. the real-world challenges. The “State-Of-The-Art” (SOTA) performance based on certain benchmark datasets should never become the motivation and holy grail of research, especially in fields like ABSA where the real use cases are often complex and even SOTA models do not generalise far beyond the training datasets. More attention and effort should be paid to analysing the limitations and mistakes of ABSA solutions, and drawing from the ideas of other disciplines and areas to fill the gaps.

5.3 Limitations

We acknowledge the following limitations of this review: First, our sample scope is by no means exhaustive, as it only includes primary studies from four peer-reviewed digital databases and only those published in the English language. Although this can be representative of a core proportion of ABSA research, it does not generalise beyond this without assumptions. The “peer-reviewed” criteria also meant that we overlooked preprint servers such as arXiv.org that more closely track the latest development of ML and NLP research. Second, no search string is perfect. Our database search syntax and auto-screening keywords represent our best effort in capturing ABSA primary studies, but may have missed some relevant ones, especially with the artificial “total pages \(< 3\) ” and “total keyword (except SA, OM) outside Reference \(< 5\) ” exclusion criteria. Moreover, our search completeness might have been affected by the performance of the database search engines. This is evidenced by the significant number of extracted search results that were entirely irrelevant to the search keywords, as well as our abandonment of the 2024 SpringerLink search due to interface issues. Enhancements in digital database search capabilities could significantly improve the effectiveness and reliability of future literature review studies, particularly SLRs. Third, we may have missed datasets, paradigms, and approaches that are not clearly described in the primary studies, and our categorisation of them is also subject to the limitations of our knowledge and decisions. Future review studies could consider a more innovative approach to enhance analytical precision and efficiency, such as applying ABSA and text summarisation alongside the screening and reviewing process. Fourth, we did not compare solution performance across studies due to the review focus, sample size, and the variability in experimental settings across studies. Evaluating the effectiveness of comparable methods and the suitability of evaluation metrics would enhance our findings and offer more valuable insights.

6 Conclusion

ABSA research is riding the wave of the explosion of online digital opinionated text data and the rapid development of NLP resources and ideas. However, its context- and domain-dependent nature and the complexity and inter-relations among its subtasks pose challenges to improving ABSA solutions and applying them to a wider range of domains. In this review, we systematically examined existing ABSA literature in terms of their research application domain, dataset domain, and research methodologies. The results suggest a number of potential systemic issues in the ABSA research literature, including the predominance of the “product/service review” dataset domain among the majority of studies that did not have a specific research application domain, coupled with the prevalence of dataset-reliant methods such as supervised machine learning. We discussed the implication of these issues to ABSA research and applications, as well as their implicit effect in shaping the future of this research field through the mutual reinforcement between resources and methodologies. We suggested areas that need future research attention and proposed ideas for exploration.

Our PDF mining for automatic review screening code is available at https://doi.org/10.5281/zenodo.12872948 .

This new database search followed the same procedures and criteria as the SLR, except that we aborted the SpringerLink search due to persistent database interface search result navigation issues during our data collection period.

https://github.com/aesuli/SentiWordNet .

https://mpqa.cs.pitt.edu/ .

https://conceptnet.io/ .

https://wordnet.princeton.edu/ .

https://alt.qcri.org/semeval2014/task4/ .

https://alt.qcri.org/semeval2015/task12/ .

https://pypi.org/project/PyMuPDF/ .

https://pdfminersix.readthedocs.io/en/latest/faq.html .

Akhtar MS, Ekbal A, Bhattacharyya P (2016) Aspect based sentiment analysis in Hindi: Resource creation and evaluation. In: Calzolari N, Choukri K, Declerck T, et al (eds) Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). European Language Resources Association (ELRA), Portoroˇz, Slovenia, pp 2703–2709. https://aclanthology.org/L16-1429

Akhtar MS, Gupta D, Ekbal A et al (2017) Feature selection and ensemble construction: a two-step method for aspect based sentiment analysis. Knowl Based Syst 125:116–135. https://doi.org/10.1016/j.knosys.2017.03.020 . https://linkinghub.elsevier.com/retrieve/pii/S095070511730148X

Akhtar MS, Ekbal A, Bhattacharyya P (2018) Aspect based sentiment analysis: category detection and sentiment classification for Hindi. In: Gelbukh A (ed) Computational linguistics and intelligent text processing, vol 9624. Lecture Notes in Computer Science. Springer, Cham, pp 246–257. https://doi.org/10.1007/978-3-319-75487-1_19

Chapter   Google Scholar  

Akhtar MS, Garg T, Ekbal A (2020) Multi-task learning for aspect term extraction and aspect sentiment classification. Neurocomputing 398:247–256. https://doi.org/10.1016/j.neucom.2020.02.093 . https://linkinghub.elsevier.com/retrieve/pii/S0925231220302897

Almatrafi O, Johri A (2022) Improving MOOCs using information from discussion forums: an opinion summarization and suggestion mining approach. IEEE Access 10:15565–15573. https://doi.org/10.1109/ACCESS.2022.3149271 . https://ieeexplore.ieee.org/document/9706374/

Alyami S, Alhothali A, Jamal A (2022) Systematic literature review of Arabic aspect-based sentiment analysis. J King Saud Univ Comput Inf Sci 34(9):6524–6551. https://doi.org/10.1016/j.jksuci.2022.07.001 . https://linkinghub.elsevier.com/retrieve/pii/S1319157822002282

Amin MM, Mao R, Cambria E et al (2024) A wide evaluation of chatgpt on affective computing tasks. IEEE Trans Affect Comput 1–9. https://doi.org/10.1109/TAFFC.2024.3419593 . https://ieeexplore.ieee.org/document/10572294

Asghar MZ, Khan A, Zahra SR et al (2019) Aspect-based opinion mining framework using heuristic patterns. Cluster Comput 22(S3):7181–7199. https://doi.org/10.1007/s10586-017-1096-9

Article   Google Scholar  

Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari N, Choukri K, Maegaard B et al (eds) Proceedings of the seventh international conference on language resources and evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta. http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf

Bommasani R, Hudson DA, Adeli E et al (2022) On the opportunities and risks of foundation models. arXiv:2108.07258

Brauwers G, Frasincar F (2023) A survey on aspect-based sentiment classification. ACM Comput Surv 55(4):1–37. https://doi.org/10.1145/3503044

Brown TB, Mann B, Ryder N et al (2020) Language models are few-shot learners. arXiv:2005.14165

Cambria E, Poria S, Bajpai R et al (2016) SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: Matsumoto Y, Prasad R (eds) Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers. The COLING 2016 Organizing Committee, Osaka, Japan, pp 2666–2677. https://aclanthology.org/C16-1251

Castellanos M, Dayal U, Hsu M, et al (2011) LCI: A social channel analysis platform for live customer intelligence. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, SIGMOD’11. Association for Computing Machinery, New York, pp 1049–1058. https://doi.org/10.1145/1989323.1989436

Cavalcanti D, Prudêncio R (2017) Aspect-based opinion mining in drug reviews. In: Oliveira E, Gama J, Vale Z et al (eds) Progress in artificial intelligence, vol 10423. Lecture Notes in Computer Science. Springer, Cham, pp 815–827. https://doi.org/10.1007/978-3-319-65340-2_66

Chauhan GS, Agrawal P, Meena YK (2019) Aspect-based sentiment analysis of students’ feedback to improve teaching-learning process. In: Satapathy SC, Joshi A (eds) Information and communication technology for intelligent systems, vol 107. Smart Innovation, Systems and Technologies. Springer Singapore, Singapore, pp 259–266. https://doi.org/10.1007/978-981-13-1747-7_25

Chebolu SUS, Dernoncourt F, Lipka N et al (2023) Survey of aspect-based sentiment analysis datasets. arXiv:2204.05232

Chen S, Fnu G (2022) Deep learning techniques for aspect based sentiment analysis. In: 2022 14th International conference on computer research and development (ICCRD), pp 69–73. https://doi.org/10.1109/ICCRD54409.2022.9730443 . https://ieeexplore.ieee.org/document/9730443

Chen Z, Liu B (2014) Topic modeling using topics from many domains, lifelong learning and big data. In: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32. JMLR.org, ICML’14, p II–703–II–711. https://proceedings.mlr.press/v32/chenf14.html

Chen Z, Qian T (2022) Retrieve-and-edit domain adaptation for end2end aspect based sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process 30:659–672. https://doi.org/10.1109/TASLP.2022.3146052 . https://ieeexplore.ieee.org/document/9693267/

Chen M, Wu W, Zhang Y et al (2021) Combining adversarial training and relational graph attention network for aspect-based sentiment analysis with BERT. In: 2021 14th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI), pp 1–6. https://doi.org/10.1109/CISP-BMEI53629.2021.9624384 . https://ieeexplore.ieee.org/document/9624384

Chen F, Yang Z, Huang Y (2022) A multi-task learning framework for end-to-end aspect sentiment triplet extraction. Neurocomputing 479:12–21. https://doi.org/10.1016/j.neucom.2022.01.021 . https://linkinghub.elsevier.com/retrieve/pii/S0925231222000406

Chu JSG, Evans JA (2021) Slowed canonical progress in large fields of science. Proc Natl Acad Sci 118(41):e2021636118. https://doi.org/10.1073/pnas.2021636118 . https://pnas.org/doi/full/10.1073/pnas.2021636118

Cortis K, Davis B (2021) Over a decade of social opinion mining: a systematic review. Artif Intell Rev 54(7):4873–4965. https://doi.org/10.1007/s10462-021-10030-2

Cruz I, Gelbukh AF, Sidorov G (2014) Implicit aspect indicator extraction for aspect based opinion mining. Int J Comput Linguistics Appl 5(2):135–152. https://www.semanticscholar.org/paper/Implicit-Aspect-Indicator-Extraction-for-Aspect-Cruz-Gelbukh/8768fc3374b27c0ac023f5bf60da9ab50714b37e

Dang TV, Hao D, Nguyen N (2024) Vi-AbSQA: multi-task prompt instruction tuning model for Vietnamese aspect-based sentiment quadruple analysis. ACM Trans Asian Low-Resour Lang Inf Process. https://doi.org/10.1145/3676886 , just Accepted

Da’u A, Salim N, Rabiu I et al (2020) Weighted aspect-based opinion mining using deep learning for recommender system. Expert Syst Appl 140:112871. https://doi.org/10.1016/j.eswa.2019.112871 . https://linkinghub.elsevier.com/retrieve/pii/S0957417419305810

Devlin J, Chang M, Lee K et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2–7, 2019, volume 1 (long and short papers). Association for Computational Linguistics, pp 4171–4186. https://doi.org/10.18653/V1/N19-1423

Do HH, Prasad P, Maag A et al (2019) Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst Appl 118:272–299. https://doi.org/10.1016/j.eswa.2018.10.003 . https://www.sciencedirect.com/science/article/pii/S0957417418306456

Dong L, Wei F, Tan C, et al (2014) Adaptive recursive neural network for target-dependent Twitter sentiment classification. In: Toutanova K, Wu H (eds) Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, pp 49–54. https://doi.org/10.3115/v1/P14-2009 . https://aclanthology.org/P14-2009

Dong Q, Li L, Dai D et al (2024) A survey on in-context learning. arXiv:2301.00234

Dragoni M, Federici M, Rexha A (2019) An unsupervised aspect extraction strategy for monitoring real-time reviews stream. Inf Process Manag 56(3):1103–1118. https://doi.org/10.1016/j.ipm.2018.04.010 . https://linkinghub.elsevier.com/retrieve/pii/S0306457317305174

Du C, Wang J, Sun H et al (2021) Syntax-type-aware graph convolutional networks for natural language understanding. Appl Soft Computi 102:107080. https://doi.org/10.1016/j.asoc.2021.107080 . https://linkinghub.elsevier.com/retrieve/pii/S156849462100003X

Ettaleb M, Barhoumi A, Camelin N et al (2022) Evaluation of weakly-supervised methods for aspect extraction. Proc Comput Sci 207:2688–2697. https://doi.org/10.1016/j.procs.2022.09.327 . https://linkinghub.elsevier.com/retrieve/pii/S1877050922012169

Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Conference on empirical methods in natural language processing. https://api.semanticscholar.org/CorpusID:53080156

Federici M, Dragoni M (2016) A knowledge-based approach for aspect-based opinion mining. In: Sack H, Dietze S, Tordai A et al (eds) Semantic web challenges, vol 641. Communications in Computer and Information Science. Springer, Cham, pp 141–152. https://doi.org/10.1007/978-3-319-46565-4_11

Fei H, Chua TS, Li C et al (2023) On the robustness of aspect-based sentiment analysis: rethinking model, data, and training. ACM Trans Inf Syst 41(2):1–32. https://doi.org/10.1145/3564281 . https://dl.acm.org/doi/10.1145/3564281

Fei H, Ren Y, Zhang Y et al (2023) Nonautoregressive encoder-decoder neural framework for end-to-end aspect-based sentiment triplet extraction. IEEE Trans Neural Netw Learn Syst 34(9):5544–5556. https://doi.org/10.1109/TNNLS.2021.3129483 . https://ieeexplore.ieee.org/document/9634849/

Fernando J, Khodra ML, Septiandri AA (2019) Aspect and opinion terms extraction using double embeddings and attention mechanism for Indonesian hotel reviews. In: 2019 International conference of advanced informatics: concepts, theory and applications (ICAICTA). IEEE, Yogyakarta, pp 1–6. https://doi.org/10.1109/ICAICTA.2019.8904124 . https://ieeexplore.ieee.org/document/8904124/

FiQA (2018) Financial opinion mining and question answering. https://sites.google.com/view/fiqa/home

Freitas C, Motta E, Milidi´u RL, et al (2014) Sparkling vampire...lol! annotating opinions in a book review corpus. New language technologies and linguistic research: a two-way Road pp 128–146. https://www.researchgate.net/publication/271836545_Sparkling_Vampire_lol_Annotating_Opinions_in_a_Book_Review_Corpus

Fukumoto F, Sugiyama H, Suzuki Y et al (2016) Exploiting guest preferences with aspect-based sentiment analysis for hotel recommendation. In: Fred A, Dietz JL, Aveiro D et al (eds) Knowledge discovery, knowledge engineering and knowledge management, vol 631. Communications in Computer and Information Science. Springer, Cham, pp 31–46. https://doi.org/10.1007/978-3-319-52758-1_3

Ganganwar V, Rajalakshmi R (2019) Implicit aspect extraction for sentiment analysis: a survey of recent approaches. Proc Comput Sci 165:485–491. https://doi.org/10.1016/j.procs.2020.01.010 . https://www.sciencedirect.com/science/article/pii/S1877050920300181 , 2nd International Conference on Recent Trends in Advanced Computing ICRTAC-DISRUP - TIV INNOVATION, 2019 November 11–12, 2019

Ganu G, Elhadad N, Marian A (2009) Beyond the stars: improving rating predictions using review text content. In: International workshop on the web and databases. https://api.semanticscholar.org/CorpusID:18345070

García-Pablos A, Cuadros M, Rigau G (2018) W2vlda: Almost unsupervised system for aspect based sentiment analysis. Expert Syst Appl 91:127–137. https://doi.org/10.1016/j.eswa.2017.08.049 . https://linkinghub.elsevier.com/retrieve/pii/S0957417417305961

Go A (2009) Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1-12. https://api.semanticscholar.org/CorpusID:18635269

Gojali S, Khodra ML (2016) Aspect based sentiment analysis for review rating prediction. In: 2016 International conference on advanced informatics: concepts, theory and application (ICAICTA). IEEE, Penang, pp 1–6. https://doi.org/10.1109/ICAICTA.2016.7803110 . http://ieeexplore.ieee.org/document/7803110/

Gong Z, Li B (2022) Emotional text generation with hard constraints. In: 2022 4th International conference on frontiers technology of information and computer (ICFTIC), pp 68–73. https://doi.org/10.1109/ICFTIC57696.2022.10075091 . https://ieeexplore.ieee.org/document/10075091

