David
 James
 Brunner

A new version of my paper with Brad Staats, Mike Tushman, and Dave Upton on how deliberate perturbation can sustain innovation in mature organizations is available for download from SSRN.  Here is the abstract:

Organizations struggle to balance simultaneous imperatives to exploit and explore, yet theorists differ as to whether exploitation undermines or enhances exploration. The debate reflects a gap: the missing theoretical mechanism by which organizations break free of old routines and discover new ones. We propose that the missing link is perturbation: novel stimuli that disrupt the execution of specialized routines. Perturbation creates opportunities for organizations to invoke exploratory, general-purpose problem-solving routines. In mature organizations, exogenous perturbations become increasingly scarce to the point that exploration is stifled and inertia sets in. We theorize that mature organizations can sustain exploration by deliberately inducing perturbations in their own processes. Our theory yields testable hypotheses about the relationships between exploitation, perturbation, and exploration. We provide illustrations from The Toyota Motor Company to show how deliberate perturbation enables efficient exploration in the midst of intense exploitation.

Over the last several decades, maximizing shareholder value has become widely accepted as the duty and legitimate purpose of business managers.  Many scholars propounded this ideology, and probably none more forcefully than Milton Friedman.  In a well-known article in the New York Times Magazine in 1970, he argued that “The Social Responsibility of Business is to Increase its Profits“.  In his book From Higher Aims to Hired Hands, Harvard Business School professor Rakesh Khurana describes how this view replaced a conception of the business manager as a steward of the public interest responsible for skillfully balancing the interests of corporate stakeholders to sustain the enterprise and create value for society.  William Lazonick and Mary O’Sullivan provide a complementary perspective.

Unfortunately, while maximizing shareholder value squares nicely with neoclassical economic theory, it can cause serious problems in practice.  In my research project on Public Interest Capitalism, we link shareholder value maximization to inequity, short-termism and underinvestment, and deterioration of social capital.  In a nutshell, there are three fundamental problems with relying on shareholder value maximization as the criterion for corporate management.  First, market incentives (hence profit) may correlate only slightly or even negatively with social welfare (on this point, see David Grewal’s book Network Power).  Second, markets can impede innovation (Mary O’Sullivan provides the details in Contests for Corporate Control).  Third, by reducing the purpose of the company to generating as much profit as possible for a diffuse external constituency, shareholder value maximization decrease the identification of employees to organizational goals, thereby hindering learning and efficient operation.  For a different but complementary perspective, see Simons, Mintzberg and Basu’s ”Memo to: CEOs“.

Fortunately, it appears that the shareholder value maximization movement may have run its course–albeit after bringing the global economy to the brink of collapse.  A group of MBA students at Harvard Business School, concerned about business ethics and inspired by Rakesh’s work, have started MBA Oath, a movement to professionalize management.  The New York Times has the story: “A Promise to Be Ethical in an Era of Immorality“.  This is a very encouraging development.

One of my colleagues asked me what to read to learn about knowledge. The answer requires a bit of explanation.

There are two approaches to knowledge. On one hand, there are the epistemologists. The epistemologists have spent many centuries developing criteria for evaluating whether a belief qualifies as knowledge. On the other hand, there are the computer scientists and organization theorists, who tend to focus on how knowledge affects the performance of problem solving systems (i.e., humans, computers, organizations). These two approaches can be reconciled as follows.

For the computer scientists and organization theorists, knowledge is anything that improves the performance of a problem solving system, except for information processing capacity. If two systems execute the same number of symbolic operations but one system gets a better answer, it must know something the other system doesn’t.

Epistemologists, by contrast, want perfect knowledge that will never lead a problem solving system to act in ways that betray its own goals. Such perfect knowledge is difficult to obtain, and perhaps even more difficult to define. After a few millennia, epistemologists still haven’t come up with a satisfactory definition. This is not to say that the field has failed: epistemology can help us evaluate the quality of knowledge and acquire better knowledge.

A short example may help clarify the matter. To a computer scientist, “what goes up, must come down” is a reasonably good piece of knowledge. It tells a problem solving system not to throw a water balloon straight up in the air. To an epistemologist, this isn’t knowledge at all, because it isn’t true. If I launch a rocket into space, it doesn’t need to come down. In fact, up and down are not even valid except within very limited frames of reference. The computer scientist has a tolerant, inclusive philosophy of knowledge, while epistemologists have an exacting, exclusive philosophy of knowledge.

