The case for workplace inefficiency

IMAGINE for a moment that you are not a human with discerning media habits. Instead you have been turned into a gazelle and have just spotted a predator. Your first instinct would be to quickly get back into the car, until you remember that you are a gazelle and cannot drive. Your second instinct would be to run away as fast as possible.

You would probably not choose to bounce up and down with all four legs extended, using up energy while making yourself visible to the thing that wants to eat you. And yet “stotting” – or, even more delightfully, “pronking” – is a behaviour that gazelles and other prey animals routinely exhibit.

Why they do so is a matter of debate among biologists. It may well be a useful signal to the predator: that an animal is sufficiently fit and healthy that it’s not going to be easy to catch, or that the source of danger has been seen.

It might be for a different audience: a signal that a specimen with this much energy is going to make another gazelle very happy. But it entails risks, too: gazelles tend to skip stotting and start scarpering when a predator gets sufficiently close.

The idea of costly signalling – incurring a penalty of some kind in order to send a message – is not just of interest to evolutionary biologists. The human equivalents of pronking are important in workplaces, too.

If a manager wants to thank a team member for their work, for example, there are various options. They can send a quick text or e-mail. Or they can use an employee-engagement platform to send an industrialised message of appreciation.

These options are a lot better than silence, and they have the advantage of being quick. But nothing beats a handwritten note for the very reason that it is inconvenient. Someone has taken the time and trouble to do something wildly countercultural: find a pen and paper, write a note, perhaps even put it in an envelope. The costs that handwriting impose on the sender produce a clear signal that they really are grateful.

Meetings are another example. It is normal for large numbers of participants to be paying absolutely no attention to what is happening in a meeting because they are working on other tasks.

Some get-togethers are worse than others: research conducted by Cao Hancheng, now of Emory University, and his co-authors found that multitasking goes up when meetings are remote, when more people attend and when they drag on.

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But people routinely check phones, send messages and monitor e-mails in almost every gathering. Paying full attention to a meeting, especially a pointless one, may be a waste of time. But it signals commitment. It is white-collar pronking.

What you say can also be stotting. Candour carries risks in hierarchical organisations: every boss says they want honest feedback until you make the mistake of giving it. But saying what you think is also a way of earning trust and standing out. The willingness to say something distinctive, even if it risks giving offence, may become more valuable as artificial intelligence (AI) tools make everyone sound amiably alike.

AI will change the calculus behind other signals, too. Ethan Mollick of the Wharton School of the University of Pennsylvania says that if he asks his students whether they would want him to pen a letter of recommendation for them or have an AI produce a better reference, they all plump for the AI version.

However logical that choice may be, Prof Mollick says there is a point to writing his recommendation letters by hand. “The whole point is it takes 45 minutes. I’m setting my time on fire as a signal that I care about a student.”

Recruitment in general is a process infested by bots. AI tools send in applications on behalf of candidates; AI tools screen them on behalf of employers. The costs of having humans read through every application or conduct every interview are obvious, but there are benefits, too.

A paper published last year by Agata Mirowska of Neoma Business School and Jbid Arsenyan of Rennes School of Business examined people’s reactions to different types of screening technologies. Using AI to evaluate pre-recorded video interviews is seen as a signal that an employer is not that bothered about its people; having humans analyse these video interviews seems to be better for an organisation’s image.

Using up human time on a task that could be done better by a machine may seem inefficient, but it can be worthwhile. Optimisation usually makes sense. But sometimes it pays to pronk.

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