marketing dissertation literature review

How To Write An A-Grade Literature Review

3 straightforward steps (with examples) + free template.

By: Derek Jansen (MBA) | Expert Reviewed By: Dr. Eunice Rautenbach | October 2019

Quality research is about building onto the existing work of others , “standing on the shoulders of giants”, as Newton put it. The literature review chapter of your dissertation, thesis or research project is where you synthesise this prior work and lay the theoretical foundation for your own research.

Long story short, this chapter is a pretty big deal, which is why you want to make sure you get it right . In this post, I’ll show you exactly how to write a literature review in three straightforward steps, so you can conquer this vital chapter (the smart way).

Overview: The Literature Review Process

• Understanding the “ why “
• Finding the relevant literature
• Cataloguing and synthesising the information
• Outlining & writing up your literature review
• Example of a literature review

But first, the “why”…

Before we unpack how to write the literature review chapter, we’ve got to look at the why . To put it bluntly, if you don’t understand the function and purpose of the literature review process, there’s no way you can pull it off well. So, what exactly is the purpose of the literature review?

Well, there are (at least) four core functions:

• For you to gain an understanding (and demonstrate this understanding) of where the research is at currently, what the key arguments and disagreements are.
• For you to identify the gap(s) in the literature and then use this as justification for your own research topic.
• To help you build a conceptual framework for empirical testing (if applicable to your research topic).
• To inform your methodological choices and help you source tried and tested questionnaires (for interviews ) and measurement instruments (for surveys ).

Most students understand the first point but don’t give any thought to the rest. To get the most from the literature review process, you must keep all four points front of mind as you review the literature (more on this shortly), or you’ll land up with a wonky foundation.

Okay – with the why out the way, let’s move on to the how . As mentioned above, writing your literature review is a process, which I’ll break down into three steps:

• Finding the most suitable literature
• Understanding , distilling and organising the literature
• Planning and writing up your literature review chapter

Importantly, you must complete steps one and two before you start writing up your chapter. I know it’s very tempting, but don’t try to kill two birds with one stone and write as you read. You’ll invariably end up wasting huge amounts of time re-writing and re-shaping, or you’ll just land up with a disjointed, hard-to-digest mess . Instead, you need to read first and distil the information, then plan and execute the writing.

Step 1: Find the relevant literature

Naturally, the first step in the literature review journey is to hunt down the existing research that’s relevant to your topic. While you probably already have a decent base of this from your research proposal , you need to expand on this substantially in the dissertation or thesis itself.

Essentially, you need to be looking for any existing literature that potentially helps you answer your research question (or develop it, if that’s not yet pinned down). There are numerous ways to find relevant literature, but I’ll cover my top four tactics here. I’d suggest combining all four methods to ensure that nothing slips past you:

Method 1 – Google Scholar Scrubbing

Google’s academic search engine, Google Scholar , is a great starting point as it provides a good high-level view of the relevant journal articles for whatever keyword you throw at it. Most valuably, it tells you how many times each article has been cited, which gives you an idea of how credible (or at least, popular) it is. Some articles will be free to access, while others will require an account, which brings us to the next method.

Method 2 – University Database Scrounging

Generally, universities provide students with access to an online library, which provides access to many (but not all) of the major journals.

So, if you find an article using Google Scholar that requires paid access (which is quite likely), search for that article in your university’s database – if it’s listed there, you’ll have access. Note that, generally, the search engine capabilities of these databases are poor, so make sure you search for the exact article name, or you might not find it.

Method 3 – Journal Article Snowballing

At the end of every academic journal article, you’ll find a list of references. As with any academic writing, these references are the building blocks of the article, so if the article is relevant to your topic, there’s a good chance a portion of the referenced works will be too. Do a quick scan of the titles and see what seems relevant, then search for the relevant ones in your university’s database.

Method 4 – Dissertation Scavenging

Similar to Method 3 above, you can leverage other students’ dissertations. All you have to do is skim through literature review chapters of existing dissertations related to your topic and you’ll find a gold mine of potential literature. Usually, your university will provide you with access to previous students’ dissertations, but you can also find a much larger selection in the following databases:

• Open Access Theses & Dissertations
• Stanford SearchWorks

Keep in mind that dissertations and theses are not as academically sound as published, peer-reviewed journal articles (because they’re written by students, not professionals), so be sure to check the credibility of any sources you find using this method. You can do this by assessing the citation count of any given article in Google Scholar. If you need help with assessing the credibility of any article, or with finding relevant research in general, you can chat with one of our Research Specialists .

Alright – with a good base of literature firmly under your belt, it’s time to move onto the next step.

Need a helping hand?

Step 2: Log, catalogue and synthesise

Once you’ve built a little treasure trove of articles, it’s time to get reading and start digesting the information – what does it all mean?

While I present steps one and two (hunting and digesting) as sequential, in reality, it’s more of a back-and-forth tango – you’ll read a little , then have an idea, spot a new citation, or a new potential variable, and then go back to searching for articles. This is perfectly natural – through the reading process, your thoughts will develop , new avenues might crop up, and directional adjustments might arise. This is, after all, one of the main purposes of the literature review process (i.e. to familiarise yourself with the current state of research in your field).

As you’re working through your treasure chest, it’s essential that you simultaneously start organising the information. There are three aspects to this:

• Logging reference information
• Building an organised catalogue
• Distilling and synthesising the information

I’ll discuss each of these below:

2.1 – Log the reference information

As you read each article, you should add it to your reference management software. I usually recommend Mendeley for this purpose (see the Mendeley 101 video below), but you can use whichever software you’re comfortable with. Most importantly, make sure you load EVERY article you read into your reference manager, even if it doesn’t seem very relevant at the time.

2.2 – Build an organised catalogue

In the beginning, you might feel confident that you can remember who said what, where, and what their main arguments were. Trust me, you won’t. If you do a thorough review of the relevant literature (as you must!), you’re going to read many, many articles, and it’s simply impossible to remember who said what, when, and in what context . Also, without the bird’s eye view that a catalogue provides, you’ll miss connections between various articles, and have no view of how the research developed over time. Simply put, it’s essential to build your own catalogue of the literature.

I would suggest using Excel to build your catalogue, as it allows you to run filters, colour code and sort – all very useful when your list grows large (which it will). How you lay your spreadsheet out is up to you, but I’d suggest you have the following columns (at minimum):

• Author, date, title – Start with three columns containing this core information. This will make it easy for you to search for titles with certain words, order research by date, or group by author.
• Categories or keywords – You can either create multiple columns, one for each category/theme and then tick the relevant categories, or you can have one column with keywords.
• Key arguments/points – Use this column to succinctly convey the essence of the article, the key arguments and implications thereof for your research.
• Context – Note the socioeconomic context in which the research was undertaken. For example, US-based, respondents aged 25-35, lower- income, etc. This will be useful for making an argument about gaps in the research.
• Methodology – Note which methodology was used and why. Also, note any issues you feel arise due to the methodology. Again, you can use this to make an argument about gaps in the research.
• Quotations – Note down any quoteworthy lines you feel might be useful later.
• Notes – Make notes about anything not already covered. For example, linkages to or disagreements with other theories, questions raised but unanswered, shortcomings or limitations, and so forth.

If you’d like, you can try out our free catalog template here (see screenshot below).

2.3 – Digest and synthesise

Most importantly, as you work through the literature and build your catalogue, you need to synthesise all the information in your own mind – how does it all fit together? Look for links between the various articles and try to develop a bigger picture view of the state of the research. Some important questions to ask yourself are:

• What answers does the existing research provide to my own research questions ?
• Which points do the researchers agree (and disagree) on?
• How has the research developed over time?
• Where do the gaps in the current research lie?

To help you develop a big-picture view and synthesise all the information, you might find mind mapping software such as Freemind useful. Alternatively, if you’re a fan of physical note-taking, investing in a large whiteboard might work for you.

Step 3: Outline and write it up!

Once you’re satisfied that you have digested and distilled all the relevant literature in your mind, it’s time to put pen to paper (or rather, fingers to keyboard). There are two steps here – outlining and writing:

3.1 – Draw up your outline

Having spent so much time reading, it might be tempting to just start writing up without a clear structure in mind. However, it’s critically important to decide on your structure and develop a detailed outline before you write anything. Your literature review chapter needs to present a clear, logical and an easy to follow narrative – and that requires some planning. Don’t try to wing it!

Naturally, you won’t always follow the plan to the letter, but without a detailed outline, you’re more than likely going to end up with a disjointed pile of waffle , and then you’re going to spend a far greater amount of time re-writing, hacking and patching. The adage, “measure twice, cut once” is very suitable here.

In terms of structure, the first decision you’ll have to make is whether you’ll lay out your review thematically (into themes) or chronologically (by date/period). The right choice depends on your topic, research objectives and research questions, which we discuss in this article .

Once that’s decided, you need to draw up an outline of your entire chapter in bullet point format. Try to get as detailed as possible, so that you know exactly what you’ll cover where, how each section will connect to the next, and how your entire argument will develop throughout the chapter. Also, at this stage, it’s a good idea to allocate rough word count limits for each section, so that you can identify word count problems before you’ve spent weeks or months writing!

PS – check out our free literature review chapter template…

3.2 – Get writing

With a detailed outline at your side, it’s time to start writing up (finally!). At this stage, it’s common to feel a bit of writer’s block and find yourself procrastinating under the pressure of finally having to put something on paper. To help with this, remember that the objective of the first draft is not perfection – it’s simply to get your thoughts out of your head and onto paper, after which you can refine them. The structure might change a little, the word count allocations might shift and shuffle, and you might add or remove a section – that’s all okay. Don’t worry about all this on your first draft – just get your thoughts down on paper.

Once you’ve got a full first draft (however rough it may be), step away from it for a day or two (longer if you can) and then come back at it with fresh eyes. Pay particular attention to the flow and narrative – does it fall fit together and flow from one section to another smoothly? Now’s the time to try to improve the linkage from each section to the next, tighten up the writing to be more concise, trim down word count and sand it down into a more digestible read.

Once you’ve done that, give your writing to a friend or colleague who is not a subject matter expert and ask them if they understand the overall discussion. The best way to assess this is to ask them to explain the chapter back to you. This technique will give you a strong indication of which points were clearly communicated and which weren’t. If you’re working with Grad Coach, this is a good time to have your Research Specialist review your chapter.

Finally, tighten it up and send it off to your supervisor for comment. Some might argue that you should be sending your work to your supervisor sooner than this (indeed your university might formally require this), but in my experience, supervisors are extremely short on time (and often patience), so, the more refined your chapter is, the less time they’ll waste on addressing basic issues (which you know about already) and the more time they’ll spend on valuable feedback that will increase your mark-earning potential.

Literature Review Example

In the video below, we unpack an actual literature review so that you can see how all the core components come together in reality.

Let’s Recap

In this post, we’ve covered how to research and write up a high-quality literature review chapter. Let’s do a quick recap of the key takeaways:

• It is essential to understand the WHY of the literature review before you read or write anything. Make sure you understand the 4 core functions of the process.
• The first step is to hunt down the relevant literature . You can do this using Google Scholar, your university database, the snowballing technique and by reviewing other dissertations and theses.
• Next, you need to log all the articles in your reference manager , build your own catalogue of literature and synthesise all the research.
• Following that, you need to develop a detailed outline of your entire chapter – the more detail the better. Don’t start writing without a clear outline (on paper, not in your head!)
• Write up your first draft in rough form – don’t aim for perfection. Remember, done beats perfect.
• Refine your second draft and get a layman’s perspective on it . Then tighten it up and submit it to your supervisor.

Psst… there’s more!

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

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38 Comments

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

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

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

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research.

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarise sources – it analyses, synthesises, and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

Why write a literature review, examples of literature reviews, step 1: search for relevant literature, step 2: evaluate and select sources, step 3: identify themes, debates and gaps, step 4: outline your literature review’s structure, step 5: write your literature review, frequently asked questions about literature reviews, introduction.

• Quick Run-through
• Step 1 & 2

When you write a dissertation or thesis, you will have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and scholarly context
• Develop a theoretical framework and methodology for your research
• Position yourself in relation to other researchers and theorists
• Show how your dissertation addresses a gap or contributes to a debate

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

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

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research objectives and questions .

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

Make a list of keywords

Start by creating a list of keywords related to your research topic. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list if you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can use boolean operators to help narrow down your search:

Read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

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

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

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models and methods? Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible, and make sure you read any landmark studies and major theories in your field of research.

You can find out how many times an article has been cited on Google Scholar – a high citation count means the article has been influential in the field, and should certainly be included in your literature review.

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

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

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It’s important to keep track of your sources with references to avoid plagiarism . It can be helpful to make an annotated bibliography, where you compile full reference information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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

Prevent plagiarism, run a free check.

To begin organising your literature review’s argument and structure, you need to understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly-visual platforms like Instagram and Snapchat – this is a gap that you could address in your own research.

There are various approaches to organising the body of a literature review. You should have a rough idea of your strategy before you start writing.

Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarising sources in order.

Try to analyse patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organise your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text, your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

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

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, make sure to follow these tips:

• Summarise and synthesise: give an overview of the main points of each source and combine them into a coherent whole.
• Analyse and interpret: don’t just paraphrase other researchers – add your own interpretations, discussing the significance of findings in relation to the literature as a whole.
• Critically evaluate: mention the strengths and weaknesses of your sources.
• Write in well-structured paragraphs: use transitions and topic sentences to draw connections, comparisons and contrasts.

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

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

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

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

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your  dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

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McCombes, S. (2022, June 07). What is a Literature Review? | Guide, Template, & Examples. Scribbr. Retrieved 27 May 2024, from https://www.scribbr.co.uk/thesis-dissertation/literature-review/

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Writing the Dissertation - Guides for Success: The Literature Review

• Writing the Dissertation Homepage
• Overview and Planning
• The Literature Review
• The Methodology
• The Results and Discussion
• The Conclusion
• The Abstract
• Getting Started
• Research Gap
• What to Avoid

Overview of writing the literature review

Conducting a literature review enables you to demonstrate your understanding and knowledge of the existing work within your field of research. Doing so allows you to identify any underdeveloped areas or unexplored issues within a specific debate, dialogue or field of study. This, in turn, helps you to clearly and persuasively demonstrate how your own research will address one or more of these gaps.

Disciplinary differences

Please note: this guide is not specific to any one discipline. The literature review can vary depending on the nature of the research and the expectations of the school or department. Please adapt the following advice to meet the demands of your dissertation and the expectations of your school or department. Consult your supervisor for further guidance; you can also check out  Writing Across Subjects guide .

Guide contents

As part of the Writing the Dissertation series, this guide covers the most common expectations for the literature review chapter, giving you the necessary knowledge, tips and guidance needed to impress your markers!  The sections are organised as follows:

• Getting Started  - Defines the literature review and presents a table to help you plan.
• Process -  Explores choosing a topic, searching for sources and evaluating what you find.
• Structure  - Presents key principles to consider in terms of structure, with examples to illustrate the concepts.
• Research gap - Clarifies what is meant by 'gap' and gives examples of common types of gaps.
• What to Avoid  - Covers a few frequent mistakes you'll want to...avoid!
• FAQs  - Answers to common questions about research gaps, literature availability and more.
• Checklist  - Includes a summary of key points and a self-evaluation checklist.

Training and tools

• The Academic Skills team has recorded a Writing the Dissertation workshop series to help you with each section of a standard dissertation, including a video on writing the literature review .
• Check out the library's online Literature Review: Research Methods training.
• Our literature reviews summary guide provides links to further information and videos.
• The dissertation planner tool can help you think through the timeline for planning, research, drafting and editing.
• iSolutions offers training and a Word template to help you digitally format and structure your dissertation.

What is the literature review?

The literature review of a dissertation gives a clear, critical overview of a specific area of research. Our main Writing the Dissertation - Overview and Planning guide explains how you can refine your dissertation topic  and begin your initial research; the next tab of this guide, 'Process', expands on those ideas. In summary, the process of conducting a literature review usually involves the following:

• Conducting a series of strategic searches to identify the key texts within that topic.
• Identifying the main argument in each source, the relevant themes and issues presented and how they relate to each other.
• Critically evaluating your chosen sources and determining their strengths, weaknesses, relevance and value to your research along with their overall contribution to the broader research field.
• Identifying any gaps or flaws in the literature which your research can address.

Literature review as both process and product

Writers should keep in mind that the phrase 'literature review' refers to two related, but distinct, things:

• 'Literature review' refers, first, to the  active process  of discovering and assessing relevant literature.
• 'Literature review' refers, second, to the  written product  that emerges from the above process.

This distinction is vital to note because  every  dissertation requires the writer to engage with and consider existing literature (i.e., to undertake the active  process ). Research doesn't exist in a void, and it's crucial to consider how our work builds from or develops existing foundations of thought or discovery. Thus, even if your discipline doesn't require you to include a chapter titled 'Literature Review' in your submitted dissertation, you should expect to engage with the process of reviewing literature.

Why is it important to be aware of existing literature?