Gui L, He Y (2021) Understanding patient reviews with minimum supervision. Artif Intell Med 120:102160. https://doi.org/10.1016/j.artmed.2021.102160 . https://www.sciencedirect.com/science/article/pii/S0933365721001536

Gunes O (2016) Aspect term and opinion target extraction from web product reviews using semi-Markov conditional random fields with word embeddings as features. In: Proceedings of the 6th international conference on web intelligence, mining and semantics. ACM, Nìmes, pp 1–5. https://doi.org/10.1145/2912845.2936809

Guo L, Jiang S, Du W et al (2018) Recurrent neural CRF for aspect term extraction with dependency transmission. In: Zhang M, Ng V, Zhao D et al (eds) Natural language processing and Chinese computing, vol 11108. Lecture Notes in Computer Science. Springer, Cham, pp 378–390. https://doi.org/10.1007/978-3-319-99495-6_32

He R, McAuley J (2016) Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, WWW ’16, p 507–517. https://doi.org/10.1145/2872427.2883037

He R, Lee WS, Ng HT et al (2019) An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, pp 504–515. https://doi.org/10.18653/v1/P19-1048 . https://www.aclweb.org/anthology/P19-1048

Hoang CD, Dinh QV, Tran NH (2022) Aspect-category-opinion-sentiment extraction using generative transformer model. In: 2022 RIVF international conference on computing and communication technologies (RIVF), pp 1–6. https://doi.org/10.1109/RIVF55975.2022.10013820 . https://ieeexplore.ieee.org/document/10013820

Hoti MH, Ajdari J, Hamiti M et al (2022) Text mining, clustering and sentiment analysis: a systematic literature review. In: 2022 11th Mediterranean conference on embedded computing (MECO). IEEE, Budva, Montenegro, pp 1–6. https://doi.org/10.1109/MECO55406.2022.9797203 . https://ieeexplore.ieee.org/document/9797203/

Howard P, Ma A, Lal V et al (2022) Cross-domain aspect extraction using transformers augmented with knowledge graphs. In: Proceedings of the 31st ACM international conference on information & knowledge management. ACM, Atlanta, pp 780–790. https://doi.org/10.1145/3511808.3557275

Huan H, He Z, Xie Y et al (2022) A multi-task dual-encoder framework for aspect sentiment triplet extraction. IEEE Access 10:103187–103199. https://doi.org/10.1109/ACCESS.2022.3210180 . https://ieeexplore.ieee.org/document/9903619/

Hu M, Liu B (2004a) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, Seattle, pp 168–177. https://doi.org/10.1145/1014052.1014073

Hu M, Liu B (2004b) Mining opinion features in customer reviews. In: AAAI conference on artificial intelligence. https://api.semanticscholar.org/CorpusID:5724860

J AK, Trueman TE, Cambria E (2021) A convolutional stacked bidirectional LSTM with a multiplicative attention mechanism for aspect category and sentiment detection. Cogn Comput 13(6):1423–1432. https://doi.org/10.1007/s12559-021-09948-0

Jiang Q, Chen L, Xu R et al (2019) A challenge dataset and effective models for aspect-based sentiment analysis. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, pp 6279–6284. https://doi.org/10.18653/v1/D19-1654 . https://www.aclweb.org/anthology/D19-1654

Jo Y, Oh AH (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, New York, NY, USA, WSDM ’11, p 815–824. https://doi.org/10.1145/1935826.1935932

Kang T, Kim S, Yun H et al (2022) Gated relational encoder-decoder model for target-oriented opinion word extraction. IEEE Access 10:130507–130517. https://doi.org/10.1109/ACCESS.2022.3228835 . https://ieeexplore.ieee.org/document/9982601

Kitchenham BA, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf . Backup Publisher: Keele University and Durham University Joint Report

Krishnakumari K, Sivasankar E (2018) Scalable aspect-based summarization in the hadoop environment. In: Aggarwal VB, Bhatnagar V, Mishra DK (eds) Big data analytics, vol 654. Advances in Intelligent Systems and Computing. Springer Singapore, Singapore, pp 439–449. https://doi.org/10.1007/978-981-10-6620-7_42

Kumar A, Gupta D (2021) Sentiment analysis as a restricted NLP problem:. In: Pinarbasi F, Taskiran MN (eds) Advances in business information systems and analytics. IGI Global, pp 65–96. https://doi.org/10.4018/978-1-7998-4240-8.ch004 . http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-4240-8.ch004

Kumar A, Seth S, Gupta S et al (2022) Sentic computing for aspect-based opinion summarization using multi-head attention with feature pooled pointer generator network. Cogn Comput 14(1):130–148. https://doi.org/10.1007/s12559-021-09835-8

Lee SK, Kim JH (2023) Sener: Sentiment element named entity recognition for aspect-based sentiment analysis. In: ICASSP 2023—2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1–5. https://doi.org/10.1109/ICASSP49357.2023.10095101 . https://ieeexplore.ieee.org/document/10095101

Lee C, Lee H, Kim K et al (2024) An efficient fine-tuning of generative language model for aspect-based sentiment analysis. In: 2024 IEEE international conference on consumer electronics (ICCE), pp 1–4. https://doi.org/10.1109/ICCE59016.2024.10444216 . https://ieeexplore.ieee.org/document/10444216

Lewis M, Liu Y, Goyal N et al (2020) BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Jurafsky D, Chai J, Schluter N et al (eds) Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Online, pp 7871–7880. https://doi.org/10.18653/v1/2020.acl-main.703 . https://aclanthology.org/2020.acl-main.703

Li X, Wang B, Li L et al (2020) Deep2s: Improving aspect extraction in opinion mining with deep semantic representation. IEEE Access 8:104026–104038. https://doi.org/10.1109/ACCESS.2020.2999673 . https://ieeexplore.ieee.org/document/9107147/

Li Z, Li L, Zhou A et al (2021) JTSG: A joint term-sentiment generator for aspect-based sentiment analysis. Neurocomputing 459:1–9. https://doi.org/10.1016/j.neucom.2021.06.045 . https://www.sciencedirect.com/science/article/pii/S0925231221009693

Li J, Zhao Y, Jin Z et al (2022a) SK2: Integrating implicit sentiment knowledge and explicit syntax knowledge for aspect-based sentiment analysis. In: Proceedings of the 31st ACM international conference on information & knowledge management. ACM, Atlanta, pp 1114–1123. https://doi.org/10.1145/3511808.3557452

Li Y, Lin Y, Lin Y et al (2022) A span-sharing joint extraction framework for harvesting aspect sentiment triplets. Knowl Based Syst 242:108366. https://doi.org/10.1016/j.knosys.2022.108366 . https://linkinghub.elsevier.com/retrieve/pii/S0950705122001381

Li Y, Wang C, Lin Y et al (2022) Span-based relational graph transformer network for aspect-opinion pair extraction. Knowl Inf Syst 64(5):1305–1322. https://doi.org/10.1007/s10115-022-01675-8

Li S, Zhang Y, Lan Y et al (2023) From implicit to explicit: a simple generative method for aspect-category-opinion-sentiment quadruple extraction. In: 2023 international joint conference on neural networks (IJCNN), pp 1–8. https://doi.org/10.1109/IJCNN54540.2023.10191098 . https://ieeexplore.ieee.org/document/10191098

Liang B, Su H, Gui L et al (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl Based Syst 235:107643. https://doi.org/10.1016/j.knosys.2021.107643 . https://linkinghub.elsevier.com/retrieve/pii/S0950705121009059

Ligthart A, Catal C, Tekinerdogan B (2021) Systematic reviews in sentiment analysis: a tertiary study. Artif Intell Rev 54(7):4997–5053. https://doi.org/10.1007/s10462-021-09973-3

Lil Z, Yang Z, Li X et al (2023) Two-stage aspect sentiment quadruple prediction based on MRC and text generation. In: 2023 IEEE International conference on systems, man, and cybernetics (SMC), pp 2118–2125. https://doi.org/10.1109/SMC53992.2023.10394369 . https://ieeexplore-ieee-org.ezproxy.auckland.ac.nz/document/10394369

Lim KW, Buntine W (2014) Twitter opinion topic model: extracting product opinions from tweets by leveraging hashtags and sentiment lexicon. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, Shanghai, pp 1319–1328. https://doi.org/10.1145/2661829.2662005

Lin B, Cassee N, Serebrenik A et al (2022) Opinion mining for software development: a systematic literature review. ACM Trans Softw Eng Methodol 31(3):1–41. https://doi.org/10.1145/3490388

Liu Q, Gao Z, Liu B, et al (2015) Automated rule selection for aspect extraction in opinion mining. In: Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, IJCAI' 15, p 1291–1297. https://doi.org/10.5555/2832415.2832429

Liu Y, Ott M, Goyal N et al (2019) Roberta: a robustly optimized BERT pretraining approach. arXiv:1907.11692

Liu H, Chatterjee I, Zhou M et al (2020) Aspect-based sentiment analysis: a survey of deep learning methods. IEEE Trans Comput Soc Syst 7(6):1358–1375. https://doi.org/10.1109/TCSS.2020.3033302 . https://ieeexplore.ieee.org/document/9260162/

Liu J, Chen T, Guo H et al (2024) Exploiting duality in aspect sentiment triplet extraction with sequential prompting. IEEE Trans Knowl Data Eng 1–12. https://doi.org/10.1109/TKDE.2024.3391381 . https://ieeexplore.ieee.org/document/10505831

López D, Arco L (2019) Multi-domain aspect extraction based on deep and lifelong learning. In: Nyström I, Hernández Heredia Y, Milián Núñez V (eds) Progress in pattern recognition, image analysis, computer vision, and applications, vol 11896. Lecture Notes in Computer Science. Springer, Cham, pp 556–565. https://doi.org/10.1007/978-3-030-33904-3_52

Luo H, Li T, Liu B et al (2019) Improving aspect term extraction with bidirectional dependency tree representation. IEEE/ACM Trans Audio Speech Lang Process 27(7):1201–1212. https://doi.org/10.1109/TASLP.2019.2913094 . https://ieeexplore.ieee.org/document/8698340/

Ma Y, Chen G, Wei Q (2017) Finding users preferences from large-scale online reviews for personalized recommendation. Electron Commer Res 17(1):3–29. https://doi.org/10.1007/s10660-016-9240-9

Maitama JZ, Idris N, Abdi A et al (2020) A systematic review on implicit and explicit aspect extraction in sentiment analysis. IEEE Access 8:194166–194191. https://doi.org/10.1109/ACCESS.2020.3031217 . https://ieeexplore.ieee.org/document/9234464/

Manning CD (2022) Human language understanding & reasoning. Daedalus 151(2):127–138. https://doi.org/10.1162/daed_a_01905 . https://direct.mit.edu/daed/article/151/2/127/110621/Human-Language-Understanding-amp-Reasoning

Marstawi A, Sharef NM, Aris TNM, et al (2017) Ontology-based aspect extraction for an improved sentiment analysis in summarization of product reviews. In: Proceedings of the 8th international conference on computer modeling and simulation, ICCMS’17. Association for Computing Machinery, New York, pp 100–104. https://doi.org/10.1145/3036331.3036362

McAuley J, Leskovec J, Jurafsky D (2012) Learning attitudes and attributes from multi-aspect reviews. In: Proceedings of the 2012 IEEE 12th International Conference on Data Mining. IEEE Computer Society, USA, ICDM ’12, p 1020–1025. https://doi.org/10.1109/ICDM.2012.110

McAuley J, Targett C, Shi Q, Van Den Hengel A (2015) Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval Association for Computing Machinery, New York, NY, USA, SIGIR'15, p 43–52. https://doi.org/10.1145/2766462.2767755

Mitchell M, Aguilar J, Wilson T, et al (2013) Open domain targeted sentiment. In: Yarowsky D, Baldwin T, Korhonen A, et al (eds) Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Seattle, Washington, USA, pp 1643–1654. https://aclanthology.org/D13-1171

Mughal N, Mujtaba G, Shaikh S et al (2024) Comparative analysis of deep natural networks and large language models for aspect-based sentiment analysis. IEEE Access 12:60943–60959. https://doi.org/10.1109/ACCESS.2024.3386969 . https://ieeexplore.ieee.org/document/10504711

Nawaz A, Awan AA, Ali T et al (2020) Product’s behaviour recommendations using free text: an aspect based sentiment analysis approach. Clust Comput 23(2):1267–1279. https://doi.org/10.1007/s10586-019-02995-1

Nazir A, Rao Y (2022) IAOTP: An interactive end-to-end solution for aspect-opinion term pairs extraction. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. ACM, Madrid, pp 1588–1598. https://doi.org/10.1145/3477495.3532085

Nazir A, Rao Y, Wu L et al (2022) IAF-LG: an interactive attention fusion network with local and global perspective for aspect-based sentiment analysis. IEEE Trans Affect Comput 13(4):1730–1742. https://doi.org/10.1109/TAFFC.2022.3208216 . https://ieeexplore.ieee.org/document/9896931/

Nazir A, Rao Y, Wu L et al (2022) Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans Affect Comput 13(2):845–863. https://doi.org/10.1109/TAFFC.2020.2970399 . https://ieeexplore.ieee.org/document/8976252/

Obiedat R, Al-Darras D, Alzaghoul E et al (2021) Arabic aspect-based sentiment analysis: a systematic literature review. IEEE Access 9:152628–152645. https://doi.org/10.1109/ACCESS.2021.3127140 . https://ieeexplore.ieee.org/document/9611271/

OpenAI (2023) Chatgpt (mar 14 version) [large language model]. https://chat.openai.com/chat

Pathan AF, Prakash C (2022) Cross-domain aspect detection and categorization using machine learning for aspect-based opinion mining. Int J Inf Manag Data Insights 2(2):100099. https://doi.org/10.1016/j.jjimei.2022.100099 . https://www.sciencedirect.com/science/article/pii/S2667096822000428

Peng H, Ma Y, Li Y, et al (2018) Learning multi-grained aspect target sequence for chinese sentiment analysis. Knowledge-Based Syst 148:167–176. https://doi.org/10.1016/j.knosys.2018.02.034 . https://www.sciencedirect.com/science/article/pii/S0950705118300972

Phan MH, Ogunbona PO (2020) Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Online, pp 3211–3220. https://doi.org/10.18653/v1/2020.acl-main.293 . https://www.aclweb.org/anthology/2020.acl-main.293

Pontiki M, Galanis D, Pavlopoulos J et al (2014) SemEval-2014 task 4: aspect based sentiment analysis. In: Nakov P, Zesch T (eds) Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014). Association for Computational Linguistics, Dublin, pp 27–35. https://doi.org/10.3115/v1/S14-2004 . https://aclanthology.org/S14-2004

Pontiki M, Galanis D, Papageorgiou H et al (2015) SemEval-2015 task 12: aspect based sentiment analysis. In: Nakov P, Zesch T, Cer D et al (eds) Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015). Association for Computational Linguistics, Denver, pp 486–495. https://doi.org/10.18653/v1/S15-2082 . https://aclanthology.org/S15-2082

Pontiki M, Galanis D, Papageorgiou H et al (2016) SemEval-2016 task 5: aspect based sentiment analysis. In: Bethard S, Carpuat M, Cer D et al (eds) Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016). Association for Computational Linguistics, San Diego, pp 19–30. https://doi.org/10.18653/v1/S16-1002 . https://aclanthology.org/S16-1002

Poria S, Chaturvedi I, Cambria E et al (2016) Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. In: 2016 International joint conference on neural networks (IJCNN). IEEE, Vancouver, pp 4465–4473. https://doi.org/10.1109/IJCNN.2016.7727784 . http://ieeexplore.ieee.org/document/7727784/

Prather J, Becker BA, Craig M et al (2020) What do we think we think we are doing?: Metacognition and self-regulation in programming. In: Proceedings of the 2020 ACM conference on international computing education research. ACM, Virtual Event New Zealand, pp 2–13. https://doi.org/10.1145/3372782.3406263

Presannakumar K, Mohamed A (2021) An enhanced method for review mining using n-gram approaches. In: Raj JS, Iliyasu AM, Bestak R et al (eds) Innovative data communication technologies and application, vol 59. Lecture Notes on Data Engineering and Communications Technologies. Springer Singapore, Singapore, pp 615–626. https://doi.org/10.1007/978-981-15-9651-3_51

