Want to Publish a Literature Review? Think of It as an Empirical Paper

By Tatiana Andreeva ?

When you’ve been reading a lot on a particular topic – for example, reviewing the literature for your research project or for your PhD – at some point it looks like you have enough material and reflections to publish this piece of work as a separate paper. Recognize this? If you ever tried it, you might know that publishing a literature review paper in an academic journal is a tricky task. The literature review publications come in so many forms, and there is no single cheat-sheet or established format like for empirical papers that you could follow to ensure success in publication.

Through my own journey of trial-and-error on this path, as well as through reviewing for journals and for PhD students who take my classes, I came up with an idea that will help you to increase the chances of publishing a literature review: think of a literature review as simply another empirical research project. Think of it as an empirical study, in which your data comes not from your usual fieldwork but from the articles that you review.

Many literature reviews can be thought of as a qualitative empirical study, in which the papers included in the review substitute interviews or field observations that you would usually collect and code. Some literature reviews, e.g., meta-analyses, are more like a quantitative empirical paper, in which various numbers you extract from the papers in your dataset substitute your survey data.

Seeing literature review in this way has three important implications for how we think about our literature review, and how we can design it to increase its chances of being interesting to others - that is, of being published.

Start with a relevant research problem and an interesting research question

We learn early in our academic career that any empirical paper should have a clear research problem and a clear research question. We frequently hear from journal editors and reviewers that just having a gap in the literature, or the fact that something has not been researched before, are not good enough to justify doing yet another empirical study. They say: you need to have a problem that your study can address, and you need to have a question that we currently don’t have an answer to. Only then your empirical study can add value to existing research.?

When we think of a literature review as of an empirical study, just with the different type of data at hand, we realize that the very same rationale applies. From this perspective the arguments that I often see in literature reviews – that there is no literature review in this particular area or that the existing literature reviews are quite dated – are not sufficient in the journal’s eyes to justify the publication of a literature review on a topic. If you aim to publish your literature review, start by thinking – what is the problem I would like to address? What would be my research question about this problem, that other readers would find interesting?

Design a methodologically-sound data collection and analysis protocol

When we think of any empirical study, we know that if we want to have reliable findings that will be accepted by our peers as trustworthy, we need to follow a transparent and well-thought data collection protocol. We also need to carefully choose and correctly apply relevant data analysis method. This goes without saying, right?

The same applies to the literature review! If we want our readers to trust our conclusions from the literature review, we need to make sure that the data we collect speaks to our research question, is of good quality, representative of the field, etc. The growing attention in business and management field to the systematic approach to literature reviews (Denyer & Tranfield, 2009; Rojon et al., 2021) reflects the rising expectations of the quality of the data used in literature review papers. Indeed, this approach offers exactly that: a clear data collection protocol, transparently communicated, so that someone else could replicate your study. For example, do the very same thing in 10 years and see how thinking on the topic has changed.

In the literature on doing literature reviews you will read that systematic literature review is only?one?of the types of literature reviews. Yet all recommendations on doing different types of the literature reviews share the idea that the data that you base your conclusions on has to be collected in a rigorous and transparent way (e.g., Callahan, 2014).?You may find these papers useful to understand how to ensure that your literature review “data collection” protocol meets the quality expectations: Denyer and Tranfield (2009), Rousseau et al. (2008) and Tranfield et al. (2003) introduce the systematic approach to identifying the articles to review, while Harari et al. (2020), Hiebl (2023) and Rojon et al. (2021) review current practices of systematic reviews in management research and provide advice on how to improve them.

So now you have all the papers you have carefully selected, how do you go about analysing them, so that peer academics would recognize your conclusions as reliable and robust? This is the trickiest part, and we have limited methodological advice published on this. Here are some of the methods / tools for literature analysis: conceptual matrices (Webster and Watson, 2002), Publish or Perish software (Harzing, 2017a & 2017b), content analysis (Gaur and Kumar, 2018), bibliometric methods such as co-citation, co-author and co-word analysis (Zupic and ?ater, 2015), citation context analysis (Anderson and Lemken, 2023), computational methods (Antons et al., 2023), multi-step categorization (Dwertmann and Knippenberg, 2021) and a method for synthesis of qualitative studies (Habersang et al., 2019).

For example, I found that a sophisticated coding rubric leveraged our literature analysis to a different level (Sergeeva & Andreeva, 2016), but must acknowledge that developing this rubric was one of the most challenging tasks of this review paper. In O’Higgins et al. (2021) we used a combination of qualitative content analysis with Pearson’s chi-squared (χ2) goodness of fit test in order to validate some of our conclusions. The trick is - as with any empirical study - your choice of the analytical method needs to fit with your research question. In sum, the message is: choose your method for analysis of the selected literature carefully, apply it rigorously, and explain it transparently.

Think of the theoretical contribution beyond description of the findings

When we think of our usual empirical work, be it qualitative or quantitative, we are well-aware that just the description of our data wouldn’t do. We know that we need to leverage what our data shows to explain how it informs the broader theory, how it compares to previous studies, what is new that we see from this data?

Again, the same logic applies to the literature reviews. In practice though, we often find it difficult to apply this advice to our literature review papers, because the description of the field in itself seems to be novel, especially if nobody did such a review before. In my experience, this argument does not persuade editors and reviewers of the journals, and often rightfully so.

For example, think of a typical quantitative empirical paper: a descriptive statistics table must be provided, but no one would claim a contribution based on it, right? Cropanzano (2009: 1306-1307) offers a good exercise that explains why reviewers often don’t buy the description of the field as a novel contribution. He suggests: imagine somebody who read all the primary articles in your dataset, would they still learn anything from your literature review? And if the answer is “no”, then it’s likely that your review paper doesn’t have yet the level of contribution that is needed to turn it into a publication.

I think this exercise can also help to stimulate your thinking of what a theoretical contribution of your literature review could be. For example, think – what it is that I see in this literature that others are not likely to see? Here are some papers that offer insights on how to leverage your literature review to have a theoretical contribution: Breslin and Gatrell (2023), Cropanzano (2009), Elsbach and van Knippenberg (2020), LePine and Wilcox-King (2010), Post et al. (2020) and Torraco (2016).

Remember: fit matters

Finally, one of the core ideas we learn about designing a high-quality empirical study, is that all elements discussed above - research question, types of the data, methods of data analysis and contributions - should fit with each other. The same applies to a literature review project! Kunisch et al. (2023) provide a detailed guidance on how to connect these pieces of a literature review together (see Table 2, pp. 20-21). ?

References

Anderson, M.H., Lemken, R.K. (2023) Citation context analysis as a method for conducting rigorous and impactful literature reviews. Organizational Research Methods, 26(1), 77-106. https://doi.org/10.1177/1094428120969905

Antons, D., Breidbach, C. F., Joshi, A. M., & Salge, T. O. (2023). Computational Literature Reviews: Method, Algorithms, and Roadmap. Organizational Research Methods, 26(1), 107-138. https://doi.org/10.1177/1094428121991230?

Breslin, D., Gatrell, C. (2023). Theorizing through literature reviews: The Miner-Prospector continuum. Organizational Research Methods, 26(1), 139-167. ?https://doi.org/10.1177/1094428120943288 ??????

Callahan, J.L. (2014). Writing literature reviews: A reprise and update.?Human Resource Development Review, 13(3), 271–275.?https://doi.org/10.1177/1534484314536705

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Disclaimer:

An earlier version of this article was published at Harzing.com: https://harzing.com/blog/2021/04/want-to-publish-a-literature-review-think-of-it-as-an-empirical-paper

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