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A friend of mine was given a troubleshooting job at a top software company precisely because she didn’t have much experience with programming — the company had learned that when it hired technical whizzes for that troubleshooting position, the whizzes would come up with inventive solutions that no one else could understand, which made the software impossible for anyone else to fix when something else went wrong. The company just wanted someone who would follow the rules and do exactly as they were told in each given situation.

Furthermore, the fact that bureaucracies (officially, at least) don’t discriminate can be for the better. The fact that loans are now based on credit scores means that someone can’t be given a loan for being a “good guy,” but it also means that someone can’t be denied a loan because of their race or sex.

Still, being caught in a bureaucracy can seem uncomfortably impersonal — because, after all, it is. That’s why Weber called rationality an “iron cage.” (See Chapter 3 for more on Weber and his “iron cage.”)

Rational Systems: Bureaucracy at its Purest

One of the best sociological books on the subject of social organization is Richard W. Scott’s Organizations: Rational, Natural, and Open Systems. In the next three sections of this chapter, I borrow Scott’s insightful typology to discuss these three different ways that sociologists have come to understand organizations. Every organization is rational, natural, and open; I’ll explain what each of those terms mean, one by one. First, I describe “rational systems.”

In Scott’s typology, “rational,” “natural,” and “open” are three different ways to understand organizations, not three different kinds of organizations. Be careful not to be confused and think that some organizations are “rational,” others are “natural,” and still others are “open” — every organization has all of those qualities.

Measuring the shovels: Efficiency!

Efficiency!

The term rational system refers to the fact that any organization is typically designed to work as efficiently as possible at accomplishing some set task. A bureaucracy as described by Weber (see previous section) is a classic rational system: an organization that works like a machine. In this sense, almost all formal organizations — that is, organizations with rules and defined membership — are rational systems. Just because they’re not always

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rational doesn’t mean they aren’t designed with a goal in mind, or that they don’t generally accomplish that goal in some form or to some extent.

An engineer-turned-management consultant named Frederick Taylor was the all-time greatest proponent of rational systems. He recognized that organizations weren’t always rational, but he thought that was a problem that ought to be fixed. In Chapter 3, I explain that the first sociologists believed that sociological research could help to design the ideal society; if we were to understand exactly how societies work, we could fix social problems like we repair cars and TVs. Taylor had exactly this view of organizations, and in the early 20th century he actually undertook to study particular organizations and help their owners or administrators make them work better.

Taylor’s approach became known as “scientific management” or “Taylorism.” He would visit organizations and study every detail of how they worked, then recommend how their operations could be revised to make them work better. He had four main principles:

Instead of guessing at how to do a task most efficiently, study the task scientifically and choose the method proven to be most effective.

Select employees based on precise qualifications, and train them in the most efficient work practices rather than leaving them to figure things out for themselves.

Supervise workers closely, and retrain them when necessary.

Give managers the job of designing efficient work methods and supervising the workers who actually accomplish the tasks.

Most famously, Taylor observed steel mills and offered recommendations as to how to improve every aspect of the steel production process — from the specific tasks each worker was given to the communication systems used in mills to the way the mill floor was laid out and the way material moved from one part of the mill to another. He even went so far as to study the size of shovels used by workers and determine what size shovel allowed a worker to scoop the most coal over the course of a workday.

Tensions between labor and management were high in Taylor’s time, and just as Comte believed that sociology could bring peace to society, Taylor believed that his version of scientific management could resolve labor disputes: If the ideal work conditions were determined by objective scientific observation rather than by the whims of managers, how could workers possibly argue with that?

Quite easily, as it turned out. Taylor appreciated the role of financial incentives and argued that his system fairly compensated dedicated workers — he held up one particular steel worker, Schmidt, as an example, and pointed out that Schmidt was working more efficiently under Taylor’s system and making more money to boot. The steel company profited, and so did Schmidt . . .

thus, scientific management was a win-win proposition.

