Fraud and the Road to Abilene

Over the weekend, an (anonymized) interview was published in a Dutch national newspaper with the three “whistle blowers” who exposed the enormous fraud of Professor Diederik Stapel. Stapel had gained stardom status in the field of social psychology but, simply speaking, had been making up all his data all the time. There are two things that struck me:

First, in a previous post I wrote about the fraud, based on a flurry of newspaper articles and the interim report that a committee examining the fraud has put together, I wrote that it eventually was his clumsiness faking the data that got him caught. Although that general picture certainly remained – he wasn’t very good at faking data; I think I could have easily done a better job (although I have never even tried anything like that, honest!) – but it wasn’t as clumsy as the newspapers sometimes made it out to be.

Specifically, I wrote “eventually, he did not even bother anymore to really make up newly faked data. He used the same (fake) numbers for different experiments, gave those to his various PhD students to analyze, who then in disbelief slaving away in their adjacent cubicles discovered that their very different experiments led to exactly the same statistical values (a near impossibility). When they compared their databases, there was substantial overlap”. Now, it now seems the “substantial overlap” was merely a part of one column of data. Plus, there were various other things that got him caught.

I don’t beat myself too hard over the head with my keyboard about repeating this misrepresentation by the newspapers (although I have given myself a small slap on the wrist – after having received a verbal one from one of the whistlers) because my piece focused on the “why did he do it?” rather than the “how did he get caught”, but it does show that we have to give the three whistle blowers (quite) a bit more credit than I – and others – originally thought.

The second point that caught my attention is that, since the fraught was exposed, various people have come out admitting that they had “had suspicions all the time”. You could say “yeah right” but there do appear to be quite a few signs that various people indeed had been having their doubts for a longer time. For instance, I have read an interview with a former colleague of Stapel at Tilburg University credibly admitting to this, I have directly spoken to people who said there had been rumors for longer, and the article with the whistle blowers suggests even Stapel’s faculty dean might not have been entirely dumbfounded that it had all been too good to be true after all... All the people who admit to having doubts in private state that they did not feel comfortable raising the issue while everyone just seemed to applaud Stapel and his Science publications.

This reminded me of the Abilene Paradox, first described by Professor Jerry Harvey, from the George Washington University. He described a leisure trip which he and his wife and parents made in Texas in July, in his parents’ un-airconditioned old Buick to a town called Abilene. It was a trip they had all agreed to – or at least not disagreed with – but, as it later turned out, none of them had wanted to go on. “Here we were, four reasonably sensible people who, of our own volition, had just taken a 106-mile trip across a godforsaken desert in a furnace-like temperature through a cloud-like dust storm to eat unpalatable food at a hole-in-the-wall cafeteria in Abilene, when none of us had really wanted to go”

The Abilene Paradox describes the situation where everyone goes along with something, mistakenly assuming that others’ people’s silence implies that they agree. And the (erroneous) feeling to be the only one who disagrees makes a person shut up as well, all the way to Abilene.

People had suspicions about Stapel’s “too good to be true” research record and findings but did not dare to speak up while no-one else did.

It seems there are two things that eventually made the three whistle blowers speak up and expose Stapel: Friendship and alcohol.

They had struck up a friendship and one night, fuelled by alcohol, raised their suspicions to one another. And, crucially, decided to do something about it. Perhaps there are some lessons in this for the world of business. For example, Jim Westphal, who has done extensive, thorough research on boards of directors, showed that boards often suffer from the Abilene Paradox, for instance when confronted with their company’s new strategy. Yet, Jim and colleagues also showed that friendship ties within top management teams might not be such a bad thing. We are often suspicious of social ties between boards and top managers, fearful that it might cloud their judgment and make them reluctant to discipline a CEO. But it may be that such friendship ties – whether fuelled by alcohol or not – might also help to lower the barriers to resolving the Abilene Paradox. So perhaps we should make friendships and alcohol mandatory – religion permitting – both during board meetings and academic gatherings. It would undoubtedly help making them more tolerable as well.

Bias (or why you can’t trust any of the research you read)

Researchers in Management and Strategy worry a lot about bias – statistical bias. In case you’re not such an academic researcher, let me briefly explain.

Suppose you want to find out how many members of a rugby club have their nipples pierced (to pick a random example). The problem is, the club has 200 members and you don’t want to ask them all to take their shirts off. Therefore, you select a sample of 20 of them guys and ask them to bare their chests. After some friendly bantering they agree, and then it appears that no fewer than 15 of them have their nipples pierced, so you conclude that the majority of players in the club likely have undergone the slightly painful (or so I am told) aesthetic enhancement.