Radford A, Wu J, Child R et al (2019) Language models are unsupervised multitask learners. https://api.semanticscholar.org/CorpusID:160025533

Raffel C, Shazeer N, Roberts A et al (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(1). https://dl.acm.org/doi/abs/10.5555/3455716.3455856

Rahman MA, Kumar Dey E (2018) Datasets for aspect-based sentiment analysis in bangla and its baseline evaluation. Data 3(2). https://doi.org/10.3390/data3020015 . https://www.mdpi.com/2306-5729/3/2/15

Rana TA, Cheah YN (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46:459–483. https://api.semanticscholar.org/CorpusID:24401592

Rani S, Kumar P (2019) A journey of Indian languages over sentiment analysis: a systematic review. Artif Intell Rev 52(2):1415–1462. https://doi.org/10.1007/s10462-018-9670-y

Ruskanda FZ, Widyantoro DH, Purwarianti A (2019) Sequential covering rule learning for language rule-based aspect extraction. In: 2019 International conference on advanced computer science and information systems (ICACSIS). IEEE, Bali, pp 229–234. https://doi.org/10.1109/ICACSIS47736.2019.8979743 . https://ieeexplore.ieee.org/document/8979743/

Sabeeh A, Dewang RK (2019) Comparison, classification and survey of aspect based sentiment analysis. In: Luhach AK, Singh D, Hsiung PA et al (eds) Advanced informatics for computing research. Springer Singapore, Singapore, pp 612–629. https://doi.org/10.1007/978-981-13-3140-4_55

Saeidi M, Bouchard G, Liakata M, et al (2016) SentiHood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods. In: Matsumoto Y, Prasad R (eds) Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, Osaka, Japan, pp 1546–1556. https://aclanthology.org/C16-1146

Sanders NJ (2011) Sanders-twitter sentiment corpus. Sanders Analytics LLC

Satyarthi S, Sharma S (2023) Identification of effective deep learning approaches for classifying sentiments at aspect level in different domain. In: 2023 IEEE International conference on paradigm shift in information technologies with innovative applications in global scenario (ICPSITIAGS), pp 496–508. https://doi.org/10.1109/ICPSITIAGS59213.2023.10527695 . https://ieeexplore-ieee-org.ezproxy.auckland.ac.nz/document/10527695

Sharma A, Shekhar H (2020) Intelligent learning based opinion mining model for governmental decision making. Proc Comput Sci 173:216–224. https://doi.org/10.1016/j.procs.2020.06.026 . https://linkinghub.elsevier.com/retrieve/pii/S1877050920315301

Socher R, Perelygin A, Wu J, et al (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Yarowsky D, Baldwin T, Korhonen A, et al (eds) Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Seattle, Washington, USA, pp 1631–1642. https://aclanthology.org/D13-1170

Soni PK, Rambola R (2022) A survey on implicit aspect detection for sentiment analysis: terminology, issues, and scope. IEEE Access 10:63932–63957. https://doi.org/10.1109/ACCESS.2022.3183205 . https://ieeexplore.ieee.org/document/9796523

Suchrady RZ, Purwarianti A (2023) Indo LEGO-ABSA: a multitask generative aspect based sentiment analysis for Indonesian language. In: 2023 International conference on electrical engineering and informatics (ICEEI), pp 1–6. https://doi.org/10.1109/ICEEI59426.2023.10346852 . https://ieeexplore-ieee-org.ezproxy.auckland.ac.nz/document/10346852

Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th international conference on neural information processing systems, NIPS’14, vol 2. MIT Press, Montreal, pp 3104–3112

Su H, Wang X, Li J et al (2024) Enhanced implicit sentiment understanding with prototype learning and demonstration for aspect-based sentiment analysis. IEEE Trans Comput Soc Syst 1–16. https://doi.org/10.1109/TCSS.2024.3368171 . https://ieeexplore-ieee-org.ezproxy.auckland.ac.nz/document/10584152

Team TPD (2023) pandas-dev/pandas: Pandas. https://doi.org/10.5281/ZENODO.3509134 . https://zenodo.org/record/3509134

Toprak C, Jakob N, Gurevych I (2010) Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, USA, ACL ’10, p 575–584. https://doi.org/10.5555/1858681.1858740

Tran TU, Hoang HTT, Huynh HX (2020) Bidirectional independently long short-term memory and conditional random field integrated model for aspect extraction in sentiment analysis. In: Satapathy SC, Bhateja V, Nguyen BL et al (eds) Frontiers in intelligent computing: theory and applications, vol 1014. Advances in Intelligent Systems and Computing. Springer Singapore, Singapore, pp 131–140. https://doi.org/10.1007/978-981-13-9920-6_14

Tubishat M, Idris N, Abushariah M (2021) Explicit aspects extraction in sentiment analysis using optimal rules combination. Futur Gener Comput Syst 114:448–480. https://doi.org/10.1016/j.future.2020.08.019 . https://linkinghub.elsevier.com/retrieve/pii/S0167739X1933081X

Vasanthi A, Kumar H, Karanraj R (2022) An RL approach for ABSA using transformers. In: 2022 6th International conference on trends in electronics and informatics (ICOEI), pp 354–361. https://doi.org/10.1109/ICOEI53556.2022.9776915 . https://ieeexplore.ieee.org/document/9776915

Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, NIPS’17. Curran Associates Inc., Red Hook, pp 6000–6010. https://doi.org/10.5555/3295222.3295349

Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, KDD ’10, p 783–792. https://doi.org/10.1145/1835804.1835903

Wang H, Lu Y, Zhai C (2011) Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, KDD ’11, p 618–626. https://doi.org/10.1145/2020408.2020505

Wang Y, Huang Y, Wang M (2017) Aspect-based rating prediction on reviews using sentiment strength analysis. In: Benferhat S, Tabia K, Ali M (eds) Advances in artificial intelligence: from theory to practice, vol 10351. Lecture Notes in Computer Science. Springer, Cham, pp 439–447. https://doi.org/10.1007/978-3-319-60045-1_45

Wang W, Pan SJ, Dahlmeier D (2018) Memory networks for fine-grained opinion mining. Artif Intell 265:1–17. https://doi.org/10.1016/j.artint.2018.09.002 . https://linkinghub.elsevier.com/retrieve/pii/S000437021830599X

Wang J, Xu B, Zu Y (2021a) Deep learning for aspect-based sentiment analysis. In: 2021 International conference on machine learning and intelligent systems engineering (MLISE), pp 267–271. https://doi.org/10.1109/MLISE54096.2021.00056 . https://ieeexplore.ieee.org/document/9611705

Wang L, Zong B, Liu Y et al (2021b) Aspect-based sentiment classification via reinforcement learning. In: 2021 IEEE international conference on data mining (ICDM), pp 1391–1396. https://doi.org/10.1109/ICDM51629.2021.00177 . https://ieeexplore.ieee.org/document/9679112

Wang X, Liu P, Zhu Z et al (2022) Interactive double graph convolutional networks for aspect-based sentiment analysis. In: 2022 International joint conference on neural networks (IJCNN). IEEE, Padua, Italy, pp 1–7. https://doi.org/10.1109/IJCNN55064.2022.9892934 . https://ieeexplore.ieee.org/document/9892934/

Wang Z, Xia R, Yu J (2024) Unified ABSA via annotation-decoupled multi-task instruction tuning. IEEE Trans Knowl Data Eng 1–13. https://doi.org/10.1109/TKDE.2024.3392836 . https://ieeexplore.ieee.org/document/10507027

Wankhade M, Rao ACS, Kulkarni C (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intel Rev 55(7):5731–5780. https://doi.org/10.1007/s10462-022-10144-1

Wikipedia (2023) SemEval. https://en.wikipedia.org/wiki/SemEval

William, Khodra ML (2022) Generative opinion triplet extraction using pretrained language model. In: 2022 9th International conference on advanced informatics: concepts, theory and applications (ICAICTA), pp 1–6. https://doi.org/10.1109/ICAICTA56449.2022.9933004 . https://ieeexplore.ieee.org/document/9933004

Wu S, Fei H, Ren Y et al (2021) High-order pair-wise aspect and opinion terms extraction with edge-enhanced syntactic graph convolution. IEEE/ACM Trans Audio Speech Lang Process 29:2396–2406. https://doi.org/10.1109/TASLP.2021.3095672 . https://ieeexplore.ieee.org/document/9478183/

Xing X, Jin Z, Jin D et al (2020) Tasty burgers, soggy fries: probing aspect robustness in aspect-based sentiment analysis. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics, Online, pp 3594–3605. https://doi.org/10.18653/v1/2020.emnlp-main.292 . https://www.aclweb.org/anthology/2020.emnlp-main.292

Xu K, Zhao H, Liu T (2020) Aspect-specific heterogeneous graph convolutional network for aspect-based sentiment classification. IEEE Access 8:139346–139355. https://doi.org/10.1109/ACCESS.2020.3012637 . https://ieeexplore.ieee.org/document/9152016/

Xu Q, Zhu L, Dai T et al (2020) Non-negative matrix factorization for implicit aspect identification. J Ambient Intell Humaniz Comput 11(7):2683–2699. https://doi.org/10.1007/s12652-019-01328-9

Yan K, Tang L, Wu M et al (2023) Aspect-based sentiment analysis method using text generation. In: Proceedings of the 2023 7th international conference on big data and internet of things, BDIOT’23. Association for Computing Machinery, New York, pp 156–161. https://doi.org/10.1145/3617695.3617709

Yauris K, Khodra ML (2017) Aspect-based summarization for game review using double propagation. In: 2017 International conference on advanced informatics, concepts, theory, and applications (ICAICTA). IEEE, Denpasar, pp 1–6. https://doi.org/10.1109/ICAICTA.2017.8090997 . http://ieeexplore.ieee.org/document/8090997/

You L, Han F, Peng J et al (2022) ASK-RoBERTa: a pretraining model for aspect-based sentiment classification via sentiment knowledge mining. Knowl Based Syst 253:109511. https://doi.org/10.1016/j.knosys.2022.109511 . https://linkinghub.elsevier.com/retrieve/pii/S0950705122007584

Yu C, Wu T, Li J et al (2023a) Syngen: A syntactic plug-and-play module for generative aspect-based sentiment analysis. In: ICASSP 2023–2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1–5. https://doi.org/10.1109/ICASSP49357.2023.10094591 . https://ieeexplore.ieee.org/document/10094591

Yu Y, Zhao M, Zhou S (2023b) Boosting aspect sentiment quad prediction by data augmentation and self-training. In: 2023 International joint conference on neural networks (IJCNN), pp 1–8. https://doi.org/10.1109/IJCNN54540.2023.10191634 . https://ieeexplore.ieee.org/document/10191634

Zarindast A, Sharma A, Wood J (2021) Application of text mining in smart lighting literature—an analysis of existing literature and a research agenda. Int J Inf Manag Data Insights 1(2):100032. https://doi.org/10.1016/j.jjimei.2021.100032 . https://linkinghub.elsevier.com/retrieve/pii/S2667096821000252

Zhang Y, Xu B, Zhao T (2020) Convolutional multi-head self-attention on memory for aspect sentiment classification. IEEE/CAA J Automatica Sinica 7(4):1038–1044. https://doi.org/10.1109/JAS.2020.1003243 . https://ieeexplore.ieee.org/document/9128078/

Zhang W, Deng Y, Li X et al (2021a) Aspect sentiment quad prediction as paraphrase generation. In: Moens MF, Huang X, Specia L et al (eds) Proceedings of the 2021 conference on empirical methods in natural language processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, pp 9209–9219. https://doi.org/10.18653/v1/2021.emnlp-main.726 . https://aclanthology.org/2021.emnlp-main.726

Zhang W, Li X, Deng Y et al (2021b) Towards generative aspect-based sentiment analysis. In: Zong C, Xia F, Li W et al (eds) Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 2: short papers). Association for Computational Linguistics, Online, pp 504–510. https://doi.org/10.18653/v1/2021.acl-short.64 . https://aclanthology.org/2021.acl-short.64

Zhang H, Chen Z, Chen B et al (2022) Complete quadruple extraction using a two-stage neural model for aspect-based sentiment analysis. Neurocomputing 492:452–463. https://doi.org/10.1016/j.neucom.2022.04.027 . https://www.sciencedirect.com/science/article/pii/S0925231222003939

Zhang W, Li X, Deng Y et al (2022b) A survey on aspect-based sentiment analysis: tasks, methods, and challenges. arXiv:2203.01054

Zhang W, Li X, Deng Y et al (2022) A survey on aspect-based sentiment analysis: tasks, methods, and challenges. IEEE Trans on Knowl and Data Eng 35(11):11019–11038. https://doi.org/10.1109/TKDE.2022.3230975

Zhang X, Xu J, Cai Y et al (2023) Detecting dependency-related sentiment features for aspect-level sentiment classification. IEEE Trans Affect Comput 14(1):196–210. https://doi.org/10.1109/TAFFC.2021.3063259 . https://ieeexplore.ieee.org/document/9368987/

Zhang W, Zhang X, Cui S, et al (2024a) Adaptive data augmentation for aspect sentiment quad prediction. In: ICASSP 2024—2024 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 11176–11180. https://doi.org/10.1109/ICASSP48485.2024.10447700

Zhang W, Zhang X, Cui S et al (2024b) Adaptive data augmentation for aspect sentiment quad prediction. In: ICASSP 2024—2024 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 11176–11180. https://doi.org/10.1109/ICASSP48485.2024.10447700 . https://ieeexplore-ieee-org.ezproxy.auckland.ac.nz/document/10447700

Zhao H, Yang M, Bai X et al (2024) A survey on multimodal aspect-based sentiment analysis. IEEE Access 12:12039–12052. https://doi.org/10.1109/ACCESS.2024.3354844 . https://ieeexplore.ieee.org/document/10401113

Zhou J, Huang JX, Chen Q et al (2019) Deep learning for aspect-level sentiment classification: survey, vision, and challenges. IEEE Access 7:78454–78483. https://doi.org/10.1109/ACCESS.2019.2920075 . https://ieeexplore.ieee.org/document/8726353

Zhou C, Wu Z, Song D et al (2024) Span-pair interaction and tagging for dialogue-level aspect-based sentiment quadruple analysis. In: Proceedings of the ACM on web conference 2024, WWW’24. Association for Computing Machinery, New York, pp 3995–4005. https://doi.org/10.1145/3589334.3645355

Download references

Open Access funding enabled and organized by CAUL and its Member Institutions.

Author information

Authors and affiliations.

School of Computer Science, The University of Auckland, Auckland, New Zealand

Yan Cathy Hua, Paul Denny, Jörg Wicker & Katerina Taskova

You can also search for this author in PubMed   Google Scholar

Contributions

Y. C. H designed, conducted, and wrote this review. P. D., J. W., and K. T. guided the design of the review methodology and review protocol, and reviewed and provided feedback on the manuscript.

Corresponding author

Correspondence to Yan Cathy Hua .

Ethics declarations

Conflict of interest.

The authors have no Conflict of interest as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A: Aspect-based sentiment analysis (ABSA)

1.1 appendix a.1: definition and examples.

Aspect-based sentiment analysis (ABSA) is a sub-domain of fine-grained SA (Nazir et al 2022a ). ABSA focuses on identifying the sentiments towards specific entities or their attributes/ features called aspects (Nazir et al 2022a ; Akhtar et al 2020 ). An aspect can be explicitly expressed in the text ( explicit aspect ) or absent from the text but implied from the context ( implicit aspects ) (Maitama et al 2020 ; Xu et al 2020b ). Moreover, the aspect-level sentiment could differ across aspects and be different from the overall sentiment of the sentence or the document (e.g. Akhtar et al 2020 , 2017 ; Li et al 2022a ). Some studies further distinguish aspect into aspect term and aspect category , with the former referring to the aspect expression in the input text (e.g. “pizza”), and the latter a latent construct that is usually a high-level category across aspect terms (e.g. “food”) that are either identified or given (Chauhan et al 2019 ; Akhtar et al 2018 ).

