steps in conducting systematic literature review

Systematic Review

Library Help
What is a Systematic Review (SR)?

Steps of a Systematic Review

Framing a Research Question
Developing a Search Strategy
Searching the Literature
Managing the Process
Meta-analysis
Publishing your Systematic Review

Forms and templates

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PICO Template
Inclusion/Exclusion Criteria
Database Search Log
Review Matrix
Cochrane Tool for Assessing Risk of Bias in Included Studies

• PRISMA Flow Diagram - Record the numbers of retrieved references and included/excluded studies. You can use the Create Flow Diagram tool to automate the process.

• PRISMA Checklist - Checklist of items to include when reporting a systematic review or meta-analysis

PRISMA 2020 and PRISMA-S: Common Questions on Tracking Records and the Flow Diagram

PROSPERO Template
Manuscript Template
Steps of SR (text)
Steps of SR (visual)
Steps of SR (PIECES)

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from the UMB HSHSL Guide. (26 min) on how to conduct and write a systematic review from RMIT University from the VU Amsterdam . , (1), 6–23. https://doi.org/10.3102/0034654319854352

. (1), 49-60. . (4), 471-475.

(2020) (2020) - Methods guide for effectiveness and comparative effectiveness reviews (2017) - Finding what works in health care: Standards for systematic reviews (2011) - Systematic reviews: CRD’s guidance for undertaking reviews in health care (2008)


	entify your research question. Formulate a clear, well-defined research question of appropriate scope. Define your terminology. Find existing reviews on your topic to inform the development of your research question, identify gaps, and confirm that you are not duplicating the efforts of previous reviews. Consider using a framework like or to define you question scope. Use to record search terms under each concept. It is a good idea to register your protocol in a publicly accessible way. This will help avoid other people completing a review on your topic. Similarly, before you start doing a systematic review, it's worth checking the different registries that nobody else has already registered a protocol on the same topic. - Systematic reviews of health care and clinical interventions - Systematic reviews of the effects of social interventions (Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies) - The protocol is published immediately and subjected to open peer review. When two reviewers approve it, the paper is sent to Medline, Embase and other databases for indexing. - upload a protocol for your scoping review - Systematic reviews of healthcare practices to assist in the improvement of healthcare outcomes globally - Registry of a protocol on OSF creates a frozen, time-stamped record of the protocol, thus ensuring a level of transparency and accountability for the research. There are no limits to the types of protocols that can be hosted on OSF. - International prospective register of systematic reviews. This is the primary database for registering systematic review protocols and searching for published protocols. . PROSPERO accepts protocols from all disciplines (e.g., psychology, nutrition) with the stipulation that they must include health-related outcomes. - Similar to PROSPERO. Based in the UK, fee-based service, quick turnaround time. - Submit a pre-print, or a protocol for a scoping review. - Share your search strategy and research protocol. No limit on the format, size, access restrictions or license. outlining the details and documentation necessary for conducting a systematic review: , (1), 28.
	Clearly state the criteria you will use to determine whether or not a study will be included in your search. Consider study populations, study design, intervention types, comparison groups, measured outcomes. Use some database-supplied limits such as language, dates, humans, female/male, age groups, and publication/study types (randomized controlled trials, etc.).
	Run your searches in the to your topic. Work with to help you design comprehensive search strategies across a variety of databases. Approach the grey literature methodically and purposefully. Collect ALL of the retrieved records from each search into , such as , or , and prior to screening. using the and .
- export your Endnote results in this screening software	Start with a title/abstract screening to remove studies that are clearly not related to your topic. Use your to screen the full-text of studies. It is highly recommended that two independent reviewers screen all studies, resolving areas of disagreement by consensus.
	Use , or systematic review software (e.g. , ), to extract all relevant data from each included study. It is recommended that you pilot your data extraction tool, to determine if other fields should be included or existing fields clarified.
Risk of Bias (Quality) Assessment - (download the Excel spreadsheet to see all data)	Use a Risk of Bias tool (such as the ) to assess the potential biases of studies in regards to study design and other factors. Read the to learn about the topic of assessing risk of bias in included studies. You can adapt ( ) to best meet the needs of your review, depending on the types of studies included.
- - -	Clearly present your findings, including detailed methodology (such as search strategies used, selection criteria, etc.) such that your review can be easily updated in the future with new research findings. Perform a meta-analysis, if the studies allow. Provide recommendations for practice and policy-making if sufficient, high quality evidence exists, or future directions for research to fill existing gaps in knowledge or to strengthen the body of evidence. For more information, see: . (2), 217–226. https://doi.org/10.2450/2012.0247-12 - Get some inspiration and find some terms and phrases for writing your manuscript - Automated high-quality spelling, grammar and rephrasing corrections using artificial intelligence (AI) to improve the flow of your writing. Free and subscription plans available.
- -	8. Find the best journal to publish your work. Identifying the best journal to submit your research to can be a difficult process. To help you make the choice of where to submit, simply insert your title and abstract in any of the listed under the tab.

Adapted from A Guide to Conducting Systematic Reviews: Steps in a Systematic Review by Cornell University Library

This diagram illustrates in a visual way and in plain language what review authors actually do in the process of undertaking a systematic review.

This diagram illustrates what is actually in a published systematic review and gives examples from the relevant parts of a systematic review housed online on The Cochrane Library. It will help you to read or navigate a systematic review.

Source: Cochrane Consumers and Communications (infographics are free to use and licensed under Creative Commons )

Check the following visual resources titled " What Are Systematic Reviews?"

Video with closed captions available
Animated Storyboard

Image:

- the methods of the systematic review are generally decided before conducting it.
- searching for studies which match the preset criteria in a systematic manner
- sort all retrieved articles (included or excluded) and assess the risk of bias for each included study
- each study is coded with preset form, either qualitatively or quantitatively synthesize data.
- place results of synthesis into context, strengths and weaknesses of the studies
- report provides description of methods and results in a clear and transparent manner

Source: Foster, M. (2018). Systematic reviews service: Introduction to systematic reviews. Retrieved September 18, 2018, from

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Last Updated: Aug 26, 2024 12:37 PM
URL: https://lib.guides.umd.edu/SR

Reference management. Clean and simple.

How to write a systematic literature review [9 steps]

What is a systematic literature review?

Where are systematic literature reviews used, what types of systematic literature reviews are there, how to write a systematic literature review, 1. decide on your team, 2. formulate your question, 3. plan your research protocol, 4. search for the literature, 5. screen the literature, 6. assess the quality of the studies, 7. extract the data, 8. analyze the results, 9. interpret and present the results, registering your systematic literature review, frequently asked questions about writing a systematic literature review, related articles.

A systematic literature review is a summary, analysis, and evaluation of all the existing research on a well-formulated and specific question.

Put simply, a systematic review is a study of studies that is popular in medical and healthcare research. In this guide, we will cover:

the definition of a systematic literature review
the purpose of a systematic literature review
the different types of systematic reviews
how to write a systematic literature review

➡️ Visit our guide to the best research databases for medicine and health to find resources for your systematic review.

Systematic literature reviews can be utilized in various contexts, but they’re often relied on in clinical or healthcare settings.

Medical professionals read systematic literature reviews to stay up-to-date in their field, and granting agencies sometimes need them to make sure there’s justification for further research in an area. They can even be used as the starting point for developing clinical practice guidelines.

A classic systematic literature review can take different approaches:

Effectiveness reviews assess the extent to which a medical intervention or therapy achieves its intended effect. They’re the most common type of systematic literature review.
Diagnostic test accuracy reviews produce a summary of diagnostic test performance so that their accuracy can be determined before use by healthcare professionals.
Experiential (qualitative) reviews analyze human experiences in a cultural or social context. They can be used to assess the effectiveness of an intervention from a person-centric perspective.
Costs/economics evaluation reviews look at the cost implications of an intervention or procedure, to assess the resources needed to implement it.
Etiology/risk reviews usually try to determine to what degree a relationship exists between an exposure and a health outcome. This can be used to better inform healthcare planning and resource allocation.
Psychometric reviews assess the quality of health measurement tools so that the best instrument can be selected for use.
Prevalence/incidence reviews measure both the proportion of a population who have a disease, and how often the disease occurs.
Prognostic reviews examine the course of a disease and its potential outcomes.
Expert opinion/policy reviews are based around expert narrative or policy. They’re often used to complement, or in the absence of, quantitative data.
Methodology systematic reviews can be carried out to analyze any methodological issues in the design, conduct, or review of research studies.

Writing a systematic literature review can feel like an overwhelming undertaking. After all, they can often take 6 to 18 months to complete. Below we’ve prepared a step-by-step guide on how to write a systematic literature review.

Decide on your team.
Formulate your question.
Plan your research protocol.
Search for the literature.
Screen the literature.
Assess the quality of the studies.
Extract the data.
Analyze the results.
Interpret and present the results.

When carrying out a systematic literature review, you should employ multiple reviewers in order to minimize bias and strengthen analysis. A minimum of two is a good rule of thumb, with a third to serve as a tiebreaker if needed.

You may also need to team up with a librarian to help with the search, literature screeners, a statistician to analyze the data, and the relevant subject experts.

Define your answerable question. Then ask yourself, “has someone written a systematic literature review on my question already?” If so, yours may not be needed. A librarian can help you answer this.

You should formulate a “well-built clinical question.” This is the process of generating a good search question. To do this, run through PICO:

Patient or Population or Problem/Disease : who or what is the question about? Are there factors about them (e.g. age, race) that could be relevant to the question you’re trying to answer?
Intervention : which main intervention or treatment are you considering for assessment?
Comparison(s) or Control : is there an alternative intervention or treatment you’re considering? Your systematic literature review doesn’t have to contain a comparison, but you’ll want to stipulate at this stage, either way.
Outcome(s) : what are you trying to measure or achieve? What’s the wider goal for the work you’ll be doing?

Now you need a detailed strategy for how you’re going to search for and evaluate the studies relating to your question.

The protocol for your systematic literature review should include:

the objectives of your project
the specific methods and processes that you’ll use
the eligibility criteria of the individual studies
how you plan to extract data from individual studies
which analyses you’re going to carry out

For a full guide on how to systematically develop your protocol, take a look at the PRISMA checklist . PRISMA has been designed primarily to improve the reporting of systematic literature reviews and meta-analyses.

When writing a systematic literature review, your goal is to find all of the relevant studies relating to your question, so you need to search thoroughly .

This is where your librarian will come in handy again. They should be able to help you formulate a detailed search strategy, and point you to all of the best databases for your topic.

➡️ Read more on on how to efficiently search research databases .

The places to consider in your search are electronic scientific databases (the most popular are PubMed , MEDLINE , and Embase ), controlled clinical trial registers, non-English literature, raw data from published trials, references listed in primary sources, and unpublished sources known to experts in the field.

➡️ Take a look at our list of the top academic research databases .

Tip: Don’t miss out on “gray literature.” You’ll improve the reliability of your findings by including it.

Don’t miss out on “gray literature” sources: those sources outside of the usual academic publishing environment. They include:

non-peer-reviewed journals
pharmaceutical industry files
conference proceedings
pharmaceutical company websites
internal reports

Gray literature sources are more likely to contain negative conclusions, so you’ll improve the reliability of your findings by including it. You should document details such as:

The databases you search and which years they cover
The dates you first run the searches, and when they’re updated
Which strategies you use, including search terms
The numbers of results obtained

➡️ Read more about gray literature .

This should be performed by your two reviewers, using the criteria documented in your research protocol. The screening is done in two phases:

Pre-screening of all titles and abstracts, and selecting those appropriate
Screening of the full-text articles of the selected studies

Make sure reviewers keep a log of which studies they exclude, with reasons why.

➡️ Visit our guide on what is an abstract?

Your reviewers should evaluate the methodological quality of your chosen full-text articles. Make an assessment checklist that closely aligns with your research protocol, including a consistent scoring system, calculations of the quality of each study, and sensitivity analysis.

The kinds of questions you'll come up with are:

Were the participants really randomly allocated to their groups?
Were the groups similar in terms of prognostic factors?
Could the conclusions of the study have been influenced by bias?

Every step of the data extraction must be documented for transparency and replicability. Create a data extraction form and set your reviewers to work extracting data from the qualified studies.

Here’s a free detailed template for recording data extraction, from Dalhousie University. It should be adapted to your specific question.

Establish a standard measure of outcome which can be applied to each study on the basis of its effect size.

Measures of outcome for studies with:

Binary outcomes (e.g. cured/not cured) are odds ratio and risk ratio
Continuous outcomes (e.g. blood pressure) are means, difference in means, and standardized difference in means
Survival or time-to-event data are hazard ratios

Design a table and populate it with your data results. Draw this out into a forest plot , which provides a simple visual representation of variation between the studies.

Then analyze the data for issues. These can include heterogeneity, which is when studies’ lines within the forest plot don’t overlap with any other studies. Again, record any excluded studies here for reference.

Consider different factors when interpreting your results. These include limitations, strength of evidence, biases, applicability, economic effects, and implications for future practice or research.

Apply appropriate grading of your evidence and consider the strength of your recommendations.

It’s best to formulate a detailed plan for how you’ll present your systematic review results. Take a look at these guidelines for interpreting results from the Cochrane Institute.

Before writing your systematic literature review, you can register it with OSF for additional guidance along the way. You could also register your completed work with PROSPERO .

Systematic literature reviews are often found in clinical or healthcare settings. Medical professionals read systematic literature reviews to stay up-to-date in their field and granting agencies sometimes need them to make sure there’s justification for further research in an area.

The first stage in carrying out a systematic literature review is to put together your team. You should employ multiple reviewers in order to minimize bias and strengthen analysis. A minimum of two is a good rule of thumb, with a third to serve as a tiebreaker if needed.

Your systematic review should include the following details:

A literature review simply provides a summary of the literature available on a topic. A systematic review, on the other hand, is more than just a summary. It also includes an analysis and evaluation of existing research. Put simply, it's a study of studies.

The final stage of conducting a systematic literature review is interpreting and presenting the results. It’s best to formulate a detailed plan for how you’ll present your systematic review results, guidelines can be found for example from the Cochrane institute .

steps in conducting systematic literature review

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Cochrane Cochrane Interactive Learning

Cochrane interactive learning, module 1: introduction to conducting systematic reviews, about this module.

Part of the Cochrane Interactive Learning course on Conducting an Intervention Review, this module introduces you to what systematic reviews are and why they are useful. This module describes the various types and preferred format of review questions, and outlines the process of conducting systematic reviews.

45-60 minutes

What you can expect to learn (learning outcomes).

This module will teach you to:

Recognize features of systematic reviews as a research design
Recognize the importance of using rigorous methods to conduct a systematic review
Identify the types of review questions
Identify the elements of a well-defined review question
Understand the steps in a systematic review

Authors, contributors, and how to cite this module

Module 1 has been written and compiled by Dario Sambunjak, Miranda Cumpston and Chris Watts, Cochrane Central Executive Team .

A full list of acknowledgements, including our expert advisors from across Cochrane, is available at the end of each module page.

This module should be cited as: Sambunjak D, Cumpston M, Watts C. Module 1: Introduction to conducting systematic reviews. In: Cochrane Interactive Learning: Conducting an intervention review. Cochrane, 2017. Available from https://training.cochrane.org/interactivelearning/module-1-introduction-conducting-systematic-reviews .

Update and feedback

The module was last updated on September 2022.

We're pleased to hear your thoughts. If you have any questions, comments or feedback about the content of this module, please contact us .

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Easy guide to conducting a systematic review

Corresponding Author

Patrina HY Caldwell

[email protected]
orcid.org/0000-0003-1124-6578

Discipline of Child and Adolescent Health, University of Sydney, Sydney, New South Wales, Australia

Department of Nephrology, The Children's Hospital at Westmead, Sydney, New South Wales, Australia

Trish Bennett

Education Department, The Children's Hospital at Westmead, Sydney, New South Wales, Australia

A systematic review is a type of study that synthesises research that has been conducted on a particular topic. Systematic reviews are considered to provide the highest level of evidence on the hierarchy of evidence pyramid. Systematic reviews are conducted following rigorous research methodology. To minimise bias, systematic reviews utilise a predefined search strategy to identify and appraise all available published literature on a specific topic. The meticulous nature of the systematic review research methodology differentiates a systematic review from a narrative review (literature review or authoritative review). This paper provides a brief step by step summary of how to conduct a systematic review, which may be of interest for clinicians and researchers.

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Citing Literature

Volume 56 , Issue 6

Pages 853-856

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A-Z Publications

Annual Review of Psychology

Volume 70, 2019, review article, how to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses.

Andy P. Siddaway 1 , Alex M. Wood 2 , and Larry V. Hedges 3
View Affiliations Hide Affiliations Affiliations: 1 Behavioural Science Centre, Stirling Management School, University of Stirling, Stirling FK9 4LA, United Kingdom; email: [email protected] 2 Department of Psychological and Behavioural Science, London School of Economics and Political Science, London WC2A 2AE, United Kingdom 3 Department of Statistics, Northwestern University, Evanston, Illinois 60208, USA; email: [email protected]
Vol. 70:747-770 (Volume publication date January 2019) https://doi.org/10.1146/annurev-psych-010418-102803
First published as a Review in Advance on August 08, 2018
Copyright © 2019 by Annual Reviews. All rights reserved

Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information. We outline core standards and principles and describe commonly encountered problems. Although this guide targets psychological scientists, its high level of abstraction makes it potentially relevant to any subject area or discipline. We argue that systematic reviews are a key methodology for clarifying whether and how research findings replicate and for explaining possible inconsistencies, and we call for researchers to conduct systematic reviews to help elucidate whether there is a replication crisis.

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Article Type: Review Article

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Systematic Reviews

What is a Systematic Review?

What are the Steps of a Systematic Review?

An Overview of the Systematic Review Process
1. Choose the Right Kind of Review
2. Formulate Your Question
3. Establish a Team
4. Develop a Protocol
5. Conduct the Search
6. Select Studies
7. Extract Data
8. Synthesize Your Results
9. Disseminate Your Report
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How to Write a Systematic Review of the Literature

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1 1 Texas Tech University, Lubbock, TX, USA.
2 2 University of Florida, Gainesville, FL, USA.
PMID: 29283007
DOI: 10.1177/1937586717747384

This article provides a step-by-step approach to conducting and reporting systematic literature reviews (SLRs) in the domain of healthcare design and discusses some of the key quality issues associated with SLRs. SLR, as the name implies, is a systematic way of collecting, critically evaluating, integrating, and presenting findings from across multiple research studies on a research question or topic of interest. SLR provides a way to assess the quality level and magnitude of existing evidence on a question or topic of interest. It offers a broader and more accurate level of understanding than a traditional literature review. A systematic review adheres to standardized methodologies/guidelines in systematic searching, filtering, reviewing, critiquing, interpreting, synthesizing, and reporting of findings from multiple publications on a topic/domain of interest. The Cochrane Collaboration is the most well-known and widely respected global organization producing SLRs within the healthcare field and a standard to follow for any researcher seeking to write a transparent and methodologically sound SLR. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), like the Cochrane Collaboration, was created by an international network of health-based collaborators and provides the framework for SLR to ensure methodological rigor and quality. The PRISMA statement is an evidence-based guide consisting of a checklist and flowchart intended to be used as tools for authors seeking to write SLR and meta-analyses.

Keywords: evidence based design; healthcare design; systematic literature review.

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Steps in Conducting a Literature Review

What is a literature review?

A literature review is an integrated analysis -- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question. That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.