• You are expected to explain how your research fits in with other research in your field and, perhaps, within the wider academic community.
• You will be expected to contribute something new, or slightly different, so you need to know what has already been done.
• Assessing the existing literature on your topic helps you to identify any gaps or flaws within the research field. This, in turn, helps to stimulate new ideas, such as addressing any gaps in knowledge, or reinforcing an existing theory or argument through new and focused research.

Not all literature reviews are the same. For example, in many subject areas, you are expected to include the literature review as its own chapter in your dissertation. However, in other subjects, the dissertation structure doesn't include a dedicated literature review chapter; any literature the writer has reviewed is instead incorporated in other relevant sections such as the introduction, methodology or discussion.

For this reason, there are a number of questions you should discuss with your supervisor before starting your literature review. These questions are also great to discuss with peers in your degree programme. These are outlined in the table below (see the Word document for a copy you can save and edit):

• Dissertation literature review planning table

Literature review: the process

Conducting a literature review requires you to stay organised and bring a systematic approach to your thinking and reading. Scroll to continue reading, or click a link below to jump immediately to that section:

Choosing a topic

The first step of any research project is to select an interesting topic. From here, the research phase for your literature review helps to narrow down your focus to a particular strand of research and to a specific research question. This process of narrowing and refining your research topic is particularly important because it helps you to maintain your focus and manage your material without becoming overwhelmed by sources and ideas.

Try to choose something that hasn’t been researched to death. This way, you stand a better chance of making a novel contribution to the research field.

Conversely, you should avoid undertaking an area of research where little to no work has been done. There are two reasons for this:

• Firstly, there may be a good reason for the lack of research on a topic (e.g. is the research useful or worthwhile pursuing?).
• Secondly, some research projects, particularly practice-based ones involving primary research, can be too ambitious in terms of their scope and the availability of resources. Aim to contribute to a topic, not invent one!

Searching for sources

Researching and writing a literature review is partly about demonstrating your independent research skills. Your supervisor may have some tips relating to your discipline and research topic, but you should be proactive in finding a range of relevant sources. There are various ways of tracking down the literature relevant to your project, as outlined below.

Make use of Library Search

One thing you don’t want to do is simply type your topic into Google and see what comes up. Instead, use Library Search to search the Library’s catalogue of books, media and articles.

Online training for 'Using databases' and 'Finding information' can be found here . You can also use the Library's subject pages to discover databases and resources specific to your academic discipline.

Engage with others working in your area

As well as making use of library resources, it can be helpful to discuss your work with students or academics working in similar areas. Think about attending relevant conferences and/or workshops which can help to stimulate ideas and allows you to keep track of the most current trends in your research field.

Look at the literature your sources reference

Finding relevant literature can, at times, be a long and slightly frustrating experience. However, one good source can often make all the difference. When you find a good source that is both relevant and valuable to your research, look at the material it cites throughout and follow up any sources that are useful. Also check if your source has been cited in any more recent publications.

Think of the bibliography/references page of a good source as a series of breadcrumbs that you can follow to find even more great material.

Evaluating sources

It is very important to be selective when choosing the final sources to include in your literature review. Below are some of the key questions to ask yourself:

• If a source is tangentially interesting but hasn’t made any particular contribution to your topic, it probably shouldn’t be included in your literature review. You need to be able to demonstrate how it fits in with the other sources under consideration, and how it has helped shape the current state of the literature.
• There might be a wealth of material available on your chosen subject, but you need to make sure that the sources you use are appropriate for your assignment. The safest approach to take is to use only academic work from respected publishers. However, on occasions, you might need to deviate from traditional academic literature in order to find the information you need. In many cases, the problem is not so much the sources you use, but how you use them. Where relevant, information from newspapers, websites and even blogs are often acceptable, but you should be careful how you use that information. Do not necessarily take any information as factual. Instead be critical and interpret the material in the context of your research. Consider who the writer is and how this might influence the authority and reliability of the information presented. Consult your supervisor for more specific guidance relating to your research.
• The mere fact that something has been published does not automatically guarantee its quality, even if it comes from a reputable publisher. You will need to critique the content of the source. Has the author been thorough and consistent in their methodology? Do they present their thesis coherently? Most importantly, have they made a genuine contribution to the topic?

Keeping track of your sources

Once you have selected a source to use in your literature review, it is useful to make notes on all of its key features, including where it comes from, what it says, and what its main strengths and weaknesses are. This way you can easily re-familiarise yourself with a source without having to re-read it. Keeping an annotated bibliography is one way to do this.

Alternately, below is a table you can copy and fill out for each source (see the Word document to save an editable copy for yourself). Software such as EndNote also allows you to keep an electronic record of references and your comments on them.

• Source evaluation table

Writing your literature review

As we explored in the 'Getting Started' tab, the literature review is both a process you follow and (in most cases) a written chapter you produce. Thus, having engaged the review process, you now need to do the writing itself. Please continue reading, or click a heading below to jump immediately to that section.

Guiding principles

The structure of the final piece will depend on the discipline within which you are working as well as the nature of your particular research project. However, here are a few general pieces of advice for writing a successful literature review:

• Show the connections between your sources. Remember that your review should be more than merely a list of sources with brief descriptions under each one. You are constructing a narrative. Show clearly how each text has contributed to the current state of the literature, drawing connections between them.
• Engage critically with your sources. This means not simply describing what they say. You should be evaluating their content: do they make sound arguments? Are there any flaws in the methodology? Are there any relevant themes or issues they have failed to address? You can also compare their relative strengths and weaknesses.
• Signpost throughout to ensure your reader can follow your narrative.  Keep relating the discussion back to your specific research topic.
• Make a clear argument. Keep in mind that this is a chance to present your take on a topic. Your literature review showcases your own informed interpretation of a specific area of research. If you have followed the advice given in this guide you will have been careful and selective in choosing your sources. You are in control of how you present them to your reader.

There are several different ways to structure the literature review chapter of your dissertation. Two of the most common strategies are thematic structure and chronological structure (the two of which can also be combined ). However you structure the literature review, this section of the dissertation normally culminates in identifying the research gap.

Thematic structure

Variations of this structure are followed in most literature reviews. In a thematic structure , you organise the literature into groupings by theme (i.e., subtopic or focus). You then arrange the groupings in the most logical order, starting with the broadest (or most general) and moving to the narrowest (or most specific).

The funnel or inverted pyramid

To plan a thematic structure structure, it helps to imagine your themes moving down a funnel or inverted pyramid  from broad to narrow. Consider the example depicted below, which responds to this research question:

What role did the iron rivets play in the sinking of the Titanic?

The topic of maritime disasters is the broadest theme, so it sits at the broad top of the funnel. The writer can establish some context about maritime disasters, generally, before narrowing to the Titanic, specifically. Next, the writer can narrow the discussion of the Titanic to the ship's structural integrity, specifically. Finally, the writer can narrow the discussion of structural integrity to the iron rivets, specifically. And voila: there's the research gap!

The broad-to-narrow structure is intuitive for readers. Thus, it is crucial to consider how your themes 'nest inside' one another, from the broad to the narrow. Picturing your themes as nesting dolls is another way to envision this literature review structure, as you can see in the image below.

As with the funnel, remember that the first layer (or in this case, doll) is largest because it represents the broadest theme. In terms of word count and depth, the tinier dolls will warrant more attention because they are most closely related to the research gap or question(s).

The multi-funnel variation

The example above demonstrates a research project for which one major heading might suffice, in terms of outlining the literature review. However, the themes you identify for your dissertation might not relate to one another in such a linear fashion. If this is the case, you can adapt the funnel approach to match the number of major subheadings you will need.

In the three slides below, for example, a structure is depicted for a project that investigates this (fictional) dissertation research question: does gender influence the efficacy of teacher-led vs. family-led learning interventions for children with ADHD? Rather than nesting all the subtopics or themes in a direct line, the themes fall into three major headings.

The first major heading explores ADHD from clinical and diagnostic perspectives, narrowing ultimately to gender:

• 1.1 ADHD intro
• 1.2 ADHD definitions
• 1.3 ADHD diagnostic criteria
• 1.4 ADHD gender differences

The second major heading explores ADHD within the classroom environment, narrowing to intervention types:

• 2.1 ADHD in educational contexts
• 2.2 Learning interventions for ADHD
• 2.2.1 Teacher-led interventions
• 2.2.2 Family-led interventions

The final major heading articulates the research gap (gender differences in efficacy of teacher-led vs. family-led interventions for ADHD) by connecting the narrowest themes of the prior two sections.

Multi-funnel literature review structure by Academic Skills Service

To create a solid thematic structure in a literature review, the key is thinking carefully and critically about your groupings of literature and how they relate to one another. In some cases, your themes will fit in a single funnel. In other cases, it will make sense to group your broad-to-narrow themes under several major headings, and then arrange those major headings in the most logical order.

Chronological structure

Some literature reviews will follow a  chronological structure . As the name suggests, a review structured chronologically will arrange sources according to their publication dates, from earliest to most recent.

This approach can work well when your priority is to demonstrate how the research field has evolved over time. For example, a chronological arrangement of articles about artificial intelligence (AI) would allow the writer to highlight how breakthroughs in AI have built upon one another in sequential order.

A chronological structure can also suit literature reviews that need to capture how perceptions or understandings have developed across a period of time (including to the present day). For example, if your dissertation involves the public perception of marijuana in the UK, it  could  make sense to arrange that discussion chronologically to demonstrate key turning points and changes of majority thought.

The chronological structure can work well in some situations, such as those described above. That being said, a purely chronological structure should be considered with caution.  Organising sources according to date alone runs the risk of creating a fragmented reading experience. It can be more difficult in a chronological structure to properly synthesize the literature. For these reasons, the chronological approach is often blended into a thematic structure, as you will read more about, below.

Combined structures

The structures of literature reviews can vary drastically, and for any given dissertation there will be many valid ways to arrange the literature.

For example, many literature reviews will  combine  the thematic and chronological approaches in different ways. A writer might match their major headings to themes or subtopics, but then arrange literature chronologically within the major themes identified. Another writer might base their major headings on chronology, but then assign thematic subheadings to each of those major headings.

When considering your options, try to imagine your reader or audience. What 'flow' will allow them to best follow the discussion you are crafting? When you are reading articles, what structural approaches do you appreciate in terms of ease and clarity?

Identifying the gap

The bulk of your literature review will explore relevant points of development and scholarly thought in your research field: in other words, 'Here is what has been done so far, thus here is where the conversation now stands'. In that way, you position your project within a wider academic discussion.

Having established that context, the literature review generally culminates in an articulation of what remains to be done: the  research gap  your project addresses. See the next tab for further explanation and examples.

Demystifying the research gap

The term research gap   is intimidating for many students, who might mistakenly believe that every single element of their research needs to be brand new and fully innovative. This isn't the case!

The gap in many projects will be rather niche or specific. You might be helping to update or re-test knowledge rather than starting from scratch. Perhaps you have repeated a study but changed one variable. Maybe you are considering a much discussed research question, but with a lesser used methodological approach.

To demonstrate the wide variety of gaps a project could address, consider the examples below. The categories used and examples included are by no means comprehensive, but they should be helpful if you are struggling to articulate the gap your literature review has identified.

***P lease note that the content of the example statements has been invented for the sake of demonstration. The example statements should not be taken as expressions of factual information.

Gaps related to population or geography

Many dissertation research questions involve the study of a specific population. Those populations can be defined by nationality, ethnicity, gender, sexuality, socioeconomic class, political beliefs, religion, health status, or other factors. Other research questions target a specific geography (e.g. a country, territory, city, or similar). Perhaps your broader research question has been pursued by many prior scholars, but few (or no) scholars have studied the question in relation to your focal population or locale: if so, that's a gap.

• Example 1:  As established above, the correlations between [ socioeconomic status ] and sustainable fashion purchases have been widely researched. However, few studies have investigated the potential relationship between [ sexual identity ] and attitudes toward sustainable fashion. Therefore...
• Example 2:  Whilst the existing literature has established a clear link between [ political beliefs ] and perceptions of socialized healthcare, the influence of [ religious belief ] is less understood, particularly in regards to [ Religion ABC ].
• Example 3:  Available evidence confirms that the widespread adoption of Technology XYZ in [ North America ] has improved manufacturing efficiency and reduced costs in the automotive sector. Using predictive AI models, the present research seeks to explore whether deployment of Technology XYZ could benefit the automotive sector of [ Europe ] in similar ways.

Gaps related to theoretical framework

The original contribution might involve examining something through a new lens.  Theoretical framework  refers, most simply, to the theory or theories a writer will use to make sense of and shape (i.e., frame ) their discussion. Perhaps your topic has been analysed in great detail through certain theoretical lenses, but you intend to frame your analysis using a theory that fewer scholars have applied to the topic: if so, that's a gap.

• Example 1:  Existing discussions of the ongoing revolution in Country XYZ frame the unrest in terms of [ theory A ] and [ theory B ]. The present research will instead analyse the situation using [ theory C ], allowing greater insight into...
• Example 2:  In the first section of this literature review, I examined the [ postmodern ], [ Marxist ], and [ pragmatist ] analyses that dominate academic discussion of The World According to Garp.  By revisiting this modern classic through the lens of [ queer theory ], I intend to...

Gaps related to methodological approach

The research gap might be defined by differences of methodology (see our Writing the Methodology guide for more). Perhaps your dissertation poses a central question that other scholars have researched, but they have applied different methods to find the answer(s): if so, that's a gap.

• Example 1:  Previous studies have relied largely upon the [ qualitative analysis of interview transcripts ] to measure the marketing efficacy of body-positive advertising campaigns. It is problematic that little quantitative data underpins present findings in this area. Therefore, I will address this research gap by [ using algorithm XYZ to quantify and analyse social-media interactions ] to determine whether...
• Example 2: Via [ quantitative and mixed-methods studies ], previous literature has explored how demographic differences influence the probability of a successful match on Dating App XYZ. By instead [ conducting a content analysis of pre-match text interactions ] on Dating App XYZ, I will...

Scarcity as a gap

Absolutes such as never  and always  rarely apply in academia, but here is an exception: in academia, a single study or analysis is  never  enough. Thus, the gap you address needn't be a literal void in the discussion. The gap could instead have to do with  replicability  or  depth/scope.  In these cases, you are adding value and contributing to the academic process by testing emerging knowledge or expanding underdeveloped discussions.

• Example:  Initial research points to the efficacy of Learning Strategy ABC in helping children with dyslexia build their reading confidence. However, as detailed earlier in this review, only four published studies have tested the intervention, and two of those studies were conducted in a laboratory. To expand our growing understanding of how Learning Strategy ABC functions in classroom environments, I will...

Elapsed time as a gap

Academia values up-to-date knowledge and findings, so another valid type of gap relates to elapsed time. Many factors that can influence or shape research findings are ever evolving: technology, popular culture, and political climates, to name just a few. Due to such changes, it's important for scholars in most fields to continually update findings. Perhaps your dissertation adds value by contributing to this process.

For example, imagine if a scholar today were to rely on a handbook of marketing principles published in 1998. As good as that research might have been in 1998, technology (namely, the internet) has advanced drastically since then. The handbook's discussion of online marketing strategies will be laughably outdated when compared to more recent literature.

• Example:  A wide array of literature has explored the ways in which perceptions of gender influence professional recruitment practices in the UK. The bulk of said literature, however, was published prior to the #MeToo movement and resultant shifts in discourse around gender, power imbalances and professional advancement. Therefore...

What to avoid

This portion of the guide will cover some common missteps you should try to avoid in writing your literature review. Scroll to continue reading, or click a heading below to jump immediately to that section.

Writing up before you have read up

Trying to write your literature review before you have conducted adequate research is a recipe for panic and frustration. The literature review, more than any other chapter in your dissertation, depends upon your critical understanding of a range of relevant literature. If you have only dipped your toe into the pool of literature (rather than diving in!), you will naturally struggle to develop this section of the writing. Focus on developing your relevant bases of knowledge before you commit too much time to drafting.

Believing you need to read everything

As established above, a literature review does require a significant amount of reading. However, you aren't expected to review  everything ever written  about your topic. Instead, aim to develop a more strategic approach to your research. A strategic approach to research looks different from one project to the next, but here are some questions to help you prioritise:

• If your field values up-to-date research and discoveries, carefully consider the 'how' and 'what' before investing time reading older sources: how will the source function in your dissertation, and what will it add to your writing?
• Try to break your research question(s) down into component parts. Then, map out where your literature review will need to provide extensive detail and where it can instead present quicker background. Allocate your research time and effort accordingly. 

Omitting dissenting views or findings

While reviewing the literature, you might discover authors who disagree with your central argument or whose own findings contradict your hypothesis. Don't omit those sources: embrace them! Remember, the literature review aims to explore the academic dialogue around your topic: disagreements or conflicting findings are often part of that dialogue, and including them in your writing will create a sense of rich, critical engagement. In fact, highlighting any disagreements amongst scholars is a great way to emphasise the relevance of, and need for, your own research.

Miscalculating the scope

As shown in the funnel structure (see 'Structure' tab for more), a literature review often starts broadly and then narrows the dialogue as it progresses, ultimately bringing the reader to the dissertation's specific research topic (e.g. the funnel's narrowest point).

