desk research vs literature review

A Quick Overview: Differences Among Desk, Literature, and Learning Reviews

November 12, 2020

By: Chelsie Kuhn, MEL Associate, Headlight Consulting Services, LLP

This is the first post in a series of two about Learning Reviews .

In order to chart the wisest path forward, we need to understand where we have been. Reflecting on past learning can ensure more effective and efficient efforts in the future, regardless of discipline or field. But different information needs require different tools. Literature, Desk, and Learning Reviews are three ways to integrate evidence into decision-making and design processes. Each tool uses varying degrees of information and rigor, and each is best suited for different applications, as described in the visual below.

A Literature Review traditionally focuses on academic journal articles and published books, giving readers a theoretical or case-based frame of reference. A Literature Review may be appropriate for researchers looking to set up an experiment or randomized control trial in a location or those looking at theoretical development over time. This kind of review is all about synthesis of what we know research-wise up to the current point, and what potential gaps exist yet to be filled.

Another type of review widely known is a Desk Review, which serves to provide readers with an introduction into a project’s context and priorities, but often not the past learnings or in-depth challenges needed to inform strategy development. A Desk Review can also serve as an entry point to understanding a particular market or an effective way to organize and summarize disparate types of information. Doing a Desk Review might be appropriate to bring a new team member up to speed on projects or learn about the current state and environment concerning a particular type of intervention.

While Literature and Desk Reviews may be more commonly known, one of the offerings that Headlight specializes in is a Learning Review. A Learning Review is a way to systematically look at past assessments, evaluations, reports, and any other learning documentation in order to inform recommendations and strategy, program, or activity design efforts. Unlike Desk Reviews, Learning Reviews focus on coding and analyzing data instead of summarizing it. With layers of triangulation and secondary analysis built into the process, we can confidently draw findings and conclusions knowing that the foundation of the process is built with rigor. Recommendations stemming from these findings and conclusions serve as the best use of an existing evidence base in designing or revisiting strategies, programs, and activities. Each of these three tools are useful at different points, but as we see more and more emphasis placed on learning and adaptive management, Learning Reviews offer a more rigorous and application focused use of available evidence.

As a synthesis of past evaluations and assessments, Learning Reviews should also be used to feed into new MEL or CLA plans. Having extra information on what has worked in the past, what information was useful, and where more-nuanced information would be beneficial enables us to set better targets and understand potential barriers to measurement. Recommendations may even point to specific indicators to consider or CLA actions to integrate into programming moving forward. Learning Reviews can also be used to appropriately scope and identify future evaluative efforts that will evolve the evidence base.

In the next post in the series, we will expand further on Learning Reviews as a process and walk readers step-by-step through how to conduct one. If you need help implementing any of these above tools, but in particular a Learning Review, Headlight would love to support you! We have the breadth and depth of expertise, experience, and toolbox to tailor-meet your needs. For more information about our services please email [email protected] . Headlight Consulting Services, LLP is a certified women-owned small business and therefore eligible for sole source procurements. We can be found on the Dynamic Small Business Search or on SAM.gov via our name or DUNS number (081332548).

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What Is Desk Research?

Desk research, also known as secondary research or documentary research, is a type of research that relies on data that has already been collected and published by others. Its data sources include public libraries, websites, reports, surveys, journals, newspapers, magazines, books, podcasts, videos, and other sources.

When performing desk research, you are not gathering new information from primary sources such as interviews, observations, experiments, or surveys. The information gathered will then be used to make informed decisions.

The most common use cases for desk research are market research , consumer behavior , industry trends , and competitor analysis .

How Is Desk Research Used?

Here are the most common use cases for desk research:

Exploring a new topic or problem
Identifying existing knowledge gaps
Reviewing the literature on a specific subject
Finding relevant data and statistics
Analyzing trends and patterns
Evaluating competitors and market trends
Supporting or challenging hypotheses
Validating or complementing primary research

Types of Desk Research Methods

There are two main types of desk research methods: qualitative and quantitative.

Qualitative Desk Research

Analyzing non-numerical data, such as texts, images, audio, or video. Here are some examples of qualitative desk research methods:

Content analysis – Examining the content and meaning of texts, such as articles, books, reports, or social media posts. It uses data to help you identify themes, patterns, opinions, attitudes, emotions, or biases.

Discourse analysis – Studying the use of language and communication in texts, such as speeches, interviews, conversations, or documents. It helps you understand how language shapes reality, influences behavior, constructs identities, creates power relations, and more.

Narrative analysis – Analyzing the stories and narratives that people tell in texts, such as biographies, autobiographies, memoirs, or testimonials. This allows you to explore how people make sense of their experiences, express their emotions, construct their identities, or cope with challenges.

Quantitative Desk Research

Analyzing numerical data, such as statistics, graphs, charts, or tables.

Here are common examples of quantitative desk research methods:

Statistical analysis : This method involves applying mathematical techniques and tools to numerical data, such as percentages ratios, averages, correlations, or regressions.

You can use statistical analysis to measure, describe, compare, or test relationships in the data.

Meta-analysis : Combining and synthesizing the results of multiple studies on a similar topic or question. Meta-analysis can help you increase the sample size, reduce the margin of error, or identify common findings or discrepancies in data.

Trend analysis : This method involves examining the changes and developments in numerical data over time, such as sales, profits, prices, or market share. It helps you identify patterns, cycles, fluctuations, or anomalies.

Examples of Desk Research

Here are some real-life examples of desk research questions:

What are the current trends and challenges in the fintech industry?
How do Gen Z consumers perceive money and financial services?
What are the best practices for conducting concept testing for a new fintech product?
Documentary on World War II and its effect on Austria as a country

You can use the secondary data sources listed below to answer these questions:

Industry reports and publications

Market research surveys and studies
Academic journals and papers
News articles and blogs
Podcasts and videos
Social media posts and reviews
Government and non-government agencies

How to Choose the Best Type of Desk Research

The main factors for selecting a desk research method are:

Research objective and question
Budget and deadlines
Data sources availability and accessibility.
Quality and reliability of data sources
Your data analysis skills

Let’s say your research question requires an in-depth analysis of a particular topic, a literature review may be the best method. But if the research question requires analysis of large data sets, you can use trend analysis.

Differences Between Primary Research and Desk Research

The main difference between primary research and desk research is the source of data. Primary research uses data that is collected directly from the respondents or participants of the study. Desk research uses data that is collected by someone else for a different purpose.

Another key difference is the cost and time involved. Primary research is usually more expensive, time-consuming, and resource-intensive than desk research. However, it can also provide you with more specific, accurate, and actionable data that is tailored to your research goal and question.

The best practice is to use desk-based research before primary research; it refines the scope of the work and helps you optimize resources.

Read Also – Primary vs Secondary Research Methods: 15 Key Differences

How to Conduct a Desk Research

Here are the four main steps to conduct desk research:

Define Research Goal and Question

What do you want to achieve with your desk research? What problem do you want to solve or what opportunity do you want to explore? What specific question do you want to answer with your desk research?

Identify and Evaluate Data Sources

Where can you find relevant data for your desk research? How relevant and current are the data sources for your research? How consistent and comparable are they with each other?

You can evaluate your data sources based on factors such as-

– Authority: Who is the author or publisher of the data source? What are their credentials and reputation? Are they experts or credible sources on the topic?

– Accuracy: How accurate and precise is the data source? Does it contain any errors or mistakes? Is it supported by evidence or references?

– Objectivity: How objective and unbiased is the data source? Does it present facts or opinions? Does it have any hidden agenda or motive?

– Coverage: How comprehensive and complete is the data source? Does it cover all aspects of your topic? Does it provide enough depth and detail?

