Beware the Hawthorne Effect, notes Kate Cooper
In the 1950s, social scientist Henry A Landsberger knuckled down on one of the most valued meta studies – or research of research – ever carried out in the field of work. Essentially, Landsberger carried out a forensic review of numerous industrial experiments that his forerunner George Elton Mayo had conducted in the 1920s and 1930s.
Staged at a Chicago factory called the Hawthorne Works, Mayo’s experiments had aimed to discover the extent to which workers’ performance was influenced by their immediate surroundings. So, Mayo’s team made a variety of tweaks to the employees’ working conditions to see whether they would affect productivity
and, if so, how it would be affected.
When Landsberger cast his eyes over the findings, something stood out: it didn’t seem to matter whether Mayo’s tweaks involved the amount of light beamed on to the factory floor, the layout of the assembly lines, or the cleanliness of the workstations… each difference seemed to produce a result that was a) measurable, but not significant, and b) decidedly short-term. Landsberger concluded that the results had stemmed largely from the workers’ awareness that they were being watched. It was observation itself that had produced the outcomes. That phenomenon came to be known as the ‘Hawthorne Effect’.
It makes sense that if you’re into measuring, and have lots of different metrics for a whole range of workplace functions, then you will take advantage of those metrics and report to your senior leaders that you use them. And, because you’ve decided upon what you’re measuring, and you’re focusing on that with particularly keen attention to detail, you will clearly see some improvements over time.
As the Hawthorne Effect demonstrates, the problem with an overt attachment to analytics is that it can lead you to focus very narrowly – or even exclusively – on what is measurable and quantifiable. And you will essentially get what you measure. The risk is that you will base your decisions only upon known quantities – while a host of often extremely interesting unknown ones will pass by unnoticed.
The key is to provide clarity around what you expect from people. But, while the fulfilment of some of your expectations may be measurable, the meeting of others – particularly in the interpersonal realm – may not be so easy to pin down.
In the era of so-called ‘gig’ work, several key aspects remain unknown. Managers of such workers are often largely unaware of other demands on their time; how they like to manage their workload; and competing bids from other clients for their attention. Leaders might be able to specify the desired outputs in a particular timeframe, but they have very little insight into the impact their requests might have on the individual’s welfare or, indeed, the quality of the work they are likely to produce for us. In a traditional office setting we gain a clearer insight into ‘what else is going on’. Getting similar insight for the more isolated gig worker is far more challenging.
Consideration of emerging ways of working draws attention to the limitations of too much measuring of that we can measure. It reminds us not to lose sight of the more subjective ‘deliverables’. Paramount among these is the sustainability of good working relationships, whatever the employment status of those working with us.
There remains a role for measurement and monitoring in some areas. Leaders are encouraged to assess people’s abilities to complete specific tasks, and the time it takes them to do so. But beware the temptation to diminish the importance of more challenging metrics. Intangibles are often the most critical factors of all.