The problem is, there is a chance that you’re wrong. There is a chance that due to sheer coincidence you happened to select 15 pierced pairs of nipples where among the full set of 200 members they are very much the minority. For example, if in reality out of the 200 rugby blokes only 30 have their nipples pierced, due to sheer chance you could happen to pick 15 of them in your sample of 20, and your conclusion that “the majority of players in this club has them” is wrong.

Now, in our research, there is no real way around this. Therefore, the convention among academic researchers is that it is ok, and you can claim your conclusion based on only a sample of observations, as long as the probability that you are wrong is no bigger than 5%. If it ain’t – and one can relatively easily compute that probability – we say the result is “statistically significant”. Out of sheer joy, we then mark that number with a cheerful asterisk * and say amen.

Now, I just said that “one can relatively easily compute that probability” but that is not always entirely true. In fact, over the years statisticians have come up with increasingly complex procedures to correct for all sorts of potential statistical biases that can occur in research projects of various natures. They treat horrifying statistical conditions such as unobserved heterogeneity, selection bias, heteroscedasticity, and autocorrelation. Let me not try to explain to you what they are, but believe me they’re nasty. You don’t want to be caught with one of those.

Fortunately, the life of the researcher is made easy by standard statistical software packages. They offer nice user-friendly menus where one can press buttons to solve problems. For example, if you have identified a heteroscedasticity problem in your data, there are various buttons to press that can cure it for you. Now, note that it is my personal estimate (but notice, no claims of an asterisk!) that about 95 out of a 100 researchers have no clue what happens within their computers when they press one of those magical buttons, but that does not mean it does not solve the problem. Professional statisticians will frown and smirk at the thought alone, but if you have correctly identified the condition and the way to treat it, you don’t necessarily have to fully understand how the cure works (although I think it often would help selecting the correct treatment). So far, so good.

Here comes the trick: All of those statistical biases are pretty much irrelevant. They are irrelevant because they are all dwarfed by another bias (for which there is no life-saving cure available in any of the statistical packages): publication bias.

The problem is that if you have collected a whole bunch of data and you don’t find anything or at least nothing really interesting and new, no journal is going to publish it. For example, the prestigious journal Administrative Science Quarterly proclaims in its “Invitation to Contributors” that it seeks to publish “counterintuitive work that disconfirms prevailing assumptions”. And perhaps rightly so; we’re all interested in learning something new. So if you, as a researcher, don’t find anything counterintuitive that disconfirms prevailing assumptions, you are usually not even going to bother writing it up. And in case you’re dumb enough to write it up and send it to a journal requesting them to publish it, you will swiftly (or less swiftly, dependent on what journal you sent it to) receive a reply that has the word “reject” firmly embedded in it.

Yet, unintended, this publication reality completely messes up the “5% convention”, i.e. that you can only claim a finding as real if there is only a 5% chance that what you found is sheer coincidence (rather than a counterintuitive insight that disconfirms prevailing assumptions). In fact, the chance that what you are reporting is bogus is much higher than the 5% you so cheerfully claimed with your poignant asterisk. Because journals will only publish novel, interesting findings – and therefore researchers only bother to write up seemingly intriguing counterintuitive findings – the chance that what they eventually are publishing is BS unwittingly is vast.

A recent article by Simmons, Nelson, and Simonsohn in Psychological Science (cheerfully entitled “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant”) summed it up prickly clearly. If a researcher, running a particular experiment, does not find the result he was expecting, he may initially think “that’s because I did not collect enough data” and collect some more. He can also think “I used the wrong measure; let me use the other measure I also collected” or “I need to correct my models for whether the respondent was male or female” or “examine a slightly different set of conditions”. Yet, taking these (extremely common) measures raises the probability that what the researcher finds in his data is due to sheer chance from the conventional 5% to a whopping 60.7%, without the researcher realising it. He will still cheerfully put the all-important asterisk in his table and declare that he has found a counterintuitive insight that disconfirms some important prevailing assumption.

In management and strategy research we do highly similar things. We for instance collect data with two or three ideas in mind in terms of what we want to examine and test with them. If the first idea does not lead to a desired result, the researcher moves on to his second idea and then one can hear a sigh of relief behind a computer screen that “at least this idea was a good one”. In fact, you might only be moving on to “the next good idea” till you have hit on a purely coincidental result: 15 bulky guys with pierced nipples.