– Currency: How current and up-to-date is the data source? When was it published or updated? Is it still relevant to your topic?

Collect and Analyze Your Data

How can you collect your data efficiently and effectively? What tools or techniques can you use to organize and analyze your data? How can you interpret your data with your research goal and question?

Present and Report Your Findings

How can you communicate your findings clearly and convincingly? What format or medium can you use to accurately record your findings?

You can use spreadsheets, presentation slides, charts, infographics, and more.

Advantages of Desk Research

Cost Effective

It is cheaper and faster than primary research, you don’t have to collect new data or report them. You can simply analyze and leverage your findings to make deductions.

Prevents Effort Duplication

Desk research provides you with a broad and thorough overview of the research topic and related issues. This helps to avoid duplication of efforts and resources by using existing data.

Improves Data Validity

Using desk research, you can compare and contrast various perspectives and opinions on the same topic. This enhances the credibility and validity of your research by referencing authoritative sources.

Identify Data Trends and Patterns

It helps you to identify new trends and patterns in the data that may not be obvious from primary research. This can help you see knowledge and research gaps to offer more effective solutions.

Disadvantages of Desk Research

Outdated Information

One of the main challenges of desk research is that the data may not be relevant, accurate, or up-to-date for the specific research question or purpose. Desk research relies on data that was collected for a different reason or context, which may not match the current needs or goals of the researcher.

Limited Scope

Another limitation of desk research is that it may not provide enough depth or insight into qualitative aspects of the market, such as consumer behavior, preferences, motivations, or opinions.

Data obtained from existing sources may be biased or incomplete due to the agenda or perspective of the source.

Read More – Research Bias: Definition, Types + Examples

Data Inconsistencies

It may also be inconsistent or incompatible with other data sources due to different definitions or methodologies.

Legal and Technical Issues

Desk research data may also be difficult to access or analyze due to legal, ethical, or technical issues.

How to Use Desk Research Effectively

Here are some tips on how to use desk research effectively:

Define the research problem and objectives clearly and precisely.
Identify and evaluate the sources of secondary data carefully and critically.
Compare and contrast different sources of data to check for consistency and reliability.
Use multiple sources of data to triangulate and validate the findings.
Supplement desk research with primary research when exploring deeper issues.
Cite and reference the sources of data properly and ethically.

Desk research should not be used as a substitute for primary research, but rather as a complement or supplement. Combine it with primary research methods, such as surveys, interviews, observations, experiments, and others to obtain a more complete and accurate picture of your research topic.

Desk research is a cost-effective tool for gaining insights into your research topic. Although it has limitations, if you choose the right method and carry out your desk research effectively, you will save a lot of time, money, and effort that primary research would require.

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How to avoid a desk reject: do’s and don’ts

Published: 17 June 2024

Cite this article

Sjoerd Beugelsdijk 1 &
Allan Bird 2

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Avoid common mistakes on your manuscript.

Introduction

The number of manuscripts submitted to academic journals has increased significantly, and along with that the desk-reject rate also, that is, the rate at which manuscripts are rejected at the very first stage of the review process (Ansell & Samuels, 2021 ). At the Journal of International Business Studies ( JIBS ), roughly 65% of submissions are desk-rejected. In other words, the authors of nearly two-thirds of the manuscripts sent to this journal will not see their submissions reach an area editor or reviewers. Obviously, no one wants to receive a rejection letter; when it is a desk reject, authors may well feel they never even got a fair hearing through a peer-review process. Not only has the number of submissions risen but so has their overall quality. It was inevitable that the desk-reject bar would be raised. Not doing so would have risked overburdening editorial teams and the pool of qualified and dedicated reviewers on which they rely. Already, across all fields of science, potential reviewers are more frequently declining invitations to review, and this further increases the pressure on reviewing editors to desk-reject manuscripts (Dance, 2023 ). Footnote 1

Getting past the desk-reject stage is critical because even if a manuscript is not eventually accepted for publication, the suggestions and comments of an area editor and reviewers— invariably acknowledged experts in the field—can be immeasurably valuable in making improvements for submission to another journal. A desk reject differs from rejection later in the review process because the objective and the process of the two are quite different. Reviewing editors decide by themselves the fate of a manuscript, while peer review is a shared responsibility. The workload of reviewing editors is such that they need to rely on heuristics to make their decisions. Hence, they hone in on specific elements which, if not present, will result in a desk reject. In this editorial, we describe these elements under two headings: (1) effective communication, and (2) theory- and method-related rigor. Our goal is to relay what reviewing editors look for when deciding whether to forward a manuscript to the next level. Our tips and actionable suggestions are summarized in the Appendix, where we also provide a list of 80 questions that serve as a ‘checklist’ of do’s and don’ts. These suggestions are based on more than 4000 Journal of International Business Studies desk-reject decisions between 2016 and 2024. Many of the suggestions and recommendations we provide apply equally to overall guidance about research in international business.

The role of reviewing editors

Reviewing editors are tasked with (1) conserving the time, attention, and energies of area editors, reviewers, and submitting authors, and (2) maintaining the focus and integrity of the journal mission as embodied in its statement of aims and scope. The mechanics of a desk review are straightforward: their purpose is to assess whether a submission meets the journal’s fit, quality, and contribution thresholds. In addition to determining if a manuscript meets those three content criteria, reviewing editors are charged with ensuring that the review process is fair, specifically that it is free of bias, ethical lapses and errors, and that, to a reasonable extent, author concerns or requests are accommodated. More details can be found in the Journal of International Business Studies guidelines for reviewers. Footnote 2

The first step in the desk-review process entails reading the cover letter. Although not required, many authors submit them. They can be used to explain distinctive aspects of the manuscript, for example, a unique approach taken in framing the research question. A cover letter might also be used to make a request, such as for a reviewer knowledgeable about a new analytical approach. We recommend that authors provide pertinent information, such as the names of persons who have previously read and commented on the manuscript, thereby avoiding the possibility of compromising the double-blind peer review should the manuscript move forward in the review process. Footnote 3 Authors should also alert the reviewing editor if other manuscripts or published articles by the author and co-authors address the same topic or draw upon the same dataset as the submitted manuscript. Footnote 4 We recommend submitting a detailed overview of any overlap and differences between the submitted manuscript and the authors’ existing work in the same vein (sometimes referred as an originality matrix), so as to help the reviewing editor assess the contribution of the manuscript. There is an expectation that authors will be transparent with editors.

Reviewing editors are anxious to avoid making type I or type II errors. They do not want to desk reject a manuscript that might end up being a high-quality, impactful article. Making the wrong decision can mean a loss for the journal as well as deny the authors timely publication. On the other hand, forwarding for full review a manuscript that does not meet fit, quality, and contribution thresholds and has little chance of reaching publication is an inefficient use of the time, attention, and effort of editors and reviewers. It also bogs down authors who end up having devoted time pursuing an ultimately fruitless review process rather than improving the manuscript and submitting it elsewhere.

Because there are recurring patterns in the types of issues that lead to a desk reject, reviewing editors use heuristics in making their assessments. In general, a manuscript is desk-rejected if there is not a good fit with the aims and scope of the journal. For the Journal of International Business Studies , this implies the topic has to address an international business topic as explained in the editorial guidelines. Footnote 5 Manuscripts should address topics from an international comparative and/or cross-border angle. This means that ‘just’ analyzing a cross section of countries is not sufficient to be considered for this journal. Similarly, ‘just’ adding some country-specific variables as control variables is not sufficient to qualify as making a contribution to international business. Single-country studies without an IB dimension are a substantial portion of all desk-rejected articles. The heuristics that reviewing editors use can be categorized into two main domains: (1) effective communication and (2) theory- and method-related rigor. Each domain consists of a series of do’s and don’ts. These do’s and don’ts are summarized in the Appendix.