A literature review may be a stand alone work or the introduction to a larger research paper, depending on the assignment. Rely heavily on the guidelines your instructor has given you.

Why is it important?

A literature review is important because it:

Explains the background of research on a topic.
Demonstrates why a topic is significant to a subject area.
Discovers relationships between research studies/ideas.
Identifies major themes, concepts, and researchers on a topic.
Identifies critical gaps and points of disagreement.
Discusses further research questions that logically come out of the previous studies.

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1. Choose a topic. Define your research question.

Your literature review should be guided by your central research question. The literature represents background and research developments related to a specific research question, interpreted and analyzed by you in a synthesized way.

Make sure your research question is not too broad or too narrow. Is it manageable?
Begin writing down terms that are related to your question. These will be useful for searches later.
If you have the opportunity, discuss your topic with your professor and your class mates.

2. Decide on the scope of your review

How many studies do you need to look at? How comprehensive should it be? How many years should it cover?

This may depend on your assignment. How many sources does the assignment require?

3. Select the databases you will use to conduct your searches.

Make a list of the databases you will search.

Where to find databases:

use the tabs on this guide
Find other databases in the Nursing Information Resources web page
More on the Medical Library web page
... and more on the Yale University Library web page

4. Conduct your searches to find the evidence. Keep track of your searches.

Use the key words in your question, as well as synonyms for those words, as terms in your search. Use the database tutorials for help.
Save the searches in the databases. This saves time when you want to redo, or modify, the searches. It is also helpful to use as a guide is the searches are not finding any useful results.
Review the abstracts of research studies carefully. This will save you time.
Use the bibliographies and references of research studies you find to locate others.
Check with your professor, or a subject expert in the field, if you are missing any key works in the field.
Ask your librarian for help at any time.
Use a citation manager, such as EndNote as the repository for your citations. See the EndNote tutorials for help.

Review the literature

Some questions to help you analyze the research:

What was the research question of the study you are reviewing? What were the authors trying to discover?
Was the research funded by a source that could influence the findings?
What were the research methodologies? Analyze its literature review, the samples and variables used, the results, and the conclusions.
Does the research seem to be complete? Could it have been conducted more soundly? What further questions does it raise?
If there are conflicting studies, why do you think that is?
How are the authors viewed in the field? Has this study been cited? If so, how has it been analyzed?

Tips:

Review the abstracts carefully.
Keep careful notes so that you may track your thought processes during the research process.
Create a matrix of the studies for easy analysis, and synthesis, across all of the studies.
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Last Updated: Jun 20, 2024 9:08 AM
URL: https://guides.library.yale.edu/YSNDoctoral

How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions

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Open access
Published: 12 May 2023
Volume 17 , pages 1899–1933, ( 2023 )

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Philipp C. Sauer ORCID: orcid.org/0000-0002-1823-0723 1 &
Stefan Seuring ORCID: orcid.org/0000-0003-4204-9948 2

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Systematic literature reviews (SLRs) have become a standard tool in many fields of management research but are often considerably less stringently presented than other pieces of research. The resulting lack of replicability of the research and conclusions has spurred a vital debate on the SLR process, but related guidance is scattered across a number of core references and is overly centered on the design and conduct of the SLR, while failing to guide researchers in crafting and presenting their findings in an impactful way. This paper offers an integrative review of the widely applied and most recent SLR guidelines in the management domain. The paper adopts a well-established six-step SLR process and refines it by sub-dividing the steps into 14 distinct decisions: (1) from the research question, via (2) characteristics of the primary studies, (3) to retrieving a sample of relevant literature, which is then (4) selected and (5) synthesized so that, finally (6), the results can be reported. Guided by these steps and decisions, prior SLR guidelines are critically reviewed, gaps are identified, and a synthesis is offered. This synthesis elaborates mainly on the gaps while pointing the reader toward the available guidelines. The paper thereby avoids reproducing existing guidance but critically enriches it. The 6 steps and 14 decisions provide methodological, theoretical, and practical guidelines along the SLR process, exemplifying them via best-practice examples and revealing their temporal sequence and main interrelations. The paper guides researchers in the process of designing, executing, and publishing a theory-based and impact-oriented SLR.

The burgeoning role of literature review articles in management research: an introduction and outlook

On being ‘systematic’ in literature reviews

On being ‘systematic’ in literature reviews in IS

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1 Introduction

The application of systematic or structured literature reviews (SLRs) has developed into an established approach in the management domain (Kraus et al. 2020 ), with 90% of management-related SLRs published within the last 10 years (Clark et al. 2021 ). Such reviews help to condense knowledge in the field and point to future research directions, thereby enabling theory development (Fink 2010 ; Koufteros et al. 2018 ). SLRs have become an established method by now (e.g., Durach et al. 2017 ; Koufteros et al. 2018 ). However, many SLR authors struggle to efficiently synthesize and apply review protocols and justify their decisions throughout the review process (Paul et al. 2021 ) since only a few studies address and explain the respective research process and the decisions to be taken in this process. Moreover, the available guidelines do not form a coherent body of literature but focus on the different details of an SLR, while a comprehensive and detailed SLR process model is lacking. For example, Seuring and Gold ( 2012 ) provide some insights into the overall process, focusing on content analysis for data analysis without covering the practicalities of the research process in detail. Similarly, Durach et al. ( 2017 ) address SLRs from a paradigmatic perspective, offering a more foundational view covering ontological and epistemological positions. Durach et al. ( 2017 ) emphasize the philosophy of science foundations of an SLR. Although somewhat similar guidelines for SLRs might be found in the wider body of literature (Denyer and Tranfield 2009 ; Fink 2010 ; Snyder 2019 ), they often take a particular focus and are less geared toward explaining and reflecting on the single choices being made during the research process. The current body of SLR guidelines leaves it to the reader to find the right links among the guidelines and to justify their inconsistencies. This is critical since a vast number of SLRs are conducted by early-stage researchers who likely struggle to synthesize the existing guidance and best practices (Fisch and Block 2018 ; Kraus et al. 2020 ), leading to the frustration of authors, reviewers, editors, and readers alike.

Filling these gaps is critical in our eyes since researchers conducting literature reviews form the foundation of any kind of further analysis to position their research into the respective field (Fink 2010 ). So-called “systematic literature reviews” (e.g., Davis and Crombie 2001 ; Denyer and Tranfield 2009 ; Durach et al. 2017 ) or “structured literature reviews” (e.g., Koufteros et al. 2018 ; Miemczyk et al. 2012 ) differ from nonsystematic literature reviews in that the analysis of a certain body of literature becomes a means in itself (Kraus et al. 2020 ; Seuring et al. 2021 ). Although two different terms are used for this approach, the related studies refer to the same core methodological references that are also cited in this paper. Therefore, we see them as identical and abbreviate them as SLR.

There are several guidelines on such reviews already, which have been developed outside the management area (e.g. Fink 2010 ) or with a particular focus on one management domain (e.g., Kraus et al. 2020 ). SLRs aim at capturing the content of the field at a point in time but should also aim at informing future research (Denyer and Tranfield 2009 ), making follow-up research more efficient and productive (Kraus et al. 2021 ). Such standalone literature reviews would and should also prepare subsequent empirical or modeling research, but usually, they require far more effort and time (Fisch and Block 2018 ; Lim et al. 2022 ). To achieve this preparation, SLRs can essentially a) describe the state of the literature, b) test a hypothesis based on the available literature, c) extend the literature, and d) critique the literature (Xiao and Watson 2019 ). Beyond guiding the next incremental step in research, SLRs “may challenge established assumptions and norms of a given field or topic, recognize critical problems and factual errors, and stimulate future scientific conversations around that topic” (Kraus et al. 2022 , p. 2578). Moreover, they have the power to answer research questions that are beyond the scope of individual empirical or modeling studies (Snyder 2019 ) and to build, elaborate, and test theories beyond this single study scope (Seuring et al. 2021 ). These contributions of an SLR may be highly influential and therefore underline the need for high-quality planning, execution, and reporting of their process and details.

Regardless of the individual aims of standalone SLRs, their numbers have exponentially risen in the last two decades (Kraus et al. 2022 ) and almost all PhD or large research project proposals in the management domain include such a standalone SLR to build a solid foundation for their subsequent work packages. Standalone SLRs have thus become a key part of management research (Kraus et al. 2021 ; Seuring et al. 2021 ), which is also underlined by the fact that there are journals and special issues exclusively accepting standalone SLRs (Kraus et al. 2022 ; Lim et al. 2022 ).

However, SLRs require a commitment that is often comparable to an additional research process or project. Hence, SLRs should not be taken as a quick solution, as a simplistic, descriptive approach would usually not yield a publishable paper (see also Denyer and Tranfield 2009 ; Kraus et al. 2020 ).

Furthermore, as with other research techniques, SLRs are based on the rigorous application of rules and procedures, as well as on ensuring the validity and reliability of the method (Fisch and Block 2018 ; Seuring et al. 2021 ). In effect, there is a need to ensure “the same level of rigour to reviewing research evidence as should be used in producing that research evidence in the first place” (Davis and Crombie 2001 , p.1). This rigor holds for all steps of the research process, such as establishing the research question, collecting data, analyzing it, and making sense of the findings (Durach et al. 2017 ; Fink 2010 ; Seuring and Gold 2012 ). However, there is a high degree of diversity where some would be justified, while some papers do not report the full details of the research process. This lack of detail contrasts with an SLR’s aim of creating a valid map of the currently available research in the reviewed field, as critical information on the review’s completeness and potential reviewer biases cannot be judged by the reader or reviewer. This further impedes later replications or extensions of such reviews, which could provide longitudinal evidence of the development of a field (Denyer and Tranfield 2009 ; Durach et al. 2017 ). Against this observation, this paper addresses the following question:

Which decisions need to be made in an SLR process, and what practical guidelines can be put forward for making these decisions?

Answering this question, the key contributions of this paper are fourfold: (1) identifying the gaps in existing SLR guidelines, (2) refining the SLR process model by Durach et al. ( 2017 ) through 14 decisions, (3) synthesizing and enriching guidelines for these decisions, exemplifying the key decisions by means of best practice SLRs, and (4) presenting and discussing a refined SLR process model.

In some cases, we point to examples from operations and supply chain management. However, they illustrate the purposes discussed in the respective sections. We carefully checked that the arguments held for all fields of management-related research, and multiple examples from other fields of management were also included.

2 Identification of the need for an enriched process model, including a set of sequential decisions and their interrelations

In line with the exponential increase in SLR papers (Kraus et al. 2022 ), multiple SLR guidelines have recently been published. Since 2020, we have found a total of 10 papers offering guidelines on SLRs and other reviews for the field of management in general or some of its sub-fields. These guidelines are of double interest to this paper since we aim to complement them to fill the gap identified in the introduction while minimizing the doubling of efforts. Table 1 lists the 10 most recent guidelines and highlights their characteristics, research objectives, contributions, and how our paper aims to complement these previous contributions.

The sheer number and diversity of guideline papers, as well as the relevance expressed in them, underline the need for a comprehensive and exhaustive process model. At the same time, the guidelines take specific foci on, for example, updating earlier guidelines to new technological potentials (Kraus et al. 2020 ), clarifying the foundational elements of SLRs (Kraus et al. 2022 ) and proposing a review protocol (Paul et al. 2021 ) or the application and development of theory in SLRs (Seuring et al. 2021 ). Each of these foci fills an entire paper, while the authors acknowledge that much more needs to be considered in an SLR. Working through these most recent guidelines, it becomes obvious that the common paper formats in the management domain create a tension for guideline papers between elaborating on a) the SLR process and b) the details, options, and potentials of individual process steps.

Our analysis in Table 1 evidences that there are a number of rich contributions on aspect b), while the aspect a) of SLR process models has not received the same attention despite the substantial confusion of authors toward them (Paul et al. 2021 ). In fact, only two of the most recent guidelines approach SLR process models. First, Kraus et al. ( 2020 ) incrementally extended the 20-year-old Tranfield et al. ( 2003 ) three-stage model into four stages. A little later, Paul et al. ( 2021 ) proposed a three-stage (including six sub-stages) SPAR-4-SLR review protocol. It integrates the PRISMA reporting items (Moher et al. 2009 ; Page et al. 2021 ) that originate from clinical research to define 14 actions stating what items an SLR in management needs to report for reasons of validity, reliability, and replicability. Almost naturally, these 14 reporting-oriented actions mainly relate to the first SLR stage of “assembling the literature,” which accounts for nine of the 14 actions. Since this protocol is published in a special issue editorial, its presentation and elaboration are somewhat limited by the already mentioned word count limit. Nevertheless, the SPAR-4-SLR protocol provides a very useful checklist for researchers that enables them to include all data required to document the SLR and to avoid confusion from editors, reviewers, and readers regarding SLR characteristics.

Beyond Table 1 , Durach et al. ( 2017 ) synthesized six common SLR “steps” that differ only marginally in the delimitation of one step to another from the sub-stages of the previously mentioned SLR processes. In addition, Snyder ( 2019 ) proposed a process comprising four “phases” that take more of a bird’s perspective in addressing (1) design, (2) conduct, (3) analysis, and (4) structuring and writing the review. Moreover, Xiao and Watson ( 2019 ) proposed only three “stages” of (1) planning, (2) conducting, and (3) reporting the review that combines the previously mentioned conduct and the analysis and defines eight steps within them. Much in line with the other process models, the final reporting stage contains only one of the eight steps, leaving the reader somewhat alone in how to effectively craft a manuscript that contributes to the further development of the field.

In effect, the mentioned SLR processes differ only marginally, while the systematic nature of actions in the SPAR-4-SLR protocol (Paul et al. 2021 ) can be seen as a reporting must-have within any of the mentioned SLR processes. The similarity of the SLR processes is, however, also evident in the fact that they leave open how the SLR analysis can be executed, enriched, and reflected to make a contribution to the reviewed field. In contrast, this aspect is richly described in the other guidelines that do not offer an SLR process, leading us again toward the tension for guideline papers between elaborating on a) the SLR process and b) the details, options, and potentials of each process step.

To help (prospective) SLR authors successfully navigate this tension of existing guidelines, it is thus the ambition of this paper to adopt a comprehensive SLR process model along which an SLR project can be planned, executed, and written up in a coherent way. To enable this coherence, 14 distinct decisions are defined, reflected, and interlinked, which have to be taken across the different steps of the SLR process. At the same time, our process model aims to actively direct researchers to the best practices, tips, and guidance that previous guidelines have provided for individual decisions. We aim to achieve this by means of an integrative review of the relevant SLR guidelines, as outlined in the following section.

3 Methodology: an integrative literature review of guidelines for systematic literature reviews in management

It might seem intuitive to contribute to the debate on the “gold standard” of systematic literature reviews (Davis et al. 2014 ) by conducting a systematic review ourselves. However, there are different types of reviews aiming for distinctive contributions. Snyder ( 2019 ) distinguished between a) systematic, b) semi-systematic, and c) integrative (or critical) reviews, which aim for i) (mostly quantitative) synthesis and comparison of prior (primary) evidence, ii) an overview of the development of a field over time, and iii) a critique and synthesis of prior perspectives to reconceptualize or advance them. Each review team needs to position itself in such a typology of reviews to define the aims and scope of the review. To do so and structure the related research process, we adopted the four generic steps for an (integrative) literature review by Snyder ( 2019 )—(1) design, (2) conduct, (3) analysis, and (4) structuring and writing the review—on which we report in the remainder of this section. Since the last step is a very practical one that, for example, asks, “Is the contribution of the review clearly communicated?” (Snyder 2019 ), we will focus on the presentation of the method applied to the initial three steps:

(1) Regarding the design, we see the need for this study emerging from our experience in reviewing SLR manuscripts, supervising PhD students who, almost by default, need to prepare an SLR, and recurring discussions on certain decisions in the process of both. These discussions regularly left some blank or blurry spaces (see Table 1 ) that induced substantial uncertainty regarding critical decisions in the SLR process (Paul et al. 2021 ). To address this gap, we aim to synthesize prior guidance and critically enrich it, thus adopting an integrative approach for reviewing existing SLR guidance in the management domain (Snyder 2019 ).

(2) To conduct the review, we started collecting the literature that provided guidance on the individual SLR parts. We built on a sample of 13 regularly cited or very recent papers in the management domain. We started with core articles that we successfully used to publish SLRs in top-tier OSCM journals, such as Tranfield et al. ( 2003 ) and Durach et al. ( 2017 ), and we checked their references and papers that cited these publications. The search focus was defined by the following criteria: the articles needed to a) provide original methodological guidance for SLRs by providing new aspects of the guideline or synthesizing existing ones into more valid guidelines and b) focus on the management domain. Building on the nature of a critical or integrative review that does not require a full or representative sample (Snyder 2019 ), we limited the sample to the papers displayed in Table 2 that built the core of the currently applied SLR guidelines. In effect, we found 11 technical papers and two SLRs of SLRs (Carter and Washispack 2018 ; Seuring and Gold 2012 ). From the latter, we mainly analyzed the discussion and conclusion parts that explicitly developed guidance on conducting SLRs.

(3) For analyzing these papers, we first adopted the six-step SLR process proposed by Durach et al. ( 2017 , p.70), which they define as applicable to any “field, discipline or philosophical perspective”. The contrast between the six-step SLR process used for the analysis and the four-step process applied by ourselves may seem surprising but is justified by the use of an integrative approach. This approach differs mainly in retrieving and selecting pertinent literature that is key to SLRs and thus needs to be part of the analysis framework.

While deductively coding the sample papers against Durach et al.’s ( 2017 ), guidance in the six steps, we inductively built a set of 14 decisions presented in the right columns of Table 2 that are required to be made in any SLR. These decisions built a second and more detailed level of analysis, for which the single guidelines were coded as giving low, medium, or high levels of detail (see Table 3 ), which helped us identify the gaps in the current guidance papers and led our way in presenting, critically discussing, and enriching the literature. In effect, we see that almost all guidelines touch on the same issues and try to give a comprehensive overview. However, this results in multiple guidelines that all lack the space to go into detail, while only a few guidelines focus on filling a gap in the process. It is our ambition with this analysis to identify the gaps in the guidelines, thereby identifying a precise need for refinement, and to offer a first step into this refinement. Adopting advice from the literature sample, the coding was conducted by the entire author team (Snyder 2019 ; Tranfield et al. 2003 ) including discursive alignments of interpretation (Seuring and Gold 2012 ). This enabled a certain reliability and validity of the analysis by reducing the within-study and expectancy bias (Durach et al. 2017 ), while the replicability was supported by reporting the review sample and the coding results in Table 3 (Carter and Washispack 2018 ).

(4) For the writing of the review, we only pointed to the unusual structure of presenting the method without a theory section and then the findings in the following section. However, this was motivated by the nature of the integrative review so that the review findings at the same time represent the “state of the art,” “literature review,” or “conceptualization” sections of a paper.

4 Findings of the integrative review: presentation, critical discussion, and enrichment of prior guidance

4.1 the overall research process for a systematic literature review.