Things get really “funny” when one realises that what is considered interesting and publishable is different in different fields in Business Studies. For example, in fields like Finance and Economics, academics are likely to be fairly skeptical whether Corporate Social Responsibility is good for a firm’s financial performance. In the subfield of Management people are much more receptive to the idea that Corporate Social Responsibility should also benefit a firm in terms of its profitability. Indeed, as shown by a simple yet nifty study by Marc Orlitzky, recently published in Business Ethics Quarterly, articles published on this topic in Management journals report a statistical relationship between the two variables which is about twice as big as the ones reported in Economics, Finance, or Accounting journals. Of course, who does the research and where it gets printed should not have any bearing on what the actual relationship is but, apparently, preferences and publication bias do come into the picture with quite some force.

Hence, publication bias vastly dominates any of the statistical biases we get so worked up about, making them pretty much irrelevant. Is this a sad state of affairs? Ehm…. I think yes. Is there an easy solution for it? Ehm… I think no. And that is why we will likely all be suffering from publication bias for quite some time to come.

Most People Don't Know Their Business (so asking them is useless)

I’ll admit it; I am rapidly becoming a skeptic when it comes to interview-based data. And the reason is that people (interviewees) just don’t know their business – although, of course, they think they do.

For example, in an intriguing research project with my (rather exceptional) PhD student Amandine Ody, we asked lots of people in the Champagne industry whether different Champagne houses paid different prices for a kilogram of their raw material: grapes. The answer was unanimously and unambiguously “no”; everybody pays more or less the same price. But when we looked at the actual data (which are opaque at first sight and pretty hard to get), the price differences appeared huge: some paid 6 euros for a kilogram, others 8, and yet other 10 or even 12. Thinking it might be the (poor) quality of the data, we obtained a large sample of similar data from a different source: supplier contracts. Which showed exactly the same thing. But the people within the business really did not know; they thought everybody was paying about the same price. They were wrong.

Then Amandine asked them which houses supplied Champagne for supermarket brands (a practice many in the industry thoroughly detest, but it is very difficult to observe who is hiding behind those supermarket labels). They mentioned a bunch of houses, both in terms of the type of houses and specific named ones, who they “were sure were behind it”. And they quite invariably were completely wrong. Using a clever but painstaking method, Amandine deduced who was really supplying the Champagne to the supermarkets, and she found out it was not the usual suspects. In fact, the houses that did it were exactly the ones no-one suspected, and the houses everyone thought were doing it were as innocent as a newborn baby. They were – again – dead wrong.

And this is not the only context and project where I have had such experiences, i.e. it is not just a French thing. With a colleague at University College London – Mihaela Stan – we analyzed the British IVF industry. One prominent practice in this industry is the role of a so-called integrator; one medical professional who is always “the face” towards the patient, i.e. a patient is always dealing with one and the same doctor or nurse, and not a different one very time the treatment is in a different stage. All interviewees told us that this really had no substance; it was just a way of comforting the patient. However, when we analyzed the practice’s actual influence – together with my good friend and colleague Phanish Puranam – we quickly discovered that the use of such an integrator had a very real impact on the efficacy of the IVF process; women simply had a substantially higher probability of getting pregnant when such an integrator, who coordinates across the various stages of the IVF cycle, was used. But the interviewees had no clue about the actual effects of the practice.*

My examples are just conjectures, but there is also some serious research on the topic. Olav Sorenson and David Waguespack published a study on film distributors in which they showed that these distributors’ beliefs about what would make a film a success were plain wrong (they just made them come true by assigning them more resources based on this belief). John Mezias and Bill Starbuck published several articles in which they showed how people do not even know basic facts about their own companies, such as the sales of their own business unit, error rates, or quality indicators. People more often than not were several hundreds of percentages of the mark, when asked to report a number.

Of course interviews can sometimes be interesting; you can ask people about their perceptions, why they think they are doing something, and how they think things work. Just don’t make the mistake of believing them.

Much the same is true for the use of questionnaires. They are often used to ask for basic facts and assessments: e.g. “how big is your company”, “how good are you at practice X”, and so on. Sheer nonsense is the most likely result. People do not know their business, both in terms of the simple facts and in terms of the complex processes that lead to success or failure. Therefore, do yourself (and us) a favor: don’t ask; get the facts.