Within that structure, it's common for writers to miscalculate the scope required. They might open the literature review far too broadly, dedicating disproportionate space to developing background information or general theory; alternately, they might rush into the narrowest part of the discussion, failing to develop any sense of surrounding context or background, first.

It takes trial and error to determine the appropriate scope for your literature review. To help with this...

• Imagine your literature review subtopics cascading down a stairwell,  as in the illustration below.
• Place the broadest concepts on the highest steps, then narrow down to the most specific concepts on the lowest steps: the scope 'zooms in' as you move down the stairwell.
• Now, consider which step is the most logical starting place for your readers. Do they need to start all the way at the top, or should you 'zoom in'?

The illustration above shows a stairwell diagram of a dissertation that aims to analyse Stephen King's horror novel  The Stand  through the lens of a specific feminist literary theory.

• If the literature review began on one of the bottom two steps, this would feel rushed and inadequate. The writer needs to explore and define the relevant theoretical lens before they discuss how it has been applied by other scholars.
• If the literature review began on the very top step, this would feel comically broad in terms of scope: in this writing context, the reader doesn't require a detailed account of the entire history of feminism!

The third step, therefore, represents a promising starting point: not too narrow, not too broad.

The 'islands' structure

Above all else, a literature review needs to synthesize a range of sources   in a logical fashion. In this context, to  synthesize  means to bring together, connect, weave, and/or relate. A common mistake writers make is failing to conduct such synthesis, and instead discussing each source in isolation. This leads to a disconnected structure, with each source treated like its own little 'island'. The island approach works for very few projects.

Some writers end up with this island structure because they confuse the nature of the  literature review  with the nature of an annotated bibliography . The latter is a tool you can use to analyse and keep track of individual sources, and most annotated bibliographies will indeed be arranged in a source-by-source structure. That's fine for pre-writing and notetaking, but to structure the literature review, you need to think about connections and overlaps between sources rather than considering them as stand-alone works.

If you are struggling to forge connections between your sources, break down the process into tiny steps:

• e.g. Air pollution from wood-burning stoves in homes.
• e.g.  Bryant and Dao (2022) found that X% of small particle pollution in the United Kingdom can be attributed to the use of wood-burning stoves.
• e.g.  A study by Williams (2023) reinforced those findings, indicating that small particle pollution has...
• e.g.  However, Landers (2023) cautions that factor ABC and factor XYZ may contribute equally to poor air quality, suggesting that further research...

The above exercise is  not  meant to suggest that you can only write one sentence per source: you can write more than that, of course! The exercise is simply designed to help you start synthesizing the literature rather than giving each source the island treatment.

Q: I still don't get it - what's the point of a literature review?

A: Let's boil it down to three key points...

• The literature review provides a platform for you, as a scholar, to demonstrate your understanding of how your research area has evolved. By engaging with seminal texts or the most up-to-date findings in your field, you can situate your own research within the relevant academic context(s) or conversation(s).
• The literature review allows you to identify the research gap your project addresses: in other words, what you will add to discussions in your academic field.
• Finally, the literature review justifies the reason for your research. By exploring existing literature, you can highlight the relevance and purpose of your own research.

Q: What if I don't have a gap?

A:  It's normal to struggle with identifying a research gap. This can be particularly true if you are working in a highly saturated research area, broadly speaking: for example, if you are studying the links between nutrition and diabetes, or if you are studying Shakespeare.

The 'What to Avoid' tab explained that  miscalculating the scope  is a common mistake in literature reviews. If you are struggling to identify your gap, scope might be the culprit, particularly if you are working in a saturated field. Remember that the gap is the narrowest part of the funnel, the smallest nesting doll, the lowest step: this means your contribution in that giant academic conversation will need to be quite 'zoomed in':

This is not a valid gap →  Analysing Shakespeare's sonnets.

This might be a valid gap →  Conducting an ecocritical analysis of the visual motifs of Shakespeare's final five procreation sonnets (e.g. sonnets number thirteen to seventeen).

In the above example, the revised attempt to articulate a gap 'zooms in' by identifying a particular theoretical lens (e.g. ecocriticism), a specific convention to analyse (e.g. use of visual motifs), and a narrower object (e.g. five sonnets rather than all 150+). The field of Shakespeare studies might be crowded, but there is nonetheless room to make an original contribution.

Conversely, it might be difficult to identify the gap if you are working not in a saturated field, but in a brand new or niche research area. How can you situate your work within a relevant academic conversation if it seems like the 'conversation' is just you talking to yourself?

In these cases, rather than 'zooming in', you might find it helpful to 'zoom out'. If your topic is niche, think creatively about who will be interested in your results. Who would benefit from understanding your findings? Who could potentially apply them or build upon them? Thinking of this in interdisciplinary terms is helpful for some projects.

Tip:  Venn diagrams and mind maps are great ways to explore how  your research connects to, and diverges from, the existing literature.

Q: How many references should I use in my literature review?

A:  This question is risky to answer because the variations between individual projects and disciplines make it impossible to provide a universal answer. The fact is that one dissertation might have 50 more references than another, yet the two projects could be equally rigorous and successful in fulfilling their research aims.

With that warning in mind, let's consider a 'standard' dissertation of around 10K words. In that context, referencing 30 to 40 sources in your literature review tends to work well. Again, this is  not  a universally accurate rule, but a ballpark figure for you to contemplate. If the 30 to 40 estimate seems frighteningly high to you, do remember that many sources will be used sparingly rather than being mulled over at length. Consider this example:

In British GP practices, pharmaceutical treatment is most often prescribed for Health Condition XYZ ( Carlos, 2019; Jones, 2020 ; Li, 2022 ). Lifestyle modifications, such as physical exercise or meditation practices, have only recently...

When writing critically, it's important to validate findings across studies rather than trusting only one source. Therefore, this writer has cited three recent studies that agree about the claim being made. The writer will delve into other sources at more length, but here, it makes sense to cite the literature and move quickly along.

As you search the databases and start following the relevant trails of 'research bread crumbs', you will be surprised how quickly your reference list grows.

Q: What if there isn't enough relevant literature on my topic?

A: Think creatively about the literature you are using and engaging with. A good start is panning out to consider your topic more broadly: you might not identify articles that discuss your  exact  topic, but what can you discover if you shift your focus up one level?

Imagine, for example, that Norah is researching how artificial intelligence (AI) can be used to provide dance instruction. She discovers that no one has written about this topic. Rather than panicking, she breaks down her research question into its component parts to consider what research  might  exist.

• First, dance instruction: literature on how dance has traditionally been taught (i.e., not with AI) is still relevant because it will provide background and context. To appreciate the challenges or opportunities that transition to AI instruction might bring, we need to understand the status quo. Norah might also search for articles that analyse how other technological shifts have affected dance instruction: for example, how YouTube popularized at-home dance study, or how live video services like Zoom enabled real-time interaction between dance pupils and teachers despite physical distance.
• Next, artificial intelligence used for instruction: Norah can seek out research on, and examples of, the application of AI for instructive purposes. Even if those purposes don't involve dance, such literature can contribute to illustrating the broader context around Norah's project.
• Could it be relevant to discuss the technologies used to track an actor's real-life movements and convert them into the motions of a video game character? Perhaps there are parallels!
• Could it be relevant to explore research on applications of AI in creative writing and visual art? Could be relevant since dance is also a creative field!

In summary, don't panic if you can't find research on your  exact  question or topic. Think through the broader context and parallel ideas, and you will soon find what you need.

Q: What if my discipline doesn't require a literature review chapter?

A: This is a great question. Whilst many disciplines dictate that your dissertation should include a chapter called Literature Review , not all subjects follow this convention. Those subjects will still expect you to incorporate a range of external literature, but you will nest the sources under different headings.

For example, some disciplines dictate an introductory chapter that is longer than average, and you essentially nest a miniature literature review inside the introduction, itself. Although the writing is more condensed and falls under a subheading of the introduction, the techniques and principles of writing a literature review (for example, moving from the broad to the narrow) will still prove relevant.

Some disciplines include chapters with names like Background , History , Theoretical Framework , etc. The exact functions of such chapters differ, but they have this in common: reviewing literature. You can't provide a critical background or history without synthesizing external sources. To illustrate your theoretical framework, you need to synthesize a range of literature that defines the theory or theories you intend to use.

Therefore, as stated earlier in this guide, you should be prepared to review and synthesize a range of literature regardless of your discipline. You can tailor the purpose of that synthesis to the structure and demands of writing in your subject area.

Q: Does my literature review need to include every source I plan to use in my discussion chapter?

A: The short answer is 'no' - there are some situations in which it is okay to use a source in your discussion chapter that you didn't integrate into your literature review chapter.

Imagine, for example, that your study produced a surprising result: a finding that you didn't anticipate. To make sense of that result, you might need to conduct additional research. That new research will help you explain the unexpected result in your discussion chapter.

More often, however, your discussion will  draw on, or return to, sources from your literature review. After all, the literature review is where you paint a detailed picture of the conversation surrounding your research topic. Thus, it makes sense for you to relate your own work to that conversation in the discussion.

The literature review provides you an opportunity to engage with a rich range of published work and, perhaps for the first time, critically consider how your own research fits within and responds to your academic community. This can be a very invigorating process!

At the same time, it's likely that you will be juggling more academic sources than you have ever used in a single writing project. Additionally, you will need to think strategically about the focus and scope of your work: figuring out the best structure for your literature review might require several rounds of re-drafting and significant edits.

If you are usually a 'dive in without a plan and just get drafting' kind of writer, be prepared to modify your approach if you start to feel overwhelmed. Mind mapping, organising your ideas on a marker board, or creating a bullet-pointed reverse outline can help if you start to feel lost.

Alternately, if you are usually a 'create a strict, detailed outline and stick to it at all costs' kind of writer, keep in mind that long-form writing often calls for writers to modify their plans for content and structure as their work progresses and evolves. It can help such writers to schedule periodic 'audits' of their outlines, with the aim being to assess what is still working and what else needs to be added, deleted or modified.

Here’s a final checklist for writing your literature review. Remember that not all of these points will be relevant for your literature review, so make sure you cover whatever’s appropriate for your dissertation. The asterisk (*) indicates any content that might not be relevant for your dissertation. You can save your own copy of the checklist to edit using the Word document, below.

• Literature review self-evaluation checklist

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How to Write a Dissertation Literature Review – Steps & Tips

Published by Anastasia Lois at August 12th, 2021 , Revised On October 17, 2023

From an academic standpoint, a dissertation literature review can be defined as a survey of the thesis, journal articles, books, and other academic resources on any given research title . This article provides comprehensive guidelines on how to write a dissertation literature review.

A literature review in a dissertation is of critical importance primarily because it provides insight into the key concepts, advancements, theories, and results of your research questions  or  research problem .

However, it is essential to note that; a first-class dissertation literature review focuses on summarizing the academic sources used for research and analysing, interpreting, and assessing them to determine the gaps and differences in opinions, judgments, themes, and developments.

A good literature review will further elaborate on existing knowledge concerning the research hypothesis or questions.

View dissertation literature review examples here.  

When do you Write a Dissertation Literature Review?

Depending on your university’s guidelines, you might be required to include a literature review in the theoretical framework or the introduction.

Or you could also be asked to develop a standalone literature review chapter that appears before  the methodology  and  the findings  chapters of the dissertation.

In either case, your primary aim will be to review the available literature and develop a link between your research and the existing literature on your chosen topic.

Sometimes, you might be designated a literature review as a separate assignment . Regardless of whether you need to write a literature review for your dissertation or as a standalone project, some general guidelines for conducting literature will remain unchanged.

Here are the steps you need to take to write the literature review for a dissertation if you cannot write the literature review.

Steps of Writing a Literature Review

1. gather, assess, and choose relevant literature.

The first seed to take when writing your dissertation or thesis is to choose a fascinating and manageable research topic . Once a topic has been selected, you can begin searching for relevant academic sources.

If you are  writing a literature review for your dissertation, one way to do this is to find academic sources relevant to your  research problem or questions.

Without fully understanding current knowledge in the chosen study area, giving the correct direction to your research aim and objectives will be hard.

On the other hand, you will be expected to guide your research by developing a central question if you are writing a literature review as an individual assignment.

A notable difference here compared to the dissertation literature review is that you must answer this central question without conducting primary research (questionnaires, surveys, interviews). You  will be expected to address the question using only the existing literature.

Dissertation Literature Review Research Question

How can company “A” improve its brand value through social media marketing?

Literature Review Research Question

What is the connection between social media marketing and brand value?

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Use Keywords and References to Find Relevant Literature

Create a list of keywords that are relevant to the topic of research. Find journals, articles, and books using these keywords. Here are links to some recognised online academic libraries and databases;

• Inspec, (Computer science, engineering, physics, chemistry)
• EconLit, (Economics)
• Google Scholar
• Your university’s online research database

Finding relevant academic sources from “the reference list” of an article you have already found in a research database effectively discovers relevant studies.

Consider noting frequently appearing references as they are likely to be highly authentic and important publications even though they didn’t appear in the keyword search.

Journal articles or books that keep appearing with different keywords and phrases are the ones you should manually look out for.

The more times an article has been referenced, the more influential it is likely to be in any research field. Google Scholar lets users quickly determine how often a particular article has been referenced.

Also Read:  How to Best Use References in a Dissertation

2. Weighing and Selecting Academic Sources

It won’t be possible for you to read every publication related to your topic. An excellent way to select academic sources for your dissertation literature review is to read the abstract , which will help you decide whether the source is supportive and relevant to your research hypothesis or research questions.

To help you select sources relevant to your study, here are some questions for you to consider before making the decision.

• What research questions has the author answered with their research work?
• What fundamental concepts have been defined by the author?
• Did the researcher use an innovative methodology or existing frameworks to define fundamental methods, models, and theories?
• What are the findings, conclusion, and recommendations in the source book or paper?
• What is the relevance between the existing literature and the academic sources you are evaluating?
• Does the source article challenge, confirm, or add to existing knowledge on the topic?
• How does the publication contribute to your understanding of the topic? What are its key insights and arguments?
• What are the strengths and weaknesses of the research?

Any breakthrough studies and key theories relevant to your research topic should be recorded as you search for highly credible and authentic academic sources.

The method of your review of literature depends on your academic subject. If your research topic is in the sciences, you must find and review up-to-date academic sources.

On the other hand, you might look into old and historical literature and recent literature if your research topic is in the humanities field.

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Recording Information and Referencing Sources

It is recommended that you start to write your literature review as you read articles, journals, and books. Take notes which can be later merged into the text of the literature review. Avoid plagiarism  and record all sources used along with references.

A good way of recording information is to analyse each source, summarise the key concepts or theories and compile a complete list of references in the form of an annotated bibliography.

This is a beneficial practice as it helps to remember the key points in each academic source and saves you valuable time as you start the literature review write-up.

3. Identify Key Themes and Patterns

The next step is to look for themes and patterns in the chosen sources that would enable you to establish similarities and differences between their  results and interpretations .

This exercise will help you to determine the  structure and argument for your literature review. Here are some questions that you can think of when reading and recording information;

• Are any gaps in the existing literature?
• What are the weaknesses of the current literature that should be addressed?
• Were you able to identify any landmark research work and theories that resulted in the topic’s change of direction?
• What are the similarities and disagreements between these sources? Were you able to identify any contradictions and conflicts?
• What trends and themes were you able to identify? Are there any results, methods, or theories that lost credibility over time?

Also Read: How to Write a Dissertation – Step-by-Step Guide

4. Structure of Literature Review

There is no acclaimed  literature review structure , which means that you can choose from a range of approaches (thematic, chronological, methodological, and theoretical) when deciding on the structure of the literature review .

However, before you begin to  write the literature review , it is important to figure out the strategy that would work best for you. For long literature reviews, you might decide to use a combination of these strategies. For example, you could discuss each of the themes chronologically.

1: Theoretical

You can discuss various significant concepts, models, and theories in your literature review to form the basis of a theoretical framework . You could also combine a range of theoretical approaches to develop your theoretical framework or debate the significance of a particular theoretical framework.

2: Methodological

The methodological approach will require you to relate the findings of studies conducted in different research areas and use different research methods .

• You might discover that results from the quantitative research approach are not the same as qualitative research.
• You might split the selected academic sources based on their discipline – engineering, and sciences.

3: Thematic

You may also deploy a thematic approach, especially if you identified repeating key themes and patterns. If that is the case, you will be expected to put each aspect of the topic into different subsections within your literature review.

For example, if your research topic is “employment issues in the UK for international students,” you can divide the key themes into subsections; legal status, poor language skills, immigration policy, and economic turmoil.

4: Chronological

The most straightforward approach is to trace the development of the topic over time. However, if you choose this strategy, avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points, and critical debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

5. Writing your Literature Review 

Whether it’s a dissertation literature review or a standalone literature review assignment you have been assigned, you will be expected to divide your literature review into three larger sections – introduction, main body, and a conclusion.

What you write under these three segments will depend on the aim of your study.

Section 1 – Introduction

Here you will be required to state the objectives of the literature review clearly;

Introduction to Dissertation Literature Review Recapitulate your research problem or questions with a summary of the sources you reviewed when the literature review is for your thesis or dissertation. Consider highlighting gaps in existing knowledge and stress the suitability of your topic.