Even within our sample of only 13 guidelines, there are four distinct suggestions for structuring the SLR process. One of the earliest SLR process models was proposed by Tranfield et al. ( 2003 ) encompassing the three stages of (1) planning the review, (2) conducting a review, and (3) reporting and dissemination. Snyder ( 2019 ) proposed four steps employed in this study: (1) design, (2) conduct, (3) analysis, and (4) structuring and writing the review. Borrowing from content analysis guidelines, Seuring and Gold ( 2012 ) defined four steps: (1) material collection, (2) descriptive analysis, (3) category selection, and (4) material evaluation. Most recently Kraus et al. ( 2020 ) proposed four steps: (1) planning the review, (2) identifying and evaluating studies, (3) extracting and synthesizing data, and (4) disseminating the review findings. Most comprehensively, Durach et al. ( 2017 ) condensed prior process models into their generic six steps for an SLR. Adding the review of the process models by Snyder ( 2019 ) and Seuring and Gold ( 2012 ) to Durach et al.’s ( 2017 ) SLR process review of four papers, we support their conclusion of the general applicability of the six steps defined. Consequently, these six steps form the backbone of our coding scheme, as shown in the left column of Table 2 and described in the middle column.

As stated in Sect. 3 , we synthesized the review papers against these six steps but experienced that the papers were taking substantially different foci by providing rich details for some steps while largely bypassing others. To capture this heterogeneity and better operationalize the SLR process, we inductively introduced the right column, identifying 14 decisions to be made. These decisions are all elaborated in the reviewed papers but to substantially different extents, as the detailed coding results in Table 3 underline.

Mapping Table 3 for potential gaps in the existing guidelines, we found six decisions on which we found only low- to medium-level details, while high-detail elaboration was missing. These six decisions, which are illustrated in Fig. 1 , belong to three steps: 1: defining the research question, 5: synthesizing the literature, and 6: reporting the results. This result underscores our critique of currently unbalanced guidance that is, on the one hand, detailed on determining the required characteristics of primary studies (step 2), retrieving a sample of potentially relevant literature (step 3), and selecting the pertinent literature (step 4). On the other hand, authors, especially PhD students, are left without substantial guidance on the steps critical to publication. Instead, they are called “to go one step further … and derive meaningful conclusions” (Fisch and Block 2018 , p. 105) without further operationalizations on how this can be achieved; for example, how “meet the editor” conference sessions regularly cause frustration among PhDs when editors call for “new,” “bold,” and “relevant” research. Filling the gaps in the six decisions with best practice examples and practical experience is the main focus of this study’s contribution. The other eight decisions are synthesized with references to the guidelines that are most helpful and relevant for the respective step in our eyes.

The 6 steps and 14 decisions of the SLR process

4.2 Step 1: defining the research question

When initiating a research project, researchers make three key decisions.

Decision 1 considers the essential tasks of establishing a relevant and timely research question, but despite the importance of the decision, which determines large parts of further decisions (Snyder 2019 ; Tranfield et al. 2003 ), we only find scattered guidance in the literature. Hence, how can a research topic be specified to allow a strong literature review that is neither too narrow nor too broad? The latter is the danger in meta-reviews (i.e., reviews of reviews) (Aguinis et al. 2020 ; Carter and Washispack 2018 ; Kache and Seuring 2014 ). In this respect, even though the method would be robust, the findings would not be novel. In line with Carter and Washispack ( 2018 ), there should always be room for new reviews, yet over time, they must move from a descriptive overview of a field further into depth and provide detailed analyses of constructs. Clark et al. ( 2021 ) provided a detailed but very specific reflection on how they crafted a research question for an SLR and that revisiting the research question multiple times throughout the SLR process helps to coherently and efficiently move forward with the research. More generically, Kraus et al. ( 2020 ) listed six key contributions of an SLR that can guide the definition of the research question. Finally, Snyder ( 2019 ) suggested moving into more detail from existing SLRs and specified two main avenues for crafting an SLR research question that are either investigating the relationship among multiple effects, the effect of (a) specific variable(s), or mapping the evidence regarding a certain research area. For the latter, we see three possible alternative approaches, starting with a focus on certain industries. Examples are analyses of the food industry (Beske et al. 2014 ), retailing (Wiese et al. 2012 ), mining and minerals (Sauer and Seuring 2017 ), or perishable product supply chains (Lusiantoro et al. 2018 ) and traceability at the example of the apparel industry (Garcia-Torres et al. 2019 ). A second opportunity would be to assess the status of research in a geographical area that composes an interesting context from a research perspective, such as sustainable supply chain management (SSCM) in Latin America (Fritz and Silva 2018 ), yet this has to be justified explicitly, avoiding the fact that geographical focus is taken as the reason per se (e.g., Crane et al. 2016 ). A third variant addresses emerging issues, such as SCM, in a base-of-the-pyramid setting (Khalid and Seuring 2019 ) and the use of blockchain technology (Wang et al. 2019 ) or digital transformation (Hanelt et al. 2021 ). These approaches limit the reviewed field to enable a more contextualized analysis in which the novelty, continued relevance, or unjustified underrepresentation of the context can be used to specify a research gap and related research question(s). This also impacts the following decisions, as shown below.

Decision 2 concerns the option for a theoretical approach (i.e., the adoption of an inductive, abductive, or deductive approach) to theory building through the literature review. The review of previous guidance on this delivers an interesting observation. On the one hand, there are early elaborations on systematic reviews, realist synthesis, meta-synthesis, and meta-analysis by Tranfield et al. ( 2003 ) that are borrowing from the origins of systematic reviews in medical research. On the other hand, recent management-related guidelines largely neglect details of related decisions, but point out that SLRs are a suitable tool for theory building (Kraus et al. 2020 ). Seuring et al. ( 2021 ) set out to fill this gap and provided substantial guidance on how to use theory in SLRs to advance the field. To date, the option for a theoretical approach is only rarely made explicit, leaving the reader often puzzled about how advancement in theory has been crafted and impeding a review’s replicability (Seuring et al. 2021 ). Many papers still leave related choices in the dark (e.g., Rhaiem and Amara 2021 ; Rojas-Córdova et al. 2022 ) and move directly from the introduction to the method section.

In Decision 3, researchers need to adopt a theoretical framework (Durach et al. 2017 ) or at least a theoretical starting point, depending on the most appropriate theoretical approach (Seuring et al. 2021 ). Here, we find substantial guidance by Durach et al. ( 2017 ) that underlines the value of adopting a theoretical lens to investigate SCM phenomena and the literature. Moreover, the choice of a theoretical anchor enables a consistent definition and operationalization of constructs that are used to analyze the reviewed literature (Durach et al. 2017 ; Seuring et al. 2021 ). Hence, providing some upfront definitions is beneficial, clarifying what key terminology would be used in the subsequent paper, such as Devece et al. ( 2019 ) introduce their terminology on coopetition. Adding a practical hint beyond the elaborations of prior guidance papers for taking up established constructs in a deductive analysis (decision 2), there would be the question of whether these can yield interesting findings.

Here, it would be relevant to specify what kind of analysis is aimed for the SLR, where three approaches might be distinguished (i.e., bibliometric analysis, meta-analysis, and content analysis–based studies). Briefly distinguishing them, the core difference would be how many papers can be analyzed employing the respective method. Bibliometric analysis (Donthu et al. 2021 ) usually relies on the use of software, such as Biblioshiny, allowing the creation of figures on citations and co-citations. These figures enable the interpretation of large datasets in which several hundred papers can be analyzed in an automated manner. This allows for distinguishing among different research clusters, thereby following a more inductive approach. This would be contrasted by meta-analysis (e.g., Leuschner et al. 2013 ), where often a comparatively smaller number of papers is analyzed (86 in the respective case) but with a high number of observations (more than 17,000). The aim is to test for statistically significant correlations among single constructs, which requires that the related constructs and items be precisely defined (i.e., a clearly deductive approach to the analysis).

Content analysis is the third instrument frequently applied to data analysis, where an inductive or deductive approach might be taken (Seuring et al. 2021 ). Content-based analysis (see decision 9 in Sect. 4.6 ; Seuring and Gold 2012 ) is a labor-intensive step and can hardly be changed ex post. This also implies that only a certain number of papers might be analyzed (see Decision 6 in Sect. 4.5 ). It is advisable to adopt a wider set of constructs for the analysis stemming even from multiple established frameworks since it is difficult to predict which constructs and items might yield interesting insights. Hence, coding a more comprehensive set of items and dropping some in the process is less problematic than starting an analysis all over again for additional constructs and items. However, in the process of content analysis, such an iterative process might be required to improve the meaningfulness of the data and findings (Seuring and Gold 2012 ). A recent example of such an approach can be found in Khalid and Seuring ( 2019 ), building on the conceptual frameworks for SSCM of Carter and Rogers ( 2008 ), Seuring and Müller ( 2008 ), and Pagell and Wu ( 2009 ). This allows for an in-depth analysis of how SSCM constructs are inherently referred to in base-of-the-pyramid-related research. The core criticism and limitation of such an approach is the random and subjectively biased selection of frameworks for the purpose of analysis.

Beyond the aforementioned SLR methods, some reviews, similar to the one used here, apply a critical review approach. This is, however, nonsystematic, and not an SLR; thus, it is beyond the scope of this paper. Interested readers can nevertheless find some guidance on critical reviews in the available literature (e.g., Kraus et al. 2022 ; Snyder 2019 ).

4.3 Step 2: determining the required characteristics of primary studies

After setting the stage for the review, it is essential to determine which literature is to be reviewed in Decision 4. This topic is discussed by almost all existing guidelines and will thus only briefly be discussed here. Durach et al. ( 2017 ) elaborated in great detail on defining strict inclusion and exclusion criteria that need to be aligned with the chosen theoretical framework. The relevant units of analysis need to be specified (often a single paper, but other approaches might be possible) along with suitable research methods, particularly if exclusively empirical studies are reviewed or if other methods are applied. Beyond that, they elaborated on potential quality criteria that should be applied. The same is considered by a number of guidelines that especially draw on medical research, in which systematic reviews aim to pool prior studies to infer findings from their total population. Here, it is essential to ensure the exclusion of poor-quality evidence that would lower the quality of the review findings (Mulrow 1987 ; Tranfield et al. 2003 ). This could be ensured by, for example, only taking papers from journals listed on the Web of Science or Scopus or journals listed in quartile 1 of Scimago ( https://www.scimagojr.com/ ), a database providing citation and reference data for journals.

The selection of relevant publication years should again follow the purpose of the study defined in Step 1. As such, there might be a justified interest in the wide coverage of publication years if a historical perspective is taken. Alternatively, more contemporary developments or the analysis of very recent issues can justify the selection of very few years of publication (e.g., Kraus et al. 2022 ). Again, it is hard to specify a certain time period covered, but if developments of a field should be analyzed, a five-year period might be a typical lower threshold. On current topics, there is often a trend of rising publishing numbers. This scenario implies the rising relevance of a topic; however, this should be treated with caution. The total number of papers published per annum has increased substantially in recent years, which might account for the recently heightened number of papers on a certain topic.

4.4 Step 3: retrieving a sample of potentially relevant literature

After defining the required characteristics of the literature to be reviewed, the literature needs to be retrieved based on two decisions. Decision 5 concerns suitable literature sources and databases that need to be defined. Turning to Web of Science or Scopus would be two typical options found in many of the examples mentioned already (see also detailed guidance by Paul and Criado ( 2020 ) as well as Paul et al. ( 2021 )). These databases aggregate many management journals, and a typical argument for turning to the Web of Science database is the inclusion of impact factors, as they indicate a certain minimum quality of the journal (Sauer and Seuring 2017 ). Additionally, Google Scholar is increasingly mentioned as a usable search engine, often providing higher numbers of search results than the mentioned databases (e.g., Pearce 2018 ). These results often entail duplicates of articles from multiple sources or versions of the same article, as well as articles in predatory journals (Paul et al. 2021 ). Therefore, we concur with Paul et al. ( 2021 ) who underline the quality assurance mechanisms in Web of Science and Scopus, making them preferred databases for the literature search. From a practical perspective, it needs to be mentioned that SLRs in management mainly rely on databases that are not free to use. Against this limitation, Pearce ( 2018 ) provided a list of 20 search engines that are free of charge and elaborated on their advantages and disadvantages. Due to the individual limitations of the databases, it is advisable to use a combination of them (Kraus et al. 2020 , 2022 ) and build a consolidated sample by screening the papers found for duplicates, as regularly done in SLRs.

This decision also includes the choice of the types of literature to be analyzed. Typically, journal papers are selected, ensuring that the collected papers are peer-reviewed and have thus undergone an academic quality management process. Meanwhile, conference papers are usually avoided since they are often less mature and not checked for quality (e.g., Seuring et al. 2021 ). Nevertheless, for emerging topics, it might be too restrictive to consider only peer-reviewed journal articles and limit the literature to only a few references. Analyzing such rapidly emerging topics is relevant for timely and impact-oriented research and might justify the selection of different sources. Kraus et al. ( 2020 ) provided a discussion on the use of gray literature (i.e., nonacademic sources), and Sauer ( 2021 ) provided an example of a review of sustainability standards from a management perspective to derive implications for their application by managers on the one hand and for enhancing their applicability on the other hand.

Another popular way to limit the review sample is the restriction to a certain list of journals (Kraus et al. 2020 ; Snyder 2019 ). While this is sometimes favored by highly ranked journals, Carter and Washispack ( 2018 ), for example, found that many pertinent papers are not necessarily published in journals within the field. Webster and Watson ( 2002 ) quite tellingly cited a reviewer labeling the selection of top journals as an unjustified excuse for investigating the full body of relevant literature. Both aforementioned guidelines thus discourage the restriction to particular journals, a guidance that we fully support.

However, there is an argument to be made supporting the exclusion of certain lower-ranked journals. This can be done, for example, by using Scimago Journal quartiles ( www.scimagojr.com , last accessed 13. of April 2023) and restricting it to journals in the first quartile (e.g., Yavaprabhas et al. 2022 ). Other papers (e.g., Kraus et al. 2021 ; Rojas-Córdova et al. 2022 ) use certain journal quality lists to limit their sample. However, we argue for a careful check by the authors against the topic reviewed regarding what would be included and excluded.

Decision 6 entails the definition of search terms and a search string to be applied in the database just chosen. The search terms should reflect the aims of the review and the exclusion criteria that might be derived from the unit of analysis and the theoretical framework (Durach et al. 2017 ; Snyder 2019 ). Overall, two approaches to keywords can be observed. First, some guides suggest using synonyms of the key terms of interest (e.g., Durach et al. 2017 ; Kraus et al. 2020 ) in order to build a wide baseline sample that will be condensed in the next step. This is, of course, especially helpful if multiple terms delimitate a field together or different synonymous terms are used in parallel in different fields or journals. Empirical journals in supply chain management, for example, use the term “multiple supplier tiers ” (e.g., Tachizawa and Wong 2014 ), while modeling journals in the same field label this as “multiple supplier echelons ” (e.g., Brandenburg and Rebs 2015 ). Second, in some cases, single keywords are appropriate for capturing a central aspect or construct of a field if the single keyword has a global meaning tying this field together. This approach is especially relevant to the study of relatively broad terms, such as “social media” (Lim and Rasul 2022 ). However, this might result in very high numbers of publications found and therefore requires a purposeful combination with other search criteria, such as specific journals (Kraus et al. 2021 ; Lim et al. 2021 ), publication dates, article types, research methods, or the combination with keywords covering domains to which the search is aimed to be specified.

Since SLRs are often required to move into detail or review the intersections of relevant fields, we recommend building groups of keywords (single terms or multiple synonyms) for each field to be connected that are coupled via Boolean operators. To determine when a point of saturation for a keyword group is reached, one could monitor the increase in papers found in a database when adding another synonym. Once the increase is significantly decreasing or even zeroing, saturation is reached (Sauer and Seuring 2017 ). The keywords themselves can be derived from the list of keywords of influential publications in the field, while attention should be paid to potential synonyms in neighboring fields (Carter and Washispack 2018 ; Durach et al. 2017 ; Kraus et al. 2020 ).

4.5 Step 4: selecting the pertinent literature

The inclusion and exclusion criteria (Decision 6) are typically applied in Decision 7 in a two-stage process, first on the title, abstract, and keywords of an article before secondly applying them to the full text of the remaining articles (see also Kraus et al. 2020 ; Snyder 2019 ). Beyond this, Durach et al. ( 2017 ) underlined that the pertinence of the publication regarding units of analysis and the theoretical framework needs to be critically evaluated in this step to avoid bias in the review analysis. Moreover, Carter and Washispack ( 2018 ) requested the publication of the included and excluded sources to ensure the replicability of Steps 3 and 4. This can easily be done as an online supplement to an eventually published review article.

Nevertheless, the question remains: How many papers justify a literature review? While it is hard to specify how many papers comprise a body of literature, there might be certain thresholds for which Kraus et al. ( 2020 ) provide a useful discussion. As a rough guide, more than 50 papers would usually make a sound starting point (see also Paul and Criado 2020 ), while there are SLRs on emergent topics, such as multitier supply chain management, where 39 studies were included (Tachizawa and Wong 2014 ). An SLR on “learning from innovation failures” builds on 36 papers (Rhaiem and Amara 2021 ), which we would see as the lower threshold. However, such a low number should be an exception, and anything lower would certainly trigger the following question: Why is a review needed? Meanwhile, there are also limits on how many papers should be reviewed. While there are cases with 191 (Seuring and Müller 2008 ), 235 (Rojas-Córdova et al. 2022 ), or up to nearly 400 papers reviewed (Spens and Kovács 2006 ), these can be regarded as upper thresholds. Over time, similar topics seem to address larger datasets.

4.6 Step 5: synthesizing the literature

Before synthesizing the literature, Decision 8 considers the selection of a data extraction tool for which we found surprisingly little guidance. Some guidance is given on the use of cloud storage to enable remote team work (Clark et al. 2021 ). Beyond this, we found that SLRs have often been compiled with marked and commented PDFs or printed papers that were accompanied by tables (Kraus et al. 2020 ) or Excel sheets (see also the process tips by Clark et al. 2021 ). This sheet tabulated the single codes derived from the theoretical framework (Decision 3) and the single papers to be reviewed (Decision 7) by crossing out individual cells, signaling the representation of a particular code in a particular paper. While the frequency distribution of the codes is easily compiled from this data tool, the related content needs to be looked at in the papers in a tedious back-and-forth process. Beyond that, we would strongly recommend using data analysis software, such as MAXQDA or NVivo. Such programs enable the import of literature in PDF format and the automatic or manual coding of text passages, their comparison, and tabulation. Moreover, there is a permanent and editable reference of the coded text to a code. This enables a very quick compilation of content summaries or statistics for single codes and the identification of qualitative and quantitative links between codes and papers.

All the mentioned data extraction or data processing tools require a license and therefore are not free of cost. While many researchers may benefit from national or institutional subscriptions to these services, others may not. As a potential alternative, Pearce ( 2018 ) proposed a set of free open-source software (FOSS), including an elaboration on how they can be combined to perform an SLR. He also highlighted that both free and proprietary solutions have advantages and disadvantages that are worthwhile for those who do not have the required tools provided by their employers or other institutions they are members of. The same may apply to the literature databases used for the literature acquisition in Decision 5 (Pearce 2018 ).

Moreover, there is a link to Step 1, Decision 3, where bibliometric reviews and meta-analyses were mentioned. These methods, which are alternatives to content analysis–based approaches, have specific demands, so specific tools would be appropriate, such as the Biblioshiny software or VOSviewer. As we will point out for all decisions, there is a high degree of interdependence among the steps and decisions made.