Effective communication

Writing a good manuscript involves reading prior research, data analysis, sense-making, writing, re-analyzing, presenting to colleagues, re-writing, and eventually accepting a certain degree of imperfection. A positive correlation exists between manuscript quality and the time spent on it, but that correlation is far from 1. Certainly, as some seem to believe, a manuscript does not merit review simply because the author claims a lot of time has been spent on it. Underestimation of the importance of effectively communicating with readers is at the root of many desk rejects. We discuss five of them here.

Develop a story

Human beings are pattern-seeking, sense-making, story-telling animals (Leamer, 2009 ). A good manuscript tells a story, one that is believable and memorable. The story may be based on a phenomenological observation or be a theory-based narrative, but a good story is critical to scholarly understanding because storytelling is a cognitive process with sense-making at its core.

It is a mistake to think that storytelling in science is limited to manuscripts using interviews, for which it is a recommended theory-development strategy. It is also a critical part of effective communication for manuscripts based on secondary data where there is often a focus on statistical relationships without a clear understanding of underlying processes. To minimize the probability that a regression result becomes merely a statistical artefact, authors should understand what is driving the statistically significant relationships between the variables. If they do, their story is much better than that of authors who rely on statistical software packages to tell the story for them. In other words, a coefficient that differs significantly from zero is never the essence of the story, but only a part of it. This is one important reason why authors who first analyze the data and then develop hypotheses on that basis (i.e., who practice what is called harking – h ypothesizing a fter r esults a re k nown) are generally not good storytellers. Harking is not only unscientific, but it also results in unpersuasive stories.

What does make for a good story? In a word, focus. We do not mean honing in on detail to such an extent that the result is a marginal contribution. Far from it. Still, the most valuable contributions are typically very focused. By focus we mean that the core concept is succinctly stated and concisely explained in just a few sentences. The key takeaway should be delivered in plain English understandable to a non-academic audience. Preparing 15-min mock presentations and rehearsing—out loud—the opening and concluding sections can be especially useful in developing a focused story.

Focus alone will not suffice. Delivery is extremely important. Good writing enhances storytelling. We hasten to add that reviewing editors do not reject manuscripts out of hand because of low readability—although obviously the manuscript must be intelligible. Nonetheless, there can be a horn’s effect. A carelessly put together manuscript with typos, misspellings, and grammatical errors that could have easily been caught by running a spelling and grammar check, or table and figure headings that do not match content, or referencing that is incomplete, inconsistent or not applicable, raise doubts about the rigor and precision with which theory is developed and data analyzed. Authors need to take the time to polish their manuscripts; even established researchers spend a considerable amount of time doing that. Poor writing can be fixed by careful language editing down the line.

It is also a mistake to overcomplicate the story by trying to do too much. This typically happens when an author tries to eclectically mix different theories. Reviewing editors are not likely to forward manuscripts in which authors use multiple theories, e.g., the resource-based view of the firm, transaction costs theory, population ecology, and institutional theory. First, each theory comes with its own set of assumptions, causal mechanisms, and boundary conditions, and these can be hard—if not impossible—to integrate into one overarching framework. Second, combining multiple theories tends to result in convoluted arguments with no real punchline.

Another mistake is to center the story around the use of a different method or a distinctive sample to empirically examine relationships that have already been studied extensively. While that strategy might work when submitting to a second-tier journal, top journals expect there to be a clear theoretical contribution and novelty beyond a new method or distinctive sample. Showing that a relationship already examined in other studies holds when expanding the sample, e.g., to different countries or perhaps by using an alternative method, will trigger interest only if there is an unusual theoretical rationale for using the new method or sample. For example, suppose a specific theory has been tested primarily in economically developed countries and that good theoretical arguments exist for why the theory may not apply outside that context; then expanding the sample to less economically developed countries makes sense. The same holds true for a manuscript that an author attempts to ‘sell’ based on the use of a new method. With the exception of method-focused journals such as Organizational Research Methods , most reviewing editors will only forward a method-focused manuscript if the method element has interesting theoretical implications.

What makes for a good story is to some extent time-specific. Management trends come and go, and so does what is seen as a legitimate story. For a long time, authors specified what was called a ‘gap’ in the literature. They would claim to have uncovered a theoretical hole and then outline it in the introduction of their manuscript. Their story was essentially based on their observation that aspect A of theory X had not yet been addressed. As time has passed, phenomenological research has become popular and it is now increasingly legitimate for authors to start their story with a new, interesting, even odd empirical observation. With that, a good story has become one that piques the interest of readers and makes them curious about what comes out. It leaves them thinking to themselves, “Good point. Why didn’t I think of that?” An effective way of gauging what is trending in a particular community of scholars is to read the introductions of conference papers and recently published articles to see what kind of ‘hook’ is used.

Write a clear introduction that explains the what, so what, and now what

The introduction can be a make-or-break point. A desk reject is likely if the introduction is not clear. The reviewing editor will look for focus, a good story, convincing theorizing, and tight empirical tests. There is no universal template for a high-quality introduction, but that does not mean that crafting one is a random process. The best introductions include several recurring elements (Grant & Pollock, 2011 ). The introduction of articles published in top journals may differ from the pattern explained below because of differences in topic, method, data, field, research tradition, and findings. Still, we can discuss several elements that all reviewing editors look for when reading an introduction. Often those elements correspond to the four paragraphs that we propose should form the introduction.

The first paragraph should set the scene. It should include (1) the topic, (2) why it matters, and (3) what is already known about it, including theories used. Writing the opening paragraph is quite challenging because the author must summarize in just a few sentences the state-of-affairs in a field. The second paragraph discusses what we do not yet know about the topic. This can be theory or phenomenon-driven. For example, there is well-established and vast literature on why people resign and change jobs, but we do not yet really understand the recently identified phenomenon of quiet quitting. Describing why quiet quitting could be important is key because it provides the motivation behind the manuscript. The third paragraph describes what the author does to address the question, specifically the theory used, key characteristics of the data (e.g., sample size and country context), as well as the method used. In this paragraph the author should also summarize the findings. In the fourth and final paragraph, authors should circle back to the broader topic – in our example, quiet quitting. They should show why their findings matter as well as the implications. The contribution should be as explicit as possible, not just repeat the empirical findings, but discuss their broader meaning. The last paragraph often ends with a road map indicating how the manuscript is structured.

The typical introduction in management journal articles is around 600 words, divided more or less equally between the paragraphs described above. This means that in each case the material to be covered is handled in just six to eight sentences. The first and last sentences of each paragraph are critical. If those two alone convey the message, the manuscript is probably properly focused. In fact, one way of checking whether a paragraph makes sense is to read those sentences, ignoring the ones in between, to see if the core message is still conveyed. If so, the manuscript is focused and the storyline clear. Another test is to string together the opening sentence of each paragraph. There should be a coherent story supported by a clear line of reasoning. Obviously there are many variations in the way successful authors craft introductions. We describe here what we, as reviewing editors, have found effective introductions have in common.

Know your audience and the language they speak

Imagine entering a room in which the ten most-cited scholars in your area are debating the very topic on which you are writing. They turn to look at you. You have their attention. What can you say about your manuscript that would interest them? Would it impress them if you were to say that you show that the relation between X, Y, and Z—something which they have already analyzed – holds true using your data? What if instead you were able to tell them a powerful story in field-specific language, words that carry a particular connotation and labels with well-known associations? The point is, in a twist to the normal advice to use your own words, you need to tell the story in their kinds of words.

