2012 Strategic Management Society Conference in Prague

Just a reminder to anyone interested (or out of the loop): the deadline to submit something for the October 5-7 Strategic Management Society conference is in two days (Feb 23). This year’s theme is “Strategy in Transition” (here’s the call for proposals).  I like SMS’s approach of requesting paper “proposals” (essentially extended abstracts rather than full papers): easier on both the reviewers and authors.

My verdict on SMS?  I like the conference.  It is quite pricey (the conference fee is a hefty $1000+), but generally the sessions are good.  And most of all, it’s fun to interact and meet up with strategy colleagues, co-authors and friends in a somewhat smaller setting (not quite the zoo that the Academy of Management can be — SMS is far more targeted).

And, as a bonus, the locations tend to be excellent.  I attended the Rome SMS conference in 2010 and this year’s conference will be held in Prague.  Maybe we’ll live blog from the conference this year.


B-School Disruption Update

Want to be an entrepreneur? Enstitute is bringing back apprenticeships

This is the answer to those who think we will keep our research-based MBAs above water by making the curriculum more “relevant in the real world” … by which people seem to mean sacrificing academic content for: external projects with business sponsors, “living” case studies, 1st summer internships, support services for personal grooming, etc. As I have long argued, research faculty are not efficient providers of substitute “real world” experiences.

Apropos this discussion, last week, E[nstitute] launched in NYC by founders Kane Sarhan and Shaila Ittycheria. The idea is to pick up promising candidates with a high school diploma and put them through a two-year apprenticeship program mentored by some of NYC’s top entrepreneurs. Impressive.

And, it isn’t just business schools this program threatens — in a recent article, Brad Mcarty, editor at Insider points out, “… the average public university (in the US) will set you back nearly $80,000 for a 4-year program. And a private school will cost in excess of $150,000. At the end of that time, you have a bellybutton,” he writes. “Oh sure, you might have a piece of paper that says you have a Bachelor of Science or Art degree but what you actually have is something that has become so ubiquitous that it’s really not worth much more than the lint inside your own navel.”

That’s strong stuff and, sadly, uncomfortably close to the truth. Moreover, it speaks to strong potential demand for apprenticeship-style entrepreneurship programs like the one mentioned above. Personally, I think it’s terrific. The existence of programs like this create more value at the society level. From the b-school foxhole, they also force research-based MBA providers to think more carefully about what, if any, comparative advantage we have vis the many non-traditional competitors we now see invading our industry.

Hint: the answer will have to involve our research. This is what we do. And, contrary to the whining and hand-wringing of so many traditional MBA providers, teaching young people cutting-edge general principles (i.e., research-based knowledge) has substantial market value. We just stopped doing it a couple of decades ago.

 


B-school tuition bubble update

Business dull, 65 B-schools across India to shut down

This is what happens when the b-school market has excess capacity. ROI for students is negative, enrolment declines and, at some point, it is literally the case that the value of the land the school is built upon becomes more valuable in some alternative use.


Error propagation and extinction

One of my minor neuroses is an aversion to propagating errors of fact or logic. Indeed, I have to apply teeth to tongue at times when witnessing others propagating error. Managing this quirk productively is an important part of pedagogy, as experienced MBA instructors will immediately recognize. (Note that you will have many more opportunities to correct errors than to answer questions, because part of not understanding something is often not realizing it.)

Knowing when to pull the trigger on a correction to an error is the most subtle aspect. The first-best solution is another student immediately chiming in with an on-point critique, but that happens rarely. A lightly guided discussion that eventually corrects the error is next best, but there are practical challenges here as well, since a) limited class time may be available to deal with the topic, b) it can become aggravating for the students to play “guess what the professor is thinking,”, and c) the longer the uncorrected statement lies there the more likely that students will internalize the error and repeatedly spout it back in future classes, on exams, etc.

Assuming one has let the error go uncorrected as long as seems prudent and decided to directly intervene, it’s still often a challenge to a) precisely recognize the nature of an error and b) quickly come up with a concise, memorable, and understandable correction that will persistently displace the erroneous idea from the audience’s minds. Of course, experience helps, because errors tend to fall into repetitive patterns, allowing you to build up an internal database of diagnoses and appropriate responses. Here are some classic sallies with proposed responses below the fold. Suggested improvements to these responses (as well as additional examples of “favorite errors”) are welcomed in the comments.