For those of us concerned with understanding the performance of problem solving systems, the problems raised by epistemologists are not of primary importance.We are better served with an inclusive definition of knowledge that asks not whether the knowledge is true, but whether it is useful. Those interested in this view of knowledge may find the following books and articles useful.

How organizations represent and exploit knowledge

March, J. G. and H. A. Simon. Organizations. 2nd ed. Cambridge, MA: Blackwell, 1993.

Simon, H. A. The Sciences of the Artificial. 2d ed. Cambridge, MA: MIT P, 1981.

Organizations and The Sciences of the Artificial are essential introductions to the science of problem solving systems (equivalently, symbol systems or information processing systems). Chapters 6 and 7 of Organizations are especially important, because they describe the functioning of performance programs (equivalently, routines), which are one of the most important ways that problem solving systems represent knowledge. Make sure to get the second edition, which has useful commentary after each chapter. Read these books several times.

How organizations learn

Mukherjee, A. S. and R. Jaikumar. “Managing Organizational Learning: Problem Solving Modes Used on the Shop Floor.” 1992.

Bohn, R. and R. Jaikumar. “The Structure of Technological Knowledge in Manufacturing.” Working paper. 1992.

Clark, K. B., R. Henderson, and R. Jaikumar. “A Perspective on Computer Integrated Manufacturing Tools.” Boston, MA, 1988.

Jaikumar had a wonderfully precise grasp of how knowledge, learning, and problem solving interact and drive system performance. The first paper describes the mechanics of unstructured problem solving, which is closely related to learning. The second paper demonstrates how theoretical models can be used to investigate the way knowledge functions. The third paper sheds light on how computers influence learning. Although the studies focus on manufacturing, the principles generalize. Unfortunately, these excellent papers are not easily available

Darr, E. D., L. Argote, and D. Epple. “The Acquisition, Transfer, and Depreciation of Knowledge in Service Organizations: Productivity in Franchises.” Management Science 41, no. 11 (1995): 1750-62.

Edward Feigenbaum often says that knowledge usually comes in thousands of grains of gold dust rather than in large nuggets. This elegant empirical study beautifully captures this aspect of organizational knowledge, and provides an example of one technique for quantitatively analyzing knowledge, learning, and knowledge decay.

Epistemological perspectives

Newell, A. “The Knowledge Level.” AI Magazine 2, no. 2 (1981): 1-20, 33.

Nonaka, I. “A Dynamic Theory of Organizational Knowledge Creation.” Organization Science 5, no. 1 (1994): 14-37.

Lenat, D. B. and E. A. Feigenbaum. “On the thresholds of knowledge.” Artificial Intelligence 47, no. 1-3 (January 1991): 185 – 250.

These three articles by leading experts on problem solving and knowledge provide theoretical foundations for the inclusive, computer science/organization theory approach to knowledge. None provides a complete theory, but when read together they provide a great deal of insight. The significance of the ideas cannot be grasped without considerable reflection. It may help to read them repeatedly, perhaps interspersed with the other readings on the list.

Mechanics of knowledge systems

Davis, R, H. Shrobe and P. Szolovitz. “What Is a Knowledge Representation.” AI Magazine, Spring (1993), 17-33.

Feigenbaum, E. A., B. G. Buchanan, and J. Lederberg. “On Generality and Problem Solving: A Case Study Using the DENDRAL Program.” In Machine Intelligence, edited by B. Meltzer and D. Michie, 165-90: Edinburgh UP, 1971.

Feigenbaum, E. A. “Knowledge Engineering: The Applied Side of Artificial Intelligence.” Proc. of a symposium on Computer culture: the scientific, intellectual, and social impact of the computer. New York Academy of Sciences, 1984.

To understand the mechanics of knowledge, one must dig into questions of representation and inference. Davis’s article provides a useful overview of the issues involved in representation. Feigenbaum’s articles on DENDRAL and knowledge engineering describe the nuts and bolts of working with knowledge.

In my doctoral dissertation, I develop a theory of computer-assisted organizing that links the rise of computer systems to the shift from integrated organizational capabilities to decentralized capability ecosystems.  Although I believe the predictions of the theory probably hold true in aggregate, Apple’s recent move to bring chip design in-house illustrates how firm-level strategic considerations influence the division of information processing tasks across ecosystem participants.  Presumably Apple won’t in-source all chip design: they’ll focus on domains where they can develop capabilities that strongly complement the firm’s software and hardware design.