Decision 9 looks at conducting the data analysis, such as coding against (pre-defined) constructs, in SLRs that rely, in most cases, on content analysis. Seuring and Gold ( 2012 ) elaborated in detail on its characteristics and application in SLRs. As this paper also explains the process of qualitative content analysis in detail, repetition is avoided here, but a summary is offered. Since different ways exist to conduct a content analysis, it is even more important to explain and justify, for example, the choice of an inductive or deductive approach (see Decision 2). In several cases, analytic variables are applied on the go, so there is no theory-based introduction of related constructs. However, to ensure the validity and replicability of the review (see Decision 11), it is necessary to explicitly define all the variables and codes used to analyze and synthesize the reviewed material (Durach et al. 2017 ; Seuring and Gold 2012 ). To build a valid framework as the SLR outcome, it is vital to ensure that the constructs used for the data analysis are sufficiently defined, mutually exclusive, and comprehensively exhaustive. For meta-analysis, the predefined constructs and items would demand quantitative coding so that the resulting data could be analyzed using statistical software tools such as SPSS or R (e.g., Xiao and Watson 2019 ). Pointing to bibliometric analysis again, the respective software would be used for data analysis, yielding different figures and paper clusters, which would then require interpretation (e.g., Donthu et al. 2021 ; Xiao and Watson 2019 ).

Decision 10, on conducting subsequent statistical analysis, considers follow-up analysis of the coding results. Again, this is linked to the chosen SLR method, and a bibliographic analysis will require a different statistical analysis than a content analysis–based SLR (e.g., Lim et al. 2022 ; Xiao and Watson 2019 ). Beyond the use of content analysis and the qualitative interpretation of its results, applying contingency analysis offers the opportunity to quantitatively assess the links among constructs and items. It provides insights into which items are correlated with each other without implying causality. Thus, the interpretation of the findings must explain the causality behind the correlations between the constructs and the items. This must be based on sound reasoning and linking the findings to theoretical arguments. For SLRs, there have recently been two kinds of applications of contingency analysis, differentiated by unit of analysis. De Lima et al. ( 2021 ) used the entire paper as the unit of analysis, deriving correlations on two constructs that were used together in one paper. This is, of course, subject to critique as to whether the constructs really represent correlated content. Moving a level deeper, Tröster and Hiete ( 2018 ) used single-text passages on one aspect, argument, or thought as the unit of analysis. Such an approach is immune against the critique raised before and can yield more valid statistical support for thematic analysis. Another recent methodological contribution employing the same contingency analysis–based approach was made by Siems et al. ( 2021 ). Their analysis employs constructs from SSCM and dynamic capabilities. Employing four subsets of data (i.e., two time periods each in the food and automotive industries), they showed that the method allows distinguishing among time frames as well as among industries.

However, the unit of analysis must be precisely explained so that the reader can comprehend it. Both examples use contingency analysis to identify under-researched topics and develop them into research directions whose formulation represents the particular aim of an SLR (Paul and Criado 2020 ; Snyder 2019 ). Other statistical tools might also be applied, such as cluster analysis. Interestingly, Brandenburg and Rebs ( 2015 ) applied both contingency and cluster analyses. However, the authors stated that the contingency analysis did not yield usable results, so they opted for cluster analysis. In effect, Brandenburg and Rebs ( 2015 ) added analytical depth to their analysis of model types in SSCM by clustering them against the main analytical categories of content analysis. In any case, the application of statistical tools needs to fit the study purpose (Decision 1) and the literature sample (Decision 7), just as in their more conventional applications (e.g., in empirical research processes).

Decision 11 regards the additional consideration of validity and reliability criteria and emphasizes the need for explaining and justifying the single steps of the research process (Seuring and Gold 2012 ), much in line with other examples of research (Davis and Crombie 2001 ). This is critical to underlining the quality of the review but is often neglected in many submitted manuscripts. In our review, we find rich guidance on this decision, to which we want to guide readers (see Table 3 ). In particular, Durach et al. ( 2017 ) provide an entire section of biases and what needs to be considered and reported on them. Moreover, Snyder ( 2019 ) regularly reflects on these issues in her elaborations. This rich guidance elaborates on how to ensure the quality of the individual steps of the review process, such as sampling, study inclusion and exclusion, coding, synthesizing, and more practical issues, including team composition and teamwork organization, which are discussed in some guidelines (e.g., Clark et al. 2021 ; Kraus et al. 2020 ). We only want to underline that the potential biases are, of course, to be seen in conjunction with Decisions 2, 3, 4, 5, 6, 7, 9, and 10. These decisions and the elaboration by Durach et al. ( 2017 ) should provide ample points of reflection that, however, many SLR manuscripts fail to address.

4.7 Step 6: reporting the results

In the final step, there are three decisions on which there is surprisingly little guidance, although reviews often fail in this critical part of the process (Kraus et al. 2020 ). The reviewed guidelines discuss the presentation almost exclusively, while almost no guidance is given on the overall paper structure or the key content to be reported.

Consequently, the first choice to be made in Decision 12 is regarding the paper structure. We suggest following the five-step logic of typical research papers (see also Fisch and Block 2018 ) and explaining only a few points in which a difference from other papers is seen.

(1) Introduction: While the introduction would follow a conventional logic of problem statement, research question, contribution, and outline of the paper (see also Webster and Watson 2002 ), the next parts might depend on the theoretical choices made in Decision 2.

(2) Literature review section: If deductive logic is taken, the paper usually has a conventional flow. After the introduction, the literature review section covers the theoretical background and the choice of constructs and variables for the analysis (De Lima et al. 2021 ; Dieste et al. 2022 ). To avoid confusion in this section with the literature review, its labeling can also be closer to the reviewed object.

If an inductive approach is applied, it might be challenging to present the theoretical basis up front, as the codes emerge only from analyzing the material. In this case, the theory section might be rather short, concentrating on defining the core concepts or terms used, for example, in the keyword-based search for papers. The latter approach is exemplified by the study at hand, which presents a short review of the available literature in the introduction and the first part of the findings. However, we do not perform a systematic but integrative review, which allows for more freedom and creativity (Snyder 2019 ).

(3) Method section: This section should cover the steps and follow the logic presented in this paper or any of the reviewed guidelines so that the choices made during the research process are transparently disclosed (Denyer and Tranfield 2009 ; Paul et al. 2021 ; Xiao and Watson 2019 ). In particular, the search for papers and their selection requires a sound explanation of each step taken, including the provision of reasons for the delimitation of the final paper sample. A stage that is often not covered in sufficient detail is data analysis (Seuring and Gold 2012 ). This also needs to be outlined so that the reader can comprehend how sense has been made of the material collected. Overall, the demands for SLR papers are similar to case studies, survey papers, or almost any piece of empirical research; thus, each step of the research process needs to be comprehensively described, including Decisions 4–10. This comprehensiveness must also include addressing measures for validity and reliability (see Decision 11) or other suitable measures of rigor in the research process since they are a critical issue in literature reviews (Durach et al. 2017 ). In particular, inductively conducted reviews are prone to subjective influences and thus require sound reporting of design choices and their justification.

(4) Findings: The findings typically start with a descriptive analysis of the literature covered, such as journals, distribution across years, or (empirical) methods applied (Tranfield et al. 2003 ). For modeling-related reviews, classifying papers against the approach chosen is a standard approach, but this can often also serve as an analytic category that provides detailed insights. The descriptive analysis should be kept short since a paper only presenting descriptive findings will not be of great interest to other researchers due to the missing contribution (Snyder 2019 ). Nevertheless, there are opportunities to provide interesting findings in the descriptive analysis. Beyond a mere description of the distributions of the single results, such as the distribution of methods used in the sample, authors should combine analytical categories to derive more detailed insights (see also Tranfield et al. 2003 ). The distribution of methods used might well be combined with the years of publication to identify and characterize different phases in the development of a field of research or its maturity. Moreover, there could be value in the analysis of theories applied in the review sample (e.g., Touboulic and Walker 2015 ; Zhu et al. 2022 ) and in reflecting on the interplay of different qualitative and quantitative methods in spurring the theoretical development of the reviewed field. This could yield detailed insights into methodological as well as theoretical gaps, and we would suggest explicitly linking the findings of such analyses to the research directions that an SLR typically provides. This link could help make the research directions much more tangible by giving researchers a clear indication of how to follow up on the findings, as, for example, done by Maestrini et al. ( 2017 ) or Dieste et al. ( 2022 ). In contrast to the mentioned examples of an actionable research agenda, a typical weakness of premature SLR manuscripts is that they ask rather superficially for more research in the different aspects they reviewed but remain silent about how exactly this can be achieved.

We would thus like to encourage future SLR authors to systematically investigate the potential to combine two categories of descriptive analysis to move this section of the findings to a higher level of quality, interest, and relevance. The same can, of course, be done with the thematic findings, which comprise the second part of this section.

Moving into the thematic analysis, we have already reached Decision 13 on the presentation of the refined theoretical framework and the discussion of its contents. A first step might present the frequencies of the codes or constructs applied in the analysis. This allows the reader to understand which topics are relevant. If a rather small body of literature is analyzed, tables providing evidence on which paper has been coded for which construct might be helpful in improving the transparency of the research process. Tables or other forms of visualization might help to organize the many codes soundly (see also Durach et al. 2017 ; Paul and Criado 2020 ; Webster and Watson 2002 ). These findings might then lead to interpretation, for which it is necessary to extract meaning from the body of literature and present it accordingly (Snyder 2019 ). To do so, it might seem needless to say that the researchers should refer back to Decisions 1, 2, and 3 taken in Step 1 and their justifications. These typically identify the research gap to be filled, but after the lengthy process of the SLR, the authors often fail to step back from the coding results and put them into a larger perspective against the research gap defined in Decision 1 (see also Clark et al. 2021 ). To support this, it is certainly helpful to illustrate the findings in a figure or graph presenting the links among the constructs and items and adding causal reasoning to this (Durach et al. 2017 ; Paul and Criado 2020 ), such as the three figures by Seuring and Müller ( 2008 ) or other examples by De Lima et al. ( 2021 ) or Tipu ( 2022 ). This presentation should condense arguments made in the assessed literature but should also chart the course for future research. It will be these parts of the paper that are decisive for a strong SLR paper.

Moreover, some guidelines define the most fruitful way of synthesizing the findings as concept-centric synthesis (Clark et al. 2021 ; Fisch and Block 2018 ; Webster and Watson 2002 ). As presented in the previous sentence, the presentation of the review findings is centered on the content or concept of “concept-centric synthesis.” It is accompanied by a reference to all or the most relevant literature in which the concept is evident. Contrastingly, Webster and Watson ( 2002 ) found that author-centric synthesis discusses individual papers and what they have done and found (just like this sentence here). They added that this approach fails to synthesize larger samples. We want to note that we used the latter approach in some places in this paper. However, this aims to actively refer the reader to these studies, as they stand out from our relatively small sample. Beyond this, we want to link back to Decision 3, the selection of a theoretical framework and constructs. These constructs, or the parts of a framework, can also serve to structure the findings section by using them as headlines for subsections (Seuring et al. 2021 ).

Last but not least, there might even be cases where core findings and relationships might be opposed, and alternative perspectives could be presented. This would certainly be challenging to argue for but worthwhile to do in order to drive the reviewed field forward. A related example is the paper by Zhu et al. ( 2022 ), who challenged the current debate at the intersection of blockchain applications and supply chain management and pointed to the limited use of theoretical foundations for related analysis.

(5) Discussion and Conclusion: The discussion needs to explain the contribution the paper makes to the extant literature, that is, which previous findings or hypotheses are supported or contradicted and which aspects of the findings are particularly interesting for the future development of the reviewed field. This is in line with the content required in the discussion sections of any other paper type. A typical structure might point to the contribution and put it into perspective with already existing research. Further, limitations should be addressed on both the theoretical and methodological sides. This elaboration of the limitations can be coupled with the considerations of the validity and reliability of the study in Decision 11. The implications for future research are a core aim of an SLR (Clark et al. 2021 ; Mulrow 1987 ; Snyder 2019 ) and should be addressed in a further part of the discussion section. Recently, a growing number of literature reviews have also provided research questions for future research that provide a very concrete and actionable output of the SLR (e.g. Dieste et al. 2022 ; Maestrini et al. 2017 ). Moreover, we would like to reiterate our call to clearly link the research implications to the SLR findings, which helps the authors craft more tangible research directions and helps the reader to follow the authors’ interpretation. Literature review papers are usually not strongly positioned toward managerial implications, but even these implications might be included.

As a kind of normal demand, the conclusion should provide an answer to the research question put forward in the introduction, thereby closing the cycle of arguments made in the paper.

Although all the works seem to be done when the paper is written and the contribution is fleshed out, there is still one major decision to be made. Decision 14 concerns the identification of an appropriate journal for submission. Despite the popularity of the SLR method, a rising number of journals explicitly limit the number of SLRs published by them. Moreover, there are only two guidelines elaborating on this decision, underlining the need for the following considerations.

Although it might seem most attractive to submit the paper to the highest-ranking journal for the reviewed topic, we argue for two critical and review-related decisions to be made during the research process that influence whether the paper fits a certain outlet:

The theoretical foundation of the SLR (Decision 3) usually relates to certain journals in which it is published or discussed. If a deductive approach was taken, the journals in which the foundational papers were published might be suitable since the review potentially contributes to the further validation or refinement of the frameworks. Overall, we need to keep in mind that a paper needs to be added to a discussion in the journal, and this can be based on the theoretical framework or the reviewed papers, as shown below.

Appropriate journals for publication can be derived from the analyzed journal papers (Decision 7) (see also Paul and Criado 2020 ). This allows for an easy link to the theoretical debate in the respective journal by submitting it. This choice is identifiable in most of the papers mentioned in this paper and is often illustrated in the descriptive analysis.

If the journal chosen for the submission was neither related to the theoretical foundation nor overly represented in the body of literature analyzed, an explicit justification in the paper itself might be needed. Alternatively, an explanation might be provided in the letter to the editor when submitting the paper. If such a statement is not presented, the likelihood of it being transferred into the review process and passing it is rather low. Finally, we want to refer readers interested in the specificities of the publication-related review process of SLRs to Webster and Watson ( 2002 ), who elaborated on this for Management Information Systems Quarterly.

5 Discussion and conclusion

Critically reviewing the currently available SLR guidelines in the management domain, this paper synthesizes 14 key decisions to be made and reported across the SLR research process. Guidelines are presented for each decision, including tasks that assist in making sound choices to complete the research process and make meaningful contributions. Applying these guidelines should improve the rigor and robustness of many review papers and thus enhance their contributions. Moreover, some practical hints and best-practice examples are provided on issues that unexperienced authors regularly struggle to present in a manuscript (Fisch and Block 2018 ) and thus frustrate reviewers, readers, editors, and authors alike.

Strikingly, the review of prior guidelines reported in Table 3 revealed their focus on the technical details that need to be reported in any SLR. Consequently, our discipline has come a long way in crafting search strings, inclusion, and exclusion criteria, and elaborating on the validity and reliability of an SLR. Nevertheless, we left critical areas underdeveloped, such as the identification of relevant research gaps and questions, data extraction tools, analysis of the findings, and a meaningful and interesting reporting of the results. Our study contributes to filling these gaps by providing operationalized guidance to SLR authors, especially early-stage researchers who craft SLRs at the outset of their research journeys. At the same time, we need to underline that our paper is, of course, not the only useful reference for SLR authors. Instead, the readers are invited to find more guidance on the many aspects to consider in an SLR in the references we provide within the single decisions, as well as in Tables 1 and 2 . The tables also identify the strongholds of other guidelines that our paper does not want to replace but connect and extend at selected occasions, especially in SLR Steps 5 and 6.

The findings regularly underline the interconnection of the 14 decisions identified and discussed in this paper. We thus support Tranfield et al. ( 2003 ) who requested a flexible approach to the SLR while clearly reporting all design decisions and reflecting their impacts. In line with the guidance synthesized in this review, and especially Durach et al. ( 2017 ), we also present a refined framework in Figs. 1 and 2 . It specifically refines the original six-step SLR process by Durach et al. ( 2017 ) in three ways:

Enriched six-step process including the core interrelations of the 14 decisions

First, we subdivided the six steps into 14 decisions to enhance the operationalization of the process and enable closer guidance (see Fig. 1 ). Second, we added a temporal sequence to Fig. 2 by positioning the decisions from left to right according to this temporal sequence. This is based on systematically reflecting on the need to finish one decision before the following. If this need is evident, the following decision moves to the right; if not, the decisions are positioned below each other. Turning to Fig. 2 , it becomes evident that Step 2, “determining the required characteristics of primary studies,” and Step 3, “retrieving a sample of potentially relevant literature,” including their Decisions 4–6, can be conducted in an iterative manner. While this contrasts with the strict division of the six steps by Durach et al. ( 2017 ), it supports other guidance that suggests running pilot studies to iteratively define the literature sample, its sources, and characteristics (Snyder 2019 ; Tranfield et al. 2003 ; Xiao and Watson 2019 ). While this insight might suggest merging Steps 2 and 3, we refrain from this superficial change and building yet another SLR process model. Instead, we prefer to add detail and depth to Durach et al.’s ( 2017 ) model.

(Decisions: D1: specifying the research gap and related research question, D2: opting for a theoretical approach, D3: defining the core theoretical framework and constructs, D4: specifying inclusion and exclusion criteria, D5: defining sources and databases, D6: defining search terms and crafting a search string, D7: including and excluding literature for detailed analysis and synthesis, D8: selecting data extraction tool(s), D9: coding against (pre-defined) constructs, D10: conducting a subsequent (statistical) analysis (optional), D11: ensuring validity and reliability, D12: deciding on the structure of the paper, D13: presenting a refined theoretical framework and discussing its contents, and D14: deriving an appropriate journal from the analyzed papers).

This is also done through the third refinement, which underlines which previous or later decisions need to be considered within each single decision. Such a consideration moves beyond the mere temporal sequence of steps and decisions that does not reflect the full complexity of the SLR process. Instead, its focus is on the need to align, for example, the conduct of the data analysis (Decision 9) with the theoretical approach (Decision 2) and consequently ensure that the chosen theoretical framework and the constructs (Decision 3) are sufficiently defined for the data analysis (i.e., mutually exclusive and comprehensively exhaustive). The mentioned interrelations are displayed in Fig. 2 by means of directed arrows from one decision to another. The underlying explanations can be found in the earlier paper sections by searching for the individual decisions in the text on the impacted decisions. Overall, it is unsurprising to see that the vast majority of interrelations are directed from the earlier to the later steps and decisions (displayed through arrows below the diagonal of decisions), while only a few interrelations are inverse.

Combining the first refinement of the original framework (defining the 14 decisions) and the third refinement (revealing the main interrelations among the decisions) underlines the contribution of this study in two main ways. First, the centrality of ensuring validity and reliability (Decision 11) is underlined. It becomes evident that considerations of validity and reliability are central to the overall SLR process since all steps before the writing of the paper need to be revisited in iterative cycles through Decision 11. Any lack of related considerations will most likely lead to reviewer critique, putting the SLR publication at risk. On the positive side of this centrality, we also found substantial guidance on this issue. In contrast, as evidenced in Table 3 , there is a lack of prior guidance on Decisions 1, 8, 10, 12, 13, and 14, which this study is helping to fill. At the same time, these underexplained decisions are influenced by 14 of the 44 (32%) incoming arrows in Fig. 2 and influence the other decisions in 6 of the 44 (14%) instances. These interrelations among decisions to be considered when crafting an SLR were scattered across prior guidelines, lacked in-depth elaborations, and were hardly explicitly related to each other. Thus, we hope that our study and the refined SLR process model will help enhance the quality and contribution of future SLRs.