1. “The company has a cost advantage because it makes more products and there are economies of scope in this industry.”

2. “The company has a cost advantage because it’s more vertically integrated (and Porter says that reduces costs).”

3. “The company has a cost advantage because it outsources more activities.”

4. “There are strong entry barriers because small companies can’t afford to pay the capital costs to operate in the industry.”

5. “The company needs to give better deals to its loyal customers.”

6. “The big growth in this industry comes from this new segment X, so the company should focus its resources on penetrating X.”

Read the rest of this entry »


“The Best Degree for Start-up Success”

“So you want to start a company. You’ve finished your undergraduate degree and you’re peering into the haze of your future. Would it be better to continue on to an MBA or do an advanced degree in a nerdy pursuit like engineering or mathematics? Sure, tech skills are hugely in demand and there are a few high-profile nerd success stories, but how often do pencil-necked geeks really succeed in business? Aren’t polished, suited and suave MBA-types more common at the top? Not according to a recent white paper from Identified, tellingly entitled “Revenge of the Nerds.”

Interested? Yes, it does sound intriguing, doesn’t it? It is the start of an article, written by a journalist, based on a report by a company called “Identified”. In the report, you can find that “Identified is the largest database of professional information on Facebook. Our database includes over 50 million Facebook users and over 1.2 billion data points on professionals’ work history, education and demographic data”.

In the report, based on the analysis of data obtained from Facebook, under the header “the best degree for start-up success”, Identified says to present some “definitive conclusions” about “whether an MBA is worth the investment and if it really gets you to the top of the corporate food chain”. Let me no longer hold you in suspense (although I think by now you do see this one coming from a mile or two, like a Harry and Sally romance), their definitive conclusion is: “that if you want to build a company, an advanced degree in a subject like engineering beats an MBA any day”.

So I have read the report…

[insert deep sigh]

and – how shall I put it – I have a few doubts… ( = polite English euphemism). I think there is no way (on earth) that the authors can reach this conclusion based on the data that they’ve got. Allow me to explain: 

Unjustified conclusion

Although Identified has “assembled a world class team of 15 engineers and data scientists to analyse this vast database and identify interesting trends, patterns and correlations” I am not entirely sure that they are not jumping to a few unwarranted conclusions. ( = polite English euphemism)

So, when they dig up from Facebook all the profiles of anyone listed as “CEO” or “founder”, they find that about ¾ are engineers and a mere ¼ are MBAs. (Actually, they don’t even find that, but let me not get distracted here). I have no quibbles with that; I am sure they do find what they find; after all, they do have “a world class team of 15 engineers and data scientists”, and a fact is a fact. What I have more quibbles with is how you get from that to the conclusion that if you want to build a company, an advanced degree in a subject like engineering beats an MBA any day.

Perhaps it may seem obvious and a legitimate conclusion to you: more CEOs have an engineering degree than an MBA, so surely getting an engineering degree is more likely to enable you to become a CEO? But, no, that is where it goes wrong; you cannot draw this conclusion from those data. Perhaps “a world class team of 15 engineers and data scientists [able] to analyse this vast database and identify interesting trends, patterns and correlations” are superbly able at digging up the data for you but, apparently, they are less skilled in drawing justifiable conclusions. (I am tempted to suggest that, for this, they would have been better off hiring an MBA, but will fiercely resist that temptation!)

The problem is, what we call, “unobserved heterogeneity”, coupled with some “selection bias”, finished with some “bollocks” (one of which is not a generally accepted statistical term) – and in this case there is lots of it. For example – to start with a simple one – perhaps there are simply a lot more engineers trying to start a company than MBAs. If there are 20 engineers trying to start a company and 9 of them succeed, while there are 5 MBAs trying it and 3 of them succeed, can you really conclude that an engineering degree is better for start-up success than an MBA?

But, you may object, why would there be more engineers who are trying to start a business? Alright then, since you insist, suppose out of the 10 engineers 9 succeed and out of the 10 MBAs only 3 do, but the 9 head $100,000 businesses and the three $100 million ones? Still so sure that an engineering degree is more useful to “get you to the top of the corporate food chain”? What about if the MBA companies have all been in existence for 15 years while all the engineering start-ups never make it past year 2?

