With thousands of new business titles published every year, it can be difficult to know what to pay attention to. One way to sort out what is useful and accurate from the noise is to take a page from the philosophy of science. In his 2010 book Nonsense on Stilts, Massimo Pigliuicci points out that the type of evidence one adduces depends entirely on the sort of argument one hopes to make. If, for example, a theory is intended merely to be useful – that is, instrumental in achieving a desired outcome – then one needs to demonstrate predictive accuracy. In other words, theories are useful if they tell us what will happen next, and the most useful theories are simply the ones that do that best.
Assessing predictive accuracy requires very carefully controlled and repeated experiments, and at times a remarkably high tolerance for experimental error. Physics, the queen of the hard sciences, has risen to this challenge time and again and as a result that discipline’s long-term project has made enormous progress. We have abandoned theories of phlogiston and the ether for the Standard Theory of quantum mechanics thanks to a careful accumulation of data under increasingly well-controlled conditions. It is a long and complex chain from theoretical formulation to controlled experiments to the everyday world of middle-sized, middle-distance objects. But every link holds, for the predictive power of physics manifests itself continually through successful application in applied fields such as engineering.
In contrast, explanatory frameworks deal address a fixed and unchanging past. We cannot test a proposed explanation of what has already happened by turning back the clock and seeing if history plays out the same way again. We must therefore decide what wins based on the completeness of the explanation, the weight of circumstantial evidence, and wherever possible insist on the sort of evidence that Pigliucci calls a “smoking gun”: one or two critical facts that no other competing theory can plausibly account for.
Because of these differences in the nature of their supporting evidence, theories that have explanatory power can make no claims to predictive usefulness, and vice versa.
Consider now the last management book you read. What kind of evidence did it provide in support of its central claims? For most of you it very likely relied for evidence on an analysis of case studies, and out of that analysis emerged a framework purporting to explain why events turned out as they did – why a given company succeeded or failed, or why a given product was a hit or a flop.
Very often, however, the explicit claim is that the principles that have been extracted from an analysis of the past can be used to shape future outcomes in desired ways. In other words, most every management book I am familiar with – and certainly all the best-sellers – makes predictive claims based on explanatory power. Whether deliberate or not, it is a most unfortunate and damaging form of conceptual bait-and-switch.
Is there any way to avoid this, though? After all, the subject matter of management research – actual organizations functioning in the real world – does not lend itself to the kinds of carefully controlled experiments that allow us to test predictive accuracy in the usual ways. Perhaps we can do no better than simply infer predictive power on the basis of explanatory persuasiveness.
I disagree. A central objective of my book is to demonstrate that Disruption—a theory of innovation, of how particular products and services come to achieve success or dominance in markets, often at the expense of incumbent providers—has true predictive power. I hope to show this using the only kind of evidence one can when it comes to prediction: controlled experiments. My hope is that you will find these data sufficiently compelling that you will conclude that of all the management theories you are familiar with Disruption is uniquely useful.
The evidence in support of Disruption’s predictive power consists of controlled “laboratory” experiments. I use a portfolio of 48 new businesses funded and launched by Intel Corporation, one of the most successful and admired companies in the world, in which the company invested in total tens of millions of dollars.
To summarize the results, using Disruption to predict survival increases predictive accuracy by as much as 50% over Intel’s track record. Specifically, investments in the portfolio had a survival rate of 10%, a number comfortingly consistent with the rule of thumb for success in venture capital generally. In contrast, those using Disruption theory to pick winners out of that population had a success rate of up to 15%.
Of course, neither the data nor the experimental design is perfect, but perfection is the wrong benchmark. All I hope to convince you of is that Disruption can make you better than you are with respect to one critically important decision: assessing which businesses will live or die.
The bottom line of my argument is that if you understand and apply the principles described in this book you can, if you are as smart as your average MBA student, improve the frequency with which you pick winners – whether you invest in them or create and launch them yourself – by up to five percentage points.
To put those five percentage points in a broader context, it is worth remembering that even physics – our queen of the hard sciences , so impressive in its predictive and explanatory power – is a long way from having everything figured out. In addition to the long-standing difficulties of reconciling quantum mechanics and general relativity, current thinking is that we actually do not understand what the universe is made of.
Galaxies are rotating so fast that the gravitational force of the stars within them is insufficient to keep those galaxies from flying apart. To account for their coherence, physicists have invoked the notion of “dark matter”, which is really just a plug for whatever it is that is generating the additional gravitational force unaccounted for by the mass of the stars. At the same time, the universe is expanding, not contracting, which is what it should be doing thanks to all that dark matter that is supposedly out there. So, to counter-act the effects of the dark matter, cosmologists have ginned up “dark energy”, which is whatever is overcoming the dark matter and pushing the universe outward.
When you put it all together, according to current estimates, the universe is made up of 24% dark matter (whatever that is), 72% dark energy (whatever that is), and only 4% matter – the bit we actually think we understand. And yet, with our arms around barely 4% of the universe, look what we have been able to accomplish.
Maybe five percentage points is pretty good, after all.