For example:

Research problem A has been debated in many recent studies.

While the topic has been explored concerning A, the B aspect has not yet been explored.

Individual Literature Review Project When reviewing literature for an individual literature review assignment, make sure that you clearly state the purpose of the research and debate the scope of the literature (how recent or old are the academic sources you are reviewing).

Section 2 – Main Body

As previously mentioned, you can divide this section into further subsections depending on your literature review’s length. You can also have a separate heading for each research method, theme, or theory to help your readers better understand your research.

Here are some tips for you to write a flawless main body of literature review;

• Summarize and Combine ; Highlight the main findings from each academic source and organise them into one whole piece without losing coherence.
• Evaluate and interpret; Make sure you are giving opinions and arguments of your own where possible. Simply rephrasing what others have said will undermine your work. You will be expected to debate and discuss other studies’ results about your research questions or aim.
• Analytical Evaluation; It is essential to unmistakably present the literature you have reviewed and the merits and weaknesses of the literature.
• Make Use of  Topic Sentences and Transitions; in organized subsections within the literature review to establish conflicts, differences, similarities, and relationships.

Example of How to Write a Dissertation Literature Review

The below example belongs to the body of a literature review on the effectiveness of e-recruitment in small and medium-sized enterprises in the United Kingdom’s IT sector.

E-recruitment means explicitly using digital technologies to recruit, select, and orient employees. The benefits of e-recruitment in the literature have been studied: increased access to a pool of candidates, time and cost savings, and greater flexibility for the organisation.

In contrast, the literature states that e-recruitment might not properly achieve the goal of retaining the workforce with the required skills to participate in the work environment (Lad & Das, 2016). Also, e-recruitment might be based on a flawed website design or poor application process, which might deter potential employees (Anand & Devi, 2016) .

This section of the study will focus on the existing studies linked to the effectiveness of e-recruitment. Human resource management is an essential function of business organisations because it manages the workforce.

The goal of HR should be to develop a strategic approach in which the organisation’s strategic goals can be attained efficiently and effectively. The advent of digital technologies has helped transform human resource management’s nature concerning recruiting and selecting employees for organisations.

The Internet’s benefits have reduced search time for candidates and significant cost savings for organisations. Finally, it offers a transparent method for obtaining information about specific candidates. E-recruitment helps organisations hire people from any part of the world as it promotes opportunities and benefits the organisation efficiently.

Sharma (2014) argues that 75% of human resource professionals in developed countries are now using e-recruitment to hire employees for their organisations. Additionally, some 2 out of 4 job seekers will use the Internet to source job opportunities.

Another evidence to support the rise of e-recruitment is a study by Holm (2014), which found that all Fortune 100 companies will be using some form of e-recruitment to advertise vacant positions.

The implications are that e-recruitment is a popular strategy for various positions, from blue-collar roles to white-collar and professional positions. The benefits of e-recruitment have been identified in the literature. Girard & Fallery (2009) argues that e-recruitment helps to save time for organisations and employees.

Employers can use several methods to post jobs in as little as 20 minutes. There are no limits to ad size, and they can receive resumes immediately. In contrast, the traditional methods require some time to appear, for example, in a newspaper, and might be there for a limited period.

Section 3 – Conclusion

When writing the dissertation literature review conclusion, you should always include a summary of the key findings which emerged from the literature and their relevance and significance to your research objectives.

Literature Review for Dissertation

If you are writing a dissertation literature review, you will be required to demonstrate how your research helped to fill an evident gap in research and contributed to the current knowledge in the field. Similarly, you can explain how you used the existing patterns, themes, and theories to develop your research framework.

Literature Review as an Individual Assignment

You can summarize your review of your literature’s significance and implications and provide recommendations for future work based on the gaps in existing knowledge you acknowledged.

6. Proofread

Finally, thoroughly proofread your literature review for grammatical, structural, spelling, and factual errors before submitting it to your university.

If you are unable to proofread and edit your paper, then you could take advantage of our  editing and proofreading service , which is designed to ensure that your completed literature review satisfies each of your module or project’s requirements. We have Masters and PhD qualified writers in all academic subjects, so you can be confident that they will edit and improve the quality of your to 2:1 or First Class standard, as required.

Valuable Tips for Writing Dissertation Literature Review

Your literature review must systematically comply with your research area. Underneath, we are stating some essential guidelines for a compact literature review.

Contribute to the Literature

After carefully reviewing the literature, search for the gaps in knowledge and state how you have analysed the literature with a different perspective and contributed to your research area.

Keep your Argument Systematic & Consistent

Your arguments must be consistent and systematic while discussing theories and controversial and debatable content. Be logical in your review and avoid vague statements, not to make it complex for the readers.

Provide Adequate References

Don’t forget to provide references, as they are the soul of the dissertation. While discussing different aspects of the research, provide a reasonable number of references, as your discussion and interpretations must be backed up by relevant evidence. You can see an example provided in the sample paragraph above.

Be Precise While Writing a Review

You aren’t required to write every inch of information you have studied while reviewing the literature. You will be able to find tons of information that will correlate with your research area.

Be precise while writing the review, as writing unnecessary, irrelevant information won’t give a good impression. State the most reliable sources in your review without jumping into every possible source.

Don’t go Excessively for Direct Quotes

Direct quoting is required to make a point more impactful, but you should opt for it to a specific limit. Making excessive use of it won’t be a good idea.

The direct quote is mainly used when you think that the words being used by the actual author are so authentic in their meaning that you can’t replace or rephrase them. Try to avoid relying too much on a single author/s work.

Discussing their contributions and keeping the review going briefly would be better. While mentioning the points discussed by the prior researchers – link your arguments with their discussion. Don’t write the crux of their discussion, yet tell if your argument goes along with them.

Express your Analysis

The literature review is written to summarize your perspective, which should be backed up in light of the literature. Critically analyze literature with a rational approach and express your opinion on it.

Use the Correct Referencing Style

While referencing, one must use proper referencing styles, i.e., Harvard reference style, etc. Different referencing styles are used for in-text citations, while different for end-text citations.

Feeling overwhelmed by your literature review? Still unsure about how to write a dissertation literature review? There is no need to panic. Whether you are an undergraduate, postgraduate, or PhD student, our literature review writing service  can help you have your literature review to the highest academic quality.

All papers completed by our writers are delivered along with a free anti-plagiarism report. We will amend your paper for free as many times as needed until you are delighted with the contents and the works’ quality as long as your original instructions and requirements remain unchanged.

FAQs About Dissertation Literature Review

How to find relevant literature for reference in a dissertation.

You must note down keywords related to the title of your dissertation and search journals, articles, and books using them. 

How to select academic sources?

If you have found plenty of academic resources, you can select a few of them by reading the abstract of all the papers and separating the most relevant ones. 

How to quote academic references?

It is recommended that you start to write your literature review as you read articles, journals and books. Take notes which can be later merged into the text of the literature review. 

How should a literature review dissertation be written?

You should divide your literature review into three sections: 

Introduction, main body, and conclusion. 

You May Also Like

Anyone who supports you in your research should be acknowledged in dissertation acknowledgments. Learn more on how to write dissertation acknowledgements.

A list of glossary in a dissertation contains all the terms that were used in your dissertation but the meanings of which may not be obvious to the readers.

How to Structure a Dissertation or Thesis Need interesting and manageable Finance and Accounting dissertation topics? Here are the trending Media dissertation titles so you can choose one most suitable to your needs.

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Undertaking a literature review in marketing

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N2 - Writing a literature review is often considered to be one of the most difficult tasks a student will undertake and is often the most significant part of the academic contribution of essays and theses. Academia is a broad and inclusive community and all research takes place within the context of others work. It is therefore imperative that we review current knowledge before we can advance. This article provides guidelines for the construction of a literature review in marketing and focuses upon three key tasks. The first is the identification and sourcing of literature. The second is the interpretation and critical understanding of what has been collected and the third is the preparation and writing of the final document. Within these three tasks a number of suggestions and examples are given to aid anyone starting to prepare a marketing literature review.

AB - Writing a literature review is often considered to be one of the most difficult tasks a student will undertake and is often the most significant part of the academic contribution of essays and theses. Academia is a broad and inclusive community and all research takes place within the context of others work. It is therefore imperative that we review current knowledge before we can advance. This article provides guidelines for the construction of a literature review in marketing and focuses upon three key tasks. The first is the identification and sourcing of literature. The second is the interpretation and critical understanding of what has been collected and the third is the preparation and writing of the final document. Within these three tasks a number of suggestions and examples are given to aid anyone starting to prepare a marketing literature review.

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Forecasting e-commerce consumer returns: a systematic literature review

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• Published: 21 May 2024

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• David Karl   ORCID: orcid.org/0000-0002-0326-5982 1  

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The substantial growth of e-commerce during the last years has led to a surge in consumer returns. Recently, research interest in consumer returns has grown steadily. The availability of vast customer data and advancements in machine learning opened up new avenues for returns forecasting. However, existing reviews predominantly took a broader perspective, focussing on reverse logistics and closed-loop supply chain management aspects. This paper addresses this gap by reviewing the state of research on returns forecasting in the realms of e-commerce. Methodologically, a systematic literature review was conducted, analyzing 25 relevant publications regarding methodology, required or employed data, significant predictors, and forecasting techniques, classifying them into several publication streams according to the papers’ main scope. Besides extending a taxonomy for machine learning in e-commerce, this review outlines avenues for future research. This comprehensive literature review contributes to several disciplines, from information systems to operations management and marketing research, and is the first to explore returns forecasting issues specifically from the e-commerce perspective.

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1 Introduction

E-commerce has witnessed substantial growth rates in recent years and continues growing by double-digit margins (National Retail Federation/Appriss Retail 2023 ). However, lenient consumer return policies have resulted in $212 Billion worth of merchandise being returned to online retailers in the U.S. in 2022, accounting for 16.5% of online sales (National Retail Federation/Appriss Retail 2023 ). While high rates of consumer returns mainly concern specific sectors and product categories, online fashion retailing is particularly affected (Diggins et al. 2016 ). Recent studies report average shipment-related return rates for fashion retailers in the 40–50% range (Difrancesco et al. 2018 ; Karl and Asdecker 2021 ). In addition to missed sales and reduced profits (Zhao et al. 2020 ), consumer returns pose operational challenges (Stock and Mulki 2009 ), including unavoidable processing costs (Asdecker 2015 ) and uncertainties regarding logistics capacities, inventory management, procurement decisions, and marketing activities. Hence, effectively managing consumer returns is an essential part of the e-commerce business model (Urbanke et al. 2015 ).

Similar to the research conducted by Abdulla et al. ( 2019 ), this work focuses on consumer returns in online retailing (e-commerce), excluding the larger body of closed-loop supply chain (CLSC) management, which encompasses product returns related to end-of-life and end-of-use scenarios involving raw material recycling or remanufacturing. In contrast to CLSC returns, retail consumer returns are typically sent or given back unused or undamaged shortly after purchase, without any quality-related defects. These returns should be reimbursed to the consumer and are intended to be resold “as new” (de Brito et al. 2005 ; Melacini et al. 2018 ; Shang et al. 2020 ).

Regarding forecasting aspects, demand forecasting is a crucial activity for successful retail management (Ge et al. 2019 ). In contrast to demand and sales, returns constitute the “supply” side of the return process (Frei et al. 2022 ). Consequently, forecasting becomes a complex task and a significant challenge in managing returns due to the inherently uncertain nature of customer decisions regarding product retention (Frei et al. 2022 ). Moreover, return forecasts are interconnected with sales forecasts and promotional activities (Govindan and Bouzon 2018 ; Tibben-Lembke and Rogers 2002 ). Hence, forecasting objectives may vary, encompassing return quantities, timing (Hachimi et al. 2018 ), and even individual return probabilities. Minimizing return forecast errors is critical to reduce and minimize reactive planning (Hess and Mayhew 1997 ). Accurate forecasts rely on (1) comprehensive data collection, e.g., regarding consumer behavior, and (2) information and communications technology (ICT) for data processing, such as big data analytics. Despite extensive research in supply chain management (SCM), Barbosa et al. ( 2018 ) noted a lack of relevant publications exploring the "returns management" process of SCM in conjunction with big data analytics. Specifically, “the topic of forecasting consumer returns has received little attention in the academic literature” (Shang et al. 2020 ). Nonetheless, precise return forecasts positively impact reverse logistics activities’ economic, environmental, and social performance, primarily concerning quantity, quality, and timing predictions (Agrawal and Singh 2020 ). Hence, forecasting returns holds significant relevance across various supply chain stages.

1.1 Previous meta-research

Hess and Mayhew ( 1997 ) emphasized the need for extensive data analysis concerning reverse flows, which forms the basis for returns forecasting. Subsequently, research on consumer returns and reverse logistics has proliferated. Thus, before collecting data and reviewing the topic of consumer returns forecasting, we first examined existing reviews and meta-studies relevant to the subject matter. To accomplish this, we referred to Web of Science, Business Source Ultimate via EBSCOhost, JSTOR and the AIS Electronic Library as primary sources of knowledge (search term: "literature review" AND "return*" AND "forecast*”). As a secondary source, we appended the results of Google Scholar, Footnote 1 for which a different search term was used (intitle:"literature review" ("product return" OR "consumer return" OR "retail return" OR "e-commerce return") forecast) due to unavailable truncations and to reduce the vast amount of literature with financial focus the search term “return” would lead to. Table 1 presents the most pertinent literature reviews related to the scope of this paper.

Agrawal et al. ( 2015 ) identified research gaps within the realm of reverse logistics, finding “forecasting product returns” as a crucial future research path. However, among 21 papers focusing on “forecasting models for product returns”, the emphasis was predominantly on CLSC, reuse, remanufacturing, and recycling, which do not align with the aim of this review. Agrawal et al. also noted a lack of comprehensive analysis of underlying factors in returns forecasting, such as demographics or consumer behavior.

Similarly, Hachimi et al. ( 2018 ) addressed forecasting challenges within the broader context of reverse logistics. They classified their literature using various forecasting approaches: time series and machine learning, operations research methods, and simulation programs. The research gaps they identified included a limited number of influencing factors taken into account, the absence of established performance indicators, and methodological issues related to dynamic lot-sizing with returns. Although this review focused on reverse logistics, the call for research into predictors of future returns is equally applicable to consumer returns in e-commerce.

The review of Abdulla et al. ( 2019 ) centers on consumer returns within the retail context, particularly in relation to return policies. While they discuss consumer behavior and planning and execution of returns, they do not present any sources explicitly focused on forecasting issues.

Micol Policarpo et al. ( 2021 ) reviewed the literature on the use of machine learning (ML) in e-commerce, encompassing common goals of e-commerce studies (e.g., purchase prediction, repurchase prediction, and product return prediction) and the ML techniques suitable for supporting these goals. Their primary contribution is a novel taxonomy of machine learning in e-commerce, covering most of the identified goals. However, within the taxonomy developed, the aspect of return predictions is disregarded.

The most exhaustive literature review to date regarding product returns, conducted by Ambilkar et al. ( 2021 ), analyzed 518 papers and adopted a holistic reverse logistics approach encompassing all supply chain stages. The authors categorized the papers into six categories, including “forecasting product returns”, for which they found and concisely described 13 papers. Due to the broader research scope, none of the analyzed papers focused on consumer returns within the retail context.

The review by Duong et al. ( 2022 ) employed a hybrid approach combining machine learning and bibliometric analysis. Regarding forecasts of product returns, they identified three relevant papers (Clottey and Benton 2014 ; Cui et al. 2020 ; Shang et al. 2020 ) within the “operations management” category. They explicitly call for further research on predicting customer returns behavior in the pre-purchase stage, highlighting the importance of a better understanding of online product reviews and customers’ online interactions.

1.2 Research gaps and research questions

Why is a systematic literature review necessary for investigating consumer returns and forecasting? On the one hand, there are empirical and conceptual papers that touch upon this topic, including brief literature reviews that align with the subject’s focus (e.g., Hofmann et al. 2020 ). However, narrative reviews lack transparency and replicability (Tranfield et al. 2003 ) and often induce selection bias (Srivastava and Srivastava 2006 ) as they tend to approach a field from a specific perspective. In contrast, systematic reviews strive to present a holistic, differentiated, and more detailed picture, incorporating the complete available literature (Uman 2011 ). On the other hand, existing systematic reviews provide structured yet relatively superficial overviews of literature on end-of-use and end-of-life forecasting (Shang et al. 2020 ), but they do not specifically address consumer returns. Furthermore, we contend that a review dedicated to general reverse logistics forecasting would not adequately capture the distinctive context and requirements inherent in the consumer-retailer relationship within the realm of e-commerce (Abdulla et al. 2019 ).

Consequently, based on existing reviews and papers, we have identified research gaps worth examining more in detail: (1) Returns forecasting techniques and relevant predictors for the respective underlying purposes, especially in the context of e-commerce (RQ1 and RQ2); (2) the integration of return forecasts into an existing but incomplete taxonomy of machine learning in e-commerce (Micol Policarpo et al. 2021 ; RQ3); and (3) future research directions pertaining to e-commerce returns forecasting (RQ4). Therefore, this review aims to shed more light on consumer returns forecasting in the retail context. The following research questions outline the primary objectives:

RQ1: What key research problems (e.g., forecasting purposes, technological approaches) have been addressed in the literature on forecasting consumer returns over time?