The Los Angeles Times is reporting “China relaxes business regulations“.  The article describe government efforts to prevent laws and regulations from hampering business:

The Industry and Commerce Administration of Zhejiang province … earlier this year released what local media called the “three noes” policy. Two of the noes have to do with minor licensing and registration issues. The third one, though, states that there should be no punishment for businesspeople who make “common violations that don’t directly cause harmful consequences.” Instead they should be given suggestions and admonitions to correct their errant behavior, officials said.  …

In China’s southeast industrial hub of Guangdong province … the government cautioned investigators about detaining or taking other action against entrepreneurs or key company managers that could disrupt business. Even if authorities have gathered all the evidence, action may be delayed until the manager has finished conducting business.

On the environmental front, there is evidence of regulatory capture:

In China’s southeast, Jiangxi Copper Corp. is expected to begin work next month on a $730-million lead-zinc smelting operation along the Yangtze River.  … Jiangxi province’s environmental protection bureau boasted that it had finished the environmental-impact review in just three days

According to the article,  favorable treatment for capitalists is nothing new:

Even during ordinary economic times, giving privileged policies to businesspeople in China is common.  Some smaller locales have offered investors immunity for traffic violations and other misdemeanors.

It’s easy to see how these policies could boost economic growth in the short term by removing constraints on business activity.  Whether these policies serve the broader public interest, however, seems questionable.

A Public Interest Capitalism perspective raises three questions.

1. Are these policies sustainable?  Even leaving aside concerns about environmental destruction, economic theory gives us compelling reasons to believe that the rule of law is essential to advanced economic development.
2. Are these policies equitable?  Perhaps investors and entrepreneurs merit special privileges.  I tend to think not, but the US tax system gives preferential treatment to capital gains.  Policies that relax regulatory enforcement are particularly problematic from an equity standpoint, however, because they benefit most those who are profiting from illegal, socially destructive activity.
3. Will these policies promote innovation?  To the extent that these policies protect incumbent businesses, they create obstacles for creative destruction.  This may not be too much of a problem when the economy is growing rapidly and generating entrepreneurial opportunities, but it could hinder readjustment when growth slows.

In an article titled “Does H.P. Need a Dose of Anarchy?”, the New York Times asks whether Mark Hurd’s management is causing exploitation to drive out exploration–the classic “productivity dilemma“. From the article:

Mr. Hurd, hired four years ago in the wake of Carleton S. Fiorina’s tumultuous departure as chief executive, forced a steady, boring diet of performance benchmarks, heavy-handed cost-cutting and data-mining down H.P.’s corporate throat.  …

But with the most brutal cuts behind it, H.P. faces a fresh set of challenges as the second stage of Mr. Hurd’s tenure begins. Most pressing is widespread concern that Mr. Hurd has built an inflexible, solipsistic giant so obsessed with schematics and data-driven fiscal machinations that it has lost the ability to deliver that prized and perennial Silicon Valley trick: to surprise and astound.

This is a clear statement of what my colleagues Brad Staats, Mike Tushman, and Dave Upton and I label the “conflict school” in our working paper “Wellsprings of Creation: How Deliberate Perturbation Sustains Exploration in Mature Organizations”.  Conflict school theorists argue that the very tools organizations use to exploit their accumulated knowledge–standardized, stable, and streamlined operating procedures–also squelch innovation.  Consequently, the most efficient and productive organizations adapt poorly to environmental changes, leaving them vulnerable to attacks by more creative (albeit perhaps less streamlined) competitors.  In HP’s case, those competitors appear to include Apple, Amazon, and Acer:

It arrived late with a line of netbooks, the low-cost, compact laptops that have taken the world by storm, opening doors for its rival Acer.  …

With its software gurus, its newfound penchant for design and its deep ties to retailers, H.P. might have been expected to disrupt the cellphone market with new devices or even to concoct an electronic book reader that would complement its printer business. Instead, it’s Apple and Amazon that built vibrant new businesses around such products. 

According to the article, Mr. Hurd is not too worried:

“In spite of the fact that there are things we could always do a better job on, innovating and so forth, I don’t think we have ever felt stronger about our portfolio of products and services and our opportunity to serve the market,” Mr. Hurd says. “I don’t think we think we’re confused about what the market wants.”

Perhaps he should be more concerned: academic research suggests that the productivity dilemma is very difficult to overcome.  Our research on Toyota suggests that organizations can sustain exploration in the midst of intense exploitation, but it’s extremely hard to do.  Unless HP is tackling the problem head on, rigidity and inflexibility may be real risks.