Authors need to immerse themselves in the language used in their area. They need to read the classic articles and books as well as the latest ones on the topic, bearing in mind that there is a significant time lag between manuscript submission and final publication. They need to stay on top of what is happening in the scholarly community in other ways as well. Taking part in academic conferences is one of them—attending panels, observing debates, engaging in discussions, especially delivering papers—all help in understanding where a field is heading. Topics, methods, approaches, and terminology are ‘in the air’ at workshops and during webinars. All of this is part of knowing the audience. Despite all recent advancements in artificial intelligence (AI), this aspect of targeting your audience has so far not been successfully integrated in existing AI tools.

One less tacit, more formal aspect of audience expectations is understanding the style and format in which core ideas are communicated. Journals have set limits on the number of words used. It is important for authors to stick to them. Reviewing editors do sometimes wade through manuscripts that are considerably longer than the norm, but they are ever mindful of the contribution-to-length ratio. Authors should not try to be exhaustive in providing references. Peppering a text with references, especially when placed mid-sentence, reduces readability. Reviewing editors are familiar with a wide range of research areas. They will catch careless referencing, such as backing up a statement with a reference to an article or book in which no such support can be found, or misattributing a contribution. Inaccurate or excessive referencing reflects badly on the scholarship of a submitting author and may lead to a desk reject. It is important to use current references, as submissions with references ending 15 or 20 years ago signal the manuscript is outdated. Finally, there is no formal rule regarding what particular works authors should reference, but if none of the references have been published in the journal to which they are submitting, it is likely to be taken as evidence of not being in touch with ongoing discussions in the journal, thereby raising the question of fit.

Avoid vague wording

Words matter. Scientific research requires precision. Formal modeling provides it in economics, finance, operations research, and some subfields in sociology and political science. Social sciences, including business and management, rely on precise, unambiguous language. Unfortunately, many authors are not so meticulous. Reviewing editors are not taken in by meaningless jargon or pretentious verbiage. Rather, such language might be taken as an indication that an author has not totally grasped the topic or is attempting to oversell the contribution.

Consider the following seven examples taken from actual manuscripts—followed by our critical comments. (1) We show that an integrated approach is required. This kind of generic statement holds for virtually all topics . (2) We provide a nuanced picture of the complex relationship between X and Y. Attempting to add nuance to a complex concept is an endless exercise—not a goal in itself. The goal should be to make the complex simple without making it simplistic. We mean E = mc 2 simple. (3) Managers should take care of their international HR function. No study is needed to reach this obvious conclusion. (4) We discuss some implications of… Some? Are there others? Vague statements like these make us wonder what is left unsaid, or unresearched, or if the author is unsure of what the implications might be or how to explain them. (5) We uncover heterogeneity that has not been addressed before. To our knowledge, we are the first to analyze... An author may have found something of importance that escaped all others, but maybe it is not sufficiently interesting or indeed even relevant enough to merit publication. (6) We draw upon… What exactly does this mean? Does the author intend to take – in whole or in part – elements from a theory and eclectically combine them? (7) The relation between subsidiary and headquarters: some insights from country X . This last example has to do with crafting meaningful titles. The manuscript title, as well as those of the figures and tables, should be precise and specific and convey meaningful information.

Finally, a word of caution about acknowledging limitations. It is not a recommended strategy to discuss all possible limitations, especially when done at the end of a manuscript, as this may leave readers wondering why they have taken the time to read something the authors themselves think is significantly flawed. Two types of limitations should be identified, but not necessarily addressed in a specific section labeled as such and found at the end of the manuscript. Methodological limitations are ideally addressed in the Method section along with steps taken to mitigate or overcome them. Theoretical limitations relate specifically to what interpretations or conclusions can be drawn from the empirical findings. Rather than listing them as limitations, they can be framed as future lines of inquiry opened up as a result of what was learned from the study.

Write a clear self-standing abstract

Many authors underestimate the importance of the abstract. This is hard to understand because a good abstract gets the attention of potential readers and can entice them to continue reading. An article read is possibly one cited. The abstract is also important in the review process. It is the first thing that a reviewing editor reads. The abstract should give the topic and research question (the motivation), the theoretical angle taken, what the author does (the empirical setting if relevant), the findings, and why the study matters (the contribution). In short, it must convey a considerable amount of information. Writing one takes time and attention, and the abstract should not be the last quick thing authors attend to before submission. All too often abstracts are overly technical and hard to understand without having read the full manuscript.

What can authors do to be sure that what they write in the abstract is meaningful? One way of testing is to draw a line through the key construct named in the abstract and put in its place some other construct in the field. If the abstract makes just as much sense after plugging in that randomly chosen construct, the original abstract is probably uninformative and unconvincing. Let us illustrate the point with a concrete example. Do the following test on this hypothetical abstract: “Institutions have been recognized as a crucial topic in international business research with wide-ranging implications for internationalizing firms. As a result, there are a wide variety of studies in different contexts, using different methods, a diverse set of theories, and a variety of empirical measures. In this article, we review the existing literature, evaluate current approaches critically, and highlight directions for future research.” Now, suppose ‘institutions’ were to be substituted by ‘headquarter–subsidiary relationships’. There is nothing jarring about the resulting version, a sign of an abstract that is too generic.

Theory- and method-related rigor

Distinguish between theory and literature review.

Authors sometimes confuse the literature review with the theory section. Whereas a literature review provides an overview of established findings thereby providing the frame into which a manuscript fits, a theory section provides a set of arguments (embedded in underlying assumptions) that logically lead to a proposition or testable hypothesis. A theory is about the arrows linking construct A to construct B (Thomas et al., 2011 ). In short, theories explain relationships. But rather than providing an integrated framework based on causal theoretical arguments, the theory section in many manuscripts is just a literature review that provides an overview of what other authors have argued or found in their empirical studies. The lack of a strong theory section is an important reason for a desk reject.

Theoretical arguments are often not precise because authors work with overly broad concepts. The result is loosely linked arguments. Another common mistake is to mix arguments from different schools of thought, leading to theoretical imprecision. This, as noted before, leads to poor stories. Reviewing editors are senior scholars and thus aware of the most important differences between the core theories used in a field. This does not mean that manuscripts need only develop narrow arguments derived from a single theoretical framework, but it is generally recognized that combining lenses is challenging (Okhuysen & Bionardi, 2011 ).

Finally, reviewing editors are likely to desk-reject a manuscript when the author excessively uses quotations. Instead of relying on others to say what you want to argue, it is far better to explain the mechanisms directly and explicitly in your own words. There is a risk of misstating what the cited author means to say because quotations are snapshots of broader arguments, and often individual sentences are taken from longer paragraphs.

Spell out the theoretical mechanisms

Ultimately, the theoretical contribution lies in highlighting the set of mechanisms that logically explain the relationship between A and B. Hypotheses are testable predictions derived from a set of arguments that causally and logically relate to one another. Often authors present hypotheses as the result of a set of empirical findings. This leads to truisms—claims that are so self-evident that they are too obvious to mention. In these cases, reviewing editors are inclined to reject manuscripts. Examples can be an effective way to present arguments, but they are no substitute for clear theoretical argumentation. In other words, the plural of anecdote may be data, but data cannot by themselves be the basis for hypotheses.