RQ2: What are the …

Publication outlets and research disciplines,

Research types and methodologies,

Product categories and industries,

Data sources and characteristics,

Relevant forecasting predictors,

Techniques and algorithms

… used to address these key problems?

RQ3: How can returns forecasting be integrated into a taxonomy of machine learning in e-commerce?

RQ4: What are promising or emerging future research directions regarding forecasting consumer returns?

The paper is organized as follows: Sect.  2 describes selected fundamental concepts and the delimitation of the research field on consumer returns forecasting. Section  3 contains the methodology for the review, drawing on the PRISMA guideline (Page et al. 2021 ) while integrating the approaches of Denyer and Tranfield ( 2009 ) and Webster and Watson ( 2002 ). Section  4 presents the review’s main results, answering RQs 1 (Sect.  4.1 ), RQ2 (Sects.  4.2 – 4.5 ), and RQ 3 (Sect.  4.6 ). A research framework developed in Sect.  5 structures the discussion regarding future research directions (RQ4). Section  6 subsumes the overall contribution of this review.

2 Consumer returns and forecasting

2.1 consumer returns and return reasons.

Reverse product flows, commonly referred to as product returns, can be classified into three categories: manufacturing returns, distribution returns, and consumer returns (Shaharudin et al. 2015 ; Tibben-Lembke and Rogers 2002 ). Among these, consumer returns are further differentiated between returns in brick-and-mortar retail or mail-order/e-commerce returns (Tibben-Lembke and Rogers 2002 ) and are also known as commercial returns (de Brito et al. 2005 ) or retail (product) returns (Bernon et al. 2016 ). With sky-rocketing e-commerce sales, online consumer returns have emerged as the dominant segment, making them a highly relevant field of research (Abdulla et al. 2019 ; Frei et al. 2020 ). Additionally, the digitization of retail provides numerous opportunities for data collection, as digital customer accounts facilitate more efficient analytical monitoring of customer behavior (Akter and Wamba 2016 ). Simultaneously, as competitive pressures intensify in e-commerce due to increased price transparency and substitution possibilites, retailers aiming to stimulate impulse purchases face hightened return rates (Cook and Yurchisin 2017 ; Karl et al. 2022 ).

The spatial decoupling of supply and demand introduces a higher level of uncertainty for e-commerce customers regarding various product attributes compared to bricks-and-mortar retailing (Hong and Pavlou 2014 ). As consumers are unable to physically assess the products they order, this translates into returns being essential part of the e-commerce business model. Besides fit uncertainty, other reasons for returns exist. Stöcker et al. ( 2021 ) classify the drivers triggering consumer returns into consumer behavior related reasons (e.g., impulsive purchases, showrooming), fulfillment/service related reasons (e.g., wrong/delayed delivery) and information gap related reasons (product fit, insufficient visualization). By mitigating customers’ return reasons, retailers try to reduce the return likelihood (“return avoidance”) (Rogers et al. 2002 ). Another, but less promising way of reducing returns, is preventing customers who intend to return from actually doing so (e.g., by incurring additional effort or by rejecting returns) (Rogers et al. 2002 ).

Adapted from Abdulla et al. ( 2019 ) and Vakulenko et al. ( 2019 ), a simplified parallel process of a return transaction from the consumer’s and retailer’s perspective is visualized in Fig.  1 . Retailers can use forecasting in all transaction phases (Hess and Mayhew 1997 ). Targeting customer interventions pre-purchase (real-time forecasting) could be implemented by using dynamically generated (Dalecke and Karlsen 2020 ) digital nudging elements (Kaiser 2018 ; Thaler and Sunstein 2009 ; Zahn et al. 2022 ) in case of a predicted high return propensity. In the post-purchase phase, forecasting could stimulate different interventions (e.g., customer support) or can be helpful for logistics and inventory planning activities (Hess and Mayhew 1997 ). In the phase after the return decision, data analysis, including segmentation on different levels, e.g., for customers, products, or brands (Shang et al. 2020 ), can support managerial decision-making regarding assortment or (individualized) return policies for future orders (Abdulla et al. 2019 ). In other words, forecasting (or modeling) of returns in later phases of the process can substantiate interventions in earlier phases of the process (e.g., a temporary return policy change, or the suspension of product promotions due to particular forecasts). However, such data-driven interventions itself also represent an influencing factor to be taken into account in future forecasts; thus, different forecasting purposes can be linked, at least when it comes to the data required. All these interdependencies hint at the circularity of the returns process, with an adequate management of returns representing an opportunity for generating customer satisfaction and retention (Ahsan and Rahman 2016 ; Röllecke et al. 2018 ).

Purchase and return process concerning forecasting issues (adapted from Abdulla et al. 2019 ; Vakulenko et al. 2019 )

Although primarily focussing on the online retailers’ process, it is worth noting that the issue at hand is equally applicable to brick-and-mortar retail (Santoro et al. 2019 ), which can benefit from the application of advanced data analysis techniques for forecasting purposes (Hess and Mayhew 1997 ).

2.2 Forecasting purposes and corresponding techniques

Accurate forecasting holds significant importance in the realm of e-commerce. Precise demand forecasts (“predictions”) play a pivotal role in inventory planning, pricing, and promotions and ultimately impact the commercial success of retailers (Ren et al. 2020 ). Forecasting consumer returns affects similar business aspects and resorts to comparable existing technical procedures. The data science and statistics literature offers diverse methods and algorithms for forecasting consumer returns. The choice of approach depends on the specific objective, with the outcome variable being scaled accordingly. For instance, when forecasting whether a single product will be returned, the dependent variable is either binary or expressed as a propensity value ranging form 0 to 1. On the other hand, forecasting the quantitay or timing of returns entails continuous outcome variables. As a result, various techniques, from time-series forecasting to machine learning approaches can be applied, which will be briefly outlined in the subsequent sections.

2.2.1 Return classifications and propensities

A naïve method for determining the propensity or return decision forecast is using lagged (historical) return information (return rates), either for a given product, a given customer, or any other reference, to calculate a historical return probability (Hess and Mayhew 1997 ). Return rate forecasts are a reference-specific variant of forecasting return propensities.

Simple causal models based on statistical regression methods utilize one or more independent exogenous variables. The logistic regression (logit model) is employed when the dependent variable is binary or contains more nominal outcomes (multinomial logistic regression). For each observation, the binary logistic regression assesses the probability that the dependent variable takes the value “1” (Hastie et al. 2017 ). Consequently, this approach finds application for return decisions and return propensities. Comparatively, linear discriminant analysis (Fisher 1936 ) bears a resemblance to logistic regression by generating a linear combination of independent variables to best classify available data. This classification process involves determining a score for each observation, subsequently compared to a critical discriminant score threshold, and distinguishing between return and keep.

More sophisticated machine learning (ML) techniques such as neural networks, decision tree-based methods, ensemble learning, and boosting methods are highly suitable for this forecasting purpose. For a general exposition of ML techniques in the domain of e-commerce, we refer to Micol Policarpo et al. ( 2021 ). Additionally, for a comparative study of several state-of-the-art ML classification techniques, see Fernández-Delgado et al. ( 2014 ). Artificial Neural Networks (NN) consist of interconnected nodes (“neurons”) organized in layers, exchanging signals to ascertain a function that accurately assigns input data to corresponding outputs. Typically, supervised learning techniques such as backpropagation compare the network outputs with known actual values (Hastie et al. 2017 ). Notably, neural networks are the most popular machine learning algorithm in last years’ e-commerce research (Micol Policarpo et al. 2021 ), and deep learning extensions like Long Short-Term Memory (Bandara et al. 2019 ) are gaining attention. Decision Trees (DT) manifest as hierarchical structures of branches representing conjunctions of specific characteristics and leaf nodes denoting class labels. This approach endeavors to construct an optimal decision tree for classifying available observations. Many decision tree algorithms have been introduced to serve this purpose (e.g., Breiman et al. 1984 ; Pandya and Pandya 2015 ). Ensemble learning methods adopt a voting mechanism involving multiple algorithms to enhance predictive performance (Polikar 2006 ). Analogously, boosting and bagging techniques are incorporated in algorithms like AdaBoost or the tree-based Random Forest (RF) to augment the input data, aiming at more generalizable forecasting models less prone to overfitting issues (Hastie et al. 2017 ). Support Vector Machines (SVM) stand as another example of a supervised ML algorithm, having demonstrated efficacy in tackling classification problems within e-commerce (Micol Policarpo et al. 2021 ).

2.2.2 Return timing and volume forecasts

For product returns, timing is crucial in forecasting end-of-life, end-of-use, or remanufacturing returns that can occur years after the initial purchase (Petropoulos et al. 2022 ). In contrast, for consumer returns, the possible time window in which products are regularly returned in new condition with the aim of a refund is much shorter (usually less than 100 days and mostly less than 30 days), and priorities are more on forecasting return volumes. Forecasting return volumes can be multi-faceted, ranging from forecasting the total return volume a retailer has to process within its logistics department through forecasting product-specific return numbers up to forecasting costly return shares, e.g., return fraud volume. Because returns depend on fluctuating sales, time-series forecasting of return volumes performs only well with constant sales volumes or under risk-pooling (Petropoulos et al. 2022 ). Thus, for a naïve return volume forecast, sales forecasts for a given timeframe are multiplied by the lagged return rate (historical data of products/consumers or any other reference). Possible algorithms for estimating historical return rates include time series forecasting to causal predictions comprising ML approaches (Hachimi et al. 2018 ).

Time-series techniques, e.g., single exponential smoothing (SES) or Holt-Winters-approaches (HW), are based on the assumption that the future development of an outcome variable (e.g., return volume) is dependent on its past numbers, while time acts as the only predictor. Most of these models can be generalized as autoregressive moving averages (ARIMA) models, for which numerous extensions are available. These models can approximate more complex temporal relationships. Similarly, time-series regression models use univariate linear regression with time as a single exogenous variable.

The mentioned multivariate regression models are essential statistical tools and can predict metric variables such as return volume or time. The logic is to fit a linear function of a given set of input variables (“features”) to the outcome variable with the criteria of minimizing the residual sum of squares (Hastie et al. 2017 ). Many variants of regression models are derived from this logic (e.g., generalized linear models), and various extensions are built upon this base (e.g., LASSO for variable selection, Tibshirani 1996 ).

Emerging from more complex statistical methods and using the possibilities of continuously increasing computing power, IT-based machine learning (ML) approaches were developed. Some of these approaches have already been presented in Sect. 2.2.1, being suitable for predicting metric variables in addition to classification tasks, e.g., neural networks, decision tree algorithms, and especially ensemble techniques like random forests.

3 Methodology

Methodologically, the research process of this review follows the PRISMA guideline (Page et al. 2021 ) where applicable and is structured in five steps (Denyer and Tranfield 2009 ; Webster and Watson 2002 ): (1) question formulation; (2) locating studies; (3) study selection and evaluation; (4) (concept-centric) analysis and synthesis; and (5) reporting and using the results for defining an agenda for future research.

The first step refers to the research questions already formulated in the introduction. The second step involves selecting the databases and defining the search terms. In that respect, five scientific databases were selected, aiming at journal as well as conference publications: AIS Electronic Library (AISeL), Business Source Ultimate (BS) via EbscoHost, JSTOR (JS), Science Direct (SD), and Web of Science (WoS). To ensure inclusivity and to account for potential variations in spelling or phrasing, the final search strings incorporate truncations where applicable. The search query utilized in this review comprises two key components. Firstly, it pertains to consumer returns, encompassing products returned by consumers, primarily in the context of e-commerce, to the retailer. While it is recommended to use reasonably general search terms, the term “return” alone would yield results for various stages of reverse logistics and a vast amount of financial literature. Therefore, we conducted a more specific search using the phrase “consumer return*” and the related terms “e-commerce return*”, “product return*”, “return* product”, “customer return*”, and “retail return*”. Secondly, this paper specifically focuses on forecasting (“forecast*”), which can be alternately referred to as “predict*” or “prognos*”. The combination of these terms was searched for in the Title, Abstract and Keywords fields.

The search includes results up to the middle of 2022 and resulted in 725 initial search hits (see Fig.  2 ). As this review aims to identify papers dealing with consumer returns and forecasting, the inclusion criteria for eligibility were:

The title or keywords referred to consumer returns or forecasting (in a broader sense, including data preparation). A connection to the respective subject area and applicability to the retail domain should at least be plausible.

Manuscript in English: No important study would be written and published in a language different than English.

The paper has undergone a single- or double-blind peer-review process, either as a journal publication or as a publication in peer-reviewed conference proceedings.

Research process flow diagram

In the third step, duplicates were removed, resulting in a set of 650 unique records. Subsequently, the papers underwent screening based on title, keywords, and language to determine whether they warranted further examination. This preliminary screening phase reduced the number of papers to 85. These papers’ abstracts and full texts were thoroughly reviewed to assess their relevance. This step encompasses all papers pertaining to returns forecasting for retailers or direct-selling manufacturers while excluding those focused on closed-loop supply chain management or remanufacturing, recycling, and end-of-life returns. Ultimately, a final sample of 20 publications was identified, serving as a foundation for identifying additional relevant papers (vom Brocke et al. 2009 ; Webster and Watson 2002 ) through a forward search using Google Scholar and snowballing via backward search. This process yielded an additional five papers, resulting in a total of 25 papers included for review (Table  2 ).

The fourth step comprises the analysis and synthesis of the relevant papers. Data, including bibliographic statistics, were collected in accordance with the research questions. A two-way concept-centric analysis, as described by Webster and Watson ( 2002 ), was conducted, encompassing confirmatory aspects based on the fundamentals outlined in Sect.  2 of this paper, as well as exploratory elements aimed at enriching existing categories and concepts. The objective was to comprehensively describe the relevant concepts, approaches, and dimensions discussed in the literature.

Moving on to the fifth and final step (Denyer and Tranfield 2009 ), the results are presented. Initially, the main scope of the papers included in the analysis is presented. Next, bibliographic data pertaining to the included papers are provided to offer a concise overview of the research area and its recent developments, followed by a content analysis and synthesis of the relevant literature to delve into the current state of research and highlight key findings. Finally, Sect.  5 outlines a research agenda for the domain (vom Brocke et al. 2009 ).

4 Results of the systematic review

After outlining the main scope of the relevant publications (4.1), a short bibliographic characterization (4.2) is given. Next, this section presents the results of the systematic review, focussing on the methodology and datasets used (4.3), predictors used for returns forecasting (4.4), and forecasting techniques employed (4.5). The integration of consumer returns forecasting into an existing taxonomy for e-commerce and machine learning (Micol Policarpo et al. 2021 ) summarizes and concludes the presentation of the results.

4.1 Overview and main scope of the relevant publications

Table 3 provides an overview of the forecasting purpose of the papers, the data source for the forecasting, the algorithms employed, and the predictors used in the forecasting models. The contributions of the respective papers regarding forecasting issues are summarized in the Appendix.

For identifying research streams, the publications are analyzed regarding the intention and main scope, as described in the abstract, the respective research questions, and the remainder of the papers. Most papers were assigned to an unequivocal research scope, while some contributed to two key topics (Fig.  3 ).

Classification of main scopes (n = 25; not mutually exclusive)

At first, we identified a stream of literature regarding the comparison of different forecasting models and algorithms (Asdecker and Karl 2018 ; Cui et al. 2020 ; Drechsler and Lasch 2015 ; Heilig et al. 2016 ; Hess and Mayhew 1997 ; Hofmann et al. 2020 ; Imran and Amin 2020 ). These papers use existing approaches, adapt them for individual forecasting purposes, apply models to one or more datasets, and compare and evaluate the resulting forecasting performance. One paper claims that the difference in forecasting accuracy of easily interpretable algorithms is relatively small compared to more sophisticated ML algorithms (Asdecker and Karl 2018 ). This statement is partially confirmed (Cui et al. 2020 ), as the ML algorithms show advantages over simpler models in the training data set but have lower prediction quality due to overfitting issues in the test data. Nevertheless, fine-tuned ML approaches (e.g., deep learning with TabNet) outperform simpler models and gain accuracy when correcting class imbalances during the data preparation phase (Imran and Amin 2020 ). When confronted with large class imbalances (e.g., low return rates), boosting algorithms like Gradient Boosting work well without oversampling (Hofmann et al. 2020 ). Fundamentally, ensemble models incorporating different techniques show the maximum possible accuracy (Asdecker and Karl 2018 ; Heilig et al. 2016 ). Forecasting of return timing is more erroneous than return decisions, and split-hazard-models outperform simple OLS approaches (Hess and Mayhew 1997 ). Time series prediction only works reliably when return rates do not fluctuate heavily (Drechsler and Lasch 2015 ).