* Although this was not necessarily a “direct effect”; the impact of the practice is more subtle than that.

The Lying Dutchman: Fraud in the Ivory Tower

The fraud of Diederik Stapel – professor of social psychology at Tilburg University in the Netherlands – was enormous. His list of publications was truly impressive, both in terms of the content of the articles as well as its sheer number and the prestige of the journals in which it was published: dozens of articles in all the top psychology journals in academia with a number of them in famous general science outlets such as Science. His seemingly careful research was very thorough in terms of its research design, and was thought to reveal many intriguing insights about fundamental human nature. The problem was, he had made it all up…

For years – so we know now – Diederik Stapel made up all his data. He would carefully review the literature, design all the studies (with his various co-authors), set up the experiments, print out all the questionnaires, and then, instead of actually doing the experiments and distributing the questionnaires, made it all up. Just like that.

He finally got caught because, eventually, he did not even bother anymore to really make up newly faked data. He used the same (fake) numbers for different experiments, gave those to his various PhD students to analyze, who then in disbelief slaving away in their adjacent cubicles discovered that their very different experiments led to exactly the same statistical values (a near impossibility). When they compared their databases, there was substantial overlap. There was no denying it any longer; Diederik Stapel, was making it up; he was immediately fired by the university, admitted to his lengthy fraud, and handed back his PhD degree.

In an open letter, sent to Dutch newspapers to try to explain his actions, he cited the huge pressures to come up with interesting findings that he had been under, in the publish or perish culture that exist in the academic world, which he had been unable to resist, and which led him to his extreme actions.

There are various things I find truly remarkable and puzzling about the case of Diederik Stapel.
• The first one is the sheer scale and (eventually) outright clumsiness of his fraud. It also makes me realize that there must be dozens, maybe hundreds of others just like him. They just do it a little bit less, less extreme, and are probably a bit more sophisticated about it, but they’re subject to the exact same pressures and temptations as Diederik Stapel. Surely others give in to them as well. He got caught because he was flying so high, he did it so much, and so clumsily. But I am guessing that for every fraud that gets caught, due to hubris, there are at least ten other ones that don’t.
• The second one is that he did it at all. Of course because it is fraud, unethical, and unacceptable, but also because it sort of seems he did not really need it. You have to realize that “getting the data” is just a very small proportion of all the skills and capabilities one needs to get published. You have to really know and understand the literature; you have to be able to carefully design an experiment, ruling out any potential statistical biases, alternative explanations, and other pitfalls; you have to be able to write it up so that it catches people’s interest and imagination; and you have to be able to see the article through the various reviewers and steps in the publication process that every prestigious academic journal operates. Those are substantial and difficult skills; all of which Diederik Stapel possessed. All he did is make up the data; something which is just a small proportion of the total set of skills required, and something that he could have easily outsourced to one of his many PhD students. Sure, you then would not have had the guarantee that the experiment would come out the way you wanted them, but who knows, they could.
• That’s what I find puzzling as well; that at no point he seems to have become curious whether his experiments might actually work without him making it all up. They were interesting experiments; wouldn’t you at some point be tempted to see whether they might work…?
• Truly amazing I also find the fact that he never stopped. It seems he has much in common with Bernard Madoff and his Ponzi Scheme, or the notorious traders in investments banks such as 827 million Nick Leeson, who brought down Barings Bank with his massive fraudulent trades, Societe Generale’s 4.9 billion Jerome Kerviel, and UBS’s 2.3 billion Kweku Adoboli. The difference: Stapel could have stopped. For people like Madoff or the rogue traders, there was no way back; once they had started the fraud there was no stopping it. But Stapel could have stopped at any point. Surely at some point he must have at least considered this? I guess he was addicted; addicted to the status and aura of continued success.
• Finally, what I find truly amazing is that he was teaching the Ethics course at Tilburg University. You just don’t make that one up; that’s Dutch irony at its best.

What's wrong with senior executive pay – lots in my view

There are three things I do not like about top management pay: 1) they usually get paid too much, 2) way too large a part is flexible, performance-related pay, 3) often, a very sizeable chunk of it is paid through stock options.

I used to think - naively - that high top management pay was high simply due to supply and demand: these smart people with lots of business acumen and experience are hard to come by; therefore you have to pay them lots. These grumpy anti-corporates claiming their pay is too high are just envious and naive. Turns out I was (maybe not envious, but certainly naive).