Hypotheses make testable statements on the relationship between abstract constructs. A good hypothesis is the logical outcome of proper theorizing (Santangelo & Verbeke, 2022 ). Because they are unable to examine theoretical relationships directly, researchers rely on empirical proxies, e.g., patent filings as a proxy for firm innovation, and return on investment for firm performance. It is not uncommon for authors to shift focus from constructs to proxies, and to make statements on relationships between empirical proxies while overlooking the theoretical constructs the proxies are purported to represent. As a general rule of thumb, one should not discuss measurement-related issues (e.g., the variables used as proxies) in the theory section. This makes it possible to keep it as clean as possible and reduces the risk of conflating the theoretical argument supporting hypotheses with the empirical tools used to test them.

Many phenomena in international business are multi-level by nature. For example, country-level variables, such as national cultural differences, may moderate lower-level relationships, such as the dynamic between team leaders and team members. When data are nested in countries, firms, teams, and individuals, one needs to use multi-level methods to disentangle the impact of variations at each level. The real challenge is often not in using multi-level methods, but in developing multi-level theories. Reviewing editors look for a description of the mechanisms linking the micro and the macro levels. If they are not made explicit, a desk reject is likely. To avoid that, authors should make sure they discuss the causal relationships between the different levels.

As a rule, authors should also avoid hypotheses that involve more than one relationship. For example, a model where an increase in A is theorized to cause a decrease in B and the A–B relationship is moderated by C should have two hypotheses, not one. Compound hypotheses are inherently complex and consequently often poorly worded, and this may lead to a desk reject.

Isolate the theoretical channels empirically

In addition to clearly specifying the nature of the theoretical argument, empirical tests of hypothesized relationships need to get as close as possible to a direct test of the proposed mechanisms. This is done by providing convincing theoretical arguments and a series of empirical tests that serve two goals. First, to show that the mechanism that is theorized exists empirically. Second, to rule out alternative plausible explanations. Ruling out alternative explanations is at least as important as providing evidence for the theoretical mechanisms. This should be taken into account when designing the study and prior to data collection. A number of methods are available to identify mechanisms, including—but not limited to—instrumental variables, natural or quasi experiments, regression discontinuity design, difference-in-difference analysis, randomized control trials, propensity score matching, and longitudinal studies.

Increasingly, authors combine multiple methods to corroborate the main effects found, combining quantitative and qualitative methods, including AI. In all cases, it is critical to explain why a specific method was used, the problem it addresses, and how it helps us better understand the theoretical mechanisms. Reviewing editors will evaluate whether the methods used are adequate to test the proposed theoretical relationship between constructs. If the answer is no, a desk reject is likely. Using multiple inadequate methods does not substitute for using a (single) adequate one.

Although theorizing is all about developing causal arguments, establishing causation is often empirically difficult. Authors should therefore avoid mentioning causation unless they can empirically test for it. Language should be precise and distinguish between association, e.g., an increase in political risk is associated with a decrease in foreign direct investment, and causation, e.g., an increase in an MNE’s foreign investments reduces its organizational slack. Note that all journals prefer to see evidence of causality, but will often accept association.

Match construct and empirical measure

Empirical research relies on proxy measures for theoretical constructs. More often than not, proxy measures are imperfect. The alignment between construct and measurement is critical in empirical research, and ideally already addressed at the design stage of a research project. Researchers doing survey-based studies typically develop custom-made measurement instruments, other researchers using those instruments in later studies need to make sure that the instruments align definitionally with their own theoretical constructs. Similarly, secondary data-based research often relies on data collected for other purposes, and hence the variables used to measure the theoretical constructs are often imperfect proxies.

One way to check if proxies are distal is to write the definition of a construct and the way the construct is measured on separate pieces of paper, and to then, without looking at the rest of the text, ask whether the two are aligned. With survey instruments, it can be useful to examine the individual items used to measure the construct. For example, research using Hofstede’s power distance dimension might compare Hofstede’s definition of the power distance construct with the original items used to measure it. Distal proxies are relatively easy for reviewing editors to detect, and are a common reason for desk rejects. Harking not only leads to poor stories, as explained earlier, but also to the use of distal proxies as authors try to retrofit an already-existing measure to a theoretical construct.

Link research question, theory, hypotheses, and implications

By the time the reviewing editor reaches the Discussion section, the primary focus is on the third criterion—contribution. It is not enough to provide a convincing answer to the research question. Authors must demonstrate that the answer contributes to a broader or deeper understanding of theoretical concerns or practical phenomena. Often described in terms of ‘implications’, what the Discussion section ideally accomplishes is an explanation of how the findings of the study should be understood, i.e., what the findings mean. A failure to position a manuscript’s contribution into a broader theoretical context may lead the reviewing editor to conclude that the manuscript’s contribution is narrow or trivial.

Theoretical implications are difficult to describe, yet doing so well is essential. One way to elicit them is by asking what changes should be made to extant theory to account for the empirical results found. When stating theoretical and empirical implications, it is best not to overreach and claim overly bold implications that do not logically follow from the findings. To sum up: reviewing editors look for a logical fit between the research question, the hypotheses, and the overall theoretical implications; and they expect the implications to be substantive.

Authors as prosecuting attorneys

The metaphor of trying a case in a court of law is useful when conceptualizing the challenges facing authors in getting their manuscripts published. Authors are like prosecuting attorneys in that they must have a convincing story supported by reliable witnesses and credible evidence. Prosecuting attorneys need to relate the various elements of a crime—motive, means, and opportunity—in a compellingly persuasive way. Likewise, authors must craft a story that explains a phenomenon, gather evidence—primary and secondary data, elicit reliable testimony from unimpeachable witnesses—authors of other relevant research, and finally, validate their closing arguments using quantitative and qualitative analytical tools. In essence, both prosecuting attorneys and authors are saying, “This is my story and I can back it up, so believe me.” In this metaphor, reviewing editors act like judges overseeing preliminary hearings in that they weigh the validity of the case before them. Is it strong enough, i.e., sufficiently credible, to warrant proceeding further? If a manuscript does not communicate persuasively that it is sufficiently compelling in terms of theory, method, analysis, and conclusion, the answer will be no, a desk reject.

We have attempted to demystify the desk-review stage of the review process by sharing our insights and the heuristics we use as reviewing editors. We trust that authors will find our suggestions helpful and look forward to reviewing their manuscripts. Our suggestions are subject to some limitations. Most of the articles published in the Journal of International Business Studies , and business and management journals more broadly, are hypothesis-testing. Thus, our recommendations are predominantly derived from reviewing such manuscripts. Relatedly, most manuscripts submitted to social science journals, including the Journal of International Business Studies , fall within the domain of the logical positivist tradition. Despite these limitations, we believe that following our suggestions can increase the probability a manuscript will pass the desk-review stage, which is a critical step towards publication.

Appendix: How to minimize the probability of a desk rejection

Can I explain the story of my paper in 2 min in non-academic language?

Suppose I take out the first and final sentence of each paragraph, do the two sentences make sense?

Is my story focused, straightforward, and not complicated?

Is my story about a theory or practice, not about a sample or method?

If I have a story on method or sample, do I explain why this matters theoretically?

Did I present the paper before submitting it?

Did I rehearse a 15-min presentation out loud?

Do figures and diagrams add substantively to descriptions and explanations in the text?

Write a clear introduction

Is my introduction in the range of 500 to 750 words?

Can I explain in one sentence why the topic matters to non-academics? (Don’t answer “yes,” write out the sentence).

Does my first paragraph clearly: (1) identify the topic, (2) explain why it matters, (3) describe what is already known?

Select the first and final sentence of each paragraph, do those two sentences make sense? And do those eight to ten sentences from the paragraphs in the Introduction pull the reader in?

Know your audience

Can I write down three names of scholars that I would like to read the article?

Can I explain why I selected these three names?

Did I check if members of the editorial team have recently published on the topic of my paper?

Do I stay within the recommended word length of the journal?