What HP probably doesn’t need, however, is anarchy.  Anarchy would simply negate the impressive efficiency gains that the company has made over the past few years.  Instead, we would recommend deliberate perturbation: selectively and strategically destabilizing processes throughout the organization.  The mechanics of deliberate perturbation are not yet well understood, but we try to provide some ideas in our paper.

The New York Times offers an example of a Japanese island that has developed a communitarian form of capitalism.  Residents share government jobs, watch free cable television, enjoy immaculate parks and public toilets, and limit displays of wealth to prevent jealousy from undermining community solidarity.

Under Hime’s system, village employees earn about a third less pay than public servants elsewhere in Japan, though they work the same hours. This has allowed the village to create more jobs: it now directly or indirectly employs a fifth of all working islanders. Most of the rest are engaged in fishing, also government-subsidized. In fact, village officials say, there are few fully private-sector jobs on the island.

This is an interesting case study, because it shows how a community can engineer capitalism to their own liking.  Creating such communitarian economic institutions probably requires a certain amount of social closure, so being an island may help.  (Just to be clear: Hime Island’s economy is certainly capitalist; individuals living there have considerable freedom to deploy their human and financial capital as they please, and the entrepreneurially-minded open restaurants or small hotels for tourists.)  The whole world cannot be Hime Island any more than Hime Island can be Silicon Valley, but the example suggests that communities may have more control over their destinies than neoclassical economics would predict.  My friend David Grewal explains why such freedom requires collective action in his book Network Power.

The financial crisis provides an opportunity to revisit the assumptions of capitalism and recognize many possible varieties of capitalism, many of which have yet to emerge.

In a dense passage in a thick book (On Organizational Learning, 2nd ed.), Chris Argyris makes a point that should deeply concern social scientists including, perhaps especially, economists and organization theorists.

It is important for social sicentists to study double-loop change because if they focus only on single-loop change, they may unwittingly become servants of the status quo…

This consequence holds negative outcomes for social science as a science.  It is becoming evident that there may be a paradox embedded in the goal that social science should be descriptive of the world as it is.  If social scientists aspire to study individuals and systems as they are, they will inevitably fall short of their goal: a complete description of things as they are would have to include a valid description of the capacity to make significant changes, and of the mechanisms by which these changes will occur.  Knowledge of these mechanisms will also produce valid generalizations about constraints to double-loop organizational change.  Such significant changes require changes in the organizational governing variables and master programs, that is, double-loop changes.  But double-loop changes cannot occur without unfreezing the models of organizational structures and processes now in good currency.  These models, in turn, cannot be unfrozen without a model of a significantly different organizational state of affairs: otherwise, toward what is the organization to change?  If these models are genuinely new, then they do not now exist.  if they do not now exist, then their invention and their use is an act of proscription, a normative stance.  Yet if the logic is correct, the normative stance is needed to get at the inner nature of the present double-loop features and potentials of the organization.  Hence, a full description of the world as is requires the intervention of stimuli from a world that presently is largely theoretical. (70)

By “double-loop change” or “double-loop learning”, Argyris means “questioning or altering the underlying values of the system” (68).  He uses the term in opposition to “single-loop learning” which “is designed to identify and correct errors so that the job gets done and the action remains within stated policy guidelines” (151-2).  In single-loop learning, “the underlying program is not questioned” (151).

To paraphrase: the potential range of social system behavior can be known only by construction, not by description.  Purely descriptive approaches to social science will underestimate the range of the possible and, to the extent that these descriptions are used by agents within the system to shape its development, description may ultimately constrain the possible.

The Journal of Operations Management recently published this invited article that I co-authored with Paul Adler, Mary Benner, John Paul MacDuffie, Emi Osono, Brad Staats, Hiro Takeuchi, Mike Tushman, and Sid Winter.  The article revisits the so-called “productivity dilemma” identified by Abernathy in 1978:  attempts to increase efficiency in the short term tend to inhibit innovation in the long term.  Mike and Mary attribute the problem to the “dynamic conservatism” of tightly coupled systems, while Paul Adler emphasizes the tendency of profit-seeking to undermine the trust required for cooperative innovation.  Brad and I argue that overcoming the problem requires selectively re-introducing variance into mature processes, a phenonenon that we term “deliberate perturbation”.  We are working on a theory paper with Mike that further develops the concept of deliberate perturbation.  [The paper, entitled "Wellsprings of Creation: How Perturbation Sustains Exploration in Mature Organizations", is now available here -- DJB, 24 Aug 2009]

This weekend’s Wall Street Journal has an essay entitled “Greed is Good” about the role of bonuses in the financial services industry.  The article doesn’t really argue that greed is good–it doesn’t really consider the topic of greed at all, leaving me to wonder if the Journal came up with the catchy title–but the framing invites the question, Wall Street incentive structures aside, of whether greed actually is good.