Pay level

Because digging into the rigorous research on the topic - and there is quite a bit of it - I learned that there is really not much of a relationship between firm performance and top management pay. These guys (mostly guys) get paid a lot whether or not their company's performance is any good. Moreover, I learned what sort of factors push up top managers' remuneration - and it ain't supply and demand. It has much more to do with selecting the right company directors (to serve on your remumeration committee) and making sure you are well networked and socialized into the business elite.* Now I have to conclude: top management pay is generally too high, and quite a bit too high.

Flexible pay

Secondly: where does this absurd idea come from that 80+ percent of these guys' remuneration has to be performance related?! "To reward them for good performance and stimulate them to act in the best interest of the company and its shareholders" you might say? To which I would reply "oh, come on!?" If your CEO is the type of guy who needs 90 percent performance-related pay or otherwise he won't act in the best interest of the company, I would say the perfect time to get rid of him is yesterday. You and I do not need 90 percent performance related pay to do our best, do we? So why would it be allowed to hold for top managers? As Henry Mintzberg put it: "Real leaders don't take bonuses".

Moreover, one should only pay performance-related remuneration if you can actually measure the person's performance. And that is - especially for top managers - actually pretty darn hard to do. The strategic decisions one takes this year will often only be felt 5 or 10 years from now, if not longer. Moreover, the performance of the company - which we always take to proxy the CEO's performance - is influenced by a whole bunch of other things; many not under a CEO's control. Hence, short term financial performance figures are a terrible indicator of a top manager's performance in the job and long-term performance contracts all but impossible to specify. If you can't reliably measure performance, don't have performance-related pay, and certainly not 80+ percent of it. We know from ample research that humans start manipulating their performance when you tie their remuneration to some strange metric and, guess what, CEOs are pretty human (at least in that respect); they do too.

Options

Finally: stock options... Once again, I have to say "oh, come on...". We pretty much take for granted that we pay top managers by awarding them options, but don't quite realize any more why. When I ask this question to my students or the executives in my lecture room ("why do we actually pay them in options...?") usually a stunned silence follows after which someone mumbles "because they are cheap to hand out...?". I usually try to remain polite after such an answer but why would they be cheap; cheaper than cash, or shares for that matter? True, it does not cost you anything out of pocket if you give them an option to buy shares for say 100 one year from now, while your present share price is 90, but if the share price by that time is 150 it does cost you 50. Moreover, you could have sold that stock option to someone who would have happily paid you good money for it, so in terms of opportunity costs it is realy money too. No, stock options are not cheaper than cash, shares, or whatever.

We give them options to stimulate them to take more risk. "Risk?! We want them to take more risk?!" thou might think. Yes, that's what you are doing if you give them options. If the share price is 150 at the time the option expires, the CEO can buy the shares at 100 and thus make 50. However, if the share price is 90 the option is worthless, and the CEO does not make anything. However, the trick is that the CEO then does not care whether the share price is 90 or, say, 50 - in either case he does not make any money; worthless is worthless. As a consequence, when his options (i.e. the right to buy shares at 100) are about to expire and the company's share price is still 90, he has a great incentive to quickly take a massive amount of risk. Going to a roulette table would already be a rational to do.

Because if you placed the company's capital on red, and the ball hits red, share price may jump from 90 to 130, and suddenly your options are worth a lot of money (130-100 to be precise). However, if your bet fails, the ball hits black and you lose a ton of money, who cares; the share price may fall from 90 to 50, but your options were worthless anyway. Hence, options give a top manager the upside risk, as we say, but do not give them the downside risk. Therefore, we incentivize them to take risk. You might think "I seldom see herds of CEOs in a casino by the time options expire, so this grumpy Vermeulen guy must be exaggerating" but I'd reply we have seen quite a lot of casino-type strategy in various businesses lately (e.g. banks). More importantly, we know from research that CEOs do take excessive risk due to stock options (see for instance Sanders and Hambrick, 2007; Zhang e.a. 2008). I think it would be naive to think that we give CEOs 90 percent performance related pay and most of it in stock options, and then think that they will not start acting in the way the remuneration system stimulates them to do. Of course it influences their decisions, and if it didn't, there would be no reason left to make their pay flexible and based on options, now would there?

Therefore, I would say, out with the performance-related pay for top managers (a good bottle of wine at Christmas and, if you insist, a small cheque like the rest of us would do). And while we're at it, let's try to reduce the level as well.