If I exceed the word length, do I provide an explanation for why in the accompanying cover letter?

Did I check the latest editorials in the journal?

Did I check if there are relevant forthcoming articles published on the website already?

Do I refer to articles published in the journal to which I am submitting?

Did I read the journal’s style guide and prepare my manuscript accordingly?

Am I explicit about what is novel in my paper?

Did I perform a search in the journal to which I am submitting using the key terms in my manuscript?

Is each sentence in the entire manuscript no longer than two lines?

Do I limit the number of abbreviations and acronyms in my article?

If I use an abbreviation, do I explain it the first time I introduce it?

Are figures and diagrams comprehensible without reference to the written text?

Are my tables and figures logically numbered and put at the end of the manuscript, not embedded in the main text?

Write a clear abstract

Does the abstract tell the story in the manuscript?

Does the abstract give the topic, research question (motivation), theoretical approach, empirical setting (if relevant), findings, and why the study matters (contribution)?

In the abstract, if I replace the key construct of the manuscript with some other key construct, does the abstract no longer make sense?

Did I ask colleagues to read my abstract without them knowing the entire paper?

Distinguish between literature review and theory

Does the literature review clearly frame my research question in terms of prior research?

Is my literature review focused on work relevant to my specific research question, the key constructs, and chosen theoretical lens?

Do I identify a specific theory, define key constructs, and delineate relevant premises/assumptions?

Do all references used in the text refer to the statement made in that particular sentence? (In other words, do I make sure there are no ‘casual’ references?)

Spell out theoretical mechanisms

Do I rely on a well-defined theoretical model?

Do I present a compelling logic, e.g., line of reasoning, rather than rely on references to prior empirical works to support my hypotheses?

If I combine multiple theories, do I explain how the assumptions of these theories are compatible?

Do I rule out alternative explanations for the findings I report?

Do my hypotheses have a counterfactual? Put differently, can my hypotheses also not be true?

Do I avoid hypotheses that include more than one relationship?

Do I minimize the use of quotations to make my argument?

Isolate theoretical channels empirically

Are my hypotheses predicated on a theoretical argument? Alternatively: Do I make sure my hypotheses are not predicated on empirical findings (i.e., merely a retest with a different data set of prior empirical findings?)

Do my hypotheses constitute tests of theoretical (as opposed to empirical) relationships?

If I test for moderating/interaction effects, do I discuss the economic effect size of the total effect (e.g., plot the marginal effects in a graph)?

Do I address endogeneity?

Do I discuss how my methods and measures are suitable to test for the mechanisms I theorize?

Do I describe how I arrive at my sample?

Do I explain why my sample is appropriate for answering my research question and testing my hypotheses?

Do I provide a table with the characteristics of the observations and possible subsamples (e.g., countries, firms per country, number of teams, etc.)?

If my data are nested, do I control for the nested structured of my data, for example using multi-level methods?

If I use multi-level methods, do I provide the intra-class correlations?

Do I include a correlation table?

Is each empirical proxy I use in my analysis closely aligned with its respective abstract construct in my theoretical model?

Do I explain how a measure that was developed and used in other studies is appropriate for use in my study?

If I adapt existing measures to my study context, do I explicitly explain why and how?

If my dependent and independent variables are from the same survey instrument, do I address and mitigate common method variance?

Do I provide a list of variables I use in my analysis (e.g., in the appendix)?

Do I write down the names of the variables in full in the tables and figures?

Do I provide data sources for all variables (in the text and in the appendix)?

If I use AI tools to collect my data, am I transparent on the process and coding?

Do I include references to the data sources in the paper (main text, footnote, reference)?

Do I provide references of scholars who have used the same measures?

Do I provide a discussion of the economic effect size?

Do I explain novelty in a consistent manner in the abstract, introduction, and discussion sections?

Do I identify theoretical implications of my findings (being careful not to extrapolate beyond what the method and data allow)?

Do I identify practical implications of my findings, i.e., specific, actionable options?

If I read the practical implications independent of the rest of the manuscript, are they meaningful? (In other words, do I make sure my implications are not obvious/generic?)

Do I clearly describe what I can explain and what I cannot explain (sometimes referred to as ‘limitations’) of my study?

Miscellaneous

If I submitted this manuscript before to another journal and it was rejected after review, did I incorporate the comments provided?

Did I prepare a cover letter?

Do I have a possible conflict of interest (e.g., colleagues who have reviewed the manuscript before, or an editor with whom I am close friends, or an editor who has been my co-author)? If yes, am I transparent about that in my cover letter?

If my manuscript is based on data I used in other manuscripts (published or not), do I explain this in my cover letter?

If my manuscript is based on data I used in other manuscripts (published or not), can I explain the difference in theory and/or variables used?

If this paper is part of a series of studies on a related topic, do I make sure there is no textual overlap between this new manuscript and other ones?

Did I check if all in-text references are listed?

Are all references in the same style and format and does that format comply with journal’s requirements?

Do I acknowledge the limits of using AI tools in my efforts to speak to the audience I have in mind?

Am I transparent about how, when, and where I have used AI in my study (e.g., literature review or analytical tools)?

Journals differ in who they nominate to handle the desk-reject stage. Sometimes it is the Editor-in-Chief, sometimes the Managing Editor, and sometimes, like at this journal, desk rejects are handled by dedicated reviewing editors.

See https://www.palgrave.com/gp/journal/41267/authors/review-process

Section 3.3.5 of the Journals Code of Ethics of the Academy of International Business provides helpful examples of potential conflicts of interest between authors and an editor or reviewer: “(1) one of the Authors is at the same institution as the nominated Editor or Reviewer; (2) one of the Authors was a member of the Editor or Reviewer’s dissertation committee, or vice versa; or (3) one of the Authors, and the Editor or Reviewer, are currently Co‐Authors on another manuscript or have been Co‐Authors on a manuscript within the past three years.”

See https://www.palgrave.com/gp/journal/41267/authors/frequently-asked-questions for a sample originality matrix.

https://www.palgrave.com/gp/journal/41267/authors/editorial-policy

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Beugelsdijk, S., Bird, A. How to avoid a desk reject: do’s and don’ts. J Int Bus Stud (2024). https://doi.org/10.1057/s41267-024-00712-8

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What is desk research?

Desk research vs. literature reviews, desk research as a research method, how do you properly tackle desk research, where do you find information for desk research.

Desk research means that you use previously collected data for your research instead of collecting it yourself. You answer your research question based on the existing data you analyze. This might include previous literature, company information, or other available data. What exactly is desk research and how do you conduct it properly?

When you do desk research, you collect existing data and use it to learn more about your research topic. You are not collecting quantitative or qualitative data yourself through things like surveys, interviews or observations.

In desk research, you work with secondary data (data collected by someone else). In field research, on the other hand, you work with primary data (data you collected yourself).

Desk research is an especially relevant research method if a lot of information on a topic is already available and/or if it is difficult to collect this data yourself. This type of research is less appropriate if you are one of the first to research the topic.

Often the terms "desk research" and "literature review" are used interchangeably. However, they don't mean exactly the same thing.

A literature review (also called "narrative review") is designed to gain more theoretical knowledge about a topic.

In desk research you collect existing research results or factual results in order to use them to explain a certain phenomenon. In doing so, you often answer an explanatory research question. You investigate a possible connection between variables.

You can use desk research as a research method on its own in your thesis. Your entire thesis research will then consist of desk research. In that case, you describe the results of the desk research in the results chapter. In the method chapter you explain how you approached the research.

It is also possible for you to use desk research as a stepping stone to a study that you will conduct yourself with data you have collected yourself. You arrive at hypotheses or theories through desk research that you then test through your own data collection. When you use desk research in this way, you incorporate its results into the theoretical framework. This type of research is called deduction .

