The recent and continuing issues that our industry has with business ethics – from bribery to off-label selling – remind us that good intentions can have unintended consequences. Trying to increase sales can result in significant reputational damage.
Some of my research into the evolution of our industry is explaining this phenomenon of unintended consequences in scientific terms. It has significant practical implications, which I’d like to explain by beginning with the theory from both biological and management science.
In biology, we know that the genotype shapes the phenotype in all sorts of complex, interconnected ways. The correlations between the two have emerged from the evolutionary process of variation, selection and replication. Much of the current research in evolutionary biology is aimed at understanding the connections between genes and the traits that evolution ‘intended’. I use inverted commas here because the implication that evolution has a deliberate intent is metaphorical, not real.
An interesting sub-stream in this work is that which looks at pleiotropy, the phenomenon where a change in the genome influences more than one phenotypic trait. Pleiotropy is, in effect, the unintended consequence of gene variation and is implicated in many complex diseases. Sickle cell anaemia, for example, is a pleiotropic effect of a genetic mutation that favours resistance to malaria.
In organisations, the equivalent of genes are organisational routines, the stable collections of activities by which we do things. Just as organisms’ genomes vary, organisations vary their routines in order to gain some positive outcome. But my research with life science companies is revealing pleiotropic effects that are directly analogous to those in biology. There seem to be many examples of this, so allow me to illustrate my point with two interesting examples from my recent case studies.
Unexpected outcomes
In the first, the firm was heavily engaged in ‘mutating’ its organisational routines for how cross-functional brand teams work. In short, the change was from relatively autocratic to strongly collaborative decision-making. The firm believed that this led to better decisions and helped engage its highly-educated people. It also hoped for a better fit with the values of a workforce that is now more female than male. In fact, I observed some evidence of better decisions but also significant evidence of an unexpected outcome. In short, the fact that everyone shared in the team’s decisions led to no one feeling strong individual ownership of the brand strategy. Every decision was a compromise that no one felt strong commitment to. The collaborative decision-making gene had created the pleiotropic effect of reducing individual commitment to brand strategy.
In the second example, a failing launch programme was being reviewed to identify causes and corrective action. Aided by a relatively new information system and new products from its market information provider, the working group developed new routines for analysing data that included large data sets and some new algorithms. The intent, obviously, was to gain new, actionable insight on why the launch was failing. A little of this emerged but, towards the end of the process, a sales manager on the team offered her intuitive views on the problem. Immediately, she was challenged to present ‘data, not anecdote’. In her role, and with the resources available to her, this was impossible; so, her views, based on more customer-facing time than the rest of the group combined, were discounted and dismissed. The gene for rigorous data analysis had created the pleiotropic effect of rejecting any information that wasn’t quantitative, whatever its provenance.
These two examples show us that the complex relationship between the replicators (genes or routines) and the interactor (organism or organisation) often leads to unintended, pleiotropic effects. This is something that biologists seem to be more aware of than executives, who often dive into the organisational equivalent of genetic engineering with a naïve recklessness that their scientific counterparts would never contemplate.
Searching for answers
So what’s the practical implication? My work certainly doesn’t suggest that we should never attempt to engineer organisational routines to get better outcomes. But it does say we should consider pleiotropy. In both the examples I’ve described, there was a lot of good research available about organisational commitment and knowledge management that would have predicted the unintended consequences and allowed the firms to avoid them. But the executives involved didn’t know about the research and weren’t interested in looking for it. They would have achieved better outcomes if they had understood that pleiotropy is common and had built an organisational routine for using management science to predict it and manage it.