In the mid-1920s at Western Electric’s manufacturing plant in Cicero, Illinois, the management began an experiment. The lighting in an area occupied by one set of workers was increased so there was better illumination to help them see the telephone relays they were building. Perhaps not surprisingly, workers who had more light were able to assemble relays faster.
Other changes were then made: Employees were given rest breaks. Their productivity increased. They were allowed to work shorter hours. Again, they were more efficient during those hours.
But then something weird happened. The lighting was cut back to normal … and productivity still went up. In fact, just about every change the company made had only one effect: increased worker productivity. After months of tinkering, the work conditions were returned to the original state, and workers built more relays than they did in the exact same circumstances at the start of the experiment.
What was happening? Why was it that no matter what the Hawthorne plant managers did, the workers just performed better? Researchers puzzled over the results, and some still doubt the details of the experiment’s protocols. But the study gave rise to what’s known in sociology as the Hawthorne effect.
The gist of the idea is that people change their behavior—often for the better—when they are being observed (which is why it’s sometimes called the observer effect). Those workers at Western Electric didn’t build more relays because there was more or less light or because they had more or fewer breaks. The Hawthorne effect posits that they built more relays simply because they knew someone was keeping track of how many relays they built.
It can already be difficult to quantify the exact ROI of Business Intelligence, but imagine the potential increase in service quality from implementing a system in a call center that allows a company to study detailed metrics down to the person level. According to the Hawthorne Effect, there may be improved performance not just from optimizing the number of staff working at a peak times or identifying call topics with long average call times that may indicate a need for additional training, but simply from the employees knowing that they can be more effectively measured and, in turn, held accountable. How does one quantify that?
Very interesting stuff. Wired is already my favorite magazine, and the focus of several articles (and the cover) of the latest issue is data and measurement, so I highly recommend it.