You can also use desk research to supplement, for example, surveys, interviews , an experiment, or observations. In these cases, desk research can help explain the results you found.

If you are going to conduct desk research, you need to go through a number of stages to do so. For example, it is important that you select the right sources and report on the sources in a logical way. To do this, take the following steps:

Determine appropriate search terms. First, determine what search terms you will use to find sources through, for example, your educational institution's online library or Google Scholar. Often, you will use terms that appear in your problem statement or research question. Search for English search terms and any other languages you may want to include.

Find appropriate sources. You do this with the chosen search terms, but also, for example, by looking in the list of found sources. Perhaps there is another useful source cited by one of the articles you read through. Save all sources in one convenient folder and put them in your bibliography so you don’t forget them.

Determine which sources are relevant. Not all sources found are equally relevant to your topic. You also want to avoid having so many sources that you cannot see the forest for the trees. Check whether the sources you have found actually match your problem, research question, and research goal. Also check the reliability of the source. Ideally you should mostly use sources from leading journals and from authors who are affiliated with a scientific institute.

Incorporate the sources you found into your text. Are you doing a quantitative analysis? If so, you will first use SPSS or Excel. Are you referring verbatim to content from the sources? Then it's mainly a matter of putting relevant content together correctly in your thesis. Make sure you create a logical thread, for example by discussing sources by topic or in chronological order.

Review the bibliography. Make sure all sources from your desk research are correctly listed in the bibliography. Also, check the source citation. Make sure your sources are formatted in APA style (or the source citation style that applies to your course).

The sources you use must be relevant and reliable. You cannot use sources like Wikipedia. In terms of sources, consider, for example:

scholarly articles (which you can find through Google Scholar and your university or college's online library, among others);

statistics from organizations such as the Central Bureau of Statistics or other reputable research institutions;

LexisNexis: a database of newspapers where you can find all kinds of news sources;

reliable databases within your field;

collections published with an academic publisher;

annual reports or corporate reports;

reports from other agencies;

literature;

documents from archives;

reports from the municipality, for example;

photographs or art objects.

Sometimes you can use very different types of sources for your research. For example, are you doing research on Instagram posts? Then, of course, you can collect existing Instagram posts on social media for that purpose and they count as sources.

Getting your sources checked

For desk research you often use a large number of sources. Unfortunately, it is easy to make an error when citing your sources. Do you want to prevent errors from creeping in unnoticed? Have your sources checked by one of our editors. They will review every source manually and ensure that all sources are correctly listed in your bibliography.

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World Leaders in Research-Based User Experience

Secondary research in ux.

February 20, 2022 2022-02-20

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You don’t have to do all the user-research work yourself. If somebody else already ran a study (and published it), grab it!

Have you ever completed a project only to find out that something very similar has already been done in your organization a couple of years ago? That situation is common, especially with rising employee-churn rates, and fueled the popularity of research repositories (e.g., Microsoft Human Insights System) and the growth of the research-operations community . It should also inspire practitioners to do more secondary research.

Secondary research, also known as desk research or, in academic contexts, literature review, refers to the act of gathering prior research findings and other relevant information related to a new project. It is a foundational part of any emerging research project and provides the project with background and context. Secondary research allows us to stand on the shoulders of giants and not to reinvent the wheel every time we initiate a new program or plan a study.

This article provides a step-by-step guide on how to conduct secondary research in UX. The key takeaway is that this type of research is not solely an intellectual exercise, but a way to minimize research costs, win internal stakeholders and get scaffolding for your own projects.

Academic publications include a literature review at the beginning to showcase context or known gaps and to justify the motivation for the research questions. However, the task of incorporating previous results is becoming more and more challenging with a growing number of publications in all fields. Therefore, practitioners across disciplines (for instance in eHealth, business, education, and technology) develop method guidelines for secondary research.

Secondary research should be a standard first step in any rigorous research practice, but it’s also often cost-effective in more casual settings. Whether you are just starting a new project, joining an existing one, or planning a primary research effort for your team, it is always good to start with a broad overview of the field and existent resources. That would allow you to synthesize findings and uncover areas where more research is needed.

Secondary research shows which topics are particularly popular or important for your organization and what problems other researchers are trying to solve. This research method is widely discussed in library and information sciences but is often neglected in UX. Nonetheless, secondary research can be useful to uncover industry trends and to inspire further studies. For example, Jessica Pater and her colleagues looked at the foundational question of participant compensation in user studies. They could have opted for user interviews or a costly large-scale survey, yet through secondary research, they were able to review 2250 unique user studies across 1662 manuscripts published in 2018-2019. They found inconsistencies in participant compensation and suggested changes to the current practices and further research opportunities.

Secondary research can be divided into two main types: internal and external research.

Internal secondary research involves gathering all relevant research findings already available in your organization. These might include artifacts from the past primary research projects, maps (e.g., customer-journey map , service blueprint ), deliverables from external consultants, or results from different kinds of workshops (e.g., discovery, design thinking, etc.). Hopefully, these will be available in a research repository .

External secondary research is focused on sources outside of your organization, such as academic journals, public libraries, open data repositories, internet searches, and white papers published by reputable organizations. For example, external resources for the field of human-computer interaction (HCI) can be found at the Association for Computing Machinery (ACM) digital library , Journal of Usability Studies (JUS ), or research websites like ours . University libraries and labs like UCSD Geisel Library , Carnegie Mellon University Libraries , MIT D-Lab , Stanford d.school , and specialized portals like Google Scholar offer another avenue for directed search.

Our goal is to have the necessary depth, rigor, and usefulness for practitioners. Here are the 4 steps for conducting secondary research:

Choose the topic of research & write a problem statement .

Write a concise description of the problem to be solved. For example, if you are doing a website redesign, you might want to both learn the current standards and look at all the previous design iterations to avoid issues that your team already identified.

Identi fy external and internal resources.

Peer-reviewed publications (such as those published in academic journals and conferences) are a fairly reliable source. They always include a section describing methods, data-collection techniques, and study limitations. If a study you plan to use does not include such information, that might be a red flag and a reason to further scrutinize that source. Public datasets also often present some challenges because of errors and inclusion criteria, especially if they were collected for another purpose.

One should be cautious of the seemingly reputable “research” findings published across different websites in a form of blog posts, which could be opinion pieces, not backed up by primary research. If you encounter such a piece, ask yourself — is the conclusion of the writeup based on a real study? If the study was quantitative, was it properly analyzed (e.g., at the very least, are confidence intervals reported, and was statistical significance evaluated?). For all studies, was the method sound and nonbiased (e.g., did the study have internal and external validity )?

A more nuanced challenge involves evaluating findings based on a different audience, which might not be always generalizable to your situation, but may form hypotheses worthy of investigating. For example, if a design pattern is found okay to use by young adults, you may still want to know if this finding will also be valid for older generations.

Collect and analyze data from external and internal resources.

Remember that secondary research involves both the existing data and existing research. Both of those categories become helpful resources when they are critically evaluated for any inherent biases, omissions, and limitations. If you already have some secondary data in your organization, such as customer service logs or search logs, you should include them in secondary research alongside any existent analysis of such logs and previous reports. It is helpful to revisit previous findings, compare how they have or have not been implemented to refresh institutional memory and support future research initiatives.

Refine your problem statement and determine what still needs to be investigated.

Once you collected the relevant information, write a summary of findings, and discuss them with your team. You might need to refine your problem statement to determine what information you still need to answer your research questions. Next time your team is planning to adopt a trendy new design pattern, it may be a good idea to go back and search the web or an academic database for any evaluations of that pattern.