* e.g. O’Reilly, Main, and Crystal, 1988; Porac, Wade, and Pollock, 1999; Westphal and Zajac, 1995.

Can entrepreneurship be taught?

“Entrepreneurship can only be self-taught. There are many ways to do it right and even more wrong, but it cannot be processed, bottled, packaged, and delivered from a lectern”, one of my readers – Michael Marotta – commented on an earlier post.

I am not sure I agree with the suggestion of that statement, namely that "entrepreneurship can only be self-taught". Of course we hear it more often - "you cannot teach entrepreneurship" - but I have yet to see any evidence of it. Granted, this is a weak statement, since the evidence that business education helps with anything is rather scarce (although there is some)!

However, the fact that the majority of entrepreneurs did not have formal business education does not tell me anything. Suppose out of 1000 attempted entrepreneurs indeed only 100 had formal business education. It might still be very possible that out of the 100, 50 of them became successful, where out of the 900 others only 300 became successful. This means that out of the 350 successful entrepreneurs, a mere 50 had formal business education. However, 50% of business educated entrepreneurs became successful, while only 1/3 of entrepreneurs without business education did.

My feeling about the potentially influence of business education on the odds of becoming a successful entrepreneur are quite the opposite of Marotta’s. I see quite a few attempted entrepreneurs with good business ideas and energy, however, they make some basic mistakes when attempting to build it into a business. The sheer logic of how to set up a viable business - once you have had a good idea - is something that is open to being "processed, bottled, packaged, and delivered from a lectern" (although that is hardly what we do in B-school).

Having a great idea and ample vision and energy perhaps is a necessary condition for becoming a successful entrepreneur, but it is not sufficient; this requires many other skills, and for some of them, education helps. Out of the 10 different skills needed to become a successful entrepreneur, perhaps only 5 can be taught or enhanced through business education, but those 5 will clearly improve your odds of making it.

Perhaps the majority of successful entrepreneurs do not have formal business education, but I have yet to meet a successful enterpreneur who did go to business school who proclaims his/her education was not a great help in becoming a success. Invariably, those people claim their education helped them a lot. In fact, many of such business school alumni donate generously to their alma mater. For example, one of London Business School's successful alumni entrepreneurs, Tony Wheeler (founder of Lonely Planet travel guides) regularly donates very substantial amounts of money to the School, because he believes his education there helped him greatly in making his business a success, and he wants others to have the same experience and opportunity.

In the absence of any formal evidence on whether business school education helps or hinders becoming a successful entrepreneur, I am inclined to rely on their judgement: business school education helps, if you want to become a successful entrepreneur.

Steve Jobs’ deification serves a very basic and fundamental human need

“I am not that surprised that an academic of entrepreneurship (are you kidding me?) would lead a story about one of the world's best innovators and CEO's about that he actually and in fact ! OMG had body odour as a teenager because of his diet, not to mention the rest of your embarrassing piece. Forbes would be best sticking with writers that are inspired by such great entrepreneurs as Steve Jobs, and not with writers such as this, who are unhappy they have not had the courage to 'live the life they love and not settle' and so sit in front of their computer with not much else to do but trying to bring others down. Shame on you Mr Vermeulen”.

This is just one of the comments I received on my earlier piece “Steve Jobs – the man was fallible” (also published on my Forbes blog). Of course, this was not unanticipated; having the audacity to suggest that, in fact, the great man did not possess the ability to walk on water was the closest thing to business blasphemy. And indeed a written stoning duly followed.

But why is suggesting that a human being like Steve Jobs was in fact fallible – who, in the same piece, I also called “a management phenomenon”, “fantastically able”, “a legend”, and “a great leader” – by some considered to be such an act of blasphemy? All I did was claim that he was “fallible”, “not omnipotent”, and “not always right”, which as far as I can see comes with the definition of being human?

And I guess that’s exactly it; in life and certainly in death Steve Jobs transcended the status of being human and reached the status of deity. A journalist of the Guardian compared the reaction (especially in the US) to the death of Steve Jobs with the reaction in England to the death of Princess Diana; a collective outpour of almost aggressive emotion by people who only ever saw the person they are grieving about briefly on television or at best in a distance. Suggesting Princess Diana was fallible was not a healthy idea immediately following her death (and still isn’t); nor was suggesting Steve Jobs was human.