The following examples illustrate the ABSA terminologies:

(From a restaurant review Footnote 7 ): “ The restaurant was expensive, but the menu was great. ” This sentence has one explicit aspect “menu” (sentiment term: “great”, sentiment polarity: positive), one implicit aspect “price” (sentiment term: “expensive”, sentiment polarity: negative). Depending on the target/given categories, the aspects can be further classified into categories, such as “menu” into “general” and “price” into “price”.

(From a laptop review Footnote 8 ): “ It is extremely portable and easily connects to WIFI at the library and elsewhere. ” This sentence has two implicit aspects: “portability” (sentiment term: “portable”, sentiment polarity: positive), “connectivity” (sentiment term: “easily”, sentiment polarity: positive). The aspects can be further classified into categories, such as both under “laptop” (as opposed to “software” or “support”).

(Text from a course review): “ It was too difficult and had an insane amount of work, I wouldn’t recommend it to new students even though the tutorial and the lecturer were really helpful .” The two explicit aspects in Example 3 are “tutorial” and “lecturer” (sentiment terms: “helpful”, polarities: positive). The implicit aspects are “content” (sentiment term: “too difficult”, sentiment polarity: negative), “workload” (sentiment term: “insane amount”, sentiment polarity: negative), and “course” (sentiment term: “would not recommend”, sentiment polarity: negative). An illustration of aspect categories would be assigning the aspect “lecturer” to the more general category “staff” and “tutorial” to the category “course component”.

As demonstrated above, the fine granularity makes ABSA more targetable and informative than document- or sentence-level SA. Thus, ABSA can precede downstream applications such as attribute weighting in overall review ratings (e.g. Da’u et al 2020 ), aspect-based opinion summarisation (e.g. Yauris and Khodra 2017 ; Kumar et al 2022 ; Almatrafi and Johri 2022 ), and automated personalised recommendation systems (e.g. Ma et al 2017 ; Nawaz et al 2020 ).

Compared with document- or sentence-level SA, while being the most detailed and informative, ABSA is also the most complex and challenging (Huan et al 2022 ). The most noticeable challenges include the number of ABSA subtasks, their interrelations and context dependencies, and the generalisability of solutions across topic domains.

1.2 Appendix A.2: ABSA Subtasks

A full ABSA solution has more subtasks than coarser-grained SA. The most fundamental ones (Li et al 2022a ; Huan et al 2022 ; Li et al 2020 ; Fei et al 2023b ) include:

Aspect (term) extraction/identification (AE) , which has a slight variation in meaning depending on the overall ABSA approach. Some authors (e.g. (Zhang et al 2023 ; Luo et al 2019 ; Ruskanda et al 2019 )) consider AE as identifying the attribute or entity that is the target of an opinion expressed in the text and sometimes call it “opinion target extraction” (Guo et al 2018 ). In these cases, opinion terms were often identified in order to find their target aspect terms. Others (e.g. Akhtar et al 2020 ; Gunes 2016 ; Li et al 2020 ; Ettaleb et al 2022 ; Tran et al 2020 ) define AE as identifying the key or all attributes of entities mentioned in the text. Implicit-Aspect Extraction (IAE) is often mentioned as a task by itself due to its technical challenge.

Opinion (term) Extraction/Identification (OE) , which relates to identifying the “opinion terms” or the sentiment expression of a specific entity/aspect (e.g. Li et al 2022a ; Wang et al 2018 ; Fernando et al 2019 ; Fei et al 2023b ). In Example 1 above, an OE task would extract the sentiment terms “great” (associated with the aspect term “menu”) and “expensive” (associated with the implicit aspect “price”).

Aspect-Sentiment Classification (ASC) , which refers to obtaining the sentiment polarity category (e.g. negative, neutral, positive, conflict) or sentiment score (e.g. 1 to 5 or \(-1\) to 1 along the scale from negative to positive) associated with a given aspect or aspect category (e.g. Akhtar et al 2020 ; Gojali and Khodra 2016 ; Castellanos et al 2011 ). This is often done via evaluating the associated opinion term(s), and sentiment lexicon resources such as the SentiWordNet (Baccianella et al 2010 ) and SenticNet (Cambria et al 2016 ) can be used to assign polarity scores (Gojali and Khodra 2016 ). Sentiment scores can be further aggregated across opinion terms for the same aspect, or across aspect terms to generate higher-level ratings, such as aspect-category ratings within or across documents (Gojali and Khodra 2016 ; Castellanos et al 2011 ).

As an extension of AE, some studies also involve Aspect-Category Detection (ACD) and Aspect Category Sentiment Analysis (ACSA) when the focus of sentiment analysis is on (often pre-defined) latent topics or concepts and requires classifying aspect terms into categories (Pathan and Prakash 2022 ).

Traditional full ABSA solutions often perform the subtasks in a pipeline manner (Li et al 2022b ; Nazir and Rao 2022 ) using one or more of the linguistic (e.g. lexicons, syntactic rules, dependency relations), statistical (e.g. n-gram, Hidden Markov Model (HMM)), and machine-learning approaches (Maitama et al 2020 ; Cortis and Davis 2021 ; Federici and Dragoni 2016 ). For instance, for AE and OE, some studies used linguistic rules and sentiment lexicons to first identify opinion terms and then the associated aspect terms of each opinion term, or vice versa (e.g. You et al 2022 ; Cavalcanti and Prudêncio 2017 ), and then moved on to ASC or ACD using a supervised model or unsupervised clustering and/or ontology (Nawaz et al 2020 ; Gojali and Khodra 2016 ). Hybrid approaches are common given the task combinations in a pipeline.

With the rise of multi-task learning and deep learning (Chen et al 2022 ), an increasing number of studies explore ABSA under an End-to-end (E2E) framework that performs multiple fundamental ABSA subtasks in one model to better capture the inter-task relations (Liu et al 2024 ), and some combine them into a single composite task (Huan et al 2022 ; Li et al 2022b ; Zhang et al 2022b ). These composite tasks are most commonly formulated as a sequence- or span-based tagging problem (Huan et al 2022 ; Li et al 2022b ; Nazir and Rao 2022 ). The most common composite tasks are: Aspect-Opinion Pair Extraction (AOPE) , which directly outputs {aspect, opinion} pairs from text input (Nazir and Rao 2022 ; Li et al 2022c ; Wu et al 2021 ) such as “ \(\langle \) menu, great \(\rangle \) ” from Example 1; Aspect-Polarity Co-Extraction (APCE) (Huan et al 2022 ; He et al 2019 ), which outputs {aspect, sentiment polarity} pairs such as “ \(\langle \) menu, positive \(\rangle \) ”; Aspect-Sentiment Triplet Extraction (ASTE) (Huan et al 2022 ; Li et al 2022b ; Du et al 2021 ; Fei et al 2023b ), which outputs {aspect, opinion, sentiment category} triplets, such as “ \(\langle \) menu, great, positive \(\rangle \) ”; and Aspect-Sentiment Quadruplet Extraction/Prediction (ASQE/ASQP) (Zhang et al 2022a ; Lim and Buntine 2014 ; Zhang et al 2021a , 2024a ) that outputs {aspect, opinion, aspect category, sentiment category} quadruplets, such as “ \(\langle \) menu, great, general, positive \(\rangle \) ”.

1.3 Appendix A.3: Other ABSA reviews

As this review focuses on trends instead of detailed solutions and methodologies, we refer interested readers to existing review papers that provide comprehensive and in-depth summaries of common ABSA subtask solutions and approaches, for example:

Explicit and implicit AE : Rana and Cheah ( 2016 ), Ganganwar and Rajalakshmi ( 2019 ), Soni and Rambola ( 2022 ), Maitama et al ( 2020 ).

Deep learning (DL) methods for ABSA : Do et al ( 2019 ), Liu et al ( 2020 ), Wang et al ( 2021a ), Chen and Fnu ( 2022 ), Zhang et al ( 2022c ), Mughal et al ( 2024 ). Specifically:

DL methods for ASC : Zhou et al ( 2019 ), Satyarthi and Sharma ( 2023 ).

E2E ABSA, composite tasks, and pre-trained Large Language Models (LLMs) in ABSA : Zhang et al ( 2022c ) provided a comprehensive review and shared extensive reading lists and dataset resource links via https://github.com/IsakZhang/ABSA-Survey . Mughal et al ( 2024 ) introduced common benchmark datasets, including more challenging ones for composite ABSA tasks. They also reviewed and tested the ABSA task performance of representative RNN-based models and pre-trained LLMs.

Multimodal ABSA : Zhao et al ( 2024 ).

Appendix B: Full SLR methodology

This section provides a complete, detailed description of the SLR methodology and procedures.

1.1 Appendix B.1: Research identification

To obtain the files for review, we conducted database searches between 24–25 October 2022, when we manually queried and exported a total of 4191 research papers’ PDF and BibTeX (or the equivalent) files via the web interfaces of four databases. Table  9 details the search string, search criteria, and the PDF files exported from each database.

Given the limited search parameters allowed in these digital databases, we adopted a “search broad and filter later” strategy. These database search strings were selected based on pilot trials to capture the ABSA topic name, the relatively prevalent yet unique ABSA subtask term (“extraction”), and the interchangeable use between ABSA and opinion mining; while avoiding generating false positives from the highly active, broader field of SA. The “filter later” step was carried out during the “selection of primary studies” stage introduced in the next section, which aimed at excluding cases where the keywords are only mentioned in the reference list or sparsely mentioned as a side context, and opinion mining studies that were at document or sentence levels.

1.2 Appendix B.2: Selection of primary studies

After obtaining the 4191 initial search results, we conducted a pilot manual file examination of 100 files to refine the pre-defined inclusion and exclusion criteria. We found that some search results only contained the search keywords in the reference list or Appendix, which was also reported in Prather et al ( 2020 ). In addition, there are a number of papers that only mentioned ABSA-specific keywords in their literature review or introduction sections, and the studies themselves were on coarser-grained sentiment analysis or opinion mining. Lastly, there were instances of very short research reports that provided insufficient details of the primary studies. Informed by these observations, we refined our inclusion and exclusion criteria to those in Table  1 in Sect.  3 . Note that we did not include popularity criteria such as citation numbers so we can better identify novel practices and avoid mainstream method over-dominance introduced by the citation chain (Chu and Evans 2021 ).

To implement the inclusion and exclusion criteria, we first applied PDF mining to automatically exclude files that meet the exclusion criteria, and then refined the selection with manual screening under the exclusion and inclusion criteria. Both of these processes are detailed below. Our PDF mining for automatic review screening code is also available at https://doi.org/10.5281/zenodo.12872948 .

The automatic screening consists of a pipeline with two Python packages: Pandas (Team 2023 ) and PyMuPDF. Footnote 9 We first used Pandas to extract into a dataframe (i.e. table) all exported papers’ file locations and key BibTex or equivalent information including title, year, page number, DOI, and ISBN. Next, we used PyMuPDF to iterate through each PDF file and add to the dataframe multiple data fields: whether the file was successfully decoded Footnote 10 for text extraction (if marked unsuccessful, the file was marked for manual screening), the occurrence count of each Regex keyword pattern listed below, and whether each keyword occurs after the section headings that fit into Regex patterns that represent variations of “references” and “bibliography” (referred to as “non-target sections” below). We then marked the files for exclusion by evaluating the eight criteria listed under “Auto-excluded” in Table  10 against the information recorded in the dataframe. Each of the auto-exclusion results from Steps 1–4 and 7 in Table  10 were manually checked, and those under Steps 5, 6, and 8 were spot-checked. These steps excluded 3277 out of the 4194 exported files.

Below are the regex patterns used for automatic keyword extraction and occurrence calculation:

PDF search keyword Regex list: [’absa’, ’aspect \(\backslash \) W+base \(\backslash \) w*’, ’aspect \(\backslash \) W+extrac \(\backslash \) w*’, ’aspect \(\backslash \) W+term \(\backslash \) w*’, ’aspect \(\backslash \) W+level \(\backslash \) w*’, ’term \(\backslash \) W+level’, ’sentiment \(\backslash \) W+analysis’, ’opinion \(\backslash \) W+mining’]

For the 914 files filtered through the auto-exclusion process, we manually screened them individually according to the inclusion and exclusion criteria. As shown in the second half of Table  10 , this final screening step refined the review scope to 519 papers.

1.3 Appendix B.3: Data extraction and synthesis

In the final step of the SLR, we manually reviewed each of the 519 in-scope publications and recorded information according to a pre-designed data extraction form. The key information recorded includes each study’s research focus, research application domain (“research domain” below), ABSA subtasks involved, name or description of all the datasets directly used, model name (for machine-learning solutions), architecture, whether a certain approach or paradigm is present in the study (e.g. supervised learning, deep learning, end-to-end framework, ontology, rule-based, syntactic-components), and the specific approach used (e.g. attention mechanism, Naïve Bayes classifier) under the deep learning and traditional machine learning categories.

After the data extraction, we performed data cleaning to identify and fix recording errors and inconsistencies, such as data entry typos and naming variations of the same dataset across studies. Then we created two mappings for the research and dataset domains described below.

For each reviewed study, its research domain was defaulted to “non-specific” unless the study mentioned a specific application domain or use case as its motivation, in which case that domain description was recorded instead.

The dataset domain was recorded and processed at the individual dataset level, as many reviewed studies used multiple datasets. We standardised the recorded dataset names, checked and verified the recorded dataset domain descriptions provided by the authors or the source web-pages, and then manually categorised each domain description into a domain category. For published/well-known datasets, we unified the recorded naming variations and checked the original datasets or their descriptions to verify the domain descriptions. For datasets created (e.g. web-crawled) by the authors of the reviewed studies, we named them following the “[source] [domain] (original)” format, e.g. “Yelp restaurant review (original) ”, or “Twitter (original)” if there was no distinct domain, and did not differentiate among the same-name variations. In all of the above cases, if a dataset was not created with a specific domain filter (e.g. general Twitter tweets), then it was classified as “non-specific”.

The recorded research and dataset domain descriptions were then manually grouped into 19 common domain categories. We tried to maintain consistency between the research and dataset domain categories. The following are two examples of possible mapping outcomes:

A study on a full ABSA solution without mentioning a specific application domain and using Yelp restaurant review and Amazon product review datasets would be assigned a research domain of “non-specific” and a dataset domain of “product/service review”.

A study mentioning “helping companies improve product design based on customer reviews” as the motivation would have a research domain of “product/service review”, and if they used a product review dataset and Twitter tweets crawled without filtering, the dataset domains would be “product/service review” and “non-specific”.

After applying the above-mentioned standardisation and mappings, we analysed the synthesised data quantitatively using the Pandas (Team 2023 ) library to obtain an overview of the reviewed studies and explore the answers to our RQs.

Appendix C: Additional results

See Figs.  9 ,  10 and Tables  11 ,  12 ,  13 ,  14 ,  15 .

Number of included studies by publication year and type ( \(\textrm{N}=519\) ). Note Although our original search scope included journal articles, conference papers, newsletters, and magazine articles, the final 519 in-scope studies consist of only journal articles and conference papers. Conference papers noticeably outnumbered journal articles in all years until 2022, with the gap closing since 2016. We think this trend could be due to multiple factors, such as the fact that our search was conducted in late October 2022 when some conference publications were still not available; the publication lag for journal articles due to a longer processing period; and potentially a change in publication channels that is outside the scope of this review

Number of included studies with the top 5 dataset languages by publication year

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. 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/4.0/ .

Reprints and permissions

About this article

Hua, Y.C., Denny, P., Wicker, J. et al. A systematic review of aspect-based sentiment analysis: domains, methods, and trends. Artif Intell Rev 57 , 296 (2024). https://doi.org/10.1007/s10462-024-10906-z

Download citation

Accepted : 06 August 2024

Published : 17 September 2024

DOI : https://doi.org/10.1007/s10462-024-10906-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Aspect-based sentiment analysis
• Systematic literature review
• Natural language processing
• Find a journal
• Publish with us
• Track your research

SYSTEMATIC LITERATURE REVIEW : HUBUNGAN GAYA KEPEMIMPINAN KEPALA RUANGAN DENGAN MOTIVASI KERJA PERAWAT DI RUMAH SAKIT

AJI ARI JUANDA, - (2024) SYSTEMATIC LITERATURE REVIEW : HUBUNGAN GAYA KEPEMIMPINAN KEPALA RUANGAN DENGAN MOTIVASI KERJA PERAWAT DI RUMAH SAKIT. S1 thesis, Universitas Pendidikan Indonesia.