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Many workers and labor advocates, though, saw Taylor’s methods as dehumanizing and brutal. In breaking down jobs into their individual components and telling each worker precisely how to do his job, his critics said, Taylor was taking away workers’ autonomy and making their work miserable. Plus, it seemed to assume they were too stupid to know how to do their own jobs. The famous muckraker Upton Sinclair pointed out that yes, Schmidt was making 61 percent more pay under Taylor’s system . . . but for doing 362 percent more work. When workers at one Massachusetts plant were subjected to Taylor’s methods, they went on strike and inspired Congressional hearings where Taylor spent four days defending his methods.

Sociologists today agree that Taylor wasn’t fundamentally mistaken in his belief that evaluating work processes objectively, especially in situations where tasks are rote, is a better strategy for efficient production than allowing each worker to independently create a work plan. Still, they also agree that Taylor made a basic error in thinking that it was a good idea to treat human workers like machines.

Even when there’s fair pay for efficient work, people are not machines and they’re apt to balk when it seems like there’s no room for individuality, variety, or personal initiative in their work. Taylorism may not be wrong . . . but it’s not exactly right, either.

The bounds of reason

Taylor’s approach makes most sense when work is predictable and easily routinized — but of course, for many jobs that’s not the case. In many cases, organizations and the people who work for them have to deal with ambiguous situations that change from week to week, day to day, or even moment to moment. In those situations, it’s just not reasonable to expect a worker to follow a strict set of rules. Workers have to be flexible and make decisions based on changing circumstances.

So what can you do if you want to make someone in a job like that as efficient as possible? Well, you can take a few actions:

You can equip them with general principles about what decisions to make in which circumstances. For example, you might tell a manager not to give any customers special accommodations unless it seems like they’re about to switch their business to another company.

You can try to localize the decision-making, so that the same people make the same kinds of decisions. For example, many stores have designated returns departments with people who are specially trained to accept returns — so busy cashiers don’t have to make decisions about whether or not to accept returns.

You can have people work in teams to make complex decisions, so no one worker is given all the decision-making power.

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Even so, dealing with uncertainty is a major problem for most organizations; they spend a lot of effort trying to design and maintain systems that will stand up to different circumstances. (I explain more about the general problem of dealing with the environment in the section on open systems.)

Especially for the very complex decisions that are made by the highestranking members of an organization, a particular challenge is to decide how much information to gather before making a decision. Imagine you’re running the coffee business I describe in the first section of this chapter, and you’re trying to decide where to open your next location. You have to consider rents, local laws, the location of competitors, the potential supply of workers and customers, and many other factors. How do you know when you’ve gathered enough information to make the best decision about where to open your next store?

Herbert Simon, one of the all-time great scholars of organizations, coined the term “bounded rationality” to refer to the fact that the rationality of human decision-makers is “bounded” by the amount of time they have to gather information and make decisions — as well as, needless to say, by the limited computing power of their human brains. (For more on decision-making by individuals, see Chapter 6.)

Simon’s work has been a major influence on management consultants, who are today’s Frederick Taylors. Management consultants are specialists in organizational behavior — many consultants, in fact, have been trained as sociologists — who are hired by companies, as Taylor was, to serve as outside evaluators who can help a company design its operations as effectively and efficiently as possible.

Not only do management consultants have to consider the bounds on the rationality of the people at the companies they’re studying, they need to consider the bounds on their own rationality. My friend became a management consultant after graduating from college, and I was surprised when he told me that his company might spend as little as two weeks studying a company before making a series of recommendations. When I asked why they didn’t spend more time, he explained that they could spend a lot more time — but that probably wouldn’t result in recommendations that were dramatically more useful. Two weeks, his firm had learned, is enough time to gather most of the important information about a company’s operations.

The success of management consultants would seem to vindicate Taylor and his overall approach: Objective, scientific study can help improve business operations by a great deal. But why do businesses need to hire outside evaluators? Why hire a 21-year-old who knows nothing about your company to make management recommendations, rather than turning to a company veteran who knows all the organization’s ins and outs?