It is important to note that secondary research is not a substitute for primary research. It is always better to do both. Although secondary research is often cost-effective and quick, its quality depends to a large extent on the quality of your sources. Therefore, before using any secondary sources, you need to identify their validity and limitations.

Secondary (or desk) research involves gathering existing data from inside and outside of your organization. A literature review should be done more frequently in UX because it is a viable option even for researchers with limited time and budget. The most challenging part is to persuade yourself and your team that the existing data is worth being summarized, compared, and collated to increase the overall effectiveness of your primary research.

Jessica Pater, Amanda Coupe, Rachel Pfafman, Chanda Phelan, Tammy Toscos, and Maia Jacobs. 2021. Standardizing Reporting of Participant Compensation in HCI: A Systematic Literature Review and Recommendations for the Field. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Article 141, 1–16. https://doi.org/10.1145/3411764.3445734

Hannah Snyder. 2019. Literature review as a research methodology: An overview and guidelines. Journal of business research 104, 333-339. DOI: https://doi.org/10.1016/j.jbusres.2019.07.039.

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How To Do Secondary Research or a Literature Review

What is secondary research, why is secondary research important.

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Literature Review
Step 1: Develop topic
Step 2: Develop your search strategy
Step 3. Document search strategy and organize results
Systematic Literature Review Tips
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Secondary research, also known as a literature review , preliminary research , historical research , background research , desk research , or library research , is research that analyzes or describes prior research. Rather than generating and analyzing new data, secondary research analyzes existing research results to establish the boundaries of knowledge on a topic, to identify trends or new practices, to test mathematical models or train machine learning systems, or to verify facts and figures. Secondary research is also used to justify the need for primary research as well as to justify and support other activities. For example, secondary research may be used to support a proposal to modernize a manufacturing plant, to justify the use of newly a developed treatment for cancer, to strengthen a business proposal, or to validate points made in a speech.

Because secondary research is used for so many purposes in so many settings, all professionals will be required to perform it at some point in their careers. For managers and entrepreneurs, regardless of the industry or profession, secondary research is a regular part of worklife, although parts of the research, such as finding the supporting documents, are often delegated to juniors in the organization. For all these reasons, it is essential to learn how to conduct secondary research, even if you are unlikely to ever conduct primary research.

Secondary research is also essential if your main goal is primary research. Research funding is obtained only by using secondary research to show the need for the primary research you want to conduct. In fact, primary research depends on secondary research to prove that it is indeed new and original research and not just a rehash or replication of somebody else’s work.

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> How to Do Research
> Desk research

Book contents

Frontmatter
Acknowledgements
Introduction: Types of research
Part 1 The research process
Part 2 Methods
9 Introducing research methods
10 Desk research
11 Analysing desk research
12 Collecting quantitative data
13 Analysing quantitative data
14 Collecting qualitative data
15 Analysing qualitative data
16 Sources of further reading
Appendix The market for information professionals: A proposal from the Policy Studies Institute

10 - Desk research

from Part 2 - Methods

Published online by Cambridge University Press: 09 June 2018

Not all research is about collecting new data – or primary data as it is known in the trade. A great deal can be achieved by working with data that have already been collected and processed by others. Indeed, most good research begins with a review of what has gone before. This type of research is often referred to as desk research.

Some projects are solely concerned with desk research, relying entirely on the re-analysis of other people's research or on secondary analysis of data that have been collected by others. Even the research that is based on the collection of primary data usually has an element of desk research built in. Few researchers, for example, feel able to manage without some form of literature review or contextual work to position their research.

Desk research covers a range of activities. Literature reviews are the most common. Increasingly the term ‘literature’ needs to be expanded to include material found on the internet. Closely allied to these reviews, and growing in importance, are research reviews which focus on the analysis of actual research findings from a number of different studies. There is also secondary analysis of data where the focus is firmly on the reworking of existing data sets to develop new insights into issues.

This chapter concentrates on the collection of the material used in desk research. The analytical techniques will be dealt with in Chapter 11.

Literature and internet searching

This is a very important part of nearly all research projects, yet it is something that is often dealt with superficially.

No research project exists in isolation. Each piece of work relates in some way to the environment within which the research takes place, to the theories and concepts that have been developed to explain the environmental conditions and to other research on the topic. If your work is to have coherence and relevance you should take full account of what has gone before and what is going on around you. You therefore need to make sure you are fully aware of all the relevant literature on the subject.

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Desk research
Book: How to Do Research
Online publication: 09 June 2018
Chapter DOI: https://doi.org/10.29085/9781856049825.011

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IMAGES

Desk Research: Complete Guide & Best Practices
How to Write a Stellar Literature Review
90 Background Of The Study Vs Literature Review For FREE
90 Background Of The Study Vs Literature Review For FREE
15 Literature Review Examples (2024)
90 Background Of The Study Vs Literature Review For FREE

VIDEO

Difference Between Studies and Literature
LITERATURE DESK BEAT COIV
How to Construct and Interact with a Research Desk
How to Do a Good Literature Review for Research Paper and Thesis
Karur Book Festival 2024
The Power of a Systematic Literature Review: Unearthing Hidden Insights

COMMENTS

A Quick Overview: Differences Among Desk, Literature, and ...
Literature, Desk, and Learning Reviews are three ways to integrate evidence into decision-making and design processes. Each tool uses varying degrees of information and rigor, and each is best suited for different applications, as described in the visual below.
Desk Research: Definition, Types, Application, Pros & Cons
Reviewing the literature on a specific subject. Finding relevant data and statistics. Analyzing trends and patterns. Evaluating competitors and market trends. Supporting or challenging hypotheses. Validating or complementing primary research. Types of Desk Research Methods.
How to avoid a desk reject: do’s and don’ts | Journal of ...
Distinguish between theory and literature review. Authors sometimes confuse the literature review with the theory section. Whereas a literature review provides an overview of established findings thereby providing the frame into which a manuscript fits, a theory section provides a set of arguments (embedded in underlying assumptions) that logically lead to a proposition or testable hypothesis.
Analysing desk research (Chapter 11) - How to Do Research
If, on the other hand, you are analysing the results of a literature or internet search or you are conducting a review of research, you will need to adopt approaches that draw heavily on qualitative research.
Desk Research: The Essential Guide for Designers & UX ...
Nov 25, 2023. Despite its potential to provide valuable insights and help designers gain stakeholder confidence, desk research is often overlooked and undervalued. It can be a critical starting point for any design process. Photo by Marvin Meyer on Unsplash.
AthenaCheck - Desk research | What is it and how do you ...
Often the terms "desk research" and "literature review" are used interchangeably. However, they don't mean exactly the same thing. A literature review (also called "narrative review") is designed to gain more theoretical knowledge about a topic.
Literature review as a research methodology: An overview and ...
This paper discusses literature review as a methodology for conducting research and offers an overview of different types of reviews, as well as some guidelines to how to both conduct and evaluate a literature review paper.
Secondary Research in UX - Nielsen Norman Group
Secondary (or desk) research involves gathering existing data from inside and outside of your organization. A literature review should be done more frequently in UX because it is a viable option even for researchers with limited time and budget.
How To Do Secondary Research or a Literature Review
Secondary research, also known as a literature review, preliminary research, historical research, background research, desk research, or library research, is research that analyzes or describes prior research. Rather than generating and analyzing new data, secondary research analyzes existing research results to establish the boundaries of ...
Desk research (Chapter 10) - How to Do Research
Desk research covers a range of activities. Literature reviews are the most common. Increasingly the term ‘literature’ needs to be expanded to include material found on the internet.