Baca Full Text klik disini

Kepemimpinan merupakan proses mempengaruhi atau memberikan contoh kepada pengikutnya melalui komunikasi, dengan tujuan mencapai target organisasi. Masalah motivasi perawat di rumah sakit tetap menjadi isu yang aktual. Ini bukan berarti bahwa para perawat kurang berkinerja, tetapi mungkin dipengaruhi oleh kepemimpinan yang kurang efektif, yang dapat memengaruhi tingkat motivasi kerja mereka. Tujuan penelitian ini adalah untuk mengevaluasi korelasi antara gaya kepemimpinan kepala ruangan dengan motivasi kerja perawat di rumah sakit dan mengidentifikasi gaya kepemimpinan yang umum digunakan oleh para kepala ruangan. Metode yang digunakan dalam penelitian ini adalah systematic literature review. Sampel penelitian melibatkan seluruh perawat pelaksana di rumah sakit. Hasil review dari 11 artikel penelitian, yang berasal dari Indonesia, Timor-leste, dan Jordania dengan rentang tahun 2018-2023, menunjukkan bahwa terdapat korelasi antara gaya kepemimpinan kepala ruangan dengan motivasi kerja perawat di rumah sakit. Gaya kepemimpinan demokratis merupakan gaya kepemimpinan yang sering digunakan oleh kepala ruangan di rumah sakit, sebagaimana teridentifikasi dalam penelitian tersebut. Kata kunci: gaya kepemimpinan, kepala ruangan, motivasi kerja, perawat. Leadership is the process of influencing or setting an example for followers through communication, with the aim of achieving organizational goals. The issue of nurse motivation in hospitals remains a current concern. This does not necessarily mean that nurses are underperforming, but it may be influenced by ineffective leadership, which can affect their level of work motivation. The objective of this research is to assess the correlation between the leadership style of ward heads and the work motivation of nurses in hospitals, as well as to identify the leadership styles commonly used by ward heads. The research method employed is systematic literature review. The research sample involves all practicing nurses in hospitals. The review of 11 research articles, spanning from 2018 to 2023 and originating from Indonesia, Timor-leste, and Jordan, indicates a correlation between the leadership style of ward heads and the work motivation of nurses in hospitals. The democratic leadership style is frequently used by ward heads in hospitals, as identified in the study. Keywords: head of room, leadership style, nurse, work motivation.

Actions (login required)

• Open access
• Published: 18 September 2024

Toward a roadmap for sustainable lean adoption in hospitals: a Delphi study

• Maria M. Van Zyl-Cillié 1 , 2 ,
• Desirée H. van Dun 2 &
• Hanneke Meijer 1  

BMC Health Services Research volume  24 , Article number:  1088 ( 2024 ) Cite this article

Metrics details

The benefits of lean adoption in healthcare include improved process efficiency and quality of patient care. However, research indicates that lean implementation in healthcare, and specifically hospitals, is often not sustained. Furthermore, there is a need for maturity models that guide lean implementation, specifically in hospitals. This study develops a prescriptive maturity model named the Sustaining of Lean Adoption in Hospitals Roadmap (SOLAR) that acts as a practical guideline for the sustainable adoption of lean in hospitals.

The SOLAR has three theoretical foundations, namely lean implementation success factors in hospitals, implementation science, and change management theory. A systematic literature review was conducted to determine the lean implementation success factors in hospitals as the first building block. Secondly, practices from implementation science were used to create the action items in the SOLAR. Ten change steps were elicited from change management theory as the third theoretical building block of the roadmap. We refined the roadmap through three Delphi rounds that verified its useability in hospitals.

The final SOLAR consists of four maturity phases (prepare, plan, experiment and learn, and sustain) and includes action items for each phase related to the hospital’s strategy, resources, engaging of people, and culture. The action items and change management steps shown in the SOLAR are not intended as an exhaustive list but provide guidelines on aspects hospitals must consider when they aim to adopt lean sustainably.

Conclusions

The strong theoretical base of the SOLAR enables hospitals to safely experiment and learn which implementation methods are best suited to their unique environment. The SOLAR is, therefore, an actionable guideline that informs both academics and practitioners involved in lean adoption in hospitals. This roadmap can guide future retrospective longitudinal or action research.

Peer Review reports

Hospitals, also called inpatient care services, experience many operational challenges. Delivering healthcare services efficiently whilst improving the flow and reducing patients’ waiting time is one of these challenges [ 1 ]. Lean management, which originated in the manufacturing industry, has proven to drive improved efficiencies in the healthcare sector in general and in hospitals in particular [ 2 ] as well as improved quality of patient care and overall performance gains [ 3 ]. Many hospitals have implemented lean in recent years due to these benefits. Lean implementation requires a complete change in organisational culture and thinking, but adopting lean tools does not ensure that the implementation is sustainable or has been adopted as part of the organisation’s culture. This is confirmed by several researchers, like Van Rossum et al. [ 4 ] who argued that lean implementation in healthcare organisations is not always maintained. In the healthcare environment, lean adoption is only seen as successful if the implementation thereof permanently improves the quality of service and patient satisfaction [ 5 ]. Van Beers et al. [ 3 ] further argued that lean implementation in hospitals often does not achieve the desired results and is a lengthy process. Indeed, Akugizibwe and Clegg [ 6 ], observed that healthcare providers (such as hospitals) struggle to sustain the success achieved after initial lean implementation.

Implementing continuous improvement interventions such as lean, Total Quality Management and Six Sigma, is often challenging due to the organisational change management process it requires [ 7 ]. In addition, such implementations are complicated due to, amongst other things, the complexity of healthcare organisations [ 8 , 9 , 10 ]. These complexities include the typical organisational structures of hospitals where different units often function in isolation as their own profit and loss entities, with little motivation for functioning across silos. In addition, hospitals have strict hierarchical structures [ 11 ] and not all stakeholders involved in a patient’s journey (such as physicians) are employed by the hospital, making it difficult to ensure that they buy into the hospital’s lean journey.

Models and frameworks that guide the successful implementation of continuous improvement initiatives in organisations do exist. Despite the availability of such maturity or implementation models [ 12 ], continuous improvement implementation initiatives have a high failure rate [ 7 ]. In line with this, researchers contend that there is minimal evidence of lean healthcare implementations sustained over the long term [ 13 ]. Indeed, D’Andreamatteo et al. [ 14 ] found that although the factors that contribute toward successful lean implementation in healthcare are established in the literature, research on adopting lean sustainably and the implementation process of lean in healthcare is lacking. Henrique et al. [ 15 ] made a first attempt to aggregate key factors that might influence the sustainability of lean interventions in hospitals. Furthermore, Kunnen et al. [ 16 ] thematically analysed the barriers and facilitators that influence the sustainable adoption of lean in healthcare organisations, but not specifically in hospitals.

Lameijer et al. [ 7 ] found that while implementation readiness factors often form part of implementation guidelines or maturity models, factors related to the sustainability of results are lacking. Furthermore, the available guidelines do not address contextual factors such as the industry or environment. Indeed, Andersen et al. [ 17 ] emphasise the importance of tailoring lean specifically for hospitals. Similarly, Antony et al. [ 18 ], Zanon et al. [ 19 ], and our own literature review identified the lack of a fully developed framework and assessment methodology for lean implementation, specifically at the hospital level. In addition, although prescriptive maturity models can provide organisations with the general direction for deploying lean, they do not necessarily guide implementation using clear action items [ 12 ]. Lameijer et al. [ 7 ] argued that there is thus a need for industry- and implementation-specific guidelines or maturity models to boost the success and durability of lean initiatives.

In sum, although lean can address prominent challenges in hospitals there is a gap in the literature on how to sustain lean in hospitals [ 14 ]. With many hospitals facing pressure to improve their financial performance, efficiency and patient care quality, there is a critical need for guidelines on sustaining lean in such settings. This research aims to design a prescriptive maturity model, the S ustaining o f L ean A doption in Hospitals R oadmap (SOLAR), that will help guide practitioners and scholars alike towards sustainable lean implementation in a hospital environment. The first research phase entailed developing the SOLAR from solid theoretical principles: The known success factors for lean implementation, change management theory, and the relatively novel theory of implementation science. In the second research phase, the proposed model was tested utilising a three-round Delphi study, during which feedback from lean healthcare expert practitioners and academics was obtained.

The resulting roadmap is intended to guide the lean adoption process in hospitals through action items throughout the change management process. Furthermore, the SOLAR contributes to the literature by integrating known lean implementation success factors and change management theory with implementation science. The resulting multidisciplinary model takes various prominent features of the hospital setting into account, including the risk-aversity of hospital staff members and the hierarchical, siloed organisational structure, requiring many stakeholders’ involvement beyond only identifying customer/patient value.

The next section provides an overview of the theory on which the initial SOLAR is built. The methodology section explains how the SOLAR was developed in dialogue with experts across the globe. The results section then discusses the content of the SOLAR, after which the theoretical and practical implications are drawn in the final discussion section.

Initial SOLAR development: literature review

The first phase of developing the SOLAR was to establish the building blocks from the literature. A brief background to the purpose and use of maturity models is provided, after which lean implementation success factors, implementation science, and change management theory are reviewed.

• Maturity models

Becker et al. [ 20 ] summarised a maturity model as a guide to organisational transformation from an initial to a desired state, where the model offers the maturity levels to guide organisational transformation. Maturity models are generally applied for two reasons. Firstly, to determine the current maturity level of an organisation [ 21 ]. Maturity models in this context are called descriptive maturity models [ 22 ] and are used to assess an organisation’s progress to achieve a desired level of maturity. Secondly, to guide the organisation’s journey to the desired state, i.e. prescriptive maturity models [ 21 ] that typically include detailed actions developed from historical data to prescribe organisational transformation [ 22 ].

Maturity models can be used in lean deployment to guide organisations on what steps to take to achieve sustainable lean adoption or to assist organisations in assessing how far along the journey towards complete lean adoption they are. Yet, lean adoption is a long-term venture, and many argue that it has no clear ‘end’ because it aims for continuous improvement. Some authors refer to the level at which an organisation has adopted lean as ‘leanness’, i.e. the extent to which lean practices have been adopted and the resulting performance achievements [ 19 ]. Ways to assess the extent to which lean has been infused into an organisation, include benchmarking [ 23 ], storytelling [ 24 ] and assessment tools such as the ‘Lean Enterprise Self-Assessment’ [ 25 ]. Maturity models can also act as evaluation tools to determine an organisation’s current state and guide toward achieving a desired state [ 26 ].

A review by Zanon et al. [ 19 ] revealed 19 lean maturity models that are presented in the literature. All models assess the general adoption of lean in “phases” or “milestones”, both of which are synonymous with “maturity levels”, and the extent to which maturity has been achieved is measured against different criteria. These 19 maturity models are predominantly descriptive. In order to determine the maturity levels of the SOLAR, we investigated the terms used in the models presented by Zanon et al. [ 19 ] and two models [ 22 , 23 ] from our own review of lean maturity models. The six lean maturity models with their respective descriptions of maturity phases are summarised in Table  1 . It was found that all of the models described progressive phases with unique, diverse labels. The phases of maturity are described in intervals of between four and eight steps.

Because of this diversity, Zanon et al. [ 19 ] proposed that lean maturity levels be described as follows:

Level 1 is associated with some (small) lean initiatives being undertaken, which are not fully integrated into the organisation. This level description is similar to, amongst others, level 1 (initial stage, limited awareness) of Verrier et al. [ 31 ] as well as level 1 (adopt lean paradigm) presented by Tortorella et al. [ 29 ]. During this level, preparation for adopting lean in the organisation, typically occurs.

Level 2 is the phase during which customer value is identified and improvements and lean implementation are directed towards isolated areas in the organisation [ 19 ]. This level corresponds to levels 2 and 3 (basic lean implementation and strategic lean implementation) of Jørgensen et al. [ 28 ] as well as levels 3 and 4 of Tortorella et al. [ 29 ] (define value and identify flow of value). In essence, this phase focuses on planning the lean adoption of the organisation and how the lean adoption will realise value.

Level 3 is described by Zanon et al. [ 19 ] as the phase during which improvement initiatives are aligned, and stakeholders can observe how process improvements contribute towards performance metrics. This description is similar to level 4 (quantitatively managed stage) of Verrier et al. [ 31 ], level 3 of Marsilio et al. [ 23 ] (expanding to other units and getting traction) as well as level 4 (proactive lean culture) as presented in the work of Jørgensen et al. [ 28 ].

The final level of lean maturity is characterised by the continuous use of lean concepts throughout the organisation and focuses on sustaining lean adoption in the organisation [ 19 ]. Verrier et al. [ 31 ] describe this level as optimisation (continuous improvement through lean). Marsilio et al. [ 23 ] refer to this level of maturity as “mature transformational performance improvement” and Maier et al. [ 22 ] as “maintenance”.

Furthermore, maturity levels are typically associated with capabilities and activities that an organisation needs to perform or are measured against as they progress on a maturity path [ 32 ]. While investigating such progress of improvement, Netland and Ferdows [ 33 ] observed that an S-shaped operational performance improvement occurs in phases over time. During the initial phases of lean implementation, operational improvement occurs slowly, followed by a drastic and rapid improvement, whereafter the improvement gradually tapers off [ 33 ]. This non-linearity of business performance improvement during lean adoption was confirmed by Negrão et al. [ 34 ]. At the saturation point lean adoption is mature and can be sustained if the correct focus is maintained.

In sum, in keeping with the notion that lean maturity is achieved in phases whereby there must be room for continuous improvement to sustain lean adoption over time, we developed our SOLAR as a prescriptive maturity model comprising four phases deduced from our overview of lean maturity models, as shown in Fig.  1 : Prepare, Plan, Experiment and Learn, and Sustain.

Sustainable lean hospital adoption roadmap maturity phases

Lean implementation success factors

The second building block of the SOLAR is informed by literature-based factors that could influence the successful adoption of lean in a hospital environment. These factors, amongst others, are described as barriers , facilitators , challenges , readiness factors , success factors , inhibitors , and managerial attributes [ 35 , 36 , 37 , 38 , 39 , 40 ]. We refer to them as success factors for brevity. In terms of lean deployment, success factors are those that enable employees to adopt lean thinking in their everyday routines [ 41 ] and can be seen as part of a change-implementation strategy that influences the sustainability of the change [ 8 ]. It is, therefore, critical to incorporate success factors into a lean healthcare adoption maturity model.

This research follows a similar approach to that of Kunnen et al. [ 16 ] but is specific to a hospital environment. Hence, a systematic literature review (SLR) was conducted at the start of this study in 2019 to determine the success factors necessary for lean implementation and adoption in hospitals, and integrated into the SOLAR by addressing the following research question: What factors influence lean implementation success within a hospital environment?

In conducting the SLR following the PRISMA statement [ 42 ], nineteen articles on lean implementation success factors were selected following the systematic approach proposed by Siddaway et al. [ 43 ]. The search terms used in the search databases Scopus and EBSCOhost (which included databases such as Academic Search Premier and MEDLINE) were as follows:

(“lean” OR “continu* improvement”) AND (“implement” OR “deploy*” OR “adopt” OR “adapt” OR “appl*” OR “conscious*” OR “integrat*”) AND (“health care” OR “healthcare” OR “hospital” OR “clinic” OR “health cent*” OR “medical service” OR “medical care environment” OR “medical facility*” OR “medical cent*”) AND (“success factor*” OR “success” OR “critical factor*” OR “change factor*” OR “driver” OR “important factor” OR “facilitate*” OR “sustain” OR “long term” OR “long term” OR “read* factor*” OR “failure factor*” OR “challenge” OR “barrier” OR “lesson*” OR “issue”).