Data availablity

The data generated during this research is summarized in Table 3 and the analyzed papers are publicly available. They are clearly identified in Table 3 and the reference list.

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Sauer, P.C., Seuring, S. How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions. Rev Manag Sci 17 , 1899–1933 (2023). https://doi.org/10.1007/s11846-023-00668-3

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A step by step guide for conducting a systematic review and meta-analysis with simulation data

Gehad Mohamed Tawfik 1 , 2 ,
Kadek Agus Surya Dila 2 , 3 ,
Muawia Yousif Fadlelmola Mohamed 2 , 4 ,
Dao Ngoc Hien Tam 2 , 5 ,
Nguyen Dang Kien 2 , 6 ,
Ali Mahmoud Ahmed 2 , 7 &
Nguyen Tien Huy 8 , 9 , 10

Tropical Medicine and Health volume 47 , Article number: 46 ( 2019 ) Cite this article

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The massive abundance of studies relating to tropical medicine and health has increased strikingly over the last few decades. In the field of tropical medicine and health, a well-conducted systematic review and meta-analysis (SR/MA) is considered a feasible solution for keeping clinicians abreast of current evidence-based medicine. Understanding of SR/MA steps is of paramount importance for its conduction. It is not easy to be done as there are obstacles that could face the researcher. To solve those hindrances, this methodology study aimed to provide a step-by-step approach mainly for beginners and junior researchers, in the field of tropical medicine and other health care fields, on how to properly conduct a SR/MA, in which all the steps here depicts our experience and expertise combined with the already well-known and accepted international guidance.

We suggest that all steps of SR/MA should be done independently by 2–3 reviewers’ discussion, to ensure data quality and accuracy.

SR/MA steps include the development of research question, forming criteria, search strategy, searching databases, protocol registration, title, abstract, full-text screening, manual searching, extracting data, quality assessment, data checking, statistical analysis, double data checking, and manuscript writing.

Introduction

The amount of studies published in the biomedical literature, especially tropical medicine and health, has increased strikingly over the last few decades. This massive abundance of literature makes clinical medicine increasingly complex, and knowledge from various researches is often needed to inform a particular clinical decision. However, available studies are often heterogeneous with regard to their design, operational quality, and subjects under study and may handle the research question in a different way, which adds to the complexity of evidence and conclusion synthesis [ 1 ].

Systematic review and meta-analyses (SR/MAs) have a high level of evidence as represented by the evidence-based pyramid. Therefore, a well-conducted SR/MA is considered a feasible solution in keeping health clinicians ahead regarding contemporary evidence-based medicine.

Differing from a systematic review, unsystematic narrative review tends to be descriptive, in which the authors select frequently articles based on their point of view which leads to its poor quality. A systematic review, on the other hand, is defined as a review using a systematic method to summarize evidence on questions with a detailed and comprehensive plan of study. Furthermore, despite the increasing guidelines for effectively conducting a systematic review, we found that basic steps often start from framing question, then identifying relevant work which consists of criteria development and search for articles, appraise the quality of included studies, summarize the evidence, and interpret the results [ 2 , 3 ]. However, those simple steps are not easy to be reached in reality. There are many troubles that a researcher could be struggled with which has no detailed indication.

Conducting a SR/MA in tropical medicine and health may be difficult especially for young researchers; therefore, understanding of its essential steps is crucial. It is not easy to be done as there are obstacles that could face the researcher. To solve those hindrances, we recommend a flow diagram (Fig. 1 ) which illustrates a detailed and step-by-step the stages for SR/MA studies. This methodology study aimed to provide a step-by-step approach mainly for beginners and junior researchers, in the field of tropical medicine and other health care fields, on how to properly and succinctly conduct a SR/MA; all the steps here depicts our experience and expertise combined with the already well known and accepted international guidance.

Detailed flow diagram guideline for systematic review and meta-analysis steps. Note : Star icon refers to “2–3 reviewers screen independently”

Methods and results

Detailed steps for conducting any systematic review and meta-analysis.

We searched the methods reported in published SR/MA in tropical medicine and other healthcare fields besides the published guidelines like Cochrane guidelines {Higgins, 2011 #7} [ 4 ] to collect the best low-bias method for each step of SR/MA conduction steps. Furthermore, we used guidelines that we apply in studies for all SR/MA steps. We combined these methods in order to conclude and conduct a detailed flow diagram that shows the SR/MA steps how being conducted.

Any SR/MA must follow the widely accepted Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA checklist 2009) (Additional file 5 : Table S1) [ 5 ].

We proposed our methods according to a valid explanatory simulation example choosing the topic of “evaluating safety of Ebola vaccine,” as it is known that Ebola is a very rare tropical disease but fatal. All the explained methods feature the standards followed internationally, with our compiled experience in the conduct of SR beside it, which we think proved some validity. This is a SR under conduct by a couple of researchers teaming in a research group, moreover, as the outbreak of Ebola which took place (2013–2016) in Africa resulted in a significant mortality and morbidity. Furthermore, since there are many published and ongoing trials assessing the safety of Ebola vaccines, we thought this would provide a great opportunity to tackle this hotly debated issue. Moreover, Ebola started to fire again and new fatal outbreak appeared in the Democratic Republic of Congo since August 2018, which caused infection to more than 1000 people according to the World Health Organization, and 629 people have been killed till now. Hence, it is considered the second worst Ebola outbreak, after the first one in West Africa in 2014 , which infected more than 26,000 and killed about 11,300 people along outbreak course.

Research question and objectives

Like other study designs, the research question of SR/MA should be feasible, interesting, novel, ethical, and relevant. Therefore, a clear, logical, and well-defined research question should be formulated. Usually, two common tools are used: PICO or SPIDER. PICO (Population, Intervention, Comparison, Outcome) is used mostly in quantitative evidence synthesis. Authors demonstrated that PICO holds more sensitivity than the more specific SPIDER approach [ 6 ]. SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) was proposed as a method for qualitative and mixed methods search.

We here recommend a combined approach of using either one or both the SPIDER and PICO tools to retrieve a comprehensive search depending on time and resources limitations. When we apply this to our assumed research topic, being of qualitative nature, the use of SPIDER approach is more valid.

PICO is usually used for systematic review and meta-analysis of clinical trial study. For the observational study (without intervention or comparator), in many tropical and epidemiological questions, it is usually enough to use P (Patient) and O (outcome) only to formulate a research question. We must indicate clearly the population (P), then intervention (I) or exposure. Next, it is necessary to compare (C) the indicated intervention with other interventions, i.e., placebo. Finally, we need to clarify which are our relevant outcomes.

To facilitate comprehension, we choose the Ebola virus disease (EVD) as an example. Currently, the vaccine for EVD is being developed and under phase I, II, and III clinical trials; we want to know whether this vaccine is safe and can induce sufficient immunogenicity to the subjects.

An example of a research question for SR/MA based on PICO for this issue is as follows: How is the safety and immunogenicity of Ebola vaccine in human? (P: healthy subjects (human), I: vaccination, C: placebo, O: safety or adverse effects)

Preliminary research and idea validation

We recommend a preliminary search to identify relevant articles, ensure the validity of the proposed idea, avoid duplication of previously addressed questions, and assure that we have enough articles for conducting its analysis. Moreover, themes should focus on relevant and important health-care issues, consider global needs and values, reflect the current science, and be consistent with the adopted review methods. Gaining familiarity with a deep understanding of the study field through relevant videos and discussions is of paramount importance for better retrieval of results. If we ignore this step, our study could be canceled whenever we find out a similar study published before. This means we are wasting our time to deal with a problem that has been tackled for a long time.

To do this, we can start by doing a simple search in PubMed or Google Scholar with search terms Ebola AND vaccine. While doing this step, we identify a systematic review and meta-analysis of determinant factors influencing antibody response from vaccination of Ebola vaccine in non-human primate and human [ 7 ], which is a relevant paper to read to get a deeper insight and identify gaps for better formulation of our research question or purpose. We can still conduct systematic review and meta-analysis of Ebola vaccine because we evaluate safety as a different outcome and different population (only human).

Inclusion and exclusion criteria

Eligibility criteria are based on the PICO approach, study design, and date. Exclusion criteria mostly are unrelated, duplicated, unavailable full texts, or abstract-only papers. These exclusions should be stated in advance to refrain the researcher from bias. The inclusion criteria would be articles with the target patients, investigated interventions, or the comparison between two studied interventions. Briefly, it would be articles which contain information answering our research question. But the most important is that it should be clear and sufficient information, including positive or negative, to answer the question.

For the topic we have chosen, we can make inclusion criteria: (1) any clinical trial evaluating the safety of Ebola vaccine and (2) no restriction regarding country, patient age, race, gender, publication language, and date. Exclusion criteria are as follows: (1) study of Ebola vaccine in non-human subjects or in vitro studies; (2) study with data not reliably extracted, duplicate, or overlapping data; (3) abstract-only papers as preceding papers, conference, editorial, and author response theses and books; (4) articles without available full text available; and (5) case reports, case series, and systematic review studies. The PRISMA flow diagram template that is used in SR/MA studies can be found in Fig. 2 .

PRISMA flow diagram of studies’ screening and selection

Search strategy

A standard search strategy is used in PubMed, then later it is modified according to each specific database to get the best relevant results. The basic search strategy is built based on the research question formulation (i.e., PICO or PICOS). Search strategies are constructed to include free-text terms (e.g., in the title and abstract) and any appropriate subject indexing (e.g., MeSH) expected to retrieve eligible studies, with the help of an expert in the review topic field or an information specialist. Additionally, we advise not to use terms for the Outcomes as their inclusion might hinder the database being searched to retrieve eligible studies because the used outcome is not mentioned obviously in the articles.

The improvement of the search term is made while doing a trial search and looking for another relevant term within each concept from retrieved papers. To search for a clinical trial, we can use these descriptors in PubMed: “clinical trial”[Publication Type] OR “clinical trials as topic”[MeSH terms] OR “clinical trial”[All Fields]. After some rounds of trial and refinement of search term, we formulate the final search term for PubMed as follows: (ebola OR ebola virus OR ebola virus disease OR EVD) AND (vaccine OR vaccination OR vaccinated OR immunization) AND (“clinical trial”[Publication Type] OR “clinical trials as topic”[MeSH Terms] OR “clinical trial”[All Fields]). Because the study for this topic is limited, we do not include outcome term (safety and immunogenicity) in the search term to capture more studies.

Search databases, import all results to a library, and exporting to an excel sheet

According to the AMSTAR guidelines, at least two databases have to be searched in the SR/MA [ 8 ], but as you increase the number of searched databases, you get much yield and more accurate and comprehensive results. The ordering of the databases depends mostly on the review questions; being in a study of clinical trials, you will rely mostly on Cochrane, mRCTs, or International Clinical Trials Registry Platform (ICTRP). Here, we propose 12 databases (PubMed, Scopus, Web of Science, EMBASE, GHL, VHL, Cochrane, Google Scholar, Clinical trials.gov , mRCTs, POPLINE, and SIGLE), which help to cover almost all published articles in tropical medicine and other health-related fields. Among those databases, POPLINE focuses on reproductive health. Researchers should consider to choose relevant database according to the research topic. Some databases do not support the use of Boolean or quotation; otherwise, there are some databases that have special searching way. Therefore, we need to modify the initial search terms for each database to get appreciated results; therefore, manipulation guides for each online database searches are presented in Additional file 5 : Table S2. The detailed search strategy for each database is found in Additional file 5 : Table S3. The search term that we created in PubMed needs customization based on a specific characteristic of the database. An example for Google Scholar advanced search for our topic is as follows:

With all of the words: ebola virus

With at least one of the words: vaccine vaccination vaccinated immunization

Where my words occur: in the title of the article

With all of the words: EVD

Finally, all records are collected into one Endnote library in order to delete duplicates and then to it export into an excel sheet. Using remove duplicating function with two options is mandatory. All references which have (1) the same title and author, and published in the same year, and (2) the same title and author, and published in the same journal, would be deleted. References remaining after this step should be exported to an excel file with essential information for screening. These could be the authors’ names, publication year, journal, DOI, URL link, and abstract.

Protocol writing and registration

Protocol registration at an early stage guarantees transparency in the research process and protects from duplication problems. Besides, it is considered a documented proof of team plan of action, research question, eligibility criteria, intervention/exposure, quality assessment, and pre-analysis plan. It is recommended that researchers send it to the principal investigator (PI) to revise it, then upload it to registry sites. There are many registry sites available for SR/MA like those proposed by Cochrane and Campbell collaborations; however, we recommend registering the protocol into PROSPERO as it is easier. The layout of a protocol template, according to PROSPERO, can be found in Additional file 5 : File S1.

Title and abstract screening

Decisions to select retrieved articles for further assessment are based on eligibility criteria, to minimize the chance of including non-relevant articles. According to the Cochrane guidance, two reviewers are a must to do this step, but as for beginners and junior researchers, this might be tiresome; thus, we propose based on our experience that at least three reviewers should work independently to reduce the chance of error, particularly in teams with a large number of authors to add more scrutiny and ensure proper conduct. Mostly, the quality with three reviewers would be better than two, as two only would have different opinions from each other, so they cannot decide, while the third opinion is crucial. And here are some examples of systematic reviews which we conducted following the same strategy (by a different group of researchers in our research group) and published successfully, and they feature relevant ideas to tropical medicine and disease [ 9 , 10 , 11 ].

In this step, duplications will be removed manually whenever the reviewers find them out. When there is a doubt about an article decision, the team should be inclusive rather than exclusive, until the main leader or PI makes a decision after discussion and consensus. All excluded records should be given exclusion reasons.

Full text downloading and screening

Many search engines provide links for free to access full-text articles. In case not found, we can search in some research websites as ResearchGate, which offer an option of direct full-text request from authors. Additionally, exploring archives of wanted journals, or contacting PI to purchase it if available. Similarly, 2–3 reviewers work independently to decide about included full texts according to eligibility criteria, with reporting exclusion reasons of articles. In case any disagreement has occurred, the final decision has to be made by discussion.

Manual search

One has to exhaust all possibilities to reduce bias by performing an explicit hand-searching for retrieval of reports that may have been dropped from first search [ 12 ]. We apply five methods to make manual searching: searching references from included studies/reviews, contacting authors and experts, and looking at related articles/cited articles in PubMed and Google Scholar.

We describe here three consecutive methods to increase and refine the yield of manual searching: firstly, searching reference lists of included articles; secondly, performing what is known as citation tracking in which the reviewers track all the articles that cite each one of the included articles, and this might involve electronic searching of databases; and thirdly, similar to the citation tracking, we follow all “related to” or “similar” articles. Each of the abovementioned methods can be performed by 2–3 independent reviewers, and all the possible relevant article must undergo further scrutiny against the inclusion criteria, after following the same records yielded from electronic databases, i.e., title/abstract and full-text screening.

We propose an independent reviewing by assigning each member of the teams a “tag” and a distinct method, to compile all the results at the end for comparison of differences and discussion and to maximize the retrieval and minimize the bias. Similarly, the number of included articles has to be stated before addition to the overall included records.

Data extraction and quality assessment

This step entitles data collection from included full-texts in a structured extraction excel sheet, which is previously pilot-tested for extraction using some random studies. We recommend extracting both adjusted and non-adjusted data because it gives the most allowed confounding factor to be used in the analysis by pooling them later [ 13 ]. The process of extraction should be executed by 2–3 independent reviewers. Mostly, the sheet is classified into the study and patient characteristics, outcomes, and quality assessment (QA) tool.

Data presented in graphs should be extracted by software tools such as Web plot digitizer [ 14 ]. Most of the equations that can be used in extraction prior to analysis and estimation of standard deviation (SD) from other variables is found inside Additional file 5 : File S2 with their references as Hozo et al. [ 15 ], Xiang et al. [ 16 ], and Rijkom et al. [ 17 ]. A variety of tools are available for the QA, depending on the design: ROB-2 Cochrane tool for randomized controlled trials [ 18 ] which is presented as Additional file 1 : Figure S1 and Additional file 2 : Figure S2—from a previous published article data—[ 19 ], NIH tool for observational and cross-sectional studies [ 20 ], ROBINS-I tool for non-randomize trials [ 21 ], QUADAS-2 tool for diagnostic studies, QUIPS tool for prognostic studies, CARE tool for case reports, and ToxRtool for in vivo and in vitro studies. We recommend that 2–3 reviewers independently assess the quality of the studies and add to the data extraction form before the inclusion into the analysis to reduce the risk of bias. In the NIH tool for observational studies—cohort and cross-sectional—as in this EBOLA case, to evaluate the risk of bias, reviewers should rate each of the 14 items into dichotomous variables: yes, no, or not applicable. An overall score is calculated by adding all the items scores as yes equals one, while no and NA equals zero. A score will be given for every paper to classify them as poor, fair, or good conducted studies, where a score from 0–5 was considered poor, 6–9 as fair, and 10–14 as good.

In the EBOLA case example above, authors can extract the following information: name of authors, country of patients, year of publication, study design (case report, cohort study, or clinical trial or RCT), sample size, the infected point of time after EBOLA infection, follow-up interval after vaccination time, efficacy, safety, adverse effects after vaccinations, and QA sheet (Additional file 6 : Data S1).

Data checking

Due to the expected human error and bias, we recommend a data checking step, in which every included article is compared with its counterpart in an extraction sheet by evidence photos, to detect mistakes in data. We advise assigning articles to 2–3 independent reviewers, ideally not the ones who performed the extraction of those articles. When resources are limited, each reviewer is assigned a different article than the one he extracted in the previous stage.

Statistical analysis

Investigators use different methods for combining and summarizing findings of included studies. Before analysis, there is an important step called cleaning of data in the extraction sheet, where the analyst organizes extraction sheet data in a form that can be read by analytical software. The analysis consists of 2 types namely qualitative and quantitative analysis. Qualitative analysis mostly describes data in SR studies, while quantitative analysis consists of two main types: MA and network meta-analysis (NMA). Subgroup, sensitivity, cumulative analyses, and meta-regression are appropriate for testing whether the results are consistent or not and investigating the effect of certain confounders on the outcome and finding the best predictors. Publication bias should be assessed to investigate the presence of missing studies which can affect the summary.

To illustrate basic meta-analysis, we provide an imaginary data for the research question about Ebola vaccine safety (in terms of adverse events, 14 days after injection) and immunogenicity (Ebola virus antibodies rise in geometric mean titer, 6 months after injection). Assuming that from searching and data extraction, we decided to do an analysis to evaluate Ebola vaccine “A” safety and immunogenicity. Other Ebola vaccines were not meta-analyzed because of the limited number of studies (instead, it will be included for narrative review). The imaginary data for vaccine safety meta-analysis can be accessed in Additional file 7 : Data S2. To do the meta-analysis, we can use free software, such as RevMan [ 22 ] or R package meta [ 23 ]. In this example, we will use the R package meta. The tutorial of meta package can be accessed through “General Package for Meta-Analysis” tutorial pdf [ 23 ]. The R codes and its guidance for meta-analysis done can be found in Additional file 5 : File S3.

For the analysis, we assume that the study is heterogenous in nature; therefore, we choose a random effect model. We did an analysis on the safety of Ebola vaccine A. From the data table, we can see some adverse events occurring after intramuscular injection of vaccine A to the subject of the study. Suppose that we include six studies that fulfill our inclusion criteria. We can do a meta-analysis for each of the adverse events extracted from the studies, for example, arthralgia, from the results of random effect meta-analysis using the R meta package.