We are inclined to deify successful people in the public eye, and in our time that certainly includes CEOs. In the past, in various cultures, it may have been ancient warriors, Olympians, or saints. They became mythical and transcended humanity, quite literally reaching God-like status.

Historians and geneticists argue that this inclination for deification is actually deeply embedded in the human psyche, and we have evolved to be prone to worship. There is increasing consensus that man came to dominate the earth – and for instance drive out Neanderthalers, who were in fact stronger, likely more intelligent, and had more sophisticated tools – because of our superior ability to organize into larger social systems. And a crucial role in this, fostering social cohesion, was religion, which centers on myths and deities. This inclination for worship very likely became embedded into our genetic system, and it is yearning to come out and be satisfied, and great people such as Jack Welch, Steve Jobs, and Lady Di serve to fulfill this need.

But that of course does not mean that they were infallible and could in fact walk on water. We just don’t want to hear it. Great CEOs realize that their near deification is a gross exaggeration, and sometimes even get annoyed by its suggestion – Amex’s Ken Chenault told me that he did not like it at all, and I have seen that same reaction in Southwest’s Herb Kelleher. Slightly less-great CEOs do start to believe their own status, and people like Enron’s Jeff Skilling or Ahold’s Cees van der Hoeven come to mind; not coincidentally they are often associated with spectacular business downfalls. I have never spoken to Steve Jobs, but I am guessing he might not have disagreed with the qualifications “not omnipotent”, “not always right” and, most of all, “human”.

Steve Jobs – the man was fallible

As a student, at Reed College, Steve Jobs came to believe that if he ate only fruits he would eliminate all mucus and not need to shower anymore. It didn’t work. He didn’t smell good. When he got a job at Atari, given his odor, he was swiftly moved into the night shift, where he would be less disruptive to the nostrils of his fellow colleagues.

The job at Atari exposed him to the earliest generation of video games. It also exposed him to the world business and what it meant build up and run a company. Some years later, with Steve Wozniak, he founded Apple in Silicon Valley (of course in a garage) and quite quickly, although just in his late twenties, grew to be a management phenomenon, featuring in the legendary business book by Tom Peters and Bob Waterman “In Search of Excellence”.

But, in fact, shortly after the book became a bestseller, by the mid 1980s, Apple was in trouble. Although their computers were far ahead of their time in terms of usability – mostly thanks to the Graphical User Interface (based on an idea he had cunningly copied from Xerox) – they were just bloody expensive. Too expensive for most people. For example, the so-called Lisa retailed for no less than $10,000 (and that is 1982 dollars!). John Sculley – CEO – recalled “We were so insular, that we could not manufacture a product to sell for under $3,000.” Steve Jobs was fantastically able to assemble and motivate a team op people that managed to invent a truly revolutionary product, but he also was unable to turn it into profit.

When Jobs was fired from Apple – in 1985 – CEO John Sculley took control. Sculley is often described as a bit of a failure, because “nothing revolutionary came out of Apple under his watch”, “he could have done so much more with the company” and especially for “being stupid enough to boot out a genius like Steve Jobs”. However, the years after Sculley took over were some of Apple’s most profitable. The man did something right, and that was focus on exploiting the competitive advantage that Apple had built up.

In management research, following terminology cornered by the legendary Stanford professor Jim March, we often say that firms have to balance exploration with exploitation. Exploration refers to developing new sources of competitive advantage and growth. Exploitation refers to making money out of them. Steve Jobs was “insanely great” at exploration, but not – at the time – at exploitation. Sculley was.

Now Steve Jobs is a legend. And rightly so; our world literally would have looked different without him. However, what Steve Jobs’ legendary status also tells me is that we – mere mortals – are inclined to overestimate the omnipotence of CEOs. We overdo it when we ascribe the failure of an entire company to just one man or woman (e.g. Enron’s Jeff Skilling) but also when we ascribe the entire success of a company to one individual.

Steve Jobs wasn’t omnipotent (John Sculley had qualities Jobs didn’t) and he wasn’t always right (eating only fruits does not eliminate the need for an occasional shower). His day-to-day influence on Apple over the last years must have been limited, given his rapidly and severely deteriorating health. If anything, he simply would not have been able to be around enough to control and take care of everything. Nevertheless, the company did well in spite of his absence. And of course that is his laudable achievement too; he managed to build a company that could do well without him. And perhaps that may prove to be his best business lesson after all: how a great leader eventually makes himself superfluous.