As inclusion criteria, only English papers with available full texts, published in accredited journals or established (peer-reviewed) conference proceedings, and focused on one or more factors influencing lean implementation in a hospital setting were selected by one author (HM), and then independently checked by the first author (MVZ-C). These inclusion criteria meant to account for the relevance and quality of the included papers. Book chapters and studies executed outside of a hospital environment, in non-service parts of the hospital, or those concerned with implementing lean in combination with another methodology, such as Six Sigma, were excluded. In particular, studies combined with Six Sigma were excluded due to their specific focus on quantitative statistical process control initiatives and not primarily on lean success factors. The final selection of papers was then determined by the entire author team (including also DVD); to avoid any omissions, the papers were discussed elaborately.

Before analysing the selected studies in more depth, the author team screened the journal impact factors as well as methods used and rigour to account for the quality of the corpus. SCImago Journal Ranking indicator, which assesses the impact and influence of journals independently, was consulted, and we found that 12 of the 19 articles in our sample were published in the top 25-50% (quartiles one and two) journals. Four articles were published in quartile three (top 75%) journals, two in peer-reviewed conference proceedings, and one in a quartile four journal. The journal ‘Quality Management in Health Care’ (quartile two journal) contributed the most articles (3 articles). The methods followed in our sample ranged from semi-structured interviews (7 articles), literature reviews (6 articles), field observations (2 articles), and quantitative methods such as structural equation modelling (4 articles). The diversity of the sample of selected papers, both in terms of methodology and countries of data collection ranging from Sweden to Iran, is proposed to curb any remaining biases in the selected studies, allowing for high-quality insights. The SLR approach, following the PRISMA statement, is summarized in Fig.  2 .

Systematic literature review approach to determine lean adoption success factors, following the PRISMA statement

In terms of content analysis, any mention of factors influencing the success of lean implementation within a healthcare environment was extracted from the selected studies. To minimize bias and ensure that all relevant factors were collected, we followed the 21-item ENTREQ guidelines [ 44 ]. Firstly, one author (HM) extracted factors influencing successful lean implementation from the selected studies. Then, a second author (MVZ-C) reviewed the selection of factors and compared them to the nineteen selected studies to ensure a balanced view. In line with Kunnen et al. [ 16 ], inductive reasoning was used to create labels for similar factors. The two authors further refined the factor labels with the third author (DVD) whereafter the factors were grouped under four themes: (1) strategy, (2) resources, (3) engaging people, and (4) organisational culture. Table  2 depicts each theme, corresponding lean adoption success factors, and the original sources which identified them.

The success factors listed in Table  2 were used in conjunction with a well-researched framework from implementation science, as discussed in the next section, to develop the proposed action items of the SOLAR under each maturity phase.

• Implementation science

Implementation science, an emerging field in healthcare evidence-based standard practices adoption, was used as the theory that informs the third element of the SOLAR. Implementation science is concerned with the study of methods that aim to diffuse research findings and evidence-based practices into an organisation’s routine [ 57 ]. May and Finch [ 58 ] further defined implementation as a deliberate effort to introduce something new to an environment to bring about change.

According to the theory of implementation science, this change is realised in organisations through a diffusion-dissemination-implementation continuum [ 55 ], which implies an ever-evolving change process. This diffusion-dissemination-implementation continuum is valuable to improving the spread of research findings that could improve a healthcare environment [ 59 ]. Diffusion is the inactive part of imparting knowledge about new practices [ 55 ], whereas dissemination requires more action and actively communicating new practices to the target group to ‘helping it happen’ [ 59 , 60 ]. Implementation is the deliberate action of ensuring that research findings are truly incorporated into the environment’s everyday practices [ 55 ]; in other words, ‘making it happen’ [ 60 ].

A key framework in the field of implementation science that guides the diffusion-dissemination-implementation process is the Quality Implementation Framework (QIF) [ 59 ]. This framework is suitable for informing the action items included in the SOLAR because the QIF may be generalised for any environment, it provides clear process steps for its application, and is widely cited and frequently used.

The QIF lists 14 critical steps in a four-phased approach that contributes towards a quality implementation where fidelity of the innovation is maintained throughout the implementation process [ 61 ]. Examples of these critical steps are determining the organisation’s current state regarding needs and resources, creating implementation teams, ensuring a supportive feedback system, and learning from the experience of implementing the change. Furthermore, the framework provides questions under each critical step the researcher needs to consider when implementing a change intervention. The proposed action items in the SOLAR were thus further developed by incorporating the QIF and its 14 critical steps.

Change management theory

In organisational behaviour literature, it is contended that planned organisational change is more likely to succeed if the change process considers all organisational stakeholders, whereby change needs to occur in a group where individuals’ behaviour and reaction to change is a function of the group environment [ 62 ]. The theory of change management uses frameworks and mechanisms to manage change in an organisation whilst causing minimal negative disruption to the workforce [ 63 ].

Although many useful change management methods and theories have been developed, the variability in each organisation and change environment may require adjustment according to their specific context [ 64 , 65 ]. Al-Haddad and Kotnour [ 62 ] explained the taxonomy of change in literature as consisting of change types, methods, enablers, and outcomes. The change type is classified in terms of the scale and duration of the change. Once the change type is defined, the most appropriate change method can be determined; these methods, in turn, are divided between systematic change methods and change management methods. Systematic change methods include processes and tools that assist organisational change agents (such as managers) to take change-related decisions [ 62 ]. These systematic change methods are cyclical and integrative, as opposed to some traditional change theories that mainly suggest management-driven change through incremental process adjustment. Examples of systematic change methods include Six Sigma, Total Quality Management and process re-engineering. On the other hand, change management methods are more conceptual and broader [ 62 ], as they assist management in aligning the change initiative with the overall organisational strategy and mission and embed the change into the organisational culture.

Al-Haddad and Kotnour [ 62 ] further argued that certain factors increase the probability of successful change and are known as organisational change enablers. Some examples of such enablers include setting a shared vision and direction for the change, clearly communicating the benefit and clarifying the roles of the employees involved in the change [ 63 ]. Notably, training employees and measuring the evolution of organisational change will also increase the probability of sustainable change [ 66 ]. Change outcomes, as depicted by Al-Haddad and Kotnour’s [ 62 ], relate to measuring the change’s performance from both a customer and organisational perspective. Errida and Lotfi [ 67 ] emphasise the importance of setting goals for such performance measures that are continuously tracked.

Furthermore, Stouten et al. [ 64 ] highlighted seven prescriptive change management models. These models (see Table  3 ) guide the management team through sequential steps in executing change interventions in their organisations. Some of the models corresponded with both the change management methods and systematic change methods [ 62 ]. Although lean implementation in a hospital environment will evolve organically and iteratively, it must be embedded in the hospital culture [ 63 ] which tends to be a large change stretched over an extended period. Therefore, change management methods [ 62 ] would be appropriate to guide lean implementation in hospitals, especially the prescriptive ones which provide specific guidance on steps to take. Hence, we focused on the prescriptive change management models classified by Stouten et al. [ 64 ]. In selecting the appropriate models to inform the SOLAR, those prescriptive change management models were filtered to ensure that they were also classified as change management methods by Al-Haddad and Kotnour [ 62 ]. Table  2 shows the result of the filtering process and the subsequent four change management models that are used to inform the SOLAR: (i) Lewin’s three-phase process method, (ii) Judson’s five steps, (iii) Kanter, Stein and Jick’s ten commandments, and (iv) Kotter’s eight-step model.

Stouten et al. [ 64 ] argued that many of the prescriptive models have similar practices and processes. The models also have a flow that acknowledges the start of the change intervention followed by the dissemination and, finally implementation or adoption of the change. As such, Stouten et al. [ 64 ] synthesised these prescriptive change management models into ten change steps, starting with assessing the opportunity to motivate the change and ending with institutionalising the change in the organisational culture and practices. Given the overlap with Al-Haddad and Kotnour [ 62 ], we contend these ten change steps are a comprehensive synthesis of prescriptive change management models and change management methods included in this SOLAR.

Research design

Given the exploratory aim of the research, a Delphi study was conducted where the initial literature-inspired design of the prescriptive maturity model was refined through feedback from lean healthcare experts. The Delphi method elicits the opinion of a panel of experts over multiple rounds on a specific research subject [ 68 , 69 ]. Expert feedback was collected from two rounds of online surveys and from narrative interviews in the third and final round, whereby the initial model was amended after each round. The surveys and the questions used in the narrative interviews were designed based on the approach followed by Tortorella et al. [ 70 ] and further refined after several dry-runs among the author team. They can be found in Additional File 1. The result of the Delphi study is the model we named ‘SOLAR’, presented herein.

Sampling approach and sample description

Delphi study respondents were selected to complete the first-round survey based on their knowledge and experience in implementing lean in hospital environments and their availability and willingness to participate [ 71 , 72 ]. A purposive expert sampling technique was followed, complemented by snowball sampling to avoid selection bias [ 73 ]. Thus, members from the Southern African Industrial Engineering (SAIIE) society were contacted via e-mail. Respondents with experience in academia, public healthcare, and private healthcare were thus identified to form a heterogeneous lean expert group. The respondents were requested to forward the survey to other potential respondents who met the inclusion criteria thereby completing the snowball sampling process. For the second Delphi round, the same method was followed and the recruitment list was expanded to include lean healthcare experts from the Netherlands. Since the third Delphi round was used to validate the SOLAR, respondents from South Africa and the Netherlands who participated in the second round were selected to participate in this final round.

During the first round, 14 participants responded to the online survey. Their experience was balanced between private and public healthcare and academia. The majority of respondents (10 out of 14) were male and six of the respondents had more than 10 years of experience. The second round also elicited responses from 14 individuals, five of whom also participated in the first round. Most respondents of this second Delphi round indicated their lean in healthcare experience as private healthcare, nine were male and five female. All four respondents (three males, one female) who participated in the third round also participated in the second round, and one of them also took part in the first round. The respondents’ experience in lean in healthcare was equally represented by public and private healthcare as well as academia. Table  4 summarises the respondent data for all three Delphi rounds.

Data collection

Delphi round 1 – approach and outcomes.

The initial prescriptive maturity model was presented to respondents in an explanatory video, followed by an online survey (Supplementary Table 1, Additional file 1) which consisted of multiple closed-ended questions. Specifically, respondents were asked to indicate to what degree they agreed with the statement: ‘ Although initial lean implementations in hospitals might be successful , it is often not sustained’ and: ‘The maturity model contributes towards the sustainability of lean implementation in a hospital’ . Respondents rated their level of agreement on a five-point scale ranging from ‘strongly disagree’, ‘disagree’, ‘undecided’, ‘agree’, or ‘strongly agree’. The survey also contained an open field for suggestions for improvement of the maturity model.

Ten out of 14 respondents agreed that hospitals often do not sustain lean implementation. Although 11 of the 14 respondents agreed that the initial maturity model contributed towards lean sustainability in hospitals, suggestions for improvement were also made. One respondent noted that the original naming of the four maturity phases (i.e., prepare, plan, implement and sustain) did indicate a clear implementation path but did not indicate how maturity evolved. Another respondent argued that the lean implementation strategy needs to be aligned with the hospital’s strategy. Another point of feedback was that the model’s action items should be more descriptive to be more actionable. Based on this feedback the model was altered incorporating change management theory, renaming the maturity phases, and refining the action items to be more descriptive and aligned with respondents’ feedback.

Delphi round 2 – approach and outcomes

The amended model was presented to respondents in a second Delphi round, using the same method as round one. The survey questions for the second round can be found in Supplementary Table 2, Additional file 1. Although some questions were similar to the first round, to evaluate the model’s usefulness, new questions were posed, such as ‘ Do you agree that the action items of the maturity model address all the relevant steps that need to be taken to successfully implement and sustain Lean in a hospital?’

The results from this round indicated that seven out of 14 respondents agreed that lean implementation in hospitals is often not sustained. Twelve respondents agreed that, once the four phases of the maturity model and the corresponding action items were completed, lean implementation in a hospital would be sustained over the long term. Furthermore, ten respondents indicated that the model could be applied to any hospital setting. Some suggested changes regarding how the change steps were integrated within each model phase whereas others noted that actions within lean implementation were ‘ongoing , iterative , and circular’ . Respondents also commented that it was a ‘very elaborate and well thought through model’ and ‘I can see that a well-structured , scientific method was followed’. The feedback from this second round helped alter the model to clarify how change steps were associated with maturity levels and to rename the third maturity level to “Experiment and Learn”. Action items were further refined.

Delphi round 3 – approach and outcomes

During the one-on-one online interviews of the third round, the final prescriptive maturity model was shared with the four respondents who took part in the second round and offered differing viewpoints. During these interviews, the researcher(s) presented the final SOLAR and the revisions based on the second round. (Supplementary Table 3, Additional File 1). The first question we asked was ‘ Do you agree with the naming of the model? ’. We also asked whether ‘the presentation of the phases of the maturity model was clearer’ . These questions stimulated an open conversation. The narrative that followed generally indicated that respondents were now clear that the aim of the prescriptive maturity model was to act as a guideline rather than a set of instructions. All respondents agreed that the final SOLAR was sound. Respondents also supported naming the third phase as ‘experiment and learn’, saying that ‘it’s very clear now that it’s cyclical’. Regarding the model’s usefulness, respondents said they ‘really thought this made sense from a theoretical and practical standpoint’ and ‘it is a useful model and the updates are practical’ . The final SOLAR, the result of a thorough theoretical investigation and three Delphi rounds, is presented in the next section.

The final SOLAR is a prescriptive maturity model consisting of four phases: Prepare, Plan, Experiment and Learn, and Sustain. The underlying action items are informed by lean implementation success factors, as discussed in Sect. 2.2, and by the 14 critical steps of the QIF discussed in Sect. 2.3. The action items of each phase are presented under four themes, namely strategy, resources, engaging people, and culture. The final element of the SOLAR is change management theory: The ten change steps, derived from Stouten et al. [ 64 ] are highlighted and incorporated during each phase and theme of the SOLAR. The action items and change management steps shown in the SOLAR are not intended as an exhaustive list but provide guidelines on aspects one must consider for a hospital that aims to adopt lean sustainably. Table  5 depicts the final SOLAR, which is discussed here in relation to the literature.

Phase 1: Prepare

As suggested by Zanon et al. [ 19 ], the first phase (Prepare) is associated with minor changes and setting the scene for lean implementation. In terms of the ‘strategy’ action items, following Grove et al. [ 37 ] and Lorden et al. [ 51 ] it is essential for a hospital to specify its (lean) strategic direction and improvement needs. It is key to contextualise how lean would fit into the hospital’s operating environment, the stakeholders of the lean adoption, and how they would benefit from lean adoption. Some stakeholders benefit more directly, such as patients, and others more indirectly such as suppliers. Furthermore, researching prior continuous improvement efforts and their successes and failures in a specific hospital is critical to setting the lean adoption strategy [ 14 , 56 ]. These actions contribute to fulfilling Stouten et al.’s change step 1 [ 64 ].

‘Resources’ such as technology and trained lean staff members are required for a successful lean implementation in a hospital [ 52 ]. This implies the need to identify staff with previous exposure to lean in the form of training or practical lean experience. In addition, assessing whether other stakeholders are currently adopting lean is recommended to ensure alignment with their efforts and possibly leveraging from them. One must also identify technology currently in place that may ease team communication and enable aspects such as visual (performance) management in wards.

An initial engagement with people on lean and the value that may be realised will set the scene for the change initiative. In terms of ‘engaging people’, further involving management, staff members, and other stakeholders is characterised by change step 2 [ 64 ]. It is important to obtain management commitment for lean adoption at an early stage [ 51 ]. The underlying action items of this theme resonate with the ‘strategy’-related action items in that management needs to align the strategy of the organisation and hospital with the strategy of lean adoption. Moreover, communicating a sense of urgency to staff and introducing the lean philosophy will mobilise energy for change during the preparation phase.

