Lower unemployment rates mean that many industries, including hospitality, need ways to attract and retain more talent. Higher minimum wage laws in many states and cities have likely encouraged people to stay in jobs they may have previously left. But, what about using automation to get them to stay?
The typical assumption is that automation leads to fewer workers, which makes sense in many cases. The cotton gin took people out of the fields and it does not take as many people to put together a car now as it did 30 years ago. What automation also does is offload boring tasks so that people can do more interesting work. We see that in offices (no longer lots of people mindlessly typing memos all day) and now we are seeing a bit of it in the hospitality sector. Granted, most of the turnover in restaurants is due to still crappy pay and low benefits. But an employer quoted in the article thinks that it is partly due to the work itself (note, I was unable to find another dataset that confirmed this, but it makes for an interesting argument). From this perspective, a restaurant can provide more value to the employee (and, presumably the customer) by having that person deliver food instead of taking orders (which customers are doing themselves from kiosks or smart phones). Perhaps these are both minimum wage tasks and the former is more interesting for the worker than the latter.
The idea of reducing turnover by making the work more interesting goes back to the 1970’s. It is pretty simple: Most people do not want to do boring and repetitive tasks and they will be more satisfied and engaged with their work (e.g., more likely to stay) if it is not mundane. This is not rocket science. However, giving people more tasks and more autonomy may also require a different skill set. Where employers who choose this approach (either through job redesign or automation) miss the boat is when they implement these changes without considering whether employees have the skills sets necessary.
Most organizational change efforts I have observed save the planning for new selection systems or training until the end (if they are thought of at all). For instance, if I have always asked workers to follow one single process but now I am giving them the autonomy to override it, I need to understand that these are two different sets of performance expectations. If you asking for new behaviors from those in a job title, you need to be sure you are hiring people with those abilities using validated tests and/or provide them with proper training.
At some point, all of us are in a meeting where a discussion breaks out over whether a particular business initiative should be implemented. Someone will say, “I heard about it on a podcast/TedTalk,” or “A friend of mine at XYZ company did it and it worked for them,” or something similar. The question then is how do we really know that it will work under a given set of circumstances? While we never have 100% of the information we would like to have before making such a decision, we do have tools to help guide us.
Dennis Adsit and I entered such a discussion about intentionally letting go the bottom 10% of a company’s workforce annually a while back. This was one of Jack Welch’s tactics and it became known as “Rank and Yank” (R&Y). The idea behind it was that the amount of resources spent on better performers has a higher return on investment than putting them towards the lowest performers. After a bit of back and forth, we decided to test this the best way we could. The result was an article in Consulting Psychology Journal: Research and Practice.
There are two main takeaways from the article:
1) Under certain circumstances, R&Y may be a very viable option for improving organizational effectiveness. Dennis summarizes this well in this post.
2) When management comes to your team with “I’ve got a great idea…” you must be prepared to develop an analysis to respond to the request. It is this that I want to address a bit more.
People sometimes confuse having all of the information and having an evidenced-based recommendation. In our paper we simulate an outcome based upon a set of assumptions. We talked quite a bit about those assumptions before we accepted them. There were also cases where we thought different assumptions were important, so we ran the numbers under different conditions. This allowed us to draw better conclusions from the data.
In the article we chose to model call center agents for several reasons. Among them were that we knew from experience with clients that their job performance (after training) is consistent on a week-to-week and can be measured objectively. This helped in estimating the impact of turnover. But, we also found that others had measured the “softer” costs of turnover on agent performance. This served as an excellent reminder that with enough diligence and care there are many aspects to productivity that can be measured, but that are not. HR brings a lot of value to table when it rolls up its sleeves and digs into these issues.
It did not really matter than we chose to simulate the effectiveness of R&Y. It could have been a selection system, a management development program, or a training class. What is important is taking the time and effort to listen to others and work through the data. That allows HR to have significant value and impact.