And these are of course only very crude examples. There are likely more subtle processes going on as well. For instance, the same type of qualities that might make someone choose to do an engineering degree could prompt him or her to start a company, however, this same person might have been better off (in terms of being able to make the start-up a success) if s/he had done an MBA. And if you buy none of the above (because you are an engineer or about to be engaged to one) what about the following: people who chose to do an engineering degree are inherently smarter and more able people than MBAs, hence they start more and more successful companies. However, that still leaves wide open the possibility that such a very smart and able person would have been even more successful had s/he chosen to do an MBA before venturing.

What can you conclude from their findings?

I could go on for a while (and frankly I will) but I realise that none of my aforementioned scenarios will be the right one, yet the point is that there might very well be a bit going on of several of them. You cannot compare the ventures started by engineers with the ventures headed by MBAs, you can’t compare the two sets of people, you can’t conclude that engineers are more successful founding companies, and you certainly cannot conclude that getting an engineering degree makes you more likely to succeed in starting a business. So, what can you conclude from the finding that more CEOs/founders have a degree in engineering than an MBA? Well… precisely that; that more CEOs/founders have a degree in engineering than an MBA. And, I am sorry, not much else.

Real research (into such complex questions such as “what degree is most likely to lead to start-up success?) is more complex. And so will likely have to be the answer. For some type of businesses an MBA might be better, and for others an engineering degree. And some type of people might be more helped with an MBA, where other types are better off with an engineering degree. There is nothing wrong with deriving some interesting statistics from a database, but you have to be modest and honest about the conclusions you can link to them. It may sound more interesting if you claim that you find a definitive conclusion about what degree leads to start-up success – and it certainly will be more eagerly repeated by journalist and in subsequent tweets (as happened in this case) – but I am afraid that does not make it so.


In the mail: Guitar Zero: A Neuroscientist Debunks the Myth of “Music Instinct”

When you read this, you realize just how little we really know about learning. Major implications for the study of strategy, of course. But, also, a glimmer of hope that I may one day pursue that long-abandoned rock career. Article here: Guitar Zero: A Neuroscientist Debunks the Myth of “Music Instinct” | Brain Pickings. Book here.


Higher Ed Tuition Bubble Update

The drumbeat continues: MIT launches free onine “fully automated” course. Aside from the fact that these innovations have major implications for the livelihoods of my friends and I, the economics are interesting per se.

With the elimination of capacity constraints on the distribution side, will brick-and-mortar education providers go the way of Blockbuster and Borders? The market does not like brick-and-morter. It is inefficient – costly and inconvenient.

What happens when one professor can serve the entire market? Will superstars play an even larger role in academia? Will there be a market for top researchers (scarce) or good teachers (less so)? The same question holds at the institution level. Will everyone get a degree (and work for) HBS one day?

UPDATE: Megan McArdle provides a more thoughtful essay on this event at the Atlantic.


Jeremy Lin and Moneyball: The Problem of Identifying Talent

After watching Jeremy Lin (Knicks) score 38 points against the Lakers tonight, I’m now on the Lin bandwagon.  I don’t really even follow basketball that closely, but this seems like an intriguing story.

How on earth did someone like this go unnoticed?   Seriously.  He happened to get an opportunity to show his stuff as Carmelo Anthony and Amare Stoudemire are injured – and boy has he delivered.

Here’s a kid who didn’t get recruited for college ball, despite a tremendous record in high school.  He was a superstar at Harvard but went undrafted by the NBA after graduating from Harvard (in economics) in 2010.  He played a few games for Golden State and Houston, but was cut by both.  He has played D-league basketball this year, until a few weeks ago.  As of last week, he did not have a contract.

But come on: is basketball truly this inefficient at identifying and sorting talent?  The comparisons and transfer of ability across “levels” (high school-college-professional) of course is tricky, though you would think that with time there would be increased sophistication.

Now, four games of course doesn’t make anyone a star.  But even if Lin proves to “just” be a solid bencher, it seems that talent scouts clearly undervalued Lin (who lived in his brother’s apartment until recently).  How much latent talent is out there?  (I think that at the quarterback position in professional football – there are significant problems in identifying talent, but that’s another story.)

There are of course also some very interesting player-context/team-fit, interaction-type issues here, and I’m not sure that this really gets carefully factored beyond just individual contribution (thus not recognizing emergent positive, or negative, player*player effects).  It’ll be interesting to see what happens, for example, when Carmelo Anthony is added back into the mix.