During this initial engagement with employees, their readiness for change can be assessed [ 45 ]. A clear indication of employees’ change readiness is their realisation that the hospital needs process improvement [ 67 ]. Simultaneously the extent to which the hospital’s culture aligns with the lean philosophy will highlight behaviour that is not conducive to a lean culture. This will guide the implementation team in determining where to place their change efforts as the lean implementation progresses. Altogether, these action items allow an organisation to move on to the next phase.

Phase 2: Plan

The planning phase is characterised by (initially) isolated lean improvements in the organisation [ 19 ]. The development of change-related knowledge and abilities is predominant in this phase [ 64 ]. With a clear company strategy in place from the preparation phase, the lean adoption strategy should be determined and set out in a clear adoption plan co-created by leaders at various hierarchical levels [ 3 ], for instance, by setting up monthly lean performance meetings at the top management level. Moreover, the specific value for various stakeholders anticipated by the lean adoption must be identified along with the criteria for measuring this value [ 74 ]. The value of lean in, for example, reducing waste such as waiting time that often occurs across all specialisations, can be articulated in this phase [ 1 ].

The planning phase provides the opportunity to list outstanding supporting resources and enlist external experts’ services to provide employees with the required lean knowledge and capability training specific to healthcare [ 17 , 48 ] aligned with, change step 7 [ 64 ]. The engagement of people across the organisation is a priority during this phase [ 45 ]. This includes appointing a lean adoption team, ideally consisting of lean champions and other front-line staff. Since hospitals often have clear hierarchies in place that may limit teamwork [ 52 ], staff members from all organisational levels must be included as lean practitioners to curb any communication barriers. These employees must be informal leaders and have an inherent mindset of critical thinking and questioning the status quo [ 45 ]. This lean adoption team’s supportive roles, processes, and responsibilities must also be specified during this phase. The variability of patient demand often leads to the last-minute acute engagement of front-line staff in patient care and during scheduled lean activities. Hence, during the planning phase, the roles, processes and responsibilities in such scenarios must be clarified. Furthermore, the lean adoption team must be empowered to lead the lean change by providing them with training on lean, leadership, and change management principles. Altogether these change steps are clearly aligned with change steps 2, 6 and 7 [ 64 ].

As part of ‘engaging people’, the shared vision for lean and common direction that was determined during the preparation phase must now be communicated clearly (i.e., Stouten et al.’s [ 64 ] change step 4). Because this should lead to initial acceptance of lean (and not resistance), in the context of a fast-paced hospital environment, it should emphasise how value will be added and waste eliminated [ 75 ], allowing healthcare workers to focus on the quality of patient care.

Also measuring the progress of lean adoption will contribute to engaging people. Indeed, Noori [ 49 ] contends that quick wins are essential to motivate hospital staff towards lean adoption. Developing an organisational performance feedback system enables the measurement of the relationship between lean adoption and performance improvement across all levels of the organisation. The performance should be discussed at time intervals that align with strategic, tactical, and operational performance meetings. Bhasin [ 76 ] noted that such a lean performance management and measurement system needs to fit each organisational level to promote positive organisational behaviour and change acceptance. Possible performance indicators include reduced patient waiting time, improved resource utilisation, and patient satisfaction [ 76 ].

The measurement of lean adoption might also identify certain behaviours that are not conducive to a lean culture, leading to interventions to build a more supportive continuous improvement lean culture [ 45 ]. Once the change readiness of most employees has been determined and that the lean philosophy aligns with the cultural preferences of the hospital, the planning phase can be used to start establishing a supportive culture of continuous improvement and to manage resistance to change [ 52 ] by giving positive attention to those employees who embrace change.

Phase 3: Experiment and learn

Each hospital has a unique operating environment and case mix [ 77 ]. A lean implementation maturity model must thus be contextualised as highlighted in the preparation phase. Therefore, the third phase has the longest duration, and this phase is associated with adapting lean according to the hospital’s specific requirements. This phase of lean maturity focuses on experimenting with lean adoption in various areas and proactively learning from this adoption by reviewing performance metrics.

From a strategic perspective, it is critical that top management support the lean adoption process and change its behaviour accordingly during this phase [ 50 ]. This may include revising some key performance indicators (KPIs) such as bed utilisation measures that management traditionally promotes [ 78 ]. Should such measurements prove to promote non-lean behaviour, top management needs to be proactive and change such KPIs. Installing lean performance meetings on a tactical and operational level will further assist in continuously learning from the lean adoption. These meetings provide a platform for discussing the measurement of lean’s value for stakeholders using the measurement criteria established in the planning phase [ 74 ]. Lastly, lean performance meetings will facilitate Stouten et al.’s [ 64 ] change step 4, 5, and 8. It is also beneficial to precisely plan and create short-term wins during this phase; those short-term successes can be used to reinforce the lean transformation (change step 8).

Change step 2 can be further executed by developing internal lean experts [ 52 ] through establishing a guiding coalition consisting of internal staff members. Although external experts enlisted during the previous phase may still be involved in the experiment and learn phase, their involvement will diminish over time as internal experts are developed [ 79 ] who could then start training other staff members during this phase. Using staff to train other employees on aspects of lean (such as the use of the unique lean vocabulary applicable to healthcare) can be a valuable tool in accelerating the adoption of lean while empowering staff to identify waste in their respective areas [ 35 ], which will mobilise the change and develop the required knowledge and abilities that promote the general acceptance of lean throughout the organisation (as described in Stouten et al.’s [ 64 ] change step 5 and 7). Inter-departmental cooperation is a hospital-specific aspect to establish for the realisation of this acceptance [ 36 ]. This tends to be a challenge, given the highly specialised disciplines in a hospital as well as the subsequent organisational silos that this creates [ 80 ].

Internal experts are referred to as lean champions, and the development of these resources is closely related to change step 6. Other supporting resources, such as software enabling knowledge management must also be provided [ 81 ]. Certain processes may need to be changed in such a way that they are aligned with the change vision set out in the preparation phase of the strategy theme. If, for example, the change vision of the hospital was set in the preparation phase to include collaboration between suppliers such as pathology and radiology services, process adaptations may include regular lean meetings between the front-line hospital staff and the supply staff.

The performance measurement system installed during the previous phase will act as a support tool through which the adoption team’s performance can regularly be evaluated. This action item also enables teams across units in the hospital to benchmark their performance in terms of achieving lean goals set out during the earlier phases, which will assist in eliminating organisational silos typical in hospitals whilst also embedding the lean adoption [ 70 ]. The feedback system must trigger remedial action so hospital staff can learn from mistakes [ 14 ] and make changes accordingly. Furthermore, feedback on lean adoption must be communicated throughout the hospital [ 51 ], providing all stakeholders with information on the implementation progress. From the above, it is clear that change steps 5, 6, 7 and 8 are addressed in the action items.

Most employees will experience changes in the hospital during the experiment and learn phase. It is important to reinforce the lean organisational culture of continuous improvement as the phase continues [ 53 ], whereby management needs to display exemplary lean behaviour [ 82 ]. Change steps 6 and 9 are clearly aligned with those actions. Finally, the experiment and learn phase is iterative in nature. As lean implementation and the associated action items of the SOLAR are progressing, it is important to modify actions to fit in with the specific hospital environment. For example, redefining the value that lean unlocks for some stakeholders may be necessary. This implies that some aspects of the planning phase should be amended.

Phase 4: Sustain

The final phase of the SOLAR is characterised by the continuous monitoring of process improvements [ 19 ]. Change steps 9 and 10 promote the monitoring and institutionalisation of the change and are associated with this final phase [ 64 ]. Change will be institutionalised by maintaining the initial strategy and common direction [ 36 ]. It also remains important during this phase to continue to set lean goals and measure the value that lean realises for all stakeholders.

Resources such as technology and specific software need to be kept up-to-date, and changes to processes institutionalised by continuously updating standard operating procedures and staff structures [ 56 ]. Allocating resources to amend the reporting structure of certain units may be necessary. Furthermore, some KPIs, such as waiting time and its definition [ 1 ], may change as the hospital environment evolves. It remains critical to continue with lean training during this final phase whilst normalising the supportive lean culture of continuous improvement [ 48 ].

The high failure rate of continuous improvement initiatives [ 7 ] and lean implementation in hospitals [ 13 ] indicate a latent need for more clarity on how to adopt lean in a hospital setting. So far, however, there was no lean maturity model specific to healthcare or a hospital environment [ 19 ]. This is problematic because the hierarchical nature of healthcare is often a barrier to bottom-up improvement and the adoption of lean throughout the hospital (system-wide) instead of applying tools and techniques in isolation [ 11 ]. The SOLAR developed herein, therefore, responds to the need for a hospital-wide lean maturity model that takes into account the complexities of healthcare. In developing the SOLAR, relevant aspects from the literature were synthesised. As such, this research expands on the prolific lean implementation in healthcare literature by combining the known success factors with implementation science and change management theory. This makes the SOLAR simultaneously unique, comprehensive, and more practical.

A prominent complexity covered by the SOLAR is the primary goal of healthcare workers in hospitals to ensure the quality of patient care. Due to the sensitive nature of hospital settings and the human lives that are often at stake, hospital staff are more risk averse. A hospital is not the ideal setting for ‘trial and error’, often part of regular lean adoptions [ 70 ]. Continuous improvement initiatives may, therefore – initially – seem counter-intuitive for healthcare workers. However, since a key building block of the SOLAR is evidence-based implementation science, hospital staff are more likely to have confidence in using the SOLAR to guide them along their lean adoption journey. Furthermore, the SOLAR provides guided experimentation and learning in the third phase of sustainable lean implementation. The action items in this phase of the SOLAR allow hospitals to tailor implementation methods that are best suited to their unique operating context through guided experimentation and learning.

A second hospital-specific complexity is covered by integrating change management theory [ 64 ] throughout the SOLAR. Specialisation silos and hierarchies are often a barrier to sustainable lean implementation in hospitals [ 83 ]. The change coalition that is established in the planning phase of the SOLAR consists of staff from all units across all levels of the hospital which enables the permeation of barriers that existed because of hierarchies and silos. Consequently, the SOLAR emphasises the importance of identifying the stakeholders throughout the hospital that will be impacted by lean adoption in the preparation phase, as well as the subsequent defining of stakeholder value, and measuring and evaluating how lean adds value throughout the hospital. The concept of ‘value’, which is often conceptualised solely from the customer’s (the patient’s) perspective, is also determined for the different stakeholders. Ensuring that the perspectives of multiple stakeholders are proactively taken into account also reduces the risk of focusing on internal lean goals such as efficiency and cost reduction, which is often seen in public service settings [ 84 ].

In sum, in conjunction with the solid theoretical base, the SOLAR utilises input from lean healthcare practitioners and academics. As confirmed by them, the SOLAR is based on relevant theory and yet remains practical.

Practical implications

The SOLAR was developed to be used by practitioners and academics as a practical guideline to test their approach on implementing lean in hospitals against. In particular, we envisage that top managers of hospitals, strategic advisors, and those in organisational development and continuous process improvement roles will find the SOLAR useful to tailor their hospital’s lean adoption approach. For example, using the SOLAR as an inspiration, data on the lean adoption progress can be captured by the lean adoption team and then discussed during periodical lean adoption strategy meetings. This data will be useful to (top) managers since it drives their learning process and informs decisions on support required to sustainably adopt lean.

The SOLAR also addresses the critical aspects pertaining to strategy, resources, the engagement of people, and organisational culture throughout lean adoption in hospitals. As mentioned, the SOLAR guides the implementation approach by emphasizing certain actions along the phases of the lean implementation journey in a hospital. This has proven to be a suitable characteristic of the SOLAR since respondents to the Delphi study agreed on its usefulness. Although the maturity phases of the SOLAR have been presented sequentially, there may be a need for hospitals implementing lean to revisit some of the phases as insights are gained during their lean implementation journey. Such tailoring to the unique hospital environment also enables hospital staff to accept lean adoption [ 83 , 85 ]. While balancing between ‘theorising’ and ‘generalising’ as called for by Åhlström et al. [ 86 ], the model is further adaptable to local hospital environments.

Limitations and future research

Creating an exhaustive list of items that need to be completed in a lean adoption journey is impossible, given that different contexts might require slightly different foci and organizational change is a dynamic process. And although we followed a systematic approach to reviewing the literature and Delphi study respondents have screened the SOLAR in various rounds, we may still have missed certain points. We propose that for purposive expert sampling, one could also consider approaching formal interest groups and associations with members who specialise in lean (i.e. the Lean Institute Africa, the Dutch Lean Healthcare community united in the ‘Lean in de Zorg’ (LIDZ) foundation, and the Lean Global Network).

Because some respondents in the Delphi study expressed the need for a more descriptive maturity model, this may be another valuable extension of our research. Reponen et al. [ 87 ] proposed a conceptual framework that can be used to benchmark lean performance in healthcare environments against best practices whilst taking the context of the environment into account. Since the inclusion of specific instructions on how to implement aspects such as training, communicating the strategy, and organising resources were not included in the aim of this research, the authors recommend that future research should include these aspects.

The next step is to validate the SOLAR in a hospital setting by further testing and possibly refining it. This can either be done retrospectively through a longitudinal study of hospitals that have implemented lean or as an intervention study following the action research approach [ 88 , 89 ]. In the case of action research, positioning the SOLAR as a guideline for the lean implementation will be the starting point. Post-implementation focus groups can subsequently be used as a further validation tool of the SOLAR. We further propose to assess to what extent the lean intervention is brought about by the further operationalizing the action items of the SOLAR. One way to assess this is by using the PARTI (Participatory Action Research, Translation, and Implementation) model underpinned by implementation science [ 90 ].

Hospitals are unique service environments that provide an essential and critical service to the community. Furthermore, hospitals tend to be high-pressure environments with variable demand and specialised services. These specialisations often result in silo structures which are hierarchical in nature and associated with waste and inefficiencies. Lean implementation in hospitals has, however, been proven to result in significant process improvements and enhanced quality of patient care. To address lean implementation efforts that are often not sustained in hospitals, we have developed the SOLAR: A unique maturity model that can act as a guideline for hospitals embarking on a lean implementation journey. After gathering expert feedback in three Delphi rounds, the SOLAR is suitable for use by academics and practitioners involved in lean deployment in hospitals, particularly because of its strong underpinning by implementation science and change management theory.

Availability of data and materials

The data used for the Systematic Literature Review was retrieved from publicly available internet databases as specified in the manuscript and is available from the authors upon reasonable request. The dataset for the Delphi study is not publicly available to protect the identity of respondents.

Abbreviations

Enhancing Transparency in Reporting the Synthesis of Qualitative Research

Lean in de Zorg

Participatory Action Research, Translation, and Implementation

Preferred Reporting Items for Systematic reviews and Meta-Analyses

Quality Implementation Framework

Systematic Literature Review

Sustaining of Lean Adoption in Hospitals Roadmap

Van Zyl-Cillié M, Demirtas D, Hans E. Wait!What does that mean? Eliminating ambiguity of delays in healthcare from an OR/MS perspective. Health Syst. 2023;12(1):3–21.

Vanichchinchai A. Relationships among lean, service quality expectation and performance in hospitals. Int J Lean Six Sigma. 2022;13(2):457–73.

Article   Google Scholar  

Van Beers JCAM, Van Dun DH, Wilderom CPM. Effective hospital-wide lean implementation: top-down, bottom-up or through co-creative role modeling? Int J Lean Six Sigma. 2022;13(1):46–66.

Van Rossum L, Aij KH, Simons FE, Van der Eng N, Ten Have WD. Lean healthcare from a change management perspective: the role of leadership and workforce flexibility in an operating theatre. J Health Organ Manag. 2016;30(3):475–93.

Stelson P, Hille J, Eseonu C, Doolen T. What drives continuous improvement project success in healthcare? Int J Health Care Qual Assur. 2017;30(1):43–57.

Akugizibwe AM, Clegg DR. Lean implementation: an evaluation from the implementers’ perspective. Int J Lean Enterp Res. 2014;1(2):132–61.

Lameijer BA, Boer H, Antony J, Does RJMM. Continuous improvement implementation models: a reconciliation and holistic metamodel. Prod Plan Control. 2021;34(11):1062–81.