The second stream we identified focuses on feature generation or selection and dataset preparation (Ahmed et al. 2016 ; Ding et al. 2016 ; Hofmann et al. 2020 ; Rezaei et al. 2021 ; Samorani et al. 2016 ; Urbanke et al. 2015 , 2017 ). Besides this central topic, some papers also compare different forecasting algorithms (Ahmed et al. 2016 ; Hofmann et al. 2020 ; Rezaei et al. 2021 ; Urbanke et al. 2015 , 2017 ). For example, random oversampling of data with large class imbalances can improve the performance of different forecasting algorithms, while models based only on sales/return history perform worse than models with more features (Hofmann et al. 2020 ). Two similar approaches are based on product, basket, and clickstream data, using different algorithms for feature extraction (Urbanke et al. 2015 , 2017 ). The first developed a Mahalanobis Feature Extraction algorithm, proving superior to other algorithms like principal component analysis or non-negative matrix factorization (Urbanke et al. 2015 ). The second develops a NeuralNet algorithm to extract interpretable features from a high-dimensional dataset, showing superior performance and giving reasonable interpretability of the most important factors (Urbanke et al. 2017 ). For the automated integration of different data sources into single flat tables and the generation of discriminating features, a rolling-path algorithm is developed, improving performance when data is imbalanced (Ahmed et al. 2016 ). Similarly, the software “Dataconda” can automatically generate and integrate relational attributes from different sources into a flat table, which is often the required prerequisite for forecasting algorithms (Samorani et al. 2016 ). A different selection approach clusters the features into groups and applies selection algorithms to the groups, aiming to select a smaller set of attributes (Rezaei et al. 2021 ). As quite an offshoot, one paper predicts a seller’s overall daily return volume dependent on his current “reputation” measured by tweets (Ding et al. 2016 ), which needs sentiment analysis to be integrated into the forecast.

A quite heterogenous research stream belongs to the development of algorithms, heuristics, and models that go beyond a straightforward adaption of existing approaches (Fu et al. 2016 ; Joshi et al. 2018 ; Li et al. 2018 ; Potdar and Rogers 2012 ; Rajasekaran and Priyadarshini 2021 ; Shang et al. 2020 ; Sweidan et al. 2020 ; Zhu et al. 2018 ). Potdar and Rogers ( 2012 ) developed a methodology for forecasting product returns based on reason codes and consumer behavior data. Fu et al. ( 2016 ) developed a conditional probability-based statistical model for predicting return propensities while revealing return reasons and outperforming some baseline benchmark models. Li et al. ( 2018 ) describe their “HyperGo” approach as a ‘framework’ and develop an algorithm for forecasting return intention after basket composition. Zhu et al. ( 2018 ) describe a “LoGraph” random walk algorithm for predicting returned customer/product combinations within their framework. Although Joshi et al. ( 2018 ) label their approach as a “framework”, they describe a specific two-stage algorithm for forecasting return decisions based on network science and ML. Rajasekaran and Priyadarshini ( 2021 ) developed a hybrid metaheuristic-based regression approach to predict return propensities.

Seven papers deal with concepts, meta-models, or substantial frameworks for returns forecasting (Fu et al. 2016 ; Fuchs and Lutz 2021 ; Heilig et al. 2016 ; Hofmann et al. 2020 ; Li et al. 2018 ; Shang et al. 2020 ; Zhu et al. 2018 ). A generic framework for a scalable cloud-based platform, which enables a vertical and horizontal adjustment of resources, could enable the practical real-time use of computationally intensive ML algorithms for forecasting returns in an e-commerce platform (Heilig et al. 2016 ). Two papers (Fuchs and Lutz 2021 ; Hofmann et al. 2020 ) are based on design science research (DSR, Hevner et al. 2004 ) for developing artifacts like meta models and frameworks. The first also refers to CRISP-DM, the “Cross Industry Standard Process for Data Mining” (Wirth and Hipp 2000 ), and develops a shopping-basket-based general forecasting approach suitable across different industries without domain knowledge and attributes needed (Hofmann et al. 2020 ). In a similar approach, based on the basket composition and user interactions, a generic model for real-time return prediction and intervention is developed (Fuchs and Lutz 2021 ) and prepared for integration into an ERP system. Fu et al. ( 2016 ) present a generalized return propensity latent model framework by decomposing returns into different inconsistencies (unmet product expectations, shipping issues, and both factors combined) and enriching the derived propensities with product features and customer profiles. Li et al. ( 2018 ) developed a “HyperGo” framework for forecasting the return intention in real-time after basket composition, including a hypergraph representation of historical purchase and return information. Similarly, Zhu et al. ( 2018 ) developed a “HyGraph” representation of historical customer behavior and customer/product similarity, combined with a “LoGraph” random-walk-based algorithm for predicting customer/product combinations that will be returned. Shang et al. ( 2020 ) discuss two opposing forecasting concepts, demonstrating that their predict-aggregate framework is superior to common and more naïve aggregate-predict approaches.

The last stream covers the detection and forecasting of return fraud and abuse (Drechsler and Lasch 2015 ; John et al. 2020 ; Ketzenberg et al. 2020 ; Li et al. 2019 ). On the employees’ side, one paper tries to automatically predict fraudulent return behavior of agents (employees), e.g., regarding unjustified refunds, by a penalized logit model, enabling a lift in detection (John et al. 2020 ). On the customers’ side, misused returns as a cost-incurring problem are the forecasting purpose of different time series prediction models (Drechsler and Lasch 2015 ). Instead of focussing on fraudulent transactions, a trust-aware random walk model identifies consumer anomalies, enabling retailers to apply targeted measures to specific customer groups (selfish, honest, fraud, and irrelevant customers) (Li et al. 2019 ). Similarly, returning customers can be categorized into abusive, legitimate, and nonreturners (Ketzenberg et al. 2020 ). Based on the characterization of abusive return behavior, a neural network classifier recaptures almost 50% of lost profits due to return abuse (Ketzenberg et al. 2020 ).

One paper (Sweidan et al. 2020 ) could not be assigned to the other scopes. It applies a single algorithm (RF) to a given dataset, and it contributes to the idea that only forecasted return decisions with high confidence should be used for targeted interventions due to their overproportional reliability.

4.2 Bibliographic literature analysis

Forecasting consumer returns has gained more research attention since 2016 (Fig.  4 ). The majority of the sample are conference publications, a couple of years ahead of the rise in journal publications. Compared to the publications on returns forecasting in the broader context of reverse logistics, which emerged in 2006 (Agrawal et al. 2015 ), the research on consumer returns moved into the spotlight about ten years later. This development is linked to a massive increase in e-commerce sales pre- and in-pandemic (Alfonso et al. 2021 ).

Publication trend by publication outlet

Out of 9 journal publications in the final sample, only two are published in the same journal (Journal of Operations Management). Out of 16 conference papers, 6 are published at conferences of the Association for Information Systems. In total, 16 of the 25 papers found are published in Information Systems (IS) and related outlets. Others can be assigned to the Management Science / Operations Research discipline (3), Strategy & Management in a broader sense (4), Marketing (1), and Research Methods (1) (Fig.  5 ).

Distribution of publication disciplines

Regarding the researchers’ geographical perspective, one paper was jointly published by authors from the US and China, 10 of 25 papers were authored from North America, followed by authors from Germany (7), India (3), China (1), and one paper each from Bangladesh, Singapore, and Sweden.

The most cited paper (200 external citations Footnote 2 ) from Hess and Mayhew ( 1997 ) could be thought of as the root of this research field (Table  4 ). However, only 10 out of 24 papers reference this work. Although Urbanke et al. ( 2015 ) received only 15 citations in total, within the sample, it is the second most cited paper (8 citations) and could eventually be classified as a research strand and origin of returns forecasting in the IS domain. Concerning the remaining papers, no unique strands of literature are recognizable based on citation analysis.

4.3 Methodology and data characterization

Regarding methodology, most of the papers start with a short narrative literature review regarding their respective focus. Not a single paper was based on interviews, surveys, questionnaires, or field experiments. 3 out of 25 papers formulated and tested conventional hypotheses. All of the publications use quantitative data for analysis and forecasting in a “case study” style, including numerical experiments based on real or simulated data.

Table 5 lists further details about the data used in the publications. 4 out of 25 papers rely on simulated data, and 23 out of 25 integrate actual data gained from a retailer. Two papers use both data types. 5 papers use more than one dataset (Ahmed et al. 2016 ; Cui et al. 2020 ; Rezaei et al. 2021 ; Samorani et al. 2016 ; Shang et al. 2020 ). The most frequently studied industry is fashion/apparel (10 papers), followed by five consumer electronics datasets. Two publications are based on data from a Taobao cosmetics retailer, and two datasets originate from general and wide assortment retailers. Two datasets incorporate building material and hardware store articles, and the detailed products are not named for three publications. Based on the previous studies, it is evident that consumer returns forecasting is most relevant for e-commerce, as 19 of the 25 publications refer to e-tailers. Nevertheless, 7 publications refer to brick-and-mortar retailing. Direct selling/marketing is represented in 2 data sets.

4.4 Predictors for consumer returns

There is an individual stream of research into factors that influence or help avoid consumer returns (e.g., Asdecker et al. 2017 ; De et al. 2013 ; Walsh and Möhring 2017 ), which is not part of this review. Nevertheless, the forecasting literature gives insights into return drivers, as the input variables (features, predictors, exogenous variables) for forecasting models represent some of these factors. Table 6 presents the most used predictors and tries to map these to the return driver categorization from Sect.  2.2 (Stöcker et al. 2021 ).

Although only a part of the publications interprets the predictors, some insights can be extracted. For total return volume , sales volume is the most critical predictor (Cui et al. 2020 ; Shang et al. 2020 ). Historical return volume trends can include behavioral aspects (e.g., impulse purchases) in a given timeframe (Cui et al. 2020 ; Shang et al. 2020 ). The product type significantly impacts the volume of returns (Cui et al. 2020 ), confirmed by widely varying return rates between different industries/sectors. Adding transaction-, customer-, or product-level predictors led to a surprisingly small forecasting accuracy gain (4% reduction of RMSE, Shang et al. 2020 ). The latter input variables may be more critical in forecasting return decisions and propensities.

Regarding product attributes , product or order price is one of the most common predictors, while some papers also include price discounts. In most models, price is hypothesized to increase returns (e.g., Asdecker and Karl 2018 ; Hess and Mayhew 1997 ). Promotional (discounted) orders also seem to result in more returns (Imran and Amin 2020 ), which could be explained by the stimulation of impulse purchases. Footnote 3 Brand perception influences return decisions (positive brands, lower returns) (Samorani et al. 2016 ). The order and return history of products are also relevant for predicting future orders and returns (Hofmann et al. 2020 ). Fit importance as a product attribute does not significantly change return propensities (Hess and Mayhew 1997 ).

Concerning customer attributes , gender seems essential, as female customers return significantly more items than men (Asdecker and Karl 2018 ; Fu et al. 2016 ). Younger customers show a slightly lower propensity to return (Asdecker and Karl 2018 ), but age played a more prominent role in predicting return fraud among employees than in customers (John et al. 2020 observed more fraud among younger employees). Customers with low credit scores returned more (Fu et al. 2016 ). The return history of a customer is possibly the most important predictor of future return behavior (Samorani et al. 2016 ). Some papers argue that consumer attributes, including purchase and return history (e.g., number and value of orders), are more relevant predictors than product or transaction profiles, reflecting more or less stable consumer preferences (Li et al. 2019 ).

Basket interactions are significant (Urbanke et al. 2017 ) in returns prediction. E.g., the larger the basket, the higher the return propensity will be (Asdecker and Karl 2018 ). Selection orders (same product in different sizes or colors) increase the return propensity (Li et al. 2018 ). Logistics attributes like delivery times only show minor effects (Asdecker and Karl 2018 ). Regarding the payment method, prepaid products are sent back less frequently than those with post-delivery payment options (Imran and Amin 2020 ), confirming other research results (Asdecker et al. 2017 ).

One literature stream focuses on the automated generation of features , as different and large-scale data sources need to be integrated and prepared for forecasting algorithms. Thus, possible interrelationships are complex to find manually, and ML approaches might outperform human analysts (Rezaei et al. 2021 ). While some approaches generate a large number of features that are hard to make sense of (Ahmed et al. 2016 ), the approach of Urbanke et al. ( 2017 ) aims to maintain the interpretability of automatically generated input variables. Some unexpected but meaningful interrelations might be found by automatic feature generation, e.g., the price of the last returned orders (Samorani et al. 2016 ). Nevertheless, automatic feature generation might be computation-intensive; thus, a parallel integration of feature selection could be advantageous for large data sets (Rezaei et al. 2021 ).

A remarkable research path based on artificial intelligence is integrating qualitative information like product reviews as predictors, going beyond numerical feedback (Rajasekaran and Priyadarshini 2021 ) or tweets. These data can be processed and made accessible for forecasting with ML-based sentiment analysis techniques (Ding et al. 2016 ).

4.5 Forecasting techniques and algorithms

To describe the techniques and algorithms employed, we sorted the papers by forecasting purpose as described in Sect.  2 , then assigned them to different algorithms, either from time series forecasting, statistical techniques, or ML algorithms. Table 7 lists all papers for which an assignment was possible, and the respective techniques used. If a comparison was possible, the best-performing algorithm is marked in this table.

The approaches listed in Table  7 are overlap-free, but some papers use more than one version of an approach, i.e., more than one algorithm from a category. E.g., TabNet is a DeepLearning version of neural networks (NN), and different variants of GradientBoosting are compared in one paper (CatBoost/LightGBM, not differentiated in the table below) (Imran and Amin 2020 ).

The algorithm used most frequently (Fig.  6 ) is the Random Forest algorithm (RF, 10 papers), followed by Support Vector Machines (SVM, 8 papers), Neural Networks (NN, 6 papers), logistic regression (Logit, 6 papers), GradientBoosting (5 papers), Ordinary Least Squares regression (OLS, 4 papers), Adaptive Boosting (AdaBoost), Linear Discriminant Analysis (LDA), and CART (Classification and Regression Trees, 3 papers each).

Most frequently used algorithms (used in at least three papers)

The papers focusing on return volume use time series forecasts like (AutoRegressive) Moving Averages (MA), Single Exponential Smoothing (SES), and Holt-Winters Smoothing (HWS) more frequently than ML algorithms. Nevertheless, when considering a predict-aggregate approach as proposed by Shang et al. ( 2020 ), these ML techniques could be helpful in forecasting return decisions first and cumulating the propensity results for the volume prediction in the second step.

In forecasting binary return decisions, Random Forests (RF) (Ahmed et al. 2016 ; Heilig et al. 2016 ; Ketzenberg et al. 2020 ), Neural Networks (NN) (Imran and Amin 2020 ; Ketzenberg et al. 2020 ), as well as Adaptive Boosting (AdaBoost) (Urbanke et al. 2015 , 2017 ) showed high prediction performance. The performance of different algorithms varies depending on the data set, the implementation, and the parameterization used. For this reason, it is hardly possible to make a generally valid statement regarding performance levels. Combining several algorithms in ensembles (Asdecker and Karl 2018 ; Heilig et al. 2016 ) seems advantageous, at least for retrospective analytical purposes, when the required computing resources are less relevant.

When evaluating different forecasting algorithms for return decisions, imbalanced classes (especially evident for low return shares in non-fashion datasets) seem to be handled differently depending on the algorithms. Class imbalances might distort comparison results in some publications. Random oversampling as a measure of data preparation can solve this problem (Hofmann et al. 2020 ).

High-performance algorithms are needed for real-time predictions, e.g., graph and random-walk-based (Li et al. 2018 ; Zhu et al. 2018 ). According to Li et al. ( 2018 ), the proposed algorithm “HyperGo” performs best for most performance metrics.

4.6 E-Commerce and machine learning taxonomy extension

In their literature review regarding the use of ML techniques in e-commerce, Micol Policarpo et al. ( 2021 ) propose a taxonomy to visualize specific ML algorithms in the context of e-commerce platforms. This novel kind of taxonomy is based on direct acyclic graphs, i.e., all input variables need to be fulfilled to reach the target. The first level of the taxonomy represents different target goals for the use of ML in e-commerce. While returns forecasting (“product return prediction”) is identified as an essential goal among others (purchase prediction, repurchase prediction, customer relationship management, discovering relationships between data, fraud detection, and recommendation systems), it was excluded from the taxonomy they developed, possibly because the review comprised only two relevant papers on this topic (Micol Policarpo et al. 2021 ). The review at hand proposes an extension of Micol Policarpo’s taxonomy, renaming the goal to “consumer returns forecasting”. This extension reflects and synthesizes the consumer returns forecasting studies reviewed.

The middle level of the taxonomy represents properties and features that support this superordinate goal. On this level, our extension does not include return fraud detection, which we propose to be integrated into the existing category of “fraud detection”, separated into transaction analysis and consumer analysis (Micol Policarpo et al. 2021 ). Circles represent the necessary data to execute the analysis, referring to categories introduced in (Micol Policarpo et al. 2021 ), with an additional “return history” category. The bottom level presents the algorithms described frequently, while some streamlining is required regarding the tools and approaches that seem the most common or most appropriate.