From the results shown in Additional file 3 : Figure S3, we can see that the odds ratio (OR) of arthralgia is 1.06 (0.79; 1.42), p value = 0.71, which means that there is no association between the intramuscular injection of Ebola vaccine A and arthralgia, as the OR is almost one, and besides, the P value is insignificant as it is > 0.05.

In the meta-analysis, we can also visualize the results in a forest plot. It is shown in Fig. 3 an example of a forest plot from the simulated analysis.

Random effect model forest plot for comparison of vaccine A versus placebo

From the forest plot, we can see six studies (A to F) and their respective OR (95% CI). The green box represents the effect size (in this case, OR) of each study. The bigger the box means the study weighted more (i.e., bigger sample size). The blue diamond shape represents the pooled OR of the six studies. We can see the blue diamond cross the vertical line OR = 1, which indicates no significance for the association as the diamond almost equalized in both sides. We can confirm this also from the 95% confidence interval that includes one and the p value > 0.05.

For heterogeneity, we see that I 2 = 0%, which means no heterogeneity is detected; the study is relatively homogenous (it is rare in the real study). To evaluate publication bias related to the meta-analysis of adverse events of arthralgia, we can use the metabias function from the R meta package (Additional file 4 : Figure S4) and visualization using a funnel plot. The results of publication bias are demonstrated in Fig. 4 . We see that the p value associated with this test is 0.74, indicating symmetry of the funnel plot. We can confirm it by looking at the funnel plot.

Publication bias funnel plot for comparison of vaccine A versus placebo

Looking at the funnel plot, the number of studies at the left and right side of the funnel plot is the same; therefore, the plot is symmetry, indicating no publication bias detected.

Sensitivity analysis is a procedure used to discover how different values of an independent variable will influence the significance of a particular dependent variable by removing one study from MA. If all included study p values are < 0.05, hence, removing any study will not change the significant association. It is only performed when there is a significant association, so if the p value of MA done is 0.7—more than one—the sensitivity analysis is not needed for this case study example. If there are 2 studies with p value > 0.05, removing any of the two studies will result in a loss of the significance.

Double data checking

For more assurance on the quality of results, the analyzed data should be rechecked from full-text data by evidence photos, to allow an obvious check for the PI of the study.

Manuscript writing, revision, and submission to a journal

Writing based on four scientific sections: introduction, methods, results, and discussion, mostly with a conclusion. Performing a characteristic table for study and patient characteristics is a mandatory step which can be found as a template in Additional file 5 : Table S3.

After finishing the manuscript writing, characteristics table, and PRISMA flow diagram, the team should send it to the PI to revise it well and reply to his comments and, finally, choose a suitable journal for the manuscript which fits with considerable impact factor and fitting field. We need to pay attention by reading the author guidelines of journals before submitting the manuscript.

The role of evidence-based medicine in biomedical research is rapidly growing. SR/MAs are also increasing in the medical literature. This paper has sought to provide a comprehensive approach to enable reviewers to produce high-quality SR/MAs. We hope that readers could gain general knowledge about how to conduct a SR/MA and have the confidence to perform one, although this kind of study requires complex steps compared to narrative reviews.

Having the basic steps for conduction of MA, there are many advanced steps that are applied for certain specific purposes. One of these steps is meta-regression which is performed to investigate the association of any confounder and the results of the MA. Furthermore, there are other types rather than the standard MA like NMA and MA. In NMA, we investigate the difference between several comparisons when there were not enough data to enable standard meta-analysis. It uses both direct and indirect comparisons to conclude what is the best between the competitors. On the other hand, mega MA or MA of patients tend to summarize the results of independent studies by using its individual subject data. As a more detailed analysis can be done, it is useful in conducting repeated measure analysis and time-to-event analysis. Moreover, it can perform analysis of variance and multiple regression analysis; however, it requires homogenous dataset and it is time-consuming in conduct [ 24 ].

Conclusions

Systematic review/meta-analysis steps include development of research question and its validation, forming criteria, search strategy, searching databases, importing all results to a library and exporting to an excel sheet, protocol writing and registration, title and abstract screening, full-text screening, manual searching, extracting data and assessing its quality, data checking, conducting statistical analysis, double data checking, manuscript writing, revising, and submitting to a journal.

Availability of data and materials

Not applicable.

Abbreviations

Network meta-analysis

Principal investigator

Population, Intervention, Comparison, Outcome

Preferred Reporting Items for Systematic Review and Meta-analysis statement

Quality assessment

Sample, Phenomenon of Interest, Design, Evaluation, Research type

Systematic review and meta-analyses

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Acknowledgements

This study was conducted (in part) at the Joint Usage/Research Center on Tropical Disease, Institute of Tropical Medicine, Nagasaki University, Japan.

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Additional files

Additional file 1:.

Figure S1. Risk of bias assessment graph of included randomized controlled trials. (TIF 20 kb)

Additional file 2:

Figure S2. Risk of bias assessment summary. (TIF 69 kb)

Additional file 3:

Figure S3. Arthralgia results of random effect meta-analysis using R meta package. (TIF 20 kb)

Additional file 4:

Figure S4. Arthralgia linear regression test of funnel plot asymmetry using R meta package. (TIF 13 kb)

Additional file 5:

Table S1. PRISMA 2009 Checklist. Table S2. Manipulation guides for online database searches. Table S3. Detailed search strategy for twelve database searches. Table S4. Baseline characteristics of the patients in the included studies. File S1. PROSPERO protocol template file. File S2. Extraction equations that can be used prior to analysis to get missed variables. File S3. R codes and its guidance for meta-analysis done for comparison between EBOLA vaccine A and placebo. (DOCX 49 kb)

Additional file 6:

Data S1. Extraction and quality assessment data sheets for EBOLA case example. (XLSX 1368 kb)

Additional file 7:

Data S2. Imaginary data for EBOLA case example. (XLSX 10 kb)

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Tawfik, G.M., Dila, K.A.S., Mohamed, M.Y.F. et al. A step by step guide for conducting a systematic review and meta-analysis with simulation data. Trop Med Health 47 , 46 (2019). https://doi.org/10.1186/s41182-019-0165-6

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Assessing the certainty of the evidence in systematic reviews: importance, process, and use.

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Romina Brignardello-Petersen, Gordon H Guyatt, Assessing the Certainty of the Evidence in Systematic Reviews: Importance, Process, and Use, American Journal of Epidemiology , 2024;, kwae332, https://doi.org/10.1093/aje/kwae332

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When interpreting results and drawing conclusions, authors of systematic reviews should consider the limitations of the evidence included in their review. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach provides a framework for the explicit consideration of the limitations of the evidence included in a systematic review, and for incorporating this assessment into the conclusions. Assessments of certainty of evidence are a methodological expectation of systematic reviews. The certainty of the evidence is specific to each outcome in a systematic review, and can be rated as high, moderate, low, or very low. Because it will have an important impact, before conducting certainty of evidence, reviewers must clarify the intent of their question: are they interested in causation or association. Serious concerns regarding limitations in the study design, inconsistency, imprecision, indirectness, and publication bias can decrease the certainty of the evidence. Using an example, this article describes and illustrates the importance and the steps for assessing the certainty of evidence and drawing accurate conclusions in a systematic review.

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Speaker 1: The first step of doing a systematic literature review is coming up with a review question, like what do you actually want to know about the world and how can you phrase that as a simple question. You can write down all of the questions you want and then choose from the best one or a combination but I like to go to ChatGPT and use them as like a sounding board and a research assistant so that they can help me really sort of refine what I actually want to do a systematic literature review on. So here we are, we head over and we say, help me define a systematic literature review research question about beards and their smell. Maybe that's what I was interested in. My beard smells lovely. It smells like Australian sandalwood at the moment. Beautiful. It says a systematic literature review research question should be specific blah blah blah. And then it comes up with one. How do microbial communities in beards influence blah blah blah. And it gives me kind of a first start. The one thing I found about any AI that you're asking, it makes a lot of assumptions about what you want to know. So I highly recommend that you go in and you sort of like re-prompt it and you say, I like this bit, but I don't like this bit, or this bit's good, but you're a little bit off on this area. That is how you kind of use this as a research assistant as like a sounding board for all of your ideas. Then once you've got a research question and you need to spend probably most of the time of the first bit of searching on this because it's so very important. Come up with a definitive but broad, and I know that is so contradictory, but you need to come up with something that is focused enough that it will give you sort of like a good outcome but not too broad that all of a sudden, you know, like you're dealing with thousands and thousands of papers. So that is the challenge, and use ChatGPT to get that balance. Now, you can also use frameworks. There's different frameworks that you can use which will help you with this first sort of like step. And I just asked ChatGPT. I'm familiar with some of these, but some of these were new to me as well. I said, what frameworks for a systematic literature review can be used for this question? And it says Prisma, it used Cochrane Handbook for systematic reviews, it's got the Joanna Briggs Institute Methodology, Spyder and Pico. One of the most famous ones arguably is Pico where you say, okay, I've got this P, population, I've got this I, intervention that I'm looking at, I've got this C, comparison of all of the things that I found and O, outcome. Then what happened when they did these things? And quite often the C stands for comparison because it's a quantitative measurement of comparing it to say like a placebo if you're doing a lot of health stuff or another sort of intervention. So that's how we use frameworks to start thinking about our research question. What population are we gonna look at? What intervention are we looking at? What comparison, if any, are we gonna look at? And we're gonna look for the outcomes within those systems and structures that we set in place. So that's step one. Step two, actually, is what defines a literature review from a systematic literature review? Let's get into that. This is so very important for a systematic literature review because we need to know what methods we are going to use to filter all of the different stuff that we're gonna come across. We wanna know stuff like what procedure are we gonna go through to find the literature. We wanna know what keywords we're gonna use, what semantic search terms we're gonna use in certain databases to find the literature. Now, I like to head over to something like Search Smart. This will give you sort of like the best databases to search for your systematic literature review. And so all you need to do is look for scholarly records or clinical trials if you want, put in the subjects or the keywords and then sort of like define whether or not you want systemic keyword searching, backwards citation, forwards, all of that sort of stuff and also non-paywall databases and you click Start Comparison and it will go off and give you all of the different databases that you can look at. Then, keywords. Keywords are so very important because we often find research based on how they're described like in the abstract or the title. So be very specific with your keywords. By the way, I have another video, go check it out here, where I talk about how to find all of the literature that you'll ever need using different approaches, AI, Boolean searches, old school keyword searches, and that video will allow you to find everything you need in your systematic review. But databases are very important. Where are you gonna search? what keywords are you gonna search for, what semantic search questions, and that's new for this sort of like era of AI because it allows us to actually just put our research question into a database and have it sort of understand that question and give us results back. So now we're on to the exciting part which is finding the research papers. The one thing I like to do first and foremost, and that's only possible now because of AI's semantic search. I love it so much. Let's head over to the three tools that I think you would wanna use. The first one is Elicit. Ask a research question. Beards and, ooh, not bears, and smells. Let's see, that's not really a research question, but let's see what it comes up with. But it's that sort of stuff that you need to sort of like thinking about. Like, is that a keyword combination that you want to put in all of the databases or not? Whatever you decide using your meat brain. So, here we go. Here's all of the different papers that I could talk about. Brilliant. The next one is consensus. Beards and smell. Then we can go off and find all of the papers here using that sort of semantic keyword search as well. And we've also got size space. I can go here, beards and smell. And this is where I like to find all of my stuff using keywords and semantic search. So making sense, oh, this hasn't really done too well with beards, beards and issues, blah, blah, blah. So overall, you can see that we've got a little bit of discrepancy between what these pick up. So it's very important, I think, that you try a few to see what works best for you. And then finally, we gotta head over to something like Google Scholar, and we wanna say, okay, what keywords are we gonna put in? This isn't semantic search, this is just putting in beards and smell. And we can use Boolean operators to make sure that we're actually gonna get the papers that are relevant for us. So we can go beards, and then and, because we want and, smell. There we are. So then we're gonna come up with all of the smell and beard articles that it's going to come up with. The smell report, shame and glory. Only the beards, even after beards became merely rather than daring, the rather radical, oh my God, I don't like this one. The British Journal of Sociology, come on now, you can do better than that. But that is where you can go and actually find all of this information. And so semantic, keywords, databases, and Boolean operators to have a look at what you're excluding and including in your search is very, very important. So that is the step three. Yeah, step three, that is searching for the paper. And now we need to filter and screen and read. Once we've ended up with a load of papers from our searching based on the criteria and the methods we set out in step two, we've now got like an exclusion and inclusion protocol where we need to say, okay, we've got all of these studies, Which ones are we going to include and which ones are we going to exclude? And it's a really sort of like simple process of just filtering. This is why you need a load of papers at the top. Put loads of papers at the top and then they have to filter down to the useful papers down the bottom. And it may only be a small fraction of all of the papers you found, but this is what a systematic review is all about. It's about making sure that we include the papers that are relevant for your research question and not just like general themes, which is like a normal literature review where we just sort of say, oh yeah, there's this theme and this theme and this theme. No, this one's much more focused, so we need to filter it. I like to use the Prisma flowchart to work out which ones I'm getting rid of and keep track of the ones I've got rid of and how much I've filtered it down. So a Prisma flowchart looks like this. We've got identification in the top here and then we've got records identified through database searching. In this case, they had 96. and then we've got other additional identified through other sources, and this was none in this bit. Then they removed duplicates, so there was two that were the same, so they removed one of them, and then they said, okay, we've got this many in screen, 95, and eligibility, full text articles assessed for eligibility, there was only five, and all of these were actually excluded because it didn't meet their criteria that they'd set out in part of their exclusion or inclusion criteria. So you can see we've got like examines treatment, not prevention. So this was like obviously like a health study where they were looking at treatment and not the prevention or something. So that was most of them, that was 52. Then one was pediatric, one was irrelevant. Oh no, loads were irrelevant, 37 were irrelevant. So you can see we've gone from 96 all the way down to five at this point. And then full text articles not included. Well, there was none there, which is great. but here we've got four which studies included in quantitative synthesis or a meta-analysis was only four, they got rid of 92 of them because they didn't meet the specific search and exclusion and inclusion criteria that they set. That is so important and that is very, very typical of a systematic review. So now it's about taking those special studies that you found and getting all of the important stuff out of them. you should read them, especially if there's only four. You should read them from end to beginning. No, don't read them like that. Read them however you want, normally with abstract, then to conclusions, then to introduction, then to method, anyway, you get the idea. Do you know what, actually, I've got another video on how to read like a PhD. Go check out that one there. It's much better than what I just said. But now you need to read them and you need to start thinking about how these studies are influencing your research question sort of response. Are they for it? Are they against it? Do they give you a new insight? Is there something sneaky in there when you look at them all together that is surprising? It's those sort of things that really should be sort of milling around in your head. We're not looking for any sort of definitive stuff just yet, but we just need to read, analyze, refine, understand, all of those stuff. Those words are very important, put them there. But now, we've got a couple of new ways that we can actually talk to all of our documents. So one place I really like is docanalyzer.ai and what you can do is upload your documents and tag them as, in this case I've got literature review, you can see I've got one, two, three, four, five, six here. So then we can go to labels and we can go chat with these six documents. And the one thing I love about docanalyzer is that it doesn't like try to make stuff up. If it doesn't understand what you're asking or it can't identify it in the documents that you've given it, it will just say, hey, I don't really know, can you give me a bit more information? It doesn't sort of like BS its way into chat, which I really like. So, for example here, it says to identify the important parts of the document, I would need more specific keywords or topics of interest. That's what I want from an AI, something that isn't just gonna make stuff up. Another thing you can do is head back over to size space, And in SciSpace, you can actually get results from my library. So if you put those very specific studies that you've filtered and found into your library, you can then ask it questions across that library, which I think is really, really fantastic. So not only do you read it all, if you can, if it's a sensible amount of papers, but then you can start chatting to all of the documents together in something like DocAnalyzer and SciSpace, and then you can get sort of further connections, further deeper inquiry into things that maybe you have missed. Or maybe there's just a question, you've read them all, and there's a question sort of in your mind. You're like, actually, does this apply to all of the papers or not? Put it into something like this and it will search across all of your documents. I absolutely love, I'm doing this today, Chef's Kiss, it's my new favorite thing. Chef's Kiss, yum, yum, yum, yum, yum. But doing that means that you're not gonna miss out on anything because you're going to use old school tactics by just reading, read, read, read, read, read, and new school tactics by using AI, AI, AI, AI. Together, they are the perfect combination, yes. And then it's all about writing it up, making sure that you actually talk about what your research question is, the methods you've used, the filtration criteria, and the exclusion and inclusion criteria, the keywords you search for, then what you've found, how they all sort of like relate together, and the outcome. What is the outcome of this literature review? Does it support your research questions? Does it give you a new insight? That is how you write this. That is the structure. It is so very sort of systematic. A systematic literature review has to be systematic, otherwise you'll just end up being completely lost in all of the papers. Oh, so many papers, so many papers. Filter them out, find the good ones, write it out. Brilliant. All right, if you like this video, Go check out this one where I talk about how to write an exceptional literature review with AI. It's going to be a great sort of addition to what you've learned in here. Go check it out.

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v.8(11); 2016 Nov

How to Conduct a Systematic Review: A Narrative Literature Review

Nusrat jahan.

1 Psychiatry, Mount Sinai Chicago

Sadiq Naveed

2 Psychiatry, KVC Prairie Ridge Hospital

Muhammad Zeshan

3 Department of Psychiatry, Bronx Lebanon Hospital Icahn School of Medicine at Mount Sinai, Bronx, NY

Muhammad A Tahir

4 Psychiatry, Suny Upstate Medical University, Syracuse, NY

Systematic reviews are ranked very high in research and are considered the most valid form of medical evidence. They provide a complete summary of the current literature relevant to a research question and can be of immense use to medical professionals. Our goal with this paper is to conduct a narrative review of the literature about systematic reviews and outline the essential elements of a systematic review along with the limitations of such a review.

Introduction and background

A literature review provides an important insight into a particular scholarly topic. It compiles published research on a topic, surveys different sources of research, and critically examines these sources [ 1 ]. A literature review may be argumentative, integrative, historical, methodological, systematic, or theoretical, and these approaches may be adopted depending upon the types of analysis in a particular study [ 2 ].

Our topic of interest in this article is to understand the different steps of conducting a systematic review. Systematic reviews, according to Wright, et al., are defined as a “review of the evidence on a clearly formulated question that uses systematic and explicit methods to identify, select and critically appraise relevant primary research, and to extract and analyze data from the studies that are included in the review” [ 3 ]. A systematic review provides an unbiased assessment of these studies [ 4 ]. Such reviews emerged in the 1970s in the field of social sciences. Systematic reviews, as well as the meta-analyses of the appropriate studies, can be the best form of evidence available to clinicians [ 3 ]. The unsystematic narrative review is more likely to include only research selected by the authors, which introduces bias and, therefore, frequently lags behind and contradicts the available evidence [ 5 ].

Epidemiologist Archie Cochrane played a vital role in formulating the methodology of the systematic review [ 6 ]. Dr. Cochrane loved to study patterns of disease and how these related to the environment. In the early 1970s, he found that many decisions in health care were made without reliable, up-to-date evidence about the treatments used [ 6 ].

A systematic review may or may not include meta-analysis, depending on whether results from different studies can be combined to provide a meaningful conclusion. David Sackett defined meta-analysis as a “specific statistical strategy for assembling the results of several studies into a single estimate” [ 7 - 8 ].

