Use Technical Language Selectively

“Every human pastime—music, cooking, sports, art, theoretical physics—develops an argot to spare its enthusiasts from having to say or type a long-winded description every time they refer to a familiar concept in each other’s company. The problem is that as we become proficient at our job or hobby we come to use these catchwords so often that they flow out of our fingers automatically, and we forget that our readers may not be members of the clubhouse in which we learned them.”

- Pinker 2014, p. 51

As Pinker notes, technical language can help experts more efficiently communicate with insiders. Technical language, colloquially known as “jargon,” refers to words, phrases, or acronyms that are used by a particular profession or group but not by everyone else. Researchers develop technical terms so they don’t need to repeat a longwinded phrase each time they refer to something (Pinker 2014a). Incongruity is faster than writing “things that don’t fit together,” just as marketization uses fewer words than writing “a country transitioning from a planned economy to a market economy.” Technical language lets experimentalists discuss p-hacking or the results from their ANOVA without having to spell out the meaning of these terms to one another. An econometrician can simply shout “endogeneity!” while other quantitative researchers either nod knowingly or nervously try to defend their model, depending on whether they are in the audience or at the podium.

Technical language may be a good way to speak to the other researchers at a boutique conference, but it will make your writing harder for everyone else to understand. If you need to use technical language, start by explaining the word (or phrase) with concrete examples. Müller-Stewens and colleagues (2017) do this to introduce the phrase gamified information presentation:

“… firms have started to use games to present information about product innovations. For instance, Nike used a basketball video game called Nike Shox to convey information about its new “Shox” line of athletic shoes (Bogost 2007). In this application of gamified information presentation, consumers had the opportunity to configure a pair of shoes for themselves and to then experience the product’s characteristics in the video game.” (Müller-Stewens et al. 2017, 8)

Before they mention gamified information presentation, Müller-Stewens and colleagues explain what it means using simple language (“firms have started to use games to present information”) and a concrete example (Nike Shox). As a result, when readers first encounter the technical term, they already have a good sense of what it means.

Many technical terms are not specific to a research sub-discipline, and you can usually replace them with more familiar synonyms. Instead of imparting erroneous information to participants, you can lie to them. Instead of precluding people from retaining their remuneration, you can simply not pay them. A thesaurus or dictionary can sometimes help you simplify technical language. Changing elucidate to clarify, or explicate to explain, will make your writing easier to understand without being any less precise.

We suggest being especially vigilant about technical language in your title and abstract. These are the first things that readers will see, and you won’t have time to explain your technical language to them. A title like “The Interactive Effects of Brand Transgression and Personality on Digital Engagement” is less inviting than “When Good Brands Do Bad” (Aaker, Fournier, and Brasel 2004). Similarly, “The Effects of Resource Equality and Ease of Mapping Preferences on Perceptions of Equity” will appeal to a smaller audience than “Deciding Who Gets What Fairly” (Shaddy and Shah 2018).

We made up the technically written versions of these titles, but it is easy to find real examples of good research with baffling titles. Consider two forthcoming papers at Management Science: “The Effect of Patent Protection on Inventory Mobility” (Melero, Palomeras, and Wehrheim 2020), and “The Normalization of Consumer Valuations: Context-Dependent Preferences from Neurobiological Constraints” (Webb, Glimcher, and Louie 2020). Do you understand what these papers are about? We did not until we read the articles. These titles would be easier to understand and might attract more readers if the authors had used more familiar language. Perhaps, “Inventors Who Get More Patents Are Less Likely to Leave Their Job,” and “A Better Way to Model How People’s Preferences Depend on the Options in Front of Them,” respectively.

Writing is supposed to build a bridge between your reader and your ideas. Building that bridge with too much technical language is like building a bridge out of lace: it might look impressive, but there’s a good chance your readers won’t make it across.

Additional Reading

Made to Stick (Heath & Heath 2007): Ch. 1, “Simple”

Writing Tools (Clark 2008): Tool 11, “Prefer the simple over the technical”

Writing Science (Schimel 2012): Ch. 15, “Words”

Writing Without Bullshit (Bernoff 2016): Ch 7, “Replace Jargon”

“Why Academics Stink at Writing” (Pinker 2014b)

“Consequences of Erudite Vernacular Utilized Irrespective of Necessity” (Oppenheimer 2006)