Well, it’ll be interesting to see how all this plays out.  There is in fact a sabermetrics-type, stats-heavy, Moneyball-like thing in basketball as well – called ABPRmetrics.  I would be curious to know whether there are ways to statistically identify Lin-type undervaluation and potential, and whether phenoms like this lead to better metrics for identifying talent.

UPDATE: Here’s ONE analyst/statistician who saw Lin’s potential in 2010.


How much rationality is enough?

Last week I had the great good fortune to attend the Max Planck Institute at Leipzig’s first conference on Rigorous Theories of Business Strategies in a World of Evolving Knowledge. The conference spanned and intense four days of presentations, exploration, and discussion on formal approaches to business strategy. Participants were terrific and covered the scholarly spectrum: philosophers, psychologists, game theorists, mathematicians, and physicists. Topics included cooperative game theory, unawareness games, psychological micro-foundations of decision making, and information theory. It was heartening to see growth in the community of formal theorists interested in strategy and my guess is that the event will spawn interesting new research projects and productive coauthoring partnerships. (Thanks to our hosts, Jurgen Jost and Timo Ehrig for organizing and sponsoring the conference!)

If one had to pick a single, overarching theme, it would have to be the exploration of formal approaches to modeling agents with bounded rationality. For example, I presented on subjective equilibrium in repeated games and its application to strategy. Others discussed heuristic-based decision making, unawareness, ambiguity, NK-complexity, memory capacity constraints, the interaction of language and cognition, and dynamic information transmission.

Over the course of the conference, it struck me just how offensive so many of my colleagues find the rationality assumptions so commonly used in economic theory. Of course, rational expectations models are the most demanding of their agents and, as such, seem to generate the greatest outrage. What I mean to convey is the sense that displeasure with these kinds of modeling choices go beyond dispassionate, objective criticism and into indignation and even anger. If you are a management scholar, you know what I mean.

Thus, at a conference such as this, we spend a lot of time reminding ourselves of all the research that points to all the limitations of human cognition. We detail how humans suffer from decision processes that are emotional, memory constrained, short-sighted, logically inconsistent, biased, bad at even rudimentary probability assessment, and so on. Then, we explore ways to build formal models in which our agents are endowed with “more realistic” cognitive abilities.

Perhaps contrary to your intuition, this is heady stuff from a modeler’s point-of-view: formalizing stylized facts about real cognition is seen as a worthy challenge … and discovering where the new assumptions lead is always amusing. From the perspective of many management scholars, such theories are more realistic, better able to explain observations of shockingly stupid decisions by business practitioners and, hence, superior to the silly, overly simplistic models that employ a false level rationality.

I am not mocking the sentiment. In fact, I agree with it. Indeed, none of the economists I know dispute the fact that human cognition is quite limited or that perfect rationality is an extreme and unrealistic assumption. (This isn’t to say there aren’t those who believe otherwise but, if there are, they are not acquaintances of mine.) On the contrary, careers have been made in game theory by finding clever ways to model some observed form of irrationality and using it to explain some observed form of decision failure. If this is the research agenda then, surely, we have hardly scratched the surface.

Yet, as I thought about it during the MPI conference last week, it dawned on me that our great preoccupation with irrational agents is misdirected. That animals as cognitively limited as us often, if not typically, fail to achieve rational consistency in our endeavors is no puzzle. What else would you expect? Rather, the deep mystery is how agents so limited in rational thought invent democracy, create the internet, land on the moon, and run purposeful organizations that succeed in a free market. Casual empiricism suggests that the pattern of objective-oriented progress in the history of mankind is too pervasive to ascribe to dumb luck. Even at the individual level, in spite of their many cognitive failings, the majority of people lead purposeful, productive lives.

This leads me to remind readers that economists invented the rational expectations model precisely because it was the only option that came anywhere close to explaining observed patterns in economy-level reactions to changes in government policies. This, even though the perfect rationality assumption is axiomatically false. There you have it.

Which leaves open the challenge of identifying which features of human cognition lead to persistent patterns of success in highly unstable environments. I conjecture that our refined pattern recognition abilities play a role in this apparent miracle. Other candidates include our determination to see causality everywhere we look as well as our incredible mental flexibility. Social factors and institutions must be involved — and, somewhere in there, a modicum of rationality and logic. After all, we did invent math.


Does This School Make Me Look Fat?