While the systematic review has several advantages, it has several limitations which can affect the conclusion. Inadequate literature searches and heterogeneous studies can lead to false conclusions. Similarly, the quality of assessment is an important step in systematic reviews, and it can lead to adverse consequences if not done properly.

The purpose of this article is to understand the important steps involved in conducting a systematic review of all kinds of clinical studies. We conducted a narrative review of the literature about systematic reviews with a special focus on articles that discuss conducting reviews of randomized controlled trials. We discuss key guidelines and important terminologies and present the advantages and limitations of systematic reviews.

Narrative reviews are a discussion of important topics on a theoretical point of view, and they are considered an important educational tool in continuing medical education [ 9 ]. Narrative reviews take a less formal approach than systematic reviews in that narrative reviews do not require the presentation of the more rigorous aspects characteristic of a systematic review such as reporting methodology, search terms, databases used, and inclusion and exclusion criteria [ 9 ]. With this in mind, our narrative review will give a detailed explanation of the important steps of a systematic review.

Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) checklist

Systematic reviews are conducted based on predefined criteria and protocol. The PRISMA-P checklist, developed by Moher, et al., contains 17 items (26 including sub-items) comprising the important steps of a systematic review, including information about authors, co-authors, their mailing and email addresses, affiliations, and any new or updated version of a previous systematic review [ 9 ]. It also identifies a plan for documenting important protocol amendments, registry names, registration numbers, financial disclosures, and other support services [ 10 ]. Moher, et al. also state that methods of systematic reviews involve developing eligibility criteria and describing information sources, search strategies, study selection processes, outcomes, assessment of bias in individual studies, and data synthesis [ 10 ].

Research question

Writing a research question is the first step in conducting a systematic review and is of paramount importance as it outlines both the need and validity of systematic reviews (Nguyen, et al., unpublished data). It also increases the efficiency of the review by limiting the time and cost of identifying and obtaining relevant literature [ 11 ]. The research question should summarize the main objective of a systematic review.

An example research question might read, “How does attention-deficit/hyperactivity disorder (ADHD) affect the academic performance of middle school children in North America?” The question focuses on the type of data, analysis, and topic to be discussed (i.e., ADHD among North American middle school students). Try to avoid research questions that are too narrow or broad—they can lead to the selection of only a few studies and the ability to generalize results to any other populations may be limited. An example of a research question that is too narrow would be, “What is the prevalence of ADHD in children and adolescents in Chicago, IL?” Alternately, if the research question is too broad, it can be difficult to reach a conclusion due to poor methodology. An example of a research question that is too broad in scope would be, “What are the effects of ADHD on the functioning of children and adolescents in North America?”

Different tools that can be used to help devise a research question, depending on the type of question, are: population, intervention, comparator, and outcomes (PICO); sample, phenomenon of interest, design, evaluation, and research type (SPIDER); setting, perspective, intervention, comparison, and evaluation (SPICE); and expectation, client group, location, impact, professionals, and service (ECLIPSE).

The PICO approach is mostly used to compare different interventions with each other. It helps to formulate a research question related to prognosis, diagnosis, and therapies [ 12 ].

Scenario: A 50-year-old white woman visited her psychiatrist with a diagnosis of major depressive disorder. She was prescribed fluoxetine, which she feels has been helpful. However, she experienced some unpleasant side effects of nausea and abdominal discomfort. She has recently been told by a friend about the use of St. John’s wort in treating depression and would like to try this in treating her current depression. (Formulating research questions, unpublished data).

In the above-mentioned scenario, the sample population is a 50-year-old female with major depressive disorder; the intervention is St. John’s wort; the comparison is fluoxetine; and the outcome would be efficacy and safety. In order to see the outcome of both efficacy and safety, we will compare the efficacy and safety of both St. John’s wort and fluoxetine in a sample population for treating depression. This scenario represents an example where we can apply the PICO approach to compare two interventions.

In contrast, the SPIDER approach is focused more on study design and samples rather than populations [ 13 ]. The SPIDER approach can be used in this research question: “What is the experience of psychiatry residents attending a transgender education?” The sample is psychiatry residents; the phenomenon of interest is transgender education; the design is a survey; the evaluation looks at the experience; and the research type is qualitative.

The SPICE approach can be used to evaluate the outcome of a service, intervention, or project [ 14 ]. The SPICE approach applies to the following research question: “In psychiatry clinics, does the combined use of selective serotonin reuptake inhibitor (SSRI) and psychotherapy reduce depression in an outpatient clinic versus SSRI therapy alone?” The setting is the psychiatry clinic; the perspective/population is the outpatient; the intervention is combined psychotherapy and SSRI; the comparison is SSRI alone; and the evaluation is reduced depression.

The ECLIPSE approach is useful for evaluating the outcome of a policy or service (Nguyen, et al., unpublished data). ECLIPSE can apply in the following research question: “How can a resident get access to medical records of patients admitted to inpatient from other hospitals?” The expectation is: “What are you looking to improve/change to increase access to medical records for patients admitted to inpatient?” The client group is the residents; the location is the inpatient setting; the impact would be the residents having easy access to medical records from other hospitals; and the professionals in this scenario would be those involved in improving the service experiences such as hospital administrators and IT staff.

Inclusion and exclusion criteria

Establishing inclusion and exclusion criteria come after formulating research questions. The concept of inclusion and exclusion of data in a systematic review provides a basis on which the reviewer draws valid and reliable conclusions regarding the effect of the intervention for the disorder under consideration [ 11 ]. Inclusions and exclusion are based on preset criteria for specific systematic review. It should be done before starting the literature search in order to minimize the possibility of bias.

Eligibility criteria provide the boundaries of the systematic review [ 15 ]. Participants, interventions, and comparison of a research question provide the basis for eligibility criteria [ 15 ]. The inclusion criteria should be able to identify the studies of interest and, if the inclusion criteria are too broad or too narrow, it can lead to an ineffective screening process.

Protocol registration

Developing and registering research protocol is another important step of conducting a systematic review. The research protocol ensures that a systematic review is carefully planned and explicitly documented before the review starts, thus promoting consistency in conduct for the review team and supporting the accountability, research integrity, and transparency of the eventually completed review [ 10 ]. PROSPERO and the Cochrane Database of Systematic Reviews are utilized for registering research protocols and research questions, and they check for prior existing duplicate protocols or research questions. PROSPERO is an international database of prospectively registered systematic reviews related to health care and social sciences (PRISMA, 2016). It is funded by the National Institute for Health Research. The Cochrane Collaboration concentrates on producing systematic reviews of interventions and diagnostic test accuracy but does not currently produce reviews on questions of prognosis or etiology [ 16 ].

A detailed and extensive search strategy is important for the systematic review since it minimizes bias in the review process [ 17 ].

Selecting and searching appropriate electronic databases is determined by the topic of interest. Important databases are: MEDLARS Online (MEDLINE), which is the online counterpart to the Medical Literature Analysis and Retrieval System (MEDLARS); Excerpta Medica Database (EMBASE); and Google Scholar. There are multiple electronic databases available based on the area of interest. Other important databases include: PsycINFO for psychology and psychiatry; Allied and Complementary Medicine Database (AMED) for complementary medicine; Manual, Alternative, and Natural Therapy Index System (MANTIS) for alternative medical literature; and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for nursing and allied health [ 15 ].

Additional studies relevant for the review may be found by looking at the references of studies identified by different databases [ 15 ]. Non-indexed articles may be found by searching the content of journals, conferences proceedings, and abstracts. It will also help with letters and commentaries which may not get indexed [ 15 ]. Reviewing clinical trial registries can provide information about any ongoing trials or unpublished research [ 15 ]. A gray literature search can access unpublished papers, reports, and conference reports, and it generally covers studies that are published in an informal fashion, rather than in an indexed journal [ 15 ]. Further search can be performed by selecting important key articles and going through in-text citations [ 15 ].

Using Boolean operators, truncation, and wildcards

Boolean operators use the relationship between different search words to help with the search strategy. These are simple words (i.e., AND, OR, and NOT) which can help with more focused and productive results (poster, Jahan, et al.: How to conduct a systematic review. APPNA 39th Summer Convention. Washington, DC. 2016). The Boolean operator AND finds articles with all the search words. The use of OR broadens the focus of the search, and it will include articles with at least one search term. The researchers can also ignore certain results from the records by using NOT in the search strategy.

An example of AND would be using “depression” AND “children” in the search strategy with the goal of studying depression in children. This search strategy will include all the articles about both depression and children. The researchers may use OR if the emphasis of the study is mood disorders or affective disorders in adolescents. In that case, the search strategy will be “mood disorders” OR “affective disorders” AND “adolescents.” This search will find all the articles about mood disorders or affective disorders in adolescents. The researchers can use NOT if they only want to study depression in children and want to ignore bipolar disorder from the search. An example search in this scenario would be “depression” NOT “bipolar disorder” AND “children.” This will help ignore studies related to bipolar disorder in children.

Truncation and wildcards are other tools to make search strategy more comprehensive and focused. While the researchers search a database for certain articles, they frequently face terminologies that have the same initial root of a word but different endings. An example would be "autism," "autistic," and "autism spectrum disorder." These words have a similar initial root derived from “autis” but they end differently in each case. The truncation symbol (*) retrieves articles that contain words beginning with “autis” plus any additional characters. Wildcards are used for words with the same meanings but different spellings due to various reasons. For the words with spelling variations of a single letter, wildcard symbols can be used. When the researcher inputs “M+N” in the search bar, this returns results containing both “man” or “men” as the wildcard accounts for the spelling variations between the letters M and N.

Study selection

Study selection should be performed in a systematic manner, so reviewers deal with fewer errors and a lower risk of bias (online course, Li T, Dickersin K: Introduction to systematic review and meta-analysis. 2016. https://www.coursera.org/learn/systematic-review #). Study selection should involve two independent reviewers who select studies using inclusion and exclusion criteria. Any disagreements during this process should be resolved by discussion or by a third reviewer [ 10 ]. Specific study types can be selected depending on the research question. For example, questions on incidence and prevalence can be answered by surveys and cohort studies. Clinical trials can provide answers to questions related to therapy and screening. Queries regarding diagnostic accuracy can be answered by clinical trials and cross-sectional studies (online course, Li T, Dickersin K: Introduction to systematic review and meta-analysis. 2016. https://www.coursera.org/learn/systematic-review #). Prognosis and harm-related questions should use cohort studies and clinical trials, and etiology questions should use case-control and cohort studies (online course, Li T, Dickersin K: Introduction to systematic review and meta-analysis. 2016. https://www.coursera.org/learn/systematic-review #).

Data screening and data extractions are two of the major steps in conducting a systematic review [ 18 ]. Data screening involves searching for relevant articles in different databases using keywords. The next step of data screening is manuscript selection by reviewing each manuscript in the search results to compare that manuscript against the inclusion criteria [ 18 ]. The researchers should also review the references of the papers selected before selecting the final paper, which is the last step of data screening [ 18 ].

The next stage is extracting and appraising the data of the included articles [ 18 ]. A data extraction form should be used to help reduce the number of errors, and more than one person should record the data [ 17 ]. Data should be collected on specific points like population type, study authors, agency, study design, humanitarian crisis, target age groups, research strengths from the literature, setting, study country, type(s) of public health intervention, and health outcome(s) addressed by the public health intervention. All this information should then be put into an electronic database [ 18 ].

Assessing bias

Bias is a systematic error (or deviation from the truth) in results or inferences. Biases can change the results of any study and lead to an underestimation or overestimation of the true intervention effect [ 19 ]. Biases can impact any aspect of a review, including selecting studies, collecting and extracting data, and making a conclusion. Biases can vary in magnitude; some are small, with negligible effect, but some are substantial to a degree where an apparent finding may be entirely due to bias [ 19 ]. There are different types of bias, including, but not limited to, selection, detection, attrition, reporting, and performance.

Selection bias occurs when a sample selected is not representative of the whole general population. If randomization of the sample is done correctly, then chances of selection bias can be minimized [ 20 ].

Detection bias refers to systematic differences between groups in how outcomes are determined. This type of bias is based on knowledge of the intervention provided and its outcome [ 19 ].

Attrition bias refers to systematic differences between groups in withdrawals from a study [ 19 ]. The data will be considered incomplete if some subjects are withdrawn or have irregular visits during data collection.

Reporting bias refers to systematic differences between reported and unreported findings, and it is commonly seen during article reviews. Reporting bias is based on reviewer judgment about the outcome of selected articles [ 20 ].

Performance bias develops due to the knowledge of the allocated interventions by participants and personnel during the study [ 20 ]. Using a double-blind study design helps prevent performance bias, where neither the experimenter nor the subjects know which group contains controls and which group contains the test article [ 14 ].

Last step of systematic review: discussion

The discussion of a systematic review is where a summary of the available evidence for different outcomes is written and discussed [ 10 ]. The limitations of a systematic review are also discussed in detail. Finally, a conclusion is drawn after evaluating the results and considering limitations [ 10 ].

Discussion of the current article

Systematic reviews with or without a meta-analysis are currently ranked to be the best available evidence in the hierarchy of evidence-based practice [ 21 ]. We have discussed the methodology of a systematic review. A systematic review is classified in the category of filtered information because it appraises the quality of the study and its application in the field of medicine [ 21 ]. However, there are some limitations of the systematic review, as we mentioned earlier in our article. A large randomized controlled trial may provide a better conclusion than a systematic review of many smaller trials due to their larger sample sizes [ 22 ], which help the researchers generalize their conclusions for a bigger population. Other important factors to consider include higher dropout rates in large studies, co-interventions, and heterogeneity among studies included in the review.

As we discussed the limitations of the systematic review and its effect on quality of evidence, there are several tools to rate the evidence, such as the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system [ 22 ]. GRADE provides a structured approach to evaluating the risk of bias, serious inconsistency between studies, indirectness, imprecision of the results, and publication bias [ 22 ]. Another approach used to rate the quality of evidence is a measurement tool to assess systematic reviews (AMSTAR) [ 23 ]. It is also available in several languages [ 23 ].

Conclusions

Despite its limitations, a systematic review can add to the knowledge of the scientific community especially when there are gaps in the existing knowledge. However, conducting a systematic review requires different steps that involve different tools and strategies. It can be difficult at times to access and utilize these resources. A researcher can understand and strategize a systematic review following the different steps outlined in this literature review. However, conducting a systematic review requires a thorough understanding of all the concepts and tools involved, which is an extensive endeavor to be summed up in one article.

The Cochrane Handbook for Systematic Reviews of Interventions and the Center for Reviews and Dissemination (CRD) provide excellent guidance through their insightful and detailed guidelines. We recommend consulting these resources for further guidance.

Given that our article is a narrative review of the scholarly literature, it contains the same limitations as noted for any narrative review. We hope that our review of the means and methods for conducting a systematic review will be helpful in providing basic knowledge to utilize the resources available to the scientific community.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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Drivers and consequences of land degradation on livestock productivity in sub-saharan africa: a systematic literature review.

1. Introduction

2. materials and methods, 2.1. study design, 2.2. pertinence and state of the matter studied, 2.3. literature search, 2.4. inclusion and exclusion criteria.

Criteria	Included	Excluded	Justification for Criteria Application
Language publication	English	All other languages	To increase readability and due to the researchers’ proficiency in the English language
Country or location of study	Sub-Saharan Africa-related papers	Non-sub-Saharan African papers	To remain within the scope of the systematic review
Article availability	Fully available paper using University of Fort Hare’s library subscription	Full paper not accessible	Access- related issues
Date of publication	Any article published before 30 June 2024	-	Used available papers from selected databases to have a contemporary perspective on drivers and the consequences of land degradation on livestock productivity
Research focus	Papers that included “drivers and consequences of land degradation in livestock” in general	Research focusing solely on agricultural crops without addressing livestock	To remain within the focused scope of the systematic review
Type of article	Peer-reviewed research journal articles, conference papers, book chapters, review papers	Gray literature, including reports and theses, unless they provided substantial empirical data	To increase the validity of the study findings

2.5. Data Extraction and Synthesis

2.6. data analysis, 3. results and discussion, 3.1. primary drivers of land degradation in sub-saharan rangelands.

Reference	Location	Biophysical Drivers	Socio-Economic Drivers	Methodology	Key Findings
[ ]	Botswana	Soil erosion, overgrazing, drought	Poverty, land tenure issues	Field survey, remote sensing	Local people identified drought as the main cause of increasing resource depletion, which impedes vegetation regeneration and induces land degradation. The situation is exacerbated by widespread poverty and inappropriate perceptions of solutions.
[ ]	Ethiopia	Bush encroachment, drought, water scarcity	Ban on traditional practices, increasing practice of crop cultivation on the rangelands	Survey	All respondents reported a dramatic decline in rangeland conditions, attributing it to past development policies based on equilibrium theories that opposed communal and traditional range management. Issues such as bush encroachment, bans on traditional burning practices, recurrent droughts, and the increasing practice of crop cultivation on rangelands were identified as serious threats to livestock production and traditional resource management.
[ ]	South Africa	Heavy grazing	-	Remote sensing, statistical analysis	Rainfall and degradation accounted for 38% and 20% of the AVHRR ZNDVI variance and 50% and 33% of the MODIS ZNDVI variance, respectively, indicating that degradation significantly influences long-term vegetation productivity. This challenges the nonequilibrium model, which predicts a negligible long-term grazing impact.
[ ]	South Africa	Land-use/land-cover change (LULCC), declining livestock, cultivation, renewable energy installations	-	Analysis of large data sets, repeat photographs	More than 95% of the Karoo has remained classified as natural and stable since 1990, with significant declines in cultivation and livestock over the last century. Vegetation productivity trends have remained unchanged over 90% of the biomes, with notable increases in nearly 10%, necessitating continuous monitoring to assess future LULCC impacts.
[ ]	Ethiopia, Kenya, Malawi	Soil texture, surface slope, rainfall	Market access, human and livestock population densities	High-resolution geospatial data analysis	Conservation agriculture (CA) aims to reduce soil degradation, conserve water, and enhance crop productivity. The study identified potential recommendation domains (RDs) for CA, with 39%, 12%, and 5% of cultivated areas in Malawi, Kenya, and Ethiopia, respectively, showing high potential, highlighting significant areas for CA adoption that are influenced by biophysical and socio-economic conditions.
[ ]	Ethiopia	Rainfall variability, land degradation, low soil fertility	Market access, human and livestock population densities	Field survey, IDSS tools (SWAT, APEX)	Rainfed agriculture in sub-Saharan Africa faces constraints from rainfall variability, land degradation, and low soil fertility. Small-scale irrigation in Ethiopia’s Robit and Dangishta watersheds shows potential for dry-season vegetable production, but groundwater recharge is insufficient; mulching and soil conservation can optimize irrigation by reducing soil evaporation.
[ ]	South Africa	Vegetation change	Expansion of human settlements	Survey	The study examined local people’s perceptions of rangeland resources in three communal grasslands, finding that locals view vegetation changes primarily in terms of species richness, diversity, and abundance, unlike ecologists who link them to degradation. Abiotic, biotic, and institutional factors were identified as primary drivers, while human settlement expansion poses a threat by reducing and fragmenting grazing resources.
[ ]	Namibia	Shrub encroachment, overgrazing	High livestock densities	Dynamic vegetation modeling	High livestock densities lead to shrub encroachment and severe decreases in fodder biomass, causing up to 100% losses in land productivity. Wildlife-based land use with a 40% browser to 60% grazer ratio is beneficial for plant structural and species diversity, enhancing ecosystem sustainability and resilience.
[ ]	South Africa	Decades of overstocking with small livestock, historical ploughing for fodder, climate change	Reduced land-use options, vulnerability to environmental and economic stressors, costs of restoration	Local-scale participatory restoration trial, assessment of regional-scale restoration costs	Ecological restoration is difficult and expensive; climate change exacerbates challenges; holistic land management actions needed to sustain livelihoods
[ ]	South Africa	Assumptions of overstocking and degradation, ecological models from large-scale commercial farming	Assumptions that increasing livestock sales and commercial farming improve productivity, belief that communal tenure causes degradation and that privatization is the solution	Examination of current policy, review of ecological and economic assumptions, analysis of the effectiveness of existing models	Current policies based on large-scale commercial farming models are inappropriate for rangeland commons; effective policy should support multiple livelihoods, strengthen common property management, and use diverse ecological and economic models for different contexts
[ ]	Zimbabwe	Changes in rangeland use and productivity, cropland conversion affecting feed resources	Local knowledge of rangeland resources, role of new institutions for cropland use, changes in common property management	Participatory rural appraisals, household surveys	User communities categorize rangelands by feed resources and changes over time, view rangelands as diverse and dynamic; croplands have become dual-purpose for food security and livestock feed; new institutions govern cropland use while those for common rangelands have weakened, presenting ecological challenges but also opportunities for innovative feed resource management
[ ]	Namibia	Overgrazing and climate change	Lack of grazing lands and feed followed by water scarcity and recurring droughts	Household surveys, focus group discussions	Respondents in all villages indicated that lack of grazing lands and feed followed by water scarcity and recurring droughts were the primary and secondary constraints of livestock production. Older respondents regarded overgrazing and climate change as the primary cause of rangeland degradation. Hence, the study concludes that communal rangelands are degraded and that degradation has resulted in gradual livestock population declining trends over the past years in communal areas due to feed shortages.
[ ]	Kenya	Soil nutrient decline, land degradation, low nutrient levels (decline of 1.7 kg P and 5.4 kg K ha half year ), low phosphorus and potassium stocks	Rising population, poverty (all households below the poverty line of 1 USD/day), low farm economic returns, low livestock productivity, and low yields of staple food crops	Soil nutrient monitoring, household surveys	Soil nutrient decline rates are low compared with macro-scale data, but low farm productivity and economic returns threaten sustainability; intercropping systems (maize–beans) improve the nutrient balance and household incomes; the study highlights the need to encourage intercropping and to consider localized sustainability strategies