The schematic above (Fig.  7 ) is to be read as follows: In the context of E-Commerce  +  Artificial Intelligence (Layer 1), Consumer Return Forecasting (Layer 2) is an essential goal among six other goals. Layer 3 presents different purposes of analysis, which are the base for return forecasting. Realtime Basket Analysis is based on clickstream data and basket composition (browsing activities) to target interventions. Basket analysis benefits from customer and product information (dotted line). Graph-based approaches (Li et al. 2018 ; Zhu et al. 2018 ) are promising for real-time analysis due to their lower computing requirements, although cloud-based implementation of more complex algorithms or ensemble models might be feasible (Fuchs and Lutz 2021 ; Heilig et al. 2016 ; Hofmann et al. 2020 ). Customer Analysis and Product Analysis (e.g., Potdar and Rogers 2012 ) require adequate Data Preparation in the sense of input variable generation, extraction, and selection (Urbanke et al. 2015 , 2017 ). For these purposes, data regarding return history (e.g., Hofmann et al. 2020 ; Ketzenberg et al. 2020 ), purchase history (e.g., Cui et al. 2020 ; Fu et al. 2016 ), customer personal information (e.g., Heilig et al. 2016 ; Ketzenberg et al. 2020 ), clickstream data, and browsing activities are required as input (shown by cross-hatched circles). For each purpose, one or more possible algorithms are shown.

Proposed consumer returns forecasting extension to the E-commerce and Machine Learning techniques taxonomy of Micol Policarpo et al. ( 2021 , p. 13)

Compared to predicting purchase intention, return predictions seem to require more levels of data. Nevertheless, even simple rule-based interventions can promise benefits, e.g., selection orders that inevitably lead to a return shipment can be easily recognized (Hofmann et al. 2020 ; Sweidan et al. 2020 ). Different ML techniques are helpful for data preparation and input variable (feature) extraction and generation when considering more complex interrelations. NeuralNet is one example of an automatic selection of relevant features (Urbanke et al. 2017 ). These approaches are not only able to enhance forecasting accuracy (Rezaei et al. 2021 ) but can also render the many possible variables interpretable about their content.

5 Discussion

The analysis of the papers above revealed that research in this discipline seems heterogeneous and partly fragmented, and clear-cut research strands are still hard to identify. Thus, the existing literature calls for further publications to render this research field more comprehensive. Below, research opportunities are derived and embedded in a conceptual research framework derived from the results of the existing literature, also integrating the extension of the E-Commerce and Machine Learning taxonomy (Fig.  7 ). A conceptual framework improves the understanding of a complex topic by naming and explaining key concepts and their relationships important to a specific field (Jabareen 2009 ; Miles et al. 2020 ). Thus, this framework aims to organize problems and solutions discussed in the consumer returns forecasting literature and to embed and classify potential future research topics in the existing knowledge base (Ravitch and Riggan 2017 ). The subsections following the framework outline some potential research avenues (P1–P6) that have been touched on in the past but still leave considerable opportunities for further insights. These proposals should not be seen as comprehensive due to numerous other research opportunities in this field but rather as prioritization based on the current literature.

The framework derived (Fig.  8 ) underlines the interdisciplinary nature of this research field, integrating different perspectives (information systems research, marketing and operations perspective, and strategy and management perspective). From a managerial point of view, the literature included in this review is biased towards the information systems perspective. Thus, in contrast to the framework developed by Cirqueira et al. ( 2020 ) for purchase prediction, we do not take a process perspective but instead emphasize the interdependencies and interactions between research topics and highlight the managerial need to take a strategical perspective similar to the framework developed by Winklhofer et al. ( 1996 ). Consequently, a meta-layer on forecasting frameworks and practices includes the mainly technical development frameworks in this review but also accentuates the need for further research regarding actual organizational forecasting practices (e.g., P2, P5, P6). Around this meta-layer, some related research strands are linked in order to embed the topic of returns forecasting in the research landscape. E.g., in general, forecasting purchases and returns could be linked (P6), also effecting inventory decisions.

Conceptual Consumer Return Forecasting Framework

The center of the framework consists of three dimensions, namely purposes and tasks, predictors, and techniques. Depending on the strategical purpose, tasks are derived that determine (1) the data (predictors) needed and (2) the usable techniques to execute the forecasting. Different forecasting techniques require an individual set of predictors, whereas the availability of specific data allows and determines the use of more or less sophisticated algorithms.

In the literature, some forecasting purposes were more pronounced (return decisions or propensities), while others have gained less attention (return timing, P1). Regarding the data necessary for accurate forecasting, the return predictors discussed often were hardly comparable, as they originated from different data sources, different industries, were related to different dimensions, or were aggregated in another way. Systematically linking forecasting predictors and research on return drivers and reasons could contribute significant insights (P4) that, from a marketing perspective, may support the development of effective preventive instruments. Furthermore, the literature mainly refers to the fashion or consumer electronics industry, leaving room to validate the findings in the context of other industries (P3).

When (automatically) selecting or creating predictors, the boundaries between predictors and prediction techniques are blurred as machine learning algorithms prepare the input data before executing a forecasting model. Regarding forecasting techniques, time series forecasting was seldom used in recent publications. Machine learning algorithms were the most popular subject of investigation, with random forests, support vector machines, and neural networks as the most popular implementations. Classical statistical models like logit models for return decisions or OLS regression gained less research attention. Literature on end-of-life return forecasting could complement the research on techniques and their accuracy. Most publications used technical indicators for assessing the accuracy of forecasting models, which is the information systems perspective. From a managerial position, evaluating (monetary) performance outcomes (e.g., Ketzenberg et al. 2020 ) of forecasting systems should be more relevant.

5.1 Research proposal P1: return timing for consumer returns

Toktay et al. ( 2004 ) encouraged the integrated forecasting of the return rate and the return time lag. In line with this, Shang et al. ( 2020 ) criticize the missing focus on the timing of return forecasts. The reviewed literature confirms that forecasting return propensities and decisions are more prominent than timing and volume forecasts. While the knowledge of when a return is expected is vital in managing end-of-life returns that occur over the years, for retail consumer returns, return periods are mostly 14–30 days. Thus, the variability of return timing seems limited compared to end-of-life returns in this context, which makes this forecasting purpose less critical. Nevertheless, some retailers offer up to 100 days of free returns (e.g., Zalando). Consequently, more studies about the importance of return timing forecasts in the e-commerce context from a business and planning perspective and their interdependence with return processing or warehousing issues could shed light on this topic and complement the current literature (Toktay et al. 2004 ; Shang et al. 2020 ).

5.2 Research proposal P2: realtime forecasting systems

Another research gap became apparent regarding the real-time use of forecasting systems and the associated activities and interventions, building on the initial research and the frameworks already published (e.g., Heilig et al. 2016 ; Urbanke et al. 2015 ). The generic framework developed by Fuchs and Lutz ( 2021 ) could serve as a launching pad for this stream of research.

The paper from Ketzenberg et al. ( 2020 ) could act as a stimulus and inspiration for a similar approach, not only focusing on return abuse as already examined but on return forecasting in general, the possible associated interventions for various consumer groups, and the resulting consequences for the retailer’s profit. Even the methodology of customer classification could be helpful for many retailers in targeting interventions.

Before real-time return forecasting is implemented, associated preventive return management instruments need to be designed and evaluated. Many of these measures are discussed (e.g., Urbanke et al. 2015 ; Walsh et al.  2014 ), but an overview of which preventive measures (for some examples, see Walsh and Möhring 2017 ) are effective in general (1) and how forecasting accuracy interdepends with their usefulness (2) is still missing, to substantially link the topics of forecasting and interventions. No answers could be found to the call by Urbanke et al. ( 2015 ) for field experiments to investigate such a link.

Thanks to cloud and parallelization technologies and the associated scalability of computing power (Bekkerman et al. 2011 ), algorithm runtimes are becoming less relevant. However, especially for real-time use, it should be evaluated which algorithms and underlying datasets exhibit an appropriate relationship between the targeted forecasting accuracy, the expected benefit, and the required computing power.

Recommendations concerning the algorithms and techniques can be derived (Urbanke et al. 2015 ), and a generic implementation framework was developed (Fuchs and Lutz 2021 ). However, from a business perspective, no contributions could be found regarding the actual implementation of real-time forecasting systems, the interventions involved, and their impact on consumer behavior or profit (also see proposal P5). In addition, the implementations of such systems need to be analyzed concerning the cost-effectiveness of the required investments.

5.3 Research proposal P3: cross-industry and multiple dataset studies

Many publications rely on a single data set from a specific industry or retailer. Only a few compare several retailers (e.g., Cui et al. 2020 ). Studies including and comparing different countries are missing, which is especially interesting since legal regulations for returns vary. For example, in contrast to the U.S., citizens within the EU are granted a 14-day right of withdrawal for distance selling purchases. Footnote 4 Although in most developed countries, liberal and broadly comparable returns policies are standard in practice due to competitive pressure, the generalizability of the results is frequently limited. One remedy for this problem is to use multiple data sets from different retailers (e.g., electronics vs. jewelry, Shang et al. 2020 ). Admittedly, it is challenging to simultaneously collaborate with several retailers and to combine different data sets, due to reasons of preserving corporate privacy and synchronizing various data sources. Nevertheless, research needs to draw conclusions from single data points, as well as logically replicate or falsify those results by integrating more data points to find patterns of similarities and differences, either within or cross-study (Hamermesh 2007 ). Therefore, we suggest that future studies acquire industry-related datasets from several retailers at once or replicate existing studies, which aligns with the aim and scope of Management Review Quarterly (Block and Kuckertz 2018 ). Cross-industry or cross-country manuscripts, which go beyond the mere assertion of an industry-agnostic approach (Hofmann et al. 2020 ) and jointly investigate data from several sectors, would promise an additional gain in knowledge and could be less challenging from a privacy perspective.

5.4 Research proposal P4: extended study of relevant predictors in forecasting applications

Although not the main focus of this review, predictors of consumer returns are especially interesting for marketing and e-commerce research, for example, regarding preventive measures for avoiding returns. In the past, many consumer return papers highlighted single aspects or a limited selection of return drivers or preventive measures employed but rarely attempted to model return behavior as comprehensively as possible. However, the latter is the very objective of returns forecasting, which is why the findings on influencing factors in articles with a forecasting focus tend to be more holistic, although not sufficiently complete (Hachimi et al. 2018 ). Some return reasons named in the literature (e.g., Stöcker et al. 2021 ) have not yet been included in forecasting approaches, and vice versa, only a part of the influencing factors investigated could be mapped to a return reason categorization. The reason categories assigned (Sect.  4.4 , Table  6 ) still contain some uncertainty. For example, a customer’s product return history may reflect the general returning behavior of a customer to some extent, while it can not be ruled out that repeated logistical problems caused the returns. Product attributes may reflect information gaps that consumers can only assess after physically inspecting the product, whereas product price–frequently cited and influential product attribute—is only related to information gaps when considering the price-performance ratio (Stöcker et al. 2021 ). Technical information about the web browser or device used by the customer is difficult to categorize, as it may reflect behavioral (impulse-driven mobile shopping) as well as informational (small display with few visible information) aspects. The payment method chosen by a customer, for example, could not be linked to one of the reason categories.

This reasoning should serve as a basis for linking forecasting predictors and return reasons more closely in the future. For example, the respective relative weighting of return drivers is more likely to be obtained considering as many factors involved as possible, minimizing the unexplained variation. From the reviewed literature, we extracted 18 different return predictor categories. For instance, seven papers (Cui et al. 2020 ; Fu et al. 2016 ; Ketzenberg et al. 2020 ; Li et al. 2018 , 2019 ; Urbanke et al. 2015 , 2017 ) integrated more than five predictor categories. But even though some papers integrate more than 5,000 features for automated feature selection (Ketzenberg et al. 2020 ), there are still combinations of input variable categories that have not been investigated and, more importantly, interpreted yet. Therefore, we call for more comprehensive research on return predictors and their interpretation, including associated preventive return measures, in the context of return forecasting.

5.5 Research proposal P5: descriptive case studies and business implementations surveys

This review identified a lack of publications regarding the actual benefit and the diffusion of consumer returns forecasting systems in different scopes and industries, building on the papers presenting return forecasting frameworks. In 2013, less than half of German retailers analyzed the likelihood of returns (Pur et al. 2013 ). Most of those who did were using naïve approaches that might be outperformed by the models presented in this review. Still, we do not know the status quo regarding the degree of adoption and implementation of forecasting systems for consumer returns in e-commerce firms (e.g., see Mentzer and Kahn 1995 for sales forecasting systems), country-specific and internationally.

Furthermore, the impact of return forecasting practices on company performance should be examined not only based on modeling, but on retrospective data (e.g., see Zotteri and Kalchschmidt 2007 for a similar study on demand forecasting practices in manufacturing). A possible hypothesis to examine might be that accuracy measures like RMSE or precision/recall and subsequently even the choice of the most accurate machine learning algorithm (e.g., see Asdecker and Karl 2018 ) are less relevant from a business perspective: (1) No algorithm clearly outperforms all other algorithms, and (2) the correlation between technical indicators and business value is unstable (Leitch and Tanner 1991 ). Methodologically, implementations of consumer returns forecasting in e-commerce should thus be surveyed and analyzed with multivariate statistical methods to examine critical factors and circumstances of return forecasting systems – similar to publications on reverse logistics performance (Agrawal and Singh 2020 ).

5.6 Research proposal P6: holistic forward and backward forecasting framework for e-tailers

Some publications present frameworks for forecasting returns (Fuchs and Lutz 2021 ). Nevertheless, in the past, forecasting in retail and especially e-commerce commonly focused more on demand (Micol Policarpo et al. 2021 ) than returns. Current approaches for demand forecasting try to predict individual purchase intentions based on click-stream data, online session attributes, and customer history (e.g., Esmeli et al. 2021 ). Our systematic approach could not identify any paper that connects and integrates both directions in e-commerce forecasting, neither conceptual (frameworks) nor with a quantitative or case-study-like approach. Nevertheless, first implementations of return predictions in inventory management are presented (e.g., Goedhart et al. 2023 ). Subsequently, similar to Goltsos et al. ( 2019 ), we call for research addressing both demand and return uncertainties by providing a holistic forecasting framework in the context of e-commerce.

6 Conclusion

To date, no systematic literature review has undertaken an in-depth exploration of the topic of forecasting consumer returns in the e-commerce context. Previous reviews have primarily focused on product returns forecasting within the broader context of reverse logistics or closed-loop supply chain management (Agrawal et al. 2015 ; Ambilkar et al. 2021 ; Hachimi et al. 2018 ). Regrettably, the interdisciplinary nature of this subject has often been overlooked, also neglecting the inclusion of results from information systems research.

The review first aims to provide an overview of the existing literature (Kraus et al. 2022 ) on forecasting consumer returns. The findings confirm that this once novel topic has significantly evolved in recent years. Consequently, this review is timely in examining current gaps and establishing a robust foundation for future research, which forms a second goal of systematic reviews (Kraus et al. 2022 ). The current body of work encompasses various aspects from different domains, including marketing, operations management/research, and information systems research, highlighting the interdisciplinary nature of e-commerce analytics and research. As a result, future studies can find suitable publication outlets in domain-specific as well as methodologically oriented journals and conferences.

Scientifically, the algorithms and predictors investigated in previous research serve as a foundational reference for subsequent publications and informed decisions regarding research design, ensuring that specific predictors and techniques are not overlooked. Researchers can utilize this review and the research framework developed as a structuring guide, e.g., regarding relevant publications on already examined algorithms or predictors.

Managerially, the extended taxonomy for machine learning in e-commerce (Micol Policarpo et al. 2021 ) can serve as a guideline for implementing forecasting systems for consumer returns. This review classifies possible prediction purposes, allowing businesses to apply them based on their respective challenges. Exploring the most frequently used predictors reveals the data that must be collected for the respective purposes. This review also offers valuable insights into data (pre-)processing and highlights popular algorithms. Furthermore, frameworks are outlined that support the design and implementation phase of such forecasting systems, supporting analytical purposes or enabling direct interventions during the online shopping process flow. As an exemplary and promising application, return policies could be personalized (Abbey et al. 2018 ) by identifying opportunistic or fraudulent basket compositions or high-returning customers, thereby reducing unwanted returns (Lantz and Hjort 2013 ).

Finally, a limitation of this review is the exclusion of forecasting algorithms for end-of-use returns, which could potentially be applicable to forecasting shorter-term retail consumer returns. However, the closed-loop supply chain and reverse logistics literature has been systematically excluded. Hence, future reviews could synthesize previous reviews on reverse logistics forecasting with the more detailed findings presented in this paper.

The use of Google Scholar for systematic scientific information search is controversely discussed (e.g., Halevi et al. 2017 ) due to the missing quality control and indexing guidelines, as well as limited advanced search options. But as an additional database for an initial search, the wide coverage of this search system can enrich the results.

External citations according to Google Scholar, which is preferable for citation tracking over controlled databases (Halevi et al. 2017 ).

Other literature also describes a counteracting effect of a reduced price due to lowered quality expectations or a higher perceived value of the “deal” itself (e.g., Sahoo et al. 2018 ).

It should be noted that the relevance of the forecasting topic depends on the maturity of the e-commerce sector. In most developing countries, B2C e-commerce is comparatively young and consumer returns are not yet a common phenomenon, which is why research on return forecasts is relatively insignificant for these countries.