It appears that selling “competitive” foods–often called junk foods–in schools has little market-expanding effect, at least if we use childhood obesity as a measure. The authors of this study look to have used pretty robust methods and found no link between attending a middle school where such marketed foods are sold and obesity. So firms’ efforts to penetrate these schools probably represent zero-sum market-share battles among brands, not a means of stimulating overall long-term consumption of these products.

Bonus question: If food firms make competitive bids to schools in order to get exclusive access (and I have no idea whether that is true–I’m analogizing from the many college campus exclusive soft-drink deals), then how would they feel about regulations banning them from school premises? Hint: Think about the impact of taking cigarette advertising off of TV on cigarette firm profits.


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.


Kodak managers learn gobbledygook theory of strategy, firm folds

Many of you will have heard by now that Kodak is likely to file for Chapter 11 bankruptcy sometime soon. Their present strategy appears to be to wind down the business by selling off many of their patents. I guess my main surprise upon seeing them back in the news was that they were still in business. Apparently, it takes an extended period for these behemoths to fold for good.

The source of my surprise was the fact that I used to teach Kodak managers in both the executive and part-time MBA programs at the Simon School in Rochester, the home of Kodak’s headquarters. Just to be clear, these men and women were great students … bright, curious, open-minded, and typically well-trained. My purpose here is not to jump on the current bandwagon and blame Kodak’s present troubles on the stupid, selfish, rapacious tendencies of  its 1%-er senior managers. Quite the opposite. Rather, I’d like to question what role, if any, the things they learned in b-school strategy classes played in the formation of, ultimately, misguided business plans.

Harking back to the late 90s, when I was teaching EMBA classes that were populated with about 1/3 senior managers from Kodak, I vividly remember initiating class discussions about the disruption digital technology was going to have on Kodak’s legacy business. Unlike the automobile manufacturers of the 70s, who really missed the significance of Japanese competition, Kodak managers fully understood that the new digital technologies were going to change their industry forever. Sure, there was a lot of uncertainty about the speed and path by which transformation would occur. But, it wasn’t the case that these smart people didn’t see it coming. They got it. And they were optimistic and dedicated, in my experience to a person, to implementing strategies that would permit Kodak to successfully ride the new technological wave.

Why were they so optimistic? When challenged to discuss it in class, they proudly explained that Kodak’s “core competency” was “color”. The reasoning went something like, “We understand color and its application to photography better than any other firm. This knowledge will be as important for success in digital applications as it was in analog film. Therefore, we are wonderfully positioned for whatever challenges the market presents.” The problem, from my perspective was two-fold: a) the thinking did not seem to go much deeper than this; and, b) the strategy literature did not have much to offer to help them think deeper than this.

Many have complained that the RBV, which is the source of this core-competency thinking, is a tautology: core competencies are unique resources that cause a firm to persistently outperform its peers; all firms that persistently outperform their peers have core competencies. I don’t agree with this complaint. Indeed, my sense that the pioneers of the RBV were on to something substantially influenced my desire to study strategy. That said, the “theory” underlying the RBV doesn’t go much more than one step beyond the tautology. And, much of where it goes is wrong (e.g., having resources that are inimitable is neither necessary nor sufficient for persistent performance advantage).

So, my energetic, smart, dedicated EMBA students, when presented with a strategy theory that was frustratingly close to a tautology, developed a strategic conceptualization of their firm that was – not surprisingly – frustratingly close to one as well. At the end of the day, it seemed to be an article of faith among my students that “knowledge resources about color” were going to save the day. (As we are all only too aware, smart people are masters at locking onto a favored idea and finding all kinds of arguments to support it.) As a teacher, it was incredibly difficult to push them deeper into a critical analysis of how, specifically, this “color know-how” was going to be their lifeline. New competitors, new product distribution channels,  radical changes to how photographs are shared and consumed? No problem — we know color!

Part of my teaching frustration, which became part of my research motivation, was that the extant literature did not offer much in the way of tools to help these folks think about such issues in a complete, consistent, and efficacious way. Worse, in my judgement, teaching those folks a shallow set of ideas actually facilitated their transition into a dangerous state of groupthink. Holding up a piece of tautological thinking as the pinnacle of scholarly theory doesn’t exactly encourage students to think beyond tautology.

Our field has more than its share of interesting conjectures (i.e., informally generated speculations). What we need now are more scholars who are willing to roll up their sleeves and dig into the details. And patience. Lots of patience.