3.2. Impact of Land Degradation on Livestock Health, Productivity, and Mortality

References	Study Areas	Health Impacts	Productivity Impacts	Mortality Rates	Methodology	Key Findings
[ ]	South Africa	Increased disease incidence	Reduced milk and meat yield	Higher calf mortality	Field experiments, veterinary records	Increased land degradation correlates with higher disease incidence and reduced productivity, leading to higher mortality.
[ ]	Namibia	Poor nutritional status	Decreased weight gain	Increased adult livestock deaths	Longitudinal study, surveys	Poor forage quality from degraded lands leads to poor nutrition, weight loss, and increased mortality.
[ ]	Botswana	Higher parasite loads	Lower reproductive rates	Elevated young livestock mortality	Cross-sectional study, lab analysis	Land degradation results in higher parasite burdens and lower reproductive success, increasing young livestock deaths.
[ ]	Kenya	Increased respiratory and digestive issues	Decline in wool and milk production	Higher lamb mortality	Observational study, interviews	Dust and poor vegetation from degraded lands contribute to respiratory and digestive problems, reducing wool and milk production, and increasing lamb mortality.
[ ]	Ethiopia	Malnutrition and weakened immunity	Lower overall herd productivity	Spike in drought-related deaths	Survey, field observation	Degradation-related malnutrition weakens immunity, reducing herd productivity and increasing mortality during drought periods.
[ ]	Tanzania	Reduced fertility rates	Lowered birth rates	Increased perinatal mortality	Case study, veterinary reports	Nutrient-deficient forage due to land degradation leads to reduced fertility and higher perinatal mortality, directly impacting herd sustainability.
[ ]	Zambia	Stress-related health conditions	Decreased milk yield	Higher incidence of miscarriages	Mixed-methods approach	Environmental stress from land degradation contributes to stress-related conditions, reducing milk yield and increasing miscarriage rates among pregnant livestock.
[ ]	Malawi	Increased susceptibility to zoonotic diseases	Decline in meat quality	Rising deaths during dry season	Field surveys, health monitoring	Land degradation exacerbates exposure to zoonotic diseases, affecting meat quality and increasing death rates during dry seasons due to limited resources.
[ ]	Zimbabwe	Compromised immune response	Lower weaning weights	Increased mortality during disease outbreaks	Longitudinal health monitoring	Land degradation results in compromised immune responses, leading to lower weaning weights and increased mortality during disease outbreaks, particularly in young livestock.

3.3. Socio-Economic Consequences of Reduced Livestock Productivity

References	Study Areas	Impact on Livelihoods	Impact on Food Security	Methodology	Key Findings
[ ]	Kenya	Reduced income from livestock sales	Increased food insecurity	Household surveys, economic analysis	Lower livestock productivity directly reduces household income and food security.
[ ]	Zimbabwe	Increased poverty	Reliance on food aid	Mixed methods, focus groups	Decreased livestock productivity exacerbates poverty, leading to a higher dependence on food aid.
[ ]	Ethiopia	Migration to urban areas	Nutritional deficiencies	Longitudinal survey, interviews	Reduced livestock yields lead to rural–urban migration and higher rates of nutritional deficiencies.
[ ]	South Africa	Loss of traditional livelihoods	Decline in dietary diversity	Case studies, participatory rural appraisal	Land degradation and reduced livestock productivity force communities to abandon traditional pastoral livelihoods, leading to a decline in dietary diversity and food security.
[ ]	Tanzania	Increased vulnerability to economic shocks	Lower access to animal-source foods	Cross-sectional survey, economic modeling	Declining livestock productivity heightens household vulnerability to economic shocks, reducing access to nutritious animal-source foods and worsening food insecurity.
[ ]	Zambia	Diversification into non-agricultural work	Reduced protein intake	Household surveys, livelihood assessments	As livestock productivity decreases, households diversify into non-agricultural work, leading to reduced protein intake due to the lower availability of animal products.

3.4. Effectiveness of Mitigation and Adaptation Strategies

References	Study Areas	Intervention	Effectiveness	Methodology	Key Findings
[ ]	Zambia	Rotational grazing	High	Controlled experiment, field observations	Rotational grazing significantly improves rangeland health and livestock productivity.
[ ]	Tanzania	Agroforestry	Moderate	Case studies, participatory research	Agroforestry practices help reduce soil erosion and improve forage quality with moderate success.
[ ]	Kenya	Soil conservation techniques	High	Field trials, farmer surveys	Soil conservation techniques, including terracing and mulching, show high effectiveness in reducing degradation and improving livestock yields.
[ ]	Malawi	Integrated livestock–crop systems	Moderate	Mixed methods, longitudinal study	Integrated livestock–crop systems enhance soil fertility and provide supplementary feed, but require careful management to be sustainable.
[ ]	Zimbabwe	Controlled burning	Low to moderate	Experimental plots, historical data	Controlled burning helps manage bush encroachment and improve grazing conditions, but its effectiveness varies based on the fire frequency and intensity.
[ ]	Botswana	Water harvesting techniques	High	Case studies, community workshops	Water harvesting techniques, such as small dams and ponds, significantly improve water availability for livestock during dry seasons, boosting productivity.
[ ]	Ethiopia	Community-based rangeland management	High	Participatory rural appraisal, interviews	Community-based rangeland management fosters collective action in rangeland restoration, leading to improved forage availability and livestock health.
[ ]	Uganda	Livestock restocking programs	Moderate	Household surveys, program evaluation	Livestock restocking programs help rebuild herds after droughts or disease outbreaks, with moderate success depending on follow-up support and training.
[ ]	Kenya	Drought-resistant forage species	High	Field trials, laboratory analysis	Introduction of drought-resistant forage species enhances rangeland resilience, ensuring consistent livestock feed during drought periods, leading to sustained productivity.
[ ]	Tanzania	Pasture improvement programs	Moderate to high	Experimental designs, participatory approaches	Pasture improvement programs, including reseeding and fertilization, show moderate to high effectiveness in increasing biomass and supporting livestock growth.
[ ]	Eswatini	Livestock health monitoring	High	Veterinary surveys, health records	Regular livestock health monitoring and vaccination programs significantly reduce disease incidence and improve overall herd productivity and survival rates.

3.5. Key Themes and Insights from the Word Cloud on Land Degradation, Rangelands, and Livestock in Sub-Saharan Africa

3.6. insights from the co-occurrence network diagram on land degradation, rangelands, and livestock in sub-saharan africa, 4. recommendations for policy makers in charge of these problems and future research directions, 5. potential limitations, 6. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Slayi, M.; Zhou, L.; Dzvene, A.R.; Mpanyaro, Z. Drivers and Consequences of Land Degradation on Livestock Productivity in Sub-Saharan Africa: A Systematic Literature Review. Land 2024 , 13 , 1402. https://doi.org/10.3390/land13091402

Slayi M, Zhou L, Dzvene AR, Mpanyaro Z. Drivers and Consequences of Land Degradation on Livestock Productivity in Sub-Saharan Africa: A Systematic Literature Review. Land . 2024; 13(9):1402. https://doi.org/10.3390/land13091402

Slayi, Mhlangabezi, Leocadia Zhou, Admire Rukudzo Dzvene, and Zolisanani Mpanyaro. 2024. "Drivers and Consequences of Land Degradation on Livestock Productivity in Sub-Saharan Africa: A Systematic Literature Review" Land 13, no. 9: 1402. https://doi.org/10.3390/land13091402

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Author Contributions
Registration can be done on platforms like PROSPERO 5 for health and social care reviews or Cochrane 3 for interventions. Step 3: search. In the process of conducting a systematic review, a well-organized literature search is a pivotal step.
Steps of a Systematic Review
Image by TraceyChandler. Steps to conducting a systematic review. Quick overview of the process: Steps and resources from the UMB HSHSL Guide. YouTube video (26 min); Another detailed guide on how to conduct and write a systematic review from RMIT University; A roadmap for searching literature in PubMed from the VU Amsterdam; Alexander, P. A. (2020).
Five steps to conducting a systematic review
Reasons for inclusion and exclusion should be recorded. Step 3: Assessing the quality of studies. Study quality assessment is relevant to every step of a review. Question formulation (Step 1) and study selection criteria (Step 2) should describe the minimum acceptable level of design.
How to Do a Systematic Review: A Best Practice Guide for Conducting and
The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information.
How to write a systematic literature review [9 steps]
Screen the literature. Assess the quality of the studies. Extract the data. Analyze the results. Interpret and present the results. 1. Decide on your team. When carrying out a systematic literature review, you should employ multiple reviewers in order to minimize bias and strengthen analysis.
Module 1: Introduction to conducting systematic reviews
This module will teach you to: Recognize features of systematic reviews as a research design. Recognize the importance of using rigorous methods to conduct a systematic review. Identify the types of review questions. Identify the elements of a well-defined review question. Understand the steps in a systematic review.
How-to conduct a systematic literature review: A quick guide for
Abstract. Performing a literature review is a critical first step in research to understanding the state-of-the-art and identifying gaps and challenges in the field. A systematic literature review is a method which sets out a series of steps to methodically organize the review. In this paper, we present a guide designed for researchers and in ...
Guidance on Conducting a Systematic Literature Review
Literature reviews establish the foundation of academic inquires. However, in the planning field, we lack rigorous systematic reviews. In this article, through a systematic search on the methodology of literature review, we categorize a typology of literature reviews, discuss steps in conducting a systematic literature review, and provide suggestions on how to enhance rigor in literature ...
Easy guide to conducting a systematic review
The meticulous nature of the systematic review research methodology differentiates a systematic review from a narrative review (literature review or authoritative review). This paper provides a brief step by step summary of how to conduct a systematic review, which may be of interest for clinicians and researchers. References, . . ; ...
How to Do a Systematic Review: A Best Practice Guide for Conducting and
Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to ...
Easy guide to conducting a systematic review
The meticulous nature of the systematic review research methodology differentiates a systematic review from a narrative review (literature review or authoritative review). This paper provides a brief step by step summary of how to conduct a systematic review, which may be of interest for clinicians and researchers. Keywords: research; ...
PDF Conducting a Systematic Review: Methodology and Steps
meta-analysis is necessarily in a systematic review.4The main purpose of this document is to provide guidelines, recommendations and propose a methodology for conducting mixed-method systematic reviews for evidence synthesis for "gender in agricult. re and food systems" for the CGIAR GENDER Platform. In this document we highlight some of ...
What are the Steps of a Systematic Review?
A comprehensive, transparent, and reproducible search of the literature is key to the validity of a systematic review's conclusions. Select studies . Appropriate studies will need to be selected from your search results based on the inclusion and exclusion criteria defined in your protocol.
Systematic Reviews: Steps in a Systematic Review
Use a spreadsheet, or systematic review software, to extract all relevant data from each included study. It is recommended that you pilot your data extraction tool, to determine if other fields should be included or existing fields clarified. Evaluate the risk of bias of included studies. Use a Risk of Bias tool (such as the Cochrane RoB Tool ...
How to Write a Systematic Review of the Literature
This article provides a step-by-step approach to conducting and reporting systematic literature reviews (SLRs) in the domain of healthcare design and discusses some of the key quality issues associated with SLRs. SLR, as the name implies, is a systematic way of collecting, critically evaluating, integrating, and presenting findings from across ...
Steps in Conducting a Literature Review
A literature review is an integrated analysis-- not just a summary-- of scholarly writings and other relevant evidence related directly to your research question.That is, it represents a synthesis of the evidence that provides background information on your topic and shows a association between the evidence and your research question.
A step by step guide for conducting a systematic review and meta
Detailed steps for conducting any systematic review and meta-analysis. We searched the methods reported in published SR/MA in tropical medicine and other healthcare fields besides the published guidelines like Cochrane guidelines {Higgins, 2011 #7} to collect the best low-bias method for each step of SR/MA conduction steps. Furthermore, we used ...
How to conduct systematic literature reviews in management research: a
Systematic literature reviews (SLRs) have become a standard tool in many fields of management research but are often considerably less stringently presented than other pieces of research. The resulting lack of replicability of the research and conclusions has spurred a vital debate on the SLR process, but related guidance is scattered across a number of core references and is overly centered ...
How-to conduct a systematic literature review: A quick guide for
Method details Overview. A Systematic Literature Review (SLR) is a research methodology to collect, identify, and critically analyze the available research studies (e.g., articles, conference proceedings, books, dissertations) through a systematic procedure [12].An SLR updates the reader with current literature about a subject [6].The goal is to review critical points of current knowledge on a ...
LSBU Library: Literature Reviews: What is a Literature Review?
The research, the body of current literature, and the particular objectives should all influence the structure of a literature review. It is also critical to remember that creating a literature review is an ongoing process - as one reads and analyzes the literature, one's understanding may change, which could require rearranging the literature ...
Steps for Creating a Literature Review
Steps for Cr eating Literature Reviews. Formulating Research Questions and Objectives: Define the scope and focus of the review.; Searching the Literature: Conduct a thorough search of academic databases and other sources.; Screening for Inclusion: Select relevant studies based on predefined criteria.; Assessing Quality: Evaluate the quality and reliability of the selected studies.
A step by step guide for conducting a systematic review and meta
Detailed steps for conducting any systematic review and meta-analysis. We searched the methods reported in published SR/MA in tropical medicine and other healthcare fields besides the published guidelines like Cochrane guidelines {Higgins, 2011 #7} [] to collect the best low-bias method for each step of SR/MA conduction steps.Furthermore, we used guidelines that we apply in studies for all SR ...
Assessing the Certainty of the Evidence in Systematic Reviews
Serious concerns regarding limitations in the study design, inconsistency, imprecision, indirectness, and publication bias can decrease the certainty of the evidence. Using an example, this article describes and illustrates the importance and the steps for assessing the certainty of evidence and drawing accurate conclusions in a systematic review.
Competitive Advantages of Sustainable Startups: Systematic Literature
We seek to fill this gap by conducting a systematic literature review on the competitive advantages of sustainable startups. We used the PRISMA 2020 protocol to conduct a comprehensive search in the Scopus and Web of Science databases, which led to the inclusion of 44 articles in the final review. The results indicate that sustainable startups ...
Integrating City Master Plans with Sustainable and Smart Urban ...
Urban configurations have substantial impacts on lifestyles, behaviors, and people's daily lives. Elaborating urban plans based on smart and sustainable initiatives is a challenging task as it encompasses numerous multidisciplinary premises due to the dynamics of the urban context. This research aims to conduct a systematic review of the literature in three axes: sustainable urban ...
Mastering Systematic Literature Reviews: Steps, Tools, and AI
Speaker 1: The first step of doing a systematic literature review is coming up with a review question, like what do you actually want to know about the world and how can you phrase that as a simple question. You can write down all of the questions you want and then choose from the best one or a combination but I like to go to ChatGPT and use them as like a sounding board and a research ...
Systematic reviews: Structure, form and content
Topic selection and planning. In recent years, there has been an explosion in the number of systematic reviews conducted and published (Chalmers & Fox 2016, Fontelo & Liu 2018, Page et al 2015) - although a systematic review may be an inappropriate or unnecessary research methodology for answering many research questions.Systematic reviews can be inadvisable for a variety of reasons.
The Impact of Speed Limit Change on Emissions: A Systematic Review of
In the pursuit of sustainable mobility and the decarbonization of transport systems, public authorities are increasingly scrutinizing the impact of travel speed on emissions within both low-speed and high-speed environments. This study critically examines the evidence concerning emission impacts associated with speed limit changes in different traffic environments by conducting a systematic ...
How to Conduct a Systematic Review: A Narrative Literature Review
Our goal with this paper is to conduct a narrative review of the literature about systematic reviews and outline the essential elements of a systematic review along with the limitations of such a review. Keywords: systematic reviews, meta-analysis, narrative literature review, prisma checklist. 1 2.
Land
Land degradation is a major threat to sub-Saharan Africa rangelands, which are crucial for livestock farming and the livelihood of millions of people in the region. This systematic review aims to provide a comprehensive understanding of the causes and effects of land degradation, as well as to evaluate the effectiveness of different mitigation strategies.

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1. Choose a topic. Define your research question.

2. Decide on the scope of your review

3. Select the databases you will use to conduct your searches.

4. Conduct your searches to find the evidence. Keep track of your searches.

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How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions

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The burgeoning role of literature review articles in management research: an introduction and outlook

On being ‘systematic’ in literature reviews

On being ‘systematic’ in literature reviews in IS

1 Introduction

2 Identification of the need for an enriched process model, including a set of sequential decisions and their interrelations

3 Methodology: an integrative literature review of guidelines for systematic literature reviews in management

4 Findings of the integrative review: presentation, critical discussion, and enrichment of prior guidance

4.2 Step 1: defining the research question

4.3 Step 2: determining the required characteristics of primary studies

4.4 Step 3: retrieving a sample of potentially relevant literature

4.5 Step 4: selecting the pertinent literature

4.6 Step 5: synthesizing the literature

4.7 Step 6: reporting the results

5 Discussion and conclusion

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Literature Reviews

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A step by step guide for conducting a systematic review and meta-analysis with simulation data

Introduction