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Appendix: Author-centric content summary (with focus on forecasting issues)

1.1 journal publications.

Hess and Mayhew ( 1997 ) describe a forecasting approach, taking the example of a direct marketer for apparel with a lenient consumer return policy (free returns anytime). The analysis can plausibly be applied to a general retailer, although return time windows are somewhat different. A regression approach and a hazard model are compared. The regression approach itself is split into an OLS estimation of return timing (with poor fit) and a logit model of return propensities, which is in turn used for the split function of the box-cox-hazard approach for estimating the probability of a return over time. The accuracy was measured by fit statistics regarding the absolute deviation from the actual cumulative return proportion, with the split-hazard model outperforming the regression model. Besides price, the importance of fit of the respective product is used as a predictor.

Potdar and Rogers ( 2012 ) propose a method using reason codes combined with consumer behavior data for forecasting returns volume in the consumer electronics industry, aiming at the retailer stage as well as the preceding supply chain stages. The subject of their study is an offline retailer, which allows generalization for e-tailers due to a similar return policy (14 days free returns with no questions asked). In a multi-step approach, the authors are using essential statistical methods (moving averages, correlations, and linear regression), but use sophisticated domain and product knowledge like product features or price in relation to past return numbers, aiming to rank different competing products regarding their quality, and to predict the volume of returns for a given product for each given period of time.

Fu et al. ( 2016 ) derive a framework for the forecasting of product- and consumer-specific return propensities, i.e., the return propensity for individual purchases. Their study is directed at online shopping and is evaluated using the data from an online cosmetic retailer selling via Taobao.com. The predictors are categorized into inconsistencies in the buying and in the shipping phase of a transaction. A latent factor model is introduced for return propensities capturing differences between expectations and performance. This model is extended by product (e.g., warranty) and customer information (e.g., gender, credit score). The model is based on conditional probabilities, and an iterative expectation–maximization approach derives its parameters. MAE and RMSE, precision/recall, and AUC metrics assess the forecast accuracy. As benchmark models, two matrix factorization models and two memory-based models (historical consumer or product return rates) are compared, while the proposed model outperforms the references. Furthermore, this model allows identifying various return reasons, e.g., return abuse and fraud.

Building on the work of Fu et al. ( 2016 ), Li et al. ( 2019 ) investigate underlying reasons for consumer returns, taking the example and data of an online cosmetic retailer via Taobao.com. They examine the customers’ return propensity for product types, aiming at detecting abnormal returns suspecting abuse. Different from purchase decisions, they find customer profile data to be more important predictors for return decisions than product information or transaction details. The authors detect “selfish” or “fraud” consumers based on this rationale. For estimating return propensities for a given consumer and product, they calculate the return behavior depending on the return decision of similar consumers (“trust network”) and the amount of trust in these other consumers. MAE and precision-recall-measures are used to assess the prediction of different random walk models. The employed trust-based random walk model outperforms the other models on most indicators, building the basis for anomaly detection of consumers to cluster them into groups (honest/selfish/fraud) and individually address the return issues of these groups.

Although the paper from Cui et al. ( 2020 ) aims at product return forecasts from the perspective of the manufacturer, their case can be generalized for classic e-tailers, as the manufacturer is responsible for the return handling in their scenario—a task often performed by the retailer. They used a comprehensive data set from an automotive accessories manufacturer aiming to forecast return volume for sales channels and different products. The observed return rates lower than 1% are uncommonly low, and therefore the results must be interpreted with caution. First, a hierarchical OLS regression step-by-step incorporates up to 40 predictors regarding sales, time, product type, sales channel, and product details, including return history. The full model shows a significantly increased performance measured by a more than 50% decrease of MSE, which was used as the primary performance measure. Interestingly, relatively small differences in model quality (R 2 ) led to overproportional changes in the MSE. Using a machine-learning approach for predictor selection (“LASSO”), another MSE reduction of about 10% was achieved. Data Mining approaches (random forest, gradient boosting) could not outperform the LASSO approach. Forecasting performance was strongly dependent on the variation of the data. The two best predictors for return volume were past sales volume and lagged return statistics. The authors were wondering about the importance of lagged return information, failing to acknowledge that this predictor includes the consumer reaction to detailed product information, which has not been a significant predictor.

Ketzenberg et al. ( 2020 ) segment customers and target detecting the small number of abusive returners, as these are unprofitable for the retailer and generate significant losses over a long time. In general, high-returning customers are usually more profitable. The data used for this study is from a department store retailer with various product groups in the assortment. Predictors are transactional data and customer attributes. For classification, different algorithms like logit, Support Vector Machines (SVM), Random Forests (RF), Neural Networks (NN) are used in combination with different shrinkage methods like LASSO, ridge regression, and elastic net. Random Forests and especially Neural Networks outperform the other algorithms, assessed by sensitivity, precision, and AUC. In conclusion, a low rate of false positives could assure retailers of using abuse detection systems.

Shang et al. (Shang et al. 2020 ) developed a predict-aggregate (P-A) model adaptable both for retailers and manufacturers for forecasting return volume in a continuous timeframe, in contrast to commonly used aggregate-predict (A-P) models. Instead of aggregating data first (i.e., sales volume and returns volume), they first aggregate product-specific return probabilities and then aggregate the purchases by addition of the individual probabilities. As predictors, they only use timestamps and lagged return information. They tune and assess their models on two datasets from an offline electronics and an online jewelry retailer. ARIMA and lagged return models known from end-of-life forecasting (de Brito et al. 2005 ) are used as benchmarks, using RMSE as an assessment criterion. The authors show that even a basic version of their approach outperforms the benchmark models in almost all observed cases by up to 19%, though using only lagged returns and timestamps as input. Different extensions, e.g., including more predictor variables, can easily be integrated and are shown to further improve the forecasting performance.

John et al. ( 2020 ) try to predict the rare event of return fraud from customer representatives that make use of exactly knowing the e-commerce company’s return policy framework and buying and returning items fraudulently. Therefore, predictors range from transaction details to customer service agent attributes. A penalized likelihood logit model was chosen by the authors and was evaluated by precision and recall, focussing on maximizing recall and minimizing false negatives. The most important predictors were communication type and reason for interaction.

The paper by Rezaei et al. ( 2021 ) introduces a new algorithm to automatically select attributes from high-dimensional databases for forecasting purposes. As a demonstration sample, they use simulated data as well as the publicly available ISMS Durable Goods dataset (Ni et al. 2012 ) for consumer electronics. The results are assessed by AUC, precision, recall, and f1-score. They compare different configurations. For the simulated data, LASSO as shrinkage method generally works best, outperforming RF and BaggedTrees. For real-world data, based on a forecast with a logit model, they show that the proposed selection algorithm performs similar or better compared to LASSO, SVM, and RF, while the complexity of the chosen variables is lower.

1.2 Conference publications

Urbanke et al. ( 2015 ) describe a decision support system to better direct return-reducing interventions at e-commerce purchases with highly likely returns. They compare different approaches for extracting input variables for return propensity forecasting. They use a large dataset from a fashion e-tailer, aiming to reduce the input variables regarding consumer profile, product profile, and basket information from over 5,000 binary variables to 10 numeric variables by different algorithms (e.g., principal component analysis, non-negative matrix factorization, etc.). The results are then used to predict return propensities with a wide variety of state-of-the-art algorithms (AdaBoost, CART, ERT, GB, LDA, LR, RF, SVM), thus also revealing both feature selection and prediction performance. The proposed Mahalanobis feature extraction algorithm used as input for AdaBoost outperforms all other combinations presented, while interestingly, a logit model with all original inputs delivers relatively precise forecasts.

Building on some parts of this study, the paper of Urbanke et al. ( 2017 ) presents a return decision forecasting approach and aims at two targets, (1) high predictive accuracy and (2) interpretability of the model. Based on real-world data of a fashion and sports e-tailer, they first hand-craft 18 input variables and then use NN to extract more features and compare this approach to other feature extraction algorithms based on different forecasting algorithms. For assessment, they measure correlations between out-of-sample-predictions and class labels and AUC. The best performing classifier was AdaBoost, while the contribution of NN-based feature extraction shows interpretability as well as superior predictive performance.

Ahmed et al. ( 2016 ) focus on the automatic aggregation and integration of different data sources to generate input variables (features). They use return forecasting just as an exemplary classification problem for their data preparation approach, using various ML algorithms, e.g., RF, NN, DT-based algorithms, to detect returned purchases of an electronics retailer. Based on AUC measure, the results of their GARP-approach are superior to not using aggregations while generating an extensive amount of features with no pruning approach. In general, SVM and RF work best in combination with the proposed GARP approach. The data is based on the publicly available ISMS durable goods data sets (Ni et al. 2012 ).

A similar group of authors published another paper (Samorani et al. 2016 ), again using the aforementioned ISMS dataset as an example for data preparation and automatic attribute generation. Besides forecasting performance, in this paper, they want to generate knowledge about important return predictors; e.g., a higher price is associated with more returns, but only as long price levels are below a 1,500$ threshold. AUC is used to assess different levels of data integration, confirming that overfitting might happen when too many attributes are used.

Heilig et al. ( 2016 ) describe a Forecasting Support System (FSS) to predict return decisions in a real environment. First, they compare different forecasting approaches for data from a fashion e-tailer, assessed by AUC and accuracy metrics. The ensemble selection approach outperforms all other classifiers, with RF being the closest competitor. Computational times grow exponentially when using more data. Based on these results, they secondly describe a cloud framework for implementing such ensemble models for live use in a real shop environment.

Ding et al. ( 2016 ) present an approach to predict the daily return rate of an e-commerce company based on sentiment analysis of tweets regarding this company in the categories of news, experience, products, and service. Therefore, they use sophisticated text mining technologies, while the forecasting approach of an econometric vector autoregression is more or less common. The emotion of posts regarding different variables (news, product, service) impacts the returns rate negatively, while the emotion of purchasing experience impacts it positively, showing that the prediction accuracy enhances through classifying social network posts.

Drechsler and Lasch ( 2015 ) aim at forecasting the volume of fraudulent returns in e-commerce over several periods of time. They present different approaches multiplying the sales volume and the relative return rate, the first referring to Potdar and Rogers ( 2012 ), estimating the rate of misused returns directly based on time-lag-specific return rates. In a second approach referring to Toktay et al. ( 2000 ), they estimate the overall returns rate and multiply it by the time-specific ratio of fraudulent returns. The return rates were forecasted by moving averages and exponential smoothing techniques. Assessment criteria for performance comparison based on simulated data were MAE, MAPE, and TIC, showing the first approach to be superior, but both methods are not sufficiently robust. Therefore, the authors include further time-specific information (like promotions or special events, which could foster fraudulent returns) in a model using a Holt-Winters approach, showing superior performance. All of the models are highly dependent on low fluctuation in return rates, showing a shortcoming of these more or less naive forecasting techniques.

Asdecker and Karl ( 2018 ) compare the performance of different algorithms for forecasting binary return decisions: logit, linear discriminant analysis, neuronal networks, and a decision-tree-based algorithm (C5.0). Their analysis is based on the data of a fashion e-tailer, including price, consumer information, and shipment information (number of articles in shipment, delivery time). For the assessment of different algorithms, they use the total absolut error (TAE) and relative error. An ensemble learning approach performs best and similar to the C5.0 algorithm. Though, differences in performance are relatively small, while only about 68% of return decisions are forecasted correctly.

Li et al. ( 2018 ) propose a hypergraph representation of historical purchase and return information combined with a random-walk-based local graph cut algorithm to forecast return decisions on order (basket) level as well as on product level. By this, they aim to detect the underlying return causes. They use data from two omnichannel fashion e-tailers from the US and Europe to assess the performance of their approach, using precision/recall/F 0.5 /AUC metrics while arguing that precision is the most important indicator for targeted interventions. Three similarity-based approaches (e.g., a k-Nearest Neighbor model) are used as reference. The proposed approach performs best regarding AUC, precision, and F 0.5 metrics.

Zhu et al. ( 2018 ) developed a weighted hybrid graph algorithm representing historical customer behavior and customer/product similarity, combined with a random-walk-based algorithm for predicting customer/product combinations that will be returned. They report an experiment based on data from a European fashion e-tailer suffering from return rates as high as 50%. For assessment, they use precision, recall, and F 0.5 metrics. Their approach is superior to two reference competitors (similarity-based and a bipartite graph algorithm). As predictors, they use product similarities and historical return information, while their approach can be enriched with detailed customer attributes.

Joshi et al. ( 2018 ) model the return decisions based on the data of an Indian e-commerce company, especially dealing with returns for apparel due to fit issues. In a two-step approach, they first model return probabilities using concepts from network science based on a customer’s historical purchase and return decisions, and secondly use a SVM implementation with return probabilities as a single input to classify for the return decision. Assessed by F 1 /precision/recall scores, their approach is superior to a reference random-walk baseline model.

Imran and Amin ( 2020 ) compare different forecasting algorithms (XGBoost, CatBoost, LightGBM, TabNet) for return classification based on the data of a general e-commerce retailer from Bangladesh. As input variables, only order attributes, including payment method and order medium, are used. For evaluation, they use metrics like true negative rate, false-positive rate, false-negative rate, true positive rate, AUC, F 2 -score, precision, and accuracy. In the end, they chose TPR, AUC, and F 2 -score, claiming that misclassifying high return probability objects were the first thing to avoid. According to these metrics, TabNet as a deep learning algorithm outperforms the other models. The most important predictors were payment method, order location, and promotional orders.

As returns are most prominent in fashion e-commerce, most of the forecasting papers take this industry as an example, as forecasting models are more precise when returns are more frequent. Hofmann et al. ( 2020 ) develop a more generalized order-based return decision forecasting approach, appropriate for different industries and suitable also for low return rates. For their analysis, they use a dataset from a german technical wholesaler with a return rate as low as 5%. Input variables were just basket composition and return information. For assessment, they used precision and recall metrics. RF did not perform superior to a statistical baseline approach, nor with oversampling as data preparation, to deal with the group imbalance. The DART algorithm makes use of the group imbalance correction by random oversampling. In general, gradient boosting performs best with imbalanced groups, also without oversampling, but forecasting quality is lower than with more specialized forecasting approaches as described for fashion. Furthermore, results were more accurate on basket level than on single-item level.

Fuchs and Lutz ( 2021 ) use Design Science Research (DSR) principles to design a meta-model for the real-time prediction of returns. The goal is to influence consumer decisions by triggering a feedback system based on the basket composition and its return probability. For forecasting, which is not the primary focus of their paper, they build upon a gradient boosting model taken from existing research (Hofmann et al. 2020 ) and describe possible implementations into an ERP system regarding asynchronous communication requirements and possible architecture.

The paper by Sweidan et al. ( 2020 ) evaluates the forecasting performance of a random forest model for a shipment-based return decision, using real-world data of a fashion e-tailer. For their model, they use customer (e.g., lagged return rate) and order information as inputs. They find that predictions with high confidence are very precise (i.e., low false-positive rate). Thus, interventions can be targeted at such orders already when the items are in the consumers’ basket without risk of a misdirected intervention. For assessment, accuracy, AUC, precision, recall and specificity are used. Regarding the predictors, they note that selection orders (a product in different sizes) are the best predictor for order-based returns.

Rajasekaran and Priyadarshini ( 2021 ) develop a metaheuristic for forecasting the product-based return probabilities. In the first step, they determine return probabilities based on product feedback, time, and product attributes regarding manufacturer return statistics. Secondly, they compare different algorithms (OLS, RF, Gradient Boosting) by MAE, MSE, and RMSE metrics. Interestingly, linear regression performs best in all metrics, but no explanation and a misinterpretation regarding the best algorithm are given.

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Karl, D. Forecasting e-commerce consumer returns: a systematic literature review. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00436-x

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Received : 24 August 2023

Accepted : 12 April 2024

Published : 21 May 2024

DOI : https://doi.org/10.1007/s11301-024-00436-x

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Rethinking Consumption: A Conceptual Paper on Circular Economy from a Marketing Perspective

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The circular economy is rapidly expanding and many practitioners are becoming aware of this growth. Consumer demand for circular economy products has motivated businesses and stakeholders to transform their business and marketing operations into a circular form. Therefore, the aim of this research is to explore the factors that influence consumer purchase intention related to the circular economy. The study proposes a conceptual model that depicts multiple factors such as attitudes, subjective norms, perceived behavioral control, convenience, environmental impact, and cause-related marketing that can possibly influence purchase intention. The methodology proposed involves collecting data from past studies on a similar research theme and developing a robust model related to circular economy from a marketing perspective. The proposed model of this study will be  analysed using structural equation modelling software in the future. Literature review reveals that these factors possess unique attributes that can accelerate consumer purchase intention. The study further suggests that empirical research can be conducted in the future to test components of the factors included in the model. Therefore, the expected results can help green marketers and policymakers understand the factors that influence purchase intention and promote the development of a circular economy. The study concludes that consumers can significantly contribute to the growth of a circular economy by purchasing circular products.

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Muhammad tahir jan.

Associate Professor, Department of Business Administration, Kulliyyah of Economics and Management Sciences, International Islamic University Malaysia, Malaysia

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