 

 

 


Urban Strategy Fads

Mario Polese provides a nice short history (up to the present) of oversold urban revitalization strategies in City Journal. Interestingly, these theories succeed with municipal decision makers for the same kinds of reasons that pop-strategy notions flourish with company managers: They fit the zeitgeist, they flatter the preconceptions and prejudices of the decision-making class, they claim to magically bypass the obstacles to success, and they enable the rent seeking of powerful coalitions. Their obvious theoretical and empirical drawbacks as all-purpose nostrums have little effect on their propagation, and their promoters often flourish despite a complete lack of proven efficacy.

One useful thought exercise for assessing urban development strategies is to imagine yourself the monopoly landowner in a city and think about what policies would maximize the value of your holdings (or rent stream). It quickly becomes apparent that for cities of any size or complexity, your chances of picking sectoral, much less firm-level, “winners” are very low, unlike the owner of, say, a shopping mall. The peculiar difficulty is that cities have both the “internal” complexity of closed systems and the “external” complexity of open systems in a turbulent environment.

Centrally planning complementarities and synergies within the city overwhelms the monopoly landowner’s knowledge and modeling prowess, because 1) the interactions are manifold and hard to decompose and 2) the city itself is what Hayek called an order (or cosmos) with different people pursuing different objectives, not an organization (or taxis) where a single hierarchy of objectives can be imposed; the denizens of the city don’t work for the landowner and are not deployable resources. The best you can do is provide the most effective sector-neutral institutions and infrastructure you can think of given your geographic and historic legacy. Any “natural” advantages a city has in specific sectors can be accommodated by policy (e.g., tourism-friendly policing in a natural tourist area), but trying to create such advantages from scratch seems foolhardy.

Deliberately positioning the city as a competitor against other cities then becomes something of a fool’s errand. The very sort of maneuverable, focused tradeoff-making needed to pursue competitive “good strategy” as an open system with shared objectives (a taxis) in a turbulent environment conflicts with the efficient policy neutrality needed to manage the city’s internal complexity as a cosmos.

Interesting question: How big does a piece of land have to be before planned synergy-mongering and focused strategy should give way to neutral governance? There are large master-planned communities put up by real-estate companies that include residential, commercial, and office components. I conjecture that that size is about the limit of effectiveness for guided, synergy-conscious development strategy.


Why you really 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.


Our Top Posts for 2012

10. Strategies in the new European barter economy.

9. Tom Friedman: Why bubbles are  far-sighted industrial policy when undertaken by bureaucrats.

8. Radical-disruptive-agile-entrepreneurial strategy implications of thought-controlled smartphones.

7. The Rose Bowl as case-discussion classroom: UCLA’s innovative response to online MBA competition.

6. Sorry we got WordPress shut down with that link to one of Russ’s videos—#!%& SOPA.

5. Harvard Business School replaces Ohio as the Cradle of Presidents.

4. Cuneiform Case Studies–archaeologists discover Babylonian analysis of the five forces. (“Gilgamesh had a decision to make…”)

3. “Sustainability” voted official cant word of the decade by the Academy of BS.

2. Facebook’s decision to display users’ Social Security numbers–bid for ad revenue or is Zuckerberg now just screwing with us for fun?

1. New SEC and FASB regulations on precise use of strategy and business buzzwords create “analyst apocalypse” and “consulting catastrophe.”


Using Mechanical Turk for behavioral experiments

I’m seeing more and more work using Mechanical Turk as a subject pool.  Here’s another piece discussing some of the features, advantages and problems with Mechanical Turk – Rand, D (2011), The promise of mechanical turk: how online labor markets can help theorists run behavioral experiments, Journal of Theoretical Biology.

Abstract

Combining evolutionary models with behavioral experiments can generate powerful insights into the evolution of human behavior. The emergence of online labor markets such as Amazon Mechanical Turk (AMT) allows theorists to conduct behavioral experiments very quickly and cheaply. The process occurs entirely over the computer, and the experience is quite similar to performing a set of computer simulations. Thus AMT opens the world of experimentation to evolutionary theorists. In this paper, I review previous work combining theory and experiments, and I introduce online labor markets as a tool for behavioral experimentation. I review numerous replication studies indicating that AMT data is reliable. I also present two new experiments on the reliability of self-reported demographics. In the first, I use IP address logging to verify AMT subjects’ self-reported country of residence, and find that 97% of responses are accurate. In the second, I compare the consistency of a range of demographic variables reported by the same subjects across two different studies, and find between 81% and 98% agreement, depending on the variable. Finally, I discuss limitations of AMT and point out potential pitfalls. I hope this paper will encourage evolutionary modelers to enter the world of experimentation, and help to strengthen the bond between theoretical and empirical analyses of the evolution of human behavior.


