Freek Vermeulen reveals what is on his research agenda.
What is the first focus of your current research?
Academics look at markets and what determines prices. And economists, of course, say supply and demand, market power, differentiation and so on. But, together with my former PhD student Amandine Ody, who is now an assistant professor at Yale, I have been looking at the champagne market where emotion has enormous consequences. The sellers of grapes sell them to houses that turn them into champagne. They have certain houses that they like and certain houses that they don’t like. This says a lot about the workings of markets.
What surprised you about the research?
What did surprise me is that it’s largely unconsciously. People don’t think, I don’t like you, therefore, I will charge you more. But it happens. We interviewed a lot of grape growers and even though they might charge more to people they don’t like, they don’t think that others do so. Indeed, they even think that everyone pays about the same price for a kilo of grapes. That’s absolutely not true. So, it’s almost an unconscious, bottom-up process that this happens. You like certain houses, therefore, they get better prices.
On another front you have been looking at some management myths and why they persist.
Yes, another title for this strand of research is how bad practice prevails. There are some management practices which are simply harmful for firms. Established theory suggests all those firms will eventually die and shrink, and others will grow. So the harmful practice will gradually disappear.
The reality is that harmful management practices may spread and persist and sometimes never disappear.
One example I use is the size of newspapers. For hundreds of years they were enormous, although it’s inefficient and impractical for readers. It took centuries for newspaper companies to get their heads around the fact that people would actually appreciate smaller, more practically-sized newspapers.
That inefficient practice was eventually changed. But look at marketing for pharmaceutical companies, something called detailing, where they run around with a suitcase with pills and ointments trying to convince doctors to prescribe their drugs. There’s quite a bit of research showing that this is completely ineffective. But still, every company continues doing it.
Your research also looks at fertility clinics.
Yes, together with Mihaela Stan from UCL, I have just completed a study of fertility clinics in the UK. What is interesting here is that the British government decided, in the name of customer choice and transparency, that all fertility clinics had to publish their success rates. There is now a website which publishes the percentage of treatments that result in pregnancy for each clinic. Most people interested in using the clinics go online and check out the success rate.
This has had one unintended consequence: it is increasingly common throughout this industry that the clinics select who they let in. Some women become pregnant more easily than others. So if a 47-year-old with one ovary comes to a clinic they may well suggest that another clinic is more appropriate. Every 23-year-old in perfect health, they will let in.
The clinics that select heavily have higher success rates. Nothing surprising there. However, then we measured, in various ways, what we call the learning curve. All companies become better with experience and we call that the learning curve. What we found is very simple. The clinics which select women who are more likely to become pregnant perform better, but learn little. In contrast, the clinics which don’t select learn a great deal.
In fact, they learn so much more that, after a year or so, their success rate will overtake those of the clinics which are highly selective. Over time, the clinics which are doing all the difficult patients have higher success rates. They learn a lot from the difficult cases and what they learn also improves their performance in standard cases.
The really interesting thing is that no one in the industry is aware of this. They’re all aware of the short-term effect of being more selective and think that, commercially, they should select. In fact, they should not because, in the long run, you’re better off doing the difficult cases.
So what is the broader message from this research?
It points to the value of good academic research, because everybody in the industry can see the short-term effects, and that’s what they base their decisions on. But they don’t necessarily see the long-term effects demonstrated by our research.
This ties in with studies on the effects of outsourcing, for instance by my former colleague Markus Reitzig. Outsourcing boosts performance, but in the long run it leads to less innovation and so on. People base their decisions on the short-term effects. They don’t necessarily see the causal connection with the long-term effects, and that’s why you need research.
And this phenomena can be seen elsewhere.
Indeed, in a lot of industries. Law firms always say look at the percentage of cases we have won. But, some only take on cases that are very winnable. You don’t learn a lot from those, necessarily. Or look at where governments hire agencies to work with the long-term unemployed and pay them on the percentage of people that eventually get a job. What matters is how their chance of getting a job is improved rather than whether they actually get a job. But we simply measure the output and that can be very misleading.
In related research I have been looking at innovation. Of course, You can’t open a business book without being told that innovation is good and every organisation should be innovative. And yet, I can think of contexts where trying to innovate will harm you.
My research looked at the Chinese pharmaceutical industry. It showed that the average firm that tries to innovate in that industry is worse off than the ones that don’t. So, saying that innovation is good for everyone and everyone should try it, is a gross oversimplification.
It is interesting that research into innovation tends to be done in industries where there’s a lot of uncertainty and change as a result of innovation – industries like robotics or semiconductors. Basically they’re saying, innovation is important in environments where innovation is important.
Can you explain more?
So, here, let me show you a context where innovation is harmful, for instance. I picked the Chinese pharmaceutical industry because there’s a lot of change and uncertainty, but it is not due to innovation. It’s due to the changing institutional context in China, macroeconomic developments. In healthcare in China there is also a lot of change with competition from traditional Chinese medicine, and so on.
In such an environment, innovation may well cause harm. The reason is that innovation is a long-term thing. Internally it can make an organisation quite rigid, because it set itself on a path that it has to follow for a few years to bring the innovation to the market. In a fast-changing environment, that will actually put you on the back foot.
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