Al-Balushi S, Sohal AS, Singh PJ, Hajri A, Al, Farsi YMA, Abri RA. Readiness factors for lean implementation in healthcare settings - a literature review. J Health Organ Manag. 2014;28:135–53.

Stanković AK. Developing a lean consciousness for the clinical laboratory. J Med Biochem. 2008;27:354.

Aherne J, Whelton J. Applying lean in healthcare. A collection of international case studies. J Chem Inform Modell. 2010. CRC Press.

Lindsay CF, Aitken J. Using Programme Theory to evaluate lean interventions in healthcare. Prod Plan Control. 2024;35(8):824–41.

Lameijer BA, De Mast J, Does R. Lean six Sigma deployment and maturity models: a critical review. Qual Manag J. 2017;24(4):6–20.

Hallam CRA, Contreras C. Lean healthcare: scale, scope and sustainability. Int J Health Care Qual Assur. 2018;31(7):684–96.

D’Andreamatteo A, Ianni L, Lega F, Sargiacomo M. Lean in healthcare: a comprehensive review. Health Policy. 2015;119:1197-209.

Henrique DB, Filho MG, Marodin G, Jabbour ABL, de Chiappetta Jabbour S. A framework to assess sustaining continuous improvement in lean healthcare. Int J Prod Res. 2021;59(10):2885–904.

Kunnen YS, Roemeling OP, Smailhodzic E. What are barriers and facilitators in sustaining lean management in healthcare? A qualitative literature review. BMC Health Serv Res. 2023;23(1):958.

Andersen H, Røvik KA. Lost in translation: a case-study of the travel of lean thinking in a hospital. BMC Health Serv Res. 2015;15(1):1–9.

Antony J, Sunder MV, Sreedharan R, Chakraborty A, Gunasekaran A. A systematic review of lean in healthcare: a global perspective. Int J Qual Reliab Manag. 2019;36:1370–91 Emerald Group Holdings Ltd.

Zanon LG, Ulhoa TF, Esposto KF. Performance measurement and lean maturity: congruence for improvement. Prod Plan Control. 2021;32(9):760–74.

Becker J, Knackstedt R, Pöppelbuß J. Developing maturity models for IT management. Bus Inf Syst Eng. 2009;1(3):213–22.

Röglinger M, Pöppelbuß J. What makes a useful maturity model? a framework of general design principles for maturity models and its demonstration in business process management. 2011.

Maier AM, Moultrie J, Clarkson PJ. Assessing organisational capabilities: reviewing and guiding the development of maturity grids. IEEE Trans Eng Manag. 2012;59(1):138–59.

Marsilio M, Pisarra M, Rubio K, Shortell S. Lean adoption, implementation, and outcomes in public hospitals: benchmarking the US and Italy health systems. BMC Health Serv Res. 2022;22(1):122.

do Mourão CAM, de Miranda Filho AN, Nogueira RN, de Barros Neto JP, da Costa JM. Using storytelling to understand a company’s lean journey. In: IGLC 2021–29th Annual Conference of the International Group for Lean Construction - Lean Construction in Crisis Times: Responding to the Post-Pandemic AEC Industry Challenges. Department of Engineering, Civil Engineering Division, Pontificia Universidad Catolica del Peru; 2021: 423–32.

Jayaraman R. Lean management maturity in the Indian industry: results from a longitudinal exploratory study using exploratory factor analysis. Int J Oper Quant Manag. 2021;27(2):81–110.

Google Scholar  

Jami Pour M, Jafari SM. Toward a maturity model for the application of social media in healthcare: the health 2.0 roadmap. Online Inf Rev. 2019;43(3):404–25.

Tortorella GL, Vergara LGL, Ferreira EP. Lean manufacturing implementation: an assessment method with regard to socio-technical and ergonomics practices adoption. Int J Adv Manuf Technol. 2017;89(9–12):3407–18.

Jørgensen F, Matthiesen R, Nielsen J, Johansen J. Lean Maturity, Lean Sustainability. In: Advances in Production Management Systems: International IFIP TC 5, WG 57 Conference on Advances in Production Management Systems. Linkoping: Springer; 2007: 371–8.

Tortorella GL, Marodin GA, Fogliatto FS, Miorando R. Learning organisation and human resources management practices: an exploratory research in medium-sized enterprises undergoing a lean implementation. Int J Prod Res. 2015;53(13):3989–4000.

Tortorella GL, Fogliatto FS. Method for assessing human resources management practices and organisational learning factors in a company under lean manufacturing implementation. Int J Prod Res. 2014;52(15):4623–45.

Verrier B, Rose B, Caillaud E. Sustainable manufacturing through lean and green approach: best practices and indicators. In: Volume 2A: 33rd Computers and Information in Engineering Conference. American Society of Mechanical Engineers; 2013.

Mettler T, Rohner P. Situational maturity models as instrumental artifacts for organisational design. In: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, DESRIST ’09. 2009.

Netland TH, Ferdows K. The S-curve effect of lean implementation. Prod Oper Manag. 2016;25(6):1106–20.

Negrão LLL, Lopes de Sousa Jabbour AB, Latan H, Godinho Filho M, Chiappetta Jabbour CJ, Ganga GMD. Lean manufacturing and business performance: testing the S-curve theory. Prod Plan Control. 2020;31(10):771–85.

Aij KH, Simons FE, Widdershoven GAM, Visse M. Experiences of leaders in the implementation of lean in a teaching hospital - barriers and facilitators in clinical practices: a qualitative study. BMJ Open. 2013;3(10):e003605.

de Souza LB, Pidd M. Exploring the barriers to lean health care implementation. Public Money Manag. 2011;31(1):59–66.

Grove AL, Meredith JO, Macintyre M, Angelis J, Neailey K. UK health visiting: challenges faced during lean implementation. Leadersh Health Serv. 2010;23(3):204–18.

Mazur LM, Rothenberg L, McCreery JK. Measuring and understanding change recipients’ buy-in during lean program implementation efforts. In: 61st Annual IIE Conference and Expo Proceedings. 2011.

Sobek IIDK. Lean healthcare implementation: critical success factors. 61st Annual IIE Conference and Expo Proceedings. 2011.

Steed A. An exploration of the leadership attributes and methods associated with successful lean system deployments in acute care hospitals. Qual Manag Health Care. 2012;21(1):48–58.

Maijala R, Eloranta S, Reunanen T, Ikonen TS. Successful implementation of lean as managerial principle in health care: a conceptual analysis from systematic literature review. Int J Technol Assess Health Care. 2018;34(2):134–46.

Article   PubMed   Google Scholar  

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Siddaway AP, Wood AM, Hedges LV. How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annu Rev Psychol. 2019;70(1):747–70.

Tong A, Flemming K, McInnes E, Olivier S, Craig J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. 2012;12:1–8.

Jain V, Ajmera P. Modelling of the factors affecting lean implementation in healthcare using structural equation modelling. Int J Syst Assur Eng anag. 2019;10(4):563–75.

Noori B. Identifying critical issues in lean implementation in hospitals. Hosp Top. 2015;93(2):44–52.

Ajmera P, Jain V. A fuzzy interpretive structural modeling approach for evaluating the factors affecting lean implementation in Indian healthcare industry. Int J Lean Six Sigma. 2020;11(2):376–597.

Régis TKO, Gohr CF, Santos LC. Lean healthcare implementation: experiences and lessons learned from Brazilian hospitals. RAE Revista De Administracao De Empresas. 2018;58(1):30–43.

Noori B. The critical success factors for successful lean implementation in hospitals. Intl J Prod Qual Manag. 2015;15(1):108–26.

Patri R, Suresh M. Factors influencing lean implementation in healthcare organisations: an ISM approach. Int J Health Manag. 2018;11(1):25–37.

Lorden AL, Zhang Y, Lin SH, Côté MJ. Measures of success: the role of human factors in lean implementation in healthcare. Qual Manag J. 2014;21(3):26–37.

Harrison MI, Paez K, Carman KL, Stephens J, Smeeding L, Devers KJ, et al. Effects of organisational context on lean implementation in five hospital systems. Health Care Manage Rev. 2016;41(2):127–44.

Ulhassan W, Sandahl C, Westerlund H, Henriksson P, Bennermo M, Von Thiele Schwarz U et al. Antecedents and characteristics of lean thinking implementation in a Swedish hospital: a case study. Qual Manag Health Care. 2013;22(1):48–61.

Poksinska B. The current state of lean implementation in health care: literature review. Qual Manag Health Care. 2010;19(4):319–29.

Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10(1):53–79.

Holden RJ. Lean thinking in emergency departments: a critical review. Ann Emerg Med. 2011;57(3):265–73.

Eccles MP, Mittman BS. Welcome to implementation science. Implement Sci. 2006;1:1.

May C, Finch T. Implementing, embedding, and integrating practices: an outline of normalisation process theory. Sociology. 2009;43(3):535–54.

Tabak RG, Khoong EC, Chambers DA, Brownson RC. Bridging research and practice: models for dissemination and implementation research. Am J Prev Med. 2012;43:337–50.

Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organisations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629.

Article   PubMed   PubMed Central   Google Scholar  

Meyers DC, Durlak JA, Wandersman A. The quality implementation framework: a synthesis of critical steps in the implementation process. Am J Community Psychol. 2012;50(3–4):462–80.

Al-Haddad S, Kotnour T. Integrating the organisational change literature: a model for successful change. J Organ Chang Manag. 2015;28(2):234–62.

Harrison R, Fischer S, Walpola RL, Chauhan A, Babalola T, Mears S, et al. Where do models for change management, improvement and implementation meet? A systematic review of the applications of change management models in healthcare. J Healthc Leadersh. 2021;13:85–108 Dove Medical Press Ltd.

Stouten J, Rousseau DM, De Cremer D. Successful organisational change: integrating the management practice and scholarly literature. Acad Manag Annals. 2018;12(2):752–88.

Langley A, Smallman C, Tsoukas H, Van De Ven AH. Process studies of change in organisation and management: unveiling temporality, activity, and flow. Acad Manag J. 2013;56(1):1069–70.

Rousseau DM, ten Have S. Evidence-based change management. Organ Dyn. 2022;51(3):100899.

Errida A, Lotfi B. The determinants of organisational change management success: literature review and case study. Int J Eng Bus Manag. 2021;13:1847970211016273.

Martin N, Fischer DA, Kerpedzhiev GD, Goel K, Leemans SJJ, Röglinger M, et al. Opportunities and challenges for process mining in organisations: results of a Delphi study. Bus Inf Syst Eng. 2021;63(5):511–27.

Skinner R, Nelson RR, Chin WW, Land L. The Delphi method research strategy in studies of information systems. Commun Association Inform Syst. 2015;37:31–63.

Prado-Prado JC, Fernández-González AJ, Mosteiro-Añón M, García-Arca J. Increasing competitiveness through the implementation of lean management in healthcare. Int J Environ Res Public Health. 2020;17(14):1–26.

Skulmoski GJ, Hartman FT, Krahn J. The Delphi method for graduate research. J Inf Technol Educ Res. 2007;6(1):1–21.

Adler M, Ziglio E. Gazing into the oracle: The Delphi method and its application to social policy and public health. Jessica Kingsley; 1996.

Trochim W, Donnelly JP. The research methods knowledge base. 3rd ed. Thompson Publishing Group; 2007.

Franken JC, van Dun DH, Wilderom CP. Kaizen event process factors for operational performance improvement: an archival study. Prod Plan Control 2024:1–5.

Bakke AL, Johansen A. Implementing of lean - challenges and lessons learned. Procedia Computer Science. Elsevier B.V.; 2019. pp. 373–80.

Bhasin S. Lean and performance measurement. J Manuf Technol Manag. 2008;19(5):670–84.

Hulshof PJH, Kortbeek N, Boucherie RJ, Hans EW, Bakker PJM. Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS. Health Syst. 2012;1(2):129–75.

Khalifa M, Khalid P. Developing strategic health care key performance indicators: a case study on a tertiary care hospital. Procedia Computer Science. Elsevier B.V.; 2015. pp. 459–66.

Holmemo MDQ, Powell DJ, Ingvaldsen JA. Making it stick on borrowed time: the role of internal consultants in public sector lean transformations. TQM J. 2018;30(3):217–31.

Drupsteen J, Van der Vaart T, Van Donk DP. Integrative practices in hospitals and their impact on patient flow. Int J Oper Prod Manag. 2013;33(7):912–33.

Zhang B, Niu Z, Liu C. Lean tools, knowledge management, and lean sustainability: the moderating effects of study conventions. Sustain (Switz). 2020;12(3):956.

Tortorella G, Van Dun DH, De Almeida AG. Leadership behaviors during lean healthcare implementation: a review and longitudinal study. J Manuf Technol Manage. 2020;31(1):193–215.

Rosa A, Marolla G, Lega F, Manfredi F. Lean adoption in hospitals: the role of contextual factors and introduction strategy. BMC Health Serv Res. 2021;21(1):1–8.

Radnor Z, Johnston R. Lean in UK Government: internal efficiency or customer service. Prod Plan Control. 2013;24(10–11):903–15.

Hung D, Gray C, Martinez M, Schmittdiel J, Harrison MI. Acceptance of lean redesigns in primary care. Health Care Manage Rev. 2017;42(3):203–12.

Åhlström P, Danese P, Hines P, Netland TH, Powell D, Shah R, et al. Is lean a theory? Viewpoints and outlook. Int J Oper Prod Manag. 2021;41(12):1852–78.

Reponen E, Rundall TG, Shortell SM, Blodgett JC, Juarez A, Jokela R et al. Benchmarking outcomes on multiple contextual levels in lean healthcare: a systematic review, development of a conceptual framework, and a research agenda. BMC Health Serv Res. 2021;21(1):1–18.

Coughlan P, Coghlan D. Action research for operations management. Int J Oper Prod Manag. 2002;22(2):220–40.

Oliva R. Intervention as a research strategy. J Oper Manag. 2019;65(7):710–24.

Fitzgerald A, Hayes K, Curry J, Eljiz K, Radford K. Balancing Yin and Yang: the development of a framework using participatory action research for the translation and implementation (part 1) of new practices. Asia Pac J Health Manag. 2016;11:14–24.

Download references

Acknowledgements

Not applicable.

The authors received no financial support for conducting this research.

Open access funding provided by North-West University.

Author information

Authors and affiliations.

Faculty of Engineering, North-West University, 11 Hoffman Street, Potchefstroom, South Africa

Maria M. Van Zyl-Cillié & Hanneke Meijer

Faculty of Behavioural, Management and Social Sciences, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands

Maria M. Van Zyl-Cillié & Desirée H. van Dun

You can also search for this author in PubMed   Google Scholar

Contributions

The authors’ contributions are as follows: MVZ-C: Conceptualization, Methodology and research design, Data analysis, Writing-Review and Editing; DVD: Conceptualisation, Methodology and research design, Writing-Review and Editing; HM: Conceptualization, Methodology and research design, Data analysis. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Maria M. Van Zyl-Cillié .

Ethics declarations

Ethics approval and consent to participate.

This study has been approved by the Ethics Committee of the Faculty of Engineering at the North-West University (South Africa). The assigned ethics number is NWU 00283-19-A1. Participants were informed in the recruitment e-mail that participation is voluntary and that their responses would be anonymously incorporated into the research. Informed consent was obtained from respondents by notifying them in the recruitment e-mail that submitting the survey on Google-forms would serve as informed consent.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1.  delphi study survey questions, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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/4.0/ .

Reprints and permissions

About this article

Cite this article.

Van Zyl-Cillié, M.M., van Dun, D.H. & Meijer, H. Toward a roadmap for sustainable lean adoption in hospitals: a Delphi study. BMC Health Serv Res 24 , 1088 (2024). https://doi.org/10.1186/s12913-024-11529-4

Download citation

Received : 31 January 2024

Accepted : 03 September 2024

Published : 18 September 2024

DOI : https://doi.org/10.1186/s12913-024-11529-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Lean in healthcare
• Lean implementation
• Change management

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]