Disrupting education

Lots of talk about education being disrupted (here are some previous links).  Here are a few links:

Here’s some info from a year in EdTech Trends:

Million Dollar Moments

Yes, we saw big M&A deals this year: Pearson plunked down $230 million for Schoolnet and another $400 million for Connections Education. Permira Fund wrestled PlatoLearning for the privilege of paying $455 million for RenaissanceLearning. ePals debuted on the Toronto exchange. Edtech M&A activity was north of $1.6 billion this year.

Here are a dozen top edtech investments this past year:

  • $33M                      Knewton
  • $32.5M                   2tor
  • $30M                      Kno
  • $20M                      CampusBookRentals
  • $17M                      Inkling
  • $17M                      Zeebo
  • $13M                      Edmodo
  • $11M                      Dreambox
  • $10M                      ConnectEDU
  • $10M                      MyEDU
  • $8M                        Instructure
  • $7M                        Grockit

Duly Noted: Solving the Principal-Agent Problem in Firms:The dumbest idea in the world?

This article in Forbes argues that a new book by the Dean of Rotman School provides an antidote to the rampant excesses of modern day capitalism.  The principle swipe is against the landmark paper (over 29000 Google Scholar citations)  by Jensen and Meckling on both the prevalence of the principal agent problem in the governance of firms and the various solutions to overcome it – including creating incentives that maximize shareholder value.  Quoting Jack Welch, former CEO of GE, the article says that maximizing shareholder value is the dumbest idea in the world.  I my self am not sure if this is THE dumbest idea in the world – in fact there are many more that would easily surpass P-A problem resolution – but I am sure this will ignite a debate about why firm’s exist – what is the best governance mechanism for them and the role of economic theory and action in our lives.  I for one need to go back and read the article and then read the book.


Ronald Coase: 101 years (and still publishing)

Via Karim’s twitter feed: Ronald Coase turned 101 years old today.  Congratulations!

(My new goal is to be publishing at 101.  That’s got to be a record of some sort.)


The ubiquity of organizations: Herbert Simon’s telescope

I’m working on a theory of the firm-related paper over the holiday break.  One of the pieces I enjoy revisiting is Herbert Simon’s (1991) article “Organizations and Markets,” Journal of Economic Perspectives.  What I like is the intriguing thought experiment in that paper (frankly, I think thought experiments are a VERY under-utilized tool in strategy and organization theory).  To illustrate the “ubiquity of organizations” Simon asks us to imagine seeing the globe from above and envisioning market exchanges as red lines and firm-related exchanges as green lines. Clearly, the green dominates.  Thus Simon never developed a comparative theory of governance (markets versus hierarchy) and focused on organizations themselves (the basis of the behavioral theory of the firm).   I tend to think that the comparative aspects are fundamental, though naturally The Behavioral Theory also has a place in the canon.

For anyone interesting, here’s the first couple paragraphs of the thought experiment:

A mythical visitor from Mars, not having been apprised of the centrality of
markets and contracts, might find the new institutional economics rather
astonishing. Suppose that it (the visitor I’ll avoid the question of its sex)
approaches the Earth from space, equipped with a telescope that reveals social
structures. The firms reveal themselves, say, as solid green areas with faint
interior contours marking out divisions and departments. Market transactions
show as red lines connecting firms, forming a network in the spaces between
them. Within firms (and perhaps even between them) the approaching visitor
also sees pale blue lines, the lines of authority connecting bosses with various
levels of workers. As our visitor looked more carefully at the scene beneath, it
might see one of the green masses divide, as a firm divested itself of one of its
divisions. Or it might see one green object gobble up another. At this distance,
the departing golden parachutes would probably not be visible.

No matter whether our visitor approached the United States or the Soviet
Union, urban China or the European Community, the greater part of the space
below it would be within the green areas, for almost all of the inhabitants would
be employees, hence inside the firm boundaries. Organizations would be the
dominant feature of the landscape. A message sent back home, describing the
scene, would speak of “large green areas interconnected by red lines.” It would
not likely speak of “a network of red lines connecting green spots.”


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