What is greed?

Any answer has to begin with a definition of greed.  The Oxford English Dictionary suggests “Inordinate or insatiate longing, esp. for wealth”, which isn’t very helpful since it simply forces us to define “inordinate”.  I’d suggest the following: longing for wealth becomes greed when it causes someone to sacrifice the welfare of others to enhance his own.  A simple prisoner’s dilemma game provides an illustration.  Consider two business partners, Albert and Bernard.  Both have a choice to contribute their efforts and capital ($2 each) toward a new innovation, or extract the assets of the their partnership through an unfortunately un-prosecutable fraud.  The innovation requires the contributions of both partners, so if either partner extracts, the business collapses.  The partnership has accumulated assets of $4, so when both partners extract (EE), they both take half ($2) and the business collapses.  When one partner contributes and one extracts, the extracting partner takes the accumulated assets ($4) and the contribution of the other partner ($2) for a total of $6, and the other partner is left with a loss of $2.  When both contribute, the innovation yields a net return of $6, so the partners each have payoffs of $5.  The image below depicts the game, the payoffs to each player, and the total wealth.

Under standard assumptions, both players will choose to extract, and the outcome will be EE.  Consider the case, however, when Bernard expects Alfred to contribute.  Bernard will still be better off by extracting, but doing so would be greedy: it increases Alfred’s payoff at the expense of a considerable drop in social welfare (the partners collectively lose $6 in wealth as a result of failing to innovate) and a crushing loss for Bernard (from the $5 he expected, evidently naively, to -$2).  All of this for a very modest increase in Alfred’s payoff.

Thus defining greed enables us to draw a distinction between enlightened self interest and greedy self interest.  Enlightened self interest increases one’s payoff while also increasing–or, at least, not decreasing–the payoffs of others.  Greedy self interest sacrifices the welfare of others for one’s own benefit.  One might also distinguish between weak greed, where one redistributes wealth toward oneself while leaving total social welfare unchanged, and strong greed, where social welfare suffers a real or opportunity loss.  Such a model would suggest that there be nothing wrong with insatiable desire for wealth–indeed, it might indeed fuel innovation and economic development–provided that one factors the effects on the welfare of others into one’s decisions.

So what’s wrong with greed?

Prominent economists have argued that economic actors–at least corporate actors–should take any legal actions that maximize profits.  This amounts to a claim that “greed is good”, and tells both Albert and Bernard that they are justified in choosing to extract, provided they can find a legal way to do so.  If society desires to prevent the extract/extract outcome, then laws should be passed to forbid it.

The problem with this position is that legal systems are costly to build and use, difficult to change, and inevitably imperfect.  No legal system will rule out all greedy, or even all strongly greedy, actions.  Even given perfect laws, the high cost of litigation will effectively permit actions that are prevented in theory.  If Alfred can afford a team of talented lawyers and Bernard cannot, the chances are that Alfred can figure out a way to expropriate Bernard with impunity.

Value systems that condemn greed as immoral will pressure ambitious actors toward socially constructive ways of acquiring wealth.  Desirable contribute/contribute equilibria will be relatively more probable.  Conversely, a society that draws no distinction between greed and enlightened self interest, even legimitimating greed by loudly proclaiming it good on the pages of prominent national media, will increase the likelihood of destructive contribute/extract and extract/extract types of equilibria.

The damage caused by greed goes beyond the outcomes of particular games.  As Fukuyama notes (and the Albert-Bernard example suggests), economic activity rests on a foundation of Trust.  When Albert contributes and Bernard extracts, Albert feels betrayed; he will surely be less likely to contribute next time.  If greed actions become common, everyone will expect others to extract instead of contribute, and protect themselves by extracting.  Collaborative action will become increasingly difficult.  Social capital will erode.

[A note on attribution: I'm not aware of whether others have developed these ideas, but they seem straightfoward enough that I'd expect someone has.  If there's anyone whom I should be acknowledging, please let me know.]